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All Journal International Journal of Multidisciplinary Sciences and Arts Jurihum
Hafiz Khawar Hussain
DePaul University Chicago, Illinois,USA
Computer Science & IT Economics, Econometrics & Finance Languange, Linguistic, Communication & Media

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Using AI to Increase Heat Exchanger Efficiency: An Extensive Analysis of Innovations and Uses Shahrukh Khan Lodhi; Hafiz Khawar Hussain; Ibrar Hussain
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 4 (2024): International Journal of Multidisciplinary Sciences and Arts, Article October 2
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v3i4.4617

Abstract

Artificial intelligence (AI) has made significant strides toward cost reduction and performance optimization in heat exchanger technologies. Artificial intelligence (AI) methods in machine learning, deep learning, and expert systems provide significant advancements in diagnostics, performance optimization, and predictive maintenance. While deep learning is superior at recognizing intricate patterns, machine learning offers flexibility through data analysis. Expert systems use domain expertise to make decisions, although they might not be as flexible as data-driven methods. Hybrid approaches integrate these strategies to improve flexibility and performance. New developments include smart heat exchangers with IoT capabilities for real-time monitoring, compact designs for a variety of applications, and new materials and coatings that improve durability and efficiency. Reducing environmental effect is also reflected in sustainable solutions like waste heat recovery. Nevertheless, issues like computing costs, data quality, and interaction with current systems still need to be resolved. Optimized computational methodologies, modular integration, and sophisticated sensor technology are required to address these problems. AI has the power to completely transform heat exchanger technology by enhancing sustainability and efficiency. Future breakthroughs will be fueled by ongoing improvements in materials, designs, and AI approaches, offering more complex solutions to satisfy changing environmental and performance requirements.
AI-POWERED HEART FAILURE PREDICTION AND MONITORING TOOLS Roman Khan; Arbaz Haider Khan; Hira Zainab; Hafiz Khawar Hussain
JURIHUM : Jurnal Inovasi dan Humaniora Vol. 2 No. 3 (2024): JURIHUM : Jurnal Inovasi dan Humaniora
Publisher : CV. Shofanah Media Berkah

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Abstract

Recently, a chronic and severe form of cardiovascular diseases – heart failure (HF) – became preventable with the aid of artificial intelligence (AI). In this article, we explore the multiple ways in which AI is employed to enhance the care of patients with heart failure: remote real-time supervision systems, individualized interventions, risk assessment models. AI’s ability to review massive amounts of data from Wearables, electronic health, and record checking tools may aid heart failure early detection, risk elevation, and preventive treatments. This enhances the patients’ quality of life, and also reduces the client’s expenditure on healthcare. Several challenges remain relating to: AI availability and data quality; algorithm explain ability; legal and regulatory aspects; and patient engagement, even if there are positive preliminary signs for the broad development of AI-based solutions in the health field. Even bigger promises for the improvement of precision and individualized heart failure therapy are seen in future developments of AI through application of big data, genomics, and remote touchscreen monitors. The work on the improvement of the explainable AI models and expanded international cooperation will also help solve these problems and enhance the efficiency as well as equity of heart failure treatment. With rapid advancements in Artificial Intelligence, it is expected that the care of patients with heart failure will be transformed, both in terms of time, efficiency, and individual patient needs.
Co-Authors Arbaz Haider Khan Hira Zainab Ibrar Hussain Roman Khan Shahrukh Khan Lodhi