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
Smart cities, intelligent transportation systems, and industrial infrastructures increasingly rely on IoT, edge computing, and AI to enable real-time monitoring and predictive maintenance. However, centralized machine learning raises concerns regarding data privacy, communication overhead, security, and data ownership. 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. Edge devices collaboratively share encrypted model updates using secure aggregation, differential privacy, and adaptive encryption techniques to preserve confidentiality. The framework also incorporates edge-cloud collaboration to balance computational efficiency, model accuracy, and network resource utilization. Designed to support heterogeneous sensor environments across transportation, energy, industrial, and urban systems, FL-PSIM optimizes global learning while maintaining local data privacy. Experimental results demonstrate improved monitoring accuracy, anomaly detection, communication efficiency, scalability, and resilience against cyber threats compared with centralized AI approaches. The proposed framework provides a secure, privacy-preserving, and scalable foundation for next-generation smart infrastructure, supporting sustainable digital transformation, smart cities, and Industry 5.0 applications.
Mahabala H.N· International Journal of Mod...· 0 citations
Rapid urbanization, increasing energy demand, and stringent environmental regulations have accelerated the adoption of smart building management systems. However, conventional Building Energy Management Systems (BEMS) rely on static control strategies and limited predictive capabilities, resulting in inefficient energy utilization. This paper proposes 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. Environmental and operational data from sensors, including temperature, humidity, occupancy, lighting, CO₂ concentration, and energy meters, are processed at the edge and synchronized with a cloud-based Digital Twin for real-time monitoring and predictive analytics. The framework forecasts energy demand, occupant behavior, and HVAC performance while optimizing building operations to reduce energy consumption and operational costs without compromising occupant comfort. Continuous interaction between the physical building and its virtual counterpart enables predictive maintenance, adaptive control, intelligent fault diagnosis, and renewable energy integration, delivering scalable, sustainable, and energy-efficient smart building management with improved reliability and decision-making.
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
Artificial Intelligence (AI) has significantly advanced predictive analytics across domains such as healthcare, finance, manufacturing, cybersecurity, and smart cities. While machine learning and deep learning models achieve strong predictive performance, they often lack structured knowledge integration and semantic reasoning. Knowledge Graphs (KGs) provide structured representations of entities and relationships but face challenges such as incomplete knowledge and limited adaptability. Conversely, Large Language Models (LLMs) offer powerful language understanding and contextual reasoning but may generate hallucinations and lack transparent reasoning. Hybrid Knowledge Graph–Large Language Model (KG–LLM) architectures address these limitations by combining symbolic reasoning with neural intelligence. 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. The proposed approach supports applications including disease prediction, fraud detection, financial forecasting, predictive maintenance, customer analytics, and cybersecurity. Performance is evaluated using metrics such as Accuracy, Precision, Recall, F1-Score, AUC, and MAE, demonstrating superior results compared with standalone ML, KG, and LLM models. The study also discusses challenges, scalability, computational requirements, and future directions, including multimodal knowledge graphs, federated learning, explainable AI, and autonomous knowledge reasoning for trustworthy predictive analytics.
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