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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Risk-Constrained Trade Sizing via Calibrated ML Probabilities and Linear Programming on SMC/ICT-Driven Structural Signals
Smart Money Concepts (SMC) and the Inner Circle Trader (ICT) provide a rich vocabulary of market-structure events, but translating these discretionary ideas into robust, cost-aware execution remains challenging. This paper proposes a structure-aware trading system that (i) detects SMC/ICT events on OHLC data, (ii) learns calibrated probabilities of favorable resolution over a fixed horizon, and (iii) maps the probabilistic edge to a risk-aware position via linear programming (LP) with exposure, turnover, and Conditional Value-at-Risk (CVaR) constraints. On XAUUSD H1 data, the hybrid method achieves a cost-inclusive development return of 46.7% with Sharpe 1.18 and maximum drawdown (MDD) 6.8%, outperforming rules-only (38.6%, Sharpe 0.86) and ML-only (34.1%, Sharpe 0.79) baselines. On a held-out validation window, calibration and tail risk improve further, reaching Brier score [32] 0.186, Expected Calibration Error (ECE) 6.1%, ES0.95 0.036, and MDD 6.3%. Ablations indicate that probability calibration and the CVaR constraint contribute materially to risk-adjusted performance, while session conditioning improves robustness across volatility regimes.
Copyright (c) 2026 Mohamed Hassan Oukhouya, Aboutabit Noureddine, Hafidi Imad
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.
Nonlinear Function Fitting and Prediction Using Newton-CG Optimization and Transformer Architecture
This study proposes a nonlinear function-fitting prediction model combining the Newton-CG optimization algorithm with the Transformer architecture to address the limited accuracy and generalization of high-dimensional nonlinear data. Traditional methods often struggle with slow convergence and overfitting when dealing with complex nonlinear relationships. In this paper, the Transformer's multi-head self-attention mechanism is used to capture long-term dependencies in the data, and the Newton-CG method is used to accelerate parameter optimization during model training, thereby significantly improving fitting accuracy and computational efficiency. In the experimental part, three typical nonlinear functions and two public high-dimensional data sets are selected for verification, and the model's average test-set fitting error is reduced to 0.023, which is about 71.5% and 68.2% higher than those of the traditional LSTM and BP network methods. At the same time, the introduction of the Newton-CG method reduces the number of training iterations by about 40% and the average convergence time by 60%. The results show that the proposed model achieves high accuracy and strong generalization in nonlinear function fitting, providing an effective solution to the prediction problem in complex systems.
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.
Knowledge-Augmented Deep Learning Framework for Chinese Literary Text Analysis Using ARO-HA-BiLSTM
Chinese literary texts spanning classical, modern, and contemporary periods present significant challenges for computational interpretation due to their deep semantic structures, metaphorical richness, and historical context. Traditional NLP approaches often fail to capture hierarchical narrative dependencies and culturally embedded meanings. This study proposes a Knowledge-Augmented Literary Text Analysis Framework that integrates ChatGLM-based knowledge-enriched embeddings with a Hierarchical Attention-based Bidirectional Long Short-Term Memory (HA-Bi-LSTM) network, optimized using the Adaptive Remora Optimization (ARO) algorithm. A corpus of 2000 Chinese literary texts was pre-processed using tokenization, rule-guided normalization, and structure-aware segmentation to preserve linguistic and contextual integrity. The proposed ARO-HA-BiLSTM model achieved an accuracy of 0.9724, precision of 0.9851, recall of 0.9718, and F1-score of 0.9734, outperforming existing models such as EL-GCN (accuracy 0.8855) and TextCNN (accuracy 0.8613). Ablation analysis further confirms the effectiveness of knowledge-enriched embeddings and adaptive hyperparameter optimization in enhancing semantic and thematic classification. The results demonstrate that integrating knowledge- augmented Chinese large language models with hierarchical deep learning significantly improves cross- era literary interpretation. This framework provides a robust computational methodology for digital humanities research and supports scalable, machine-assisted analysis of culturally rich literary corpora.
Hybrid Early Warning System for Financial Crises in Listed Companies Using Attention Mechanism and LightGBM
Aiming to address the accuracy and interpretability needs of early warning systems for financial crises in listed companies, this paper proposes a hybrid early warning model that integrates an attention mechanism and LightGBM. When traditional financial early warning models deal with high-dimensional and nonlinear financial data, it is often difficult to capture the differential influence of key indicators, and they are sensitive to quasi-imbalanced data. This study combines the attention mechanism and the LightGBM classifier to perform weighted processing on key financial indicators and trains the model to handle large-scale imbalanced data. The experiment uses data from ST companies and non-ST companies in the Chinese A-share market from 2015 to 2020, constructing a panel dataset of 2,560 listed companies. The model's warning accuracy on the test set is 88.67%, with an AUC value of 0.942, which is 3.2%, 4.8%, and 5.7% higher than the accuracy rates achieved by using LightGBM, XGBoost, and random forest models alone. The attention weights enhance the model's interpretability and identify key warning indicators, such as cash flow ratios and return on assets.
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.
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.
Feature Adaptive Distillation and Attention-Enhanced Siamese Network for Intelligent Network Intrusion Detection and Proactive Response
To address the problems of high-dimensional feature redundancy, weak generalization ability of small- sample attacks, and insufficient detection-response coordination in network intrusion detection, this study proposes an end-to-end intelligent detection and proactive response framework based on Feature Adaptive Distillation-Attention Enhanced Siamese Network (FAD-AESN). We adopt a three-stage methodological approach: (1) a Feature Adaptive Distillation (FAD) module for adaptive dimensionality reduction, which integrates feature discriminativeness, correlation and attack relevance to dynamically adjust distillation temperature and feature weights, realizing redundant feature removal and core information preservation; (2) an Attention-Enhanced Siamese Network (AESN) module with an embedded channel-space attention mechanism and improved triplet loss function to enhance the differentiated expression of small-sample attack features; (3) a dynamic proactive response mechanism constructed based on detection confidence and multidimensional threat-level assessment, forming a closed-loop detection-response coordination system. We conduct comprehensive experiments on the CSE-CIC-IDS2018 and UNSW-NB15 datasets with 5 independent experimental runs for each algorithm; the full-sample dataset is split into training and test sets at a 7:3 ratio, and the small-sample dataset is split into support and query sets at a 6:4 ratio, with all results reported as the mean ± standard deviation. Statistical significance is verified via two-tailed t-tests (p<0.001) for all performance improvements over baseline methods. Experimental results show that FAD-AESN achieves a detection accuracy of 98.7±0.2% and a small-sample F1 score of 92.3±0.5% on the two datasets, representing improvements of 12%-18% and 3.5%-8.9% respectively compared to traditional machine learning and mainstream deep learning algorithms. The detection latency is as low as 42±3ms with a false-positive rate (FPR) of 1.2±0.1%, and the dynamic response mechanism achieves a blocking success rate of 89.3±1.2%- 98.2±0.5%. This study realizes the integrated optimization of efficient detection and dynamic response, providing an end-to-end solution for proactive network security defense, and outperforms the state-of- the-art (SOTA) network intrusion detection models of 2026 in both detection performance and real-time response capability.
Copyright (c) 2026 Yong Wang, Fan Jia, Yi Ren, Ming Wang, Jun Li
End-to-End Network Security Prediction via Dual Attention- Enhanced Situation Assessment and IHHO-GResNeSt Integration
To address the limitations of existing cybersecurity prediction methods in terms of insufficient feature extraction and low parameter optimization efficiency, this study proposes an end-to-end cybersecurity prediction method based on Channel-Spatial Dual Attention (CS-DA) to enhance situation assessment and integrating an improved Harris Hawks Optimization algorithm and a Global Context Residual Split-Attention Network (IHHO-GResNeSt). This method first introduces CS-DA to enhance the extraction of key threat features during the cybersecurity status assessment stage, and then uses IHHO to globally optimize the assessment model parameters. Subsequently, quantified situation values are used as prior input, and GResNeSt is introduced for temporal feature learning. The Sparrow Search Algorithm (SSA) is then combined with the GResNeSt algorithm to optimize the prediction hyperparameters, achieving integrated end-to-end security situation prediction that combines assessment and prediction. Experimental results on three public benchmark datasets-NSL-KDD, UNSW- NB15, and CICIDS2017 show that the proposed method achieves a false negative rate of 0.05 and a false positive rate of 0.03 in the security assessment stage, with a situational assessment bias of 0.04 and an inference latency of only 32.16 ms, demonstrating good assessment accuracy and real-time performance. In the prediction stage, the RMSE (0.08) of the SSA-tuned model is superior to CNN- LSTM (0.17), XGBoost (0.20), and Transformer (0.15). These results demonstrate that the proposed method can achieve high-precision, robust, and efficient cybersecurity prediction in complex and dynamic network environments, providing reliable support for security early warning and decision- making.
Software-Defined Networking for IoT-Based Motor Bearing Fault Diagnosis
With the acceleration of industrial intelligence, motor bearings, as the core components of mechanical equipment, are the key to ensuring stable industrial production through real-time and accurate fault diagnosis. This article aims to address the issues of poor stability, weak anti-interference, and insufficient real-time performance in traditional fault diagnosis methods, and develop an innovative solution for motor bearing fault diagnosis that integrates the advantages of the Internet of Things (IoT) and Software Defined Networking (SDN). Build a software defined IoT architecture in terms of methodology, and collect motor bearing operating parameters based on IoT wide area connectivity characteristics. Relying on the centralized control advantage of SDN to enhance diagnostic flexibility, integrating CNN-LSTM model to strengthen feature extraction capability, using Z-Score normalization, 3 σ criterion, etc. to complete data preprocessing, and optimizing model parameters through grid search. The experiment was validated based on the CWRU standard dataset, and the results showed that the proposed model improved prediction accuracy by 13.5 and 10.1 percentage points respectively compared to traditional methods, increased F1 scores by 0.156 and 0.120, decreased MAE by 0.119 and 0.091, reduced inference delay by 58ms and 25ms, and exhibited good scalability in multi motor parallel scenarios. This model effectively achieves real-time monitoring and efficient diagnosis of the operating status of motor bearings, solving the core pain points of traditional methods. It can be seamlessly integrated into industrial predictive maintenance systems and is suitable for multi domain motor operation and maintenance, providing reliable technical support for industrial motor bearing fault diagnosis.
Quantitative Analysis of Soil Bearing Capacity Using Image-Based Morphology and Capacitive Moisture Sensing
This study proposes a rapid Pedon-scale screening framework for in-situ soil bearing capacity by integrating image-based soil morphology and capacitive moisture sensing. Six disturbed soil samples were collected from KIPP IKN (Nusantara), Mandalika (Central Lombok), Pulau Seram (Central Maluku), Desa Mayang (Sukoharjo), Penajam Paser Utara, and Ciracas (East Jakarta). Morphological structure was quantified from smartphone images acquired under controlled back-illumination using grayscale conversion, adaptive histogram equalization, Otsu thresholding, and morphological opening to obtain an image-derived porosity index treated as a void-ratio proxy. Moisture content was measured using a low-cost capacitive probe and mapped to percentage water content. Laboratory references followed ASTM standards (e.g., ASTM D2216 and D854) to compute gravimetric water content and void ratio. Agreement between rapid estimators and laboratory values was assessed using RMSE/NRMSE, normality testing, and Pearson correlation. The capacitive sensor achieved RMSE = 2.4366% (NRMSE = 7.56%) with r = 0.9956, while the image-based void-ratio proxy achieved RMSE = 0.0255 (NRMSE = 18.89%) with r = 0.8670. A rule-based fusion of porosity–moisture classes aligned fully with geotechnical bearing-capacity classification across sites. The proposed workflow offers a portable, low-cost alternative for rapid preliminary subgrade and foundation screening, while acknowledging uncertainty sources (illumination variability, heterogeneity, and sensor drift) for future field-scale validation.
Copyright (c) 2026 Febry Mandasari, Eri prasetyo Wibowo, Tubagus Maulana Kusuma, Sri Wulandari
Dynamic Tangent Search-Driven Graph Neural Networks for Cross- Border E-Commerce Sales Forecasting
The rapid expansion of cross-border e-commerce has intensified the need for accurate and computationally efficient sales forecasting models capable of capturing complex user–product interactions and dynamic market behavior. Traditional machine learning (ML) approaches often fail to model multi-hop relational dependencies and volatile browsing patterns across international platforms. To address these challenges, this study proposes a Dynamic Tangent Search–driven Graph Neural Network (DTS-GNN) framework for cross-border e-commerce sales forecasting. The proposed methodology integrates comprehensive data preprocessing, including noise removal, missing value imputation, Min–Max normalization, and one-hot encoding, followed by Principal Component Analysis (PCA) for dimensionality reduction and feature extraction. A Graph Neural Network (GNN) is employed to model relational structures among users and products, while Dynamic Tangent Search (DTS) is used to adaptively optimize graph weights and hyperparameters, improving convergence stability and prediction accuracy. The model is evaluated using a real-world cross-border e-commerce dataset comprising 2,100 records of user browsing behavior, product attributes, and transactional data. Experimental results demonstrate that the proposed DTS-GNN significantly outperforms existing models, achieving a Mean Absolute Error (MAE) of 1.8420, Root Mean Square Error (RMSE) of 2.2165, Normalized RMSE of 0.0568, Mean Absolute Percentage Error (MAPE) of 10.3924, and a correlation coefficient (R) of 0.9448. Additionally, the framework shows improved reliability (24.6), reduced uncertainty (17.3), and faster search time (10.9 s). These results confirm the effectiveness, robustness, and computational efficiency of the DTS-GNN framework for accurate sales forecasting in dynamic cross- border e-commerce environments.
Financial Forecasting and Budget Management Based on Machine Learning
This article focuses on the application of machine learning technology in financial forecasting and budget management to improve the intelligence level and decision-making efficiency of enterprise financial management. Given the shortcomings of traditional financial models in terms of data processing capability, prediction accuracy, and response speed, an improved Sparse Denoising Autoencoder (SDAE) neural network is introduced as the core modeling tool. And by using interval discretization method to construct a robust financial prediction model to achieve anomaly smoothing. Through comparative experiments, the performance of the improved SDAE model was compared with standard SDAE and traditional backpropagation neural networks (BPNN). The results indicate that the improved SDAE outperforms the other two models in terms of prediction accuracy and stability, and can complete prediction tasks within milliseconds even when processing tens of thousands of records. In addition, system concurrency testing has shown that it has good scalability and resource scheduling capabilities, and can support multi departmental parallel usage scenarios.
A Virtualized BB84 QKD Simulator in Python Using Qiskit Aer for Education and Prototyping
With the rapid advancement of quantum technologies, quantum key distribution (QKD) has become a key approach to secure communication. This study presents a software-only implementation of the BB84 QKD protocol developed in Python using IBM’s Qiskit Aer simulator. The proposed framework is designed for educational and prototyping purposes rather than for hardware-level security evaluation. The simulator models the core stages of BB84: random bit/basis generation, state preparation, basis-dependent measurement, sifting, and parameter estimation via QBER, followed by conceptual post-processing. Evaluation reports (i) the expected sifted-key ratio under random basis selection, (ii) the ideal-channel QBER behavior under noise-free Aer execution, and (iii) the theoretical intercept–resend detection probability derived from standard BB84 analysis. For sequences of ≥100 bits, the implementation matches standard BB84 expectations under ideal Aer execution: an expected sifted-key ratio of ≈50%, QBER → 0 in the absence of adversarial interference, and an intercept–resend detection probability above 99.9% for m = 100 disclosed verification bits. These results demonstrate that an idealized virtual environment can reproduce the BB84 measurement–disturbance trade-off in the information-theoretic sense, without claiming physical decoherence modeling unless an explicit Aer noise model is enabled. The analysis focuses on idealized BB84 behavior, where disturbance arises solely from basis mismatch and intercept– resend measurements rather than from physical decoherence or hardware noise models. The framework provides a reproducible platform for teaching and early-stage experimentation, with future work targeting realistic noise models and advanced post-processing.
A Unified Taxonomy and Empirical Review of Recent Proximal Policy Optimization Variants and Their Real-World Applications
Artificial Intelligence (AI) algorithms such as Proximal Policy Optimization (PPO) help train agents for sequential decision-making tasks. Existing surveys already provide good coverage of the original PPO and its early developments but fell short of capturing the rapid evolution of PPO-based methods over the recent few years. Since 2023, a wave of algorithmic variants has emerged. These variants address different objectives, regularization approaches, exploration methods, training pipelines, and hybrid architectures across diverse applications. However, there has been no systematic effort to organize, compare, or critically assess these advances. This review addresses that research gap. This review analyzes 32 peer- reviewed studies (2023–2025) to evaluate 15+ PPO variants across six innovation categories and ten application domains. The study revealed that PPO delivers a typical performance improvement of 15– 44% over baselines across metrics, including improved safety constraint satisfaction (+15%), computational efficiency (+18% SLA compliance), and sim-to-real transfer (+23% task success). The study analyzed advancements and developments by proposing a unified taxonomy focused on algorithmic advances and their performance in real-world scenarios. Three critical dimensions considered for evaluation are: generalization across tasks and environments, robustness and safety in deployment, and computational efficiency in training and inference. The review also identifies recurring limitations, inconsistent evaluation practices, and underexplored directions. It exposes gaps between simulation benchmarks and real-world deployment conditions, including operational constraints and challenges. By connecting theoretical improvements to empirical outcomes, this work serves as both a practical reference for engineers and researchers applying PPO today. The synthesized taxonomy provides a structured reference for analyzing recent PPO variants and their empirical trade-offs.
SAGE: A Unified Evaluation Framework for Data Augmentation and Few-Shot Learning on Small and Imbalanced Tabular Datasets
In critical domains such as healthcare and finance, structured data often suffers from sample scarcity and class imbalance, undermining the traditional machine learning assumption that training data adequately reflects the true distribution. To address this challenge, this study proposes SAGE (Small-sample Adaptive Generalization Evaluation), a unified framework for systematically comparing data-driven augmentation methods with model-driven few-shot learning (FSL) approaches. The framework integrates a standardized data conditioning pipeline, a comprehensive spectrum of 12 models (including 6 classical classifiers and 6 FSL architectures), and multi-dimensional evaluation metrics. Experimental validation was conducted on three diverse datasets: UCI Heart Disease (297 samples), Hepatitis (155 samples), and Glass Identification (214 samples), covering medical and forensic domains. Results demonstrate the complementary strengths of both paradigms. For data-driven methods, CatBoost augmented with Large Language Models (LLMs) achieved a Macro-F1 of 0.4219 on the heart disease dataset, significantly outperforming traditional oversampling methods like SMOTE (p<0.001). However, for extreme scarcity, model-driven approaches proved superior; Siamese Networks achieved the highest Macro-F1 of 0.5959 on heart disease and maintained robustness across datasets, specifically attaining an F1-score of 0.64 on the rarest class (Class 4). Furthermore, SHAP analysis confirmed that the best-performing models successfully captured clinically relevant features, such as albumin levels in hepatitis prediction. The SAGE framework thus provides empirical evidence to guide paradigm selection: FSL for extreme scarcity and LLM-based augmentation for enhancing ensemble classifiers.
Copyright (c) 2026 Yuhao Yan, Linlu Chen, Houyan Zhang, Chong Chen, Leran Liang, Meng Yang
Analyzing Public Opinion Bias through Social Media Using a Hybrid RoBERTa-BiGRU-DPCNN Sentiment Analysis Framework
During the development of public opinion, effective guidance through social media is conducive to the formation of a positive public opinion bias, while incorrect guidance may lead to a further loss of control over public opinion. This paper designed a sentiment analysis algorithm called robustly optimized bidirectional encoder representations from transformers pretraining approach-bidirectional gated recurrent unit-deep pyramid convolutional neural network (RoBERTa-BiGRU-DPCNN) to analyze the sentiment categories of texts. The algorithm first used RoBERTa-wwm-ext to obtain the vector representation of a text, then employed BiGRU and DPCNN to extract features, and finally classified sentiment through a softmax layer. Then, the guiding effect of social media was analyzed based on changes in sentiment categories in public opinion events. The sentiment classification performance of the algorithm was evaluated using the SMP2020 dataset, which contains 22,019 samples in the training set and 4,010 in the test set. The Micro F1 value was used to measure the classification performance. It was found that the RoBERTa-BiGRU-DPCNN algorithm achieved a micro F1 value of 73.58%, outperforming algorithms such as TextCNN. The algorithm was applied to analyze two public opinion events, and it was found that the social media guidance effect was poor and failed to form a positive public opinion bias. These results verify the effectiveness of the proposed algorithm in reflecting the guiding effect of social media indirectly.
Design and Edge-Enabled Implementation of Smart Acousto-Optic Safety Wearables for Power IoT Environments
With the rapid development of the power Internet of Things, the safety risks faced by power facility maintenance personnel, such as high-voltage electric shock, equipment failure, and severe weather, have become increasingly prominent. Traditional protective measures are no longer able to meet the safety needs in complex environments. To this end, this article designs an intelligent sound and light alarm workwear based on the power Internet of Things. The workwear integrates infrared detection, microwave perception, video monitoring, and AI intelligent recognition technology to build a comprehensive monitoring system. Real time data is collected from multiple sensors, and edge AI technology is used for localized analysis to achieve active and accurate identification of dangerous behaviors. Once a risk is detected, the system automatically triggers an audible and visual alarm, and supports dual mode operation day and night, significantly improving the night work protection capability. To verify its effectiveness, this study designed three control experiments: the basic group (only electrical sensors+threshold alarm), the advanced group (multi-sensor+basic fusion+simplified edge AI), and the experimental group (multi-dimensional sensors+deep fusion+complete edge AI). The experiment was conducted in two typical scenarios: a 729kW factory area (strong electromagnetic environment) and an outdoor high-voltage inspection area (temperature, humidity, and light gradient changes). The sensor complementarity and blind spot coverage were verified by synchronously collecting 100 operating condition data (including 50 anomalies), and the recognition accuracy, local calculation proportion (≥ 80%), and alarm delay (timing error ≤ 0.01s) of edge AI were tested based on 1500 standard datasets. The results showed that the experimental group performed the best in key indicators such as detection accuracy and false alarm rate. The safety level of operation and maintenance personnel during daytime and nighttime operations was improved by 30% and 40% respectively, effectively verifying the significant effectiveness of intelligent work clothes in improving the safety protection ability of power maintenance personnel and having good promotion and application value.
Copyright (c) 2026 Xi Fang, Ying Liu, Jialong Wu, Jing Lv, . Galvin
A Grid-Coupled Clustering Algorithm with Soft Constraints for Mixed-Attribute Data Streams
Mixed-attribute data contains both numerical and categorical attributes, posing challenges for traditional clustering algorithms in managing its dynamics and concept drift. This article proposes a hybrid attribute data stream clustering algorithm that combines soft constraints. Firstly, normalize the mixed-attribute data stream and apply local linear embedding for dimensionality reduction; Secondly, design a mixed-attribute sliding window, based on the idea of grid coupling update, to analyze changes in grid centroids to adapt to dynamic data flows; Finally, fuzzy mathematics is introduced to set soft constraints on interval boundaries (width) and grid cell density, restricting high-frequency cluster shifts. In the experimental section, a comparison was made between the time dimension feature extraction method based on unsupervised learning and the dual interactive generative adversarial network method. On the Forest Cover Type, GMD-4C2D800 Linear, and KDD CUP 99 datasets, the proposed method achieved a minimum CMM value of 0.894 and a minimum Purity value of 0.856, with an accuracy of up to 99.94% and a maximum NMI value of 1, all of which were superior to the comparison methods. The results indicate that the proposed algorithm can effectively adapt to changes in data flow distribution, enhance both clustering accuracy and computational efficiency.
Research on Fine-Tuned BERT-CRF Joint Labeling Model for Contractual Syntactic and Lexical Ambiguity Resolution in Business
Ambiguity in business English contracts (e.g., lexical polysemy, syntactic nesting) seriously undermines accurate understanding and execution efficiency. To address this, we propose a Fine-Tuned BERT-CRF joint labeling model, with the following key methodological details: 1) Corpus Construction: A 12,800- sample contract corpus (covering 8 business domains) is built, with unified labeling for 4 ambiguity types (lexical, syntactic, reference, pragmatic) and 9,750 annotated ambiguity instances. 2) Model Design: BERT is adaptively fine-tuned via “term-aware masking” (prioritizing contract-specific terms); CRF is integrated as a sequence labeling layer with a domain-prior transition matrix to optimize label generation. 3) Training Strategies: Adversarial training (FGSM/PGD perturbations) enhances robustness, and incremental online learning (importance sampling) enables dynamic adaptation. Experimental results demonstrate: For lexical ambiguity, the model achieves 67.5% accuracy, 78.9% recall, and 72.8% F1; boundary labeling accuracy for the clause “USD 90.1 million” improves from 56% to 79%; syntactic ambiguity F1 is 62.3%. Lightweight optimization (hidden layer dimension=34) reduces parameters by 45% (to 12.78 M) but requires 89 training rounds to balance performance. This model outperforms generic BERT-CRF by 9.2% in F1 for complex contract processing, verifying its effectiveness in resolving contractual syntactic and lexical ambiguity.
Copyright (c) 2026 Xiaohong Huang, Xiao Tao
A Dual Modeling Framework for Music Recommendation Via GraphSAGE and Deep Interest Networks (DIN)
This study proposes a music recommendation system that combines the GraphSAGE and DIN algorithms to improve the accuracy and personalization of recommendations. Using a dataset of 50,000 users, 100,000 songs, and 3 million listening records, the system evaluates performance using metrics like AUC and HitRate. The GraphSAGE algorithm extracts users' long-term interest preferences from heterogeneous user-song graphs, while DIN dynamically adjusts users' short-term interests through attention mechanisms. Experimental results show that the hybrid model outperforms standalone GraphSAGE and DIN, with a recommendation accuracy of 92%, which is 7% higher than GraphSAGE and 4% higher than DIN. The model also shows significant improvements in user satisfaction (4.5 vs. 4.1 for GraphSAGE and 4.3 for DIN), demonstrating its effectiveness in handling large-scale music recommendation tasks with dynamic user preferences. Through this dual modeling method of structure + dynamics, this study effectively makes up for the shortcomings of a single algorithm in dealing with complex user behaviors. For the experiment of recommendation coverage, the GraphSAGE algorithm shows a higher recommendation coverage, especially when the number of music exceeds 100, the recommendation coverage is stable at around 75%, which is significantly better than DIN's 65%. At the same time, this paper also analyzes the relationship between system response time and performance. Experimental results show that GraphSAGE can maintain high recommendation accuracy when the response time is short, while DIN achieves the best performance when the response time is 10 hours. The performance advantages of the two in different situations complement each other. The research in this paper not only proposes a new music recommendation system architecture, but also proves through experiments that the effective combination of GraphSAGE and DIN can significantly improve the accuracy, coverage and user satisfaction of the recommendation system. This research provides a new idea and technical path for the application of recommendation system in complex data and multi-dimensional interest modeling scenarios.
Integration of Deep Reinforcement Learning and Cloud Computing for Enhanced Sports Performance Prediction and Training Guidance
Improving sports performance with smart technology is becoming more important, especially in highly competitive fields like swimming. Many current training methods do not provide real-time feedback, lack flexibility, and fail to meet the unique needs of each swimmer. To solve these problems, the SwimInsight Reinforcement Learning Training Framework SIRLTF is introduced. SIRLTF integrates Long Short Term Memory LSTM for temporal modeling and Deep Reinforcement Learning algorithms including Deep Q Network DQN and Proximal Policy Optimization PPO for personalized feedback generation in a cloud- based environment. The cloud infrastructure enables fast data processing, large storage capacity, and remote accessibility, making the system efficient and scalable. SIRLTF is designed for use in professional swimming centers, sports academies, and athlete rehabilitation programs. It helps coaches and swimmers track progress, adjust training plans dynamically, and improve overall performance with intelligent data- driven suggestions. The framework also adapts continuously to changes in a swimmer's condition and training response. Comparative experiments as described in Section 4 show a 19 point 4 percent improvement in prediction accuracy and a 21 point 7 percent gain in training efficiency over baseline models such as CNN SVM and EC DRLMS. These results demonstrate the benefits of combining deep learning with cloud computing to enhance sports training. Overall, SIRLTF offers a smart, reliable, and scalable solution to improve swimming performance and can be extended to other sports for better athlete development.
Research on Error Correction Algorithm for Intelligent Energy Metering System Based on Deep Learning
Smart energy metering systems enable energy operators to allocate energy efficiently. However, these metering systems are not exempt from hardware impracticalities, environmental effect, and communication errors that sometimes produce, as a result, potentially erroneous data. For these reasons, this research proposes an approach to deep learning (DL) error mitigation framework (C-ESA-LSTMAE), incorporating various factors into the training model focused upon the object of the Chaotic-Enriched Seahorse Algorithm-Mutated LSTM Autoencoder. This research utilizes energy consumption logs consisting of voltage, current, frequency, and power from the Kaggle smart meter dataset. The preprocessing of the dataset took many forms, to include identifying missing value, chats data with outliers, identifying duplicated values, Min-Max normalizing, and applying a Discrete Fourier transform (DFT) to all time-domain features as well frequency-domain features to best leverage use of features for purposes of anomaly detection and anomaly mitigation. The C-ESA-LSTMAE proposed framework also made a comparison to various baseline contenders and other prior traditional error correction methods, as well tested with a standard LSTM Autoencoder for the sake of comparison. The results were significant and lend a lot of promise as to the proposed C-ESA-LSTMAE approach and framework. C-ESA-LSTMAE produced errors considerably lower than the baseline (MAPE = 0.01413, RMSE = 0.02011, MAE = 0.01242, MSE = 0.2642), and in comparison to the baseline's, C-ESA-LSTMAE produced errors that were 18-25% lower in different scenarios (with comparable error levels) claiming it was able to increase the reliability and accuracy of smart meter data. Paired t-tests using IBM SPSS 26.0 were conducted between the baseline models and the proposed C-ESA-LSTMAE framework on the same dataset splitsThe C-ESA-LSTMAE framework represents a considerable opportunity for developing a real-world error abatement solution for Active Power distribution networks, which maxim IZES efficiencies in operational costs AND, ultimately, the auditing of energy consumption.
Copyright (c) 2026 Tianfu Huang, Chunguang Wang, Tongyao Lin, Xiaoxu Hu
Hybrid GRA-GRM Model for Spatiotemporal Forecasting in Port Logistics Using Grey System Theory and Nonlinear Feature Selection
Accurate and timely port logistics demand forecast is essential for increasing operational efficiency, cutting costs, and raising customer satisfaction in logistics and transportation networks in the age of artificial intelligence and quickly changing supply chain dynamics. This study introduces a new hybrid model, called the GRA-GRM combination model, that combines Grey Relational Analysis (GRA) and Grey Relational Modelling (GRM) to successfully handle the difficulties of feature selection and nonlinear pattern extraction that are present in port logistics demand forecasting. The algorithm outperforms traditional techniques in terms of predicted accuracy by using a comprehensive real- world dataset from five major Chinese cities, which includes over 10 million package transactions and comprehensive courier trajectories. The model's GRA component reduces dimensionality and improves interpretability by thoroughly identifying the key variables influencing port logistics demand. The GRM then models intricate nonlinear interactions using these chosen characteristics, improving prediction accuracy. Performance evaluation shows that the suggested GRA-GRM model outperforms baseline models like standalone GRM (MAE 8.43, RMSE 10.25, MAPE 12.3%, R² 0.841) and Long Short-Term Memory networks (LSTM) (MAE 7.92, RMSE 9.38, MAPE 10.7%, R² 0.865) in terms of predictive metrics, with Mean Absolute Error (MAE) of 5.91 minutes, Root Mean Square Error (RMSE) of 7.42 minutes, Mean Absolute Percentage Error (MAPE) of 7.6%, and coefficient of determination (R²) of 0.918. Additional studies include error distribution histograms that demonstrate the resilience and dependability of the model, time-series comparisons of the projected and real logistical demand, and geographical heatmaps that show the accuracy of regional demand forecast across many Areas of Interest (AOIs). Logistics managers and policymakers may take action based on feature significance rankings, which show that port container throughput and courier delivery frequency are important factors influencing demand swings. Even while the model performs well, issues like its susceptibility to input noise and real-time flexibility point to areas for further study, such as integrating with IoT data streams and sophisticated deep learning architectures. All things considered, this research offers an AI-driven forecasting framework that is scalable, interpretable, and efficient, facilitating more intelligent resource allocation and operational decision-making in intricate port logistics settings.
Copyright (c) 2026 Yanyan Wu, Yang Yang Zhanlong Li
Predicting Tourist Flow and Economic Impact Using a Transformers- Based Deep Learning Model with Multi-Modal Data Integration
Copyright (c) 2026 Jiajie Liu, Dianwei Zhang
Comparative Analysis for Requirement Classification Using Transformer based Pre-trained Models for Digital Governance RFPs
Digital Governance deals with vast data. One of the domains in digital governance is tendering, which uses multiple documents, including a Request for Proposal (RFP). These documents contain extensive information, particularly detailing the project's expected requirements. For these RFP documents, the identification and classification of requirements are essential. One of the existing datasets under the software engineering domain is the PROMISE dataset for software requirements; this work takes inspiration from the PROMISE dataset and curates a dataset for digital governance software development-related RFP documents. The curated domain-specific dataset for text classification uses a pre-trained language model to classify functional and non-functional requirements. Experiments were performed to compare the Transformer's model performance with the baseline dataset, the curated DigiGov RFP dataset, and the concatenated PROMISE + DigiGov RFP datasets. The model's statistical performance across the datasets is assessed using an ANOVA test. The work focuses on automating RFP document statement classification using transformer-based pre-trained models through transfer learning, increasing productivity and accuracy in the field of digital governance. The research shows that using state-of-the-art techniques for RFP documents can effectively enhance the quality of the bidding process. This technique can bring automation to requirement analysis in the bidding process, strengthening the digital governance process.
Copyright (c) 2026 Manisha Tiwari, Shagun Srivastava, Padmaja Joshi
Predicting Critical Submergence in Horizontal Circular Intakes Using Meta-Heuristic Optimized Gradient Boosting Ensembles
This study evaluates critical submergence depth (Sc) in horizontal circular intakes using a dataset compiled from two controlled laboratory experiments conducted under varying hydraulic and geometric configurations. A total of 324 experimental measurements were obtained across two intake-clearance scenarios: (i) zero bottom clearance (C = 0) and (ii) partial elevation (C = d_i/2). The dataset included four primary input variables—intake diameter, approach flow velocity, Froude number, and bottom clearance ratio—which were used as predictors of the dimensionless submergence depth. To enhance predictive accuracy, an automated machine-learning framework was developed using Gradient Boosting (GB) and Extreme Gradient Boosting (XGB), combined with five meta-heuristic optimizers: Artificial Hummingbird Algorithm (AHA), Victoria Amazonica Optimization (VAO), Turbulent Flow of Water- based Optimization (TFWO), Smell Agent Optimization (SAO), and Tasmania Devil Optimizer (TDO). Model training used a 70/30 train–test split, with 10-fold cross-validation applied exclusively to the training portion for hyperparameter tuning. A custom empirical adjustment equation was incorporated to further enhance physical consistency and model stability. Performance evaluation based on the independent test set showed that VAO-enhanced models provided the highest predictive accuracy. The XGVO model achieved R² = 0.993 and RMSE = 0.082 for the C = 0 configuration, while the GBVA model achieved R² = 0.993 and RMSE = 0.118 for the C = d_i/2 case. These findings demonstrate that hybrid ensemble techniques can reliably estimate Sc across varying hydraulic scenarios and outperform standard boosting algorithms.