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3rd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2022

作者： Jaiswal, Kriti Dahiya, Anshul Saxena, Surabhi Singh, Virendra Singh, Archita Kushwaha, Arun University of Lucknow Department of Computer Science Lucknow India Mangalmay Institute of Engineering and Technology Department of Computer Science and Engineering Greater Noida India PES University Department of Computer Applications Bengaluru India Babasahab Bhimrao Ambedkar University Department of Computer Engineering Lucknow India Babu Banarasi das University Department of Computer Application Lucknow India Babu Banarasi das University Department of Computer Applications Lucknow India

ISBN: (数字)9781665453196

ISBN: (纸本)9781665453196

The Internet of Things (IoT) has become a fundamental and omnipresent component of modern life, such as the infrastructure of intelligent transport systems and smart cities. All the next-generation IoT systems necessitate cognitive data processing that could be accomplished promptly through satellite communication. The edge server performs data processing in satellite communication, which is predicted to minimize service delay. However, in prevailing satellite technology, substantial transmission latency, significant route loss, and the resource, as well as energy limitations of IoT devices, all pose challenges to the rigorous service demands for latency and throughput in the upcoming 6G age. To tackle these challenges, 6G IoT will rely heavily on technologies such as space-Air-ground integrated networks (SAGINs), edge computing, deep learning, machine learning, blockchain, and energy harvesting. Combining communication technology with Unmanned Aerial Vehicle (UAV) has recently been identified as one of the potential strategies in the future network. UAVs may provide a variety of services;however, the UAV's onboard computational resources and battery backup are constrained. The Researchers in this article examine satellites as well as UAVs to deliver wireless IoT device cloud computing and edge computing services, respectively. Grounded IoT devices can conduct operations locally or offload them to UAV-based edge servers as well as distant cloud servers via satellites. The major emphasis is on the computational offloading dilemma and considering deep learning methods to optimize the task success rate considering energy dynamics and channel circumstances. The suggested AI-based offloading approach outperformed conventional methods, indicating that it can determine the most energy-efficient offloading policy. © 2022 IEEE.

关键词： Satellites

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Evaluating the Effectiveness of Object Detection Algorithms for Autonomous Parking

Evaluating the Effectiveness of Object Detection Algorithms ...

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2024 International Conference on computer, Electronics, Electrical engineering and their applications, IC2E3 2024

作者： Sharma, Utkarsh Subramonian, Krishnan Dsouza, Aaron Sughosh, P. Savitha, G. Girisha, S. Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal576104 India Manipal Institute of Technology Manipal Academy of Higher Education Department of Civil Engineering Manipal576104 India

ISBN: (纸本)9798350388534

In response to the challenges posed by increasing urbanization, traditional parking systems face numerous issues, such as inaccurate slot detection and manual tracking, which exacerbate traffic congestion and environmental strain. This paper presents an autonomous parking system as a solution to these challenges. By leveraging advanced object detection algorithms, including YOLO-NAS, YOLOv8, and DETR models, this system revolutionizes parking space identification, freeing drivers from the task of manual searching. Our research focused on identifying the most suitable object detection algorithm for our dataset and objectives. Utilizing the 'PKLot' Dataset, we trained and fine-tuned our models, ensuring consistency in hyperparameters across algorithms to isolate their impact on performance. We evaluated algorithmic efficacy using metrics like mean average precision (mAP) and latency, measuring accuracy in discerning occupied and vacant spaces alongside real-time processing efficiency. Our evaluation results highlight the performance differences among algorithms, guiding future research. This research advocates for an intelligent, autonomous approach to parking management, aligning with goals of optimizing operations and promoting sustainable urban development. © 2024 IEEE.

关键词： Traffic congestion

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Balancing And Risks: Managing Machine Learning In Mental Health applications 15

Balancing And Risks: Managing Machine Learning In Mental Hea...

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

作者： Upendra, Sheela Verma, Pushpendra Kumar Gupta, Prerana Kulanthaivel, M. Karthigaikumar, P. Maral, Vikas B. Department of Symbiosis College of Nursing Pune India Iimt University School of Computer Science and Applications Meerut India Tmu Department of Psychitary India Karpagam Academy of Higher Education Department of Microbiology Tamil Nadu Coimbatore India Karpagam College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Vishwakarma Institute of Information Technology Computer Engineering Pune India

ISBN: (纸本)9798350370249

The Evaluation Technique, which uses AI, has the potential to completely change the way mental health services are provided. An overview of artificially intelligent technology (AI) and its most recent developments in the medical industry is provided in this article. It also reviews current research on mental health, emphasizing how AI may help with diagnosis and therapy while taking into account its present drawbacks, unexplored territory, and ethical ramifications. New Developments: We have taken a look at psychological wellbeing analyses and evaluations that classify or split psychological problems like discomfort, extreme personality disorder, and various mental illnesses, such suicide ideas and attempts, into different categories. These evaluations make use of medical records, scales for measuring mood, neuroimaging data, cutting-edge monitoring systems, and information from social media sites. The promise of statistical machine learning to treat issues with mental health is shown by these study findings, but it is crucial to identify the methods that provide the greatest outcomes. Together, these investigations add to the expanding corpus of research that shows how AI may be used in psychiatric treatments. As AI methods develop, they hold the potential to help mental health professionals define psychotic symptoms accurately, recognize chronic illnesses at an earlier, asymptomatic stage when measures can be more effective, and customize treatment plans based on unique patient characteristics. However, care must be taken to avoid extrapolating too soon from preliminary results. To close the gap between clinical therapy and behavioral health research, further study is necessary. © 2024 IEEE.

关键词： AI in healthcare and therapy computational intelligence data mining Diagnosis emotional wellness Innovative monitoring systems Mental health Neuroimaging data Personalized therapy

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Comparing Convolutional Neural Networks for Covid-19 Detection in Chest X-Ray Images 10

Comparing Convolutional Neural Networks for Covid-19 Detecti...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Varshney, Neeraj Madan, Parul Shrivastava, Anurag Kumar, C Praveen Mehta, Jyoti Kumar, Anil Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

The study has been conducted to understand the effectiveness of CNN in the case of 'COVID-19' detection by using all the X-ray images of the chest. This helps the health sector to facilitate medical performance and conduct the analytical procedure by using ML. The aim and objective of the study have been properly mentioned in the first segment of the study. The issues have also been identified and the second chapter has identified all the previous work related to the study. The methodology segment has illustrated all the methods that have been applied to implement the project. The resulting segment has illustrated all the themes with proper evaluation in a comprehensive way. The last chapter represented a conclusion segment with proper recommendations and future work that can be implemented later. © 2023 IEEE.

关键词： CNN COVID-19 pandemic situation Social Repercussions X-ray images

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Detection of Different Stages of Alzheimer’s Disease Using CNN Classifier

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computers, Materials & Continua 2023年第9期76卷 3933-3948页

作者： S M Hasan Mahmud Md Mamun Ali Mohammad Fahim Shahriar Fahad Ahmed Al-Zahrani Kawsar Ahmed Dip Nandi Francis M.Bui Department of Computer Science American International University-Bangladesh(AIUB)Dhaka1229Bangladesh Centre for Advanced Machine Learning and Applications(CAMLAs) DhakaBangladesh Department of Software Engineering Daffodil International UniversityDhakaBangladesh Department of Computer Engineering Umm Al-Qura UniversityMecca24381Saudi Arabia Department of ECE University of SaskatchewanSaskatoonSK S7N5A9Canada Group of Biophotomatiχ Department of ICTMBSTUSantoshTangail1902Bangladesh

Alzheimer’s disease(AD)is a neurodevelopmental impairment that results in a person’s behavior,thinking,and memory *** common symptoms ofADare losingmemory and early *** addition to these,there are several serious impacts ***,the impact ofADcanbemitigatedby early-stagedetection though it cannot be cured ***-stage detection is the most challenging task for controlling and mitigating the impact of *** study proposes a predictive model to detect AD in the initial phase based on machine learning and a deep learning approach to address the *** build a predictive model,open-source data was collected where five stages of images of AD were available as Cognitive Normal(CN),Early Mild Cognitive Impairment(EMCI),Mild Cognitive Impairment(MCI),Late Mild Cognitive Impairment(LMCI),and *** stage of AD is considered as a class,and then the dataset was divided into three parts binary class,three class,and five *** this research,we applied different preprocessing steps with augmentation techniques to efficiently *** integrates a random oversampling technique to handle the imbalance problem from target classes,mitigating the model overfitting and *** three machine learning classifiers,such as random forest(RF),K-Nearest neighbor(KNN),and support vector machine(SVM),and two deep learning methods,such as convolutional neuronal network(CNN)and artificial neural network(ANN)were applied on these *** analyzing the performance of the used models and the datasets,it is found that CNN with binary class outperformed 88.20%*** result of the study indicates that the model is highly potential to detect AD in the initial phase.

关键词： Alzheimer’s disease early detection convolutional neural network data augmentation random oversampling machine learning

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Edge Computing And Convolutional Neural Networks For Real-Time Object Detection In Healthcare Iot 1

Edge Computing And Convolutional Neural Networks For Real-Ti...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kumar, Rakesh Pundir, Sumit Shrivastava, Anurag Kumar, C Praveen Rao, A.L.N. Sankhyan, Akhil Gla University Department of Computer Engineering & Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

In order to provide real-time identification of objects in Internet of Things (IoT) software for the healthcare industry, this research examines the incorporation of cutting-edge computing with convolutional neural networks (CNNs). A deductive method is used to methodically optimize CNN representations for constrained by resources edge devices, and it is based on an interpretivist mindset. To give a thorough explanation of the technical complexities concerned, a design based on descriptive statistics is used. The analysis is built on secondary data gathered from reliable sources. The topics covered range from enhancing CNN machine learning models on edge devices to putting secure processing of information practices in place, analyzing the effects on medical diagnostics particularly monitoring, and examining scalability across various healthcare contexts. Results indicate significant gains in object detection precision and decreased latency, demonstrating the viability of implementing Edge-CNN methods. Critical study underlines the possibility for live installations and the necessity for specific approaches to integration in broader healthcare organizations. Conducting clinical trials, modifying the framework for particular healthcare areas, and investigating cost-benefit assessments are all recommendations. Enhanced optimization methods, adaptive safeguards, and additional scalability investigations should all be the focus of future research. Future research around real-time analytics gives an intriguing opportunity to go beyond recognizing objects as well. Overall, this research revolutionizes real-time object identification in medical IoT for better patient care by advancing the practical use of edge technologies and CNNs. © 2023 IEEE.

关键词： applications bandwidth Convolutional neural networks hierarchical framework IoT device

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A Review of Web Services Research: Current Trends and Future Directions in computer Data Security 2

A Review of Web Services Research: Current Trends and Future...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kumar, Gaurav Singh, Kamred Udham Pandey, Saroj Kumar Singh, Teekam Kumar, Ankit Kadu, Rakesh K. Gla University Department of Computer Engineering & Applications UP Mathura India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Shri Ramdeobaba College of Engineering and Management Nagpur India

ISBN: (纸本)9798350369960

The Internet and Web services have developed significantly since their inception. Web Services standardize data flow between programs. Service-Oriented architectural (SOA) is a software architectural strategy for designing applications that use network services like the web to convey data. I'll introduce web services and SOA theory well. Web services offer standardized data flow between applications, improving system compatibility and integration. Web services using SOA are designed as a set of services that other applications may utilize inside and outside an organization's network. This technique allows services to be designed, deployed, and updated separately and reused across applications, increasing flexibility and scalability. When exchanging data between applications, two standards - REST HTTP and SOAP/WSDL web services - are used. The former provides a uniform interface, self-descriptive messages, and resource identification through URIs, while the latter uses an XML-based protocol and is platform- and language-agnostic. This study carefully compares SOAP and REST web service delivery techniques and examines security issues. This article addresses web service WSDL document security issues and provides a cryptographic way to protect them using the WSSecurity standard. WSDL documents contain sensitive information, hence web service security must be addressed. Cryptography and WS-Security can protect application data. © 2023 IEEE.

关键词： Cryptography REST Security SOA SOAP UDDI Web services WSDL

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Behavioral and Clinical Data Analysis for Autism Spectrum Disorder Screening with Machine Learning 10

Behavioral and Clinical Data Analysis for Autism Spectrum Di...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Kumar, Rakesh Bordoloi, Dibyhash Shrivastava, Anurag Kumar, C Praveen Kumari, Vandna Kumar, Anil Gla University Department of Computer Engineering & Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350382471

The study of appropriate and accurate classification for 'autism spectrum disorder (ASD)' is crucial, and this study, 'Behavioral and Clinical Data Analysis for Autism Spectrum Disorder Screening with Machine Learning,' aims to fulfil this requirement. The study integrates both 'quantitative and qualitative methodologies' through an integrated approach and accessible study philosophy. Approaches for gathering data include compiling datasets, reviewing relevant research, and obtaining EEG, emotions, and eye motion data. In order to boost the accuracy of ASD screening, statistical models including 'logistic regression, neural networks, and support vector machines have been created.' This quantitative analysis is enhanced by a thematic approach, which pinpoints recurrent themes and characteristics. Data protection and permission from subjects are given top priority in this study's ethical concerns. The theoretical and practical divide, the studies hope to improve effective ASD diagnosis and treatments. © 2023 IEEE.

关键词： Autism Spectrum Disorder (ASD) Electroencephalogram (EEG) Logistic Regression Neural network restricted and repetitive behaviours SVM

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A Prediction of Stock Time Series Data Using Single Layer Long Short-Term Memory 2

A Prediction of Stock Time Series Data Using Single Layer Lo...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Al-Fatlawy, Ramy Riad Lande, Jayapal Sundaram, S. Meenakshi. Saranya, N. Naga Podaralla, Rakesh College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq School of Computer Science & Artificial Intelligence Sr University Warangal India Nitte Meenakshi Institute of Technology Department of Cse Bengaluru India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India School of Computing and Information Technology Reva University Bangalore India

ISBN: (纸本)9798350373110

Financial and cash flow data is a particular kind of past temporal sequence information which gives a huge quantity of data which is employed often in the task including data analysis. For investors and financial professionals, the major question is how to forecast stock time series data. Stock prices forecasting is a challenging task because of time series data volatility, non-linearity and significant noise on the value of stocks. The earlier study only focused on alone parameter stock called close cost, hence, Single Layer Long Short-term Memory (LSTM) model is proposed. This model suggests the contribution as stock information and it predicts two stocks characteristics namely high cost and close cost for the day after. The dataset for Shanghai composite index is utilized for conducting experiments and by the use of existing techniques the comparisons are done. From the results, it clearly shows that proposed single layer LSTM achieved MAE of 26.961, RMSE of 12.112 and R2 of 0.987 which is better when compared with the existing models. © 2024 IEEE.

关键词： Long short-term memory

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Sustainable Energy Generation from Speed Breakers for Smart Street Lighting Application

Sustainable Energy Generation from Speed Breakers for Smart ...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： P. Vijayakumar M. Pyingkodi S. Karuppusamy J. M. Dhyaneshvar Prasaath S. Jeysaravanan V. Manojkumar Department of Computer Applications Kongu Engineering College Perundurai Erode India Department of Computer Science and Engineering Nandha Engineering College Erode India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

This research explores the potential of integrating rack and pinion mechanisms with electromagnetic induction to generate electricity for street light control systems. By converting mechanical energy into electrical energy, this approach offers a sustainable and environmentally friendly solution. The proposed system utilizes Light Dependent Resistors (LDRs) and a Raspberry Pi microcontroller to dynamically control street lights based on ambient light conditions. This intelligent control strategy optimizes energy consumption and reduces operational costs. This research study analyzes the theoretical foundations, design considerations, and implementation methodologies of the proposed system. Experimental validation demonstrates the feasibility and effectiveness of the approach. By promoting the use of renewable energy sources and reducing reliance on conventional power grids, this innovative solution contributes to the development of sustainable and resilient urban infrastructure.

关键词： Image sensors Renewable energy sources Technological innovation Urban areas Sensor systems Real-time systems Sensors Calibration Internet of Things Intelligent sensors

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