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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Hari Krishna, S.M. Jahnavi, Konda Abdulla, Akula Sumanth, H. Gaur, Akshat Tejaswini, V. M S Ramaiah University of Applied Sciences Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665450461

In recent years, the production of Electric Vehicles (EVs) has increased significantly due to their environmentally friendly nature and as substitutes for cost-effective petrol and diesel vehicles. With the increase in penetration of these electric vehicles and various user preferences, Charging Stations also need to install in appropriate locations and satisfy the various requirements of EV users. However, with the current infrastructure of these charging stations in major cities of India charging these massive loads of Battery, eats important time for EV users waiting in Queue or finding suitable charging station for their vehicles. This paper explains the mobile application that was designed and built to help EV users in locating different charging stations near them and find out the variety of charging modes available at their preferred charging point. This data has been gathered from various APIs provided by the different EV companies and included in this application using Google maps SDK and API to display charging station locations and directions to it. One of the main features of this application is to provide users an option to add themselves to the waiting list at a charging station of user choice instead of going there and waiting in the queue until their turn comes. © 2022 IEEE.

关键词： Cost effectiveness

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Refining Blockchain-Powered Industrial Internet-of-Things Architecture for Improved Performance 5

Refining Blockchain-Powered Industrial Internet-of-Things Ar...

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5th International Conference on Blockchain Computing and Applications, BCCA 2023

作者： Gambo, Ishaya Ogundare, Timilehin Magreola, Michael Ajayi, Temitope Obafemi Awolowo University Department of Computer Science and Engineering Ile-Ife Nigeria University of the Witwatersrand Computer Science and Applied Mathematics Johannesburg South Africa University of Arkansas Little Rock Department of Computer Science 2801 S. University Ave Little RockAR72204 United States

ISBN: (纸本)9798350339239

This paper introduces a high-performance blockchain solution to enhance Industrial Internet of Things (IIoT) operations, effectively addressing the challenge of reconciling blockchain's computational demands with the limited capabilities of IIoT devices. Through quantitative research, we present an IIoT blockchain-powered deep reinforcement learning performance optimization protocol. Our evaluation covers key aspects such as latency, scalability, security, and decentralization, demonstrating enhanced scalability without compromising security or latency. We establish a scalable and robust blockchain architecture tailored for high-processing IIoT systems, fine-tuning its components using deep reinforcement learning. Our simulations illustrate the framework's capacity to significantly boost the performance of blockchain-powered IIoT systems while adapting to their diverse operational dynamics. © 2023 IEEE.

关键词： Reinforcement learning

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Measuring Electromagnetic Performance and Configuration of Networks in High Interference Environments 15

Measuring Electromagnetic Performance and Configuration of N...

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

作者： Khan, A.Z. Panwar, Neeraj Rayaguru, N.K. Shelke, Priya M. Giri, Rakesh Kumar Jaishankar, P. Yeshwantrao Chavan College of Engineering Applied Physics Department Nagpur India University Gehu Dehradun Department of School of Computing India Vel Tech Rangarajan Dr sagunthala R & D Institute of Science and Technology Department of Eee India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Sunrise University Department of Computer Science & Engineering Alwar India Sastra Deemed to be University Department of School of Civil Engineering India

ISBN: (纸本)9798350370249

Measuring the electromagnetic overall performance and configuration of networks in high-interference environments can be challenging. In those environments, the indicators of hobby can be infected with unwanted heritage indicators, i.e., noise, leading to degraded overall performance and misguided size readings. This paper investigates the most reliable techniques for measuring networks' electromagnetic performance and configuration in these problematic environments, utilizing introducing a unique multistage framework that combines sign-processing strategies with some community parameters. In particular, the framework includes four degrees: sign reception, sensitivity analysis, pass-correlation, and community configuration optimization-an analysis of the results of varying environmental conditions on sign reception and sensitivity change into conduct. A cross-correlation approach has evolved to decide the extent of correlation among one-of-a-kind alerts and to locate the specific configurations of the particular networks. Sooner or later, an optimization set of rules was carried out to locate the network's optimum configuration. The final result of this research is a reliable method for measuring the electromagnetic performance and configuration of networks in high-interference environments. The proposed technique is anticipated to help researchers and engineers better understand the electromagnetic behaviors of complicated networks in actual-world scenarios. © 2024 IEEE.

关键词： Configuration Electromagnetic Environmental Performance Technique

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Fuzzy Logic-Based Deep Learning for Human-Machine Interaction and Gesture Recognition in Uncertain and Noisy Environments 1

Fuzzy Logic-Based Deep Learning for Human-Machine Interactio...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Anand, John Thamilarasi, V. Rayal, Ashish Gupta, Himanshu Kumar Jyothi, Kamboji Vishwakarma, Pramatma Greater Noida201310 India Department of Computer Science & Engineering Tamilnadu Salem-16 India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun248007 India Bipin Tripathi Kumaon Institute of Technology Department of Electrical Engineering Almora Dwarahat India Chaitanya Bharathi Institute of Technology Department of Mathematics AP Proddatur India Lovely Professional University Department of Computer Aplication Punjab 144411 India

ISBN: (纸本)9798331518578

Conventional deep learning methods have considerable hurdles when it comes to Human-Machine Interaction (HMI) and gesture identification in noisy and uncertain situations. A Fuzzy Logic-Based Deep Learning (Fuzzy DL) method is presented in this work to improve the resilience, accuracy, and adaptability of human gesture recognition in a variety of scenarios. We test several algorithms on various datasets, lighting situations, and noise levels, such as CNN, LSTM, Transformer, and Fuzzy DL. The outcomes show that Fuzzy DL functions better than other systems, retaining 91.0% accuracy at high noise levels and reaching 97.5% accuracy under typical circumstances. In comparison to traditional structures, it also shows faster inference times and a lower mean squared error. Its superiority is further demonstrated by comparative analysis on precision, recall, and F1-score. This study presents a robust and noise-resilient solution for real-world HMI applications, including as assistive systems and virtual interfaces. The outcomes indicate that incorporating fuzzy logic with DL substantially enhances gesture recognition in difficult environments. © 2025 IEEE.

关键词： Contrastive Learning

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A Novel-based Swin Transfer Based Diagnosis of COVID-19 Patients

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Intelligent Automation & Soft Computing 2023年第1期35卷 163-180页

作者： Yassir Edrees Almalki Maryam Zaffar Muhammad Irfan Mohammad Ali Abbas Maida Khalid K.S.Quraishi Tariq Ali Fahad Alshehri Sharifa Khalid Alduraibi Abdullah AAsiri Mohammad Abd Alkhalik Basha Alaa Alduraibi M.K.Saeed Saifur Rahman Department of Medicine Division of RadiologyMedical CollegeNajran UniversityNajran11001Saudi Arabia Department of Computer Science and Information Technology Ibadat International UniversityIslamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran11001Saudi Arabia Department of Chemical Engineering PIEAS UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University Islamabad(CUI)Sahiwal Campus57000Pakistan Department of Radiology College of MedicineQassim UniversityBuraydah52571Saudi Arabia Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran11001Saudi Arabia Faculty of Human Medicine Zagazig University44631Egypt

The numbers of cases and deaths due to the COVID-19 virus have increased daily all around the *** X-ray is considered very useful and less time-consuming for monitoring COVID *** doubt,X-ray is considered as a quick screening method,but due to variations in features of images which are of X-rays category with Corona confirmed cases,the domain expert is *** address this issue,we proposed to utilize deep learning *** this study,the dataset of COVID-19,lung opacity,viral pneumonia,and lastly healthy patients’images of category X-rays are utilized to evaluate the performance of the Swin transformer for predicting the COVID-19 patients *** performance of the Swin transformer is compared with the other seven deep learning models,including ResNet50,DenseNet121,InceptionV3,EfficientNetB2,VGG19,ViT,CaIT,Swim transformer provides 98%recall and 96%accuracy on corona affected images of the X-ray *** proposed approach is also compared with state-of-the-art techniques for COVID-19 diagnosis,and proposed technique is found better in terms of *** system could support clin-icians in screening patients for COVID-19,thus facilitating instantaneous treatment for better effects on the health of COVID-19 ***,this paper can contribute to saving humanity from the adverse effects of trials that the Corona virus might bring by performing an accurate diagnosis over Corona-affected patients.

关键词： Biomedical systems chest X-ray images CNN COVID-19 swin transformer image processing

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Quasi-Bound States in the Continuum and Enhanced Light-Matter Interactions in GaN/InGaN Epitaxial Metasurfaces

Quasi-Bound States in the Continuum and Enhanced Light-Matte...

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2024 Laser science, LS 2024

作者： Xu, Yuan Liu, Jiangnan Singh, Anshuman Tsai, Cheng-Chia Malek, Stephanie C. Shin, Walter Jin Wu, Jiahao Zhu, Freddie Liu, David Yu, Nanfang Mi, Zetian Soltani, Mohammad Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States Raytheon BBN Technologies CambridgeMA02138 United States

We fabricated GaN metasurfaces doped with InGaN quantum dots by templated molecular beam epitaxy (MBE) that support tunable high Q-factor quasi-bound states in the continuum (q-BICs) and demonstrated efficient optical... 详细信息

关键词： Gallium nitride

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Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

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computer Systems science & engineering 2023年第3期44卷 2243-2257页

作者： B.Kalpana S.Dhanasekaran T.Abirami Ashit Kumar Dutta Marwa Obayya Jaber S.Alzahrani Manar Ahmed Hamza Department of Information Technology RMD Engineering CollegeChennai601206India Department of Information Technology Kalasalingam Academy of Research and EducationSrivilliputtur626126India Department of Information Technology Kongu Engineering CollegeErode638060India Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Biomedical data classification has become a hot research topic in recent years,thanks to the latest technological advancements made in ***-dical data is usually examined by physicians for decision making process in patient *** manual diagnosis is a tedious and time consuming task,numerous automated models,using Artificial Intelligence(AI)techniques,have been presented so *** this motivation,the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI(BDC-CMBOAI)*** aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical ***,the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection(CMBO-FS)technique to derive a useful subset of *** addition,Ridge Regression(RR)model is also utilized as a classifier to identify the existence of *** novelty of the current work is its designing of CMBO-FS model for data ***,CMBO-FS technique is used to get rid of unwanted features and boosts the classification *** results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.

关键词： Artificial intelligence biomedical data feature selection cat and mouse optimizer ridge regression

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Use of digital platforms for enhancing academic performance. Systematic Review 7

Use of digital platforms for enhancing academic performance....

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Costescu, Cristina Anamaria Şogor, Mǎlina Pǎlimariu, Sabina Katona, Jozsef Tufar, Ioana Avram, Carla Maria Babe-Bolyai University Special Education Department Cluj-Napoca Romania Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary

ISBN: (纸本)9798331531119

The purpose of this study was to determine the effect of technology-based interventions on academic performance. To achieve this purpose a systematic review was conducted. Within the scope of this study EBSCO, Springer, ProQuest, science Direct, Wiley Online Library, Taylor and Francis Online, databases were scanned, and 18 studies were included in the review. Our results show that the iPad is the most preferred digital device for enhancing math and reading skills and significant results can be seen when used. Similar positive effects were observed with other technological devices, reinforcing the effectiveness of technology as a tool for supporting academic performance. This evidence underscores the value of incorporating digital devices in educational strategies, particularly for improving key academic skills. © 2024 IEEE.

关键词： academic performance children digital devices

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Transformative Applications and Key Challenges of Generative AI 7

Transformative Applications and Key Challenges of Generative...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power engineering, CANDO-EPE 2024

作者： Kovari, Attila Katona, Jozsef Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary University of Dunaújváros Department of Institute of Engineering Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary

ISBN: (纸本)9798331531119

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative AI, such as OpenAI, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative AI in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative AI tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative AI capabilities through AI. © 2024 IEEE.

关键词： Generative adversarial networks

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AutoML: A systematic review on automated machine learning with neural architecture search

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Journal of Information and Intelligence 2024年第1期2卷 52-81页

作者： Imrus Salehin Md.Shamiul Islam Pritom Saha S.M.Noman Azra Tuni Md.Mehedi Hasan Md.Abu Baten Department of Computer Engineering Dongseo University47 Jurye-roSasang-guBusan 47011Republic of Korea Department of Computer Science and Engineering Daffodil International UniversityDhaka 1216Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and TechnologyDhaka 1216Bangladesh Faculty of Computer Science and Engineering Frankfurt University of Applied SciencesFrankfurt 60318Germany Department of Computer Science and Engineering University of Asia PacificDhaka 1205Bangladesh Department of Computer Science and Engineering Ahsanullah University of Science and TechnologyDhaka 1208Bangladesh Department of Computer Science and Engineering Northern University BangladeshDhaka 1215Bangladesh

AutoML(Automated Machine Learning)is an emerging field that aims to automate the process of building machine learning *** emerged to increase productivity and efficiency by automating as much as possible the inefficient work that occurs while repeating this process whenever machine learning is *** particular,research has been conducted for a long time on technologies that can effectively develop high-quality models by minimizing the intervention of model developers in the process from data preprocessing to algorithm selection and *** this semantic review research,we summarize the data processing requirements for AutoML approaches and provide a detailed *** place greater emphasis on neural architecture search(NAS)as it currently represents a highly popular sub-topic within the field of *** methods use machine learning algorithms to search through a large space of possible architectures and find the one that performs best on a given *** provide a summary of the performance achieved by representative NAS algorithms on the CIFAR-10,CIFAR-100,ImageNet and wellknown benchmark ***,we delve into several noteworthy research directions in NAS methods including one/two-stage NAS,one-shot NAS and joint hyperparameter with architecture *** discussed how the search space size and complexity in NAS can vary depending on the specific problem being *** conclude,we examine several open problems(SOTA problems)within current AutoML methods that assure further investigation in future research.

关键词： AutoML Neural architecture search Advance machine learning Search space Hyperparameter optimization

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