Qubahan Techno Journal (QTJ) is a quarterly academic journal published by the Qubahan, QTJ publishes original researches in all fields of Technology (Computer Science, Computer Engineering, Information Technology, Artificial Intelligent, Security, computer Graphics and Data Science ). QTJ is a Peer-Reviewed Open Access journal. QTJ provides immediate, worldwide, barrier-free access to the full text of research articles without requiring a subscription to the journal.
AI–Driven Educational Ecosystems: Integrating Learning Analytics, Adaptive Assessment, and Intelligent Feedback for Sustainable Student Performance
The rapid expansion of artificial intelligence in educational settings has led to the deployment of diverse yet largely fragmented systems, including learning analytics, adaptive assessment, intelligent tutoring systems, and AI-based feedback tools. While each of these technologies has demonstrated measurable benefits for teaching and learning, their isolated implementation often limits long-term impact and raises unresolved ethical concerns related to privacy, bias, and accountability. This study addresses this gap by conceptualizing an integrated AI-driven educational ecosystem designed to support sustainable student performance across educational contexts. Drawing on recent systematic reviews and ethical analyses from both higher education and K–12 domains, the paper proposes a holistic framework that unifies core AI functionalities—data-driven learning analytics, predictive modeling, adaptive assessment, intelligent feedback, and tutoring—within a coherent architectural and governance structure. Central to the framework is an embedded ethical oversight module that ensures transparency, fairness, and responsible data use. Through conceptual modeling and literature-based synthesis, the study demonstrates how integrated ecosystems can move beyond short-term performance gains toward sustained improvements in engagement, learning outcomes, and institutional decision-making. The proposed ecosystem offers a strategic foundation for future empirical validation and provides practical guidance for researchers, educators, and policymakers seeking to deploy AI technologies in a scalable, ethical, and educationally meaningful manner.
Copyright (c) 2026 Qubahan Techno Journal https://creativecommons.org/licenses/by-nc-nd/4.0
Leveraging Technological Innovations in Cloud Kitchens: A Case Study on Enhancing Customer Retention
The global food service industry has been impacted by the explosive growth of cloud kitchens—food production facilities focused solely on delivery. The purpose of this research, entitled "Leveraging Technology Innovations in Cloud Kitchens to Support Growing Customer Loyalty through Case-Based Approaches," is to explore how new technologies such as Artificial Intelligence (AI), IoT and Data Analytics, Automated Customer Relationship Management (CRM) systems, Support Sustainability-Growth of Associated Businesses and Consumer Loyalty through Case-Based Analysis. The research utilizes a mixed-method approach (face-to-face interviews combined with quantitative survey data) to analyze and understand how predictive analytics improve the demand forecasting process, how automation can increased the operational efficiencies of businesses as well as how Personalized Algorithms can expand Customer Engagement with the Brand. Overall, this study found that the strategic implementation of these new technological innovations within Cloud Kitchens positively impacts creating a satisfied and re-purchasing clientele through providing enhanced customer services and creating a more ideal digital shopping experience for Clients. The study concludes with the proposal of a general framework for the technology-related growth of customer retention in Cloud-Based Food Enterprises.
Artificial Intelligence–Augmented Statistical Inference: Integrating Machine Learning with Classical Estimation and Hypothesis Testing
The convergence of machine learning and classical statistical inference has given rise to a new paradigm in data analysis, enabling researchers to address high-dimensional, complex, and non-linear data structures that challenge traditional inferential techniques. While classical methods such as maximum likelihood estimation and hypothesis testing provide strong theoretical guarantees and interpretability, they often struggle in modern data-rich environments. Conversely, machine learning models offer remarkable predictive power but frequently lack transparency and rigorous uncertainty quantification. This study examines artificial intelligence–augmented statistical inference as a principled integration of these two approaches, aiming to preserve statistical rigor while enhancing flexibility and performance. By synthesizing theoretical foundations and empirical evidence across diverse application domains—including neuroimaging, model-based deep learning systems, and survival analysis—the paper demonstrates how hybrid frameworks can improve estimation accuracy, hypothesis testing robustness, and interpretability. The analysis highlights the role of model-based deep learning, penalized estimation, and machine learning–assisted feature selection in maintaining inferential validity and uncertainty quantification. Overall, this work establishes AI-augmented statistical inference as a robust and interpretable framework for advancing modern data-driven science while retaining the foundational principles of classical statistics.
Copyright (c) 2025 Qubahan Techno Journal https://creativecommons.org/licenses/by-nc-nd/4.0
AI Integration in Legal Decision-Making: Innovations and Challenges
The accelerating integration of artificial intelligence into legal decision-making is reshaping how justice is administered, interpreted, and legitimized across judicial and regulatory systems. While AI-driven tools offer substantial gains in efficiency, consistency, and analytical capacity, their deployment in high-stakes legal contexts simultaneously raises critical concerns regarding transparency, accountability, fairness, and due process. This study provides a comprehensive analysis of AI integration in legal decision-making by examining its conceptual foundations, technical architectures, and regulatory implications. Drawing on comparative legal frameworks, empirical case studies, and recent developments in explainable artificial intelligence, the paper explores how algorithmic systems interact with traditional legal reasoning and institutional safeguards. Particular attention is given to challenges such as algorithmic bias, the black-box problem, attribution of legal responsibility, and the tension between transparency requirements and privacy or intellectual property protections. The analysis demonstrates that sustainable and legitimate AI-assisted legal decision-making requires hybrid governance models that combine algorithmic support with robust human oversight, standardized auditing mechanisms, and adaptive regulatory frameworks. By synthesizing legal theory, technological innovation, and policy-oriented perspectives, this work contributes to ongoing debates on how artificial intelligence can be responsibly integrated into legal systems without undermining fundamental rights or the rule of law.
Impact of Digital Technologies on Legal Theory and Practice
The rapid diffusion of digital technologies—including artificial intelligence, big data analytics, blockchain, and digital platforms—has fundamentally reshaped both legal theory and legal practice, challenging long-established doctrines, institutional structures, and modes of legal reasoning. While these technologies have enhanced efficiency, access to justice, and data-driven decision-making within courts, regulatory bodies, and legal services, they have simultaneously introduced profound normative, ethical, and accountability challenges. This study provides an integrated examination of how digital technologies transform legal systems at structural, theoretical, and practical levels. Drawing on comparative regulatory perspectives and empirical case studies from multiple jurisdictions, the paper analyzes the reconfiguration of legal reasoning under algorithmic governance, the tension between computational logic and human interpretation, and the evolving role of legal professionals in increasingly automated environments. Particular attention is devoted to issues of algorithmic bias, transparency, accountability, and access to justice, highlighting the risks of discrimination and democratic erosion alongside the promise of innovation. The analysis demonstrates that sustainable digital transformation in law requires hybrid decision-making models, adaptive regulatory frameworks, and interdisciplinary collaboration that reconcile technological efficiency with normative legitimacy and fundamental rights protection.
Evaluating the Effectiveness of LinkedIn in Screening and Shortlisting the Right Talent Based on Business Requirements in B2B Organizations.
LinkedIn has become a powerful platform for recruitment purposes in today's digital world. It provides B2B businesses with the ability to find, screen and shortlist candidates. The purpose of this conceptual paper is to discuss how B2B recruitment agencies utilize the analytical functions of LinkedIn to improve the effectiveness of their recruitment efforts. The author discusses how recruitment agencies in B2B sectors use LinkedIn’s advanced search functions, AI-recommended matches, and Career Insights dashboards to narrow down the selection of candidates who meet their business needs. The integration of LinkedIn’s analytical capabilities and employer branding initiatives allows for evidence-based hiring practices to reduce the time-to-hire and increase the quality of candidates hired and fit for an organization's culture. The article discusses the role that analytical tools have played in changing the manner in which companies recruit candidates and allows organizations to use analytical capabilities to predict labor market trends and identify future talent needs. Additionally, the article discusses how employer branding serves as the relationship-building component between organizations and potential employees to help shape perceptions about an organization's attractiveness and values. The paper concludes by stressing the importance of ethical considerations, particularly with respect to algorithmic transparency and bias mitigation, when using data analytics to inform recruitment decisions.
Toward Human-Centered Artificial Intelligence in Education: Adaptive Learning Models for Personalized and Equitable Academic Outcomes
This research paper examines the integration of human-centered principles into adaptive learning systems with the objective of achieving personalized and equitable academic outcomes. The study addresses technical, ethical, and pedagogical aspects of utilizing artificial intelligence (AI) in education. Through a comprehensive literature review and mixed-methods research—including quantitative surveys, qualitative interviews, and analysis of system logs—the paper demonstrates how adaptive learning models can enhance student engagement, improve academic performance, and reduce disparities in learning access. The ethical challenges of privacy, algorithmic transparency, digital inequity, and potential biases are carefully analyzed, and mitigation strategies are proposed. Our findings indicate that while AI-based educational tools offer substantial benefits in personalization and efficiency, a human-centered approach that integrates ethical oversight and stakeholder engagement is essential for ensuring equitable outcomes. The paper concludes with recommendations for policymakers and educators, proposing a framework for future research that centers human values and equitable resource distribution.
From Data to Pedagogy: Leveraging Explainable Artificial Intelligence to Enhance Trust, Transparency, and Effectiveness in Intelligent Learning Systems
The increasing integration of artificial intelligence into educational technologies has amplified concerns regarding transparency, trust, and pedagogical validity, particularly as complex machine learning models are deployed in high-stakes learning contexts. While contemporary intelligent learning systems demonstrate strong predictive capabilities, their black-box nature often limits educator trust and constrains meaningful instructional use. This study addresses this gap by proposing and empirically evaluating a human-centered framework that embeds explainable artificial intelligence (XAI) into intelligent learning systems to bridge the divide between data-driven prediction and pedagogical decision-making. The proposed architecture integrates instance-level explainability mechanisms—such as SHAP and ceteris-paribus analyses—with predictive models and an interactive teacher dashboard, enabling educators to interpret, validate, and act upon AI-generated insights. Using real-world learning management system data and mixed-methods evaluation, the study demonstrates that instance-level explanations significantly enhance interpretability, reduce mispredictions, and strengthen teacher trust without compromising predictive performance. Empirical findings further indicate that explainable feedback supports targeted pedagogical interventions and contributes to measurable improvements in student outcomes. By situating explainability within theories of learning, trust, and human–AI collaboration, this work advances the design of transparent, trustworthy, and pedagogically grounded intelligent learning systems, offering practical and theoretical contributions to the evolving field of AI in education.
Securing the Quantum Era: A Comprehensive Review of Post-Quantum Cryptography, Threat Models, Algorithmic Foundations, and Standardization Pathways
The advent of fault-tolerant quantum computing precipitates a foundational threat to the security of global digital infrastructure by rendering obsolete the mathematical assumptions underlying classical public-key cryptography. Widely deployed algorithms, including RSA, ECDSA, and Diffie-Hellman, which rely on the computational intractability of integer factorization and discrete logarithm problems, are vulnerable to polynomial-time attacks via Shor's algorithm. Concurrently, Grover's algorithm imposes a quadratic reduction in the security strength of symmetric primitives. In response, Post-Quantum Cryptography (PQC) has emerged as a critical field of research, dedicated to developing cryptographic systems secure against both classical and quantum attacks, while remaining deployable on existing classical hardware. This paper presents a comprehensive and in-depth examination of PQC, analyzing the five principal families: lattice-based, code-based, multivariate, hash-based, and isogeny-based cryptography. Each family is scrutinized through rigorous mathematical exposition, conceptual analysis, comparative performance evaluations, and contemporary security assessments. The study situates PQC within the evolving global threat landscape, provides a detailed analysis of the National Institute of Standards and Technology (NIST) PQC standardization process, and addresses critical implementation challenges such as constrained environments, migration strategies, hybrid cryptographic modes, and the imperative for cryptographic agility. The paper concludes by delineating essential future research directions vital for constructing a robust, quantum-resilient global cryptographic infrastructure.
Securing the Future: A Comprehensive Review of Post-Quantum Cryptography in the Modern Threat Landscape
The accelerating development of quantum computing represents one of the most significant paradigm shifts in the history of information security. Cryptographic systems that currently underpin global digital infrastructure—including RSA, Diffie–Hellman, and Elliptic Curve Cryptography—rely on mathematical assumptions that collapse under quantum algorithms such as Shor’s and Grover’s. Post-Quantum Cryptography (PQC) emerges as the most viable and immediate solution to secure communications in the quantum era, offering cryptographic primitives that can be deployed on today’s classical hardware while resisting both classical and quantum attacks. This review provides a comprehensive, detailed examination of PQC, analyzing its mathematical foundations, algorithmic families, standardization progress, and deployment challenges. Special emphasis is placed on the NIST PQC standardization process, the risk posed by “harvest-now-decrypt-later” adversaries, the complexities of global cryptographic migration, and the implications for critical digital infrastructures such as cloud computing, blockchain systems, long-lifespan data archives, IoT ecosystems, and national cybersecurity. Through standalone equations, tables, and figures—including a conceptual visual provided by the user—this paper offers a complete, cohesive, and rigorous synthesis. The findings underscore that the transition to PQC is not optional but imperative, requiring coordinated scientific, governmental, and industrial effort to safeguard the digital future.
Design and Implementation of a Durable and Secure Enterprise Service Bus Framework for Modern Web Applications
This project presents the design, implementation, and evaluation of a secure, durable, and service-oriented web application built on an Enterprise Service Bus (ESB) architecture. Motivated by the growing need for scalable, modular, and security-hardened online systems, the study integrates principles of Service-Oriented Architecture (SOA) with UMLsec-driven secure design and Java EE’s Role-Based Access Control (RBAC). The ESB serves as the integration backbone, enabling seamless communication across heterogeneous services while supporting dynamic orchestration and protocol interoperability. The project employs a hybrid lifecycle model combining agile and plan-driven approaches to accommodate evolving requirements without compromising system discipline. Durability and quality-in-use characteristics—evaluated using multi-criteria decision-making frameworks—demonstrate high trustworthiness, maintainability, usability, and long-term operational resilience. Comprehensive testing, including security, performance, usability, and durability assessments, confirms that the system meets critical functional and non-functional requirements. The results validate that an ESB-based SOA architecture, enhanced with secure development methodologies and user-centric design, provides a robust foundation for enterprise-level web applications requiring reliability, extensibility, and sustained user satisfaction
Recent Advances and Real-World Implementations of the Whale Optimization Algorithm
The Whale Optimization Algorithm (WOA) has rapidly evolved as a novel bio-inspired metaheuristic technique, drawing significant attention in the optimization community since its inception in 2016. Inspired by the bubble-net hunting behavior of humpback whales, WOA begins with a simple yet effective mechanism that balances exploration and exploitation in search space. Over the years, numerous enhancements, hybridizations, and applications of WOA have been proposed, addressing complex constrained engineering design problems, renewable energy systems management, feature selection in machine learning, resource allocation in wireless networks, and hyperparameter tuning in deep neural networks. In this review, we provide an in-depth analysis of recent advances in WOA from 2016 to 2024, highlighting algorithmic developments and real-world implementations. We categorize the variants into parameter-controlled, hybridized, binary, and multi-objective forms, and we examine their performance against benchmark functions and practical scenarios in industrial applications. Performance comparisons are supported by convergence studies and transient response analysis from applications such as automatic generation control of modern power systems and photovoltaic system optimization. The review further discusses theoretical aspects such as convergence properties, computational complexity, and stability, while also identifying challenges like premature convergence and scalability issues. Finally, future research directions including self-adaptive strategies, quantum-inspired frameworks, and explainable optimization methods are discussed. The comprehensive overview presented here is intended to inform researchers and practitioners about the recent trends, successes, and remaining challenges of WOA in real-world applications, thereby contributing to further research and development in metaheuristic optimization techniques.
A Comprehensive Framework for Predicting Student Performance Using Machine Learning in Online Education
The rapid expansion of online education has created an urgent need for data-driven tools to predict student performance and enable proactive support. This paper presents a comprehensive machine learning framework designed to model and forecast academic outcomes using behavioral, cognitive, and social metrics. Validated on two large-scale datasets—the Open University Learning Analytics Dataset (OULAD) and Kalboard 360—our framework integrates a rigorous pipeline for data preprocessing, feature engineering, and model interpretation. We engineer novel quantitative features, including a Weekly Engagement Consistency Index (WECI) and an Assignment Timeliness Score (ATS), and evaluate multiple algorithms. The XGBoost model achieved superior performance with an AUC of 0.92 and an F1-Score of 0.88. Crucially, we integrate SHAP (SHapley Additive exPlanations) for model interpretability, revealing WECI and ATS as the most salient predictors. This work demonstrates that a systematic framework leveraging engineered features and ensemble methods can accurately identify at-risk students, providing a scalable solution for early intervention and personalized learning pathways in digital education.
Developing an Intelligent Tutoring System for Personalized Skill Development Using Reinforcement Learning
The one-size-fits-all model of traditional education is increasingly inadequate for addressing diverse learner needs. Intelligent Tutoring Systems (ITS) offer a solution but are often limited by static, hand-crafted pedagogical rules that cannot optimize long-term learning trajectories. This paper presents the design, implementation, and empirical validation of RL-Tutor, a novel ITS that leverages Deep Reinforcement Learning (RL) to provide dynamic, personalized instruction. RL-Tutor integrates a Deep Knowledge Tracing model based on a Dynamic Key-Value Memory Network (DKVMN) to maintain a rich, continuous representation of the student's knowledge state. This state serves as the input to a Proximal Policy Optimization (PPO) agent, which functions as the pedagogical module, selecting optimal actions from a hierarchical space including problem selection, hint provision, and instructional review. A critical contribution is the formulation of a multi-faceted reward function that balances immediate performance, learning efficiency, and long-term knowledge retention. Due to the sample inefficiency of RL, the agent was first trained in a high-fidelity simulated environment with a population of 10,000 synthetic students. The trained system was then evaluated against a rule-based tutor and a static tutor in a between-subjects human study (N=90) in the domain of introductory Python programming. Results show that RL-Tutor led to significantly higher normalized learning gains (0.72 vs. 0.58 for rule-based and 0.45 for static, p < 0.01) and better retention in a one-week delayed post-test. Analysis of the learned policy revealed emergent, pedagogically sound strategies such as adaptive hinting and implicit spaced repetition. This work establishes that RL can autonomously discover complex, effective teaching policies that are tailored to individual learners and outperform traditional ITS architectures.
Portable Few-Shot Learning for Early Warning Systems in Small Private Online Courses: A CNN-Based Predictive Framework for Student Performance
This study develops a portable early warning system designed to predict student academic performance in Small Private Online Courses (SPOCs) by leveraging few-shot learning techniques and convolutional neural networks (CNNs). Addressing the persistent challenges posed by limited sample sizes and the absence of face-to-face interactions in asynchronous online environments, the research explores whether small-sample behavioral data derived from multiple SPOCs can support reliable and transferable predictive models. The dataset comprises more than 4.4 million LMS log entries collected from four online courses sharing similar instructional designs and taught by a single instructor. After comprehensive preprocessing—encompassing feature extraction, weekly aggregation, and normalization—18 week-specific CNN models were trained to capture the temporal progression of student learning behaviors. The results indicate that meaningful prediction accuracy emerges by the fifth week, with performance exceeding 80% from week eight onward. Portability was further validated by applying the model to an additional course, where accuracy remained at or above 81%, confirming its robustness under consistent instructional conditions. The findings highlight the potential of few-shot learning to sustain predictive performance despite limited training samples, offering educators a viable foundation for timely interventions and institutional adoption of precision-driven academic support systems.
Copyright (c) 2024 Qubahan Techno Journal https://creativecommons.org/licenses/by-nc-nd/4.0
A Unified Multi-Layer Framework for Detecting and Mitigating Web Application Attacks in Cloud-Native Environments
Cloud-native architectures have introduced unprecedented scalability and flexibility for modern web applications, yet they have simultaneously expanded the attack surface and exposed systems to increasingly sophisticated intrusion patterns. Existing security approaches—such as provenance-based anomaly detection and machine-learning-driven web intrusion detection—have shown promising performance individually but suffer from scalability, contextual gaps, and limited visibility when deployed in isolation. This study proposes a unified, multi-layer security framework that integrates runtime provenance analysis with optimized SVM-based web intrusion detection to deliver comprehensive protection for containerized and orchestrated cloud-native systems. The framework correlates application-level HTTP feature extraction, container-level provenance graph analysis, and orchestration-level event aggregation, enabling early recognition of both rapid, high-volume attacks and stealthy low-and-slow Advanced Persistent Threats. Experimental evaluation using web intrusion datasets and provenance-based APT traces demonstrates that the combined model significantly enhances detection accuracy, reduces false alarms, and improves the timeliness of automated mitigation actions such as container isolation. By bridging cross-layer visibility and leveraging machine-learning optimization, the unified framework offers a scalable and robust security architecture tailored to the demands of modern cloud-native deployments.
Copyright (c) 2022 Qubahan Techno Journal https://creativecommons.org/licenses/by-nc-nd/4.0
Adaptive Deep Learning Architectures for Real-Time Data Streams in Edge Computing Environments
The increasing reliance on real-time analytics within edge computing environments has underscored the need for adaptive deep learning architectures capable of handling continuous data streams under limited computational and energy resources. Unlike traditional cloud-based frameworks, edge computing shifts computation closer to the data source, minimizing latency, preserving data privacy, and supporting responsive decision-making in dynamic contexts. This paper comprehensively examines adaptive deep learning models that autonomously adjust their structure and parameters in response to evolving data distributions and resource constraints. The study explores several key methodologies—such as master–surrogate deep neural networks, context–adaptive DNN atom partitioning, distributed inference with fused layer partitioning, and reinforcement learning–driven resource scheduling. Comparative analyses demonstrate that these adaptive frameworks achieve substantial performance gains, including up to 23.31% accuracy improvement, 62.14% latency reduction, and over 50% energy savings across diverse edge devices. Furthermore, the paper evaluates real-world deployments in smart cities, healthcare monitoring, Industry 4.0 automation, autonomous vehicles, and environmental sensing, illustrating the scalability and robustness of adaptive architectures in heterogeneous edge ecosystems. The discussion concludes by outlining current challenges—such as hardware diversity, continual learning, and energy constraints—and highlights future research directions including federated adaptation, neuromorphic hardware integration, and standardized benchmarking for edge AI.
Blockchain-Driven Security Models for Privacy Preservation in IoT-Based Smart Cities
The convergence of blockchain technology and the Internet of Things (IoT) has emerged as a transformative paradigm for developing secure, resilient, and privacy-preserving infrastructures in smart cities. Despite the rapid adoption of IoT across urban environments, centralized data management models continue to expose significant vulnerabilities, including single points of failure and data privacy breaches. This study proposes a blockchain-driven security framework that addresses these issues through decentralized identity verification, privacy-preserving data sharing, and scalability-enhancing mechanisms. The model integrates a Proof-of-Internet-of-Things (PIoT) consensus protocol with local peer networks and double-blind consent-driven data sharing to achieve both security and efficiency. Through simulation-based evaluations and real-world case studies in healthcare, traffic, and energy management, the proposed architecture demonstrates superior performance in transaction throughput, latency reduction, and energy efficiency compared with traditional blockchain systems. The results indicate that blockchain can substantially enhance data integrity and trust while maintaining privacy compliance under urban-scale IoT deployments. This research contributes to the growing body of knowledge by bridging the gap between theoretical blockchain constructs and practical, resource-constrained IoT applications, offering a scalable blueprint for secure smart city ecosystems.
Copyright (c) 2023 Qubahan Techno Journal https://creativecommons.org/licenses/by-nc-nd/4.0
Metaheuristics in Sustainable and Green Optimization
The accelerating global pursuit of sustainability has placed optimization at the forefront of achieving environmental, economic, and social balance. This study presents a comprehensive review of metaheuristic algorithms as powerful computational tools for addressing sustainable and green optimization challenges. By examining a broad range of classical and modern metaheuristics—including bio-inspired, physics-based, swarm intelligence, and hybrid models—this work explores how these algorithms are utilized to minimize energy consumption, carbon emissions, and resource waste across key sectors such as renewable energy systems, smart grids, sustainable manufacturing, and green logistics. The paper emphasizes the role of hybrid and intelligent adaptive metaheuristics in enhancing convergence speed, robustness, and scalability in complex, multi-objective optimization scenarios. Comparative analyses reveal the superiority of hybrid models in achieving accurate, energy-efficient, and environmentally responsible outcomes. Furthermore, the study highlights persistent challenges related to computational cost, parameter sensitivity, and real-time adaptability. By consolidating current findings and identifying open research directions—such as self-adaptive learning-based frameworks, unified benchmarking standards, and quantum-inspired metaheuristics—this review underscores the transformative potential of metaheuristic optimization in advancing the global sustainability agenda.
Systematic Review of Regression Algorithms for Predictive Analytics
This systematic review provides an in-depth examination of regression algorithms applied in predictive analytics across multiple domains, including engineering, transportation, and education. The study synthesizes developments in both traditional statistical and modern machine learning (ML) regression models, outlining their evolution, taxonomy, and comparative performance. Traditional multiple linear regression (MLR) has been foundational for predictive analysis but struggles with scalability and computational efficiency in the era of big data. To address these limitations, adaptations such as MapReduce-based MLR have been proposed, enabling distributed computation for large-scale data processing. Simultaneously, hybrid and ML-based regression models—such as support vector regression (SVR), regression trees (CART), and integrated linear–deep learning frameworks—demonstrate superior performance in modeling complex, non-linear relationships. This review establishes a structured taxonomy differentiating regression approaches based on assumptions, computational properties, and domain-specific requirements, highlighting the balance between model interpretability and predictive power. Comparative evaluations using metrics such as variance explained (R²), mean squared error (MSE), and computational scalability reveal that hybrid regression models often outperform conventional techniques in accuracy and adaptability. The review also identifies persistent challenges related to big data management, model interpretability, data harmonization, and real-time scalability. Emerging research trends emphasize explainable AI (XAI), ensemble regression strategies, and enhanced distributed computing frameworks to overcome these obstacles. Ultimately, this study contributes a holistic understanding of regression-based predictive analytics, guiding researchers and practitioners toward developing scalable, interpretable, and domain-adaptive models for next-generation data-driven decision-making.
An Experimental Assessment of AI-Based Legal Decision-Making Systems in Contract Analysis and Risk Detection
This comprehensive experimental study evaluates the performance, reliability, and practical applicability of AI-based legal decision-making systems in contract analysis and risk detection. Utilizing a corpus of 5,247 contracts with expert-validated annotations from 12 legal professionals, we benchmark four classes of AI systems—rule-based, supervised machine learning (XGBoost), fine-tuned transformer models (Legal-BERT), and large language models (GPT-4, Claude 3)—across multiple dimensions critical to legal practice. The Key Findings of this research : a) Performance Variability: Fine-tuned Legal-BERT achieved the highest overall clause classification F1-score (0.923, 95% CI [0.917, 0.929]), but exhibited significant degradation in cross-jurisdictional applications (28.4% performance drop from US to UK contracts). b) Risk Detection Gaps: All systems demonstrated decreasing recall with increasing risk severity. GPT-4 missed 18.2% of high-severity risks (severity ≥4), while Legal-BERT missed 12.3% of total risk severity weight (FNRP metric). c)Decision Inconsistency: LLMs showed substantial inconsistency, with GPT-4 achieving only 0.81 intra-model Jaccard similarity across identical inputs and 14.7% decision variation on identical clause phrasings. d)Domain-Specific Performance: Rule-based systems performed adequately on standardized agreements (NDA: F1=0.812) but failed catastrophically on complex contracts (M&A: F1=0.432). e) Cost-Effectiveness: Local fine-tuned models provided 92.3% of GPT-4's performance at 3.5% of the cost ($0.0087 vs $0.2478 per document).We introduce two novel legal-specific metrics—False-Negative Risk Penalty (FNRP) and Severity-Weighted F1 (SwF1)—that better capture the asymmetric cost structure of legal errors. Based on our empirical findings, we propose a three-tier human-in-the-loop deployment framework that reduces attorney review time by 64% while maintaining 99.7% risk coverage. The study establishes evidence-based performance thresholds for safe deployment, recommending against autonomous use of any system with FNRP > 0.15 or cross-jurisdiction performance degradation > 25%. Our findings challenge optimistic claims of AI autonomy in legal decision-making and provide a rigorous, reproducible framework for evaluating legal AI systems in practice.
Responsible AI-Powered Learning Architectures for Long-Term Educational Equity
The growing adoption of artificial intelligence in education has intensified debates surrounding fairness, transparency, and long-term equity, particularly as AI-driven systems increasingly influence assessment, personalization, and learner support. While existing AI-powered learning platforms have demonstrated notable gains in efficiency and performance, their benefits are often undermined by ethical risks related to data privacy, algorithmic bias, and unequal access. This study addresses these challenges by advancing a comprehensive framework for responsible AI-powered learning architectures that explicitly prioritizes educational equity over the long term. Grounded in established ethical principles and human-centered design paradigms, the proposed architecture integrates adaptive learning models, learner modeling, bias mitigation mechanisms, and robust data governance within a human-in-the-loop framework. Drawing on empirical evidence from prior studies and illustrative case deployments across higher education and K–12 contexts, the analysis demonstrates that responsible AI architectures can enhance personalization and academic outcomes while safeguarding fairness, accountability, and transparency. By aligning technical innovation with ethical governance and sustained human oversight, this work contributes a principled foundation for designing AI-enabled learning environments that are not only effective but also socially just and inclusive.
Computer Forensics: Evolution and Its Pivotal Role in Modern Investigations
The preservation, identification, retrieval, documentation, and evaluation of laptop facts are the honest primary goals of computer forensics. Many computer forensics guidelines and approaches that want to be described and established are tested in this have a look at. Data have to be undamaged and available for evaluation. Authenticity of the information is likewise guaranteed. As a end result, pc forensic specialists extract and take a look at facts from storage devices used on the scene of a digital crime the usage of contemporary strategies and device. Examining some of these forensic technologies is the number one objective of this take a look at. The aim of the cutting-edge take a look at is to evaluate and comparison the key variations and similarities across the extraordinary forensic equipment as a way to determine which characteristics need to be progressed for effective garage device autopsy.
Designing Trustworthy Educational Artificial Intelligence: A Systemic Framework for Explainability, Adaptivity, and Ethical Learning Analytics
The accelerating integration of artificial intelligence into educational systems has underscored the necessity of designing learning technologies that are not only effective but also trustworthy, transparent, and ethically grounded. Although adaptive and data-driven educational AI systems have demonstrated substantial potential for personalization and performance enhancement, their widespread adoption remains constrained by concerns related to explainability, ethical learning analytics, data privacy, and algorithmic bias. This study proposes a systemic framework for trustworthy educational artificial intelligence that unifies explainability, adaptivity, and ethical governance within a coherent architectural model. Grounded in interdisciplinary theoretical foundations and supported by evidence from systematic literature reviews and empirical case studies, the framework emphasizes human-centered design, transparent decision-making, and continuous ethical oversight. The analysis illustrates how explainable and adaptive AI systems, when coupled with responsible learning analytics, can enhance learner engagement, improve academic outcomes, and foster sustained trust among educators and students. By articulating design principles and architectural layers aligned with established ethical frameworks, this work contributes a robust foundation for developing educational AI systems that balance technological innovation with accountability, fairness, and long-term educational value.
Regulating Intelligent Systems in Digital Governance and Legal Transformation
The accelerating deployment of intelligent systems across public administration, economic regulation, and legal processes has profoundly reshaped the landscape of digital governance, raising complex challenges for accountability, transparency, and the protection of fundamental rights. While artificial intelligence offers significant opportunities for efficiency, predictive capacity, and data-driven governance, it simultaneously exposes limitations within traditional legal frameworks that were not designed to regulate autonomous, adaptive, and cross-border technologies. This study provides a comprehensive analysis of how intelligent systems are driving legal transformation in the context of digital governance, with particular emphasis on regulatory lag, algorithmic opacity, jurisdictional fragmentation, and evolving liability regimes. Drawing on comparative regulatory approaches—including the European Union’s rights-based model, market-oriented frameworks in the United States, and state-centered strategies in parts of Asia—the paper advances an adaptive and principle-based regulatory paradigm. Central to this paradigm are transparency obligations, explainable AI, human- and society-in-the-loop oversight mechanisms, and regulatory sandboxes for high-risk applications. By synthesizing theoretical insights, regulatory practices, and empirical case evidence from sectors such as healthcare, finance, and public administration, the study argues that effective governance of intelligent systems requires dynamic, interdisciplinary, and participatory legal frameworks capable of reconciling innovation with democratic values and fundamental rights protection.
Comprehensive Review and Hybrid Evolution of Teaching–Learning-Based Optimization
Teaching–Learning-Based Optimization (TLBO) stands out as a novel population‐based metaheuristic inspired by the pedagogical process in a classroom, in which a teacher imparts knowledge to learners and the learners enhance their performance by mutual interaction. This paper provides a comprehensive review of TLBO along with its extensive hybrid and intelligent extensions developed over recent years. We examine the fundamental algorithmic principles of TLBO—its parameter‐free nature, two-phase (teacher and learner) approach, and inherent simplicity—and contrast its performance across a range of benchmarks and real-world engineering optimization problems. In addition, we survey various taxonomic categories such as adaptive TLBO, multi-objective TLBO, discrete variants, and hybrids that integrate TLBO with other metaheuristics including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Robust Tabu Search (RTS), and Harmony Search (HS)6. Special attention is given to recent innovations in hybrid frameworks, such as the TLBO–RTS and TLBO–CO algorithms, where complementary search techniques enhance global exploration while preserving the rapid convergence property. Finally, the paper discusses theoretical aspects including convergence properties, computational complexity, and outlines current challenges and promising directions for future research.
5G-Enabled Internet of Things: Latency Optimization through AI-Assisted Network Slicing
The confluence of Fifth-Generation (5G) wireless technology and the Internet of Things (IoT) heralds a new era of hyper-connectivity, enabling transformative applications from autonomous vehicles and industrial automation to extended reality and remote surgery. A critical performance indicator for many of these applications is ultra-reliable low-latency communication (URLLC), where delays must be bounded within milliseconds. However, the heterogeneous and dynamic nature of IoT traffic presents a monumental challenge to consistently meeting these stringent latency requirements. Traditional network management paradigms, which are largely static and reactive, are ill-suited for this task.This paper posits that the synergy of two cornerstone 5G technologies—Network Slicing and Artificial Intelligence (AI)—provides the foundational architecture and intelligent control mechanism necessary to achieve dynamic latency optimization at scale. Network Slicing allows for the creation of multiple logical, end-to-end virtual networks on a shared physical infrastructure, each tailored to specific service requirements. Meanwhile, AI and Machine Learning (ML) offer the predictive and adaptive capabilities to manage these slices proactively.This comprehensive review and analytical paper delves into the architecture of 5G-standalone (SA) systems to elucidate the enablers of low latency. It then provides a detailed exposition of network slicing as a resource isolation mechanism. The core of the paper is a thorough investigation into how various AI/ML paradigms—including supervised learning, reinforcement learning, and deep learning—can be integrated into the network control loop to predict traffic surges, dynamically allocate resources, and proactively reconfigure slices. We present a conceptual framework for an AI-assisted Network Slicing orchestration system, detailing its functional components and operational workflow. Furthermore, we analyze the significant challenges impeding widespread deployment, such as data collection, model training, security, and standardization. Through this analysis, we demonstrate that AI-assisted network slicing is not merely an enhancement but a critical imperative for realizing the full potential of latency-critical 5G-IoT ecosystems.
Automated Software Testing through Large Language Models: Opportunities and Challenges
The rapid evolution of software systems and the increasing complexity of modern applications have driven the need for innovative testing methodologies. Traditional techniques, often burdened by manual intervention and limited scalability, are increasingly challenged by the demands for speed, flexibility, and comprehensive quality assurance. Large Language Models (LLMs) offer a promising avenue for transforming automated software testing. This research examines the opportunities and challenges of integrating LLMs into testing workflows, supported by extensive literature, empirical surveys, and real-world case studies.Key opportunities include automated test-case generation, program repair, test-oracle creation, debugging assistance, and the development of self-healing testing systems. For instance, surveys show that approximately 51% of studies focus on test-case generation, while 26% address program repair, 11% focus on test-oracle creation, and 12% on debugging assistance. Concurrently, several challenges hinder the seamless adoption of LLM-based testing. These include the risk of hallucinated outputs, context window limitations, security and privacy issues, high computational costs, integration complexities, and potential biases in generated tests.This paper comprehensively reviews the theoretical underpinnings and practical implementations of LLM-driven test automation while proposing pathways for future research and development. Ultimately, LLMs are positioned as transformative tools whose effective integration could greatly enhance testing efficiency and reliability. However, careful design and human oversight remain critical to overcome the inherent challenges.
Hybrid Big Data Analytics: Integrating Structured and Unstructured Data for Predictive Intelligence
Hybrid big data analytics has emerged as a compelling paradigm for predictive intelligence, yet most operational pipelines still privilege a single modality—either structured relational data or unstructured text—thereby under-exploiting complementary signals. This paper proposes a unified framework that integrates structured records (e.g., time-series sensors, tabular attributes) with unstructured corpora (e.g., clinical narratives, web-scale text) through a multi-modal deep learning architecture coupled with scalable clustering and query optimization. The method fuses static encoders, temporal CNN/LSTM modules, and text representations (e.g., document embeddings with BiLSTM/CNN) in a learned fusion layer, and augments inference with a Gaussian Mixture Model optimized by a bio-inspired Salp Swarm Algorithm for low-latency, distributed querying. Experiments across two representative domains—infectious-disease forecasting and Industry 4.0 cycle-time projection—demonstrate consistent gains over single-modality baselines in AUROC, F1, MAE, and AUPRC, while preserving near real-time responsiveness on commodity GPU/CPU clusters. We discuss integration complexity, interpretability challenges, and deployment constraints, and delineate practical pathways for edge-side execution, transfer learning across domains, and explainability overlays. By systematically bridging structured and unstructured modalities, the study evidences material performance improvements and offers a robust template for multimodal analytics in high-stakes environments.