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A Novel Hybrid Clustering Based Transmission Protocol for Wireless Body Area Networks

computers, Materials & Continua 2021年第11期69卷 2459-2473页

作者： Neelam Sharma Harshita Chadha Karan Singh B.M.Singh Nitish Pathak Research Scholar Computer Science and EngineeringUttarakhand Technical University(UTU)DehradunIndia Computer Science and Engineering Maharaja Agrasen Institute of TechnologyNew DelhiIndia School of Computer&Systems Sciences Jawaharlal Nehru University(JNU)DelhiIndia College of Engineering Roorkee UTUDehradunIndia Department of Information Technology KIET Group of InstitutionsGhaziabadIndia

Wireless sensor networks are a collection of intelligent sensor devices that are connected to one another and have the capability to exchange information packets amongst *** recent years,this field of research has become increasingly popular due to the host of useful applications it can potentially serve.A deep analysis of the concepts associated with this domain reveals that the two main problems that are to be tackled here are throughput enhancement and network security *** present article takes on one of these two issues namely the throughput *** the purpose of improving network productivity,a hybrid clustering based packet propagation protocol has been *** protocol makes use of not only clustering mechanisms of machine learning but also utilizes the traditional forwarding function approach to arrive at an optimum *** result of the simulation is a novel transmission protocol which significantly enhances network productivity and increases throughput value.

关键词： Forwarding function wireless sensor networks WSNs WBANs energy characteristics clustering hybrid mechanism dissipated energy network stability

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New hybrid decentralized evolutionary approach for DIMACS challenge graph coloring & wireless network instances

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 259-265页

作者： Balakrishnan, S. Suresh, Tamilarasi Marappan, Raja Venkatesan, R. Sabri, Abdelouahed Research Scholar Department of Computer Science and Engineering St. Peter's Institute of Higher Education and Research Tamilnadu India Department of Information Technology St. Peter's Institute of Higher Education and Research Tamilnadu India Vellore Institute of Technology Chennai600127 India SASTRA Deemed University Thanjavur India Faculty of Sciences Dhar El Mahraz University Sidi Mohamed Ben Abdellah Fez Morocco

The Graph Coloring Problem is an NP-hard combinatorial optimization problem, and it is being used in different real-world environments. The chromatic integer is determined using different probabilistic methods. This paper explores a new hybrid decentralized evolutionary approach that applies the fixed colors and reduces the edge conflicts iteratively using greedy, split and conquer strategies. This article explores a new hybrid decentralized stochastic methodology for solving graph coloring. The method is constructed by combining the following strategies: Greedy heuristics, split & conquer, and decentralized strategy with an advanced & enhanced global search evolutionary operator. These hybrid design strategies are exhibited on complex DIMACS challenge benchmark graphs and wireless network instances. The proposed approach minimizes the complexity and converges to the optimal solution within a minimal time. The minimum percentage of successful runs obtained for the DIMACS benchmarks lies in (82%, 85%) except for the difficult instance latin_square_***, the vertices count n = 900 and edges count m = 307350. For the latin_square_*** graph, the minimum color is reduced to 97 compared to other methods with less successful runs percentage. For the difficult instance flat1000_76_*** graph, the minimum color is reduced to 76 compared to other methods, resulting in a better successful run. The method obtains the minimum color as χ(G) for the difficult instances *** and *** graphs compared to other methods. The time taken to execute the developed technique is compared with the competing methods, and the proposed method outperforms very competitively in finding the minimum color for large graphs and also in finding the better solution with the high frequency of convergence (> 98%) in the channel allocation of wireless networks compared to the current methods. © 2023

关键词： Combinatorial optimization

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Enhancing IoT Ecosystems through Effective Master Data Management 11

Enhancing IoT Ecosystems through Effective Master Data Manag...

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11th IEEE International Conference on Signal Processing and Integrated Networks, SPIN 2024

作者： Pansara, Ronak Ravjibhai Kasula, Balaram Yadav Gupta, Piyush Kumar Khan, Tabrej Ahamad Alam, Naved Whig, Pawan Master Data Specialist Tesla United States University of the Cumberlands Dept. of Information Technology WilliamsburgKY United States Jamia Hamdard Department of Computer Science and Engineering New Delhi India Research Scholar Jamia Millia Islamia New Delhi India Whig Research Scientist Threws VIPS-TC New Delhi India

ISBN: (纸本)9798350308433

This study explores the quantitative impact of Master Data Management (MDM) implementation on Internet of Things (IoT) ecosystems. Employing a comparative analysis methodology, pre-And post-MDM integration metrics were assessed within the IoT environment. Results showcased a notable enhancement in data accuracy by 20%, attributed to minimized errors and improved consistency following MDM adoption. Operational efficiency exhibited a significant 30% reduction in data integration time across diverse IoT platforms, contributing to streamlined processes. Financially, operational costs decreased by 15%, primarily due to reduced data cleansing efforts post-MDM implementation. Decision-making processes benefited from a 25% faster response time to real-Time analytics derived from IoT data insights. Additionally, the IoT ecosystem's scalability increased by 40%, indicating amplified adaptability to accommodate higher data volumes without compromising performance. These quantitative findings highlight the substantial impact of MDM on fortifying the reliability, efficiency, and scalability of IoT ecosystems, providing empirical evidence for the transformative potential of MDM in optimizing IoT applications. © 2024 IEEE.

关键词： Decision making

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis

Character Classification and Actor Recognition in Yakshagana...

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： Anantha Murthy Prathwini Sanjeev Kulkarni Savitha G Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala and and Research Scholar Institute of Computer Science and Information Science” Srinivas University India Department of Master of Computer Applications NMAM Institute of Technology NITTE (Deemed to be University) Nitte Karkala India Department of Computer Science and Engineering Srinivas University Institute of Engineering and Technology Mangalore India Department of Data Science and Computer Applications Manipal Academy of higher Education Manipal Institute of Technology Manipal

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350609

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form.

关键词： YOLO Accuracy Image recognition Recurrent neural networks Machine learning Documentation Logic gates Character recognition Cultural differences Image classification

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Editorial

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Computational Intelligence 2023年第4期39卷 530-531页

作者： Er, Meng Joo Ju, Zhaojie Ferrein, Alexander Changjiang Scholar Distinguished Director of Institute of Artificial Intelligence and Marine Robotics College of Marine Electrical Engineering Dalian Maritime University Dalian China Reader in Machine Learning and Robotics School of Computing University of Portsmouth Portsmouth United Kingdom Department of Electrical Engineering and Information Technology Mobile Autonomous Systems & Cognitive Robotics Institute Robotics and Foundations of Computer Science FH Aachen - University of Applied Sciences Aachen Germany

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Internet of things: present state of the art, applications, protocols and enabling technologies 3

Internet of things: present state of the art, applications, ...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Pasalkar, Jayashree C. Kaur, Kamaljeet Munagala, Nvlm Krishna Banu, N. Sharmila Hussein, Ali Alazzam, Malik Bader Aissms Institute of Information Technology Research Scholar in Aissms Coe Department of Information Technology India Chandigarh University Mohali India Gitam Deemed University Department of Electrical Electronics and Communication Engineering Andhra Pradesh Visakhapatnam India School of Computer Science and Artificial Intelligence Sr University Telangana Warangal India Medical Technical College Al-Farahidi University Baghdad 10021 Iraq Information Technology College Ajloun National University Jordan

ISBN: (纸本)9798350343274

The Internet of Things (IoT) has transformed into a dynamic paradigm that connects a wide range of systems and devices in many fields. The current status of IoT is thoroughly examined in this research study, including its applications, communication protocols, supporting technologies, including prospects for the future. Researchers methodically examine how the Internet of Things (IoT) has an influence on many businesses, and social issues, including daily life using Jupyter Notebook for data analysis. In the Jupyter Notebook environment, the study highlights the value of structured data pretreatment, exploratory data analysis, and statistical inference, including visual depiction of findings. The future of IoT research is in the integration of developing technologies, better interoperability, and increased security methods. © 2023 IEEE.

关键词： Internet of things

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RETRACTED ARTICLE: Explainable context-aware IoT framework using human digital twin for healthcare

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Multimedia Tools and Applications 2023年第22期83卷 62489-62490页

作者： Vats, Tarun Singh, Sunil K. Kumar, Sudhakar Gupta, Brij B. Gill, Shabeg Singh Arya, Varsha Alhalabi, Wadee UG Scholar Department of Computer Science and Engineering Chandigarh College of Engineering and Technology Chandigarh India Department of Computer Science and Engineering Chandigarh College of Engineering and Technology Chandigarh India Department of Computer Science and Information Engineering Asia University Taichung Taiwan Kyung Hee University Seoul Korea Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India CSSS Indraprastha Institute of Information Technology (IIIT) New Delhi India Department of Business Administration Asia University Taichung Taiwan Lebanese American University Beirut Lebanon Chandigarh University Chandigarh India Department of Computer Science Immersive Virtual Reality Research Group King Abdulaziz University Jeddah Saudi Arabia

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Integrating Convolutional and Recurrent Networks for Image Caption Generation: A Unified Approach

Integrating Convolutional and Recurrent Networks for Image C...

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International Conference on Computing and Networking Technology (ICCNT)

作者： I Nandhini L Leo Prasanth T Nagalakshmi D Manjula Department of Electronics & Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Research Scholar Department of Information Science and Technology Anna University Chennai India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering VIT University Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Within the realm of natural language processing and computer vision, the synergy between Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) systems has emerged as some powerful paradigm for image captioning. This paper presents a comprehensive solution for visual and temporal understanding in the image captioning domain by presenting a unified framework that efficiently blends the strengths of CNN and LSTM architectures. Our proposed model takes advantage of the spatial hierarchies captured by CNNs in images and the sequential dependencies modeled by LSTMs in natural language. The CNN component efficiently retrieves high-degree features to result in coherent and contextually rich image captions. The hybridization of both of these networks creates a symbiotic relationship, addressing the challenges associated with both spatial and temporal aspects of image interpretation. We carried out in-depth tests on benchmark datasets, demonstrating the superiority of our hybrid CNN-LSTM architecture over standalone models. Our results showcase improved captioning exactness, fluency, and contextual relevance. Our proposed model achieved highest BLEU score $\mathbf{0. 8 1}$ when compared with existing methods. In addition, we investigated the interpretability of our model, shedding light on the visual and temporal cues learned by the hybrid architecture.

关键词： Symbiosis Visualization Fitting computer architecture Search engines User experience Natural language processing Convolutional neural networks Long short term memory Optimization

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Cloud Computing, Machine Learning, and Secure Data Sharing enabled through Blockchain 5

Cloud Computing, Machine Learning, and Secure Data Sharing e...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Singh, Rajesh Chandra, Akkaraju Sailesh Bbhagat, Babeetta Panduro-Ramirez, Jeidy Gaikwad, Anil Pandurang Pant, Bhasker Uttaranchal Institute of Technology Uttaranchal University Uttarakhand India University Doctoral Scholar School of Business and Management Karnataka Bangalore India Chhatrapati Shivaji Maharaj University Department of Computer Science & Information Technology Navi Mumbai India Universidad Tecnológica Del Perú Department of Business Peru Savitribai Phule Pune University JSPM'S JSCOE-MCA Department Maharashtra Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

Blockchain technologies are sweeping the globe. Cloud computing & secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection & processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms & cloud computing, ML on-device information learning is now a reality. IoT gadgets may outsource training duties to cloud computing services to enable AI at the network's perimeter. Furthermore, these dispersed edges intelligence architectures bring additional issues, also including consumer confidentiality & information safety. Blockchain has been proposed as a viable alternative to these issues. Blockchain, as a dispersed intelligent database, has evolved as a revolutionary innovation for the future phase of multiple industries' uses due to its decentralized, accessible, & safe structure. This system also includes trustworthy automatic scripting running & unchangeable information recordings. As quantum technologies have proven more viable in the latest days, blockchain has faced prospective challenges from quantum computations. In this paper, we summarize the existing material in the study fields of blockchain-based cloud computing, machine learning, and secure data sharing, as well as a basic orientation to post-quantum blockchain to offer a summary of the existing state-of-the-art in these cutting-edge innovations. © 2022 IEEE.

关键词： Blockchain

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Pioneering Healthcare Transformation: Deep Learning and Radiographic Imaging for Rapid COVID-19 Diagnosis

Pioneering Healthcare Transformation: Deep Learning and Radi...

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International Conference on Innovative Mechanisms for Industry Applications (ICIMIA)

作者： L. Megaan Leo B. Prabha K.M. Gopinath C. Tamilselvi Mohanaprakash T A Naveen P Department of ECE Sathyabama Institute of Science and technology Chennai Tamilnadu India Research Scholar SRM Institute of Science and Technology Kattankulathur Chennai India Department of ECE Panimalar Engineering College Chennai India Department of Information Technology Dr. MGR Educational and Research Institute Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Artificial Intelligence and Data Science St.joseph’s College of Engineering OMR Chennai India

The COVID-19 pandemic has underscored the critical need for innovative diagnostic tools, prompting an exploration of the transformative potential of combining radiographic imaging with deep learning. Traditional diagnostic methods face challenges in accessibility, time constraints, and symptom variability, necessitating advanced technologies. Deep learning, a subset of artificial intelligence, emerges as a solution by employing complex algorithms to analyze radiographic images, such as chest X-rays and CT scans. Recent advancements in deep learning and its successful application in medical imaging are examined, emphasizing its proficiency in recognizing unique patterns associated with COVID-19. The proposed model, based on convolutional neural networks (CNNs), facilitates swift and accurate identification of infected individuals, overcoming limitations of current diagnostic approaches. The study also delves into the integration of extensive datasets and collaborative efforts to train robust deep learning models capable of handling diverse patient populations and imaging conditions. Ethical considerations, including data privacy and bias in AI models, are addressed, stressing responsible deployment in healthcare settings. Comparative analyses showcase the superior performance and efficiency of the proposed approach compared to traditional methods. This study concludes by highlighting the potential impact on healthcare systems, underscoring the importance of early and accurate diagnosis in controlling virus spread.

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