Graph-based data engineering has become a powerful approach for managing and analyzing highly interconnected data across enterprise systems, IoT, social media, healthcare, finance, and scientific domains. Unlike traditional relational databases, graph-based models represent data as interconnected nodes and edges, enabling efficient relationship analysis, semantic understanding, and knowledge discovery. This paper surveys recent advances in graph databases, knowledge graphs, graph neural networks (GNNs), and distributed graph analytics, and proposes an integrated framework for scalable graph construction, semantic enrichment, graph analytics, and AI-driven knowledge extraction. The framework emphasizes scalability, semantic consistency, explainable AI, and continuous graph evolution. Experimental evaluation demonstrates improved relationship discovery, query performance, and knowledge extraction compared with conventional relational approaches, making the proposed framework suitable for intelligent applications in healthcare, cybersecurity, finance, smart manufacturing, and enterprise knowledge management.
Mahabala H.N· International Journal of Dat...· 0 citations
The rapid growth of IoT, 5G, AI, and cyber-physical systems has accelerated the development of smart applications that require low-latency, reliable, and intelligent computing. Traditional cloud computing faces challenges in meeting these demands due to latency, bandwidth, and privacy limitations. Intelligent Edge–Cloud collaboration addresses these issues by combining edge computing with cloud resources for efficient workload distribution, AI-driven resource management, and adaptive service orchestration. This paper reviews recent advances in collaborative architectures, distributed AI, intelligent orchestration, and resource optimization. It also highlights key challenges, including interoperability, security, heterogeneous resource management, and sustainable computing, while demonstrating the potential of Edge–Cloud collaboration to improve computational efficiency, response time, energy efficiency, scalability, and privacy for next-generation smart applications.
Mahabala H.N· International Journal of Eme...· 0 citations
The enormous increases in global retail has pushed automation levels to new extremes, with autonomous mobile robots (AMRs) increasingly becoming a centerpiece of modern logistics infrastructure. One of the major challenges faced by traditional multiagorithm design and line models, due to static routing charts or offline algorithmic updates, when it has to be deployed in highly dynamic unpredectable working environment within an intra-logistics setting. This paper offers a resilient, adaptive routing framework for the well-timed delivery of independent logistics robots over uniquely temporary traffic and sudden tangible obstructions. Synthesizing localized real-time sensory perception and distributed topological map updates, the architecture dynamically re-calculates optimal travel trajectories making systemic deadlocks impossible and minimizing idle times drastically. Results from computational evaluations across simulated warehouse layouts with varying spatial complexity show that the adaptive framework improves fleet-wide operational efficiency (by up to 24.3%) in comparison to conventional fixed-path planning configurations and simultaneously leads to lower total energy expenditure. Together these results provide evidence of clinical feasibility for inclusion of decentralized, reactive real-time routing models into heavy-duty industrial automation applications.
Mahabala H.N· International Journal of Int...· 0 citations
Intelligent robotic systems increasingly require predictive health monitoring to ensure reliability, safety, and operational efficiency. Traditional maintenance methods cannot accurately predict failures or estimate component lifespan. This paper proposes an AI-driven predictive health monitoring framework that integrates multi-sensor data, intelligent feature engineering, deep learning, anomaly detection, fault diagnosis, and Remaining Useful Life (RUL) estimation. Using data from vibration, temperature, motor current, torque, acoustic signals, batteries, and controller logs, the framework continuously assesses robot health and predicts failures in real time. It supports intelligent maintenance scheduling, resource optimization, and autonomous maintenance decisions through adaptive learning. The proposed approach improves fault detection accuracy, reduces downtime and maintenance costs, enhances robot reliability and productivity, and supports the development of self-aware robotic systems for Industry 4.0 and Industry 5.0 smart manufacturing environments.
Mahabala H.N· International Journal of Int...· 0 citations
Artificial Intelligence (AI) has revolutionized decision-making systems of today, allowing automated data analysis, intelligent prediction, and real-time decision-making in a variety of application areas, including healthcare, finance, transportation, manufacturing, cybersecurity, and public administration. While deep learning and other advanced machine learning techniques have been able to deliver impressive results, numerous AI models can be considered as ‘black-box’ models, meaning that they give very accurate predictions without actually offering understandable explanations for their decisions. This lack of transparency has generated a number of concerns about trust, accountability, fairness, ethical compliance, and regulatory acceptance. Explainable Artificial Intelligence (XAI) is thus becoming an indispensable research field which aims to reconcile the predictive power and human interpretability. By explaining the reasoning behind AI system output, model importance, feature impact, and confidence scores, XAI helps users gain insights into how the system is working. This is done to build trust among stakeholders and promote responsible AI governance and decision-making. This paper offers a detailed overview of the concept of Explainable AI in contemporary decision-making processes, covering its theoretical underpinnings, its development, prominent explainability methods, implementation in practice, hurdles, and prospects. A methodology is advanced to embed explainability in the AI decision-making process, starting from data preprocessing to generating explanations and human evaluation. The paper also delves into the implications of explainability on decision quality, user trust, model reliability, and regulatory compliance. The results highlight the potential of explainability to enhance human comprehension and foster responsible use of AI systems in high-stakes decision-making scenarios.
Mahabala H.N· International Journal of Mod...· 0 citations
This paper proposes a Federated Learning Framework for Privacy-Preserving Smart Infrastructure Monitoring (FL-PSIM), which enables decentralized model training without transferring raw infrastructure data and optimizes global learning while maintaining local data privacy.
Mahabala H. N.· International Journal of Mod...· 0 citations
A Hybrid Digital Twin–IoT Framework that integrates real-time IoT sensing, cloud-edge computing, machine learning, and Digital Twin simulation for intelligent energy optimization for scalable, sustainable, and energy-efficient smart building management with improved reliability and decision-making is proposed.
Mahabala H.N· International Journal of Mod...· 0 citations
Autonomous robots play a crucial role in industrial manufacturing, healthcare, transportation, logistics, agriculture, disaster response, planetary exploration, and service robotics. Reliable visual perception is essential for enabling robots to recognize objects, understand scenes, localize themselves, and navigate safely in dynamic environments. Although CNN-based vision models have significantly improved perception accuracy, they often struggle to capture long-range dependencies and generalize to complex or unseen environments. Recent advances in Transformer-based vision models address these limitations by employing self-attention mechanisms to learn both local visual features and global contextual relationships. Architectures such as Vision Transformer (ViT), Swin Transformer, DETR, SAM, and Mask2Former have achieved remarkable performance in object detection, semantic segmentation, SLAM, localization, obstacle avoidance, and autonomous navigation. This paper presents a comprehensive review and proposes the Transformer-Based Visual Perception Models for Autonomous Robots (TBVPM-AR) framework. The framework integrates RGB cameras, depth sensors, LiDAR, IMUs, multimodal sensor fusion, transformer-based feature extraction, contextual reasoning, and edge-cloud computing to achieve robust perception in dynamic environments. Mathematical formulations for self-attention, positional encoding, and feature embedding provide the theoretical foundation of the architecture. Experimental evaluations demonstrate that the proposed framework outperforms CNN-based and hybrid approaches on standard robotic perception benchmarks, achieving over 98% visual perception accuracy with improved scene understanding, localization, obstacle detection, navigation, and computational efficiency. The proposed architecture offers a scalable, explainable, and adaptable solution for future Industry 5.0, collaborative robotics, autonomous vehicles, and smart cyber-physical systems.
Mahabala H.N· International Journal of Int...· 0 citations
This paper presents a comprehensive framework integrating graph embeddings, retrieval-augmented generation (RAG), transformer-based reasoning, attention mechanisms, and contextual embedding fusion to improve prediction accuracy, explainability, and robustness.
Mahabala H.N· International Journal of Int...· 0 citations
This study proposes a Federated Predictive Learning with Privacy-Aware Model Aggregation (FPL-PAMA) framework, suitable for applications including healthcare, IoT, smart manufacturing, transportation, and financial fraud detection, providing a secure and scalable solution for next-generation distributed intelligent systems.
Mahabala H. N., Seshagiri N· International Journal of Mac...· 0 citations
