Journal of Smart Computing and Data Intelligence
AI-Driven Edge Computing Architecture for Real-Time Data Analysis in Next-Generation Smart Applications
The rapid growth of IoT, AI, 5G/6G, and smart applications has created a strong need for fast, secure, and intelligent real-time data processing. This study proposes an AI-Driven Edge Computing Architecture (AIDECA) that integrates edge computing, artificial intelligence, IoT, cloud collaboration, federated learning, explainable AI, and security mechanisms for next-generation smart applications. The architecture enables localized data processing, low-latency decision-making, efficient resource utilization, and privacy-aware analytics. Experimental evaluation using 8.7 million heterogeneous sensor records demonstrates improvements in processing latency, throughput, bandwidth efficiency, scalability, and resource utilization, while achieving 97.2% prediction accuracy. The findings highlight the potential of AI-driven edge computing to support intelligent and reliable applications in healthcare, autonomous systems, smart cities, industrial IoT, and intelligent energy systems.
Copyright (c) 2026 Author https://creativecommons.org/licenses/by-nc-nd/4.0
Natural Language Processing-Based Intelligent Information Retrieval System Using Transformer Models
The rapid growth of digital information has increased the demand for intelligent information retrieval systems capable of understanding user intent and delivering semantically relevant results. Traditional keyword-based retrieval methods often fail to capture contextual meaning and complex natural language queries. This study proposes a Natural Language Processing-Based Intelligent Information Retrieval System (NLP-IIRS) using advanced Transformer models, including BERT, RoBERTa, DistilBERT, SBERT, T5, GPT-based encoders, and Retrieval-Augmented Generation (RAG). The framework integrates semantic indexing, dense vector retrieval, hybrid ranking, knowledge graphs, and explainable AI to improve retrieval performance. Experimental evaluation on a large multilingual dataset demonstrates improvements in retrieval accuracy, semantic relevance, query efficiency, and user satisfaction over conventional approaches. The findings indicate that Transformer-based intelligent retrieval systems provide an effective foundation for next-generation semantic search, while future research should address scalability, multilingual adaptation, privacy, and responsible AI deployment.
Blockchain-Enabled Secure Data Management System for Intelligent Computing Networks
The rapid expansion of intelligent computing networks, including cloud computing, edge computing, Internet of Things (IoT), cyber-physical systems, smart cities, industrial automation, and artificial intelligence (AI), has resulted in unprecedented growth in distributed data generation and exchange. As digital infrastructures become increasingly interconnected, ensuring secure, transparent, and trustworthy data management has emerged as a critical challenge. Conventional centralized data management systems are vulnerable to cyberattacks, unauthorized access, single points of failure, data tampering, and privacy breaches, limiting their suitability for next-generation intelligent computing environments. Blockchain technology, with its decentralized architecture, immutable ledger, cryptographic security, and consensus mechanisms, offers a promising solution for secure and trusted data management across distributed computing networks. This study proposes a Blockchain-Enabled Secure Data Management System (BSDMS) for intelligent computing networks by integrating blockchain technology, artificial intelligence (AI), edge computing, cloud computing, smart contracts, zero-trust security, decentralized identity (DID), InterPlanetary File System (IPFS), and Explainable Artificial Intelligence (XAI) within a unified architecture. The proposed framework consists of six interconnected layers: Data Acquisition Layer, Edge Intelligence Layer, Blockchain Security Layer, Distributed Storage Layer, AI-Based Decision Support Layer, and User Application Layer. The architecture enables secure data sharing, decentralized authentication, tamper-resistant record management, intelligent anomaly detection, and transparent auditability across heterogeneous computing environments. Experimental evaluation was conducted using a distributed dataset containing 8.1 million transaction records collected from healthcare systems, financial institutions, industrial IoT platforms, smart city infrastructures, intelligent transportation systems, and cloud computing environments during 2021–2025. The framework was implemented using Hyperledger Fabric, Ethereum, IPFS, Docker, Kubernetes, TensorFlow, Python, Apache Kafka, PostgreSQL, and Apache Spark. Performance evaluation considered transaction throughput, latency, scalability, storage efficiency, security resilience, consensus performance, fault tolerance, energy efficiency, and attack resistance. Experimental results demonstrate that the proposed architecture improves data integrity by 39%, enhances security resilience by 34%, increases transaction reliability by 31%, reduces unauthorized access incidents by 29%, and improves overall system scalability by 32% compared with conventional centralized data management systems. AI-driven anomaly detection and explainable security analytics further strengthen proactive cyber defence and decision transparency. The study identifies challenges including blockchain scalability, consensus overhead, interoperability, energy consumption, regulatory compliance, privacy preservation, and quantum-era cybersecurity. The findings indicate that blockchain-enabled secure data management provides a robust technological foundation for trustworthy intelligent computing networks. Future research should explore cross-chain interoperability, blockchain-based federated learning, confidential computing, quantum-resistant cryptography, decentralized autonomous organizations (DAOs), and AI-enabled autonomous blockchain governance.
Federated Learning-Based Privacy-Preserving Framework for Distributed Smart Data Intelligence
The rapid growth of the Internet of Things (IoT), edge computing, cloud services, mobile applications, and cyber-physical systems has led to the generation of massive volumes of distributed data across heterogeneous environments. Traditional centralized machine learning approaches require transferring raw data to a central server for model training, creating significant concerns regarding privacy, security, communication overhead, regulatory compliance, and data ownership. These challenges have accelerated interest in Federated Learning (FL), a distributed machine learning paradigm that enables collaborative model training without requiring direct sharing of sensitive data. By keeping data locally on participating devices while exchanging only model parameters, federated learning provides an effective mechanism for privacy-preserving intelligent data analytics. This study proposes a Federated Learning-Based Privacy-Preserving Framework (FLPPF) for distributed smart data intelligence. The proposed framework integrates Federated Learning, Edge Computing, Differential Privacy (DP), Secure Multi-Party Computation (SMPC), Homomorphic Encryption (HE), Blockchain-assisted model verification, Explainable Artificial Intelligence (XAI), and Artificial Intelligence-driven adaptive optimization into a unified architecture. The framework is designed to support scalable, secure, and privacy-aware collaborative learning across geographically distributed environments while maintaining high predictive performance. Experimental evaluation was conducted using a heterogeneous distributed dataset containing 7.8 million data records collected from healthcare institutions, industrial IoT systems, financial transaction networks, intelligent transportation systems, smart city infrastructures, and mobile edge devices during 2021–2025. The proposed framework was implemented using TensorFlow Federated, PyTorch, Flower Framework, Kubernetes, Docker, Apache Spark, Hyperledger Fabric, and Python. Performance was evaluated using classification accuracy, communication efficiency, convergence rate, privacy preservation score, model robustness, scalability, energy consumption, latency, and attack resistance. Experimental results demonstrate that the proposed framework improves privacy protection by 38%, increases distributed learning efficiency by 30%, reduces communication overhead by 26%, enhances system scalability by 33%, and maintains prediction accuracy above 96% compared with conventional centralized machine learning architectures. The study also examines challenges related to non-IID data distribution, client heterogeneity, communication bottlenecks, adversarial attacks, model poisoning, fairness, and regulatory compliance. The findings indicate that federated learning represents a transformative paradigm for privacy-preserving intelligent computing and distributed AI systems. Future research should focus on hierarchical federated learning, federated foundation models, green federated AI, quantum-secure federated learning, digital twins, and autonomous edge intelligence.
Cloud-Based Intelligent Data Analytics Architecture for Scalable and Secure Computing Applications
The rapid growth of cloud computing, Internet of Things (IoT), artificial intelligence (AI), and big data technologies has fundamentally transformed the way organizations collect, store, process, and analyse massive volumes of digital information. Modern enterprises, healthcare systems, financial institutions, smart manufacturing environments, and government organizations increasingly rely on cloud infrastructures to support intelligent data analytics and real-time decision-making. However, conventional cloud-based data processing architectures face significant challenges related to scalability, security, privacy preservation, latency, resource optimization, and heterogeneous data integration. These limitations highlight the need for intelligent cloud architectures capable of delivering scalable, secure, and efficient data analytics services. This study proposes a Cloud-Based Intelligent Data Analytics Architecture (CIDAA) that integrates cloud computing, edge computing, artificial intelligence (AI), machine learning (ML), big data analytics, containerized microservices, and zero-trust security mechanisms for scalable and secure computing applications. The proposed architecture consists of six interconnected layers: Data Acquisition Layer, Edge Intelligence Layer, Cloud Analytics Layer, AI Decision Support Layer, Security and Governance Layer, and User Service Layer. The framework enables distributed processing, intelligent workload scheduling, automated resource provisioning, predictive analytics, and secure multi-tenant data management. Experimental evaluation was conducted using a heterogeneous dataset containing 8.4 million records collected from healthcare information systems, industrial IoT platforms, financial transaction networks, smart city infrastructures, cybersecurity monitoring systems, and e-commerce platforms between 2021 and 2025. The architecture was implemented using Apache Spark, Hadoop Distributed File System (HDFS), Kubernetes, Docker, TensorFlow, PyTorch, PostgreSQL, Apache Kafka, and Microsoft Azure Cloud. Performance was evaluated using throughput, latency, scalability, resource utilization, prediction accuracy, security resilience, fault tolerance, and cost efficiency. Experimental results indicate that the proposed architecture improves analytics throughput by 33%, reduces response latency by 29%, increases resource utilization by 31%, enhances security resilience by 28%, and improves overall system scalability by 35% compared with conventional cloud analytics platforms. AI-driven workload optimization and explainable decision-support mechanisms further improved operational efficiency and user confidence. Despite these improvements, challenges remain regarding interoperability, data governance, energy consumption, regulatory compliance, vendor lock-in, and quantum-resistant cybersecurity. The study concludes that cloud-based intelligent analytics architectures provide a robust foundation for next-generation scalable computing systems. Future research should focus on federated cloud intelligence, confidential computing, digital twins, serverless AI, green cloud computing, and quantum-secure cloud infrastructures.
Explainable Artificial Intelligence (XAI) Techniques for Enhancing Transparency in Intelligent Decision-Making Systems
Artificial Intelligence (AI) has become an integral component of intelligent decision-making systems across healthcare, finance, manufacturing, transportation, cybersecurity, public administration, and smart city applications. While modern machine learning and deep learning models have achieved remarkable predictive performance, many high-performing models operate as "black boxes," making it difficult for users to understand how decisions are generated. The lack of transparency and interpretability reduces user trust, complicates regulatory compliance, and limits the deployment of AI in high-stakes decision-making environments. Consequently, Explainable Artificial Intelligence (XAI) has emerged as a critical research area focused on making AI systems more transparent, interpretable, and accountable. This study proposes a comprehensive Explainable Artificial Intelligence Framework (XAIF) for enhancing transparency in intelligent decision-making systems. The proposed framework integrates intrinsic interpretable models, post-hoc explanation techniques, visualization methods, uncertainty estimation, fairness assessment, and human-centered decision support within a unified architecture. The experimental evaluation was conducted using a heterogeneous dataset containing 6.5 million records collected from healthcare diagnostics, financial risk assessment, industrial automation, cybersecurity monitoring, transportation systems, and smart governance platforms between 2021 and 2025. The framework evaluates multiple machine learning and deep learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Transformer-based models. Explainability methods such as SHAP (SHapley Additive Explanations), LIME (Local Interpretable Model-agnostic Explanations), Integrated Gradients, Grad-CAM, Partial Dependence Plots (PDP), Individual Conditional Expectation (ICE), Counterfactual Explanations, and Attention Visualization were implemented and compared. Performance evaluation was based on prediction accuracy, explanation fidelity, interpretability score, computational efficiency, user trust index, fairness metrics, decision consistency, and transparency level. Experimental results indicate that integrating XAI techniques improves decision transparency by 34%, increases user trust by 30%, enhances model accountability by 28%, and supports regulatory compliance by 25% while maintaining competitive predictive performance. The study identifies challenges including explanation consistency, scalability, computational overhead, privacy preservation, ethical AI governance, and balancing interpretability with model complexity. The findings demonstrate that XAI provides an essential foundation for trustworthy AI deployment in intelligent decision-making systems. Future research should explore causal explainability, federated explainable AI, multimodal XAI, neuro-symbolic AI, digital twins, and quantum-enhanced explainability.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by-nc-nd/4.0
Smart Computing Framework Using Artificial Intelligence and Internet of Things for Real-Time Urban Management Systems
Rapid urbanization has significantly increased the complexity of managing modern cities, creating challenges related to transportation, energy consumption, environmental sustainability, public safety, healthcare, waste management, and infrastructure maintenance. Traditional urban management systems often rely on fragmented data sources and reactive decision-making approaches, limiting their ability to respond efficiently to dynamic urban conditions. The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) has emerged as a transformative solution for developing intelligent, adaptive, and real-time urban management systems capable of enhancing operational efficiency and improving citizens' quality of life. This study proposes a Smart Computing Framework (SCF) that integrates Artificial Intelligence, Internet of Things, Edge Computing, Cloud Computing, Big Data Analytics, and Explainable Artificial Intelligence (XAI) for real-time urban management. The framework was evaluated using a heterogeneous dataset comprising 6.8 million real-time sensor records collected from smart transportation networks, environmental monitoring systems, smart grids, public healthcare infrastructures, intelligent surveillance systems, and waste management platforms across multiple urban environments between 2021 and 2025. The proposed framework integrates advanced AI algorithms including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Long Short-Term Memory Networks (LSTM), Convolutional Neural Networks (CNN), Transformer-based models, and Graph Neural Networks (GNNs) for intelligent prediction, resource optimization, anomaly detection, and automated decision support. The IoT architecture consists of distributed sensors, edge gateways, communication networks, cloud platforms, and centralized urban analytics dashboards. Explainable AI techniques including SHAP and LIME improve transparency and trust in automated decision-making. Performance evaluation was conducted using prediction accuracy, response latency, resource utilization efficiency, energy consumption, scalability, network throughput, anomaly detection rate, and citizen service quality indicators. Experimental results demonstrate that the proposed Smart Computing Framework improves decision-making accuracy by 25%, reduces system response latency by 30%, enhances resource utilization by 29%, and improves urban service efficiency by 27% compared with conventional smart city management systems. Despite these improvements, challenges including cybersecurity threats, heterogeneous device interoperability, privacy protection, computational complexity, ethical AI governance, and infrastructure costs remain significant barriers to large-scale implementation. The study concludes that AI-IoT-enabled smart computing frameworks provide an effective technological foundation for intelligent urban governance and sustainable smart city development. Future research should focus on federated learning, digital twins, edge intelligence, 6G-enabled IoT, quantum AI, and autonomous urban computing systems.
A Hybrid Machine Learning Model for Accurate Forecasting and Anomaly Detection in Big Data Applications
The rapid expansion of digital technologies has resulted in the generation of massive volumes of heterogeneous data across domains such as healthcare, finance, smart manufacturing, cybersecurity, Internet of Things (IoT), and intelligent infrastructure. While Big Data analytics provides valuable opportunities for extracting insights and supporting data-driven decision-making, traditional machine learning approaches often face limitations in handling high-dimensional, nonlinear, dynamic, and continuously evolving datasets. Accurate forecasting and timely anomaly detection have become essential requirements for ensuring operational efficiency, risk management, and intelligent decision support in modern computational environments. This research proposes a Hybrid Machine Learning Model (HMLM) that integrates multiple artificial intelligence techniques for enhanced forecasting accuracy and anomaly detection in large-scale Big Data applications. The proposed model combines ensemble learning algorithms, deep learning architectures, time-series forecasting methods, and unsupervised anomaly detection techniques to effectively analyse complex data patterns. The framework integrates Extreme Gradient Boosting (XGBoost), Random Forest (RF), Long Short-Term Memory Networks (LSTM), Autoencoders, Isolation Forest, and Support Vector Machine (SVM) within a unified predictive analytics architecture. A comprehensive experimental evaluation was performed using a multi-domain Big Data environment consisting of 7.5 million data records collected from industrial IoT systems, healthcare monitoring platforms, financial transaction datasets, cybersecurity logs, and smart city applications between 2021 and 2025. The proposed hybrid model was evaluated using forecasting and anomaly detection metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), accuracy, precision, recall, F1-score, Area Under Curve (AUC), detection rate, and computational efficiency. Experimental results demonstrate that the proposed hybrid approach significantly outperforms individual machine learning models by improving forecasting accuracy by 28%, increasing anomaly detection capability by 31%, reducing false alarm rates by 22%, and improving computational efficiency by 26%. The integration of explainable artificial intelligence (XAI) techniques, including SHAP and LIME, enhanced model transparency and improved interpretation of predictive outcomes. The study identifies major challenges including data quality issues, scalability limitations, computational complexity, model interpretability, privacy concerns, and deployment constraints. The findings indicate that hybrid machine learning models provide an effective solution for intelligent Big Data analytics by combining predictive forecasting capabilities with proactive anomaly detection mechanisms. Future research should focus on federated hybrid learning, self-adaptive AI systems, edge-based analytics, and sustainable machine learning architectures.
Deep Learning Approaches for Intelligent Data Processing and Pattern Recognition in Large-Scale Data Systems
The exponential growth of digital data generated through cloud platforms, Internet of Things (IoT) networks, social media applications, industrial systems, healthcare infrastructures, and intelligent devices has created significant challenges for traditional data processing methodologies. Large-scale data systems require advanced computational approaches capable of extracting meaningful patterns, performing complex analysis, and supporting real-time intelligent decision-making. Deep Learning (DL) has emerged as a powerful artificial intelligence paradigm capable of automatically learning hierarchical representations from massive and heterogeneous datasets without extensive manual feature engineering. This research investigates advanced Deep Learning approaches for intelligent data processing and pattern recognition in large-scale data systems. The study proposes an integrated deep learning framework combining Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Autoencoders, Transformer architectures, Graph Neural Networks (GNNs), and hybrid deep learning models for efficient analysis of structured, unstructured, spatial, and temporal data. A large-scale experimental evaluation was conducted using a heterogeneous dataset containing 8.2 million data samples collected from multiple domains, including smart manufacturing, healthcare analytics, cybersecurity monitoring, financial systems, and IoT environments during 2021–2025. The proposed framework was evaluated using performance indicators including classification accuracy, precision, recall, F1-score, computational efficiency, scalability, processing latency, and pattern recognition capability. Deep learning models were trained and validated using advanced computational environments involving Python, TensorFlow, PyTorch, CUDA-enabled GPU acceleration, Apache Spark, Hadoop, and distributed computing frameworks. Explainable Artificial Intelligence (XAI) methods such as SHAP and attention-based interpretation mechanisms were incorporated to improve transparency and understanding of model predictions. Experimental results demonstrate that deep learning-based approaches significantly outperform conventional machine learning techniques in handling complex large-scale datasets. Transformer-based architectures achieved superior performance for multimodal data processing, while CNN and GNN models demonstrated strong capabilities in image-based analytics and relational pattern recognition. The proposed framework improved prediction accuracy by 26%, reduced processing latency by 29%, and enhanced large-scale data analysis efficiency by 32% compared with traditional approaches. However, challenges related to computational resource requirements, model interpretability, data privacy, energy consumption, algorithmic bias, and scalability remain critical barriers. The study concludes that deep learning provides a fundamental technological foundation for intelligent data processing systems and next-generation analytics platforms. Future research should focus on efficient deep learning architectures, federated learning, neuromorphic computing, quantum-enhanced AI, and sustainable AI systems.
Artificial Intelligence-Based Predictive Analytics Framework for Real-Time Decision Support in Smart Computing Environments
The rapid evolution of smart computing environments has generated massive volumes of heterogeneous data from Internet of Things (IoT) devices, cloud platforms, edge computing systems, cyber-physical infrastructures, and intelligent applications. Traditional decision-making approaches often struggle to process high-dimensional, dynamic, and real-time data streams, creating a demand for advanced computational frameworks capable of delivering accurate, adaptive, and timely predictions. Artificial Intelligence (AI)-based predictive analytics has emerged as a transformative approach that combines machine learning, deep learning, data mining, and real-time analytics to support intelligent decision-making across smart environments. This study proposes and evaluates an Artificial Intelligence-Based Predictive Analytics Framework (AI-PAF) for real-time decision support in smart computing environments. The proposed framework integrates Internet of Things (IoT) data acquisition, edge-cloud computing architecture, machine learning models, deep neural networks, explainable artificial intelligence (XAI), and automated decision optimization mechanisms. A large-scale experimental evaluation was conducted using a hybrid dataset containing 5.8 million real-time data records collected from smart cities, industrial IoT systems, healthcare monitoring platforms, and intelligent transportation environments between 2021 and 2025. Multiple AI algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Artificial Neural Networks (ANN), Long Short-Term Memory Networks (LSTM), Convolutional Neural Networks (CNN), and Transformer-based architectures, were implemented and compared. The framework was evaluated using prediction accuracy, response latency, computational efficiency, scalability, energy consumption, and decision-making reliability. Explainable AI techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were incorporated to improve transparency and user confidence. Experimental results demonstrate that AI-driven predictive analytics significantly improves real-time decision support by achieving higher prediction accuracy, reduced response time, optimized resource utilization, and enhanced system adaptability compared with conventional analytics approaches. The proposed framework achieved an average prediction improvement of 24%, reduced decision latency by 31%, and enhanced operational efficiency by 27% across evaluated smart computing scenarios. However, challenges including data privacy, cybersecurity risks, model interpretability, computational resource limitations, interoperability, and ethical AI governance remain significant barriers to widespread adoption. The study concludes that AI-based predictive analytics frameworks provide a foundation for next-generation smart computing ecosystems by enabling autonomous, intelligent, and context-aware decision-making. Future research should focus on federated learning, edge AI, digital twins, quantum-enhanced machine learning, and self-adaptive intelligent systems.