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crossrefElectronics2025-10-29Cited by 8

A Comprehensive Review of DDoS Detection and Mitigation in SDN Environments: Machine Learning, Deep Learning, and Federated Learning Perspectives

Sidra Batool, Muhammad Aslam, Edore Akpokodje, Syeda Fizzah Jilani

Software-defined networking (SDN) has reformed the traditional approach to managing and configuring networks by isolating the data plane from control plane. This isolation helps enable centralized control over network resources, enhanced programmability, and the ability to dynami…

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crossrefElectronics2025-06-26Cited by 9

Machine Learning and Deep Learning Approaches for Predicting Diabetes Progression: A Comparative Analysis

Oluwafisayo Babatope Ayoade, Seyed Shahrestani, Chun Ruan

The global burden of diabetes mellitus (DM) continues to escalate, posing significant challenges to healthcare systems worldwide. This study compares machine learning (ML) and deep learning (DL) methods, their hybrids, and ensemble strategies for predicting the health outcomes of…

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crossrefElectronics2025-11-14Cited by 4

Modern Approaches to Software Vulnerability Detection: A Survey of Machine Learning, Deep Learning, and Large Language Models

Md. Shazzad Hossain Shaon, Mst Shapna Akter

Software vulnerabilities pose significant risks to the security and reliability of modern systems, making automated vulnerability detection an essential research area. Traditional static and rule-based approaches are limited in scalability and adaptability, motivating the adoptio…

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crossrefElectronics2026-04-01Cited by 1

Hybrid Deep Learning Techniques Integrated with Machine Learning for Foreign Exchange Rate Forecasting

Yu Cui, Jingjing Jiang

Foreign exchange is a significant financial market that attracts investors and countries seeking profitable investments. Despite the numerous techniques available for exchange rate forecasting and trend analysis, there is still a need for an automated, intelligent model to unders…

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crossrefElectronics2026-05-07

Application of Deep Machine Learning in Compressed Sensing Reconstruction of Shift-Invariant Spaces

Chenyu Ling, Junyi Luo, Kaibo Shi, Lusheng Liu

This paper proposes a structure-constrained deep reconstruction framework for compressed sensing in shift-invariant spaces (SISs). The reconstruction is formulated as an inverse operator estimation problem derived from the matrix factorization H(ω)=W(ω)A and approximated using a…

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crossrefElectronics2025-01-19Cited by 17

AI on Wheels: Bibliometric Approach to Mapping of Research on Machine Learning and Deep Learning in Electric Vehicles

Adrian Domenteanu, Liviu-Adrian Cotfas, Paul Diaconu, George-Aurelian Tudor, Camelia Delcea

The global transition to sustainable energy systems has placed the use of electric vehicles (EVs) among the areas that might contribute to reducing carbon emissions and optimizing energy usage. This paper presents a bibliometric analysis of the interconnected domains of EVs, arti…

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