Search Results (245)

The integration of molecular features into histopathological diagnoses has become central to the World Health Organization (WHO) classification of central nervous system (CNS) tumors, improving prognostic accuracy and supporting precision medicine. However, unequal access to molecular testing limits the universal application of integrated diagnosis. To address this, artificial intelligence (AI) models are being developed to predict molecular alterations directly from histological data. In gliomas, deep learning applied to whole-slide images (WSIs) of permanent sections achieves neuropathologist-level accuracy in predicting biomarkers such as IDH mutation and 1p/19q co-deletion, as well as in molecular subtype classification and outcome prediction. Recent advances extend these approaches to intraoperative cryosections, enabling real-time glioma grading, molecular prediction, and label-free tissue analysis using modalities such as stimulated Raman histology and domain-adaptive image translation. Beyond gliomas, AI-powered histology is being explored in other brain tumors, including morphology-based molecular classification of spinal cord ependymomas and intraoperative discrimination of gliomas from primary CNS lymphomas. This review summarizes current progress in AI-assisted molecular profiling prediction of brain tumors from tissue, highlighting opportunities for rapid, accurate, and globally accessible diagnostics. The integration of histology and computational methods holds promise for the development of smart AI-assisted neuro-oncology. Full article

Major Depressive Disorder (MDD) poses a significant global health burden, characterized by a complex and heterogeneous pathophysiology insufficiently targeted by conventional single-treatment approaches. This review presents an integrative framework incorporating network pharmacology, artificial intelligence (AI), and multi-omics technologies to advance a systems-level understanding and management of MDD. Its central contribution lies in moving beyond reductionist methods by embracing a holistic perspective that accounts for dynamic interactions within biological networks. The primary objective is to demonstrate how AI-powered integration of multi-omics data—spanning genomics, proteomics, and metabolomics—can enable the construction of predictive network models. These models are designed to uncover fundamental disease mechanisms, identify clinically relevant biotypes, and reveal novel therapeutic targets tailored to specific pathological contexts. Methodologically, the review examines the microbiota–gut–brain (MGB) axis as an illustrative case study, detailing its pathogenic roles through neuroimmune alterations, metabolic dysfunction, and disrupted neuro-plasticity. Furthermore, we propose a translational roadmap that includes AI-assisted biomarker discovery, computational drug repurposing, and patient-specific “digital twin” models to advance precision psychiatry. Our analysis confirms that this integrated framework offers a coherent route toward mechanism-based personalized therapies and helps bridge the gap between computational biology and clinical practice. Nevertheless, important challenges remain, particularly pertaining to data heterogeneity, model interpretability, and clinical implementation. In conclusion, we stress that future success will require integrating prospective longitudinal multi-omics cohorts, high-resolution digital phenotyping, and ethically aligned, explainable AI (XAI) systems. These concerted efforts are essential to realize the full potential of precision psychiatry for MDD. Full article

This study presents an intelligent modeling framework for predicting fabric nesting efficiency (NE) based on geometric descriptors of garment patterns, offering a rapid alternative to conventional nesting software. A synthetic dataset of 1000 layouts was generated using a custom Python algorithm that simulates realistic garment-like shapes within a fixed fabric size. Each layout was characterized by five geometric descriptors: number of pieces (NP), average piece area (APA), average aspect ratio (AAR), average compactness (AC), and average convexity (CVX). The relationship between these descriptors and NE was modeled using a Sugeno-type Adaptive Neuro-Fuzzy Inference System (ANFIS). Various membership function (MF) structures were examined, and the configuration 3-3-2-2-2 was identified as optimal, yielding a mean relative error of −0.1%, with high coefficient of determination (R² > 0.98). The model was validated through comparison between predicted NE values and results obtained from an actual nesting process performed with Deepnest.io, demonstrating strong agreement. The proposed method enables efficient estimation of NE directly from CAD-based parameters, without requiring computationally intensive nesting simulations. This approach provides a valuable decision-support tool for fabric and apparel designers, facilitating rapid assessment of material utilization and supporting design optimization toward reduced fabric waste. Full article

The integration of artificial intelligence (AI) into intelligent control systems has advanced significantly, enabling improved adaptability, robustness, and performance in nonlinear and uncertain environments. This study conducts a PRISMA-2020-compliant systematic mapping of 188 peer-reviewed articles published between 2000 and 15 January 2025, identified through fully documented Boolean queries across IEEE Xplore, ScienceDirect, SpringerLink, Wiley, and Google Scholar. The screening process applied predefined inclusion–exclusion criteria, deduplication rules, and dual independent review, yielding an inter-rater agreement of κ = 0.87. The resulting synthesis reveals three dominant research directions: (i) control model strategies (36.2%), (ii) parameter optimization methods (45.2%), and (iii) adaptability mechanisms (18.6%). The most frequently adopted approaches include fuzzy logic structures, hybrid neuro-fuzzy controllers, artificial neural networks, evolutionary and swarm-based metaheuristics, model predictive control, and emerging deep reinforcement learning frameworks. Although many studies report enhanced accuracy, disturbance rejection, and energy efficiency, the analysis identifies persistent limitations, including overreliance on simulations, inconsistent reporting of hyperparameters, limited real-world validation, and heterogeneous evaluation criteria. This review consolidates current AI-enabled control technologies, compares methodological trade-offs, and highlights application-specific outcomes across renewable energy, robotics, agriculture, and industrial processes. It also delineates key research gaps related to reproducibility, scalability, computational constraints, and the need for standardized experimental benchmarks. The results aim to provide a rigorous and reproducible foundation for guiding future research and the development of next-generation intelligent control systems. Full article

This study introduces two hybrid forecasting models that integrate the Marine Predators Algorithm (MPA) with Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Feed-Forward Neural Networks (FFNN) for short-term Bitcoin price prediction. Daily Bitcoin data from 2022 were converted into supervised time-series structures with multiple input configurations. The proposed hybrid models were evaluated against six well-known metaheuristic algorithms commonly used for training intelligent forecasting systems. The results show that MPA consistently yields lower prediction errors, faster convergence, and more stable optimization behavior compared with alternative algorithms. Both ANFIS-MPA and FFNN-MPA maintained their advantage across all tested structures, demonstrating reliable performance under varying model complexities. All experiments were repeated multiple times, and the hybrid approaches exhibited low variance, indicating robust and reproducible behavior. Overall, the findings highlight the effectiveness of MPA as an optimizer for improving the predictive performance of neuro-fuzzy and neural network models in financial time-series forecasting. Full article

This paper presents a neuro-symbolic approach for constructing distributed knowledge graphs to facilitate cooperation through communication among spatially proximate agents. We develop a graph autoencoder (GAE) that learns rich representations from heterogeneous modalities. The method employs density-adaptive k-nearest neighbor (k-NN) construction with Gabriel pruning to build the proximity graphs that balance local density awareness with geometric consistency. When the agents enter the bridging zone, their individual knowledge graphs are aggregated into hypergraphs using a construction algorithm, for which we derive the theoretical bounds on the minimum number of hyperedges required for connectivity under arity and locality constraints. We evaluate the approach in PettingZoo’s communication-oriented environment, observing improvements of approximately 10% in episode rewards and up to 40% in individual agent rewards compared to Deep Q-Network (DQN) baselines, while maintaining comparable policy loss values. The explicit graph structures may offer interpretability benefits for applications requiring auditability. This work explores how structured knowledge representations can support cooperation in distributed multi-agent systems with heterogeneous observations. Full article

English argumentative writing is a cornerstone of academic and professional communication, yet it remains a significant challenge for second-language (L2) learners. While Large Language Models (LLMs) show promise as components in automated feedback systems, their responses are often generic and lack the structural insight necessary for meaningful improvement. Existing Automated Essay Scoring (AES) systems, conversely, typically provide holistic scores without the kind of actionable, fine-grained advice that can guide concrete revisions. To bridge this systemic gap, we introduce ARGUS (Argument Understanding and Structured-feedback), a novel neuro-symbolic system that synergizes the semantic understanding of LLMs with the structured reasoning of Graph Neural Networks (GNNs). The ARGUS system architecture comprises three integrated modules: (1) an LLM-based parser transforms an essay into a structured argument graph; (2) a Relational Graph Convolutional Network (R-GCN) analyzes this symbolic structure to identify specific logical and structural flaws; and (3) this flaw analysis directly guides a conditional LLM to generate feedback that is not only contextually relevant but also pinpoints precise weaknesses in the student’s reasoning. We evaluate ARGUS on the Argument Annotated Essays corpus and on an additional set of 150 L2 persuasive essays collected from the same population to augment training of both the parser and the structural flaw detector. Our argument parsing module achieves a component identification F1-score of 90.4% and a relation identification F1-score of 86.1%. The R-GCN-based structural flaw detector attains a macro-averaged F1-score of 0.83 across the seven flaw categories, indicating that the enriched training data substantially improves its generalization. Most importantly, in a human evaluation study, feedback generated by the ARGUS system was rated as consistently and significantly more specific, accurate, actionable, and helpful than that from strong baselines, including a fine-tuned LLM and a zero-shot GPT-4. Our work demonstrates a robust systems engineering approach, grounding LLM-based feedback in GNN-driven structural analysis to create an intelligent teaching system that provides targeted, pedagogically valuable guidance for L2 student writers engaging with persuasive essays. Full article

As intelligent systems become increasingly embedded in industrial ecosystems, the demand for transparency, reliability, and interpretability has intensified. This study investigates how explainable artificial intelligence (XAI) contributes to enhancing accountability, trust, and human–machine collaboration across industrial contexts transitioning from Industry 4.0 to Industry 5.0. To achieve this objective, a systematic bibliometric literature review (LRSB) was conducted following the PRISMA framework, analysing 98 peer-reviewed publications indexed in Scopus. This methodological approach enabled the identification of major research trends, theoretical foundations, and technical strategies that shape the development and implementation of XAI within industrial settings. The findings reveal that explainability is evolving from a purely technical requirement to a multidimensional construct integrating ethical, social, and regulatory dimensions. Techniques such as counterfactual reasoning, causal modelling, and hybrid neuro-symbolic frameworks are shown to improve interpretability and trust while aligning AI systems with human-centric and legal principles, notably those outlined in the EU AI Act. The bibliometric analysis further highlights the increasing maturity of XAI research, with strong scholarly convergence around transparency, fairness, and collaborative intelligence. By reframing artificial intelligence through the lens of transparent machine reasoning, this study contributes to both theory and practice. It advances a conceptual model linking explainability with measurable indicators of trustworthiness and accountability, and it offers a roadmap for developing responsible, human-aligned AI systems in the era of Industry 5.0. Ultimately, the study underscores that fostering explainability not only enhances functional integrity but also strengthens the ethical and societal legitimacy of AI in industrial transformation. Full article

Data incompleteness is a common problem in real-life datasets. This is caused by acquisition problems, sensor failures, human errors, and so on. Missing values and their subsequent imputation can significantly affect the performance of data-driven models and can also distort the interpretability of explainable artificial intelligence (XAI) models, such as fuzzy models. This paper presents a novel imputation algorithm based on granular computing. This method benefits from the local structure of the dataset, explored using the granular approach. The method elaborates a set of granules that are then used to impute missing values in the dataset. The method is evaluated on several datasets and compared with several state-of-the-art imputation methods, both directly and indirectly. The direct evaluation compares the imputed values with the original data. The indirect evaluation compares the performance of fuzzy models built with TSK and ANNBFIS neuro-fuzzy systems. This enables not only the evaluation of the quality of numerically imputed values but also their impact on the interpretability of the constructed fuzzy models. This paper is accompanied by numerical experiments. The implementation of the method is available in a public GitHub repository. Full article

The digital transformation in the treatment of mental health and emotional disharmony requires artificial intelligence architectures that overcome the limitations of purely neural approaches, such as temporal inconsistency, opacity, and lack of theoretical foundations. Assuming the existence and use of generalist LLMs currently used in clinical settings and considering the appropriate limitations indicated by experts, this article aims to offer clinicians an alternative Neuro-symbolic-Psychological multi-agent architecture (NSPA-AI), which integrates archetypal symbolic reasoning with neurobiological modelling, based on our established framework of artificial neurotransmitters for the modelling and analysis of affective-emotional stimuli to enable interpretable AI-assisted psychological intervention. The system implements a hub-and-spoke topology that coordinates five specialized agents (symbolic, psychological, neurofunctional, decision fusion, learning) that process heterogeneous information via SPADE protocols. Seven archetypal constructs from Jungian psychology and narrative identity theory provide stable symbolic frameworks for longitudinal therapeutic consistency. An empirical study of 156 university students demonstrated significant improvements in depression (Cohen’s d = 1.03), stress (d = 0.89), and narrative identity integration (d = 0.75), which were maintained at a 12-week follow-up and superior to GPT-4 controls (d = 0.34). Neurofunctional correlations—downregulation of cortisol (r = 0.71 with stress reduction), increase in serotonin (r = −0.68 with depression improvement)—validated the neurobiological basis of the entropy-energy framework. Qualitative analysis revealed the following four mechanisms of improvement: symbolic emotional support (93%), increased self-awareness through neurotransmitter visualization (84%), non-judgmental AI interaction (98%), and archetypal narrative organization (87%). The results establish that neuro-symbolic architectures are viable alternatives to large language models for digital mental health, providing the interpretability and clinical validity essential for adoption in the healthcare sector. Full article

This paper proposes a novel intelligent switching control strategy for a five-bar lower limb exoskeleton. First, during the support phase, terminal sliding mode control (TSMC) is employed to ensure robust stability and high-torque amplification capabilities. Then, during the swing phase, a hybrid controller combining proportional-integral-derivative (PID) control and the adaptive neuro-fuzzy inference system (ANFIS) is implemented to generate natural and compliant leg movements. Finally, to achieve smooth transitions between phases, an intelligent switching algorithm based on multi-sensor information fusion is proposed. Simulation results demonstrate that the proposed strategy keeps trajectory tracking errors below 0.05 across all gait phases and achieves stable torque amplification ratios ranging from 1:6 to 1:10. This performance significantly reduces the user’s physical exertion. These findings validate the effectiveness of this control framework in improving the stability and comfort of human–machine interaction. Full article

Hierarchical Reinforcement Learning (HRL) is effective for long-horizon and sparse-reward tasks by decomposing complex decision processes, but its real-world application remains limited due to instability between levels, inefficient subgoal scheduling, delayed responses, and poor interpretability. To address these challenges, we propose Timed and Bionic Circuit Hierarchical Reinforcement Learning (TBC-HRL), a biologically inspired framework that integrates two mechanisms. First, a timed subgoal scheduling strategy assigns a fixed execution duration $\tau$ to each subgoal, mimicking rhythmic action patterns in animal behavior to improve inter-level coordination and maintain goal consistency. Second, a Neuro-Dynamic Bionic Circuit Network (NDBCNet), inspired by the neural circuitry of C. elegans, replaces conventional fully connected networks in the low-level controller. Featuring sparse connectivity, continuous-time dynamics, and adaptive responses, NDBCNet models temporal dependencies more effectively while offering improved interpretability and reduced computational overhead, making it suitable for resource-constrained platforms. Experiments across six dynamic and complex simulated tasks show that TBC-HRL consistently improves policy stability, action precision, and adaptability compared with traditional HRL, demonstrating the practical value and future potential of biologically inspired structures in intelligent control systems. Full article

The increasing ubiquity of wearable computing and multimodal interaction technologies has created unprecedented opportunities for natural and seamless human–computer interaction. However, most existing systems adapt only to external user actions such as speech, gesture, or gaze, without considering internal cognitive or affective states. This limits their ability to provide intelligent and empathetic adaptations. This paper addresses this critical gap by proposing the Neuro-Adaptive Multimodal Architecture (NAMI), a principled, modular, and reproducible framework designed to integrate behavioral and neurophysiological signals in real time. NAMI combines multimodal behavioral inputs with lightweight EEG and peripheral physiological measurements to infer cognitive load and engagement and adapt the interface dynamically to optimize user experience. The architecture is formally specified as a three-layer pipeline encompassing sensing and acquisition, cognitive–affective state estimation, and adaptive interaction control, with clear data flows, mathematical formalization, and real-time performance on wearable platforms. A prototype implementation of NAMI was deployed in an augmented reality Java programming tutor for postgraduate informatics students, where it dynamically adjusted task difficulty, feedback modality, and assistance frequency based on inferred user state. Empirical evaluation with 100 participants demonstrated significant improvements in task performance, reduced subjective workload, and increased engagement and satisfaction, confirming the effectiveness of the neuro-adaptive approach. Full article

The review “Symbolic Approaches for Explainable Artificial Intelligence” discusses the potential of symbolic AI to improve transparency, contrasting it with opaque deep learning systems. Though connectionist models perform well, their poor interpretability means that they are of concern for bias and trust in high-stakes fields such as healthcare and finance. The authors integrate symbolic AI methods—rule-based reasoning, ontologies, and expert systems—with neuro-symbolic integrations (e.g., DeepProbLog). This paper covers topics such as scalability and integrating knowledge, proposing solutions like dynamic ontologies. The survey concludes by advocating for hybrid AI approaches and interdisciplinary collaboration to reconcile technical innovation with ethical and regulatory demands. Full article

Runway surface roughness is recognized as a principal cause of passenger aircraft vibration during taxiing, adversely affecting ride comfort, safety, and even human health. Effective mitigation of such vibrations is therefore essential for improving passenger experience and operational reliability. Previous studies have investigated passive, semi-active, and intelligent controllers such as PID, H∞, and ANFIS; however, the comprehensive application of a robust adaptive neuro-fuzzy inference system (RANFIS) to active landing-gear control has not yet been addressed. The novelty of this work lies in combining robustness with adaptive learning of fuzzy rules and neural network parameters, thereby filling this critical gap in the literature. To investigate this, a six-degrees-of-freedom aircraft dynamic model was developed, and three controllers were comparatively evaluated: model-based neural network (MBNN), adaptive neuro-fuzzy inference system (ANFIS), and the proposed RANFIS. Performance was assessed in terms of rise time, settling time, peak value, and steady-state error under stochastic runway excitations. Simulation results show that while MBNN and ANFIS provide satisfactory control, RANFIS achieved superior performance, reducing vibration peaks to ≤0.3–1.0 cm, shortening settling times to <1.5 s, and decreasing steady-state errors to <0.05 cm. These findings confirm that RANFIS offers a more effective solution for enhancing comfort, safety, and structural durability in next-generation active landing-gear systems. Full article