Journal of Smart Computing and Data Intelligence
type: channel
language: en
publication name: Journal of Smart Computing and Data Intelligence
items: AI-Driven Edge Computing Architecture for Real-Time Data Analysis in Next-Generation Smart Applications
items: Natural Language Processing-Based Intelligent Information Retrieval System Using Transformer Models
items: Blockchain-Enabled Secure Data Management System for Intelligent Computing Networks
items: Federated Learning-Based Privacy-Preserving Framework for Distributed Smart Data Intelligence
items: Cloud-Based Intelligent Data Analytics Architecture for Scalable and Secure Computing Applications
items: Explainable Artificial Intelligence (XAI) Techniques for Enhancing Transparency in Intelligent Decision-Making Systems
items: Smart Computing Framework Using Artificial Intelligence and Internet of Things for Real-Time Urban Management Systems
items: A Hybrid Machine Learning Model for Accurate Forecasting and Anomaly Detection in Big Data Applications
items: Deep Learning Approaches for Intelligent Data Processing and Pattern Recognition in Large-Scale Data Systems
items: Artificial Intelligence-Based Predictive Analytics Framework for Real-Time Decision Support in Smart Computing Environments

AI-Driven Edge Computing Architecture for Real-Time Data Analysis in Next-Generation Smart Applications
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description: 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.
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creator: Dr. Pathan Ahmed Khan
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 33
ending page: 41

Natural Language Processing-Based Intelligent Information Retrieval System Using Transformer Models
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description: 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.
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creator: Cynthia Rudin
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 25
ending page: 32

Blockchain-Enabled Secure Data Management System for Intelligent Computing Networks
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description: 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 infrastruct…
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creator: Prof. José Machado
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 17
ending page: 24

Federated Learning-Based Privacy-Preserving Framework for Distributed Smart Data Intelligence
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description: 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.…
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creator: Robert Tibshirani
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 9
ending page: 16

Cloud-Based Intelligent Data Analytics Architecture for Scalable and Secure Computing Applications
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description: 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, TensorFlo…
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creator: John A. List
rights: Copyright (c) 2026 Author https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 1
ending page: 8

Explainable Artificial Intelligence (XAI) Techniques for Enhancing Transparency in Intelligent Decision-Making Systems
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description: 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 (PD…
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creator: David B. Audretsch
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-26
publication date: 2025-12-26
starting page: 42
ending page: 49

Smart Computing Framework Using Artificial Intelligence and Internet of Things for Real-Time Urban Management Systems
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description: 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 trus…
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creator: Regina Barzilay
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-26
publication date: 2025-12-26
starting page: 32
ending page: 41

A Hybrid Machine Learning Model for Accurate Forecasting and Anomaly Detection in Big Data Applications
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description: 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 computa…
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creator: Bonnie Berger
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-29
publication date: 2025-12-29
starting page: 23
ending page: 31

Deep Learning Approaches for Intelligent Data Processing and Pattern Recognition in Large-Scale Data Systems
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description: 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) method…
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creator: Prof. Luís Paulo Reis
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-26
publication date: 2025-12-26
starting page: 12
ending page: 22

Artificial Intelligence-Based Predictive Analytics Framework for Real-Time Decision Support in Smart Computing Environments
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description: 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 Mode…
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creator: Prof. João Paulo Carvalho
rights: Copyright (c) 2025 Author https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-26
publication date: 2025-12-26
starting page: 1
ending page: 11