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Human Factors engineering Simulated Analysis in Administrative, Operational and Maintenance Loops of Nuclear Reactor Control Unit Using artificial intelligence and machine learning Techniques

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SSRN 2024年

作者： Khamaj, Abdulrahman Ali, Abdulelah M. Saminathan, Rajasekaran Shanmugasundaram, M. Industrial Engineering Department College of Engineering Jazan University Jazan Saudi Arabia Mechanical Engineering Department College of Engineering Jazan University Jazan Saudi Arabia Department of Artificial Intelligence and Machine Learning BMS Institute of Technology and Management Bangalore India

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective neural network models for machine learning program are suggested for administrative, operational and maintenance loops of nuclear reactor control unit. © 2024, The Authors. All rights reserved.

关键词： machine learning

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Revelatory Insights into Parkinson’s: Hand Gestures Deciphering with Mobilenet SSD 8th

Revelatory Insights into Parkinson’s: Hand Gestures Deciphe...

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8th IFIP TC 12 International Conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Balaji, V.S. Sangeetha, K. Anand, V. Aatrey Velayutharaj, J. Abishek Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9783031736162

Hand gestures have evolved into a natural and intuitive means of engaging with technology. Parkinson’s disease is a neurological ailment that worsens with time. Sophisticated deep learning-based system for the precise identification of Parkinson’s Disorder is developed through the interpretation and analysing of hand gestures exhibited by affected individuals. Relevant features are extracted from the images, employing computer vision techniques, enabling the analysis of hand movements. A deep learning model is trained to recognize distinctive motor symptoms associated with the disorder. Object detection algorithms are employed to pinpoint specific deviations in finger and hand positions, thereby quantifying tremors, bradykinesia, and other relevant motor symptoms. The approach lies in the utilisation of a dataset comprising 40 images for each Parkinson’s hand gesture, enabling robust model training. The Mobilenet SSD algorithm for object detection to find the specific deviations in finger and hand positions. The model exhibits a high level of accuracy in distinguishing between individuals with the possibility of Parkinson’s disorder and those without, based on the subtle motor symptoms exhibited in their hand gestures. © IFIP International Federation for Information Processing 2025.

关键词： Object detection

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Precision Exercise Monitoring Through Advanced Body Language Detection Using Computer Vision 8th

Precision Exercise Monitoring Through Advanced Body Language...

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8th IFIP TC 12 International Conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Balaji, V.S. Sangeetha, K. Ganapathy, P. S. Anirudh Banu, M. Shafiya Kumar, S. Dinesh Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9783031736162

The paper aims to assist individuals in performing physical exercises with precision and safety. This model delivers real-time feedback on an individual’s workout style by utilising powerful computer vision and machine learning technology. It uses deep learning models for position estimation to recognise and track essential body features while exercising. It classifies workouts and their phases, such as "down" and "up," based on a continual monitoring of the user’s body position. Advanced computer vision algorithms are used to analyse video frames from the camera feed in order to extract critical data about the user’s form. It can recognise and classify certain workouts such as the deadlift, squat, and push-up by continually monitoring the user’s body position. The system provides useful information such as the current stage of the exercise, the number of repetitions, and the chance of correct performance. The model can recognize and detect the exercises performed by the person and it also iterates the number of times the exercises are done and it also detects the position of the body of the person. Healthcare professionals who want to promote safe and successful exercise regimens. © IFIP International Federation for Information Processing 2025.

关键词： Deep learning

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AI-Powered Mental Health Assessment using Emotion Detection for Real-Time Analysis

AI-Powered Mental Health Assessment using Emotion Detection ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Vijay, K. Raghakeerthana, R. Milinda, Renata Rachael Thusheel, S. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331512965

This paper is an AI-driven mental health assessment tailored to each patient's distinct emotional needs, addressing the global mental health crisis. Using advanced machine learning and natural language processing, it aims to deliver personalized guidance for psychological and physical wellness activities, along with tools for tracking individual progress. The papers employ exploratory case studies and knowledge discovery techniques to evaluate the effectiveness of these interventions. Data mining methods reveal hidden patterns within mental health datasets, with the potential to reshape therapeutic paradigms. Expected outcomes include enhanced accessibility to mental health resources, early intervention, personalized progress monitoring, improved quality of life, and reduced stigma. By integrating sophisticated data processing, machine learning, and empathetic user interaction, it operates by redefining mental health support. The system's architecture is designed to promote both emotional and cognitive well-being, fostering positive change in mental health care delivery. © 2025 IEEE.

关键词： Computer Vision conversational bot Convolution neural network Emotion detection machine learning natural language processing Real-Time Analysis

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Enhancing Operational Efficiency and Security in Multi-Sectioned Businesses with Edge AI through Real-Time Analytics

Enhancing Operational Efficiency and Security in Multi-Secti...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Vijay, K. Jeyanth, V. Kamalesh, S. Dheva Prasath, D. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522988

The goal of this project is to disrupt the way businesses monitor their operations by integrating IoT and machine learning to gain a deeper understanding of customer behavior and improve operational efficiency. Traditional surveillance systems create lots of data to store and analyze, but they do not provide actionable intelligence. CCTV cameras and edge devices strategically placed throughout a facility capture real-time footage, identify visitor movement patterns, and represent critical heat zones on a heat map. This data-driven process enhances resource allocation, enhances customer experience, and enables proactive improvements in operational workflow. Edge devices enable real-time anomaly detection, such as theft or staff sitting, without compromising privacy, as the device retains all information and performs all processing locally. Our next-gen solution provides businesses with an all-inclusive monitoring system that solves today's most pressing security and operational issues, ultimately delivering better business results. © 2024 IEEE.

关键词： Resource allocation

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Smartsight: Android-Based Retinal Disease Detection using Retinal Fundus Images with XAI 8

Smartsight: Android-Based Retinal Disease Detection using Re...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Bagewadi, Manisha Reshmi, B.M. Kalligud, Nagaraj Badiger, Krutika Sonagaj, Sakshi Giraddi, Sindurani Basaveshwar Engineering College Department of Artificial Intelligence and Machine Learning Bagalkot India

ISBN: (纸本)9798331505462

Diabetic retinopathy (DR) is a serious eye condition affecting diabetic patients, stemming from microvascular damage to retinal blood vessels. If not diagnosed and treated early, DR can lead to blindness. Traditional diagnostic methods involve manual examination of retinal images by ophthalmologists, which is labor-intensive and time-consuming. This highlights the need for automated, efficient, and accurate diagnostic tools. This study proposes a deep learning-based framework using ResNet50 architecture enhanced with Gradient-weighted Class Activation Mapping (Grad-CAM) for the automatic detection and classification of diabetic retinopathy from retinal images obtained via a smartphone-based fundus camera. The ResNet50 model, known for its deep residual learning capabilities, was trained on a dataset of retinal images to differentiate between healthy and DR-affected eyes. Incorporating Grad-CAM provides visual explanations by highlighting the retinal image regions most indicative of DR, thereby improving model interpretability. The proposed model demonstrated high performance, achieving 98% accuracy on the training dataset, 91% on the validation dataset, and 96% on the test dataset. These results highlight the model's robustness and potential for facilitating early DR detection, especially in resource-constrained environments with limited access to specialized ophthalmic care. © 2024 IEEE.

关键词： Diagnosis

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Determining the Underlying Factors of Chronic Liver Disease through Factor Analysis 2

Determining the Underlying Factors of Chronic Liver Disease ...

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2nd International Conference on artificial intelligence and machine learning Applications, AIMLA 2024

作者： Balaji, V.S. Mahi, A.R. Aashif, M. Manju, M. Indira Priya, P. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

Chronic liver disease is a significant health concern, necessitating a comprehensive understanding of its underlying causes. The work aims to identify the key factors contributing to chronic liver disease using Factor Analysis, a statistical technique that explores the inter-relationships among a set of variables. The dataset, comprising various biochemical parameters and demographic details, is subjected to rigorous preprocessing, including imputation and standardisation. The suitability of data is determined using KMO measure value. The factors are represented by combinations of things that include the biochemical parameters protein, aminotransferase, globulin, phosphatase and *** research provides valuable insights, potentially aiding in early diagnosis and effective treatment strategies for chronic liver disease. This research underscores the utility of Factor Analysis in medical research, particularly in understanding complex diseases like chronic liver disease. The overview of Factor Analysis would include the loadings of each factor for each variable on each factor, which indicate how much each variable contributes to each factor and further help to identify which variables are most strongly associated with each underlying factor. The communalities and specific variances of the factors are *** could potentially aid in early diagnosis and effective treatment strategies for chronic liver disease. The complex relationship among various attributes of the dataset is examined thereby providing the contribution of a vital parameter to liver disease. © 2024 IEEE.

关键词： Factor analysis

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Insight Retina: A Deep learning Approach to Diagnose Diabetic Retinopathy

Insight Retina: A Deep Learning Approach to Diagnose Diabeti...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Akshaya, V. Sashank, S. Subhash, G.M. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350359688

The greatest cause of blindness in the world and a frequent cause of diabetes melitus is Diabetic-Retinopathy (DR). To stop vision loss in afflicted people, early identification and decision making are essential. Diabetic - Retinopathy is a eye disorder that is currently being diagnosed manually by ophthalmologists. The diagnosis and recovery takes a great deal of time, effort and money, sometimes even a risk of misdiagnosis. Deep-learning has been the most highly emerging popular method that performs better in many fields, especially in the process analysing data and as well as classifying images from medical imaging. The IdX-DR and EyeArt AI are some existing systems that are legally approved and widely used in many places. Convolution neural networks and transfer learning are widely used as they are more effective. The dataset called Asia Pacific Tele-Ophthalmology Society (APTOS) is a large collection, which contains images acquired using Fundus Imaging and is available in open-source which is used extensively. The Insight Retina uses Contrast Limited Adaptive Histogram Equalization for enhancement of images and pre-trained model Res-Net for detection, thus providing improved performance and a reliable 0.893 Kappa accuracy score from the DR model. Insight Retina then generates a report for the input image providing accurate information about the stage of the Diabetic-Retinopathy. © 2024 IEEE.

关键词： Contrastive learning

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Intelligent Image Captioning with InceptionV3, LSTM, and PySpark Integration 3

Intelligent Image Captioning with InceptionV3, LSTM, and PyS...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Kurian, Thanu Mathew, Jimsha K. Syam Dev, R.S. Anand, P.K. Vignesh, A. Rakshith, B.S. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331515911

Image captioning is a challenging task in artificial intelligence that involves generating descriptive captions for images automatically. In this project, we propose a novel approach leveraging advanced technologies such as PySpark, LSTM, and InceptionV3 to develop an effective image captioning system. We harness the power of PySpark, a distributed computing framework, to efficiently process large-scale image datasets and extract high-level features from images using the InceptionV3 convolutional neural network (CNN) model. These features capture the semantic information present in the images and serve as input to the caption generation model. The caption generation model utilizes LSTM neural network as the decoder component. LSTM is well-suited for sequential data processing and is capable of generating coherent and contextually relevant captions based on the extracted image features. The InceptionV3 model acts as the encoder, extracting meaningful visual features from input images, while the LSTM decoder generates captions by decoding these features into natural language descriptions. This multimodal approach enables the model to understand and describe the content of images accurately. Through experimentation and evaluation on diverse image datasets, our proposed system demonstrates promising results in generating accurate and human-like captions for a wide range of images. By integrating PySpark, LSTM, and InceptionV3, our image captioning system achieves state-of-the-art performance, highlighting the effectiveness of leveraging advanced technologies in solving complex AI tasks. © 2025 IEEE.

关键词： Convolutional neural networks

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Background initialization in video data using singular value decomposition and robust principal component analysis

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International Journal of Computers and Applications 2023年第9期45卷 600-609页

作者： Gowda, Vishruth B. Gopalakrishna, M.T. Megha, J. Mohankumar, Shilpa Department of Information Science and Engineering SJB Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning SJB Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Bengaluru India

Background initialization is used in video processing applications to extract a scene without the foreground scene. In recent times, the issue of background initialization has gained researchers’ attention in different fields such as video surveillance, video segmentation, computational photography, and so on. The initialization of the background is affected due to different complex dissimilarities such as shadow, intermittent movement, illumination, camera jitter, and clutter. To overcome the aforementioned issues, this paper proposes a decomposition using the combination of the Singular Value Decomposition (SVD) and Robust Principal Component Analysis (RPCA) for Singular Spectrum Analysis (SSA) to perform an effective background initialization. The incorporation of RPCA in SVD is used to overcome the issues related to non-Gaussian noise and it also uses an effective structural knowledge of the video input i.e. sparse and low rank matrix which improves the Peak-Signal-to-Noise-Ratio (PSNR) of the background image. The SBI dataset was used to analyze the performances of SSA-SVDRPCA. The SSA-SVDRPCA is analyzed using MultiScale Structural Similarity Index (MSSSIM), Average gray-level error (AGE), Percentage of clustered error pixels (pCEPS), Percentage of error pixels (pEPs), and PSNR. The existing approaches such as Background Initialization Singular Spectrum Analysis (BISSA) and Quaternion-based Dynamic Mode Decomposition (Q-DMD) are used to compare with the SSA-SVDRPCA method. The PSNR of the SSA-SVDRPCA for Board class is 30.39 dB which is higher than the BISSA and Q-DMD. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Singular value decomposition

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