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Scalable and transparent mental health support via XAI-LLM

International Journal of Information Technology 2025年 1-9页

作者： Nimala, K. Shieh, Chin-Shiuh Nareshkumar, R. Murugan, V. Senthil Department of Networking and Communications School of Computing College of Engineering and Technology (CET) SRM Institute of Science and Technology Chennai India Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Professor Department of Computer Engineering CMR University Bengaluru India

The accurate evaluation and efficient treatment of mental health problems, such as depression, continue to be difficult due to the complicated underlying causes and diverse signs and symptoms. In this study, the integration of Large Language Models (LLMs) and Explainable AI (XAI) offers a transformative approach to mental health care, surpassing traditional systems in conversational relevance, empathy, and risk detection. The proposed hybrid framework leverages LLMs’ contextual understanding and XAI’s transparency to deliver inclusive, personalized, and trustworthy mental health interventions. BioBERT (bio-medical bidirectional encoder representations from transformers) is being used as LLM which produces vector representations for questions and answers, encapsulating the semantic content and context of biomedical language. These vector representations are then saved for future use, guaranteeing efficient retrieval during the query phase. It is followed by utilization of bi-directional gated recurrent unit (Bi-GRU) to analyze vector representations in both forward and backward directions, therefore capturing temporal connections essential for comprehending emotions and contextual refinements.

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A Comprehensive Review on Detection of Dacrocytes from Blood Smear Images using ML Algorithms

A Comprehensive Review on Detection of Dacrocytes from Blood...

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International Conference on Recent Trends in Advance Computing (ICRTAC)

作者： D. Kavitha B. Padmavathi Shakthi Priya S Sanjana N Sidesh S Department of Computer Science and Engineering Assistant Professor of Easwari Engineering College Chennai India Department of Computer Science and Engineering Student of Easwari Engineering College Chennai India

A nation’s capacity to develop is greatly impacted by the prevalence of anemia, a major public health issue. Despite this, it is a sickness that many developing countries choose to overlook. A million people die each year from iron deficiency, which is the underlying cause of anemia in at least half of those cases. Approximately 75% of these fatalities take place in Southeast Asia and Africa. One of the leading causes of death on a global scale is iron deficiency anemia (IDA). The use of machine learning techniques for the prediction of anemia was the focus of this work. Light Gradient Boosting Machines, Logistic Regression, Gaussian Naive Bayes, Support Vector Machines, and K-Nearest Neighbors were the five machine learning models studied in the study. The voting classifier method is used to merge various models in order to improve the accuracy of predictions. The need of accurate sickness forecasting by medical practitioners is highlighted by the study. Being able to predict when anemia may occur is crucial for effective prevention and treatment of the condition. This study proves that specific machine learning algorithms can improve disease prevention and treatment by predicting the occurrence of anemia. Using advanced algorithms and data processing techniques can help doctors make better predictions and decisions, which in turn improves patient outcomes. According to the results, the vote classifier has a 99.95% accuracy rate.

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Study on Limited Data Problem - Semantic Segmentation of Retinal Fundus Images

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Procedia computer science 2024年 233卷 782-792页

作者： Uday A Nuli Shrinivas D Desai Gururaj N Bhadri Research Scholar School of Computer Science & Engineering KLE Technological University Hubli 580031 D.K.T.E. Society's Textile and Engineering Institute Ichalkaranji INDIA Professor School of Computer Science & Engineering KLE Technological University Hubli 580031 Professor Department of Mathematics KLE Technological University Hubli 580031

Medical images captured through various imaging modalities are key to the diagnosis and treatment of diseases, and these images serve as a major input to AI driven models but due to the scarcity of medical experts and their quality research time, the quantum of data required for AI models is limited. Moreover, medical image analysis is a laborious and error prone task. Early detection of retinal diseases by means of AI based semantic segmentation model has been boon to the diagnostic system. However, performing semantic segmentation with limited data of retinal fundus image is quite challenging. The present study is primarily focused on investigation of effectiveness of data augmentation with gamma corrected images to alleviate the problem of limited annotated data in deep learning models. The most widely accepted U-Net model in medical domain for image segmentation is trained employing retinal fundus image dataset augmented with gamma corrected images on all image channels and then tested against major publicly available datasets. The proposed method has outperformed other complex contemporary methods in terms of sensitivity and has also shown better generalizability across datasets from different institutions.

关键词： Limited Annotated Data Segmentation Medical Imaging Training Retinal Fundus Image

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Automatic Creation of Quality Images from Text using Multiple Generative Adversial Network

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Procedia computer science 2023年 230卷 955-963页

作者： M.S. Geetha Devasana V. Krishna Kumar R. Selvaraj T. Satheesh Assistant Professor Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore-641022 India Assistant Professor Department of Computer Science and Engineering Dr.N.G.P Institute of Technology Coimbatore-641048 India Assistant Professor Department of Artificial Intelligence and Data Science Dr.N.G.P Institute of Technology Coimbatore-641048 India

Automatic creation of realistic images is a tedious process even though the state-of-the-art AI/ML algorithms are employed. There is a lot of demand for such automatic image generators that could create high quality images. Many have this problem of visualizing things from the explanations they hear about. Thus, the text to image generation problem is necessary because it has significant applications in CAD, art generation and many more. This is a challenging task since the image should be realistic and consistent with the text. One of the most common uses of modern conditional generative models is the generation of visuals from natural language. Viewing an image makes us easily understand what that image is, rather than hearing someone describing the image. To bridge the semantic gap between text and image, Generative Adversarial Network (GAN) systems are used to achieve high accuracy. The proposed system helps in generation of superior quality images that are meaningfully consistent with the text.

关键词： Machine Learning Generative Adversarial Network (GAN) Text to Image and Natural Language Processing

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Cloud-Driven Wearables using Naive Bayes Algorithm and Advanced Sensors in Smart Fabrics

Cloud-Driven Wearables using Naive Bayes Algorithm and Advan...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： M.D. Anto Praveena Nukala Sujata Gupta V. Bini Marin J Gnana Jeslin R. Meenakshi M. Muthulekshmi Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Assistant Professor Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Assistant Professor Faculty of Management Studies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Professor Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Adjunct Professor Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

In the realm of fashion technology, the introduction of smart textiles and wearable sensors may be attributed to the fast progression of technology and the rising need for customized, interactive, and intelligent apparel. This study investigates the interface of electronics and textiles to develop cutting-edge, multifunctional, and flexible apparel. This research digs into the fascinating world of smart textiles. It investigates the many different kinds of sensors, such as temperature sensors, heart rate monitors, motion detectors, and chemical sensors that may be easily incorporated into the fabric. These sensors make a wide variety of applications possible, such as improved user experiences, monitoring of health conditions, and data collecting on the surrounding environment. In addition, to assure the practicability and usage of smart textiles, it addresses the problems and possibilities connected with various power sources, connection choices, and data processing units. The purpose of this study is to contribute to the continuous development of intelligent clothing, which has the potential to revolutionize the fashion industry by producing clothes that not only represent an individual's sense of style but also give useful insights, functionality, and connectedness to the digital world.

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Emotion Recognition in Real Time for Personalized Healthcare Using Digital Twin Approach

Emotion Recognition in Real Time for Personalized Healthcare...

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International Conference on Advances in Information Technology (ICAIT)

作者： B. Karthikeyani D. Yuvaraj T. Parimalam P. Parthasarathi Nandha Arts and Science College & Assistant Professor @ Shree Venkateshwara Arts and Science (Co Education) CollegeGobi Department of Computer Science and Design Kongu Engineering College Perundurai Department of Computer Science Nandha Arts and Science College Perundurai Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Facial Emotional Recognition (FER) in healthcare can help to monitoring the patient by analyzing the emotional state of the patient through various emotional states. In medical industry it can help to trace the emotional state changes and diagnosis the pattern which are mapped respective to the patient's history. Digital twin technology is transforming healthcare systems and improving patient care, clinical operations, predictive analytics, and training and simulation by integrating virtual simulations and emerging analytic mechanism through ML and DL techniques. By allocating resources, monitoring workflow, and providing individualized patient care, these algorithms reduce healthcare operations and assist in determining an individual's risk. Since FER has lot of technical challenges like illumination, obstruction issues also high implementation cost. To solve these issues, the FER system will embed with the digital twin technology to provide a better result from real time sensing cameras also analyzing the result and guiding for a better treatment of an individual. We propose a system of end to end framework which produces best results in minimum amount of time with maximum accuracy. This real time implementation will help all the healthcare centers/industry to study an individual's health status as well as early prediction of diseases with effective treatment steps.

关键词： Training Emotion recognition Face recognition Medical services Speech recognition Cameras Real-time systems Digital twins Medical diagnostic imaging Monitoring

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Reading Pixels Parallel and Processing Pixels Parallel (RPP-PPP): An Approach to Detecting and Tracking Objects in Static and Dynamic camera

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Procedia computer science 2023年 218卷 1475-1484页

作者： K. Sivakumar P. Shanmugapriya Research Scholar Dept. of Computer Scienc and Engineering SCSVMV Kanchipuram Tamilnadu India Associate Professor Department of Computer Science and Engineering SCSVMV Kanchipuram Tamilnadu India

Object detection and Tracking are important tasks in both moving and static camera applications like unmanned vehicles, human-assisted vehicles, product parts inspection, video surveillance, and many more. These applications are required to detect moving objects and track static and dynamic cameras. It also needed to process these tasks with rate and lesser time. Many computer vision applications are always used to read the image/frame pixels one by one from row by column or column by row pattern. Reading pixels one by one from the image/frame took more time in real-time applications. In this proposed method we used to read the pixels from the entire image/frame in parallel using Parallel. For utilities and process these pixels in parallel. In the proposed work we used the background subtraction model for object detection and tracking and noted the difference in both sequential and parallel processes and compared the output with the existing background subtraction model. In this work analyzed the time taken to read the entire image using various tools and languages. The work was implemented using C#.NET parallel. For other thread-based functions to minimize the pixel reading time and processing time for object detection and tracking. Finally, the proposed work gave a considerable performance and compared with the existing system.

关键词： Background subtraction parallel Computing Thread Task Pixels processing static dynamic camera

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Improving Knowledge Retention in a Systems Programming Course Through Confirmatory Multiple-Choice Questions

教育研究与发展期刊

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教育研究与发展期刊 2022年第1期18卷 107-127页

作者：賴建宏鍾斌賢夏延德林聰武 Assistant Professor Department of Information and Computer Engineering Chung Yuan Christian University Professor Department of Information and Computer Engineering Chung Yuan Christian University Professor Department of Computer Science and Information Management Soochow University

Multiple-choice questions (MCQs) are often used in assessments. Although MCQs can be designed to test higher-order cognitive skills such as analysis, synthesis, and evaluation, they are often used to measure recall. This may encourage students to only learn material superficially. To resolve this problem, several variations of MCQs have been proposed to improve learning. We proposed a modified type of MCQ called a ＂confirmatory MCQ＂ (CMCQ). Optional online CMCQ tests were more effective than MCQs and confidence-based marking tests in helping students study material from a systems programming course because they prompted students to thoroughly evaluate each answer.

关键词： ultiple choice knowledge retention assessment evaluation methodology learning strategy

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Identification of Congenital Heart Defects in Ultrasound Images using U-Net Segmentation 1

Identification of Congenital Heart Defects in Ultrasound Ima...

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

作者： Pandey, Saroj Kumar Diwan, Ritesh Rose, Anthony Singh, Kamred Udham Singh, Teekam Somasekar, J. Gla University Department of Computer Engineering & Apllications UP Mathura India Department of Ele. & Tele. Raipur Institute of Technology Raipur India Pune India School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Professor of Cse Department Bangalore India

ISBN: (纸本)9798350330915

In this study, the use of a U-Net segmentation model is measured for the accurate detection of congenital heart abnormalities in newborn ultrasound pictures. The technique includes U-Net model training, sophisticated picture preprocessing, as well as exhaustive dataset gathering. As a result of the model's considerable accuracy, sensitivity, and specificity, it has the potential to become a crucial diagnostic tool. A thorough analysis found both advantages, such as reliable performance, and disadvantages, which include the requirement for a larger and more varied dataset. Enlarging the dataset, addressing equipment variability, real-time clinical application, multidisciplinary cooperation, and ethical issues, including model improvement are among the key recommendations. In order to promote newborn healthcare, future work will require ongoing model development, real-time clinical integration, and ethical adherence. The work advances the development of accurate diagnostics in newborn care. © 2023 IEEE.

关键词： Congenital heart defects diagnostic accuracy medical image analysis neonatal ultrasound pediatric cardiology U-Net segmentation

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An Efficient Algorithm for Secure Key Management in Cloud Environment

An Efficient Algorithm for Secure Key Management in Cloud En...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： J. Jospin Jeya S. Raja Ratna G. Ganga Devi M. Priya C. Sivasankar Department of Computer Science and Engineering SRM Institute of Science and Technology Professor/CSE Saveetha School of Engineering SIMATS Chennai

Cloud infrastructure is efficient and scalable in all aspects; hence an enhanced method of safety measures is needed for secured data transfer. Security solution for cloud centric PC’s was reliable in providing the on-demand cloud services. The common way to protect file before sending the file is encrypting the file. So safe key generation and allocation to the client for file decryption is a big task. The significant method we proposed is to receive the secret key secretly by the allowed user. We proposed wellorganized algorithm to share out the secret key in cloud computing environment. In this paper the safety method used depends on two things. One is improving the security by using secure socket shell channel and the second one is sharing the secret key to the allocated user using well organized key distribution algorithm.

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