Year:

  1.  4
    Discriminating Healthy Wheat Grains From Grains Infected with Fusarium Graminearum Using Texture Characteristics of Image-Processing Technique, Discriminant Analysis, and Support Vector Machine Methods.Yousef Abbaspour-Gilandeh, Hamed Ghadakchi-Bazaz & Mahdi Davari - 2019 - Journal of Intelligent Systems 29 (1):1576-1586.
    Among agricultural plants, wheat, with valuable foodstuffs such as proteins, vitamins, and minerals, provides about 25% of the world’s food calories. Hence, providing its health conditions and quality is of great importance. One of the most important wheat diseases that causes a lot of damages to this product is Fusarium head blight. In most areas, the causal agent of disease is Fusarium graminearum. This disease not only decreases product quality and efficiency but also has harmful effects on humans and animals (...)
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  2.  11
    Hybridizing the Cuckoo Search Algorithm with Different Mutation Operators for Numerical Optimization Problems.Bilal H. Abed-Alguni & David J. Paul - 2019 - Journal of Intelligent Systems 29 (1):1043-1062.
    The Cuckoo search algorithm is an efficient evolutionary algorithm inspired by the nesting and parasitic reproduction behaviors of some cuckoo species. Mutation is an operator used in evolutionary algorithms to maintain the diversity of the population from one generation to the next. The original CS algorithm uses the Lévy flight method, which is a special mutation operator, for efficient exploration of the search space. The major goal of the current paper is to experimentally evaluate the performance of the CS algorithm (...)
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  3.  23
    Harmony Search Algorithm for Patient Admission Scheduling Problem.Iyad Abu Doush, Mohammed Azmi Al-Betar, Mohammed A. Awadallah, Abdelaziz I. Hammouri, Ra’ed M. Al-Khatib, Saba ElMustafa & Habes ALkhraisat - 2019 - Journal of Intelligent Systems 29 (1):540-553.
    The patient admission scheduling problem is an optimization problem in which we assign patients automatically to beds for a specific period of time while preserving their medical requirements and their preferences. In this paper, we present a novel solution to the PAS problem using the harmony search algorithm. We tailor the HS to solve the PAS problem by distributing patients to beds randomly in the harmony memory while respecting all hard constraints. The proposed algorithm uses five neighborhood strategies in the (...)
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  4.  11
    Self-Adaptive Mussels Wandering Optimization Algorithm with Application for Artificial Neural Network Training.Ahmed A. Abusnaina, Rosni Abdullah & Ali Kattan - 2019 - Journal of Intelligent Systems 29 (1):345-363.
    The mussels wandering optimization is a recent population-based metaheuristic optimization algorithm inspired ecologically by mussels’ movement behavior. The MWO has been used successfully for solving several optimization problems. This paper proposes an enhanced version of MWO, known as the enhanced-mussels wandering optimization algorithm. The E-MWO aims to overcome the MWO shortcomings, such as lack in explorative ability and the possibility to fall in premature convergence. In addition, the E-MWO incorporates the self-adaptive feature for setting the value of a sensitive algorithm (...)
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  5.  1
    A Hybrid of Deep CNN and Bidirectional LSTM for Automatic Speech Recognition.Rajesh Kumar Aggarwal & Vishal Passricha - 2019 - Journal of Intelligent Systems 29 (1):1261-1274.
    Deep neural networks have been playing a significant role in acoustic modeling. Convolutional neural networks are the advanced version of DNNs that achieve 4–12% relative gain in the word error rate over DNNs. Existence of spectral variations and local correlations in speech signal makes CNNs more capable of speech recognition. Recently, it has been demonstrated that bidirectional long short-term memory produces higher recognition rate in acoustic modeling because they are adequate to reinforce higher-level representations of acoustic data. Spatial and temporal (...)
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  6.  5
    An Efficient Quality Inspection of Food Products Using Neural Network Classification.Syed Sumera Ershad Ali & Sayyad Ajij Dildar - 2019 - Journal of Intelligent Systems 29 (1):1425-1440.
    Currently, there is a necessity for the expansion of precise, rapid, and intentional quality assurance with respect to the character of food and horticultural food items, because it is difficult to maintain and organize food products in an elevated quality and secure manner for the increasing population. In this article, we propose a procedure to resolve difficulties and to categorize food as either a broken or quality product. Therefore, the proposed process encompasses four segments, such as preprocessing, segmentation of broken (...)
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  7.  8
    Optimizing the Self-Organizing Team Size Using a Genetic Algorithm in Agile Practices.Wael Almadhoun & Mohammad Hamdan - 2019 - Journal of Intelligent Systems 29 (1):1151-1165.
    In agile software processes, the issue of team size is an important one. In this work we look at how to find the optimal, or near optimal, self-organizing team size using a genetic algorithm which considers team communication efforts. Communication, authority, roles, and learning are the team’s performance characteristics. The GA has been developed according to performance characteristics. A survey was used to evaluate the communication weight factors, which were qualitatively assessed and used in the algorithm’s objective function. The GA (...)
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  8.  14
    Aspect-Oriented Sentiment Analysis: A Topic Modeling-Powered Approach.V. S. Anoop & S. Asharaf - 2019 - Journal of Intelligent Systems 29 (1):1166-1178.
    Because of exponential growth in the number of people who purchase products online, e-commerce organizations are vying for each other to offer innovative and improved services to its customers. Current platforms give its customers innovative services such as product recommendations based on their purchase histories and location, product comparison, and most importantly, a platform for expressing their experience and feedback. It is important for any e-commerce organization to analyze this feedback and to find out the sentiment of the customers for (...)
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  9.  12
    Database Creation and Dialect-Wise Comparative Analysis of Prosodic Features for Punjabi Language.Shipra J. Arora & Rishipal Singh - 2019 - Journal of Intelligent Systems 29 (1):1275-1282.
    The paper represents a Punjabi corpus in the agriculture domain. There are various dialects in the Punjabi language and the main concentration is on major dialects, i.e. Majhi, Malwai and Doabi for the present study. A speech corpus of 125 isolated words is taken into consideration. These words are uttered by 100 speakers, i.e. 60 Malwi dialect speakers, 20 Majhi dialect speakers and 20 Doabi dialect speakers. Tonemes, adhak and nasal words are selected from the corpus. Recordings have been processed (...)
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  10.  2
    Image Compression Based on Block SVD Power Method.Khalid El Asnaoui - 2019 - Journal of Intelligent Systems 29 (1):1345-1359.
    In recent years, the important and fast growth in the development and demand of multimedia products is contributing to an insufficiency in the bandwidth of devices and network storage memory. Consequently, the theory of data compression becomes more significant for reducing data redundancy in order to allow more transfer and storage of data. In this context, this paper addresses the problem of lossy image compression. Indeed, this new proposed method is based on the block singular value decomposition power method that (...)
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  11.  5
    Automatically Assess Day Similarity Using Visual Lifelogs.Khalid El Asnaoui & Petia Radeva - 2019 - Journal of Intelligent Systems 29 (1):298-310.
    Today, we witness the appearance of many lifelogging cameras that are able to capture the life of a person wearing the camera and which produce a large number of images everyday. Automatically characterizing the experience and extracting patterns of behavior of individuals from this huge collection of unlabeled and unstructured egocentric data present major challenges and require novel and efficient algorithmic solutions. The main goal of this work is to propose a new method to automatically assess day similarity from the (...)
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  12.  9
    Software Effort Estimation Using Modified Fuzzy C Means Clustering and Hybrid ABC-MCS Optimization in Neural Network.Hussain Azath, Marimuthu Mohanapriya & Somasundaram Rajalakshmi - 2019 - Journal of Intelligent Systems 29 (1):251-263.
    In a software development process, effective cost estimation is the most challenging activity. Software effort estimation is a crucial part of cost estimation. Management cautiously considers the efforts and benefits of software before committing the required resources to that project or order for a contract. Unfortunately, it is difficult to measure such preliminary estimation, as it has only little information about the project at an early stage. In this paper, a new approach is proposed; this is based on reasoning by (...)
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  13.  1
    Clay-Based Brick Porosity Estimation Using Image Processing Techniques.Mohamed Azrour, Mohamed El Amraoui, Mohammed Ouanan, Brahim Aksasse, Hassan Ouallal & Safa Jida - 2019 - Journal of Intelligent Systems 29 (1):1226-1234.
    This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of (...)
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  14.  5
    FCNB: Fuzzy Correlative Naive Bayes Classifier with MapReduce Framework for Big Data Classification.Chitrakant Banchhor & N. Srinivasu - 2019 - Journal of Intelligent Systems 29 (1):994-1006.
    The term “big data” means a large amount of data, and big data management refers to the efficient handling, organization, or use of large volumes of structured and unstructured data belonging to an organization. Due to the gradual availability of plenty of raw data, the knowledge extraction process from big data is a very difficult task for most of the classical data mining and machine learning tools. In a previous paper, the correlative naive Bayes classifier was developed for big data (...)
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  15.  8
    Ensembles of Text and Time-Series Models for Automatic Generation of Financial Trading Signals From Social Media Content.Omar A. Bari & Arvin Agah - 2019 - Journal of Intelligent Systems 29 (1):753-772.
    Event studies in finance have focused on traditional news headlines to assess the impact an event has on a traded company. The increased proliferation of news and information produced by social media content has disrupted this trend. Although researchers have begun to identify trading opportunities from social media platforms, such as Twitter, almost all techniques use a general sentiment from large collections of tweets. Though useful, general sentiment does not provide an opportunity to indicate specific events worthy of affecting stock (...)
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  16.  4
    Linear Regression Supporting Vector Machine and Hybrid LOG Filter-Based Image Restoration.D. Khalandar Basha & T. Venkateswarlu - 2019 - Journal of Intelligent Systems 29 (1):1480-1495.
    The image restoration technique is a part of image processing to improve the quality of an image that is affected by noise and blur. Thus, IR is required to attain a better quality of image. In this paper, IR is performed using linear regression-based support vector machine. This LR-SVM has two steps: training and testing. The training and testing stages have a distinct windowing process for extracting blocks from the images. The LR-SVM is trained through a block-by-block training sequence. The (...)
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  17.  5
    A One-Pass Approach for Slope and Slant Estimation of Tri-Script Handwritten Words.Suman Kumar Bera, Radib Kar, Souvik Saha, Akash Chakrabarty, Sagnik Lahiri, Samir Malakar & Ram Sarkar - 2019 - Journal of Intelligent Systems 29 (1):688-702.
    Handwritten words can never complement printed words because the former are mostly written in either skewed or slanted form or in both. This very nature of handwriting adds a huge overhead when converting word images into machine-editable format through an optical character recognition system. Therefore, slope and slant corrections are considered as the fundamental pre-processing tasks in handwritten word recognition. For solving this, researchers have followed a two-pass approach where the slope of the word is corrected first and then slant (...)
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  18.  3
    Spectral Graph-Based Features for Recognition of Handwritten Characters: A Case Study on Handwritten Devanagari Numerals.Mohammad Idrees Bhat & B. Sharada - 2019 - Journal of Intelligent Systems 29 (1):799-813.
    Interpretation of different writing styles, unconstrained cursiveness and relationship between different primitive parts is an essential and challenging task for recognition of handwritten characters. As feature representation is inadequate, appropriate interpretation/description of handwritten characters seems to be a challenging task. Although existing research in handwritten characters is extensive, it still remains a challenge to get the effective representation of characters in feature space. In this paper, we make an attempt to circumvent these problems by proposing an approach that exploits the (...)
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  19.  7
    Oppositional Gravitational Search Algorithm and Artificial Neural Network-Based Classification of Kidney Images.S. M. K. Chaitanya & P. Rajesh Kumar - 2019 - Journal of Intelligent Systems 29 (1):485-496.
    Ultrasound imaging has been broadly utilized as part of kidney diagnosis because of its ability to show structural abnormalities like cysts, stones, and infections as well as information about kidney function. The main aim of this research is to effectively classify normal and abnormal kidney images through US based on the selection of relevant features. In this study, abnormal kidney images were classified through gray-scale conversion, region-of-interest generation, multi-scale wavelet-based Gabor feature extraction, probabilistic principal component analysis-based feature selection and adaptive (...)
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  20.  4
    Selector: PSO as Model Selector for Dual-Stage Diabetes Network.Ramalingaswamy Cheruku & Damodar Reddy Edla - 2019 - Journal of Intelligent Systems 29 (1):475-484.
    Diabetes is a chronic disease caused by insulin deficiency, and it should be detected in the early stages for effective treatment. In this paper, the Diabetes-Network is proposed to increase diabetes predictive accuracy. The proposed Dia-Net is a dual-stage network. It combines both optimized probabilistic neural network and optimized radial basis function neural network in the first stage. Hence, Dia-Net possesses the advantages of both the models. In the second stage, the linear support vector machine is used. As the dataset (...)
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  21. Design and Evaluation of Outlier Detection Based on Semantic Condensed Nearest Neighbor.Nagaraju Devarakonda & M. Rao Batchanaboyina - 2019 - Journal of Intelligent Systems 29 (1):1416-1424.
    Social media contain abundant information about the events or news occurring all over the world. Social media growth has a greater impact on various domains like marketing, e-commerce, health care, e-governance, and politics, etc. Currently, Twitter was developed as one of the social media platforms, and now, it is one of the most popular social media platforms. There are 1 billion user’s profiles and millions of active users, who post tweets daily. In this research, buzz detection in social media was (...)
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  22.  9
    An Improved Robust Fuzzy Algorithm for Unsupervised Learning.Amina Dik, Khalid Jebari & Aziz Ettouhami - 2019 - Journal of Intelligent Systems 29 (1):1028-1042.
    This paper presents a robust, dynamic, and unsupervised fuzzy learning algorithm that aims to cluster a set of data samples with the ability to detect outliers and assign the numbers of clusters automatically. It consists of three main stages. The first stage is a pre-processing method in which possible outliers are determined and quarantined using a concept of proximity degree. The second stage is a learning method, which consists in auto-detecting the number of classes with their prototypes for a dynamic (...)
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  23.  4
    Gbest-Guided Artificial Bee Colony Optimization Algorithm-Based Optimal Incorporation of Shunt Capacitors in Distribution Networks Under Load Growth.Mukul Dixit, Prasanta Kundu & Hitesh R. Jariwala - 2019 - Journal of Intelligent Systems 29 (1):202-222.
    In this work, a new technique is introduced for optimal incorporation of shunt capacitors in distribution networks. This technique has been compared to other sensitivity-based approaches such as loss sensitivity factor, index vector method, power loss index, and index of voltage stability. In the proposed technique, the optimal positions as well as the ratings of SCs are identified through an optimization algorithm. In sensitivity-based approaches, the positions of SCs are determined through a sensitivity approach and the optimal ratings of SCs (...)
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  24.  2
    Discriminative Training Using Noise Robust Integrated Features and Refined HMM Modeling.Mohit Dua, Rajesh Kumar Aggarwal & Mantosh Biswas - 2019 - Journal of Intelligent Systems 29 (1):327-344.
    The classical approach to build an automatic speech recognition system uses different feature extraction methods at the front end and various parameter classification techniques at the back end. The Mel-frequency cepstral coefficients and perceptual linear prediction techniques are the conventional approaches used for many years for feature extraction, and the hidden Markov model has been the most obvious selection for feature classification. However, the performance of MFCC-HMM and PLP-HMM-based ASR system degrades in real-time environments. The proposed work discusses the implementation (...)
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  25.  6
    Optimizing Integrated Features for Hindi Automatic Speech Recognition System.Mohit Dua, Rajesh Kumar Aggarwal & Mantosh Biswas - 2019 - Journal of Intelligent Systems 29 (1):959-976.
    An automatic speech recognition system translates spoken words or utterances into text format. State-of-the-art ASR systems mainly use Mel frequency cepstral coefficient, perceptual linear prediction, and Gammatone frequency cepstral coefficient for extracting features in the training phase of the ASR system. Initially, the paper proposes a sequential combination of all three feature extraction methods, taking two at a time. Six combinations, MF-PLP, PLP-MFCC, MF-GFCC, GF-MFCC, GF-PLP, and PLP-GFCC, are used, and the accuracy of the proposed system using all these combinations (...)
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  26.  7
    Modeling and Optimization of a Liquid Flow Process Using an Artificial Neural Network-Based Flower Pollination Algorithm.Pijush Dutta & Asok Kumar - 2019 - Journal of Intelligent Systems 29 (1):787-798.
    Controlling liquid flow is one of the most important parameters in the process control industry. It is challenging to optimize the liquid flow rate for its highly nonlinear nature. This paper proposes a model of liquid flow processes using an artificial neural network and optimizes it using a flower pollination algorithm to avoid local minima and improve the accuracy and convergence speed. In the first phase, the NN model was trained by the dataset obtained from the experiments, which were carried (...)
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  27.  9
    Improved Adaptive Neuro-Fuzzy Inference System Using Gray Wolf Optimization: A Case Study in Predicting Biochar Yield.Ahmed A. Ewees & Mohamed Abd Elaziz - 2019 - Journal of Intelligent Systems 29 (1):924-940.
    This paper presents an alternative method for predicting biochar yields from biomass thermochemical processes. As biochar is considered a renewable and sustainable energy source, it has received more attention. Several methods have been presented to predict biochar, such as neural network and least square support vector machine. However, each of them has its own drawbacks, such as getting stuck in a local optimum, which occurs in NN, and lack of uncertainty and time complexity, as in LS-SVM. Therefore, this paper avoids (...)
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  28.  6
    An Improved Particle Swarm Optimization Algorithm for Global Multidimensional Optimization.Rkia Fajr & Abdelaziz Bouroumi - 2019 - Journal of Intelligent Systems 29 (1):127-142.
    This paper introduces a new variant of the particle swarm optimization algorithm, designed for global optimization of multidimensional functions. The goal of this variant, called ImPSO, is to improve the exploration and exploitation abilities of the algorithm by introducing a new operation in the iterative search process. The use of this operation is governed by a stochastic rule that ensures either the exploration of new regions of the search space or the exploitation of good intermediate solutions. The proposed method is (...)
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  29.  3
    An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm.Nacer Farajzadeh & Asgarali Bouyer - 2019 - Journal of Intelligent Systems 29 (1):1-18.
    Among the data clustering algorithms, the k-means algorithm is one of the most popular clustering techniques because of its simplicity and efficiency. However, KM is sensitive to initial centers and it has a local optima problem. The k-harmonic means clustering algorithm solves the initialization problem of the KM algorithm, but it also has a local optima problem. In this paper, we develop a new algorithm for solving this problem based on a modified version of particle swarm optimization algorithm and KHM (...)
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  30. Tangramob: An Agent-Based Simulation Framework for Validating Urban Smart Mobility Solutions.Giorgio Forcina, Jacopo de Berardinis, Carlo Castagnari, Andrea Polini, Francesco De Angelis & Flavio Corradini - 2019 - Journal of Intelligent Systems 29 (1):1188-1201.
    Estimating the effects of introducing a range of smart mobility solutions within an urban area is a crucial concern in urban planning. The lack of a simulator for the assessment of mobility initiatives forces local public authorities and mobility service providers to base their decisions on guidelines derived from common heuristics and best practices. These approaches can help planners in shaping mobility solutions; however, given the high number of variables to consider, the effects are not guaranteed. Therefore, a solution conceived (...)
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  31.  2
    Polarity Analysis of Customer Reviews Based on Part-of-Speech Subcategory.Ayman S. Ghabayen & Basem H. Ahmed - 2019 - Journal of Intelligent Systems 29 (1):1535-1544.
    Nowadays, sentiment analysis is a method used to analyze the sentiment of the feedback given by a user in an online document, such as a blog, comment, and review, and classifies it as negative, positive, or neutral. The classification process relies upon the analysis of the polarity features of the natural language text given by users. Polarity analysis has been an important subtask in sentiment analysis; however, detecting correct polarity has been a major issue. Different researchers have utilized different polarity (...)
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  32.  1
    Hybrid Approach for Face Recognition From a Single Sample Per Person by Combining VLC and GOM.Ahmed Ghorbel, Walid Aydi, Imen Tajouri & Nouri Masmoudi - 2019 - Journal of Intelligent Systems 29 (1):1523-1534.
    This paper proposes a new face recognition system based on combining two feature extraction techniques: the Vander Lugt correlator and Gabor ordinal measures. The proposed system relies on the execution speed of VLC and the robustness of GOM. In this system, we applied the Tan and Triggs and retina modeling enhancement techniques, which are well suited for VLC and GOM, respectively. We evaluated our system on the standard FERET probe data sets and on extended YaleB database. The obtained results exhibited (...)
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  33.  1
    Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection.Manosij Ghosh, Ritam Guha, Imran Alam, Priyank Lohariwal, Devesh Jalan & Ram Sarkar - 2019 - Journal of Intelligent Systems 29 (1):1598-1610.
    Feature selection is a technique which helps to find the most optimal feature subset to develop an efficient pattern recognition model under consideration. The use of genetic algorithm and particle swarm optimization in the field of FS is profound. In this paper, we propose an insightful way to perform FS by amassing information from the candidate solutions produced by GA and PSO. Our aim is to combine the exploitation ability of GA with the exploration capacity of PSO. We name this (...)
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  34.  9
    M-HMOGA: A New Multi-Objective Feature Selection Algorithm for Handwritten Numeral Classification.Ritam Guha, Manosij Ghosh, Pawan Kumar Singh, Ram Sarkar & Mita Nasipuri - 2019 - Journal of Intelligent Systems 29 (1):1453-1467.
    The feature selection process is very important in the field of pattern recognition, which selects the informative features so as to reduce the curse of dimensionality, thus improving the overall classification accuracy. In this paper, a new feature selection approach named Memory-Based Histogram-Oriented Multi-objective Genetic Algorithm is introduced to identify the informative feature subset to be used for a pattern classification problem. The proposed M-HMOGA approach is applied to two recently used feature sets, namely Mojette transform and Regional Weighted Run (...)
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  35.  6
    An Efficient Technique for Three-Dimensional Image Visualization Through Two-Dimensional Images for Medical Data.Ganesan Gunasekaran & Meenakshisundaram Venkatesan - 2019 - Journal of Intelligent Systems 29 (1):100-109.
    The main idea behind this work is to present three-dimensional image visualization through two-dimensional images that comprise various images. 3D image visualization is one of the essential methods for excerpting data from given pieces. The main goal of this work is to figure out the outlines of the given 3D geometric primitives in each part, and then integrate these outlines or frames to reconstruct 3D geometric primitives. The proposed technique is very useful and can be applied to many kinds of (...)
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  36.  6
    Speech Signal Compression Algorithm Based on the JPEG Technique.Tariq A. Hassan, Rageed Hussein Al-Hashemy & Rehab I. Ajel - 2019 - Journal of Intelligent Systems 29 (1):554-564.
    The main objective of this paper is to explore parameters usually adopted by the JPEG method and use them in speech signal compression. Speech compression is the technique of encoding the speech signal in some way that allows the same speech parameters to represent the whole signal. In other words, it is to eliminate redundant features of speech and keep only the important ones for the next stage of speech reproduction. In this paper, the proposed method is to adopt the (...)
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  37. Distributed Multi-Agent Bidding-Based Approach for the Collaborative Mapping of Unknown Indoor Environments by a Homogeneous Mobile Robot Team.Abdelfetah Hentout, Abderraouf Maoudj, Nesrine Kaid-Youcef, Djamila Hebib & Brahim Bouzouia - 2019 - Journal of Intelligent Systems 29 (1):84-99.
    This paper deals with the problem of the collaborative mapping of unknown indoor environments by a homogeneous mobile robot team. For this aim, a distributed multi-agent coordination approach is proposed for the mapping process to offer a global view of the entire environment. First, the scheme starts by assigning the most suitable robots to the different zones of the environment to be mapped based on a bidding strategy. Then, while a Robot agent of the group explores its local surroundings and (...)
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  38.  3
    Trapezoidal Linguistic Cubic Fuzzy TOPSIS Method and Application in a Group Decision Making Program.Shah Hussain, Muhammad Aslam, Fazli Amin, Saleem Abdullah & Aliya Fahmi - 2019 - Journal of Intelligent Systems 29 (1):1283-1300.
    The aim of this paper is to define some new operation laws for the trapezoidal linguistic cubic fuzzy number and Hamming distance. Furthermore, we define and use the trapezoidal linguistic cubic fuzzy TOPSIS method to solve the multi criteria decision making method. The new ranking method for trapezoidal linguistic cubic fuzzy numbers are used to rank the alternatives. Finally, an illustrative example is given to verify and prove the practicality and effectiveness of the proposed method.
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  39.  2
    The Use of Natural Language Processing Approach for Converting Pseudo Code to C# Code.Ayad Tareq Imam & Ayman Jameel Alnsour - 2019 - Journal of Intelligent Systems 29 (1):1388-1407.
    Although current computer-aided software engineering tools support developers in composing a program, there is no doubt that more flexible supportive tools are needed to address the increases in the complexity of programs. This need can be met by automating the intellectual activities that are carried out by humans when composing a program. This paper aims to automate the composition of a programming language code from pseudocode, which is viewed here as a translation process for a natural language text, as pseudocode (...)
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  40.  4
    A Color Image Encryption Technique Based on Bit-Level Permutation and Alternate Logistic Maps.Priyanka Jaroli, Shelza Dua, Mohit Dua & Ankita Bisht - 2019 - Journal of Intelligent Systems 29 (1):1246-1260.
    The paper presents an approach to encrypt the color images using bit-level permutation and alternate logistic map. The proposed method initially segregates the color image into red, green, and blue channels, transposes the segregated channels from the pixel-plane to bit-plane, and scrambles the bit-plane matrix using Arnold cat map. Finally, the red, blue, and green channels of the scrambled image are confused and diffused by applying alternate logistic map that uses a four-dimensional Lorenz system to generate a pseudorandom number sequence (...)
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  41.  6
    I-Vector-Based Speaker Verification on Limited Data Using Fusion Techniques.T. R. Jayanthi Kumari & H. S. Jayanna - 2019 - Journal of Intelligent Systems 29 (1):565-582.
    In many biometric applications, limited data speaker verification plays a significant role in practical-oriented systems to verify the speaker. The performance of the speaker verification system needs to be improved by applying suitable techniques to limited data condition. The limited data represent both train and test data duration in terms of few seconds. This article shows the importance of the speaker verification system under limited data condition using feature- and score-level fusion techniques. The baseline speaker verification system uses vocal tract (...)
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  42.  2
    Leaf Disease Segmentation From Agricultural Images Via Hybridization of Active Contour Model and OFA.Muniram Gajendra Singh Jayanthi & Dandinashivara Revanna Shashikumar - 2019 - Journal of Intelligent Systems 29 (1):35-52.
    In this paper, an alternative active contour model driven by an oppositional fruit fly algorithm is presented. Unlike the traditional ACM variant, which is frequently caught in a local minimum, this methodology helps the focalizing of control points toward the global least of the energy function. In the proposed system, energy minimization is performed through a fruit fly algorithm, and every control point is compelled in a local search window. As for the local search window, the rectangular-shaped approach has been (...)
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  43.  5
    Automatic Genetic Fuzzy C-Means.Khalid Jebari, Abdelaziz Elmoujahid & Aziz Ettouhami - 2019 - Journal of Intelligent Systems 29 (1):529-539.
    Fuzzy c-means is an efficient algorithm that is amply used for data clustering. Nonetheless, when using this algorithm, the designer faces two crucial choices: choosing the optimal number of clusters and initializing the cluster centers. The two choices have a direct impact on the clustering outcome. This paper presents an improved algorithm called automatic genetic fuzzy c-means that evolves the number of clusters and provides the initial centroids. The proposed algorithm uses a genetic algorithm with a new crossover operator, a (...)
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  44. Predicting Automatic Trigger Speed for Vehicle-Activated Signs.Diala Jomaa & Siril Yella - 2019 - Journal of Intelligent Systems 29 (1):1079-1091.
    Vehicle-activated signs are speed-warning signs activated by radar when the driver speed exceeds a pre-set threshold, i.e. the trigger speed. The trigger speed is often set relative to the speed limit and is displayed for all types of vehicles. It is our opinion that having a static setting for the trigger speed may be inappropriate, given that traffic and road conditions are dynamic in nature. Further, different vehicle classes behave differently, so a uniform trigger speed of such signs may be (...)
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  45.  4
    Enhanced Twitter Sentiment Analysis Using Hybrid Approach and by Accounting Local Contextual Semantic.Nisheeth Joshi & Itisha Gupta - 2019 - Journal of Intelligent Systems 29 (1):1611-1625.
    This paper addresses the problem of Twitter sentiment analysis through a hybrid approach in which SentiWordNet -based feature vector acts as input to the classification model Support Vector Machine. Our main focus is to handle lexical modifier negation during SWN score calculation for the improvement of classification performance. Thus, we present naive and novel shift approach in which negation acts as both sentiment-bearing word and modifier, and then we shift the score of words from SWN based on their contextual semantic, (...)
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  46.  7
    An Integrated Intuitionistic Fuzzy AHP and TOPSIS Approach to Evaluation of Outsource Manufacturers.Cengiz Kahraman, Başar Öztayşi & Sezi Çevik Onar - 2019 - Journal of Intelligent Systems 29 (1):283-297.
    Outsourcing is the action of contracting a specific task, function, or process to an external company instead of using an organisation’s resources. The history of outsourcing goes back to the 1980s when it was used for cost reduction in non-core business operations. Over time, outsourcing has moved to more strategic areas and has become an important factor in business performance. The selection of the best alternative among alternative outsource manufacturers is a multi-criteria decision-making problem. In this study, the fuzzy set (...)
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  47.  24
    Feature Pair Index Graph for Clustering.N. Karthika & B. Janet - 2019 - Journal of Intelligent Systems 29 (1):1179-1187.
    Text documents are significant arrangements of various words, while images are significant arrangements of various pixels/features. In addition, text and image data share a similar semantic structural pattern. With reference to this research, the feature pair is defined as a pair of adjacent image features. The innovative feature pair index graph is constructed from the unique feature pair selected, which is constructed using an inverted index structure. The constructed FPIG is helpful in clustering, classifying and retrieving the image data. The (...)
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  48.  8
    Pythagorean Hesitant Fuzzy Information Aggregation and Their Application to Multi-Attribute Group Decision-Making Problems.Muhammad Sajjad Ali Khan, Saleem Abdullah, Asad Ali & Khaista Rahman - 2019 - Journal of Intelligent Systems 29 (1):154-171.
    In this paper, we introduce the concept of the Pythagorean hesitant fuzzy set, which is the generalization of the intuitionistic hesitant fuzzy set under the restriction that the square sum of its membership degrees is ≤1. In decision making with PHFSs, aggregation operators play a key role because they can be used to synthesize multidimensional evaluation values represented as Pythagorean hesitant fuzzy values into collective values. Under PHFS environments, Pythagorean hesitant fuzzy ordered weighted averaging and Pythagorean fuzzy ordered weighted geometric (...)
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  49. A Blind Medical Image Watermarking for Secure E-Healthcare Application Using Crypto-Watermarking System.Polurie Venkata Vijay Kishore & Puvvadi Aparna - 2019 - Journal of Intelligent Systems 29 (1):1558-1575.
    A reliable medical image management must provide proper security for patient information. Protecting the medical information of the patients is a major concern in all hospitals. Digital watermarking is a procedure prevalently used to secure the confidentiality of medical information and maintain them, which upgrades patient health awareness. To protect the medical information, the robust and lossless patient medical information sharing system using crypto-watermarking method is proposed. The proposed system consists of two phases: embedding and extraction. In this paper, we (...)
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  50.  1
    Fractional Fuzzy Clustering and Particle Whale Optimization-Based MapReduce Framework for Big Data Clustering.Omkaresh Kulkarni, Sudarson Jena & C. H. Sanjay - 2019 - Journal of Intelligent Systems 29 (1):1496-1513.
    The recent advancements in information technology and the web tend to increase the volume of data used in day-to-day life. The result is a big data era, which has become a key issue in research due to the complexity in the analysis of big data. This paper presents a technique called FPWhale-MRF for big data clustering using the MapReduce framework, by proposing two clustering algorithms. In FPWhale-MRF, the mapper function estimates the cluster centroids using the Fractional Tangential-Spherical Kernel clustering algorithm, (...)
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  51.  6
    A New Feature Selection Method for Sentiment Analysis in Short Text.H. M. Keerthi Kumar & B. S. Harish - 2019 - Journal of Intelligent Systems 29 (1):1122-1134.
    In recent internet era, micro-blogging sites produce enormous amount of short textual information, which appears in the form of opinions or sentiments of users. Sentiment analysis is a challenging task in short text, due to use of formal language, misspellings, and shortened forms of words, which leads to high dimensionality and sparsity. In order to deal with these challenges, this paper proposes a novel, simple, and yet effective feature selection method, to select frequently distributed features related to each class. In (...)
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  52.  2
    Noise Reduction Using Modified Wiener Filter in Digital Hearing Aid for Speech Signal Enhancement.Madam Aravind Kumar & Kamsali Manjunatha Chari - 2019 - Journal of Intelligent Systems 29 (1):1360-1378.
    Speech signals are usually affected by noises during the communication process. For suppressing the noise signal that is combined with the speech signal, a Wiener filter is adapted in digital hearing aids. Weiner filter plays an important role in noise suppression and enhancement by estimating the relation between the power spectra of the noise-affected speech signal and the noise signal. Power consumption and the hardware requirement are the important problems in adapting Weiner filter for major communication systems. In this work, (...)
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  53.  13
    A Novel Weakest T-Norm Based Fuzzy Fault Tree Analysis Through Qualitative Data Processing and Its Application in System Reliability Evaluation.Mohit Kumar - 2019 - Journal of Intelligent Systems 29 (1):977-993.
    The quantification of the fuzzy fault tree analysis is based on fuzzy arithmetic operations. It is well known that the weakest t-norm -based fuzzy arithmetic operations have some advantages. The Tw-based fuzzy arithmetic operations provide fuzzy results with less fuzziness and preserve the shape of fuzzy numbers. The purpose of this study is to develop a Tw-based fuzzy fault tree analysis to assess system reliability when only qualitative data such as expert opinions or decisions are available and described in linguistic (...)
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  54.  8
    An Overview of Segmentation Algorithms for the Analysis of Anomalies on Medical Images.Subbiahpillai Neelakantapillai Kumar, Alfred Lenin Fred & Paul Sebastin Varghese - 2019 - Journal of Intelligent Systems 29 (1):612-625.
    Human disease identification from the scanned body parts helps medical practitioners make the right decision in lesser time. Image segmentation plays a vital role in automated diagnosis for the delineation of anatomical organs and anomalies. There are many variants of segmentation algorithms used by current researchers, whereas there is no universal algorithm for all medical images. This paper classifies some of the widely used medical image segmentation algorithms based on their evolution, and the features of each generation are also discussed. (...)
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  55.  12
    A Flame Detection Method Based on Novel Gradient Features.Zhu Liping, Li Hongqi, Wang Fenghui, Lv Jie, Sikandar Ali & Zhang Hong - 2019 - Journal of Intelligent Systems 29 (1):773-786.
    In this study, we present a novel approach to efficiently detect the flame in multiple scenes in an image. The method uses a set of parametric representation named as Gradient Features, to learn the features of flame color changes in the image. Different from the traditional color features of the flame, GF represents the color changes in RGB channels for further consideration. In this study, support vector machine was applied to generate a set of candidate regions and the decision tree (...)
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  56. A 4D Trajectory Prediction Model Based on the BP Neural Network.Lan Ma, Shan Tian & Zhi-Jun Wu - 2019 - Journal of Intelligent Systems 29 (1):1545-1557.
    To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was (...)
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  57.  5
    Development of a Two-Stage Segmentation-Based Word Searching Method for Handwritten Document Images.Samir Malakar, Manosij Ghosh, Ram Sarkar & Mita Nasipuri - 2019 - Journal of Intelligent Systems 29 (1):719-735.
    Word searching or keyword spotting is an important research problem in the domain of document image processing. The solution to the said problem for handwritten documents is more challenging than for printed ones. In this work, a two-stage word searching schema is introduced. In the first stage, all the irrelevant words with respect to a search word are filtered out from the document page image. This is carried out using a zonal feature vector, called pre-selection feature vector, along with a (...)
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  58.  5
    Sparse Decomposition Technique for Segmentation and Compression of Compound Images.V. N. Manju & A. Lenin Fred - 2019 - Journal of Intelligent Systems 29 (1):515-528.
    Compression of compound records and images can be more cumbersome than the original information since they can be a mix of text, picture and graphics. The principle requirement of the compound record or images is the nature of the compressed data. In this paper, diverse procedures are used under block-based classification to distinguish the compound image segments. The segmentation process starts with separation of the entire image into blocks by spare decomposition technique in smooth blocks and non smooth blocks. Gray (...)
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  59.  3
    Using an Efficient Optimal Classifier for Soil Classification in Spatial Data Mining Over Big Data.Aakunuri Manjula & G. Narsimha - 2019 - Journal of Intelligent Systems 29 (1):172-188.
    This article proposes an effectual process for soil classification. The input data of the proposed procedure is the Harmonized World Soil Database. Preprocessing aids to generate enhanced representation and will use minimum time. Then, the MapReduce framework divides the input dataset into a complimentary portion that is held by the map task. In the map task, principal component analysis is used to reduce the data and the outputs of the maps are then contributed to reduce the tasks. Lastly, the proposed (...)
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  60.  5
    A Genetic Algorithm Approach for Group Recommender System Based on Partial Rankings.Ritu Meena & Kamal K. Bharadwaj - 2019 - Journal of Intelligent Systems 29 (1):653-663.
    Many recommender systems frequently make suggestions for group consumable items to the individual users. There has been much work done in group recommender systems with full ranking, but partial ranking where items are partially ranked still remains a challenge. The ultimate objective of this work is to propose rank aggregation technique for effectively handling the PR problem. Additionally, in real applications, most of the studies have focused on PR without ties. However, the rankings may have ties where some items are (...)
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  61.  9
    Group Recommender Systems – An Evolutionary Approach Based on Multi-Expert System for Consensus.Ritu Meena & Sonajharia Minz - 2019 - Journal of Intelligent Systems 29 (1):1092-1108.
    Recommender systems have focused on algorithms for a recommendation for individuals. However, in many domains, it may be recommending an item, for example, movies, restaurants etc. for a group of persons for which some remarkable group recommender systems has been developed. GRSs satisfy a group of people optimally by considering the equal weighting of the individual preferences. We have proposed a multi-expert scheme for group recommendation using genetic algorithm MES-GRS-GA that depends on consensus techniques to further improve group recommendations. In (...)
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  62.  1
    A New Algorithm Based on Magic Square and a Novel Chaotic System for Image Encryption.Sadiq A. Mehdi & Rageed Hussein Al-Hashemy - 2019 - Journal of Intelligent Systems 29 (1):1202-1215.
    This article introduces a simple and effective new algorithm for image encryption using a chaotic system which is based on the magic squares. This novel 3D chaotic system is invoked to generate a random key to encrypt any color image. A number of chaotic keys equal to the size of the image are generated by this chaotic system and arranged into a matrix then divided into non-overlapped submatrices. The image to be encrypted is also divided into sub-images, and each sub-image (...)
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  63.  6
    Handwritten Indic Script Recognition Based on the Dempster–Shafer Theory of Evidence.Anirban Mukhopadhyay, Pawan Kumar Singh, Ram Sarkar & Mita Nasipuri - 2019 - Journal of Intelligent Systems 29 (1):264-282.
    In a multilingual country like India, script recognition is an important pre-processing footstep necessary for feeding any document to an optical character recognition engine, which is, in general, script specific. The present work evaluates the performance of an ensemble of two MLP classifiers, each trained on different feature sets. Here, two complementary sets of features, namely, gray-level co-occurrence matrix and Gabor wavelets transform coefficients are extracted from each of the handwritten text-line and word images written in 12 official scripts used (...)
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  64.  9
    Non-Word Attributes’ Efficiency in Text Mining Authorship Prediction.Tareef Kamil Mustafa - 2019 - Journal of Intelligent Systems 29 (1):1408-1415.
    Literature scripts can be compared to paintings, in an artistic way as well as in the perspective of financial value, whereas the value of these scripts rise and fall depending on their author’s popularity. Authors’ scripts represent a specific style of writing that can be measured and compared using a text mining field called Stylometric. Stylometric analysis depends on some features called authorship attributes, and these attributes or features can be used in special algorithms and methods to reach that aim. (...)
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  65.  5
    Improving Image Search Through MKFCM Clustering Strategy-Based Re-Ranking Measure.A. K. Naveena & N. K. Narayanan - 2019 - Journal of Intelligent Systems 29 (1):497-514.
    The main intention of this research is to develop a novel ranking measure for content-based image retrieval system. Owing to the achievement of data retrieval, most commercial search engines still utilize a text-based search approach for image search by utilizing encompassing textual information. As the text information is, in some cases, noisy and even inaccessible, the drawback of such a recovery strategy is to the extent that it cannot depict the contents of images precisely, subsequently hampering the execution of image (...)
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  66.  5
    Combined Multi-Agent Method to Control Inter-Department Common Events Collision for University Courses Timetabling.Jalil Nourmohammadi-Khiarak, Yashar Zamani-Harghalani & Mohammad-Reza Feizi-Derakhshi - 2019 - Journal of Intelligent Systems 29 (1):110-126.
    University course timetabling is the scheduling of courses at different time slots in a university. The two important issues in this process are the allocation of all events to resources in a semester, and maximizing the satisfaction of common events among multiple departments. Accumulating evidences in university course timetabling problems suggest dividing the problem into several sub-problems. This study attempted to investigate the appropriateness of using the genetic algorithm and the imperialist competitive algorithm. The proposed technique consists of two steps: (...)
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  67.  11
    A Hybrid Grey Wolf Optimiser Algorithm for Solving Time Series Classification Problems.Heba Al Nsour, Mohammed Alweshah, Abdelaziz I. Hammouri, Hussein Al Ofeishat & Seyedali Mirjalili - 2019 - Journal of Intelligent Systems 29 (1):846-857.
    One of the major objectives of any classification technique is to categorise the incoming input values based on their various attributes. Many techniques have been described in the literature, one of them being the probabilistic neural network. There were many comparisons made between the various published techniques depending on their precision. In this study, the researchers investigated the search capability of the grey wolf optimiser algorithm for determining the optimised values of the PNN weights. To the best of our knowledge, (...)
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  68.  1
    AGCS Technique to Improve the Performance of Neural Networks.Suresh Pabboju & Kishor Kumar Katha - 2019 - Journal of Intelligent Systems 29 (1):1235-1245.
    In this paper, a fresh method is offered regarding training of particular neural networks. This technique is a combination of the adaptive genetic and cuckoo search algorithms, called the AGCS method. The intention of training a particular artificial neural network is to obtain the finest weight load. With this protocol, a particular weight is taken into account as feedback, which is optimized by means of the hybrid AGCS protocol. In the beginning, a collection of weights is initialized and the similar (...)
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  69.  7
    Deadline Constrained Task Scheduling Method Using a Combination of Center-Based Genetic Algorithm and Group Search Optimization.Sellaperumal Parthasarathy & Chinnasami Jothi Venkateswaran - 2019 - Journal of Intelligent Systems 29 (1):53-70.
    The present paper describes a hybrid group search optimization and center-based genetic algorithm -based model for task scheduling in cloud computing. The proposed hybrid model combines the GSO, which has been successful in its application in task scheduling, with the use of the CBGA. The basic scheme of our approach is to utilize the benefits of both the GSO algorithm and CBGA excluding their disadvantages. In our work, we introduce the hybrid clouds, which are needed to determine which task to (...)
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  70.  4
    Enriching Documents by Linking Salient Entities and Lexical-Semantic Expansion.Mohsen Pourvali & Salvatore Orlando - 2019 - Journal of Intelligent Systems 29 (1):1109-1121.
    This paper explores a multi-strategy technique that aims at enriching text documents for improving clustering quality. We use a combination of entity linking and document summarization in order to determine the identity of the most salient entities mentioned in texts. To effectively enrich documents without introducing noise, we limit ourselves to the text fragments mentioning the salient entities, in turn, belonging to a knowledge base like Wikipedia, while the actual enrichment of text fragments is carried out using WordNet. To feed (...)
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  71.  4
    Optimizing Software Modularity with Minimum Possible Variations.Amarjeet Prajapati & Jitender Kumar Chhabra - 2019 - Journal of Intelligent Systems 29 (1):1135-1150.
    Poor design choices at the early stages of software development and unprincipled maintenance practices usually deteriorate software modularity and subsequently increase system complexity. In object-oriented software, improper distribution of classes among packages is a key factor, responsible for modularity degradation. Many optimization techniques to improve the software modularity have been proposed in the literature. The focus of these optimization techniques is to produce modularization solutions by optimizing different design quality criteria. Such modularization solutions are good from the different aspect of (...)
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  72. Early Detection of Parkinson’s Disease by Using SPECT Imaging and Biomarkers.Bhanu Prasad, T. N. Nagabhushan & Gunjan Pahuja - 2019 - Journal of Intelligent Systems 29 (1):1329-1344.
    Precise and timely diagnosis of Parkinson’s disease is important to control its progression among subjects. Currently, a neuroimaging technique called dopaminergic imaging that uses single photon emission computed tomography with 123I-Ioflupane is popular among clinicians for detecting Parkinson’s disease in early stages. Unlike other studies, which consider only low-level features like gray matter, white matter, or cerebrospinal fluid, this study explores the non-linear relation between different biomarkers using deep learning and multivariate logistic regression. Striatal binding ratios are obtained using 123I-Ioflupane (...)
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  73.  7
    Extreme Learning Machine for Credit Risk Analysis.Mais Haj Qasem & Loai Nemer - 2019 - Journal of Intelligent Systems 29 (1):640-652.
    Credit risk analysis is important for financial institutions that provide loans to businesses and individuals. Banks and other financial institutions generally face risks that are mostly of financial nature; hence, such institutions must balance risks and returns. Analyzing or determining risk levels involved in credits, finances, and loans can be performed through predictive analytic techniques, such as an extreme learning machine. In this work, we empirically evaluated the performance of an ELM for credit risk problems and compared it to naive (...)
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  74.  6
    Graded Soft Expert Set as a Generalization of Hesitant Fuzzy Set.Afshan Qayyum & Tanzeela Shaheen - 2019 - Journal of Intelligent Systems 29 (1):223-236.
    Hesitant fuzzy sets play a vital role in decision analysis. Although they have been proved to be a landmark in evaluating information, there are certain deficiencies in their structure. Also, in decision analysis with the aid of hesitant fuzzy sets, the relative importance of the decision makers according to their area of expertise is ignored completely, which may be misleading in some situations. These sorts of issues have been resolved in this work by using graded soft expert sets. The proposed (...)
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  75.  12
    Pythagorean Fuzzy Einstein Hybrid Averaging Aggregation Operator and its Application to Multiple-Attribute Group Decision Making.Khaista Rahman, Saleem Abdullah, Asad Ali & Fazli Amin - 2019 - Journal of Intelligent Systems 29 (1):736-752.
    Pythagorean fuzzy set is one of the successful extensions of the intuitionistic fuzzy set for handling uncertainties in information. Under this environment, in this paper, we introduce the notion of Pythagorean fuzzy Einstein hybrid averaging aggregation operator along with some of its properties, namely idempotency, boundedness, and monotonicity. PFEHA aggregation operator is the generalization of Pythagorean fuzzy Einstein weighted averaging aggregation operator and Pythagorean fuzzy Einstein ordered weighted averaging aggregation operator. The operator proposed in this paper provides more accurate and (...)
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  76.  13
    Some Interval-Valued Pythagorean Fuzzy Einstein Weighted Averaging Aggregation Operators and Their Application to Group Decision Making.Khaista Rahman, Saleem Abdullah & Muhammad Sajjad Ali Khan - 2019 - Journal of Intelligent Systems 29 (1):393-408.
    In this paper, we introduce the notion of Einstein aggregation operators, such as the interval-valued Pythagorean fuzzy Einstein weighted averaging aggregation operator and the interval-valued Pythagorean fuzzy Einstein ordered weighted averaging aggregation operator. We also discuss some desirable properties, such as idempotency, boundedness, commutativity, and monotonicity. The main advantage of using the proposed operators is that these operators give a more complete view of the problem to the decision makers. These operators provide more accurate and precise results as compared the (...)
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  77.  6
    Prediction of User Future Request Utilizing the Combination of Both ANN and FCM in Web Page Recommendation.V. Raju & N. Srinivasan - 2019 - Journal of Intelligent Systems 29 (1):583-595.
    This paper explains about the web page recommendation system. This procedure encompasses consumers’ upcoming demand and web page recommendations. In the proposed web page recommendation system, potential and non-potential data can be categorized by use of the Levenberg–Marquardt firefly neural network algorithm, and forecast can be made by using the K-means clustering algorithm. Consequently, the projected representation demonstrates the infrequent contact format with the help of the representation that integrates the comparable consumer access model data that belong to the further (...)
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  78.  8
    A Grey Wolf Optimizer for Text Document Clustering.Hasan Rashaideh, Ahmad Sawaie, Mohammed Azmi Al-Betar, Laith Mohammad Abualigah, Mohammed M. Al-Laham, Ra’ed M. Al-Khatib & Malik Braik - 2019 - Journal of Intelligent Systems 29 (1):814-830.
    Text clustering problem is a leading process in many key areas such as information retrieval, text mining, and natural language processing. This presents the need for a potent document clustering algorithm that can be used effectively to navigate, summarize, and arrange information to congregate large data sets. This paper encompasses an adaptation of the grey wolf optimizer for TCP, referred to as TCP-GWO. The TCP demands a degree of accuracy beyond that which is possible with metaheuristic swarm-based algorithms. The main (...)
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  79.  1
    Proximal Support Vector Machine-Based Hybrid Approach for Edge Detection in Noisy Images.Rajendra K. Ray, Manoj Thakur, Deepak Kumar & Subit K. Jain - 2019 - Journal of Intelligent Systems 29 (1):1315-1328.
    We propose a novel edge detector in the presence of Gaussian noise with the use of proximal support vector machine. The edges of a noisy image are detected using a two-stage architecture: smoothing of image is first performed using regularized anisotropic diffusion, followed by the classification using PSVM, termed as regularized anisotropic diffusion-based PSVM method. In this process, a feature vector is formed for a pixel using the denoised coefficient’s class and the local orientations to detect edges in all possible (...)
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  80.  7
    Presentation of ACT/R-Rbf Hybrid Architecture to Develop Decision Making in Continuous and Non-Continuous Data.Nader Rezazadeh & Touraj Banirostam - 2019 - Journal of Intelligent Systems 29 (1):596-611.
    Computational models are based on symbolic architecture. For this reason, computational models function problematically in dynamic, noisy, and continuous environments. The ACT/r model is also problematic, as it is purely based on symbolic architecture like other computational models. The ACT/r decision-making process is based on the production operator on the input subject set. This approach firstly does not make a non-linear mapping between input and the decision-making result in ACT/r. Secondly, it is not possible to decide on the input subjects (...)
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  81.  6
    A Bayesian Multiresolution Approach for Noise Removal in Medical Magnetic Resonance Images.Sima Sahu, Harsh Vikram Singh, Basant Kumar & Amit Kumar Singh - 2019 - Journal of Intelligent Systems 29 (1):189-201.
    A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging. In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the signal parameters, (...)
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  82.  1
    Implementation of Improved Ship-Iceberg Classifier Using Deep Learning.Vadivel Sangili & Ankita Rane - 2019 - Journal of Intelligent Systems 29 (1):1514-1522.
    The application of synthetic aperture radar for ship and iceberg monitoring is important to carry out marine activities safely. The task of differentiating the two target classes, i.e. ship and iceberg, presents a challenge for operational scenarios. The dataset comprising SAR images of ship and iceberg poses a major challenge, as we are provided with a small number of labeled samples in the training set compared to a large number of unlabeled test samples. This paper proposes a semisupervised learning approach (...)
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  83.  10
    Blind Restoration Algorithm Using Residual Measures for Motion-Blurred Noisy Images.Mayana Shah & U. D. Dalal - 2019 - Journal of Intelligent Systems 29 (1):626-639.
    Image de-blurring is an inverse problem whose intent is to recover an image from the image affected badly with different environmental conditions. Usually, blurring can happen in various ways; however, de-blurring from a motion problem with or without noise can pose an important problem that is difficult to solve with less computation task. The quality of the restored image in iterative methods of blind motion de-blurring depends on the regularization parameter and the iteration number, which can be automatically or manually (...)
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  84.  10
    Opposition Intensity-Based Cuckoo Search Algorithm for Data Privacy Preservation.G. K. Shailaja & C. V. Guru Rao - 2019 - Journal of Intelligent Systems 29 (1):1441-1452.
    Privacy-preserving data mining is a novel approach that has emerged in the market to take care of privacy issues. The intention of PPDM is to build up data-mining techniques without raising the risk of mishandling of the data exploited to generate those schemes. The conventional works include numerous techniques, most of which employ some form of transformation on the original data to guarantee privacy preservation. However, these schemes are quite multifaceted and memory intensive, thus leading to restricted exploitation of these (...)
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  85.  17
    Cubic Ordered Weighted Distance Operator and Application in Group Decision-Making.Muhammad Shakeel, Saleem Abdullah & Rehan Ahmed - 2019 - Journal of Intelligent Systems 29 (1):440-458.
    Group decision-making is a very useful technique for ranking the group of alternatives. The ordered weighted distance operator is a new tool in group decision-making problems. In this paper, we apply the OWD operator on cubic information. We develop a new operator, the so-called cubic OWD operator, and study the different properties of it. We also discuss some particular cases of COWD. Finally, we develop a general algorithm for group decision-making problems using the COWD operator and give an application to (...)
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  86.  2
    Power Average Operators of Trapezoidal Cubic Fuzzy Numbers and Application to Multi-Attribute Group Decision Making.Muhammad Shakeel, Saleem Abdullah, Fazli Amin & Aliya Fahmi - 2019 - Journal of Intelligent Systems 29 (1):1643-1661.
    Trapezoidal cubic fuzzy numbers are an extraordinary cubic fuzzy set on a real number set. TzCFNs are useful for dealing with well-known quantities in decision data and decision making problems themselves. This paper is about multi-attribute group decision making problems in which the attribute values are stated with TzCFNs, which are solved by developing a new decision method based on power average operators of TzCFNs. The new operation laws for TzCFNs are given. Hereby, the power average operator of real numbers (...)
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  87.  10
    Approach to Multiple Attribute Group Decision Making Based on Hesitant Fuzzy Linguistic Aggregation Operators.Minghua Shi & Qingxian Xiao - 2019 - Journal of Intelligent Systems 29 (1):423-439.
    Inspired by the nonlinear weighted average operator, this paper proposes several generalized power average operators to aggregate hesitant fuzzy linguistic decision information. It is worth noting that the new operators take both the location and date weight information and the relative closeness of the decision-making information into consideration, a characteristic that results in objectivity and fairness in a group decision making. Moreover, we demonstrate some useful properties of the operators and discuss their associations. A new approach based on the designed (...)
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  88.  7
    Efficient Classification of DDoS Attacks Using an Ensemble Feature Selection Algorithm.Khundrakpam Johnson Singh & Tanmay De - 2019 - Journal of Intelligent Systems 29 (1):71-83.
    In the current cyber world, one of the most severe cyber threats are distributed denial of service attacks, which make websites and other online resources unavailable to legitimate clients. It is different from other cyber threats that breach security parameters; however, DDoS is a short-term attack that brings down the server temporarily. Appropriate selection of features plays a crucial role for effective detection of DDoS attacks. Too many irrelevant features not only produce unrelated class categories but also increase computation overhead. (...)
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  89.  10
    A Modified Jaya Algorithm for Mixed-Variable Optimization Problems.Prem Singh & Himanshu Chaudhary - 2019 - Journal of Intelligent Systems 29 (1):1007-1027.
    Mixed-variable optimization problems consist of the continuous, integer, and discrete variables generally used in various engineering optimization problems. These variables increase the computational cost and complexity of optimization problems due to the handling of variables. Moreover, there are few optimization algorithms that give a globally optimal solution for non-differential and non-convex objective functions. Initially, the Jaya algorithm has been developed for continuous variable optimization problems. In this paper, the Jaya algorithm is further extended for solving mixed-variable optimization problems. In the (...)
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  90.  6
    Universal Liver Extraction Algorithm: An Improved Chan–Vese Model.Sangeeta K. Siri & Mrityunjaya V. Latte - 2019 - Journal of Intelligent Systems 29 (1):237-250.
    Liver segmentation is important to speed up liver disease diagnosis. It is also useful for detection, recognition, and measurement of objects in liver images. Sufficient work has been carried out until now, but common methodology for segmenting liver image from CT scan, MRI scan, PET scan, etc., is not available. The proposed methodology is an effort toward developing a general algorithm to segment liver image from abdominal computerized tomography scan and magnetic resonance imaging scan images. In the proposed algorithm, pixel (...)
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  91.  10
    A Framework for Image Alignment of TerraSAR-X Images Using Fractional Derivatives and View Synthesis Approach.B. Sirisha, B. Sandhya, Chandra Sekhar Paidimarry & A. S. Chandrasekhara Sastry - 2019 - Journal of Intelligent Systems 29 (1):364-377.
    Conventional integer order differential operators suffer from poor feature detection accuracy and noise immunity, which leads to image misalignment. A new affine-based fractional order feature detection algorithm is proposed to detect syntactic and semantic structures from the backscattered signal of a TerraSAR-X band stripmap image. To further improve the alignment accuracy, we propose to adapt a view synthesis approach in the standard pipeline of feature-based image alignment. Experiments were performed to test the effectiveness and robustness of the view synthesis approach (...)
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  92.  10
    An Efficient Lossless ROI Image Compression Using Wavelet-Based Modified Region Growing Algorithm.P. Sreenivasulu & S. Varadarajan - 2019 - Journal of Intelligent Systems 29 (1):1063-1078.
    Nowadays, medical imaging and telemedicine are increasingly being utilized on a huge scale. The expanding interest in storing and sending medical images brings a lack of adequate memory spaces and transmission bandwidth. To resolve these issues, compression was introduced. The main aim of lossless image compression is to improve accuracy, reduce the bit rate, and improve the compression efficiency for the storage and transmission of medical images while maintaining an acceptable image quality for diagnosis purposes. In this paper, we propose (...)
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  93.  8
    Fault Signal Recognition in Power Distribution System Using Deep Belief Network.T. C. Srinivasa Rao, S. S. Tulasi Ram & J. B. V. Subrahmanyam - 2019 - Journal of Intelligent Systems 29 (1):459-474.
    Nowadays, electrical power system is considered as one of the most complicated artificial systems all over the globe, as social and economic development depends on intact, consistent, stable and economic functions. Owing to diverse random causes, accidental failures occur in electrical power systems. Considering this issue, this article aimed to propose the use of deep belief network in detecting and classifying fault signals such as transient, sag and swell in the transmission line. Here, wavelet-decomposed fault signals are extracted and the (...)
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  94.  1
    Cloud Security: LKM and Optimal Fuzzy System for Intrusion Detection in Cloud Environment.S. S. Sujatha & S. Immaculate Shyla - 2019 - Journal of Intelligent Systems 29 (1):1626-1642.
    In cloud security, intrusion detection system is one of the challenging research areas. In a cloud environment, security incidents such as denial of service, scanning, malware code injection, virus, worm, and password cracking are getting usual. These attacks surely affect the company and may develop a financial loss if not distinguished in time. Therefore, securing the cloud from these types of attack is very much needed. To discover the problem, this paper suggests a novel IDS established on a combination of (...)
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  95. Histopathological Image Segmentation Using Modified Kernel-Based Fuzzy C-Means and Edge Bridge and Fill Technique.Hosahally Narayangowda Suresh & Faiz Mohammad Karobari - 2019 - Journal of Intelligent Systems 29 (1):1301-1314.
    Histopathological lung cancer segmentation using region of interest is one of the emerging research area in the field of health monitoring system. In this paper, the histopathological images were collected from the database Stanford Tissue Microarray Database. After image collection, pre-processing was performed using a normalization technique, which enhances the quality of the histopathological image by eliminating unwanted noise. After pre-processing, segmentation was carried out using the modified kernel-based fuzzy c-means clustering approach along with the edge bridge and fill technique. (...)
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  96. Secure Communication Through MultiAgent System-Based Diabetes Diagnosing and Classification.Kiran Tangod & Gururaj Kulkarni - 2019 - Journal of Intelligent Systems 29 (1):703-718.
    The main objective of the research is to provide a multi-agent data mining system for diagnosing diabetes. Here, we use multi-agents for diagnosing diabetes such as user agent, connection agent, updation agent, and security agent, in which each agent performs their own task under the coordination of the connection agent. For secure communication, the user symptoms are encrypted with the help of Elliptic Curve Cryptography and Optimal Advanced Encryption Standard. In Optimal Advanced Encryption Standard algorithm, the key values are optimally (...)
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  97.  4
    A Novel Bio-Inspired Algorithm Based on Social Spiders for Improving Performance and Efficiency of Data Clustering.Ravi Chandran Thalamala, A. Venkata Swamy Reddy & B. Janet - 2019 - Journal of Intelligent Systems 29 (1):311-326.
    Since the last decade, the collective intelligent behavior of groups of animals, birds or insects have attracted the attention of researchers. Swarm intelligence is the branch of artificial intelligence that deals with the implementation of intelligent systems by taking inspiration from the collective behavior of social insects and other societies of animals. Many meta-heuristic algorithms based on aggregative conduct of swarms through complex interactions with no supervision have been used to solve complex optimization problems. Data clustering organizes data into groups (...)
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  98.  9
    Predict Forex Trend Via Convolutional Neural Networks.Yun-Cheng Tsai, Jun-Hao Chen & Jun-Jie Wang - 2019 - Journal of Intelligent Systems 29 (1):941-958.
    Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts. This study uses the characteristics of deep learning to train computers in imitating this kind of intuition in the context of trading charts. The main goal of our approach is combining the time-series modeling and convolutional neural networks to build a trading model. We propose three steps to build the trading model. First, we preprocess the input data from quantitative data to images. (...)
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  99.  1
    Secure Fingerprint Authentication Using Deep Learning and Minutiae Verification.S. Vadivel, Saad Bayezeed & V. M. Praseetha - 2019 - Journal of Intelligent Systems 29 (1):1379-1387.
    Nowadays, there has been an increase in security concerns regarding fingerprint biometrics. This problem arises due to technological advancements in bypassing and hacking methodologies. This has sparked the need for a more secure platform for identification. In this paper, we have used a deep Convolutional Neural Network as a pre-verification filter to filter out bad or malicious fingerprints. As deep learning allows the system to be more accurate at detecting and reducing false identification by training itself again and again with (...)
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  100.  5
    Fuzzy Adaptive Genetic Algorithm for Improving the Solution of Industrial Optimization Problems.Marco Vannucci, Valentina Colla, Stefano Dettori & Vincenzo Iannino - 2019 - Journal of Intelligent Systems 29 (1):409-422.
    In the industrial and manufacturing fields, many problems require tuning of the parameters of complex models by means of exploitation of empirical data. In some cases, the use of analytical methods for the determination of such parameters is not applicable; thus, heuristic methods are employed. One of the main disadvantages of these approaches is the risk of converging to “suboptimal” solutions. In this article, the use of a novel type of genetic algorithm is proposed to overcome this drawback. This approach (...)
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  101. Analogy-Based Approaches to Improve Software Project Effort Estimation Accuracy.S. Vijayalakshmi & V. Resmi - 2019 - Journal of Intelligent Systems 29 (1):1468-1479.
    In the discipline of software development, effort estimation renders a pivotal role. For the successful development of the project, an unambiguous estimation is necessitated. But there is the inadequacy of standard methods for estimating an effort which is applicable to all projects. Hence, to procure the best way of estimating the effort becomes an indispensable need of the project manager. Mathematical models are only mediocre in performing accurate estimation. On that account, we opt for analogy-based effort estimation by means of (...)
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  102.  1
    Intelligent Systems for Structural Damage Assessment.Eleni Vrochidou, Petros-Fotios Alvanitopoulos, Ioannis Andreadis & Anaxagoras Elenas - 2019 - Journal of Intelligent Systems 29 (1):378-392.
    This research provides a comparative study of intelligent systems in structural damage assessment after the occurrence of an earthquake. Seismic response data of a reinforced concrete structure subjected to 100 different levels of seismic excitation are utilized to study the structural damage pattern described by a well-known damage index, the maximum inter-story drift ratio. Through a time-frequency analysis of the accelerograms, a set of seismic features is extracted. The aim of this study is to analyze the performance of three different (...)
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  103.  5
    Improvements in Spoken Query System to Access the Agricultural Commodity Prices and Weather Information in Kannada Language/Dialects.Thimmaraja G. Yadava & H. S. Jayanna - 2019 - Journal of Intelligent Systems 29 (1):664-687.
    In this paper, the improvements in the recently developed end to end spoken query system to access the agricultural commodity prices and weather information in Kannada language/dialects is demonstrated. The spoken query system consists of interactive voice response system call flow, automatic speech recognition models and agricultural commodity prices, and weather information databases. The task specific speech data used in the earlier spoken query system had a high level of background and other types of noises as it is collected from (...)
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  104.  11
    Video Steganography Using Knight Tour Algorithm and LSB Method for Encrypted Data.Zeyad Safaa Younus & Ghada Thanoon Younus - 2019 - Journal of Intelligent Systems 29 (1):1216-1225.
    This paper aims to propose a method for data hiding in video by utilizing the least significant bit method and improving it by utilizing the knight tour algorithm for concealing the data inside the AVI video file and using a key function encryption method for encrypting the secret message. First, the secret message is encrypted by utilizing a mathematical equation. The key used in the equation is a set of random numbers. These numbers differ in each implementation to warrant the (...)
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  105.  12
    A Kernel Probabilistic Model for Semi-Supervised Co-Clustering Ensemble.Yinghui Zhang - 2019 - Journal of Intelligent Systems 29 (1):143-153.
    Co-clustering is used to analyze the row and column clusters of a dataset, and it is widely used in recommendation systems. In general, different co-clustering models often obtain very different results for a dataset because each algorithm has its own optimization criteria. It is an alternative way to combine different co-clustering results to produce a final one for improving the quality of co-clustering. In this paper, a semi-supervised co-clustering ensemble is illustrated in detail based on semi-supervised learning and ensemble learning. (...)
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  106.  4
    License Plate Recognition in Urban Road Based on Vehicle Tracking and Result Integration.Liping Zhu, Shang Wang, Chengyang Li & Zhongguo Yang - 2019 - Journal of Intelligent Systems 29 (1):1587-1597.
    Multiple surveillance cameras provide huge video resources that need further mining to collect traffic stream data such as license plate recognition. However, these surveillance cameras have limited spatial resolution, which may not always suffice to precisely recognize license plates by existing LPR systems. This work is focused on the LPR method in low-quality images from surveillance video screenshots on urban road. The methodology we proposed is based on vehicle tracking and result integration, and we recognize the plate with an end-to-end (...)
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  107.  2
    Mitral Regurgitation Severity Analysis Based on Features and Optimal HE (OHE) with Quantification Using PISA Method.Pinjari Abdul Khayum & Reddy Pogu Sudheer Babu - 2019 - Journal of Intelligent Systems 28 (5):777-789.
    Heart disease is the foremost reason for death and also the main source of incapability in the developed nations. Mitral regurgitation is a typical heart disease that does not bring about manifestations until its end position. In view of the hidden etiologies of heart distress, functional MR can be partitioned into two subgroups, ischemic and no ischemic MR. A procedure is progressed for jet area separation and quantification in MR evaluation in arithmetical expressions. Thus, a strategy that depends on echocardiography (...)
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  108. Mathematical Model Using Soft Computing Techniques for Different Thermal Insulation Materials.Anil Kumar Dixit, Manmatha K. Roul & Bikash C. Panda - 2019 - Journal of Intelligent Systems 28 (5):821-833.
    The property of low thermal transmission of the small air gap between the constituents of combined material has been utilized to obtain energy-efficient wall sections. Ferro-cement is a highly versatile form of reinforced concrete made up of wire mesh, sand, water, and cement, which possesses unique qualities of strength and serviceability. The significant intention of the proposed technique is to frame a mathematical model with the aid of optimization techniques. Mathematical modeling is done by minimizing the cost and time consumed (...)
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  109.  2
    Precursor Selection for Sol–Gel Synthesis of Titanium Carbide Nanopowders by a New Cubic Fuzzy Multi-Attribute Group Decision-Making Model.Aliya Fahmi, Saleem Abdullah, Fazli Amin & Asad Ali - 2019 - Journal of Intelligent Systems 28 (5):699-720.
    In this paper, we construct an extended version of the TOPSIS method by using cubic information, and provide a numerical application to verify and demonstrate the practicality of the method. A new extension of the gray relation analysis method is introduced by using cubic information. We also propose the cubic fuzzy multi-attribute group decision-making model, and the relation between the cubic TOPSIS method and the cubic gray relation analysis method is introduced. Finally, the proposed method is used for selection in (...)
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  110.  8
    Substation Equipment 3D Identification Based on KNN Classification of Subspace Feature Vector.Weiying Guo, Yong Ji, Yong Luo & Yan Zhou - 2019 - Journal of Intelligent Systems 28 (5):807-819.
    Aiming to realize rapid and efficient three-dimensional identification of substation equipment, this article proposes a new method in which the 3D identification of substation equipment is based on K-nearest neighbor classification of subspace feature vector. First of all, the article uses octree encoding to reduce and denoise the point cloud data obtained by a 3D laser scanner. Secondly, position calibration and size standardization are used for the point cloud after pretreatment. Then, the normalized point cloud is divided into a number (...)
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  111.  1
    Prediction Method of Railway Freight Volume Based on Genetic Algorithm Improved General Regression Neural Network.Zhi-da Guo & Jing-Yuan Fu - 2019 - Journal of Intelligent Systems 28 (5):835-848.
    Railway freight transportation is an important part of the national economy. The accurate forecast of railway freight volume is significant to the planning, construction, operation, and decision-making of railways. Railway freight volume forecasting methods are complex and nonlinear due to the imbalance of supply and demand in the railway freight market as well as the complicated and different influences of various factors on freight volume. The relation between some information is easily ignored when the traditional method of railway freight volume (...)
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  112.  8
    Tree Physiology Optimization in Benchmark Function and Traveling Salesman Problem.A. Hanif Halim & I. Ismail - 2019 - Journal of Intelligent Systems 28 (5):849-871.
    Nature has the ability of sustainability and improvisation for better survival. This unique characteristic reflects a pattern of optimization that inspires the computational intelligence toward different scopes of optimization: a nondeterministic optimization approach or a nature-inspired metaheuristic algorithm. To date, there are many metaheuristic algorithms introduced with good promising results and also becoming a powerful method for solving numerous optimization problems. In this paper, a new metaheuristic algorithm inspired from a plant growth system is proposed, which is defined as tree (...)
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  113.  3
    PCI-PSO: Preference-Based Component Identification Using Particle Swarm Optimization.Seyed Mohammad Hossein Hasheminejad & Shabnam Gholamshahi - 2019 - Journal of Intelligent Systems 28 (5):733-748.
    Nowadays, component identification is one of the main challenges of software analysis and design. The component identification process aims at clustering classes into components and subcomponents. There are a number of methods to identify components in the literature; however, most of them cannot be customized to software architect’s preferences. To address this limitation, in this paper, we propose a preference-based method by the name of preference-based component identification using particle swarm optimization to identify logical components. PCI-PSO provides a novel method (...)
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  114. Particle Swarm Optimization-Enhanced Twin Support Vector Regression for Wind Speed Forecasting.Essam H. Houssein - 2019 - Journal of Intelligent Systems 28 (5):905-914.
    Wind energy is considered one of the renewable energy sources that minimize the cost of electricity production. This article proposes a hybrid approach based on particle swarm optimization and twin support vector regression for forecasting wind speed. To enhance the forecasting accuracy, TSVR was utilized to forecast the wind speed, and the optimal settings of TSVR parameters were optimized by PSO carefully. Moreover, to estimate the performance of the suggested approach, three wind speed benchmark data of OpenEI were used as (...)
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  115.  1
    Modified and Optimized Method for Segmenting Pulmonary Parenchyma in CT Lung Images, Based on Fractional Calculus and Natural Selection.S. Pramod Kumar & Mrityunjaya V. Latte - 2019 - Journal of Intelligent Systems 28 (5):721-732.
    Computer-aided diagnosis of lung segmentation is the fundamental requirement to diagnose lung diseases. In this paper, a two-dimensional Otsu algorithm by Darwinian particle swarm optimization and fractional-order Darwinian particle swarm optimization is proposed to segment the pulmonary parenchyma from the lung image obtained through computed tomography scans. The proposed method extracts pulmonary parenchyma from multi-sliced CT. This is a preprocessing step to identify pulmonary diseases such as emphysema, tumor, and lung cancer. Image segmentation plays a significant role in automated pulmonary (...)
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  116.  9
    Performance Evaluation of Modified Color Image Steganography Using Discrete Wavelet Transform.Vijay Kumar & Dinesh Kumar - 2019 - Journal of Intelligent Systems 28 (5):749-758.
    Steganography is the foremost influential approach to hide data. Images serve as the most appropriate cover media for steganography. This paper intends to do a performance evaluation of color images and its comparison with the recently proposed approaches, using the modified technique already proposed for grayscale images, by the authors. This approach hides large data in color image using the blocking concept. The blocking process is applied on approximation coefficients of secret image and detail coefficients of red, green and blue (...)
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  117.  1
    Forecasting Air Quality Index Using an Ensemble of Artificial Neural Networks and Regression Models.S. Sankar Ganesh, Pachaiyappan Arulmozhivarman & Rao Tatavarti - 2019 - Journal of Intelligent Systems 28 (5):893-903.
    Air is the most essential constituent for the sustenance of life on earth. The air we inhale has a tremendous impact on our health and well-being. Hence, it is always advisable to monitor the quality of air in our environment. To forecast the air quality index, artificial neural networks trained with conjugate gradient descent, such as multilayer perceptron, cascade forward neural network, Elman neural network, radial basis function neural network, and nonlinear autoregressive model with exogenous input along with regression models (...)
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  118.  1
    Design and Development of a Multiobjective Cost Function for Robust Video Watermarking Using Wavelet Transform.Amir M. U. Wagdarikar & Ranjan K. Senapati - 2019 - Journal of Intelligent Systems 28 (5):873-891.
    Watermarking is the process of concealing the secret message into multimedia sources, such as audio, image, and video. Rather than other steganography, video watermarking is mainly focused on the robustness of the system. In this paper, we propose the multiobjective cost function for video watermarking. Initially, the cover image is subjected into cost function computation. Then, the cost function is newly designed and developed by various objectives, such as intensity, energy, edge, coverage, and brightness. Subsequently, the wavelet transform is applied (...)
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  119.  3
    Non-Dominated Sorting Genetic Algorithms for a Multi-Objective Resource Constraint Project Scheduling Problem.Xixi Wang, Farouk Yalaoui & Frédéric Dugardin - 2019 - Journal of Intelligent Systems 28 (5):791-806.
    The resource constraint project scheduling problem has attracted growing attention since the last decades. Precedence constraints are considered as well as resources with limited capacities. During the project, the same resource can be required by several in-process jobs and it is compulsory to ensure that the consumptions do not exceed the limited capacities. In this paper, several criteria are involved, namely makespan, total job tardiness, and workload balancing level. Our problem is firstly solved by the non-dominated sorting genetic algorithm-II as (...)
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  120.  7
    Pythagorean Hesitant Fuzzy Hamacher Aggregation Operators in Multiple-Attribute Decision Making.Guiwu Wei & Mao Lu - 2019 - Journal of Intelligent Systems 28 (5):759-776.
    The Hamacher product is a t-norm and the Hamacher sum is a t-conorm. They are good alternatives to the algebraic product and the algebraic sum, respectively. Nevertheless, it seems that most of the existing hesitant fuzzy aggregation operators are based on algebraic operations. In this paper, we utilize Hamacher operations to develop some Pythagorean hesitant fuzzy aggregation operators: Pythagorean hesitant fuzzy Hamacher weighted average operator, Pythagorean hesitant fuzzy Hamacher weighted geometric operator, Pythagorean hesitant fuzzy Hamacher ordered weighted average operator, Pythagorean (...)
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  121.  12
    A Hybrid Cuckoo Search and Simulated Annealing Algorithm.Faisal Alkhateeb & Bilal H. Abed-Alguni - 2019 - Journal of Intelligent Systems 28 (4):683-698.
    Simulated annealing proved its success as a single-state optimization search algorithm for both discrete and continuous problems. On the contrary, cuckoo search is one of the well-known population-based search algorithms that could be used for optimizing some problems with continuous domains. This paper provides a hybrid algorithm using the CS and SA algorithms. The main goal behind our hybridization is to improve the solutions generated by CS using SA to explore the search space in an efficient manner. More precisely, we (...)
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  122. Multiple-Reservoir Scheduling Using Β-Hill Climbing Algorithm.Emad Alsukni, Omar Suleiman Arabeyyat, Mohammed A. Awadallah, Laaly Alsamarraie, Iyad Abu-Doush & Mohammed Azmi Al-Betar - 2019 - Journal of Intelligent Systems 28 (4):559-570.
    The multi-reservoir systems optimization problem requires defining a set of rules to recognize the water amount stored and released in accordance with the system constraints. Traditional methods are not suitable for complex multi-reservoir systems with high dimensionality. Recently, metaheuristic-based algorithms such as evolutionary algorithms and local search-based algorithms are successfully used to solve the multi-reservoir systems. β-hill climbing is a recent metaheuristic local search-based algorithm. In this paper, the multi-reservoir systems optimization problem is tackled using β-hill climbing. In order to (...)
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  123.  4
    Extracting Conceptual Relationships and Inducing Concept Lattices From Unstructured Text.V. S. Anoop & S. Asharaf - 2019 - Journal of Intelligent Systems 28 (4):669-681.
    Concept and relationship extraction from unstructured text data plays a key role in meaning aware computing paradigms, which make computers intelligent by helping them learn, interpret, and synthesis information. These concepts and relationships leverage knowledge in the form of ontological structures, which is the backbone of semantic web. This paper proposes a framework that extracts concepts and relationships from unstructured text data and then learns lattices that connect concepts and relationships. The proposed framework uses an off-the-shelf tool for identifying common (...)
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  124.  8
    Nurse Scheduling with Opposition-Based Parallel Harmony Search Algorithm.Ece Cetin Yagmur & Ahmet Sarucan - 2019 - Journal of Intelligent Systems 28 (4):633-647.
    One of the advances made in the management of human resources for the effective implementation of service delivery is the creation of personnel schedules. In this context, especially in terms of the majority of health-care systems, creating nurse schedules comes to the fore. Nurse scheduling problem is a complex optimization problem that allows for the preparation of an appropriate schedule for nurses and, in doing so, considers the system constraints such as legal regulations, nurses’ preferences, and hospital policies and requirements. (...)
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  125.  6
    Fusion Algorithm of Multi-Focus Images with Weighted Ratios and Weighted Gradient Based on Wavelet Transform.Wei-bin Chen, Mingxiao Hu, Lai Zhou, Hongbin Gu & Xin Zhang - 2019 - Journal of Intelligent Systems 28 (4):505-516.
    Multi-focus image fusion means fusing a completely clear image with a set of images of the same scene and under the same imaging conditions with different focus points. In order to get a clear image that contains all relevant objects in an area, the multi-focus image fusion algorithm is proposed based on wavelet transform. Firstly, the multi-focus images were decomposed by wavelet transform. Secondly, the wavelet coefficients of the approximant and detail sub-images are fused respectively based on the fusion rule. (...)
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  126. A Multi-Agents System for Solving Facility Layout Problem: Application to Operating Theater.Abdelahad Chraibi, Said Kharraja, Ibrahim H. Osman & Omar Elbeqqali - 2019 - Journal of Intelligent Systems 28 (4):601-619.
    Facility layout problem has a great impact on the efficiency of any organization. It is concerned with defining the optimal location for each facility in order to optimize the supply chain productivity. In this kind of problems, the choice of resolution approach depends on the complexity and the size of the problem. Operating theaters are generally big structures containing a lot of facilities, which makes the conception of their layout a complex problem. In the literature, exact methods are powerless when (...)
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  127.  4
    Fuzzy Approach to Decision Support System Design for Inventory Control and Management.Mohuya Dev, Prabjot Kaur & Kandarpa Kumar Sarma - 2019 - Journal of Intelligent Systems 28 (4):549-557.
    The ubiquitous nature of inventory and its reliance on a reliable decision support system is crucial for ensuring continuous availability of goods. The DSS needs to be designed in a manner that enables it to highlight its present status. Further, the DSS should be able to provide indications about subtle and large-scale variations that are likely to occur in the supply chain within the context of the decision-making framework and inventory management. However, while dealing with the parameters of the system, (...)
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  128. Global Research Trends of Intuitionistic Fuzzy Set: A Bibliometric Analysis.Xiaorong He & Yingyu Wu - 2019 - Journal of Intelligent Systems 28 (4):621-631.
    Despite the fast growth of intuitionistic fuzzy publications, only a small part of these groundbreaking researches have significantly impacted the field. The main purpose of this paper was to identify and investigate the 100 most cited publications in the intuitionistic fuzzy field. Topic search based on the keyword “intuitionistic fuzzy” in the Science Citation Index and Social Sciences Citation Index databases was conducted to identify the 100 most cited articles. Bibliometric analysis methods were employed to describe these articles from different (...)
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  129.  5
    An Efficient Adaptive Filter for Fetal ECG Extraction Using Neural Network.Abdullah Mohammed Kaleem & Rajendra D. Kokate - 2019 - Journal of Intelligent Systems 28 (4):589-600.
    Fetal electrocardiogram checking is a strategy for acquiring critical data about the state of the fetus during pregnancy and labor. This is done by measuring electrical signals created by the fetal heart as measured from multichannel potential recordings on the mother’s body surface. In any case, extraction of fetal signal is difficult because the signal is marred by the mother’s heartbeat signal. Subsequently, in this paper, a powerful versatile filtering strategy is utilized to eliminate the mother’s heartbeat signal with the (...)
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  130.  2
    Some Innovative Types of Fuzzy Ideals in AG-Groupoids.Faiz Muhammad Khan, Hidayat Ullah Khan, Safyan Mukhtar, Asghar Khan & Nor Haniza Sarmin - 2019 - Journal of Intelligent Systems 28 (4):649-667.
    AG-groupoids are basic structures in Flocks theory. This theory mainly focuses on distance optimization, motion replication, and leadership maintenance with a wide range of applications in physics and biology. In this paper, we define some new types of fuzzy ideals of AG-groupoids called -fuzzy bi-ideals, -fuzzy interior ideals, -fuzzy bi-ideals, and -fuzzy interior ideals, where α, β∈{∈γ, qδ, ∈γ∨qδ, ∈γ∧qδ} and ᾱ, β̄∈{⋶γ, q̄δ, ⋶γ∨q̄δ, ⋶γ∧q̄δ}, with α≠∈γ∧qδ and β̄≠⋶γ∧q̄δ. An important milestone achieved by this paper is providing the connection (...)
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  131.  9
    Combining Wavelet Texture Features and Deep Neural Network for Tumor Detection and Segmentation Over MRI.Srinivasalu Preethi & Palaniappan Aishwarya - 2019 - Journal of Intelligent Systems 28 (4):571-588.
    A brain tumor is one of the main reasons for death among other kinds of cancer because the brain is a very sensitive, complex, and central portion of the body. Proper and timely diagnosis can prolong the life of a person to some extent. Consequently, in this paper, we have proposed a brain tumor classification scheme on the basis of combining wavelet texture features and deep neural networks. Normally, the system comprises four modules: feature extraction, feature selection, tumor classification, and (...)
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  132.  2
    A Novel Approach to Extract Exact Liver Image Boundary From Abdominal CT Scan Using Neutrosophic Set and Fast Marching Method.Sangeeta K. Siri & Mrityunjaya V. Latte - 2019 - Journal of Intelligent Systems 28 (4):517-532.
    Liver segmentation from abdominal computed tomography scan images is a complicated and challenging task. Due to the haziness in the liver pixel range, the neighboring organs of the liver have the same intensity level and existence of noise. Segmentation is necessary in the detection, identification, analysis, and measurement of objects in CT scan images. A novel approach is proposed to meet the challenges in extracting liver images from abdominal CT scan images. The proposed approach consists of three phases: preprocessing, CT (...)
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  133.  7
    A Fast Segmentation and Efficient Slice Reconstruction Technique for Head CT Images.A. A. Haseena Thasneem, M. Mohamed Sathik & R. Mehaboobathunnisa - 2019 - Journal of Intelligent Systems 28 (4):533-547.
    The three-dimensional reconstruction of medical images usually requires hundreds of two-dimensional scan images. Segmentation, an obligatory part in reconstruction, needs to be performed for all the slices consuming enormous storage space and time. To reduce storage space and time, this paper proposes a three-stage procedure, namely, slice selection, segmentation and interpolation. The methodology will have the potential to 3D reconstruct the human head from minimum selected slices. The first stage of slice selection is based on structural similarity measurement, discarding the (...)
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  134.  10
    Neural Network-Based Architecture for Sentiment Analysis in Indian Languages.Rupal Bhargava, Shivangi Arora & Yashvardhan Sharma - 2019 - Journal of Intelligent Systems 28 (3):361-375.
    Sentiment analysis refers to determining the polarity of the opinions represented by text. The paper proposes an approach to determine the sentiments of tweets in one of the Indian languages. Thirty-nine sequential models have been created using three different neural network layers [recurrent neural networks, long short-term memory, convolutional neural network ] with optimum parameter settings. These sequential models have been investigated for each of the three languages. The proposed sequential models are experimented to identify how the hidden layers affect (...)
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  135.  5
    Composite Sequential Modeling for Identifying Fake Reviews.Rupal Bhargava, Anushka Baoni & Yashvardhan Sharma - 2019 - Journal of Intelligent Systems 28 (3):409-422.
    This paper presents a comprehensive analysis and comparison of various proposed sequential models based on different deep networks such as the convolutional neural network, long short-term memory, and recurrent neural network. The different sequential models are analyzed based on the number of layers, the number of output dimensions, order, and the combination of different deep network architectures. The proposed approach is compared to a baseline model based on traditional machine learning techniques.
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  136.  8
    Deep Learning-Based Language Identification in English-Hindi-Bengali Code-Mixed Social Media Corpora.Anupam Jamatia, Amitava Das & Björn Gambäck - 2019 - Journal of Intelligent Systems 28 (3):399-408.
    This article addresses language identification at the word level in Indian social media corpora taken from Facebook, Twitter and WhatsApp posts that exhibit code-mixing between English-Hindi, English-Bengali, as well as a blend of both language pairs. Code-mixing is a fusion of multiple languages previously mainly associated with spoken language, but which social media users also deploy when communicating in ways that tend to be rather casual. The coarse nature of code-mixed social media text makes language identification challenging. Here, the performance (...)
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  137.  7
    Development of Telugu-Tamil Transfer-Based Machine Translation System: An Improvization Using Divergence Index.Parameswari Krishnamurthy - 2019 - Journal of Intelligent Systems 28 (3):493-504.
    Building an automatic, high-quality, robust machine translation system is a fascinating yet an arduous task, as one of the major difficulties lies in cross-linguistic differences or divergences between languages at various levels. The existence of translation divergence precludes straightforward mapping in the MT system. An increase in the number of divergences also increases the complexity, especially in linguistically motivated transfer-based MT systems. This paper discusses the development of Telugu-Tamil transfer-based MT and how a divergence index is built to quantify the (...)
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  138.  6
    An Overview of the Shared Task on Machine Translation in Indian Languages – 2017.M. Anand Kumar, B. Premjith, Shivkaran Singh, S. Rajendran & K. P. Soman - 2019 - Journal of Intelligent Systems 28 (3):455-464.
    In recent years, the multilingual content over the internet has grown exponentially together with the evolution of the internet. The usage of multilingual content is excluded from the regional language users because of the language barrier. So, machine translation between languages is the only possible solution to make these contents available for regional language users. Machine translation is the process of translating a text from one language to another. The machine translation system has been investigated well already in English and (...)
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  139.  12
    Deep Learning Based Part-of-Speech Tagging for Malayalam Twitter Data.S. Kumar, M. Anand Kumar & K. P. Soman - 2019 - Journal of Intelligent Systems 28 (3):423-435.
    The paper addresses the problem of part-of-speech tagging for Malayalam tweets. The conversational style of posts/tweets/text in social media data poses a challenge in using general POS tagset for tagging the text. For the current work, a tagset was designed that contains 17 coarse tags and 9915 tweets were tagged manually for experiment and evaluation. The tagged data were evaluated using sequential deep learning methods like recurrent neural network, gated recurrent units, long short-term memory, and bidirectional LSTM. The training of (...)
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  140.  8
    MTIL2017: Machine Translation Using Recurrent Neural Network on Statistical Machine Translation.Sainik Kumar Mahata, Dipankar Das & Sivaji Bandyopadhyay - 2019 - Journal of Intelligent Systems 28 (3):447-453.
    Machine translation is the automatic translation of the source language to its target language by a computer system. In the current paper, we propose an approach of using recurrent neural networks over traditional statistical MT. We compare the performance of the phrase table of SMT to the performance of the proposed RNN and in turn improve the quality of the MT output. This work has been done as a part of the shared task problem provided by the MTIL2017. We have (...)
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  141.  9
    Neural Machine Translation for Indian Languages.Amarnath Pathak & Partha Pakray - 2019 - Journal of Intelligent Systems 28 (3):465-477.
    Machine Translation bridges communication barriers and eases interaction among people having different linguistic backgrounds. Machine Translation mechanisms exploit a range of techniques and linguistic resources for translation prediction. Neural machine translation, in particular, seeks optimality in translation through training of neural network, using a parallel corpus having a considerable number of instances in the form of a parallel running source and target sentences. Easy availability of parallel corpora for major Indian language forms and the ability of NMT systems to better (...)
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  142.  12
    Sentiment Polarity Detection in Bengali Tweets Using Deep Convolutional Neural Networks.Kamal Sarkar - 2019 - Journal of Intelligent Systems 28 (3):377-386.
    Sentiment polarity detection is one of the most popular sentiment analysis tasks. Sentiment polarity detection in tweets is a more difficult task than sentiment polarity detection in review documents, because tweets are relatively short and they contain limited contextual information. Although the amount of blog posts, tweets and comments in Indian languages is rapidly increasing on the web, research on sentiment analysis in Indian languages is at the early stage. In this paper, we present an approach that classifies the sentiment (...)
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  143.  5
    Verb Phrases Alignment Technique for English-Malayalam Parallel Corpus in Statistical Machine Translation Special Issue on MTIL 2017.Mary Priya Sebastian & G. Santhosh Kumar - 2019 - Journal of Intelligent Systems 28 (3):479-492.
    Machine translation from English to foreign languages is a fast developing area of research, and various techniques of translation are discussed in the literature. However, translation from English to Malayalam, a Dravidian language, is still in the rising stage, and works in this field have not flourished to a great extent, so far. The main reason of this shortcoming is the non-availability of linguistic resources and translation tools in the Malayalam language. A parallel corpus with alignment is one of such (...)
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  144.  7
    Neural Machine Translation System for English to Indian Language Translation Using MTIL Parallel Corpus.K. P. Soman, M. Anand Kumar & B. Premjith - 2019 - Journal of Intelligent Systems 28 (3):387-398.
    Introduction of deep neural networks to the machine translation research ameliorated conventional machine translation systems in multiple ways, specifically in terms of translation quality. The ability of deep neural networks to learn a sensible representation of words is one of the major reasons for this improvement. Despite machine translation using deep neural architecture is showing state-of-the-art results in translating European languages, we cannot directly apply these algorithms in Indian languages mainly because of two reasons: unavailability of the good corpus and (...)
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  145.  8
    Segmentation of Brain Tumour Based on Clustering Technique: Performance Analysis.Ritu Agrawal, Manisha Sharma & Bikesh Kumar Singh - 2019 - Journal of Intelligent Systems 28 (2):291-306.
    Manual detection and analysis of brain tumours is an exhaustive and time-consuming process. Further, it is subject to intra-observer and inter-observer variabilities. Automated brain tumour segmentation and analysis has thus gained much attention in recent years. However, the existing segmentation techniques do not meet the requirements of real-time use due to limitations posed by poor image quality and image complexity. This article proposes a hybrid approach for image segmentation by combining biorthogonal wavelet transform, skull stripping, fuzzy c-means threshold clustering, Canny (...)
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  146.  10
    Analysis of the Use of Background Distribution for Naive Bayes Classifiers.Daniel Andrade, Akihiro Tamura & Masaaki Tsuchida - 2019 - Journal of Intelligent Systems 28 (2):259-273.
    The naive Bayes classifier is a popular classifier, as it is easy to train, requires no cross-validation for parameter tuning, and can be easily extended due to its generative model. Moreover, recently it was shown that the word probabilities estimated from large unlabeled corpora could be used to improve the parameter estimation of naive Bayes. However, previous methods do not explicitly allow to control how much the background distribution can influence the estimation of naive Bayes parameters. In contrast, we investigate (...)
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  147.  11
    Evaluation of Flexible Manufacturing Systems Using a Hesitant Group Decision Making Approach.Beyzanur Cayir Ervural, Bilal Ervural & Özgür Kabak - 2019 - Journal of Intelligent Systems 28 (2):245-258.
    Flexible manufacturing systems are capable of processing various parts, styles, and quantities of production in manufacturing systems. It is a quite complex process for companies to decide the appropriate FMS design as it involves multiple and conflicting criteria and multiple decision makers under various uncertainties. The fuzzy set theory offers an efficient tool to cope with vagueness and to define performance measurement of FMS in a multi-attribute group decision making framework. In this study, we present a MAGDM approach based on (...)
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  148.  7
    An Improved Correlation Coefficient of Intuitionistic Fuzzy Sets.Han-Liang Huang & Yuting Guo - 2019 - Journal of Intelligent Systems 28 (2):231-243.
    The intuitionistic fuzzy set is a useful tool to deal with vagueness and uncertainty. Correlation coefficient of the intuitionistic fuzzy sets is an important measure in intuitionistic fuzzy set theory and has great practical potential in a variety of areas, such as decision making, medical diagnosis, pattern recognition, etc. In this paper, an improved correlation coefficient of the intuitionistic fuzzy sets is defined, and it can overcome some drawbacks of the existing ones. The properties of this correlation coefficient are discussed. (...)
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  149.  7
    Interval-Valued Intuitionistic Fuzzy Confidence Intervals.Cengiz Kahraman, Basar Oztaysi & Sezi Cevik Onar - 2019 - Journal of Intelligent Systems 28 (2):307-319.
    Confidence intervals are useful tools for statistical decision-making purposes. In case of incomplete and vague data, fuzzy confidence intervals can be used for decision making under uncertainty. In this paper, we develop interval-valued intuitionistic fuzzy confidence intervals for population mean, population proportion, differences in means of two populations, and differences in proportions of two populations. The developed IVIF intervals can be used in cases of both finite and infinite population sizes. The developed fuzzy confidence intervals are equivalent decision-making tools to (...)
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  150.  3
    Fully Automated Segmentation of Lung Parenchyma Using Break and Repair Strategy.S. Pramod Kumar & Mrityunjaya V. Latte - 2019 - Journal of Intelligent Systems 28 (2):275-289.
    The traditional segmentation methods available for pulmonary parenchyma are not accurate because most of the methods exclude nodules or tumors adhering to the lung pleural wall as fat. In this paper, several techniques are exhaustively used in different phases, including two-dimensional optimal threshold selection and 2D reconstruction for lung parenchyma segmentation. Then, lung parenchyma boundaries are repaired using improved chain code and Bresenham pixel interconnection. The proposed method of segmentation and repairing is fully automated. Here, 21 thoracic computer tomography slices (...)
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  151.  6
    Preventive Maintenance Optimization and Comparison of Genetic Algorithm Models in a Series–Parallel Multi-State System.Imane Maatouk, Iman Jarkass, Eric Châtelet & Nazir Chebbo - 2019 - Journal of Intelligent Systems 28 (2):219-230.
    In this research, different optimization models are developed to solve the preventive maintenance optimization problem in a maintainable multi-state series–parallel system. The objective is to determine for each component in the system the maintenance period minimizing a cost function under the constraint of required availability and for a specified horizon of time. Four genetic models based on the cost associated with maintenance schedule and availability characteristic parameters are constructed and analyzed. They are genetic algorithm, hybridization GA and local search, fuzzy (...)
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  152.  7
    An Optimized Face Recognition System Using Cuckoo Search.Preeti Malhotra & Dinesh Kumar - 2019 - Journal of Intelligent Systems 28 (2):321-332.
    The development of an effective and efficient face recognition system has always been a challenging task for researchers. In a face recognition system, feature selection is one of the most vital processes to achieve maximum accuracy by removing irrelevant and superfluous data. Many optimization techniques, such as particle swarm optimization, genetic algorithm, ant colony optimization, etc., have been implemented in face recognition systems mainly based on two feature extraction methods: discrete cosine transform and principal component analysis. In this research, a (...)
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  153.  12
    Iterated Local Search for Time-Extended Multi-Robot Task Allocation with Spatio-Temporal and Capacity Constraints.Hakim Mitiche, Dalila Boughaci & Maria Gini - 2019 - Journal of Intelligent Systems 28 (2):347-360.
    We propose a method for task allocation to multiple physical agents that works when tasks have temporal and spatial constraints and agents have different capacities. Assuming that the problem is over-constrained, we need to find allocations that maximize the number of tasks that can be done without violating any of the constraints. The contribution of this work is the study of a new multi-robot task allocation problem and the design and the experimental evaluation of our approach, an iterated local search (...)
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  154.  12
    A Bi-Objective Genetic Algorithm Optimization of Chaos-DNA Based Hybrid Approach.Shelza Suri & Ritu Vijay - 2019 - Journal of Intelligent Systems 28 (2):333-346.
    The paper implements and optimizes the performance of a currently proposed chaos-deoxyribonucleic acid -based hybrid approach to encrypt images using a bi-objective genetic algorithm optimization. Image encryption is a multi-objective problem. Optimizing the same using one fitness function may not be a good choice, as it can result in different outcomes concerning other fitness functions. The proposed work initially encrypts the given image using chaotic function and DNA masks. Further, GA uses two fitness functions – entropy with correlation coefficient, entropy (...)
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  155.  2
    Elite Opposition-Based Cognitive Behavior Optimization Algorithm for Global Optimization.Shaoling Zhang, Yongquan Zhou & Qifang Luo - 2019 - Journal of Intelligent Systems 28 (2):185-217.
    This paper presents an elite opposition-based cognitive behavior optimization algorithm. The traditional COA is divided into three stages: rough search, information exchange and share, and intelligent adjustment process. In this paper, we introduce the elite opposition-based learning in the third stage of COA, with a view to avoid the latter congestion as well as to enhance the convergence speed. ECOA is validated by 23 benchmark functions and three engineering design problems, and the experimental results have proven the superior performance of (...)
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  156.  17
    Simulation-Based Analysis of Intelligent Maintenance Systems and Spare Parts Supply Chains Integration.Enzo M. Frazzon, Tulio H. Holtz, Lucas S. Silva & Matheus C. Pires - 2019 - Journal of Intelligent Systems 28 (1):31-42.
    Production systems are composed of increasingly complex components with unique specifications. Therefore, since holding safety stocks of each component would be prohibitive, maintenance activities rely on the proper delivery of spare parts, making it available at the right time and place. Equipments monitored by sensors as well as the transmission of sensors data to the spare part supply chain represent an interesting venue for dealing with this contemporaneous industrial challenge. In this direction, this paper applies a simulation model derived from (...)
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  157.  1
    Retinal Fundus Image for Glaucoma Detection: A Review and Study.Shilpa Sameer Kanse & Dinkar Manik Yadav - 2019 - Journal of Intelligent Systems 28 (1):43-56.
    Glaucoma is one of the severe visual diseases that lead to damage the eyes irreversibly by affecting the optic nerve fibers and astrocytes. Consequently, the early detection of glaucoma plays a virtual role in the medical field. The literature presents various techniques for the early detection of glaucoma. Among the various techniques, retinal image-based detection plays a major role as it comes under noninvasive methods of detection. While detecting glaucoma disorder using retinal images, various medical features of the eyes, such (...)
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  158.  14
    Fuzzy Mutual Information-Based Intraslice Grouped Ray Casting.R. Mehaboobathunnisa, A. A. Haseena Thasneem & M. Mohamed Sathik - 2019 - Journal of Intelligent Systems 28 (1):77-86.
    The traditional ray casting algorithm has the capability to render three-dimensional volume data in the viewable two-dimensional form by sampling the color data along the rays. The speed of the technique relies on the computation incurred by the huge volume of rays. The objective of the paper is to reduce the computations made over the rays by eventually reducing the number of samples being processed throughout the volume data. The proposed algorithm incorporates the grouping strategy based on fuzzy mutual information (...)
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  159.  15
    A Novel Word Clustering and Cluster Merging Technique for Named Entity Recognition.Rakesh Patra & Sujan Kumar Saha - 2019 - Journal of Intelligent Systems 28 (1):15-30.
    In this paper, we present a novel word clustering technique to capture contextual similarity among the words. Related word clustering techniques in the literature rely on the statistics of the words collected from a fixed and small word window. For example, the Brown clustering algorithm is based on bigram statistics of the words. However, in the sequential labeling tasks such as named entity recognition, longer context words also carry valuable information. To capture this longer context information, we propose a new (...)
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  160.  13
    Design of Optimized Multiobjective Function for Bipedal Locomotion Based on Energy and Stability.Manish Raj & Gora C. Nandi - 2019 - Journal of Intelligent Systems 28 (1):133-152.
    This paper presents a novel analytical method to develop the multiobjective function including energy and stability functions. The energy function has been developed by unique approach of orbital energy concept and the stability function obtained by modifying the pre-existing zero moment point trajectory. These functions are optimized using real coded genetic algorithm to produce an optimum set of walk parameters. The analytical results show that, when the energy function is optimized, the stability of the robot decreases. Similarly, if the stability (...)
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  161.  10
    A Wheelchair Control System Using Human-Machine Interaction: Single-Modal and Multimodal Approaches.Mohamed K. Shahin, Alaa Tharwat, Tarek Gaber & Aboul Ella Hassanien - 2019 - Journal of Intelligent Systems 28 (1):115-132.
    Recent research studies showed that brain-controlled systems/devices are breakthrough technology. Such devices can provide disabled people with the power to control the movement of the wheelchair using different signals. With this technology, disabled people can remotely steer a wheelchair, a computer, or a tablet. This paper introduces a simple, low-cost human-machine interface system to help chaired people to control their wheelchair using several control sources. To achieve this paper’s aim, a laptop was installed on a wheelchair in front of the (...)
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  162.  11
    An Effective Technique to Track Objects with the Aid of Rough Set Theory and Evolutionary Programming.Kumaraperumal Shanmugapriya & RajaMani Suja Mani Malar - 2019 - Journal of Intelligent Systems 28 (1):1-13.
    Due to its wide range of applications, the impact of multimedia in the real world has shown stupendous growth. Texts, images, audio, and video are the different forms of multimedia which are utilized by humans in various applications such as education and surveillance applications. A wide range of research has been carried out, and here in this paper, we propose an object racking with the aid of rough set theory in combination with the eminent soft computing technique evolutionary programming. Initially, (...)
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  163.  3
    An Efficient Compound Image Compression Using Optimal Discrete Wavelet Transform and Run Length Encoding Techniques.Priya Vasanth Sundara Rajan & A. Lenin Fred - 2019 - Journal of Intelligent Systems 28 (1):87-101.
    Reduction in file size leads to reduction in the number of bits required to store it. When data is compressed, it must be decompressed into its original form bit for bit. Compound images are defined as images that contain a combination of text, natural images and graphic images. Here, compression is the process of reducing the amount of data required to represent information. Image compression is done on the basis of various loss and lossless compression algorithms. This research work deals (...)
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  164.  4
    Hybridization of Genetic and Group Search Optimization Algorithm for Deadline-Constrained Task Scheduling Approach.Nazneen Taj & Anirban Basu - 2019 - Journal of Intelligent Systems 28 (1):153-171.
    Cloud computing is an emerging technology in distributed computing, which facilitates pay per model as per user demand and requirement. Cloud consists of a collection of virtual machines, which includes both computational and storage facility. In this paper, a task scheduling scheme on diverse computing systems using a hybridization of genetic and group search optimization algorithm is proposed. The basic idea of our approach is to exploit the advantages of both genetic algorithm and group search optimization algorithms while avoiding their (...)
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  165.  17
    An Effective Optimization-Based Neural Network for Musical Note Recognition.Allabakash Isak Tamboli & Rajendra D. Kokate - 2019 - Journal of Intelligent Systems 28 (1):173-183.
    Musical pitch estimation is used to recognize the musical note pitch or the fundamental frequency of an audio signal, which can be applied to a preprocessing part of many applications, such as sound separation and musical note transcription. In this work, a method for musical note recognition based on the classification framework has been designed using an optimization-based neural network. A broad range of survey and research was reviewed, and all revealed the methods to recognize the musical notes. An OBNN (...)
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  166.  22
    A Fast Internal Wave Detection Method Based on PCANet for Ocean Monitoring.Shengke Wang, Qinghong Dong, Lianghua Duan, Yujuan Sun, Muwei Jian, Jianzhong Li & Junyu Dong - 2019 - Journal of Intelligent Systems 28 (1):103-113.
    Research on internal waves in the coastal ocean is one of the most important tasks both in physical oceanography and ocean monitoring network. Currently, how to quickly and accurately detect the ocean internal waves from the huge ocean surface is still a challenging issue. In this paper, we model the ocean internal wave detection as a task of region classification for texture images and then propose a rapid internal waves detection method based on a deep learning framework. In the proposed (...)
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  167.  7
    Task Reallocating for Responding to Design Change in Complex Product Design.Meng Wei, Yu Yang, Jiafu Su, Qiucheng Li & Zhichao Liang - 2019 - Journal of Intelligent Systems 28 (1):57-76.
    In the real-world complex product design process, task allocating is an ongoing reactive process where the presence of unexpected design change is usually inevitable. Therefore, reallocating is necessary to respond to design change positively as a procedure to repair the affected task plan. General reallocating literature addressed the reallocating versions with fixed executing time. In this paper, a multi-objective reallocation model is developed with a feasible assumption that the task executing time is controllable. To illustrate this idea, a compressing executing (...)
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