Informatica (Print ISSN 0350-5596, Online ISSN 1854-3871) is an international, peer-reviewed journal based in Europe. It publishes scholarly work in computer science, informatics, and related areas, serving researchers and practitioners across academic and applied computing.
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Kernel‐PCA + RBM Feature Extraction with Optimised Naïve Bayes for Intrusion Detection in Impaired Wireless Sensor Networks
The high-dimensional communication data generated by wireless sensor networks often contains substantial redundant and irrelevant information, which hampers the effective retention of critical features. Consequently, the characteristics of network impairment states and abnormal intrusion behaviors become intertwined and difficult to distinguish, ultimately compromising the accuracy of intrusion detection. Therefore, this paper studies the method of abnormal intrusion detection of wireless sensor network communication under network impairment. First, global node perception is achieved through the wireless sensor network networking model to obtain high-dimensional communication data. Second, the kernel principal component analysis (KPCA) method is used to perform nonlinear dimensionality reduction on the data, significantly reducing the data dimension and computational complexity while retaining the key information in the data. Subsequently, a restricted Boltzmann machine (RBM) is introduced to extract the deep features of the dimensionality-reduced data to distinguish the feature differences between network impairment states and abnormal intrusions. Finally, a high-precision abnormal intrusion detection is achieved through an optimized naive Bayes classifier. This classifier effectively improves the anti-interference ability under network impairment states by feature weighting and micro conditional probability optimization, highlights key features, and realizes abnormal intrusion detection. The experiment was conducted on a WSN dataset containing 50000 records, simulating a damaged scenario with a 30% packet loss rate and a 40% bandwidth limitation. The results showed that the proposed method reduced the data dimensionality from 90 to 15 dimensions, with a variance retention rate of 94.7%; In the detection of 10 types of attacks, the F1 value reaches 0.92, which is better than CNN (0.60) and association rules (0.62); At a 75% network damage rate, the false positive rate is only 5%, with accuracy and recall rates of 0.94 and 0.86, respectively, and a single sample prediction time of only 0.21 ms. This method maintains high detection accuracy while having low computational overhead and strong robustness, making it suitable for WSN security protection in complex damaged environments.
Copyright (c) 2026 Shengguo Guo, Dandan Xing
Leveraging Local and Global Features: A CNN-Transformer Hybrid for fMRI-Based Autism Diagnosis
Autism Spectrum Disorder (ASD) diagnosis remains challenging because of its heterogeneity and reliance on subjective behavioral assessments. Resting-state functional MRI (fMRI) presents a compelling opportunity avenue for identifying objective biomarkers, but decoding its complex spatiotemporal patterns requires advanced computational models. While Deep Learning (DL) approaches have progressed, many struggle to concurrently capture local neural dynamics and global temporal dependencies. A novel end-to-end CNN-Transformer hybrid framework designed for fMRI-based autism diagnosis is proposed to address this. Our model leverages a convolutional module to extract localized spatiotemporal features, which are then processed by a Transformer encoder to model long-range, global dependencies through a Multi-Head Self-Attention (MHSA) mechanism. Evaluated on the large multi-site ABIDE-I dataset (N=1,035), the suggested model achieved state-of-the-art performance with an accuracy of 77.85%, a sensitivity of 76.52%, a specificity of 78.90%, and an F1-score of 77.71%. Ablation studies confirmed the critical contribution of each architectural component, and comparisons with pre-trained CNNs and other leading methods demonstrated superior and statistically significant performance (p<0.05). Despite an observed performance drop in site-specific evaluations, underscoring the challenge of scanner heterogeneity, our results affirm that the synergistic integration of local feature learning and global contextual modeling is a powerful paradigm for neuroimaging-based diagnostic applications.
A Hybrid LSTM-Prophet Model for Sales Forecasting and Inventory Optimization in E-Commerce Time Series
With the rapid development of e-commerce, accurately predicting product sales and achieving dynamic inventory control have become key challenges for enterprises to optimize supply chain management. The traditional Prophet model excels at capturing long-term trends and seasonal characteristics in time series, but has limited modeling capabilities for complex nonlinear relationships. Although LSTM neural networks can effectively learn the dynamic dependencies of sequential data, they tend to overlook explicit temporal patterns. This study constructs an LSTM-Prophet fusion model, first utilizing Prophet to decompose the trends, seasonality, and holiday effects of sales data, and then inputting the residuals into LSTM for nonlinear correction. Finally, the prediction accuracy is improved through weighted fusion. Experiments based on 150,000 daily sales data from an e-commerce platform over three years show that the average absolute error of the fusion model is reduced to 8.7, which is 29.3% and 17.1% lower than that of the single Prophet (12.3) and LSTM (10.5) models, respectively, and the root mean square error decreases by 22.6%. In inventory control simulations, this model drives an 18.4% increase in inventory turnover rate and reduces the out-of-stock rate to 3.2%, effectively balancing prediction accuracy and computational efficiency. However, the model still relies on high-quality historical data, has high computational complexity, and has limited adaptability to new product launches and emergencies.
EdgeRopeNet: Lightweight Neural Network for Real-Time Wire Rope Tension Monitoring Using FBG Sensors in Edge-Fog Mining Systems
Wire rope tension monitoring in mining hoist systems demands real-time, high-accuracy detection to mitigate catastrophic failure risks, yet existing cloud-based solutions remain constrained by 300–800 ms latency and network dependence, and conventional FBG sensing lacks embedded intelligence at the edge. To address these limitations, EdgeRopeNet utilizes a compact GRU-based neural architecture with two dense layers (64 and 32 neurons) deployed on Raspberry Pi 4 edge devices (4 GB RAM), supported by fog-layer aggregation on Intel i7 hardware. Sensor data from FBG arrays undergo Savitzky–Golay filtering and Min–Max normalization prior to inference, enabling 19 ms real-time latency and 97.8% prediction accuracy on synthetic datasets emulating mining shaft dynamics. Performance was rigorously benchmarked against ten baselines: five traditional models (Linear Regression, SVM, Random Forest, k-NN, Naïve Bayes) and five deep learning methods (CNN, LSTM, GRU, CNN–LSTM hybrid, Transformer) sing an 80:20 train–test split across 100 epochs with Adam optimization. EdgeRopeNet delivered 97.8% accuracy, 97.4% precision, 98.1% recall, a 97.7% F1-score, and MAE of 0.012, surpassing CNN–LSTM (95.2% accuracy, MAE 0.029) and Transformer models (96.1% accuracy, MAE 0.023). Parameter-pruning reduced model size by 60% while preserving 97.4% precision and 98.1% recall, with edge inference sustained at 0.019 seconds per prediction. Overall, EdgeRopeNet achieves a 94% reduction in latency relative to cloud-based platforms while maintaining superior accuracy, providing a scalable, autonomous, and edge-resilient solution for safety-critical mining infrastructure. Keywords: Edge computing, wire rope tension monitoring, FBG sensors, lightweight neural networks, mining hoist systems, real-time calibration.
Physics-Informed Cold Diffusion Framework with Time-Conditioned U-Net for Multi-Coil Brain MRI Reconstruction
Magnetic Resonance Imaging (MRI) reconstruction from undersampled multi-coil k-space data remains a central challenge for accelerating clinical scans. This paper proposes a cold diffusion based reconstruction framework that integrates a deterministic k-space forward operator with a time-conditioned U-Net denoiser. Unlike conventional diffusion models that rely on stochastic Gaussian noise, the proposed approach explicitly models the physical undersampling process, yielding interpretable and data-consistent reconstructions. The model was trained and evaluated on the fastMRI multi-coil brain dataset using 2D slices center-cropped to 320×320 and normalized in complex form (real + imaginary channels). Ablation studies compare the proposed method against a supervised U-Net baseline and a Gaussian DDPM trained under identical conditions. Quantitatively, the cold diffusion model with 500 diffusion steps achieved 37.8 dB PSNR, 0.42 SSIM, and 0.46 HFEN, outperforming both the DDPM (32.8 dB, 0.33 SSIM, 0.60 HFEN) and supervised U-Net (28.9 dB, 0.29 SSIM, 0.71 HFEN) while reducing inference time by 70% relative to conventional diffusion sampling. Qualitative results indicate improved anatomical sharpness, stable convergence and consistent reconstruction across slices, suggesting that the physics-informed diffusion framework is a reliable and interpretable approach for accelerated MRI reconstruction.
Copyright (c) 2026 Nives Grčić, Marija Habijan, Krešimir Matić, Irena Galić
DCNN-FLAME: A Dual-Supervised Style Transfer-Based Method for 3D Animated Character Expression Reconstruction
This study aims to explore a method capable of generating high-naturalness expressions for animated characters, thereby enhancing the audience’s emotional resonance. This study proposes a 3D face reconstruction system named Deep Convolutional Neural Network-Faces Learned with an Articulated Model and Expressions (DCNN-FLAME). DCNN-FLAME consists of an identity encoder, a mapping network, a Facial Landmark Embedding Model (FLAME) geometric decoding module, and a detail reconstruction network, forming an end-to-end processing pipeline from input images to 3D meshes and appearance textures. A style transfer module is constructed based on Deep Convolutional Neural Network (DCNN). It uses a pre-trained convolutional network to achieve effective separation of content and style, providing high-level semantic constraints for texture detail modeling of animated character expressions. On this basis, a dual-branch feature supervision mechanism composed of expression classification features and facial Action Units (AU) detection features is designed. Expression classification features provide global emotional semantic constraints to ensure macro-expression consistency. AU detection features guide local muscle movements from an anatomical perspective to enhance the realism of expression details. Experiments are conducted based on the large-scale face dataset VGGFace2. Systematic comparisons are performed with four 3D face reconstruction algorithms: 3D Morphable Model Fitting (3DMM-Fitting), RingNet, Detailed Expression Capture and Animation (DECA), and Fourier Analysis Networks-3D (FAN-3D). The proposed DCNN-FLAME model achieves a mean value of 1.29 in non-metric evaluation and 1.72 in metric evaluation. Both indicators are lower than those of all baseline methods, demonstrating higher geometric reconstruction accuracy and facial alignment quality. In the overall expression restoration evaluation, the F1 score of the proposed method reaches 0.564, reflecting comprehensive advantages in complex expression modeling. When both the expression classification branch and facial AU detection branch are enabled, the expression classification accuracy rate is 0.571 and the F1 score is 0.563, which are significantly better than the configuration using only a single feature for supervision. This verifies the key role of the dual-branch feature supervision mechanism in improving the naturalness and controllability of animated character expressions. This study provides an effective technical path integrating geometric reconstruction and texture enhancement for 3D animated character expression generation, and also offers new ideas and practical basis for the field of unsupervised 3D face reconstruction.
AIRADL: A Deep Learning-Based Framework for Multimodal Physiological and Biomechanical Injury Risk Classification in Athletes
Athlete performance and injury prevention depend on computational biology to evaluate physiological responses and biomechanics to investigate movement. Incorporating these areas with deep learning improves risk evaluation, allowing for data-driven training tactics. Conventional injury risk evaluations are frequently incorrect, depending on subjective evaluations. Existing models do not capture intricate interactions between biological and biomechanical factors, requiring a more accurate, data-driven method. Objectives: This research proposes the AIRADL (Athlete Injury Risk Assessment using Deep Learning) Framework to forecast injury risk by incorporating physiological and biomechanical data, enhancing classification accuracy, and assisting training decisions. The proposed AIRADL model is trained on the Athlete Health & Motion Analysis Dataset (AHMAD), which contains physiological data such as heart rate, oxygen level, lactate level, and muscle fatigue, in addition to biomechanical factors such as stride length, joint flexibility, and movement symmetry. The data preprocessing steps include mean imputation for missing numerical values, mode imputation for categorical values, label encoding for categorical features, Min-Max scaling for normalization, and Chi-Square feature selection to maintain the most pertinent predictors. The dataset is split into 80% training and 20% testing. A DL4J MLP Classifier is employed to learn trends and classify performance risk levels. The AIRADL framework employs a three-hidden-layer DL4J MLP architecture consisting of 64–128–64 neurons with ReLU activation, Adam optimizer, and early stopping to prevent overfitting. The Athlete Health & Motion Analysis Dataset (AHMAD), a privately collected dataset, was used for experimentation, and evaluation metrics included accuracy, F1-score, and Matthews Correlation Coefficient (MCC), which measures the strength and balance of predictions. Experimental validation confirms model stability, demonstrating consistent convergence behaviour and superior performance compared to baseline methods. The AIRADL model attained 92.3% accuracy in high-risk classification, with precision, recall, and F1-score of 91.8%, 89.6%, and 90.7%, respectively. The MCC score was 89.2%, indicating excellent predictive ability, with lactate level, muscle fatigue, and movement symmetry being important risk indicators. Conclusion: AIRADL shows deep learning's capability in athlete risk prediction and provides a powerful tool for injury prevention.
A Multi-layer Deep Learning Framework for Real-Time Fault Detection and RUL Prediction in Avionics Systems
To address the challenge of real-time fault detection in avionics equipment, we proposed a comprehensive framework consisting of data , model, and decision layers. Data is collected in real time from multiple IoT sensors and processed based on a standardized framework. The data model layer utilizes three models for electronic equipment anomaly detection, multi-category fault identification, and remaining equipment life prediction . The model layer contains three core modules: (1) an unsupervised anomaly detection model based on stacked denoising autoencoders (SDAEs) to identify early latent faults using reconstruction errors; (2) a long short-term memory network (LSTM-Attention) with an attention mechanism for accurate classification of multi-class faults; and (3) a Wiener degradation process model based on Bayesian updates to achieve probabilistic prediction of RUL. Experiments were conducted based on the NASA C-MAPSS dataset and real flight data provided by partner airlines. The results showed that the framework achieved an F1 score of 0.969 in the anomaly detection task, an average accuracy of 97.2% for multi-class fault classification, and a RUL prediction interval coverage of 91.3%. After quantization and compression, the model inference latency was only 18.3 milliseconds, meeting the stringent requirements of airborne equipment for lightweight and real-time performance. This method not only improves the economy and safety of aviation maintenance but also provides an interpretable, low-latency end-to-end solution for intelligent health management.
Multi-Teacher Knowledge Distillation for Lightweight Speech Interaction in Embedded Educational Robots
Educational robots have significant potential in improving learning experience and efficiency through their natural real-time voice interaction capabilities. However, existing mainstream end-to-end voice interaction models have problems with large parameter quantities and high computational costs, making it difficult to deploy efficiently on resource limited embedded educational robot platforms. The average inference delay is 810ms, which seriously affects real-time interaction. Moreover, traditional compression methods sacrifice understanding accuracy in complex scenarios, and the representation ability of small-scale models is limited; To this end, this study proposes a method for constructing a lightweight speech interaction system based on knowledge distillation. A deep neural network pre trained on a large-scale general corpus is used as the teacher model, and a multi-level knowledge transfer mechanism is established through differential masking to guide key feature learning, relationship information extraction module to obtain global correlations, and hierarchical loss function to balance distillation weights. The core knowledge of the teacher model is extracted into a lightweight student model driven by educational scenarios. The final student model contains only 20% of the parameters of the teacher model and maintains high accuracy on a benchmark test set simulating real educational environments. The speech recognition error rate is as low as 15.8% (12.6 percentage points lower than directly training small models of the same scale), and the inference delay is reduced from 810ms to 500ms By reducing by 38% and breaking through the real-time threshold of educational human-computer interaction, the model storage space has been compressed by over 80% (<350MB). It can run efficiently on low-power hardware platforms, effectively solving the balance between accuracy and efficiency in educational robot voice interaction, improving real-time interaction, robustness, and practicality, and providing reliable technical support for its wide application in various educational scenarios.
Adaptive Multi-Scale Image Stitching Using an Attention-Enhanced BiFPN With Contrast-Aware Optimization
To address the challenges of low stitching accuracy and limited robustness in complex scenes, this study proposed an image stitching model based on an improved Bi-directional Feature Pyramid Network (BiFPN). The model enhances performance through three key optimizations. First, an adaptive weighting mechanism dynamically balances the global and local contributions of multi-scale features. Second, a Squeeze-and-Excitation (SE) attention mechanism strengthens feature extraction in critical stitching regions such as edges and textures. Third, a global contrast enhancement module mitigates illumination variation effects on feature matching through multi-scale histogram equalization and adaptive calibration. Experiments were conducted on two benchmark datasets: Microsoft Common Objects in Context (MS COCO) and the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI). From MS COCO, 1,500 image pairs were selected (500 with illumination variations and 500 with scale variations). From KITTI, 1,500 image pairs were selected (800 static scenes and 700 dynamic targets). Each dataset was split into training and validation sets with an 8:2 ratio. Training used a batch size of 16, 50 epochs, and an initial learning rate of 0.001 with a 50% decay every 10 epochs. Comparative methods included traditional algorithms such as Oriented FAST and Rotated BRIEF (ORB) and Scale-Invariant Feature Transform (SIFT), as well as deep learning approaches including Vision Transformer-Large/16 (ViT-L/16) and the Stitch Generative Adversarial Network. The proposed model outperformed all baselines in complex scenarios. On the MS COCO dataset with illumination variations, the mean squared error (MSE) reached 1.12×10⁻²—69.09% lower than ORB and 39.46% lower than ViT-L/16. The peak signal-to-noise ratio (PSNR) increased to 34.89 dB, improving by 5.11 dB over SIFT and 2.75 dB over other models. The structural similarity index (SSIM) reached 0.946, exceeding competing methods by 7.26%. On the KITTI dataset with dynamic targets, the feature matching accuracy reached 92.3%, a 17.95% improvement over SIFT, while the stitching time decreased to 1.78 s, 30.47% faster than other models. The model maintained high robustness under parallax and motion blur conditions, providing precise and efficient image stitching for vision-based control and automation tasks such as robotic navigation and industrial monitoring.
Copyright (c) 2026 Guiqiang Zhang, Huihui Han
Improved YOLOv8s with Swin Transformer and Depthwise Convolutions for Small-Target Pepper Detection and Localization in Agricultural Robotics
A recognition and localization system for chili picking robots was developed based on an improved YOLOv8s model and a RealSense depth camera. The proposed model integrates the Swin Transformer, DW Conv, and C2 modules into the YOLOv8s framework to enhance small-target detection and reduce computational complexity. A dataset containing 2,000 field images of Chaotian pepper (Capsicum frutescens L.) was collected under varying lighting and occlusion conditions, and divided into training, validation, and test sets (7:2:1). To validate the effectiveness of the proposed approach, comparative experiments were conducted against YOLOv5, YOLOv6, YOLOv7, and the original YOLOv8s models. Ablation studies demonstrated that each added component improved model performance, with the combined integration achieving the best results. The improved YOLOv8s model reached a mean Average Precision (mAP) of 82.7%, Recall (R) of 93.0%, and Precision (P) of 79.0%, representing respective increases of 3.4%, 3.0%, and 5.7% compared with the baseline YOLOv8s. These results confirm that the improved YOLOv8s model achieves accurate and efficient chili recognition and localization suitable for robotic harvesting applications.
Frog-Leaping Search-Optimized BiLSTM-Attention Network with GNN for News Hotspot Prediction and Dissemination Path Optimization
The exponential growth of online news and social media platforms necessitates intelligent systems capable of predicting emerging news hotspots and optimizing dissemination pathways. This research proposes a novel deep learning framework, Frog-Leaping Search-Mutated BiLSTM with Attention Net (FLS-BiLSTM AttNet), designed to identify potential news hotspots and enhance information propagation accurately. The dataset was collected from multiple reliable sources, including the Kaggle repository, Google News API, Twitter feeds, and various online news portals. Preprocessing involves removing stop words, punctuation, and irrelevant symbols, followed by tokenization to convert text into meaningful word sequences. TF-IDF is applied to extract significant keywords, quantifying their relevance within individual documents and across the dataset. The Bidirectional Long Short-Term Memory (BiLSTM) network captures temporal dependencies in news sequences, while the Attention Net (AttNet) highlights critical features for improved prediction. Frog-Leaping Search (FLS) optimizes weight initialization, enhancing convergence speed and hotspot detection performance. To optimize dissemination pathways, Graph Neural Networks (GNNs) are employed to identify efficient propagation routes, reducing latency and maximizing coverage. Implemented in Python, the model achieves an accuracy of 98.92%, a precision of 98.45%, a recall of 98.41%, an F1 score of 98.57%, a coverage of of of 94.5%, and an NDCG of of of 82.6%, demonstrating substantial improvement over traditional methods. The proposed framework successfully integrates temporal modeling, attention mechanisms, optimization strategies, and graph-based propagation, making it highly applicable to real-world digital journalism, early warning systems, and media strategy planning, while maintaining robustness and adaptability across diverse news datasets.
Dynamic Elderly Care Resource Allocation using SHBM-Tuned Deep Q-Networks (SHBM-RDQN) Hua
The growing demand for elderly care services necessitates intelligent, efficient, and adaptive resource management strategies. This research presents a dynamic allocation strategy for optimizing elderly care resources using reinforcement learning (RL), addressing the complexity of nursing home resource management. This research proposes a Dynamic Honeybees Mating–tuned Resource-based Deep Q-Network (DHBM-RDQN) for elderly care resource allocation. The approach models the care environment as a Markov Decision Process (MDP), where states capture patient acuity levels, staff availability, daily admissions, and care workload, while actions correspond to dynamic allocation of nursing staff and support resources. The system uses a comprehensive Elderly Care Staffing & Quality Dataset, comprising time-series records at daily or shift-level granularity from multiple long-term care facilities. Preprocessing includes Z-score normalization and missing value imputation, while feature extraction applies Independent Component Analysis (ICA) and Discrete Wavelet Transform (DWT) to capture latent health patterns and temporal fluctuations. The DHBM-RDQN employs a Deep Q-Network with two fully connected layers (256 and 128 neurons) and ReLU activations, trained using the Adam optimizer. The Honeybee Mating optimization layer dynamically tunes learning rate, exploration parameters, and reward weights to prevent premature convergence. Experimental evaluation were implemented in python. Results show 96.5% accuracy, 90.5% resource efficiency, 1.0 s response time, and an adaptability score of 0.925, demonstrating robust adaptability under fluctuating patient demand and staffing. This research introduces a novel RL framework combining deep learning and bio-inspired optimization to achieve superior performance, rapid decision-making, and improved care quality in dynamic long-term elderly care environments.
Dynamic Sub-Model Aggregation and Clustering for Intelligence Data via Hierarchical Federated Learning with Pre-Training
To address the challenges of slow convergence speed, poor dynamic adaptability, and low communication efficiency in intelligence data processing, a dynamic integration and clustering method for intelligence data based on an improved federated learning algorithm is proposed. First, an improved federated learning algorithm combining decomposition and combination is designed, where the global model is decomposed into multiple sub-models for local training, and a dynamic combination strategy is applied to integrate these sub-models, thereby improving the adaptability and accuracy of the global model. Then, a pre-training mechanism is introduced to initialize the global model using feature information from historical data, enhancing the model's initialization performance in dynamic data environments and accelerating convergence. Experiments are conducted on the MNIST and CIFAR-10 datasets, with comparisons made against baseline methods including FedAvg, FedProx, and ScaFFL. The results show that the proposed algorithm achieves accuracies of 98.69% and 90.26% on the pathological heterogeneity client, and 98.14% and 89.87% on the actual scenario heterogeneity client, respectively, on the two datasets. The normalized mutual information values of the proposed intelligence dynamic data integration and clustering method are 0.91 and 0.79, respectively. In a practical medical Internet of Things scenario test, the running time and memory usage of the proposed method are 18.23s and 1681MB, respectively. Our research denotes that the designed method can effectively improve the quality of dynamic integration of intelligence data and reduce resource consumption, providing a feasible solution for efficient processing of multi-source heterogeneous intelligence data.
Copyright (c) 2026 Jianfeng Wang, Lin Ma, Xinyan Pei, Ruonan Shi, Qi Jing, Chen Yang
SEH-ConGAN: A Scalable GAN-based Framework for Robot-Assisted Automation in Animation Production
The animation production process is traditionally labor-intensive, requiring extensive manual effort in character motion design, scene composition, and post-production editing. To overcome these limitations, this research introduces a robot-assisted automation system integrated with artificial intelligence (AI) to streamline and accelerate animation development. The system incorporates a motion capture interface for acquiring human movement data, a feedback-enabled robotic arm to replicate and analyze motions, and a simulation environment for virtual testing. Preprocessing includes missing-value handling and Z-score normalization, after which structured motion sequences (3D joint coordinates, robotic servo positions, and torque data) are provided as input to the Scalable Elephant Herding-tuned Conditional Generative Adversarial Network (SEH-ConGAN). The model generates refined outputs such as smooth motion trajectories, facial expression synthesis, and context-aware style transfer. Statistical analysis using a paired t-test, 95% confidence intervals, and Cohen’s d effect size was performed to confirm the significant performance improvement of SEH-ConGAN over baseline models Performance is evaluated using 5-fold cross-validation and achieves an accuracy of 0.96, precision of 0.97, recall of 0.96, and F1-score of 0.96. Comparative analysis of motion generation metrics shows that SEH-ConGAN surpasses existing models achieving the best MPJPE (16.7), FID (11.3), Smoothness (0.028), and Diversity (0.72), demonstrating superior motion accuracy, trajectory smoothness, and animation realism. . The findings demonstrate that combining robotics with SEH-ConGAN provides a scalable solution for producing high-quality animations with reduced time, cost, and manual intervention.
Hybrid Panel Data Forecasting for Coastal Flood Hazard Assessment: A Spatial Data Mining Approach Using Exponential Smoothing and Neural Networks
Due to climate change–induced sea level rise, coastal flood risk is increasing significantly, creating an urgent need for improved flood risk assessment and mitigation strategies. Effective flood analysis requires both time series forecasting and hazard risk evaluation, including the determination of appropriate weights for flood-related parameters such as elevation, runoff, distance to the shoreline, and sea level. Traditional forecasting methods like exponential smoothing are limited in capturing relationships between variables, while neural networks can model non-linear interactions but are less commonly applied to long-term forecasting. To address these limitations, this study proposes a hybrid method that integrates Exponential Smoothing (ES) and Neural Networks (NN) for panel data analysis, where ES identifies trends and seasonal patterns and its output is used as additional input for NN. The NN is also employed to objectively determine parameter weights, reducing the subjectivity of conventional AHP-based approaches. The method integrates geographic and climatic variables, including wind speed, temperature, sea surface pressure, and rainfall, and is applied to coastal areas in Semarang, Demak, and Jepara, Indonesia. Results show that the hybrid model outperforms standard ES and NN methods, achieving flood forecasting errors (MAPE) between 3.5% and 8.3% and parameter weighting accuracy of 88–94%, contributing to a more holistic and reliable flood risk analysis.
Copyright (c) 2026 Dimara Kusuma Hakim, Rahmat Gernowo, Anang Widhi Nirwansyah
AI-SLMS: An AI-Integrated Framework for Predictive Maintenance, Intelligent Scheduling, and Access Control in University Laboratory Management Systems
In recent years, a smart, safe, and efficient way to run university labs has become increasingly popular. Traditional management systems rely on manual processes that are error-prone, slow, and offer limited adaptability. To address these challenges, this study proposes a Smart Laboratory Management System (AI-SLMS) that optimizes operations, improves safety, and enhances the user experience in academic labs. AI-SLMS integrates predictive maintenance, intelligent scheduling, and secure access control using machine learning and the Internet of Things (IoT). The system employs Random Forest and Logistic Regression models, trained on integrated datasets (Kaggle Predictive Maintenance and TON_IoT), to anticipate equipment failures. For resource allocation, an intelligent scheduling module utilizes genetic algorithms for optimization. The system also enforces role-based access through RFID and biometric authentication. Experimental validation over three months in a university setting demonstrated significant improvements across key metrics: a 71.2% reduction in equipment downtime, a 78.7% decrease in scheduling conflicts, a 53.5% improvement in resource utilization, and 98.3% authentication accuracy. In conclusion, AI-SLMS offers a scalable and intelligent framework that significantly enhances the efficiency, security, and responsiveness of university laboratory management systems.
Copyright (c) 2026 Bingcan Wang, Jin Zhang
Adaptive Multidimensional Fusion Network with Dynamic Decision Trees for Financial Market Trend Forecasting
This study proposes an Adaptive Multidimensional Fusion Network (AMFN) for financial market trend forecasting. The model integrates heterogeneous data sources through a multidimensional data fusion module, combining historical price and volume data with external information such as macroeconomic indicators and sentiment indices. An adaptive temporal processing module is employed to model time-varying dependencies and regime shifts in market behavior, while a dynamic decision-tree prediction module captures nonlinear patterns in the fused representations. Experiments are conducted on multiple financial datasets, including the S&P 500 Index, China A-share market, and Gold Futures, using a rolling time-window evaluation to avoid information leakage. The AMFN model achieves lower MSE and MAE and higher R² than traditional SVM and LSTM baselines, with up to 24.4% relative improvement in forecasting accuracy. These results demonstrate that AMFN provides interpretable, stable, and robust trend predictions across diverse market environments.
AE-LSTM-Based Multimodal Sensing System for Real-Time Monitoring of Children’s Play Behavior on Edge Devices
Children’s play is a fundamental activity that supports emotional, cognitive, and social development. However, capturing and analyzing play behavior in real time is challenging due to its spontaneous, multimodal, and dynamic nature. Traditional observation methods are time-consuming, subjective, and lack real-time responsiveness. This research aims to design and implement a multimodal sensing and feedback platform that leverages edge computing and real-time Artificial Intelligence (AI) to monitor, interpret, and support children’s play behavior. The platform collects multimodal play behavior datasets from various sensors, including action and posture recognition, microphones for speech and voice tone analysis, motion sensors to track physical activity, and wearable devices. An Autoencoder-based Long Short-Term Memory (AE-LSTM) network is used to analyze behavior in real time. Feature extraction is performed using a lightweight ResNet model to extract features. Data is pre-processed using Kalman filtering and normalization techniques to reduce noise and improve consistency. The entire system is deployed on edge devices to ensure low-latency processing, local storage, and privacy preservation. The system also provides real-time feedback through visual and haptic cues to enhance engagement. Implemented in Python, experiments have demonstrated that the proposed AE-LSTM model outperforms baseline architectures like LSTM, GRU, and BiLSTM+Attention, and the proposed model achieves higher results according to the F1-score (0.959), accuracy (0.975), recall (0.964), and precision (0.968). These findings offer robust performance in naturalistic settings and provide valuable applications for educators, therapists, and researchers who intend to support and understand child development through intelligent, responsive play environments.
GAN-Based Model for Multi-Instrument Collaborative Music Generation Using Deep Learning
The intelligent development of music creation promotes the application of artificial intelligence in multi-instrument collaborative composition. In this study, we propose a multi-instrument music generation model based on a conditional Generative Adversarial Network (cGAN) that explicitly learns different instrument performance patterns and their coordination. The model is trained on a dataset of 19,000 multi-instrument music excerpts collected from Muse Score, Magenta, Spottily and a self-built corpus, covering classical, pop, jazz, electronic and orchestral styles. Audio is converted to a unified format and sampling rate, denoised, and represented by a fused feature set that combines short-time Fourier transform (STFT) spectrograms with Mel-frequency cepstral coefficients (MFCCs) to capture both harmonic structure and timbral characteristics. The generator adopts a multi-layer convolutional and transposed-convolutional architecture conditioned on instrument labels to synthesize multi-track audio segments, while a multi-branch discriminator jointly evaluates global musical coherence, instrument-wise timbre consistency and style conformity. Model parameters are optimized using gradient-based training combined with a genetic search over key hyperparameters to enhance training stability and audio realism.Quantitative experiments show that the proposed model achieves a mean pitch prediction error of 0.42 semitones, a chord recognition accuracy of 92.3%, and a rhythm synchronization rate of 95.1% across common instrument combinations such as piano–violin and guitar–bass. Subjective listening tests with 20 experienced musicians report an average score of 4.3/5 for melody fluency, 4.2/5 for timbre matching and 4.1/5 for perceived instrument coordination. The model performs particularly well in generating melodically fluent lines, harmonically consistent chord progressions and rhythmically stable ensemble parts, and can more accurately simulate collaborative performance effects among different instruments. However, there remains room for improvement in handling highly complex chord transformations and in integrating electronic synthesizer timbres with traditional instruments. Moreover, computational cost and training stability still constrain large-scale practical deployment, indicating that improving generation efficiency and robustness is an important direction for enhancing the application value of AI-based multi-instrument music composition models.
Microsimulation-Based Evaluation of Fixed-Time and Vehicle-Actuated Traffic Signal Control Strategies Using PTV Vissim in an Urban Corridor
Urban traffic congestion at signalized intersections can be mitigated by enhanced signal control strategies. This paper presents a microsimulation-based comparative analysis of two strategies applied to a heavily congested corridor in Tirana, Albania. Scenario 1 implements conventional fixed-time traffic signals, and Scenario 2 deploys fully vehicle-actuated signals. Four intersections along Tirana’s main ring road were modeled in PTV Vissim under morning peak-hour conditions. The simulation uses a 50-run Monte Carlo approach, and the model was calibrated to field-measured volumes and queues for realism. A custom Vehicle Actuated Programming (VAP) logic with inductive-loop detectors was developed to emulate real controller behavior. Key performance metrics (queue lengths, vehicle delay, and network throughput) were evaluated for each scenario. The results show that vehicle-actuated control yields significant reductions in queue lengths and delays compared to the fixed-time plan. For example, at the most congested approach, fixed-time control caused over 2200 s/vehicle average delay (37 min), whereas actuated control reduced this by about 39% to 1346 s/vehicle. Similarly, queue lengths on major approaches dropped by up to 70% during the first peak hour under actuated control. Network-wide, the actuated strategy improved average travel speed by 33% and reduced total travel time by 25% relative to fixed timings. These findings underscore the effectiveness of vehicle-actuated signals in dynamically allocating green time based on real-time demand, thereby improving intersection throughput and reducing congestion. The paper further examines implementation considerations such as detector placement and maintenance. The adaptive Scenario 2 is found to be highly beneficial for urban traffic management in a growing city like Tirana.
Copyright (c) 2026 Veranda Syla, Algenti Lala
Cardiovascular Disease Prediction via Hybrid SVM–SMOTE and Sparse Autoencoder Feature Reduction with Deep MLP Classification
Cardiovascular diseases remain the leading global cause of death, demanding diagnostic systems that are accurate, interpretable, and computationally efficient. Traditional machine learning approaches frequently struggle with class imbalance, high-dimensional noise, and restricted generalization in clinical datasets. To tackle such issues, we propose a hybrid framework that combines SVM–SMOTE and neighborhood cleaning rule (NCL) for class rebalancing, a sparse autoencoder (SAE) with random forest (RF) selection for non-linear feature optimization, and a class-weighted multilayer perceptron (MLP) for final classification. We validate our framework on the Z-Alizadeh Sani (54 features) and Cleveland (13 features) datasets under stratified fivefold cross-validation, the model attains mean accuracies of 94.02 ± 2.77 % and 94.36 ± 1.47 %, with AUC–ROC = 0.988 and 0.982, outperforming prior baselines [4, 10, 14] by 7.6%–20.8%, and Bootstrap 95% confidence intervals and McNemar/DeLong tests (p < 0.001) confirms significance. Noteably, the ablation study demonstrates the contribution of each module (e.g., a 12% accuracy improvement without sampling). The optimized MLP reduced false negatives to ~5%, while training 40% faster than CNN–LSTM alternatives. The proposed framework provides a statistically robust and interpretable solution for predicting cardiovascular disease.
Dynamic BERT-Reinforcement Learning Model for Intent Recognition in Medical Dialogue Systems
This study proposes a dynamic reasoning model of medical dialogue intentions that integrates BERT and reinforcement learning, aiming to solve the recognition difficulties caused by complex multiple rounds of interaction contexts and changeable user intentions in medical scenarios. Although the traditional BERT model is excellent in semantic modeling, it has limitations such as poor adaptability and static response strategy in the face of dynamic changes in intention expression in medical dialogue. Therefore, this paper introduces reinforcement learning mechanism, and realizes dynamic intention reasoning and policy optimization through state modeling, reward function and policy network. The experimental results highlight the robustness of our model in complex and dynamic medical dialogue scenarios. In high-complexity intent recognition tasks, our model achieved an accuracy improvement of 8.5%. Moreover, in extended multi-round dialogues, the BRL model demonstrated a significant increase in recognition accuracy—from 32% in the 70th round to 60% in the 140th round. This performance was notably better than that of the BLN model, which achieved about 40% accuracy. These improvements underscore the effectiveness of integrating reinforcement learning to adapt to evolving user intents and provide more accurate and contextually relevant responses in long-duration medical dialogues. In the sensitivity analysis of reward function, different reward functions have a significant impact on the model performance. Among them, RWA and RWF perform best when the weight numbers are 2 and 4, with an accuracy rate of more than 70%, while RWN and RWS are often below 40%. To sum up, the model combining BERT and reinforcement learning not only improves semantic understanding capabilities, but also realizes dynamic strategy adaptation, providing an efficient and intelligent intentional reasoning solution for medical dialogue systems.
Dual-Channel Convolutional Neural Network with Mask Guidance for Digital Media Image Super-Resolution
To address the issues of low resolution and detail loss in digital media visual image reconstruction, this paper proposes a super-resolution reconstruction algorithm based on a dual-channel mask-guided convolutional neural network (CNN). This algorithm innovatively designs a parallel guidance channel that utilizes edge masks generated from the input image as prior knowledge to enhance the main channel's ability to extract high-frequency structural features. Through multi-layer feature fusion and improved Adam optimizer, the model achieves a balance between reconstruction quality and efficiency. Tested on the CIFAR-10 and ILSVRC2020 datasets, and validated using image quality assessment metrics, it was found that under super-resolution tasks, the peak signal-to-noise ratio of the research method improved by an average of about 1.1 dB, with a maximum of 37.12 dB. The structural similarity index improved by an average of about 2.1%, with a maximum of 0.9682. At the same time, the algorithm demonstrated excellent efficiency advantages while maintaining high performance, with model parameters reduced to 1.2M, which was only 1/35 of advanced models in the same category. The average inference time for a single image has been reduced to about 20 ms, which was about 45% faster than the baseline method and demonstrated superior overall performance. The improved algorithm offers improved reconstruction quality and achieves a balance between quality and efficiency when the number of parameters is limited. Improving CNN through dual channel collaborative design and feature reuse mechanisms can provide new technological solutions for digital media image processing.
Spatial-Temporal Graph Convolutional Network for Fault Diagnosis in Weak Electrical Systems
Weak electrical systems, such as low-voltage distribution grids and embedded sensor networks, are highly susceptible to faults due to their complex topology and limited fault tolerance. Accurate failure analysis and diagnosis in such systems are essential for maintaining operational reliability and safety. However, traditional diagnostic methods—such as rule-based systems or shallow machine learning—struggle with non-linear relationships, dynamic system behavior, and distributed component interactions. These limitations result in delayed or inaccurate fault detection, particularly in noisy or rapidly changing environments. To overcome these challenges, this paper proposes a failure diagnosis framework based on the Spatial-Temporal Graph Convolutional Network (ST-GCN), comprising a multi-channel CNN feature extractor for spatial pattern learning and an attention-guided temporal module for capturing temporal dependencies across system nodes. The architecture allows the model to learn complex spatiotemporal interactions and adapt to multi-modal sensor inputs effectively. The proposed ST-GCN achieves 96.7% accuracy, 0.95 F1-score, and 93.6% fault localization accuracy, significantly outperforming traditional methods. It also demonstrates sub-10 ms detection latency, 95.4% actual positive rate in the confusion matrix, and a Precision–Recall AUC of 0.96, while converging within 25 epochs and showing only 1.4% accuracy drop when scaled from a 33-bus to a 123-bus system. These results highlight the robustness, real-time applicability, and methodological effectiveness of ST-GCN for fault diagnosis in weakly meshed and low-voltage distribution grids.
GA-BPNN-Based Multi-objective Optimization Framework for Synchronous Grouting in Shield Tunneling
The performance optimization of shield synchronous grouting materials is a key technical problem in controlling stratum deformation and ensuring structural stability in tunnel engineering. Traditional proportioning design methods rely on empirical trial and error and have bottlenecks such as poor multi-objective synergy and weak dynamic adaptability. This study proposes a collaborative optimization strategy of genetic algorithm (GA) and BP neural network (BPNN), which achieves a multi-objective dynamic balance of fluidity, strength and timeliness of grouting materials by fusing global search and local optimization mechanisms. Based on 126 sets of experimental data on grouting materials, a multi-modal data set was constructed, covering the proportion parameters and corresponding performance indicators of the cement-fly ash-bentonite system. Combined with dynamic boundary conditions such as propulsion speed and grouting pressure in shield construction, a GA-BP collaborative optimization framework was designed. Using a genetic algorithm and BP neural network series coupling strategy, based on twelve sets of experimental data, a multi-objective optimization study of synchronous grouting materials was conducted, and the optimal solution was obtained through fifty iterations of simulation. Experiments show that the optimal ratio scheme (water-binder ratio 0.38, bentonite content 12%) generated by the synergistic strategy in clay formation makes the slurry fluidity reach 248mm, 17.6% higher than the empirical ratio. The 3d compressive strength is increased by 22.4% to 1.85 MPa, and the initial setting time is shortened to 5.8 h. Through multi-objective Pareto solution set analysis, the solution space coverage of the collaborative strategy is increased to 89.2%, which is 29.7% higher than that of the single genetic algorithm, and the number of convergence iterations is reduced by 41.3%. In the field verification, the optimized scheme controls the segment staggering amount within 2.3 mm, which is reduced by 36.1% compared with the traditional method, and the standard deviation of surface settlement is reduced from 4.5 mm to 2.1 mm. Given the sudden working conditions of gravel formation, the adaptive adjustment response time of the model is shortened to 7.5 min, the slurry utilization rate is increased to 92.4%, and the single-ring grouting cost is saved by 13.8 yuan. The research confirms that the collaborative strategy effectively solves the problems of performance imbalance and engineering adaptability in multi-objective optimization of grouting materials and provides a new technical path for intelligent construction of shield tunnels.
A CNN-LSTM-Attention-Based Decision Support Model for Land Use and Agricultural Investment Optimization
The rapid growth of the global population and urbanization has made the efficient utilization of land resources and agricultural investment decision-making critical for sustainable agricultural development. This study focuses on the application of machine learning, an advanced artificial intelligence technology, for land use optimization and agricultural investment decision-making, where its data analysis and prediction capabilities offer significant potential for improving decision-making processes. Based on machine learning algorithms, this paper studies the construction of land use and agricultural investment decision-making models, aiming at improving the allocation efficiency of agricultural resources through data-driven methods and providing scientific basis for agricultural investment decisions. This paper focuses on the construction and evaluation of a hybrid CNN-LSTM-Attention model for land use forecasting and agricultural investment decision-making. The model is compared against traditional machine learning algorithms such as Random Forest, Support Vector Machine, and Gradient Boosting Machine to evaluate performance. In the experimental part, multi-dimensional data from agricultural zones of China were collected, including land use type, climate data, soil conditions, and crop yield (measured as annual crop production per hectare) from 2015 to 2020. The dataset of 10,000 samples spans the Eastern, Southern, and Western agricultural zones of China and is sourced from national agricultural surveys and publicly available environmental databases. The performance of the CNN-LSTM-Attention model was evaluated alongside the baseline models, with results showing that the CNN-LSTM-Attention model outperforms Random Forest, SVM, and GBM in land use change forecasting, achieving an accuracy of 96.8%. The study demonstrates the effectiveness of hybrid machine learning models for optimizing land use and making more accurate agricultural investment decisions. Additionally, the machine learning model predicted an average annual return of 12% for the best agricultural investment portfolio. In terms of agricultural investment decision-making, by combining land use forecasting and crop return data, the machine learning model successfully predicted the expected rate of return under different portfolios, with the best portfolio having an average annual return of 12%. This study shows that machine learning algorithms can effectively optimize land use structure and provide accurate predictions for agricultural investment decisions. The research results not only provide new ideas for the sustainable utilization of land resources, but also provide data support and decision-making basis for agricultural investors, and promote the development of agricultural modernization and intelligence.
TriFactNet: A Multi-Modal Neural Architecture for Fake News Detection Using Text, Source Credibility, and Stance
The spread of misinformation on online platforms has made fake news detection systems more essential and demanding. TriFactNet is introduced in this study as an innovative multi-factor deep learning approach combining semantic textual features, credibility of sources, and synthetic stance vectors for improved fake news accuracy and reliability. The model is trained and tested on an equal subset of the ISOT Fake News Detection Dataset consisting of 1,000 real and fake news articles labeled accordingly. For enhancing input representation, credibility of sources is synthesized using ground-truth ratings and 32-dimensional random stance vectors are added to mimic alignment of context with surrounding claims. The textual information is represented using lightweight transformer model—prajjwal1/bert-tiny—while the auxiliary features are processed using parallel dense layers. These representations are combined and fed into fully connected layers for binary classification. The AdamW optimizer is used in training and ten epochs are used to test using accuracy as well as precision, recall, F1-score, and confusion matrix. Experimentation shows high performance in classification with overall accuracy being 97.5%, class-wise balanced metrics, and harmonized training-validation curves. Modular nature of the architecture and processing of multiple signals of information highlight its applicability to real-world disinformation detection. Future research will investigate the application of semantically derived stance vectors and large datasets to enhance scalability and generalizability.
A Transformer-CNN-SVM Based Architecture for Legal Risk Assessment in Data Privacy Infringements
Under the wave of digitalization, privacy rights and data protection face severe challenges. This study focuses on the application of artificial intelligence in this legal issue and constructs an intelligent legal privacy protection system (ILPPS). By integrating real data from multiple fields such as finance, e-commerce, social networking, medical care, and government affairs and corresponding legal provisions, an experimental system is built. Using accuracy, recall, and F1 value as evaluation indicators, ILPPS is compared with models such as C4.5, Naive Bayes, BERT-SVM, CNN-LSTM, and GRU-SVM. The experimental results show that ILPPS performs well on data sets in various fields. For example, in the financial field, the accuracy rate is 0.87, the recall rate is 0.84, and the F1 value is 0.85; comprehensive analysis of various fields shows that ILPPS has an average accuracy rate of 0.86, a recall rate of 0.83, and an F1 value of 0.84. This shows that ILPPS is significantly superior to traditional models in data privacy risk assessment and infringement judgment, providing enterprises and legal institutions with more effective data privacy protection tools, enriching the research in the intersection of computer technology and law, and promoting the healthy development of social digitalization.
DualSecure Key Exchange (DSKE): A Hybrid ECDH-SIDH Protocol for Post-Quantum Cryptography
Quantum computing threatens classical key exchange protocols such as Elliptic Curve Diffie–Hellman (ECDH). Post-quantum schemes like Supersingular Isogeny Diffie–Hellman (SIDH) offer quantum resistance but at notable computational and communication costs. We propose DualSecure Key Exchange (DSKE), a hybrid protocol that integrates the classical security of ECDH with the quantum resistance of SIDH. Methodology: DSKE jointly derives two shared secrets -KECDH via elliptic-curve scalar multiplication over a 256-bit prime field and KSIDHvia supersingular isogeny mappings—then fuses them using a secure KDF (e.g., SHA-3): K=KDF(KECDH || KSIDH). We formalize core operations and asymptotics (ECDH O(n3); SIDH O(mlog m)), specify key materials, and fix parameter choices aligned with established baselines (e.g., Curve25519 for ECDH and standard SIKE/SIDH parameter sets) to ensure reproducibility. Experimental settings: Evaluations were conducted on an Intel Core i7 with 16 GB RAM using Python-based cryptographic libraries, with repeated trials for timing stability. Results: DSKE achieves runtime = 6.6 ms versus ECDH = 1.2 ms and SIDH = 5.4 ms; communication = 512 bytes versus ECDH = 128 bytes and SIDH = 384 bytes; and key size 1024 bits (hybrid) versus 256 bits (ECDH) and 768 bits (SIDH). Comparative analysis against SIKE further contextualizes DSKE’s efficiency–security trade-off. Security strength follows the minimum of the constituent levels; with a 256-bit prime for ECDH (=128-bit classical) and standard SIDH/SIKE parameters (targeting =128-bit quantum), the fused key maintains an effective 128-bit level under the stated assumptions and KDF construction. These results indicate that DSKE offers a balanced pathway toward post-quantum readiness, particularly for long-lived, security-critical deployments that can tolerate modest overheads for dual-layer protection.
Copyright (c) 2026 Veera Reddy Dasari, Padmaja Madugula,