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Adapting the Education System to Support Remote Monitoring and Elderly Care Needs

Adapting the Education System to Support Remote Monitoring a...

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international conference on Engineering Education (ICEED)

作者： Nor Afirdaus Zainal Abidin Raihah Aminuddin Alya Geogiana Buja College of Computing Informatics and Mathematics University Teknologi Mara Melaka Malaysia

ISBN: (数字)9798350367416

ISBN: (纸本)9798350367423

Telehealth, which uses digital and telecommunication technology to provide healthcare services, is now a crucial component of modern healthcare systems. It covers a broad spectrum of uses, including the use of digital communication channels for consultation and support as well as the monitoring of vital signs and chronic illnesses. Effective remote monitoring and care solutions are in more demand as the world's senior population continues to rise. In response, the educational system has created customized curricula and training materials to address the requirements and difficulties related to senior remote care. This abstract examines how the educational system addresses these crucial issues, emphasizing methods and materials meant to offer caregivers and medical professionals the know-how and abilities required to deliver high-quality remote care. It would assist in offering chances for practical clinical experience with remote care and real patients. Thus, through placements and practical experience, learners acquire clinical competence in remote care, making them ready for real-world situations. Interviews from health workers and IT experts will help in planning strategies such as curriculum integration in the technology training and elder care education, develop online courses focused on elderly care and technology or include training on health policy and advocacy to prepare students to influence policies that support remote monitoring and elderly care.

关键词： Training Telemedicine Medical services Digital communication Communications technology Planning Older adults Interviews Engineering education Remote monitoring

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A Preliminary Lightweight Random Forest Approach-Based Image Classification for Plant Disease Detection

A Preliminary Lightweight Random Forest Approach-Based Image...

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computing (ICOCO), ieee international conference on

作者： Mashitah Ibrahim Muzaffar Hamzah Mohammad Fadhli Asli Faculty of Computing and Informatics Universiti Malaysia Sabah Sabah Malaysia

ISBN: (纸本)9781665489973

In recent years, the rapid development of environmental sensors and artificial intelligence is changing the traditional mode of agricultural production and moving towards intelligent and efficient precision agriculture. According to the demand of developing precision agriculture, this study plans to carry out comprehensive improvise research on the intelligent unmanned plant disease detection technology for agricultural ecosystems. The production can be adversely affected if plant disease problems cannot be detected in the early stage. Therefore, the biggest challenge in disease detection is the accurate early diagnosis for loss prevention. However, achieving high accuracy requires a computationally intensive approach to the system, which can cause overhead and high technical costs. Random Forest is a special kind of ensemble learning technique and it turns out to perform very well compared to other classification algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN). In this study, we modified the structure of, RF model to improve the overall accuracy and accessibility, to transform it into a lightweight detection system. This lightweight framework is for cost-effective distribution with high performance without requiring extensive computational resources or complex algorithms. With that, this system can be more practical and easier to use.

关键词： Support vector machines Radio frequency Plants (biology) Production Artificial neural networks Transforms Agriculture

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$p$-ClustVal: A Novel $p$-Adic Approach for Enhanced Clustering of High-Dimensional scRNASeq Data (Extended Abstract)

$p$-ClustVal: A Novel $p$-Adic Approach for Enhanced Cluster...

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international conference on Data Science and Advanced Analytics (DSAA)

作者： Parichit Sharma Sarthak Mishra Hasan Kurban Mehmet Dalkilic Computer Science Luddy School of Informatics Computing & Engineering Indiana University Bloomington IN USA College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

This paper introduces $p$ -ClustVal, a novel data transformation technique inspired by p-adic number theory that significantly enhances cluster discernibility in genomics data, specifically Single Cell RNA Sequencing (scRNASeq). By lever-aging p-adic-valuation, $p$ -ClustVal integrates with and augments widely used clustering algorithms and dimension reduction techniques, amplifying their effectiveness in discovering meaningful structure from data. The transformation uses a data-centric heuristic to determine optimal parameters, without relying on ground truth labels, making it more user-friendly. $p$ -ClustVal reduces overlap between clusters by employing alternate metric spaces inspired by p-adic-valuation, a significant shift from conventional methods. Our comprehensive evaluation spanning 30 experiments and over 1200 observations, shows that $p$ -ClustVal improves performance in 91% of cases, and boosts the performance of classical and state of the art (SOTA) methods. This work contributes to data analytics and genomics by introducing a unique data transformation approach, enhancing downstream clustering algorithms, and providing empirical evidence of $p$ -ClustVal's efficacy.

关键词： Dimensionality reduction Sequential analysis Parameter estimation RNA Genomics Clustering algorithms Data science Extraterrestrial measurements Bioinformatics Tuning

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e-CommerceShield: A Framework for Enhanced Security in E-Commerce with Awareness, DNS Matching, and Blockchain Integration

e-CommerceShield: A Framework for Enhanced Security in E-Com...

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international conference on Telecommunications (ICT)

作者： Aymen Belghith Computer Science Department College of Informatics and Computing Saudi Electronic University Saudi Arabia

ISBN: (数字)9798350356694

ISBN: (纸本)9798350356700

Security attacks on e-commerce platforms are increasingly prevalent, posing significant risks to both users and organizations. These attacks aim to cause financial harm or disrupt e-commerce services, highlighting the critical need for robust security measures. This study investigates key security attacks targeting e-commerce platforms and evaluates the efficacy of various attack detection methods. Through a comprehensive literature review and descriptive analysis, the study identifies financial frauds, phishing, and Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks as the major threats to e-commerce systems. Furthermore, the study examines different detection methods, revealing that blockchain-based security systems emerge as the most effective solution against a range of identified security attacks. Building upon these findings, this paper proposes a novel framework, dubbed e-CommerceShield, which integrates user awareness, DNS matching, and blockchain technology to enhance security in e-commerce environments. The framework prioritizes proactive security measures, empowers users with awareness and education, and leverages advanced technologies to detect and prevent security threats effectively. By adopting the e-CommerceShield framework, e-commerce platforms can fortify their security posture and safeguard the integrity of online transactions, thereby fostering trust and confidence among users in the digital marketplace.

关键词： Phishing Refining Organizations Machine learning User experience Real-time systems Blockchains

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Machine Learning Analysis of Heartbeat Sounds Using Weka for Classification

Machine Learning Analysis of Heartbeat Sounds Using Weka for...

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Annual ieee Systems conference

作者： Rafeeq Muhammad T. Maheswara Reddy Yenumula Tanner Mergist Tytiana James Ali Harimi Kasem Khalil Ashok Kumar School of Computing and Informatics University of Louisiana at Lafayette Lafayette USA Electrical and Computer Engineering University of Mississippi Mississippi USA

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

Classifying heartbeat sounds is a critical step in improving cardiovascular diagnostics, especially as reliance on traditional stethoscope assessments can be limited by human subjectivity. In this study, we explored the application of machine learning techniques to the PhysioNet Heartbeat Sound Database, aiming to differentiate normal from abnormal heart sounds with both accuracy and consistency. Our approach integrates unsupervised K-means clustering, which identifies natural groupings within the data, with supervised classification models to validate these groupings and enhance predictive power. Key acoustic features-such as pitch, RMS energy, MFCCs, and formants-were extracted to provide a comprehensive representation of the heart sounds. To streamline classification, we applied the InfoGain algorithm, selecting the 50 most significant features from an initial set of 238. This process ensured that the models focused on the most relevant information, improving efficiency and interpretability. We evaluated four classifiers-SVM (SMO), Decision Tree (J48), Naive Bayes, and k-NN (IBK)-using a 10-fold cross-validation approach. SMO and J48 demonstrated strong performance, achieving accuracies of 97.4% and 96.02%, respectively, with high Kappa statistics, showcasing their reliability in distinguishing heart sound categories. These results highlight the potential of machine learning in addressing critical healthcare challenges, particularly when labeled data is scarce. Future work will focus on expanding the dataset, incorporating neural network architectures, and exploring additional clustering methods to further improve generalizability and diagnostic accuracy.

关键词： Heart Support vector machines Accuracy Heart beat Databases Machine learning Stethoscope Feature extraction Acoustics Decision trees

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Machine Learning-Based Security Solutions for Healthcare: An Overview 2nd

Machine Learning-Based Security Solutions for Healthcare: An...

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2nd international conference on Emerging Technologies for computing, Communications, and Smart Cities, ETCCS 2021

作者： Arora, Pallavi Kaur, Baljeet Teixeira, Marcio Andrey IK Gujral Punjab Technical University Punjab Kapurthala India Guru Nanak Dev Engineering College Punjab Ludhiana India Department of Informatics Federal Institute of Education Science and Technology of São Paulo SP Catanduva15808-305 Brazil

ISBN: (纸本)9789811902833

The eruption of chronic diseases such as COVID-19 has re-emphasized the need for people all over the world to have access to urgent healthcare. The latest pandemic has shown the flaws in the conventional healthcare system, namely that hospitals and clinics alone are incapable of dealing with such a crisis. Smart and integrated wearables are one of the main technologies that favor modern healthcare solutions (Godi, B., Viswanadham, S., Muttipati, A.S., Prakash Samantray, O., Gadiraju student, S.R.: E-Healthcare Monitoring System using IoT with Machine Learning Approaches. In: 2020 international conference on Computer Science, Engineering and Applications (ICCSEA). pp. 1–5. ieee, Gunupur, India (2020). https://***/10.1109/ICCSEA49143.2020.9132937 ). These wearables can now gather data on an unparalleled scale thanks to advancements in the Internet of Things (IoT). Healthcare is among many fields that have been transformed by IoT, with the Internet of Medical Things (IoMT) systems being introduced as an IoT branch. Patients with chronic diseases can be monitored remotely using IoMT systems. As a result, it can offer prompt diagnostics to patients, potentially saving their lives in the event of an emergency. However, protection in these vital systems is a major issue that has arisen as a result of their widespread use. This paper presents an overview of the technologies that are being used on IoMT as well as some security problems found in the literature. This survey will give an insight to the readers regarding the importance of security in healthcare and the different machine learning methods used to address that issue. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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CNN-NSVM Architecture for Skin Lesion Classification Using Non-Dermoscopic Digital Image 10

CNN-NSVM Architecture for Skin Lesion Classification Using N...

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Joint 10th international conference on informatics, Electronics and Vision, ICIEV 2021 and 2021 5th international conference on Imaging, Vision and Pattern Recognition, icIVPR 2021

作者： Kabir, Md Mohsin Mehera, Parisa Saha, Aloke Kumar Mridha, M.F. Hamid, Md Abdul Monowar, Muhammad Mostafa Department of Computer Science and Engineering Bangladesh University of Business & Technology Dhaka Bangladesh Department of Computer Science and Engineering University of Asia Pacific Dhaka Bangladesh Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

ISBN: (纸本)9781665449212

Skin cancer has become a severe problem for medical diagnosis. The adoption of Artificial Intelligence (AI) in pharmaceutical diagnosis is constantly improving. Lately, AI-based computer-aided diagnostics explications for the diagnosis of skin disease have been of prominent concern. Notwithstanding its importance, skin lesion segmentation inhabits an unresolved difficulty of variability in shade, texture, patterns, and obscure borders. Melanoma, precisely called malignant melanoma, is the deadliest kind of skin cancer. It is much more spread to other body organs if it remains undiagnosed and is not treated early. Hence, early screening has the utmost importance in enhancing the cure probability. The proposed algorithms significantly impacted skin cancer classification until today, but they decayed classification rates while using Non-Dermoscopic Digital Images. This paper presents a new approach of classifying skin lesions from non-dermoscopic digital images using Convolutional Neural Network and Neutrosophic Logic Support Vector Machine (CNN-NSVM) combined approach. CNN extracts the features from the images, and the neutrosophic logic employed with SVM classifies the skin lesion types. The proposed methodology is evaluated on the well-known malignant lesion image dataset from the digital image archive of the Department of Dermatology of the University Medical Center Groningen (UMCG). The obtained accuracy of the proposed algorithm is 91% which outperformed MED-NODE by 10% on a similar dataset. Contribution-The paper introduced a skin cancer classification method from non-dermoscopic digital images based on Convolutional Neural Network and Neutrosophic Logic SVM (CNN-NSVM) to classify skin lesions into two types: Melanoma and Naevus. © 2021 ieee

关键词： Support vector machines

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LawKey — Law Constitution Chatbot

LawKey — Law Constitution Chatbot

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Artificial Intelligence (SLAAI-ICAI), SLAAI international conference on

作者： W Shamini Fernando Mandinu Handapangoda Nividula Samindi Maneth Gamage Dileeka Alwis School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331509170

ISBN: (纸本)9798331509187

In response to a pervasive lack of legal awareness among Sri Lanka, this research introduces a law constitution chatbot utilizing Natural Language Processing (NLP). The chatbot is designed to offer precise legal guidance by extracting pertinent laws from the Sri Lankan constitution based on user input. Building on existing studies at the intersection of NLP and the legal domain, this chatbot addresses the lack of relevant legal context for Sri Lanka. Reinforcement learning is integrated to refine the chatbot’s behavior through user feedback, ensuring optimal selection of the most relevant laws. An exploration of applications in countries like Canada and India, where NLP and machine learning have successfully addressed legal information gaps, focuses on various legal domains in Sri Lanka with the aim of empowering the public. The research methodology involves leveraging the Sri Lankan constitution as the dataset, extracting keywords from legal documents, and employing various NLP models, and limitations of the application. A comprehensive literature review reveals the potential energy of NLP techniques in legal chatbots. The global analysis of legal chatbots significantly contributes to the theoretical foundation of this project. In summary, this project seeks to advance legal chatbot technology in Sri Lanka, utilizing NLP and reinforcement learning techniques to empower citizens with legal knowledge and enhance overall legal awareness, thereby addressing the critical issue of legal unawareness and fostering a more informed society.

关键词： Potential energy Law Buildings Reinforcement learning Chatbots Mobile applications Encryption Security ieee Constitution Systematic literature review

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Evaluating Multi-Level Checkpointing for Distributed Deep Neural Network Training

Evaluating Multi-Level Checkpointing for Distributed Deep Ne...

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ieee/ACM 7th international Workshop on Heterogeneous High- Performance Reconfigurable computing (H2RC) Part of international conference for High Performance computing, Networking, Storage and Analysis (SC)

作者： Anthony, Quentin Dai, Donglai X ScaleSolut LLC Columbus OH 43221 USA Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781665483889

Deep learning (DL) applications are becoming one of the most important applications for HPC and cloud systems. The massive datasets and deep neural networks (DNN) used by DL applications introduce many HPC challenges. Therefore, HPC checkpoint/restart tools are an attractive choice. However, most data-parallel DL training jobs use a naive scheme, called root checkpointing, which is subject to blocking semantics and straggling forward progress. In this work, we apply a multi-level checkpointing tool (SCR-Exa) to distributed DL applications. We examine the performance of two DNN models at scale on Lassen (a leading TOP500 system), while ensuring the DNN's accuracy is maintained after restart from simulated system failures. Our test results show that multi-level checkpointing schemes are able to achieve nearly constant overhead at scale. To the best our knowledge, this study presents the first evaluation to demonstrate strong scalability of a checkpointing scheme for distributed DL without making framework-specific changes.

关键词： Deep Learning Neural Networks Faulttolerance Checkpointing High Performance computing Performance Characterization MVAPICH2 MPI PyTorch Horovod

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Ensemble Based Machine Learning Model for Heart Disease Prediction 3

Ensemble Based Machine Learning Model for Heart Disease Pred...

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3rd international conference on Communications, Information, Electronic and Energy Systems, CIEES 2022

作者： Ambrews, Ashley Bryan Gubin Moung, Ervin Farzamnia, Ali Yahya, Farashazillah Omatu, Sigeru Angeline, Lorita Universiti Malaysia Sabah Faculty of Computing And Informatics Kota Kinabalu Sabah Malaysia Universiti Malaysia Sabah Faculty of Engineering Kota Kinabalu Sabah Malaysia Education Research Centre Higashi-Hiroshima739-0046 Japan Taylor's University School of Computer Science Faculty of Innovation And Technology Selangor Malaysia

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

The World Health Organization reports that more than 10 million people worldwide die each year due to heart disease. In this project, an ensemble-based machine learning (ML) model is proposed for the prediction of heart disease. The proposed method was evaluated on two datasets: UCI heart disease and Framingham heart study. Various ML such as K-Nearest Neighbors, Naïve Bayes, Decision Tree, Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF) were carried out to predict the outcome of heart disease. Additionally, the Stacking and Voting ensemble approaches are also employed for heart disease prediction. All ML classifiers evaluated on both datasets have their performance metrics analyzed and compared. The results of all ML classifiers assessed on the Framingham dataset is inferior than that of the UCI heart disease dataset. For individual ML classifier, SVM achieved the highest accuracy for UCI heart disease with 91.09%, whereas LR achieved the highest accuracy for the Framingham heart study with 85.38%. However, it should be noted that RF trained on the UCI dataset is the best ML for prioritizing Sensitivity (92.52%). Finally, with the exception of Sensitivity, the Voting approach is strongly suggested because it outperforms the other classifiers noticeably where it achieved Accuracy (91.96%), F1 (91.69%), Sensitivity (91.72%), Specificity (90.77%), and Precision (92.40%) on UCI dataset. © 2022 ieee.

关键词： Forecasting

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