Frontiers in Emerging Computer Science and Information Technology
Frontiers in Emerging Computer Science and Information Technology (2978-3712) is a peer-reviewed, open access international journal committed to publishing cutting-edge research and innovative advancements in computer science and information technology. The journal serves as a scholarly platform for academics, researchers, industry professionals, and students to disseminate knowledge and promote the development of next-generation technologies.
Authors retain the copyright of their articles published in this journal. All articles are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly cited.
Intelligent Framework For Twitter-Based Sentiment Analysis Using Machine Learning Techniques
The rapid growth of social media platforms has transformed the dynamics of digital communication, enabling individuals, organizations, and governments to express opinions and interact in real time. Among these platforms, Twitter has emerged as a significant source of public sentiment data due to its high user engagement and concise textual structure. The increasing volume of Twitter-generated content has intensified the need for intelligent sentiment analysis systems capable of extracting meaningful insights from unstructured textual information. This study presents an intelligent framework for Twitter-based sentiment analysis using machine learning techniques with the objective of improving sentiment classification accuracy, scalability, and contextual interpretation. The research integrates preprocessing strategies, feature extraction models, machine learning algorithms, and deep learning approaches into a unified analytical framework. The study critically evaluates supervised learning methods, lexicon-based techniques, and neural network architectures for sentiment classification. Comparative examination of previous studies reveals substantial progress in sentiment detection; however, challenges related to sarcasm detection, contextual ambiguity, multilingual tweets, and data imbalance remain significant. The proposed framework emphasizes intelligent data handling, adaptive classification, and hybrid learning mechanisms to enhance predictive performance. Results indicate that machine learning-based sentiment analysis frameworks outperform traditional rule-based systems in handling dynamic social media environments. The research contributes to the development of scalable sentiment analytics systems applicable in business intelligence, healthcare communication, public policy monitoring, cybersecurity, and digital marketing. The study further highlights ethical concerns, data privacy implications, and future opportunities in AI-driven sentiment intelligence systems.
Copyright (c) 2026 Dr. Mika Tanielu, Dr. Leilani Fepuleai https://creativecommons.org/licenses/by/4.0
Optimization Strategies And Infrastructure Models In Contemporary Web Engineering: Evaluating Docker And Kubernetes Ecosystems
The rapid evolution of cloud-native computing has significantly transformed contemporary web engineering through the adoption of containerization and orchestration technologies. Docker and Kubernetes have emerged as foundational technologies for scalable, resilient, and automated software infrastructures. This research-review article critically evaluates optimization strategies, infrastructure models, and deployment architectures associated with Docker and Kubernetes ecosystems. The study synthesizes existing literature to examine container orchestration frameworks, microservices scalability, CI/CD integration, security enforcement, workload optimization, and cloud-native operational models. The article further investigates how Kubernetes improves resource scheduling, service reliability, and high-availability architectures within distributed systems. Special attention is given to modern security automation, including LLM-enhanced application testing and privacy validation mechanisms in containerized environments. The research identifies critical gaps involving orchestration complexity, resource overhead, latency optimization, and security governance. Findings indicate that Docker and Kubernetes significantly improve deployment consistency, scalability, infrastructure portability, and operational automation, although challenges remain regarding security orchestration, cluster management complexity, and observability optimization. The paper contributes a consolidated analytical perspective on contemporary container ecosystems and proposes strategic directions for future cloud-native infrastructure engineering
Copyright (c) 2026 Dr. Arvind Ramdin, Dr. Kavita Bhojoo https://creativecommons.org/licenses/by/4.0
Intelligent Framework For Real-Time Anomaly Detection In Distributed Data Engineering Systems
The rapid expansion of distributed data engineering ecosystems has significantly increased the complexity of real-time anomaly detection across cloud-native infrastructures, Internet of Things (IoT) platforms, enterprise analytics pipelines, and decentralized computational environments. Traditional anomaly detection techniques are increasingly ineffective in addressing high-volume, heterogeneous, and continuously evolving data streams generated by modern distributed architectures. This research proposes an intelligent framework for real-time anomaly detection in distributed data engineering systems by integrating machine learning, adaptive stream analytics, distributed event processing, security-driven architectures, and AI-assisted predictive modeling. The framework focuses on improving detection accuracy, reducing latency, minimizing false positives, and enhancing scalability across multi-source data environments. The study synthesizes recent developments in AI-driven monitoring, blockchain-enabled security, quantum-aware cybersecurity models, predictive maintenance systems, and scalable ETL infrastructures. A layered methodological architecture is introduced to support anomaly identification, automated classification, distributed coordination, and response optimization. The findings indicate that intelligent distributed anomaly detection systems significantly improve operational reliability, cyber resilience, and infrastructure scalability when compared to static rule-based monitoring models. Furthermore, the study identifies critical implementation challenges related to model drift, distributed synchronization, computational overhead, and data governance. The proposed framework contributes to research on intelligent distributed analytics by establishing a scalable, adaptive, and security-oriented anomaly detection paradigm suitable for contemporary enterprise and cloud environments.
Copyright (c) 2026 Dr. Daniel Rolle, Dr. Melissa Cartwright https://creativecommons.org/licenses/by/4.0
Intelligent Machine Learning Framework For Detecting Mobile Money Fraud Through SMS Message Analysis
The rapid expansion of mobile money ecosystems has transformed financial inclusion across emerging economies by enabling secure and accessible digital transactions. However, this transformation has also generated substantial cybersecurity risks, particularly SMS-based fraud schemes targeting financially vulnerable users. Fraudulent SMS messages involving phishing links, impersonation attacks, social engineering tactics, and deceptive financial alerts have become increasingly sophisticated, creating significant challenges for mobile money providers and regulatory institutions. This study proposes an intelligent machine learning framework for detecting mobile money fraud through SMS message analysis using data mining, semantic processing, and predictive classification techniques. The research synthesizes theoretical foundations from fraud analytics, machine learning, and mobile financial security literature to construct a scalable fraud detection architecture. The proposed framework integrates text preprocessing, feature engineering, semantic clustering, probabilistic classification, and deep learning-based pattern recognition to improve fraud detection accuracy and adaptability. The study evaluates the effectiveness of supervised and deep learning approaches in identifying fraudulent SMS behavior while examining operational constraints such as dataset imbalance, linguistic variability, and adversarial manipulation. Findings indicate that hybrid machine learning architectures combining semantic analysis with neural network-based classifiers provide superior performance in identifying fraudulent communication patterns. The research contributes a structured analytical model for intelligent fraud detection in mobile money systems and highlights the importance of adaptive machine learning in strengthening digital financial security infrastructures. The paper further discusses implementation limitations, practical implications, and future research directions for intelligent fraud prevention systems in developing economies.
Copyright (c) 2026 Dr. Devendra Singh, Dr. Alana Fraser https://creativecommons.org/licenses/by/4.0