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International Conference on Computing and Networking technology (ICCNT)

作者： K.G Chandi Priya P Savitha V. Sujatha A. Sabarivani M Dinesh Department of General Engineering R.M.K College of Engineering and technology Chennai Department of CSE Nandha Engineering College Perundurai Erode Master of Computer Applications S.A. Engineering College Chennai Department of Biomedical Engineering Sathyabama Institute of Science and Technology Chennai Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep residual algorithms succeeded wildly in different computer vision problems (e.g., Image Super resolution). Deep neuraenablersraredesign and residual learning are great enablers that define how these architectures overcome limitations in traditional image resolution models. This article implements a deep residual algorithm representing the image resolution model performance research. This paper aims to synthesize HR images from their low-resolution (LR) counterparts by training the proposed model using large-scale image pairs across different resolutions. The model is tested using multiple evaluation criteria and compared with classical resolution models. Experimental results show that the deep residual model performs better in peak signal-to-noise ratio(PSNR), structural similarity index(SSIM), and visual quality metrics. It examines the influence of various network architectures, training strategies, and datasets on how well this algorithm can recognize dialog acts. Experiments demonstrate the superiority of the proposed model with a deep residual algorithm in terms of improving Resolution compared to traditional models.

关键词： Training Measurement Visualization Computational modeling Superresolution Network architecture Performance analysis Signal resolution Image reconstruction Signal to noise ratio

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Resilient Framework for Next-Generation Heterogeneous Wireless Networks in Disaster Management

Resilient Framework for Next-Generation Heterogeneous Wirele...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Ajisha Mathias J. Relin Francis Raj S R Raja R. Santhana Krishnan S. Balamurugan G.Vinoth Rajkumar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Associate professor Master of Computer Applications Hindustan Institute of Technology and Science Chennai India Associate Professor Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Mathematics Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The growing occurrence and concentration of natural and artificial disasters necessitate the development of robust communication systems capable of maintaining reliable and efficient connectivity under adverse conditions. Heterogeneous wireless networks (HSNs) have emerged as a promising solution, offering the flexibility to integrate various wireless technologies and adapt to changing environments. However, the inherent complexity and dynamic nature of HSNs present significant challenges in ensuring resilience and reliability, particularly in disaster scenarios where network infrastructure may be severely compromised. This research investigates a Resilient Framework for Next Generation Heterogeneous Wireless Networks in Disaster Management, focusing on enhancing communication reliability and efficiency during critical scenarios. The study introduces Network Coding Enhanced COPE (NC-COPE), a novel algorithm integrating network coding principles into the COPE framework. Simulation analysis compares NC-COPE with existing algorithms: Optimized Link State Routing (OLSR), Coding-Aware Routing (CAR), and Dynamic Source Routing (DSR) using metrics such as Packet Delivery Ratio (PDR), Routing Overhead, and Resilience to Node Failures. NC-COPE demonstrates superior performance in maintaining high PDR, reducing routing overhead, and enhancing network resilience under node failures. The findings underscore NC-COPE's potential to mitigate communication challenges in disaster management scenarios, highlighting its role in future wireless network deployments.

关键词： Wireless networks Heuristic algorithms Disasters Focusing Disaster management Network coding Routing Automobiles Next generation networking Resilience

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Melanoma Malignancy Prognosis Using Deep Transfer Learning

Melanoma Malignancy Prognosis Using Deep Transfer Learning

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Artificial Intelligence and Applications (ICAIA), International Conference on, technology Conference (ATCON-1), Alliance

作者： R. Shobarani R. Sharmila Mathur Nadarajan Kathiravan A. Anbarasa Pandian Ch Narasimha Chary K. Vigneshwaran Computer Science and Engineering Dr.M.G.R Edu. & Research Institute Maduravoyal Tamilnadu India Master of Computer Application Dhanalakshmi Srinivasan Engg. College Perambalur Tamilnadu India Department of Biotechnology Dr. N. G. P Arts and Science College Coimbatore Tamilnadu India Computer Science & Engineering Panimalar Engineering College Poonamallae Tamilnadu India Computer Science and Engineering Sriindu College of Engg. & Technology Hyderabad Telangana India Electronics & Commu. Engineering K.Ramakrishnan College of Engineering Trichy Tamilnadu India

Melanoma is a type of Skin cancer that spreads rapidly and has a significant death risk if it is not detected early and treated. A prompt and accurate diagnosis can improve the patient’s chances of survival. The creation of a skin cancer diagnostic support system based on computer technologies is highly essential. This study suggests a unique deep transfer learning model for categorizing melanoma malignancy. The proposed system comprises of three main phases including image preprocessing, feature extraction and melanoma classification. The preprocessing phase employs image filters such as mean, median, gaussian and non-local means filter along with histogram equalization techniques to obtain the preprocessed images. Feature extraction and classification are performed using pre-trained Convolutional Neural Network architectures such as DenseNet121, Inception-Resnet-V2 and Xception. Using the ISIC 2020 dataset, the suggested deep learning model’s effectiveness is assessed. The experimental findings show that, in terms of precision and computational expense, the suggested deep transfer learning model performs better than cutting-edge deep learning algorithms.

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Revisiting Fuel Subsidies in Indonesia using K-Means, PAM, and CLARA

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2023年第3期50卷 858页

作者： Prasetyo, Fajar Agung Caraka, Rezzy Eko Kim, Yunho Goldameir, Noor Ell Sulistyowati Tyasti, Avia Enggar Gio, Prana Ugiana Anggoro, Faisal Ramadhani, Muthia Pardamean, Bens Faculty of Economics and Business Universitas Indonesia Indonesia Indonesia Department of Mathematics Ulsan National Institute of Science and Technology Korea Republic of Department of Mathematics Statistics Study Program Riau University Riau Pekanbaru Indonesia Institut Teknologi Adhi Tama Surabaya East Java Indonesia International Trade of ASEAN and RRT Region Polytechnic of APP DKI Jakarta Indonesia Department of Mathematics Universitas Sumatera Utara Medan20155 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

Indonesia is one of the countries in the world that still applies subsidies for fuel oil. By the law, the Indonesian government must ensure the supply and distribution of fuel for all Indonesian people. To implement this policy properly, understanding the pattern of fuel consumption is fundamental. In this study, clustering will be used to determine the categories of districts and cities based on subsidized fuel consumption patterns. Our research used several methods to compare which cluster method is the most optimal;the methods include k-means, Partitioning Around Medoids (PAM), and Clustering Large Applications (CLARA). The results show that k-means is the best clustering method with the highest Dunn index and Silhouette coefficient compared to others. The optimum cluster number we get is two and represents fuel consumption from several districts and cities. Some districts and cities had under-average consumption and needed to be monitored. © (2023), (International Association of Engineers). All Rights Reserved.

关键词： K-means clustering

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Adaptive Multi-Resolution Framework for Scalable and Efficient Big Data Processing

Adaptive Multi-Resolution Framework for Scalable and Efficie...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： V. Asha Yagateela Pandu Rangaiah Ginni Nijhawan Ali Albawi K. Pushpa Rani Susmita Kalyani Master of Computer Application New Horizon College of Engineering Bangalore India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The increasing applicability of the big data in different domains require the formulation of effective data collection and analysis framework that can accommodate complex and large datasets. This paper proposes the new DRAF that enhance both data aggregation and mining for large datasets. The Dynamic Resolution Adaptive Framework (DRAF) takes advantage of using multiple levels of representation of data, which makes it easy to expand the workload or scale down depending on the needs of the user. This proposed framework employs methods like hierarchical clustering, branching multi-level indexing and Adaptive Resolution Switching in order to optimize the trade-off of data accuracy and computational cost. Moreover, high level of detail is maintained in the DRAF, which supports mining necessary data and model details at multiple resolutions, making the DRAF specifically appropriate for operational environments where data come from numerous different sources and types. Substantial experiments in the book prove the efficiency of the DRAF-based algorithms in terms of speed and accuracy in comparison with other existing methods. The study establishes potentiality of the DRAF toward the application of big data problems in areas of high level of resolution and immediacy of decision making.

关键词： Accuracy Computational modeling Weapons Switches Big Data Data models Data mining Informatics Indexing Load modeling

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How to design the biofeedback training for the rehabilitation of PTSD patients?

How to design the biofeedback training for the rehabilitatio...

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2023 IEEE Ural-Siberian Conference on Computational Technologies in Cognitive science, Genomics and Biomedicine, CSGB 2023

作者： Bazanova, Olga Zakharov, Alexander Larkova, Irina Maryanovskaya, Tatiana Melnikov, Andrey Ermolaeva, Sargylana Nikolenko, Ekaterina Shirolapov, Igor The Research Institute of Molecular Biology and Biophysics Laboratory of Computer Systems of Biofeedback Dolgoprudny Russia The Moscow Institute of Physics and Technology Educational and Scientific Center for Humanities and Social Science Novosibirsk Russia Research Institute of Neurosciences Samara State Medical University Samara Russia Lugansk Republican Center for Emergency Medical Care and Disaster Medicine Laboratory of Psychophysiological Support Lugansk Russia Novosibirsk National Research State University Mechanic-Mathematic Department Novosibirsk Russia The Russian University of Sport «gtsolifk» Department of Physiology Moscow Russia The Moscow Institute of Physics and Technology Master's Degree in Applied Data Analysis in the Medical Field Dolgoprudny Russia Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Department of Clinical Neuroscience Behavior and Neurotechnology Novosibirsk Russia

ISBN: (纸本)9798350307979

Rationale for this review is the pressing medical and social problem of the posttraumatic stress disorder (PTSD) treatment. It is known that biofeedback technology is one of the most effective methods of treatment and rehabilitation for emotional profile disorders. In order to develop biofeedback aimed to PTSD rehabilitation, we conducted a literature search on this problem. However, it appears that at the moment there is not enough unambiguous data on this type of biofeedback design. The pathogenesis of PTSD is closely related to impaired efficiency of sensorimotor integration (SMI). The effectiveness of therapy for psychosomatic disorders in patients with PTSD can be increased by restoring normal sensorimotor integration. The review examines various autonomic, electrophysiological and postural markers of destroyed sensorimotor integration (SMI) in individuals with PTSD. We have found that the most informative indicators of SMI in norm include: an increase in EEG power in the individually determined high-frequency alpha subrange, a decrease in the speed of body sway and energy demands to maintain a vertical posture, and a decrease in EMG activity of muscles not involved in cognitive or psychomotor performance. We intend to use these indicators in diagnostic purposes and to develop neurofeedback technology for SMI recovery in patients with PTSD. © 2023 IEEE.

关键词： Electroencephalography

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Advancing Image Segmentation, Analysis, and Feature Extraction Techniques

Advancing Image Segmentation, Analysis, and Feature Extracti...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： V. Asha Yagateela Pandu Rangaiah Ginni Nijhawan Susmita Kalyani K. Pushpa Rani Ali Albawi Master of Computer Application New Horizon College of Engineering Bangalore India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study centers on review, feature extraction, and image segmentation, integrated through the Dual-Path Convolutional Neural Network (DP-CNN). The DP-CNN is designed to accurately identify objects in images, even in the presence of intricate lines and discontinuities. The MAMAM technique efficiently prioritizes critical image regions, expediting the review process and enhancing accuracy in image analysis. Additionally, feature extraction is refined using the Adaptive Feature Selection (AFS) method, facilitating precise and adaptable image processing. Each component of this approach is systematically developed to meet the research objectives. The combined methodology demonstrates robustness and versatility, proving effective across diverse and challenging data scenarios, thereby presenting a promising solution for rapid and accurate complex image processing tasks.

关键词： Image segmentation Image analysis Accuracy Reviews Feature extraction Solids Robustness Object recognition Convolutional neural networks Informatics

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Intelligent road crack detection and analysis based on improved YOLOv8

arXiv

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arXiv 2025年

作者： Zuo, Haomin Li, Zhengyang Gong, Jiangchuan Tian, Zhen School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou China Master of Science in Computer Science DigiPen Institute of Technology RedmondWA United States Hebei Normal University Hebei Shijiazhuang China James Watt School of Engineering University of Glasgow Glasgow United Kingdom

As urbanization speeds up and traffic flow increases, the issue of pavement distress is becoming increasingly pronounced, posing a severe threat to road safety and service life. Traditional methods of pothole detection rely on manual inspection, which is not only inefficient but also costly. This paper proposes an intelligent road crack detection and analysis system, based on the enhanced YOLOv8 deep learning framework. A target segmentation model has been developed through the training of 4029 images, capable of efficiently and accurately recognizing and segmenting crack regions in roads. The model also analyzes the segmented regions to precisely calculate the maximum and minimum widths of cracks and their exact locations. Experimental results indicate that the incorporation of ECA and CBAM attention mechanisms substantially enhances the model's detection accuracy and efficiency, offering a novel solution for road maintenance and safety monitoring. © 2025, CC BY.

关键词： Image segmentation

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Maximum Energy Productivity for Concurrent Wireless Data and Power Shifting-Enabled IoT Network with Energy Coordination

Maximum Energy Productivity for Concurrent Wireless Data and...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering, ICECONF 2023

作者： Joseph, Sneha Sankar, R. Royappa, Angelina Anandakumar, D. Gururaj, D. Karthikeyan, K.V. Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai India Department of Master of Computer Application S.A Engineering College Chennai India Department of Electronics and Communication Engineering Velammal Engineering College Chennai India Department of Electronics and Communication Engineering Nandha Engineering College Erode India Department of Electronics and Communication Engineering Rajalakshmi Engineering College Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai600119 India

ISBN: (纸本)9798350334357

The online phase may be successfully extended by simultaneous wireless data, internet of things (IoT) components, and sophisticated technologies. The development support base station is developed to accomplish the exchange of renewable electricity to manage the volatility of power generation by the hybrid access points. In this research, we jointly investigate the cooperative SWIPT-enabled IoT systems. While maximizing the program's energy consumption, we must also adhere to maximize the transmission limits, thermoelectric generator restrictions, and customer quality of service (QoS) requirements. We collaborate to find solutions to the challenges of power-sharing, period shifting, and ecological collaboration. The incessant algorithm is employed to address the load distribution and duration swapping problems, the matching algorithm is employed to fix the cooperation agreement issue, and since this trouble is a nonlinear optimization issue, it is challenging to address directly. Instead, we are using the interchanging differential technique. The outcomes of the simulations demonstrate that the suggested algorithm performs with a considerable advantage in terms of energy conservation compared to the comparative method. Also, it has been shown that using energy collaboration technologies can reduce the amount of power a system uses and make it run better. © 2023 IEEE.

关键词： Energy utilization

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Preliminary Staff Collaboration and Artificial Intelligence

Preliminary Staff Collaboration and Artificial Intelligence

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： Arvind Kumar Pandey Mahesh T R Veena Christy Mohsin Shaikh Preksha Yadav Saptorshi Das Department of Computer Science ARKA JAIN University Jamshedpur Jharkhand India Department of Computer Science and Engineering JAIN(Deemed-to-be University) Bangalore India Department of Business Administration Bharat Institute of Higher Education and Research India Department of School of Management Dr. Vishwanath Karad MIT World Peace University Pune India Master of Business Administration CMR Institute of Technology Bangalore India KIIT School of Management KIIT Deemed to be University India

Artificial Intelligence is viewed as by a few contemporary associations all over the planet to foster able labor forces, where the ways of behaving and abilities of the workers are assisting those associations with acquiring upper hands. The administration experts of the contemporary associations are recognizing that the representatives can work cooperatively because of AI and prescient information examination. Utilization of Artificial Intelligence in the representative administration exercises is assisting a few contemporary firms with driving positive representative commitment as a result of the improvement of durable workplace. This exploration work is centered around the positive effect of Artificial Intelligence on representative commitment to this contemporary worldwide business *** models and hypothetical structures on worker commitment are applied in this review. Then again, unique significant hypothetical parts of Artificial Intelligence in forming representative commitment likewise have been examined in this exploration work. The tone and heading of the exploration show that utilization of Artificial Intelligence decidedly affects the contemporary representative commitment exercises. An exploratory review configuration is created and considered for the conduction of this examination. A subjective philosophy has been viewed as in this examination relying upon the exploratory idea of the review to meet the exploration objective. A likelihood inspecting approach has been embraced by the scientist to choose a significant example size and gather expected information. Essential data collection process has been taken on in this review to upgrade legitimate examination result. The discoveries and conversations have demonstrated toward the end that Artificial Intelligence is significant for the contemporary associations to shape representative commitment to a positive way.

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