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2023 International Symposium on Devices, Circuits and Systems, ISDCS 2023

作者： Kano, Kaede Johguchi, Koh Graduare School of Science and Technology Shinshu University Electrical and Computer Engineering Division Department of Engineering Nagano Japan Shinshu University Electrical and Computer Engineering Division Faculty of Engineering Nagano Japan

ISBN: (纸本)9798350315042

A prototype of wearable vital sensing system has been developed with Fiber-Bragg-Gratings senor and 180-nm CMOS image sensor process chip. The system can acquire vital signs by capturing reflecting light with a photodiode. The designed test chip contains two types analog-front-end circuit. The feasibility for a vital sensing was verified with measurements with both of current-voltage conversion type and integral analog-to-digital convertor type. © 2023 IEEE.

关键词： Photodiodes

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Social Media Analytics for Disaster Management using BERT Model 1

Social Media Analytics for Disaster Management using BERT Mo...

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Varghese, Sherin R Juliet, Sujitha Anitha, J. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359756

Disasters, both natural and human-induced, continue to affect the global population in multiple ways. In the past decade, social media has evolved and has seen a rapid, exponential increase in the number of users and their data. The manner in which communities anticipate, react to, and recover from natural and man-made disasters have been entirely shifted by rapid information transfer, public sentiment analysis, and real-time communication. There is now a high demand for social media based disaster management systems. This research project introduces a sentiment analysis framework tailored to Twitter data, incorporating BERT as a core component. Additionally, we employ a range of NLP methodologies for evaluation and comparative analysis. The results indicate that the employment of BERT yields a noteworthy enhancement in accuracy, surpassing alternative models by 5% to 7%. In summary, our research accentuates the effectiveness of employing BERT-based sentiment analysis to optimize disaster management strategies. © 2023 IEEE.

关键词： Sentiment analysis

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Disease Classification in X-Ray Images using Convolutional Neural Networks 2

Disease Classification in X-Ray Images using Convolutional N...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Allen Matthew, T.J. Shanish Roshan, K. Paul Jerome Immanuel, K. Mithun, S. Jemima Jebaseeli, T. Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350340235

Convolutional Neural Networks (CNNs) have emerged as a revolutionary tool in the domain of disease categorization, fundamentally changing the landscape of medical imaging diagnostics. This study deeply investigates the application of CNNs to automatically and with high precision classify diseases using medical imagery, including X-rays, MRIs, and CT scans. The utilization of CNNs in this research takes advantage of their proficiency in recognizing intricate patterns and features within medical images, thereby simplifying the disease diagnosis process. The incorporation of CNNs in disease classification represents a promising advancement in the healthcare sector. Through extensive training on extensive datasets, these neural networks can discern intricate patterns and subtle irregularities in medical images, resulting in improved precision and efficiency in disease diagnosis. By automating the categorization process, healthcare providers can minimize the likelihood of human errors and enhance the speed of diagnosis, ultimately leading to improved patient outcomes. This study emphasizes the potential of CNNs to reshape disease classification, offering a glimpse into a healthcare future where advanced technology plays a pivotal role in ensuring accurate and timely disease identification. It also underscores the ongoing necessity for research and development in the field of medical image analysis, as CNNs continue to reshape the healthcare landscape with their ability to revolutionize disease diagnostics. © 2023 IEEE.

关键词： Automated diagnosis Deep learning Medical imaging Neural Networks Xray

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Investigation of Handwritten Image-To-Speech Using Deep Learning

Investigation of Handwritten Image-To-Speech Using Deep Lear...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Manju, S. Anitha, J. Sujitha Juliet, D. Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

In the realm of accessibility technology, this paper introduces a pioneering method for converting handwritten images to speech. The work primarily focuses on recognizing handwritten text and subsequently converting it into audible speech. The work comprises two fundamental phases: initially, a deep learning algorithm is employed to recognize and extract text from handwritten images precisely. Following this, the recognized text is converted into speech using the pyttsx3 library which offers flexibility in voice selection, speech rate, and other parameters, making it highly customizable for different applications. To ascertain the most effective algorithm for the text recognition phase, a comparative analysis is conducted among the top three algorithms-Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models - based on their accuracy in recognizing handwritten text. Experimental results indicate that CNNs surpass the other two algorithms, achieving an accuracy of 92%. This work offers a pragmatic approach to converting handwritten content into speech, with potential applications in aiding the visually impaired and enhancing accessibility to written information. © 2024 IEEE.

关键词： Long short-term memory

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Machine Learning for Detecting Subtle Signs of EyeDisease 4

Machine Learning for Detecting Subtle Signs of EyeDisease

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Dafny Neola, J. Ebenezer, V. Edwin, E.Bijolin Kirubakaran, S.Stewart Thanka, M.Roshni Yona Davincy, R. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Coimbatore India

ISBN: (纸本)9798350385793

Eye disorders can have serious repercussions, such as blindness or visual impairment, if they are not identified or addressed, Treatment that works and preventing permanent harm depend on early diagnosis and prediction. Using cutting-edge clinical observations, this method looks for diseases such diabetic retinopathy, cataracts, myopia, glaucoma, age-related eye illnesses, and hypertension-related eye disorders. This study presents an innovative method for early detection of eye health disorders: The Eye Health Station. It uses machine learning algorithm, by leveraging EfficientNet's superior performance in image classification tasks, our system analyzes ocular images, such as retinal scans, to identify potential indicators of eye disorders at an early stage., The proposed system seeks to discover possible markers of eye problems by evaluating several factors taken from ocular pictures and patient data, including retinal scans and medical history. The system is able to learn from various datasets and gradually increase its accuracy since machine learning techniques are included into the system. © 2024 IEEE.

关键词： Ophthalmology

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Multi-Criteria-based Graph Neural Networks for a Medical Emergency Response System 7

Multi-Criteria-based Graph Neural Networks for a Medical Eme...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Prakash, J. Princess, P. Joyce Beryl Karunya Institute of Technology And Science Division of Computer Science And Engineering Coimbatore India Karunya Institute of Technology And Science Division of Artificial Intelligence And Machine Learning Coimbatore India

ISBN: (纸本)9798350359299

The ability to get an immediate response from medical emergencies quickly and more effectively is essential for saving both lives and ensuring people's well-being. In the Traditional medical emergency response systems often struggle to correctly assess the seriousness and urgency of emergency response, leading to ineffective resource allocation and prolonged response times. This research work suggests a unique method for medical emergency response systems based on a multi-criteria-based graph neural network to resolve such problems in recent times. The process of choosing the best hospital based on a variety of conditions is optimized by the graph neural network (GNN), which makes use of the graph structure with hospital nodes and accident location nodes. The GNN will examine several conditions to choose the best hospital after identifying the nearby hospitals. It evaluates whether each nearby hospital has the medical resources required to meet the needs of the patient, including available beds, emergency gear, and doctor availability to get a quick medical treatment. To promote quick transportation, the GNN also confirms the availability of ambulances connected to each nearby hospital and alerts the hospital management to arrange needs for patients. The above approach improves the effectiveness and efficiency of medical emergency response systems, guaranteeing that injured persons receive immediate and appropriate medical treatment. © 2024 IEEE.

关键词： Hospitals

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Time Complexity of Population-Based Metaheuristics

Mendel

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Mendel 2023年第2期29卷 255-260页

作者： Omran, Mahamed G. H. Engelbrecht, Andries Computer Science Department Gulf University for Science & Technology Kuwait Department of Industrial Engineering and Computer Science Division Stellenbosch University South Africa Centre for Applied Mathematics and Bioinformatics Gulf University for Science & Technology Kuwait

This paper is a brief guide aimed at evaluating the time complexity of metaheuristic algorithms both mathematically and empirically. Starting with the mathematical foundational principles of time complexity analysis, key notations and fundamen-tal concepts necessary for computing the time efficiency of a metaheuristic are introduced. The paper then applies these principles on three well-known meta-heuristics, i.e. differential evolution, harmony search and the firefly algorithm. A procedure for the empirical analysis of metaheuristics’ time efficiency is then presented. The procedure is then used to empirically analyze the computational cost of the three aforementioned metaheuristics. The pros and cons of the two approaches, i.e. mathematical and empirical analysis, are discussed. © 2023, Brno University of technology. All rights reserved.

关键词： Optimization

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Pneumonia Disease Detection Using Hyperparameter Tuned Convolutional Neural Network from Chest X-Ray Images 10

Pneumonia Disease Detection Using Hyperparameter Tuned Convo...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Jascar Benish, P. Manoranjitham, R. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Artificial Intelligence and Machine Learning Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

Pneumonia is a common and possibly fatal respiratory illness, particularly affecting children, and is now one of the most destructive and fatal respiratory conditions. By obtaining a chest X-ray, the proposed model assists in determining whether or not an individual is suffering from pneumonia. This model seeks to deliver accurate and timely results. The proposed approach uses Convolutional Neural Network with random search algorithm to achieve higher accuracy, reaching a rate of 97%. Compared to current models, this proposed model demonstrates a significant improvement in prediction accuracy. This model has the potential for wider application in the medical domain and could be developed into a web application or program in the future. Its capacity to raise the accuracy of medical treatments, lessen the strain on healthcare systems, and aid in the battle against antibiotic resistance makes it an invaluable tool in the continuous quest for better respiratory care. © 2024 IEEE.

关键词： Convolutional neural networks

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CNN Based Model for Underwater Image Classification and Enhancement

CNN Based Model for Underwater Image Classification and Enha...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Jamandlamudi, Sahit Isravel, Deva Priya Dhas, Julia Punitha Malar Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Coimbatore India Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350371567

The challenge of enhancing underwater images is difficult because of several issues, including color distortion, inadequate illumination, and dispersion from suspended particles in the water. Conventional picture enhancing methods frequently find it difficult to resolve these problems. Thanks to its ability to learn intricate representations and pick up on minute features, deep learning has become a potent tool for picture improvement tasks in recent years. In this work, we suggest a novel deep learning-based method for improving underwater images. To automatically learn and improve the visual quality of underwater scenes, we use a convolutional neural network architecture that is specifically made to handle underwater photos. A sizable dataset of underwater photos is used to train the network, with an emphasis on contrast improvement and color correction. We compare our method with many state-of-the-art techniques and undertake extensive tests on a benchmark dataset to evaluate our approach. The outcomes show that our strategy performs much better than current approaches both in terms of subjective visual quality and objective criteria. Our technique improves visibility, minimizes noise, and successfully restores colors in underwater photos. By presenting a deep learning-based method that enhances the visual quality of underwater photographs, this work advances the field of underwater image processing and aids in marine research, underwater exploration, and surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT-Based Plant Health Monitor Using NodeMCU and ESP8266 5

IoT-Based Plant Health Monitor Using NodeMCU and ESP8266

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Tandel, Rajat Ebenezer, V. Santhiya, P. Edwin, Bijolin Stewart Kirubakaran, S. Thanka, Roshni Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Data Sciences and Cyber Security Coimbatore India

ISBN: (纸本)9798331518097

Plant Monitor Using NodeMCU(ESP8266)is a technology that is advanced;it uses IoT-Internet of Things to measure and monitor different plant phenomena. This system, which is based on sensors that collect data on different factors such as soil type, temperature, humidity, and other vital metrics vital for plant growth, is also integrated. Bringing IoT into the picture, the system can remotely sense the metrics and having control over other factors which include watering, shading, and even security provision for the plants to make it thrive. Besides that, it will also send notifications to users, hence allowing them to engage in the monitoring process through mobile applications with support from their smartphones. The Smart Plant Monitoring System though creates an impact on the efficiency of plant care also impacts on water conservation, automation of plant care process and better agricultural practices. © 2024 IEEE.

关键词： Efficiency

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