https://journals.sagescience.org/index.php/ssraml/issue/feed 2024-02-27T12:06:52-07:00 Open Journal Systems <p>The Sage Science Review of Applied Machine Learning is a peer-reviewed academic journal that publishes original research articles, reviews, and case studies in the field of applied machine learning. The journal aims to provide a platform for researchers, practitioners, and scholars to share their innovative research and applications in the field of machine learning.</p> <p>The journal covers a broad range of topics related to applied machine learning, including machine learning algorithms and models, deep learning and neural networks, natural language processing, computer vision, and image processing, data mining and knowledge discovery, statistical learning, reinforcement learning, transfer learning, explainable and interpretable machine learning, and machine learning for healthcare, finance, social media, and other applications.</p> https://journals.sagescience.org/index.php/ssraml/article/view/120 2024-01-24T07:32:41-07:00 Prakash Sharma Sharmaprakash@gmail.com

<p>The integration of quantum computing into drug formulation and design represents a significant paradigm shift, augmenting computational capabilities in processing complex datasets far beyond the scope of classical computing systems. This evolution is particularly consequential in the realm of drug design, where the intrinsic nature of molecular interactions and reactions is fundamentally quantum mechanical. Central to this development is the role of quantum computing in enhancing molecular simulation processes. Traditional drug design methodologies frequently employ molecular dynamics simulations to predict the behavior of molecules. However, the accuracy of these simulations is often hampered when conducted on classical computers, especially when dealing with large and complex molecular systems. Quantum computers, by leveraging the principles of quantum mechanics, are capable of simulating molecular interactions with a higher degree of precision. This enhanced accuracy is vital for comprehensively understanding drug interactions at the molecular level, a critical factor in ensuring both the efficacy and safety of pharmaceuticals. Furthermore, quantum computing facilitates a more efficient approach to computational drug discovery. The process of identifying new drug candidates involves navigating a vast chemical space to discover compounds that bind effectively to specific biological targets. Quantum algorithms have the potential to expedite this process by rapidly evaluating the potential effectiveness of a multitude of compounds. This capability not only reduces the time associated with drug discovery but also diminishes the overall cost. Another significant application of quantum computing is in the optimization of drug formulations. By calculating the most effective molecular structures and combinations, quantum computing aids in optimizing both pharmacokinetics and pharmacodynamics. This optimization is crucial for enhancing drug efficacy while minimizing adverse effects. The move towards personalized medicine also benefits from the advent of quantum computing. As treatments become increasingly tailored to individual genetic profiles, the processing of immense genomic data sets becomes imperative. Quantum computing stands to revolutionize this aspect of drug design, offering processing speeds unattainable by classical computers, thus enabling the development of personalized treatment regimes and drugs. Despite these promising advancements, the application of quantum computing in drug design is still in its early stages. Challenges such as the current limitations in quantum hardware, which hinder stability and scalability, and the ongoing development of algorithms that effectively harness quantum mechanics for drug design, remain significant barriers.</p>

2024-01-08T00:00:00-07:00 Copyright (c) 2024 Author https://journals.sagescience.org/index.php/ssraml/article/view/133 2024-02-27T12:06:52-07:00 Samantha Reyes samantha.reyes@utarawa.ki Michael Patel michael.patel@kit.edu.ki

<p class="keys" style="text-align: justify;"><span style="font-family: 'Times New Roman',serif;">Supply chain management and logistics are vital to business success in today's globalized and technology-driven world. The advent of big data and predictive analytics provides unprecedented opportunities to optimize supply chain operations, reduce costs, and gain competitive advantage. This paper examines the role of big data and predictive analytics in supply chain management and logistics optimization. It reviews relevant technologies and analytical techniques, key application areas, implementation challenges, and ethical considerations. Three illustrative case studies demonstrate measurable benefits from using big data analytics, such as improved demand forecasting, optimized inventory and production levels, reduced supply chain risks, and enhanced logistics network design. Tables and quantitative results are provided to highlight the positive impacts. The paper concludes with a discussion of emerging trends, best practices, and a future outlook for leveraging big data analytics further as a key driver of supply chain excellence.</span></p>

2024-01-11T00:00:00-07:00 Copyright (c) 2024 Authors