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Machine Learning-Based Routing Protocol in Flying Ad Hoc Networks: A Review

computers, Materials & Continua 2025年第2期82卷 1615-1643页

作者： Priyanka Manjit Kaur Deepak Prashar Leo Mrsic Arfat Ahmad Khan Department of Computer Science and Engineering(CSE) Lovely Professional UniversityPhagwara144411India Department for Program Engineering Algebra UniversityGradiscanska 24Zagreb10000Croatia Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

“Flying Ad Hoc Networks(FANETs)”,which use“Unmanned Aerial Vehicles(UAVs)”,are developing as a critical mechanism for numerous applications,such as military operations and civilian *** dynamic nature of FANETs,with high mobility,quick node migration,and frequent topology changes,presents substantial hurdles for routing protocol *** the preceding few years,researchers have found that machine learning gives productive solutions in routing while preserving the nature of FANET,which is topology change and high *** paper reviews current research on routing protocols and Machine Learning(ML)approaches applied to FANETs,emphasizing developments between 2021 and *** research uses the PRISMA approach to sift through the literature,filtering results from the SCOPUS database to find 82 relevant *** research study uses machine learning-based routing algorithms to beat the issues of high mobility,dynamic topologies,and intermittent connection in *** compared with conventional routing,it gives an energy-efficient and fast decision-making solution in a real-time environment,with greater fault tolerance *** protocols aim to increase routing efficiency,flexibility,and network stability using ML’s predictive and adaptive *** comprehensive review seeks to integrate existing information,offer novel integration approaches,and recommend future research topics for improving routing efficiency and flexibility in ***,the study highlights emerging trends in ML integration,discusses challenges faced during the review,and discusses overcoming these hurdles in future research.

关键词： FANET protocol machine learning

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ALCTS—An Assistive Learning and Communicative Tool for Speech and Hearing Impaired Students

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computers, Materials & Continua 2025年第5期83卷 2599-2617页

作者： Shabana Ziyad Puthu Vedu Wafaa A.Ghonaim Naglaa M.Mostafa Pradeep Kumar Singh Computer Science Department College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl Kharj11942Saudi Arabia Faculty of Science Al-Azhar UniversityCairo12111Egypt Department of Mathematics Faculty of ScienceAl-Azhar University(Girl’s Branch)Cairo12111Egypt Department of Computer Science and Engineering Central University of JammuJammu and Kashmir181143India

Hearing and Speech impairment can be congenital or *** and speech-impaired students often hesitate to pursue higher education in reputable institutions due to their ***,the development of automated assistive learning tools within the educational field has empowered disabled students to pursue higher education in any field of *** learning devices enable students to access institutional resources and facilities *** proposed assistive learning and communication tool allows hearing and speech-impaired students to interact productively with their teachers and *** tool converts the audio signals into sign language videos for the speech and hearing-impaired to follow and converts the sign language to text format for the teachers to *** educational tool for the speech and hearing-impaired is implemented by customized deep learning models such as Convolution neural networks(CNN),Residual neural Networks(ResNet),and stacked Long short-term memory(LSTM)network *** assistive learning tool is a novel framework that interprets the static and dynamic gesture actions in American Sign Language(ASL).Such communicative tools empower the speech and hearing impaired to communicate effectively in a classroom environment and foster *** deep learning models were developed and experimentally evaluated with the standard performance *** model exhibits an accuracy of 99.7% for all static gesture classification and 99% for specific vocabulary of gesture action *** two-way communicative and educational tool encourages social inclusion and a promising career for disabled students.

关键词： Sign language recognition system ASL dynamic gestures facial key points CNN LSTM ResNet

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BAAP-FIoT: Blockchain Assisted Authentication Protocol for Fog-enabled Internet of Things Environment

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IEEE Internet of Things Journal 2025年第11期12卷 15681-15696页

作者： Ponnuru, Raveendra Babu Kumar, Sathish A.P. Azab, Mohamed Alavalapati, Goutham Reddy Virginia Tech Virginia Military Institute and Computer Science Department of Computer and Information Science BlacksburgVA United States Cleveland State University Department of Electrical Engineering and Computer Science ClevelandOH44115 United States Virginia Military Institute Department of Computer Science and Information Sciences LexingtonVA24450 United States University of Illinois Springfield One University Plaza Department of Computer Science SpringfieldIL62703 United States

The proliferation of Internet of Things (IoT) devices across multiple domains has heralded an era of unprecedented connectivity and data exchange. Fog computing enhances edge-network processing, enabling real-time data analysis and prompt responses. However, ensuring secure and trustworthy communication among these devices remains a paramount concern. In fog-enabled IoT environments, securing communication among users,IoT devices, gateways, and fog nodes is of paramount importance to prevent unauthorized access and ensure data integrity and confidentiality. Additionally, users can control and deliver instructions to IoT devices remotely. Hence, we propose a blockchain-assisted authentication protocol tailored specifically for fog-enabled IoT environments to verify the user's identity prior to accessing the IoT devices. The proposed protocol leverages cutting-edge crypto primitives like elliptic curve cryptography, hash functions, and blockchain to establish secure communication between users and IoT devices. Furthermore, we evaluate the proposed scheme through formal (scyther) and informal analysis, demonstrating its efficacy in mitigating well-known attacks. On the other hand, the proposed protocol exhibits robustness against relevant protocols in terms of communication and computational aspects, as well as reliability for real-world fog-enabled IoT applications. © 2014 IEEE.

关键词： Authentication Protocol

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CLS-3D: Content-Wise LiDAR-Camera Fusion and Slot Reweighting Transformer for 3D Object Detection in Autonomous Vehicles

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IEEE Access 2025年 13卷 69840-69856页

作者： Mushtaq, Husnain Latif, Sohaib Saad Bin Ilyas, Muhammad Mohsin, Syed Muhammad Ali, Mohammed Central South University School of Computer Science and Engineering Changsha410083 China Grand Asian University Department of Computer Science and Software Engineering Sialkot51040 Pakistan The University of Chenab Department of Computer Science Gujranwala50700 Pakistan COMSATS University Islamabad Department of Computer Science Islamabad45550 Pakistan Virtual University of Pakistan Standard College of Modern Sciences Lahore55150 Pakistan King Khalid University Department of Computer Science Abha61421 Saudi Arabia Abha61421 Saudi Arabia

Accurate 3D object detection is vital in autonomous driving. Single-modal detectors, using either camera or LiDAR, struggle with issues like limited depth perception or difficulty in distinguishing semantically similar objects. While multimodal approaches aim to address these limitations by combining LiDAR and camera data, they often face complexities in integrating sparse and uneven point cloud distributions, resulting in inefficient feature fusion. To tackle these challenges, we propose CLS-3D (Content-wise LiDAR-Camera Fusion and Slot Reweighting Transformer for 3D Object Detection). This novel framework fuses LiDAR and camera features using a single multi-modal backbone and augments them with the semantic probabilities obtained from the image stream. Our method captures local and global spatial relationships through a slot reweighting mechanism and incorporates I3C-IoU loss for precise box regression. The semantically augmented features, via a single multi-modal backbone, are embedded using a content-based transformer and processed through a slot-wise auto-encoder structure with channel-wise positional embeddings and a feed-forward MLP network. Our model improves temporal consistency and detection accuracy by dynamically adjusting feature relevance through slot-wise reweighting. We further define a I3C-IoU metric, considering centre, overlap, and scale for enhanced box regression accuracy. This mechanism allows the model to focus on significant temporal information, enhancing its ability to learn complex sequences and improving the overall performance of 3D object detection, especially in challenging scenarios such as occlusion and long-range detection. Extensive experiments on the KITTI and nuScenes benchmark demonstrate that CLS-3D achieves state-of-the-art performance, with 89.52% 3D mAP and 94.08% BEV mAP, outperforming existing methods. © 2013 IEEE.

关键词： Depth perception

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Assessing U-Net Architectures for Effective Brain Tumor Segmentation in MRI

Assessing U-Net Architectures for Effective Brain Tumor Segm...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Verma, Akash Yadav, Arun Kumar Department of Computer Science and Engineering India

ISBN: (纸本)9798331529833

Accurately segmenting brain tumors using MRI scans is essential for diagnosing, treatment planning and monitoring treatment. This research compares several U-Net based architectures, including U-Net DIU-Net, DFP-ResUNet, BU-Net, and SEResU-Net, which are implemented and evaluated using BraTS 2018 dataset. Each model integrates unique enhancements to address challenges in brain tumor segmentation, like vanishing gradients, poor feature extraction, & class imbalance and trained using BraTS 2018 dataset, and their performance is evaluated using Accuracy, Dice score, and IoU score. The comparative analysis reveals that while each model has specific advantages, the SEResU-Net and BU-Net architectures attain the highest performance metrics, especially in segmenting TC and ET regions. This evaluation of U-Net variants segment brain tumor highlights their strengths and limitations, offering key insights for advancing brain imaging research and clinical. © 2025 IEEE.

关键词： Deep learning

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A Survey for Blockchain-Enabled Resource Management in Edge Networks

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IEEE Access 2025年 13卷 77252-77277页

作者： Boamah, Sharon A. Louis, Jean D. McNair, Janise Gilbert, Juan E. University of Florida Department of Electrical and Computer Engineering GainesvilleFL32611 United States University of Florida Department of Computer and Information Science and Engineering GainesvilleFL32611 United States

As more people and devices come online with the rapid growth of the internet, the need for secure and efficient network resource management becomes increasingly important. The boom of these connections on Next Generation Wireless Networks through the advent of technologies like the Internet of Things has changed the landscape of traditional edge networks. This necessitates a drastic change in the existing edge network infrastructure. As these devices come online, make requests, and go offline, the allocation and management of edge network resources become paramount. Blockchain, with its growing popularity, presents itself as a potential solution. The inherent features of blockchain, such as decentralization, transparency, security, and immutability, make it possible for network providers to manage network resources efficiently. This survey paper delves into the diverse applications of blockchain in edge network resource management, offering a comprehensive discussion of its potential to revolutionize network infrastructure. Unlike other surveys, we focus this work on three specific contexts of blockchain integration for network resource management: Cloud and Edge Computing, Radio Access Networks in Next Generation Wireless Networks, Reinforcement Learning, and Federated Learning. We provide an extensive analysis of recent research advances in these areas, and the main findings are highlighted. Understanding the current use of blockchain and network resource management opens the door to new research and more efficient and secure networks. The open research issues that arise from the combination of blockchain with the management of edge network resources are identified. Finally, the future directions to fuel innovative research are presented in this survey. © 2013 IEEE.

关键词： Blockchains Surveys Cloud computing Network resource management Internet of Things Edge computing Federated learning Peer-to-peer computing Network slicing Wireless networks

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Elevating E-Service Standards in Bangladesh: Merging FAHP and TOPSIS for Strategic Online Business Solutions

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IEEE Access 2025年 13卷 83765-83780页

作者： Tasnim, Farzana Mahmud, Tanjim Johara Chowdhury, Shefayatuj Fahrin Choity, Tasnia Ahmed, Aisha Saeed Hasan Chowdhury, Mohammad Shahadat Hossain, Mohammad Andersson, Karl International Islamic University Chittagong Department of Computer Science and Engineering Chittagong4318 Bangladesh Rangamati Science and Technology University Department of Computer Science and Engineering Rangamati4500 Bangladesh International Islamic University Chittagong Department of Electrical and Electronic Engineering Chittagong4318 Bangladesh University of Chittagong Department of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Skellefteå931 87 Sweden

The rapid expansion of Bangladesh’s digital economy has unlocked new opportunities for online businesses, but it has also heightened the challenges of delivering high-quality e-services in an increasingly competitive environment. While previous studies have largely focused on customer satisfaction and consumer experiences, the perspective of business owners—those managing daily operations and strategic decisions—has been notably underexplored. Addressing this gap, the present study aims to identify and prioritize the critical success factors (CSFs) influencing the performance of online businesses in Bangladesh from the owners’ viewpoint.A structured survey has been conducted among 57 online business owners, collecting data across ten essential dimensions, including website infrastructure, information sharing, delivery practices, responsiveness, customization, customer service, reliability, payment security, return policies, and service quality challenges. To ensure a data-driven analysis, the Random Forest algorithm is utilized for feature importance extraction, followed by pairwise comparisons through fuzzy triangular matrices. Subsequently, FAHP and TOPSIS techniques are applied to systematically rank the factors and alternatives. Model validation using the XGBoost classifier results in 93% accuracy and a 93% F1-score, demonstrating the robustness of the proposed approach. The results reveal that service quality concerns, payment security, and effective customer support are the most influential determinants of business success. Beyond offering a methodological advancement that integrates machine learning with MCDM, this study delivers actionable insights for business owners, digital strategists, and policymakers aiming to enhance the competitiveness and resilience of Bangladesh’s e-commerce landscape. © 2013 IEEE.

关键词： Marketplaces

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RoPe-Door: Toward Robust and Persistent Backdoor Data Poisoning Attacks in Federated Learning

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IEEE Network 2025年第3期39卷 302-310页

作者： Liu, Ye Chang, Shan Li, Denghui Shi, Shaohuai Li, Bo Jiangxi Normal University School of Computer and Information Engineering Nanchang330022 China Donghua University School of Computer Science and Technology Shanghai201620 China Harbin Institute of Technology School of Computer Science and Technology Shenzhen518057 China The Hong Kong University of Science and Technology Department of Computer Science and Engineering Sai Kung Hong Kong

Federated Learning (FL) enables privacy-preserving collaborative training and builds a federation through exchanges of immutable data such as model parameters or gradient updates. FL remains vulnerable to a variety of attacks during critical processes like local model training and parameter transmission, in which the backdoor attack is particularly evident. In this paper, we propose a novel backdoor data poisoning attack method, RoPe-Door, using a trigger generation algorithm to improve the robustness and persistence of attacks even under Byzantine aggregation methods. We conduct extensive experiments on four image classification tasks to evaluate the effectiveness of RoPe-Door. The experimental results demonstrate that, compared to backdoor attacks using random triggers, RoPe-Door exhibits significant advantages in robustness, persistence, and attack effectiveness under both IID and Non-IID data settings. © 1986-2012 IEEE.

关键词： Federated learning

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THE IMPACT OF USING AI AND CHATGPT IN ORGANIZATIONS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2025年第1期87卷 53-64页

作者： Tudose, Cătălin Boiangiu, Costin Anton Antal, Jasmyna Cristina Bănăţeanu, Cristiana Breazu, Laura Piele, Mihai Computer Science and Engineering Department National University of Science and Technology POLITEHNICA Bucharest Romania

This article investigates the impact of Artificial Intelligence (AI) and ChatGPT in the business sector. It highlights the evolution of AI, focusing on the integration and applications of technologies like machine learning, natural language processing, and ChatGPT in various business areas. A central aspect of the study is a survey that reflects professionals' views on AI's effectiveness, data security concerns, and its influence on workplace dynamics across multiple industries. The research reveals both the benefits, such as improved efficiency and innovation, and challenges, including data security risks and potential overreliance on technology. Concluding, the article emphasizes the need for a balanced approach to AI adoption in businesses, advocating for a combination of technological and human expertise for optimal results. This study provides key insights for companies looking to leverage AI for sustainable growth and competitive advantage. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Adversarial machine learning

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A Magnetic Signal Based Device Fingerprinting Scheme in Wireless Charging

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2943-2958页

作者： Li, Jiachun Meng, Yan Zhang, Le Chen, Guoxing Tian, Yuan Zhu, Haojin Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Electrical and Computer Engineering Department Institute for Technology Los AngelesCA90095 United States

Wireless charging is widely used to charge smart devices with limited battery capacity. However, it is susceptible to the identity spoofing attack, where adversaries can impersonate malicious devices as legitimate ones to gain unauthorized access and potentially disrupt the wireless charging system (e.g., resulting in incorrect billing, overheating, or even explosions). Device fingerprinting is a classical method for defending against identity spoofing attacks. However, applying existing schemes in wireless charging scenarios has drawbacks such as inconvenience (e.g., requiring specialized devices or user participation) and ineffectiveness (e.g., vulnerability to spoofing). Thus, we design a novel passive, effective, and robust device fingerprinting scheme called MagID for wireless charging systems. The insight of MagID lies in the fact that during wireless charging, the magnetic signal around a device can reflect inherent hardware differences. These differences can be extracted as unique fingerprints for authentication purposes. MagID leverages a novel scheme, SUPER-ARRAY, to precisely measure magnetic data and generate effective fingerprints for authenticating a device's identity before starting charging progress. Experimental results demonstrate that MagID achieves an accuracy rate of 98.14% across various charging devices. We have also tested its performance under different impact factors and verified its compatibility with various wireless charging pads. © 2004-2012 IEEE.

关键词： Charging stations

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