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Securing FANET using federated learning through homomorphic matrix factorization

International Journal of Information technology (Singapore) 2025年第1期17卷 17-36页

作者： Banerjee, Aiswaryya Mahato, Ganesh Kumar Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote 791113 India

As Flying Ad Hoc Networks (FANETs) continue to advance, ensuring robust security, privacy, and data reliability remains a significant challenge. This research presents a novel framework known as HE-FSMF-short for Homomorphic Encrypted Federated Secure Matrix Factorization-specifically designed to tackle these challenges. HE-FSMF integrates matrix factorization with federated learning and homomorphic encryption to enhance both security and efficiency in FANET environments. Matrix factorization, commonly used in recommendation systems, is adapted here to address the unique complexities of FANETs. By leveraging detailed feature extraction through the VGG-16 model, HE-FSMF ensures precise and secure data processing even in dynamic and high-mobility settings. The incorporation of homomorphic encryption protects data throughout cloud-based computations, maintaining privacy and integrity without compromising performance. Additionally, HE-FSMF features mechanisms to verify the accuracy and authenticity of results, which is crucial for establishing trust in distributed systems. This framework not only enhances learning efficiency and improves data transmission rates but also provides strong safeguards for sensitive information. HE-FSMF offers a robust solution for advancing FANET capabilities, making it a valuable tool for secure and efficient communication in the increasingly interconnected and rapidly evolving landscape of networked systems. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： FANET Federated learning Homomorphic encryption UAV

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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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Blockchain-based layered architecture for traceability in dairy supply chain management

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International Journal of Information technology (Singapore) 2025年 1-8页

作者： Sharma, Deepti Singh, Gurpreet Kaur, Amanpreet Department of Computer Science & Engineering Giani Zail Singh Campus College of Engineering and Technology Maharaja Ranjit Singh Punjab Technical University (MRSPTU) Punjab Bathinda India Department of Computer Science & Engineering Punjab Institute of Technology Rajpura (MRSPTU Bathinda) Punjab Rajpura India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

Securing the availability of high-quality and safe food for people is the primary goal of food supply chain systems. Traditional food supply systems have inherent difficulties such as the possibility of data loss, consistency problems with the product, quality compromises, and single points of failure. Blockchain technology exhibits significant potential because of its auditability, immutability, and decentralization. Thus, blockchain-based layered architecture is proposed in the given paper to guarantee traceability of dairy products and information sharing across supply chain participants. The proposed architecture is based on the concept of sharding, which is based on the idea of parallel processing. Other components of the dairy supply chain, including quality assurance, automated payment settlements, sales analysis, and consumption monitoring, are also supported by the proposed architecture. For overall implementation, Hyperledger Fabric is used, and the algorithms for all the chain codes used for traceability of dairy items are described. A comparative analysis of Hyperledger-based systems and other existing approaches is presented, which shows the suitability of the platform for supply chain networks. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Blockchain Chain codes Dairy supply chain management Smart Contracts Traceability

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UETT4K Anti-UAV: A Large Scale 4K Benchmark Dataset for Vision-based Drone Detection in High-Resolution Imagery

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IEEE Access 2025年 13卷 73553-73568页

作者： Awan, Mughees Sarwar Zaidi, Syed Azhar Ali Mir, Junaid Dr. A. Q. Khan Institute of Computer Sciences and Information Technology Kahuta Pakistan University of Engineering and Technology Department of Electronics Engineering Taxila47080 Pakistan University of Engineering and Technology Department of Electrical Engineering Taxila47080 Pakistan

In recent years, incidents involving unmanned aerial vehicles (UAVs) have increased significantly, raising concerns over security and privacy, especially concerning civilian and military facilities. Vision-based approaches, especially those employing deep convolutional neural networks (DCNNs), show great promise in addressing the need for an accurate and cost-effective UAV detection system. However, DCNNs rely heavily on extensive and well-labeled datasets, which are essential for achieving high accuracy and effectiveness. For drone detection tasks, a dataset of high-resolution images is especially valuable, as it provides more contextual information for DCNNs, enabling more accurate drone detection. This work presents a new drone detection dataset composed of 4K resolution images named University of engineering and technology Taxila 4K Anti-UAV (UETT4K Anti-UAV). The dataset is created by obtaining real-world videos of different types of drones in diverse environmental and challenging conditions. A custom dataset of 33601 images, manually annotated with hand-labeled bounding boxes, is created from these *** used our dataset to train, validate, and test eight state-of-the-art YOLOv6v3 algorithm models. The test results and qualitative analysis guide selecting the most suitable model for drone detection applications. The proposed extensive 4K dataset offers a valuable resource for effectively training deep learning models to detect drones across diverse conditions accurately. © 2013 IEEE.

关键词： Convolutional neural networks

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Bi-SMOTE: a novel framework for handling imbalanced datasets using machine learning techniques

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International Journal of Information technology (Singapore) 2025年第1期17卷 431-445页

作者： Tigga, Onima Pal, Jaya Mustafi, Debjani Department of Computer Science & amp Engineering Birla Institute of Technology Jharkhand Mesra Ranchi India

Working with Imbalance data in real-world problems is not so easy due to the different cardinality of classes. Several machine learning Techniques have been used to overcome this kind of problem for 100% original data for classification. In this research work, the proposed Bi-SMOTE approach on the imbalanced Red wine and Glass datasets using machine learning techniques, Random Forest (RF), K Nearest Neighbor (KNN), Logistic Regression (LR), and Decision Tree (DT) have been used. Moreover, preprocessing steps are done by Binarization and standardization. The accuracy and F1-score are calculated of without SMOTE / with SMOTE / proposed Bi-SMOTE approaches with classifiers RF, KNN, LR, and DT for 95% & 100% of original datasets. Here the Bi-SMOTE approach gives better results up to 12%, and up to 22% for accuracy and up to 29% and up to 28% for F1-score for the Red wine and the Glass dataset respectively. Moreover, mean squared error (MSE) is continuously decreased in these machine learning techniques with original data with SMOTE and Bi-SMOTE. Thus, the proposed approach Bi-SMOTE yields the highest values in all performance measures, including accuracy, and F1-score for 95% data than 100% data. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Accuracy Binarization Class Imbalance Machine learning (ML) SMOTE (Synthetic minority over-sampling technique)

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A Tolerant and Energy Optimization Approach for Internet of Things to Enhance the QoS Using Adaptive Blended Marine Predators Algorithm

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computers, Materials & Continua 2025年第5期83卷 2449-2479页

作者： Vijaya Krishna Akula Tan Kuan Tak Pravin Ramdas Kshirsagar Shrikant Vijayrao Sonekar Gopichand Ginnela Department of Information Technology G.Narayanamma Institute of Technology and Science for WomenHyderabad500104India Engineering Cluster Singapore Institute of Technology10 Dover DriveSingapore138683Singapore Department of Electronics and Telecommunication Engineering J.D.College of Engineering&ManagementNagpur441501India Department of Computer Science and Engineering J.D.College of Engineering&ManagementNagpur441501India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014TamilnaduIndia

The rapid expansion of Internet of Things(IoT)networks has introduced challenges in network management,primarily in maintaining energy efficiency and robust connectivity across an increasing array of *** paper introduces the Adaptive Blended Marine Predators Algorithm(AB-MPA),a novel optimization technique designed to enhance Quality of Service(QoS)in IoT systems by dynamically optimizing network configurations for improved energy efficiency and *** results represent significant improvements in network performance metrics such as energy consumption,throughput,and operational stability,indicating that AB-MPA effectively addresses the pressing needs ofmodern IoT *** are initiated with 100 J of stored energy,and energy is consumed at 0.01 J per square meter in each node to emphasize energy-efficient *** algorithm also provides sufficient network lifetime extension to a resourceful 7000 cycles for up to 200 nodes with a maximum Packet Delivery Ratio(PDR)of 99% and a robust network throughput of up to 1800 kbps in more compact node *** study proposes a viable solution to a critical problem and opens avenues for further research into scalable network management for diverse applications.

关键词： Internet of things trust energy marine predators algorithm(MPA) differential evolution(DE) nodes throughput lifetime

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Improving Machine Translation Formality with Large Language Models

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computers, Materials & Continua 2025年第2期82卷 2061-2075页

作者： Murun Yang Fuxue Li School of Computer Science and Engineering Northeastern UniversityShenyang110819China College of Electrical Engineering Yingkou Institute of TechnologyYingkou115014China

Preserving formal style in neural machine translation (NMT) is essential, yet often overlooked as an optimization objective of the training processes. This oversight can lead to translations that, though accurate, lack formality. In this paper, we propose how to improve NMT formality with large language models (LLMs), which combines the style transfer and evaluation capabilities of an LLM and the high-quality translation generation ability of NMT models to improve NMT formality. The proposed method (namely INMTF) encompasses two approaches. The first involves a revision approach using an LLM to revise the NMT-generated translation, ensuring a formal translation style. The second approach employs an LLM as a reward model for scoring translation formality, and then uses reinforcement learning algorithms to fine-tune the NMT model to maximize the reward score, thereby enhancing the formality of the generated translations. Considering the substantial parameter size of LLMs, we also explore methods to reduce the computational cost of INMTF. Experimental results demonstrate that INMTF significantly outperforms baselines in terms of translation formality and translation quality, with an improvement of +9.19 style accuracy points in the German-to-English task and +2.16 COMET score in the Russian-to-English task. Furthermore, our work demonstrates the potential of integrating LLMs within NMT frameworks to bridge the gap between NMT outputs and the formality required in various real-world translation scenarios.

关键词： Neural machine translation formality large language model text style transfer style evaluation reinforcement learning

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Federated Synergy: Hierarchical Multi-Agent Learning for Sustainable Edge Computing in IIoT

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IEEE Access 2025年 13卷 68311-68322页

作者： Benila, S. Devi, K. Vellore Institute of Technology Chennai Campus School of Computer Science and Engineering Tamil Nadu 600127 India

The Industrial Internet of Things (IIoT) presents significant challenges in task offloading, resource allocation, and energy efficiency, necessitating intelligent, scalable, and adaptive solutions. This paper introduces the Hierarchical Multi-Agent Federated Actor-Critic (HMAFAC) algorithm. This novel approach integrates hierarchical reinforcement learning with multi-agent federated learning to enhance decision-making in dynamic edge computing environments. It employs multi-agent reinforcement learning to optimize resource allocation and task offloading by leveraging local Actor-Critic models on IoT edge devices. A Deep Q-Network (DQN) agent manages task offloading decisions based on real-time system states, ensuring efficient task execution. To maintain data privacy and reduce communication overhead, federated learning aggregates model updates at the edge server, eliminating the need for raw data exchange. Additionally, a hierarchical framework enables adaptive scheduling, allowing flexible computational scaling from edge to cloud servers based on task complexity and network conditions. By effectively reducing latency and energy consumption, enhancing system adaptability, and maintaining computational efficiency, HMAFAC overcomes key limitations of existing IIoT frameworks. Simulation results validate its superior performance compared to baseline methods regarding resource utilization, energy conservation, and model convergence. These findings establish HMAFAC as a robust and scalable framework for optimizing IIoT applications in distributed and resource-constrained environments. Future work will further explore advanced federated learning techniques, enhanced privacy mechanisms, and real-world deployment in industrial systems to improve efficiency and scalability. © 2013 IEEE.

关键词： Federated learning

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White shark optimizer via support vector machine for video-based gender classification system

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Multimedia Tools and Applications 2025年 1-17页

作者： Oyediran, Mayowa Oyedepo Ajagbe, Sunday Adeola Ojo, Olufemi Samuel Alshahrani, Reem Awodoye, Olufemi O. Adigun, Matthew O. Department of Computer Engineering Ajayi Crowther University Oyo Nigeria Department of Computer Science University of Zululand Kwadlangezwa3886 South Africa Department of Computer Engineering Abiola Ajimobi Technical University Ibadan200255 Nigeria Department of Computer Science College of Computers and IT Taif University P.O.Box 11099 Taif21944 Saudi Arabia Department of Computer Engineering Ladoke Akintola University of Technology Ogbomoso Nigeria

Gender identification from videos is a challenging task with significant real-world applications, such as video content analysis and social behavior research. In this study, we propose a novel approach, the White Shark Optimizer-Support Vector Machine (WSO-SVM), tailored specifically for gender identification from video data. The WSO-SVM integrates the White Shark Optimizer, a bio-inspired optimization algorithm mimicking the hunting behavior of white sharks, with the Support Vector Machine, a powerful machine learning technique for classification. By combining these two methods, we aim to exploit the advantages of both algorithms and enhance gender identification accuracy. To evaluate the performance of the WSO-SVM in gender identification, the work conducted extensive experiments using a diverse dataset of video clips containing individuals of various genders and backgrounds. The work compared the results with conventional SVM-based gender identification and state-of-the-art methods. The findings demonstrate that the WSO-SVM achieves superior accuracy in gender identification compared to traditional SVM-based approaches. The WSO-SVM's ability to efficiently explore the solution space and select optimal SVM parameters contributes to its improved performance. Moreover, the WSO-SVM exhibits robustness in handling variations in lighting conditions, poses, and facial expressions, making it well-suited for real-world video-based gender identification tasks. The outcomes derived from the SVM approach demonstrate that WSO-SVM produced an average FPR of 7.14%, Sensitivity of 93.06%, Specificity of 92.86%, Precision of 91.0%, and overall accuracy of 93.00% in 45.83 s with a recognition time of 45.83 s. © The Author(s) 2025.

关键词： Support vector machines

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Adaptive Elastic GAN for High-Fidelity Blood Cell Image Hallucination and Classification

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IEEE Access 2025年 13卷 84897-84910页

作者： Margret, Issac Neha Rajakumar, K. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore632 014 India

Automated blood cell classification is crucial for hematological analysis, yet the scarcity of annotated medical datasets challenges deep learning models. This study presents a novel semi-supervised Elastic Generative Adversarial Network that enhances classification accuracy, improves synthetic image generation quality, and reduces computational complexity. The model utilizes image hallucination, which generates artificial but realistic images to supplement datasets, addressing data imbalance and improving model generalization. The generated images exhibit high fidelity, meaning they closely resemble real blood cell images in texture, morphology, and structural details, ensuring their effectiveness in training deep learning models. Compared to the conventional semi-supervised Wasserstein Generative Adversarial Network, the proposed model achieves a 5.5% increase in classification accuracy, a 34.5% reduction in computational complexity, and a 21.0% decrease in training time, demonstrating superior efficiency and scalability. In addition, it greatly enhances Fréchet Inception Distance and Wasserstein distance, which verifies improved realism and diversity of synthetic samples. Complementing pruning-based optimization ensures reduced model deployment size, rendering it appropriate for real-time medical diagnosis. Through image hallucination, the proposed method sufficiently alleviates data deficiency and improves the reliability of automated blood cell classification, qualifying it as a prospective tool for advanced hematological studies and diagnosis purposes. © 2013 IEEE.

关键词： Generative adversarial networks

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