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All Journal International Journal of Multidisciplinary Sciences and Arts Journal Research of Social Science, Economics, and Management Jurihum
Ahmad Yousaf Gill
American National University, USA
Computer Science & IT Economics, Econometrics & Finance Environmental Science Languange, Linguistic, Communication & Media

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Healthcare Revolution: How AI and Machine Learning Are Changing Medicine Ayesha Saeed; Ali Husnain; Saad Rasool; Ahmad Yousaf Gill; Amelia Amelia
Journal Research of Social Science, Economics, and Management Vol. 3 No. 3 (2023): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v3i3.558

Abstract

This essay examines the enormous effects of machine learning and artificial intelligence (AI) on healthcare. Through data analysis, AI is transforming disease detection and prediction and improving the precision of diagnoses. By accelerating medication discovery and improving individualized treatment programs, it is revolutionizing both treatment and drug development. AI is promoting customized medicine by using genetic information to customize therapies. Through automation and optimized resource allocation, it is streamlining hospital processes. The importance of ethical considerations is significant; they center on data privacy, bias reduction, and accountability. The study highlights potential avenues for AI development, such as AI-driven drug discovery, predictive and preventative healthcare, advances in genomic medicine, enhanced medical imaging, and more robotics and automation. Predictive analytics, telehealth, AI virtual assistants, and AI in mental healthcare are all expected to grow. These developments have the potential to improve health care, streamline processes, and boost scientific inquiry. To use AI in healthcare in a fair and ethical manner, however, and usher in a future that is more patient-centric, accurate, and accessible internationally, difficulties related to data quality, ethics, regulation, and prejudice must be addressed.
3D Printing Techniques: Transforming Manufacturing with Precision and Sustainability Shahrukh Khan Lodhi; Ahmad Yousaf Gill; Ibrar Hussain
International Journal of Multidisciplinary Sciences and Arts Vol. 3 No. 3 (2024): International Journal of Multidisciplinary Sciences and Arts, Article July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/ijmdsa.v3i3.4568

Abstract

Additive manufacturing, sometimes known as 3D printing, has advanced quickly and has the potential to revolutionize a number of industries. This analysis examines the noteworthy developments and upcoming trends in 3D printing, emphasizing the effects on accuracy, eco-friendliness, and industrial uses. The first section of the book explores how advances in materials have affected 3D printing, with a focus on the creation of thermoplastics, photopolymers, metal powders, ceramics, and bio-printable materials. These developments have increased 3D printing's potential and made it possible to produce components that are biocompatible, customizable, and high-performing. The topic of sustainability is then brought up, with particular emphasis on how 3D printing helps to cut down on energy and material waste. The key components of the technology's environmental benefits are its on-demand part production capability and its utilization of environmentally benign resources like recycled and biodegradable polymers. The essay also discusses the difficulties associated with material disposal and energy use in the perspective of sustainability. Examining industrial applications, the paper emphasizes how 3D printing is revolutionizing industries like consumer products, construction, healthcare, automotive, and aerospace. The technology's versatility and effect across multiple fields are demonstrated by highlighting its role in enabling rapid prototyping, customized production, and novel design solutions. The assessment highlights major developments in 3D printing for the future, such as advances in materials science, technological advancements, and integration with other cutting-edge fields like artificial intelligence, the Internet of things, and robotics. Highlighted are the possibilities for increased customization, on-demand production, and sustainability, demonstrating how these advancements will continue to influence the market? The assessment highlights the significant effects that 3D printing will have on manufacturing and design, emphasizing how technology may spur innovation, lessen its negative effects on the environment, and adapt to changing needs in a variety of industries. Technology will play an ever-more-important role in improving sustainability and revolutionizing sectors as it develops.
AI-Powered Innovations in Contemporary Manufacturing Procedures: An Extensive Analysis Shahrukh Khan Lodhi; Ahmad Yousaf Gill; 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.v3i4.4616

Abstract

The industrial sector is undergoing a transformation thanks to artificial intelligence (AI), which is bringing revolutionary changes to a number of areas like robots and automation, supply chain efficiency, predictive maintenance, and quality control and assurance. This thorough analysis investigates AI's significant influence on contemporary manufacturing procedures. Artificial Intelligence (AI) improves machine capabilities in robotics and automation, creating more intelligent and flexible systems. Robots can now complete complicated tasks with more flexibility and precision thanks to AI-driven developments, which boosts manufacturing efficiency and human-robot cooperation. Another crucial area where AI has a big impact is predictive maintenance. With the use of machine learning algorithms and real-time data analysis, artificial intelligence (AI) helps manufacturers anticipate equipment faults before they happen. By taking a proactive stance, unplanned downtime is decreased, resource usage is optimized, and machinery longevity is increased. AI has a significant positive impact on quality assurance and control because to cutting-edge technologies like data analytics and computer vision. Artificial intelligence (AI) solutions facilitate predictive quality management, improve fault identification, and offer real-time monitoring. Higher quality standards, less waste, and more customer happiness are the outcomes of this. Artificial Intelligence (AI) tackles issues related to supplier performance, accurate forecasting, and inventory management in supply chain optimization. Automation and analytics powered by AI simplify supply chain processes, increase transparency, and facilitate improved decision-making, which lowers costs and increases flexibility. All things considered, integrating AI into manufacturing processes offers a strategic advantage by promoting increased accuracy, flexibility, and efficiency. The continued developments in AI technology have the potential to significantly influence how manufacturing develops in the future by creating new avenues for creativity and excellence in the sector.
Harnessing Predictive Power: Exploring the Crucial Role of Machine Learning in Early Disease Detection Saad Rasool; Ali Husnain; Ayesha Saeed; Ahmad Yousaf Gill; Hafiz Khawar Hussain
JURIHUM : Jurnal Inovasi dan Humaniora Vol. 1 No. 2 (2023): JURIHUM : Jurnal Riset dan Humaniora
Publisher : CV. Shofanah Media Berkah

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

Abstract

The incorporation of machine learning into healthcare has transformed the landscape of disease detection, allowing for a paradigm shift from reactive to proactive approaches. This paper investigates the transformative effect of machine learning on early disease detection by conducting a comprehensive literature review. The paper is divided into ten sections, each of which focuses on an important aspect of this developing discipline. The first section, titled "Predictive Power: Machine Learning's Role in Early Disease Detection," introduces the overall theme and significance of leveraging machine learning for proactive healthcare strategies. Subsequent sections delve into particulars, highlighting the complex relationship between machine learning and early disease detection. The article "Unleashing the Potential: How Machine Learning Enhances Early Disease Detection" analyzes the multidimensional capabilities of machine learning in analyzing complex data to identify correlations that underlie early disease symptoms. The article "A Primer on Predictive Models: Understanding the Core Concepts in Disease Detection" explains the fundamental principles of predictive models and their function in identifying patterns within data. "From Pixels to Diagnoses: The Role of Imaging Data in Machine Learning-Driven Disease Detection" demonstrates how machine learning algorithms excel at analyzing medical images to detect subtle anomalies, thereby improving diagnostic accuracy. "Challenges and Opportunities: Navigating Ethical and Technical Considerations in Predictive Disease Detection" delves into the ethical implications of data privacy, bias, interpretability, and accountability, while also addressing technical obstacles such as data quality and model validation. The following sections highlight the convergence of clinical expertise and machine learning. The article "Bridging the Gap: Integrating Clinical Expertise with Machine Learning Algorithms for Early Diagnosis" highlights the significance of collaboration between healthcare professionals and data scientists in the development of accurate and interpretable predictive models. "Beyond Diagnostics: Predictive Power of Machine Learning in Forecasting Disease Progression" examines the extension of predictive models beyond diagnosis to predict disease trajectories, thereby transforming treatment planning. "Real-World Applications: Showcasing Successful Implementation of Machine Learning for Early Disease Detection" presents case studies from various medical domains to illustrate the practical impact of machine learning in identifying early disease indicators. "A Glimpse into the Future: Emerging Trends and Prospects in Machine Learning-Driven Disease Diagnostics" envisions the future landscape by emphasizing trends such as multi-modal data fusion, explainable artificial intelligence, and real-time monitoring. This article offers a comprehensive overview of the current state and future prospects of machine learning-driven early disease detection. It highlights the significance of collaboration between healthcare professionals and data scientists, as well as ethical considerations and the potential to transform healthcare delivery. The synthesis of these sections portrays a comprehensive picture of the transformative power of predictive models in healthcare, paving the way for proactive interventions, personalized treatments, and enhanced patient outcomes.
Co-Authors Ali Husnain Amelia Amelia Ayesha Saeed Ayesha Saeed Hafiz Khawar Hussain Ibrar Hussain Ibrar Hussain Saad Rasool Saad Rasool Shahrukh Khan Lodhi