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• Neural network classification techniques for diagnostic problems
  Neural network classification techniques for diagnostic problems by Ho-ching Lau thesis 1995 xi, 97 leaves : ill. ; 30 cm Neural networks have been widely used in general classification tasks. They have the advantages of being flexible and tolerant to…Read mor…
  crossref Ho-ching Lau 2014-12-19T10:11:26Z 置信度 0.70
• Interpretable and fair generalized additive neural networks via multi-objective learning.
  Interpretability and fairness are two of the most emphasized dimensions in trustworthy artificial intelligence (AI). Various explainable AI methods have been introduced to improve interpretability. This paper focuses on neural network (NN)-based generalized ad…
  europepmc 2026 置信度 0.80
• Toward global interpretability of neural networks via Boolean transformation.
  Interpretability remains a central challenge in the deployment of deep neural networks, particularly in safety-critical and decision-sensitive fields. This work proposes a unified framework for post-hoc global interpretability by transforming general neural ne…
  europepmc 2026 置信度 0.80
• Spatiotemporal Dual-Channel Interpretable Hybrid Neural Network for HD-sEMG-Based Gesture Recognition.
  Accurate gesture recognition is crucial for precision control of upper limb prostheses. High-density surface electromyography (HD-sEMG) enhances spatial resolution and information richness of human gesture representation, thus improving myoelectric control of …
  europepmc 2026 置信度 0.80
• A Span-Prior-Guided Explainable Multimodal Neural Network Method for Final-State Quality Inspection of Hairpin Windings.
  For final-state quality inspection of three-dimensional stamped hairpin windings, existing studies still lack multimodal methods that integrate mechanical geometric constraints, prior-guided fusion, and decision interpretability. This study proposes a span-pri…
  europepmc 2026 置信度 0.80
• Interpretable Self-Attention Dynamic Inner Neural Network Model for Nonlinear Dynamic Process Monitoring.
  Nonlinearity and dynamics are important issues in industrial process monitoring. The dynamic relationships between samples are often embedded in the latent variable space and are usually nonlinear. The fitting of nonlinear dynamic relationships largely determi…
  europepmc 2026 置信度 0.80
• Physics-informed spatiotemporal graph neural network models for groundwater contaminant prediction.
  Groundwater contamination prediction is challenging because of the strong concealment, complex spatiotemporal coupling, and high parameter dependence of physical mechanism models. However, traditional data-driven models lack spatial representations. To address…
  europepmc 2026 置信度 0.80
• An interpretable omnigenic neural network architecture for the human genome
  Genetic prediction of complex phenotypes typically relies on additive linear models, which scale well but cannot capture non-additive effects or deeply integrate molecular and clinical data. Domain-specific neural networks have driven advances in images, text,…
  europepmc 2026 置信度 0.80
• Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation.
  Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep…
  europepmc 2026 置信度 0.80
• A Hybrid Perception Network for Accurate and Interpretable Brain Tumor Classification Using Multi-Modal Imaging.
  Objective Accurate classification of brain tumors is of utmost importance in early diagnosis and treatment, which can be further enhanced through advanced deep learning techniques. This work introduces HyPerNet-a Hybrid Perception Network for brain tumor class…
  europepmc 2026 置信度 0.80
• Construction and validation of a liquid neural network-based prediction model and bedside tool for spontaneous passage of common bile duct stones.
  Background To construct and externally validate a liquid neural network (LNN)-based risk prediction model for spontaneous passage of common bile duct stones (CBDS) and to develop a cross-platform AI bedside tool integrating real-time SHapley Additive exPlanati…
  europepmc 2026 置信度 0.80
• MSCT-Trans: A Multi-scale Convolutional Neural Network Token Transformer for Interpretable Ultrasound Image Classification.
  Automated ultrasound image classification is increasingly important for clinical decision support in breast, thyroid and fetal screening. However, deploying deep learning models in such safety-critical settings demands not only high predictive accuracy but als…
  europepmc 2026 置信度 0.80
• A Non-equilibrium thermodynamic framework for neural networks: A principled correspondence and parameter dynamics.
  Deep learning's opaque training dynamics preclude a unified explanation for parameter evolution and the nature of generalization. Existing physics-inspired approaches often exacerbate this issue by overlooking core thermodynamic concerns such as energy convers…
  europepmc 2026 置信度 0.80
• LMF-CP: An Interpretable Multimodal Late-Fusion Framework for Compound Carcinogenicity Prediction.
  Accurately predicting the carcinogenicity of compounds is of great significance for drug discovery, clinical drug safety, and chemical risk assessment. Traditional methods for assessing carcinogenicity rely on animal testing, which suffers from limitations suc…
  europepmc 2026 置信度 0.80
• Unveiling the Structural Determinants of PFAS Toxicity: A Graph Neural Network and Interpretability Analysis.
  The rapid proliferation of per- and polyfluoroalkyl substances (PFASs) necessitates high-throughput tools to assess safety and prevent regrettable substitution. However, conventional toxicological testing is resource-intensive, while standard computational met…
  europepmc 2026 置信度 0.80
• Development and validation of a deep neural network for predicting coronary heart disease in hypertensive patients using 24-hour ambulatory blood pressure monitoring: a retrospective study.
  Background Coronary heart disease (CHD) remains a leading cause of morbidity and mortality worldwide. Early identification of high-risk hypertensive patients is crucial for preventing cardiovascular events. While traditional risk scores rely on static clinical…
  europepmc 2026 置信度 0.80
• Robust and interpretable tabular data classification via multi-channel image conversion and channel-wise gating.
  Deep learning for tabular data remains challenging because heterogeneous feature types, missing values, and limited sample sizes complicate learning representations that are both expressive and reliable. Existing table-to-image approaches enable convolutional …
  europepmc 2026 置信度 0.80
• A Model-Driven Deep Neural Network for Simultaneous Low-Light Image Enhancement and Deblurring.
  Images captured at night are often degraded by both low-light conditions and motion blur, primarily caused by insufficient illumination and relative motion during exposure. Existing methods typically address only a single type of degradation or rely heavily on…
  europepmc 2026 置信度 0.80
• Enhancing interpretability in Metabolomics: Ranking Metabolites by their Impact on Graph Neural Network Predictions
  Abstract A key step in the biological interpretation of untargeted metabolomics data is the ranking of compounds by importance, usually using statistical significance or impact on classification as the basis for importance assignment. However, current approach…
  europepmc 2026 置信度 0.80
• Classifying electric service disruptions under deep uncertainty: A comparative analysis of algorithms and their policy outcomes.
  As machine learning finds increasing applications in energy policy, it is critical to evaluate model accuracy alongside interpretability and equity. We leverage classification of electricity disruptions within residential advanced metering infrastructure (AMI)…
  europepmc 2026 置信度 0.80
• Improved Spontaneous EEG Signal Decoding Efficiency by Function Predefined Convolutional Neural Network.
  A spontaneous electroencephalogram (EEG)-based brain-computer interface (BCI) is an ideal form of brain-computer interaction. The classical decoding methods can achieve classification by using meaningful manual features, but their performance is poor. The neur…
  europepmc 2026 置信度 0.80
• MCFusion-DDI: Multimodal cross-attention fusion of local-global features and latent drug associations for explainable DDI prediction.
  Accurate prediction of drug-drug interactions (DDIs) is critical for ensuring patient safety in polypharmacy, yet remains challenging due to the complexity of the underlying biochemical mechanisms. Existing methods are limited by inadequate fusion of heterogen…
  europepmc 2026 置信度 0.80
• A multi-view graph neural network framework for Parkinson's disease identification based on dynamic functional connectivity.
  Parkinson's disease (PD) is a common neurodegenerative disorder, and accurate diagnosis is crucial for timely intervention. Dynamic functional connectivity (DFC) analysis of resting-state functional magnetic resonance imaging (rs-fMRI) can capture the time-var…
  europepmc 2026 置信度 0.80
• Fuzzy Neural Module Network: Leveraging Univariate Models and Layer-Specific and Network-Wide Dual Learning.
  In this study, we propose an incrementally expanding fuzzy neural module network (FNMN) designed to effectively handle both low- and high-dimensional problems without relying on dimensionality reduction techniques. The proposed framework adopts a modular and h…
  europepmc 2026 置信度 0.80
• Explainable spatio-temporal transformers for schizophrenia classification and activity analysis from functional MRI.
  Background and objective Schizophrenia is a complicated neuropsychiatric disorder caused by disturbed brain connectivity and abnormal neural activation patterns. To identify these underlying neural mechanisms, computational models need to be reliable and inter…
  europepmc 2026 置信度 0.80
• Co-assistant networks by pathology foundation model and convolutional neural network for gigapixel whole slide image analysis.
  Multiple instance learning (MIL) with pre-trained models to extract patch-level features has been widely used in whole slide image (WSI) analysis to avoid expensive pixel-level annotations. Although pre-trained pathology foundation model (PFM) have achieved pr…
  europepmc 2026 置信度 0.80
• Fuzzy-TD3: A prior-knowledge guided deterministic policy gradient for interpretable control.
  Deterministic policy gradient methods with neural network actors, such as Twin Delayed Deep Deterministic Policy Gradient (TD3), offer strong performance but remain difficult to interpret. Although existing fuzzy reinforcement learning methods can improve inte…
  europepmc 2026 置信度 0.80
• Physics-Informed Neural Network with Data Augmentation for Predicting Marine Atmospheric Corrosion of Carbon Steel
  Abstract The prediction of corrosion rate of carbon steel in marine atmospheric environments faces the challenge of scarce measured data. Conventional machine learning algorithms often find it challenging to strike a balance between high prediction accuracy an…
  europepmc 2026 置信度 0.80
• Enhanced Diagnosis of Cervical Cancer Using Archer Fish Hunting Optimizer-Optimized Xception Network with AI Interpretability via Grad-CAM.
  Objective The primary objective of this study is to enhance the performance of cervical cancer classification by optimizing the Xception deep learning model using the Archer Fish Hunting Optimizer (AHO). The study aims to evaluate the effectiveness of the AHO-…
  europepmc 2026 置信度 0.80
• Task-State fMRI-Derived Whole-Brain Functional Topology-Constrained Spiking Neural Network with an Embedded Auditory Core Circuit for Speech Recognition.
  The topology of spiking neural networks (SNNs) plays an important role in determining their dynamic representation ability, recognition performance, and biological interpretability in speech recognition. However, most existing SNN reservoirs are constructed us…
  europepmc 2026 置信度 0.80
• Morphological saliency prior-guided band-aware multi-modal disease diagnostic fusion network for vibrational spectroscopy.
  Multimodal vibrational spectroscopy provides a powerful biochemical probe for non-invasive clinical diagnosis. However, when mapping high-dimensional spectral features, existing deep neural networks are highly susceptible to overfitting high-variance noise, in…
  europepmc 2026 置信度 0.80
• Explainable artificial intelligence in ICU admission prediction for methanol poisoning patients: a comparative study of deep and machine learning models.
  Background Methanol poisoning poses significant challenges due to its rapid progression and high mortality rate, necessitating timely and accurate ICU admission decisions. Explainable artificial intelligence (XAI) offers transparent insights into these decisio…
  europepmc 2026 置信度 0.80
• A Deep Learning-Based System for Prawn Hepatopancreas Identification Based on Image Classification with Interpretability.
  Accurate identification of the hepatopancreas is essential for prawn quality assessment and automated seafood processing. This study presents an explainable deep learning framework for the binary classification of prawn images into "with hepatopancreas" and "n…
  europepmc 2026 置信度 0.80
• HDGNN-Mamba2: Mamba-Based Spatiotemporal Heterogeneous Dynamic Graph Neural Network for Major Depressive Disorder Classification.
  Background : Major depressive disorder (MDD) affects 332 million people worldwide, yet diagnosis remains reliant on subjective clinical interviews with substantial inter-rater variability. Objective neuroimaging model-attributed regions offer a path toward pre…
  europepmc 2026 置信度 0.80
• Hard-Constrained and Physics-Informed Deep Learning Model for Predicting Residual Flexural Strengths of Steel Fiber Reinforced Concrete: A Multi-Study Generalisation Framework
  Accurate prediction of residual flexural strengths is essential for the structural design and performance assessment of steel fibre reinforced concrete (SFRC). While machine learning models have recently demonstrated strong predictive capability, most of the e…
  europepmc 2026 置信度 0.80
• DMG-GCN: A Dynamic Microstate-Guided Graph Convolutional Network for EEG Cognitive Workload Decoding in Air Traffic Control.
  Complex inter-subject variability induces severe distribution shifts in the physiological features of electroencephalography (EEG) for air traffic controllers (ATCOs). These inter-subject shifts limit the generalization and interpretability of passive brain-co…
  europepmc 2026 置信度 0.80
• The illusion of interpretability in biologically informed neural networks
  Biologically informed neural networks (BINNs), also known as visible neural networks (VNNs), are widely adopted in omics because their architectures mirror known biological structures, such as gene-to-pathway relationships, and are therefore often assumed to b…
  europepmc 2026 置信度 0.80
• A physics-guided neural network framework for prediction and control of spring-mass running.
  The spring-mass template acts as a fundamental bridge between animal locomotion and legged robotic platforms. However, controlling spring-mass dynamics involves a persistent trade-off: numerical integration offers accuracy but high computational cost, while ap…
  europepmc 2026 置信度 0.80
• Physics-informed LSTM neural network for personalized pharmacokinetic modeling of polymyxin B in critically Ill patients.
  Polymyxin B represents a critical last-resort antibiotic for carbapenem-resistant Gram-negative infections, yet its narrow therapeutic window and substantial inter-individual pharmacokinetic variability present significant challenges for optimal dosing in crit…
  europepmc 2026 置信度 0.80
• scKanFormer: A Transformer-KAN framework with biologically informed attention for cell type annotation in large-scale scRNA-seq data.
  A key challenge in single-cell RNA sequencing (scRNA-seq) data analysis is accurately and efficiently identifying the cell type of each cell. Cell type annotation for scRNA-seq data not only needs to overcome batch effects caused by various factors but also re…
  europepmc 2026 置信度 0.80
• Application of deep learning in prognostic prediction of non-small cell lung cancer.
  Non-small cell lung cancer (NSCLC) is characterized by high heterogeneity. Traditional prognostic evaluation methods such as TNM staging are difficult to accurately characterize the survival differences of patients, and there is an urgent clinical need for mor…
  europepmc 2026 置信度 0.80
• Reconstructing the brain’s language network from non-invasive EEG enables interpretable speech decoding
  Abstract Non-invasive EEG-based speech decoding typically treats the neural-to-text mapping as a black box, offering limited interpretability and few explicit links to the brain’s language networks. Here we present a framework that reconstructs language-networ…
  europepmc 2026 置信度 0.80
• [Medical prior-guided TabMap deep learning model for ovarian cancer prediction and interpretability analysis].
  Objectives To develop a TabMap image mapping and deep learning prediction framework that integrates medical prior knowledge to address the challenges of complex feature associations in tabular medical data and insufficient model interpretability in early ovari…
  europepmc 2026 置信度 0.80
• An Interpretability Framework for Convolutional Neural Network-Based Electroencephalography Analysis Discovers New Spatial and Spectral Epileptic Biomarkers.
  Deep learning (DL) models, particularly convolutional neural networks (CNNs), have shown promise in automated epileptic seizure detection from electroencephalogram (EEG). However, their "black-box" nature limits clinical adoption, as interpretability is critic…
  europepmc 2026 置信度 0.80
• BRB-GNN: Hybrid Belief Rule Base and Graph Neural Network Framework for Interpretable Fault Diagnosis in Wireless Sensor Networks
  Abstract Wireless Sensor Networks (WSNs) in hostile or far away environments will often be exposed to more than one type of faults at once; Stuck-At, Random Noise, Gain/Offset, etc., and this is usually due to the degradation of both data quality and network e…
  europepmc 2026 置信度 0.80
• The Deep Learning Evolution in Wireless Physical Layer Communications: Applications, Challenges, and Evolutionary Directions.
  With the continuous evolution toward sixth-generation (6G) wireless communication systems, emerging scenarios such as terahertz transmission, integrated sensing and communication (ISAC), and ultra-massive multiple-input multiple-output (MIMO) have significantl…
  europepmc 2026 置信度 0.80
• A Comprehensive Multimodal Framework for Robust Forgery Detection in Social Media Images via Adaptive Gated Fusion of Convolutional Neural Networks, Vision Transformers, and Graph Neural Network Representations
  Abstract The rapid proliferation of AI-generated synthetic content on social networks necessitates a robust approach for detecting forgery. Current unimodal techniques, which use only visual or metadata information, are not resilient to new-generation models. …
  europepmc 2026 置信度 0.80
• Process-aware physics-guided neural network framework for harmful algal bloom forecasting in lentic freshwater systems.
  Harmful algal blooms (HABs) in lentic freshwater systems pose persistent challenges to water resource management, motivating the development of forecasting tools that combine physical rigor with data-driven flexibility. Although physics-informed neural network…
  europepmc 2026 置信度 0.80
• A causal reinforcement learning framework for reliable gene regulatory network inference.
  Background Gene regulatory network inference is crucial for revealing the mechanisms of cellular functions and the causal mechanisms of disease occurrence. However, traditional correlation-based methods struggle to identify causal directions, and deep learning…
  europepmc 2026 置信度 0.80
• NeuroMimicNet: A Multimodal Neuromorphic Framework for Early Screening and Cognitive Pattern Recognition in Children with Autism.
  Background Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition which is composed of social, behavioral, and communication challenges that generally require early detection. Purpose This research proposes NeuroMimicNet, a brain-like event-d…
  europepmc 2026 置信度 0.80
• Deep-Learning: An Emerging Tool to Support Model-Informed Drug Development.
  Traditional Model-Informed Drug Development (MIDD) primarily relies on hypothesis-driven (HD) models based on biological, physiological, and physicochemical principles. While these mechanistic models have been instrumental in drug development and regulatory de…
  europepmc 2026 置信度 0.80
• Machine Learning for Coronary Heart Disease Prediction: Comparative Analysis of Framingham and Cleveland Subset of the UCI Dataset with SHAP-Based Interpretability.
  Introduction Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with coronary artery disease (CAD), also known as ischemic heart disease (IHD), responsible for approximately 13% of global deaths in 2021. Studies applying machine lea…
  europepmc 2026 置信度 0.80
• A survey of modern neural network based methods for the graph coloring problem.
  The graph coloring problem (GCP) is a fundamental NP-hard combinatorial optimization task with applications in scheduling, register allocation, frequency assignment, and resource management. Traditional heuristics and exact methods often scale poorly to large …
  europepmc 2026 置信度 0.80
• WaveCrossNet: interpretable cross-modal automatic sleep staging for OSA patients.
  Obstructive sleep apnea (OSA) patients show highly fragmented sleep with frequent stage transitions, challenging existing automatic sleep staging in feature extraction, multimodal correlation modeling, and interpretability. Current methods rely on fixed-thresh…
  europepmc 2026 置信度 0.80
• GPC-net: a generative-augmented predictive-corrective neural network for small-sample FTIR spectral quantification with application to subdural hematoma injury time.
  Quantitative analysis in biomedical Fourier-transform infrared (FTIR) spectroscopy faces a fundamental challenge known as the small sample, high dimensionality paradox. This study introduces GPC-Net, a three-stage neural framework developed for robust spectral…
  europepmc 2026 置信度 0.80
• Non-point source pollution prediction and dynamics simulation in urban runoff: a physics-informed neural network approach.
  Urban non-point source (NPS) pollution poses a significant threat to water environments, yet modeling its complex dynamics remains constrained by the trade-off between the extensive data requirements of process-based models and the limited interpretability of …
  europepmc 2026 置信度 0.80
• HRGNN: hierarchical region-aware graph neural network for interpretable EEG-Based emotion recognition.
  Objective. Electroencephalography (EEG)-based emotion recognition has increasingly adopted graph neural networks (GNNs) to model functional connectivity. However, most existing approaches operate at the electrode level without explicitly incorporating function…
  europepmc 2026 置信度 0.80
• DCL-SE: Dynamic curriculum learning for spatiotemporal encoding of brain imaging.
  High-dimensional neuroimaging analyses for clinical diagnosis are often constrained by compromises in spatiotemporal fidelity and the limited adaptability of large-scale, general-purpose models. To address these challenges, we introduce Dynamic Curriculum Lear…
  europepmc 2026 置信度 0.80
• An Explainable Computer-Aided Framework for Skin Lesion Classification Using Deep Learning.
  The utilization of deep convolutional neural networks for the purpose of diagnosing diseases in the skin area has proven to yield similar accuracy levels to those obtained by dermatologists in different studies. Nevertheless, many challenges are still present,…
  europepmc 2026 置信度 0.80
• Automated Prediction of Subsequent Miscarriage Risk in Pregnant Women by Early First-Trimester Ultrasound Characteristics Based on the Convolutional Neural Network Model.
  Predicting the risk of subsequent miscarriage during the first trimester is crucial for optimizing ultrasound surveillance and alleviating psychological distress for pregnant women. This study aims to develop a multi-input convolutional neural network (CNN) th…
  europepmc 2026 置信度 0.80
• HFG-Net: high-frequency guided multi-view graph convolution and dynamic spatio-temporal fusion for ASD diagnosis.
  Objective. Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition characterized by significant inter-subject variability in electroencephalogram (EEG) features. Existing deep learning approaches often fail to fully capture intrin…
  europepmc 2026 置信度 0.80
• Clinically Interpretable Deep Learning for Differentiating Vitiligo and Postinflammatory Hypopigmentation: Diagnostic Accuracy Study.
  Background Distinguishing vitiligo from postinflammatory hypopigmentation (PIH) is clinically challenging because both conditions may present with similar depigmented lesions. Although deep learning has shown strong potential for dermatologic image classificat…
  europepmc 2026 置信度 0.80
• ARF-GNN: Adaptive receptive field graph neural network for protein function prediction.
  Protein function prediction is one of the core challenges in bioinformatics, which plays a key role in resolving cellular mechanisms and driving drug discovery. A core challenge in this field is that protein function depends on both local structural motifs and…
  europepmc 2026 置信度 0.80
• Neuro-fuzzy systems in internet of medical things: a systematic review on applications, taxonomy, challenges and open issues.
  The growing demand for real-time, adaptive, and explainable analytics in the Internet of Medical Things (IoMT) has increased interest in neuro-fuzzy systems for connected healthcare. By combining neural learning with fuzzy inference, these systems can support …
  europepmc 2026 置信度 0.80
• Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study.
  Background Irregularly sampled data, as a common data structure in the medical field, is frequently observed in emergency clinical datasets. It poses problems such as unequal sampling time intervals and frequencies, making it difficult to align the data withou…
  europepmc 2026 置信度 0.80
• Residual bidirectional gated recurrent unit with spatiotemporal shortcuts and dual-path attention for robust interpretable runoff forecasting.
  While runoff forecasting models based on neural networks demonstrate the superior performance over traditional methods in the hydrological field, they also face challenges such as insufficient depth, rapid decay in accuracy with increasing lead times, and poor…
  europepmc 2026 置信度 0.80
• Gated edge-node interaction graph convolutional network for ADHD classification.
  Objective. Attention deficit hyperactivity disorder (ADHD) remains challenging to diagnose objectively and often relies on subjective clinical assessments. Although graph neural network-based methods have been widely used for ADHD identification, most existing…
  europepmc 2026 置信度 0.80
• TMESurv: a Tumor Microenvironment-informed Interpretable Neural Network for Survival Analysis across Multiple Cancer Types.
  Cancer survival analysis is critical for prognosis; however, integrative analyses across multiple cancer types remain limited, particularly in capturing tumor microenvironment (TME) heterogeneity. To address this, we developed TMESurv, a biologically informed …
  europepmc 2026 置信度 0.80
• Real-Time Dynamic Prediction of Mortality and Renal Replacement Therapy After Cardiac Surgery Using a Time-Series Deep Learning Model.
  Objective Predicting postoperative deterioration following cardiac surgery remains challenging. Conventional risk scores rely on static variables and fail to capture evolving physiologic trajectories. We developed a time-series deep learning model (DLM) using …
  europepmc 2026 置信度 0.80
• Modeling the interpretable geometric-performance relationship of metamaterials on small datasets using Kolmogorov-Arnold operator informed network.
  Deep learning has been extensively employed in the prediction of metamaterial properties. However, the multi-layer perceptron-kernelled methods lack interpretability and are highly dependent on large datasets, making the end-to-end mapping opaque and computati…
  europepmc 2026 置信度 0.80
• Finite-Element-Informed Pyramid Neural Network With Draco Lizard Optimizer for Accurate and Efficient Water Contamination Classification.
  Water Contamination is a major issue of concern in the aquaculture industry, and it affects the sustainability of the ecosystem as well as the health of the aquatic species. Measuring pollution clean-up in water is the key component of efficient supervision an…
  europepmc 2026 置信度 0.80
• Multi-wavelength photobleaching kinetics of endogenous fluorophores for label-free tissue characterization with potential for tumor margin estimation using machine learning.
  Autofluorescence photobleaching encodes valuable information related to tissue morphology, metabolism, and fluorophore microenvironment; however, it remains mostly underexplored as a quantitative imaging biomarker. This study investigates multi-wavelength phot…
  europepmc 2026 置信度 0.80
• NetFormer: A Dual-Stream Interpretable Transformer Autoencoder for Unsupervised Network Intrusion Detection
  Background: The growing complexity and frequency of cyberattacks demand intrusion detection systems (IDS) that accurately identify malicious activity with very low false-positive rates and minimal latency. Traditional rule-based and classical machine learning …
  europepmc 2026 置信度 0.80
• Pathway Modeling of Genomic and Tissue-Specific Transcriptomic Architecture Identifies Personalized Mechanisms of Atrial Fibrillation Risk
  Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and a major cause of cardioembolic stroke. Although polygenic risk scores (PRS) are well characterized to quantify inherited susceptibility for AF, they provide limited insight into the p…
  europepmc 2026 置信度 0.80
• Statistical and machine learning inference for depletion risk assessment in a stochastic harvesting model with weak Allee effects
  Abstract Estimating the dynamics of harvested populations is particularly difficult when weak Allee effects are present, since their influence is concentrated at low biomass levels, where observations are often scarce. We investigate this problem for a stochas…
  europepmc 2026 置信度 0.80
• Machine Learning in Nonischemic Cardiomyopathy: Phenotyping, Mechanism Discovery, and Clinical Applications.
  Nonischemic cardiomyopathy (NICM) refers to a heterogeneous group of myocardial disorders whose shared phenotypes complicate diagnosis and risk stratification. This complexity has driven growing interest in computational approaches that can integrate high-dime…
  europepmc 2026 置信度 0.80
• LysePred: A Multiscale Convolutional Neural Network for Predicting Hemolytic Activity of Antimicrobial Peptides.
  Antimicrobial peptides (AMPs) represent promising alternatives to conventional antibiotics, yet hemolytic toxicity remains a critical barrier to clinical translation, with approximately 70% of known AMPs exhibiting high or moderate hemolytic activity. Existing…
  europepmc 2026 置信度 0.80
• A hybrid approach for citrus disease detection using convolutional neural networks and fuzzy inference systems for enhanced accuracy and interpretability.
  The citrus diseases are affecting the fruit production worldwide thereby posing an economical burden. Major research is moving towards finding solutions using Artificial Intelligence (AI) and Image processing methods. Due to factors like illumination variation…
  europepmc 2026 置信度 0.80
• Interpretable graph-based models on multimodal biomedical data integration: a technical review and benchmarking.
  Integrating diverse biomedical modalities is essential for robust healthcare insights, and graph-based models are increasingly used to capture complex relational structures. Yet, their clinical translation hinges on interpretability. This review surveys interp…
  europepmc 2026 置信度 0.80
• Melanoma Prediction Using a Deep Convolutional Neural Network (CNN) Model
  Abstract Melanoma is one of the most aggressive forms of skin cancer and remains a significant global public health challenge due to its high mortality rate when diagnosed at advanced stages. Recent advances in artificial intelligence (AI), particularly deep l…
  europepmc 2026 置信度 0.80
• Multiway Autoregressive Network: A Dynamic Graph Representation Framework for Temporal Link Prediction.
  Understanding how links form and predicting future link states is of great importance in social, traffic, and many other complex temporal networks. These temporal networks are typically governed by multiple evolutionary mechanisms. However, existing dynamic gr…
  europepmc 2026 置信度 0.80
• Enhancing knowledge base question answering with AMR-driven subgraph retrieval.
  Knowledge Base Question Answering (KBQA) aims to answer natural language questions using factual triples from a knowledge graph (KG). Mainstream Graph Neural Network (GNN)-based methods rely on subgraph retrieval to reduce reasoning complexity. However, existi…
  europepmc 2026 置信度 0.80
• MTDRA-Net: graph-temporal neural network for dynamic evaluation of college students' responsibility.
  Responsibility awareness is a core psychological trait in higher education, yet traditional scale assessments suffer from low stability and limited structural interpretability. This study proposes the Multi-Task Dynamic Responsibility Awareness Network (MTDRA-…
  europepmc 2026 置信度 0.80
• From graph models to intelligent decision-making: a review of spatio-temporal graph neural networks for regional disease risk prediction and etiology mining.
  Background Regional disease risk prediction is a core component of public health early warning systems. Traditional statistical models and machine learning methods have inherent limitations in handling multi-source heterogeneous data fusion, complex spatio-tem…
  europepmc 2026 置信度 0.80
• SMFF-Net: Spatiotemporal-frequency Multi-domain Feature Fusion Network for EEG-based brain state detection.
  The timely and reliable detection of driver fatigue is crucial for reducing driving risks and improving traffic safety. EEG-based deep learning approaches for brain state detection suffer from insufficient cross-domain feature interaction and limited neurophys…
  europepmc 2026 置信度 0.80
• Predicting biomolecular interactions via a dual-stream graph neural network with motif constraint and diffusion-based regularization.
  Biomolecular interactions, such as RNA-Protein Interactions (RPIs) and Protein-Protein Interactions (PPIs), are fundamental to life; however, accurately predicting them remains a central challenge in computational biology. Current deep learning methods, despit…
  europepmc 2026 置信度 0.80
• Bayesian Convolutional Neural Networks for Uncertainty-Aware Classification of Infrasound Events.
  Accurate classification of infrasound signals is essential for nuclear-test verification, natural-hazard warning, and geophysical monitoring. Conventional convolutional neural networks (CNN) applied to this task tend to overfit small, class-imbalanced datasets…
  europepmc 2026 置信度 0.80
• Data-driven oscillatory network modeling with condition-dependent coupling laws: Identifying directed neural interactions in working memory–attention dynamics
  Learning new information in the presence of distracters and changing conditions requires the ability to adapt. In the brain, this adaptive capability has been linked to dynamic interactions between attention and working memory, which enable the selective filte…
  europepmc 2026 置信度 0.80
• Cross-Domain Transfer Learning Strategy Enhances Interpretability of Deep Learning Model Explanations
  Abstract Clinical decision-making increasingly relies on deep neural networks (DNNs), yet their deployment in practice requires transparent and interpretable predictions. Explainable artificial intelligence (xAI) methods can identify input regions relevant to …
  europepmc 2026 置信度 0.80
• Explainable Prediction of ICU Transfer in Acute Pancreatitis: A Neural Network Model With SHAP Interpretability for Clinical Decision Support.
  Objective Acute pancreatitis (AP) is a life-threatening disorder commonly observed in emergency departments. Patients with acute pancreatitis may necessitate transfer to the intensive care unit (ICU) if standard treatments prove ineffective. The development of…
  europepmc 2026 置信度 0.80
• RINAMI: Residue-attributed interpretable neural network for predicting absolute folding free energy by merging structure and sequence information.
  Recent advances in de novo protein design have enabled the generation of diverse novel proteins. However, a fundamental challenge remains: even when an amino acid sequence is designed with the target structure as the most stable conformation, there is currentl…
  europepmc 2026 置信度 0.80
• Machine learning-based prediction of SARS-CoV-2 bioactivity: integrating IC<sub>50</sub> regression and activity classification using multi-task neural networks.
  Accurate prediction of compound bioactivity is essential for accelerating antiviral drug discovery and reducing experimental costs. Machine learning (ML) methods have shown considerable promise in modeling structure-activity relationships and compound potency.…
  europepmc 2026 置信度 0.80
• Deep Learning-Based Classification of Facial Profile Convexity from Profile Photographs in Orthodontics: An Artificial Intelligence Study.
  The soft-tissue facial profile is a cornerstone of orthodontic diagnosis and treatment planning, strongly influencing facial esthetics and patient satisfaction. This study aimed to develop and evaluate a deep learning-based framework for automated classificati…
  europepmc 2026 置信度 0.80
• Multimodal Information-Driven Heterogeneous Graph Neural Networks for Protein-Ligand Binding Affinity Prediction.
  Accurate prediction of protein-ligand binding affinity (PLA) is essential for efficient drug screening. However, existing methods often inadequately model multimodal molecular interactions and their combined effects on binding affinity. To address this challen…
  europepmc 2026 置信度 0.80
• A Hybrid AI-Mathematical approach for epidemic threshold prediction in metapopulation networks: Integrating physics-guided neural networks with spectral graph theory.
  Predicting the epidemic threshold [Formula: see text] in contact networks is a central challenge in computational epidemiology. Classical structural approaches based on spectral graph theory-most notably Quenched Mean-Field (QMF) and the recently proposed KSEL…
  europepmc 2026 置信度 0.80
• A novel deep learning approach for privacy-preserving encoded EEG-based brain-computer interfaces with clinical LLM applications.
  Purpose The rise of large language models (LLMs) such as GPT-4 and DeepSeek has transformed healthcare information processing by enabling natural language-based clinical reasoning. However, the integration of LLMs with privacy-sensitive biomedical signals, par…
  europepmc 2026 置信度 0.80
• Developing an Interpretable Hybrid AI Framework that Integrates Mobility Data, Genomic Surveillance, and Symptom Reporting for Early Outbreak Signal Detection
  Background: Early detection of infectious disease outbreaks is critical for containing transmission and mitigating public health impacts. Traditional surveillance systems rely on clinical case reporting, which suffers from significant delays due to diagnostic …
  europepmc 2026 置信度 0.80
• Reliability-based mix design for concrete compressive strength using a physics-prior residual-learning surrogate with calibrated uncertainty.
  An integrated framework is presented for concrete compressive strength prediction and reliability-based mix design under data-scarce conditions, in which a physics-prior residual surrogate and uncertainty decomposition are combined. A physics-consistent baseli…
  europepmc 2026 置信度 0.80
• Topology-Constrained Saddle-Point Reconstruction of Strong-Field Photoelectron Momentum Spectra Using Physics-Informed Neural Networks
  In strong-field ionization, complex-time saddle points govern electron ionization instant and quantum-orbit interference patterns. Neverthless, not all solutions to the saddle-point equation carry physical contributions. And saddle coalescence can also invalid…
  europepmc 2026 置信度 0.80
• COLLECT: A Counterfactual Oral Lesion Library for Trustworthy AI Evaluation.
  Intraoral mucosal lesions are difficult to diagnose because they vary widely in appearance and cause. Deep-learning models perform well for oral lesion classification, but limited interpretability restricts clinical use. Counterfactual explanations, which modi…
  europepmc 2026 置信度 0.80