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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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Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

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Computers, Materials & Continua 2023年第3期74卷 5901-5914页

作者： R.Brindha S.Nandagopal H.Azath V.Sathana Gyanendra Prasad Joshi Sung Won Kim Department of Computing Technologies SRM Institute of Science and TechnologyKattankulathur603203India Department of Computing Science and Engineering Nandha College of TechnologyErode638052India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-si38541Gyeongbuk-doKorea

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in *** the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the *** this background,there is a drastic increase observed in the number of phishing emails sent to potential *** scenario necessitates the importance of designing an effective classification *** numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the *** current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)*** aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing *** the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word ***,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature ***,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing ***,the CS algorithm is used to fine-tune the parameters involved in the GRU *** performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several *** comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing *** proposed model achieved a maximum accuracy of 99.72%.

关键词： Phishing email data classification natural language processing deep learning cybersecurity

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Precise trajectory tracking of mecanum-wheeled omnidirectional mobile robots via a novel fixed-time sliding mode control approach

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Control Theory and Technology 2024年第4期22卷 596-611页

作者： Zhe Sun Zhipeng Li Hao Xie Yunjun Zheng Jinchuan Zheng Bo Chen College of Information Engineering Zhejiang University of TechnologyHangzhou310023China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthorn3122VICAustralia School of Mechanical Engineering Hefei University of TechnologyHefei230009AnhuiChina

This paper proposes a novel fixed-time sliding mode control approach for trajectory-tracking tasks of a mecanum-wheeled omnidirectional mobile ***,the idea of two-phase attractors is introduced into the domain of sliding mode control,and a new fixed-time sliding surface is ***,according to this sliding surface,a new type of nonsingular fast terminal sliding mode control algorithm is designed for the omnidirectional mobile robot,which can realize a fast fixed-time convergence *** stability of the control system is proven scrupulously,and a guideline for control-parameter tuning is ***,experiments are implemented to test the trajectory-tracking performance of the *** results demonstrate the superiority of the proposed sliding surface and the corresponding control scheme in comparison with benchmark controllers.

关键词： Omnidirectional mobile robot Trajectory tracking Sliding mode control Fixed-time convergence

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Enhancing Secure Data Transmission Through Deep Learning-Based Image Steganography and Super-Resolution Generative Adversarial Networks

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SN Computer science 2024年第8期5卷 1138页

作者： Ramesh, Sreyas Sukanth, B.N. Jaswanth, Sathyavarapu Sri Singh, Rimjhim Padam Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

In the era of multimedia technology digital images are essential and keeping them safe from unauthorised access is crucial. To address this issue, the proposed research explores the intersection of image steganography and deep learning for enhanced secure data transmission. It proposes a unique approach that leverages skip-connection-based residual network to extract semantic features and reconstruct images for secure data transmission. This paper introduces a novel steganographic approach combining LSB embedding with sprite image generation instead of directly using image features. Hence, the proposed method effectively hides data within images while maintaining high invisibility. Furthermore, Super-Resolution Generative Adversarial Network (SRGAN) is employed to improve the quality of the reconstructed images. Several other custom and state-of-art convolutional architectures have also been investigated for semantic feature extraction and image reconstruction, including Convolutional Autoencoders (CAE), Deep convolutional network (DCN), DenseNet, InceptionNet and the XceptionNet model, etc. for the comprehensive comparisons against the proposed model. The proposed technique proved to be the best performing model with an SSIM value of 1.00 and PSNR value of 33.04 on the test data. Overall, this research work combines deep learning with steganography to create a secure and hidden communication method. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data security Least significant bit Sprite image Steganography Super-resolution generative adversarial network

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Convergence of Deep Learning and Forensic Methodologies Using Self-attention Integrated EfficientNet Model for Deep Fake Detection

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SN Computer science 2024年第8期5卷 1-12页

作者： Singh, Rimjhim Padam Sree, Nichenametla Hima Reddy, Koti Leela Sai Praneeth Jashwanth, Kandukuri Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

The immensely increasing number of Deepfake technologies poses significant challenges to digital media integrity, leading to the immediate need for effective Deepfake detection methods. In light of the growing threat posed by sophisticated synthetic media, this paper tackles the urgent need for accurate deepfake detection in real-time by proposing a novel classification model employing a fine-tuned EfficientNet-B0 model with an optimally integrated Self-attention mechanism. The study also analyzes various Deepfake generation techniques by utilizing a suite of Convolutional Neural Networks namely, XceptionNet, NasNetLarge, InceptionV3, ConNextv1, ConvNext2 and EfficientB0 model on the Celeb-DF dataset for baseline model selection and performance comparison purposes. The results prove that the proposed fine-tuned EfficientNet-B0 model with integrated self-attention mechanism is the most superior at identifying complex long-range dependencies and relations in various unconnected image regions, highlighting the importance of attention mechanisms in improving deep learning models for multimedia forensics with a remarkable F-score of 97.67%. In an era marked by the proliferation of sophisticated artificial intelligence (AI)-driven synthetic media, these findings establish the foundation for strengthening digital media platform security and protecting information authenticity. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Celeb-DF dataset EfficientNetB0 Multimedia forensics NasNetLarge Self-attention mechanism Xception Deepfake detection

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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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Establishment of secure and authentic data security framework in wireless sensor network using key reconciliation

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International Journal of Information Technology (Singapore) 2024年第5期16卷 3325-3336页

作者： Devi, Suman Kumar, Avadhesh School of Computing Science and Engineering Greater Noida India Galgotias University Greater Noida India

A massive amount of data is generated in today's modern era. The world's open and interconnected nature puts this data and its privacy at constant risk. As a result, establishing a secure data exchange mechanism for large datasets in cyberspace is an extremely difficult task. To address this issue, several architectures already exist that include a safe and reliable data exchange algorithm. The focus of this research lies in the development of a Wireless Sensor Network (WSN) system that fosters secure data sharing. To achieve this, an intelligent security model that supports the WSN security measures is designed. This research assesses the performance of network security and economic incentives, while exploring alternative deployment strategies. The evaluation encompasses criteria such as design of network architecture, secure authentication, and key management including randomness, hardness, and successful key reconciliation. The main goal of this proposed scheme is to design a more secure and authentic security framework to handle large amounts of data. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Cyberspace Data Sharing Key Generation Security Wireless Sensors – Authentication

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A Multi-path Droplet Routing Protocol for Digital Microfluidic Biochip

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Informatica (Slovenia) 2024年第7期48卷 95-102页

作者： Swain, Jyotiranjan Pyne, Sumanta School of Computing and Information Technology REVA University Bangalore India Department of Computer Science and Engineering National Institute of Technology Rourkela India

Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It involves challenges like moving droplets without violating the fluid constraints. This article proposed a multi-path droplet routing protocol. Multiple routes are explored and validated using special packets. These routes are then classified based on user-defined heuristics. A single route is sequentially selected for each droplet. The routes are then compacted to generate a parallel moving sequence. The simulation result shows 2.087% and 4.952% improvement in the latest arrival time for free and virtual topology, respectively. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Biochips

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Spontaneous Infection and Periodic Evolving of Domain in a Diffusive SIS Epidemic Model

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Acta Mathematicae Applicatae Sinica 2024年第1期40卷 164-191页

作者： Qiang Wen Guo-qiang Ren Bin Liu School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan430074China

In this paper, we consider a susceptible-infective-susceptible(SIS) reaction-diffusion epidemic model with spontaneous infection and logistic source in a periodically evolving domain. Using the iterative technique,the uniform boundedness of solution is established. In addition, the spatial-temporal risk index R0(ρ) depending on the domain evolution rate ρ(t) as well as its analytical properties are discussed. The monotonicity of R0(ρ)with respect to the diffusion coefficients of the infected dI, the spontaneous infection rate η(ρ(t)y) and interval length L is investigated under appropriate conditions. Further, the existence and asymptotic behavior of periodic endemic equilibria are explored by upper and lower solution method. Finally, some numerical simulations are presented to illustrate our analytical results. Our results provide valuable information for disease control and prevention.

关键词： diffusive SIS model spontaneous infection periodically evolving domain periodic endemic equilibrium

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IoT-Based Fire Alarm System and Motion Detector: A Comprehensive Protocol Analysis for Enhanced Performance 9

IoT-Based Fire Alarm System and Motion Detector: A Comprehen...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Samudrala, Sai Santhoshi Haneesha Thambi, Jaishitha Vadluri, Sai Rishisri Rajagopal, Shinu M. Bhaskaran, Sreebha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bangalore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Fire alarm systems play a vital role in providing early warnings, facilitating prompt evacuation in emergency situations. These systems ensure the security of individuals by alerting them to possible risks and allowing rapid responses to minimize the impact of fires. The research investigates the integration of IoT devices into a Cisco Packet Tracer simulated environment for fire detectionwithin a centralized architecture using a star topology. The significance of an IoT system determines its ability to simulate and analyse diverse IoT device integrations, evaluate various protocols. The System incorporates smoke detection sensors for precise identification of fire events and motion-detecting sensors seamlessly integrated with cameras for motion capture. Performance of different protocols, including BGP, DHCP, EIGRP, HSRP, ICMP, OSPF, RIPis evaluated in terms of response time. The findings contribute valuable insights into optimizing protocol selection for enhanced real-time responsiveness in IoT-based fire alarm systems. © 2024 IEEE.

关键词： Smoke

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