Frontiers in Computer Science and Artificial Intelligence
Frontiers in Computer Science and Artificial Intelligence (FCSAI) is an international, peer-reviewed, monthly, open-access journal dedicated to advancing research and innovation in all areas of computer science and artificial intelligence. The journal provides a global platform for researchers, scholars, practitioners, and industry professionals to disseminate original and high-quality scientific contributions that address contemporary challenges and emerging trends in the digital world. FCSAI publishes cutting-edge research articles, review papers, case studies, technical reports, and short communications covering a broad spectrum of topics including—but not limited to—machine learning, deep learning, data science, natural language processing, computer vision, robotics, cybersecurity, software engineering, cloud and edge computing, intelligent systems, human–computer interaction, and emerging AI applications across disciplines. The journal is committed to promoting ethical research practices, fostering innovation, and encouraging interdisciplinary collaboration. Through a rigorous double-blind peer-review process, FCSAI ensures the publication of impactful, reliable, and scientifically sound research that contributes to the advancement of knowledge in computer science and artificial intelligence. As a monthly journal, FCSAI offers frequent publication opportunities, rapid dissemination of new findings, and up-to-date coverage of evolving research trends. The journal welcomes submissions from authors around the world and provides timely review, high visibility, and broad dissemination of published works. As part of Al-Kindi Center for Research and Development, UK, the journal benefits from a strong academic network and a commitment to supporting global scholarly communication.
Al-Kindi Center for Research and Development
Explainable and Risk-Aware AI for Autonomous Cyber Defense and Reliable Incident Response Across U.S. Public and Enterprise Digital Services: The XR-ACD Framework
The scale, velocity and complexity of cyber threats are growing, and with them there are new challenges for security teams tasked with defending public and enterprise digital services. Artificial intelligence (AI) can speed up the discovery, investigation and response process to threats, but a second risk is often overlooked: autonomous response may disrupt service, impact critical dependencies, or have irreversible effects if the assessment is wrong. The paper presents a conceptual framework for an autonomous cyber defense process, Explainable and Risk-Aware Autonomous Cyber Defense (XR-ACD), which combines explainable threat assessment, explicit action-risk evaluation, risk-proportional autonomy, and continuous governance. While some methods rely mainly on the size of the threat or on the confidence of the model to trigger automatic responses, XR-ACD differentiates between the risk of the threat and the risk of responding to the threat and considers both risks before allowing autonomous action. Action risk is represented with asset criticality, blast radius, dependency impact, operational impact, uncertainty and reversibility. These factors are linked to four response modalities: autonomous execution, bounded or supervised autonomy, human-in-the-loop decision making and human authorization. The framework also includes user-friendly explanations, policy limitations, audit reports, outcome tracking, and feedback-driven risk reassessment. In a municipal water-treatment IT/OT scenario, the same detected threat can trigger various response authorities, depending on the operational consequences of candidate actions. The paper proposes a comparative evaluation methodology that includes threat-severity-only automation, human-led response, XR-ACD without explainability and the complete XR-ACD configuration, for future empirical validation. The criteria for evaluation are response latency, containment effectiveness, inappropriate high-impact action rate, disruption to operations, human intervention, quality of explanation, and trust calibration. The paper provides a structured framework for safer autonomous cyber defense and lays the groundwork for empirical analysis of risk-proportional autonomy in public and enterprise digital services.
Copyright (c) 2026 Frontiers in Computer Science and Artificial Intelligence
Agentic Artificial Intelligence for Intelligent Business Decision-Making: A Multi-Agent Framework Integrating Large Language Models, Explainable AI, and Business Intelligence
The increasing complexity of modern business environments requires intelligent decision-support systems capable of combining predictive analytics, contextual reasoning, and transparent decision-making. This study proposes an Agentic Artificial Intelligence framework for intelligent business decision-making by integrating Large Language Models (LLMs), Explainable Artificial Intelligence (XAI), machine learning, and Business Intelligence through a collaborative multi-agent architecture. The proposed framework utilizes specialized AI agents for data processing, feature engineering, prediction, explanation generation, and business reasoning, coordinated through an orchestration agent to produce actionable insights.The framework is evaluated using the Online Shoppers Purchasing Intention Dataset from the UCI Machine Learning Repository. Multiple machine learning models, including Random Forest, XGBoost, CatBoost, Artificial Neural Network, and LightGBM, are compared, where LightGBM achieves the best standalone performance with 94.82% accuracy and 0.99 AUC. After integrating multi-agent collaboration, SHAP-based explainability, and LLM-driven reasoning, the proposed framework improves performance to 95.63% accuracy, 0.95 F1-score, and 97.1% decision consistency while providing interpretable business recommendations. The results demonstrate that the proposed Agentic AI framework effectively bridges predictive analytics and intelligent business decision-making by delivering accurate, explainable, and actionable insights. The framework provides a scalable approach for AI-driven decision support across industries such as retail, finance, healthcare, manufacturing, and supply chain management.
Agentic AI; Large Language Models; Explainable AI; Business Intelligence; Multi-Agent Systems; Predictive Analytics; LightGBM; Intelligent Decision-Making
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/
Artificial Intelligence in Education: Transforming Learning Outcomes, Academic Integrity, and Pedagogical Innovation in Higher Education
The transformative opportunities and challenges of generative AI tools, especially large language models (LLMs) such as ChatGPT, have emerged rapidly in higher education. The adoption of generative AI in undergraduate education has increased dramatically in 2024–25, whereas institutions' pedagogical approaches, institutional policies, and evidence of learning outcomes are less developed than their use. Previous research has primarily focused on the short-term acceptance of the tools or single academic integrity issues without considering them in relation to each other. Recent systematic reviews and meta-analyses (2025–2026) have begun to measure the impact of GenAI on learning outcomes and trace authorship and integrity concerns in greater detail. However, few studies have systematically investigated the impact of GenAI on learning outcomes, pedagogical design, and academic integrity across a variety of learner populations. This study will explore (1) the observable effects of AI-supported personalised learning on student learning outcomes at the undergraduate level; (2) faculty and student attitudes towards the impact of AI on pedagogical redesign; and (3) institutional policies to ensure academic integrity while supporting and facilitating AI-supported learning. The proposed convergent mixed methods design will involve validation of the survey instrument (n = 420 undergraduate and faculty students across three institutions) and a quasi-experimental pre-post assessment study. Structural equation modelling (SEM) and thematic coding of qualitative data were used in the planned analysis. This study aims to produce a theoretically sound, testable framework that will benefit evidence-based strategies for the adoption of AI in higher education, the AI-Augmented Pedagogy Integration Model (AAPIM). This document outlines the conceptual, theoretical, and methodological underpinning, which has now been further supported by a growing evidence base of 2025–2026 meta-analyses and systematic reviews, with empirical data reported when the data collection is complete
Artificial Intelligence in Education (AIEd); Generative AI (GenAI); Personalized Learning; Academic Integrity; AI Literacy; Intelligent Tutoring Systems (ITS)
Enhancing Data Privacy and Governance in Cloud-Deployed Large Language Models Using Deep Learning-Based Risk Mitigation
The growing number of problems related to the privacy and security of data in cloud computing requires the need for efficient protection schemes. In this paper, we present an innovative solution for ensuring the privacy and management of data in large language models deployed in clouds by applying risk mitigation techniques based on deep learning. The technique involves risk mitigation, secure data management, and model performance enhancement. First, data input is acquired from a synthesized cybersecurity logs dataset and filtered using data preprocessing to eliminate any form of inconsistencies. Next, data preprocessing enables the input to be directed to the feature extraction phase utilizing the Dense Channel Spatial Semantic Guidance Attention UNet (DCSGA-UNet), which allows for the extraction of relevant features, such as Log_ID, IP_Address, Request_Type, and Response_Time_ms. After that, these features undergo processing through a Bayesian Constitutive Artificial Neural Network (BCANN) classifier that helps predict the probability of a threat or non-threat occurrence. The technique is designed using Python and evaluated against current methods. The experiments show that our innovative solution exhibits a higher accuracy of 98% in a lesser amount of time of 1.07 seconds.
Cloud security, data privacy, risk mitigation, deep learning, governance
DACS: A Plaintext-Dependent Dynamic Affine–Chaotic Stream Cipher for Efficient Symmetric Encryption with Resistance to Linear and Statistical Cryptanalysis
The affine cipher is fast and almost free to run, but it breaks the moment an attacker sees two plaintext–ciphertext pairs: its parameters never change, so recovering them recovers the whole message. This paper asks what happens if those parameters stop being constant. This paper introduces DACS, a stream cipher that keeps the affine map as its core operation yet generates a new pair together with a keystream byte for each symbol; all three values are drawn from a two-dimensional coupled Logistic–Tent chaotic system. The seed of that system is bound to the message itself via a keyed synthetic-IV (HMAC–SHA-256) schedule combined with a ciphertext-feedback chain, which makes the keystream a function of the plaintext and not of the key alone. On the standard 512×512 cameraman test image, DACS attains an information entropy of 7.9992 bits, while the adjacent-pixel correlations fall to |-0.0047|, |-0.0053|, and 0.0128 in the horizontal, vertical, and diagonal directions; the cipher histogram is statistically flat as well ( , under the 293.25 threshold). Flipping one bit of the plaintext changes 99.6342% of the output (NPCR) with an average intensity change of 33.5117% (UACI); a one-bit change in the key changes 99.5979% of the output. The key-only keystream clears every NIST SP 800-22 test we ran. Most tellingly, the classical two-pair attack that defeats an ordinary affine cipher recovers nothing here—0.0% of subsequent bytes, consistent with blind guessing—because there is no longer a single key to solve for. Taken together, these measured results put DACS alongside the strongest 2024–2025 chaos-based ciphers while doing what none of them set out to do: remove the affine map’s linear weakness at its source.
stream cipher, chaotic cryptography, affine transformation, plaintext-dependent encryption, linear cryptanalysis, NPCR, UACI, NIST SP 800-22
Sustainable Apparel Supply Chains: A Review of AI-Driven Optimization, Waste Reduction, and Circular Economy Practices
The apparel industry faces persistent sustainability challenges arising from overproduction, inefficient resource utilization, textile waste, and increasingly complex global supply chains. Artificial intelligence (AI) has emerged as a promising technology for improving operational decision-making; however, evidence of its contribution to sustainability remains fragmented across research on forecasting, production, waste management, and the circular economy. This narrative review synthesizes current knowledge on AI-enabled optimization, waste reduction, and circular economy practices within apparel supply chains. Drawing on recent literature, the review examines AI applications in demand forecasting, production planning, supplier selection, logistics, risk management, and explainable decision support, and evaluates their role in reducing textile waste and supporting circular strategies such as product life extension, traceability, reverse logistics, and material recovery. The review finds that AI contributes to sustainability primarily by improving the quality of operational decisions rather than through automation alone. Although AI can enhance resource efficiency, reduce waste, and strengthen circular supply chains, these benefits depend on effective governance, high-quality data, organizational capability, and integration with circular economy practices. Based on this synthesis, an integrated conceptual framework is proposed that positions AI as a decision-support capability linking operational decisions with sustainability outcomes through continuous feedback and governance mechanisms. The review also identifies key research gaps, including the need for industrial validation, apparel-specific datasets, and responsible AI implementation. The findings provide researchers and practitioners with an integrated perspective on how AI can support the transition towards more sustainable and circular apparel supply chains.
Artificial intelligence; Apparel supply chain; Sustainable supply chain management; Circular economy; Textile waste; Decision support
Toward Circular Economy and Intelligent FSCs: A Critical Review of AI-Driven Sustainability Transformation
The global fashion industry is at a critical juncture, where increasing environmental challenges and rapid digital innovation are reshaping supply chain systems. This study provides a comprehensive review of the evolution of circular and intelligent FSCs by integrating artificial intelligence (AI) with circular economy (CE) principles. Using a narrative synthesis approach, the research analyzes 58 peer-reviewed studies published between 2020 and 2026, focusing on sustainability, supply chain management, and emerging digital technologies. The findings reveal a significant transition from traditional sustainability discourse toward AI-driven, system-level transformation. Technologies such as machine learning (ML), big data analytics, and the Internet of Things (IoT) enhance predictive capabilities, operational efficiency, and resource optimization. However, the analysis also identifies critical limitations, including a persistent gap between the theoretical potential of AI-enabled sustainability and its empirical validation in real-world applications. Furthermore, the literature demonstrates a strong emphasis on environmental sustainability, with comparatively limited attention to social dimensions such as labor conditions and ethical sourcing. To address these challenges, this study proposes the Technology–Sustainability Integration Model (TSIM), a socio-technical framework that conceptualizes sustainable transformation as the alignment and co-evolution of technological and sustainability maturity. The model highlights the importance of integrating digital capabilities with multidimensional sustainability practices to enable fully circular and intelligent supply chains. By synthesizing existing knowledge, identifying key research gaps, and offering a structured conceptual framework, this study contributes to advancing research and practice in sustainable fashion supply chain transformation.
Artificial Intelligence (AI); Circular Economy (CE); Digital Transformation; Fashion Industry; Intelligent Supply Chains; Industry 4.0/5.0; Sustainability Integration; Supply Chain Resilience; Socio-Technical Systems
Algorithmic Panic, Cultural Misalignment, and Multimodal Over Filtration in AI Processing of Obfuscated Arabic Political Image-Texts about the USA–Israel–Iran Conflict
This study examines how four AI models decode and interpret Arabic political texts containing obfuscated words embedded in 138 images extracted from YouTube and Instagram videos posted during the USA–Israel–Iran War (March–April 2026). It compares Gemini, Copilot, Claude, and Qwen 3.7 Plus, representing distinct political, cultural, and safety alignment ecosystems. The study also explores algorithmic panic behaviors triggered under uncertainty, and sheds light on cultural misalignment, geopolitical censorship, multimodal over filtration, and structural bias across the models. The 138 images contained 374 obfuscated Arabic words. Analysis of the translated texts revealed substantial variation in recognition and interpretation. Gemini translated Arabic texts in 94% of the images and correctly decoded 98% of the obfuscated words, exhibiting no signs of algorithmic panic, geopolitical censorship, or cultural misalignment. Claude correctly translated 54% of the images and 67% of the words, produced partially hallucinated translations (14%), generated faulty translations for 33%, deleted 9%, and replaced 13%. Qwen translated 54% of the images, yielded correct translations for 72% of the words, and produced fully hallucinated translations for 4%. It generated faulty translations for 27.5% of the words, including deletions (6%) and replaced words (8.5%). Claude and Qwen’s performance reflects soft moderation behaviors, including selective omission, substitution, and semantic weakening. Copilot successfully translated the Arabic texts in 65% of the images, and 72% of the words, but blocked 24% of the images and produced faulty translations for 11%, including hallucinations, deletions, and additions. The anomalous behavioral patterns exhibited by Copilot—specifically its hyper reactive filtration protocols, stochastic blocking, and systematic generation of non-semantic strings—indicate a deeper sociopolitical and structural phenomenon involving the intersection of geopolitical anxiety, keyword panic, and algorithmic censorship when processing grassroots, organically obfuscated Arabic journalistic and cultural texts. Results are discussed in terms of why Copilot’s filters blocked certain images but not others, why Claude and Qwen hallucinate instead of blocking, why they exhibit selective deletions and substitutions, and how these behaviors relate to sociocultural misalignment, Eurocentric algorithmic prejudices, keyword panic, and censorship as empirical proof of linguistic competence, among other factors.
Obfuscated Arabic text, political lexeme distortion, distorted text recognition, algorithmic moderation, multimodal AI models, social media political imagery, algorithmic panic, multimodal over filtration, geopolitical censorship, hallucinated and sanitized translation
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Decoding Digital Evasion: Recognition and Interpretation of Obfuscated Arabic Text in Images by Gemini, Copilot, Claude, and Qwen
This study examines how four AI models—Gemini, Copilot, Claude, and Qwen 3.7 Plus—decode and interpret obfuscated Arabic words embedded in 138 images extracted from YouTube and Instagram videos posted during the USA–Israel–Iran War (March–April 2026). The obfuscation relied primarily on inserting punctuation marks (periods, commas, dashes, underscores) to evade platform moderation. Across these images, the dataset contained 374 obfuscated Arabic words, which the models translated in context and in isolation. Analysis revealed variations in recognition and interpretation across the models. All four models correctly translated 30% of images; three models succeeded in 24%; and two models converged on 27%. Gemini produced uniquely correct translations for 16% of the images. All four models failed completely on 3% of the images, where root semantics were unrecoverable. Additionally, Gemini translated Arabic text in 94% of the images correctly, producing accurate translations for 98% of the words in context and 99% in isolation. Copilot succeeded on 61% of the images, correctly translating 72% in context and 98% in isolation. Qwen translated 54% of the images, yielding correct translations for 72% of the words in context and 98% in isolation. Claude also succeeded on 54% of the images, producing 67% correct words in context and 98% in isolation. Across all models, recognition accuracy was consistently higher on isolated words than within full images, indicating that failures were driven by visual noise, contextual interference, and safety‑filter activation, rather than linguistic limitations. Additional behaviors included deleted and replaced tokens, hallucinated translations, transliteration, and partial clipping. Copilot blocked few images or produced gibberish output. Gemini sometimes mirrored obfuscation patterns in English equivalents. In isolated‑word translation, the models produced identical and synonymous equivalents. Claude added feminine gender markers to some distorted words. The lexemes exhibiting the highest number of polymorphic distortion variants were Iran, Israel, war, missiles, nuclear, America, attack, Netanyahu, party/war, Gaza and Zionist. How LLMs process obfuscated Arabic text, density, complexity, and symbol typology of obfuscation, contextual vs. isolated processing, the spectrum of mimetic obfuscation, non-obfuscation hallucination, algorithmic paranoia, keyword panic, and socio-cultural misalignment are discussed in detail.
Linguistic encryption, obfuscated Arabic words, distorted text recognition, algospeak, algorithmic moderation, content moderation, multimodal AI models, OCR segmentation, political lexeme distortion, social media images
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Cultivating Tomorrow's Minds: Faculty Perspectives on Technology-Enhanced Pedagogy in Punjab's Private Universities
At a time of increasing digital transformation and a global shift in policy regarding the integration of artificial intelligence (AI) into the education sector, it is essential to comprehend the engagement of faculty with new technologies to build equitable, future-ready learning systems. This study set in the interpretative qualitative paradigm explores the perception and experience of augmented minds through the integration of AI and AR within the pedagogical practices of the faculty members of private university of Punjab, Pakistan. The study will achieve the desired aims through 50 in-depth semi-structured interviews of the faculty members belonging to different academic disciplines. Thematic analysis consisting of the six-step framework of Braun & Clarke will be utilized to analyse data. Research shows that there is widespread optimism regarding the pedagogical potential of AI and AR in particular, enhancing student engagement, visualizing complex concepts, personalized learning paths, and collaborative knowledge construction. According to the participants of the study, through the use of immersive AR and adaptable AI systems, more interactive, learner-centered environments can be created in alignment with constructivist principles. Significant institutional and contextual barriers were also identified, such as constraints in technological infrastructure; limited specialized professional development; and academic integrity, cognitive dependence, and equity of access concerns. Faculty members also expressed concern about the changing nature of their work in AI classrooms. This research provides context-specific insights into “Augmented Minds”, thereby enriching the global discourse on educational innovation in a developing country context. It shows that technology enhancement and faculty empowerment may be possible with continued institutional support. The findings are directly useful to educational policymakers, curriculum developers, and university administrators interested in integrating AI/AR-enhanced teaching frameworks in various higher education environments.
Augmented Minds; higher education; Pakistan; faculty perceptions; artificial intelligence; technology integration
Architecting AI-Ready, HIPAA-Compliant CI/CD Pipelines in Healthcare IT: Balancing Interoperability, Legacy Constraints, and Operational Stability
The convergence of artificial intelligence, regulatory compliance requirements, and the persistent complexity of healthcare information technology infrastructure has created a critical imperative: the development of CI/CD (Continuous Integration / Continuous Delivery) pipelines that are simultaneously AI-ready, HIPAA-compliant, and capable of operating within the constraints of legacy clinical systems. This paper presents a comprehensive architectural framework addressing this challenge. Drawing on enterprise architecture principles, DevSecOps methodologies, and healthcare-specific regulatory requirements, the framework delineates five architectural pillars: Secure Pipeline Design, AI Model Lifecycle Integration, Legacy System Bridging, Interoperability Orchestration, and Operational Stability Engineering. The paper examines how each pillar addresses the unique tensions inherent in healthcare CI/CD: the velocity demands of AI deployment versus the rigour requirements of HIPAA; the agility aspirations of DevOps versus the change-resistance of legacy clinical systems; and the innovation imperative versus the patient safety obligation. Through architectural analysis and illustrative deployment scenarios, the paper demonstrates that well-structured healthcare CI/CD pipelines can achieve AI model deployment lead times of under 72 hours while maintaining full HIPAA compliance posture, HL7 FHIR R4 interoperability, and zero-downtime production deployments. Guidance is provided for healthcare CIOs, clinical informatics architects, DevSecOps engineers, and AI/ML operations practitioners navigating this complex domain.
CI/CD, DevSecOps, HIPAA Compliance, AI/ML Pipelines, Healthcare Interoperability, FHIR, Legacy Systems, MLOps, Clinical AI Safety, Zero-Downtime Deployment
Token Optimization and AI Efficiency Patterns for Healthcare IT: A Practical Architecture for Cost, Performance, and HIPAA-Aware Deployment
Healthcare organizations are rapidly adopting large language model (LLM) capabilities for clinical documentation, revenue cycle operations, prior authorization workflows, and IT service management. However, production-scale deployment is constrained by token cost, latency, governance risk, and privacy requirements. This paper presents a practical architecture for token optimization in healthcare IT environments, emphasizing prompt compression, context minimization, retrieval tuning, structured output constraints, and observability-driven continuous improvement. We describe a layered implementation model, domain-relevant pipeline patterns, operational metrics, and business impact estimation methods. The framework aligns with HIPAA's minimum necessary principle by reducing unnecessary protected health information (PHI) exposure while improving throughput and cost efficiency. We also provide implementation examples and KPI guidance to support enterprise rollout.
Healthcare AI, large language models, token optimization, prompt engineering, retrieval-augmented generation, HIPAA, AI governance, cost optimization
Comparing AI-Based and Traditional Epidemiological Models for Epidemic Forecasting in the USA. A Comparative Study of AI Models Versus Conventional Statistical Public Health Methods.
Artificial intelligence has emerged as a transformative force in epidemic forecasting, offering new opportunities to complement and extend traditional epidemiological modeling approaches. This study presents a comparative analysis of AI-based models and conventional public health forecasting methods for epidemic prediction in the United States. The research examines the performance and practical implications of mechanistic and statistical techniques alongside modern machine learning frameworks within a unified analytical setting. The methodological framework incorporates traditional epidemiological models, including SEIR and ARIMA approaches, together with Random Forest and Long Short-Term Memory architectures representing contemporary artificial intelligence methods. A standardized preprocessing pipeline, common evaluation criteria, and consistent experimental procedures are employed to ensure fair comparisons across forecasting paradigms. Beyond predictive accuracy, the study considers interpretability, robustness, computational efficiency, and operational applicability as critical dimensions of effective epidemic forecasting. The findings indicate that AI-driven models exhibit stronger capabilities in capturing nonlinear transmission patterns and adapting to rapidly evolving epidemic conditions. Deep learning methods, particularly recurrent architectures, demonstrate enhanced performance during complex outbreak waves characterized by changing behavioral and intervention dynamics. Traditional epidemiological approaches, however, retain important advantages in interpretability, transparency, and policy communication, providing mechanistic insights that remain essential for public health decision-making. The results further suggest that methodological diversity contributes to greater forecasting resilience and supports more reliable epidemic preparedness strategies. The study concludes that artificial intelligence should not be viewed as a replacement for conventional epidemiological modeling but rather as a complementary set of tools capable of strengthening existing public health infrastructures. Future forecasting systems are likely to benefit from hybrid and collaborative frameworks that integrate mechanistic understanding with data-driven learning capabilities. Such an approach offers a promising pathway toward more accurate, interpretable, and operationally effective epidemic forecasting in the United States.
Privacy-Preserving AI Architectures for Salesforce Healthcare CRM: A Human-Centered Framework for Trustworthy Intelligence in Regulated Care Operations
Artificial intelligence is becoming deeply embedded in healthcare customer relationship management platforms, where it now supports case summarization, care navigation, intelligent search, workflow recommendations, and member-service guidance. In healthcare environments, however, the promise of intelligence is inseparable from the obligation to protect privacy. Salesforce-based healthcare CRM platforms often sit close to highly sensitive operational data, including protected health information, care interactions, benefit questions, service histories, and authorization contexts. This paper proposes a human-centered architecture for privacy-preserving artificial intelligence in Salesforce healthcare CRM. The framework is designed to help healthcare enterprises use advanced AI services without allowing those services to overreach into sensitive data, weaken compliance discipline, or erode user trust. The paper introduces a layered architecture built on data minimization, consent-aware controls, tokenization, safe prompt handling, encryption, human review, and governed operational release. The central argument is that privacy in healthcare AI should not be treated as a legal afterthought or a filtering rule added at the edge. Instead, privacy must be built into the architecture itself so that intelligence remains both useful and worthy of trust.
Privacy-preserving AI, Salesforce healthcare CRM, protected health information, trustworthy AI, healthcare cloud architecture, human-centered governance
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A Sequence-Connection Oriented Scheme for Detecting Gray/Black Hole Attacks in Mobile Ad-Hoc Networks
Security of routing protocols for Mobile Ad Hoc Networks (MANETs) have been studied extensively. Malicious nodes habitually intervene the process of route establishment and make it complicated to build a reasonable route. In the literature, various mechanisms presented which claim to build secure routes are prone to gray and black hole attacks. In this paper, a novel method is proposed that efficiently detects gray hole attack in addition to detecting black hole attacks. The proposed technique uses two modes, connection oriented and connection-less oriented mode depending on a sequence number sent in the route request packets. A deep and extensive analysis is performed to prove the efficiency of the proposed method as well as show how it prevents malicious nodes from deteriorating the packet forwarding process. The analysis encompasses both presenting the attacks on routing protocols and proofs of how this suggested protocol detects and prevents these attacks. According to the theoretical analysis, the proposed method is expected to maintain great ratio of packet delivery even under gray/black hole attack conditions. This expectation is based on the rigorous prevention of the attacks which leads to build more successful routes.
MANETs; Gray Hole Attack; Blackhole Attack; Security of Routing in MANETs; DoS
Artificial Intelligence-Driven Financial Data Analytics for Revenue Optimization, Risk Management, and Sustainable Growth of Small Businesses in the United States.
Small businesses are a vital driver of economic growth, innovation, and employment in the United States. However, many small businesses face significant challenges related to financial management, market uncertainty, resource constraints, and increasing competition. The emergence of Artificial Intelligence (AI) and advanced financial data analytics has created new opportunities for small businesses to improve operational efficiency, enhance decision-making, and achieve sustainable growth. This study explores the role of AI and financial data analytics in supporting the growth and competitiveness of small businesses in the United States. The research examines how AI-powered technologies, including machine learning, predictive analytics, and intelligent automation, can be utilized to analyze financial data, forecast revenue trends, optimize resource allocation, identify business opportunities, and mitigate financial risks. Furthermore, the study investigates the impact of data-driven decision-making on profitability, customer retention, and long-term business performance. By leveraging financial and operational data, AI systems can generate actionable insights that enable business owners to make more informed strategic decisions and respond effectively to changing market conditions. The findings suggest that the integration of artificial intelligence and financial data analytics can significantly improve business growth, financial sustainability, and competitive advantage for small enterprises. This study contributes to the growing body of knowledge on digital transformation and provides practical implications for entrepreneurs, financial managers, policymakers, and technology providers seeking to promote small business development in the United States.
Artificial Intelligence, Financial Data Analytics, Small Business Growth, Predictive Analytics, Machine Learning, Business Performance, United States Economy
Hybrid Cybersecurity: AI Models for Predicting Threats Across Multi-Cloud for U.S. Business Environment
The presence of multipurpose cloud solutions has presented emerging cybersecurity concerns and an incentive to use more sophisticated methods, including threat prediction and mitigation. The above writing presents the integration of artificial intelligence (AI) hybrid models in multipurpose cloud infrastructures to achieve better threat prediction and detection. The hybrid AI models, achieved by implementing supervised and unsupervised learning algorithms in parallel with the fusion of data across multiple cloud platforms help to achieve new heights of accuracy, scalability, and flexibility of the cybersecurity systems. The study mentions use of deep learning, extensive data management and reinforcement learning as a real-time detection mechanism not just to detect known threats but also detect new threats. The findings indicate that hybrid AI solutions could be relevant in achieving substantial performance in terms of the threat detection rate, low false alarms, and quicker response rates in running multipurpose cloud setups. These findings confirm again the strength of hybrid AI models to cope with the dynamic nature of cybersecurity threats and, hence, enhance the system resilience and offer more efficient protection in various cloud environments. The article analyzes the embodiment of artificial intelligence (AI) models to their optimal in multi-cloud entities to enhance its security risk detection and prediction measures. Both the integration of the guided and uncontrolled learning approaches as well as the integration of various cloud data and the use of hybrid AI approach allow for the increase in precision and flexibility of threat detection systems. Thus, based on the analysis presented in the paper, it becomes clear that there are certain ways of applying AI to identify both known and newly formed threats. These AI methods include such approaches as deep learning, reinforcement learning, and anomaly detection. The application of hybrid AI models allows for the successful identification of changes within the dynamic multi-cloud environment.
Hybrid AI, Cybersecurity, Multi-cloud Environments, Threat Prediction, Machine Learning, Deep Learning, Anomaly Detection, Reinforcement Learning, Data Fusion, Cloud Security
Diagnosis Everywhere: Lightweight AI That Detects Disease from Scans and Records on Limited Hospital Hardware
Federated learning enables collaborative medical model training without centralizing raw institutional data, yet its practical use remains constrained by non IID distributions, communication overhead, limited computing resources, and the need to support multiple diagnostic tasks across different modalities. This paper presents the proposed model, an efficiency aware federated multi task learning framework for medical imaging and clinical text applications. For imaging tasks, the model uses a frozen SqueezeNet encoder with task specific prediction heads and applies entropy adaptive structured pruning, where each client’s pruning rate is adjusted according to local label distribution heterogeneity. For clinical text, BioClinicalBERT is adapted using LoRA modules inserted into the query and value projections, followed by structured top 3 layer pruning to reduce inference and communication cost. Experiments are conducted on fundus based eye disease classification, COVID 19 radiography classification, and five unified clinical text datasets. Under non IID conditions, the proposed model achieves 90.27 ± 0.44% accuracy and 0.903 F1 for eye disease classification, and 78.51 ± 0.53% accuracy and 0.784 F1 for COVID 19 radiography. Compared with FedAvg, FedProx, SCAFFOLD, FedBN, AutoFLIP, and fixed pruning, the adaptive variant provides the strongest imaging performance while reducing communication to 3.14 MB per round and active parameters to 0.87M. In clinical text modeling, FLUTE based LoRA fine tuning achieves 91.76 ± 0.36% accuracy and 0.913 F1. With top 3 layer pruning, the model retains 90.94 ± 0.40% accuracy while reducing parameters to 86M, FLOPs to 79.4G, latency to 79 ms, communication to 0.85 MB per round, and estimated energy demand by 22.6%. These results indicate that adaptive compression and parameter efficient adaptation can improve federated medical learning under heterogeneous and resource constrained conditions.
Federated learning, multitask learning, medical imaging, clinical text diagnosis, adaptive pruning, LoRA
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Smarter Health for Everyone with an AI System That Detects Four Diseases and Gives Easy-to-Understand Clinical Advice
Medical-image artificial intelligence models are commonly developed for single diseases or isolated imaging modalities, which limits shared representation learning, calibration-aware prediction, and clinically usable decision-support reporting. This study proposes PolyDx-Mamba, a unified multi-disease medical image classification framework designed strictly for clinician-assisted decision support rather than autonomous diagnosis. The framework combines modality-specific stem and patch embedding, a shared Vision-Mamba hierarchical encoder, per-stage channel-spatial attention, cross-disease selective state-space global context aggregation, gated multi-scale feature fusion, uncertainty-aware latent representation learning, and task-specific classification heads. PolyDx-Mamba was evaluated across four clinically distinct imaging tasks: brain tumor MRI classification, skin lesion classification using dermoscopy, COVID-19 chest X-ray classification, and diabetic retinopathy grading from fundus images. A confidence-conditioned Gemini-1.5-Pro retrieval-augmented generation advisor was further incorporated to generate clinician-facing recommendation reports grounded in retrieved clinical context. PolyDx-Mamba achieved accuracies of 0.9926, 0.9209, 0.9880, and 0.8718 across the four datasets, with an average accuracy of 0.9433. Macro-F1 scores were 0.9917, 0.9151, 0.9863, and 0.8597, yielding an average macro-F1 of 0.9382. The model also demonstrated favorable calibration, with expected calibration error values of 0.0162, 0.0307, 0.0208, and 0.0421 across the respective tasks. Statistical testing confirmed significant performance improvements over all evaluated baselines, including the strongest Mamba-based comparator. The Gemini-1.5-Pro confidence-RAG advisor produced clinically relevant and guideline-aligned recommendation reports, with high factual accuracy, low hallucination rate, and strong safety compliance. These findings suggest that PolyDx-Mamba provides a calibrated and statistically supported multi-disease image classification framework with retrieval-grounded clinical advisory support. However, all predictions and generated recommendations require clinician confirmation, and external prospective validation remains necessary before any clinical deployment.
PolyDx Mamba, medical image classification, Vision Mamba, clinical decision support, uncertainty calibration, retrieval augmented generation
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Crypto-Agile Zero Trust Architecture for IT-OT Convergence in Industrial Control Systems: A Digital Twin-Driven Framework over 5G/TSN with Post-Quantum Cryptography
The convergence of Information Technology (IT) and Operational Technology (OT) in Industrial Control Systems (ICS) has dissolved the implicit trust boundaries that traditionally protected critical infrastructure. While Zero Trust Architecture (ZTA) is increasingly advocated as the successor to perimeter-based defense for converged industrial environments, existing ZT proposals for ICS rarely address three concurrent realities: (i) the rise of high-fidelity Digital Twins (DTs) as the natural locus of process-aware trust evaluation, (ii) the migration of industrial transport to 5G non-public networks and Time-Sensitive Networking (TSN) with deterministic timing budgets that cannot accommodate naive authentication round-trips, and (iii) the imminent obsolescence of classical asymmetric cryptography due to cryptographically relevant quantum computers, which regulatory frameworks such as NIS2, the EU Cyber Resilience Act, and IEC 62443 already require to be mitigated through crypto-agility. This paper proposes QZT-ICS, a unified ZT framework for IT-OT convergence that (a) uses a synchronized Digital Twin as the policy decision point for continuous, process-aware trust scoring; (b) embeds policy enforcement at the 5G slice and TSN bridge layers to preserve sub-millisecond determinism; and (c) integrates a crypto-agile post-quantum key-establishment and signature layer based on NIST-standardized ML-KEM and ML-DSA, with hybrid classical-PQC modes for legacy field devices. The framework is evaluated through a simulated water-treatment SCADA testbed against false data injection, command injection, and lateral-movement attack classes. Results quantify the trade-off between PQC overhead, TSN scheduling slack, and DT-driven trust evaluation latency, and demonstrate viable deployment paths for brownfield ICS. The paper concludes with a maturity model for staged adoption and open research challenges.
Zero Trust Architecture, IT-OT convergence, Industrial Control Systems, Digital Twin, 5G non-public networks, Time-Sensitive Networking, Post-Quantum Cryptography, Critical Infrastructure, Cyber-Physical Systems, IEC 62443, NIST SP 800-207
Deep Learning Models for Early Detection of Chronic Diseases Using Multimodal Healthcare Data
Early detection of chronic diseases is critical for improving patient outcomes and reducing healthcare burdens. The increasing availability of heterogeneous healthcare data, including electronic health records, medical imaging, and wearable sensor measurements, offers unprecedented opportunities for predictive modeling, yet poses significant challenges in integration and interpretation. In this study, we develop a multimodal deep learning framework that combines structured EHR data, time-series signals from wearable devices, and medical images to predict the onset of chronic conditions. Our methodology incorporates attention-based fusion mechanisms and hybrid architectures, including CNN-LSTM networks, to capture both spatial and temporal patterns across modalities. Extensive evaluations demonstrate that the multimodal approach substantially outperforms unimodal models, yielding robust, interpretable predictions even in the presence of missing or noisy data. The results highlight the potential of integrating diverse healthcare data streams to enable timely clinical interventions. This work contributes a systematic framework for multimodal disease prediction, demonstrating practical strategies for model development, ablation analysis, and sensitivity testing in complex real-world healthcare scenarios.
Generative AI-Driven Predictive Quality Control for Smart Manufacturing Systems
With the accelerated development of smart manufacturing and Industry 4.0, there is a substantial need for intelligent quality control mechanisms that can guarantee the production of reliable products with sustainable manufacturing processes. Conventional quality control mechanisms, which mainly involve physical checks and statistical control methods, tend to be reactive in nature and encounter problems coping with the highly complicated and data-driven manufacturing environments. While advancements in artificial intelligence and machine learning have enabled predictive maintenance and automatic inspection, current mechanisms still exhibit certain constraints, such as low adaptability and dependency on previous datasets. In light of these challenges, Generative Artificial Intelligence emerges as a novel approach that holds immense potential in enhancing predictive quality control processes. This research study aims at examining the significance of Generative AI for predictive quality control of smart manufacturing systems and presenting a theoretical model that incorporates Generative AI, IIoT, predictive analytics, digital twin technology, and human-centered decision-making. This theoretical model will be based on real-time sensor data collection, artificial intelligence-based anomaly prediction, simulation through digital twin technology, and autonomous feedback loops for proactive quality control purposes. Some of the technologies considered for the development of this theoretical model include LLMs, GANs, and diffusion models. Moreover, the paper elaborates on the practical uses of the suggested framework in the industries of automobiles, additive manufacturing, aviation industry, electronics, and pharmaceutics, focusing on such areas as enhanced defect reduction, economic efficiency, and sustainability. Moreover, the challenges faced during the implementation associated with cybersecurity, explainability of artificial intelligence, computing needs, and integration issues are considered in detail. Based on the research outcomes, it is possible to say that Generative AI-based quality control prediction has the potential to foster the emergence of Industry 5.0 through establishing intelligent and adaptable human-centered ecosystems of production.
Generative AI, Smart Manufacturing, Predictive Quality Control, Digital Twin, Industry 5.0
Investigating the Security Risks of Gen-AI-Powered Phishing Attacks on University Students
Generative Artificial Intelligence (GenAI) is a paradigm shift in the cyber-threat environment as it allows generating hyper-realistic, personalised and scalable phishing campaigns. This is a study that explores how this technological development has intersected with the human susceptibility that has been there in the realm of higher education. This paper, which targets university students as a target population with a high level of digital exposure but possibly low levels of security awareness, also adopts a positivist, quantitative approach in its attempt to investigate empirically the security risks that AI-driven phishing presents to them. It was a cross-sectional online survey (N=63), in which a simulated GenAI phishing mail was used as a behavioural stimulus. Descriptive and inferential statistics demonstrate a crucial lack of connection: self-reported confidence in detection was at a moderate level (M=3.13/5), whilst behavioural susceptibility to it is alarmingly high, with 81.7 percent stating that they were likely to choose to click the malicious link. It was discovered that there was a deep institutional training deficit, as 90.1% of respondents had not been trained in cybersecurity at university and analyses (kh2(1)) demonstrated that prior training had no significant protective effect on phishing experience (kh2(1) =0.948, p=.330). Moreover, existing guidance in universities was perceived as insufficient by 76.1% of them. The results highlight a severe overconfidence paradox and a systemic defect in the modern pedagogical models to help counteract AI-improved threats. This paper has determined that the human firewall within the academic context is highly misaligned and recommends an immediate, strategic move to compulsory, simulation-based training programmes that are specifically crafted to take into account the advanced affordances of GenAI in social engineering attacks.
AI-Powered Phishing, Generative AI, Cybersecurity Awareness, University Students, Human Factor, Susceptibility, Cybersecurity Training
Machine Learning Framework for Liver Cirrhosis Stage Prediction Using Clinical and Biochemical Features
Chronic liver disease is characterized by liver cirrhosis, a progressive condition associated with high morbidity and mortality rates globally. Accurate classification of disease stage is essential for timely clinical intervention, comprehensive disease assessment, and the development of individualized treatment plans. This study proposes an interpretable machine learning model to predict the stage of liver cirrhosis using routinely collected clinical and biochemical parameters. A comprehensive exploratory data analysis was conducted to identify patterns and relationships among variables and to examine feature distributions across different stages of disease progression. After data preprocessing, including handling missing values, feature encoding, and normalization, multiple machine learning algorithms were evaluated, including Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, Gradient Boosting and Extreme Gradient Boosting (XGBoost). Model performance was assessed using accuracy, precision, recall, F1-score, and confusion matrix analysis. Experimental results demonstrated that ensemble-based models outperformed traditional machine learning methods, with XGBoost achieving the highest performance in multi-class stage prediction. Feature importance analysis identified clinical blood parameters such as bilirubin, albumin, platelet count, prothrombin time, and cholesterol as significant predictors of disease severity. The proposed framework is reliable, interpretable, and suitable for automated liver cirrhosis stage classification, offering valuable support to clinicians in early diagnosis and risk stratification. Results show the promising contributions of machine learning methods to decision support systems in healthcare, as well as their applicability to enhance the management of chronic liver diseases via leveraging data and predictive modelling.
Liver Cirrhosis, Disease Stage Prediction, Clinical Decision Support System, Healthcare Analytics, Machine Learning, Multi-Class Classification, XGBoost.
Autonomous Decision Intelligence for Secure and Resilient Digital Enterprises
Few forces have reshaped organizational life as quickly as digital transformation. The way firms create value, manage risk, and hold their competitive ground now depends on systems that grow more entangled with one another every year. A typical enterprise sits at the center of a constant flow of data drawn from its operations, its cloud platforms, the sensors embedded in its products, its planning systems, and the many places where it meets its customers. Artificial intelligence (AI), machine learning, big data analytics, blockchain, and cybersecurity have each made it easier to turn that flow into useful judgment. Yet most organizations still adopt these tools one at a time, and the habit quietly erodes the strategic payoff that integration could deliver. This paper sets out an Autonomous Decision Intelligence (ADI) framework that gathers AI, cybersecurity, big data analytics, blockchain, and management information systems (MIS) into one coherent architecture built for resilient digital enterprises. The argument rests on a synthesis of recent work in decision intelligence, predictive analytics, business intelligence, federated learning, cloud computing, blockchain governance, cyber threat intelligence, and enterprise risk management. From that body of evidence, we construct a conceptual model for organizational decision-making that is trustworthy and capable of improving itself over time. The framework gives weight to secure data governance, explainable AI, privacy-preserving analytics, blockchain-based trust, cyber-resilience, and intelligent automation. It then asks how such a design might reinforce critical infrastructure protection, supply chain resilience, economic sustainability, IT project governance, and day-to-day agility. The contribution is at once theoretical and practical, because it shows how converging technologies can turn conventional decision-support systems into adaptive ecosystems that learn. Organizations that pair AI-driven analytics with strong security and decentralized trust look best placed to absorb uncertainty, keep operating under stress, and pursue digital transformation that lasts.
Autonomous Decision Intelligence; Artificial Intelligence; Big Data Analytics; Blockchain; Cybersecurity; Management Information Systems; Digital Transformation; Business Intelligence; Enterprise Resilience
Artificial Intelligence in Crime Scene Reconstruction: Using Machine Learning for Predictive Analysis and Scenario Simulation
Crime scene reconstruction remains a critical yet complex component of forensic investigation, requiring the integration of heterogeneous evidence sources to infer plausible sequences of criminal events. Traditional reconstruction methods rely heavily on expert interpretation, which can introduce subjectivity and limitations when dealing with large-scale or incomplete forensic data. This study proposes a machine learning-based framework for automated crime scene reconstruction, integrating predictive analytics and scenario simulation to enhance investigative decision-making. The framework leverages structured forensic features, including evidence distribution, temporal response characteristics, and scene complexity indicators, to model reconstruction outcomes using a combination of classical machine learning, ensemble methods, and deep learning architectures. In addition, sequential models and hybrid neural networks are employed to capture temporal dependencies and local evidence patterns, enabling more robust interpretation of event sequences. Scenario simulation is incorporated to generate multiple plausible crime narratives under varying evidence conditions, supporting probabilistic reasoning in uncertain investigative environments. The findings indicate that advanced ensemble and hybrid deep learning models offer superior capability for capturing nonlinear and temporal relationships in forensic datasets, leading to more consistent and reliable reconstruction outcomes. The proposed approach demonstrates the potential of artificial intelligence to augment forensic investigation processes by improving reconstruction accuracy, enhancing scenario exploration, and supporting more structured evidence interpretation in complex crime scenes.
Cost-Aware Prompt Engineering: A Governance Architecture for Optimizing Enterprise AI Agents
As enterprises deploy large language model (LLM)-based and agentic AI systems across critical business functions, prompt engineering has emerged as a significant operational bottleneck. Prompts that are poorly structured, excessively verbose, or manually tuned without systematic comparison introduce hidden costs: elevated token consumption, increased inference latency, inconsistent output quality, and deployment risk. Despite its importance, prompt engineering in most enterprise environments remains an informal, trial-and-error discipline without governance, reproducibility, or measurable quality criteria. This paper proposes a Cost-Aware Prompt Engineering Governance Architecture (CAPEGA) designed for enterprise production environments. The architecture integrates four core components: iterative prompt optimization using SELF-REFINE techniques, structured prompt compression with instruction-preservation controls, metric-driven prompt comparison with cost and latency artifact generation, and CI/CD-ready governance workflows for prompt promotion and rollback. CAPEGA's contribution is architectural integration rather than novel algorithmic techniques: it combines established optimization methods into a unified, production-ready governance pipeline. Together, these components transform prompt engineering from an ad hoc craft into a reproducible, auditable engineering discipline. The framework is model-agnostic in design, model-specific in execution, and intended for integration into existing enterprise AI release pipelines. By establishing measurable quality gates for prompts, CAPEGA enables organizations to reduce token overhead, improve output consistency, control inference cost, and generate audit-ready evidence for production deployment decisions. An implementation architecture with component-level design is presented alongside the conceptual framework.
Prompt Engineering, Enterprise AI, Cost-Aware AI, SELF-REFINE, LLM Optimization, AI Governance, CI/CD, Agentic AI, Token Efficiency, Production Readiness
Deep Learning and Explainable Benchmarking for Early Parkinson’s Disease Detection Using Speech Signals in the United States
Early-stage Parkinson’s disease (Early PD) detection using speech analysis has emerged as a promising and non-invasive approach for improving neurological healthcare in the United States. However, existing studies remain difficult to compare due to variations in datasets, speech tasks, languages, evaluation strategies, and definitions of Early PD. To address these limitations, this study proposes a comprehensive benchmark framework for speech-based Early PD detection using speaker-independent evaluation protocols to ensure fair, reproducible, and clinically reliable comparisons. The proposed benchmark evaluates multiple speech tasks under different training-resource settings and provides multidimensional performance analysis based on dataset characteristics, gender, aggregation level, and disease severity. Experimental findings offer actionable insights into the robustness and generalizability of speech-based Parkinson’s detection systems. The proposed benchmark establishes a reliable reference framework for advancing explainable, scalable, and clinically meaningful Early PD detection technologies within modern U.S. healthcare and neurological diagnostic systems.
Early-Stage Parkinson’s Disease, Parkinson’s Disease Detection, Speech-Based Diagnosis, Neurological Disorder Detection, Machine Learning, Deep Learning, Speech Analysis, Explainable Artificial Intelligence (XAI), Benchmark Framework, Speaker-Independent Evaluation, Healthcare AI, U.S. Healthcare System, Clinical Decision Support, Voice Biomarkers, Neurological Healthcare
Explainable Deep Fusion Transfer Learning for Automated Lung Disease Classification in the U.S. Healthcare Environment
Lung diseases continue to impose a significant healthcare burden in the United States, making early and accurate diagnosis essential for effective treatment and patient management. This study proposes an explainable fusion-based transfer learning framework for automated lung disease classification using chest X-ray and CT scan images. The proposed system integrates state-of-the-art deep learning architectures, including ResNet50, ResNet101, and EfficientNetB0, along with feature-level and decision-level fusion strategies to enhance classification accuracy and robustness. Experimental results demonstrate that the hybrid fusion models outperform standalone architectures, achieving 95.68% accuracy for chest X-ray classification and 99.61% accuracy for CT scan classification. To improve transparency and clinical reliability, Explainable Artificial Intelligence (XAI) methods such as SHAP, LIME, and Integrated Gradients were incorporated to visualize diagnostically relevant regions. The proposed framework offers an accurate, interpretable, and reliable AI-driven solution for pulmonary disease diagnosis in modern U.S. healthcare systems.
Lung Disease Classification, Chest X-ray, CT scan, Transfer Learning, Deep Learning, Explainable Artificial Intelligence
AI-Based Schedule Overrun Prediction in Technical Projects Using Gradient Boosting Machine for Decision Support
The increasing complexity of technical projects has challenged the effectiveness of traditional project management approaches, particularly in accurately predicting schedule performance. Conventional methods often rely on expert judgment and static planning techniques, limiting their ability to capture dynamic project conditions and complex relationships between variables. This study investigates the role of Artificial Intelligence, specifically the Gradient Boosting Machine (GBM), in enhancing schedule management through a data-driven approach. A predictive model was developed using a publicly available project management dataset obtained from Kaggle, consisting of 4517 project instances. The dataset includes key variables such as project duration, total cost, team size, risk factor, and client satisfaction. The proposed model was applied to predict Schedule Overrun (%) as a measure of project performance. The results demonstrate strong predictive performance, with an R² value of 0.835, RMSE of 7.128, and MAE of 5.946. These findings indicate that the model is capable of capturing complex patterns and relationships within project data. Feature importance analysis revealed that risk factor and project duration are the most influential variables affecting schedule performance, followed by client satisfaction. These results highlight the importance of managing project uncertainty and timeline characteristics to improve overall project outcomes. The study confirms that AI-based models can provide valuable predictive insights to support proactive, data-driven decision-making in project management. Future research may focus on expanding the dataset, incorporating additional contextual variables, and exploring advanced modeling techniques to further enhance predictive accuracy and practical applicability.
Artificial Intelligence; Machine Learning; Project Management; Schedule Overrun; Gradient Boosting Machine; Predictive Modeling; Data-Driven Decision-Making