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Machine learning and intelligent Network System based Covid 19 Health Care System 3

Machine Learning and Intelligent Network System based Covid ...

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3rd international conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Basha, Amjath Ali Jakul Saravanan, K. Mangayarkarasi, T. Balarengadurai, C. University of Technology and Applied Sciences Ibra Sultanate of Oman RCC - ENGG Department Computer Engineering Electrical and Electronic Section Department of Engineering Oman R.M.D Engineering College Department of Information Technology Tamil Nadu Chennai India Sri Sairam Engineering College Department of Instrumentation and Control Engineering Tamil Nadu Chennai India Vidyavardhaka College of Engineering Department of Computer Science and Engineering Karnataka Mysuru India

ISBN: (纸本)9781665462754

COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management Systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency. © 2022 IEEE.

关键词： COVID-19

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An Efficient Deep learning based Approach for Pneumonia Detection

An Efficient Deep Learning based Approach for Pneumonia Dete...

引用

intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Shirisha M Sri Sai Durga M Bhanu Keerthan Reddy G Hariharan Shanmugasundaram Bandar Vaishnavi Karuppiah Natarajan Artificial Intelligence and Data science Engineering Vardhaman College of Engineering Hyderabad India Electrical and Electronics Engineering Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Pneumonia is a major global cause of death, hence early detection and precise diagnosis are essential for successful management. Conventional techniques for detecting pneumonia, like radiological interpretation and clinical assessment, are frequently laborious and prone to variation amongst observers. Deep learning models have transformed medical imaging by offering automated, reliable, and accurate diagnostic instruments. The goal of this paper is to use data (image) pre-processing techniques to increase the accuracy of the deep learning model, EfficientNetB0 for pneumonia identification. The dataset utilized in this paper consists of annotated chest X-ray images from the publically accessible source “Chest X-Ray Images (Pneumonia)”; Paul Mooney submitted 5,863 photos in 2 categories on Kaggle. To enhance the model's resilience and ability to generalize, the images undergo preprocessing using techniques such as normalization, gray scaling, median filtering, CLAHE, and data augmentation. A standardized evaluation framework is used to evaluate each model's performance, and measures like Fl score and confusion matrix analysis are used. The results of this paper demonstrate how pre-processing the data can increase the accuracy of the deep learning model and can enhance medical imaging diagnostic accuracy and dependability.

关键词： Deep learning Pneumonia Accuracy Observers data models Reliability Internet of Things X-ray imaging Biomedical imaging Resilience

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Real-Time Underwater Trash Object Detection Using Enhanced YOLOv8 3

Real-Time Underwater Trash Object Detection Using Enhanced Y...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Pratap, Adimulam Raghuvira Reddy, Thumu Rampraveen Nikhila, Lanke Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798331523893

The accumulation of trash in aquatic and marine environments has severe and significant consequences on marine ecosystems, resulting in a persistent environmental hazard. While volunteer trash pickup efforts have grown in recent years, they still demand considerable time, effort, and resources. To effectively address these challenges, the implementation of automated solutions is essential. This project aims to develop an efficient object detection deep learning model which helps in effectively identifying the underwater trash objects. The key challenges of underwater image-based localization come from object detection under low light conditions, also the presence of numerous small and overlapping targets, which significantly increase the complexity. In this project we have used improved Yolov8n object detection model achieving an impressive accuracy rate of mAP of 0.951. We have utilized diverse datasets covering a range of environmental conditions, including challenging low-light scenarios. Also, the integration of advanced preprocessing techniques and data augmentation has further enhanced our model's performance, ensuring reliable predictions in various underwater environments, thereby improving detection performance without introducing additional computational complexity. © 2025 IEEE.

关键词： Marine pollution

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data-Driven Approaches for Personalized Sleep Disorder Diagnosis

Data-Driven Approaches for Personalized Sleep Disorder Diagn...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： R. Asmita T.H. Hrithik K. H. Akhil Nisha Mishra V. S. Kirthika Devi Department of Electrical and Electronics Engineering Amrita School of Engineering Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

These days, sleeping disorders are a concern for young people and people with a variety of health issues. It is important to keep in mind that sleep problems are not a recent occurrence and should not be sought after or accepted as a trend, even though it is true that issues related to sleep have garnered more attention and discussion in recent years due to growing knowledge and research. Numerous problems, such as anxiety, depression, and mental health strain, can be brought on by this illness. Machine learning (ML) can handle a wide range of tasks, including classification, prediction, and decision-making. To analyze and identify sleeping disorders using ML and can also be employed to gauge a person's level of stress. This paper presents the DL models and ML algorithms that have been deployed to sleep stage classification in the literature to date. The effectiveness of Adaboost models is investigated in this work. These models' research findings suggest the ideal parameter combination for improved performance.

关键词： Support vector machines Accuracy Machine learning algorithms Search methods Prediction algorithms Classification algorithms Bayes methods Tuning Stress Strain

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Root Cause Classification of Temperature-related Failure Modes in a Hot Strip Mill 3

Root Cause Classification of Temperature-related Failure Mod...

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3rd international conference on Innovative intelligent Industrial Production and Logistics, IN4PL 2022

作者： Latham, Samuel Giannetti, Cinzia Faculty of Science and Engineering Swansea University Fabian Way Wales Swansea United Kingdom

data is one of the most valuable assets a manufacturing company can possess. Historical data in particular has much potential for use in automated data-driven decision-making which can result in more efficient and sustainable processes. Although the technology and research behind data-driven systems for Root Cause Analysis has developed vastly over decades, their use for real time automated detection of root causes within steel manufacturing has been limited. Typically, root cause analysis still involves a lot of human interaction both in the pre-processing and data analysis phases, which can lead to variability in results and cause delay when devising corrective actions. In this paper, an application for automated Root Cause Analysis in an Hot Strip Mill is proposed for the purpose of demonstrating the effectiveness of such an approach against a manual approach. The proposed approach classifies temperature defects of steel strip Width Pull using a variety of machine learning algorithms in conjunction with k-fold cross validation. Copyright © 2022 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

关键词： data Analytics

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Enhancing English Language Assessment in Educational Settings using Natural Language Processing Techniques 3

Enhancing English Language Assessment in Educational Setting...

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3rd international conference on intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Jayakumar, V Moses Rajakumari, R. Alapati, Purnachandra Rao Otero-Potosi, Santiago Malleswari, D. Naga Karthik, M. SIMATS Department of English Saveetha School of Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Department of English Saveetha School of Engineering Tamil Nadu Chennai India Prasad V Potluri Siddhartha Institute of Technology Department of English Andhra Pradesh India Instituto Superior Tecnológico Liceo Aduanero Departament of Investigation Ibarra Ecuador Koneru Lakshmaiah Education Foundation Dept. of CSE Andhra Pradesh Vaddeswaram India K. Ramakrishnan College of Engineering Department of AI & DS Trichy India

ISBN: (纸本)9798331523893

Various approaches to testing the ENL learners in educational contexts have tended to experience some limitations concerning validity, reliability, practicality, and sustainability. These limitations can be well mitigated by NLP techniques especially in areas where automated assessment can be made and personalized feedback given to learners. This particular work will contribute to the improvement of methods for teaching and testing in the sphere of English language by employing narrow components of NLP to create the base for the automated assessment that matches the need of a learner or academical curriculum and offers accurate and comprehensive feedback. In contrast with more conventional forms of assessment that involve paper grading or multiple choice tests the proposed approach leverages NLP to 'interpret' the student texts for the purposes of assessment thus encompassing multiple aspects of languages than can simply be graded out of out 100. The type of data used in the present study includes datasets like the Twitter Sentiment Analysis dataset;this is a dataset that comprises labeled tweets that are used to train and test NLP models. An ideal approach, like BERT-CNN is used for feature extraction, preprocessing and training/validation to achieve higher levels of student outputs analysis. K-fold validation and parameter tuning strategies are used for effectiveness and overfitting avoidance of the proposed approaches. The proposed NLP-based assessment system will have a test accuracy of 0.99, and it will prove to be more accurate, efficient and scalable than traditional systems. The system shows its ability to fairly assess the language skills of the learners and offer the feedback to them;therefore, it underpins the potential for introducing changes in the language assessment in learning institutions. This is a major innovation in the area of English language assessment since this work provides a new approach of using NLP in education measurement. The proposed

关键词： Natural language processing systems

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The Usage of Machine learning on Penetration Testing Automation

The Usage of Machine Learning on Penetration Testing Automat...

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Electronic and Electrical engineering and intelligent System (ICE3IS), international conference on

作者： Clintswood Delbert Giovanni Lie Lexiandy Kuswandana Nadia Said Achmad Derwin Suhartono Computer Science Department Cyber Security Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Penetration testing is a method to assess the security within a network by performing or simulating a real-world cyber-attack on the network. It has been one of the best ways preferred by organizations to strengthen their network defenses against cyber threats. However, penetration testing proves that it has several drawbacks such as requiring a significant number of skills and time to perform. A survey made by The international Information System Security Certification Consortium also shows that the world is lacking cybersecurity workforces. To address this problem, this paper conducts research regarding the usage of machine learning to automate penetration testing activity. To start off, automated penetration testing tools will be created using machine learning algorithms, specifically reinforcement learning and deep reinforcement learning algorithms. The second step is to put the tools in a learning stage where it will be provided with the MITRE ATT&CK Framework. This framework will be the base for the attacks and exploitations used within the tools. The third step is to conduct a series of automated penetration testing over different target networks. The result will be then compared and analyzed with a manual penetration testing report to see how efficient and better it is compared to a manual penetration test. Lastly, the final data will then be collected for future developments and recommendations.

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Lung Diseases Detection Based on ResNet and Deep learning Method

Lung Diseases Detection Based on ResNet and Deep Learning Me...

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Computer, Control and Robotics (ICCCR), international conference on

作者： Yiqian Zhang Yan Ding Yifei Zhang School of Information and Communication Engineering. University of Electronic Science and Technology of China Chengdu China

Usually, a doctor can judge whether a patient has lung disease by X-ray. With the spread of the COVID-19 virus, the number of patients continues to increase while manual work increases. Thus, it's a challenge now to detect what kind of lung diseases the patient has with radiography., in an accurate and fast way. With the development of deep learning and the emergence of CNN (Convolutional Neural Network), the devices equipped with neural network can become more intelligent and automated. In this article, we adopted ResNet to be our network, and used 3 data augmentation methods and data regularization methods to process the data and get higher accuracy. In this article, we reached 93.39% accuracy based on the experiments.

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ClipXpert: automated Clip Mining from Video data for High-Demand Content

ClipXpert: Automated Clip Mining from Video Data for High-De...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Rajdeep Chatterjee Sudip Chakrabarty Pappu Bishwas School of Computer Engineering KIIT Deemed to be University Bhubaneswar India School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

ClipXpert is a new system that automates the extraction of relevant clips from YouTube videos using user-defined keywords or frequently used word or comment analysis. The system uses advanced transcription models to search a pre-existing database for lines associated with the provided keywords. If the database doesn’t contain the keywords, it dynamically identifies and stores relevant content for future use. ClipXpert also automates identifying frequently used nouns, adjectives, and adverbs to generate focused content highlights without user intervention. It also performs sentiment analysis on YouTube comments to understand audience engagement and reception. ClipXpert enhances video content extraction efficiency and accuracy, catering to the growing demand for targeted, high-value clips in the digital media landscape.

关键词： Sentiment analysis Video on demand Accuracy Machine learning Media Real-time systems Web sites data mining intelligent systems Videos

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Plant Leaf Disease Identification Based on Machine and Deep learning Methods

Plant Leaf Disease Identification Based on Machine and Deep ...

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intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： P. Kalaiarasi R. Ramalakshmi V. Kannan A. Bhuvanesh Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Agriculture Kalasalingam Academy of Research and Education Krishnankoil India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The substantial effects of plant diseases on agriculture, which result in losses in production of crops, financial losses, and food insecurity, highlight the necessity for plant leaf disease detection and categorization. In order to effectively control illnesses, stop them from spreading to other plants, and develop tailored treatment plans, early and precise disease identification is essential. Various strategies are used for plant leaf disease diagnosis, such as manual symptom assessment, computer image processing, and visual examination by skilled professionals. Traditional methods rely on human expertise to visually identify disease symptoms based on characteristic patterns, discolorations, or lesions on plant leaves. These techniques, however, are laborintensive, time-consuming, and sensitive to bias and mistakes. The development of automated and computer-based techniques for the identification of plant leaf disease through the use of ML (machine learning) algorithms and digital imaging technology has garnered increasing attention in recent years. There are still a number of issues with the DL (deep learning) and ML algorithms. In order to identify research gaps and address them in the future while developing a unique framework, this study presents an examination of ML and DL models.

关键词： Deep learning Visualization Plant diseases Machine learning algorithms Microorganisms Plants (biology) Production Manuals Medical diagnosis Diseases

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