Real-World AI Systems

Abstract: This study presents a structured investigation into the recent advancements and practical applications of Artificial Intelligence (AI), Deep Learning (DL), Machine Learning (ML), and Computer Vision (CV), with a specific focus on their integration in domains such as healthcare, autonomous transportation, and intelligent surveillance. Through a comprehensive available knowledge investigation and thematic analysis of expert... More

Abstract: This paper investigates the transformative impact of deep learning technologies on medical imaging diagnosis within the healthcare sector. We explore how deep learning algorithms, particularly convolutional neural networks (CNNs), have significantly improved the accuracy, speed, and consistency of detecting diseases such as cancer, neurological disorders, and cardiovascular conditions. The paper also addresses the practical challenges... More

Abstract: This paper presents a comprehensive overview of the application of artificial intelligence (AI) in medical image diagnosis, highlighting real-world insights and recent breakthroughs. We examine how AI technologies, particularly deep learning and computer vision, are revolutionizing diagnostic accuracy, efficiency, and accessibility across various medical fields such as radiology, oncology, and cardiology. The paper also discusses... More

Abstract: This paper explores the real-world applications of artificial intelligence (AI), highlighting the complexities and innovations that define its current landscape. We examine how AI technologies are being integrated into diverse sectors such as healthcare, finance, transportation, and education, addressing challenges like data privacy, ethical governance, and system robustness. The paper also discusses key innovations driving... More

Abstract: This paper delves into the real-world impact of artificial intelligence (AI), examining its transformative effects across industries, societies, and daily life. We explore major challenges in AI deployment, including issues of bias, transparency, scalability, and ethical responsibility. Through a review of current applications in sectors such as healthcare, finance, transportation, and education, we highlight how... More