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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.

Global emergency departments and urgent care facilities regularly experience prolonged waiting periods that surpass patient expectations, primarily driven by unexpected spikes in patient volumes. The resulting delays caused by insufficient staffing during these high-demand intervals have been associated with detrimental clinical consequences. Prior investigations into predicting patient volumes have predominantly relied on traditional statistical approaches. Furthermore, these efforts have mainly focused on short-range predictions, which offer minimal utility for the long-term scheduling of medical staff. This paper contributes to a growing domain of research aimed at investigating the capacity of machine learning frameworks to produce precise patient volume projections. Utilizing a decade's worth of data from two major urgent care centers, this study constructs long-range patient flow predictions extending up to one quarter in advance by evaluating a selection of modern, high-performing algorithms. A unique aspect of this research is the integration of eXplainable Artificial Intelligence (XAI) frameworks, specifically LIME and Shapley, to conduct a comprehensive assessment of internal model dynamics that are typically obscured. These diagnostic instruments allowed us to examine how effectively the predictive frameworks adjusted to volatile demand shifts induced by COVID-19 pandemic lockdowns, while also determining the most significant predictor variables to gain deep operational insights. The empirical findings indicate that an innovative ensemble combining gradient boosting techniques with advanced univariate frameworks like Prophet yielded the most precise and dependable performance overall. On average, this methodology produced a 16%–30% improvement over the current internal forecasting techniques utilized for predicting daily patient volumes 90 days into the future.

Unchecked discharges of untreated water from combined sewer systems pose critical hazards to public health and ecosystems. To address this, regional sanitation authorities like the Metropolitan Sewer District of Greater Cincinnati (MSDGC) have begun collecting massive streams of hydrological telemetry, looking toward deep learning (DL) methodologies—specifically recurrent neural networks (RNN)—to forecast these spill events. Crucially, determining whether a DL model is truly suitable for this domain requires a structured analysis of the environment in which it operates. This paper introduces a requirements engineering methodology grounded in problem frames to map out and organize stakeholder concerns. This approach systematically evaluates real-world conditions where standard assumptions of high-quality data break down, translating these insights into actionable software testing benchmarks known as metamorphic relations, which govern both data input adjustments and output validations. Deploying this methodology within MSDGC’s operational context provides a rigorous, standardized baseline to assess how effectively competing RNN designs fulfill core operational mandates.

The reliability and safety of an entire hydraulic system are compromised by the occurrence of cavitation in axial piston pumps. Because vibration signals can indicate cavitation states within these pumps, they have been increasingly coupled with machine learning techniques for detection purposes. Nevertheless, raw vibration signals captured under actual operating conditions are frequently corrupted by noise, complicating the precise identification of cavitation within noisy environments. To address this issue, this study introduces an intelligent methodology designed to identify cavitation in axial piston pumps under high-noise conditions. Initially, a convolutional neural network (CNN) is trained utilizing spectrogram images that are converted from raw, unstructured vibration datasets across varying cavitation states. Subsequently, the gradient-weighted class activation mapping (Grad-CAM) technique is applied to highlight and visualize the class-discriminative zones within these spectrogram images. Ultimately, a novel image processing approach leveraging the Grad-CAM heatmap is introduced to automatically filter out background noise and amplify the key class features in the spectrograms. The empirical outcomes demonstrate that this strategy substantially boosts the diagnostic efficacy of the CNN framework under noisy conditions. Specifically, the accuracy of cavitation condition classification is elevated from 0.50 to 0.89 at a signal-to-noise ratio of 4 dB, and from 0.80 to 0.92 at 6 dB.

Artificial intelligence (AI) has swiftly emerged as a core technology for securing a competitive advantage. Nonetheless, there are escalating anxieties regarding bias within AI models, given that AI developers are susceptible to introducing bias both inadvertently and intentionally. Utilizing a qualitative methodology, this study explored how AI developers can actively foster the creation of equitable AI models. The primary results indicate that the danger of bias stems predominantly from a deficiency in social and gender diversity within AI development teams, alongside pressure from AI managers to rapidly deliver highly anticipated outcomes. Furthermore, the integrity of AI developers is critical, as they possess the ability to hide biases from management and other relevant AI stakeholders. The testing phase preceding model deployment is also prone to bias because it seldom reflects the diverse social demographics that could be impacted.This research offers actionable recommendations across four core dimensions: governance, social, technical, and training and development procedures. Responsible organizations must implement deliberate measures to guarantee that their AI developers follow equitable protocols during AI creation; AI developers need to put ethical considerations first and evaluate the societal impacts of their models; collaborative partnerships ought to be formed among AI developers, AI stakeholders, and the communities likely to be affected by these models; and AI developers must emphasize transparency and explainability within their models while ensuring robust bias testing and remediation prior to deployment. Lastly, providing emotional intelligence training to AI developers is recommended to equip them for constructive dialogue with stakeholders outside the immediate development team.

A multitude of studies demonstrate that robust predictive models for identifying microorganisms can be successfully developed by combining Raman micro-spectroscopy with chemometric techniques, frequently yielding highly accurate outcomes. Progress in machine learning alongside increased software availability has simplified the process for researchers to construct predictive frameworks from intricate data arrays. Conversely, the underlying reasons for the high accuracy of these classifications remain under-investigated. This study analyzes Raman spectroscopic datasets from fungal spores and carotenoid-producing microorganisms to demonstrate that successful differentiation is frequently independent of peak positions or subtle variations in spectral band ratios caused by minor biochemical differences among the organisms. Instead, distinctive baseline variations in biochemically related microorganisms, which may be amplified by specific data preprocessing routines, play a critical role; furthermore, seemingly neutral spectral intervals can significantly influence a convolutional neural network. By applying a technique known as Gradient-weighted Class Activation Mapping, we investigate the internal mechanics of convolutional neural networks within microbiological domains to determine the precise Raman spectral bands driving successful classification.