International Journal of Applied Data Science & Modern Computing
Intelligent Data Synchronization Strategies for Geo-Distributed Systems
Employee welfare facilities are essential components of human resource management that significantly influence employee well-being, motivation, and job satisfaction. In educational institutions, the provision of adequate welfare facilities is particularly important for creating a supportive work environment and enhancing institutional effectiveness. The present study examines employee welfare facilities and their impact on job satisfaction among employees in educational institutions. Specifically, the study evaluates six dimensions of welfare facilities, namely transport facilities, canteen facilities, parking facilities, safety and security, basic amenities, and grievance redressal mechanisms. The study adopted a quantitative and empirical research design. Primary data were collected from 199 teaching and non-teaching staff members using a structured questionnaire and analyzed using descriptive and inferential statistical techniques. The study employed the Employee Welfare Index (EWI), mean ranking analysis, gap analysis, correlation analysis, multiple regression analysis, and a Welfare Strategic Matrix to benchmark welfare facilities and identify critical welfare drivers of job satisfaction. The findings reveal that employees generally perceive the welfare facilities provided by educational institutions positively, with an overall Employee Welfare Index indicating a good level of welfare provision. Safety and Security emerged as the most satisfactory and influential welfare dimension, followed by Basic Amenities and Grievance Redressal Mechanisms. The gap analysis identified Canteen Facilities and Transport Facilities as areas requiring improvement. Correlation and regression analyses confirmed that employee welfare facilities significantly and positively influence job satisfaction, with Safety and Security emerging as the strongest predictor of job satisfaction. The study concludes that employee welfare facilities are strategic determinants of employee satisfaction and institutional effectiveness. The findings provide valuable insights for educational administrators in prioritizing welfare interventions and developing employee-centric welfare policies that promote employee well-being, satisfaction, and long-term institutional sustainability.
Self-Adaptive Distributed Computing Models for High-Performance Analytics
The rapid growth of Big Data, IoT, cloud computing, edge intelligence, and AI has increased the demand for scalable and efficient analytical infrastructures. Traditional distributed computing systems often rely on fixed resource allocation and execution strategies, leading to performance issues, resource underutilization, higher latency, and limited scalability under dynamic workloads. To address these challenges, self-adaptive distributed computing models enable systems to autonomously monitor, analyze, and optimize operations in real time. The proposed framework integrates adaptive resource management, intelligent workload scheduling, dynamic task migration, predictive analytics, and machine learning-based optimization to improve computational efficiency and responsiveness. The architecture includes monitoring layers, decision engines, adaptation controllers, distributed resource managers, and analytics execution frameworks. Machine learning techniques such as reinforcement learning, deep neural networks, and predictive modeling support proactive adaptation by forecasting workload demands and optimizing scheduling decisions. Experimental results demonstrate significant improvements in resource utilization, throughput, scalability, fault tolerance, and execution time compared to traditional approaches. Self-healing capabilities further enhance resilience against node failures and network disruptions. Overall, self-adaptive distributed computing provides a scalable, intelligent, and resilient foundation for next-generation high-performance analytics and data-intensive applications.
Copyright (c) 2025 International Journal of Applied Data Science & Modern Computing
AI-Powered Digital Twin Frameworks for Industrial Process Optimization
Industry 4.0 technologies are transforming manufacturing into intelligent, interconnected, and data-driven systems. Among these innovations, Digital Twin (DT) technology creates virtual replicas of physical assets, processes, and systems to enable real-time monitoring and optimization. When integrated with Artificial Intelligence (AI), Digital Twins provide a powerful framework for predictive maintenance, anomaly detection, process optimization, and autonomous control. By leveraging IoT sensors, edge computing, cloud platforms, and machine learning algorithms, AI-driven Digital Twins continuously synchronize physical and virtual environments to support intelligent decision-making. The proposed framework includes data acquisition, preprocessing, digital modeling, AI analytics, simulation, optimization, and decision support layers. Techniques such as Artificial Neural Networks (ANNs), Deep Learning (DL), Reinforcement Learning (RL), and predictive analytics are used to identify operational patterns and optimize industrial performance. Key performance indicators, including production efficiency, resource utilization, product quality, energy efficiency, downtime reduction, and system reliability, demonstrate the effectiveness of the framework. Experimental results indicate that AI-enabled Digital Twins can improve industrial productivity by over 20%, reduce maintenance costs, and enhance decision-making accuracy. The study highlights the transformative potential of AI-driven Digital Twins in enabling autonomous industrial operations, sustainable manufacturing practices, and the development of future smart factories.
Explainable Deep Learning Models for Critical Infrastructure Monitoring
Critical infrastructure systems such as power, transportation, water, oil and gas, manufacturing, and smart cities increasingly rely on advanced monitoring technologies to manage complex operations. Deep Learning (DL) techniques, including CNNs, RNNs, LSTMs, Autoencoders, and Transformers, have demonstrated strong capabilities in fault detection, anomaly detection, predictive maintenance, and operational optimization. However, their "black-box" nature limits adoption in safety-critical environments where transparency and trust are essential. Explainable Artificial Intelligence (XAI) addresses this challenge by providing interpretable insights into model decisions. This study presents a comprehensive survey of explainable deep learning approaches for critical infrastructure monitoring, focusing on SHAP, LIME, Attention Mechanisms, Gradient-Based Attribution, and Feature Visualization techniques. A framework integrating sensor data acquisition, deep learning-based anomaly detection, explainability generation, and decision support is proposed. Experimental findings indicate that incorporating XAI improves user trust, fault diagnosis, decision transparency, and maintenance planning while preserving predictive accuracy. The study also discusses implementation challenges and emerging research directions, including federated explainable learning, digital twins, and edge intelligence. The results highlight explainable deep learning as a key enabler for next-generation intelligent infrastructure monitoring systems.
Explainable Artificial Intelligence (XAI)
Intelligent Workflow Orchestration in Containerized Cloud Environments
This study presents an Intelligent Workflow Orchestration (IWO) Framework for containerized cloud environments that integrates Artificial Intelligence (AI), Machine Learning (ML), predictive analytics, and autonomous decision-making. Traditional orchestration methods often struggle to manage dynamic workloads efficiently due to their reliance on static scheduling and fixed resource allocation. The proposed framework addresses these limitations through AI-based workload forecasting, adaptive scheduling, intelligent autoscaling, resource-aware orchestration, container migration, and automated fault recovery. The architecture consists of monitoring, analytics, orchestration intelligence, and execution management layers that enable real-time workflow optimization. Machine learning models predict workload demands, while reinforcement learning supports optimal resource allocation decisions. Experimental results demonstrate improvements in workflow completion time, resource utilization, service availability, scalability, and operational efficiency compared to conventional orchestration approaches. The framework also enhances system resilience through automated fault detection and recovery, providing a scalable and adaptive solution for next-generation cloud-native applications and autonomous cloud infrastructure management.
Machine Learning-Based Fault Prediction and Recovery in Distributed Systems
Distributed systems play a vital role in modern cloud computing, edge computing, IoT, and enterprise applications by providing scalable and reliable services. However, these systems are vulnerable to failures caused by hardware faults, software defects, network issues, resource limitations, and workload fluctuations. Traditional fault management approaches, which rely on rule-based monitoring and reactive recovery, are often inadequate for handling the complexity of large-scale distributed environments. This study presents a Machine Learning (ML)-based Fault Prediction and Recovery Framework that enables proactive fault management through the analysis of system logs, performance metrics, resource utilization data, and network telemetry. The framework integrates data collection, feature engineering, fault prediction, anomaly detection, and autonomous recovery mechanisms. Supervised learning models classify known faults, while unsupervised learning techniques detect unknown anomalies. Reinforcement learning agents further optimize recovery actions through continuous interaction with the system environment. Experimental results demonstrate that the proposed framework achieves fault prediction accuracy above 95%, reduces downtime by more than 40%, lowers Mean Time to Recovery (MTTR), and improves overall system availability. The findings highlight the effectiveness of machine learning in enabling self-healing distributed systems and resilient cloud-native applications. Future research will focus on federated learning, explainable AI, and edge-cloud integration for enhanced intelligent fault management.
Large Language Model Integration for Enterprise Knowledge Management Platforms
Enterprise Knowledge Management Platforms (EKMPs) help organizations capture, organize, share, and utilize knowledge to improve decision-making and operational efficiency. Traditional knowledge management systems often face challenges such as data silos, unstructured information, limited contextual understanding, and ineffective search capabilities. The integration of Large Language Models (LLMs) addresses these limitations by enabling intelligent search, semantic understanding, automated content generation, and conversational interfaces. This study proposes an LLM-powered knowledge management framework that combines Retrieval-Augmented Generation (RAG), semantic embeddings, enterprise-specific language models, and vector databases to transform enterprise data into actionable knowledge. The framework includes data ingestion pipelines, knowledge repositories, embedding generation modules, retrieval systems, and conversational AI interfaces while emphasizing security, privacy, governance, scalability, and explainability. It also addresses challenges such as hallucination reduction, domain adaptation, and knowledge freshness. Experimental results demonstrate that LLM-based systems significantly improve retrieval accuracy, response relevance, knowledge reuse, and user satisfaction compared with traditional keyword-based approaches. These advancements enhance employee productivity, decision quality, collaboration, and innovation, positioning LLM-driven knowledge management systems as a key enabler of enterprise digital transformation.
Context-Aware AI Models for Dynamic Resource Management in Cloud Systems
Cloud computing provides scalable and cost-effective resources for modern digital enterprises, but increasing workload diversity, changing user demands, and complex infrastructures make resource management challenging. Traditional resource allocation methods often fail to adapt to dynamic cloud environments, resulting in inefficient resource usage, SLA violations, and higher operational costs. This study proposes a Context-Aware AI framework for dynamic cloud resource management that incorporates workload patterns, user behavior, network conditions, infrastructure health, and business objectives. The framework combines context acquisition, real-time analytics, Long Short-Term Memory (LSTM) workload prediction, Deep Reinforcement Learning (DRL)-based optimization, and adaptive orchestration. Experimental results show improved resource utilization, response time, energy efficiency, cost reduction, and service reliability. The framework supports autonomous cloud management and provides a foundation for future technologies such as edge computing, IoT, 6G networks, and intelligent enterprise applications.
Autonomous Data Pipeline Optimization Using Reinforcement Learning Techniques
Modern large-scale data pipelines support analytics, AI, ML, and real-time applications but face challenges related to scalability, resource utilization, reliability, and changing workloads. This paper proposes a reinforcement learning (RL)-based autonomous optimization framework that integrates RL agents with data orchestration platforms to continuously monitor pipeline states and optimize operations. The framework uses system metrics such as workload patterns, queue lengths, execution delays, resource consumption, and failure rates to make intelligent decisions on task scheduling, resource allocation, workload balancing, and fault recovery. Three RL algorithms—Q-learning, Deep Q-Networks (DQN), and Proximal Policy Optimization (PPO)—are evaluated. Experimental results demonstrate improved throughput, reduced latency, enhanced fault tolerance, and better resource efficiency compared to traditional rule-based approaches. The proposed framework enables adaptive, self-managing data pipelines that improve scalability, resilience, and operational efficiency across enterprise, cloud, and edge environments.
Energy-Efficient Distributed Machine Learning in Edge Computing Architectures
The rapid growth of IoT devices, smart sensors, and real-time intelligent applications has increased the demand for distributed machine learning (DML) in edge computing environments. Traditional cloud-based machine learning approaches often suffer from high latency, excessive bandwidth consumption, privacy concerns, and increased energy costs. Edge computing addresses these challenges by enabling data processing closer to data sources, thereby reducing response time and network congestion. However, deploying machine learning on resource-constrained edge devices introduces challenges related to energy efficiency, computational limitations, communication overhead, and model synchronization. This study investigates energy-efficient distributed machine learning techniques, including federated learning, model compression, adaptive resource management, dynamic task offloading, and communication-efficient optimization. A distributed learning framework is proposed that integrates local model training, adaptive communication scheduling, gradient compression, and workload balancing to minimize energy consumption while maintaining learning accuracy. Mathematical models are developed to evaluate energy usage, communication costs, and convergence behavior. Experimental results demonstrate significant improvements in energy efficiency, network utilization, convergence speed, and resource management compared with conventional distributed learning approaches. The findings highlight the importance of intelligent communication reduction and adaptive edge orchestration for sustainable AI deployment. The proposed framework supports scalable, privacy-preserving, and real-time analytics in large-scale IoT environments, contributing to the development of next-generation energy-efficient edge intelligence systems.
Geo-distributed systems are essential for modern cloud computing, enabling applications to run across multiple geographically distributed data centers while ensuring scalability, fault tolerance, and low latency. However, maintaining data consistency and synchronization across distributed nodes remains challenging due to network delays, bandwidth limitations, node failures, and dynamic workloads. This study proposes an intelligent data synchronization framework that integrates machine learning-based workload prediction, adaptive replication management, conflict-aware synchronization, and latency-aware consistency optimization. The framework continuously monitors system metrics such as network latency, bandwidth usage, replication delay, transaction rates, and node availability to make dynamic synchronization decisions. By forecasting future data access patterns and adapting synchronization strategies accordingly, the proposed model reduces replication traffic, synchronization conflicts, and resource consumption while maintaining consistency. Experimental results demonstrate significant improvements over traditional methods, achieving approximately 94% prediction accuracy, 35% lower synchronization overhead, 28% higher throughput, and 31% lower latency. The framework provides a scalable and efficient solution for cloud computing, edge computing, IoT, and large-scale enterprise systems. Future work includes integrating Federated Learning, autonomous synchronization orchestration, and blockchain-based consistency management for decentralized environments.
Scalable Event-Driven Architectures for Real-Time Big Data Applications
Scalable Event-Driven Architectures (EDAs) provide an efficient approach for real-time big data processing by enabling low-latency, asynchronous, and continuous handling of high-speed data streams generated from IoT devices, cloud platforms, social media, and enterprise systems. This study presents a scalable framework that integrates distributed event brokers, stream processing engines, cloud-native microservices, and scalable storage solutions. The architecture utilizes publish-subscribe communication, event sourcing, and distributed stream analytics to support real-time decision-making and dynamic scalability. Technologies such as Apache Kafka, Apache Flink, Apache Spark Streaming, and Kubernetes enhance throughput, fault tolerance, and system resilience. Key components include event producers, brokers, stream processors, consumers, and monitoring services. Performance evaluation based on throughput, latency, scalability, fault tolerance, and resource utilization demonstrates that EDAs outperform traditional batch-processing and request-response systems. The framework also addresses challenges such as event ordering, state management, event replay, and observability through checkpointing, event partitioning, distributed tracing, and container orchestration. The proposed architecture is applicable to financial analytics, smart cities, healthcare, e-commerce, cybersecurity, and industrial automation. Overall, EDAs offer a scalable, reliable, and flexible foundation for next-generation real-time analytics and big data applications.
Copyright (c) 2024 International Journal of Applied Data Science & Modern Computing
Distributed Artificial Intelligence for Smart City Infrastructure Management
Rapid urbanization has increased the need for intelligent and sustainable smart city infrastructure management. Smart cities generate massive amounts of data through IoT devices, sensors, cloud platforms, and communication networks, making traditional centralized AI systems less effective due to scalability, latency, privacy, and reliability challenges. Distributed Artificial Intelligence (DAI) addresses these issues by distributing intelligence across multiple interconnected nodes, enabling decentralized learning and decision-making. This study examines the role of DAI technologies such as multi-agent systems, edge computing, federated learning, IoT networks, and cloud-edge collaboration in managing urban services. A review of recent applications demonstrates DAI’s effectiveness in traffic management, energy distribution, water systems, predictive maintenance, public safety, and environmental monitoring. The proposed framework enhances real-time processing, resource optimization, fault tolerance, and data privacy. Results indicate that DAI outperforms centralized approaches in response time, scalability, accuracy, and reliability. The study concludes that DAI is a key enabler of future smart cities, with emerging technologies such as Explainable AI (XAI), blockchain, digital twins, and autonomous urban management expected to further improve smart city operations and citizen services.
Machine Learning Techniques for Automated Database Performance Tuning
Database Management Systems (DBMSs) are essential for modern applications such as cloud computing, e-commerce, finance, healthcare, and scientific research. As data volumes continue to grow, traditional manual database tuning methods have become inefficient, time-consuming, and unable to adapt to dynamic workloads. Machine Learning (ML) offers an intelligent solution by enabling databases to learn from historical workload patterns, resource utilization, query performance, and system metrics. This study explores the application of ML techniques for automated database performance tuning. Various algorithms, including Random Forest, Support Vector Machines, Artificial Neural Networks, Gradient Boosting, and Deep Reinforcement Learning, are utilized to optimize database parameters such as indexing, memory allocation, buffer management, query execution plans, and storage configurations. A systematic framework is proposed that incorporates performance monitoring, feature engineering, predictive modeling, and automated decision-making components to achieve self-managing database operations. Experimental results demonstrate that ML-based tuning significantly improves query response time, throughput, resource utilization, and adaptability compared to static configurations. The study also discusses challenges related to model interpretability, scalability, data quality, and real-time deployment. Furthermore, future research directions including autonomous databases, cloud-native optimization, federated learning, and AI-driven database administration are highlighted. Overall, the findings indicate that machine learning is a transformative technology for developing intelligent, self-tuning database systems with enhanced performance, reliability, and reduced operational costs.
AI-Enabled Workload Prediction for Elastic Cloud Computing Systems
In conclusion, the proposed AI-based workload prediction framework significantly enhances cloud resource management by accurately forecasting future workload demands and enabling proactive resource allocation. By utilizing machine learning and deep learning techniques such as LSTM, Random Forest Regression, and Gradient Boosting, the system improves resource utilization, reduces response time, lowers operational costs, and minimizes SLA violations. The results demonstrate superior prediction accuracy compared to traditional methods, leading to better Quality of Service (QoS) and energy efficiency. This study highlights the potential of AI-driven predictive analytics to transform cloud computing from reactive resource management to intelligent, autonomous, and adaptive cloud ecosystems. Future research can further improve performance through the integration of federated learning, reinforcement learning, and edge-cloud computing technologies.
Hybrid Knowledge-Driven and Data-Driven Approaches for Predictive Intelligence
Predictive intelligence enables systems to forecast future events using historical data, domain knowledge, and advanced analytics. Traditional approaches are either knowledge-driven, offering interpretability and reasoning, or data-driven, providing strong learning capabilities but facing challenges in explainability and adaptability. Hybrid predictive intelligence combines both paradigms to overcome their limitations. The proposed framework includes four stages: knowledge acquisition, data preprocessing, hybrid model integration, and predictive decision support. By integrating expert knowledge with machine learning techniques, it improves prediction accuracy, reliability, transparency, and decision-making. Applications span healthcare, industrial automation, cybersecurity, finance, smart cities, and intelligent transportation systems. Comparative studies show that hybrid models outperform conventional approaches in accuracy, robustness, and interpretability. Future developments in federated learning, digital twins, graph neural networks, and autonomous reasoning are expected to further enhance predictive intelligence for next-generation intelligent systems.
A Study on Work life Balance of Employees in Banking Sectors
This study investigates the critical challenges and determinants of work-life balance among employees within the banking sector. In the contemporary financial services industry, escalating operational targets, extended working hours, and the digitization of banking services have significantly intensified professional demands, often at the cost of personal well-being. Recognizing the limitations in existing literature regarding specific institutional stressors, this paper establishes a robust framework to evaluate the correlation between organizational support mechanisms and employee retention. Utilizing a systematic methodology comprising a quantitative survey design, primary data was gathered from public and private sector bank employees and analyzed using statistical tools to ensure high reproducibility and validity. The key findings reveal that workload pressure and rigid schedules are primary drivers of work-life conflict, whereas flexible timing and strong managerial support significantly enhance job satisfaction and reduce burnout. Ultimately, this research offers crucial theoretical and practical implications by providing actionable frameworks for HR policymakers in banking institutions to foster a healthier work environment. By addressing these distinct structural gaps, this study advances current knowledge, offering a clear roadmap for future investigations and practical implementations within the relevant domain.
Graph-Based Deep Learning Models for Cyber-Physical System Analytics
Cyber-Physical Systems (CPSs) integrate computational intelligence, communication networks, and physical processes to support applications such as smart manufacturing, healthcare, transportation, smart grids, and autonomous vehicles. The increasing use of IoT devices generates large volumes of dynamic and interconnected data, making traditional machine learning methods insufficient for capturing complex relationships. Graph-Based Deep Learning (GBDL) addresses this challenge by representing CPS components as nodes and their interactions as edges. Advanced models such as Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Graph Autoencoders effectively learn from graph-structured data. These techniques improve anomaly detection, fault diagnosis, predictive maintenance, cybersecurity, and decision-making. The proposed framework constructs dynamic graph representations from sensor networks and applies graph convolution and attention mechanisms to capture spatial-temporal dependencies and enhance feature extraction. Experimental results demonstrate that graph-based deep learning outperforms traditional machine learning and standard deep learning methods in terms of accuracy, precision, recall, F1-score, and anomaly detection. The architecture also provides high computational efficiency and reliability for large-scale CPS deployments. Overall, graph-based deep learning is a key enabling technology for future intelligent CPS infrastructures. Future research directions include federated graph learning, explainable GNNs, energy-efficient architectures, and secure graph analytics for Industry 5.0 applications.
AI-Assisted Resource Scheduling in Multi-Cloud Computing Environments
Cloud computing has evolved significantly, with multi-cloud environments becoming popular for improving scalability, reliability, fault tolerance, and cost efficiency. However, resource scheduling in multi-cloud systems remains challenging due to heterogeneous infrastructures, dynamic workloads, varying pricing models, network latency, and SLA requirements. Traditional scheduling methods such as Round Robin and FCFS often fail to adapt effectively to these complexities. Artificial Intelligence (AI) offers an advanced solution through intelligent resource scheduling. By leveraging machine learning, reinforcement learning, and predictive analytics, AI-based schedulers can forecast workloads, optimize resource allocation, and make autonomous scheduling decisions. This research proposes an AI-driven resource scheduling framework that integrates workload prediction, resource classification, intelligent scheduling, and continuous feedback mechanisms. The framework aims to optimize multiple objectives, including cost reduction, execution efficiency, energy consumption, and SLA compliance. Performance evaluation using metrics such as resource utilization, makespan, response time, throughput, and energy consumption demonstrates that AI-assisted scheduling outperforms traditional approaches. The results indicate improved resource utilization, better workload balancing, reduced operational costs, and enhanced service quality, highlighting the potential of AI-driven scheduling for next-generation multi-cloud resource management systems.
Intelligent Data Provenance Tracking for Large-Scale Analytics Platforms
Large-scale analytics platforms generate massive amounts of data from diverse sources, making data transparency, accountability, and governance increasingly important. Data provenance, which records the origin, transformation, ownership, and movement of data, plays a critical role in ensuring data quality, reproducibility, compliance, and trustworthiness. However, traditional provenance tracking methods often struggle to handle the complexity and scale of modern analytics environments. This study proposes an Intelligent Data Provenance Tracking Framework (IDPTF) that integrates artificial intelligence, graph-based metadata management, distributed provenance storage, and automated lineage analysis. The framework consists of four layers: Data Acquisition, Provenance Intelligence, Metadata Storage, and Analytics Governance, enabling end-to-end data traceability with minimal performance overhead. Using graph databases and machine learning techniques, the framework captures provenance information across the entire data lifecycle, detects anomalies, optimizes metadata storage, and reconstructs data lineage dynamically. Experimental results demonstrate improved provenance completeness, faster query performance, enhanced governance effectiveness, simplified compliance auditing, and greater resilience to data inconsistencies. The AI-driven approach also enables proactive risk prediction, supporting trusted analytics and reliable decision-making. Overall, the proposed framework provides a scalable and flexible solution for provenance management in modern analytics ecosystems, with future enhancements including blockchain integration, federated learning support, and autonomous governance mechanisms.
Adaptive Federated Analytics Frameworks for Distributed Enterprise Data Systems
As enterprise data grows across cloud, edge, and geographically distributed environments, traditional centralized analytics face challenges related to privacy, security, scalability, and regulatory compliance. To address these issues, this study proposes an Adaptive Federated Analytics Framework (AFAF) for distributed enterprise data systems. The framework enables collaborative analytics without sharing raw data by incorporating adaptive node selection, dynamic aggregation, privacy-preserving mechanisms, and communication optimization techniques. Unlike conventional federated approaches, AFAF dynamically evaluates node reliability, computational capacity, data quality, and network conditions to improve analytical performance. The framework integrates differential privacy, secure multi-party computation (SMPC), and encrypted aggregation to ensure enterprise-grade security and compliance. Experimental evaluations in hybrid cloud enterprise environments demonstrate improvements in analytical accuracy, aggregation efficiency, communication overhead, convergence stability, scalability, and fault tolerance compared with traditional federated systems. The framework also supports real-time analytics through adaptive participation thresholds and aggregation frequencies, enabling timely insights from distributed data sources. Results indicate that AFAF provides a scalable, secure, and efficient platform for privacy-preserving enterprise intelligence, with future enhancements including AI-driven orchestration, blockchain-based trust management, and autonomous analytics optimization.
Deep Learning-Based Network Traffic Classification for Next-Generation Networks
Network traffic classification has become essential for managing, securing, and optimizing next-generation networks (NGNs) that support IoT, cloud computing, SDN, NFV, and 5G technologies. Traditional classification methods based on port numbers and deep packet inspection are increasingly ineffective due to encrypted traffic, dynamic port allocation, and evolving application behaviors.This study presents a deep learning-based approach for intelligent network traffic classification. It reviews the evolution of traffic classification techniques and proposes a hybrid CNN-LSTM model that combines packet-level feature extraction with temporal sequence learning. The framework effectively captures spatial and temporal characteristics of network traffic, enabling accurate classification of both encrypted and non-encrypted flows. Experimental evaluation using benchmark datasets demonstrates that the proposed model outperforms conventional machine learning methods, achieving classification accuracy above 97%. Performance is validated using metrics such as accuracy, precision, recall, F1-score, and classification efficiency. The results highlight the potential of deep learning to support real-time traffic analysis, network security, QoS management, and resource optimization in modern communication networks. The proposed framework provides an intelligent and scalable solution for traffic classification in future autonomous and secure networking environments.
Scalable Load Balancing Algorithms for Cloud Infrastructures
Cloud computing has become a paradigm of providing dynamically scalable and on-demand computing in the Internet. Effective load balancing is one of the most significant issues in cloud environment that guarantees optimal resource use, low response time, high availability, and quality of service (QoS). Traditional methods of load balancing are inadequate as cloud infrastructures increase due to scale and complexity because of the dynamism and heterogeneity in their workloads. As a result, there is a need to have scalable and flexible load balancing strategies that would efficiently distribute workloads in large-scale cloud data centres. The paper provides a detailed research of the scalable load-balancing algorithms to the cloud infrastructures in terms of their architectural principles, performance indicators, scaling attributes, and fault-tolerance. Some of the classical and state of the artload balancing algorithms, such as, are statical, dynamic, heuristic, and nature inspired strategies that are reviewed in the paper. It is also suggested that a new approach to scaling hybrid load balancing methodology should be offered because it combines distributed decision-making with predictive estimation of workload. The suggested solution is intended to increase the throughput of the system, reduce the response time, and optimize the use of resources in the presence of highly changing workloads. A long analysis assessment and comparative report is carried out to reveal the efficiency of the scalable load balancing strategies at the large-scale cloud environment. The findings show that adaptive algorithm and hybrid algorithm is better in scalability, robustness and the overall performance of the system compared to the traditional centralized algorithms. The results of this paper can be helpful to researchers and practitioners during the development of the next-generation cloud load balancing mechanisms.
Copyright (c) 2023 International Journal of Applied Data Science & Modern Computing
IoT and Data Science Integration for Smart Home Automation
The Internet of Things (IoT) technologies and their high growth rate in the context of data science approaches have led to the significant shift in paradigm of smart home automation. Conventional home automation systems are mainly dependent on the rule-based measures of control and do not offer flexibility and intelligence. Modern smart homes are expected to be more automated and energy efficiency, comfortable to the user, and secure through the combination of data-driven solutions like machine learning, predictive analytics, and real-time data processing. This essay is a detailed research of the use of IoT and data science in the automation of smart homes. The suggested interface focuses on sensor-based acquisition of data, cloud storage, and the smart decision-making process based on machine learning models. Modular architecture: It is a form of architecture that is created to support interoperability and scalability among heterogeneous devices. The most common challenges include privacy of data, system consistency and efficiency. The experimental performance proves that data science-based automation is much more efficient than the conventional approaches to automation concerning energy optimization and prediction of user behavior. The results validate the fact that the combination of IoT and data science forms a strong venue of the future smart houses.
Industrial Defect Detection Using Modern Computer Vision Techniques
The manufacturing process of the industry requires very high quality standards to guarantee reliability and suitability of the product, level of customer satisfaction and economical efficiency of the product. The fact is that even small defects may cause some heavy loss of money, risks, and damaged reputation. Conventional defect inspection systems are often based on manual inspection and classical machine vision methods, which is time consuming, subjective and prone to errors and it is hard to scale into large scale production set ups today. The recent fast development of computer vision technology using the methods of deep learning and artificial intelligence has also changed the nature of automated defect detection systems dramatically. The current computer vision methods including convolutional neural networks (CNNs), transformer networks, and mixed learning systems have shown outstanding results when detecting surface defects, structural deformities and functional defects in many industrial settings. The paper is an in-depth research on the industrial defect detection with the help of the modern computer vision methodology, including the theoretical bases, the current approach methods, and the practical aspect of their implementation. The suggested framework incorporates the advanced image acquisition, preprocessing and deep features extraction, and intelligent classification algorithms to obtain a high level of detection and strong stability in adverse industrial environments. Extensive trials indicate the efficiency of the contemporary vision-based solutions to decrease false positives, accelerate the speed of inspection, and maintain the quality control. The outcomes verify that defect detection systems based on deep learning are far more effective than conventional inspection and can be utilized in practice in the industrial environment on a real-time basis. The paper ends with the determination of the current limitations and research areas to explore in the future in the field of scalable and explainable defect detectors in smart factories.
Deep Learning Enhancements Using Pretraining and Fine-Tuning
Deep learning has achieved state-of-the-art performance across domains such as computer vision, NLP, and healthcare, but it often requires large datasets and high computational resources. Pretraining and fine-tuning have emerged as effective strategies to improve performance, reduce training cost, and enhance generalization. Pretraining learns transferable representations from large-scale data, while fine-tuning adapts models to specific tasks with limited labeled data. This paper provides a comprehensive study of various pretraining methods (supervised, unsupervised, self-supervised) and fine-tuning techniques, including full-model and parameter-efficient approaches. A unified framework is proposed to integrate both processes in a deep learning pipeline. Experimental findings show that pretrained models outperform those trained from scratch in terms of accuracy, convergence speed, and robustness. The study also highlights challenges, trade-offs, and emerging trends such as foundation models and multimodal learning, emphasizing the importance of these techniques in advancing deep learning systems.
A Review of Modern Database Systems for Data Science Applications
Modern data mining applications require scalable and high-performance database systems capable of handling structured, semi-structured, and unstructured data. Traditional RDBMS, designed for transactional consistency, face limitations in scalability and performance for big data analytics. As a result, modern systems such as NoSQL, NewSQL, distributed file systems, and cloud-native platforms have emerged, offering features like horizontal scalability, schema flexibility, and real-time processing. This paper provides a comprehensive overview and comparison of these database systems, focusing on their architecture, data models, and suitability for analytical workloads. It also examines their role in data science pipelines, including data ingestion, preprocessing, model training, and deployment. Key trade-offs based on the CAP theorem are discussed, along with performance metrics such as scalability, latency, and fault tolerance. The study highlights that no single database solution fits all scenarios, and hybrid architectures with polyglot persistence are increasingly adopted. The paper concludes by identifying research gaps in database optimization for machine learning, data governance, and real-time analytics, offering guidance for selecting appropriate systems in data-driven environments.
Optimizing Cloud Storage Using Intelligent Caching Algorithms
Cloud computing has become the new foundation of the current digital network, which provides the possibility to store data with scaling and on-demand access to huge amounts of data. Nevertheless, the fast development of the cloud-based applications has posed the significant threats in terms of latency, bandwidth usage as well as storage efficiency. Conventional cloud storage platforms are very much dependent on the concept of traditional or heuristic based caching whereby caching is not always adequate to adjust to the dynamism and heterogeneity of workloads. The given paper is a complete research on the optimization of cloud storage systems by using intelligent caching algorithms. Through machine learning, predictive analytics, and adaptive replacement technologies, intelligent caching will be used to enhance the speed and efficiency of data access, minimizing network congestions, and increasing the efficiency of a system as a whole. The suggested algorithm is a combination of workload-sensitive placement of caches, predicting access pattern, and managing the cache in real time to dynamically optimize storage throughput. The high performance of the cloud of isolated simulated cloud workloads has been extensively tested and results in high improvements in cache hit ratio, response time and bandwidth use as compared to traditional caching methods. The findings support the fact that smart caching schemes offer a solid and scalable remedy to optimization of next-generation cloud storage.
Deep Learning Approaches for Speech Recognition Systems
Automatic Speech Recognition (ASR) has evolved from rule-based and statistical methods to deep learning approaches, achieving near human-level performance under certain conditions. Traditional HMM-GMM models face limitations in handling long-term dependencies, speaker variability, and noise. Modern architectures such as DNNs, CNNs, RNNs, LSTMs, and Transformers provide improved representation learning and feature extraction from speech signals. This paper presents a comprehensive review of deep learning-based ASR systems, covering their evolution, acoustic feature extraction, end-to-end modeling, and training techniques. A generalized ASR pipeline is proposed, including preprocessing, feature encoding, model design, optimization, and decoding. Performance is evaluated using metrics like Word Error Rate (WER) and Character Error Rate (CER). The study highlights key challenges such as low-resource languages, real-time processing, domain adaptation, and model interpretability. It concludes that deep learning is the standard for ASR, with future research focusing on self-supervised learning, multilingual models, and efficient edge deployment.
Computational Intelligence Models for Disease Outbreak Prediction
Disease outbreak prediction has become increasingly important due to the rise of emerging and re-emerging infectious diseases such as COVID-19, Ebola, Zika, Dengue, and Influenza. Traditional epidemiological surveillance systems rely on historical case reports and laboratory confirmations, often resulting in delayed reporting and limited spatial resolution, which hinder timely intervention and preparedness. Computational Intelligence Systems (CIS), including machine learning (ML), deep learning (DL), evolutionary computing, and fuzzy systems, offer improved prediction by identifying complex nonlinear patterns from heterogeneous data sources such as epidemiological, environmental, mobility, and social media data. This paper presents a comprehensive review of CI models for outbreak prediction, comparing supervised and unsupervised methods such as Support Vector Machines (SVM), Random Forest (RF), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and hybrid models. A multi-layered predictive framework is proposed, incorporating data preprocessing, feature engineering, dimensionality reduction, ensemble learning, and performance evaluation. A hybrid LSTM–CNN model is introduced to capture temporal and spatial dependencies in outbreak data. Experimental results demonstrate that CI models outperform traditional statistical approaches like ARIMA and regression models in short- and medium-term forecasting. Evaluation metrics including RMSE, MAPE, Precision, Recall, and F1-score confirm the superior predictive performance and robustness of hybrid models. The study highlights the importance of integrating heterogeneous data and intelligent modeling for smart disease surveillance and supports future research in explainable AI, federated learning, and real-time adaptive prediction systems.