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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Soni, Ishika Yuvaraj, S. Kaur, Ashmeet Jayaprakash, B. Kolekar, Vikas K. Yuvaraj, K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Sciences Department of Computer Science & It Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350370249

The ever-developing subject of photograph processing and pc vision has made excellent strides inside the advancement of coloration and multispectral photo retrospective analysis. This generation allows for particularly designated and accurate evaluation of images accrued from various sources. Its miles a useful asset for various industries, together with clinical imaging, facial popularity, and remote sensing. Notwithstanding the several blessings and blessings of this generation, there are still numerous demanding situations related to it. these include difficulties of appropriately processing huge amounts of information in a well timed way, the high price of acquiring and implementing the essential hardware and software program, and they want for accuracy and reliability in identifying and extracting precise features from picas. Moreover, the undertaking of dealing with regulators and highbrow assets owners of the imagery must additionally be considered. Even though these challenges persist, the terrific strides in era have enabled greater accuracy and precision, making the challenge of retrospective photograph analysis greater green and price-powerful. © 2024 IEEE.

关键词： Additionally Amounts Enabled Greater Implementing Information Precision Processing

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Robustness Analysis of EV Charging System using Random Forest Algorithm 14

Robustness Analysis of EV Charging System using Random Fores...

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14th International Conference on Computing communication and Networking Technologies, ICCCNT 2023

作者： Barre, Uma Prasanna Vishnu Satyanarayan, Swapna Reddy, Hemanjaneya Pulikala, Aparna Bajaj, Ankit National Institute of Technology Karnataka Department of Electronics and Communication Engineering Karnataka Surathkal India Mercedes-Benz Research and Development India Pvt Ltd Bengaluru India Malaviya National Institute of Technology Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798350335095

Electric cars offer numerous benefits and are considered the future of the automobile industry. However, their worldwide adoption still needs to grow. One of the primary reasons for this delay in electrification is charge anxiety, which refers to the uncertainty customers feel when connecting the charging cable to the car. To address this issue, this study analyses the performance of the charging system using a machine learning model to identify sensitive signals that influence the charging process and can cause successful charging or charge termination. The analysis will also help to define robust operating regions where the charging component can reliably function, regardless of external conditions. This study's findings will provide insights into electric vehicle charging behavior with the supply station. © 2023 IEEE.

关键词： Automotive industry

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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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computer Modeling in engineering & Sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

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The Unique Way of Designing SNN Network for Content Recommending System using Hybrid Algorithms 4

The Unique Way of Designing SNN Network for Content Recommen...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Yadav, Kanchan Sruthi, Pinamala Al-Jawahry, Hassan M. Kumar, Devender Gehlot, Anita Angadi, Sanjeev Kumar Gla University Department of Electrical Engineering Mathura India Cmr College of Engineering & Technology Department of Cse India The Islamic University Najaf Iraq Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Uttaranchal Institute of Technology Uttaranchal University Department of Electronics & Communication Engineering Dehradun248007 India Computer Science and Engineering Nutan College of Engineering and Research Pune Maharashtra Talegaon Dabhade India

ISBN: (纸本)9798350360165

In the rapidly evolving panorama of e-commerce, personalised apparel recommendation systems stand as a pinnacle of technological advancement, aiming to reflect the intricacies of human style feel and social interactions on-line. This paper offers a singular technique via using Siamese Neural Networks (SNN) to research and integrate style fashion consistency with user social circle data, thereby addressing the pervasive challenges of bloodless start and records sparsity in traditional collaborative filtering systems. Our methodology leverages huge statistics from a distinguished social fashion platform, demonstrating good sized upgrades in advice personalization and accuracy. This integration no longer handiest enhances the user experience by way of providing tailor-made fashion suggestions however additionally marks a great step forward within the application of neural networks in the realm of fashion e-commerce. © 2024 IEEE.

关键词： Collaborative filtering

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An Intensive Review on Propagative Antagonistic N/W which is used in Health Care System 4

An Intensive Review on Propagative Antagonistic N/W which is...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Badhoutiya, Arti Sivaiah, Borra Almusawi, Muntather Verma, Rajan Singh, Rajesh Angadi, Sanjeevkumar Gla University Department of Electrical Engineering Mathura India Cmr College of Engineering & Technology Department of Cse India The Islamic University Najaf Iraq Chitkara University Chitkara Center for Research and Development Himachal Pradesh 174103 India Uttaranchal University Uttaranchal Institute of Technology Department of Electronics & Communication Engineering Dehradun248007 India Nutan College of Engineering and Research Computer Science and Engineering Pune Maharashtra Talegaon Dabhade India

ISBN: (纸本)9798350360165

This article introduces a Generative Adversarial Network tailored for medical imaging, in particular designed to synthesize retinal images alongside side their segmented mask. This innovation addresses the challenge of obtaining big datasets for deep getting to know, in particular in scientific fields wherein expert annotations are high priced and time-ingesting. It not satisfactory generates fantastic synthetic scientific pics for supervised analysis but moreover achieves cutting-edge day overall performance with dice coefficients of 0.837 on the STARE dataset and 0.832 on the dataset, surpassing present techniques. This development highlights the capability of GANs in scientific imaging, deep gaining knowledge of, and style transfer applications. © 2024 IEEE.

关键词： Gallium nitride

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Simulation Environment for the I9 Vanet Platform 2

Simulation Environment for the I9 Vanet Platform

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2nd IEEE International Conference on Advances in Computing, communication and Applied Informatics, ACCAI 2023

作者： Avhankar, Madhavi Satish Pawar, JanardanAdinath Singh, Gurpreet Asokan, Anju Kaliappan, S. Purohit, Kamlesh Chandra Indira College of Commerce and Science Department of Computer Science Maharashtra Pune411033 India Department of Computer Science and Engineering Punjab 140401 India Sri Eshwar College of Engineering Department of Computer and Communication Engineering Tamil Nadu Coimbatore641202 India Lovely Professional University Division of Research and Development Punjab Jalandhar144411 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350315905

Vehicular Specially appointed Organizations (VANETs) are delegated a use of Portable Impromptu Organizations (MANETs) that has the potential in further developing street security and giving Clever Transportation Framework (ITS). Yet, VANET simulation is not the same as MANETs (portable impromptu organizations) simulation on the grounds that in VANETs vehicular environment forces new issues and necessities, like obliged street geography, multi-way blurring and side of the road obstructions, traffic flow models, trip models, varying vehicle speeds and adaptability, traffic lights, traffic congestion, and driver behavior. The vehicular security application ought to be completely tried before it is sent in a genuine world to utilize. Test system device has been liked over outside explore on the grounds that it straightforward, simple and modest. VANET expects that a traffic and organization test system ought to be utilized together to play out this test. Many apparatuses exist for this reason yet the majority of them dislike the appropriate association. In this proposal, we target reenacting vehicular organizations with outside upgrade to examine its impact on wireless correspondence yet to finish this work a decent test system is likewise required. Thus, we will initially banter on the deficiency of current test systems and think of our own suggestions to play out our simulation. Returning to VANET test systems is expected to distinguish assuming that they are as yet fit for assessing these new situations. This paper represents a refreshed audit of VANET test systems, describing their present status and abilities to evaluate novel situations in VANET research. © 2023 IEEE.

关键词： Traffic congestion

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Improving Video Search and Retrieval Through Semantic Annotation 15

Improving Video Search and Retrieval Through Semantic Annota...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Balaji, N.V. Katyal, Ashu Jaisudha, J. Parmar, Yuvraj Asha, S. More, Priyanka D. Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - UG Management Karnataka Bangalore India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

This paper affords a singular approach for enhancing video search and retrieval through the usage of semantic annotation. The proposed method includes levels of semantic annotation and semantic search and retrieval. Within the first level, semantic descriptors are mechanically generated from low-degree features of the video. Inside the 2 nd, those semantic descriptors are used to locate relevant films efficiently. Experiments are performed to evaluate the accuracy and performance of the proposed machine. Consequences indicate that the proposed gadget extensively outperforms existing techniques for video seek and retrieval, demonstrating the application of semantic annotation. © 2024 IEEE.

关键词： Annotation Demonstrating Experiments Features Outperforms Retrieval Semantic Singular

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Deep Learning Methods for Video Segmentation and Tracking 15

Deep Learning Methods for Video Segmentation and Tracking

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Deshpande, Vivek Balaji, N.V. Sood, Gourav Ramya, G. Aggarwal, Prateek Debnath, Baishakhi Vishwakarma Institute of Technology Department of Computer Engineering Maharashtra Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India School of Mangement - Ug Mangement Karnataka Bangalore India

ISBN: (纸本)9798350370249

Deep mastering strategies for video segmentation and tracking involve using convolutional neural networks (CNNs) to pick out gadgets or areas of hobby in the body of a video. Such fashions may be used to extract spatial statistics, which include an item's role, movement, and velocity from the video. Moreover, they are able to help locate changes in the environment, hit upon appearances of recent items, and tune multiple objects within the identical body. It is an effective device for video forensics and movement evaluation, permitting computerized tracking and identity of objects in a scene. Maximum contemporary procedures observe CNNs for single-frame item segmentation and monitoring, which is predicated on the excessive-stage shape and look facts of interested items. Furthermore, CNNs may be used for temporal analysis and fusion of the collection of frames for extra robust object detection and tracking. Such an approach is regularly employed for multi-item monitoring and hard item segmentation duties. In the end, using CNNs in video segmentation and monitoring can result in advanced accuracy and robustness of video information. © 2024 IEEE.

关键词： Changes Contemporary Detection Monitoring Movement Permitting Robustness Segmentation

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Advanced Urban Air Quality Control and Management Using XM and RFE Models for Enhanced Predictive Analysis 4

Advanced Urban Air Quality Control and Management Using XM a...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Sreelatha, Bolisetty Bhoopathy, V. Saravanan, B. Vattikolla, Tulasirao Asrani, Deepak Kaliappan, S. Geethanjali College of Engineering and Technology Department of Electronics and Communication Engineering Medchal India Sree Rama Engineering College Department of Cse Tirupati India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Department of Englis Hyderabad India School of Information Technology Institute of Management Studies Noida India Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798350352931

Urban air pollution, particularly in large cities, has historically been exacerbated by construction activity, resulting in significant surges. Pollutants such as Sulphur dioxide and soot have, however, been significantly reduced in the industrialized Western world as a result of developments in abatement technologies. As a result, the attention has shifted to pollutants related with mobility, such as nitrogen oxides, organic compounds, and fine particulate matter. This work uses a machine learning approach to analyze urban air quality, with a focus on feature selection and data preparation. Data preprocessing, feature selection using Principal Component Analysis (PCA), and model training are all important steps. Normalization of Life Cycle Assessment (LCA) results was also added as a preprocessing step. The model had a maximum accuracy of 91.93% in categorizing and predicting urban air quality. This outcome was greatly influenced by the selection of important features using PCA and model refining by feature deletion. The findings highlight the efficacy of sophisticated machine learning approaches, specifically PCA and normalization, in improving air quality prediction models. The findings show that, while traditional pollutants have been significantly reduced, new concerns in urban air pollution necessitate specific solutions centered on transportation-related particles and other minute pollutants. This paper establishes a paradigm for better urban air quality management and emphasizes the value of adaptive approaches in environmental monitoring. © 2024 IEEE.

关键词： Nitrogen oxides

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Software defined network (SDN) based data server computing system

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International Journal of Information Technology (Singapore) 2025年第1期17卷 607-613页

作者： Madhura, K. Sekhar, Gillala Chandra Sahu, Amaresh Karthikeyan, M.P. Khurana, Saniya Shukla, Meenu Vashisht, Nitish Information and Communication Technology Manipal Institute of Technology Mahe Bengaluru India Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad India Ajay Binay Institute of Technology Odisha India Department of Computer Science and Information Technology Jain (Deemed to be University) Karnataka Jain Bangalore India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Rajpura India

The growing clientele and broadband demand lead to several technical restrictions in ensuring stable connections between data centers. The data on existing network devices such as switches, routers, and gateways must be routed to some particular point commonly known as software-defined network (SDN). SDN is defined as a networking approach that makes use of software-based controllers or application programming interfaces (API’s) to communicate with existing hardware and provide paths to traffic in the network. In order to overcome technical restrictions in the network, the given paper introduces an application-based enabled open flow information center network layout to accomplish transport load allocation and higher efficiency. In order to mitigate noise in the network, the proposed approach employs a data centre network (DCN) and implements it using SDN controller such as OpenDaylight (ODL). ODL is an open-source platform for automating networks of any size and scale. It allocates traffic requests among the linked servers, thereby ensuing higher throughput, optimization of load balancing, and versatility in SDN’s. Lastly, the performance of the proposed approach is validated by assessing the throughput rate (Mbps) and average delay (ms) prior to load balancing (BLD) and after load balancing (ALD). The results show that the proposed approach leads to higher throughput after load balancing optimization using ODL. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： DCN Load balancing ODL SDN Throughput

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