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12,893 results

2. Machine Learning Algorithms are Used for Fake Review Detection

Author

Asaad, Wesam Hameed, Allami, Ragheed, Ali, Yossra Hussain, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, García Márquez, Fausto Pedro, editor, Jamil, Akhtar, editor, Hameed, Alaa Ali, editor, and Segovia Ramírez, Isaac, editor

Published
2024
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3. Event Categorization from News Articles Using Machine Learning Techniques

Author

Shanmugavadivel, Kogilavani, Subramanian, Malliga, Vasantharan, K., Prethish, G. A., Sankar, S., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Chakravarthi, Bharathi Raja, editor, B, Bharathi, editor, García Cumbreras, Miguel Ángel, editor, Jiménez Zafra, Salud María, editor, Subramanian, Malliga, editor, Shanmugavadivel, Kogilavani, editor, and Nakov, Preslav, editor

Published
2024
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5. Classification of Colorectal Cancer Tissue Utilizing Machine Learning Algorithms

Author

Reddy, N. Sai Satwik, Manoj, A. Venkata Siva, Sowmya, V., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Rodrigues, Joel J. P. C., editor, Gupta, Suneet Kumar, editor, Cheng, Xiaochun, editor, Sarao, Pushpender, editor, and Patel, Govind Singh, editor

Published
2024
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6. Using Data Mining Techniques to Analyze Facial Expression Motion Vectors

Author

Roshanzamir, Mohamad, Alizadehsani, Roohallah, Roshanzamir, Mahdi, Shoeibi, Afshin, Gorriz, Juan M., Khosravi, Abbas, Nahavandi, Saeid, Acharya, U. Rajendra, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Moosaei, Hossein, editor, Hladík, Milan, editor, and Pardalos, Panos M., editor

Published
2024
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8. Multiclass Classification of Online Reviews Using NLP & Machine Learning for Non-english Language

Author

Sharma, Priyanka, Parwekar, Pritee, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zaynidinov, Hakimjon, editor, Singh, Madhusudan, editor, Tiwary, Uma Shanker, editor, and Singh, Dhananjay, editor

Published
2023
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9. Application of Phonocardiogram and Electrocardiogram Signal Features in Cardiovascular Abnormality Recognition

Author

Ramani, R. Geetha, Ganesh, Abhinand, Balasubramanian, Roshni, Ramkumar, Aruna Srikamakshi, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Fernando, Xavier, editor, and Chandrabose, Aravindan, editor

Published
2023
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10. Cardiac Arrhythmia Diagnosis via Multichannel Independent Component Analysis: An Approach Towards a Better Health Care System

Author

Sarfraz, Mohammad, Khan, Mudassir Hasan, Mohammed, Duraid Yahya, Hussain, Mays Dheya, Zidan, Khamis A., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Liatsis, Panos, editor, Hussain, Abir, editor, Mostafa, Salama A., editor, and Al-Jumeily, Dhiya, editor

Published
2022
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11. Improving Teledermatology Referral with Edge-AI: Mobile App to Foster Skin Lesion Imaging Standardization

Author

Vasconcelos, Maria João M., Moreira, Dinis, Alves, Pedro, Graça, Ricardo, Franco, Rafael, Rosado, Luís, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gehin, Claudine, editor, Wacogne, Bruno, editor, Douplik, Alexandre, editor, Lorenz, Ronny, editor, Bracken, Bethany, editor, Pesquita, Cátia, editor, Fred, Ana, editor, and Gamboa, Hugo, editor

Published
2022
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12. Acute Lymphoblastic Leukemia Disease Detection Using Image Processing and Machine Learning

Author

Chavan, Abhishek D., Thakre, Anuradha, Chopade, Tulsi Vijay, Fernandes, Jessica, Gawari, Omkar S., Gore, Sonal, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Mayank, editor, Tyagi, Vipin, editor, Gupta, P. K., editor, Flusser, Jan, editor, and Ören, Tuncer, editor

Published
2022
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14. eXtreme Gradient Boosting Scheme for Fundus Vessels Separation

Author

Dhar, Shirsendu, Mukherjee, Sumit, Ghoshal, Ranjit, Dhara, Bibhas Chandra, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Mukhopadhyay, Somnath, editor, Sarkar, Sunita, editor, Dutta, Paramartha, editor, Mandal, Jyotsna Kumar, editor, and Roy, Sudipta, editor

Published
2022
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15. Video-Based Depression Detection Using Support Vector Machine (SVM)

Author

Yadav, Nishu, Singh, Astha, Kumar, Divya, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Mukhopadhyay, Somnath, editor, Sarkar, Sunita, editor, Dutta, Paramartha, editor, Mandal, Jyotsna Kumar, editor, and Roy, Sudipta, editor

Published
2022
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16. To Identify Malware Using Machine Learning Algorithms

Author

Pujari, Shivam, Mandoria, H. L., Shrivastava, R. P., Singh, Rajesh, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Chaubey, Nirbhay, editor, Thampi, Sabu M., editor, and Jhanjhi, Noor Zaman, editor

Published
2022
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17. Heuristic Learning in Domain-Independent Planning: Theoretical Analysis and Experimental Evaluation

Author

Trunda, Otakar, Barták, Roman, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rocha, Ana Paula, editor, Steels, Luc, editor, and van den Herik, Jaap, editor

Published
2021
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18. End to End Deep Neural Network Classifier Design for Universal Sign Recognition

Author

Sengar, Vartika, Rameshan, Renu M., Ponkumar, Senthil, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, De Marsico, Maria, editor, Sanniti di Baja, Gabriella, editor, and Fred, Ana, editor

Published
2020
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19. Malware Detection in Word Documents Using Machine Learning

Author

Khan, Riya, Kumar, Nitesh, Handa, Anand, Shukla, Sandeep K., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Anbar, Mohammed, editor, Abdullah, Nibras, editor, and Manickam, Selvakumar, editor

Published
2021
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20. Time and Frequency Domain Features Extraction Comparison for Motor Imagery Detection

Author

Orjuela-Cañón, Alvaro D., Archila, Juan Sebastian Ramírez, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Orjuela-Cañón, Alvaro David, editor, Lopez, Jesus, editor, Arias-Londoño, Julián David, editor, and Figueroa-García, Juan Carlos, editor

Published
2021
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22. Driving Activity Recognition of Motorcyclists Using Smartphone Sensor

Author

Raheel, Aasim, Ehatisham-ul-Haq, Muhammad, Iqbal, Anees, Ali, Hanan, Majid, Muhammad, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bajwa, Imran Sarwar, editor, Sibalija, Tatjana, editor, and Jawawi, Dayang Norhayati Abang, editor

Published
2020
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25. Automatic Multiclass Classification of Foliar Leaf Diseases Using Statistical and Color Feature Extraction and Support Vector Machine

Author

Datta, Aparajita, Dey, Abhishek, Dey, Kashi Nath, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Mandal, Jyotsna Kumar, editor, Mukhopadhyay, Somnath, editor, Dutta, Paramartha, editor, and Dasgupta, Kousik, editor

Published
2019
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26. Automated Web Test for Loophole Detection

Author

Monika, Tiwari, Vijay, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Yuan, Junsong, Founding Editor, Luhach, Ashish Kumar, editor, Jat, Dharm Singh, editor, Hawari, Kamarul Bin Ghazali, editor, Gao, Xiao-Zhi, editor, and Lingras, Pawan, editor

Published
2019
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27. The Bread Recognition System with Logistic Regression

Author

Jian, Guo-Zhang, Wang, Chuin-Mu, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Chang, Chuan-Yu, editor, Lin, Chien-Chou, editor, and Lin, Horng-Horng, editor

Published
2019
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28. A Machine Learning Approach to Employability Evaluation Using Handwriting Analysis

Author

Joshi, Prachi, Ghaskadbi, Pranav, Tendulkar, Sarvesh, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ghosh, Ashish, Series Editor, Luhach, Ashish Kumar, editor, Singh, Dharm, editor, Hsiung, Pao-Ann, editor, Hawari, Kamarul Bin Ghazali, editor, Lingras, Pawan, editor, and Singh, Pradeep Kumar, editor

Published
2019
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30. A Review Paper on Dimensionality Reduction Techniques.

Author

Mulla, Faizan Riyaz and Gupta, Anil Kumar

Subjects
*FEATURE selection, *DATA compression, *MATRIX decomposition, *MACHINE learning, *RANDOM variables, *PREDICTION models
Abstract

Dimensionality Reduction (DR) is the process of reducing the numerous features or random variables under consideration to a limited number of features by obtaining a set of principal variables. These techniques cater great values in machine learning, which come in handy to simplify a classification or a regression dataset, thereby yielding a better-performing predictive model. Techniques used for DR include Feature Selection methods, Matrix Factorization, AutoEncoder methods, and Manifold Learning. Merits of DR include data compression, reduced space of storage, and removal of redundant features. This paper attempts to review various techniques used to carry out dimensionality reduction while providing an exhaustive comparative study over the merits and demerits of each of the techniques used under the empirical experiments performed by the authors whose work is being reviewed. [ABSTRACT FROM AUTHOR]

Published
2022
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32. Learning Through Non-linearly Supervised Dimensionality Reduction

Author

Grabocka, Josif, Schmidt-Thieme, Lars, Hameurlain, Abdelkader, Editor-in-chief, Küng, Josef, Editor-in-chief, Wagner, Roland, Editor-in-chief, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bellatreche, Ladjel, editor, and Mohania, Mukesh, editor

Published
2015
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33. Visible detection of chilled beef freshness using a paper-based colourimetric sensor array combining with deep learning algorithms.

Author

Lin, Yuandong, Ma, Ji, Cheng, Jun-Hu, and Sun, Da-Wen

Subjects
*MACHINE learning, *DEEP learning, *SENSOR arrays, *PATTERN recognition systems, *MULTIVARIATE analysis, *FEATURE extraction
Abstract

• Qualitative and quantitative detection of amine gases could be achieved by CSA. • A visible detection of beef freshness using the amine-responsive CSA was proposed. • ResNet34 had the best performance for beef freshness detection based on CSA. • T-SNE could further visualize and understand the classification process of DL. This study developed an innovative approach that combines a colourimetric sensor array (CSA) composed of twelve pH-response dyes with advanced algorithms, aiming to detect amine gases and assess the freshness of chilled beef. With the assistance of multivariate statistical analysis, the sensor array can effectively distinguish five amine gases and enable rapid quantification of trimethylamine vapour with a limit of detection (LOD) of 8.02 ppb and visually monitor the fresh levels of chilled beef. Moreover, the utilization of deep learning models (ResNet34, VGG16, and GoogleNet) for chilled beef freshness evaluation achieved an overall accuracy of 98.0 %. Furthermore, t -distributed stochastic neighbour embedding (t -SNE) visualized the feature extraction process and provided explanations to understand the classification process of deep learning. The results demonstrated that applying deep learning techniques in the process of pattern recognition of CSA can help in realizing the rapid, robust, and accurate assessment of chilled beef freshness. [ABSTRACT FROM AUTHOR]

Published
2024
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36. 3‐3: Invited Paper: Prediction Model for Visual Fatigue Caused by Smartphone Display Based on EEG Multi‐dimensional Features.

Author

Shi, Yunyang, Tu, Yan, Wang, Lili, and Zhu, Nianfang

Subjects
FEATURE extraction, PREDICTION models, ELECTROENCEPHALOGRAPHY, SMARTPHONES
Abstract

In this study, a prediction model for visual fatigue is developed. As input, frequential and nonlinear features are extracted from multichannel EEG, and then dimensionally reduced. In the model, bidirectional LSTM and attention layers are combined for effective learning. As a result, 82.90% accuracy, 85.26% weighted precision, 82.90% weighted recall, and 84.02% weighted F1‐score were obtained. [ABSTRACT FROM AUTHOR]

Published
2024
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37. An efficient approach for paper submission recommendation

Author

Nguyen Huu Dac, Huynh Thanh Son, and Huynh Tan Phong

Subjects
Artificial neural network, business.industry, Process (engineering), Computer science, Deep learning, Feature extraction, 02 engineering and technology, 010501 environmental sciences, Recommender system, Machine learning, computer.software_genre, 01 natural sciences, Convolutional neural network, 0202 electrical engineering, electronic engineering, information engineering, Encyclopedia, 020201 artificial intelligence & image processing, The Internet, Artificial intelligence, business, computer, 0105 earth and related environmental sciences
Abstract

Nowadays, there is a rapidly increasing number of conferences and journals in computer science that make a lot of challenges for researchers to find an appropriate venue to submit their scientific work. There is a need for a recommendation system that can support researchers for a better process of paper submission. In this paper, we present an efficient approach for building such a recommendation model by using embedding methods, Global Vector (GloVe) 1 created by Pennington et al. [1] and FastText 2 proposed by Facebook [2], Convolutional Neural Network (CNN) [3], and LSTM. The experimental results show that the combination of CNNs and FastText, CNN + FastText, can achieve the best performance in terms of the Top 1 Accuracy compared with other techniques, including the S2RSCS model, as presented in [4]. Moreover, the performance by using GloVe or FastText is much better, faster, and more stable than S2RSCS in most cases.

Published
2020

38. Paper Citation Count Prediction Based on Recurrent Neural Network with Gated Recurrent Unit

Author

Liyun Wu, Jianping Chai, and Jiaqi Wen

Subjects
Measure (data warehouse), business.industry, Computer science, Feature extraction, Regression analysis, Machine learning, computer.software_genre, Data modeling, Recurrent neural network, Convergence (routing), Artificial intelligence, Time series, Citation, business, computer
Abstract

In recent years, with the increasing investment in scientific research, the number of papers has proliferated, and the development of scientific research has also received widespread attention from scholars. Paper citations as an indicator to measure academic influence have essential reference value for future research directions. Aiming at the future paper citation number prediction problem, we propose a citation number prediction model based on the recurrent neural network method with gated recurrent unit(GRU-CPM). In this paper, we first extract features from real data sets that are useful for predicting the number of citations in papers, and then the features were input into GRU-CPM for prediction. Finally, the prediction results are compared with other regression models. Experimental results demonstrate that the GRU-CPM has higher prediction accuracy and faster convergence speed. The time series prediction of citation count is better than existing methods.

Published
2020

39. A Review Paper on Feature Selection Techniques and Artificial Neural Networks Architectures Used in Thermography for Early Stage Detection of Breast Cancer

Author

Kumod Kumar Gupta, Pallavi Pahadiya, and Ritu Vijay

Subjects
medicine.diagnostic_test, Artificial neural network, business.industry, Computer science, Feature extraction, Feature selection, Machine learning, computer.software_genre, medicine.disease, Reduction (complexity), ComputingMethodologies_PATTERNRECOGNITION, Breast cancer, Feature (computer vision), medicine, Mammography, Artificial intelligence, Sensitivity (control systems), business, computer
Abstract

For primary stage breast cancer detection, mammography techniques are being used for a long time. The disadvantage associated with the technology is taking a long time required by the radiologist to diagnose each image pattern. Moreover, this technique is not suitable for the detection of cancer in younger women. At present, the technique has been upgraded by using the technology-based classifiers, which adds the accuracy, specificity, sensitivity, and time requirement by differentiating malignant and benign. Only limited reviews are available, that focuses on features reduction algorithm and artificial neural networks architecture to enhance the detection techniques. The purpose of this paper is to collect the recently used techniques, algorithms, and future scope related to thermography. The author further suggests the technique to divide the features according to different age groups and to find a unique feature for all.

Published
2020

40. Review Paper on Leaf Diseases Detection and Classification Using Various CNN Techniques

Author

Manjeet Singh and Twinkle Dalal

Subjects
Disease detection, business.industry, Computer science, fungi, Feature extraction, Direct observation, food and beverages, Machine learning, computer.software_genre, Training methods, Convolutional neural network, restrict, Agricultural economy, Artificial intelligence, business, computer, Cropping
Abstract

Majority of Indian population depends on agribusiness for its survival, and it plays a vital role in every nation’s economy. The disease is spread to other plants. Early detection of disease is a significant thing. Detection models to detect the disease are built by direct observation of every plant. This is essential as we can take parameters to restrict. Hence, healthy cropping is necessary for the growing agricultural economy. A better yield of crop is dependent on many factors including disease detection. The on time disease detection helps the farmers to save their crop yield as the remedies can be given on time. In order to solve the problem, various convolution neural network architectures have been designed and tested on labelled data to obtain high accuracy in classification and detection of disease. This work deals with the brief and detailed study of various techniques used for classification and detection of disease in plants based on feature extraction and different training methods.

Published
2020

41. A Survey Paper on Plant Disease Identification Using Machine Learning Approach

Author

Pooja Akulwar, Jyoti Shirahatti, and Rutuja Patil

Subjects
Computer science, business.industry, media_common.quotation_subject, fungi, Feature extraction, food and beverages, 020207 software engineering, Image processing, 02 engineering and technology, Disease, Machine learning, computer.software_genre, Plant disease, Support vector machine, Identification (information), Agriculture, 0202 electrical engineering, electronic engineering, information engineering, Quality (business), Artificial intelligence, business, computer, media_common
Abstract

Agriculture plays an important role in farmer’s life. Sometimes manual identification of disease is time consuming and need of labor is more. One of the most important facts that reduce the growth of plants is disease attack. Overall study about agriculture shows that quality and quantity of agricultural products may be reduced due to various factors of plant diseases. These diseases can be more easily identified by using machine learning approach as compared to manual method. Hence machine learning method can be used to identify the affected leaf images.Images captured by camera will be processed using different image processing technique. These techniques will help in identifying plant diseases thereby increasing the yield of plants. This survey paper describes plant disease identification using Machine Learning Approach and study in detail about various techniques for disease identification and classification is also done.

Published
2018

42. [Research Paper] On the Use of Machine Learning Techniques Towards the Design of Cloud Based Automatic Code Clone Validation Tools

Author

Golam Mostaeen, Chanchal K. Roy, Banani Roy, Jeffrey Svajlenko, and Kevin A. Schneider

Subjects
Source code, Syntax (programming languages), Cloning (programming), Computer science, business.industry, media_common.quotation_subject, Feature extraction, Maintainability, 020207 software engineering, 02 engineering and technology, Machine learning, computer.software_genre, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Code (cryptography), Clone (computing), Software system, Artificial intelligence, business, computer, media_common
Abstract

A code clone is a pair of code fragments, within or between software systems that are similar. Since code clones often negatively impact the maintainability of a software system, a great many numbers of code clone detection techniques and tools have been proposed and studied over the last decade. To detect all possible similar source code patterns in general, the clone detection tools work on syntax level (such as texts, tokens, AST and so on) while lacking user-specific preferences. This often means the reported clones must be manually validated prior to any analysis in order to filter out the true positive clones from task or user-specific considerations. This manual clone validation effort is very time-consuming and often error-prone, in particular for large-scale clone detection. In this paper, we propose a machine learning based approach for automating the validation process. In an experiment with clones detected by several clone detectors in several different software systems, we found our approach has an accuracy of up to 87.4% when compared against the manual validation by multiple expert judges. The proposed method shows promising results in several comparative studies with the existing related approaches for automatic code clone validation. We also present our experimental results in terms of different code clone detection tools, machine learning algorithms and open source software systems.

Published
2018

43. Supervised Papers Classification on Large-Scale High-Dimensional Data with Apache Spark

Author

Athanasios Fevgas, Leonidas Akritidis, and Panayiotis Bozanis

Subjects
Computer science, business.industry, Data manipulation language, Supervised learning, Feature extraction, 02 engineering and technology, Digital library, Machine learning, computer.software_genre, Statistical classification, Categorization, Margin (machine learning), 020204 information systems, Spark (mathematics), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, business, computer
Abstract

The problem of classifying a research article into one or more fields of science is of particular importance for the academic search engines and digital libraries. A robust classification algorithm offers the users a wide variety of useful tools, such as the refinement of their search results, the browsing of articles by category, the recommendation of other similar articles, etc. In the current literature we encounter approaches which attempt to address this problem without taking into consideration important parameters such as the previous history of the authors and the categorization of the scientific journals which publish the articles. In addition, the existing works overlook the huge volume of the involved academic data. In this paper, we expand an existing effective algorithm for research articles classification, and we parallelize it on Apache Spark –a parallelization framework which is capable of sharing large amounts of data into the main memory of the nodes of a cluster– to enable the processing of large academic datasets. Furthermore, we present data manipulation methodologies which are useful not only for this particular problem, but also for most parallel machine learning approaches. In our experimental evaluation, we demonstrate that our proposed algorithm is considerably more accurate than the supervised learning approaches implemented within the machine learning library of Spark, whereas it outperforms them in terms of execution speed by a significant margin.

Published
2018

45. Yet Another ADNI Machine Learning Paper? Paving the Way Towards Fully-Reproducible Research on Classification of Alzheimer’s Disease

Author

Jorge Samper-González, Hugo Bertin, Stanley Durrleman, Sabrina Fontanella, Theodoros Evgeniou, Olivier Colliot, Marie-Odile Habert, and Ninon Burgos

Subjects
Multiple kernel learning, Computer science, business.industry, BETA (programming language), Image (category theory), Data management, Core component, 05 social sciences, Feature extraction, Disease classification, Machine learning, computer.software_genre, 050105 experimental psychology, Support vector machine, 03 medical and health sciences, 0302 clinical medicine, 0501 psychology and cognitive sciences, Artificial intelligence, business, computer, 030217 neurology & neurosurgery, computer.programming_language
Abstract

In recent years, the number of papers on Alzheimer’s disease classification has increased dramatically, generating interesting methodological ideas on the use machine learning and feature extraction methods. However, practical impact is much more limited and, eventually, one could not tell which of these approaches are the most efficient. While over 90% of these works make use of ADNI an objective comparison between approaches is impossible due to variations in the subjects included, image pre-processing, performance metrics and cross-validation procedures. In this paper, we propose a framework for reproducible classification experiments using multimodal MRI and PET data from ADNI. The core components are: (1) code to automatically convert the full ADNI database into BIDS format; (2) a modular architecture based on Nipype in order to easily plug-in different classification and feature extraction tools; (3) feature extraction pipelines for MRI and PET data; (4) baseline classification approaches for unimodal and multimodal features. This provides a flexible framework for benchmarking different feature extraction and classification tools in a reproducible manner. Data management tools for obtaining the lists of subjects in AD, MCI converter, MCI non-converters, CN classes are also provided. We demonstrate its use on all (1519) baseline T1 MR images and all (1102) baseline FDG PET images from ADNI 1, GO and 2 with SPM-based feature extraction pipelines and three different classification techniques (linear SVM, anatomically regularized SVM and multiple kernel learning SVM). The highest accuracies achieved were: 91% for AD vs CN, 83% for MCIc vs CN, 75% for MCIc vs MCInc, 94% for AD-A\(\displaystyle \beta \)+ vs CN-A\(\displaystyle \beta \)- and 72% for MCIc-A\(\displaystyle \beta \)+ vs MCInc-A\(\displaystyle \beta \)+. The code will be made publicly available at the time of the conference (https://gitlab.icm-institute.org/aramislab/AD-ML).

Published
2017

46. Scoring based unsupervised approach to classify research papers

Author

B. Anil, N. Rajasimha, U. Rajath Kumar, K. M. Anil Kumar, and S G Gagan

Subjects
business.industry, Computer science, Feature extraction, Big data, Context (language use), computer.software_genre, Machine learning, Class (biology), Electronic mail, Unsupervised learning, Data mining, Artificial intelligence, Cluster analysis, business, computer, Clustering coefficient
Abstract

Platforms for publishing research papers are increasing largely that contribute to big data as their volume is humongous and are unstandardized. Classification of this huge chunk of data is one of the biggest challenges in Information Retrieval. In this paper we discuss a scoring based unsupervised learning approach to extract relevant features and classify the research papers according to their content on a two class dataset. Feature extraction is carried out by analyzing the sections of a research paper and scoring them, followed by a two level hybrid classification technique based on title and conceptual summary using graph clustering. Promising experimental results are observed for data set using our research paper classification method. We present the experimental results of our proposed algorithm for feature extraction and clustering and compare the same with different approaches reported in literature.

Published
2016

47. EHDC: enhanced dilated convolution framework for underwater blurred target recognition.

Author

Cai, Lei, Qin, Xiaochen, and Xu, Tao

Subjects
MACHINE learning, AUTONOMOUS underwater vehicles, RECOGNITION (Psychology), FEATURE extraction, PROBLEM solving
Abstract

The autonomous underwater vehicle (AUV) has a problem with feature loss when recognizing small targets underwater. At present, algorithms usually use multi-scale feature extraction to solve the problem, but this method increases the computational effort of the algorithm. In addition, low underwater light and turbid water result in incomplete information on target features. This paper proposes an enhanced dilated convolution framework (EHDC) for underwater blurred target recognition. Firstly, this paper extracts small target features through hybrid dilated convolution networks, increasing the perceptive field of the algorithm without increasing the computational power of the algorithm. Secondly, the proposed algorithm learns spatial semantic features through an adaptive correlation matrix and compensates for the missing features of the target. Finally, this paper fuses spatial semantic features and visual features for the recognition of small underwater blurred targets. Experiments show that the proposed method improves the recognition accuracy by 1.04% compared to existing methods when recognizing small underwater blurred targets. [ABSTRACT FROM AUTHOR]

Published
2023
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48. What Is Hidden in Clear Sight and How to Find It—A Survey of the Integration of Artificial Intelligence and Eye Tracking.

Author

Kędras, Maja and Sobecki, Janusz

Subjects
ARTIFICIAL intelligence, EYE tracking, FEATURE extraction, MACHINE learning
Abstract

This paper presents an overview of the uses of the combination of eye tracking and artificial intelligence. In the paper, several aspects of both eye tracking and applied AI methods have been analyzed. It analyzes the eye tracking hardware used along with the sampling frequency, the number of test participants, additional parameters, the extraction of features, the artificial intelligence methods used and the methods of verification of the results. Finally, it includes a comparison of the results obtained in the analyzed literature and a discussion about them. [ABSTRACT FROM AUTHOR]

Published
2023
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49. Data-Driven Method for Vacuum Prediction in the Underwater Pump of a Cutter Suction Dredger.

Author

Chen, Hualin, Yuan, Zihao, Wang, Wangming, Chen, Shuaiqi, Jiang, Pan, and Wei, Wei

Subjects
DREDGES, BIG data, MACHINE learning, DATA mining, RANK correlation (Statistics), FEATURE extraction
Abstract

Vacuum is an important parameter in cutter suction dredging operations because the equipment is underwater and can easily fail. It is necessary to analyze other parameters related to the vacuum to make real-time predictions about it, which can improve the construction efficiency of the dredger under abnormal working conditions. In this paper, a data-driven method for predicting the vacuum of the underwater pump of the cutter suction dredger (CSD) is proposed with the help of big data, machine learning, data mining, and other technologies, and based on the historical data of "Hua An Long" CSD. The method eliminates anomalous data, standardizes the data set, and then relies on theory and engineering experience to achieve feature extraction using the Spearman correlation coefficient. Then, six machine learning methods were employed in this study to train and predict the data set, namely, lasso regression (lasso), elastic network (Enet), gradient boosting decision tree (including traditional GBDT, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM)), and stacking. The comparison of the indicators obtained through multiple rounds of feature number iteration shows that the LightGBM model has high prediction accuracy, a good running time, and a generalization ability. Therefore, the methodological framework proposed in this paper can help to improve the efficiency of underwater pumps and issue timely warnings in abnormal working conditions. [ABSTRACT FROM AUTHOR]

Published
2024
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50. DETECTION OF DEEP FAKES USING DEEP LEARNING.

Author

D., ANJANI SUPUTRI DEVI, T., SAI KISHORE, V., VENKATA SRI SAI TEJASWI, G., VENKATA SUBRAHMANYA SIVARAM, K., SUBHAN SAHEB S., and K., VIKAS KUMAR

Subjects
DEEP learning, MACHINE learning, CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, RECURRENT neural networks, FEATURE extraction
Abstract

Deep learning algorithms have simplified the process of creating indistinguishable synthetic videos, or deep fakes, because of the unparalleled increase in processing power. It is concerning because these face-swapped manipulations are often used in a variety of contexts, such as blackmail and political manipulation. This paper presents a revolutionary deep learning-based approach to accurately discriminating between real and Artificial Intelligence (AI)-generated false films. Using a ResNext Convolutional Neural Network (CNN) for frame-level feature extraction, this method makes use of an automated mechanism intended to identify replacement and re-enactment deep fakes. A Recurrent Neural Network (RNN) equipped with Long Short-Term Memory (LSTM) training is utilized to classify videos and distinguish between real and modified ones. The system demonstrates the effectiveness of a straightforward and reliable methodology, in addition to utilizing complex neural network topologies. Through testing, this paper showcases how well the system can accurately identify videos playing a crucial role in ongoing initiatives to combat the increasing dangers posed by the proliferation of deep fake content in society. [ABSTRACT FROM AUTHOR]

Published
2024
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