Search Results (20,333)

Introduction: Dental malocclusion affects more than half of the global population, causing significant functional and esthetic consequences. The integration of artificial intelligence (AI) into orthodontic care for malocclusion has the potential to enhance diagnostic accuracy, treatment planning, and clinical efficiency. However, existing research remains fragmented, and recent advances have not been comprehensively synthesized. This scoping review aimed to map the current landscape of AI applications in dental malocclusion from 2020 to 2025. Methods: The review followed the Joanna Briggs Institute methodology and the PRISMA-ScR guidelines. The authors conducted a systematic search across four databases (PubMed, Scopus, Web of Science, and IEEE Xplore) to identify original, peer-reviewed research applying AI to malocclusion diagnosis, classification, treatment planning, or monitoring. The review screened, selected, and extracted data using predefined criteria. Results: Ninety-five studies met the inclusion criteria. The majority employed convolutional neural networks and deep learning models, particularly for diagnosis and classification tasks. Accuracy rates frequently exceeded 90%, with robust performance in cephalometric landmark detection, skeletal classification, and 3D segmentation. Most studies focused on Angle’s classification, while anterior open bite, crossbite/asymmetry, and soft tissue modeling were comparatively underrepresented. Although model performance was generally high, study limitations included small sample sizes, lack of external validation, and limited demographic diversity. Conclusions: AI offers the potential to support and enhance the diagnosis and management of malocclusion. However, to ensure safe and effective clinical adoption, future research must include reproducible reporting, rigorous external validation across sites/devices, and evaluation in diverse populations and real-world clinical workflows. Full article

When training a neural network, the choice of activation function can greatly impact its performance. A function with a larger derivative may cause the coefficients of the latter layers to deviate further from the calculated direction, making deep learning more difficult to train. However, an activation function with a derivative amplitude of less than one can result in the problem of a vanishing gradient. To overcome this drawback, we propose the application of pseudo-normalization to enlarge some gradients by dividing them by the root mean square. This amplification is performed every few layers to ensure that the amplitudes are larger than one, thus avoiding the condition of vanishing gradient and preventing gradient explosion. We successfully applied this approach to several deep learning networks with hyperbolic tangent activation for image classifications. To gain a deeper understanding of the algorithm, we employed interpretability techniques to examine the network’s prediction outcomes. We discovered that, in contrast to popular networks that learn picture characteristics, the networks primarily employ the contour information of images for categorization. This suggests that our technique can be utilized in addition to other widely used algorithms. Full article

Magnetic Barkhausen noise (MBN) represents a powerful non-destructive testing and material characterization methodology enabling quantitative assessment of microstructural features, mechanical properties, and stress states in ferromagnetic materials. This comprehensive review synthesizes recent advances spanning theoretical foundations, sensor design, signal processing methodologies, and industrial applications. The physical basis rooted in domain wall dynamics and statistical mechanics provides rigorous frameworks for interpreting MBN signals in terms of grain structure, dislocation density, phase composition, and residual stress. Contemporary instrumentation innovations including miniaturized sensors, multi-parameter systems, and high-entropy alloy cores enable measurements in challenging environments. Advanced signal processing techniques—encompassing time-domain analysis, frequency-domain spectral methods, time–frequency transforms, and machine learning algorithms—extract comprehensive material information from raw Barkhausen signals. Deep learning approaches demonstrate superior performance for automated material classification and property prediction compared to traditional statistical methods. Industrial applications span manufacturing quality control, structural health monitoring, railway infrastructure assessment, and predictive maintenance strategies. Key achievements include establishing quantitative correlations between material properties and stress states, with measurement uncertainties of ±15–20 MPa for stress and ±20 HV for hardness. Emerging challenges include standardization imperatives, characterization of advanced materials, machine learning robustness, and autonomous system integration. Future developments prioritizing international standards, physics-informed neural networks, multimodal sensor fusion, and wireless monitoring networks will accelerate industrial adoption supporting safe, efficient engineering practice across diverse sectors. Full article

The growing prevalence of anxiety, depression, and stress among students highlights the urgent need for school-based strategies that promote psychological well-being and timely intervention. This study explores the use of artificial intelligence (AI) as a scalable and data-driven tool to support institutional mental health initiatives in higher education. Using synthetic and real datasets derived from validated psychometric instruments (the Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI), and the Perceived Stress Scale (PSS-14)), we trained and evaluated 32 deep neural network architectures for the early detection of emotional distress. Optimized three- and four-layer dense models achieved classification accuracies exceeding 95%, demonstrating the feasibility of deploying AI-based screening tools in educational settings. Beyond prediction, this approach can support counselors and educators in identifying at-risk students and informing proactive, school-based interventions to improve mental health and resilience in post-pandemic academic environments. Full article

Unmanned Aerial Vehicles (UAVs) have become integral to modern applications, including smart agricultural robotics, where reliability is essential to ensure safe and efficient operation. It is commonly recognized that traditional fault diagnosis approaches usually rely on vibration and noise measurements acquired via onboard sensors or similar methods, which typically require continuous data acquisition and non-negligible onboard computational resources. This study presents a portable Laser Doppler Vibrometer (LDV)-based system designed for noncontact, offboard, and high-sensitivity measurement of UAV vibration signatures. The LDV measurements are analyzed using a Deep Extreme Learning-based Neural Network (DeepELM-DNN) capable of identifying both propeller fault type and severity from a single 1 s measurement. Experimental validation on a commercial quadcopter using 50 datasets across multiple induced fault types and severity levels demonstrates a classification accuracy of 97.9%. Compared to conventional onboard sensor-based approaches, the proposed framework shows strong potential for reduced computational effort while maintaining high diagnostic accuracy, owing to its short measurement duration and closed-form learning structure. The proposed LDV setup and DeepELM-DNN framework enable noncontact fault inspection while minimizing or eliminating the need for additional onboard sensing hardware. This approach offers a practical and scalable diagnostic solution for large UAV fleets and next-generation smart agricultural and industrial aerial robotics. Full article

Fine-grained sentiment analysis requires a deep understanding of emotional intensity in the text to distinguish subtle shifts in polarity, such as moving from positive to more positive or from negative to more negative, and to clearly separate emotionally neutral statements from polarized expressions, especially in short or contextually sparse texts such as social media posts. While recent advances combine deep semantic encoding with context-aware architectures, such as Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNNs), many models still struggle to detect nuanced emotional cues, particularly in short texts, due to the limited contextual information, subtle polarity shifts, and overlapping affective expressions, which ultimately hinder performance and reduce a model’s ability to make fine-grained sentiment distinctions. To address this challenge, we propose an Emotion- Aware Bidirectional Gating Network (Electra-BiG-Emo) that improves sentiment classification and subtle sentiment differentiation by learning contextual emotion representations and refining them with auxiliary emotional signals. Our model employs an asymmetric gating mechanism within a BiLSTM to dynamically capture both early and late contextual semantics. The gates are temperature-controlled, enabling adaptive modulation of emotion priors, derived from Reddit post datasets to enhance context-aware emotion representation. These soft emotional signals are reweighted based on context, enabling the model to amplify or suppress emotions in the presence of an ambiguous context. This approach advances fine-grained sentiment understanding by embedding emotional awareness directly into the learning process. Ablation studies confirm the complementary roles of semantic encoding, context modeling, and emotion modulation. Further our approach achieves competitive performance on Sem- Val 2017 Task 4c, Twitter US Airline, and SST5 datasets compared with state-of-the-art methods, particularly excelling in detecting subtle emotional variations and classifying short, semantically sparse texts. Gating and modulation analyses reveal that emotion-aware gating enhances interpretability and reinforces the value of explicit emotion modeling in fine-grained sentiment tasks. Full article

The escalating complexity, density, and agility of the modern electromagnetic environment (CME) pose unprecedented challenges to radar signal deinterleaving, a cornerstone of electronic intelligence. While traditional methods face significant performance bottlenecks, the advent of artificial intelligence, particularly deep learning, has catalyzed a paradigm shift. This review provides a systematic, comprehensive, and forward-looking analysis of the radar signal deinterleaving landscape, critically bridging foundational techniques with the cognitive frontiers. Previous reviews often focused on specific technical branches or predated the deep learning revolution. In contrast, our work offers a holistic synthesis. It explicitly links the evolution of algorithms to the persistent challenges of the CME. We first establish a unified mathematical framework and systematically evaluate classical approaches, such as PRI-based search and clustering algorithms, elucidating their contributions and inherent limitations. The core of our review then pivots to the deep learning-driven era, meticulously dissecting the application paradigms, innovations, and performance of mainstream architectures, including Recurrent Neural Networks (RNNs), Transformers, Convolutional Neural Networks (CNNs), and Graph Neural Networks (GNNs). Furthermore, we venture into emerging frontiers, exploring the transformative potential of self-supervised learning, meta-learning, multi-station fusion, and the integration of Large Language Models (LLMs) for enhanced semantic reasoning. A critical assessment of the current dataset landscape is also provided, highlighting the crucial need for standardized benchmarks. Finally, this paper culminates in a comprehensive comparative analysis, identifying key open challenges such as open-set recognition, model interpretability, and real-time deployment. We conclude by offering in-depth insights and a roadmap for future research, aimed at steering the field towards end-to-end intelligent and autonomous deinterleaving systems. This review is intended to serve as a definitive reference and insightful guide for researchers, catalyzing future innovation in intelligent radar signal processing. Full article

Medical imaging is an indispensable tool in clinical diagnosis and therapeutic decision-making, encompassing a wide range of modalities such as radiography, ultrasound, CT, and MRI. With the rapid advancement of deep learning technologies, significant progress has been made in medical image analysis. However, existing deep learning methods are often limited by dataset size, which can lead to overfitting, while traditional approaches relying on hand-crafted features lack specificity and fail to fully capture complex pathological information. To address these challenges, we propose RadioGuide-DCN, an innovative radiomics-guided decorrelated classification network. Our method integrates radiomics features as prior information into deep neural networks and employs a feature decorrelation loss mechanism combined with an anti-attention feature fusion module to effectively reduce feature redundancy and enhance the model’s capacity to capture both local details and global patterns. Specifically, we utilize a Kolmogorov–Arnold Network (KAN) classifier with learnable activation functions to further boost performance across various medical imaging datasets. Experimental results demonstrate that RadioGuide-DCN achieves an accuracy of 93.63% in BUSI image classification and consistently outperforms conventional radiomics and deep learning methods in multiple medical imaging classification tasks, significantly improving classification accuracy and AUC scores. Our study offers a novel paradigm for integrating deep learning with traditional imaging approaches and holds broad clinical application potential, particularly in tumor detection, image classification, and disease diagnosis. Full article

This article presents the design, simulation, and real-time implementation of an intelligent water level control system using Deep Reinforcement Learning (DRL) with the Deep Deterministic Policy Gradient (DDPG) algorithm. The control policy was initially trained in a MATLAB-based simulation environment, where actor–critic neural networks were trained and optimized to ensure accurate and robust performance under dynamic and nonlinear conditions. The trained policy was subsequently deployed on a low-cost embedded platform (Arduino Uno), demonstrating its feasibility for real-time embedded applications. Experimental results confirm the controller’s ability to adapt to external disturbances. Quantitatively, the proposed controller achieved a steady-state error of less than 0.05 cm and an overshoot of 16% in the physical implementation, outperforming conventional proportional–integral–derivative (PID) control by 22% in tracking accuracy. The combination of the DDPG algorithm and low-cost hardware implementation demonstrates the feasibility of real-time deep learning-based control for intelligent water management. Furthermore, the proposed architecture is directly applicable to low-cost Internet of Things (IoT)-based water management systems, enabling autonomous and adaptive control in real-world hydraulic infrastructures. This proposal demonstrates its potential for smart agriculture, distributed sensor networks, and scalable and resource-efficient water systems. Finally, the main novelty of this work is the deployment of a DRL-based controller on a resource-constrained microcontroller, validated under real-world perturbations and sensor noise. Full article

Panoramic radiography remains a cornerstone diagnostic tool in dentistry; however, its two-dimensional nature limits the visualisation of complex maxillofacial anatomy. Three-dimensional reconstruction from single panoramic images addresses this limitation by computationally generating spatial representations without additional radiation exposure or expensive cone-beam computed tomography (CBCT) scans. This systematic review and conceptual study traces the evolution of 3D reconstruction approaches, from classical geometric and statistical shape models to modern artificial intelligence-based methods, including convolutional neural networks, generative adversarial networks, and neural implicit fields such as Occudent and NeBLa. Deep learning frameworks demonstrate superior accuracy in reconstructing dental and jaw structures compared to traditional techniques. Building on these advancements, this paper proposes HoloDent3D, a theoretical framework that combines AI-driven panoramic reconstruction with real-time holographic visualisation. The system enables interactive, radiation-free volumetric inspection for diagnosis, treatment planning, and patient education. Despite significant progress, persistent challenges include limited paired 2D–3D datasets, generalisation across anatomical variability, and clinical validation. Continued integration of multimodal data fusion, temporal modelling, and holographic visualisation is expected to accelerate the clinical translation of AI-based 3D reconstruction systems in digital dentistry. Full article

Robustness to distributional shifts remains a critical limitation for deploying deep neural networks (DNNs) in real-world applications. While DNNs excel in standard benchmarks, their performance often deteriorates under unseen or perturbed conditions. Understanding how internal information representations relate to such robustness remains underexplored. In this work, we propose an interpretable framework for robustness assessment based on partial information decomposition (PID), which quantifies how neurons redundantly, uniquely, or synergistically encode task-relevant information. Analysis of PID measures computed from clean inputs reveals that models characterized by higher redundancy rates and lower synergy rates tend to maintain more stable performance under various natural corruptions. Additionally, a higher rate of unique information is positively associated with improved classification accuracy on the data from which the measure is computed. These findings provide new insights for understanding and comparing model behavior through internal information analysis, and highlight the feasibility of lightweight robustness assessment without requiring extensive access to corrupted data. Full article

Streaming media data have become pervasive in modern commercial systems. To address large-scale data processing in intelligent transportation systems (ITSs), recent research has focused on deep neural network–based (DNN-based) approaches to improve the performance of cross-modal hashing retrieval (CMHR) systems. However, due to their high dimensionality and network depth, DNN-based CMHR systems inherently suffer from vulnerabilities to malicious adversarial examples (AEs). This paper investigates the robustness of CMHR-based ITS systems against AEs. Prior work typically formulates AE generation as an optimization-driven, iterative process, whose high computational cost and slow generation speed limit research efficiency. To overcome these limitations, we propose a parallel cross-modal relevance-guided generative network (ReGeNet) that captures the semantic characteristics of the target deep hashing model. During training, we design a relevance-guided adversarial generative framework to efficiently learn AE generation. During inference, the well-trained parallel adversarial generator produces adversarial cross-modal data with effectiveness comparable to that of iterative methods. Experimental results demonstrate that ReGeNet can generate AEs significantly faster while achieving competitive attack performance relative to iterative-based approaches. Full article

Location estimation is significant in this era of the Internet of Things (IoT). Satellite and cellular signals are often blocked indoors, prompting researchers to explore alternative wireless technologies for indoor positioning. Among these, Wi-Fi Received Signal Strength (RSS) with fingerprinting is dominant in large, multi-floor buildings due to its existing infrastructure, acceptable accuracy, low cost, easy deployment, and scalability. This study aims to systematically search and review the literature on the use of real Wi-Fi RSS fingerprints for indoor localization or positioning in large, multi-floor buildings, in accordance with PRISMA guidelines, to identify current trends, performance, and gaps. Our findings highlight three main public datasets in this fields (covering areas over 10,000 sq.m). Recent trends indicate the widespread adoption of Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs) and Stacked Autoencoders (SAEs). While buildings (in the same vicinity) and their respective floors are accurately identified, the maximum average error remains around 7 m. A notable gap is the lack of public datasets with detailed room or zone information. This review intends to serve as a guide for future researchers looking to improve indoor location estimation in large, multi-floor structures such as universities, hospitals, and malls. Full article

The tasks of community detection in complex networks have garnered increasing attention from researchers. Concurrently, with the emergence of graph neural networks (GNNs), these models have rapidly become the mainstream approach for solving this task. However, GNNs frequently encounter the Laplacian oversmoothing problem, which dilutes the crucial neighborhood signals essential for community identification. These signals, particularly those from first-order neighbors, are the core source information defining community structure and identity. To address this contradiction, this paper proposes a novel training strategy focused on strengthening these key local signals. We design a multi-branch learning structure that injects a gradient into the GNN layer during backpropagation. This gradient is then modulated by the GNN’s native message-passing path, precisely supplementing the parameters of the initial layers with first-order topological information. Based on this, we construct the network structure-informed GNN (NIGNN). A large number of experiments show that the proposed method achieves a 0.6–3.6% improvement in multiple indicators compared with the basic model in the community detection task, and performs well in the t-test. The framework has good general applicability and can be effectively applied to GCN, GAT, and GraphSAGE architectures, and shows strong robustness in networks with incomplete information. This work offers a novel solution for effectively preserving core local information in deep GNNs. Full article

With the acceleration of urbanization in China, air pollution is becoming increasingly serious, especially PM_2.5 pollution, which poses a significant threat to public health. The study employed different deep learning models, including recurrent neural network (RNN), artificial neural network (ANN), convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM), Transformer, and novel hybrid interpretable CNN–BiLSTM–Transformer architectures for forecasting daily PM_2.5 concentrations on the integrated dataset. The dataset of meteorological factors and atmospheric pollutants in Qingdao City was used as input features for the model. Among the models tested, the hybrid CNN–BiLSTM–Transformer model achieved the highest prediction accuracy by extracting local features, capturing temporal dependencies in both directions, and enhancing global pattern and key information, with low root Mean Square Error (RMSE) (5.4236 μg/m³), low mean absolute error (MAE) (4.0220 μg/m³), low mean absolute percentage error (MAPE) (22.7791%) and high correlation coefficient (R) (0.9743) values. Shapley additive explanations (SHAP) analysis further revealed that PM₁₀, CO, mean atmospheric temperature, O_3, and SO₂ are the key influencing factors of PM_2.5. This study provides a more comprehensive and multidimensional approach for predicting air pollution, and valuable insights for people’s health and policy makers. Full article