Stock market volatility is a key factor influencing investment decisions, portfolio optimization, risk management, derivative pricing, and financial planning. Traditional statistical models often struggle to capture the nonlinear, dynamic, and high-dimensional nature of modern financial markets. Recent advances in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) provide more accurate and adaptive approaches for volatility prediction. This paper proposes a comprehensive predictive modeling framework that integrates financial data preprocessing, feature engineering, machine learning algorithms, and deep learning models for intelligent stock market volatility forecasting. The framework incorporates historical stock prices, technical indicators, trading volume, economic variables, financial news, and market sentiment to improve predictive performance. Advanced algorithms such as Random Forest, Support Vector Machine, Gradient Boosting, Extreme Gradient Boosting, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) are employed to capture complex market patterns and temporal dependencies. Model performance is evaluated using metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), R², precision, recall, F1-score, and prediction accuracy. The proposed framework emphasizes scalability, computational efficiency, and adaptability to rapidly changing market conditions, supporting intelligent portfolio management and financial risk assessment. Results indicate that AI-driven predictive models outperform traditional statistical approaches in stock market volatility forecasting, providing a reliable foundation for data-driven investment strategies and next-generation intelligent financial analytics systems.
Seshagiri N, Mahabala H. N.· International Journal of Com...· 0 citations
Artificial Intelligence (AI) and Machine Learning (ML) have significantly improved predictive analytics across domains such as healthcare, finance, transportation, cybersecurity, manufacturing, and smart cities. However, conventional ML models rely on statistical correlations and often fail under dynamic environments due to concept drift, distribution shifts, and changing causal relationships. Causal Machine Learning (CML) addresses these limitations by integrating causal inference techniques, including structural causal models, directed acyclic graphs (DAGs), counterfactual reasoning, intervention analysis, and invariant causal prediction, to identify true cause-and-effect relationships. This enables more interpretable, robust, and generalizable predictive models. This paper proposes a unified CML framework that combines data preprocessing, causal graph construction, structural causal modeling, causal feature optimization, predictive learning, intervention analysis, and continuous model adaptation. Mathematical formulations support causal dependency estimation, structural equation modeling, invariant risk minimization, and prediction optimization. Experimental results demonstrate improved out-of-distribution prediction, robustness, causal consistency, explainability, and computational efficiency, providing a scalable foundation for trustworthy and adaptive AI in dynamic environments.
Mahabala H. N.· International Journal of Mac...· 0 citations
Digital healthcare technologies have significantly improved patient monitoring and disease prediction; however, conventional cloud-based healthcare systems face challenges such as communication latency, bandwidth consumption, privacy concerns, and delayed clinical responses. This study proposes a Smart Healthcare Monitoring through Edge Intelligence framework that integrates Artificial Intelligence (AI), Edge Computing, the Internet of Things (IoT), and Machine Learning (ML) to enable real-time and secure healthcare services. The proposed architecture employs wearable sensors to continuously monitor vital physiological parameters, including heart rate, ECG, blood oxygen saturation (SpO₂), body temperature, blood pressure, respiratory rate, glucose level, and physical activity. Medical data is processed locally at edge devices for noise removal, anomaly detection, risk assessment, and selective cloud synchronization. AI-based edge analytics support early disease prediction, personalized healthcare recommendations, and rapid emergency alerts while reducing communication overhead and protecting patient privacy. The proposed framework demonstrates improved monitoring accuracy, lower response latency, enhanced network efficiency, better data security, and reliable healthcare decision-making. It provides a scalable and intelligent solution for telemedicine, remote patient monitoring, and next-generation smart healthcare systems.
Seshagiri N, Mahabala H. N.· International Journal of Mod...· 0 citations
Renewable energy systems, including wind, solar photovoltaic (PV), hydroelectric, biomass, and hybrid energy systems, play a vital role in sustainable energy generation and reducing greenhouse gas emissions. However, harsh operating conditions, equipment aging, and mechanical and electrical failures can significantly affect their reliability and performance. Traditional maintenance approaches often fail to detect faults at an early stage, resulting in increased costs, unexpected downtime, and reduced energy production. Artificial Intelligence (AI)-based predictive maintenance has emerged as an effective solution by combining real-time sensor data, historical records, and environmental information to predict equipment failures before they occur. Advanced AI techniques, including Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Support Vector Machines (SVM), Random Forests (RF), and Transformer models, enable accurate fault diagnosis, anomaly detection, and Remaining Useful Life (RUL) estimation. This study reviews recent AI-driven predictive maintenance approaches, identifies key research gaps, and proposes an intelligent framework integrating IoT, edge computing, cloud platforms, deep learning, and Explainable AI (XAI). The proposed framework improves fault prediction, reduces downtime, and extends equipment lifespan, and supports reliable, sustainable, and intelligent renewable energy systems for future smart grid and Industry 5.0 applications.
Mahabala H. N.· International Journal of Eme...· 1 citation
The increasing complexity of financial management due to digital transactions, globalization, and rapidly growing financial data has exposed the limitations of traditional decision-making approaches. Artificial Intelligence (AI)-Assisted Decision Support Systems (AI-DSS) have emerged as effective solutions by integrating machine learning, deep learning, predictive analytics, and optimization techniques to enhance financial decision-making. This study proposes an intelligent AI-based financial decision support framework that integrates data from enterprise resource planning systems, banking transactions, accounting software, stock markets, customer relationship management systems, and external economic indicators. Advanced data preprocessing techniques, including data cleaning, feature engineering, normalization, and anomaly detection, improve data quality prior to model training. The framework employs supervised and ensemble learning models for financial forecasting, risk assessment, fraud detection, investment evaluation, and budget optimization, while reinforcement learning continuously refines decision strategies under dynamic market conditions. Explainable Artificial Intelligence (XAI) enhances transparency by providing interpretable recommendations that improve user trust and decision confidence. Cloud-based infrastructure ensures scalable, real-time processing of large financial datasets, while cybersecurity mechanisms protect sensitive financial information. Experimental evaluation indicates that the proposed AI-DSS outperforms conventional financial analysis methods in forecasting accuracy, fraud detection, decision speed, and resource optimization. Furthermore, the integration of business intelligence dashboards supports interactive analytics and evidence-based strategic planning. Overall, the proposed framework offers a scalable, secure, and explainable solution for intelligent financial management, enabling organizations to improve financial planning, investment decisions, risk mitigation, and long-term sustainability. Future research may explore the integration of generative AI, federated learning, and quantum-inspired optimization to further enhance next-generation financial decision support systems.
Mahabala H. N.· International Journal of Com...· 0 citations
Robotic object recognition is a fundamental capability that enables autonomous robots to interact intelligently with dynamic environments. Traditional vision-based methods, such as SIFT, SURF, HOG, and template matching, perform well under controlled conditions but struggle with variations in lighting, viewpoint, occlusion, and complex backgrounds. Recent advances in deep learning have significantly improved recognition accuracy by automatically learning features from raw image data. However, individual deep learning models often face challenges related to computational cost, inference speed, and limited generalization. This paper proposes a Hybrid Deep Learning Framework that integrates Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), attention mechanisms, and multimodal sensor fusion (RGB, depth, and LiDAR) to enhance recognition accuracy and efficiency. The framework combines local and global feature extraction, adaptive feature fusion, intelligent object recognition, and robotic decision-making for real-time perception and task execution. It supports applications in industrial automation, warehouse logistics, autonomous mobile robots, healthcare, agriculture, and service robotics while improving robustness, scalability, and computational efficiency.
Seshagiri N, Mahabala H. N.· International Journal of Int...· 0 citations
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
This paper 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
A resilient, adaptive routing framework for the well-timed delivery of independent logistics robots over uniquely temporary traffic and sudden tangible obstructions is offered, providing 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
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
A methodology is advanced to embed explainability in the AI decision-making process, starting from data preprocessing to generating explanations and human evaluation, and 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
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