Gait recognition in video surveillance is still challenging because the employed gait features are usually affected by many variations. To overcome this difficulty, this paper presents a novel Deep Large Margin Nearest Neighbor (DLMNN) method for gait recognition. The proposed DLMNN trains a convolutional neural network to project gait feature onto a metric subspace, under which intra-class gait samples are pulled together as small as possible while inter-class samples are pushed apart by a large margin. We provide an (...) extensive evaluation in terms of various scenarios, namely, normal, carrying, clothing, and cross-view condition on two widely used gait datasets. Experimental results demonstrate that the proposed DLMNN achieves competitive gait recognition performances and promising computational efficiency. (shrink)

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 (...) carried out by the semantic approach using the condensed nearest neighbor (SACNN). The Twitter and Tom’s Hardware data are stored in the UC Irvine Machine Learning Repository, and this dataset is used in this research for outlier detection. The min–max normalization technique is applied to the social media dataset, and additionally, missing values were replaced by the normalized value. The condensed nearest neighbor (CNN) is used for semantic analysis of the database, and based on the optimized value provided by the proposed method, the threshold is calculated. The threshold value is used to classify buzz and non-buzz discussions in the social media database. The result showed that the SACNN achieved 99% of accuracy, and relative error is less than the existing methods. (shrink)

In the case of magnetic resonance imaging (MRI) imaging, image processing is crucial. In the medical industry, MRI images are commonly used to analyze and diagnose tumor growth in the body. A number of successful brain tumor identification and classification procedures have been developed by various experts. Existing approaches face a number of obstacles, including detection time, accuracy, and tumor size. Early detection of brain tumors improves options for treatment and patient survival rates. Manually segmenting brain tumors from a significant (...) number of MRI data for brain tumor diagnosis is a tough and time-consuming task. Automatic image segmentation of brain tumors is required. The objective of this study is to evaluate the degree of accuracy and simplify the medical picture segmentation procedure used to identify the type of brain tumor from MRI results. Additionally, this work suggests a novel method for identifying brain malignancies utilizing the Bagging Ensemble with K-Nearest Neighbor (BKNN) in order to raise the KNN’s accuracy and quality rate. For image segmentation, a U-Net architecture is utilized first, followed by a bagging-based k-NN prediction algorithm for classification. The goal of employing U-Net is to improve the accuracy and uniformity of parameter distribution in the layers. Each decision tree is fitted on a little different training dataset during classification, and the bagged decision trees are effective since each tree has minor differences and generates slightly different skilled predictions. The overall classification accuracy was up to 97.7 percent, confirming the efficiency of the suggested strategy for distinguishing normal and pathological tissues from brain MR images; this is greater than the methods that are already in use. (shrink)

With the development of communication and computer technology, the application of big data technology has become increasingly widespread. Reasonable, effective, and fast retrieval methods for querying information from massive data have become an important content of current research. This article provides an image retrieval method based on the weighted nearest neighbor label prediction for the problem of automatic image annotation and keyword image retrieval. In order to improve the performance of the test method, scientific experimental verification was implemented. (...) The nearest neighbor weights are determined by maximizing the training image annotation, and experiments are carried out from multiple angles based on the Mahalanobis metric learning integration model. The experimental results show that the proposed tag correlation prediction propagation model has obvious improvements in accuracy, recall rate, break-even point, and overall average accuracy performance compared with other widely used algorithm models. (shrink)

This work presents a novel approach to forecast streaming big time series based on nearest similar patterns. This approach combines a clustering algorithm with a classifier and the nearest neighbours algorithm. It presents two separate stages: offline and online. The offline phase is for training and finding the best models for clustering, classification and the nearest neighbours algorithm. The online phase is to predict big time series in real time. In the offline phase, data are divided into (...) clusters and a forecasting model based on the nearest neighbours is trained for each cluster. In addition, a classifier is trained using the cluster assignments previously generated by the clustering algorithm. In the online phase, the classifier predicts the cluster label of an instance, and the proper nearest neighbours model according to the predicted cluster label is applied to obtain the final prediction using the similar patterns. The algorithm is able to be updated incrementally for online learning from data streams. Results are reported using electricity consumption with a granularity of |$10$| minutes for 4-hour-ahead forecasting and compared with well-known online benchmark learners, showing a remarkable improvement in prediction accuracy. (shrink)

In this essay I explore what might be meant by the “nearest things” in Nietzsche’s philosophy. In the first part of the essay I contextualise Nietzsche’s concerns with “the closest things of all” in the “free spirit” period (1878–1882) and raise the question of how knowledge of them is possible. This idea is developed in the second part of the paper in relation to the claim that dominant (Platonic/christian) habits of thought impede our understanding of the body. In the (...) third section, I suggest that Nietzsche’s interest in Epicurean thinking in this period enables us to situate the nearest things within the political aesthetics of a transfigured physis. In the final section, I examine how Nietzsche’s 1881 notes on eternal return provide a less-well known locus for his philosophy of the nearest things, one which suggests that to “incorporate” eternal return we need to become “good neighbours” to what is close. (shrink)

Knearest neighbor search is an important problem in location-based services and has been well studied on static road networks. However, in real world, road networks are often time-dependent; i.e., the time for traveling through a road always changes over time. Most existing methods forkNN query build various indexes maintaining the shortest distances for some pairs of vertices on static road networks. Unfortunately, these methods cannot be used for the time-dependent road networks because the shortest distances always change over time. (...) To address the problem ofkNN query on time-dependent road networks, we propose a novel voronoi-based index in this paper. Furthermore, we propose a novel balanced tree, namedV-tree, which is a secondary level index on voronoi-based index to make our querying algorithm more efficient. Moreover, we propose an algorithm for preprocessing time-dependent road networks such that the waiting time is not necessary to be considered. We confirm the efficiency of our method through experiments on real-life datasets. (shrink)

Local foodshed proponents in the United States seek to change the food system through campaigns to “buy local” and to rediscover “good food” in the local foodshed. Presumably, common sense dictates that the word “local” signifies spatial proximity to the consumer. For some populations, however, both the terms “local” and “local food” signify various different meanings. The local food definition generally used by scholars and activists alike as “geographically proximate food” is unhelpfully narrow. Localist rhetoric often does not incorporate the (...) food choices and practices of immigrants, migrants, and refugees. In this paper, I report the various characterizations of local food among Filipino immigrants in San Diego, California: (1) geography-based local food; (2) (US) America-based local food; (3) community-based local food; and (4) immigrant identity-based local food. Local foodshed proponents should acknowledge those who have a starkly different definition of “local”—those who possess translocal subjectivities, for whom “local-ness” is both mobile and embodied. My study underscores how the movement of food—or its containment within a geographic space—cannot be viewed in isolation from the movement of people. (shrink)

Nearest-neighbor correlation-based similarity computation in the space of outputs of complex-type receptive elds can support robust recognition of 3D objects. Our experiments with four collections of objects resulted in mean recognition rates between 84% and 94%, over a 40 40 range of viewpoints, centered on a stored canonical view and related to it by rotations in depth. This result has interesting implications for the design of a front end to an arti cial object recognition system, and for the (...) understanding of the faculty of object recognition in primate vision. (shrink)

In _Reliable Reasoning_, Gilbert Harman and Sanjeev Kulkarni -- a philosopher and an engineer -- argue that philosophy and cognitive science can benefit from statistical learning theory, the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors -- a central topic in SLT. After discussing philosophical attempts to evade the (...) problem of induction, Harman and Kulkarni provide an admirably clear account of the basic framework of SLT and its implications for inductive reasoning. They explain the Vapnik-Chervonenkis dimension of a set of hypotheses and distinguish two kinds of inductive reasoning. The authors discuss various topics in machine learning, including nearest-neighbor methods, neural networks, and support vector machines. Finally, they describe transductive reasoning and suggest possible new models of human reasoning suggested by developments in SLT. (shrink)

In an era characterized by fast technological progress that introduces new unpredictable scenarios every day, working in the law field may appear very difficult, if not supported by the right tools. In this respect, some systems based on Artificial Intelligence methods have been proposed in the literature, to support several tasks in the legal sector. Following this line of research, in this paper we propose a novel method, called PRILJ, that identifies paragraph regularities in legal case judgments, to support legal (...) experts during the redaction of legal documents. Methodologically, PRILJ adopts a two-step approach that first groups documents into clusters, according to their semantic content, and then identifies regularities in the paragraphs for each cluster. Embedding-based methods are adopted to properly represent documents and paragraphs into a semantic numerical feature space, and an Approximated Nearest Neighbor Search method is adopted to efficiently retrieve the most similar paragraphs with respect to the paragraphs of a document under preparation. Our extensive experimental evaluation, performed on a real-world dataset provided by EUR-Lex, proves the effectiveness and the efficiency of the proposed method. In particular, its ability of modeling different topics of legal documents, as well as of capturing the semantics of the textual content, appear very beneficial for the considered task, and make PRILJ very robust to the possible presence of noise in the data. (shrink)

In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor. This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the (...) quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model. (shrink)

We have developed a new method for Japanese-to-English translation of tense, aspect, and modality that uses an example-based method. In this method the similarity between input and example sentences is defined as the degree of semantic matching between the expressions at the ends of the sentences. Our method also uses the k-nearest neighbor method in order to exclude the effects of noise; for example, wrongly tagged data in the bilingual corpora. Experiments show that our method can translate tenses, (...) aspects, and modalities more accurately than the top-level MT software currently available on the market can. Moreover, it does not require hand-craft rules. (shrink)

There are distinctive methodological and conceptual challenges in rare and severe event (RSE) forecast verification, that is, in the assessment of the quality of forecasts of rare but severe natural hazards such as avalanches, landslides or tornadoes. While some of these challenges have been discussed since the inception of the discipline in the 1880s, there is no consensus about how to assess RSE forecasts. This article offers a comprehensive and critical overview of the many different measures used to capture the (...) quality of categorical, binary RSE forecasts – forecasts of occurrence and non-occurrence – and argues that of skill scores in the literature there is only one adequate for RSE forecasting. We do so by first focusing on the relationship between accuracy and skill and showing why skill is more important than accuracy in the case of RSE forecast verification. We then motivate three adequacy constraints for a measure of skill in RSE forecasting. We argue that of skill scores in the literature only the Peirce skill score meets all three constraints. We then outline how our theoretical investigation has important practical implications for avalanche forecasting, basing our discussion on a study in avalanche forecast verification using the nearest-neighbour method (Heierli et al., 2004). Lastly, we raise what we call the “scope challenge”; this affects all forms of RSE forecasting and highlights how and why working with the right measure of skill is important not only for local binary RSE forecasts but also for the assessment of different diagnostic tests widely used in avalanche risk management and related operations, including the design of methods to assess the quality of regional multi-categorical avalanche forecasts. (shrink)

Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography -based brain-computer interface research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (...) and power spectral density, which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor, support vector machine, and random forest. We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset. (shrink)

Nowadays, online product reviews have been at the heart of the product assessment process for a company and its customers. They give feedback to a company on improving product quality, planning, and monitoring its business schemes in order to increase sale and gain more profit. They are also helpful for customers to select the right products in less effort and time. Most companies make spam reviews of products in order to increase the products sales and gain more profit. Detecting spam (...) product reviews is a challenging issue in NLP. Numerous machine learning approaches have attempted to detect and classify the product reviews as spam or nonspam. However, in order to improve the classification accuracy, this study has introduced an ensemble machine learning model that combines predictions from multilayer perceptron, K-Nearest Neighbour, and Random Forest and predicts the outcome of the review as spam or real, based on the majority vote of the contributing models. In order to accomplish the task of spam review classification, the proposed ensemble and other benchmark boosting approaches are tested with 25 statistical features extracted from mobile application reviews of Yelp Dataset. Then, three different selection techniques are exploited to diminish the feature space and filter out the top 10 optimal features. The effectiveness of the proposed ensemble, the individual models, and other benchmark boosting approaches is again evaluated with 10 optimal features in terms of classification accuracy. Experimental outcomes illustrate that the proposed ensemble model outperformed the individual classifiers and state-of-the-art boosting approaches like Generalized Boost Regression Model, Extreme Gradient Boost, and AdaBoost Regression Model in terms of classification accuracy. (shrink)

Diabetes is one of the most common metabolic diseases that cause high blood sugar. Early diagnosis of such a condition is challenging due to its complex interdependence on various factors. There is a need to develop critical decision support systems to assist medical practitioners in the diagnosis process. This research proposes developing a predictive model that can achieve a high classification accuracy of type 2 diabetes. The study consisted of two fundamental parts. Firstly, the study investigated handling missing data adopting (...) data imputation, namely, median value imputation, K-nearest neighbor imputation, and iterative imputation. Consequently, the study validated the implications of these imputations using various classification algorithms, i.e., linear, tree-based, and ensemble algorithms, to see how each method affected classification accuracy. Secondly, Artificial Neural Network was employed to model the best performing imputed data, balanced with SMOTETomek ensuring each class is represented fairly. This approach provided the best accuracy of 98% on the test data, outperforming accuracies achieved in prior studies using the same dataset. The dataset used in this study is concerned with gender and population. As a prospect, the study recommends adopting a larger population sample without geographic boundaries. Additionally, as the developed Artificial Neural Network model did not undergo any specific hyperparameter tuning, it would be interesting to explore tuning on top of normalized data to optimize accuracy further. (shrink)

Impostor syndrome or Impostor phenomenon is a belief that a person thinks their success is due to luck or external factors, not their abilities. This psychological trait is present in certain groups like women. In this paper, we propose a neutrosophic trait measure to represent the psychological concept of the trait-anti trait using refined neutrosophic sets. This study analysed a group of 200 undergraduate students for impostor syndrome, perfectionism, introversion and self-esteem: after the COVID pandemic break in 2021. Data labelling (...) was carried out using these neutrosophic trait measures. Machine learning models like Support Vector Machine(SVM), K-nearest neighbour (K-NN), and random forest were used to model the data; SVM provided the best accuracy of 92.15%. (shrink)

Data mining, as an essential part of artificial intelligence, is a powerful digital technology, which makes businesses predict future trends and alleviate the process of decision-making and enhancing customer experience along their digital transformation journey. This research provides a practical implication – a case study - to provide guidance on analyzing information and predicting repairs in home appliances after sales services business. The main benefit of this practical comparative study of various classification algorithms, by using the Weka tool, is the (...) analysis of information and the prediction of repairs in the home appliances after sales services business. The comparison of algorithms is performed considering different parameters, such as the mean absolute error, root mean square error, relative absolute error and root relative squared error, receiver operating characteristic area, accuracy, Matthews’s correlation coefficient, precision-recall curve, precision, F-measure, recall and statistical criteria. Five classification algorithms such as the Naive Bayes, J48, random forest, K-Nearest Neighbor, and logistic regression were implemented in the dataset. J48 has proved to provide the best accuracy and the lowest error among the other examined algorithms applied to a home appliances after sales services dataset to predict repairs based on product guarantee period. The extracted information and results of an after sales services business by using data mining techniques prove to alleviate the process of streamlining decision-making and provide reliable predictions, especially for the customers, as well as increase businesses’ efficiency along their digital transformation journey. (shrink)

In recent years, the biomedical field has witnessed the emergence of novel tools and modelling techniques driven by the rise of the so-called Big Data. In this paper, we address the issue of predictability in biomedical Big Data models of cancer patients, with the aim of determining the extent to which computationally driven predictions can be implemented by medical doctors in their clinical practice. We show that for a specific class of approaches, called k-Nearest Neighbour algorithms, the ability to (...) draw predictive inferences relies on a geometrical, or topological, notion of similarity encoded in a well-defined metric, which determines how close the characteristics of distinct patients are on average. We then discuss the conditions under which the relevant models can yield reliable and trustworthy predictive outcomes. (shrink)

This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit (...) was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated. (shrink)

In recent years, with the continuous improvement of urban public transportation capacity, citizens’ travel has become more and more convenient, but there are still some potential problems, such as morning and evening peak congestion, imbalance between the supply and demand of vehicles and passenger flow, emergencies, and social local passenger flow surged due to special circumstances such as activities and inclement weather. If you want to properly guide the local passenger flow and make a reasonable deployment of operating buses, it (...) is necessary to grasp the changing law of public transportation short-term passenger flow. This paper builds a short-term passenger flow prediction model for urban public transportation based on the idea of integrated learning. The goal is to use the integrated model to accurately predict the short-term passenger flow of urban public transportation, using Multivariable Linear Regression, K-Nearest Neighbor, eXtreme Gradient Boosting, and Gated Recurrent Unit as the four seed models, and then use regression algorithm to integrate the model and predict the passenger flow, station boarding and landing, and cross-sectional passenger flow data of the typical representative line 428 in the “Huitian Area” of Beijing from January 1, 2020, to May 31, 2020. Finally, the prediction results of the submodels are compared with those of the integrated model to verify the superiority of the integrated model. The research results of this paper can enrich the short-term passenger flow forecasting system of urban public transportation and provide effective data support and scientific basis for the passenger flow, vehicle management, and dispatch of urban public transportation. (shrink)

To better reflect the precise clustering results of the data samples with different shapes and densities for affinity propagation clustering algorithm, an improved integrated clustering learning strategy based on three-stage affinity propagation algorithm with density peak optimization theory was proposed in this paper. DPKT-AP combined the ideology of integrated clustering with the AP algorithm, by introducing the density peak theory and k-means algorithm to carry on the three-stage clustering process. In the first stage, the clustering center point was selected by (...) density peak clustering. Because the clustering center was surrounded by the nearest neighbor point with lower local density and had a relatively large distance from other points with higher density, it could help the k-means algorithm in the second stage avoiding the local optimal situation. In the second stage, the k-means algorithm was used to cluster the data samples to form several relatively small spherical subgroups, and each of subgroups had a local density maximum point, which is called the center point of the subgroup. In the third stage, DPKT-AP used the AP algorithm to merge and cluster the spherical subgroups. Experiments on UCI data sets and synthetic data sets showed that DPKT-AP improved the clustering performance and accuracy for the algorithm. (shrink)

In recent years, many face image feature extraction and dimensional reduction algorithms have been proposed for linear and nonlinear data, such as local-based graph embedding algorithms or fuzzy set algorithms. However, the aforementioned algorithms are not very effective for face images because they are always affected by overlaps and sparsity points in the database. To solve the problems, a new and effective dimensional reduction method for face recognition is proposed—sparse graph embedding with the fuzzy set for image classification. The aim (...) of this algorithm is to construct two new fuzzy Laplacian scattering matrices by using the local graph embedding and fuzzy k-nearest neighbor. Finally, the optimal discriminative sparse projection matrix is obtained by adding elastic network regression. Experimental results and analysis indicate that the proposed algorithm is more effective than other algorithms in the UCI wine dataset and ORL, Yale, and AR standard face databases. (shrink)

Compound Gauss-Markov random field model is one of Markov random field models for natural image restorations. An optimization algorithm was constructed by means of mean-field approximation, which is a familiar techniques for analyzing massive probabilistic models approximately in the statistical mechanics. Cluster variation method was proposed as an extended version of the mean-field approximation in the statistical mechanics. Though the mean-field approximation treat only the marginal probability distribution for every single pixel, the cluster variation method can take acount into the (...) correlation between pixels by treating the marginal probability distribution for every nearest neighbor pair of pixels. In this paper, we propose a newstatistical-mechanical iterative algorithm by means of the cluster variation method for natural image restorations in the compound Gauss-Markov random field model. In some numerical experiments, it is investigate howthe proposed algorithm improves the quality of restored images by comparing it with the algorithm constructed from the mean-field approximation. (shrink)

In this paper, a novel efficient algorithm is presented for locating and tracking object parts in low resolution videos using Lowe's SIFT keypoints with a nearest neighbor object detection approach. Our interest lies in using this information as one step in the process of automatically programming service, household, or personal robots to perform the skills that are being taught in easily obtainable instructional videos. In the reported experiments, the system looked for 14 parts of inanimate and animate objects (...) in 40 natural outdoor scenes. The scenes were frames from a low-resolution instructional video on cleaning golf clubs containing 2,405 frames of 180 by 240 pixels. The system was trained using 39 frames that were half-way between the test frames. Despite the low resolution quality of the instructional video and occluded training samples, the system achieved a recall of 49% with a precision of 71% and an F1 of 0.58, which is better than that achieved by less demanding applications. In order to verify that the reported results were not dependent on the specific video, the proposed technique was applied to another video and the results are reported. (shrink)

For the safe and economical construction of embankment dams, the mechanical behaviour of the rockfill materials used in the dam’s shell must be analyzed. The characterization of rockfill materials with specified shear strength is difficult and expensive due to the presence of particles greater than 500 mm in diameter. This work investigates the feasibility of using an extreme gradient boosting computing paradigm to estimate the shear strength of rockfill materials. To train and validate the proposed XGBoost model, a total of (...) 165 databases obtained from the literature are chosen. The XGBoost model was compared against support vector machine, adaptive boosting, random forest, and K-nearest neighbor models described in the literature. XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination, Nash–Sutcliffe coefficient, and error in the root mean square ratio to the standard deviation of the measured data. The results demonstrated that the XGBoost model has the highest prediction performance with, followed by the SVM model with, RF, the AdaBoost model with and the KNN model with. A sensitivity analysis has been conducted to ascertain the impact of each investigated input parameter. This study demonstrates that the established XGBoost model for estimating the shear strength of rockfill materials is reliable. (shrink)

Nowadays, depression is the world’s major health concern and economic burden worldwide. However, due to the limitations of current methods for depression diagnosis, a pervasive and objective approach is essential. In the present study, a psychophysiological database, containing 213 subjects, was constructed. The electroencephalogram signals of all participants under resting state and sound stimulation were collected using a pervasive prefrontal-lobe three-electrode EEG system at Fp1, Fp2, and Fpz electrode sites. After denoising using the Finite Impulse Response filter combining the Kalman (...) derivation formula, Discrete Wavelet Transformation, and an Adaptive Predictor Filter, a total of 270 linear and nonlinear features were extracted. Then, the minimal-redundancy-maximal-relevance feature selection technique reduced the dimensionality of the feature space. Four classification methods distinguished the depressed participants from normal controls. The classifiers’ performances were evaluated using 10-fold cross-validation. The results showed thatK-Nearest Neighbor had the highest accuracy of 79.27%. The result also suggested that the absolute power of the theta wave might be a valid characteristic for discriminating depression. This study proves the feasibility of a pervasive three-electrode EEG acquisition system for depression diagnosis. (shrink)

Information is considered to be the major part of an organization. With the enhancement of technology, the knowledge level is increasing with the passage of time. This increase of information is in volume, velocity, and variety. Extracting meaningful insights is the dire need of an individual from such information and knowledge. Visualization is a key tool and has become one of the most significant platforms for interpreting, extracting, and communicating information. The current study is an endeavour toward data modelling and (...) user knowledge by using a rough set approach for extracting meaningful insights. The technique has used different rough set algorithms such as K-nearest neighbours, decision rules, decomposition tree, and local transfer function classifier for an experimental setup. The approach has found its accuracy for the optimal use of data modelling and user knowledge. The experimental setup of the proposed method is validated by using the dataset available in the UCI web repository. Results of the proposed study show that the model is effective and efficient with an accuracy of 96% for KNN, 87% for decision rules, 91% for decision trees, 85.04% for cross validation architecture, and 94.3% for local transfer function classifier. The validity of the proposed classification algorithms is tested using different performance metrics such as F-score, precision, accuracy, recall, specificity, and misclassification rates. For all these performance metrics, the KNN classifier outperformed, and this high performance shows the applicability of the KNN classifier in the proposed problem. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Three Poems RICARDO PAU-LLOSA panta rhei¿Quién es tu hermano? Tu vecino más cercano. (Who is your brother? Your nearest neighbor.) —Spanish saying In emergencies, the closest will do. Love, even a few blocks away, fails when the stranger next door rises in charity unknown to him till then. The day is saved by those whose names you’ll forget: the driver in the next car, the gardener who (...) rushed into a house he’s never seen to pull, guard, rescue. All the verbs which were alien and will resume their callous grip, now storm the sudden heart that just then fled the wolf condition. But it hears the lunar groan returningly. Homo homini lupus est, teach Romans who knew so much about impenetrability. Ovid and Hobbes differ, Erasmus equivocates, Freud surrenders. A pack of thinkers have torn at the flesh of our layered psyche, wishing to find an identifying center. We are the poker hand, tossing and assembling bluff and triumph. We fold, we hold on. There is nothing Chaos, our secret god, cannot bestow. arion 28.2 fall 2020 70 three poems Proteus In the dream, the man is bathing a large dog whose breed changes as the chore unfolds, starting with a rottweiler, then bulldog, great dane.... Starts on the lawn, then shifts to the groomer’s, the man’s bathtub, a creek from his childhood.... First it was a dog he owns, then a former pet, then a friend’s, a dog from a commercial.... It is as if the dream announced this thing, then that, then changed its mind, and it is then that the man understands he cannot truly embrace the idea that the dream has a mind to make up or change, for then life would have a toolbox, a menu, program tabs, training schedule.... hence throwing into the acid of questions whether he has such powers—to alter, navigate, reflect, cure, develop.... this tumble of paradigms, themselves becoming a chain, sequence, sentence, syntax, metonymy.... because the thing —the announcement— cannot simply be a fate but a motion, a variance, an effect that churns the weather of effects, topples the still life on the table just when the painter had caught the just-so light, right before the supper, the sole ingestion point of the thing, the call to itself, begins and leaves everything else twirling. Ricardo Pau-Llosa 71 puddle 2 Samuel 14:14 My student sees filth everywhere, even in this dank, rotten-leafed, cigarette-butt poisoned tongue of late morning bronze. It laces the oak branches above it and folds the mid-day sky in green convections. Allow me to introduce you to squallor’s gorgeous mirror who taught the golden youth to catch himself in love. This silent bed whose many sheets are one mellifluous of shade, has hosted boot and quandary, astronomy and betrayal. I cannot hold it accountable for accuracy or permanence. Surely it knew from the start how short the mission and how stark the light. But linger, pupil, where an eye might brave the trough whose vapid depth dared frame the trees, their sky.... (shrink)

This paper presents an approach for monitoring exercises of hand rehabilitation for post stroke patients. The developed solution uses a leap motion controller as hand-tracking device and embeds a supervised machine learning. The K-nearest neighbor methodology is adopted for automatically characterizing the physiotherapist or helper hand movement resulting a unique movement pattern that constitutes the basis of the rehabilitation process. In the second stage, an evaluation of the patients rehabilitation exercises results is compared to the movement pattern of (...) the patient and results are presented, saved and statistically analyzed. Physicians and physiotherapists monitor and assess patients’ rehabilitation improvements through a web application, furthermore, offer medical assisted rehabilitation processes through low cost technology, which can be easily exploited at home. Recorded tracked motion data and results can be used for further medical study and evaluating rehabilitation trends according to patient’s rehabilitation practice and improvement. (shrink)

Knowledge of the patterns of location of retail stores in urban areas supports the development of effective urban planning and the reasonable allocation of commercial facilities. Using point of interest data and consumer survey data in three main commercial districts in Changchun, China, this study investigates the spatial structures of commercial districts and the patterns of distribution of retail stores to assess the determinants of the development of retail stores in commercial districts. Kernel density estimation, nearest neighbor index, (...) and Pearson’s correlation analysis were used for this study. The following conclusions are drawn. The spatial distribution of retail stores in Changchun commercial districts generates the coexistence of a concentration in the core area and diffusion in the peripheral area. The emergence of shopping malls has challenged the traditional single-center structure, resulting in the transformation of commercial districts from single-center to multicenter layouts, while also producing a hierarchical trend in development. The Chongqing Road and Hongqi Street commercial districts have a relatively high spatial concentration of retail stores. Retail stores in Guilin Road exhibit distinct characteristics, namely, stores selling textiles, clothing, and daily necessities show the highest concentration, and food, beverage, and tobacco outlets as well as integrated stores show the lowest concentration. The selected locations of the differing categories of stores on Chongqing Road strongly correlate, and textile, clothing, and daily necessity stores show a high correlation with other retail categories. Four main factors affect the development and spatial layout of retail in the commercial districts. First, the interaction between consumer behavior and location choice in retail stores promotes the evolution of retail formats and trends in the development of comprehensive, specialized, and hierarchical retail commercial spaces. Second, the retail format determines the spatial layouts and the historical inheritance of the format. Third, governmental planning and policies lead to the agglomeration and diffusion of commercial activities in different areas. Fourth, such spatial clustering effects are an external driving factor for integration and aggregation among retail formats. (shrink)

The stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning and deep learning models can be used to tackle the problem of stability prediction for the energy grid. This study elaborates on the necessity of IoT technology to make energy grid networks smart. Different prediction models, namely, logistic regression, naïve Bayes, decision tree, support vector machine, random forest, XGBoost, k-nearest neighbor, and optimized (...) artificial neural network, have been applied on openly available smart energy grid datasets to predict their stability. The present article uses metrics such as accuracy, precision, recall, f1-score, and ROC curve to compare different predictive models. Data augmentation and feature scaling have been applied to the dataset to get better results. The augmented dataset provides better results as compared with the normal dataset. This study concludes that the deep learning predictive model ANN optimized with Adam optimizer provides better results than other predictive models. The ANN model provides 97.27% accuracy, 96.79% precision, 95.67% recall, and 96.22% F1 score. (shrink)

This paper presents an approach for monitoring exercises of hand rehabilitation for post stroke patients. The developed solution uses a leap motion controller as hand-tracking device and embeds a supervised machine learning. The K-nearest neighbor methodology is adopted for automatically characterizing the physiotherapist or helper hand movement resulting a unique movement pattern that constitutes the basis of the rehabilitation process. In the second stage, an evaluation of the patients rehabilitation exercises results is compared to the movement pattern of (...) the patient and results are presented, saved and statistically analyzed. Physicians and physiotherapists monitor and assess patients’ rehabilitation improvements through a web application, furthermore, offer medical assisted rehabilitation processes through low cost technology, which can be easily exploited at home. Recorded tracked motion data and results can be used for further medical study and evaluating rehabilitation trends according to patient’s rehabilitation practice and improvement. (shrink)

Machine learning, deep learning and neural networks are extensively applied for the development of many fields. Though their technologies are improved greatly, they are often said to be opaque in terms of explainability. Their explainable neural functions will be essential to realization in the networks. In this paper, it is shown that the bio-inspired networks are useful for the explanation of tracking and classification of features. First, the asymmetric network with nonlinear functions is created based on the bio-inspired retinal network. (...) They have orthogonal properties useful for the tracking of features compared with the conventional symmetric networks, which is also proposed on the biological functions. Next, the analysis for the independence of the subspaces between the Fourier bases and the asymmetric network bases is performed. It was that the asymmetric networks have better performances in the classification compared with the symmetric ones. Further, the layered asymmetric networks generate the higher dimensional orthogonal bases that improve the classification accuracies by the replacements of bases. Finally, we classified Reuters collections data applying the explainable processing steps, which consist of the linear discriminations and the sparse coding with nearest neighbor relation for classification. (shrink)

With the development of mobile communication technology, location-based services are booming prosperously. Meanwhile privacy protection has become the main obstacle for the further development of LBS. The k-nearest neighbor search is one of the most common types of LBS. In this paper, we propose an efficient private circular query protocol with high accuracy rate and low computation and communication cost. We adopt the Moore curve to convert two-dimensional spatial data into one-dimensional sequence and encrypt the points of interest (...) information with the Brakerski-Gentry-Vaikuntanathan homomorphic encryption scheme for privacy-preserving. The proposed scheme performs the secret circular shift of the encrypted POIs information to hide the location of the user without a trusted third party. To reduce the computation and communication cost, we dynamically divide the table of the POIs information according to the value of k. Experiments show that the proposed scheme provides high accuracy query results while maintaining low computation and communication cost. (shrink)

In order to obtain continuous stratum information during TBM tunneling, using TBM tunneling parameters, stratum recognition is carried out through the K-nearest neighbor model, and the model is improved by the entropy weight method to improve the stratum recognition rate. By analyzing the correlation between TBM tunneling characteristic parameters and stratum, the tunneling characteristic parameter vector which is most sensitive to the stratum is obtained by sensitivity analysis, and the stratum recognition model based on the K-nearest (...) class='Hi'>neighbor algorithm is established. Aiming at the problem that the model has a large error in complex formation recognition, a formation recognition model based on the entropy weight K-nearest neighbor algorithm is established, and the wrong data of the K-nearest neighbor model is recalculated. The recognition rate of the stratum in the new model is increased from 90.95% to 98.55%. The results show that the K-nearest neighbor model has a better recognition effect for the interval with single stratum distribution, and the recognition rate of entropy weight K-nearest neighbor model for complex stratum is significantly improved, which provides an effective method to obtain stratum information by using tunneling characteristic parameters. (shrink)

It is well established that single crystals of B2-structured Fe-Al change from paramagnetic to ferromagnetic upon plastic deformation. This strain-induced ferromagnetism arises mostly from Fe atoms which have three or more like nearest neighbours in antiphase-boundary tubes. Such Fe atoms carry magnetic moments according to their local environment. In this study, the saturation magnetizations, M S , of cold-rolled Fe-34 at.% Al, Fe-40 at.% Al and Fe-43 at.% Al single crystals were measured in a vibrating-sample magnetometer from 77 K (...) to room temperature and transmission electron microscopy was used to confirm the presence of APB tubes. After magnetic measurements, rolled single crystals were heated in a differential scanning calorimeter at 20 K min m 1 to 973 K and the enthalpy associated with the removal of the APB tubes measured. From the enthalpy measurement, the APB tube density and the number of Fe NNs were estimated. Hence, the M S values at 0 K were calculated. These were in good agreement with the experimentally determined values. (shrink)

Depression has become one of the most common mental illnesses, and the widespread use of social media provides new ideas for detecting various mental illnesses. The purpose of this study is to use machine learning technology to detect users of depressive patients based on user-shared content and posting behaviors in social media. At present, the existing research mostly uses a single detection method, and the unbalanced class distribution often leads to a low recognition rate. In addition, a large number of (...) irrelevant or redundant features in high-dimensional data sets interfere with the accuracy of recognition. To solve this problem, this paper proposes a hybrid feature selection and stacking ensemble strategy for depression user detection. First, recursive elimination method and extremely randomized trees method are used to calculate feature importance and mutual information value, calculate feature weight vector, and select the optimal feature subset according to the feature weight. Second, naive bayes, k-nearest neighbor, regularized logistic regression and support vector machine are used as base learners, and a simple logistic regression algorithm is used as a combination strategy to build a stacking model. Experimental results show that compared with other machine learning algorithms, the proposed hybrid method, which integrates feature selection and ensemble, has a higher accuracy of 90.27% in identifying online patients. We believe this study will help develop new methods to identify depressed people in social networks, providing guidance for future research. (shrink)

To design and develop AI-based cybersecurity systems ), users can justifiably trust, one needs to evaluate the impact of trust using machine learning and deep learning technologies. To guide the design and implementation of trusted AI-based systems in IDS, this paper provides a comparison among machine learning and deep learning models to investigate the trust impact based on the accuracy of the trusted AI-based systems regarding the malicious data in IDs. The four machine learning techniques are decision tree, K (...) class='Hi'>nearest neighbour, random forest, and naïve Bayes. The four deep learning techniques are LSTM and GRU. Two datasets are used to classify the IDS attack type, including wireless sensor network detection system and KDD Cup network intrusion dataset. A detailed comparison of the eight techniques’ performance using all features and selected features is made by measuring the accuracy, precision, recall, and F1-score. Considering the findings related to the data, methodology, and expert accountability, interpretability for AI-based solutions also becomes demanded to enhance trust in the IDS. (shrink)

The preparation of layers of amorphous Se by plasma-enhanced CVD using the hydride H2Se as precursor gas is described. Information concerning the structure of the films was obtained from Raman spectroscopy. The spectra of amorphous Se indicated that the dominant molecular structure is the eight-membered ring and/or a chain with Se8 molecular fragments. This material exhibited reversible photodarkening when illuminated at 77 K. In order to explain this phenomenon, we propose a mechanism which takes into account the role of the (...) lone-pair electron orbitals of Se in their contribution to structural ordering. Illumination can cause a distortion in the normal bonding direction between nearest-neighbour Se atoms and induce in this way intrinsic defect states located at the band edges. In the photo-darkened state, optical transition will occur between these defect states. (shrink)