The rapid development of Artificial Intelligence (AI) has significantly transformed the operations and strategies of digital businesses. Initially, AI adoption focused on efficiency, automation, and extracting insights from large-scale data, but the lack of systematic innovation frameworks limited its full integration into enterprise ecosystems. This paper provides an in-depth analysis of AI-based innovation models for smart digital firms, focusing on foundational approaches. AI-driven innovation frameworks utilize machine learning, data analytics, cognitive computing, and automation to enhance business processes, customer interaction, and decision-making, enabling scalable and responsive enterprise systems. It also highlights the role of data-centric architectures, cloud computing, and algorithmic intelligence in digital transformation. The study examines the evolution from traditional IT systems to predictive, automated smart ecosystems and reviews key frameworks such as data-driven models, knowledge-based systems, and enterprise intelligence architectures. A multi-layered AI innovation model is proposed, including data acquisition, processing, intelligence, and business integration, supported by feedback mechanisms and continuous learning for adaptability and scalability. Results indicate that AI frameworks improve efficiency, reduce costs, and enhance decision accuracy and innovation. The paper concludes that structured AI frameworks are essential for sustainable innovation in digital enterprises and highlights future directions, including deep learning and autonomous systems.
Tharindu Jayasinghe, Nimal Perera· International Journal of Art...· 0 citations
Rapid urbanization and increasing vehicle numbers have led to a significant rise in road accidents, posing serious threats to human life and economic stability. Traditional accident detection systems rely on manual reporting, causing delays in emergency response. To address this, computer vision integrated with intelligent transportation systems offers an effective solution. This study analyzes pre-2018 approaches to traffic accident detection using video surveillance. The proposed system automatically detects accidents by analyzing traffic camera footage through feature extraction, motion analysis, and pattern recognition. It identifies abnormal vehicle behavior such as sudden speed drops, collisions, and irregular trajectories. Classical computer vision techniques like optical flow, background subtraction, edge detection, and machine learning methods such as Support Vector Machines (SVM) and decision trees are used. The system includes preprocessing, object detection, trajectory tracking, and classification of normal and abnormal events, supported by mathematical modeling and threshold-based decisions. Results show high detection accuracy with low false alarms, especially in controlled environments like highways and intersections. However, challenges remain in real-time implementation due to lighting, occlusion, and camera angle issues. Future work suggests incorporating deep learning and multi-sensor data fusion to improve performance. In conclusion, computer vision-based accident detection is a promising approach to enhancing road safety and reducing response time, contributing to the advancement of smart transportation systems.
Nimal Perera, Tharindu Jayasinghe· International Journal of Mod...· 0 citations
A comprehensive survey of current technologies in cloud infrastructure automation, including Infrastructure as Code (IaC), configuration management, continuous integration/continuous deployment (CI/CD), and containerization is provided.
Nimal Perera, Tharindu Jayasinghe· International Journal of Art...· 0 citations
The key design principles of an EDP, including data distribution, workload optimization, auto-scaling, and cost analytics, and how these can be implemented across multiple cloud providers are discussed.
Nimal Perera· International Journal of Dat...· 0 citations
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