Publications

Abstract:
The COVID-19 pandemic is undoubtedly one of the most formidable health crises in modern history. It has impacted hundreds of millions of people worldwide, with millions of lives lost. Research groups worldwide in artificial intelligence (AI) and machine learning (ML) have made significant strides in addressing various facets of the COVID-19 crisis. Their efforts have spanned epidemiological areas, such as prediction, control, and forecasting; molecular research, including molecular modeling and drug target identification; and medical applications, like AIdriven diagnostics and treatment development. In this work, we performed a systematic literature review on AI and ML applications in addressing various challenges in COVID-19 management. We conducted a search across major databases, including Google Scholar and Scopus, up to December 2024, using keywords related to AI, ML, and COVID-19. Our approach identified 63 studies with diverse applications of AI and ML in the fight against COVID-19. We carried out a thorough review of the identified studies based on several research questions and offered directions for future studies in this area. In particular, we highlighted lessons learned from COVID-19 for future pandemics in the South Asia context. By showing how AI and ML methods can streamline the COVID-19 response, this review will also help researchers and practitioners develop practical applications based on AI and ML models.

Abstract:
The rapid advancement of artificial intelligence (AI) has brought significant benefits across various domains, yet it has also led to increased energy consumption and environmental impact. This paper positions Green AI as a crucial direction for future research and development. It proposes a comprehensive framework for understanding, implementing, and advancing sustainable AI practices. We provide an overview of Green AI, highlighting its significance and current state regarding AI’s energy consumption and environmental impact. The paper explores sustainable AI techniques, such as model optimization methods, and the development of efficient algorithms. Additionally, we review energy-efficient hardware alternatives like tensor processing units (TPUs) and field-programmable gate arrays (FPGAs), and discuss strategies for designing and operating energy-efficient data centers. Case studies in natural language processing (NLP) and Computer Vision illustrate successful implementations of Green AI practices. Through these efforts, we aim to balance the performance and resource efficiency of AI technologies, aligning them with global sustainability goals.

Abstract:
Despite abundant research on the application of artificial intelligence (AI) in reverse logistics, no comprehensive study with bibliometric and network analysis has been conducted. This study uses bibliometric analysis to derive the prominent research statistics in AI-centric reverse logistics, considering 2929 articles from the last three decades. The most impactful contributors and countries that employ AI in reverse logistics are identified using various bibliometric tools. Also, network analysis is performed to reveal the most influential articles and emerging trends and map the relationships via clustering. The results of keyword co-occurrence and co-citation analyses reveal that machine learning and deep learning techniques have been commonly used for addressing reverse logistics challenges with higher frequency in recent years. Furthermore, a systematic review is carried out, considering the influential articles from recent years. The review is conducted following the systematic literature review framework, and 79 articles are chosen to be studied thoroughly. Subsequently, the articles are divided based on various reverse logistics processes, and the most frequently used AI techniques are identified and categorized into five distinct groups. The comprehensive investigation of AI techniques reveals the use-case scenario of AI algorithms in the reverse logistics domain. This study concludes with implications and recommendations for prospects by addressing the shortcomings of the current studies and providing future researchers and practitioners with a robust roadmap to investigate reverse logistics in their research further.

Abstract:
When it comes to logistics management, the distribution of finished goods from depots to customers is both a practical and difficult problem to solve. Because more customers can be served in a shorter period of time, better routing and scheduling decisions can result in higher levels of customer satisfaction. The objective of this study is to solve the poor vehicle routing, underutilization of vehicles, and decreased service level of Company X by finding optimal routes for distribution where the company uses vehicles that have limited capacity. In order to fulfill that objective, three heuristics (Nearest Neighbor, Sweep, Clarke & Wright) and Google OR-Tools are used to analyze, calculate and solve the Capacitated Vehicle Routing Problem (CVRP). The result of the methods are then compared, and finally, Google OR-Tool is selected as it gives the most optimal route with minimum distance among the four methods.