Abstract Overview The Industrial Revolution (IR)4.0 is driving the Digital Age, and the advancements in AI and block-chain technology are fueling their integration to bring about transformation. Research Gap :Despite the fact that there is study on the use and convergence of block-chain and AI, there is still a lack of consensus on the benefits of this integration for business. In order to close this gap, this paper attempts to describe the uses and benefits of blockchain-integrated AI systems across various corporate verticals. Aim This paper explores the potential applications of block chain integration with artificial intelligence (AI) for business, attempting to fill the identified research vacuum. In instance, this research study aims to pinpoint the unique research characteristics of the field and to clarify how, in the era of IR 4.0, the integration of block chain technology and artificial intelligence may benefit various business sectors respectively. Methodology :Based on publications, citations, and significance within the intellectual network, bibliometric analysis is used in this study to identify the most influential works on the topic. Findings: This study emphasizes the importance of doing more research to address the unanswered research issues on artificial intelligence (AI) and block chain integration from a business perspective in order to support future studies along similar lines. To sum up, this study shows that artificial intelligence (AI) and block chain integration are real, worldwide phenomena with enormous business potential.
Object detection in video and image surveillance is a well-established yet rapidly evolving task, strongly influenced by recent deep learning advancements. This review summarises modern techniques by examining architectural innovations, generative model integration, and the use of temporal information to enhance robustness and accuracy. Unlike earlier surveys, it classifies methods based on core architectures, data processing strategies, and surveillance specific challenges such as dynamic environments, occlusions, lighting variations, and real-time requirements. The primary goal is to evaluate the current effectiveness of semantic object detection, while secondary aims include analysing deep learning models and their practical applications. The review covers CNN-based detectors, GAN-assisted approaches, and temporal fusion methods, highlighting how generative models support tasks such as reconstructing missing frames, reducing occlusions, and normalising illumination. It also outlines preprocessing pipelines, feature extraction progress, benchmarking datasets, and comparative evaluations. Finally, emerging trends in low-latency, efficient, and spatiotemporal learning approaches are identified for future research.
