https://journals.threws.com/index.php/IJSCS <p><strong>International Journal of Statistical Computation and Simulation</strong></p> <p><strong>Scope:</strong></p> <p>The International Journal of Statistical Computation and Simulation is a peer-reviewed scholarly publication dedicated to advancing the field of statistical computation and simulation. This journal serves as a platform for researchers, academics, and practitioners to disseminate their innovative contributions and engage in meaningful discourse regarding statistical methods, computational techniques, and simulation applications.</p> <p><strong>Key Focus Areas:</strong></p> <ol> <li> <p><strong>Statistical Methods:</strong> The journal covers a wide array of statistical methods, from classical techniques to modern approaches. Topics include, but are not limited to, regression analysis, time series analysis, Bayesian statistics, and multivariate analysis.</p> </li> <li> <p><strong>Computational Techniques:</strong> This journal explores computational methods and algorithms used in statistical analysis. It embraces the latest advancements in numerical and computational tools, including software packages, high-performance computing, and parallel computing for statistical applications.</p> </li> <li> <p><strong>Simulation Studies:</strong> Researchers and practitioners are encouraged to submit papers related to simulation studies, encompassing Monte Carlo methods, agent-based modeling, and discrete-event simulation. This area delves into the design, analysis, and interpretation of simulation experiments.</p> </li> <li> <p><strong>Statistical Software Development:</strong> The journal welcomes contributions related to the development of statistical software, packages, and tools that facilitate statistical computation and analysis. It offers a platform for sharing open-source resources and fostering collaboration among software developers.</p> </li> <li> <p><strong>Applications:</strong> The journal acknowledges the importance of real-world applications of statistical computation and simulation. It welcomes articles that apply statistical techniques and simulations in various domains, such as economics, engineering, social sciences, and healthcare.</p> </li> <li> <p><strong>Data Analytics:</strong> The scope of the journal extends to data analytics, including big data analytics and data mining. It explores the role of statistical methods and simulations in uncovering meaningful insights from large and complex datasets.</p> </li> <li> <p><strong>Computational Statistics Education:</strong> Contributions pertaining to the pedagogy of computational statistics and simulation in academic and professional settings are encouraged. This includes innovative teaching methods, curriculum development, and educational resources.</p> </li> </ol> <p><strong>Aims and Objectives:</strong></p> <ul> <li>To provide a global platform for the exchange of knowledge and ideas in statistical computation and simulation.</li> <li>To facilitate collaboration among researchers, practitioners, and educators in the field of statistics.</li> <li>To foster innovation and advancements in statistical methods and computational techniques.</li> <li>To promote the development of open-source statistical software and tools.</li> <li>To encourage the application of statistical computation and simulation in solving real-world problems across diverse domains.</li> <li>To contribute to the growth and improvement of statistical education and training worldwide.</li> </ul> <div><strong><em><br /></em> <em>IJSCS </em> </strong>does not consider applications of statistics to other fields, except as illustrations of the use of the original statistics presented.</div> <div> </div> <div>Accepted papers should ideally appeal to a wide audience of statisticians and provoke real applications of theoretical constructions.</div> <div> </div> <div> <div align="left"><strong>Impact Factor </strong></div> <div align="left"> </div> <div align="left"><strong>International Journal of Statistical Computation and Simulation is a double-blind peer-reviewed journal indexed in several databases like google scholar, Wos, Dooj, ESCI </strong></div> <div align="left"> </div> <div align="left"> <p>JCR Impact Factor: 4.6 (2019)</p> <p>JCR Impact Factor: 5.6 (2020)</p> <p>JCR Impact Factor: 5.9 (2021)</p> <p>JCR Impact Factor : 6.1 (2022)</p> <p>JCR Impact Factor : Under Evaluation (2023)</p> </div> </div> <div align="left"> </div> <div align="left"><strong>Peer Review Policy</strong></div> <div align="left"><br />All submitted manuscripts are subject to initial appraisal by the Editors. If found suitable for further consideration, papers are subject to peer review by independent, anonymous expert referees, under the guidance of a team of expert Associate Editors. All peer-review is double-blind and submissions can be made online via the Submission Portal.</div> <p><br /><strong>Publishing Ethics</strong></p> <div align="left">The Journal adheres to the highest standards of publishing ethics, with rigorous processes in place to ensure this is achieved. We are member of the Committee of Publications Ethics (COPE) and utilizes CrossCheck for all Journals. </div> The research world en-US International Journal of Statistical Computation and Simulation https://journals.threws.com/index.php/IJSCS/article/view/238 <p>As cloud computing continues to reshape the landscape of IT infrastructure, organizations strive to strike a delicate balance between cost-effectiveness, resource efficiency, and the ability to meet dynamic workloads. This systematic review explores and synthesizes the existing body of literature on cloud architectural approaches, aiming to optimize Total Cost of Ownership (TCO), enhance resource utilization, ensure high service availability, and enable rapid elasticity. The study employs a rigorous systematic review methodology, encompassing a comprehensive search of peer-reviewed articles, conference papers, and relevant industry reports published over the last decade. The focus is on identifying architectural frameworks, design patterns, and strategies that contribute to the overarching goals of cost optimization, efficient resource utilization, and robust service availability in the cloud environment. Key themes emerging from the literature include Infrastructure as Code (IaC) practices, microservices architectures, serverless computing paradigms, and auto-scaling mechanisms. These architectural elements are examined for their impact on TCO reduction, the efficient allocation of resources, and the ability to seamlessly scale resources in response to fluctuating demand. Additionally, considerations related to fault tolerance, load balancing, and data redundancy are explored in the context of ensuring high service availability. The systematic review also sheds light on the challenges and trade-offs associated with different architectural choices. Factors such as security implications, vendor lock-in risks, and the learning curve for implementing advanced architectural patterns are discussed, providing a nuanced understanding of the complexities organizations face when optimizing their cloud infrastructure. The findings of this systematic review contribute to the current state of knowledge in cloud architecture by offering insights into proven approaches, emerging trends, and potential areas for future research. The synthesis of diverse architectural strategies provides a valuable resource for practitioners and researchers seeking guidance on designing cloud solutions that align with business objectives, emphasizing the importance of an architectural foundation that not only minimizes costs but also maximizes resource efficiency, service availability, and agility in the face of changing demands.</p> Leela Manush Gutta Copyright (c) 2024 2024-03-06 2024-03-06 16 1 1 20 https://journals.threws.com/index.php/IJSCS/article/view/240 <p>The integration of Machine Learning (ML) and Internet of Things (IoT) technologies is revolutionizing patient monitoring in healthcare. This paper presents a comprehensive review of the current advancements in applying ML algorithms to IoT-enabled healthcare systems. We explore various IoT devices used for continuous health monitoring, such as wearable sensors and smart medical equipment, and how ML techniques enhance data analysis, prediction, and decision-making. Key applications discussed include early detection of diseases, personalized treatment plans, and remote patient monitoring. We also address challenges such as data privacy, interoperability, and the need for real-time processing. Our findings highlight the potential of ML and IoT to improve patient outcomes, reduce healthcare costs, and enable proactive healthcare management.</p> <p>&nbsp;</p> <p>&nbsp;</p> Dr. Pankaj Kapoor Copyright (c) 2024 2024-06-07 2024-06-07 16 1 https://journals.threws.com/index.php/IJSCS/article/view/241 <p>Predictive analytics, powered by advanced computational methods, is transforming the healthcare sector by enabling personalized medicine and proactive patient care. This paper delves into the role of computational techniques, including machine learning, data mining, and statistical modeling, in predictive analytics for healthcare. We discuss the utilization of big data from electronic health records (EHRs), genomics, and wearable devices to develop predictive models for disease risk assessment, patient stratification, and treatment response prediction. Case studies demonstrating the successful implementation of these models in chronic disease management, oncology, and emergency care are presented. Furthermore, the paper addresses the challenges of data integration, model interpretability, and regulatory compliance. The insights provided emphasize the transformative impact of predictive analytics on enhancing healthcare delivery and patient outcomes.</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> Dr. Anu Chakarvaty Copyright (c) 2024 2024-06-07 2024-06-07 16 1