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Energy Efficient Mathematical Model for Cyber-Physical Systems: A Model for Industrial Internet of Things

SN Computer science 2023年第5期4卷 1-7页

作者： Veerachamy, Ramachandran Rao, Ganga Rama Koteswara Arivanantham, Vishnu Priya Kuppusamy, Sangeetha Velayutham, Priya Govindaraj, Rajeshkumar Department of Computer Science and Engineering GITAM School of Computing GITAM Deemed to be University Bengaluru 561203 India Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 520002 India Department of Computational Intelligence School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal 637018 India Department of Information Science and Engineering Bannari Amman Institute of Technology Tamil Nadu Sathyamagalam 638401 India

Proliferated usage of computer devices, smart objects, and electronics significantly impacts the atmosphere, which is inevitable. The existing context has engineered schemes, or Cyber-Physical Systems (CPS), that integrate physical and computation components. The extensive usage of devices has utilized more energy, creating complexity in environmental handling. The problem is improving an automotive function’s reliability, application, or functionality with time and energy constraints. By applying smart technologies, namely Deep Learning (DL) and Machine Learning (ML) in CPS and the Internet of Things (IoT), enduring the utilization of these intensive DL-based approaches has a negative effect on resource utilization. The proposed Energy Efficient Mathematical Model (EEMM) is designed with a mathematical model for IIoT, which guides in solving the drawback of the existing system. The mathematical model integrated CPS-based proposed EEMM has outperformed the existing state-of-art technique. The proposed EEMM attains minimal Energy Consumption (EC) of 521 Jules and the highest reliability rate of 0.987. The proposed EEMM in this research has attained minimal energy usage of 0.987 i for 100 tasks, which is minimal than other existing techniques. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cyber-attacks Energy IIoT IoT Mathematical model Network Smart objects

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Network Coding For Data Compression In Genomic Sequencing

Network Coding For Data Compression In Genomic Sequencing

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Alam, Sultan Agarwal, Saksham Sharma, Anshumaan School of Computing Science and Engineering Vit Bhopal University M.P. Sehore466114 India School of Computing Science Engineering and Artificial Intelligence Vit Bhopal University M.P. Sehore466114 India

ISBN: (纸本)9798331519582

The exponential growth of next-generation sequencing technologies has resulted in a massive influx of genomic data, posing significant challenges in storage, transmission, and processing. Conventional compression methods, such as gzip and bzip2, are not optimized for the unique structural patterns and redundancies of genomic sequences, leading to suboptimal performance. Addressing this gap, our research introduces a novel data compression framework that leverages network coding principles to enhance efficiency, scalability, and reliability. Network coding, widely used in communication systems, is adapted here to process and compress genomic data by employing finite field arithmetic and redundancy-aware encoding techniques. Our method achieves a compression ratio of up to 50% while maintaining a data integrity level of 99.99%, as validated through extensive mathematical analysis and experimental testing on diverse genomic datasets. Furthermore, the framework demonstrates linear scalability and robust error resilience, making it particularly suited for large-scale applications in clinical genomics and research. By bridging the gap between traditional compression algorithms and the growing demands of modern genomic data, this study lays the groundwork for more efficient and reliable data management solutions in bioinformatics. © 2025 IEEE.

关键词： Data compression ratio

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An Adaptive Community Detection Algorithm based on Density Peaks 9

An Adaptive Community Detection Algorithm based on Density P...

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9th IEEE Smart World Congress, SWC 2023

作者： Fan, Xiang Shi, Lei-Lei Liu, Lu Han, Zi-Xuan Jiang, Liang Yuan, Bo Jiangsu University School of Computer Science and Telecommunication Engineering China Jiangsu University Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace China University of Leicester School of Computing and Mathematical Sciences United Kingdom Jiangsu University of Science and Technology School of Ocean China

ISBN: (纸本)9798350319804

Community detection is an important task for analyzing the topological function and structure of a network, as well as for various application scenarios such as advertising, marketing, and influence maximization. Label propagation is a widely used community detection algorithm for complex networks due to its simplicity and fast computation. However, this algorithm has several limitations in terms of stability and the quality of discovered communities. To address these shortcomings, we propose a novel community detection algorithm based on density peaks (ACDDP) in this paper. Firstly, we improve the local density and minimum distance of nodes by using an adaptive selection of community center nodes. Secondly, we specify the node update order to enhance the robustness and performance of the algorithm. The experimental results on real networks demonstrate that our proposed algorithm achieves higher accuracy in community division while ensuring robustness compared to other popular algorithms. © 2023 IEEE.

关键词： Adaptive Selection Community detection Density Peaks

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Disentangled Hypergraph Collaborative Filtering for Social Recommendation

Disentangled Hypergraph Collaborative Filtering for Social R...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Xiao Meng, Shunmei Li, Qianmu Xu, Xiaolong Qi, Lianyong Dou, Wanchun Zhang, Jing Zhang, Xuyun Nanjing University of Science and Technology China Nanjing University State Key Lab. for Novel Software Technology China Nanjing University of Information Science and Technology School of Computer and Software China College of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Macquarie University School of Computing Australia

ISBN: (纸本)9798350304855

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collaborative filtering, to disentangle the representations of users and items at the granularity of social users' intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user's intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores. © 2023 IEEE.

关键词： Collaborative filtering

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Creating Realities: An In-Depth Study of AI-Driven Image Generation with Generative Adversarial Networks 15

Creating Realities: An In-Depth Study of AI-Driven Image Gen...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhand, Raman Sidana, Sagar Sinha, Anurag Kumar, Biresh Kumari, Shreya Madhukar Rao, G. Raj, Petheru Al-Khayyat, Ahmed Maheswaran, S. Guru Tegh Bahadur Institute of Technology Department of computer science and engineering India Maharshi Dayanand University Department of computer science and engineering Haryana Rohtak India IGNOU India School of Computing and information Science New Delhi India Amity University Department of computer science and It Jharkhand India Koneru Lakshmaiah Education Foundation Department of computer science and engineering India Reliance Jio Platforms Ltd Bangalore India Islamic University Department of computer science and engineering Iraq Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India

ISBN: (纸本)9798350370249

The field of artificial intelligence has witnessed significant advancements with the advent of Generative Adversarial Networks, revolutionizing image generation techniques. The literature review covers seminal works and recent advancements, highlighting the evolution of image generation techniques. Taxonomy is developed to classify methods based on their underlying principles and applications across various domains including art, fashion, healthcare, and entertainment. Comparative analysis evaluates the effectiveness, efficiency, and applicability of different models, utilizing metrics corresponding Inception Score and Frechet Inception Distance. By conducting a thorough review of existing literature and technologies, we explore the evolution of GAN architectures, from their inception to the latest innovations. We examine various GAN models, including DCGAN, StyleGAN, and CycleGAN, assessing their effectiveness in generating high-fidelity images across diverse applications. In addition to this comprehensive analysis, we present our own implementation of a GAN model tailored for specific image generation tasks. Our model is tested on multiple datasets to evaluate its performance, highlighting improvements in training stability and image quality. We discuss the architectural choices, optimization techniques, and training procedures employed in our implementation. Furthermore, we address the challenges faced in the field, such as mode collapse, training instability, and the need for largescale datasets. Our findings offer valuable insights into optimizing GAN performance and propose potential directions for future research. This comprehensive analysis serves as a foundational reference for researchers and practitioners aiming to leverage GANs for advanced image generation tasks, pushing the boundaries of what is visually conceivable through artificial intelligence. © 2024 IEEE.

关键词： AI Computer vision Deep Learning GANs Image generation ML

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Cache in fog computing design,concepts,contributions,and security issues in machine learning prospective

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Digital Communications and Networks 2023年第5期9卷 1033-1052页

作者： Muhammad Ali Naeem Yousaf Bin Zikria Rashid Ali Usman Tariq Yahui Meng Ali Kashif Bashir School of Science Guangdong University of Petrochemical TechnologyMaoming525000China Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Republic of Korea Department of Information and Communication Technologies Universitat Pompeu FabraBarcelonaSpain College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUnited Kingdom Woxsen School of Business Woxsen UniversityHyderabadIndia Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon

The massive growth of diversified smart devices and continuous data generation poses a challenge to communication *** deal with this problem,communication networks consider fog computing as one of promising technologies that can improve overall communication *** brings on-demand services proximate to the end devices and delivers the requested data in a short *** computing faces several issues such as latency,bandwidth,and link utilization due to limited resources and the high processing demands of end *** this end,fog caching plays an imperative role in addressing data dissemination *** study provides a comprehensive discussion of fog computing,Internet of Things(IoTs)and the critical issues related to data security and dissemination in fog ***,we determine the fog-based caching schemes and contribute to deal with the existing issues of fog ***,this paper presents a number of caching schemes with their contributions,benefits,and challenges to overcome the problems and limitations of fog *** also identify machine learning-based approaches for cache security and management in fog computing,as well as several prospective future research directions in caching,fog computing,and machine learning.

关键词： Internet of things Cloud computing Fog computing Caching Latency

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Creation of GIF dataset and implementation of a speech-to-sign language translator in Telugu

Creation of GIF dataset and implementation of a speech-to-si...

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2022 IEEE North Karnataka Subsection Flagship International Conference, NKCon 2022

作者： Reddy, Bandi Rupendra Tharun Reddy, Darukumalli Sai Sandeep Preetham, M.C. Vekkot, Susmitha Department of Computer Science and Engineering India Amrita School of Computing Department of Electronics & Communication Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9781665453424

The paper proposes the design and implementation of a model that translates live voice or audio recordings of a native Indian regional language (Telugu) to text and further matches it to sign language animations from the pre-defined Graphics Interchange Format (GIF) dataset. The speech is converted to text using Google API. If the text matches with the words in GIF dataset, it generates the respective GIF. A GUI interface is also developed which displays ISL text alphabetically if the given input is outside the range of GIF dataset. Alongside, the performance of four powerful deep learning networks viz. RNN, LSTM, Bi-LSTM, and GRU, has been compared and trained over a Telugu multi-speaker speech dataset for speech recognition. The proposed models yielded accuracies of 51% for RNN, 68% for LSTM, 90% for Bi-LSTM and 88% for GRU. On evaluating the models, Google API-based speech to sign language conversion has been most promising and consistent with precision and recall scores of 94% each and a word error rate of 4.1%. © 2022 IEEE.

关键词： Speech recognition

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BitPredict: End-to-End Context-Aware Detection of Anomalies in Bitcoin Transactions using Stack Model Network

BitPredict: End-to-End Context-Aware Detection of Anomalies ...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： K. Snigdha P. Sree Navya Reddy Deva Hema S. Gayathri Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The seamless integration of cryptocurrencies and blockchain technology in various sectors has revolutionized financial transactions. While cryptocurrencies serve as a convenient mode of payment, they have also opened avenues promoting fraudulent schemes such as Ponzi schemes, HYIPs, or money laundering activities leading to substantial financial losses. Traditional ways of anomaly detection, such as heuristic and signature-based approaches, have proven inadequate in addressing the intricacies of burgeoning fraud patterns. This paper explores the application of ensemble learning for anomaly detection in Bitcoin transactions by combining various ML techniques such as Isolation Forest, One-class SVM, and DBSCAN within a stacking framework. The proposed model harnesses the complementary strengths of each algorithm to achieve a nearly $98 \%$ accuracy rate in anomaly detection, thereby addressing the shortcomings of existing techniques. The study utilizes hyperparameter tuning techniques to enhance the effectiveness of the ensemble model and create a resilient model for detecting fraud and security threats in cryptocurrency transactions. Leveraging the cryptographic foundations of blockchain technology, the proposed method aims to create a more secure and reliable system for detecting threats and maintaining the integrity of Bitcoin transactions.

关键词： Support vector machines Measurement Accuracy Stacking Bitcoin Forestry Feature extraction

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NP-ConicJS: The JavaScript Library of General Conic in Dynamic Geometry 9

NP-ConicJS: The JavaScript Library of General Conic in Dynam...

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9th International Conference on Digital Home, ICDH 2022

作者： Chen, Wenxian Xu, Zhonglan Guan, Hao Wang, Liang Rao, Yongsheng Guangzhou University Institute of Computing Science and Technology Guangzhou China Guangzhou University School of Computer Science and Cyber Engineering Guangzhou China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

The application of computer and Web technology in teaching has become an inevitable trend of modern education. The Dynamic Geometry System brings innovation to traditional geometry teaching. Conic curves have always been one of the most important topics of geometric research. There is a lack of an algorithm library for dynamic geometric conic curves that are based on JavaScript. In this paper, an efficient and reusable conic curve algorithm library is proposed based on the general theory of conic curves. The algorithm library implements the functions of conic curve construction, transformation and related property calculation. Finally, the application in NetPad shows that the algorithm library brings convenience to the teaching of conic curve geometry and has important practical application value. © 2022 IEEE.

关键词： Geometry

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities 24

On the Effectiveness of Function-Level Vulnerability Detecto...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

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