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The rise of the digital era presents mounting difficulties for ideological and political instruction, as the swift dissemination of internet-based public sentiment and the expanding reach of social media frequently outpace conventional pedagogical approaches. Present methodologies suffer from poor precision in detecting risks and lack explicit metrics for assessing the fundamental competencies of instructors. To surmount these challenges, this paper presents a dual-core system framework. Chiefly, the Multi-modal Decision Support System (MADF-SE) merges classroom behavioral identification, semantic evaluation, and blockchain-enabled traceability to enhance both the precision and punctuality of hazard detection. Secondary to this, the Dynamic Capability Evolution Model (DCEM) quantitatively monitors and upgrades the cognitive, practical, and inventive skills of teachers over time. Furthermore, a hierarchical federated learning architecture is deployed to strike a balance between individual privacy safeguarding and inter-collegiate cooperation. Empirical testing and case analyses across more than 30 higher education institutions indicate that the introduced framework cuts the public opinion management timeframe by 50%, elevates instructor engagement in strategic choices from 20% to 60%, and boosts their collective core competencies by greater than 25% on an annual basis. These outcomes highlight a viable and scalable methodology for driving the digital evolution of ideological and political schooling.

This research examined user perceptions regarding the concept of "knowledge" in robotic systems and how this conceptualization diverges from perspectives on human knowledge. Utilizing the Justified True Belief (JTB) framework of epistemology, three experiments were executed to ascertain whether individuals apply identical criteria to a robot's epistemic states as they do to a human's. In Experiment 1, a Gettier-style scenario was presented to participants; their subsequent responses demonstrated that the prerequisites for attributing knowledge vary significantly between human and robotic agents. Experiment 2 investigated the precise threshold at which information is categorized as "knowledge," indicating that users demand a higher degree of direct verification from robots than from humans. Experiment 3 analyzed how these underlying beliefs regarding robotic knowledge, as mapped in Experiments 1 and 2, affect the moral assessments made by users. The findings indicated that when acting as an accomplice to an offense, a robot was assigned less responsibility by users than a human accomplice. Collectively, these outcomes imply that users view a robot's knowledge as rigid, dependent upon direct perception, and inherently distinct from human cognition and inference. These insights offer valuable implications for designing human–robot interaction (HRI) systems, as comprehending user perceptions of robotic knowledge may be critical for fostering trust in future human–robot collaboration.

The fraudulent application of image transformation methods to clinical data represents a severe hazard that can jeopardize patient safety. Consequently, identifying artificial alterations using deep learning frameworks is vital for mitigating the challenges posed by modified medical imagery in clinical environments. The experimental data were partitioned into an authentic fundus dataset and a manipulated dataset. The proposed framework for detecting fundus image modification utilizes a deep learning architecture rooted in a Convolutional Neural Network (CNN) structure, incorporating a concatenation mechanism to achieve rapid processing speeds and minimize the attenuation of input image weights. When evaluating authentic data, the system attained an average sensitivity of 0.08, precision of 1.00, F1-score of 0.99, and an AUC of 0.988. On the manipulated dataset, the framework achieved a sensitivity of 1.00, precision of 0.84, F1-score of 0.92, and an AUC of 0.988. In comparison, a panel of five ophthalmologists demonstrated lower average metrics on the altered images, yielding a sensitivity of 0.71, precision of 0.61, F1-score of 0.65, and an AUC of 0.822. This investigation highlights the potential for mitigating and preventing the complications arising from fabricated medical imagery within healthcare systems. The introduced methodology for identifying altered fundus photographs via a deep learning model outperforms human specialists, proving to be an efficient diagnostic strategy.

Progress in continuous glucose monitoring (CGM) technologies alongside wearable electronics is driving the evolution of automated insulin delivery systems (AIDs) toward fully automated closed-loop configurations. The ultimate goal is to provide safe, individualized, and optimized management of blood glucose concentration (BGC) for patients living with diabetes. Although model predictive control offers an adaptable architecture for constructing AIDs control logic, accurate and efficient BGC regulation demands models capable of accounting for inter- and intra-patient variability as well as perturbation uncertainties. Innovations in artificial intelligence offer novel pathways for creating data-driven, fully closed-loop AIDs. Among these approaches, deep reinforcement learning (DRL) has gained substantial interest owing to its inherent robustness against disturbances. Consequently, this article presents a comprehensive literature review focused on DRL-based BGC control methodologies within AIDs. Initially, this paper systematically evaluates the advantages of deploying DRL frameworks in AIDs. Following this, it delivers an extensive analysis of diverse DRL methods and enhancements designed to surmount obstacles linked to their integration into AIDs, addressing factors such as restricted sample availability, personalization, and safety protocols. Furthermore, an application-centric assessment of DRL-based AIDs control algorithms is provided, highlighting critical hurdles encountered during practical implementation. Lastly, the paper addresses potential remedies for relevant BGC control challenges, outlines prospects for real-world application, and identifies future research trajectories.

Merging Artificial Intelligence (AI) with Business Process Management Systems (BPMSs) has given rise to AI-Augmented Business Process Management Systems (ABPMSs). These modern setups deliver flexible adaptability, instantaneous process refinement, and superior knowledge management workflows. Yet, notable hurdles persist, especially regarding contextual clarity, user buy-in, and the incorporation of behavioral dynamics. This research introduces a novel conceptual model that unifies the Socialization, Externalization, Combination, and Internalization (SECI) knowledge paradigm with Agile workflows (specifically Scrum) and advanced AI capabilities like explainable AI (XAI), process mining, and Robotic Process Automation (RPA). This setup streamlines the formalization, validation, and dissemination of insights through structured, intuitive software and collaborative frameworks, particularly Communities of Practice (CoPs). The system highlights context-sensitive transparency, step-by-step deployment, and iterative refinement to foster productive human–AI partnerships. It delivers both conceptual and practical blueprints for linking AI strengths with corporate knowledge assets. The introduced model eases the upgrade path from legacy BPMSs to advanced ABPMSs by utilizing systematic practices and technologies. This strategy accelerates knowledge distribution and operational refinement, mapped out via exhaustive BPMN 2.0 diagrams.

Machine unlearning is vital for model optimization and privacy preservation, particularly when erasing entire data classes within classification frameworks. Nonetheless, current approaches frequently suffer from excessive computational overhead, such as calculating the inverse Hessian, or necessitate availability of the primary training set, which restricts their real-world utility. To address these limitations, we present Scrub-and-Learn, a category-aware weight adjusting framework engineered for the efficient removal of class-level information. Formulating unlearning as a sequential learning objective, our approach utilizes re-encoded labels from target class samples to steer parameter adjustments, successfully purging targeted information while maintaining the network's remaining capabilities. Empirical evaluations across several benchmarks show that our technique successfully wipes out designated categories—limiting the identification rate to under 5%—while maintaining the accuracy of the kept classes within a 4% variance compared to the baseline model.

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.

Deducing causal protein signalling networks using human immune system cellular data offers a encouraging pathway to decode both fundamental tissue signaling biology and the underlying abnormalities in pathological cells. Consequently, this domain has drawn substantial interest within bioinformatics. More recently, Bayesian network (BN) approaches have become highly prominent for reconstructing causal protein signalling pathways from high-dimensional single-cell datasets. Nevertheless, existing BN algorithms frequently suffer from elevated computational demands and overlook the interplay among signaling molecules across distinct single cells. To address these issues, this paper introduces a novel score-based BN framework named parallel discrete artificial bee colony (PDABC) for uncovering causal protein signalling architectures. Specifically, PDABC leverages a parallelized artificial bee colony framework to explore the search space and identify the globally optimal network topology maximizing the discrete K2 score metric. Performance assessments conducted on multiple synthetic benchmarks, alongside a public multi-parameter fluorescence-activated cell sorter dataset, demonstrate that the proposed PDABC framework exceeds contemporary state-of-the-art techniques regarding both structural accuracy and computational throughput.

While deep convolutional neural networks (DCNNs) have achieved substantial breakthroughs in computer vision, these models are frequently criticized for their lack of interpretability during decision-making. A core issue is that their underlying operational mechanism is generally perceived as a "black-box." To address this, the authors introduce the binary tree structure convolution (BTSC) module and control the activation thresholds of specific neurons to construct an interpretable DCNN framework. First, the authors develop the BTSC module, where every parent node branches into two distinct child layers, which are subsequently integrated into a standard DCNN architecture. The primary benefits of the BTSC module include: 1) child nodes emerging from separate parent nodes operate without mutual interference; 2) parent and child nodes are capable of knowledge inheritance. Second, by managing neural activation states, the authors design an information coding objective that directs neural nodes toward acquiring a predefined, targeted information coding scheme. Empirical evaluations demonstrate that: 1) the decisions executed by both ResNet and DenseNet models can be effectively clarified via the "decision information flow path" (termed the decision-path) established within the BTSC module; 2) this decision-path offers a logical explanation for the decision reversal mechanism (Robustness mechanism) inherent in DCNN models; 3) the reliability of a decision can be quantified by evaluating the alignment between the actual and anticipated decision-paths.

Pre-trained multilingual language models (PMLMs), including mBERT and XLM-R, have demonstrated strong capabilities in cross-lingual transfer. Nonetheless, these models are not explicitly optimized to capture cross-lingual indicators related to sentiment-bearing terms. This limitation disproportionately affects low-resource languages (LRLs), which suffer from under-representation within these architectures. To optimize the fine-tuning of these models for sentiment analysis in LRLs, this paper presents a novel intermediate task fine-tuning (ITFT) approach centered on a sentiment lexicon from a high-resource language (HRL). The authors evaluate this methodology using a 3-class sentiment classification framework across three LRLs: Sinhala, Tamil, and Bengali. The findings indicate that the proposed strategy surpasses standard vanilla fine-tuning of the PMLM, while also performing on par with or exceeding conventional ITFT techniques that utilize HRL sentiment classification datasets.

In recent years, deep learning has emerged as a promising framework for identifying Alzheimer's disease (AD) within clinical neuroimaging. Nonetheless, conventional architectures frequently fail to optimize feature extraction from medical scans and underutilize auxiliary data resources vital for diagnostic categorization. To resolve these challenges, this paper introduces a deep three-dimensional convolutional neural network architecture that integrates attention mechanisms with multi-task learning. An enhanced primary C3D network is deployed to generate initial, coarse low-level feature maps. This design implements a novel convolutional block tailored to capture the specific structural properties of magnetic resonance imaging (MRI) data, along with a secondary block configured to compute localized attention weights for distinct pixel coordinates within the feature map, which are subsequently combined with the map's output. Thereafter, a series of fully connected layers implements multi-task learning, producing three distinct outputs that encompass the primary classification objective. During the training phase, the two auxiliary outputs leverage backpropagation to refine and enhance the primary classification performance. Empirical results demonstrate that this proposed framework outperforms existing state-of-the-art models for AD detection, yielding superior classification accuracy and enhanced performance metrics on the Alzheimer's disease Neuroimaging Initiative dataset. These outcomes illustrate a compelling direction for prospective diagnostic classification research.

Offline reinforcement learning (RL) focuses on training policies exclusively from passively gathered datasets, establishing it as a data-driven decision-making framework. A major obstacle in offline RL is the phenomenon of distribution shift, which leads the algorithm to encounter out-of-distribution (OOD) samples. While constraining the divergence between the target policy and the behavior policy can alleviate distribution shift, this strategy can unduly restrict the target policy and degrade algorithmic performance, because it fails to explicitly differentiate between in-distribution and OOD samples. Furthermore, accurately learning and representing a multi-modal behavior policy is highly challenging when datasets are compiled from multiple distinct behavior policies. To resolve these limitations, the authors tackle the distribution shift problem through implicit policy constraints utilizing energy-based models (EBMs), bypassing the explicit modeling of the behavior policy. EBMs offer a powerful framework for characterizing complex multi-modal distributions while effectively distinguishing between in-distribution and OOD samples. Experimental outcomes demonstrate that this approach substantially surpasses explicit policy constraint methods and other baseline techniques. Furthermore, the trained energy model serves as an effective indicator of OOD encounters, providing alerts for potential failures.

Laminectomy represents one of the most frequently performed posterior spinal procedures. Because the lamina is situated in close proximity to critical neural structures, any operative damage can induce profound complications, potentially culminating in paralysis. To mitigate these risks and associated adverse events, surgical robotics and ultrasonic bone scalpels have been integrated into spinal laminectomy procedures, prompting extensive academic investigation into methods for detecting bone tissue states. At present, virtually all state-recognition methodologies rely on sensor signals acquired via high-precision instrumentation integrated into the end-effectors of surgical robots. Nevertheless, previous approaches have struggled to achieve highly accurate identification of spinal bone tissue states. In an innovative departure, this study frames the identification of bone tissue states as a time-series classification problem. The LSTM-FCN classification algorithm was deployed to analyze multi-sensor fusion signals combining force and cutting depth data, thereby enabling precise categorization of the laminal bone tissue state. Furthermore, experimental validation demonstrated that this approach achieves an accuracy of 98.85% in identifying bone tissue states during porcine spinal laminectomy. Crucially, the maximum penetration depth can be restricted to within 0.6 mm, confirming its safety and viability for practical clinical application.

Anticipating the movement of nearby traffic participants allows automated vehicles to formulate safe and efficient navigation strategies. This undertaking is highly intricate, given that the conduct of road users is shaped by numerous variables, resulting in a substantial variety of potential future paths (multi-modality). The majority of previous methodologies designed to tackle multi-modal trajectory forecasting rely on intricate machine learning architectures that offer minimal interpretability. Furthermore, the performance measures utilized in contemporary benchmarks fail to assess every dimension of the issue, particularly regarding the variety and validity of the generated paths.The authors intend to progress the creation of dependable trajectory forecasting systems by aligning with specific tenets established for Trustworthy Artificial Intelligence. The primary emphasis is directed toward assessment benchmarks, structural resilience, and the explainability of model outputs. Initially, existing appraisal metrics undergo a detailed examination to pinpoint the major deficiencies in current benchmarks, leading to the introduction of a new, comprehensive assessment framework. Following this, a methodology to evaluate spatial and temporal resilience is developed by replicating sensor noise within the perception pipeline. To improve output interpretability and produce more equitable outcomes across the proposed evaluation metrics, an intention forecasting layer is introduced that can be integrated into existing multi-modal trajectory models. The utility of this configuration is analyzed via a user study that investigates distinct graphical representations of multi-modal paths and driver intents. The introduced methodology and subsequent insights offer a meaningful advancement toward constructing dependable trajectory forecasting frameworks for automated vehicles, shifting the discipline toward elevated levels of safety and operational dependability.

The intricate and heterogeneous nature introduced by the decentralized architecture of the modern electric information collection system has increased the difficulty of establishing verification index systems and quality appraisal frameworks. Furthermore, discrepancies between distinct components complicate the realization of impartial and scientifically sound validation. To address these challenges, an integrated construction quality evaluation framework grounded in multi-scenario comprehensive weighted assessments was developed by exploiting graph neural networks and siamese networks. Initially, a graph neural network was formulated utilizing terminal data from the regional branches' electricity utilization information collection system alongside its link topological framework. This network was subsequently implemented at the headquarters level to directly capture terminal information and produce mirrored network inputs from the regional branch data, facilitating the real-time acquisition of diverse system performance metrics. Ultimately, the degree of correspondence between the headquarters and regional branch datasets was computed via siamese networks to derive precision compensation weights for validating and analyzing various parameters, thereby yielding comprehensive weighted quality appraisal indicators for the next-generation electric information collection systems at the branch level. Three distinct service types—namely, power data acquisition, load forecasting, and task scheduling—served as the experimental environments. The empirical findings demonstrated that the multidimensional comprehensive weighted quality appraisal, augmented by the accuracy compensation derived from the siamese network, achieved business construction quality evaluation ratings of 97.92%, 95.95%, and 99.96% within the branch system. These metrics closely approximate the quality ratings obtained through manual assessment, confirming that this methodology can reliably validate the construction quality of modern systems implemented across branches.

Deep learning (DL) architectures have established state-of-the-art capabilities across numerous fields. Unpacking their internal operations and reasoning paths is critical due to their high architectural complexity and opaque, black-box character, particularly in high-stakes fields like healthcare. Visual analytics (VA) integrated with DL methodologies are frequently employed to extract data insights, yet they regularly face challenges related to visual clutter (VC). This research introduces a streamlined neural network (NN) view design engineered to mitigate visual clutter while clarifying DL model elements for field specialists and everyday users. We applied clustering techniques to aggregate hidden neurons according to similarities in their activation profiles. This framework accommodates both macroscopic and micro-level perspectives of the generated neuron groups. Utilizing a tabular medical dataset as a practical case study, this clustered visualization strategy successfully decreased visual clutter by 54% for neuron graphics and by 88.7% for their interconnections, while successfully exposing correlated neuron activations developed throughout model training.

The reliability and security of convolutional neural networks (CNNs) have attracted growing attention, particularly in domains where safety is paramount, such as clinical medicine. Even though CNNs demonstrate high competency in categorizing images, their outcomes frequently exhibit vulnerability to minor image adjustments that remain imperceptible to human observers. Consequently, an altered, degraded image can appear identical to the authentic image to a person, yet mislead the CNN into making an incorrect classification. Throughout this study, these altered images are termed adversarial images. A widespread approach for producing adversarial images involves propagating the loss gradient backward to alter the source image. Typically, perturbations have been established using solely the gradient direction and a fixed step size (referred to as the fast gradient sign method, or FGSM), or by executing the FGSM iteratively to craft more potent perturbations capable of altering the model's categorization (i-FGSM). Conversely, if the step size is excessively large, the smallest possible image perturbation might be overlooked during the gradient exploration. To discover precise and minimal input image variations necessary to prompt a classification shift, this paper proposes initiating the FGSM with a small step size and dynamically tuning it across iterations. For evaluation, several decay algorithms from existing literature were assessed against a novel method driven by an index monitoring the state of the loss. Ultimately, three separate tracking functions were implemented for comparative analysis. The empirical findings demonstrate that our loss-adaptive decay algorithms successfully identified adversarial examples with a success rate exceeding 90% while introducing fewer distortions to mislead the CNNs.

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 printed circuit board (PCB) serves as a fundamental component across all electronic devices, and the assurance of its fabrication quality exerts a major influence on the final pricing of the product. Consequently, research and development (R&D) focused on optimizing inspection techniques has emerged as a expanding area of global interest. Among the various paradigms utilized for PCB evaluation, visual analysis constitutes the preponderance of contemporary methodologies. To that end, this survey aggregates the most influential investigations conducted over the past quarter-century, outlining their operational frameworks and historical progression up to the present day, with a particular emphasis on the integration of artificial intelligence (AI) methodologies to enhance functional efficacy and minimize operational expenditures. Additionally, this study underscores the vital role that open-access PCB datasets play in facilitating the advancement of these techniques and establishing baseline metrics for benchmarking. Lastly, the current difficulties and obstacles confronting the discipline are delineated and analyzed.

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.

The critical nature of explainable artificial intelligence and machine learning (XAI/XML) is gaining widespread recognition. This field focuses on uncovering how input variables influence automated choices, identifying systemic algorithmic bias, and detecting vulnerability to anomalies in data. Much of the existing literature centers on creating post hoc explanations tailored for opaque, black box architectures. These secondary techniques introduce novel vulnerabilities to error without correcting the fundamental flaws of the core model. Conversely, the development of inherently transparent systems receives far less attention. This paper introduces an inherently interpretable strategy rooted in the principles of ensemble learning, designated as the League of Experts (LoE) framework. We construct the initial theoretical foundations and subsequently derive a flexible, modular meta-algorithm. Although our presentation focuses primarily on classification tasks, the LoE framework adapts identically to regression scenarios. For classification specifically, we deploy traditional decision trees to form classifier ensembles as a concrete implementation. This paradigm simplifies the extraction of human-comprehensible decision rules for the underlying classification task, yielding an independent rule-induction system called RuleLoE. Beyond evaluating 12 KEEL classification datasets, we validate the LoE framework using two benchmarks from highly critical domains—healthcare and finance—to demonstrate its utility. The predictive capability of LoE matches contemporary state-of-the-art classification algorithms regarding both accuracy and rule coverage. Furthermore, LoE outputs a highly transparent group of decision rules featuring customizable complexity to map out the target classification problem. LoE provides a dependable approach for handling classification and regression problems. Its predictive precision is highly suited for environments where discovering root causal mechanisms is the primary objective, rather than merely maximizing predictive or classification performance.

Among the most critical and fatal medical conditions are brain malignancies, making early identification vital for enhancing survival rates. Currently, magnetic resonance imaging (MRI) stands as the premier diagnostic tool for early detection because of its exceptional capability to capture soft tissue details. Nevertheless, relying solely on human interpretation of brain MRI scans introduces risks of oversight, which are frequently exacerbated by the fatigue and varying expertise of radiologists. To mitigate these limitations, computer-aided diagnosis (CAD) systems have taken on an increasingly vital role. Leveraging state-of-the-art computer vision methodologies like deep learning, these systems generate highly precise diagnoses from clinical imagery, thereby boosting both precision and consistency in clinical settings. This study introduces an innovative CAD architecture designed for multi-class classification of brain malignancies. The system evaluates six pre-trained deep learning networks as its foundational architecture, integrating robust data preprocessing and augmentation protocols to optimize computational throughput. To counter the opaque "black box" nature of deep learning architectures and improve interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated to illuminate the network's deductive steps when identifying anomalies in MRI data. Furthermore, an accessible, web-based Brain Tumor Detection System was constructed using the Streamlit framework, highlighting its utility for clinical environments and offering meaningful support to medical professionals. All experimental simulations were carried out using an open benchmark repository, proving that the introduced system delivers state-of-the-art diagnostic capabilities, achieving accuracy levels near 99% with the ResNet-50, Xception, and InceptionV3 architectures.

While large language models (LLMs) demonstrate exceptional predictive capabilities, their internal reasoning mechanisms remain obscure, creating an urgent demand for enhanced interpretability in artificial intelligence. In this paper, we suggest addressing this explanatory deficit by incorporating principles from topological quantum computing (TQC), specifically the anyonic structures derived from S(2)k theories. Anyons serve as an interpolation between fermions and bosons, offering a formal mathematical paradigm that could clarify the latent configurations and decision-making pathways within LLMs. By investigating the relationships between these topological models, token interactions, and contextual dependencies within neural systems, we seek to offer an alternative viewpoint on the emergence of meaning and coherence. Drawing upon insights obtained from ChatGPT alongside an examination of low-level instances of S(2)k models, we propose that the formal machinery of modular tensor categories and topological phases could guide the development of clearer, more stable, and more resilient AI frameworks. This cross-disciplinary approach indicates that quantum-theoretic concepts may provide the foundation for a novel paradigm in explainable AI.

Graph neural networks (GNNs) have been successfully deployed across numerous practical domains, yet the precise mechanisms underlying their operations remain largely opaque. In response to this lack of clarity and to encourage the deployment of reliable AI systems, a variety of GNN explanation tools have been developed. Nonetheless, current interpretation techniques frequently encounter severe bottlenecks, including: (1) explanations that are restricted to localized individual instances; (2) poor generalizability when applied to unfamiliar graph structures; (3) the risk of creating structurally invalid graphs; and (4) confinement to specific task domains (such as only node or only graph classification). To resolve these deficiencies, we introduce a novel interpretation framework, GAN-GNNExplainer, which integrates a generator to construct explanations alongside a discriminator to supervise the production stage, thereby enhancing output dependability. Although advantageous, GAN-GNNExplainer still exhibits limitations in producing highly faithful explanations and shows suboptimal performance when tested on empirical datasets. To rectify these weaknesses, we present ACGAN-GNNExplainer, an advanced iteration that enhances GAN-GNNExplainer by utilizing a more robust discriminator to continuously monitor the generation phase, which yields explanatory outputs that are both dependable and faithful. Comprehensive empirical evaluations conducted on both artificial and empirical graph datasets confirm that our proposed methodologies outperform conventional GNN explanation frameworks.

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.

At present, the majority of healthcare spending is driven by chronic patients who experience multiple readmissions due to their underlying conditions. Implementing individualized prevention frameworks could optimize the clinical management of this demographic. This study aimed to generate localized predictive models utilizing interpretable machine learning approaches to provide early identification of individual unscheduled hospital readmissions. To achieve this, a retrospective case-control investigation was performed, utilizing patient readmission data spanning 2018–2019. Following the cleaning and curation of the initial data repository (n = 76,210), the final sample size comprised n = 29,026 participants. A machine learning analysis evaluated multiple algorithms, employing unscheduled hospital readmission as the dependent variable, alongside local model-agnostic interpretability techniques.The results demonstrated a 13% rate of unscheduled hospital readmissions. Statistically significant variations were identified regarding patient age and length of hospital stay (p < 0.001 in both instances). A logistic regression framework highlighted chronic therapy (odds ratio: 3.75), a history of diabetes mellitus (odds ratio: 1.14), and length of stay (odds ratio: 1.02) as statistically meaningful predictors. Machine learning algorithms demonstrated superior performance concerning sensitivity and complementary evaluation metrics. Within this analytical framework, length of stay and patient age emerged as the primary determinants for forecasting unscheduled readmissions. Notably, other clinical indicators, such as documented allergies and histories of adverse drug reactions, also demonstrated predictive relevance. Individualized prognostic models exhibited high sensitivity. In conclusion, this investigation identified critical determinants governing unscheduled hospital readmissions, highlighting the prominence of age and hospitalization duration. A personalized risk assessment framework capable of predicting readmissions with high accuracy was established. Future investigations should incorporate a broader array of clinical parameters to further optimize the predictive capacity of this model.

Recent investigations have demonstrated that deep neural networks are susceptible to subtly modified input data. This paper introduces a novel untargeted white-box adversarial attack termed the dynamic programming-based sub-pixel score method (SPSM) attack (DPSPSM). This technique modifies conventional gradient-based white-box adversarial methods, which are typically restricted by a predetermined Hamming distance, by incorporating a dynamic programming framework. The method is driven by a pixel score metric approach, the SPSM, which is developed herein. Unlike traditional gradient-based adversarial attacks that introduce nearly imperceptible modifications across the entire image, DPSPSM operates rapidly and maintains robustness by altering only a restricted number of input pixels. The introduced algorithm quantizes the gradient updates using a score computed for every pixel, which aggregates the contributions across all channels. Experimental outcomes indicate that the DPSPSM successfully misleads the model, achieving a success rate of 30.45% on the CIFAR-10 test set and 29.30% on the CIFAR-100 test set.

Within a student learning community, the generation of new knowledge represents a vital mechanism for expanding collective understanding and driving original synthesis. Students assume critical responsibilities when they target knowledge deficiencies and engage in cooperative efforts to refine communal concepts emerging from dialogue. Nonetheless, the standard of these concepts can frequently be constrained by resource deficits or a narrow spectrum of viewpoints. The introduction of generative Artificial Intelligence and large language models (LLMs) into educational settings has unlocked new opportunities for analyzing and enriching idea-focused debates that were formerly unachievable. Maximizing the utility of LLMs necessitates specialized competencies in prompt engineering, particularly regarding the in-context methodology termed Chain-of-Thought (CoT) to formulate and scaffold progressive concepts within student conversations. This investigation gathered a dataset of 721 conversational entries—encompassing 272 applicable question–answer dynamics and 149 distinct discussion tracks—produced by 31 participants during a 2-day student Knowledge Building Design Studio (sKBDS). The researchers augmented student inputs utilizing the CoT technique and subsequently evaluated the AI-generated outputs against the original student assertions. The outcomes are demonstrated through two explicit conversational tracks, confirming that CoT-configured prompts enable LLMs to output responses that actively advance malleable concepts during knowledge construction. This inquiry examines data from genuine classroom discourse and offers actionable insights for both educational research and instructional implementation.

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.