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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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A Framework for Deepfake Detection using Convolutional Neural Network and Deep Features

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Procedia Computer science 2025年 258卷 3640-3648页

作者： Soundarya B C Gururaj H L Naveen Kumar C M Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Business System Malnad College of Engineering Hassan India

With the advancement of Artificial Intelligence, facial recognition has become a crucial biometric feature. Deepfake technology leverages AI and can create hyper-realistic digitally manipulated videos of people appearing to say or do things that never occurred. The emergence of Generative Adversarial Networks (GANs) has further enabled the creation of fake visual content with astonishing realism. This technology has diverse applications, such as in the film industry, where it allows for video recreation without reshooting, creating awareness videos, restoring the voices of those who have lost them, and updating movie scenes at low cost. However, this rapid advancement also presents significant challenges. The proliferation of synthetic images raises severe concerns about their societal impact, particularly in terms of potential misuse for harassment and blackmail. Therefore, developing robust deepfake detection models is imperative. This study evaluates the performance of a proposed ResNet34 model in deepfake detection. We utilize the FaceForensics++ dataset to train and assess the model, incorporating images generated by four popular deepfake techniques. Our experimental results demonstrate that integrating linear ternary patterns (LTP) and edge detection-based features with the modified ResNet34 model achieves superior performance, attaining 97.5% accuracy and surpassing other approaches.

关键词： Deepfake Artificial intelligence Deep learning

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Advancing IC Fault Localization and Diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

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

作者： Rajasekar, P. Kumar, Alok Prema Latha, V. Jayapal, N. Santhoshi, Boddu Soundarya Karthik, K. Sivaramkumar, Mathiyalagan School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Guru Gobind Singh Educational Society Department of Electrical and Electronics Engineering Technical Campus Jharkhand Bokaro827013 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Trichy621215 India S.R.K.R Engineering College Department of Mechanical Engineering Andhra Pradesh Bhimavaram534204 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Saveetha University Department of Biosciences Chennai602105 India

ISBN: (纸本)9798331509828

Fault localization and diagnosis of Integrated Circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Mobility and Behavior based Trustable Routing in Mobile Wireless Sensor Network 7

Mobility and Behavior based Trustable Routing in Mobile Wire...

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Praveen Kumar, B. Umamageswaran, J. Kalpana, A.V. Dhanalakshmi, R. Department of Computer Science and Engineering KL University Andhra Pradesh Vijayawada India Department of Information Technology RMK Engineering College Tamil Nadu Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India

ISBN: (纸本)9781665464086

Mobile wireless sensor networks (MWSNs) allow the sensor nodes to move freely and transmit to each other without needing a fixed infrastructure. Usually, the routing process is very complex, and it becomes even more complicated as the sensor nodes are low-power, cost-effective mobile devices with minimum resources. MWSNs raises deployed in security-critical applications due to their resource-constrained features;they are prone to several security attacks that critically affect data transmission. The most significant innovation of trust is that it evades untrustable nodes by activating several detection routes to detect and obtain node trust, enhancing security quickly. Mobility and Behavior based Trustable Routing (MBTR) is proposed to solve these issues. This approach measures sensor node mobility based on the Received Signal Strength (RSS) between the two sensor nodes. The trust manager computes the node trust ability by the node behavior. Here, the node behavior is checked through the node energy, supportive account, and received packet account. The trust manager selects the security algorithm based on distance during data transmission. The simulation examination demonstrates lesser delay and lesser packet loss in the network. Furthermore, it improves the network throughput in the MWSN. © 2023 IEEE.

关键词： Sensor nodes

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Enhancement of Security in IoT using Secure Hash Cryptography Algorithm with Double Encryption (SHCA-DE) 9

Enhancement of Security in IoT using Secure Hash Cryptograph...

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9th International Conference on Communication and Electronics systems, ICCES 2024

作者： Uganya, G. Rajalakshmi, D. Vangapandu, Ramarao Dinesh, M. Giri, Jayant Odeh, Mahmoud Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India Vignan's Institute of Engineering for Women Department of Computer Science and Engineering Vishakhapatnam India Saveetha University Department of Vlsi Microelectronics Saveetha School of Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (纸本)9798350377972

The 'Internet of things' is the developing technology to transfer the world into smart innovation. 'IoT' security is the most problematic issue in recent applications due to its properties including 'centralized architecture', 'limited capacity devices', and 'ambiguity'. So, blockchain with 'IoT' applications provides secure data communication compared to traditional 'IoT' applications. Blockchain is a recent growing technology that has features including 'decentralized structure', 'transparency', 'immutable records', 'increased capacity', and 'quick backtracks'. The 'cryptography hash algorithm' is very important to provide secure data transmission. It converted the different size input to fixed-size unalterable output to enhance the security. But still, it needs efficient algorithm to secure data transmission with safeguard of private key. The objective of this paper is to propose a secure hash cryptography algorithm with double encryption (SHCA-DE) to protect the private key message. Then, this proposed algorithm has been analyzed with MD5, SHA-256, and SHA3-512. From this analysis, the SHA3-512 performs well with proposed algorithm. © 2024 IEEE.

关键词： Encryption algorithms

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Advancements in Artificial Intelligence for Better Image Analysis, Recognition, and Interpretation in the Field of Image Processing 2

Advancements in Artificial Intelligence for Better Image Ana...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Saravanan, V. Adaikkammai, A. Sasikala, G. Iyswariya, A. Babisha, A. Latha, M. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Nano Electronics Materials and Sensors Tamilnadu Chennai India R.M.D. Engineering College Department of Computer Science and Business Systems India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Avadi Chennai India R.M.K. Engineering College Department of Ece Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science India Srm Institute of Science and Technology Department of It India

ISBN: (纸本)9798350388916

The rapid advancement of artificial intelligence (AI) has brought about a significant revolution in image processing. This revolutionary technology has enabled the analysis, recognition, and interpretation of images in ways that were previously inconceivable. An investigation into the most recent developments in artificial intelligence techniques that have improved the efficiency and precision of image processing tasks is presented in this study. We survey the emergence of transformer architectures and the integration of deep learning models, namely Convolutional Neural Networks (CNNs), to explore how these technologies have transformed the understanding and application of images in several industries. An overview of existing image processing methods and the limits of those approaches is presented at the beginning of our research. This highlights the necessity of more advanced AI-driven solutions. After that, we examine the advancements that have been made in artificial intelligence, such as the incorporation of attention processes, the development of more effective CNN architectures, and the utilisation of generative adversarial networks (GANs) for the purpose of picture synthesis and augmentation. The purpose of this research is to investigate the impact that artificial intelligence has on particular applications of image processing, such as autonomous car navigation systems, facial recognition, medical imaging, and satellite images analysis. Specifically, we focus on the enhancements in diagnostic accuracy, environmental monitoring, security, and safety that arose as a result of the adoption of artificial intelligence in these sectors through the use of case studies. Furthermore, we discuss the issues that are associated with artificial intelligence in image processing. These challenges include the necessity for explainable AI models, the requirement for computational resources, and the protection of data privacy. A number of potential solutions to these problems

关键词： Convolutional neural networks

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Hybrid system with Modified Cuckoo Search to Design Airfoil

Hybrid System with Modified Cuckoo Search to Design Airfoil

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Thinakaran, K. Anand, M. Kumar, T.Rajesh Atham, Saira Banu Subramanian, K.M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Chennai Kattankulathur India Saveetha Institute of Medical and Technical Science Saveetha School of Engineering India Presidency University School of Cse & Is Department of Computer Science and Engineering Bangaluru India Shadan College of Engineering and Technology Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9798350348910

The proposed models can design the airfoil by Cuckoo search with Levenberg-Marquardt. The Neural Network framework has impediments due to over-fitting. This paper proposed a modified cuckoo search. here the aerodynamic coefficient as an input to produce output the airfoil coordinates. The generated airfoil is compared to know its performance metrics. The Cuckoo search with the Feedforward Neural Network model yields the lowest prediction error. © 2023 IEEE.

关键词： Airfoils

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Cybernated Watermarking Approach on Image with Cryptographic Encryption 2

Cybernated Watermarking Approach on Image with Cryptographic...

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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Sivasankari, R. Veeramakali, T. Mustafa Nawaz, S.M. Prakash, P. Umadevi, P. Alvin Ancy, A. B.S.Abdur Rahman Crescent Institute of Science and Technology Dept. of Computer Science and Engineering India Srm Institute of Science and Technology School of Computing Dept of Data Science and Business Systems Tamilnadu Kattankulathur India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Sri Sai Ram Engineering College Dept of Electronics and Communication Engineering India Kpr Institute of Engineering and Technology Department of S & H Coimbatore India Anna University Dept of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350387933

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types;a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text. © 2024 IEEE.

关键词： Cryptography

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Intelligent Caching Strategies for 5G Edge Networks using Machine Learning 4

Intelligent Caching Strategies for 5G Edge Networks using Ma...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Guduri, Naga Venkata Ramakrishna Sethu, S. Shalinirajan, R. Reena, R. Karthikeyan, M. Uthayakumar, G.S. University of Technology and Applied Sciences-Al Musannah Electrical and Electronics Section Engineering Department Oman Department of Computer Science and Engineering Tamilnadu Chennai India Panimalar Engineering College Department of Computer Science and Business System Tamilnadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India Tamilnadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350300888

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%. © 2023 IEEE.

关键词： 5G mobile communication systems

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Blockchain Driven Metaheuristic Route Planning in Secure Wireless Sensor Networks

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Computers, Materials & Continua 2023年第1期74卷 933-949页

作者： M.V.Rajesh T.Archana Acharya Hafis Hajiyev E.Laxmi Lydia Haya Mesfer Alshahrani Mohamed K Nour Abdullah Mohamed Mesfer Al Duhayyim Department of Computer Science and Engineering Aditya Engineering CollegeSurampalem533437India Department of Business Administration Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Accounting and Audit Azerbaijan State University of Economics(UNEC)BakuRepublic of Azerbaijan Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Recently,Internet of Things(IoT)has been developed into a field of research and it purposes at linking many sensors enabling devices mostly to data collection and track *** sensor network(WSN)is a vital element of IoT paradigm since its inception and has developed into one of the chosen platforms for deploying many smart city application regions such as disaster management,intelligent transportation,home automation,smart buildings,and other such IoT-based *** routing approaches were extremely-utilized energy efficient approaches with an initial drive that is,for balancing the energy amongst sensor *** clustering and routing procedures assumed that Non-Polynomial(NP)hard problems but bio-simulated approaches are utilized to a recognized time for resolving such *** this motivation,this paper presents a new blockchain with Enhanced Hunger Games Search based Route Planning(BCEHGS-RP)scheme for IoT assisted *** presented BCEHGS-RP model majorly employs BC technology for secure communication in the IoT supportedWSN *** addition,an effective multihop route planning approach was designed by the use of EHGS *** proposed EHGS technique is derived from the concept of Hill Climbing strategy(HCS)and HGS ***,a fitness function with two parameters namely residual energy(RE)and intercluster distance to elect optimal *** performance validation of the BCEHGS-RP model is experimented with under diverse number of *** experimental outcomes highlighted the better performance of the BCEHGS-RP technique on recent approaches.

关键词： Internet of things wireless sensor networks routing metaheuristics blockchain

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