Mainframe systems have a track record of reliability, but they need help in today’s fast-changing business world. These include high upkeep expenses and limited room for growth. This paper looks at three top cloud platforms—Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)—to update old mainframe programs. We compare them in detail, looking at what each does best. AWS has a wide range of tools to move data. Azure works well with cloud and on-site systems and fits into (...) company setups. GCP leads in AI and machine learning. Azure is the top option for updating mainframes, offering compatibility with various cloud configurations, integration with current company infrastructure, and sophisticated data analysis driven by AI. These conclusions provide valuable information for companies transitioning mainframe systems to the cloud, enabling expansion, reduced expenses, and future-ready IT systems. (shrink)

Modernizing workflows is imperative to address labor-intensive tasks that hinder productivity and efficiency. Convolutional Neural Networks (CNNs), a prominent technique in Artificial Intelligence, offer transformative potential for automating complex processes and streamlining operations. This study explores the application of CNNs in building accurate classification models for diverse datasets, demonstrating their ability to significantly enhance decision-making processes and operational efficiency. By leveraging a dataset of images, an optimized CNN model has been developed, showcasing high accuracy and reliability in classification tasks. The (...) findings underscore the ability of AI-powered approaches to reduce manual efforts, improve productivity, and support sustainable modernization across various domains. This study is relevant to professionals seeking to embed AI solutions into conventional workflows, offering a pathway to enhanced innovation and efficiency. (shrink)

This study presents a novel transformer-based model specifically designed for financial forecasting, integrating explainability mechanisms such as SHAP (SHapley Additive exPlanations) values and attention visualizations to enhance interpretability. Unlike previous models, which often compromise between accuracy and transparency, our approach balances predictive accuracy with interpretability, allowing stakeholders to gain deeper insights into the factors driving market changes. By revealing critical market influences through feature importance and attention maps, this model provides both robustness and transparency, catering to the needs of high-stakes (...) financial environments. (shrink)

Cloud computing has transformed how businesses design, deploy, and manage applications. With its ability to provide on-demand resources and scalability, cloud platforms offer significant benefits in terms of flexibility, cost-efficiency, and speed. However, to fully leverage these advantages, it is crucial to apply proven design patterns that ensure the cloud architecture is both scalable and secure. This paper explores key cloud architecture design patterns, including microservices, serverless architecture, multi-cloud, and hybrid cloud, along with best practices for achieving scalable and secure (...) systems. Emphasizing security and scalability, it provides guidance for organizations on how to build resilient and high-performing cloud systems. (shrink)