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1st International Conference on Software, Systems and information technology, SSITCON 2024

作者： Parthasarathi, P. Nivedha, S. Balamurugan, M. Yuvaraj, D. Siamala Devi, S. Sangeethaa, S.N. Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India Sona College of Technology Dpartment of Computer Science and Engineering Salem India Kongu Engineering College Department of Computer Science and Design Perundurai India Sri Eshwar College of Engineering Kuniamuthur Department of Information Technology Coimbatore India Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350352931

Skin melanoma, a dangerous kind of skin cancer, necessitates accurate identification and diagnosis to improve patient outcomes. The use of deep learning algorithms into medical imaging promises significant advancements in skin disease analysis, particularly melanoma diagnosis. This study looks at the usage of a novel optimisation technique, the African Gorilla Troops Optimizer (AGTO), to enhance deep learning models in the segmentation and analysis of skin images. The AGTO, which is based on African gorilla groups' social and hunting activities, is an effective approach of improving convolutional neural networks (CNNs) for accurate and reliable skin image segmentation. The proposed approach uses AGTO to distinguish melanoma lesions from benign skin disorders, which increases their identification. Extensive research on benchmark skin image datasets has confirmed the AGTO-enhanced deep learning model's durability and performance, emphasising its clinical use in dermatology and the proposed model shows 98 %accuracy. This work not only underlines the need of advanced optimisation techniques in medical image processing, but it also paves the way for more effective diagnostic tools for skin problems. © 2024 IEEE.

关键词： Image segmentation

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Confidence-Based Trustscoring System for Identification of Secure Agent Platforms

Confidence-Based Trustscoring System for Identification of S...

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

作者： Joseph, Adri Jovin John Marikkannu, P. Mariappan, Marikkannan Sri Ramakrishna Institute of Technology Department of Information Technology Coimbatore India Anna University Department of Information Technology Regional Campus Coimbatore Coimbatore India Government College of Engineering Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798331509828

Autonomous mobile agent systems can accomplish multiple tasks in an asynchronous and dynamic manner in platforms where there is excessive network load, network interruption and in slow networks. However, the decisions and execution depend on the platforms visited by the agents. Since the platforms may influence the decisions made by the agents, it is crucial that the trustable platforms are selected. Trust score is one parameter that helps mobile agents to select the appropriate platform. This work proposes a minor modification of the existing trust score calculation which has a significant impact on the safety and performance of mobile agent systems when deployed in a conventional distributed environment or in a modern cloud environment for completion of tasks. This work introduces a confidence-based approach for the five parameters namely persistence, competence, reputation, credibility, integrity which determine the trust score. This will provide a clear decision-making environment for the mobile agents to choose an appropriate platform. © 2025 IEEE.

关键词： Cloud platforms

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A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

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computer Modeling in engineering & Sciences 2024年第5期139卷 1613-1633页

作者： Jingyu Zhang Pian Zhou Jin Wang Osama Alfarraj Saurabh Singh Min Zhu School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410015China Science and Technology on Information Systems Engineering Laboratory School of Systems EngineeringNational University of Defense TechnologyChangsha410073China Computer Science Department King Saud UniversityRiyadh11437Saudi Arabia Department of Artificial Intelligence and Big Data Woosong UniversityDaejeon34606South Korea College of Information Science and Technology Zhejiang Shuren UniversityHangzhou310015China

Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the *** technology has been widely used and has developed rapidly in big data systems across various *** increasing number of users are participating in application systems that use blockchain as their underlying *** the number of transactions and the capital involved in blockchain grow,ensuring information security becomes *** the verification of transactional information security and privacy has emerged as a critical ***-based verification methods can effectively eliminate the need for centralized third-party ***,the efficiency of nodes in storing and verifying blockchain data faces unprecedented *** address this issue,this paper introduces an efficient verification scheme for transaction ***,it presents a node evaluation module to estimate the activity level of user nodes participating in transactions,accompanied by a probabilistic analysis for all ***,this paper optimizes the conventional transaction organization form,introduces a heterogeneous Merkle tree storage structure,and designs algorithms for constructing these heterogeneous *** analyses and simulation experiments conclusively demonstrate the superior performance of this *** verifying the same number of transactions,the heterogeneous Merkle tree transmits less data and is more efficient than traditional *** findings indicate that the heterogeneous Merkle tree structure is suitable for various blockchain applications,including the Internet of *** scheme can markedly enhance the efficiency of information verification and bolster the security of distributed systems.

关键词： Blockchain architecture transaction verification information security heterogeneous Merkle tree distributed systems

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Internet of things technology, research, and challenges: a survey

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Multimedia Tools and Applications 2025年第11期84卷 8455-8490页

作者： Vishwakarma, Amit Kumar Chaurasia, Soni Kumar, Kamal Singh, Yatindra Nath Chaurasia, Renu Management science and technology Khalifa University Abu Dhabi United Arab Emirates Computer science & Engineering SGT University Gurugram India Department of Information Technology IGDTUW New Delhi India Electrical Engineering IIT Kanpur Kanpur India Computer science & Engineering AIT Rooma Kanpur India

The world of digitization is growing exponentially;data optimization, security of a network, and energy efficiency are becoming more prominent. The Internet of Things (IoT) is the core technology of modern society. This paper is based on a survey of recent and past technologies used for IoT optimization models, such as IoT with Blockchain, IoT with WSN, IoT with ML, and IoT with big data analysis. Suppose anyone wants to start core research on IoT technologies, research opportunities, challenges, and solutions. In that case, this paper will help me understand all the basics, such as security, interoperability, standards, scalability, complexity, data management, and quality of service (QoS). This paper also discusses some recent technologies and the challenges in implementation. Finally, this paper discusses research possibilities in basic and applied IoT Domains. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

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computer Modeling in engineering & Sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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PULSE: Progressive Utilization of Log-Structured Techniques to Ease SSD Write Amplification in B-epsilon-tree 25

PULSE: Progressive Utilization of Log-Structured Techniques ...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Peng, Huai-De Chen, Yi-Shen Chen, Tseng-Yi Chang, Yuan-Hao Department of Computer Science and Information Engineering National Central University Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Institute of Information Science Academia Sinica Taiwan

ISBN: (纸本)9798400706356

During the unprecedented expansion of global data, efficient storage solutions are essential for processing massive datasets stored on modern storage devices. B-epsilon-tree (Bϵ-tree) is one of the most well-known techniques that provides a write-optimized structure for database file systems. With the excellent access performance and high energy efficiency of solid-state drives (SSDs), they are expected to yield promising outcomes for large-scale data computation. However, their integration into storage systems has the challenges of write amplification, which impacts SSD endurance and reliability. This work identifies significant write amplification issues with Bϵ-tree implementations on SSDs due to the complicated management of key-value pairs. To mitigate the impact of write amplification, we propose PULSE, a novel scheme that rethinks Bϵ-tree designs by leveraging log-structured techniques optimized for SSDs. Moreover, PULSE integrates auxiliary indexing and a dual flush selector to minimize write amplification. Experimental results demonstrate that PULSE significantly mitigates write amplification by more than 62.6% on SSDs for the representative benchmarks compared to the Bϵ-tree. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Flash-based SSDs

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Blockchain Inspired Secure and Reliable Data Exchange Architecture for Cyber-Physical Healthcare System 4.0

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arXiv 2023年第1期3卷 309-322页

作者： Kumar, Mohit Raj, Hritu Chaurasia, Nisha Gill, Sukhpal Singh Department of Computer Science and Engineering National Institute of Technology Punjab Jalandhar India School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Information Technology National Institute of Technology Punjab Jalandhar144011 India

A cyber-physical system is considered to be a collection of strongly coupled communication systems and devices that poses numerous security trials in various industrial applications including healthcare. The security and privacy of patient data is still a big concern because healthcare data is sensitive and valuable, and it is most targeted over the internet. Moreover, from the industrial perspective, the cyber-physical system plays a crucial role in the exchange of data remotely using sensor nodes in distributed environments. In the healthcare industry, Blockchain technology offers a promising solution to resolve most securities-related issues due to its decentralized, immutability, and transparency properties. In this paper, a blockchain-inspired secure and reliable data exchange architecture is proposed in the cyber-physical healthcare industry 4.0. The proposed system uses the BigchainDB, Tendermint, Inter-Planetary-File-System (IPFS), MongoDB, and AES encryption algorithms to improve Healthcare 4.0. Furthermore, blockchain-enabled secure healthcare architecture for accessing and managing the records between Doctors and Patients is introduced. The development of a blockchain-based Electronic Healthcare Record (EHR) exchange system is purely patient-centric, which means the entire control of data is in the owner’s hand which is backed by blockchain for security and privacy. The experimental results prove that the proposed architecture is robust to handle more security attacks and can recover the data if 2/3 of nodes are failed. The proposed model is patient-centric, and control of data is in the patient's hand to enhance security and privacy, even system administrators can’t access data without user permission. © 2023, CC0.

关键词： Blockchain

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Enabling Robust Security in MQTT-Based IoT Networks with Dynamic Resource-Aware Key Sharing 4

Enabling Robust Security in MQTT-Based IoT Networks with Dyn...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Kaganurmath, Sharadadevi Cholli, Nagaraj Department of Computer Science Engineering Global Academy of Technology Bengaluru India Department of Information Science Engineering RV College of Engineering Bengaluru India

This paper presents a novel approach aimed at developing a secure secret key-sharing system optimized for resource-constrained Internet of Things (IoT) devices. Focusing on the MQTT protocol, the research endeavors to establish secure communication channels between IoT devices and brokers, thereby enhancing the overall security of MQTT-based IoT deployments. This research introduces a novel Dynamic Lightweight Authentication for MQTT (DLA-MQTT) mechanism designed to the unique needs of IoT devices operating under the MQTT protocol. The DLA-MQTT mechanism leverages an innovative lightweight Generalized Feedback Shift Register (GFSR)-based Pseudo-Random Number Generator (PRNG) to generate ephemeral keys, ensuring secure communication while addressing the limitations of computational power and energy resources. Through a detailed comparative analysis with existing cryptographic solutions, the DLA-MQTT mechanism demonstrates superior performance in terms of computational overhead, energy consumption, and execution time, while maintaining robust security against common attacks such as Man-in-the-Middle (MitM) and Denial of Service (DoS). he proposed algorithm's adaptability and scalability are validated using the Cooja simulator, where simulated IoT networks are subjected to various threat scenarios. The results confirm the DLA-MQTT mechanism's efficacy, showcasing a significant reduction in resource utilization without compromising the strength of the security provided. © 2025 The Authors. Published by Elsevier B.V.

关键词： Shift registers

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Development of Emotion Recognition via Federated Learning: A Multi-modal Approach with Hybrid Convolutional Network

Development of Emotion Recognition via Federated Learning: A...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Kaur, Dilsheen Misra, Anuradha Vyas, O.P. Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Lucknow Campus Lucknow India Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India

ISBN: (纸本)9798350385199

Human emotion recognition is the crucial process for performing effective communication and intelligent interaction. Identifying human emotion through discrete technologies is an essential step. Affective computing is one machine learning process in which multi-task learning has gathered a great deal of attention. It leverages shared data across associated activities to enhance performance. The physiological results from the aforementioned devices may also constitute a privacy risk because they reveal extremely sensitive information. To address this issue, we develop a federated learning approach with multi-task learning to precisely recognize facial emotion from the human. The required face images and Electroencephalogram (EEG) signals are collected from online sources. The face images are considered as feature set 1. From the EEG signals, the deep features are extracted through the Restricted Boltzmann Machine (RBM) and these features are considered as feature set 2. Both feature set 1 and feature set 2 are applied to the federated learning network, where the Hybrid Convolution-based Convolutional Neural Network (HC-CNN) is adopted for the recognition of human facial emotions. The face images are applied to the 2D layer and the extracted deep features from the EEG signal are applied to the 1D layer of the hybrid convolution network to provide an effective face recognition outcome. The recognition outcome of the proposed model is compared with the traditional face emotion recognition models concerning various measures. The recommended model successfully recognizes the emotion of the human with an accuracy of 95.75 % and precision of 95.97%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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