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BiLSTM and SqueezeNet With Transfer Learning for EEG Motor Imagery classification: Validation With Own Dataset

IEEE ACCESS 2023年 11卷 136422-136436页

作者： Lazcano-Herrera, Alicia Guadalupe Fuentes-Aguilar, Rita Q. Ramirez-Morales, Adrian Alfaro-Ponce, Mariel Tecnol Monterrey Sch Engn & Sci Monterrey 64849 Mexico Tecnol Monterrey Inst Adv Mat Sustainable Mfg Monterrey 64849 Mexico UPIITA Inst Politecn Nacl Mexico City 07340 Mexico

Transfer Learning (TL) is a methodology that allows the re-train of a Machine Learning (ML) algorithm (like Neural Networks or NN's) for a new task with the advantage of the previous training acquired knowledge;with this methodology, it is possible to train NNs for a new task even if the data is scarce. The present study uses this approach to train NNs to classify Electroencephalography (EEG) signals that include Movement/Imagery (MI), first with a publicly available data set and then using it to validate the training process of a small dataset of acquired data. The first part of the article describes the methodology for acquiring EEG signals that imitated the information found in the publicly available dataset Physionet Motor/Imagery. The second part compares the training process for NNs. The first NN is a Bidirectional Long-Short Term Memory (BiLSTM) trained from scratch with the Physionet dataset, and the second NN is a CNN called SqueezeNet trained following the TL method with the small acquired dataset, reaching an accuracy of 91.25% in the BiLSTM with the scratch method and an accuracy of 92.33% with the transfer learning method for the EEG MI signal classification.

关键词： Electroencephalography Task analysis Training classification algorithms Transfer learning Prediction algorithms Machine learning algorithms BCI EEG signal processing machine learning transfer learning motor/imagery

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Framework for multi-criteria assessment of classification models for the purposes of credit scoring

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JOURNAL OF BIG DATA 2023年第1期10卷 94页

作者： Ziemba, Pawel Becker, Jaroslaw Becker, Aneta Radomska-Zalas, Aleksandra Univ Szczecin Inst Management Szczecin Poland Jacob Paradies Univ Fac Technol Gorzow Wielkopolski Poland West Pomeranian Univ Technol Fac Econ Szczecin Poland

The main dilemma in the case of classification tasks is to find-from among many combinations of methods, techniques and values of their parameters-such a structure of the classifier model that could achieve the best accuracy and efficiency. The aim of the article is to develop and practically verify a framework for multi-criteria evaluation of classification models for the purposes of credit scoring. The framework is based on the Multi-Criteria Decision Making (MCDM) method called PROSA (PROMETHEE for Sustainability Analysis), which brought added value to the modelling process, allowing the assessment of classifiers to include the consistency of the results obtained on the training set and the validation set, and the consistency of the classification results obtained for the data acquired in different time periods. The study considered two aggregation scenarios of TSC (Time periods, Sub-criteria, Criteria) and SCT (Sub-criteria, Criteria, Time periods), in which very similar results were obtained for the evaluation of classification models. The leading positions in the ranking were taken by borrower classification models using logistic regression and a small number of predictive variables. The obtained rankings were compared to the assessments of the expert team, which turned out to be very similar.

关键词： classification algorithms Model evaluation Multi-criteria decision making PROSA PROMETHEE II Credit scoring

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Robust Automatic Modulation classification in Low Signal to Noise Ratio

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IEEE ACCESS 2023年 11卷 7860-7872页

作者： An, To Truong Lee, Byung Moo Sejong Univ Seoul South Korea Sejong Univ Dept Intelligent Mechatron Engn & Convergence Engn Seoul 05006 South Korea

In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal modulation before they are demodulated. Deep learning (DL)-based AMC has been proposed as an efficient method of achieving high classification performance. However, most current DL-AMC methods have limited generalization capabilities under varying noise conditions, especially at low signal-to-noise ratios (SNRs). Therefore, these methods can not be directly applied to practical systems. In this paper, we propose a threshold autoencoder denoiser convolutional neural network (TADCNN), which consists of a threshold autoencoder denoiser (TAD) and a convolutional neural network (CNN). TADs reduce noise power and clean input signals, which are then passed on to CNN for classification. The TAD network generally consists of three components: the batch normalization layer, the autoencoder, and the threshold denoise. The threshold denoise component uses an auto-learning threshold sub-network to compute thresholds automatically. According to experiments, AMC with TAD improved classification accuracy by 70% at low SNR compared with a model without a denoiser. Additionally, our model achieves an average accuracy of 66.64% on the RML2016.10A dataset, which is 6% to 18% higher than the current AMC model.

关键词： Modulation Feature extraction Signal to noise ratio Convolutional neural networks classification algorithms Signal processing algorithms Deep learning Automatic modulation classification autoencoder denoiser denoise signal convolutional neural network automatic modulation classification

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Gauss-Mapping Black Widow Optimization With Deep Extreme Learning Machine for Android Malware classification Model

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IEEE ACCESS 2023年 11卷 87062-87070页

作者： Aldehim, Ghadah Arasi, Munya A. Khalid, Majdi Aljameel, Sumayh S. Marzouk, Radwa Mohsen, Heba Yaseen, Ishfaq Ibrahim, Sara Saadeldeen Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh 11671 Saudi Arabia King Khalid Univ Coll Sci & Arts Rijal Almaa Dept Comp Sci Abha 61421 Saudi Arabia Umm Al Qura Univ Coll Comp & Informat Syst Dept Comp Sci Mecca 21955 Saudi Arabia Imam Abdulrahman Bin Faisal Univ Coll Comp Sci & Informat Technol SAUDI ARAMCO Cybersecur Chair Dept Comp Sci Dammam 31441 Saudi Arabia Future Univ Egypt Fac Comp & Informat Technol Dept Comp Sci New Cairo 11835 Egypt Prince Sattam bin Abdulaziz Univ Dept Comp & Self Dev Deanship Preparatory Year Al Kharj 16278 Saudi Arabia

Nowadays, the malware on the Android platform is found to be increasing. With the prevalent use of code obfuscation technology, the precision of antivirus software and classical detection techniques is low. Classical detection techniques of signature matching and manual analysis have exposed issues like low accuracy and slow detection speed. Several authors have overcome the issue of Android malware detection utilizing machine learning (ML) techniques and had more research outcomes. With the growth of deep learning (DL), many researchers started to use DL methods for detecting Android malware. This article introduces a Gauss-Mapping Black Widow Optimization with Deep Learning Enabled Android Malware classification (GBWODL-AMC) model. The major intention of the GBWODL-AMC technique lies in the automated classification of Android malware. To accomplish this, the GBWODL-AMC technique involves the design of GBWO based feature selection approach to enhance the classification performance. For Android malware classification purposes, the GBWODL-AMC technique employs a deep extreme learning machine (DELM) model and its parameter are optimally selected by the ant lion optimization (ALO) algorithm. The simulation analysis of the GBWODL-AMC technique is tested on CICAndMal2017 dataset. Extensive experimental results signify the better performance of the GBWODL-AMC technique over other malware detectors with maximum accuracy of 98.95%.

关键词： Operating systems Ransomware Optimization Feature extraction classification algorithms Static analysis Heuristic algorithms Android malware machine learning cybersecurity feature selection parameter tuning

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Ensemble classification With Noisy Real-Valued Base Functions

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2023年第4期41卷 1067-1080页

作者： Ben-Hur, Yuval Goren, Asaf Klang, Da-El Kim, Yongjune Cassuto, Yuval Technion Israel Inst Technol Dept Elect & Comp Engn IL-3200003 Haifa Israel Pohang Univ Sci & Technol POSTECH Dept Elect Engn Pohang 37673 South Korea Yonsei Univ Inst Convergence Res & Educ Adv Technol Seoul 03722 South Korea

In data-intensive applications, it is advantageous to perform partial processing close to the data, and communicate intermediate results to a central processor, instead of the data itself. When the communication or computation medium is noisy, the resulting degradation in computation quality at the central processor must be mitigated. We study this problem for the setup of binary classification performed by an ensemble of base functions communicating real-valued confidence levels. We propose a noise-mitigation solution that optimizes the transmission gains and aggregation coefficients of the base functions. Toward that, we formulate a post-training gradient-based optimization algorithm that minimizes the error probability given the training dataset and the noise parameters. We further derive lower and upper bounds on the optimized error probability, and show empirical results that demonstrate the enhanced performance achieved by our approach on real data.

关键词： Noise measurement Training classification algorithms Optimization Reliability Performance evaluation Hardware Machine learning classification algorithms Index Terms boosting inference algorithms distributed computing Gaussian noise

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Performance Evaluation of Phishing classification Techniques on Various Data Sources and Schemes

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IEEE ACCESS 2023年 11卷 38721-38738页

作者： Abdillah, Rahmad Shukur, Zarina Mohd, Masnizah Murah, T. S. Mohd Zamri Oh, Insu Yim, Kangbin Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Cyber Secur CYBER Bangi 43600 Malaysia Soonchunhyang Univ Dept Informat Secur Engn Asan 31538 South Korea Soonchunhyang Univ Dept Software Convergence Engn Asan 31538 South Korea

Phishing attacks have become a perilous threat in recent years, which has led to numerous studies to determine the classification technique that best detects these attacks. Several studies have made comparisons using only specific datasets and techniques without including the most crucial aspect, which is the performance evaluation of data changes. Hence, classification techniques cannot be generalized if they only use specific datasets and techniques. Therefore, this research determined the performance of classification techniques on changing data through a subset of schemes in a dataset. It was conducted using unbalanced and balanced phishing datasets, as well as subset schemes in ratios of 90:10, 80:20, 70:30, and 60:40. The thirteen most recent classification techniques used in preliminary phishing studies were compared and evaluated against ten performance measures. The results showed that the proposed schemes successfully uncover the maximum and minimum performance obtained by a classification technique. These comparisons can provide deeper insights into phishing classification techniques than related research.

关键词： Phishing Performance evaluation Random forests Feature extraction Support vector machines Uniform resource locators Benchmark testing classification algorithms performance evaluation phishing

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When is Early classification of Time Series Meaningful?

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第3期35卷 3253-3260页

作者： Wu, Renjie Der, Audrey Keogh, Eamonn J. Univ Calif Riverside Dept Comp Sci & Engn Riverside CA 92521 USA

Since its introduction two decades ago, there has been increasing interest in the problem of early classification of time series. This problem generalizes classic time series classification to ask if we can classify a time series subsequence with sufficient accuracy and confidence after seeing only some prefix of a target pattern. The idea is that the earlier classification would allow us to take immediate action, in a domain in which some practical interventions are possible. For example, that intervention might be sounding an alarm or applying the brakes in an automobile. In this work, we make a surprising claim. In spite of the fact that there are dozens of papers on early classification of time series, it is not clear that any of them could ever work in a real-world setting. The problem is not with the algorithms per se but with the vague and underspecified problem description. Essentially all algorithms make implicit and unwarranted assumptions about the problem that will ensure that they will be plagued by false positives and false negatives even if their results suggested that they could obtain near-perfect results. We will explain our findings with novel insights and experiments and offer recommendations to the community.

关键词： Time series analysis Electrocardiography classification algorithms Predictive models Heart Diseases Data models Early classification time series analysis data mining

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Electric Vehicles Charging Sessions classification Technique for Optimized Battery Charge Based on Machine Learning

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IEEE ACCESS 2023年 11卷 52444-52451页

作者： Matrone, Silvana Ogliari, Emanuele Giovanni Carlo Nespoli, Alfredo Gruosso, Giambattista Gandelli, Alessandro Politecn Milan DOE I-20156 Milan Italy Politecn Milan Dipartimento Elettron Informaz & Bioingengeria DEI I-20156 Milan Italy

The fast increase in electric vehicle (EV) usage in the last 10 years has raised the need to properly forecast their energy consumption during charge. Lithium-ion batteries have become the major storage component for electric vehicles, avoiding their overcharge can preserve their health and prolong their lifetime. This paper proposes a Machine Learning model based on the K-Nearest Neighbors classification algorithm for EV charging session duration forecast. The model forecasts the duration of the charge by assigning the event to its correct class. Each class contains the charging events whose duration is comprised of a certain interval. The only information used by the algorithm is the one available at the beginning of the charging event (arrival time, starting SOC, calendar data). The model is validated on a real-world dataset containing records of charging sessions from more than 100 users, a sensitivity analysis is performed to assess the impact of different information given as input. The effectiveness of the model with respect to the benchmark models is demonstrated with an increase in performance.

关键词： classification algorithms electric vehicle charging forecasting machine learning

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An Attention-Based Architecture for Hierarchical classification With CNNs

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IEEE ACCESS 2023年 11卷 32972-32995页

作者： Pizarro, Ivan Nanculef, Ricardo Valle, Carlos Univ Tecn Federico Santa Maria Dept Informat Valparaiso 2390123 Chile Univ Playa Ancha Dept Data Sci & Informat Valparaiso 2360072 Chile

Branch Convolutional Neural Nets have become a popular approach for hierarchical classification in computer vision and other areas. Unfortunately, these models often led to hierarchical inconsistency: predictions for the different hierarchy levels do not necessarily respect the class-subclass constraints imposed by the hierarchy. Several architectures to connect the branches have arisen to overcome this limitation. In this paper, we propose a more straightforward and flexible method: let the neural net decide how these branches must be connected. We achieve this by formulating an attention mechanism that dynamically determines how branches influence each other during training and inference. Experiments on image classification benchmarks show that the proposed method can outperform state-of-the-art models in terms of hierarchical performance metrics and consistency. Furthermore, although sometimes we found a slightly lower performance at the deeper level of the hierarchy, the model predicts much more accurately the ground-truth path between a concept and its ancestors in the hierarchy. This result suggests that the model does learn not only local class memberships but also hierarchical dependencies between concepts.

关键词： Taxonomy Measurement Computer architecture Training Convolutional neural networks classification algorithms Predictive models Attention mechanisms deep learning hierarchical classification

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Imbalanced classification Methods for Student Grade Prediction: A Systematic Literature Review

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IEEE ACCESS 2023年 11卷 1970-1989页

作者： Bujang, Siti Dianah Abdul Selamat, Ali Krejcar, Ondrej Mohamed, Farhan Cheng, Lim Kok Chiu, Po Chan Fujita, Hamido Univ Teknol Malaysia Kuala Lumpur Malaysia Japan Int Inst Technol MJIIT Kuala Lumpur 54100 Malaysia Politekn Sultan Idris Shah Fac Informat & Commun Technol Sungai Ayer Tawar 45100 Selangor Malaysia Univ Teknol Malaysia Fac Engn Malaysia & Media & Games Ctr Excellence MagicX Sch Comp Johor Baharu 81310 Malaysia Univ Hradec Kralove Fac Informat & Management Hradec Kralove 50003 Czech Republic Univ Tenaga Nas Coll Comp & Informat Kajang 43000 Selangor Malaysia Univ Malaysia Sarawak Fac Comp Sci & Informat Technol Kota Samarahan 94300 Sarawak Malaysia Iwate Prefectural Univ Fac Software & Informat Sci Takizawa Iwate 0200693 Japan Univ Granada Andalusian Res Inst Data Sci & Comp Intelligence D Granada 18011 Spain i SOMET Inc Assoc Morioka 0200104 Japan

Student success is essential for improving the higher education system student outcome. One way to measure student success is by predicting students' performance based on their prior academic grades. Concerning the significance of this area, various predictive models are widely developed and applied to help the institution identify students at risk of failure. However, building a high-accuracy predictive model is challenging due to the dataset's imbalanced nature, which caused biased results. Therefore, this study aims to review the existing research article by providing a state-of-the-art approach for handling imbalanced classification in higher education, including the best practices of dataset characteristics, methods, and comparative analysis of the proposed algorithms, focusing on student grade prediction context problems. The study also presents the most common balancing methods published from 2015 to 2021 and highlights their impact on resolving imbalanced classification in three approaches: data-level, algorithm-level, and hybrid-level. The survey results reveal that the data-level approach using SMOTE oversampling is broadly applied in determining imbalanced problems for student grade prediction. However, the application of hybrid and feature selection methods supporting the generalization of the predictive model to boost student grade prediction performance is generally lacking. Other than that, some of the strengths and weaknesses of the proposed methods are discussed and summarized for the direction of future research. The outcomes of this review will guide the professionals, practitioners, and academic researchers in dealing with imbalanced classification, mainly in the higher education field.

关键词： Predictive models Systematics Machine learning Prediction algorithms classification algorithms Data mining Bibliographies Imbalanced classification prediction model machine learning student grade prediction education systematic literature review

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