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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.

In recent years, considerable attention has been directed toward clarifying complex, opaque systems, commonly referred to as black-box models, including deep neural networks and random forests. So-called model-agnostic techniques generally approximate the outputs of these non-transparent systems using an interpretable architecture, ignoring the internal mechanisms of the primary black-box system itself. Consequently, it remains an important inquiry whether the straightforward training of transparent, white-box architectures ought to be favored over the post-hoc approximation of these opaque models. This study presents the findings of an empirical investigation comparing post-hoc explanatory methods against inherently interpretable architectures across multiple datasets, focusing on both rule-based and feature-based frameworks. The empirical outcomes indicate that directly trained transparent models frequently approximate black-box models with an accuracy that matches or exceeds that of post-hoc surrogate models, despite lacking direct access to the underlying black-box structure.

Identifying vehicles within parking zones yields valuable insights into the spatial and temporal distribution of parking spot utilization. Because parking monitoring is conventionally conducted through manual observation, its temporal granularity remains restricted by substantial personnel expenditures. This study leverages simulated datasets alongside transfer learning to construct a dependable, real-world model tasked with detecting and categorizing vehicles via single-beam LiDAR within a roadside parking context. This work introduces a synthetically augmented transfer learning methodology designed for LiDAR-centric vehicle detection, alongside the deployment framework for synthetic LiDAR datasets. A synthetically augmented transfer learning technique was implemented to expand the constrained real-world dataset and facilitate the formulation of data management workflows. Furthermore, incorporating this synthetically augmented transfer learning methodology enhances both the resilience and the overall precision of the network. Empirical tests demonstrate that this approach enables the rapid deployment of models tailored for vehicle detection using a LiDAR sensor.

The integration of deep learning (DL) algorithms offers significant potential to enhance healthcare applications. Specifically, DL has advanced medical imaging diagnostics, therapeutic interventions, and disease management. However, deploying deep learning algorithms on sensitive medical imagery introduces substantial challenges regarding data security and patient privacy. Optimizing medical imaging performance while simultaneously ensuring patient anonymity remains a complex task. Consequently, privacy-preserving techniques designed for deep learning model training and inference are increasingly gaining traction. Currently, these medical image sequences are interpreted using state-of-the-art computer-aided detection/diagnosis (CAD) methodologies. Yet, frameworks that require uploading medical photographs to external servers inherently create privacy vulnerabilities.To address this, this article introduces a convolutional Bi-LSTM network designed to evaluate fully homomorphic-encrypted (HE) time-series medical images. Within this framework, convolutional blocks extract selective spatial features from the secure image sequences, while analytical sequence layers based on Bi-LSTM encode the temporal data. To optimize efficiency and minimize misdiagnoses, a weighted unit and sequence voting layer incorporates spatial data using varying weights. The effectiveness of this framework is demonstrated using two rigorous public benchmarks: the CheXpert and BreaKHis datasets. The proposed technique surpasses several competing methods, achieving an accuracy exceeding 0.99 across both datasets. These findings indicate that the introduced methodology can successfully extract visual representations and sequential dynamics from encrypted medical image series, thereby safeguarding patient privacy while maintaining high performance in medical image analysis.

The climate system is heavily influenced by aerosols through direct and indirect pathways, notably the absorption and scattering of radiant energy. Beyond climate impacts, these particles compromise atmospheric visibility and jeopardize public health. Specifically, human populations face exposure to fine particulate matter PM2.5, a major driver of respiratory and cardiovascular pathologies. Long-term modifications in PM levels are generally governed by emission shifts and meteorological patterns, whereas short-term fluctuations are predominantly dictated by immediate weather dynamics. Surface variations in PM2.5 levels are further driven by environmental variables such as temperature, wind velocity, relative humidity, and vegetative coverage.Air quality forecasting has advanced significantly through the application of machine learning. This research explores PM2.5 estimation by utilizing these environmental and meteorological metrics as spatiotemporal inputs. This study evaluates seven years (2014–2021) of in situ PM2.5 measurements gathered from five distinct monitoring stations across Singapore, establishing a baseline dataset for spatial modeling.The primary contribution of this paper is a novel predictive architecture tailored for temporal analysis, utilizing Random Forest (RF), Gradient Boosting (GB) regression, and the Tree-based Pipeline Optimization Tool (TP) Auto ML framework, which optimizes via a meta-heuristic genetic algorithm. The TP model demonstrated strong predictive capabilities, achieving a peak Global Performance Index (GPI) of 7.4 in August 2016 and a minimum value of −0.6 in June 2019. These metrics highlight the robust nature of the TP framework; its negative values remained superior to competing models, indicating less pessimistic errors. Finally, the underlying logic of these machine learning systems is clarified using eXplainable Artificial Intelligence (XAI) methods, which validate the integrity of feature importance and offer deeper insights into the cyclical variations of PM2.5 concentrations.

The integration of digital architectures powered by artificial intelligence (AI) into daily routines is expanding at an unprecedented rate. Within this paradigm, the twin dilemmas of accountability and openness endure as critical issues that have yet to be satisfactorily resolved. This study intends to delineate the specific domains of opacity within digital ecosystems and formulate actionable frameworks for mitigating these visibility challenges. Initially, by synthesizing existing scholarship, interrogating the dynamics of platformization, establishing a cross-disciplinary conceptualization of AI, and applying ethical AI frameworks, we map the dimensions of opacity inherent in digital environments. Subsequently, having mapped these obscure domains, we move beyond a standard literature synthesis to offer an independent perspective on mitigating these systemic vulnerabilities. Our evaluation of the literature indicates the existence of three primary spheres of opacity within digital networks: an inadequate understanding of platform content originators; a deficiency of visibility regarding the background labor force, their explicit operational outputs, and their occupational environments; and a lack of openness concerning the engineering and oversight of algorithmic models. Given the abundance of abstract, high-level ethical tenets in current scholarship that resist pragmatic implementation, this investigation represents an effort to reconcile theoretical concepts with operational execution.

We introduce and execute a transparent machine learning classification model designed for Explainable AI (XAI) that leverages expressive Boolean formulas. Potential domains of use include credit risk evaluation and medical diagnostics. The system utilizes a Boolean formula to establish a rule with adjustable complexity (or interpretability) for categorizing input data. This formula accommodates any operator applicable to single or multiple Boolean variables, offering superior expressivity relative to more rigid rule- and tree-based methodologies. Native local optimization methods are used to train the classifier, allowing for an efficient search of the feasible formula space. Fast Integer Linear Programming (ILP) or Quadratic Unconstrained Binary Optimization (QUBO) solvers—potentially enhanced by custom hardware or quantum systems—can identify shallow rules. By executing non-local moves that optimize across subtrees of the complete Boolean formula, we merge the expressivity and efficiency of the native local optimizer with the rapid processing capabilities of these devices. We present comprehensive numerical benchmarking data against multiple baseline models using recognized public datasets. The findings indicate that the native local rule classifier performs competitively with alternative models. Incorporating non-local moves yields comparable outcomes with a reduced number of iterations. Consequently, deploying specialized or quantum hardware could provide substantial acceleration by rapidly generating non-local moves.

Verbal autopsies (VA) serve as a frequent mechanism within Low- and Medium-Income Countries (LMIC) to ascertain the cause of death (CoD) for individuals who pass away outside of clinical environments, relative to the established international gold standard of physician medical certification. Field interviewers gather detailed testimonies from the families of the deceased concerning the events and circumstances preceding the fatality. This gathered information is documented in text format, referred to as VA narratives. These textual records contain granular details suitable for identifying the CoD. Nonetheless, this methodology continues to be a manual operation characterized by high costs, inconsistency, prolonged processing times, and subjectivity (making it susceptible to errors), among other limitations. Consequently, these disadvantages undermine the VA reporting infrastructure, which is otherwise crucial for defining public health agendas and populating civil registration frameworks.To address this operational gap, the present study introduces interpretable deep learning (DL) methodologies designed to analyze VA narratives and deliver CoD predictions in an efficient, transparent, economical, and precise manner. The verification of our DL models is conducted via optimization and performance accuracy machine learning (ML) curves mapped against the volume of training samples. Our models achieved training set accuracies of (LSTM = 76.11%, CNN = 76.35%, and SEDL = 82.1%), validation accuracies of (LSTM = 67.05%, CNN = 66.16%, and SEDL = 82%), and test set accuracies of (LSTM = 67%, CNN = 66.2%, and SEDL = 82%). Additionally, Local Interpretable Model-agnostic Explanations (LIME) are utilized to enhance the interpretability of the outcomes, thereby fostering confidence in automated healthcare solutions.This work offers robust deep learning strategies for CoD determination via VAs, where the stacked ensemble deep learning (SEDL) model achieved optimal performance, outperforming both Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN). Our empirical evidence suggests that ensemble DL paradigms can be successfully integrated into the CoD classification workflow to assist medical experts in reaching a diagnosis. Ultimately, this integration aims to compress the diagnostic turnaround time for physicians reviewing the narratives, while simultaneously reducing operational expenses and mitigating human error. The scope of this study was constrained by the volume of training data required for model optimization and the substantial lexical diversity present within the unstructured text.

Although Reinforcement Learning (RL) exhibits significant potential for refining intricate control and decision-making systems, Deep Reinforcement Learning (DRL) suffers from a distinct lack of explainability. This limitation restricts its deployment within highly regulated domains, including manufacturing, finance, and healthcare. The opacity of DRL decision-making introduces structural challenges that impede operational efficiency and resource management—an issue that intensifies alongside ongoing algorithmic advancements. While current trends favor transitioning from traditional Experience Replay configurations to A3C architectures, the latter imposes substantially greater resource demands. Furthermore, despite continuous efforts to refine selection mechanisms within Experience Replay, repository capacities generally remain elevated. In this study, we evaluate a Deep Convolutional Q-learning agent across 20 Atari environments while deliberately downsizing the Experience Replay capacity from 1×106 to 5×102. Our findings indicate that lowering the capacity from 1×104 to 5×103 does not meaningfully degrade performance rewards, thereby establishing a viable strategy for resource-efficient DRL frameworks. To clarify agent choices and correlate them with inherent game mechanics, we introduce a novel methodology: visualizing the Experience Replay mechanism utilizing the Deep SHAP Explainer. This framework enhances domain comprehension and delivers transparent, interpretable accounts, though capacity constraints must be managed carefully to mitigate overfitting. Ultimately, this research validates the practicality of minimizing Experience Replay storage and supports the integration of transparent explanations via the Deep SHAP Explainer to advance resource conservation.

Machine learning (ML) techniques have been applied in diverse manners to combat the COVID-19 pandemic. Predictive ML models have been constructed, for example, for both diagnostic and prognostic objectives, utilizing varying data modalities such as textual, visual, and structured formats. Because of the distinct characteristics of this disease and its temporal transformation, a comprehensive understanding of the central elements driving the clinical trajectory of COVID-19 in individual patients remains incomplete. Throughout all phases of this study, a medical specialist was actively engaged via a human-in-the-loop framework. This collaborative element represents a vital yet frequently overlooked component of the ML and knowledge extraction (KE) workflow. Our findings indicate that explainable artificial intelligence (XAI) can offer substantial reinforcement to this dimension of ML and KE. This investigation centered on employing ML for knowledge extraction across two distinct operational scenarios. In the initial scenario, we sought to determine if integrating data regarding the dominant COVID-19 variant affects the predictive performance of the ML models. In the subsequent scenario, we concentrated on prognostic classification models designed to predict a patient's requirement for intensive care unit admission, integrating various XAI methodologies. We implemented nine distinct ML techniques, specifically XGBoost, CatBoost, LightGBM, logistic regression, Naive Bayes, random forest, SGD, SVM-linear, and SVM-RBF. The predictive quality of these models was evaluated using precision, accuracy, and AUC metrics. Following this evaluation, knowledge extraction was performed on the highest-performing models through two discrete pathways: (a) automated feature extraction using forward stepwise selection (FSS); and (b) the identification of attributes and their interactions using model explainability tools. Both sets of features were evaluated against a baseline configuration of attributes selected a priori by medical professionals using domain expertise. Our empirical tests demonstrated that incorporating data on the prevailing COVID-19 variant did not alter the performance of the generated ML models. Furthermore, the results indicated that human medical experts displayed significantly higher precision in identifying meaningful attributes compared to the automated FSS approach. Explainability tools identified nearly identical sets of attributes as the medical expert, while additionally uncovering noteworthy attribute interactions that the specialist subsequently evaluated from a clinical perspective. The outcomes of this investigation and their broader implications are examined.

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.

The expansion of machine-learning frameworks into sectors with significant end-user consequences has heightened concerns regarding the equity of trained models, particularly the necessity of preventing biases that disadvantage distinct demographic sub-groups. The majority of current bias-mitigation techniques adjust the relative weight of data observations during the pre-processing phase. Because fairness depends heavily on context, we support an interactive machine-learning strategy that empowers human supervisors to offer iterative guidance for model refinement. To this end, we adapt the explanatory interactive machine-learning framework Caipi for equitable machine learning. This modified approach, FairCaipi, integrates human-in-the-loop interventions regarding both model predictions and underlying explanations to enhance model equity. Empirical outcomes indicate that FairCaipi outpaces a state-of-the-art pre-processing bias mitigation strategy in both the equity metrics and the predictive accuracy of the resulting machine-learning framework. We show that FairCaipi serves to expose and minimize algorithmic bias in machine-learning architectures while simultaneously identifying underlying human bias.

This study develops an expanded framework for extracting classification rules from neural network architectures applied to tabular data. We introduce alternative designs for learning Boolean logic operators by configuring neurons with continuous weights, including the integration of trainable NAND gates. This unified formulation ensures that distinct logical operations are trained under a singular mathematical approach, allowing developers to directly deploy established sparsity-inducing optimization routines—such as reweighted L1 regularization—to simplify the resulting network into concise rule sets. Furthermore, by utilizing De Morgan’s law, we construct an alternative network topology based on these trainable NAND components to create a NAND-NAND structure rather than the traditional AND-OR setup, both of which easily convert into discrete decision rules. Empirical evaluations show that these revised methodologies produce highly competitive decision rule models that match state-of-the-art predictive accuracy on tabular datasets.