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Multi-dimensional Task Offloading using Deep Learning for Vehicular Edge computing Networks: A Survey

IEIE Transactions on Smart Processing and computing 2024年第1期13卷 1-11页

作者： Abuthahir, S. Syed Peter, J. Selvin Paul Department of Computing Technologies SRM Institute of Science & Technology KTR Campus Tamil Nadu Chennai India

Vehicular networks must support connection ubiquity and high levels of services for a large number of vehicles. In vehicular networks, mobile edge computing (MEC) is considered a viable technique, utilizing computing resources at the edge of wireless access networks. This survey examines effectual task offloading systems forvehicular edge computing (VEC) networks. Several deep learning methods have been suggested to enable task offloading schemes. Therefore, task offloading optimality is a main research topic in fog computing. The main aim of this work is to offer readers an overview of the journey from the task offloading concept to its mathematic problem formulation. This survey introduces fog computing and the process of task offloading that emulates numerous aspects of optimization. Subsequently, a number of machine learning and deep learning methodologies are employed in task offloading,and the challenge of fog computing is discussed. This paper provides a detailed statistical analysis from papers published between 2019 and 2022. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Fog computing

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Updated deep long short-term memory with Namib beetle Henry optimisation for sentiment-based stock market prediction

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International Journal of Intelligent Information and Database Systems 2024年第3期16卷 316-344页

作者： Adikane, Nital Nirmalrani, V. Department of School of Computing Sathyabama Institute of Science and Technology Semmancheri Tamil Nadu Chennai600119 India

Stock price prediction is a challenging and promising area of research due to the volatile nature of stock markets influenced by factors like investor sentiment and market rumours. Developing accurate prediction models is difficult, given the complexity of stock data. Long short-term memory (LSTM) models have proven effective in uncovering hidden patterns, enabling precise predictions. Therefore, in this research work, an innovative approach called updated deep LSTM (UDLSTM) combined with Namib beetle Henry optimisation (BH-UDLSTM) is proposed and applied to historical stock market and sentiment analysis data. The UDLSTM model enhances prediction performance, offering stability during training and increased data accuracy. By incorporating Namib beetle and Henry gas algorithms, BH-UDLSTM further improves prediction accuracy by striking a balance between exploration and exploitation. The evaluation against existing methods demonstrates that the proposed approach achieves a higher accuracy rate (92.45%) in stock price prediction compared to state-of-the-art techniques. © 2024 Inderscience Enterprises Ltd.

关键词： Long short-term memory

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Federated learning for Healthcare 5.0: a comprehensive survey, taxonomy, challenges, and solutions

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Soft computing 2025年第2期29卷 673-700页

作者： Amin, Muhammad Sadiq Ahmad, Shabir Loh, Woong-Kee School of Electrical and Computer Science National University of Science and Technology Islamabad Pakistan School of Computing Gachon University Seongnam13120 Korea Republic of

The advent of Healthcare 5.0 heralds a groundbreaking revolution in digital healthcare, superseding the achievements of its predecessor, Healthcare 4.0. Integrating cutting-edge technologies such as the Internet of Medical Things (IoMT), smart wearables, and the extraordinary capabilities of Artificial Intelligence (AI), Healthcare 5.0 envisions a unified framework that grants seamless access to health records, fosters interconnectedness among individuals, resources, and institutions, and empowers intelligent responses to medical concerns. However, the realization of Healthcare 5.0 faces a significant challenge in the form of high-speed data transmission using smart devices. Conventional AI approaches relying on centralized data processing raise compelling concerns surrounding information privacy and scalability within the Healthcare 5.0 context. Amidst this backdrop, federated learning emerges as a beacon of hope, offering a decentralized AI paradigm that facilitates on-device machine learning without compromising end-user privacy through centralized data export. Safeguarding data integrity, federated learning holds the key to unlocking the full potential of Healthcare 5.0. In this pioneering study, we conduct an extensive survey, exploring the transformative implications of federated learning within the realm of Healthcare 5.0. By shedding light on recent advancements tailored to this paradigm, we delve into the fundamental concepts of resource-awareness, privacy preservation, incentivization, and personalization, all within the framework of federated learning. Moreover, we meticulously scrutinize key parameters including security, sparsification, quantization, robustness, scalability, and privacy, providing an authentic evaluation of the current progress in federated learning for Healthcare 5.0. This comprehensive survey serves as an indispensable cornerstone for the evolution of Healthcare 5.0, offering invaluable insights into its unique requirements and untapp

关键词： Electronic health record

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Distributed Nash Equilibrium Seeking Strategies Under Quantized Communication

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 103-112页

作者： Maojiao Ye Qing-Long Han Lei Ding Shengyuan Xu Guobiao Jia IEEE the School of Automation Nanjing University of Science and TechnologyNanjing 210094China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the Institute of Advanced Technology Nanjing University of Posts and TelecommunicationsNanjing 210023China

This paper is concerned with distributed Nash equi librium seeking strategies under quantized communication. In the proposed seeking strategy, a projection operator is synthesized with a gradient search method to achieve the optimization o players' objective functions while restricting their actions within required non-empty, convex and compact domains. In addition, a leader-following consensus protocol, in which quantized informa tion flows are utilized, is employed for information sharing among players. More specifically, logarithmic quantizers and uniform quantizers are investigated under both undirected and connected communication graphs and strongly connected digraphs, respec tively. Through Lyapunov stability analysis, it is shown that play ers' actions can be steered to a neighborhood of the Nash equilib rium with logarithmic and uniform quantizers, and the quanti fied convergence error depends on the parameter of the quan tizer for both undirected and directed cases. A numerical exam ple is given to verify the theoretical results.

关键词： Consensus distributed Nash equilibrium seeking projected gradient play quantized communication

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Inappropriate YouTube content detection and classification by using proposed novel auto-determined k-means clustering and PDBRNN architecture

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10833-10845页

作者： Faheem Nikhat, H. Sait, Saad Yunus Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

To ensure a safe and pleasant user experience while watching content on YouTube, it is necessary to identify and classify inappropriate content, especially content that is inappropriate for children. In this work, we have concentrated on establishing an efficient system for detecting inappropriate content on YouTube. Most of the work focuses on manual preprocessing;however, it takes too much time, requires manpower support, and is not ideal for solving real-time problems. To address this challenge, we have proposed an automatic preprocessing scheme for selecting appropriate frames and removing unwanted frames such as noise and duplicate frames. For this purpose, we have utilized the proposed novel auto-determined k-means (PADK-means) algorithm. Our PADK-means algorithm automatically determines the optimal cluster count instead of manual specifications. By doing this, we have solved the manual cluster count specification problem in the traditional k-means clustering algorithm. On the other hand, to improve the system’s performance, we utilized the Proposed Feature Extraction (PFE) method, which includes two pre-trained models DenseNet121 and Inception V3 are utilized to extract local and global features from the frame. Finally, we employ a proposed double-branch recurrent network (PDBRNN) architecture, which includes bi-LSTM and GRU, to classify the video as appropriate or inappropriate. Our proposed automatic preprocessing mechanism, proposed feature extraction method, and proposed double-branch RNN classifier yielded an impressive accuracy of 97.9%. © 2024 – IOS Press. All rights reserved.

关键词： Network architecture

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Informal settlement mapping from very high-resolution satellite data using a hybrid deep learning framework

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Neural computing and Applications 2024年第4期37卷 2877-2889页

作者： Prabhu, Ravi Department of Networking and Communications School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur Chennai India

Informal settlements are becoming increasingly common in the global south, and their locations are often inaccurately represented in government statistics and maps. In remote sensing (RS), classifying informal settlements from very high-resolution satellite images presents a significant challenge due to their unique spectral signatures. This article proposes a novel hybrid framework, multi-shape-multisize-morphological profiles-dual-channel convolutional neural network (MSh-MSi-MP-DC-CNN) architecture, which fully exploits the feature extraction capabilities of both morphological profiles (MP) and convolutional neural networks (CNN) for detecting informal settlements from RS images. Three different images were used to evaluate the proposed MSh-MSi-MP-DC-CNN architecture. One was taken by the WorldView-2 dataset (1.82 m resolution) of Tiruppur in India, and the other two were taken by the WorldView-3 dataset (0.31 m resolution) of Kibera in Kenya and Medellin in Colombia. By extracting both spatial and structural features, the proposed CNN architecture proves robust and scalable for RS images of complex urban slum environments. It also generates significantly higher classification accuracy with less computing time compared to other state-of-the-art CNN methods considered in this work. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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Energy-Aware Task Offloading for Rotatable STAR-RIS-Enhanced Mobile Edge computing Systems

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IEEE Internet of Things Journal 2025年第12期12卷 20239-20250页

作者： Yang, Dongdong Li, Bin Niyato, Dusit Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Huzhou Key Laboratory of Urban Multidimensional Perception and Intelligent Computing Huzhou313000 China Nanyang Technological University College of Computing and Data Science Singapore Singapore

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can expand the coverage of mobile edge computing (MEC) services by reflecting and transmitting signals simultaneously, enabling full-space coverage. The orientation of the STAR-RIS plays a crucial role in optimizing the gain of received and transmitted signals, and a rotatable STAR-RIS offers potential enhancement for MEC systems. This paper investigates a rotatable STAR-RIS-assisted MEC system, operated under three protocols, namely energy splitting, mode switching, and time switching. The goal is to minimize energy consumption for multiple moving user devices through the joint optimization of STAR-RIS configurations, orientation, computation resource allocation, transmission power, and task offloading strategies. Considering the mobility of user devices, we model the original optimization problem as a sequential decision-making process across multiple time slots. The high-dimensional, highly coupled, and nonlinear nature makes it a challenging non-convex decision-making problem for traditional optimization algorithms. Therefore, a deep reinforcement learning (DRL) approach is employed, specifically utilizing soft actor-critic algorithm to train the DRL model. Simulation results demonstrate that the proposed algorithm outperforms the benchmarks in both convergence speed and energy efficiency, while reducing energy consumption by up to 52.7% compared to the fixed STAR-RIS scheme. Among three operating protocols, the energy splitting yields the best performance. © 2014 IEEE.

关键词： Mobile edge computing

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Intelligent patient monitoring through hybrid consensus algorithm based blockchain technology

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Multimedia Tools and Applications 2025年第18期84卷 19579-19600页

作者： Priyadharshini, A. Nithiya, S. Archana, H.A. Jaganathan, Subash Chandra Bose School of Engineering and Technology Bangaluru India Department of Computing Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Agronomy SRM College of Agricultural Science Chengalpattu603201 India School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore466114 India

This research aims to integrate IoT with blockchain technology to securely manage and monitor sensitive patient health data in critical care environments, thereby improving the reliability and efficiency of patient monitoring systems. A hybrid blockchain architecture is employed to store ICU patient data securely, linking each patient's information with their respective doctors' details. This system operates without third-party intermediaries, ensuring trusted communication channels between patients and doctors. The blockchain ensures authorized access and secure transmission of medical histories and treatment plans, even allowing for remote care by doctors outside hospital premises. The integration of blockchain technology with IoT devices facilitates rapid and secure data transmission, enhancing the speed and accuracy of critical care delivery. The decentralized nature of blockchain ensures data integrity and prevents unauthorized access, while the hybrid approach allows for efficient and trusted collaboration among healthcare professionals. By harnessing the power of hybrid blockchain technology, this system not only safeguards sensitive medical information but also improves the overall quality of critical care through timely and informed medical intervention. The proposed solution holds the promise of reducing patient mortality rates by ensuring secure, efficient, and collaborative healthcare delivery. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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An Innovative Deep Learning-Based Predictive Maintenance Model for Smart Automotive System

An Innovative Deep Learning-Based Predictive Maintenance Mod...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Indumathi, V. Ashokkumar, C. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

This research presents an innovative deep learning-based predictive maintenance model designed for smart automotive systems, utilizing the EnsembleAE-Boost (EAE-Boost) algorithm. The primary objective of the proposed model is to enhance the prediction accuracy and fault detection capabilities of smart vehicles through advanced machine learning techniques. The EAE-Boost algorithm, an ensemble model integrating auto encoders and boosting, is compared with existing algorithms, namely K-Nearest Neighbors (KNN), Deep Neural Networks (DNN), and Extreme Gradient Boosting (XGBoost), based on key simulation metrics, including accuracy, precision, prediction time, and fault detection rate. Simulation results demonstrate that the EAE-Boost algorithm outperforms the existing algorithms, achieving superior performance in all metrics. Notably, the proposed method showed significant improvements in accuracy and fault detection rate, with a reduction in prediction time, highlighting its potential for real-time maintenance applications in smart automotive systems. The findings underscore the effectiveness of the proposed deep learning model in enhancing vehicle reliability, safety, and operational efficiency. © 2025 IEEE.

关键词： Predictive maintenance

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Investigation of Android Malware with Machine Learning Classifiers using Enhanced PCA Algorithm

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Computer Systems science & Engineering 2023年第3期44卷 2147-2163页

作者： V.Joseph Raymond R.Jeberson Retna Raj Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennai600119TamilnaduIndia School of Computing SRM Institute of Science and TechnologyChennai603203TamilnaduIndia

Android devices are popularly available in the commercial market at different price levels for various levels of *** Android stack is more vulnerable compared to other platforms because of its open-source *** are many android malware detection techniques available to exploit the source code andfind associated components during execution *** obtain a better result we create a hybrid technique merging static and dynamic *** this paper,in thefirst part,we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid Mul-ticollinearity problem is one of the drawbacks in the existing *** the proposed work,a novel PCA(Principal Component Analysis)based feature reduction technique is implemented with conditional dependency features by gathering the functionalities of the application which adds novelty for the given *** Android Sensitive Permission is one major key point to be considered while detecting *** select vulnerable columns based on features like sensitive permissions,application program interface calls,services requested through the kernel,and the relationship between the variables henceforth build the model using machine learning classifiers and identify whether the given application is malicious or *** goal of this paper is to check benchmarking datasets collected from various repositories like virus share,Github,and the Canadian Institute of cyber security,compare with models ensuring zero-day exploits can be monitored and detected with better accuracy rate.

关键词： Zero-day exploit hybrid analysis principal component analysis supervised learning smart cities

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