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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RL-AMHA: A Reinforcement Learning-Based Adaptive System for Personalized Mental Health Education and Intervention
Effective mental health education requires personalized recommendations and intervention strategies that align with an individual's psychological condition and learning preferences. This study introduces a Reinforcement Learning-Based Adaptive Mental Health Advisor (RL-AMHA) that selects instructional content and self-care interventions using a PPO agent and actor-critic neural architecture. The agent uses self-reports, in-app behavior, and contextual cues from a mobile mental health platform. It was trained on 1,200 anonymized user sessions using log data and validated questionnaire scores, after ethics approval and informed consent. The experiment contrasts RL-AMHA with a static rule-based curriculum and a supervised recommendation model that predicts content based on past clicks. All models share the same content pool and interaction constraints; PPO hyperparameters (learning rate, discount factor, clipping range, batch size) were tweaked on a validation split, and 500 episodes were trained until the average episodic reward converged. RL-AMHA improves engagement rate from 75.2% to 88.1% (+2.9%), user satisfaction from 4.21 to 4.85 (+0.64 on a 5-point scale), and weekly self-care activity frequency from 9.4 to 13.1 (+39%) compared to the baseline. Additionally, it enhances stress-reduction scores by 18.6% and increases continuous engagement time from 21.3 to 29.0 minutes. With minimal implementation costs, RL-AMHA demonstrates scalability, adaptability, and effectiveness for long-term psychological support across mobile health, e-learning, and clinical decision-support environments.
Multimodal Diamond Inclusion Detection and Clarity Grading via Enhanced YOLOv7 with ECA and ASFF Modules
The detection of micro-inclusions and the representation of interpretable results compatible with gemological standards are two major bottlenecks to the automation of diamond clarity grading. In this work, we propose a novel solution using an enhanced YOLOv7 model in a multimodal framework to overcome the above bottlenecks. Specifically, our contributions are threefold: Firstly, we improve the original YOLOv7 by incorporating the Efficient Channel Attention (ECA) mechanism to enhance the extracted fine-grained features and the Adaptively Spatial Feature Fusion (ASFF) module for capturing more robust multi-scale representations; secondly, we build a three-channel input consisting of optical grayscale, gray-level co-occurrence matrix (GLCM) texture linked with the optical properties of inclusions, and morphological operation-enhanced images. Thirdly, we design a traceable grading system by combining the XGBoost classifier with programmable GIA (Gemological Institute of America) rules. Our method achieves 91.3% mAP@0.5 on the Roboflow Diamond Inclusion dataset, outperforming the baseline YOLOv7 by 9.2%. The clarity grading performance on this dataset attains an accuracy of 86.7%, a Kappa coefficient of 0.82, and a weighted F1-score of 0.87, resulting in high consistency with human expert evaluations. Ablation experiments confirm that the proposed components all make individual and complementary contributions. This work represents a significant advance towards the automatic, accurate, and interpretable grading of diamonds, and it creates a practical tool for use within the jewelry industry.
A Genetic Algorithm-Based Scheduling Framework for Hospital Resource Allocation in Edge-Terminal Collaborative Networks Qinglin Shi
Under the background of increasing medical demand and digital transformation, hospital resource scheduling in edge-terminal collaborative environments faces challenges such as information delay and insufficient decision-making accuracy. This study proposes a hospital resource allocation and scheduling framework based on genetic algorithm, which integrates edge terminal collaboration architecture, and heuristic optimization logic. The edge computing node is deployed in key medical areas, with a 1000 Mbps transmission rate and a 5 ms low latency hybrid network, to achieve real-time data acquisition and preprocessing of the terminal. The core of genetic algorithm adopts resource processing capability chromosome encoding, with a crossover probability of 0.7 and a mutation probability of 0.1. It minimizes task completion time, patient waiting time, and resource conflict cost through multi-objective fitness functions (α=0.4, β=0.3, γ=0.3). The validation results based on the MIMIC-III (Medical Information Mart for Intensive Care III) dataset show that the model's basic performance (accuracy 87%, F1 value 86%) and resource management effectiveness (accuracy 89%, F1 value 88%) outperform traditional FCFS (First Come, First Served) scheduling models, with an overall performance improvement of 2% -14%. This framework effectively improves resource utilization and service efficiency, provides practical solutions for optimizing hospital resource scheduling, and expands the application scenarios of heuristic algorithms in the medical field.
A Dual-Channel Transformer-SHAP Framework for Early Detection and Interpretable Analysis of Youth Social Burnout from Multimodal Behavioral Data
An efficient and interpretable warning model is urgently needed to address the prominent risk of social burnout among adolescents in high-intensity social media interactions. Traditional machine learning methods have insufficient modeling capabilities for temporal behavior features and lack interpretability in prediction. This study proposes a warning method that integrates Transformer and SHAP: a dual channel Transformer architecture is designed, where the main channel analyzes the high-order correlation of 15-dimensional user behavior sequences through stacked encoding layers and multi head attention, and the auxiliary channel uses a recursive feature pyramid structure to enhance state sensitivity; Furthermore, fatigue risk prediction can be achieved through a fully connected network. Simultaneously constructing a tree structure optimized SHAP calculation process, while reducing high-dimensional computational complexity, verifying the rationality of feature attribution through psychological theory, and using SHAP to preprocess abnormal samples, combined with variational autoencoder (VAE) to achieve deviation detection, forming an integrated mechanism of "warning traceability". Based on a desensitization set containing 6-month social data of 5892 adolescents, the robustness was verified through missing data and data change scenarios. The results showed that the model had an accuracy rate of 92.37% (improved by 8.5% and 12.2% compared to LSTM and random forest, respectively), an F1 value of 0.891, an early fatigue recall rate of 85.2%, a false positive rate of<7.3%, and a response time of 1.8 seconds; SHAP analysis confirmed that nighttime activity exceeding 2 hours (+0.32), weekly social decline exceeding 40% (+0.28), and negative emotional words exceeding 15% (+0.41) are the core features, with a combined contribution of 68.7%, providing quantitative evidence for targeted interventions.
Real-Time Detection of Public Relations Crisis Signals Using Hybrid LSTM and Isolation Forest Model
With the widespread use of social media, public relations crises have become increasingly rapid and unpredictable. Traditional manual monitoring and public opinion analysis methods are difficult to cope with the rapidly developing information environment. To address this challenge, this study proposes a new real-time PR crisis signal detection model that effectively combines long short-term memory networks and isolation forest algorithms. This model aims to achieve early warning of potential crises by efficiently processing large amounts of social media and news data. The experiment was conducted on a real-world public relations crisis event dataset, which includes text, user interaction, and sentiment analysis data. The performance of the proposed fusion model was compared with independent LSTM and isolated forest models as baselines. The experimental results show that the fusion model performs robustly in various types of public relations crisis signals. It achieved significant high accuracy in detecting social media popularity and public opinion fluctuations, improving by 3.4% and 6.3% respectively compared to single LSTM and Isolation Forest models. In addition, the fusion model exhibits significantly enhanced robustness in high noise environments. At a pollution rate of 60%, it maintained an accuracy of 54.1% and an F1 score of 0.823, demonstrating its powerful anomaly detection capability. The proposed fusion model provides an accurate, real-time, and robust solution for PR crisis signal detection by utilizing the complementary advantages of LSTM to capture time patterns and isolation forest to identify anomalies, which has significant practical application value for enterprises and organizations.
H-CMAF: A Deep Learning-Based Cross-Modal Attention Fusion Framework for Dangerous Goods Detection from X-Ray and Visible Light Imagery
Hazardous goods identification still faces challenges such as low accuracy and difficulty identifying items in low-light, densely packed environments. Therefore, this paper combines multimodal fusion information with deep learning models to construct an identification and classification system. This aims to improve the accuracy of hazardous goods identification in the public domain and its robustness in diverse environments and conditions. Based on the attention mechanism, this study fuses feature information from X-rays and visible light to construct an H-CMAF framework model. In challenging scenarios such as low light, partial occlusion, and dense stacking, H-CMAF achieves minimal mAP degradation (6.5%, 5.9%, and 7.1%, respectively), demonstrating exceptional robustness. Furthermore, the model achieves an inference speed of 22.8 FPS on edge devices, with only 34.8M parameters and a memory footprint of 100MB, outperforming most competing models and achieving a good balance between accuracy and efficiency.
A Multi-Dimensional Empirical Evaluation of k-Anonymity, Centralized and Local Differential Privacy, and Federated Learning for Personal Data Protection in Big Data Scenarios
This study systematically evaluates the performance of mainstream personal information anonymization technologies in the big data environment. It focuses on k-anonymity family models, differential privacy (DP) and its local variants, and privacy protection mechanisms in federated learning (FL). A multi-dimensional comprehensive evaluation system is constructed in the study, measured from four aspects: privacy protection strength, data utility, computational efficiency, and scalability. Indicators such as normalized certainty penalty (NCP), area under the curve (AUC), and processing time are adopted. The experimental results show that under the condition of medium-intensity privacy protection (privacy budget ε = 1.0), the central differential privacy (CDP) mechanism exhibits the best comprehensive performance in most scenarios. In the medical data release scenario, the NCP of CDP is 0.05, the model's AUC is 0.945, and the average processing time is 25 seconds. In contrast, the k-anonymity model has higher information loss (NCP = 0.22) and lower model accuracy (AUC = 0.920). In the FL scenario of financial anti-fraud, the AUC of the global model under CDP protection is 0.890, which is significantly better than the AUC of the local differential privacy (LDP) scheme (0.842), with comparable computational overhead. In the user behavior analysis experiment involving one million users, the LDP method based on the Randomized Aggregate Privacy-Preserving Ordinal Response (RAPPOR) mechanism achieves lower mean absolute error and higher popular category recognition accuracy (99.0%). In all tests, the NCP and AUC of CDP remain stable with changes in data scale, while the processing time increases approximately linearly with data volume, showing excellent scalability. The above results quantitatively verify the significant advantages of CDP over traditional k-anonymity and LDP methods in data utility, privacy protection strength, and computational efficiency. Thus, empirical evidence can be provided for the selection of anonymization technologies in large-scale data processing scenarios.
Information-Theoretic and Algorithmic Thresholds for Network Attack Detection via Spectral and CUSUM Methods
We develop a unified theory for the detectability of network-borne attacks under two canonical observation models: (i) a static graph drawn from an Erdős–Rényi background with a planted anomalous community, and (ii) a temporal interaction network modeled by multivariate point processes (Poisson or Hawkes). Our main contribution is to match, up to universal constants, information-theoretic lower and upper bounds that govern when reliable testing is possible. In the static case, the core quantity is the accumulated edgewise signal k²·χ²(Bern(p+Δ) ‖ Bern(p)), where χ² ≈ Δ²/[p(1−p)] for small Δ; detection is impossible when this falls below c·log n, and a non-backtracking spectral statistic succeeds above C·log n. In the temporal case, detectability is controlled by the Kullback–Leibler information rate I contributed by internal edges over a window of length T, yielding a threshold T I ≳ log n; a likelihood-based cumulative-sum (CUSUM) test achieves first-order optimal delay ≈ |log α|/I at false-alarm level α. We complement these limits with concrete algorithms and simple experiments. A pruning-based non-backtracking spectral detector for static graphs and a sparsity-aware CUSUM procedure for temporal streams are given with near-linear time and memory complexity. Monte Carlo simulations on Erdős–Rényi graphs with planted dense subgraphs and on Poisson streams illustrate the predicted phase transitions: detection power increases as k²·χ²/log n crosses a constant-level boundary, and empirical detection delays closely track the log(ARL)/I prediction. We also discuss robustness under bounded edge perturbations and mild misspecification, and show how these thresholds can be used to dimension practical security monitoring systems.
Copyright (c) 2026 Abdulkader Hajjouz, Elena Avksentieva
FB2BPMN: An End-to-End Pipeline for Translating Unstructured User Feedback into BPMN Using LLM and Fuzzy Matching
Translating unstructured user feedback into Business Process Model and Notation (BPMN) is challenging due to informal language, contextual ambiguity, and the lack of explicit structural cues. We present FB2BPMN, an end-to-end pipeline that combines natural language processing (NLP), large language models (LLMs), and fuzzy string matching to automatically generate BPMN elements from raw feedback. The pipeline comprises four stages: sentence structuring, fact extraction, role-activity mapping, and fuzzy-based semantic alignment. We evaluate FB2BPMN on 125 annotated feedback instances sampled from academic journal management systems. Using expert-authored BPMN as reference, FB2BPMN attains precision 0.97, recall 0.88, and F1 0.91 on element identification and accuracy 0.85 on process flow construction, outperforming a rule-based baseline. Results indicate strong structural and semantic correspondence, showing that FB2BPMN effectively bridges informal feedback and formal process representations.
Copyright (c) 2026 Uce Indahyanti, Arif Djunaidy, Daniel Siahaan
MS-NADNet: A Multi-Stream Noise-Aware Deep Learning Framework for Microarray Image Denoising
Microarray imaging is a critical tool for large-scale gene expression analysis, yet its accuracy is often compromised by noise introduced during sample preparation, hybridization, and image acquisition. Traditional denoising approaches, including median, Wiener, and wavelet-based filtering, either rely on fixed parameters or degrade under mixed-noise conditions, while CNN-based methods treat all noise uniformly, limiting adaptability. To address these limitations, we propose MS-NADNet (Multi-Stream Noise-Aware Denoising Network), a deep-learning framework that integrates a Noise Characterization Module (NCM) to identify noise type, a set of Noise-Specific Denoising Modules (NSDMs) specialized for distinct noise distributions, and a Global Refinement Block for residual suppression. A large-scale augmented dataset was constructed from the Malignant Lymphoma Classification dataset, incorporating 17 noise variants, including Gaussian, Poisson, salt-and-pepper, speckle, and mixed combinations, to simulate realistic imaging conditions. Experimental evaluation demonstrates that MS-NADNet achieves an MSE of 0.00012, PSNR of 42.73 dB, and SSIM of 0.9861, outperforming classical filters and state-of-the-art CNN denoisers. These results confirm the robustness of MS-NADNet in handling diverse single and multi-noise environments, ensuring biologically reliable microarray image analysis and improved downstream gene expression profiling.
Copyright (c) 2026 Shreenidhi BS, R Saravana Kumar
Multi-Modal Elderly Monitoring via Wearable Devices and WiFi-Enabled Edge-Cloud Architecture with HRV-Based Fall Detection
In elderly health monitoring scenarios, events such as falls and cardiac arrhythmias are often sudden and high-risk, making traditional single-indicator monitoring methods insufficient for real-time and accurate detection in complex environments. To enhance system stability and anomaly detection capability under multi-interference conditions, this study develops a wearable monitoring system based on WiFi communication technology. The system integrates physiological signal acquisition, environmental parameter sensing, and dynamic threshold and weight adjustment mechanisms, achieving low-latency and high-reliability data processing and alert response within an edge-cloud collaborative architecture. Experiments were conducted using the SisFall Dataset and the MIT-BIH Physiological Signal Database. The K-Nearest Neighbors (KNN) classifier is employed for anomaly detection. The results showed that under optimal parameter configuration, the system achieved a maximum F1-score of 0.94, peak sensitivity of 94.8%, minimum event detection latency of 140 ms, and a minimum false alarm rate of only 3.8%. The simulation tests further verify the robustness and adaptability of the system under low, medium, and high interference environments, demonstrating its feasibility for deployment in smart elderly care and remote healthcare applications.
Copyright (c) 2026 Cui Huang, Runguo Chai
T-WGAN: A Transformer-Wasserstein GAN Approach for Melody Generation with Structural and Rhythmic Fidelity
To address the current challenges of deep learning music generation models in capturing long-range dependencies, ensuring generation diversity, and maintaining training stability, this study proposes an optimized music melody generation model—T-WGAN. The model deeply integrates Transformer and Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). This study first preprocesses the large-scale Lakh MIDI dataset, extracts single-track main melodies, and converts them into symbolic sequences using a REMI-based event representation. On this basis, the model innovatively adopts a generator based on Transformer decoder to learn the long-range structure of melodies. It also uses a critic based on Transformer encoder for stable adversarial training under the WGAN-GP framework to enhance the diversity and authenticity of generated melodies. Experimental results show that T-WGAN performs excellently on multiple key evaluation metrics. T-WGAN achieves a Rhythmic Consistency Rate (RCR) of 85.17%, significantly higher than baseline models (e.g., Transformer’s 75.68%). Its score on Fréchet Distance for Music (FDM) drops to 31.02, proving that the generated melodies are closer to real music in feature distribution. The conclusion indicates that the proposed T-WGAN model successfully addresses the three core issues in melody generation—structural integrity, diversity, and training stability—synergistically. The findings provide an effective technical approach for generating high-quality music melodies with both structural logic and innovation.
English Composition Topic and Emotion Classification Based on LDA-BERT Fusion and Dual GRU model
With the development of globalization and artificial intelligence, the traditional manual correction of English compositions has problems, such as low efficiency and a lack of standardized evaluation. Therefore, there is an urgent need for automated analysis. The study aims to develop a precise model for identifying English essay themes and classifying emotional perspectives. Latent Dirichlet allocation is used to combine Probase prior knowledge and BERT fusion technology for topic modeling, and a Bi-GRU network with an attention mechanism is used for emotion classification. The experiment was carried out based on a dataset of 66,000 English compositions, including TOEFL, IELTS, and classroom compositions, and verified the effectiveness and superiority of the joint model in topic identification and emotion classification tasks. The experiment showed that the topic recognition model had a perplexity of 803, coherence of 0.831, and inter-class distance of 3.2 when there were 25 topics. When the threshold of the emotion classification model was 0.45, the accuracy reached 80.1% and the F1 value was 73.7%. The F1 value of 2,000 tests was 76.1%, which was better than the comparison model. Research has shown that this solution effectively solves problems such as insufficient semantic understanding in existing technologies, provides scientific support for intelligent evaluation systems, and promotes the intelligent development of educational evaluation.
Tree-HP2PL: A Hierarchical Priority-Based Two-Phase Locking Protocol for Nested Transactions in Distributed Real-Time Database Systems
Modern applications require advanced concurrency control protocols to efficiently manage nested transactions within Distributed Real-Time Database Systems (DRTDBS). This paper introduces Tree-HP2PL, a Hierarchical Priority-Based Two-Phase Locking protocol designed to optimize conflict resolution within nested transactions. The proposed method employs a binary search tree structure for transaction management and incorporates a hybrid strategy combining Depth-First Search (DFS) and parallel Breadth-First Search (BFS) for efficient lock acquisition. Tree-HP2PL is capable of distinguishing and resolving various conflict scenarios, including no conflict, intra-transaction conflicts, and inter-transaction conflicts. To mitigate pseudo-priority inversion, it implements priority inheritance and transaction abort mechanisms as necessary. The protocol’s effectiveness was evaluated through experimental simulations conducted in a MATLAB simulation environment, using transaction volumes ranging from 5 to 30 across 10 iterations. Results indicate that execution time remains efficient, with a recorded time of 0.0011 seconds for 5 transactions and showing controlled growth to 0.0049 seconds at 20 transactions. Memory usage demonstrated linear scalability, increasing from 0.006 MB at 5 transactions to 0.016 MB at 30 transactions. Although system throughput decreases under heavier transaction loads, it remains competitive, dropping from 1980 transactions per second (TPS) at 5 transactions to 998 TPS at 30 transactions. Notably, the success ratio consistently maintained a value of 100%, reflecting the protocol’s high reliability. In conclusion, Tree-HP2PL effectively addresses the challenges of concurrency control in nested transaction environments by offering a scalable, structured, and conflict-aware locking mechanism. Its robust performance and methodological design validate its applicability in real-time distributed systems, marking it as a significant improvement over existing flat or unstructured locking techniques.
Real-Time Network Threat Detection in Intelligent Power Monitoring Systems Using Multiobjective Horse Herd Optimization and Online Streaming Random Forest
As the threats to critical infrastructure have grown more sophisticated, securing intelligent power monitoring systems have taken on new critical importance. The paper introduces a scenario of network security situation awareness based on Big Data particularly in the intelligent power monitoring setting. The suggested framework incorporates Multiobjective Horse Herd Optimization (MHHO) to allow optimal feature selection, and Online Streaming Random Forest (OSRF) to allow real-time threat detection so that network activities could adapt to the dynamic environment in an online scenario. Depending on the set of KDD Cup99 dataset, the system will capture and analyse security risks based on usage of huge size of heterogeneous data to detect some type of attacks like DoS, Probe, U2R and R2L. MHHO optimizes the detection channel to choose the most informative features and OSRF scales to high-velocity streaming data efficiently; they also guarantee real-time identification of changing patterns of attacks. Experimental analysis shows that the proposed approach is more accurate as 96.2 %, precise as 95.8%, has higher recall as 95.4 % and F1-score 95.6 % and lower error rates (MSE, RMSE) when compared to the previously tested intrusion detection techniques, which proves its effectiveness and flexibility in changing network conditions. The contribution of this work is a scalable, real time, and intelligent security solution able to assist in proactive decision making in power monitoring activities. Future work will concentrate on experimenting with deep learning-based hybrid detection models, graph-based threat correlation, and explainable AI approaches to the improve the level of detection and interpretability further.
Copyright (c) 2026 Lei Zhao, Yang Yu, Quan Sun, Chonghui Ge
A Dual-Engine Embedded Face Detection and Recognition Framework Using YOLO5Face and Attention-Enhanced Faster-RCNN for Surveillance Video
Embedded detection and recognition systems for surveillance video are in urgent demand in the security field. However, traditional methods face limitations, including poor real-time performance, high resource consumption, and limited generalization in complex scenarios. To this end, this study proposes a dual-engine embedded face detection and recognition framework that optimizes performance by synergistically integrating YOLO v5Face with attention-enhanced Faster Regions with Convolutional Neural Network. The system adopts a dual engine cascade architecture: YOLO5Face is responsible for fast initial face screening, while Faster Regions with Convolutional Neural Network, which integrates spatial and channel attention mechanisms, accurately recognizes key targets. By synergistically optimizing speed and accuracy through feature reuse and structural fusion techniques, and by combining the feature-extraction capabilities of the local binary pattern histogram algorithm based on hierarchical feature pyramids, a dynamic background suppression module is used to reduce false positives in complex scenes. The experimental results on the WIDER FACE and Face Detection Data Set and Benchmark datasets show that the accuracy of our system reaches 99.1%, with a loss rate as low as 0.08, significantly better than the comparison systems Visual Transformer Convolutional Neural Network Fusion (accuracy 98.16±0.23%) and Additive Marginal Soft Maximum Loss Convolutional Multi-scale Transformer (accuracy 97.42±0.34%); The system converges to a loss of less than 0.1 within 200 iterations, with a response time of only 28 ms, much faster than the fusion of Visual Transformer Convolutional Neural Network (78-85 ms). The above results show that the proposed method effectively addresses the problems of poor real-time performance, resource constraints, and insufficient scene generalization, offering efficient, lightweight new ideas for system development and promoting the intelligent and efficient development of security terminals.
Copyright (c) 2026 Qianqian Yuan, Yuping Quan, Hui Li
Emergency Rescue Path Planning for Urban Emergencies Based on Improved GA and PSO
With the acceleration of urbanization and the frequent urban emergencies, traditional rescue path planning methods have response delay and low path efficiency. To improve the efficiency of urban emergency rescue and resource scheduling capabilities, a path planning technique for urban emergency rescue based on improved Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) is built. The research introduces Tent chaotic mapping initialization particles, adaptive segmented inertia weights and exponential learning factors for the PSO algorithm to enhance global search capabilities, and adds traction acceleration terms to avoid local optimality. For the GA, a combination strategy of elite retention and roulette is used to improve selection efficiency, and the Metropolis criterion is combined to optimize the cross-mutation operation, and an adaptive variable neighborhood search mechanism is introduced to strengthen local search. In the experimental setting, the Python simulation platform is used to compare with baseline methods such as RRT*, D* Lite and MOPSO. The test indicators include response time, planning success rate, path length, number of convergence iterations, etc. Experimental results showed that under six concurrent events, the response time of the research method was 6 seconds, which was significantly better than that of the comparison method. When the dynamic obstacle density was 40/km², the planning success rate reached 90.2%. When the scene complexity was 200 nodes, the single planning calculation time was 150ms. The research method converged at the 100th iteration, and the fitness change rate was reduced to 1.3%, showing faster convergence speed and better stability. The above results show that the proposed method is superior to traditional methods in terms of timeliness, robustness and optimization capabilities, which is suitable for emergency rescue path planning in complex urban scenarios.
Adaptive Multiple-Kernel SVM with Joint Kernel-Weight and Hyperparameter Optimization for IR Spectroscopy
Infrared spectroscopy analysis technology is widely used in material identification and other fields due to its non-destructive and fast characteristics. However, the high-dimensional, nonlinear, and noisy nature of the data poses challenges to the robustness of feature extraction and classification models; Although SVM performs well in spectral classification, its performance depends on kernel function selection, and traditional single kernel processing of complex infrared spectral data has limited capabilities. To this end, this study focuses on the SVM composite kernel intelligent selection strategy and proposes a framework of integrated intelligent optimization algorithm. With the goal of maximizing classification accuracy and minimizing model complexity, the optimal weighted combination is searched in candidate kernel pools such as polynomial kernel and RBF (Radial Basis Function) kernel. The core innovation is the collaborative automatic optimization of composite kernel weight coefficients and SVM parameters; Based on the ATR-FTIR dataset of 120 organic compounds, experiments were conducted using a modified PSO (Particle Swarm Optimization) algorithm (optimizing inertia weights and learning factors, constructing a fusion fitness function for collaborative optimization) and an outer 5-fold cross validation. The results showed that the optimized composite kernel model achieved a classification accuracy of 94.1% ± 1.2%, which was 11.5 percentage points higher than the traditional RBF kernel (82.6% ± 1.8%). The convergence speed of the optimization iteration was 47% ± 3.5% higher than the standard PSO, and the prediction accuracy on the near-infrared drug/food dataset reached 96.67% ± 0.9%. The generalization and robustness were significant, providing a new paradigm for intelligent feature analysis of complex infrared spectral data and improving the accuracy and automation level of spectral substance identification.
Copyright (c) 2026 Yuchun Liu, Haiwei Wu, Xuexin Li, Jintong Tu
Hybrid GA-PSO Model for Multi-objective Dynamic Optimization of Cruise Cabin Layouts under Marine Operational Constraints
Cruise cabin space design stands as a pivotal determinant of passenger experience and operational efficiency, yet it grapples with three core challenges: irregular spatial configurations shaped by hull curvature, dynamic demands (e.g., fluctuating passenger load, real-time activity needs), and inherent multi-objective conflicts (e.g., maximizing space utilization vs. ensuring comfort). Traditional optimization methods—such as standalone heuristic algorithms or static layout planning—are constrained by limited global search capabilities, sluggish convergence in complex scenarios, and inability to adapt to maritime dynamic constraints. To address these gaps, this study innovatively proposes a hybrid GA-PSO bimodal algorithm and constructs a dynamic cruise cabin space optimization model. Marine-specific dynamic constraints are explicitly modeled, including ship motion parameters (roll/pitch angles under sea conditions 3–8) and maritime evacuation regulations (minimum channel width ≥1.2m). Four mutually restrictive objective functions are also formalized: space utilization (ratio of effective area to total area), comfort (integrated score of ergonomic spacing/noise isolation), structural feasibility (compatibility with hull load-bearing limits), and life-cycle cost (construction + maintenance expenses).Experiments utilize 12 typical cabin layouts (200–300㎡), 500+ passenger behavior datasets (18 months of real-ship data), and 30 independent runs, comparing with standard GA and PSO. Results verify superiority: GA-PSO convergence speed reaches 0.80 ± 0.05 iterations/s (mean ± std), which is 38.7% faster than GA (0.58 ± 0.08 iter/s) and 22.1% faster than PSO (0.66 ± 0.07 iter/s). The improvement is statistically significant (p < 0.01, t-test).; optimal solution comprehensive value hits 0.92 (15.2% higher than GA, 9.8% than PSO), with statistical significance (p<0.05). It maintains 0% safety violations in dynamic scenarios, providing reliable intelligent decision support for cruise cabin design.
Copyright (c) 2026 Jiping Gong, Lili Wang
A GAN-LSTM Based Framework for Dynamic Project Scheduling and Risk Prediction in Engineering Management
As the complexity of large-scale engineering projects increases, traditional schedule management methods face the dual challenges of insufficient prediction accuracy and lagging risk response in a dynamic environment. This study proposes a hybrid framework that integrates a conditional generative adversarial network (cGAN) with a bidirectional long short-term memory network (Bi-LSTM) to achieve dynamic project progress generation and real-time risk warning. The model was trained on a real-world dataset from a cross-sea bridge project, comprising 12 months and over 50,000 process records. The Bi-LSTM module captures temporal dependencies among construction processes, while the cGAN, trained with Wasserstein distance and gradient penalty, generates diverse and plausible progress scenarios conditioned on real process states. The adversarial training uses minimax loss with Adam optimization. Experimental results indicate that the proposed model reduces the mean square error (MSE) by 23.7% compared to a standalone LSTM in progress prediction, and achieves a risk identification accuracy of 91.4%, surpassing a traditional Bayesian network by 18.2%. In dynamic disturbance simulations, the model achieves an early-warning response time of 2.3 hours for emergencies such as resource shortages or extreme weather—67% faster than the baseline. Furthermore, the adversarial generation of 12,000 virtual progress samples mitigates overfitting in small-sample settings and improves the test-set F1-score to 0.89. This study demonstrates the robustness of the cGAN-BiLSTM framework in complex engineering environments and offers a data-driven solution for dynamic schedule optimization and risk management.
Copyright (c) 2026 Zhixia Fu, Qiang Su, Zhiwei Mu
LegalHANOpt: A Hierarchical Attention Network with BOHB Optimization for Predicting and Explaining Legal Case Decisions
Legal documents are often lengthy and complex, making it challenging and time-consuming for experts to accurately predict case outcomes. Older methods are not well-suited to the structure and language used in legal texts. This paper proposes a model called LegalHANOpt (Legal Hierarchical Attention Network with Optimized Parameters) to make accurate predictions about legal case decisions and explain how those decisions are made. LegalHANOpt utilizes a Hierarchical Attention Network (HAN) that analyzes legal documents by examining individual words and sentences, much like lawyers typically do. To improve the model's performance, Bayesian Optimization with Hyperband (BOHB) is utilized. This sophisticated method automatically determines the optimal settings for training the model, such as learning rate and dropout. The LegalHANOpt is trained on an extensive collection of past legal cases, including relevant facts, laws, and decisions. Results show that LegalHANOpt gives more accurate predictions than older methods achieving superior performance with an accuracy of 0.91%, macro F1-score of 0.83%, and AUC-ROC of 0.92%. It also highlights essential parts of the text, helping users understand why the model made a particular decision. In short, LegalHANOpt is a valuable and explainable tool to support legal experts in making better and faster decisions.
Optimizing Deep Learning Model Ensembles for Plant Disease Detection through Ablation and Correlation Analysis
Early detection of plant diseases is crucial for global food security. While Deep Learning ensemble techniques are widely adopted to improve performance, the assumption that simply aggregating models is always beneficial should be nuanced. This paper addresses this issue by conducting a rigorous analysis of an ensemble of four state-of-the-art architectures (Swin Transformer, Vision Transformer, EfficientNetV2, ConvNeXt) on the PlantDoc dataset, a benchmark known for its complexity. Our approach is twofold. First, we conduct a systematic ablation study to assess how each model contributes to the ensemble's performance. This analysis leads to the counter-intuitive finding that an optimized three-model subset (Swin, ViT, and EfficientNetV2) outperforms the full four-model ensemble. Quantitatively, the pruned ensemble achieves a Macro F1-score of 0.7503 and an accuracy of 0.7619, compared to 0.7409 and 0.7500 for the full set, respectively. Second, to explain this phenomenon, we perform a prediction correlation analysis. This reveals significant predictive redundancy, stemming from the architectural similarities between ConvNeXt and the Transformer-based models, as the cause for this sub-optimality. These findings suggest a key principle for ensemble design: the predictive complementarity of the models is a more critical factor than their individual performance or the complexity of the aggregation method-a finding reinforced by our benchmark showing that even advanced strategies like calibrated voting and stacking failed to outperform the pruned ensemble. Our work thus positions the methodological pairing of ablation study and correlation analysis as an essential and pragmatic approach to optimize the performance of ensembles in computer vision.
Copyright (c) 2026 Nawel Ghrieb, Tahar Guerram, Othaila Chergui
A Proximal Policy Optimization-Based Reinforcement Learning Framework for Real-Time Personalized Endurance Training
Customized sports training routines take into account individual physiology, fatigue, and recovery to maximize performance. Proximal Policy Optimization (PPO)-based reinforcement learning is used to adjust training intensity, duration, and rest in a simulated endurance-training environment for runners, using real-time wearable and performance data. The environment models athlete status utilizing heart rate variability, VO₂ max, fatigue ratings, and injury-risk indicators. PPO is trained to maximize performance gains, recovery quality, and safety over repeated sessions. Simulated policy improves performance (18.6%), injury-risk deviation (−22.4%), recovery compliance (91.3%), training load variability control (±7.2%), reward-signal evolution convergence (+41.7%), session completion rate (94.6%), personalized adaptation score (87.5%), and fatigue index stability (94.3%). Results show that a PPO-based RL setup, specifically defined by state design, reward shaping, and multi-episode training, can provide adaptive and data-driven tailored sports training.
Copyright (c) 2026 Chuanzhong Wu, Danqing Liang, Bo Yang, Li Xu, Yunlong Li
Grey Wolf Optimization-Based Decision-Control for Multi-Robot Signal Source Localization in Communication-Constrained Environments
By developing and evaluating a decision-control strategy using an Hazardous Environment method, this study addresses the issue of signal source localization utilizing a group of autonomous robots. A new grouping technique is the basis of our proposed algorithm, Optimal Weighting Grey Wolf Optimization. The OW-GWO algorithm takes into account the past, present, and future ideal positions of all grey wolves, ranks them according to these positions, and updates these positions as needed. The alpha wolf constantly estimates where the prey is, and the rest of the grey wolves follow suit. Integrating the OWGWO method with an enhanced grouping strategy and dividing the algorithm into two stages—the random walk stage with the dynamic grouping stage—allows us to address the multi-target issue of swarm robots search. In the random walk phase, grey wolves adjust their best placements based on history and travel at random. The OWGWO algorithm builds search auxiliary points throughout the dynamic grouping stage by using a better grouping approach that takes into account the previous ideal placements of individual grey wolves. In order to hunt for various prey, grey wolves use these to form groups. . A decision level along with a control level are both included in the proposed decision-control method. The decision level employs a particle filter to make educated guesses about where the signal could be coming from. The actual location of the signal source becomes closer to the predicted position as the robots move. At the level of control, a consensus controller is suggested for commanding several robots to locate a signal source according to the predicted location of the source. Additionally, in order to alleviate some of the communication load, an Hazardous Environment mechanism is developed. Lastly, experiments and simulations demonstrate that the suggested decision-control method using the Hazardous Environment strategy solves the signal source localization issue well.
Genetic Algorithm-Based Calibration of PV System Parameters Utilizing Deep Learning Architectures
This study introduces a new method for optimizing evolutionary algorithms to learn characteristics of unknown solar energy systems or subsystems. When used together, Genetic Algorithm (GA) and Deep Learning methods allow for precise PV system calibration. By combining GA's optimization powers with deep learning models' pattern recognition abilities, this technique achieves precise system calibration by fine-tuning model parameters. One way to create a connection between variables like irradiance, temperature, and voltage, which are input parameters, and variables like power production or defect detection, is to employ a model that uses deep learning, and brain networks in particular. Feature extraction is a strong suit of CNNs, making them a promising tool for image analysis in the context of spatial pattern defect detection. Using GA, one may optimize the settings of either the PV system or the deep learning model. To do this, one must define a fitness function that represents the target performance metric. This metric might be anything from maximizing power production to reducing prediction error. In order to decrease the discrepancy between the expected and actual electrical output, GA may optimize the biases and weights of a neural network. To sum up, optimizing and calibrating PV systems has never been easier than with the help of a genetic algorithm and deep learning. Together, these things have the potential to make solar energy systems more efficient, provide better results, and enhance model accuracy. This method optimizes seven parameters of a PV system based on observed PV power: nominal power, tilt, azimuth, albedo, irradiance, temperature dependence, and the DC/AC ratio, which is the proportion of nominal module power to nominal inverter power. Through the optimization of these parameters, a digital twin is generated that faithfully mimics the real characteristics and actions of the unidentified solar power plants or subsystems. In order to create this method, we used data on the PV system's on-site power output, surrounding temperature, and irradiance as collected by satellite in southwest Germany. Here, we provide a method that, when applied to nominal power, produces a mean bias error of around 10%, 3° for azimuth with tilt angles, a temperature coefficient ranging from 0.01% to 0.09%, and now-casts having an accuracy of about 6%. As a conclusion, we provide a novel approach to properly parametrize and model PV systems with little to no prior information about their characteristics and elements.
Enhanced Prediction of Manufacturing Quality Ratings Using Optimized Stacking Ensemble Modeling
The predictive modeling of quality in manufacturing is vital in improving efficiency, cutting costs, and attaining zero-defect production. This paper addresses this requirement through an empirical comparison of machine learning models to predict manufacturing quality ratings. On a simulated dataset of 3,957 samples with five important features of the process, this study compared the performance of six basic models (AdaBoost, Bagging, Decision Tree, Gradient Boosting, K-Nearest Neighbors, and XGBoost) and two more advanced ensemble models (Averaging and Stacking). The findings demonstrated that the Stacking Ensemble model outperformed all other candidates, with higher performance in terms of an R2 of 0.999 and the lowest values of error (MSE: 0.004, RMSE: 0.065, MAE: 0.016) in the test set. Moreover, SHAP analysis of the Stacking ensemble as the best model revealed that Material Transformation Metric (MTM) and Temperature (T) were the top features having a significant impact on the quality of products. The analysis finds that the Stacking Ensemble method provides a valid and very effective framework for predicting quality, which is helpful in the optimization of the manufacturing process.
Copyright (c) 2026 Lanbao Hou, Zhiqiong Zou