Issue 2 (December)

This paper explores the application of deep learning approaches in the field of remote sensing for image super-resolution (ISR). The demand for high-resolution imagery in remote sensing has been increasing, as it offers a wealth of valuable information for numerous applications such as environmental monitoring, urban planning, and agricultural assessment. However, the acquisition of high-resolution images often requires expensive sensors and time-consuming processes. Deep learning has emerged as a powerful tool for enhancing the resolution of low-quality images, and this study investigates its efficacy in remote sensing ISR. By leveraging convolutional neural networks (CNNs) and generative adversarial networks (GANs), this paper presents a comparative analysis of their performance in super-resolution tasks. The experimental results demonstrate that deep learning models, particularly those based on GANs, can significantly improve the quality of super-resolved images while maintaining the details and textures. In addition, the paper discusses the limitations of existing models and suggests potential avenues for future research, including the integration of transfer learning and the development of more efficient architectures for ISR in remote sensing.

This paper explores the advancements in deep reinforcement learning (DRL) techniques for enhancing the navigation capabilities of autonomous vehicles. With the rapid progress in the field of autonomous driving, there is an increased demand for intelligent navigation systems that can adapt to various road conditions and traffic scenarios. Deep reinforcement learning, which combines the principles of reinforcement learning with deep neural networks, has emerged as a promising approach to address these challenges. The paper presents a comprehensive review of the latest DRL algorithms and their application in autonomous vehicle navigation. It discusses the benefits and limitations of DRL-based navigation systems, highlighting the need for robustness, safety, and efficiency. Furthermore, the paper examines the impact of real-world data integration, sensor fusion, and multi-agent systems on the performance of DRL-based navigation algorithms. Through an extensive analysis of existing literature and case studies, the paper concludes with a set of recommendations for future research directions in the field of DRL for autonomous vehicle navigation.

The integration of Distributed Ledger Technologies (DLT) into supply chain management (SCM) represents a significant development in modern business operations. This paper explores the potential of DLT to enhance transparency, efficiency, and security in supply chains. By creating a decentralized and immutable ledger, DLT can facilitate end-to-end tracking of goods, reduce counterfeiting, and streamline transactions. We discuss the challenges and opportunities associated with implementing DLT in SCM, including the interoperability of different blockchains, scalability issues, and regulatory considerations. Furthermore, we highlight the various applications of DLT, such as smart contracts and provenance tracking, and their impact on the overall supply chain performance. The study concludes that while DLT presents a promising solution for SCM, its successful adoption requires careful planning, collaboration, and a supportive regulatory environment.

The integration of Edge AI technology into real-time video surveillance systems has emerged as a transformative approach in enhancing security measures and optimizing resource usage. This paper explores the implications of deploying edge-based artificial intelligence for video surveillance, focusing on the benefits, challenges, and future directions. Edge AI refers to the processing of data at the network's edge, closer to the data source, which reduces latency, bandwidth consumption, and the dependency on centralized servers. This abstract discusses how edge AI enables faster analytics and decision-making for surveillance systems, improving detection accuracy and responsiveness in real-time applications. The challenges such as power constraints, hardware limitations, and the management of diverse edge devices are also addressed. Furthermore, the paper outlines potential solutions to these challenges and suggests innovative approaches for future development, emphasizing the importance of edge AI in creating efficient, secure, and smart surveillance systems.

The use of Artificial Intelligence (AI) in credit scoring has revolutionized the financial industry by enabling institutions to assess risk more efficiently and accurately. However, the lack of transparency in AI models has raised concerns about their fairness, accountability, and trustworthiness. This paper investigates the application of explainable AI (XAI) models in credit scoring within financial institutions. We explore the challenges faced by traditional AI models in providing insights into their decision-making processes and argue for the integration of XAI techniques to enhance the interpretability of AI-driven credit scoring systems. Through a comprehensive literature review, we identify various XAI methods, including decision trees, rule-based models, and feature importance analysis. We discuss the potential benefits of XAI in improving the explainability, fairness, and trustworthiness of credit scoring models. Furthermore, we highlight the importance of regulatory compliance and ethical considerations when incorporating XAI into credit scoring practices. This paper concludes by emphasizing the need for ongoing research and development in XAI to ensure the sustainable and responsible use of AI in the financial sector.