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All Journal Journal of Social Research BULLET : Jurnal Multidisiplin Ilmu
Zainab, Hira
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Humanities Computer Science & IT Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Social Sciences

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Artificial-Intelligence Aerodynamics for Efficient Energy Systems: The Focus on Wind Turbines Nasir, Sheharyar; Zainab, Hira; Hussain, Hafiz Khawar
BULLET : Jurnal Multidisiplin Ilmu Vol. 3 No. 5 (2024): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The incorporation of AI in wind energy systems has transformed the design, operation and management of wind turbines, wind farms increasing their effectiveness, resilience and viability. This paper explores the transformative impact of AI-driven technologies across various aspects of wind energy, focusing on five key areas: Lear two main areas: in turbine engineering, advanced concepts such as fluid dynamics and blade design, while in computer sciences, major components consist of machine learning for performance assessment of turbines, monitoring of turbines on real-time basis as well as for the purpose of maintenance, and optimization of wind farms. In the specific application of improving the efficiency of turbine blade design and function, AI continues to be useful as machine learning is used in creating new and more efficient and long lasting blades while dynamic real time monitoring systems are used in making adjustments based on external conditions. AI-based predictive maintenance enables for mechanical problems identification before they evolve, thus decreasing the time a machine spends out of service and operational expenses. Also, AI enhances the design of wind farm, control of wake and load balance to enhance efficiency of wind electricity generation. It allows for a more effective intro of energy into the larger grid and hydrates therefore increasing the availability of renewable energy with stability. Based on this paper, the future of AI remains evident in future enhancement of wind energy systems, hence guaranteeing sustainable energy, efficiency, and cost-effectiveness in energy solutions for the overall energy transformation.
Deep Learning in the Diagnosis and Management of Arrhythmias Khan, Arbaz Haider; Zainab, Hira; Khan, Roman; Hussain, Hafiz Khawar
Journal of Social Research Vol. 4 No. 1 (2024): Journal of Social Research
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v4i1.2362

Abstract

Recent advancements in analyzing methods for the identification of arrhythmia based on deep learning have revealed great promise towards improving cardiac care. Probabilistic models have been used effectively to detect a number of arrhythmic disorders from ECG signals with the help of convolutional neural networks and Long Short Term Memory neural network. These models are more precise and quicker than conventional approaches to deal with the ailment in the initial stages and with diseases such as bradycardia, ventricular tachycardia, or atrial fibrillation. However, barriers such as class distribution, data sanitization, interpretability, and generalization across different types of patients remain, which hinders their clinical utilization. Actually, deep learning is used in clinical practice, especially in wearable devices and remote patient monitoring for the unceasing and real-time continuous rheological evaluation of the cardiovascular system. The subsequent advancements in this area will focus on the proper combination of the data from multiple subject areas and the application of specific treatment approaches, including the use of artificial intelligence in a more extensive medical system. Other than the diagnosis of arrhythmias, deep learning has the chances of enhancing patient prognoses, preliminary assessment, and tailor-made treatments. It is likely that deep learning-based systems will have a possibility to evolve into powerful aid for diagnosing and setting further treatment in cases of arrhythmias, though there are issues on the way to the enhance the availability and quality of the care. This will probably be facilitated by continued research and integration between academicians, practitioners, and policy makers.
Co-Authors Hussain, Hafiz Khawar Khan, Arbaz Haider Khan, Roman Nasir, Sheharyar