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Generative Adversarial Networks for Motor Imagery Classification using Wavelet Packet Decomposition and Complex Morlet Transform

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Multimedia Tools and Applications 2025年 1-24页

作者： Shirodkar, Vaishali Edla, Damodar Reddy Kumari, Annu Department of Computer Science and Engineering National Institute of Technology Goa Kottamoll Plateau Goa Cuncolim403703 India Information Technology Department Goa College of Engineering Farmagudi Goa Ponda403401 India

The area of brain-computer interface research is widely spreading as it has a diverse array of potential applications. Motor imagery classification is a boon to several people with motor impairment. Low accuracy and datasets with few trial recordings present challenges for this classification. This research offers a novel approach to these problems in electroencephalography signal classification for efficient control of neuroprosthetic devices. By using wavelet packet decomposition the proposed method divides the EEG signal into eight frequency bands. Each of these bands were then evaluated by a one-dimensional convolution neural network to find the most discriminative band. Only EEG signals filtered with these bands were used to generate time-frequency images called scalograms using complex Morlet transform. These scalograms were then classified using a two-dimensional convolution neural network optimized for spatial-temporal feature extraction and classification. To mitigate data scarcity, generative adversarial networks were used for effective data augmentation. Experimental evaluation on a benchmark dataset demonstrated substantial improvement: the two-dimensional convolution neural network achieved 93.63% accuracy, surpassing the 85% achieved by the one-dimensional convolution neural network baseline. Precision, recall, and F1-score metrics validated robust performance across motor imagery tasks. Comparative analysis with the latest methods underscored the proposed model’s superiority in accuracy and reliability. Using wavelet transformations, convolution neural network architectures, and generative adversarial network-driven data augmentation presented a promising framework for advancing brain-computer interface applications, particularly in enhancing neuroprosthetic device control. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wavelet decomposition

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A Lightweight Deep Exclusion Unfolding Network for Single Image Reflection Removal

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第6期47卷 4957-4973页

作者： Huang, Jun-Jie Liu, Tianrui Chen, Zihan Liu, Xinwang Wang, Meng Dragotti, Pier Luigi National University of Defense Technology College of Computer Science and Technology Changsha China Hefei University of Technology School of Computer Science and Information Engineering Hefei China Imperial College London Department of Electrical and Electronic Engineering London United Kingdom

Single Image Reflection Removal (SIRR) is a canonical blind source separation problem and refers to the issue of separating a reflection-contaminated image into a transmission and a reflection image. The core challenge lies in minimizing the commonalities among different sources. Existing deep learning approaches either neglect the significance of feature interactions or rely on heuristically designed architectures. In this paper, we propose a novel Deep Exclusion unfolding Network (DExNet), a lightweight, interpretable, and effective network architecture for SIRR. DExNet is principally constructed by unfolding and parameterizing a simple iterative Sparse and Auxiliary Feature Update (i-SAFU) algorithm, which is specifically designed to solve a new model-based SIRR optimization formulation incorporating a general exclusion prior. This general exclusion prior enables the unfolded SAFU module to inherently identify and penalize commonalities between the transmission and reflection features, ensuring more accurate separation. The principled design of DExNet not only enhances its interpretability but also significantly improves its performance. Comprehensive experiments on four benchmark datasets demonstrate that DExNet achieves state-of-the-art visual and quantitative results while utilizing only approximately 8% of the parameters required by leading methods. © 1979-2012 IEEE.

关键词： Blind source separation

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Advanced perspectives on big data security: Navigating challenges in cloud computing 4th

Advanced perspectives on big data security: Navigating chall...

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4th International Conference on Computational Methods in Science and technology, ICCMST 2024

作者： Verma, Richa Bhatt, Ravindara Department of Computer Engineering Jaypee University of Information Technology Solan India

ISBN: (纸本)9781032911571

Recent years have seen the rise of big data workflow management solutions as widespread data analytic platforms for handling massive amounts of data in the cloud. However, keeping information private and ensuring the integrity of workflow applications is still a major and difficult issue. This document reviews and organizes research on large data workflow management privacy and security breaches and remedies. In this case, data encryption is utilized to safeguard information at rest and in transit. These points of view would allow researchers to better comprehend crucial research areas and create novel approaches. according to research question improve parameters of security by proposed approach. In this paper review of different workflows and security issue in cloud and other frame works © 2025 the Author(s).

关键词： Cloud security

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ABL-SMOTE: A Novel Resampling Method by Handling noisy and borderline challenge for Imbalanced Dataset for Software Defect Prediction

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IEEE Access 2025年 13卷 78781-78794页

作者： Bashir, Kamal Ghorashi, Sara Abdelwahab Ahmed, Ali Khader, Abdolraheem Karary University College of Computer Science and Information Technology Department of Information Technology Omdurman12304 Sudan Princess Nourah bint Abdulrahman University P.O. Box 84428 Collage of Computer and Information Sciences Department of Computer Sciences Riyadh11671 Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Jeddah21589 Saudi Arabia Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minority class examples in feature space before preprocessing. The underlying problem leading to performance deterioration emerges from noise and boundary instances found in the minority class rather than an excessive imbalance ratio. Noise and marginal samples become problematic during oversampling operations since they reduce the performance of classification models. This paper proposes an advanced version of Borderline-SMOTE (BL-SMOTE), called ABL-SMOT approach that utilizes INFFC for noise filtering to optimize data quality in the dataset. This improvement method for minority class sample placement and noise filtering ensures higher classifier performance through ABL-SMOTE. The proposed technique receives assessment through the usage of software defect datasets along with multiple testing classifiers against conventional data sampling procedures. The experimental outcomes show that ABL-SMOTE performs better than alternative approaches in most tested datasets because it is an effective preprocessing tool for imbalanced classification problems. © 2013 IEEE.

关键词： Software testing

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Indic Language Empowerment in E-commerce: Integrating Generative Translations and Vectorized Searches 1

Indic Language Empowerment in E-commerce: Integrating Genera...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Bhosale, Parth Gopale, Kunal Taware, Karan Bewoor, Laxmi Vishwakarma Institute Of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350355611

E-commerce has revolutionized the retail landscape, offering consumers unparalleled convenience and a vast array of choices from the comfort of their homes. Enabling e-commerce in native languages is crucial for creating an inclusive shopping experience, allowing businesses to effectively reach and engage diverse linguistic communities. India is renowned for its multilingual and cultural legacy and this work is an attempt to reach a wider consumer base and increase market potential in India by providing online shopping for cultural festivals. The proposed work is an attempt to develop native language E-commerce platform for various festivals celebrated in India by using Large Language Models (LLM) to generate contextually rich and relevant contents making the user experience more intuitive and personalized. Finally, we talk about making search features better by using modern technology. By using smart algorithms and models, we help users search for products in their own language, and show them results that are most relevant to what they're looking for. Through careful testing and analysis, we show that our solution works well in improving how people interact with e-commerce websites, reaching more customers, and making more sales. Our research is important because it helps bridge the language gap online, giving businesses new opportunities to grow and making online shopping more accessible for everyone. © 2025 IEEE.

关键词： Marketplaces

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年 1-19页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Machine Learning-Based Life Expectancy Prediction in Developed and Developing Regions

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IEEE Access 2025年 13卷 69520-69531页

作者： Ahamad, Shaik Shakeel Kumar, K. Praveen Ganesh, Sankuratri Sri Alharbi, Fayez Alharby, Sultan Awad Dendukuri, Venkata S. P. Koneru, Suvarna Vani Majmaah University College of Computer and Information Sciences Department of Information Technology Al-Majmaah11952 Saudi Arabia Deemed to Be University Velagapudi Ramakrishna Siddhartha School of Engineering Siddhartha Academy of Higher Education Department of Computer Science and Engineering Andhra Pradesh Vijayawada520007 India Majmaah University College of Computer and Information Sciences Department of Computer Science Al-Majmaah11952 Saudi Arabia

The life expectancy of a population is a vital measure of its overall health and healthcare quality. This study use machine learning methods, notably XGBoost, to predict life expectancy in industrialized and emerging countries. XGBoost, or Extreme Gradient Boosting, is a robust machine-learning technique renowned for its precision and efficiency in handling structured data. The objective is to develop a precise prediction model and identify the primary factors influencing life expectancy. The experimental findings indicate that our XGBoost-based model effectively forecasts life expectancy in both industrialized and developing nations. Mean Absolute Error, Mean Squared Error, and R2 score are used for model selection. XGBoost is selected because to its mean squared error of 2.45, mean absolute error of 1.03, and R2 scores of 0.99 for the training set and 0.97 for the testing set. In this a feature significance analysis was conducted to identify the most relevant factors affecting life expectancy. Our results underscore the significance of variables such as HIV/AIDS, income composition, and adult mortality in influencing life expectancy. The model's findings may assist lawmakers and healthcare professionals in making decisions to enhance public health outcomes, allocate resources efficiently, and formulate focused solutions. © 2013 IEEE.

关键词： Adversarial machine learning

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Evaluation of Reliability in Request-segregated Clouds through Remiadiations Predicting Hardware Failure Scheme

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Recent Patents on engineering 2025年第1期19卷 112-127页

作者： Sharma, Rohit Yadav, Vibhash Singh, Raghuraj Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology for Handicapped U.P. Kanpur208024 India Department of Information Technology Rajkiya Engineering College Banda210201 India Department of Computer Science and Engineering Harcourt Butler Technical University Kanpur208002 India

Background: Cloud services have become a popular approach for offering efficient services for a wide range of activities. Predicting hardware failures in a cloud data center can minimize downtime and make the system more reliable and fault-tolerant. Objective: This research aims to analyze a predictive hardware failure model based on machine learning that anticipates the required remediations for undiagnosed failures in a cloud computing system serving multiclass requests. Methods: The model is tested on a carefully designed cloud data center that categorizes incoming requests as web, compute, storage, and dedicated server requests. To demonstrate improved reliability, a carefully designed test case is run on ReliaCloud-NS, which is a simulator for creating a CCS and computing its reliability. Results: The work found that using this model considerably enhanced the reliability of cloud computing systems when compared to not using the model. Conclusion: Although various estimation methods are patented to evaluate the system reliability of a cloud computing network, the emphasis of this study was mostly on improving the reliability of request-segregated clouds upon failing hardware resources like CPU, memory, bandwidth, and hard disc. Moreover, the prediction model might potentially be expanded to other system resources such as GPUs, software, and database packages. © 2025 Bentham Science Publishers.

关键词： Cloud platforms

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An AI-Enabled Framework for Transparency and Interpretability in Cardiovascular Disease Risk Prediction

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computers, Materials & Continua 2025年第3期82卷 5057-5078页

作者： Isha Kiran Shahzad Ali Sajawal ur Rehman Khan Musaed Alhussein Sheraz Aslam Khursheed Aurangzeb Department of Computer Science University of Engineering and TechnologyLahore39161Pakistan Dipartimento di Farmacia e Biotecnologie Alma Mater Studiorum—Universita di BolognaBologna40126Italy Department of Information Sciences University of EducationLahore54770Pakistan Department of Computer Science National Textile UniversityKarachi CampusKarachi74900Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Electrical Engineering Computer Engineeringand InformaticsCyprus University of TechnologyLimassol3036Cyprus Department of Computer Science CTL EurocollegeLimassol3077Cyprus

Cardiovascular disease(CVD)remains a leading global health challenge due to its high mortality rate and the complexity of early diagnosis,driven by risk factors such as hypertension,high cholesterol,and irregular pulse *** diagnostic methods often struggle with the nuanced interplay of these risk factors,making early detection *** this research,we propose a novel artificial intelligence-enabled(AI-enabled)framework for CVD risk prediction that integrates machine learning(ML)with eXplainable AI(XAI)to provide both high-accuracy predictions and transparent,interpretable *** to existing studies that typically focus on either optimizing ML performance or using XAI separately for local or global explanations,our approach uniquely combines both local and global interpretability using Local Interpretable Model-Agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).This dual integration enhances the interpretability of the model and facilitates clinicians to comprehensively understand not just what the model predicts but also why those predictions are made by identifying the contribution of different risk factors,which is crucial for transparent and informed decision-making in *** framework uses ML techniques such as K-nearest neighbors(KNN),gradient boosting,random forest,and decision tree,trained on a cardiovascular ***,the integration of LIME and SHAP provides patient-specific insights alongside global trends,ensuring that clinicians receive comprehensive and actionable *** experimental results achieve 98%accuracy with the Random Forest model,with precision,recall,and F1-scores of 97%,98%,and 98%,*** innovative combination of SHAP and LIME sets a new benchmark in CVD prediction by integrating advanced ML accuracy with robust interpretability,fills a critical gap in existing *** framework paves the way for more explainable and transparent decision-making in he

关键词： Artificial Intelligence cardiovascular disease(CVD) explainability eXplainable AI(XAI) interpretability LIME machine learning(ML) SHAP

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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