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Prediction and analysis of Covid-19 using the Deep Learning Models

Research on Biomedical engineering 2024年第1期40卷 183-197页

作者： Indira, V. Geetha, R. Umarani, Srikanth Priyadarshini, D. Annal Department of Computer Science and Engineering S.A. Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India

Purpose: The difficulty of diagnosing several lung disorders, including atelectasis, cardiomegaly, lung cancer, and COVID-19, is a challenging problem and needs to be addressed. These conditions exhibit some symptoms and demand advanced medical imaging process, thorough clinical assessments, and innovative procedures for accurate diagnosis. The shortage of qualified radiologists further makes the problem more complex to deal with. COVID-19 in particular has resulted in a remarkable number of fatalities around the world. Children below the age of 5 and individuals over 65 are more likely to be affected by lung disorders. It is very hard to diagnose and manage COVID-19 absolutely, but it can be identified earlier by employing computer-aided diagnosis (CAD) technologies to make timely diagnosis. Currently, radiologists adopt technologies, which are driven by artificial intelligence. By using them, medical imaging data, such as chest X-rays and CT scans, can be investigated to identify patterns to diagnose the severity of the virus. This expedites the diagnostic process and enhances accuracy, facilitating more timely and precise medical interventions. The efficiency of artificial intelligence in processing large datasets can directly help healthcare professionals in making diagnosis quicker and more accurate. The objective of the work in this paper is to design and implement deep learning model classifiers, which will effectively categorize the patterns found in the X-rays and CT scans. Methods: Three techniques for categorization are exploited to propose an entirely new hybrid convolutional neural network (CNN) model in this context. MRI and CT image categorization in the first classification method employ Fully Connected (FC) layers. The weights are calculated and tuned for training the algorithm over multiple periods to deliver the maximum precision for classification. The most accurate MRI and CT image characteristics are studied, and deep learning model classifiers

关键词： COVID-19

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Analysis of Watermarked Video Optimization and Training Based on Classification Using Deep Learning Techniques

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SN computer science 2024年第2期5卷 220页

作者： Muthulakshmi, K. Valarmathi, K. Department of Information Technology Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India

The exchange of multimedia data is secured using watermarking. Typically, embedding a watermark involves optimising scheme parameters, frequently by using meta-heuristic optimization methods. Despite the fact that DNN have achieved significant advancements in field of multimedia representation, training neural methods takes a lot of time and data. This research propose novel method in analysing watermarked video optimization and training based on DL. Here input is collected as watermarked input video and optimize and train the watermarked input video data with enhanced security using swarm optimization-based clustering fuzzy greedy trust analysis. then the optimized video has been trained using variational temporal-based u-net convolutional encoder neural networks (VTU-net ENN). Experimental analysis has been carried out in terms of accuracy, precision, recall, F-1 score, RMSE and NSE for various watermarked dataset. Accuracy attained by proposed technique is 92%, precision attained by proposed technique is 75%, recall attained is 62%, F-1 score attained 55%, RMSE obtained is 53%, NSE attained is 48% by proposed technique. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Deep learning Optimization and training Securing multimedia Watermarked video

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Human Stress Detection in and Through Sleep Patterns Using Machine Learning Algorithms

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Journal of The Institution of Engineers (India): Series B 2024年第6期105卷 1691-1713页

作者： Geetha, R. Gunanandhini, S. Srikanth, G. Umarani Sujatha, V. Department of Computer Science and Engineering S.A. Engineering College Chennai600077 India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Master of Computer Applications S.A. Engineering College Chennai600077 India

Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, and problem-solving abilities. As a result, stress detection and modeling have become important areas of study in both psychology and computer science. This study links the fields of psychology and machine learning to deal with the urgent requirement of accurate stress detection methodologies and highlights sleep patterns as a key indicator for stress detection, discussing a novel approach to understand and determine stress levels. Psychologists use affective states to measure stress, which refers to a sense of feeling an underlying emotional state. However, most stress classification work has been limited to user-dependent models, which new users cannot use without additional training. This can be a significant time burden for new users trying to predict their affective states. Therefore, it is critical to address basic mental health issues in children and adults to prevent them from developing more complex problems on account of undergoing stress. The medical field processes vast amounts of medical data;the machine learning algorithms sift through patterns that might escape the human eye. The machine learning algorithms act as detectives, able to spot correlations and bring out a sense of complex information. The machine learning algorithms reveal fine correlations and patterns, aiding in more precise and prompt diagnoses particularly to focus fundamental mental health issues in individuals of all ages. This research work deploys an enhanced Multilayer Perceptron (MLP), exhibiting an extensive feature analysis for processing medical datasets, resulting in improved effectiveness in predicting stress levels. This helps us to diagnose issues more accurately and swiftly which improves the patient outcomes. The proposed and enhanced MLP model undergoes stri

关键词： Decision trees

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Autonomous Vehicle Lane Following Network based on Multimodal Sensor Fusion and LiDAR Reconstruction in Extreme Lighting and Snowing Environments 4

Autonomous Vehicle Lane Following Network based on Multimoda...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Padmapriya, J. Meera, V. Murugavalli, S. Karthikeyan, L. Panimalar Engineering College Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai City Campus Chennai India Panimalar Engineering College Department of Mechanical Engineering Chennai India

ISBN: (数字)9798350387063

ISBN: (纸本)9798350387063

The self-driving car industry is gaining attention for its role in motion planning technology. Deep learning approaches have been implemented to plan autonomous vehicles' motion, but their effectiveness depends on the data used in training. Despite advancements, fully autonomous vehicles still face limitations, such as lighting-related issues affecting camera image accuracy, and LiDARs are negatively impacted by challenging weather circumstances like snow in varied snowfall conditions, thus becoming imperative to precisely control the motion of the Autonomous Vehicle. To address this, an architecture based on deep-learning networks is proposed for lane-following on roads, consisting of LiDAR De-snowing, Multi-modal Sensor Fusion, VGG16, and gated recurrent units (GRUs). The Point Reconstruction Network (PR-Net) rebuilds points from neighbors, while the Reconstruction Difficulty Network forecasts reconstruction difficulty. Radar measurements and de-snowed LiDAR point clouds are projected onto the front-view image plane, fed into VGG16 and GRU output layers, and processed into two fully connected layers for steering and speed parameters estimation. © 2024 IEEE.

关键词： Snow

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Transforming Fetal Health Monitoring: Advanced Machine Learning Innovations for Accurate Prognosis 7

Transforming Fetal Health Monitoring: Advanced Machine Learn...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Nancy Jenifer, A. Jabasheela, L. Subramani, Kavitha Vinmathi, M.S. Sathiya, V. Maheswari, M. Panimalar Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798331527822

Early detection of pregnancy-related illnesses, such as congenital heart defects, neural tube defects, Down syndrome, and Inborn Errors of Metabolism (IEM), is vital for ensuring the health of both the fetus and the expectant mother. Leveraging advanced ML techniques-specifically Decision Trees, KNN, and Random Forest-can significantly enhance the accuracy of predicting these conditions. Decision Trees provide a transparent, interpretable model that visualizes decision-making through a series of binary splits, enabling healthcare professionals to understand the rationale behind predictions. In contrast, KNN operates on the principle of proximity, classifying new data points based on the 'k' nearest neighbors in a multidimensional space, making it effective in scenarios with complex, non-linear relationships. Meanwhile, Random Forest improves predictive performance by aggregating the outputs of multiple Decision Trees, thus mitigating overfitting and enhancing reliability. This ensemble approach draws on the diversity of trees, each trained on random subsets of data, allowing it to capture intricate interactions among variables. Together, these ML techniques utilize extensive historical data to autonomously predict pregnancy-related conditions, forming a sophisticated decision support system that complements traditional screening methods. By integrating these advanced algorithms, healthcare providers can achieve more accurate early diagnoses, ultimately improving maternal and fetal health outcomes and paving the way for targeted interventions. © 2024 IEEE.

关键词： Fetal monitoring

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Wireless Sensor Network-Driven IoT Solution for Advanced Borewell Rescue Robotics 4

Wireless Sensor Network-Driven IoT Solution for Advanced Bor...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Meera, V. Mercia, Sharon Nesa PadmaPriya, J. Murugavalli, S. Panimalar Engineering College Artificial Intelligence And Data Science Chennai City Campus Chennai India Panimalar Engineering College Computer Science Engineering Chennai City Campus Chennai India Panimalar Engineering College Department of Computer Science Engineering Chennai India Panimalar Engineering College Department of Computer Science Engineering Chennai City Campus Chennai India

ISBN: (纸本)9798350387063

The fact that deaths from bore wells persist in India is highly alarming, particularly when young people are involved. Since 2009, there have been more than 40 documented child deaths, and the National Disaster Response Force (NDRF) reported a worrisome 70% failure rate for rescue efforts. The solution must be found quickly and effectively. In response to this humanitarian problem, this paper has proposed a novel design for a robotics device. This proposed system is a creative illustration of how human interaction and technology may be used in tandem to swiftly evaluate the victims and guarantee their safe release. The main element of this solution is a skillfully designed robotic arm that smoothly integrates Internet of Things (IoT) devices (Rasp Pi or Arduino) and is powered by high-precision servo motors. Among the many features it offers are an advanced APR (Auto Playback Recorder) system, an infrared-based CCTV camera, a strong LED lighting system, advanced sensors (Passive Infrared, Ultrasonic Distance Measurement, Heartbeat, Temperature, and Gas), and a dual-functional mask. Artificial intelligence (AI) is another tactic that will be used in the rescue operation. This paper discusses the pressing need for safe, efficient bore well rescues and offers a comprehensive analysis of the methodology. This project uses cutting edge technology and human collaboration to stop more catastrophes and increase the speed and effectiveness of life-saving procedures in emergency situations. In an effort to contribute to the creation of a more secure and safe future, this research is presented as a critical first step in lowering the death toll related to Indian bore wells. © 2024 IEEE.

关键词： Wireless sensor networks

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Comprehensive Evaluation of Federated Learning Based Models for Disease Detection in Healthcare

Comprehensive Evaluation of Federated Learning Based Models ...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Jayalakshmi, R. Tamilvizhi, T. Ramya, P. Department of Computer Science and Engineering Panimalar Engineering College Chennai India

ISBN: (纸本)9798331533137

In healthcare, the primary objective is to improve the individuals' well-being by addressing a range of health issues through accurate disease detection. Central to this process is Federated Learning (FL), a novel Machine Learning (ML) technique which aids model training across distributed devices, ensuring data privacy. FL, in conjunction with Artificial Learning (AI) algorithms, enhances disease detection, treatment, and management practices, ultimately improving patient outcomes and healthcare services. This study analyzes 50 research papers on FL and AI-based healthcare disease detection to assess existing models comprehensively. Through meticulous literature review and gap analysis, the review aims to provide insights and recommendations for robust FL-based solutions. Exploring FL's potential, challenges, and implications in healthcare, the review aims to stimulate the development of AI-assisted disease detection systems. This work involves systematic paper selection, categorization, and in-depth review, followed by chronological trend analysis, dataset review, and performance metric evaluation. The study also identifies common limitations in healthcare disease detection using AI algorithms, underlining the need for efficient FL-based approaches. ©2024 IEEE.

关键词： Federated learning

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IntelliCrop: Optimizing Crop Production with Smart Agriculture 10

IntelliCrop: Optimizing Crop Production with Smart Agricultu...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Devi, Kalpana Sri, Kavya Arundhathi Arunadevi Jennifer Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350353068

Although agriculture is the backbone of the Indian economy, it faces numerous challenges to its sustainability, including soil degradation, pollution, and the impact of pollution from corporate enterprises, which poses a significant threat. To address these challenges, predictive tools that can evaluate crop suitability to varying soil conditions and nutrient compositions are needed;however, the application of data mining and machine learning in agriculture is still relatively new. This study attempts to close this gap by utilizing a variety of regression techniques, such as random forest, linear regression, decision tree regression, polynomial regression, and support vector regression. These methodologies are used to predict crop performance. The study also explores the important topic of plant diseases, which are a major threat to crop quality, productivity, and economic viability. Advanced methods for monitoring pests are explained, including thorough methods for identifying plant pests in important crops including cotton, citrus, and rice. These surveillance systems reduce labour-intensive tasks and mitigate human error by enabling automated monitoring across large agricultural fields. This paper presents a comprehensive strategy for improving agricultural sustainability in India by combining cutting-edge pest surveillance systems with data-driven predictive models. The suggested approaches build resilience against environmental issues and advance long-term agricultural prosperity by offering farmers, policymakers, and other agricultural stakeholders practical insights. © 2024 IEEE.

关键词： Crops

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Identification of Therapeutic Plants Using Machine Learning 2

Identification of Therapeutic Plants Using Machine Learning

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Rohini, S. Keerthana Shree, M.U. Jayalakshmi, R. Malathi, M. Jabasheela, L. Panimalar Engineering College M.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College B.E Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Dept of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350328202

It takes a lot of work to develop a computerized method for classifying plants for medicinal purposes. There are countless kinds of plants found in India, and each one has its own unique set oftherapeutic advantages. Background information is necessary for manual identification and categorization because it is difficult for people to recall the proper titles of all plant species and their *** is crucial to preserve these medicinal plants since doing so will benefit several industries, including medicine, botanical research, and taxonomic studies, among others. Current technology cannot match thediversity of medicinal herb species that existin India. Utilizing textural traits that are crucial for recognizing and classifying leaves, the recommended technique makes itsimpler to categorize medicinal plants. The feature extraction, categorization, and picture enhancement stages are the three main components of the suggested method. The qualities that could be matched betweenthe leaf images were gathered using digital image processing methods after cell phones were used to snap the leaf photos. The CNN classifier is then used to create a machine learning classifier. © 2024 IEEE.

关键词： Machine learning

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Virtual AI Mouse with Biometric Authentication

Virtual AI Mouse with Biometric Authentication

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

作者： Vishnu Praba, B. Vinothini, R. Jayarathna, M. Subramani, Kavitha Sravanthi, P. Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359688

A recent study has introduced an AI-driven virtual mouse that has the potential to revolutionize Human-computer Interaction (HCI) through its powerful machine-learning capabilities. This mouse has a versatile and user-friendly pointing system that can be used with current devices, potentially transforming traditional input methods. By using our AI-powered solution, we can address accessibility issues, enhance user experience, and enable new digital connections. The AI-powered virtual mouse is revolutionizing the way people interact with devices by offering a hands-free alternative to human-computer interaction. There are concerns about privacy and security due to the extensive use of technology. This research proposes two upgrades to overcome these issues: biometric verification and encryption. These enhancements create a private and secure virtual mouse experience by reducing security threats and unauthorized access, which is especially helpful for sensitive tasks. This research effort bridges innovation with increased security, adding to the changing field of HCI. © 2024 IEEE.

关键词： Biometrics

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