An Intelligent System for Remote Monitoring of Patients Health and the Early Detection of Coronary Artery Disease

2021 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), Journal Year: 2022, Volume and Issue: unknown, P. 1 - 6

Published: Dec. 23, 2022

India is a country where number of death occur due to road accidents and thus because the bad health condition driver's. To overcome this issue remote patient monitoring system has be deployed find conditions patients update their information. A new been developed, that monitors activities user behavior, detection stress device helps in accessing all those provides better results for healthier living or patients. The ability predicting prevalence diabetes hypertension Indian women using specific thresholds indices are examined. cut off point waist circumstances >35% thresholds, 12% have more than WC threshold 13 % case diabetic body mass index value 25.02 kg/m2 34 ^% people BMI cut-point 25% diabetes. In height ratio cutoff only 1% missed 0% diabetes, from these three parameters same women. proposed decision tree.

Language: Английский

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Implementation of Machine Learning Approach for Detecting Cardiovascular Diseases

Published: June 23, 2023

The rapidly expanding discipline of data analysis has an important role to play in the medical industry. Using this knowledge, we can uncover previously concealed details that might aid early illness prediction. Predicting cardiovascular is one most pressing issues our day. community views heart disease prediction as a challenging endeavour. Machine learning for field's massive training and testing needs. Creating assessing system crucial detection treatment condition. This research uses variety machine methods predict possibility diagnose patient with or not. These include Decision Tree, K - Nearest Neighbour classifier, Support Vector Machine. Finally, study provides cardiac result, trials comparing suggested technique others have shown it may be used provide forecast patient.

Language: Английский

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Cardiovascular Disease Prediction Using LSTM Algorithm based On Cytokines

Published: May 26, 2023

The modern lifestyle's busy schedule often results in unhealthy habits that lead to anxiety and depression. To deal with stress, many people engage harmful behaviours such as heavy smoking, drinking, drug usage. Heart disease, cancer, other fatal conditions may all be traced back these bad routines. World Health Organization (WHO) reports healthcare spending is becoming unsustainable due the prevalence of cardiovascular disease. address this issue, it essential have a fast, accurate, early clinical assessment disease severity. This work proposes an effective CVD prediction approach using deep learning, which considers cytokines important feature for prediction. proposed scheme shown provide better predictions, supporting decision-making logistical planning systems.

Language: Английский

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Implementation of GUI based Vital Track Ambulance for Patient Health Monitoring

2022 7th International Conference on Communication and Electronics Systems (ICCES), Journal Year: 2023, Volume and Issue: unknown, P. 1417 - 1424

Published: June 1, 2023

Smart Ambulance and Patient Health Monitoring is a system designed to enhance the quality of medical care during patient transport. it cutting-edge technology that integrates healthcare with transportation It aims improve efficiency emergency services. This work an effort address critical issue in modern delivery. consists three major sections. First, sensors would be used detect patient's vitals; second, data sent cloud storage service; third, discovered made available for remote viewing via Java GUI. The ambulance equipped real-time communication connects database, enabling professionals remotely monitor advise on vital signs (heart rate, respiration temperature) are tracked real time by wireless devices health surveillance system. information transmitted GUI including safety parameters like Fire sensor, IR GPS tracking Gas make informed decisions regarding care, ambulance's ability reach hospital safely. outcomes providing timely accurate interventions transport may reduce between diagnosis treatment.

Language: Английский

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Future Directions and Emerging Trends in Multimodal Data Fusion for Bioinformatics

Published: Jan. 13, 2025

Bioinformatics has just been the nexus of dynamic field that amalgamation multiple data types now represents cutting-edge technology for research in biology and medicine. The present chapter addresses status, nascent trends, next steps multimodal integration bioinformatics, which provides cues about underlying tactics, messes, challenges this fast-growing field. As high-throughput increasing richness diversified biological are entrenched, fusion, should not be ignored, played a vital role exploration complex mechanisms, precision medicine, new drug development. This gives complete review covering successive incorporation genomics, proteomics, metabolomics, imaging among maybe others transformation will highlighted. It goes into depth computational strategies based on machine learning deep models, constantly limits analysis interpretation. Secondly, it solution to only tech issues but ethical dilemmas linked with fusion; thus, is passageway researchers bioinformatics practitioners. In chapter, we have put together all fragmentized knowledge whole conclusion also presented future openings; thus meant propel further improvement unfold complexities life.

Language: Английский

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Integrating IoT, Blockchain, and Quantum Machine Learning

Published: Jan. 13, 2025

This chapter discusses the transformative nature by which IoT, blockchain and QML converge to address healthcare. With technological advancements being critical factors in defining a future medical practice, these three technologies make statements as pacesetters for better patient care record keeping treatment procedures. The starts with discussion on singularities provided healthcare industry increase patients' engagements safety of their information records well optimize data-driven decision-making processes. It then analyzes synergistic capability when merged proposes new approach health gives improved efficiency unmatched security personalized medicine breakthroughs. is also about practical cases hypothetical situations, revealing possibilities achieved usage IoT or treating chronic problems including drug monitoring, remote consumer satisfaction predictive analytics disease management. considers ethical, regulatory technical challenges that have been reported result implementing generate more objective look at feasibility operation.

Language: Английский

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Challenges and Limitations of Few-Shot and Zero-Shot Learning

Advances in bioinformatics and biomedical engineering book series, Journal Year: 2024, Volume and Issue: unknown, P. 113 - 137

Published: March 22, 2024

Essential to the development of AI and machine learning, this chapter explores complex areas few-shot zero-shot learning. There have been great advancements towards more efficient adaptive systems with learning respectively, which can learn from minimal data infer particular instances without previous exposure. Nevertheless, there are several limits difficulties associated these procedures. This delves deeply into theoretical foundations both techniques, explaining how they work what problems solve in different ways. It examines semantic gap, domain adaptation problems, model bias, as well computational restrictions, overfitting, generalizability that intrinsic respectively. We may better understand ideas' potential use real-world contexts by comparing contrasting them.

Language: Английский

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A New Malware Classification Framework Based on Deep Learning Algorithms

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, Journal Year: 2024, Volume and Issue: 08(06), P. 1 - 5

Published: June 6, 2024

Recent advancements in computer technology have precipitated a shift towards virtual environments, accelerated by the COVID-19 pandemic. Cybercriminals capitalized on this trend, transitioning their activities to exploit vulnerabilities cyberspace. Malicious software (malware) has emerged as preferred tool for launching cyber-attacks, continually evolving with sophisticated obfuscation and packing techniques evade detection. Traditional machine learning (ML) algorithms, once effective identifying malware, are now struggling keep pace these advancements. In response, deep (DL) algorithms offer promising solution, leveraging ability discern intricate patterns correlations within data. This study proposes novel hybrid deep-learning-based architecture, integrating two pre-trained network models enhance classification accuracy. Through extensive evaluation datasets including Malimg, Microsoft BIG 2015, Malevis, proposed method demonstrates significant improvements accuracy, outperforming existing ML-based malware detection methods literature. Specifically, achieves an impressive accuracy of 97.78% Malimg dataset, underscoring its effectiveness combating variants. Keywords — Malware, classification, detection, variants, neural networks, transfer learning, learning.

Language: Английский

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Digital Evidence Collection and Preservation in Computer Network Forensics

Advances in library and information science (ALIS) book series, Journal Year: 2024, Volume and Issue: unknown, P. 42 - 62

Published: Jan. 18, 2024

The growing integration of information and communication technology (ICT) in today's world has led to the rise crimes digital realm, specifically those linked networks computers. This surge cybercrime presents substantial hurdles for forensic evaluation. A pivotal evidence source cyber probes, especially when pinpointing potential threats confidential data, stems from extensive data produced by network nodes. primary goal forensics is offer clear, well-documented that can stand up a courtroom. chapter intends deliver thorough overview current scholarly material, emphasizing diverse aspects endeavors. It encompasses foundational theories, prior analysis blueprints, initiatives refine methods, thereby augmenting reach, proficiency, precision structure.

Language: Английский

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AIoT Integration Advancements and Challenges in Smart Sensing Technologies for Smart Devices

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 42 - 65

Published: Jan. 26, 2024

Artificial intelligence in things (AIoT) has revolutionized the capabilities and features of smart gadgets. Technology advancements sensing have allowed for seamless integration AI IoT, increasing general efficacy devices. This chapter looks at technology's progress challenges context AIoT integration. The study begins with a brief introduction its significance device industry. It then delves into numerous technologies that aid bringing IoT together, such as environmental sensors, motion biometric more. Miniaturization, improved accuracy, lower power consumption are just few ways these sensor progressed. also highlights integrating technology. need efficient management, interoperability, complexity fusion data integration, concerns over security privacy some obstacles way.

Language: Английский

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