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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

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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.

Active Learning Enhanced Neural Networks for Aerodynamics Design in Military and Civil Aviation Nasir, Sheharyar; Hussain, Hafiz Khawar; 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.v3i4.5036

The use of adaptive neural networks in aerodynamics design has become one of the most promising recent invention in both military and civil aircraft design, providing new approaches to the solution of a number of problematic issues connected with optimization of aircraft performance. Herein, this review provides a synthesis of neural networks and aerodynamics by emphasizing their ability to facilitate advanced design engineering, expedite the design process, as well as promote the usability and effectiveness of higher performing systems. Neural networks are involved in shape optimization, drag cutting, real time aircraft modifications and other key issue areas attesting to their capability in handling aerodynamics. Employing methods like supervised learning, reinforcement learning, and physics aware neural networks these networks can simulate non-linear multidimensional systems and arrive at solutions that are impossible through ordinary methods. The usage of these tools has been pushed even more over time, due to new advancements such as High-Performance Computing and specialized hardware. The review also considers effective application of systematic adaptive neural networks in the military and civil aviation hypersonic vehicle design, stealth aircraft design and optimization, the new fuel-efficient wings, and flight efficiency systems for real time control. The results put into evidence benefits of neural networks for cutting down design cycles, boosting MPG, increasing safety, and encouraging environmentally friendly solutions. The future for aerospace engineering will be in the hands of adaptive neural networks as part of the development of the aviation industry, dictating new advancements in both military and commercial aviation.

Active Learning Enhanced Neural Networks for Aerodynamics Design in Military and Civil Aviation Nasir, Sheharyar; Hussain, Hafiz Khawar; 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.v3i4.5036

The use of adaptive neural networks in aerodynamics design has become one of the most promising recent invention in both military and civil aircraft design, providing new approaches to the solution of a number of problematic issues connected with optimization of aircraft performance. Herein, this review provides a synthesis of neural networks and aerodynamics by emphasizing their ability to facilitate advanced design engineering, expedite the design process, as well as promote the usability and effectiveness of higher performing systems. Neural networks are involved in shape optimization, drag cutting, real time aircraft modifications and other key issue areas attesting to their capability in handling aerodynamics. Employing methods like supervised learning, reinforcement learning, and physics aware neural networks these networks can simulate non-linear multidimensional systems and arrive at solutions that are impossible through ordinary methods. The usage of these tools has been pushed even more over time, due to new advancements such as High-Performance Computing and specialized hardware. The review also considers effective application of systematic adaptive neural networks in the military and civil aviation hypersonic vehicle design, stealth aircraft design and optimization, the new fuel-efficient wings, and flight efficiency systems for real time control. The results put into evidence benefits of neural networks for cutting down design cycles, boosting MPG, increasing safety, and encouraging environmentally friendly solutions. The future for aerospace engineering will be in the hands of adaptive neural networks as part of the development of the aviation industry, dictating new advancements in both military and commercial aviation.

Using AI to Increase Heat Exchanger Efficiency: An Extensive Analysis of Innovations and Uses Lodhi, Shahrukh Khan; Hussain, Hafiz Khawar; Hussain, Ibrar
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

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.

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

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.

Evaluating the Potential of Artificial Intelligence in Orthopedic Surgery for Value-based Healthcare Tariq, Aftab; Gill, Ahmad Yousaf; Hussain, Hafiz Khawar
International Journal of Multidisciplinary Sciences and Arts Vol. 2 No. 2 (2023): International Journal of Multidisciplinary Sciences and Arts, Article April 202
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v2i1.2394

The potential of artificial intelligence (AI) to transform value-based healthcare in the area of orthopedic surgery is examined in this research. Orthopedic surgeons and healthcare systems may improve patient outcomes, increase efficiency, and alter care delivery by combining AI algorithms, cutting-edge data analytics, and novel technology. Through case studies and success stories, the article provides a thorough study of the advantages and prospects provided by AI in orthopedic surgery. These instances demonstrate how AI has been successfully applied to several facets of orthopedic surgery, including as diagnosis, planning of the surgical course, surgical navigation, postoperative care, and resource allocation. The ethical and legal ramifications of using AI are also discussed in the study, with a focus on patient autonomy, privacy, accountability, and any potential effects on the healthcare workforce. The potential applications of AI in orthopedic surgery are examined, together with developments in preoperative planning, surgical robotics, remote monitoring, predictive analytics, personalised medicine, research, and innovation. The promise of AI in orthopedic surgery is obvious, despite issues with data quality, privacy, algorithm biases, and legal constraints. The ethical and appropriate application of AI technology in orthopedic surgery has the potential to significantly enhance patient outcomes, lower complications, boost efficiency, and change the way healthcare is provided. This study lays the groundwork for future study and application in the field of orthopedic surgery by offering insightful information on the role of AI in delivering value-based healthcare.

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