Major Challenges and Future Approaches in the Employment of Blockchain and Machine Learning Techniques in the Health and Medicine

In an era characterized by the growing significance of energy-efficient and human-centric environmental control systems, this research endeavors to investigate the efficacy of a Fuzzy Proportional-Integral-Derivative (PID) control approach for temperature regulation within Heating, Ventilation, and Air Conditioning (HVAC) systems. The study leverages the adaptability and robustness of fuzzy logic to dynamically tune the PID controller's parameters in response to changing environmental conditions. Through comprehensive simulations and comparative analyses, the research showcases the superior performance of the proposed fuzzy PID control system in terms of rapid response, overload avoidance, and minimal steady-state error, particularly when contrasted with conventional PID control and model predictive control (MPC) methodologies. Furthermore, the research extends its scope to assess the control system's resilience in the face of significant load variations, affirming its practical applicability in real-world HVAC scenarios. Beyond its immediate implications for HVAC systems, this research underscores the broader potential of fuzzy PID control in enhancing control precision and adaptability across various domains, including robotics, industrial automation, and process control. By advocating for future research endeavors in optimizing fuzzy membership functions, implementing real-time solutions, and exploring multi-objective optimization, among other avenues, this study seeks to contribute to the ongoing discourse surrounding advanced control strategies for achieving energy-efficient and human-centric environmental regulation.

Section: A Discussionmentioning

confidence: 98%

Intelligent Fuzzy-PID Temperature Control System for Ensuring Comfortable Microclimate in an Intelligent Building

Abdrakhmanov,

Berkimbayev,

Seitmuratov

et al. 2024

“…The fourth industrial revolution, or Industry 4.0, represents a fundamental shift in the paradigm of manufacturing and production industries, integrating advanced digital technologies and achieving enhanced connectivity and data exchange in manufacturing environments [1]. With this transformation comes the generation of unprecedented volumes of data, necessitating innovative approaches for effective data utilization.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Enhanced Framework for Big Data Modeling with Application in Industry 4.0

Kazbekova,

Ismagulova,

Zhussipbek

et al. 2024

In the dynamic milieu of Industry 4.0, characterized by the deluge of big data, this research promulgates a groundbreaking framework that harnesses machine learning (ML) to optimize big data modeling processes, addressing the intricate requirements and challenges of contemporary industrial domains. Traditional data processing mechanisms falter in the face of the sheer volume, velocity, and variety of big data, necessitating more robust, intelligent solutions. This paper delineates the development and application of an innovative ML-augmented framework, engineered to interpret and model complex, multifaceted data structures more efficiently and accurately than has been feasible with conventional methodologies. Central to our approach is the integration of advanced ML strategies-including but not limited to deep learning and neural networks-with sophisticated analytics tools, collectively capable of automated decisionmaking, predictive analysis, and trend identification in real-time scenarios. Beyond theoretical formulation, our research rigorously evaluates the framework through empirical analysis and industrial case studies, demonstrating tangible enhancements in data utility, predictive accuracy, operational efficiency, and scalability within various Industry 4.0 contexts. The results signify a marked improvement over existing models, particularly in handling high-dimensional data and facilitating actionable insights, thereby empowering industrial entities to navigate the complexities of digital transformation. This exploration underscores the potential of machine learning as a pivotal ally in evolving data strategies, setting a new precedent for data-driven decision-making paradigms in the era of Industry 4.0.

“…Big Data technologies enable the handling of large datasets beyond the ability of traditional data processing tools, providing insights that can lead to improved patient outcomes, cost reductions, and enhanced operational efficiencies [1]. The integration of Big Data analytics into healthcare processes allows for the predictive analysis of patient health, personalized medicine, and the optimization of clinical operations [2].…”

Section: Introductionmentioning

confidence: 99%

Framework for Organization of Medical Processes in Medical Institutions Based on Big Data Technologies

Zhussipbek,

Turymbetov,

Ibragimova

et al. 2024

This research paper delves into the burgeoning field of Big Data analytics in healthcare, proposing an innovative framework aimed at refining the organization and management of medical processes within healthcare institutions. Through the lens of detailed case studies, including stroke diagnosis leveraging the UNet model, and the identification of heart and respiratory diseases via machine learning algorithms applied to data from wearable devices, the study illuminates the profound capabilities of Big Data technologies in enhancing the precision of diagnostics, tailoring patient treatment, and elevating the overall efficiency of healthcare services. It meticulously interprets the outcomes of these applications, discusses the practical implications for healthcare professionals and institutions, confronts the challenges inherent in the integration of sophisticated analytics in clinical settings, and outlines potential directions for future research. Among the pivotal challenges highlighted are issues related to data privacy, security, the need for advanced infrastructure, and the imperative for ongoing training and interdisciplinary cooperation to navigate the complexities of Big Data in healthcare. The paper underscores the transformative promise of Big Data analytics, suggesting that comprehensive adoption and adept implementation could revolutionize healthcare delivery, making it more personalized, efficient, and cost-effective. Through this exploration, the paper contributes to the ongoing discourse on the integration of technology in healthcare, offering insights into how Big Data analytics can serve as a cornerstone for the next generation of medical diagnostics and patient care management, thereby enhancing health outcomes on a global scale.