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2nd International Conference on Networking, Embedded and Wireless Systems, ICNEWS 2024

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

ISBN: (纸本)9798350367461

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： Electronic health record

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Skin Cancer Prediction by Incorporating Bio-inspired Optimization in Deep Neural Network

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SN computer science 2024年第8期5卷 1-20页

作者： Mundada, Monica R. Sowmya, B.J. Supreeth, S. Prabhu, Smitha G. Mahesh, K. Vishwanath, Y. Rohith, S. Department of Computer Science and Engineering M S Ramaiah Institute of Technology Bengaluru 560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru 560054 India School of Computer Science and Engineering REVA University Bengaluru 560064 India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering Sir M Visvesvaraya Institute of Technology Bengaluru India School of Computer Science & Engineering Presidency University Bengaluru 560064 India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Bengaluru 562164 India

This study offers a unified surgical conception of skin neoplasms using deep learning and genetic optimization techniques to improve classification values and speed of diagnosis. In this case, the architecture of Convolutional Neural Networks such as MobileNet, Xception and Inception is enhanced on the basis of the creative algorithms of Particle Swarm Optimization and Bat Algorithm to adjust the hyperparameters. The Heroku hosted cloud solution permits registered users to upload pictures of skin lesions via the web interface, where the picture is then processed through a Deep Neural Network (DNN) model that classifies the lesion as either melanoma or non-melanoma. The Convolution Neural Network (CNN) models were trained and evaluated on large publicly available datasets of skin lesions like ISIC, HAM10000 and the model performance was evaluated based on the effect of varying optimizers. Among all the architectures, Xception with Bat Algorithm (BA) performed best because of the more complex and deeper network architecture and extreme inception modules, which made it possible to extract and classify more complicated features. As a cloud-based solution, this system provides dermatologists and health practitioners with a more practical and workable solution for the detection of skin cancers. Furthermore, it can be utilized in other fields, which implies that this approach could extend its applications beyond dermatological imaging or imaging of patients at risk for other diseases, thus creating an adaptable and effective tool for clinical diagnosis in many areas of medicine. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bio-inspired optimization Deep neural network (DNN) Skin cancer prediction

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Modulated-ISRJ rejection using online dictionary learning for synthetic aperture radar imagery

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Journal of Systems engineering and electronics 2024年第2期35卷 316-329页

作者： WEI Shaopeng ZHANG Lei LU Jingyue LIU Hongwei College of Oceanography and Space Informatics China University of Petroleum(East China)Qingdao 266580China School of Electronics Engineering Xidian UniversityXi’an 710071China School of Electronics and Communication Engineering Shenzhen Campus of Sun Yat-sen UniversityGuangzhou 518107China School of Computer Science and Technology Xidian UniversityXi’an 710071China

In electromagnetic countermeasures circumstances,synthetic aperture radar(SAR)imagery usually suffers from severe quality degradation from modulated interrupt sampling repeater jamming(MISRJ),which usually owes considerable coherence with the SAR transmission waveform together with periodical modulation *** paper develops an MISRJ suppression algorithm for SAR imagery with online dictionary *** the algorithm,the jamming modulation temporal properties are exploited with extracting and sorting MISRJ slices using fast-time *** dictionary learning is followed to separate real signals from jamming *** the learned representation,time-varying MISRJs are suppressed *** simulated and real-measured SAR data are also used to confirm advantages in suppressing time-varying MISRJs over traditional methods.

关键词： synthetic aperture radar(SAR) modulated interrupt sampling jamming(MISRJ) online dictionary learning

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Energy Proficient Reduced Coverage Set with Particle Swarm Optimization for Distributed Sensor Network

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computer Systems science & engineering 2023年第5期45卷 1611-1623页

作者： T.V.Chithra A.Milton Department of Computer Science and Engineering Arunachala College of Engineering for WomenManavilaiTamil Nadu629203India Department of Electronics and Communication Engineering St.Xavier’s Catholic College of EngineeringChunkankadaiTamil Nadu629003India

Retransmission avoidance is an essential need for any type of wireless *** retransmissions induce the unnecessary presence of redundant data in every accessible *** storage capacity is symmetrical to the size of the memory,less storage capacity is experienced due to the restricted size of the respective *** this proposed work,we have discussed the integration of the Energy Proficient Reduced Coverage Set with Particle Swarm Optimization(PSO).PSO is a metaheuristic global search enhancement technique that promotes the searching of the best nodes in the search *** is integrated with a Reduced Coverage Set,to obtain an optimal path with only high-power transmitting *** Proficient Reduced Coverage Set with PSO constructs a set of only best nodes based on the fitness solution,to cover the whole *** proposed algorithm has experimented with a different number of *** has been made between original and improved algorithm shows that improved algorithm performs better than the existing by reducing the redundant packet transmissions by 18%~40%,thereby increasing the network lifetime.

关键词： Wireless sensor network reduced coverage set swarm intelligence particle swarm optimization energy consumption

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Threats to medical diagnosis systems: analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis

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Soft Computing 2025年第3期29卷 1879-1896页

作者： Haque, Sheikh Burhan Ul Zafar, Aasim Haq, Sheikh Riyaz Ul Haque, Sheikh Moeen Ul Ahmad, Mohassin Roshan, Khushnaseeb School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Computer Science Aligarh Muslim University Uttar Pradesh Aligarh202002 India Department of Computer Science Engineering Guru Nanak Institution Ibrahimpatnam Telangana Hyderabad501506 India Department of Electronics and Communication Guru Nanak Institution Ibrahimpatnam Telangana Hyderabad501506 India

Deep and machine learning models have become pivotal in medical image analysis, especially for diagnosing COVID-19 using X-rays and CT scans. While these models, including transfer learning-based approaches, have achieved high accuracy, they remain highly vulnerable to adversarial attacks, which can manipulate input data and cause misclassification, posing critical risks in clinical applications. This study introduces a novel approach to addressing this issue by systematically evaluating the impact of adversarial attacks on COVID-19 diagnosis models built with two leading architectures, VGG-16 and DenseNet-121, using the Fast Gradient Sign Method (FGSM). The FGSM attack causes a dramatic drop in accuracy, reducing VGG-16’s accuracy from 95.12 to 9.97% and DenseNet-121’s from 96.51 to 10.13%. To counter these vulnerabilities, we propose a novel defense mechanism that combines adversarial training with Gaussian noise data augmentation, a dynamic approach that generates perturbations across various epsilon values during the training phase. This innovative method significantly enhances model robustness, restoring accuracy to over 92% on adversarial examples. These findings emphasize the need for strong defense mechanisms in deep learning models for COVID-19 diagnosis, ensuring reliability and security against adversarial threats in clinical environments. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Adversarial machine learning

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Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

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computer Systems science & engineering 2023年第1期44卷 205-219页

作者： J.Jean Justus U.Sakthi K.Priyadarshini B.Thiyaneswaran Masoud Alajmi Marwa Obayya Manar Ahmed Hamza Department of Computer Science&Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Sona College of TechnologySalem636005India Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer *** MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so *** existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task *** the same time,the task scheduling process is yet to be explored in the multi-core *** paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy *** GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks *** performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark *** experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy *** overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.

关键词： Task scheduling energy efficiency multi-core systems fitness function makespan

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Sustainable augmented framework using smart sensors with ATM inspired 5G technologies for enterprise networks

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1805-1812页

作者： Kagi, Shivakumar Venugopal, Anita Ramaiah, V. Subba Panda, Sunita Ambikapathy, A. MB, Abhishek Department of Computer Science and Engineering Sharnbasva University Karnataka Kalaburagi India IT Unit Dhofar University Salalah Oman Department of CSE Mahatma Gandhi Institute of Technology Telangana Hyderabad India Department of Electrical Electronics and Communication Engineering GITAM School of Technology Bengaluru Campus Karnataka Bengaluru India Department of Electronics and Communication Engineering ITS Engineering College Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bengaluru India

The primary driving force behind the development of fifth-generation (5G) applications is the necessity of Concurrent Multipath Transfer (CMT). Nevertheless, CMT systems that rely on Transport Control Protocol/Internet Protocol (TCP/IP) will continue to be the key point in 5G as multipath transport protocols are not often utilized. In today’s worldwide networks, integrating TCP/IP with asynchronous transfer mode (ATM) is a difficult structural problem since the TCP/IP protocol has to accommodate the special features of ATM. An effective mechanism for the effective use of both approaches will be a detailed characterization of TCP/IP performance via ATM. ATM network performance may be negatively impacted by improper use of TCP error and flow control methods, which were created for best-effort networks. In order to facilitate effective communication, this article offers a comprehensive and standardized examination and analysis of TCP/IP over ATM in the context of the 5G network architecture. It also proposes a tuned energy efficient transmission control protocol (TEETCP) to enhance Quality of Service (QoS) in TCP/IP applications. The performance of the approach is validated against existing recent works based on evaluation metrics such as packet delivery ratio (PDR), average throughput (AP), end-to-end delay (e2e), utilization of energy and computation overhead. The results indicate the better performance of our proposed work in all aspects. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： 5G ATM network CMT Network performance TEETCP

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Energy-Efficient Routing Protocol with Multi-Hop Fuzzy Logic for Wireless Networks

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Intelligent Automation & Soft Computing 2023年第5期36卷 2457-2471页

作者： J.Gobinath S.Hemajothi J.S.Leena Jasmine Department of Information and Communication Engineering Anna UniversityChennai600025India Department of Electronics and Communication Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India

A Wireless Sensor Network(WSN)becomes a newer type of real-time embedded device that can be utilized for a wide range of applications that make regular networking which appears *** the energy produc-tion of the nodes,WSN has major issues that may inﬂuence the stability of the *** a result,constructing WSN requires devising protocols and standards that make the most use of constrained capacity,especially the energy *** faces some issues with increased power utilization and an on going devel-opment due to the uneven energy usage between the *** has proven to be a more effective strategy in this *** the proposed work,a hybrid meth-od is used for reducing the energy consumption among CHs.A Fuzzy Logic-based clustering protocol FLUC(unequally clustered)and Fuzzy Clustering with Energy-Efﬁcient Routing Protocol(FCERP)are used.A Fuzzy Clustering with Energy Efﬁcient Routing Protocol(FCERP)reduces the WSN power usage and increases the lifespan of the *** has created a novel cluster-based fuzzy routing mechanism that uses a limit value to combine the clustering and multi-hop routing *** technique creates uneven groups by using fuz-zy logic with a competitive range to choose the Cluster Head(CH).The input variables include the distance of the nodes from the ground station,concentra-tions,and remaining *** proposed FLUC-FCERP reduces the power usage and improves the lifetime of the network compared with the existing algorithms.

关键词： Energy consumption lifetime wireless sensor network cluster head fuzzy logic unequally clustered fuzzy clustering energy-efﬁcient protocol

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Faster R-CNN based Framework For Bone Fracture Detection in Telesurgery Systems in Healthcare 4.0 5

Faster R-CNN based Framework For Bone Fracture Detection in ...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Patel, Yogi Patel, Dron Patel, Het Gupta, Rajesh Tanwar, Sudeep Nirma University Institute of Technology Department of Computer Science and Engineering India Nirma University Institute of Technology Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350376685

This paper introduces an ideal structure for the detection and treatment of bone fractures utilizing advanced deep learning and telesurgery techniques. The approach begins with the collection of medical imaging data from the X-Ray Layer, which is subsequently divided into training, validation, and testing datasets. The proposed model, ResNet50, is trained on training dataset and validated with the validation dataset to ensure robust performance. Once the model is trained, it accurately detects bone fractures in X-ray images and identifies the region of the fracture using an array of four points representing bounding box defined by four corners. Upon detecting a fracture, telesurgery is employed to treat the patient through a remote arm controlled by a doctor, providing minimally invasive procedures that reduce surgical risks. In case of no fracture, the patient is advised on precautionary measures and follow-up care. This comprehensive framework demonstrates a method for improving the diagnosis and treatment of bone fractures, offering efficient, precise, error-free, and patient-centric solutions. The combination of deep learning with telesurgery shows advancement in medical technology, potentially reducing need for in-person consultations also improving specialized care. Future advancement can include model improvement by using more diverse datasets, integrating additional imaging modalities such as CT and MRI, and expanding real-time capabilities in emergency situations. © 2024 IEEE.

关键词： Medical imaging

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Deep Convolutional Neural Networks for Early-Stage Detection and Prognostication of Lung and Colon Cancer 2

Deep Convolutional Neural Networks for Early-Stage Detection...

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2nd IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2024

作者： Laxmikant, Kumar Arthi, A. Vinodhini, V. Natarajan, B. Bhuvaneswari, R. Selvam, Prabu Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Karpagam College of Engineering Computer Science and Technology Coimbatore India University College of Engineering Department of Electronics and Communication Engineering Ariyalur India VIT University School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350317558

Lung and colon cancers are significant health issues across the world, prompting the need for inventive methods of diagnosis. This study takes the lead in introducing advanced Deep ConvNets (CNNs) to enhance the accuracy of early detection. The model is trained on extensive datasets, resulting in impressive outcomes. During training, it achieves an accuracy of 92.54% and a loss value of 0.0161. These results are mirrored in testing, where the accuracy reaches 96.59% and the loss value is 0.0441. Beyond just numerical achievements, this research revolutionizes cancer diagnostics by providing a sophisticated tool for personalized and early detection. This study surpasses the mere influence on individuals and extends its reach to bring hope for society and the economy by tackling the pervasive weight of cancer. It distinguishes itself in the realm of advanced medical investigation, affirming the efficacy of the model and inspiring additional inquiry, encompassing the utilization of more extensive sets of data and implementation in diverse medical domains. © 2024 IEEE.

关键词： Colon Cancer Convolutional Neural Network Lung Cancer

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