The proceedings contain 278 papers. The topics discussed include: a novel approach: biomedical image encryption for healthcare applications;adversarial attacks and defenses using machinelearning for cybersecurity in ...
ISBN:
(纸本)9798350375442
The proceedings contain 278 papers. The topics discussed include: a novel approach: biomedical image encryption for healthcare applications;adversarial attacks and defenses using machinelearning for cybersecurity in corporates;efficient threat detection machinelearning techniques for intrusion detection utilizing data engineering;securing the digital realm data engineering and deep learning in cybersecurity crime prevention;agro-smart crop recommendation and yield forecasting employed through machinelearning;integrating perspectives: a unified deep learning framework for human activity recognition;performance analysis of linear regression based simple reflex artificial intelligence agent to detect malwares;deep learning-augmented hybrid optimization for crop classification using hyperspectral images;and improving accuracy in network intrusion detection via machinelearning algorithms: an in-depth examination.
Sales forecasting is a useful tool used for estimating future demand and sales. It provides insightful information for strategic decision-making, resource allocation, and inventory control. This research aims to utili...
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The conversion process of one specific language to another either in a completely automatic manner or with considerable amount of human intervention by preserving the original meaning of the input source text is known...
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Cardiovascular Disease (CVD), the world's largest cause of sickness, affects millions of individuals annually, particularly in low- and middle-income countries. Early detection of CVD is crucial since conventional...
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Indeed These days, trading stocks is a vital part of the finance sector. It could be difficult to predict stock values using machinelearning because the market is continually changing. On the other hand, machine lear...
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Electronic devices are everywhere, so strong hardware security is needed to keep private data safe from dangers. A physical Unclonable Function (PUF) device constructed using magneto-resistive random-access memory (MR...
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The use of machinelearning methods is becoming more important in the dynamic environment of manufacturing as companies seek to improve their processes and tighten their quality controls. The purpose of this research ...
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The Internet of Things (IoT) is facing a growing concern regarding cyber-attacks and the need for anomaly detection. As the deployment of IoT devices rapidly expands, the number of attacks targeting these devices incr...
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This paper addresses the regression problem of predicting the resonant frequency of H -shaped microstrip antennas using theoretical data generated through an enhanced cavity model method plus, a set of measured and si...
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Traditional health systems mostly rely on rules created by experts to offer adaptive interventions to patients. However, with recent advances in artificial intelligence (AI) and machinelearning (ML) techniques, healt...
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Traditional health systems mostly rely on rules created by experts to offer adaptive interventions to patients. However, with recent advances in artificial intelligence (AI) and machinelearning (ML) techniques, health-related systems are becoming more sophisticated with higher accuracy in providing more personalized interventions or treatments to individual patients. In this paper, we present an extensive literature review to explore the current trends in ML-based adaptive systems for health and well-being. We conduct a systematic search for articles published between January 2011 and April 2022 and selected 87 articles that met our inclusion criteria for review. The selected articles target 18 health and wellness domains including disease management, assistive healthcare, medical diagnosis, mental health, physical activity, dietary management, health monitoring, substance use, smoking cessation, homeopathy remedy finding, patient privacy, mobile health (mHealth) apps finder, clinician knowledge representation for neonatal emergency care, dental and oral health, medication management, disease surveillance, medical specialty recommendation, and health awareness. Our review focuses on five key areas across the target domains: data collection strategies, model development process, ML techniques utilized, model evaluation techniques, as well as adaptive or personalization strategies for health and wellness interventions. We also identified various technical and methodological challenges including data volume constraints, data quality issues, data diversity or variability issues, infrastructure-related issues, and suitability of interventions which offer directions for future work in this area. Finally, we offer recommendations for tackling these challenges, leveraging on technological advances such as multimodality, Cloud technology, online learning, edge computing, automatic re-calibration, Bluetooth auto-reconnection, feedback pipeline, federated learning, explainabl
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