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As a leading risk factor for chronic illnesses, obesity highlights the critical need for prompt diagnosis and active management. This research introduces an innovative expert system developed to precisely categorise obesity severity and offer individualised treatment advice. Utilising the SEMMA data mining framework alongside the tidymodels ecosystem, the study assessed five machine learning algorithms: decision tree, random forest, multinomial logistic regression (MLR), Naive Bayes, and support vector machine (SVM). Achieving the highest predictive accuracy (97.48%), MLR was chosen as the definitive model. The platform incorporates an intuitive R Shiny interface, promoting seamless real-time user engagement. Treatment protocols are formulated via if-then rule-based logic to guarantee customised advice for every obesity classification. A comparative evaluation emphasises the system's exceptional diagnostic precision and its functional utility in recommending treatments. By improving accessibility—especially for marginalised rural communities—this system bolsters public health by facilitating preliminary diagnosis, specific interventions, and forward-looking obesity care.

The application of Artificial Intelligence (AI) tools to assist with systematic literature reviews (SLRs) is expanding rapidly, yet researchers still lack structured frameworks for assessing and incorporating these tools into their research practices. This paper introduces and implements a rubric-based framework designed to evaluate AI toolchains across essential SLR stages, such as literature searching, screening, and data synthesis. A mixed-methods study was executed with 63 postgraduate student researchers, who represent the early-stage investigators that serve as primary adopters of these digital workflows. The results demonstrate that workflows structured around the rubric enhanced the participants' perceived utility of AI utilities, provided organized criteria for comparative analysis, and promoted greater transparency during tool selection. Although formal indicators of inter-rater reliability (such as Cohen’s or Fleiss’ Kappa) were not calculated due to the fact that participants did not evaluate identical benchmark sets concurrently, the consistency of the rubric was qualitatively validated through pilot evaluations, iterative updates, and structured focus group discussions. These efforts confirm interpretative alignment among users while recognizing that future validation via independent assessors is required. This research contributes a validated assessment rubric, an operational example of an AI-augmented SLR workflow, and a discussion regarding the consequences for research training, study reproducibility, and cross-disciplinary translation.

The rapid evolution of generative artificial intelligence, most notably large language models like ChatGPT and GPT-4, has unlocked revolutionary opportunities for postgraduate scholarship. This systematic review evaluates how generative artificial intelligence utilities alter the quality, execution speed, ethical standards, and innovative potential of postgraduate inquiries. A thorough literature search was conducted across four major repositories: Google Scholar, Web of Science, IEEE Xplore, and Scopus. Adhering strictly to PRISMA protocols, 20 peer-reviewed articles published between 2019 and 2025 were chosen for detailed investigation based on their direct alignment with postgraduate research settings, explicit use of generative AI applications, and documented outcomes. The synthesis revealed four core dimensions: (i) research productivity and efficiency, where generative AI refines academic composition, data processing, and literature mapping; (ii) cognitive and creative assistance, where AI aids in drafting hypotheses, brainstorming novel concepts, and polishing text; (iii) academic honesty and ethical dilemmas, which highlight vulnerabilities surrounding plagiarism, data fabrication, and over-dependence on machine-generated text; and (iv) capacity shortages and skill evolution, indicating an accelerating necessity for postgraduate scholars to undergo training in AI literacy and ethics. Although these generative AI systems offer significant advantages by broadening access to analytical tools and boosting operational output for postgraduate scholars, this investigation reveals a stark lack of rigorous regulatory oversight and institutional frameworks across most universities. Furthermore, unequal regional access to state-of-the-art AI systems threatens global research equity. The review culminates in a proposal for a customized framework designed for the conscientious integration of GenAI into postgraduate studies, prioritizing institutional governance, human-machine partnerships, and ethical deployment. These insights enrich the ongoing debate regarding the trajectory of AI, offering scholars, investigators, and administrators data-driven strategies to harness AI capabilities while defending academic standards.

The rapid expansion of artificial intelligence (AI) systems across numerous sectors in recent years has drawn significant attention to the ethical, legal, and societal aspects (ELSA) of these technologies, along with the specific difficulties they introduce. This paper outlines an ELSA curriculum tailored for data scientists. The framework is designed to heighten awareness of ELSA dilemmas in professional practice, establish a shared vocabulary with domain experts to facilitate collaborative problem-solving, and ultimately imbed ELSA principles directly into data science workflows. Rather than being treated as an obstacle or an auxiliary requirement, ELSA ought to be viewed as an essential component of the Data Science Project Lifecycle. The proposed educational model utilizes the CRISP-DM (CRoss-Industry Standard Process for Data Mining) framework as its structural foundation, creating a vertical division composed of modules that align with each CRISP-DM stage. Vertically intersecting these phases is a horizontal division comprising three distinct thematic strands: ethical and societal, legal, and technical rendering knowledge units (KUs). Alongside an exhaustive breakdown of these KUs, this paper examines practical implementation factors, including course duration, delivery formats, assessment methods, and strategies for managing the diverse baseline knowledge and varying requirements of the target student body.

This review investigates the expanding influence of machine learning and deep learning methodologies within the healthcare sector, concentrating specifically on the vital challenges of interpretability and explainability inherent in black-box models. Although machine learning frameworks are increasingly deployed to assist in medical analytics and diagnostics, their intricate nature emphasizes the necessity of comprehending how these systems process and interpret data to facilitate well-informed clinical choices. This paper offers a thorough examination of the obstacles and potential solutions documented in existing scholarship, delivering a synthesis of contemporary techniques in this domain. Furthermore, it establishes precise conceptual boundaries for interpretability and explainability, seeking to untangle the differences between these notions and outline their consequences for clinical decision-making. Utilizing a dataset of 448 publications to address seven distinct research questions, our investigation demonstrates that this research area has experienced exponential growth over the past ten years. Additionally, the psychological factors governing public opinion highlight the vital need for transparent communication regarding the capabilities and constraints of artificial intelligence. To mitigate opacity, investigators are actively engineering strategies to lower model complexity and implement visual explanation tools. Nevertheless, maintaining a delicate equilibrium between high predictive performance and adequate model interpretability remains an ongoing challenge. Given the expanding role of artificial intelligence in supporting clinical diagnostics and therapeutics, the development of inherently interpretable artificial intelligence systems is critical. Within this rapidly changing paradigm, a steadfast dedication to transparency, ethical standards, and cross-disciplinary partnerships is essential to guarantee the conscientious deployment of artificial intelligence. This shared dedication is foundational to cementing long-term trust between medical professionals and patients, resolving novel problems, and promoting the educated integration of these sophisticated technologies into medical practices.

The growing demand for automation within the financial sector creates an ideal environment for utilizing artificial intelligence. However, stringent and intricate regulatory mandates alongside rapid technical progress present substantial hurdles for the creation and deployment of AI-driven financial services. To foster comprehension, safeguard consumers, and maintain service quality in this sector, the regulatory guidelines established by European authorities must be organized into highly detailed components. This categorization will offer deeper insights into regulatory priorities and provide a structured approach to engineering AI-driven solutions. This study introduces a classification framework based on a taxonomy designed to demystify current European regulatory tenets for academics, supervisory bodies, and financial enterprises. By highlighting the core thematic focal points of these regulations, our research aims to facilitate the engineering of compliant AI services.

Explainable Artificial Intelligence (XAI) represents an active discipline dedicated to clarifying the internal mechanisms of AI systems, thereby fostering stakeholder confidence and advancing the deployment of accountable technology. A primary theoretical hurdle within XAI involves engineering techniques capable of producing clear, accessible interpretations without sacrificing scalability or operational performance in intricate domains. While rule-based methodologies offer a framework for decoding AI-driven conclusions, they are prone to becoming overly complex in dense environments, which diminishes both their comprehensibility and computational scalability. Furthermore, these approaches frequently overlook contrastive logic, which leaves observers unable to discern why a specific outcome was prioritized over alternatives. To resolve this underlying scientific problem, this research investigates the fusion of computational argumentation—a specialized domain of AI that replicates cognitive processes through defeasible reasoning—with rule-based XAI architectures. Computational argumentation provides a mechanism whereby arguments derived from structured rules can be invalidated or withdrawn when confronted with conflicting evidence. Consequently, this framework offers a compelling avenue for refining rule-based systems to yield more transparent AI models. Despite the synergistic benefits of combining rule-based structures with computational argumentation, academic inquiry into their synthesis remains sparse. Accordingly, this paper also targets the practical engineering challenge of realizing this conceptual integration within functional AI software. This investigation utilizes the Logic Learning Machine (LLM)—a specialized rule-extraction methodology—and introduces a modular framework designed to map input rules directly into a structured argumentation network via state-of-the-art argumentation techniques. Empirical evaluations performed on various binary classification problems from the UCI Machine Learning Repository validate the utility of this combined model. The LLM strategy proved highly proficient at extracting a compact, interpretable collection of if-then rules characterized by minimal antecedent premises, all while sustaining strong predictive accuracy across all evaluated datasets. Concurrently, the argumentation-based models delivered classification performance comparable to the native if-then rule configurations, achieving this through a streamlined rule set while offering vastly superior explainability. Ultimately, this work delivers an innovative framework for the automated and streamlined generation of arguments and their internal conflicts directly from raw data, simultaneously addressing fundamental theoretical and practical barriers to propagate argumentation frameworks within the XAI landscape.

With the emergence of novel data collection platforms driven by the Internet of Things (IoT), this study expands beyond the conventional boundaries of traditional remote sensing technologies. The deep integration of remote sensing and computer vision has transitioned into industrial applications, enabling the utilization of Artificial Intelligence to address challenges like automated information extraction and image analysis. Nonetheless, due to the intricate architecture of IoT and the absence of standardized security protocols, remote sensing instruments are highly susceptible to privacy breaches during data transmission. Because traditional cryptographic approaches depend heavily on computational complexity, it is critical to develop security frameworks tailored for resource-constrained IoT devices. Visual Cryptography (VC) serves as an image-based threshold technique that allows direct decoding by the human visual system simply by overlaying encrypted shares. This "stacking-to-see" attribute, combined with basic Boolean decryption processes, positions VC as an optimal candidate for privacy-preserving identification within extensive IoT-based remote sensing imagery. In this investigation, the secure and productive dissemination of high-resolution remote sensing imagery is accomplished via meaningful VC. By distributing the error between the encryption block and the source block to neighboring blocks, the fidelity degradation in the reconstructed images is minimized. Furthermore, by adjusting a pre-trained model using large-scale datasets, the recognition accuracy of small, encrypted remote sensing datasets is enhanced. The empirical findings demonstrate that the introduced lightweight, privacy-preserving recognition system maintains superior identification performance while strengthening data security.

Machine learning (ML) has evolved into an indispensable technology within the defense sector, driving the creation of sophisticated mechanisms for identifying threats, supporting decision-making processes, and managing autonomous operations. Nevertheless, the expanding integration of ML into defense frameworks has generated significant ethical anxieties concerning accountability, transparency, and inherent bias. This paper offers an extensive evaluation of ML’s influence on the defense industry, detailing both the advantages and risks of implementing these methodologies across diverse domains, including surveillance operations, target pinpointing, and autonomous weapon platforms. Furthermore, we examine the ethical repercussions of deploying ML in military contexts, emphasizing problems tied to privacy preservation, responsibility attribution, and algorithmic bias. Ultimately, we outline strategic recommendations to alleviate these ethical complications, advocating for heightened operational transparency, clearer accountability structures, and the active engagement of diverse stakeholders in the architecture and deployment of ML systems within defense frameworks.

Artificial intelligence increasingly informs consequential decisions, yet high predictive performance alone does not establish that an intelligent system is worthy of trust. This systematic review examines trustworthy AI as a multidimensional, socio-technical, and lifecycle-dependent research domain. It synthesises selected peer-reviewed journal evidence addressing transparency, interpretability, explainability, robustness, safety, privacy, security, fairness, accountability, governance, and meaningful human oversight. The review applies predefined questions, eligibility principles, trustworthiness-dimension mapping, claim-level evidence extraction, and cross-dimensional synthesis without undertaking meta-analysis or inventing quantitative screening results. The evidence indicates that transparency and explanation must be assessed in relation to fidelity, stability, completeness, usefulness, stakeholder needs, contestability, recourse, and the practical authority of human overseers. Robustness and safety require evaluation under adversarial conditions, uncertainty, context shift, system failure, and residual risk, while privacy-preserving approaches must still be examined for leakage, cybersecurity vulnerabilities, and governance limitations. Fairness depends on context-sensitive definitions, bias identification, distributional consequences, documentation, responsibility allocation, auditability, and institutional mechanisms for correction. Cross-dimensional analysis reveals persistent tensions between transparency and privacy, explainability and security, automation and human control, and performance and fairness. Important gaps remain in multidimensional evaluation, realistic human-oversight studies, explanation-usefulness validation, lifecycle monitoring, accountability operationalisation, stakeholder participation, and foundation-model assurance. The review contributes an integrated evidence synthesis showing that AI worthiness of trust should be supported through coordinated technical validation, human-centred assessment, institutional governance, and continuing monitoring rather than inferred from any isolated property.