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International Conference on communication Technology (ICCT)

作者： Shichao Li Bingji Lu Hongbin Chen Fangqing Tan Mianxiong Dong Kaoru Ota School of Information and Communication Guilin University of Electronic Technology Guilin China Department of Information and Electronic Engineering Muroran Institute of Technology Muroran Japan

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

The integration of mobile edge computing (MEC) with the air-ground integrated network is viewed as a crucial technology for Internet of remote things (IoRT) devices, where the tasks of IoRT devices can be executed by the unmanned aerial vehicle (UAV) and the high altitude platform. In this paper, we study a partial task offloading and UAV trajectory design problem to minimize the total task offloading delay in the air-ground integrated MEC network. Since the problem is non-convex, we transform it into a Markov decision process (MDP). Considering the complexity of the MDP increases with the number of the IoRT devices and the UAVs, we divide the problem into two subproblems: the UAV trajectory design subproblem, and the partial task offloading and resource allocation subproblem. For these two subproblems, we utilize deep deterministic policy gradient and proximal policy optimization to solve them, respectively. Based on the two methods, a partial task offloading and UAV trajectory design (PTOUTD) algorithm is proposed. Simulation results show the proposed PTOUTD algorithm can achieve a reduction in total offloading delay.

关键词： Multi-access edge computing Simulation Transforms Autonomous aerial vehicles Trajectory Delays Air to ground communication Complexity theory Resource management Optimization

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A Crossbreed and Powerful Machine Learning Archetype for Unparalleled Safety and Effectiveness in Wireless Sensor Networks

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SN computer Science 2025年第4期6卷 1-11页

作者： Sharma, Deepak Shahnaz, K.V. Vidhya, T. Helan Srinivas, Korra Burra, Lakshmi Ramani Reddy, Yeruva Jaipal Joshi, Shubham Department of Computer Science Aryabhatta College University of Delhi New Delhi India Department of ECE Veltech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology TN Chennai India Department of ECE (Advanced Communication Technology) R.M.K Engineering College TN Kavaraipettai Thiruvallur India Department of CSE-AIML St. Martin’s Engineering College Telangana Secunderabad India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of ECE Narasaraopeta Engineering College AP Narasaraopet India Department of AI-DS Vishwakarma Institute of Technology Savitribai Phule Pune University Pune Pune India

When a Wireless Sensor Network successfully executes a transaction within a finite timeframe and with minimal resource consumption, it is deemed active and operational. The underlying challenge in all scenarios boils down to a common issue as Denial of Service. This challenge encompasses endeavours to enhance the enforcement of consumption and routing policies, striving for improved security and reliability. In this study, we leverage a machine learning approach, utilizing a covariance vector derived from eigenvalues to identify anomalous behaviour in nodes. The CDS parameter of each node proves pivotal in adapting the selection policy to the current conditions, guiding a suitable course of action. Decision values for likelihoods are dynamically adjusted, serving as either rewards or punishments based on prior actions. A real-time payment function is established as a reward mechanism, contingent on the action taken and its utility, such as whether a forwarded packet was dropped or delayed. At the second level, the same learning model is employed to discern between malicious and benign nodes. The proposed method showcases a notable enhancement, including a 98% improvement in Packet Delivery Ratio (%), 84% reduction in delay (seconds), 93.2% decrease in jitter (seconds), and an 86% boost in Goodput (Kilobits per second). These substantial improvements validate the efficacy of the proposed method when compared to existing methodologies employed in the same research context. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： DoS Traffic flow Traffic variance WSN

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SAHARA: A Cloud - based Remote Health - Vitals Monitoring System

SAHARA: A Cloud - based Remote Health - Vitals Monitoring Sy...

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International Conference on Networking, Embedded and Wireless Systems (ICNEWS)

作者： Arbind Kumar Gupta J. Nagaraja Chennagiri Rajarao Padma Mahesh Kumar N Roopa M. S Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Medical Electronics Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Electronics and Communication and Engineering Dayananda Sagar College of Engineering Bengaluru India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350367461

ISBN: (纸本)9798350367478

The healthcare landscape in India, like in many countries, faces disparities between urban and rural areas. While major cities boast advanced medical infrastructure, rural regions struggle with limited healthcare access, insufficient doctor-to-patient ratios, and poor road connectivity. The COVID-19 pandemic has emphasized the importance of robust healthcare systems. This paper proposes a cost-effective telehealth system to bridge this urban-rural healthcare gap. This paper discusses the historical development of telehealth technology, highlighting its early phases, rapid development, and current technological maturity. It then presents a novel system model "Sahara", focusing on the integration of temperature and pulse sensors to record patient health vitals. The system utilizes portable devices equipped with sensors to collect patient health data, which is wirelessly transmitted to the cloud. Doctors and caregivers can access this data through a secure web interface for remote diagnosis and treatment. The system ensures a simple user interface, is independent of electricity, and provides a low-cost solution for aggregating patient data. By leveraging readily available technology, this system has the potential to significantly improve healthcare access in rural India. The paper also reviews the evolution of telehealth technologies, highlighting the challenges and opportunities in the field. The system transfers these physiological data to the cloud using a WiFi module and stores them in Amazon Web Services (AWS). The data is accessible to authorized doctors and caretakers through a web interface. The paper also describes the implementation details of the system, both from the front-end and back-end perspectives, including user registration, appointment scheduling, and data integration. Finally, it discusses the results of the system and its potential impact on rural healthcare in India, emphasizing the role of telemedicine in improving healthcare accessibility, redu

关键词： Temperature sensors COVID-19 Costs Pandemics Telemedicine Clouds Urban areas Medical services Sensor systems Sensors

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An Eagle-Eye Vision: Advancements in Avian Species Classification

An Eagle-Eye Vision: Advancements in Avian Species Classific...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： P. Kanaga Priya M.S. Vinu M PrasannaBlessy P. Kirupa R. Gayathri N. Selvakumar Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nehru Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering RVS Technical Campus Coimbatore India Department of Computer Science and Engineering SNS College of Engineering Coimbatore India Department of Electronics and Communication Engineering Coimbatore Institute of Engineering and Technology Coimbatore India

Avian species taxonomy is an important activity in the domain of ornithology, wildlife preservation, and ecological research. Convolutional Neural Networks (CNNs) a widely used tool for automating categorization jobs in recent years. This abstract provides an overview of applying Eagle-Eye CNN to the classification of bird species. The methodology originates with image pre-processing, comprising scaling, normalization, and augmentation, to strengthen model resilience. Subsequently, the feature extraction is accomplished inside the CNN architecture layers. Moreover, the use of transfer learning harnesses the proposed CNN model, fine-tuned for the particular job of the Eagle-Eye vision engine for bird species classification. The proposed pre-trained model can also be used for other classification tasks. This technique attains a remarkable accuracy of 89% due to its large dataset and holds potential for further applications across avian taxonomy. In summary, CNNs offer an effective avenue for automated bird or avian classification, permitting more efficient and exact bird behavior study thus enhancing avian conservation attempts.

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Retraction Note: Sub-network modeling and integration for low-light enhancement of aerial images

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Optical and Quantum electronics 2024年第10期56卷 1-2页

作者： Uganya, G. Devi, C. H. Sarada Chaturvedi, Abhay Shankar, B. B. Ramesh, Janjhyam Venkata Naga Kiran, Ajmeera Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Meenakshi College of Engineering Chennai India Department of Electronics and Communication Engineering GLA University Mathura India Department of Electronics and Communication Engineering NITTE NITTE (Deemed to Be University) NMAM Institute of Technology (NMAMIT) Karkala India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad India

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An overview Scientific Cloud computing Web-based cloud services issues and challenges 23

An overview Scientific Cloud computing Web-based cloud servi...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Sayan Majumder Kommisetti Murthy Raju Nancy Arya Nazeer Shaik Sanjay Tiwari Akanksha Singh Department of cyber security The Heritage Academy India Department of Electronics and Communication Engineering Shri Vishnu Engineering College For Women India Department of Computer Science and Engineering Shree Guru Gobind Singh Tricentenary University India Department Of Computer Science and Engineering Srinivasa Ramanujan Institute Of Technology-- Autonomous- India Department of Computer Science and Engineering ARYA Institute of Engineering Technology and Management India Computer Science and Engineering Babu Banarasi Das University India

ISBN: (纸本)9798400709418

This research investigates the crucial role of cloud computing services in web development for creating scalable applications. Our study explores the benefits, challenges, and best practices associated with leveraging these services. Real-world case studies will be examined to showcase their significance. Additionally, we examine the integration of blockchain technologies in cloud-based web development and their potential impact on scalability. This paper presents a comprehensive analysis of the indispensability of cloud computing services in building scalable applications for the web. This research project delves into the transformative impact of cloud computing services on the field of web development. By providing scalable and flexible infrastructure, these services have revolutionized the way applications are built. Through an in-depth analysis of benefits, challenges, and best practices, along with real- world case studies, the project highlights the indispensable nature of cloud computing in creating scalable web applications. Furthermore, the exploration of blockchain integration within cloud-based web development offers insights into potential advancements in scalability and opens new avenues for innovation in the field. Additionally, the integration of blockchain technologies in cloud-based web development is investigated, providing insights into its potential impact on scalability and fostering innovation in the field. The research presents a comprehensive analysis of the indispensability of cloud computing services in creating scalable web applications and highlights their transformative impact on the development process.

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Low-Complexity Maximum Likelihood Decoding Algorithm for Sparse Superposition Codes

Low-Complexity Maximum Likelihood Decoding Algorithm for Spa...

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Conference on Technologies for Sustainability (Sustech)

作者： Po-Chih Hsu Tzu-Hsiu Hsu Cheng-Yu Pai Zhen-Ming Huang Chao-Yu Chen Institute of Computer and Communication Engineering National Cheng Kung University Taiwan Department of Electrical Engineering National Cheng Kung University Taiwan Department of Engineering Science National Cheng Kung University Taiwan

ISBN: (数字)9798331504311

ISBN: (纸本)9798331504328

As the demand for high-reliability and low-latency communication grows, short packet transmission has emerged as a key area of research. Recently, sparse superposition codes (SSCs) have shown outstanding performance in short-length channel coding. This extended abstract proposes a low-complexity decoding algorithm for SSCs based on Golay complementary pairs (GCPs), leveraging their properties and algebraic structures of Boolean functions. Simulation results demonstrate that proposed algorithm can effectively reduce the computational complexity while maintaining the same error rate performance as the maximum likelihood (ML) decoding algorithm, thus supporting sustainable and energy-efficient communications.

关键词： Energy consumption Codes Error analysis Simulation Green communications Energy efficiency Sustainable development Low latency communication Computational complexity Maximum likelihood decoding

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Neurodegenerative Disorder of Ageing using Neural Networks 1

Neurodegenerative Disorder of Ageing using Neural Networks

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1st International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Anand, Ankit Bolishetti, Nithin Teja, Boyapati.Sri Navya Adhikari, Sharadindu Ahmed, Ikramuddin Natarajan, Janani Lakshmi Narain College of Technology Department of Electronics and Communication Bhopal India St.mary's College of Engineering and Technology Department of Eee Hyderabad India Bharath Institute of Higher Education and Research Department of Genetic Engineering Chennai India Vit University Department of Computer Science Engineering Vellore India National Institute of Technology Calicut Department of Mechanical Engineering Calicut India Panimalar Engineering College Department of Computer Science Engineering Chennai India

ISBN: (纸本)9781665497107

Generally, a neurodegenerative disease is known as the Parkinson disease because this is a condition in which the nerve cells gradually degenerate and die. There are general symptoms and causes which include gradual slowness of moment, muscles get stiff and finally tremors. Apart from this severe symptoms there are so many other symptoms too, many of which are severe, and some may occur sometimes. It generally affects the different nervous systems and the types of neurons. However, substantial attention has been paid to neurons in brain areas linked to overt motor symptoms, which are part of a key cerebral network important for movement facilitation. Dopaminergic neurons in the substantia nigra die slowly in Parkinson's disease,May start causing the nigra which tends to malfunction and creates a problem of typical moment types. Parkinson's disease is more common in those over the age of 50. It is a slowly progressing disease. The main aim of the paper is to prove that the neurodegenerative disorder can be solved using deep learning techniques such as neural networks and get a great accuracy. © 2022 IEEE.

关键词： Neurons

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FSBNet: A Classifying Framework of Disaster Scene for Volcanic Lithology Through Deep Learning Models

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 15101-15115页

作者： Liu, Lan Xiao, Zhouyi Hu, Jianpeng Han, Jingxin Kim, Jung Yoon Sharma, Rohit Li, Chengfan Shanghai University of Engineering Science School of Electronic and Electrical Engineering Shanghai 201620 China Shanghai University School of Computer Engineering and Science Shanghai 200444 China Gachon University College of Future Industry Gyeonggi-do Seongnam-Si 13120 South Korea Department of Electronics & Communication Engineering ABES Engineering College UP Ghaziabad India

Volcanic lithology classification is one of the fundamental tasks in remote sensing investigation of disaster scenes, and deep learning can improve the accuracy and efficiency of classification. Here, a new focused squeeze-and-excitation (SE) attention and bilinear interpolation network (FSBNet) is proposed for volcanic lithology scene classification from remote sensing image. Specifically, we first visualize and recalculate the weights of volcanic lithology features in different channels using SE attention to enhance the network's sensitivity for extracting key semantic features in disaster scenes. Besides, the detailed features of volcanic lithology in remote sensing images are located and reconstructed bilinear interpolation and upsampling. And then the features are aggregated into the feature map by a 1×1 convolutional layer in prediction network. Finally, we perform extensive experiments on CMRD dataset from module, backbone, SE attention position and ratio aspects. Experimental results show that the proposed FSBNet framework achieves 94.66% accuracy of volcanic lithology scene classification on the validation set compared with three traditional machine learning methods, and assists the remote sensing investigation of volcanic disaster scenes. © 2008-2012 IEEE.

关键词： Changbaishan Mountain deep learning disaster scene classification remote sensing image Volcanic lithology

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End-to-End Classification of Transmission Lines in Fault Detection Using SW and Extreme Learning Based Approach

End-to-End Classification of Transmission Lines in Fault Det...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Cholla, Ravindra Raman Kakkar, Sushma Natarajan, Chidambararaj Gulati, Chahat Dwivedi, Vijay Kumar Rajput, Akshay Department of Cse Karnataka Bengluru India Sharda School of Engineering and Technology Sharda University Department of Electrical Electronics and Communication Engineering Greater Noida India St. Joseph's College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Chennai India Birla Institute of Technology and Science Department Ece Pilani Campus Pilani Rajasthan Vidyavihar India Vishwavidyalaya Engineering College Department of Mathematics Surguja Chhattisgarh Ambikapur India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350300857

The reliability of the power grid is dependent on transient stability. In order to swiftly restore transient stability, large disturbances, such a fault in a transmission line, must be separated. Faults in a transmission network can be located, identified, and categorized with the use of erroneous current and voltage signals. When an aberrant signal is detected, the relay immediately disables the transmission line carrying the bad news. Fault detection systems are crucial for transmission lines. The proposed technique for preprocessing makes use of a solid-state drive (SSD) detection model, a quicker RCNN detection model, and a logical reasoning in parallel mechanism. To choose features, SWELM is employed. The envisioned model is trained using SWELM in the given technique. The suggested model is tested, and the ELM and SWELM models are used for comparison. When compared to the other two varieties, its performance is far superior. © 2023 IEEE.

关键词： Electric lines

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