Results for 'Deep Learning'

1000+ found
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  1. Deep Learning: A Philosophical Introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10).
  2. Mango Classification Using Deep Learning.Alaa Soliman Abu Mettleq, Ibtesam M. Dheir, Abeer A. Elsharif & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):22-29.
    Abstract: In worldwide, there are several hundred cultivars of mango. Depending on the cultivar, mango fruit varies in size, shape, sweetness, skin color, and flesh color which may be pale yellow, gold, or orange. Where there are more than 15 types of manga. In this paper, two types Mango classification approach is presented with a dataset that contains approximately 1200 images. Convolutional Neural Network (CNN) algorithms, a deep learning technique extensively applied to image recognition was used, for this (...)
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  3. Banana Classification Using Deep Learning.Ahmed F. Al-Daour, Mohammed O. Al-Shawwa & Samy S. Abu-Naser - 2020 - International Journal of Academic Information Systems Research (IJAISR) 3 (12):6-11.
    Abstract: Banana, fruit of the genus Musa, of the family Musaceae, one of the most important fruit crops of the world. The banana is grown in the tropics, and, though it is most widely consumed in those regions, it is valued worldwide for its flavour, nutritional value, and availability throughout the year. Cavendish, or dessert, bananas are most commonly eaten fresh, though they may be fried or mashed and chilled in pies or puddings. They may also be used to flavour (...)
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  4.  62
    Deep Learning in Law: Early Adaptation and Legal Word Embeddings Trained on Large Corpora.Ilias Chalkidis & Dimitrios Kampas - 2019 - Artificial Intelligence and Law 27 (2):171-198.
    Deep Learning has been widely used for tackling challenging natural language processing tasks over the recent years. Similarly, the application of Deep Neural Networks in legal analytics has increased significantly. In this survey, we study the early adaptation of Deep Learning in legal analytics focusing on three main fields; text classification, information extraction, and information retrieval. We focus on the semantic feature representations, a key instrument for the successful application of deep learning in (...)
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  5. Plant Seedlings Classification Using Deep Learning.Belal A. M. Ashqar, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2019 - International Journal of Academic Information Systems Research (IJAISR) 3 (1):7-14.
    Agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Preceding instrument vision methods established for selective weeding have confronted with major challenges for trustworthy and precise weed recognition. In this paper, (...)
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  6. Grape Type Classification Using Deep Learning.Hosni Qasim El-Mashharawi, Samy S. Abu-Naser, Izzeddin A. Alshawwa & Mohammed Elkahlout - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):41-45.
    Abstract: A grape is a fruit, botanically a berry, of the deciduous woody vines of the flowering plant genus Vitis. it can be eaten fresh or they can be used for making jam, grape juice, jelly, grape seed extract, raisins, and grape seed oil. Grapes are a nonclimacteric type of fruit, generally occurring in clusters. Grapes are a type of fruit that grow in clusters of 15 to 300, and can be crimson, black, dark blue, yellow, green, orange, and pink. (...)
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  7.  12
    Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification.Zeynep H. Kilimci & Selim Akyokus - 2018 - Complexity 2018:1-10.
    The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system. Ensemble learning is an approach to raise the overall accuracy of a classification system by utilizing multiple classifiers. Deep learning-based methods provide better results in many applications when compared with the other conventional machine learning algorithms. Word embeddings enable representation of words learned from (...)
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  8. Avocado Classification Using Deep Learning.Mohammed N. Abu Alqumboz & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):30-34.
    Avocado is the fruit of the avocado tree, scientifically known as Persia Americana. This fruit is prized for its high nutrient value and is added to various dishes due to its good flavor and rich texture. It is the main ingredient in guacamole. These days, the avocado has become an incredibly popular food among health-conscious individuals. It’s often referred to as a superfood, which is not surprising given its health properties. Using a public dataset of 1,234 images of Avocado collected (...)
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  9. Peach Type Classification Using Deep Learning.Mohammed I. El-Kahlout & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):35-40.
    Abstract: Peach, (Prunus persica), fruit tree of the rose family (Rosaceae), grown throughout the warmer temperate regions of both the Northern and Southern hemispheres. Peaches are widely eaten fresh and are also baked in pies and cobblers; canned peaches are a staple commodity in many regions. Yellow-fleshed varieties are especially rich in vitamin A. Peach trees are relatively short-lived as compared with some other fruit trees. In some regions orchards are replanted after 8 to 10 years, while in others trees (...)
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  10. Lemon Classification Using Deep Learning.Jawad Yousif AlZamily & Samy Salim Abu Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):16-20.
    Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images (...)
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  11.  70
    Grape Type Classification Using Deep Learning.Hosni Qasim El-Mashharawi, Samy S. Abu-Naser, Izzeddin A. Alshawwa & Mohammed Elkahlout - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):41-45.
    Abstract: A grape is a fruit, botanically a berry, of the deciduous woody vines of the flowering plant genus Vitis. it can be eaten fresh or they can be used for making jam, grape juice, jelly, grape seed extract, raisins, and grape seed oil. Grapes are a nonclimacteric type of fruit, generally occurring in clusters. Grapes are a type of fruit that grow in clusters of 15 to 300, and can be crimson, black, dark blue, yellow, green, orange, and pink. (...)
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  12.  6
    Deep Learning in Law: Early Adaptation and Legal Word Embeddings Trained on Large Corpora.Ilias Chalkidis & Dimitrios Kampas - 2019 - Artificial Intelligence and Law 27 (2):171-198.
    Deep Learning has been widely used for tackling challenging natural language processing tasks over the recent years. Similarly, the application of Deep Neural Networks in legal analytics has increased significantly. In this survey, we study the early adaptation of Deep Learning in legal analytics focusing on three main fields; text classification, information extraction, and information retrieval. We focus on the semantic feature representations, a key instrument for the successful application of deep learning in (...)
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  13.  6
    A Deep Learning Approach for a Source Code Detection Model Using Self-Attention.Yao Meng & Long Liu - 2020 - Complexity 2020:1-15.
    With the development of deep learning, many approaches based on neural networks are proposed for code clone. In this paper, we propose a novel source code detection model At-biLSTM based on a bidirectional LSTM network with a self-attention layer. At-biLSTM is composed of a representation model and a discriminative model. The representation model firstly transforms the source code into an abstract syntactic tree and splits it into a sequence of statement trees; then, it encodes each of the statement (...)
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  14. Handwritten Signature Verification Using Deep Learning.Eman Alajrami, Belal A. M. Ashqar, Bassem S. Abu-Nasser, Ahmed J. Khalil, Musleh M. Musleh, Alaa M. Barhoom & Samy S. Abu-Naser - 2020 - International Journal of Academic Multidisciplinary Research (IJAMR) 3 (12):39-44.
    Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...)
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  15.  6
    Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization.Xue-Bo Jin, Hong-Xing Wang, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su & Jian-Lei Kong - 2020 - Complexity 2020:1-14.
    The power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load (...)
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  16.  27
    Deep Learning, Education and the Final Stage of Automation.Michael A. Peters - 2018 - Educational Philosophy and Theory 50 (6-7):549-553.
  17.  17
    Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation.J. M. Torres & R. M. Aguilar - 2018 - Complexity 2018:1-10.
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  18.  18
    Deep Learning and Cognitive Science.Pietro Perconti & Alessio Plebe - 2020 - Cognition 203:104365.
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  19.  6
    Deep Learning Algorithms and Multicriteria Decision-Making Used in Big Data: A Systematic Literature Review.Mei Yang, Shah Nazir, Qingshan Xu & Shaukat Ali - 2020 - Complexity 2020:1-18.
    The data are ever increasing with the increase in population, communication of different devices in networks, Internet of Things, sensors, actuators, and so on. This increase goes into different shapes such as volume, velocity, variety, veracity, and value extracting meaningful information and insights, all are challenging tasks and burning issues. Decision-making based on multicriteria is one of the most critical issues solving ways to select the most suitable decision among a number of alternatives. Deep learning algorithms and multicriteria-based (...)
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    Complex Deep Learning and Evolutionary Computing Models in Computer Vision.Li Zhang, Chee Peng Lim & Jungong Han - 2019 - Complexity 2019:1-2.
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  21.  13
    Using Deep Learning to Predict Sentiments: Case Study in Tourism.C. A. Martín, J. M. Torres, R. M. Aguilar & S. Diaz - 2018 - Complexity 2018:1-9.
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  22. Potato Classification Using Deep Learning.Abeer A. Elsharif, Ibtesam M. Dheir, Alaa Soliman Abu Mettleq & Samy S. Abu-Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):1-8.
    Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties of (...)
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  23.  22
    Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications.Fangyuan Lei, Jun Cai, Qingyun Dai & Huimin Zhao - 2019 - Complexity 2019:1-12.
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  24.  48
    Computational Functionalism for the Deep Learning Era.Ezequiel López-Rubio - 2018 - Minds and Machines 28 (4):667-688.
    Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of (...) learning, namely the extraction of successively higher level features and the multiple layer structure, which are closely related to each other. Then some indications about the framing of this heated debate are given. After that, an assessment of the value of artificial deep networks as models of the human brain is given from the similarity perspective of model representation. Finally, a new version of computational functionalism is proposed which addresses the specificity of deep neural computation better than classic, program based computational functionalism. (shrink)
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  25. Type of Grapefruit Classification Using Deep Learning.Mohammed M. Abu-Saqer, Samy S. Abu-Naser & Mohammed O. Al-Shawwa - 2020 - International Journal of Academic Information Systems Research (IJAISR) 4 (1):1-5.
    Fruit has been recognized as a good source of vitamins and minerals, and for their role in preventing vitamin C and vitamin A deficiencies. People who eat fruit as part of an overall healthy diet generally have a reduced risk of chronic diseases. Fruit are important sources of many nutrients, including potassium, fiber, vitamin C and folate (folic acid). One of important types of fruit is Grapefruit . Grapefruit is a tropical citrus fruit known its sweet and somewhat sour taste. (...)
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  26. Type of Tomato Classification Using Deep Learning.Mahmoud A. Alajrami & Samy S. Abu-Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):21-25.
    Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which (...)
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  27.  20
    Deep-Learning Networks and the Functional Architecture of Executive Control.Richard P. Cooper - 2017 - Behavioral and Brain Sciences 40.
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  28.  2
    Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers.Shan Wang, Sulaiman Khan, Chuyi Xu, Shah Nazir & Abdul Hafeez - 2020 - Complexity 2020:1-7.
    With the increase in the number of electronic devices and developments in the communication system, security becomes one of the challenging issues. Users are interacting with each other through different heterogeneous devices such as smart sensors, actuators, and many other devices to process, monitor, and communicate different scenarios of real life. Such communication needs a secure medium through which users can communicate in a secure and reliable way so that their information may not be lost. The proposed study is an (...)
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  29. Analyzing Types of Cherry Using Deep Learning.Izzeddin A. Alshawwa, Hosni Qasim El-Mashharawi, Mohammed Elkahlout, Mohammed O. Al-Shawwa & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 4 (1):1-5.
    A cherry is the fruit of many plants of the genus Prunus, and is a fleshy drupe (stone fruit), Michigan's Northwest Lower Peninsula is the largest producer of tart cherries in the United States. In fact, grow 75% of the country's variety of mighty Montmorency cherries. We use these Ruby Red Morsels of Joy in over 200 cherry products like Salsas, Chocolate Covered Cherries, Cherry Nut Mixes, and much more. Cherry fruits are rich in vitamins and minerals, and it is (...)
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  30. Image-Based Tomato Leaves Diseases Detection Using Deep Learning.Belal A. M. Ashqar & Samy S. Abu-Naser - 2019 - International Journal of Academic Engineering Research (IJAER) 2 (12):10-16.
    : Crop diseases are a key danger for food security, but their speedy identification still difficult in many portions of the world because of the lack of the essential infrastructure. The mixture of increasing worldwide smartphone dispersion and current advances in computer vision made conceivable by deep learning has cemented the way for smartphone-assisted disease identification. Using a public dataset of 9000 images of infected and healthy Tomato leaves collected under controlled conditions, we trained a deep convolutional (...)
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  31.  6
    Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model.Yang He, Shah Nazir, Baisheng Nie, Sulaiman Khan & Jianhui Zhang - 2020 - Complexity 2020:1-6.
    Mobile and pervasive computing is one of the recent paradigms available in the area of information technology. The role of pervasive computing is foremost in the field where it provides the ability to distribute computational services to the surroundings where people work and leads to issues such as trust, privacy, and identity. To provide an optimal solution to these generic problems, the proposed research work aims to implement a deep learning-based pervasive computing architecture to address these problems. Long (...)
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  32. Classification of Apple Fruits by Deep Learning.Mohammed O. Al-Shawwa & Samy S. Abu-Naser - 2020 - International Journal of Academic Engineering Research (IJAER) 3 (12):1-7.
    Abstract: Apple is a plant species that follows the apple genus, which is a fruit because it contains seeds of the pink family. It is one of the most fruit trees in terms of agriculture. The apple tree is small in length from 3 to 12 meters. Several recent studies have shown many health benefits of apples. It helps with the strengthening of the brain, heart, and stomach. It is used in the treatment of joint pain and limberness. It is (...)
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  33.  8
    DLI: A Deep Learning-Based Granger Causality Inference.Wei Peng - 2020 - Complexity 2020:1-6.
    Integrating autoencoder, long short-term memory, and convolutional neural network, we propose an interpretable deep learning architecture for Granger causality inference, named deep learning-based Granger causality inference. Two contributions of the proposed DLI are to reveal the Granger causality between the bitcoin price and S&P index and to forecast the bitcoin price and S&P index with a higher accuracy. Experimental results demonstrate that there is a bidirectional but asymmetric Granger causality between the bitcoin price and S&P index. (...)
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  34.  7
    A Systematic Review of Deep Learning Approaches to Educational Data Mining.Antonio Hernández-Blanco, Boris Herrera-Flores, David Tomás & Borja Navarro-Colorado - 2019 - Complexity 2019:1-22.
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  35.  93
    Judging Machines: Philosophical Aspects of Deep Learning.Arno Schubbach - forthcoming - Synthese 198 (2):1807-1827.
    Although machine learning has been successful in recent years and is increasingly being deployed in the sciences, enterprises or administrations, it has rarely been discussed in philosophy beyond the philosophy of mathematics and machine learning. The present contribution addresses the resulting lack of conceptual tools for an epistemological discussion of machine learning by conceiving of deep learning networks as ‘judging machines’ and using the Kantian analysis of judgments for specifying the type of judgment they are (...)
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  36.  11
    Digging Deeper on “DeepLearning: A Computational Ecology Approach.Massimo Buscema & Pier Luigi Sacco - 2017 - Behavioral and Brain Sciences 40.
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  37.  36
    Shared Decision‐Making and Maternity Care in the Deep Learning Age: Acknowledging and Overcoming Inherited Defeaters.Keith Begley, Cecily Begley & Valerie Smith - 2021 - Journal of Evaluation in Clinical Practice 27 (3):497–503.
    In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, (...)
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  38.  33
    The Unbearable Shallow Understanding of Deep Learning.Alessio Plebe & Giorgio Grasso - 2019 - Minds and Machines 29 (4):515-553.
    This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deeplearning architectures. A precise account of “success” is given, in (...)
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  39.  3
    Pedestrian Re-Recognition Algorithm Based on Optimization Deep Learning-Sequence Memory Model.Feng-Ping An - 2019 - Complexity 2019:1-16.
    Pedestrian re-recognition is an important research because it affects applications such as intelligent monitoring, content-based video retrieval, and human-computer interaction. It can help relay tracking and criminal suspect detection in large-scale video surveillance systems. Although the existing traditional pedestrian re-recognition methods have been widely applied to address practical problems, they have deficiencies such as low recognition accuracy, inefficient computation, and difficulty to adapt to specific applications. In recent years, the pedestrian re-recognition algorithms based on deep learning have been (...)
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  40.  8
    The Unbearable Shallow Understanding of Deep Learning.Alessio Plebe & Giorgio Grasso - 2019 - Minds and Machines 29 (4):515-553.
    This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deeplearning architectures. A precise account of “success” is given, in (...)
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  41.  1
    A Deep Learning Method for Latent Space Analysis of Multiple Seismic Attributes.Bradley C. Wallet & Thang N. Ha - forthcoming - Interpretation:1-40.
    Seismic attributes are a well-established method for highlighting subtle features in seismic data to improve interpretability and suitability for quantitative analysis. Seismic attributes are an enabling technology in such areas as thin-bed analysis, geobody extraction, and seismic geomorphology. Seismic attributes are mathematical functions of the data that are designed to exploit geologic and/or geophysical principles to provide meaningful information about underlying processes. Seismic attributes often suffer from an “abundance of riches” because the high dimensionality of seismic attributes may cause great (...)
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  42. Applying Deep Learning Techniques to Estimate Patterns of Musical Gesture.David Dalmazzo, George Waddell & Rafael Ramírez - 2021 - Frontiers in Psychology 11.
    Repetitive practice is one of the most important factors in improving the performance of motor skills. This paper focuses on the analysis and classification of forearm gestures in the context of violin playing. We recorded five experts and three students performing eight traditional classical violin bow-strokes: martelé, staccato, detaché, ricochet, legato, trémolo, collé, and col legno. To record inertial motion information, we utilized the Myo sensor, which reports a multidimensional time-series signal. We synchronized inertial motion recordings with audio data to (...)
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  43.  19
    Quantum Deep Learning Triuniverse.Angus McCoss - 2016 - Journal of Quantum Information Science 6 (4).
    An original quantum foundations concept of a deep learning computational Universe is introduced. The fundamental information of the Universe (or Triuniverse)is postulated to evolve about itself in a Red, Green and Blue (RGB) tricoloured stable self-mutuality in three information processing loops. The colour is a non-optical information label. The information processing loops form a feedback-reinforced deep learning macrocycle with trefoil knot topology. Fundamental information processing is driven by ψ-Epistemic Drive, the Natural appetite for information selected for (...)
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  44.  2
    Hybridized Deep Learning Model for Perfobond Rib Shear Strength Connector Prediction.Jamal Abdulrazzaq Khalaf, Abeer A. Majeed, Mohammed Suleman Aldlemy, Zainab Hasan Ali, Ahmed W. Al Zand, S. Adarsh, Aissa Bouaissi, Mohammed Majeed Hameed & Zaher Mundher Yaseen - 2021 - Complexity 2021:1-21.
    Accurate and reliable prediction of Perfobond Rib Shear Strength Connector is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression, artificial neural network, and M5 tree model. (...)
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  45.  13
    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 (...)
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  46.  1
    Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox.Karl M. Kuntzelman, Jacob M. Williams, Phui Cheng Lim, Ashok Samal, Prahalada K. Rao & Matthew R. Johnson - 2021 - Frontiers in Human Neuroscience 15.
    In recent years, multivariate pattern analysis has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging, electroencephalography, and other neuroimaging methodologies. In a similar time frame, “deep learning” has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much (...)
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  47.  2
    Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia.Nahla F. Omran, Sara F. Abd-el Ghany, Hager Saleh, Abdelmgeid A. Ali, Abdu Gumaei & Mabrook Al-Rakhami - 2021 - Complexity 2021:1-13.
    The novel coronavirus disease is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide. Because of the unpredictable outbreak nature and the virus’s pandemic intensity, people are experiencing depression, anxiety, and other strain reactions. The response to prevent and control the new coronavirus pneumonia has reached a crucial point. Therefore, it is essential—for safety and prevention purposes—to promptly predict and forecast the virus outbreak in the course of this troublesome time to have (...)
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  48.  6
    Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications.Nan Liu, Liangyu Li, Bing Hao, Liusong Yang, Tonghai Hu, Tao Xue, Shoujun Wang & Xingmao Shao - 2020 - Complexity 2020:1-11.
    In smart cities and factories, robotic applications require high accuracy and security, which depends on precise inverse dynamics modeling. However, the physical modeling methods cannot include the nondeterministic factors of the manipulator, such as flexibility, joint clearance, and friction. In this paper, the Semiparametric Deep Learning method is proposed to model robot inverse dynamics. SDL is a type of deep learning framework, designed for optimal inference, combining the Rigid Body Dynamics model and Nonparametric Deep (...) model. The SDL model takes advantage of the global characteristics of classic RBD and the powerful fitting capabilities of the deep learning approach. Moreover, the parametric and nonparametric parts of the SDL model can be optimized at the same time instead of being optimized separately. The proposed method is validated using experiments, performed on a UR5 robotic platform. The results show that the performance of SDL model is better than that of RBD model and NDL model. SDL can always provide relatively accurate joint torque prediction, even when the RBD or NDL model is not accurate. (shrink)
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  49.  10
    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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  50. Deep Learning in a Disorienting World.Jon F. Wergin - 2019 - Cambridge University Press.
    Much has been written about the escalating intolerance of worldviews other than one's own. Reasoned arguments based on facts and data seem to have little impact in our increasingly post-truth culture dominated by social media, fake news, tribalism, and identity politics. Recent advances in the study of human cognition, however, offer insights on how to counter these troubling social trends. In this book, psychologist Jon F. Wergin calls upon recent research in learning theory, social psychology, politics, and the arts (...)
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