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2024 Global Digital Health Knowledge Exchange and Empowerment Conference, gDigiHealth.KEE 2024

作者： Naji, Areen Subha, Muath Arab American University PhD in Information Technology Engineering Ramallah Palestine Arab American University Department of Computer Science PHD Coordinator Ramallah Palestine

ISBN: (纸本)9798331531331

This paper evaluates the effect of initial center selection on cluster quality and the rate of convergence in K-means clustering. In this paper, a new approach is proposed that uses random initialization and spatial distribution scores. The main objective is to enhance K-means clustering by creating a simple, novel, and efficient technique shredded for data classification and other high-level image processing tasks. Our approach relies on image color segmentation through the application of a transformation to the image's RGB color space, representing every pixel by three values of different components of color, divided into blocks, and then a calculation of the density of dots in every block. This value of density mark provided information about the distribution of color values around various parts of an image. It uses a tactical approach in selecting initial centers, namely Scored Centers Initialization, or SCI. It chooses the block with the highest score as the initial center block chosen randomly. Then, it selects subsequent centers based on the probability assessed by the Euclidean distance from the nearest one picked center and the density score of the block. It continuously does this until the desired number of centers is reached. The experimental results indicate that the clustering effect of the approach proposed was better than the traditional Random Centers Initialization method, thus proving the proposed strategy was effective. Based on the consideration of point-to-center distances and the spatial distribution score, it captured better data structures to get quicker convergence and higher quality in the cluster assignment. © 2024 IEEE.

关键词： K-means clustering

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Implementation of Secure Storage for Maintaining Characteristic Contracts in Civil Law 6

Implementation of Secure Storage for Maintaining Characteris...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Shukurillayevich, Gulomov Akmaljan Mahmudovich, Kuldashev Muhammad Ziyodullayevna, Muqimova Muslima Ananth, Christo Ananth Kumar, T. PhD in Law Samarkand State University Uzbekistan Samarkand State University Faculty of International Education Programs Uzbekistan Computer Science and Engineering IFET College of Engineering India

ISBN: (纸本)9798350304480

In public life, the contract, which is one of the oldest legal instruments, is widely used to regulate relations related to the satisfaction of certain needs of participants in civil law relations. Property relations that develop at the stage of sale of manufactured goods, performance of work, provision of services is based on the construction of the contract. Property interest is secured by an agreement. A contract is an agreement between two or more persons on the emergence, change or termination of their civil rights and obligations. It can be seen from the definition that the interested parties in the contract are counterparties directly participating in it. Civil law relations arising under this agreement apply only to both of them and create relative legal relations. According to the principle of relativity, the contract, as a rule, obliges the parties that have concluded it does not affect third parties. Contracts concluded for the benefit of third parties are one of the very important exceptions to this principle. When it comes to cyber security, the characteristic contract made for the favour of a third party may involve the exchange, processing, or storage of sensitive information. This article discusses the features of the secure storage for contract concluded in favour of a third party as a subject of research. The secure MD5 fused AES algorithm is proposed here to improve data privacy and security in the storage. © 2023 IEEE.

关键词： contract law contracts obligation security

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Classification and prediction of student performance data using various machine learning algorithms

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Materials Today: Proceedings 2023年 80卷 3782-3785页

作者： Pallathadka, Harikumar Wenda, Alex Ramirez-Asís, Edwin Asís-López, Maximiliano Flores-Albornoz, Judith Phasinam, Khongdet Manipur International University Manipur Imphal India Department of Electrical Engineering Faculty of Science and Technology State Islamic University of Sultan Syarif Kasim Riau Indonesia Management and turism Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru PhD in Computer Science and Engineering Science Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru PhD in Environmental Engineering Environmental Sciences Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru Faculty of Food and Agricultural Technology Pibulsongkram Rajabhat University Phitsanulok Thailand

In today's competitive world, it is critical for an institute to forecast student performance, classify individuals based on their talents, and attempt to enhance their performance in future tests. Students should be advised well in advance to concentrate their efforts in a specific area in order to improve their academic achievement. This type of analysis assists an institute in lowering its failure rates. Based on their prior performance in comparable courses, this study predicts students' performance in a course. Data mining is a collection of techniques used to uncover hidden patterns in massive amounts of existing data. These patterns may be valuable for analysis and prediction. Education data mining refers to the collection of data mining applications in the field of education. These applications are concerned with the analysis of data from students and teachers. The analysis might be used for categorization or prediction. Machine learning such as Nave Bayes, ID3, C4.5, and SVM are investigated. UCI machinery student performance data set is used in experimental study. Algorithms are analysed on certain parameters like- accuracy, error rate. © 2021

关键词： Forecasting

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Classification of Images using Multi-Layer Neural Network using Machine Learning 14

Classification of Images using Multi-Layer Neural Network us...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Chanchlani, Akshita S. Thakare, Vilas M. Wadhai, Vijay M. Department Computer Science and Engineering PhD Research Scholar SGBA University Amravati India Departments of CSE SGBA University Amravati India Astute Career Counseling Academy United States

This paper is focused on development of system for analysis with advanced Imaging Technologies with Machine Learning and Deep Learning. It also focuses on research and development of technological solutions for detection and analysis of brain cancer disease from health care domain. This paper focuses on implementation and analysis of convolution neural network algorithm in combination with multi-layer neural network algorithm on brain cancer MRI image dataset. This system will be useful in detection, classification, segmentation, analysis and prediction of brain cancer disease by using machine learning and deep learning technology. Proposed system is to detect whether brain tumor is cancerous (malignant) or not (benign) as well prediction of tumor grade on segmented region of interest. This implemented research work will enable all the stake holders like doctors, radiologists, neurosurgeons to detect and predict brain cancer precisely, efficiently and accurately with speedy performance. © Grenze Scientific Society, 2023.

关键词： Image analysis

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Improvement of K-Means Clustering Through Center's Hybrid Initialization

Improvement of K-Means Clustering Through Center's Hybrid In...

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Global Digital Health Knowledge Exchange & Empowerment Conference (gDigiHealth.KEE)

作者： Areen Naji Muath Subha Technology Engineering Arab American University PhD in Information Palestine Ramallah Department of Computer Science PHD Coordinator Arab American University Palestine Ramallah

ISBN: (数字)9798331531331

ISBN: (纸本)9798331531348

关键词： Image segmentation Graphical models Accuracy Image color analysis Clustering algorithms Euclidean distance Indexes Standards Distribution functions Convergence

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Pragmatic Analysis of IoMT Network Modelling Techniques from a Statistical Perspective

Pragmatic Analysis of IoMT Network Modelling Techniques from...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Fuke, Radhika P. Mahajan, R.P. Sage University Department of Information Technology Phd Scholar Indore India Sage University Department of Computer Science Engineering and It Professor Indore India

ISBN: (纸本)9781665484251

Internet of Medical Things (IoMT) networks are high precision wireless interfaces that require design of accurate sensing, actuation, and processing devices. These devices include wireless electrocardiograph (ECG) sensors, electroencephalograph (EEG) sensors, wireless blood pressure monitoring devices, etc. Due to direct patient interface, these devices are required to have superior performance in terms of sensing accuracy, processing efficiency, and communication quality of service (QoS) parameters. A wide variety of models are proposed by IoMT researchers to perform this task, but each of these models vary in terms of network size, deployment complexity, cost of deployment, processing delay, etc. These models include machine learning routing techniques, blockchain based security methods, privacy preservation methods, high-precision sensor design methods, high-performance communication interfaces, etc. Due to such a wide variation in performance, IoMT network design requires continuous validation, which increases time-to-market, thereby increasing deployment cost. In order to reduce number of validations, a statistical survey of models for IoMT network design is discussed in this text. This discussion is focussed towards evaluating various characteristics, advantages, limitations, and future research scopes in existing models. readers would be able to identify best performing model(s) for a given IoMT application. It is followed by a statistical analysis of the reviewed IoMT network design models in terms of end-to-end delay, communication QoS, network security, scalability, computational complexity, cost of deployment, and application of use. This statistical performance evaluation will further assist readers to statistically compare the reviewed methods, and identify best performing model(s) their combinations for context-specific network deployments. Due to this, readers would be able to reduce network design validation delay, which will further assist in reduci

关键词： Scalability

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A Machine Learning Model for Disease Prediction and Remote Patient Monitoring 22

A Machine Learning Model for Disease Prediction and Remote P...

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Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： Kirtida Tejas Naik Bindu Garg Computer engineering PHD Scholar Bharati vidyapeeth deemed to be university college of engineering India Computer Engineering Professor and Head of department computer science Barati vidyapeeth(Deemed to be university college of engineering pune) India

ISBN: (纸本)9781450399937

People's health reports, including diagnostic information and medical prescriptions, are delivered in the form of test-based case notes, making it impossible to determine a person's prior health issues or medications taken until he or she returns to the hospital later on. Storing all of a person's health information in the cloud as a soft copy, on the other hand, alleviates this issue. To accomplish this, each and every hospital, dispensary, and laboratory must have an internet connection for the registration of patient data. A unique Health Id will identify each patient, and all of the patient's data will be stored in the cloud, where the specific patient can only access it. In order to prevent and treat illness, it is critical to perform an accurate and timely analysis on any health-related problem. The ability to diagnose disease by obtaining all information from a linked Health ID combined with Machine Learning techniques will improve the system's ability to detect diseases. We believe that our diagnostic model can operate like a doctor in the earlier diagnosis of this disease, allowing for timely treatment and the preservation of life.

关键词： Disease Prediction Remote Health Monitoring AI Machine Learning

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Enhancing Schizophrenia Prediction Using Class Balancing and SHAP Explainability Techniques on EEG Data

Enhancing Schizophrenia Prediction Using Class Balancing and...

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Pattern Recognition Systems (ICPRS), International Conference on

作者： Javiera T. Arias César A. Astudillo PhD in Engineering Systems University of Talca Curicó Chile Department of Computer Science Faculty of Engineering Universidad de Talca Curicó Chile

Machine learning (ML) makes predictions or supports decision making based on data, achieving high accuracy, saving time and resources, and even running real-time analysis. However, one drawback of these models is the lack of transparency in complex models, reducing confidence in sensitive fields such as health. This paper analyzes electroencephalogram (EEG) data to predict schizophrenia in patients. Three classifiers are compared, considering Support Vector Machines(SVM), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost). Three metrics are used to measure the classification process, including accuracy (ACC), area under the curve (AUC) and F-l score (F1). XAI is incorporated into the pipeline to identify relevant features. XGBoost is the model with the best performance in predicting schizophrenia cases, reaching an ACC = 0.93, AUC = 0.93 and F1 score = 0.92, outperforming the SVM and AdaBoost algorithms. The SHAP explainability technique was applied on the XGBoost model, identifying the sex, IQ, delta T6, and delta Pz waves as the most relevant characteristics in the prediction processes. Based on the data analysis, we found that schizophrenia causes an alteration of the delta wave in an EEG, which is different to other mental illnesses. On the other hand, the study generates a specific impact by showing that XGBoost presents better results in 3 validation metrics (ACC, AUC, and F1 Score) compared to SVM and AdaBoost. It is shown that balancing classes is essential to obtain better ML predictions.

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[Wearable Smart Cardiac Care: Take Vipasyana for Example]

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Hu li za zhi The journal of nursing 2024年第3期71卷 13-19页

作者： Hsi-Lu Chao Sau-Hsuan Wu PhD Associate Professor Department of Computer Science National Yang Ming Chiao Tung University Taiwan ROC. hsiluchao@nycu.edu.tw. PhD Professor Institute of Communications Engineering National Yang Ming Chiao Tung University Taiwan ROC.

The most direct challenge to the healthcare system in Taiwan associated with population aging is the chronic and growing shortfall in medical and nursing practitioners. The integration of artificial intelligence, Internet of Things, and advanced 5G communication technologies is making healthcare more streamlined and intuitive to improve healthcare service efficiency, encourage cross-disciplinary cooperation to improve patient health management, and introduce and mainstream innovative service models such as telecare. To this end, the wearable smart cardiac care system 'Vipasyana', jointly developed by the interdisciplinary research team of the Departments of Electrical engineering and computer science at National Yang Ming Chiao Tung University, not only has an innovative fully functional wireless wearable electrocardiograph but also uses machine learning-based detection/prediction methods to detect and monitor for emergencies such as myocardial infarction, ventricular tachycardia/ventricular fibrillation, and heart failure. To handle the real-time streaming sensing data generated during clinical trials, a big data real-time streaming framework based on Apache Storm and microservices has also been integrated. In addition, a mobile phone app and virtual reality 3D ward round system providing a user-friendly management and monitoring tool for medical staffs were developed and integrated into the system. The 'Vipasyana' system is capable of providing precision medical services to individual patients.

关键词： Apache storm big data real-time streaming framework artificial intelligence heart failure rehabilitation care intelligent cardiac healthcare internet of things virtual reality-based 3D ward round system

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Development of 5G Cyber-Physical Production System

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International Journal of Networked and Distributed Computing 2023年第1期11卷 9-19页

作者： Huang, Hsuan-Chao Tsai, Chun-Hsien Lin, Hsiung-Cheng Department of Computer Science and Information Engineering National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan PhD program Prospective Technology of Electrical Engineering and Computer Science National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan Department of Electronic Engineering National Chin-Yi University of Technology No. 57 Sec. 2 Zhongshan Rd Taiping Dist Taichung Taiwan

The cyber-physical production system (CPPS) was developed for the interconnection between operational technology (OT) and information and communication technology (ICT) among the machines and decentralized production control. However, OT and ICT are now getting more complex in the modern industry. To enhance industrial management and production process, the CPPS based on the 5G architecture under production environments can provide a more comprehensive solution in the context of Industry 4.0. For this purpose, a 5G CPPS model to improve the interoperability of CPPS in ICT and OT is proposed to build a virtualized Industry 4.0 asset management shell system. First, real-time task artifacts and 5G communication modules are bound in a certain form of virtualization. Second, task artifacts are released to the production line in a real-time scheduling. Third, an order is placed on a production line and split into multiple tasks for production process. Single-machine single-workpiece and multi-machine and multi-workpiece processing cases are used for testing in the production line. Experimental results show that the proposed 5G CPPS can efficiently achieve multi-machine work piece processing. © 2022, The Author(s).

关键词： 5G Cyber-physical production system (CPPS) Digital twin (DT) Industry 4.0 Information technology Operational technology

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