Journal of Artificial intelligence and Machine Learning

How Nanotechnology can Revolutionize Drug Delivery

Uttam K Chowdhury — Sun, 10 Sep 2023 00:00:00 +0000

Nanotechnology has been immensely beneficial to the field of drug delivery as it has allowed pharmacologists to produce medicines that are much more effective than ever before. Read on to learn more

Transforming Healthcare: The Impact and Future of Artificial Intelligence in Healthcare

Dr. Suryakiran Navath, Ph.D. — Wed, 18 Oct 2023 00:00:00 +0000

The integration of Artificial Intelligence (AI) in healthcare has ushered in a new era, transforming the industry in unprecedented ways. This manuscript delves into the profound impact of AI on healthcare systems and envisions its future trajectory. Through a comprehensive analysis of current applications, challenges, and emerging trends, this study illuminates the revolutionary changes brought about by AI technologies. From advanced diagnostics to personalized treatment plans, AI is reshaping patient care, improving operational efficiency, and fostering innovative solutions. Furthermore, the manuscript explores ethical considerations, regulatory frameworks, and the societal implications of widespread AI adoption in healthcare. By examining the intersection of technology and human well-being, this manuscript provides a holistic view of how AI is revolutionizing healthcare and offers valuable insights into the future of medicine.

How Radio labeling is Helping Researchers Combat Covid 19

Suryakiran Navath — Thu, 21 Sep 2023 00:00:00 +0000

Want to learn more about radiolabeling? Here’s how radiolabeling is helping medical researchers combat Covid 19.

The Machine learning for predictive maintenance in supply chain management

Krishnamoorthy Selvaraj, Lakshmanan Lakshmanan — Fri, 22 Sep 2023 00:00:00 +0000

It has recently come to light that one of the most important applications of machine learning in a variety of sectors, including supply chain management, is predictive maintenance. The purpose of this research is to investigate the use of machine learning strategies for predictive maintenance within the framework of supply chain management. Traditional procedures of maintenance often cause inefficiencies and interruptions in the supply chain as a result of unanticipated breakdowns of various pieces of equipment. It is possible to greatly improve both the reliability and performance of supply chain operations via the use of predictive maintenance approaches. This article starts out by giving an overview of predictive maintenance and the role that it plays in supply chain management. The issues that are presented by unanticipated equipment failures and the cascade consequences that these failures have on the supply chain are discussed. In the context of predictive maintenance, a number of different techniques to machine learning, including supervised learning, unsupervised learning, and deep learning, are analyzed and discussed. In addition to this, the study digs into data-gathering strategies, discussing topics such as sensor data, past maintenance records, and external influences that might influence the health of equipment. In addition, the article discusses the implementation issues that are associated with installing predictive maintenance systems in supply chain environments. Some of these challenges include data quality and integration, real-time decision-making, cost concerns, and others. This paper investigates the role that edge computing and industrial Internet of Things (IoT) devices play in making data gathering, analysis, and preventative maintenance more efficient.

Machine learning-based survival prediction in glioma using large scale registry data – the importance of chemotherapy and radiation therapy management as predictive features

Krauze Andra, Zhao R, Zhuge Y, Camphausen K, Krauze Andra — Thu, 18 May 2023 00:00:00 +0000

Gliomas are the most common central nervous system tumors exhibiting poor survival, quality of life and neurological outcomes prompting significant discussion surrounding optimisation of the aggressiveness of management. The ability to estimate prognosis is crucial for both patients and providers in order to select the most appropriate treatment. Previous attempts at predicting survival outcomes have relied on clinical parameters (age, KPS, gender) and resection or methylation status and statistical models to create prognostic groups limiting survival prediction due to selection bias and tumor heterogeneity. Machine learning (ML) allows for more sophisticated approaches to survival prediction amalgamating real world clinical, molecular and imaging data. We wanted to examine clinical parameters needed to achieve superior predictive accuracy in order to help advance guidelines for the creation and maintenance of robust large-scale glioma registries.