In today's world, data has become a valuable resource for businesses, governments, researchers, and individuals alike. Technologies are evolving at a faster pace which is leading organizations to adopt newer technologies and solutions at a very faster rate.

The integration of artificial intelligence (AI) into the renewable energy sector marks a significant turning point, heralding a paradigm shift towards achieving sustainability goals while concurrently addressing pressing security concerns.

The review paper explores the evolving landscape of security challenges and solutions in the realm of adaptive video streaming at the edge. As the demand for high-quality video content continues to surge, the decentralized nature of edge computing introduces unique security considerations.

The development of the nation's states is said to be mostly facilitated by national highways and expressways. Because of the enormous cost associated with them, road traffic accidents have been identified as one of the unfavorable factors that contribute to the stifling of economic progress in developing countries.

A major worry is the increase of traffic on the roads of big cities in emerging nations like India. The fact that Mumbai is the second-most populous city after Delhi and that every resident has a two-wheeler, a four-wheeler, or both vehicles makes it difficult for traffic to move because everyone uses their own vehicle.

This study performed a performance analysis of multi-prime RSA decryption variations' applicability for various system applications. The goal of the research was to determine how suitable the various decryption variations are for different application systems with varying system requirements.

This paper explores the rise and impact of Software Point of Sale (SoftPOS) solutions within the U.S. payment processing industry. SoftPOS allows merchants to accept payments using standard mobile devices, bypassing the need for dedicated POS hardware. This research provides an in-depth analysis of the factors driving SoftPOS adoption, examines the market trends, and discusses the challenges and opportunities associated with this technology.

This is perhaps the main reason why it is now possible to build intelligent systems that can analyze loads of data in order to extract meaningful information. AWS is particularly an organization with a clear strategic direction towards becoming a cloud solution organization, and it has provided several solutions for building smart systems relying on Data lakes and AI. This paper focuses on steps to take in AWS for developing intelligent systems with specific emphasis on how data lakes can be optimized and how AI can be applied to data.

In the current dynamic business environment, the use of AI as part of BI has emerged as a key determinant of the competitiveness of a firm. This paper is aimed at discussing the possibilities of using AI for the modernization of traditional BI models to support more efficient data analysis, better forecasting, and faster decision-making in organizations. Reviewing the possibilities of AI in BI with a focus on the most essential technologies like machine learning, natural language processing, and predictive analytics, the results of this research demonstrate how the companies might improve operational performance, customer satisfaction, the overall organization’s effectiveness implementing AI strategies as a part of BI initiatives.

Migration to the cloud is a significant move for enterprises seeking to transform their IT platforms and remain relevant in a digital marketplace. Existing applications that are based on legacy frameworks are difficult to scale, adjust and maintain affordably. By moving these applications to the cloud, companies can benefit from modern cloud capabilities like on-demand scaling, faster performance and improved security.

This paper addresses the critical role of automated data quality monitoring in Machine Learning Operations (MLOps) pipelines. As organizations increasingly rely on machine learning models for decision-making, ensuring the quality and reliability of input data becomes paramount. The paper explores various types of data quality issues, including missing values, outliers, data drift, and integrity violations, and their potential impact on model performance. It then examines automated detection methods, such as statistical analysis, machine learning-based anomaly detection, rule-based systems, and data profiling.

This paper explores the application of machine learning (ML) and artificial intelligence (AI) in advancing online signature verification systems. By leveraging AI-driven methods, including neural networks and hybrid models, the proposed system enhances the ability to detect forgeries and adapt to evolving signature patterns. Integrating these advanced technologies into a distributed, event-driven architecture ensures scalability, efficiency, and robust cybersecurity. This study examines state-of-the-art techniques and demonstrates their effectiveness in achieving real-time, high-accuracy verification, thereby strengthening cybersecurity measures and minimizing vulnerabilities in digital transactions.

In the evolving landscape of modern IT infrastructure, organizations are increasingly adopting hybrid environments that combine on-premises data centers with cloud solutions to meet scalability, flexibility, and operational efficiency needs. However, managing the security and performance of such complex environments introduces unique challenges, particularly in areas like network segmentation, vulnerability management, and real-time threat detection. This case study explores the implementation of an AI-powered network infrastructure audit and security framework for a global retail company operating in both on-premises and cloud environments.

Embedded systems development often involves repetitive and time-consuming tasks such as code generation, testing, debugging, and hardware interaction. Automation of these tasks can significantly enhance development efficiency and reduce human error. Python, a versatile and powerful programming language, offers an extensive ecosystem of libraries and tools that simplify automation in embedded systems. This research explores the application of Python in various stages of embedded systems development, including firmware flashing, testing automation, hardware communication, and integration with debugging tools.

From conventional financial structures to new sophisticated ERP systems such as SAP FICO, companies experience an enormous change in financial management and processes. Persistent with existing mainframe habits, most legacy systems lack the ability to integrate process data in real time and are concerned about scalability. This paper analyses key factors of the transition from traditional systems to SAP FICO, the issues that one may come across, the approaches to follow, and the advantages that result from the process. This way, using SAP FICO, organizations are provided with solutions to make their oldest and most vital business processes leaner, faster, less error-prone, and more standard-compliant.

Serverless computing, commonly referred to as Function-as-a-Service (FaaS), has emerged as a transformative model within cloud computing, allowing developers to concentrate on coding without the necessity of managing the underlying infrastructure. This model presents several notable advantages, including cost efficiency, automatic scalability, and streamlined application management.

Cloud computing has transformed IT by offering scalability, cost efficiency, and flexibility, yet it has also introduced complex security risks, including misconfigurations, identity mismanagement, and compliance violations. Cloud Security Posture Management (CSPM) has emerged as a critical solution to address these challenges by automating the continuous assessment and remediation of cloud environments.

It is discussed that malware authors are moving towards more advanced techniques to bypass detection. Static methods of analysis, while useful, are limited by obfuscated or polymorphic malware. It has thus become necessary for dynamic malware analysis, or the real-time execution of malicious software and its observation of behaviour. There are two main approaches in this paradigm: system call tracing and API monitoring. These techniques are granular in providing visibility of malware behaviour by logging interactions with the operating system and the core services.

This research examines the evolution of network isolation techniques with-in kubernetes-based containerized environment with an emphasis on early architectural patterns and their associated security constraints. It investigates the use of iptables and ebtables for packet filtering processes, as well the use of VXLAN based overlay networks for cross node communication. While these mechanisms are instrumental in supporting basic network segmentation, they were often complex to manage at scale, and had limited integration with identity aware security models.