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International Conference on Computing and Networking technology (ICCNT)

作者： Nagarjuna Pitty N. Hemalatha K Venkata Ramana L.P Sajitha P Ramesh Babu M.Dinesh Babu Indian Institute of Science Bengaluru Karnataka India Saveetha School of Engineering (SIMATS) Chennai Tamilnadu India Department of CSE QIS College of Engineering and Technology Ongole Andrapradesh India Department of Computer Science and Business System R.M.K Engineering College Kavaraipettai Tamilnadu India Department of Computer Science College of Engineering and Technology Wollega University Nekemte Oromia Region Ethiopia Department of Mechanical Engineering Rajalakshmi Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In current years, part cloud computing systems have received huge adoption because of their capability to provide low latency and high bandwidth offerings to users. however, one of the primary challenges in these structures is green undertaking scheduling in the presence of dynamic and heterogeneous sources. To deal with this trouble, genetic set of rules (GA) optimization strategies were proposed as a potential solution. This approach utilizes genetic operators, which include mutation and crossover, to conform a populace of potential solutions and choose the best one. by way of considering various constraints and objectives, consisting of strength consumption and make span, the GA-primarily based project scheduling technique can efficaciously optimize useful resource allocation in area cloud computing structures, at the end improving their performance and resource utilization.

关键词： Cloud computing Processor scheduling Dynamic scheduling Genetics Resource management Low latency communication Biological cells Optimization Genetic algorithms Genetic operators

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Convolutional Neural Network Based Deep Neural Network Model for Electrocardiogram Records Classification 7

Convolutional Neural Network Based Deep Neural Network Model...

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7th International Conference on Future Networks and Distributed systems, ICFNDS 2023

作者： Khudhur, Ahmed Mahmood Alwan, Mohammed Hasan Al Omairi, Qayssar Mahmood, Omar Abdulkareem Hammadi, Yousif I Muthanna, Mohammed Saleh Ali Aziz, Ahmed Muthanna, Ammar Information Technology Department College Of Computer Science And Information Technology Kirkuk University Iraq Department Of Communications Engineering College Of Engineering University Of Diyala Baqubah32001 Iraq Department Of Computer Engineering College Of Engineering University Of Diyala Iraq Bilad Alrafidain University College Diyala32001 Iraq Institute Of Computer Technologies And Information Security Southern Federal University Taganrog344006 Russia Department Of Computer Science Faculty Of Computer And Artificial Intelligence Benha University Egypt Department Of International Business Management Tashkent State University Of Economics Tashkent Uzbekistan Department Of Telecommunication Networks And Data Transmission The Bonch Bruevich Saint Petersburg State University Of Telecommunications Russia Department Of Applied Probability And Informatics Peoples Friendship University Of Russia Russia

ISBN: (纸本)9798400709036

Cardiovascular diseases, also known as CVDs, currently rank as the primary incidence of mortality. The present approach for identifying illnesses involves the analysis of the Electrocardiogram (ECG), an electronic diagnostic device utilized to capture the rhythm of the heart. Regrettably, the process of seeking out specialists to conduct analysis on a substantial volume of electrocardiogram (ECG) data results in a significant depletion of medical capabilities. Hence, the utilization of deep learning algorithms for the identification of ECG characteristics has progressively gained prominence. Nevertheless, there exist certain limitations associated with these conventional approaches, which necessitate the need for manual characteristic identification, intricate models, and extensive training duration. This study presents a novel and effective approach for categorizing the five different types of heartbeats in the MIT-BIH Arrhythmia database. The proposed method involves the utilization of a 16-layer deep one-dimensional convolutional neural network, which exhibits both robustness and efficiency in its classification performance. Thus, the designed model is consisting of five-groups, first to fourth groups are the main feature extraction and mapping blocks, while the fifth group is fully connected and classification layers. The findings indicate that the model presented in this study has superior performance in terms of accuracy, precision, and F1-score. The proper classification of medical cases has a significant impact on the conservation of medical resources, hence positively influencing clinical practice. Additionally, the model training phase is characterized by its efficiency, since it does not significantly deplete resources. © 2023 ACM.

关键词： Electrocardiograms

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An Advanced Intrusion Detection Solution for Networks based on Honeypot Servers

An Advanced Intrusion Detection Solution for Networks based ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： K. Veena K. Meena Mercy Theresa M Hemalatha C D. Rajalakshmi Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering GITAM School of Technology GITAM- Bangalore India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science and Engineering Sri Sairam Institute of Technology Chennai

Network security is a critical issue in modern technology. Honey pot-based intrusion detection methods provide an additional layer of security and enhance network performance by analyzing hacker behaviour and detecting unauthorized clients. IP validation and vulnerability detection are used to ensure authorized customer access and prevent illegal client activity. A voice recognition system detects malicious attacks in stage two. If authorized, packets are passed to the server, otherwise, they are delivered to honeypot servers. Using Naive Bayes algorithm, the detection rate is 85.06% with an accuracy of 98.18% and a false alarm rate of 0.15 seconds.

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Candidate Evaluation with Multimodal data-Driven for Recruitment 27th

Candidate Evaluation with Multimodal Data-Driven for Recru...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xing Liu, Kehong Wang, Jianjia Yao, Junfeng Deng, Bin Lv, Rongqi Song, Jun School of Computer Engineering and Science Shanghai University Shanghai200444 China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of AI and Advanced Computing XJTLU Entrepreneur College Xi’an Jiaotong-Liverpool University Suzhou215412 China CSSC Seago System Technology Co. Ltd. Shanghai200010 China Science and Technology on Near-Surface Detection Laboratory Jangsu Wuxi214035 China Department of Geography Hong Kong Baptist University 999077 Hong Kong

ISBN: (纸本)9783031781858

In the field of intelligent recruitment, automated resume matching and non-contact interviews have significantly improved the efficiency of companies in finding suitable candidates. This corresponds to the techniques of person-job matching and AI interviews. However, current person-job matching methods lack substantial data support, while AI interview methods struggle to integrate deep information from multimodal data and provide comprehensive evaluations of candidates’ responses. To address these challenges, we propose a multimodal data-driven person-job evaluation model, comprising two key stages: a person-job matching method based on graph attention and a multimodal AI interview method. Using a dual-perspective graph neural network approach, we accomplish the screening of candidates and positions. In the second stage, we conduct a comprehensive evaluation of candidates’ interview performance based on text, audio, and image modalities, providing a more objective, consistent, and efficient interview assessment method. Experimental results demonstrate that our person-job matching method surpasses current popular techniques and effectively transfers features to the next stage. In our multimodal AI interview method, we achieve accurate scoring of candidate responses, assessment of intonation stress levels, and inference of their Big Five personality traits, comprehensively evaluating candidates from multiple perspectives. This confirms the superiority and efficiency of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Execution of Convolution Coding Method for FPGA in Industrial Automation using VERILOG HDL

Execution of Convolution Coding Method for FPGA in Industria...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Kanagaraj Venusamy S. Kannadhasan A. Vimalesh P Chandramohan M. Shanmugam K. Priyadarsini Department of Mechatronics Engineering Rajalakshmi Engineering College Chennai India Department of Electronics and Communication Engineering Study World College of Engineering Coimbatore Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science & Technology Chennai India

In this paper, the performance study of the FPGA-based encoder is delivered and received with little errors given the large number of applications that employ digital communication to store and retrieve data recently. Forward Error Correction units a wireless communication system region that is essential. Several types of encryption methods have been measured, and the error is completely different Convolution code writing approach, an honest technique used to rectify mistakes during the data transfer. Convolutional encoder area unit compelled to provide the output in the kind of register style with specified positions in the register. The areas in which the region is entirely arranged are likewise shown as arrows. The position of the registers provides probable state transitions wherein example, L is the register's length. Using these facts, the best way to execute of data transmission in digital communication channels is to correct errors in codes. FPGA device utilized in convolutional encoder's hardware implementation. Vivado and XILINX ISE are used for performance analysis.

关键词： Convolutional codes Convolution Forward error correction Writing Digital communication Encoding Registers

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Leveraging Edge Optimize Vision Transformer for Monkeypox Lesion Diagnosis on Mobile Devices

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Computers, Materials & Continua 2025年第5期83卷 3227-3245页

作者： Poonam Sharma Bhisham Sharma Dhirendra Prasad Yadav Surbhi Bhatia Khan Ahlam Almusharraf Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia Centre for Research Impact and Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Engineering&Applications G.L.A.UniversityMathura281406India Department of Data Science University of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Division of Research and Development Lovely Professional UniversityPhagwara144411India Department of Management College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Rapid and precise diagnostic tools for Monkeypox(Mpox)lesions are crucial for effective treatment because their symptoms are similar to those of other pox-related illnesses,like smallpox and *** morphological similarities between smallpox,chickenpox,and monkeypox,particularly in how they appear as rashes and skin lesions,which can sometimes make diagnosis *** lesions appear in many simultaneous phases and are more diffuse,often beginning on the *** contrast,monkeypox lesions emerge progressively and are typically centralized on the face,palms,and *** provide accessible diagnostics,this study introduces a novel method for automated monkeypox lesion classification using the HMTNet(Hybrid Mobile Transformer Network).The convolutional layers and Vision Transformers(ViT)are combined to enhance the spatial *** addition,we replace the classical MHSA(Multi-head self-attention)with the WMHSA(Window-based Multi-Head Self-Attention)to effectively capture long-range dependencies within image patches and depth-wise separable convolutions for local feature *** trained and validated HMTNet on the two datasets for binary and multiclass *** model achieved 98.38% accuracy for multiclass classification using cross-validation and 99.25% accuracy for binary *** findings show that the model has the potential to be a useful diagnostic tool for monkeypox,especially in environments with limited resources.

关键词： Monkeypox disease classification local global transformer

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Penetration of Deep Learning in Human Health Care and Pharmaceutical Industries;the Opportunities and Challenges 3

Penetration of Deep Learning in Human Health Care and Pharma...

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3rd International Conference on Innovative Practices in technology and Management, ICIPTM 2023

作者： Raman, Ramakrishnan Radha, H.R. Inbamalar, T.M. Subhahan, D. Abdus Kumar, Ashok Bathrinath, S. Sarkar, Swagata Symbiosis Institute of Business Management Pune & Symbiosis International Maharashtra Pune India K. S. School of Engineering and Management Department of Chemistry Karnataka Bangalore India R.M.K. College of Engineering Department of Electronics and Communication Tamil Nadu Thiruvallur India B v Raju Institute of Technology Department of Computer Science Telangana Narsapur India Banasthali Vidyapith Department of Computer Science Rajasthan India Kalasalingam Academy of Research Department of Mechanical Engineering Tamil Nadu Krishnankoil India Sri Sai Ram Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350336238

Computational medicine has emerged as a result of the advancement of medical technology, which has led to the emergence of the big data era in the biomedical area, which is supported by artificial intelligence technology. To advance the development of precision medicine, people must be able to extract the valuable information from this vast biomedical data. In the past, professionals in the field of feature engineering and domain knowledge were typically utilised to extract the features from the biological data using machine learning techniques, which took a lot of time and resources. Modern machine learning techniques like deep learning (DL) have an advantage over them in that they can automatically find strong, complex features from fresh data without the necessity for succeeding engineering. The study of DL's applications in the fields of genomics, drug development, electronic health records, and medical imaging suggests that deep learning has clear advantages in maximising the use of biomedical data. Deep learning is becoming increasingly important in the field of medicine and health due to its large range of potential applications. The lack of data, interpretability, data privacy, and heterogeneity are some of the limitations of deep learning in computational medical health. A resource for improving the use of deep learning in medical health is provided by the analysis and discussion of these difficulties. © 2023 IEEE.

关键词： Health care

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Advancements in Wireless Networks: Transition from 5G to Beyond

Advancements in Wireless Networks: Transition from 5G to Bey...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Gunasekaran Thangavel C. Malarvizhi M. Ameena Banu Arun Anthonisamy M. Hemalatha Raja Manikandan Department of Electronics and Communication Engineering University of Technology and Applied Sciences Muscat Sultanate of Oman Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chembarambakkam India Department of Electronics and Communication Engineering NPR College of Engineering and Technology Natham India Department of Computer Science and Business System Panimalar Engineering College Chennai Tamil Nadu India Department of Information Technology Nandha Arts and Science College Erode Tamil Nadu India Department of ECE K. Ramakrishnan College of Technology Trichy Tamil Nadu India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

Earlier generations of mobile technology experienced exponential growth in mobile customers, disruptive ecosystems like Internet of Things applications, and massive channel capacity needed to connect trillions of devices. 5G networks are a vital solution. 5G aims to provide 10 Gbps to a single user, as well as quality of service, increased spectrum efficiency, ultra-reliable communication, extended device battery life, massive machine-type communication (mMTC), and a 1000-fold capacity gain. Due to the heavy traffic volume and high resource consumption of 5G applications like augmented reality (AR), virtual reality (VR), and wireless body area networks, there are major barriers to channel capacity and potential service disruptions for other users. New strategies are needed to increase spectral efficiency and channel capacity. This paper explores many major new wireless technologies that aim to increase spectral efficiency and channel capacity to meet growing user demands and improve network performance. This paper also examines these technologies’ benefits and research challenges. Massive Multi-Input Multi-Output (mMIMO) technology’s effectiveness and potential distinguish it for 5G and Beyond 5G (B5G) networks, offering a variety of benefits and characteristics. This article can also assist researchers who are conducting in-depth studies, as well as wireless network operators who aim to streamline the development of 5G and B5G networks.

关键词： Technological innovation 5G mobile communication Channel capacity Spectral efficiency Array signal processing Wireless networks Signal processing algorithms Throughput Millimeter wave communication Ultra-dense networks

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Personalized Federated Learning with Enhanced Implicit Generalization

Personalized Federated Learning with Enhanced Implicit Gener...

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International Joint Conference on Neural Networks (IJCNN)

作者： Heping Liu Songbai Liu Junkai Ji Qiuzhen Lin Jianyong Chen Kay Chen Tan College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computing The Hong Kong Polytechnic University

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Integrating personalization into federated learning is crucial for addressing data heterogeneity and surpassing the limitations of a single aggregated model. Personalized federated learning excels at capturing inter-client similarities and meeting diverse client needs through custom-made models. However, even with personalized approaches, it’s essential to aggregate knowledge among clients to ensure universal benefits. This paper proposes Federated Dual Objectives and Dual Models (FedDodm), a novel approach that employs two independent models to separately address explicit personalization and implicit generalization objectives in personalized federated learning. By treating these objectives as distinct loss functions and training models accordingly, we achieve a balance between the two through a fusion method. Extensive experiments across various models and learning tasks demonstrate that FedDodm outperforms state-of-the-art federated learning approaches, marking a significant advancement in effectively integrating personalized and generalized knowledge.

关键词： Training Federated learning Aggregates Neural networks data models

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Auto capture on drug text detection in social media through NLP from the heterogeneous data

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Measurement: Sensors 2022年 24卷

作者： Lavanya, P.M. Sasikala, E. Department of Computer Science and Engineering SRM Institute of Science & Technology Kattankulathur India Department of Information Technology Easwari Engineering College Ramapuram India Department of Data Science & Business Systems SRM Institute of Science & Technology Kattankulathur India

Auto Capture Drug Text detects (ACDTD) that textual content has currently gained important activities in Pharmaceutical drugs research. The recent studies focused on the different resources of textual content-related messages including social networks. Where a huge quantity of consumers posted information about the usage of pharmaceutical drugs and it's combined and cleared neatly. Our textual content classification strategies depend on regenerating the features of a large set of designs;it represents the semantic rules from the short textual nuggets. Significantly use the detailed structured files and it is combined by instruction data from the various collections to regulate the classification approach. Social Media Online Natural Language Processing (SMONLP) with ACDTD class achieves the F-scores of 0.90, 0.738, and 0.878 for the three information files. Integrating the instruction messages from various compatible files enhances the ACDTD F-scores for the in-house information files to 0.797 and 0.854 respectively. The heterogeneous textual content training using the cases where information sets are maintained may reduce the cost and time in the future. © 2022 The Authors

关键词： Natural language processing systems

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