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Breast cancer represents a leading cause of mortality among women. Consequently, early detection is crucial for effective screening and reducing the overall death rate. Therefore, the development of computer-aided diagnosis systems is essential for identifying breast cancer in its initial stages. Because of its affordability, ultrasound serves as a vital examination tool for breast cancer diagnosis. Although numerous learning-based methods have recently been introduced to categorize breast cancer using the breast ultrasound imaging (BUSI) dataset, manual processing remains a difficult and highly time-consuming task. This study presents an XAI-based framework utilizing an EfficientNet-integrated ResNet deep network to precisely categorize breast cancer as either benign or malignant. Initially, data augmentation is applied to expand the training sample size. To achieve this, three-pixel flip mathematical equations are implemented: horizontal, vertical, and 90°. Subsequently, two pre-trained deep learning models are utilized, selectively skipping specific layers before fine-tuning. These adjusted models are then trained via a deep transfer learning process to extract features from their deeper layers. The performance of these trained models is evaluated utilizing explainable artificial intelligence (XAI). Following this, a novel feature selection approach known as cuckoo search controlled standard error mean is introduced, which is grounded in the cuckoo search algorithm. This method isolates the optimal features, which are then combined using a novel parallel zero-padding maximum correlated coefficient fusion technique. Finally, the selection algorithm is re-applied to the integrated feature vector prior to classification via machine learning algorithms. Experimental evaluations of the proposed framework, performed on the publicly accessible BUSI dataset, achieved accuracies of 98.4% and 98% across two distinct experiments. Comparisons with contemporary techniques demonstrate that the proposed framework offers enhanced accuracy. Furthermore, it requires less execution time compared to the standalone original deep learning models.

Recently, significant efforts have been dedicated to enhancing anomaly detection methods, especially concerning high-dimensional and distributed clinical datasets. The analysis of patient information in clinical environments highlights a strong emphasis on improving diagnostic precision, tailoring treatment strategies, and maximizing resource efficiency to boost health outcomes. However, this field encounters distinct difficulties, including sporadic data gathering, variable data quality, and structural differences unique to individual patients. To overcome these obstacles, this paper introduces an innovative hybrid framework that merges stochastic and heuristic techniques for detecting anomalies in clinical patient data. This approach leverages an HPO-optimized Density-Based Spatial Clustering of Applications with Noise to group patient exercise data, thereby streamlining the detection of abnormalities. Following this, an Interquartile Range-based stochastic technique filters out untrustworthy data points, guaranteeing that healthcare practitioners and diagnostic systems are supplied with only highly relevant and precise data. The main goal of this research is to provide medical experts and scholars with a reliable instrument for handling vast, high-dimensional clinical datasets, allowing for the successful separation and elimination of deviant data observations. Additionally, an advanced regression model was constructed utilizing Automated Machine Learning (AutoML) to evaluate the effectiveness of the ensemble technique for detecting abnormal patterns. Multiple statistical error evaluation methods are employed to verify the success of this hybrid strategy in conjunction with AutoML. Empirical findings indicate that applying this cutting-edge hybrid framework to patient rehabilitation data significantly boosts AutoML performance, yielding an average improvement of 0.041 in the score, thereby outperforming conventional regression models.

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

Modern technological innovations and breakthroughs in the medical sector have significantly enhanced life expectancy. While smart healthcare systems are currently being transformed by artificial intelligence (AI) and the Internet of Things (IoT), the confidentiality and security of medical data continue to be a critical challenge. Reversible data hiding (RDH) is extensively investigated within medical environments to facilitate secure information transmission, as well as in fields such as cloud computing and satellite imagery transfer. The transmission of medical images serves as a cornerstone of the smart healthcare industry.For clinical images, even a marginal distortion during reconstruction can result in diagnostic errors by physicians, thereby endangering patient safety. Consequently, while numerous RDH methods have been developed, a limited number focus specifically on clinical imagery, particularly regarding high-quality DICOM formats.The introduced AdaBPE RDH framework provides a robust solution for securely sharing patient health reports (PHRs) and other confidential data with medical professionals. This strategy introduces a methodology that establishes an optimal balance between leveraging smooth pixels for maximized intra-block data embedding and ensuring lossless image reconstruction. Within this framework, an encrypted 16-bit DICOM image serves as the host medium to conceal the patient's confidential data. The algorithm divides the cover image into equally sized, non-overlapping blocks, adaptively embedding data into these encrypted sections based on the underlying characteristics of the original pixel values via an MSB prediction error approach.Performance evaluation is conducted using both 16-bit DICOM and 8-bit natural images. The proposed method successfully meets the core RDH benchmarks of BER = 0, PSNR = ∞, and SSIM = 1, yielding an average embedding capacity of 5.7067 bpp for premium medical images and 1.6769 bpp for natural scenes. The empirical findings demonstrate substantial advantages, outperforming existing state-of-the-art approaches.

The accurate determination of genotype and the clinical outlook for individuals with glioma present notable challenges, frequently relying on subjective evaluations and minimal scientific backing. This investigation intends to establish a dependable, noninvasive, preoperative learning framework based on a multi-modal MRI transformer to forecast IDH genotyping alongside glioma outcomes. This multi-institutional investigation gathered 563 individuals with glioma to construct an interpretable categorization model that leverages diverse preoperative imaging tracks, such as T1-weighted, T2-weighted, fluid-attenuated inversion recovery, contrast-enhanced T1-weighted, and diffusion-weighted imaging. The system utilizes a multi-task learning architecture designed to isolate and merge radiomic, deep learning, and clinical variables for IDH genotyping and glioma prognosis. Furthermore, a multi-modal transformer technique is incorporated to evaluate anatomical and functional MRI scans, thereby boosting the capabilities of the model. Empirical outcomes demonstrate that the framework achieves exceptional capability, outperforming prior investigations and alternative state-of-the-art approaches. The model attains an AUC of 91.40% for the IDH genotyping objective and 93.37% for the glioma prognosis objective. Subgroup evaluations indicate that the system displays heightened sensitivity toward IDH-mutant instances and identifies low-risk cohorts with greater precision than medium- or high-risk cohorts. This investigation intends to achieve precise IDH genotyping and glioma prognosis through an efficient classification methodology, delivering helpful diagnostic perspectives for clinical workflows and accelerating therapeutic determinations.

Equatorial plasma bubbles (EPBs) represent areas of reduced electron density that develop within Earth’s ionosphere as a result of Rayleigh–Taylor instability. These formations can trigger signal scintillation, which induces signal degradation and navigation positioning inaccuracies. EPBs can be identified via imagery gathered by All-Sky Imager (ASI) networks. The present research introduces an inexpensive, automated technique for identifying EPBs within ASI datasets, suitable for real-time monitoring as well as categorization tasks. This approach couples Two-Dimensional Principal Component Analysis (2DPCA) and Recursive Feature Elimination (RFE) alongside a Random Forest machine learning classifier to construct an Explainable Artificial Intelligence (XAI) framework. This framework extracts image characteristics to automate EPB detection using minimal dimensionality, yielding a compact and exceptionally rapid-to-train model designed to discern EPBs in recorded ASI frames. A collection of 2458 images, categorized into "Event" and "Empty" classes, was assembled to construct the database. This image repository was partitioned randomly into two distinct portions: an 80% training dataset and a 20% testing dataset. The developed XAI framework achieved a marginally better detection accuracy relative to the conventional 2DPCA approach while maintaining a notably smaller structural footprint. Additionally, the execution of the proposed framework was assessed and contrasted against alternative deep learning baselines (ResNet18, Inception-V3, VGG16, and VGG19) under identical environmental conditions.

The escalating complexity of malicious digital activities highlights the necessity for dependable and transparent intrusion detection systems (IDS) to preserve network integrity. Although machine learning frameworks like Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (K-NN), and XGBoost demonstrate exceptional capabilities in identifying malicious behavior, their inner mechanics grow increasingly opaque as structural complexity and predictive precision expand, creating challenges for high-stakes safety operations. Local Interpretable Model-agnostic Explanations (LIME) is frequently deployed to mitigate this issue; nevertheless, its dependency on Gaussian distributions when generating data variations often fails to capture the non-linear and disproportionate traits of frameworks like CIC-IDS-2018. To overcome these limitations, we introduce an adapted LIME variation strategy utilizing Weibull, Gamma, Beta, and Pareto distributions to more accurately represent the attributes of network telemetry. Our framework enhances the consistency of diverse machine learning systems optimized on CIC-IDS repositories, yielding more constructive and dependable rationales for predictive outputs. These proposed algorithmic adjustments yield a gain in explanatory fidelity of up to 78% relative to the standard normal distribution framework. Variations rooted in the Pareto distribution deliver the most optimal outcomes. Across all evaluated distributions, the Pareto framework consistently generated the peak explanation fidelity and consistency, notably for K-NN (R^2 = 0.9971, S = 0.9907) and DT (R^2 = 0.9267, S = 0.9797). This demonstrates that heavy-tailed distributions align effectively with actual network operational trends, minimizing the discrepancy in attribute significance justifications and rendering them more resilient.

In clinical environments, the transparency of artificial intelligence models is paramount, as understanding the rationale behind automated choices is vital. Although black-box AI frameworks exhibit remarkable efficacy, their deployment in healthcare remains impractical unless their underlying mechanisms can be clarified for medical professionals. Deep neural networks (DNNs) achieve state-of-the-art results, yet the justifications they provide are frequently incomprehensible to human observers. Conversely, traditional machine learning approaches and manually designed features extracted to capture distinct data characteristics offer greater transparency, though human constraints in feature engineering often limit their overall effectiveness. To bridge this gap, we present ExShall-CNN, a novel and transparent shallow convolutional neural network tailored for medical image analytics. This framework builds upon hand-crafted feature methodologies to preserve human interpretability, guaranteeing clear and verifiable decision-making. We introduce the explainable shallow convolutional neural network (ExShall-CNN), which merges the high performance of sophisticated deep architectures like U-Net with the inherent clarity of hand-crafted features for segmenting medical images. Grounded in contemporary machine learning progress, ExShall-CNN integrates widely utilized kernels while maintaining structural openness, allowing medical practitioners to visually inspect and comprehend its outputs. This balanced strategy delivers the precision expected of deep learning paradigms alongside the explainability required for clinical deployment.

Artificial neural networks (ANNs) have frequently been deployed for classification objectives within functional magnetic resonance imaging (fMRI) research, though such analyses traditionally isolate only specific regional sub-volumes of the brain. Recent methodology has integrated shallow neural networks (SNNs) with explainable artificial intelligence (xAI) frameworks to uncover underlying mechanisms of neural processing. Although prior research validated this paradigm utilizing fMRI data from motor execution tasks, the current investigation implements this framework on Theory of Mind (ToM) cognitive tasks, leveraging data extracted from the Human Connectome Project (HCP) Young Adult repository. Cognitive paradigms introduce greater computational complexity due to the fundamentally non-linear dynamics of neural functions. The HCP multimodal parcellation brain atlas is utilized to partition the cortex, thereby guiding the systematic training, pruning, and subsequent retraining of an SNN. Shapley values are subsequently computed to decode the retrained architecture, with the resulting outputs benchmarked against a General Linear Model (GLM) analysis for empirical validation. The baseline network demonstrated a classification accuracy of 88.2%, which decreased to 80.0% following network pruning, and subsequently recovered to 84.7% after the retraining phase. The SHAP-derived explanations demonstrated strong convergence with both GLM observations and established neuroanatomical regions associated with ToM. This fMRI investigation effectively navigated a highly complex cognitive paradigm, illustrating the efficacy of explainability techniques in deciphering non-linear neurological dynamics. These outcomes indicate that xAI platforms, particularly knowledge extraction frameworks, offer substantial utility for progressing mental health research and neural state decoding applications.

This research investigates and refines the MINDWALC node classification algorithm, specifically targeting its capacity to extract human-interpretable decision trees from knowledge graph (KG) architectures. We propose techniques to tailor MINDWALC for environments where the knowledge graph is bifurcated into internal background knowledge (domain-specific data) and instance knowledge (specific case data). While the core logic of MINDWALC—utilizing discriminative graph walks as features—is preserved, we introduce two primary enhancements. First, we implement relation-tail merging to empower the algorithm to process relation-modified nodes. Second, we propose flexible walking depths that, when combined with the original MINDWALC strategy, improve the detection of commonalities between node instances. Our findings suggest that these flexible walks can markedly increase classification accuracy in hierarchical or incomplete tree-like graphs, though performance varies on more heterogeneous KG structures. Ultimately, this work demonstrates that our refinements significantly boost MINDWALC’s utility in tree-structured environments, allowing the system to use background knowledge to bridge gaps in instance data in a transparent way. Initial evaluations on medical toy datasets suggest these optimizations could refine clinical decision-making, particularly in sectors requiring explainable artificial intelligence.

To overcome critical constraints regarding diagnostic accuracy and model transparency in medical imaging, this paper presents a novel framework termed the Pixel-Level Interpretability (PLI) model. The investigation primarily aims to benchmark PLI against Gradient-Weighted Class Activation Mapping (Grad-CAM) to attain superior localization correctness and highly detailed explainability. Operating on a VGG19 convolutional neural network infrastructure, the methodology incorporates three open-access COVID-19 chest X-ray datasets containing over 1000 categorized images; these underwent augmentation, normalization, and resizing steps to achieve generalizability and algorithmic stability. The experimental evaluation focused on vital performance metrics: structural similarity (SSIM), mean squared error (MSE), interpretability, computational speed, and diagnostic precision. The findings indicate that PLI drastically surpasses Grad-CAM across every evaluated metric. Specifically, PLI generated fine-grained, pixel-granular heatmaps that yielded reduced MSE, superior SSIM metrics, and expedited inference rates, demonstrating an capability to reveal minute diagnostic elements without sacrificing processing speed. Conversely, the explanations produced via Grad-CAM regularly lack the detailed resolution necessary for dependable clinical integration. By incorporating fuzzy logic to refine both numeric and visual explanations, PLI delivers transparent data that corresponds with clinical logic, thereby allowing healthcare professionals to execute decisions with greater certainty. Ultimately, this research validates PLI as a dependable framework for resolving deficiencies in AI explainability and clinical adoption. By simultaneously managing the dual demands of precision and transparency, PLI advances medical AI deployment and establishes a precedent for implementation within other high-stakes environments.

The medical industry in India has undergone substantial evolution driven by progress in technology and infrastructure. Yet, prominent hurdles remain when addressing critical problems like inadequate medical infrastructure for the nation's massive population, restricted accessibility, a deficit of qualified practitioners, and high-quality services. Artificial intelligence (AI)-based interventions offer the possibility to ease the burden on the Indian medical network; nevertheless, incorporating reliable AI into this domain is still obstructed by moral and legal barriers. The objective of this research is to critically analyze the contemporary state of AI system engineering in Indian medical care and its alignment with the moral and legal dimensions of AI, while also pinpointing the obstacles and prospects for implementing reliable AI within the Indian medical domain. This investigation assessed 15 papers chosen from an initial pool of 1136 papers collected across two digital databases, PubMed and Google Scholar, alongside project sites. This inquiry utilizes the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). The findings indicate that current publications primarily deployed traditional machine learning (ML) models and artificial neural networks (ANNs) for diverse operations, including drug discovery, disease monitoring networks, early sickness identification and diagnostic precision, and medical resource administration in India. This analysis uncovers a deficiency in the integration of reliable AI within Indian medical care and various obstructions linked to it. It examines pathways for engineering reliable AI within Indian medical environments, prioritizing patient protection, information confidentiality, and adherence to moral and statutory criteria.

The connection between the cognitive and emotional components of comprehension has remained largely unexamined because of a shortage of dependable techniques for assessing feelings and emotions during educational activities. Concentrating on five distinct stages of comprehension—nascent understanding, misunderstanding, confusion, emergent understanding, and deep understanding—this investigation presents an AI-based method to assess subjective comprehension by tracking the physiological activity visible in facial expressions. To examine these stages, 103 individuals worked on 15 riddles in a remote setting while their facial expressions were video recorded. Action units (AUs) for each stage occurrence were quantified utilizing AFFDEX software. Recurring AU configurations linked to each stage were subsequently pinpointed by implementing six supervised machine learning algorithms. Unique AU configurations were identified for all five stages, with gradient boosting machine and random forest models delivering the greatest predictive precision. These outcomes indicate that physiological activity can be utilized to dependably assess comprehension. Furthermore, they propose an innovative methodology for assessing and encouraging comprehension in instructional environments, as well as designing adaptive educational technologies and tailored learning interventions. Future research should investigate how the physiological indicators of comprehension stages simultaneously mirror and impact their related cognitive workflows, alongside the generalizability of these outcomes across varied demographics and educational environments (A suite of AI tools was employed in the development of this paper: (1) ChatGPT4o (for writing clarity and reference checking), (2) Grammarly (for grammar and editorial corrections), and (3) ResearchRabbit (reference management)).

Artificial intelligence (AI) applications in mental health possess immense potential to revolutionize clinical results, yet determining the most optimal strategies remains a persistent difficulty. This research fills this gap by assessing and establishing priorities among various AI-based mental health options. The evaluation is grounded in essential benchmarks: implementation feasibility, economic efficiency, scalability, adherence to ethical standards, user satisfaction, and overall clinical efficacy. A fuzzy multi-criteria decision-making (MCDM) framework, integrating fuzzy TOPSIS and fuzzy ARAS, was utilized to determine the ranking of the alternatives. A hybrid combination of these two approaches was subsequently implemented to reconcile variations in the results, as each methodology highlights distinct evaluative dimensions. Fuzzy TOPSIS, which privileges proximity to the ideal target, positioned the personalization of care (A5) as the primary alternative, yielding a closeness coefficient of 0.50, with user engagement (A2) following at 0.45. Similarly, fuzzy ARAS, which measures aggregate performance, ranked A5 as the leading option, demonstrating a total performance score of Si = 0.90 and a utility degree of Qi = 0.92. The integration of both frameworks offered a well-rounded analysis, wherein A5 maintained its premier ranking because of its superior performance in user satisfaction and clinical outcomes. These findings highlight the critical role that personalization and active engagement play in refining AI-enabled mental health platforms. This indicates that customized, patient-centric strategies are vital for maximizing therapeutic efficacy and sustaining user commitment.

This research undertook an assessment of Large Language Models (LLMs) within the domain of clinical diagnostics, specifically investigating their capacity to interpret symptom-centered inputs and generate precise clinical assessments. The investigation focused on evaluating several prominent architectures—including GPT-4, GPT-4o, Gemini, o1 Preview, and GPT-3.5—to gauge their proficiency in identifying clinical conditions solely from presented symptomatology. To guarantee clinical validity and appropriateness, symptom profiles were compiled from verified medical literature. Each architecture underwent testing within standardized environments to quantify diagnostic accuracy, precision, recall, and underlying logical processing. Specialized clinical scenarios were structured to examine performance boundaries across both routine and critical diagnostic conditions. The findings indicated that GPT-4 delivered the highest overall diagnostic accuracy, displaying a strong synergy with established medical logic. Conversely, Gemini demonstrated superior performance in high-stakes environments where exact decision-making is vital. Both GPT-4o and o1 Preview presented equitable metrics, maintaining a stable balance between precision and recall while managing real-time diagnostic scenarios. While possessing less advanced capabilities, GPT-3.5 remained functional for standard diagnostic duties. Ultimately, this investigation outlines the current capabilities and constraints of utilizing LLMs for clinical determination. Even though architectures like GPT-4 and Gemini show significant capability, critical barriers regarding data confidentiality, ethical protocols, and training bias correction must still be overcome. These insights offer strategic directions for the ethical incorporation of LLMs into diagnostic frameworks to optimize patient care outcomes.

This research introduces an innovative prediction framework utilizing a parallel multi-input Bi-GRU structure coupled with a sliding window-based Multi-Input Multi-Output (MIMO) forecasting approach. The primary novelty centers on feature-specific Bi-GRU encoding, which independently processes historical demand, meteorological variables, and calendar indicators prior to data fusion, thereby empowering the network to more effectively capture temporal correlations across disparate data streams. In contrast to preceding models, our methodology distinctively merges a structured feature-specific Bi-GRU encoding mechanism with interpretability via SHAP analysis, providing both superior predictive fidelity and transparent algorithmic decision-making suitable for practical deployment within electrical grids. The introduced Bi-GRU MIMO architecture operates as the foundational model. To rigorously evaluate its performance, an attention-enhanced LSTM model is incorporated solely as a baseline for comparative analysis. Empirical validation using actual data from Singapore’s National Electricity Market System (NEMS) demonstrates that the Bi-GRU MIMO framework enhances forecasting accuracy by 1.64% and decreases MAE by 2.19% relative to traditional models, underscoring its capability to improve decision-making in smart grids.

The escalating value placed on organizational human capital underscores the necessity for firms to optimize their systems for sourcing, evaluating, acquiring, onboarding, deploying, and retaining premium talent. Although embedding artificial intelligence (AI) within recruitment infrastructure promises unmatched workflow acceleration, operational scalability, minimized human subjectivity, and enhanced candidate sourcing, its deployment introduces distinct friction points that can degrade candidate attitudes and behavioral trajectories. Grounded in signaling and organizational justice frameworks, the deficit of interpersonal relational dynamics paired with the opaque nature of automated screening algorithms can prompt prospective candidates to question the integrity and structural fairness of the employment screening process. These institutional challenges risk modifying not only how job seekers deduce corporate principles, but also whether they choose to abandon the submission process altogether. Consequently, this investigation explores the comparative impact of AI-mediated versus human-mediated selection pathways on applicant trust, perceived procedural equity, corporate appeal, and subsequent intent to apply. Furthermore, we map the precise pathways through which trust and structural fairness mediate the link between the initial screening modality and early-stage candidate attraction and submission intentions. Data from an experimental student questionnaire indicate that job candidates view automated hiring protocols as significantly less credible, less equitable, and less appealing. Similarly, respondents exhibited depressed motivation to pursue open roles within enterprises leveraging automated recruitment architectures. Cultivating higher levels of institutional trust during the selection pathway and designing touchpoints that allow candidates to authentically showcase their professional capabilities can constructively reshape applicant attitudes and behavioral outcomes. These insights challenge the reductive assumption that operational velocity inherently yields strategic talent management success, emphasizing instead the vital role of interpersonal markers in securing top-tier talent within an automated recruitment environment.

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