International Journal of Data Science, Bioinformatics and Cyber Security

Adaptive Replication Strategies for Latency Reduction in Geo-Distributed Databases

2026-05-16T08:26:53+00:00

Geo-distributed databases are fundamental to modern cloud-based and globally distributed applications, enabling data availability and fault tolerance across geographically dispersed regions. However, these systems frequently suffer from high access latency due to physical distance between data centers, fluctuating network conditions, and the overhead introduced by strict consistency guarantees. Traditional static replication strategies, which rely on fixed replica placement and replication factors, are poorly suited to such dynamic environments and often result in inefficient resource utilization and increased response times, particularly for latency-sensitive applications. This study proposes an adaptive replication strategy designed to minimize latency in geo-distributed database systems by dynamically adjusting both replica placement and replication factor in response to changing workload conditions. The approach continuously monitors real-time workload characteristics, including data access frequency, regional demand distribution, and network latency metrics. Predictive models are employed to identify frequently accessed (hot) data items and anticipate future access patterns, enabling proactive replication or relocation of data closer to regions with high demand. By aligning replication decisions with observed and predicted workload behaviour, the proposed strategy seeks to balance performance gains with consistency and resource overhead. The effectiveness of the proposed approach is evaluated through comprehensive simulation and experimental analysis. Key performance metrics, including read and write latency, system throughput, and consistency-related overhead, are measured and compared against conventional static replication schemes. The results demonstrate significant reductions in access latency and improved system responsiveness without incurring excessive consistency costs. These findings highlight the potential of adaptive replication as a practical and scalable solution for latency optimization in geo-distributed database deployments, offering valuable insights for the design of performance-aware distributed data management systems.

International Review of Digital Trade and Artificial Intelligence

2026-01-28T08:53:56+00:00

The acceleration of digital trade has fundamentally reshaped the global economic landscape, with Artificial Intelligence (AI) emerging as a central driver of cross-border innovation, efficiency, and competitiveness. Recent advances in AI-enabled e-commerce automation, predictive logistics, data governance, and digital financial systems have increased the velocity, trustworthiness, and scalability of international digital markets. However, the integration of AI into global trade remains fragmented due to uneven regulatory regimes, data sovereignty conflicts, infrastructure disparities, and algorithmic risks. Existing studies provide important insights into the intersection of AI and digital trade, but a holistic, unified analytical framework remains missing. This article offers a comprehensive international review of AI-enabled digital trade and introduces the AI-Driven Digital Trade Integration Framework (AIDTIF), which conceptualizes AI’s role across digital infrastructure, regulatory governance, enterprise adoption, cybersecurity, and global value chain participation. The framework is validated through comparative literature triangulation, theoretical reasoning, and scenario-based assessments grounded in existing empirical work. Findings demonstrate that AI significantly enhances digital trade performance by reducing transaction friction, enabling scalable analytics, improving cross-border logistics, supporting small-market inclusion, and strengthening digital compliance. However, risks related to algorithmic bias, cybersecurity, legal fragmentation, and unequal absorption capacity remain persistent. The article concludes by outlining strategic policy, industry, and research recommendations to build an interoperable, equitable, and sustainable AI-enabled global trade ecosystem.

Person Reidentification Using Facial and Fabric Feature Extraction

2026-02-06T12:37:36+00:00

Purpose: Person identification across surveillance cameras with non-overlapping fields of view has emerged as a highly challenging and intriguing area of study in smart surveillance. Though numerous approaches have been carried out, proposed, and developed, unresolved issues and limitations persist.

Methods: Existing re-identification methodologies generally, involve extracting feature vectors from images or video frames and applying various similarity or dissimilarity measures to compare these vectors. Some approaches rely on models based on fabric colour or facial features information, the ultimate objective of all these methods is to attain higher matching accuracy and at the same time lower computational costs. This work involves the study of person re-identification, using the classification of feature vectors for facial and fabric images, represented as a visual word dictionary, and person matching is carried out using different baseline methods. The primary novelty of this work lies in a selective and context-aware fusion of facial and fabric features within a Bag of Visual Words (BoVW) framework enhanced by Spatial Pyramid Matching (SPM) for person re-identification in non-overlapping camera environments. Unlike existing approaches that rely solely on either global appearance features or deep learning–based representations, the author's method explicitly separates and independently models facial and fabric regions, enabling robust re-identification under clothing variations and pose changes.

Results: Experiments are done with various benchmark datasets, and comparisons are done with various baseline methods, which show that the method gives 99% accuracy for the ETHZ dataset.

Conclusion: Person ReID using facial and fabric feature extraction has been carried out using Bag of Visual Words (BoVW) features. Also, to include spatial information of features, BoVW features are grouped using spatial pyramid matching, and the performance of the system has been improved.