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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Calibrated Probabilistic Stacking with Linear Meta-Learning for Admission Outcome Prediction
This paper presents a two-stage probabilistic stacking ensemble for admission outcome prediction based on calibrated heterogeneous classifiers and linear decision fusion in probability space. The proposed approach combines HistGradientBoosting, ExtraTrees, and RandomForest models to generate posterior class probabilities, while probability calibration is applied to improve the consistency and comparability of probabilistic estimates. To prevent information leakage, the meta-level training space is formed exclusively from calibrated out-of-fold probability representations. At the second stage, linear meta-models are used to aggregate probabilistic outputs and produce the final decision. The method was evaluated on a real-world dataset collected from the admission campaign of Lviv Polytechnic National University. Experimental studies using holdout validation and stratified cross-validation demonstrate that the proposed ensemble achieves high and stable predictive performance while preserving the quality of probabilistic estimates. In particular, the method reached F1-scores up to 0.990 and MCC values up to 0.979 on the holdout test set, together with low LogLoss values. Comparative analysis with baseline classifiers and standard stacking approaches confirms that calibrated probabilistic fusion improves both classification quality and the reliability of posterior probability estimates in practical decision-support tasks.
Copyright (c) 2026 Khrystyna Zub, Oksana Mulesa
In memoriam: Prof. Dr. Anton Pavel Železnikar (1928-2026)
Copyright (c) 2026 Slavko Žitnik, Matjaž Gams
Deciphering COVID-19 Narratives: A Comparative Study of ML Models (RF, MNB, GB, LR, SVM) and DL Models (CNN, Bi-LSTM) for News Article Classification
The COVID-19 pandemic has provided an unprecedented amount of information in news outlets, which include scientific, health-related, political, economic, and social narratives. This study compares the effectiveness of machine learning and deep learning algorithms for classifying text data, with a certain emphasis on how well the former handle COVID-19 news narratives. The study dataset contains news articles regarding COVID-19. To achieve the primary purpose of this research is to classify COVID19 related news, we integrate multiple datasets. The analysis reveals machine learning models exhibit superior performance in text data classification. In particular, the Random Forest model reaches a 98% accuracy rate. In contrast, with regards to deep learning models, the Bidirectional Long Short-Term Memory model with FastText integration turns out to be the best option due to its exceptional accuracy. Exploratory data techniques such as topic modeling and word cloud approaches are incorporated to uncover hidden patterns in the data. Pre-trained (e.g., deep learning) and non-pre-trained ML models are implemented highlighting the versatility of ML in text classification tasks. The specific purpose to compare to the deep learning and machine learning algorithm to classification of the new article. Notably, a predictive model employing Bi-LSTM with the FastText pre-trained model achieved an impressive 94% accuracy in classifying COVID-19 news reports.
Lightweight Vehicle Detection in Dense Traffic Scenarios Based on YOLOv8n with ContextECA2.0 and Adaptive Window Attention
To address the challenges of large vehicle scale variation, severe occlusion, and frequent target overlap in dense traffic scenarios, this paper proposes a lightweight vehicle detection method based on YOLOv8n. First, a novel ContextECA2.0 module is introduced at the detection head input, synergistically fusing local convolution features with global attention mechanisms to adaptively balance feature contributions across scales. Second, an Adaptive Window Attention (AWA) module dynamically adjusts the attention window size according to local target density, effectively balancing fine-grained small-object modeling and global contextual dependencies. Third, an attention-weighted SiOU loss function is introduced to dynamically weight regression gradients, improving the localization precision of overlapping targets. Extensive experiments were conducted on the large-scale VisDrone dataset (comprising 10,209 images) under an NVIDIA RTX 3090 GPU environment. Compared to the standard YOLOv8n baseline, the proposed method achieves significant improvements of 2.2% in mAP@0.5 and 1.5% in mAP@0.5:0.95, while maintaining a highly lightweight architecture (3.7M parameters) and real-time inference speed (120 FPS). The results validate the effectiveness, robustness, and practical deployability of the proposed method for dense vehicle detection in complex edge-computing traffic scenarios.
Cross-Attention Fusion of Scientific Text and Project Metadata for Explainable Technology Readiness Level Prediction
Technology Readiness Level (TRL) assessment is often constrained by document volume, evaluator subjectivity, and variation in institutional reporting practices. This paper presents a multimodal framework for TRL prediction that combines scientific project descriptions with structured project metadata through a cross-attention fusion layer. The dataset contains 18,247 technology projects collected from NASA TechPort, EU CORDIS, and US DOE repositories. NASA records provide explicit TRL labels, while CORDIS and DOE labels were derived using a milestone-based TRL rubric and annotator agreement analysis (κ=0.72). Textual descriptions were encoded with SciBERT, and structured variables were transformed through an XGBoost-based metadata encoder before being aligned in a cross-attention module. Evaluation was conducted using stratified 5-fold cross-validation with source-aware grouping to reduce leakage between related records. The proposed model reached 71.8% accuracy (MAE=0.82 TRL levels), 68.4% macro F1, and 89.2% adjacent accuracy, compared with 64.2% accuracy for the text-only SciBERT baseline and 54.1% accuracy for the structured-only XGBoost baseline. SHAP analysis identified project duration, funding amount, and technology domain as influential features, although these associations should be interpreted as predictive rather than causal. A trajectory extension produced 67.3% accuracy for 6-month TRL forecasting, with lower performance at longer horizons (62.8% at 12 months, 58.1% at 24 months). These findings support the use of multimodal TRL prediction as a decision-support mechanism, while acknowledging limitations related to label noise, cross-source bias, and external validation requirements.
BFS-CNN-ECA-GMP-GRU-MSP: An Enhanced Cross-Perspective GaitRecognition Model with Efficient Channel Attention and Cosine-ConsistentMetric Learning
Cross-view gait recognition remains vulnerable to viewpoint shifts and appearance changes, especially under carrying and clothing covariates. We propose BFS-CNN-ECA-GMP-GRU-MSP, an enhanced version of our previous BFS-CNN-GMP-GRU-MSP framework, by introducing two upgrades: multi-stage lightweight channel recalibration with Efficient Channel Attention (ECA) and cosine-consistent metric learning through cosine batch-hard triplet loss, a cosine classifier, and L2-normalized embeddings. Experiments are first conducted on CASIA-B under the same legacy closed-set protocol used in our earlier Informatica study (gallery: NM#01–04; probe: NM#05–06, BG#01–02, CL#01–02; same-view matches excluded). This protocol is retained to isolate architecture-level improvements and is interpreted as a within-protocol comparison rather than a subject-disjoint generalization benchmark. Under this setting, the proposed model reaches mean Rank-1 accuracies of 99.96% (NM), 99.74% (BG), and 98.38% (CL), improving the baseline by +2.96, +5.74, and +7.38 percentage points, respectively. To probe unseen-subject behavior more directly, we further report a supplementary subject-disjoint split (subjects 001–074 for training and 075–124 for testing), where the full model attains 97.73% (NM), 87.98% (BG), and 64.00% (CL). Under this stricter split, the clearest effect of ECA appears under clothing variation, where the full model exceeds w/o ECA by 1.82 percentage points on CL, while the ECA branch still introduces only 13 learnable parameters (k=3/5/5 for 64/128/256 channels). These results support the proposed modifications as a lightweight and effective enhancement for protocol-matched cross-view gait recognition, while broader multi-split subject-disjoint and open-set validation remains future work.
Copyright (c) 2026 Lijuan Gao, Jieran Liu
Difficulty-aware Dynamic Chain-of-Thought Prompting for Large Language Models via BM25 and Semantic Retrieval
Large language models (LLMs) have demonstrated exceptional capabilities across multiple domains and emerged as the core driving force in natural language processing. Their reasoning however can be associated with logical flaws and lack of stability when attempting to solve difficult problems that require multi-step deduction, cross-domain knowledge or implicit constraints, with redundant or insufficient exemplars in conventional prompts and poor fit with exemplar and target problems. To address these issues, we propose a dynamic Chain-of-Thought (CoT) prompting method based on problem difficulty assessment: first, the model performs zero-shot self-evaluation of the required solution steps to dynamically determine the number of exemplars; then, it integrates BM25 retrieval to select the most similar high-quality question-answer pairs, constructing precise Few-shot prompts. Experiments conducted on multiple datasets effectively resolve the two major limitations of traditional exemplar-based methods, enabling LLMs to obtain appropriately tailored exemplars for both multi-step mathematical reasoning and interdisciplinary question answering. Consequently, the accuracy of complex reasoning is improved to varying degrees across tasks.
Metric-Wise Comparative Analysis of Hybrid CNN–SRU/LSTM and Lightweight CNN–MIL Frameworks for Deployment-Oriented Video Anomaly Detection
Video anomaly detection is a critical component of intelligent surveillance systems, where detection accuracy, temporal stability, computational efficiency, and real-world deployment feasibility must be jointly considered. Existing studies frequently rely on ROC–AUC as the primary evaluation metric, providing limited insight into practical system performance. This study presents a structured metric-wise comparative analysis of hybrid CNN–SRU/LSTM architectures and lightweight CNN-based multiple instance learning (MIL) frameworks, based on systematically collected benchmark results from datasets such as UCF-Crime and ShanghaiTech. The analysis follows a literature-driven methodology and evaluates models across multiple dimensions, including AUC, false alarm rate (FAR), temporal stability, inference speed (FPS), computational footprint, and calibration reliability using expected calibration error (ECE). Deployment-oriented factors such as latency–performance trade-offs, cross-domain robustness, and scalability under limited labeled data are also examined. Results indicate that hybrid CNN–SRU/LSTM frameworks achieve approximately 85.9% AUC across benchmark datasets with strong temporal consistency, while CNN–MIL approaches maintain competitive accuracy (≈82–84.7%) with significantly higher efficiency (up to 72 FPS) and improved calibration (ECE reduced from ~0.17 to ~0.10). Transformer-based and vision–language models achieve slightly higher accuracy (>86% AUC) but operate at substantially lower frame rates (<12 FPS) and higher memory requirements (>800 MB). These findings highlight that marginal accuracy gains often incur substantial computational cost, emphasizing multi-metric evaluation and hardware-aware model selection for practical video anomaly detection systems.
Copyright (c) 2026 Rajat Gupta, Nidhi Tyagi
Software Framework for Mitigating Programming Plagiarism and Collusion
Many mitigation strategies for programming plagiarism and collusion focus solely on either penalising students involved in such misbehaviour or manually educating students regarding the matter. This paper combines both strategies within a software framework and automates the education strategy. It informs students about plagiarism and collusion based on code similarity and instructors' expectations through an assessment submission system with three variants. Highly similar submissions are alerted, while original submissions have their similarities simulated. In addition, the quality of the submissions is also reported through static analysis. On the due date, student submissions are checked using a similarity detector that provides human-language explanations, which has two variants. Students using our framework show greater awareness of programming plagiarism and collusion, and are less likely to engage in such misbehaviours.
ATT-LCNN: An Attention-Enhanced Lightweight CNN for Acoustic Signal-Based Municipal Pipeline Leak Detection
This study focuses on the recognition of acoustic signals from municipal pipeline leaks. An original end-to-end recognition algorithm (ATT-LCNN) based on the fusion of an attention mechanism and an improved lightweight convolutional neural network (CNN) is proposed, targeting the bottlenecks of traditional methods in minor leak detection and poor generalization. The algorithm employs a preprocessing flow of "DC component removal → improved Kalman filtering → signal normalization → segmented windowing", constructs a multi-dimensional fusion feature set across time, frequency, and time-frequency domains, and embeds an improved SE-Net attention module with depth-wise separable convolution for lightweight and high-accuracy feature learning. Experiments are conducted on a self-built simulated pipeline dataset (1250 samples, covering 3 pipe diameters, 3 leakage levels, 5 propagation distances, 5 noise intensities, with 750 leak samples and 500 non-leak samples), with 5- fold cross-validation to ensure statistical robustness. Performance is benchmarked against traditional machine learning methods (SVM, Random Forest) and standard deep learning models (vanilla CNN, LSTM). The results show that the ATT-LCNN algorithm achieves a 98.7% detection rate (recall), 98.2% precision, 98.4% F1-score for minor leaks, maintains an overall accuracy of over 97.2% at a propagation distance of 20m, and reaches 97.8% accuracy under 60 dB strong noise conditions. The model has only 1.2M parameters, with a single-sample inference time of 2.3ms on CPU, meeting embedded deployment requirements. The repeated detection accuracy fluctuation range is only 0.8%, with stable performance across various pipe diameters and materials. Compared with baseline methods, ATT-LCNN improves minor leak detection accuracy by 6.3%-12.1% under high-noise conditions, balancing recognition accuracy, anti-interference capability, and real-time performance, providing technical support for intelligent leak detection in municipal pipeline networks.
Copyright (c) 2026 Mingjie Liang, Cuimei Li
AI-Blockchain Integrated Trusted Interaction Mechanism... Informatica 50 (2026) 411–426 411 AI-Blockchain Integrated Trusted Interaction Mechanism for Enhanced Network Security
This paper proposes an AI–blockchain integrated trusted interaction mechanism for network security. The mechanism combines a deep-learning anomaly detector with a permissioned blockchain and smart contracts to provide real-time threat analysis, decentralized authentication and tamper-resistant trust management. The AI module is a two-branch neural network that processes network-traffic features and interaction-behavior graphs and outputs an anomaly score for each event. The blockchain layer adopts a consortium architecture with PBFT-style consensus, and smart contracts implement identity verification, trust update rules and access control.Experiments are conducted on labeled network-traffic traces and an enterprise-like testbed. We compare the proposed mechanism with an AI-only baseline (deep model with centralized control) and a blockchain-only baseline (rule-based smart contracts without AI) using detection-level metrics—accuracy, precision, recall, F1-score, false positive rate (FPR) and false negative rate (FNR)—and system-level metrics—response time and throughput. Under low load, the system reaches 180 ms response time and 5000 requests/s throughput; under high load, response time is 300 ms and throughput is 4200 requests/s. Compared with the strongest baseline, throughput increases by 42.8% and FPR is reduced by 60.9%, while FNR remains low across diverse attack scenarios. These results confirm that the mechanism supports trusted interaction in dynamic network environments.
Blockchain-Enhanced Anomaly Detection Algorithm for Financial Data Sharing Platforms Using u-BlockMixup
As the financial industry advances in digitalization, the security and quality of shared data have become critical. Traditional centralized anomaly detection methods face challenges regarding single points of failure and data tampering. To address these issues, this paper proposes a Blockchain-Enhanced Anomaly Detection Algorithm using u-BlockMixup. We integrate a decentralized blockchain architecture with a semi-supervised deep learning model based on the Mean Teacher framework. Specifically, we introduce the u-BlockMixup data augmentation method, which combines supervised cross-entropy loss and unsupervised consistency loss to generate high-quality synthetic samples, thereby improving generalization on limited labeled data. Experimental results on a dataset of over 1 million financial transaction records demonstrate that the proposed method outperforms traditional Isolation Forest and Autoencoder models. The algorithm achieves an accuracy improvement of 15% and an F1 score increase of 18%, with a false positive rate reduced to 2.3%. These findings confirm that combining blockchain immutability with u-BlockMixup-enhanced machine learning significantly improves the reliability and real-time detection capabilities of financial data sharing platforms.
Multi-Objective Logistics Route Optimization Using a Physics- Informed Neural Network-Assisted Genetic Algorithm
Modern logistics distribution systems require simultaneous optimization of transportation cost, delivery time, travel distance, and energy consumption under vehicle capacity and time-window constraints. Conventional genetic algorithm (GA)-based routing approaches rely on penalty-based constraint handling and static fitness evaluation, resulting in unstable convergence and reduced scalability for high- dimensional, multi-objective vehicle routing problems (VRPs). This study proposes a Physics-Informed Neural Network Assisted Genetic Algorithm for Logistics Route Optimization (PINN-GA-RouteOpt), integrating physics-regularized learning with adaptive evolutionary search. The proposed framework embeds vehicle motion dynamics, fuel–distance nonlinear relationships, and service time-window constraints into a physics-informed neural network (PINN), where the fitness function is formulated as a composite objective minimizing transportation cost, total travel time, route distance, and energy consumption. The PINN is trained using a hybrid loss function combining supervised data loss and physics residual loss derived from motion and fuel-consumption equations, thereby generating constraint- consistent fitness landscapes. An improved GA with adaptive mutation and dynamic crossover control is employed to enhance exploration–exploitation balance and accelerate convergence. Computational experiments were conducted on benchmark logistics distribution datasets with network sizes ranging from 100 to 500 nodes and fleet sizes up to 200 vehicles. Comparative evaluation against standard GA and penalty-based multi-objective GA variants demonstrates a 22% reduction in convergence iterations, 17.1% decrease in energy consumption, 12.8% reduction in total travel distance, and 15.4% improvement in average delivery time. Additionally, Pareto front dispersion variance decreased by 19.6%, indicating improved solution stability. The results confirm that PINN-GA-RouteOpt achieves computational efficiency, constraint-aware optimization, and scalability, establishing an intelligent and energy-efficient framework for large-scale logistics distribution route planning.
LC-ATSR: A Transformer-Based Approach for Semantic Retrieval in Lakehouse Data Platforms
This study proposes a Lakehouse Collaborative Adaptive Transformer Semantic Representation Algorithm (LC-ATSR) to address the core challenge of semantic retrieval of unstructured data in a Lakehouse integrated data platform. The algorithm uses a transformer as its backbone and incorporates a dual-dimensional adaptive mechanism of storage attributes and data types to construct an integrated "preprocessing-representation-retrieval" framework. The core designs include a storage attribute-enhanced attention mechanism that integrates Lake storage tags into self-attention computation to adapt to heterogeneous storage characteristics; a lightweight semantic compression and multi-modal alignment module that reduces vector dimensions (from 768 to 256) while constructing a unified semantic space; and a Lakehouse incremental semantic indexing mechanism that dynamically updates the index based on LSH hashing. The experimental results show that the LC-ATSR algorithm achieves a P@10 score of 89.7% and an F1 score of 88.2%, significantly outperforming mainstream algorithms such as BERT, DPR, and RoBERTa. The single-retrieval latency was 18.3ms, the incremental index construction time was reduced by 68.4% compared with the full-data approach, and the accuracy fluctuation was only 3.2% in heterogeneous data scenarios. The retrieval system built based on this algorithm achieves a 99.8% functional pass rate, a throughput of 286 QPS with 500 concurrent connections, and a response time of 21.3ms, meeting the engineering requirements of enterprise data platforms and providing technical support for the value mining of unstructured data.
A Tactile-Driven Hierarchical Reinforcement Learning Framework for Dexterous Robotic Manipulation
To address the challenges of robotic dexterous manipulation in complex environments, this paper proposes a hierarchical reinforcement learning (HRL) framework driven by tactile information. We design an adaptive hierarchical decision-making algorithm that integrates multimodal tactile features, dynamically adjusting hierarchical strategies and reward functions to adapt efficiently to complex tactile environments. Experiments were conducted on the PyBullet simulation platform, constructing a manipulation scenario involving 20 objects of varying shapes and materials. Two primary tasks were evaluated: basic grasping and placement, and complex assembly. Comparative results against standard DQN, PPO, and existing tactile-driven algorithms demonstrate that the proposed method achieves a success rate of 92.3% in basic tasks—outperforming DQN by 27.6% and PPO by 21.5%—while reducing the average operation time to 3.2 seconds. For complex tasks, the success rate increased to 85.7% (a 31.2% improvement over baselines), with a 40% acceleration in convergence speed. Ablation studies further validate that multimodal tactile feature fusion contributes an 18.3% increase in success rate, while the adaptive adjustment mechanism reduces strategy adjustment time by 35%. These findings confirm that the proposed framework significantly enhances robotic dexterous manipulation performance, offering a new pathway for the development of intelligent robotic systems.
Copyright (c) 2026 Gao Liu, Changyu Li, Ruchao Liao, Linkun Yu, Jianguo Zhang, Ning Ding
Multi-level Constraint-Based Two-Stage Few-Shot Knowledge Distillation for Vision-Language Models
Vision-Language Models (VLMs) exhibit excellent zero-shot and few-shot capabilities in downstream tasks by maximizing the similarity between matched image-text pairs. However, the dual-encoder structure of VLMs introduces a large number of parameters, which limits their practical deployment. Knowledge distillation transfers the knowledge of VLMs to lightweight student models to ensure computational efficiency, but the requirement of sufficient high-quality labeled data for knowledge distillation is difficult to meet in real-world scenarios. This paper proposes a Multi-level Two-stage few-shot Knowledge Distillation (MTKD) method. MTKD consists of two stages: in the first stage, a fine-tuned VLM is used as the teacher model. Under the few-shot setting, the knowledge of unlabeled data is transferred to the student model through multi-level constraints (instance-level, batch-level, and class-level) to enhance the few-shot knowledge representation. In the second stage, a small amount of labeled data is used, the student model from the first stage is frozen, and an adapter implemented by a residual structure is inserted at the end of the image encoder for supervised improvement. Ablation experiments on 6 commonly used public datasets verify the effectiveness of MTKD, and comparisons with other methods demonstrate its competitiveness. MTKD achieves an average performance improvement of 3.2% across the six public datasets, with a maximum gain of 8.6% on certain datasets. In addition, experiments on 3 medical datasets prove that MTKD also has high applicability in the field of medical image recognition, indicating that MTKD can be easily transferred to fields with significant differences from the pre-trained data distribution.
Perception-Edge-Cloud Architecture for Post-Stroke Rehabilitation: A Biosensor-Based Intelligent System for Movement Recognition and Compliance Assessment
To address the problem of distorted NC (Nursing Compliance) assessment for stroke patients during home rehabilitation, this paper proposes a four-level collaborative intelligent assessment and guidance system architecture: "Perception-Edge-Cloud-Feedback." First, upper limb movement and electromyography signals are synchronously collected using IMU (Inertial Measurement Unit) and sEMG (Surface Electromyography) sensors. Signal preprocessing, combined with wavelet denoising and SW (Sliding Window) segmentation, improves interference resistance in unstructured home environments. Second, multidimensional features are extracted from the time-frequency-spatial domain, and the input space is optimized. Finally, an attention-enhanced LSTM (Long Short-Term Memory)-Gated Recurrent Unit (GRU) hybrid architecture is constructed to adaptively capture key spatiotemporal features. Finally, a mapping mechanism between action semantics and rehabilitation prescriptions was constructed, along with a dynamically weighted compliance scoring model. The methodology integrates Daubechies wavelet denoising, sliding window segmentation, and an attention-enhanced LSTM-GRU network to process synchronous IMU and sEMG signals. Experimental validation involving 32 patients demonstrates that this system achieves an average recognition accuracy of 93.6%, an Intraclass Correlation Coefficient (ICC) of 0.89 between automatic and manual compliance scoring, and a significant improvement in Fugl- Meyer Assessment scores (p < 0.001) for the high-adherence group.
Predicting Loess Collapsibility Using Grey Relational Analysis and Transformer Networks Zeliang Chen Lanzhou New Area Urban Construction Engineering Co., Ltd., Lanzhou 73000, China
Subgrade and pavement diseases caused by loess collapsibility seriously affect the safety and durability of road engineering. Precise prediction of the collapsibility coefficient plays a pivotal role in engineering prevention and control in loess regions. To address the issues where loess collapsibility is influenced by the coupling effect of multiple factors and traditional forecasting approaches lack adequate generalization capability, this study presents a predictive model integrating grey relational analysis and Transformer network, based on the NCE7# Highway project in Lanzhou New Area. Firstly, indoor experiments were conducted to analyze the correlation between various indicators (such as sampling depth and compaction state) and the collapsibility coefficient. Grey relational degree was used to screen out saturation (0.79), natural density (0.78), void ratio (0.71), and compression coefficient (0.71) as core indicators, eliminating weakly correlated redundant information. Subsequently, an optimized Transformer model was constructed based on these core indicators, which captures the deep coupling relationships among indicators through the self-attention mechanism. Based on indoor experimental data, comparative verification was carried out with RF, BP, and SVM models. The results demonstrate that the Transformer model achieves an R² of 0.976 and an MSE of 0.0009 on the training set, and an R² of 0.953 and an MSE of 0.0015 on the test set. Compared with traditional models, the MSE is reduced by 57.1%– 67.9% and the R ² is improved by 5.7% – 8.9%. The model stabilizes after only 42 epochs, with a generalization performance decay of merely 8.3%, significantly outperforming the decay rate of over 35% observed in traditional models, effectively mitigating the overfitting problem. In summary, this study accurately screens core indicators through grey relational degree and combines the powerful feature extraction capability of the Transformer network to realize high-precision and stable prediction of the loess collapsibility coefficient. It provides a scientific basis and efficient technical support for the formulation of foundation treatment schemes, disease prevention and control, and service life extension of road engineering in loess areas.
Chain Mediation Path Optimization for Smartphone Dependence Prediction Using Graph Neural Networks and Reinforcement Learning
Smartphone dependence prediction and subjective well-being impact analysis are challenged by complex variable interactions, suboptimal mediation-chain optimization, and limited interpretability. This paper proposes a chain mediation path optimization framework that formalizes mediation as a directed causal graph, where nodes denote behavioral and psychometric variables and edges encode dependency relations. Graph convolutional neural networks learn high-level node embeddings, and an attention-based multi-scale causal embedding module enhances structural expressiveness at semantic and topological levels. For path optimization, a policy-gradient reinforcement learning module dynamically updates edge weights under a reward that balances prediction accuracy, path consistency, and computational cost. A graph autoencoder with geometric consistency constraints is introduced to stabilize reconstructed mediation chains and preserve structural coherence. The framework is evaluated on 5,724 users, with an average of 58 behavioral features and 12 psychometric indicators per sample, using stratified sampling and cross-validation. The proposed model achieves 92.1%±0.4 Accuracy, 89.4%±0.6 F1-score, and 87.8%±0.5 Topology Score, outperforming a structural equation model baseline and a deep neural network without causal-path constraints. Ablation results verify that attention, reinforcement learning, and geometric constraints jointly improve classification performance and path stability. These results indicate that graph-based mediation path modeling enables unified prediction of dependence and well-being outcomes with improved interpretability and deployability in large-scale settings.
Copyright (c) 2026 Guiping Xuan, Tianping Li
Neural Network–Based Multi-Feature Disturbance Analysis for Power Line Installation in Renewable Energy Systems
Power line communication and installation in renewable energy–based off-grid and microgrid systems are highly sensitive to electrical disturbances, environmental variability, and appliance-induced non-stationary noise. This paper proposes an RBF neural network–based multi-feature disturbance analysis framework for power line installation assessment. The model integrates power, voltage, frequency, and environmental (P–V–F–E) features into a unified learning architecture to predict disturbance severity and transmission power losses. MATLAB-based simulations were conducted for rural, urban, and industrial environments under seasonal and appliance-induced noise conditions. Compared with traditional and static assessment methods, the proposed model achieved up to 74% reduction in power losses and improved voltage stability under high-disturbance scenarios. The results demonstrate that multi-feature neural learning enables robust disturbance characterization and provides actionable decision support for disturbance-aware power line planning in renewable energy systems.
Copyright (c) 2026 Jianjun Lan, Guo Li, Dan Yang , Chunjiang Li, Xiaochuan Wei
Development of a Lightweight Convolutional Neural Network for Student Mental Health Assessment
To address the limitations and subjective biases of self-report scales in student mental health assessments, this study developed an automated mental health assessment framework based on lightweight convolutional neural networks (LCNN). This system integrates multi-source data to achieve a more objective and efficient assessment model. The main innovations of this research are threefold. First, a lightweight network structure is used to reduce computational complexity. Second, multimodal data, including text content and behavioral logs, is integrated. Finally, a dynamic rating system with real-time monitoring capabilities is built. The experiments were conducted on an automatically constructed dataset containing 5000 samples. The results show that the model achieves a precision of 94.2%, a recall of 91.8%, and an F1 score of 92.9%, which is an improvement of about 10% over traditional methods. The system has completed preliminary application validation in a campus environment, demonstrating its practical value and providing new technical support for mental health monitoring.
Development of a Federated Learning and Knowledge Graph-Based Personalized E-Commerce Recommendation System Using PTB-MFA
E-commerce platforms require accurate and personalized recommendation systems while ensuring user privacy and scalability. Traditional centralized recommendation models face challenges related to privacy risks, data heterogeneity, and limited personalization. To address these limitations, this study proposes a Painting Training-Based Optimization with Modified Federated Averaging (PTB- MFA) framework that integrates federated learning with knowledge graph embeddings for privacy- preserving personalized recommendation. The proposed method captures complex semantic relationships among users, products, and categories while enabling adaptive client-level personalization. Experiments conducted on an e-commerce personalized recommendation dataset demonstrate that PTB-MFA outperforms existing methods, achieving improvements of 16.9% in Click-Through Rate, 19.9% in user engagement, and 6.37% in Mean Average Precision. The results confirm that the proposed framework effectively balances personalization accuracy, privacy preservation, and computational efficiency for next-generation e-commerce systems.
Copyright (c) 2026 Li Jianmin, Yang Jinmei
Neuro-Integrated Residual-Attention Learning for Robust Outcome Prediction in Mobile Remote Biology Laboratories
Despite heterogeneous learner behaviors, interaction noise, and unstable session dynamics, AI-enabled remote biology laboratories are increasingly used to support scalable and flexible science education. Assessing learning outcomes in such environments is difficult. Many learning analytics algorithms fail to anticipate consistent, interpretable, and resilient outcomes under real-world operational limitations. The Quantum Residual Attentive Fusion Network (QRAF-Net) is used to provide a neuro-integrated learning outcome prediction framework for session-level categorization in mobile-based remote biology labs. A new Neuro-Integrated Learning Outcome State (NILOS) taxonomy categorizes learner achievement. QRAF-Net captures hierarchical representations and persistent behavioral dependencies via deep residual feature propagation, attention-driven behavioral interaction modeling, and quantum-inspired phase modulation. The system is tested on 254020 real-world laboratory sessions using rigorous preprocessing, dependency-based feature refinement, and hybrid parameter optimization. QRAF-Net outperforms twelve competing deep and transformer-based baseline models by 7--18% in accuracy, precision, recall, and F1-score with 97.8% accuracy. The suggested Outcome Stability Index (OSI) is 0.956, outperforming all baselines in consistency. Ablation experiments, sensitivity assessment, ROC analysis, and resilience testing under network disruptions, behavioral noise, and partial log loss indicate consistent performance decline and good generalization. Explainability findings show that learning gain and procedural consistency dominate, bolstering the framework's clarity and practicality.
Copyright (c) 2026 Xinxin Cui, Chenguang Feng; Yanlong Chen
IoT Privacy Protection Scheme Based on Hyperledger Fabric
The increasing use of Internet of Things devices has exacerbated the contradiction between data sharing and privacy protection, while existing blockchain based solutions still have significant limitations in scalability, computational efficiency, and access control. Therefore, this study proposes a Hyperledger Fabric privacy protection framework for the Internet of Things, and constructs a smart contract system that includes device management, policy management, and access control. By combining advanced encryption standard algorithms, interstellar file system storage, and Shamir secret sharing mechanism, secure storage and flexible sharing of data have been achieved. The experiment showed that the throughput of the research scheme was stable between 370 TPS and 420 TPS on both the training and testing sets, with an average delay controlled between 50 ms-72 ms, which was significantly better than other comparative schemes. The utilization rate of the central processing unit in terms of resource overhead was maintained at around 50%, with a minimum memory consumption of 505 MB. In addition, in terms of decryption performance, the decryption success rate of this scheme under various attacks was between 98% and 99%, with an average decryption cost of the lowest 45 ms. The research scheme balances performance and efficiency while ensuring privacy and security, demonstrating its application advantages in Internet of Things privacy protection scenarios.
Copyright (c) 2026 Weiyan Feng, Li Zhang, Xin Zhao
Adaptive Warehouse Task Allocation Using Stacked Surrogate Models and Active Learning with Simulation-Based Validation
Modern warehouse operations face increasing order volumes, volatile demand, and shrinking lead times, challenging conventional static task dispatching policies such as FIFO or priority-based picking. This study proposes a closed-loop intelligent warehouse management framework that integrates task-level feature engineering, lightweight policy simulation, stacked surrogate modelling, and active learning retraining to enable adaptive, real-time decision support. The surrogate model combines an attention-enhanced TensorFlow multilayer perceptron, a LightGBM classifier, and a logistic regression meta-learner to predict optimal dispatching policies for dynamically generated warehouse tasks. Experiments were conducted using a publicly available logistics warehouse dataset comprising over 3,000 inventory records, from which realistic task batches were generated and evaluated across multiple dispatching strategies. Simulation-based validation was performed using a SimPy discrete-event model capturing contention between automated guided vehicles and human pickers under varying workload and resource conditions. The stacked surrogate achieved validation accuracy exceeding 98% with balanced precision and recall across all policy classes. Simulation results showed stable throughput, reduced average waiting times, and balanced resource utilisation compared with FIFO baselines. Additional robustness analyses demonstrated consistent model behaviour under demand variability and resource disturbances. The integration of an active learning loop enables continuous recalibration of the surrogate model using observed simulation KPIs, ensuring adaptability to changing operational contexts. The proposed framework provides a computationally efficient and interpretable approach for adaptive warehouse task allocation and demonstrates broader applicability to intelligent decision-making in cyber-physical systems aligned with Industry 4.0 principles.
Genetic Algorithm-Based Intelligent Optimization for Enhancing Security in High-Performance Concrete Supply Chains
The supply chain security of high-performance concrete materials directly affects the orderly progress of engineering projects. At present, its supply chain management still faces problems such as high costs, insufficient timely supply, and poor quality stability. This article aims to construct an intelligent optimization design algorithm model to enhance the safety and management efficiency of the high- performance concrete material supply chain. In terms of methodology, firstly, based on the principles of scientificity, comprehensiveness, and operability, a supply chain security indicator system covering supply, production, transportation, and other links should be established. Subsequently, based on genetic algorithm (GA), the model improvement design was completed by optimizing the encoding method, adjusting the fitness function, and improving the genetic operator. The results were compared and verified with traditional methods through experiments. The key results show that the optimized model has an average cost lower than traditional methods by about 170200 yuan, an increase in on-time delivery rate of about 9.92%, and an increase in product qualification batch rate of about 3.1%. The conclusion indicates that the intelligent optimization design algorithm model can effectively optimize the supply chain management of high-performance concrete materials, providing reliable support for their supply chain security.
Real-Time Transmission Line Fault Monitoring Using SVRG-DDPG and Lightweight Edge Computing
In power system intelligent fault detection, real-time monitoring is critical due to grid complexity. To address the challenges posed by the complexity of continuous action selection and gradient estimation errors in transmission line fault monitoring, we developed a Deep Deterministic Policy Gradient (DDPG) algorithm enhanced by the Stochastic Variance Reduction Gradient (SVRG) method, termed SVRG-DDPG. This algorithm leverages the SVRG technique to mitigate the gradient estimation errors typically encountered in the DDPG algorithm. Utilizing the SVRG-DDPG framework, we further developed a transmission line fault monitoring model that directly employs real-time voltage sensor data from actual transmission line environments, encompassing a range of state information from normal operation to fault conditions. To achieve real-time monitoring and optimize system performance, we also propose a transmission line fault monitoring system based on a lightweight edge computing architecture. Using real-time voltage sensor data, the SVRG-DDPG-based fault monitoring model achieves a residual error within 30 kV accuracy. To enable real-time fault diagnosis in resource-constrained edge environments, we propose a lightweight edge-cloud collaborative architecture that dynamically allocates computational resources based on fault severity and sensor data volume. The framework is validated using high-fidelity simulation data from a power grid (covering 8 fault types under 12 operational conditions, e.g., humidity >90%, load fluctuations ±40%), which aligns with the dynamic resource demands of edge devices in practical systems. Finally, our method achieves 92% accuracy in fault diagnosis with 42% lower latency compared to baselines, leveraging SVRG-enhanced DDPG for adaptive edge-cloud synchronization. Experimental results on real-world 5G-V2X data validate its suitability for low-latency transmission line monitoring.
Copyright (c) 2026 MengHao Lin, Yang Ding, JinDa Lu, WeiYi Chen, TianLe Wang
2OAMN: A Deep Learning Framework for Personalized Psychological Interventions Using Multimodal Student Data
Background: Student mental health has become a global concern due to increasing academic stress, anxiety, and depression, which negatively impact learning outcomes, emotional well-being, and social interactions. Traditional assessment methods, such as static questionnaires and periodic counseling, fail to capture the dynamic nature of psychological health. Objective: This research aims to develop an intelligent deep learning (DL) framework using neural networks to predict early mental health risks and generate personalized psychological intervention strategies based on students’ evolving emotional states. Methods: The Student Mental Health and Intervention Dataset, comprising 1,000 student records collected through self-reported surveys, physiological indicators were utilized. Data preprocessing involved normalization, sentiment classification using BERT-based text embeddings, and feature extraction with Convolutional Neural Network (CNN) layers. The integrated features were input into the Octopus Optimization with Attention-Based Memory Network (2OAMN). The Bi-LSTM captures both past and future dependencies in mental health data, enhancing prediction accuracy. The attention mechanism prioritizes the most relevant emotional and behavioral signals, ensuring timely interventions. OOA optimizes model parameters by balancing exploration and exploitation, improving adaptability and predictive accuracy. The framework was implemented in Python using DL libraries to predict mental health risks and generate adaptive intervention recommendations. Results: Experimental evaluation shows that 2OAMN achieves 97.86% accuracy, 98.81% precision, 97.64% recall, and 98.22% F1-score. 2OAMN regression performance includes an RMSE of 5.92, MAE of 4.11, and R² of 0.74. Conclusion: The 2OAMN reliably predicts student mental health risks and generates effective personalized interventions, offering a practical and data-driven solution for improving student psychological well-being.
Copyright (c) 2026 Jiao Fu, Yongjuan Zhao
Bayesian Optimization with SAT Solver for Enhanced Database Security Identification
To solve the limitations of traditional database security detection, such as weak logical reasoning, delayed feature update, and high computational complexity under high-dimensional constraints, this study proposes a Bayesian optimization algorithm based on SAT. Security rules are formalized into CNF logical constraints, enabling automated reasoning and rule consistency verification via a satisfiability solver. A Gaussian process-driven Bayesian optimization framework with an improved Expected Improvement (EI) acquisition function dynamically updates feature weights and accelerates convergence toward high-risk regions, enhancing rare vulnerability detection and global solving efficiency. Experiments on enterprise database logs and national vulnerability datasets demonstrate an average detection rate of 97.5%, a defense success rate of 95.8%, and a response latency of 2.11 s, outperforming baseline methods. The system stability index reaches 0.93, with concurrent processing capability improved by 34.2%. These results confirm the algorithm’s high-precision identification, stable defense performance, and practical deployability for database security management under complex, high-dimensional attack scenarios.
Energy-Aware Clustered Federated Learning for Underwater Sensor Networks in Naval Surveillance
Underwater wireless sensor networks (UWSNs) are essential for naval operations, as they are used for ob- ject monitoring (surveillance), environmental monitoring, and tactical defense. However, their deployment faces serious challenges due to the limitations of underwater acoustic communication, such as high latency, low bandwidth, high packet loss rates, and severe energy constraints. Under these conditions, conventional centralized data processing approaches are impractical, necessitating a shift toward decentralized intelli- gence. This paper presents an Energy-Aware Clustered Federated Learning (CFL) framework specifically designed for UWSNs in naval systems. The proposed approach organizes sensor nodes into logical clusters, where local models are trained and aggregated at cluster heads before being transmitted to a central unit. To extend network lifetime, an energy-conscious participation scheme is employed, ensuring that only nodes with sufficient residual energy participate in model training. Moreover, a robust median-based aggregation strategy is introduced at the cluster level to mitigate the effects of noisy and lossy underwater communi- cation. Simulations conducted under realistic underwater conditions demonstrate that the proposed CFL framework achieves a model accuracy of up to 91.2%, which is comparable to centralized learning and outperforms conventional federated learning approaches. Furthermore, CFL improves network lifetime by approximately 40% and reduces communication overhead by nearly 68%, thereby enhancing overall energy efficiency compared to traditional federated learning methods. The results also show improved ro- bustness to packet loss and communication failures, highlighting the suitability of the proposed framework for autonomous underwater operations. Overall, this work illustrates the potential of federated learning to enable intelligent, resilient, and energy-efficient underwater sensor networks, opening new opportunities for future naval and maritime applications in challenging underwater environments.
Copyright (c) 2026 Shekhar Tyagi, Kunchanapalli Rama Krishna, Himanshu Sharma, Hridesh Gupta, Abhishek Tyagi, Hirdesh Sharma, Manoj Kumar Yadav