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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PUGWO-TEABC-MNet: A Hybrid Metaheuristic Framework for Optimal Ice-Melting Device Placement in Power Distribution Networks
Ice accumulation on power distribution lines leads to significant reliability issues, operational instability, and economic losses during extreme weather conditions. Accurate prediction and optimal placement of ice-melting devices are essential to mitigate these impacts and ensure continuous power delivery. Conventional optimization approaches often exhibit slower convergence, limited adaptability to nonlinear climatic variations, and inadequate prediction accuracy under uncertain environmental parameters. The objective to develop an intelligent and robust hybrid framework for optimal ice-melting equipment placement, enhancing prediction precision and system resilience. A new hybrid metaheuristic framework, PUGWO-TEABC-MNet, is formulated by integrating a Position-Updated Grey Wolf Optimizer (PUGWO), Tent-Elite Artificial Bee Colony (TEABC), and LSTM-based Memory Network (MNet) to ensure adaptive exploration and exploitation with dynamic memory learning. The framework optimally determines device placement by minimizing prediction error while modeling nonlinear climatic dependencies. Climatic datasets related to icing events, including wind speed, temperature, humidity, and ice thickness, were collected from Kaggle repositories. Data were normalized and partitioned into an 80:20 training–testing ratio. Principal Component Analysis (PCA) was used to retain the eight most significant components representing climatic influence. PUGWO initializes optimal search regions, TEABC refines global optima, and MNet captures sequential temporal patterns to improve predictive stability. The framework was implemented in Python using TensorFlow and Scikit-learn libraries. Performance metrics achieved include R² = 0.992, MAPE = 0.0042, and RMSE = 0.12, outperforming baseline BP and PSO-BP approaches. The proposed framework demonstrates superior forecasting accuracy and resilience, providing a cost-effective strategy for optimal ice-melting device deployment in power distribution networks.
Copyright (c) 2026 Yue Li, Hao Yang, Yaorong Hu, Anjiang Liu, Chen Dou
Blockchain-Enhanced Anomaly Detection Algorithm for Financial Data Sharing Platforms Using u-BlockMixup
As the financial industry advances in digitalization, the security and quality of shared data have become critical. Traditional centralized anomaly detection methods face challenges regarding single points of failure and data tampering. To address these issues, this paper proposes a Blockchain-Enhanced Anomaly Detection Algorithm using u-BlockMixup. We integrate a decentralized blockchain architecture with a semi-supervised deep learning model based on the Mean Teacher framework. Specifically, we introduce the u-BlockMixup data augmentation method, which combines supervised cross-entropy loss and unsupervised consistency loss to generate high-quality synthetic samples, thereby improving generalization on limited labeled data. Experimental results on a dataset of over 1 million financial transaction records demonstrate that the proposed method outperforms traditional Isolation Forest and Autoencoder models. The algorithm achieves an accuracy improvement of 15% and an F1 score increase of 18%, with a false positive rate reduced to 2.3%. These findings confirm that combining blockchain immutability with u-BlockMixup-enhanced machine learning significantly improves the reliability and real-time detection capabilities of financial data sharing platforms.
LC-ATSR: A Transformer-Based Approach for Semantic Retrieval in Lakehouse Data Platforms
This study proposes a Lakehouse Collaborative Adaptive Transformer Semantic Representation Algorithm (LC-ATSR) to address the core challenge of semantic retrieval of unstructured data in a Lakehouse integrated data platform. The algorithm uses a transformer as its backbone and incorporates a dual-dimensional adaptive mechanism of storage attributes and data types to construct an integrated "preprocessing-representation-retrieval" framework. The core designs include a storage attribute-enhanced attention mechanism that integrates Lake storage tags into self-attention computation to adapt to heterogeneous storage characteristics; a lightweight semantic compression and multi-modal alignment module that reduces vector dimensions (from 768 to 256) while constructing a unified semantic space; and a Lakehouse incremental semantic indexing mechanism that dynamically updates the index based on LSH hashing. The experimental results show that the LC-ATSR algorithm achieves a P@10 score of 89.7% and an F1 score of 88.2%, significantly outperforming mainstream algorithms such as BERT, DPR, and RoBERTa. The single-retrieval latency was 18.3ms, the incremental index construction time was reduced by 68.4% compared with the full-data approach, and the accuracy fluctuation was only 3.2% in heterogeneous data scenarios. The retrieval system built based on this algorithm achieves a 99.8% functional pass rate, a throughput of 286 QPS with 500 concurrent connections, and a response time of 21.3ms, meeting the engineering requirements of enterprise data platforms and providing technical support for the value mining of unstructured data.
A Tactile-Driven Hierarchical Reinforcement Learning Framework for Dexterous Robotic Manipulation
To address the challenges of robotic dexterous manipulation in complex environments, this paper proposes a hierarchical reinforcement learning (HRL) framework driven by tactile information. We design an adaptive hierarchical decision-making algorithm that integrates multimodal tactile features, dynamically adjusting hierarchical strategies and reward functions to adapt efficiently to complex tactile environments. Experiments were conducted on the PyBullet simulation platform, constructing a manipulation scenario involving 20 objects of varying shapes and materials. Two primary tasks were evaluated: basic grasping and placement, and complex assembly. Comparative results against standard DQN, PPO, and existing tactile-driven algorithms demonstrate that the proposed method achieves a success rate of 92.3% in basic tasks—outperforming DQN by 27.6% and PPO by 21.5%—while reducing the average operation time to 3.2 seconds. For complex tasks, the success rate increased to 85.7% (a 31.2% improvement over baselines), with a 40% acceleration in convergence speed. Ablation studies further validate that multimodal tactile feature fusion contributes an 18.3% increase in success rate, while the adaptive adjustment mechanism reduces strategy adjustment time by 35%. These findings confirm that the proposed framework significantly enhances robotic dexterous manipulation performance, offering a new pathway for the development of intelligent robotic systems.
Copyright (c) 2026 Gao Liu, Changyu Li, Ruchao Liao, Linkun Yu, Jianguo Zhang, Ning Ding
Lightweight Multi-Channel BiLSTM-Attention with Coordinated Defense for Intrusion Detection in Distributed Photovoltaic Communication Networks
To address intrusion and data tampering threats in distributed photovoltaic (PV) communication networks, this paper proposes a lightweight multi-channel BiLSTM-Attention–based intrusion detection and coordinated defense framework. An attack-oriented, risk-aware feature modeling method constructs multi-dimensional temporal inputs by combining protocol semantics, operational interaction behaviors, and deviation-based risk indicators, which are processed by a BiLSTM backbone with time-step attention and channel-level attention fusion. Model pruning and attention sparsification are adopted to obtain a compact architecture suitable for edge deployment, while a dual-threshold, detection-driven defense coordination mechanism links anomaly scores to graded response policies and closed-loop control. Experiments on a hybrid dataset composed of real PV communication logs and PV-adapted UNSW-NB15 and CIC-IDS2018 traffic show that the proposed model achieves 94.8% accuracy, 94.2% recall, and a 95.6% F1-score with an average inference time of 16.2 ms and a false positive rate below 3.6%. Compared with a vanilla BiLSTM-Attention baseline, the proposed framework improves F1-score by 3.2 percentage points and reduces inference latency by about 11%, while maintaining higher robustness under noisy and imbalanced conditions. These results indicate that the framework can provide accurate, real-time intrusion detection and coordinated security defense for distributed photovoltaic communication networks in resource-constrained deployment scenarios. Povzetek:An improved lightweight multi-channel BiLSTM-Attention framework enhances intrusion detection and coordinated security defense in distributed photovoltaic communication networks through risk-aware temporal modeling, multi-channel attention fusion, and model pruning for edge deployment.
Copyright (c) 2026 Danni Liu, Shengda Wang, Chunhui Shi, Jia Li, Xiuhong Jiang
Adaptive Weighted Federated Learning for Enhanced Data Privacy in Cross-Platform E-commerce
Aiming at the urgent need for data privacy protection of cross-border e-commerce platforms, this project proposes a privacy-enhanced federated learning algorithm based on weight fusion. The algorithm can dynamically adjust the aggregation weight of parameters according to the data volume, quality, and feature distribution of participants, and achieve a balance between privacy protection and model performance. In terms of experiments, a simulated cross-platform e-commerce database was established and compared with algorithms such as FedAvg and FedDP. The experimental results show that after 100 iterations, the accuracy of the test case set of the algorithm reaches 85.3%, which is 7.2 percentage points higher than FedAvg's 78.1%; in terms of convergence speed, the algorithm proposed in this paper converges in 52 iterations, while FedAvg needs 75 times and FedDP needs 70 times; in terms of privacy protection, under the same privacy budget conditions, the algorithm in this paper can reduce the success rate of model inversion attacks to 4.8% and 5.2% respectively, which is much lower than FedDP's 12.3% and 11.8%. Adaptive weights dynamically adjust via multi-dimensional evaluation (data scale: 40%, quality: 30%, features: 30%), while hierarchical privacy allocates ε=0.1 for sensitive data (financial) and ε=0.5 for non-sensitive (clicks), boosting accuracy by 7.2% and cutting attacks to 4.8%. The experimental results show that the algorithm proposed in this paper can effectively improve the data privacy protection level and model training effect of cross-platform e-commerce platforms, and its performance is significantly better than SOTA federated learning algorithms. This work pushes the development of federated learning in e-commerce data privacy protection by proposing a new framework of adaptive weighted aggregation combined with hierarchical differential privacy, which provides a theoretical and experimental basis for cross-platform data collaboration under strict data privacy regulations.
CAFWE: A Feature-Weighted Ensemble Classifier for Predicting Performance of Copper-Aluminum Composite Conductors
Copper-aluminum composite conductors are suitable for modern electrical applications due to their high electrical conductivity and lightweight nature; however, predicting their performance in changing environmental and electrical conditions is difficult and expensive using conventional techniques. Problem Statement: Previous models ignore the combined effects of copper-aluminum ratio, tensile strength, temperature, and current load; furthermore, metrics such as accuracy and F1-score cannot accurately reflect physical performance characteristics. A new Copper-Aluminum Feature-Weighted Ensemble (CAFWE) classifier was developed using the CopperAluminum_WirePerformance_Dataset containing 5000 samples with 10 input features and one target output (performance_level). The model integrates three base learners — Linear Classifier, k-Nearest Neighbor (k = 5), and a Decision-Stump classifier — combined through a weighted voting mechanism that assigns higher weights to copper_percentage, tensile_strength, and electrical_conductivity based on feature-sensitivity analysis. The dataset was partitioned using an 80:20 stratified split, and all results were averaged over five repeated experiments to ensure stability. Five domain-specific evaluation metrics were introduced: Conductivity Accuracy (CA), Strength Reliability (SR), Temperature Adaptation Score (TAS), Load Prediction Stability (LPS), and Composite Material Alignment (CMA), enabling alignment with real-world engineering behavior. Results: Across five independent training runs, CAFWE achieved consistent performance, with mean scores of CA = 93.5%, SR = 91.2%, TAS = 89.8%, LPS = 90.6%, and CMA = 92.4%, demonstrating superior predictive reliability under varying material and operational conditions. Feature importance analysis confirmed copper_percentage and electrical_conductivity as the most influential contributors to final predictions. The CAFWE model accurately and interpretably predicts copper-aluminum conductor performance; and provides a scalable framework to optimize hybrid material design for smart grid applications.
Copyright (c) 2026 Yinghan Xiang, Congrui Zhang, Shibo Hou, Yifu Wu, Xu Guo
A Deep Reinforcement Learning and Evolutionary Optimization- Based Collaborative Control Network for Power Systems with Multi- Terminal Information Fusion
Copyright (c) 2026 Li Deng, Lin Geng, Yu Liu, Peng Zhang, Guocheng Wang, Feng Liu
FedPPH: A Federated Personalized and Privacy-Preserving Health Model for Multi-Campus Medical Diagnosis
This study addresses the issue that multi-hospital medical data cannot be efficiently shared or jointly modeled under privacy constraints, thereby achieving a higher level of intelligent diagnosis and cross-regional collaborative services. The study proposes the Federated Personalized and Privacy-preserving Health (FedPPH) model. FedPPH introduces an adaptive gradient adjustment mechanism into global optimization. It implements noise perturbation and secure aggregation through a two-layer security strategy composed of differential privacy and homomorphic encryption. This ensures privacy constraints while enabling dynamic optimization updates of each client and improving the stability of overall convergence. The study conducts systematic verification based on four public medical datasets: Data 1, 2, 3, and 4 (Healthcare, Hospital Patient Records, Disease Diagnosis, and Medical Data and Hospital Readmissions). The verification methods include horizontal and vertical verification. In horizontal verification, the same dataset is divided into multiple subsets to simulate different campus environments, examining the model's collaborative effect under homogeneous data conditions. In vertical verification, multi-source datasets with significant structural differences test models' generalization and robustness in heterogeneous scenarios. Research results show that in horizontal validation, FedPPH controls the global variance within the range of 0.019–0.035, significantly outperforming the single-node model's range of 0.038–0.077. In vertical validation, taking the Data2&Data3 combination as an example, the client discrepancy decreases from 0.192 to 0.103, demonstrating stronger consistency. Regarding stability, FedPPH maintains an accuracy of 0.892 under perturbed conditions, which remains high compared to the 0.935 accuracy without perturbation. This study aims to provide medical institutions with a collaborative modeling solution that can improve diagnostic accuracy and ensure data security, laying a foundation for the practical implementation of multi-campus smart healthcare.
A Hierarchical Attention-Based Heterogeneous Multi-Agent PPO Framework for Distributed Warehouse Scheduling
The dynamic changes in the warehousing environment, the heterogeneity of task allocation, and the complexity of multi-agent collaboration make it difficult for traditional scheduling algorithms to meet the challenges of modern warehousing. This study proposes a heterogeneous multi-agent collaborative scheduling method based on an improved Proximal Policy Optimization (PPO) framework, which integrates a hierarchical attention-driven architecture and dynamic variance constraint algorithm to address the spatio-temporal coupling constraint problem of distributed warehousing scheduling. We designed a multi-objective reward function (considering task timeliness, energy consumption, and space utilization) and a dynamic computing resource allocation strategy to enhance the system’s efficiency and robustness in handling large-scale orders (500+ daily orders) and unexpected situations (e.g., equipment failure). Experimental data shows that compared with traditional scheduling algorithms, the completion rate of agent collaborative tasks has increased from 56.89% to 73.24%, the average task execution delay has dropped from 22.1 seconds to 15.67 seconds, and the storage space utilization rate has increased from 49.2% to 63.5%. In complex order scenarios, the framework's sorting accuracy rate for multiple types of goods reaches 94.5%, which is 37.67 percentage points higher than the baseline model, and the proportion of multi-agent communication overhead in system resources has dropped from 88.76% to 63.5%, which verifies the algorithm's optimization capabilities under resource constraints.
Hierarchical Multi-Agent Deep Reinforcement Learning for Coordinated Optimization of Aggregated Virtual Power Plants in Smart Microgrids
Computational efficiency, Data privacy, and equitable benefit assessment are some of the issues that have arisen as a result of the fast expansion of distributed energy resources (DERs), which have added complexity to the functioning of distribution networks. This paper presents a two-tiered VPP coordination architecture that takes into account the operational interests of both Distribution System Operators (DSOs) or VPPs under AC optimum power flow (AC-OPF) limitations. The goal is to solve these challenges. A penalty-function-enhanced OPF mechanism is used in the upper layer to guarantee network security in the event of voltage or branch-limit violations, and an Asynchronous Advantage Actor-Critic (A3C) multi-agent architecture is integrated into the lower layer to utilize a parameter-sharing Twin-Delayed Deep Deterministic Policy Gradient (PS-TD3) algorithm. Through lightweight parameter sharing and decentralized execution, every agent— which represents a VPP subsystem—learns optimum judgments for energy-dispatch, storage, and flexibility, resulting in dramatically reduced computational cost and preservation of data privacy. When compared to the non-cooperative TD3 baseline, traditional distributed OPF, and independent Q-learning, the suggested dual-layer MARL approach outperforms all three in simulation tests conducted on the IEEE 33-node distribution network. Thanks to parameter sharing, the PS-TD3 + A3C hybrid improves convergence speed through 42% and reduces per-step computing time by 37%. It also reduces voltage variation by 31.4%, network real-power losses by 26.7%, and operating cost by 18.2%. Since agents only share compressed gradients and not raw operational data, privacy leakage is minimized by more than 80%. In contemporary distribution systems that are rich in distributed energy resources (DERs), the findings show that the suggested framework provides a computationally efficient, scalable, and privacy-preserving approach for coordinated VPP operation.
Copyright (c) 2026 Junxiong Zhang, Jiao Wang, Qihang Wang, Qi Zhou
A Hybrid SNMP–STP-Based Framework for Topology Discovery and Energy Optimization in Heterogeneous Power Information Networks
The rapid development of new power systems and the pursuit of "dual-carbon" goals present power information systems with new challenges: diverse terminal access, dynamic network topologies, and complex load distributions. Traditional topology management and energy efficiency strategies fall short of the requirements for managing heterogeneous networks, VLAN isolation, and dynamic access efficiently. This study proposes an adaptive weight SNMP-STP hybrid topology discovery and energy-efficiency optimization framework that integrates an improved Simple Network Management Protocol (SNMP) with an enhanced Spanning Tree Protocol (STP). The algorithm first performs SNMP scanning to identify device vendor types, response delays, and MIB completeness, which are used to calculate initial weights. It then concurrently collects SNMP-layer routing and port status information along with STP link connectivity data, and constructs a topology matrix based on weight fusion. During operation, weights are dynamically updated, giving priority to high-weight vendor parsing results while incorporating MIB verification and STP status compensation to ensure topology integrity. In parallel, a correlation model between topology parameters and energy consumption is established, enabling coordinated topology recognition and energy-efficiency optimization. Experiments were conducted in a multi-vendor heterogeneous network environment, including 15 routers, 32 switches, and 50 servers. Results demonstrate that the framework achieves 96% topology identification accuracy, a link misjudgment rate below 4%, and a topology update latency of only 150 ms, significantly outperforming traditional methods. The energy-efficiency optimization strategy, based on redundant link sleep and dynamic load balancing, reduced overall system energy consumption by over 30%, with switch energy savings reaching 44.4%. These findings indicate that integrating topology information with energy-efficiency modeling not only enhances system performance and resource utilization but also provides a feasible approach for building low-carbon, efficient, and secure next-generation power information systems, highlighting both theoretical and engineering significance.
Copyright (c) 2026 Eerdun Eerdun, Jianzhong Yang, Dongxing Liu, Ke Shi, Jing Li
Attention-Guided Multimodal Signal Fusion with Transformer-Based Deep Transfer Learning for Real-Time Emotional Crisis Prediction in Students
At present, early warning of students' emotional crises mostly relies on single data sources and traditional models, making it difficult to achieve high-precision, real-time monitoring with cross-individual generalization. To address this, we propose a pipeline integrating multi-modal physiological signals and deep transfer learning: 1) Signal preprocessing (adaptive denoising via attention mechanism and z-score normalization) to improve quality of ECG, EEG, GSR, and EMG signals; 2) Attention-guided cross-modal feature fusion using a physiological behavior mapping matrix to unify feature spaces; Evaluation metrics include accuracy, true positive rate (TPR), false positive rate (FPR), and single-sample processing latency. Baseline models for comparison include traditional CNN-LSTM and standard BERT-BASE. System tests show that the accuracy of feature extraction of the heart rate signal is 78.2%, and that of the skin electro cutaneous signal is 34.5%. After deep transfer learning optimization, the emotional crisis early warning accuracy on the small-sample cross-subject dataset (n=80) increased from 45.3% (baseline) to 88.1%, the false positive rate (FPR) dropped to 12.75%, the true positive rate (TPR) reached 87.7%, and the false negative rate (FNR) was 12.3%. The single-sample processing latency was 23.45 ms.
Copyright (c) 2026 Wanhao Gao, Yaxin Wang
Secure IoT Data Sharing via Semi-Supervised Clustering Federated Learning with Fuzzy Multi-Objective Decision-Making and SecureBoost Integration
The Internet of Things enables digital transactions and data sharing, but poses significant security risks during data transmission. To address the issues of weak data sharing security and stability, this study proposes a data sharing technology for Internet of Things. The framework integrates semi-supervised clustering with fuzzy multi-objective decision making and SecureBoost encryption, evaluated on USPS and synthetic datasets. Experimental results show that the Semi-Supervised Clustering Federated Learning algorithm achieves 94.6% accuracy on synthetic datasets, outperforming Multi-View Deep Subspace Clustering Networks (90.3%) and Mid-level Deep Pattern Mining (84.6%). Furthermore, evaluation of the proposed fusion data sharing technology reveals that the key encryption time remains within 250 ms for files smaller than 10 MB. For a 1 MB file, the decryption time is 19 ms. These results demonstrate that the proposed technology prevents data leakage and enables secure multi-party transactions. This study contributes to future secure access to diverse resource data in Internet of Things and ensures fair data sharing.
Copyright (c) 2026 Lihong Zhang, Kai Yan, Xia Yang
Hybrid LSTM-Transformer Model for Sequential and Context- Aware Tourism Destination Recommendation
With the vigorous development of big data and artificial intelligence technology, personalized recommendation systems have been deeply integrated into the tourism industry, aiming to improve user experience through accurate travel recommendations. However, traditional recommendation algorithms often struggle to capture the dynamic changes of user interests and long-tail preferences when dealing with complex sequential data. To this end, this study constructs a hybrid architecture that integrates long short-term memory (LSTM) and transformer to build an efficient tourist destination recommendation system. Among them, the LSTM module focuses on mining the time-dependent features of user behavior, and the Transformer captures the long-range dependencies in behavior by relying on the self-attention mechanism. The experiment was conducted based on a real dataset of more than 100 million user browsing records, and the results showed that the prediction accuracy of the system was improved by 23% and the recall rate was increased by 18% compared with the baseline method. User feedback analysis further showed that the new system's recommendations were more relevant to actual needs, and the average satisfaction score increased by 15%. These results not only confirm the great potential of deep learning in optimizing the personalized recommendation system for tourism, but also provide key technical support for the intelligent upgrading of the tourism industry.
MLSTM: A GraphSAGE-based Android Malware Detection Method Integrating Mean Aggregation and LSTM
With the popularity of smartphones and mobile applications, the threat of Android malware is increasingly serious. To realize the efficient and accurate detection of Android malware, a detection method combining mean aggregator and long-term short-term memory (MLSTM) has been proposed. This method is based on the graph sample and aggregate framework. Construct an isomorphic graph of an application based on permission requests and third-party library call features. MLSTM first aggregates the average features of adjacent nodes, and then uses long-term short-term memory (LSTM) to process sequence information, generating the final node embeddings for classification. The test results on AndroZoo and VirusShare datasets show that compared with baseline models including average aggregator, LSTM aggregator, max pool aggregator, graph convolution network, and gated recurrent unit (GRU), MLSTM has the smallest average absolute error and root mean square error, which are 3.84 and 6.26, respectively. Its detection performance is the best. In terms of permission features and third-party library features, the accuracy of MLSTM is (98.85 ± 0.12)% and (92.58 ± 0.25)%, respectively, significantly higher than GRU under the same permission features. In addition, under the projected gradient descent attack, the success rate of MLSTM attack is 35.28%. This model exhibits good robustness against adversarial attacks. The proposed method has good detection performance, enhanced robustness and stability. Applied to actual Android devices can improve the security and privacy protection level of user data. This method ensures the enhanced efficiency and stability, and provides a certain reference direction for Android malware detection.
Copyright (c) 2026 Yi Liu, Md Gapar Md Johar, Jacquline Tham
NEAT-ID: A Novel Method for Enhancing Threat Detection Process DDoS in Cybersecurity
Cyberattacks, especially Distributed Denial-of-Service (DDoS) attacks, are highly dangerous to online infrastructure, as they use network resources and cause disruption of services. It is also hard to detect such attacks in real-time because the traditional rule-based intrusion detection system (IDS) and single machine-based learning models fail to contend with threat variations. In this paper, NEAT-ID (Neuro- Symbolic Ensemble of Anomaly-based Threat Detection) is described, which is a hybrid framework that combines both network and biometric signals to enhance the accuracy and interpretability of the detection. NEAT-ID is based on a wavelet-transformed feature extractor of temporal network patterns, a Transformer encoder with attention on biometric feature integration, a rulefit model of symbolic reasoning, a stacked ensemble of five classifiers (TabNet, LightGBM, Histogram-based GB, Naive Bayes, Logistic Regression), and an XGBoost meta-learner to provide the final prediction. The framework was tested on the CIC-dDoS2019 dataset, with NEAT-ID scoring 96% accuracy, 97% F1-score, and 0.9949 ROC-AUC, which is better than baseline IDS models and shows robust, interpretable, and high- performance intrusion detection.
BE-RAGAN: A Bayesian Ensemble GAN Framework with Black- Scholes Risk Feature Integration for Scalable Financial Fraud Detection
Fraud detection in financial transactions remains a critical challenge due to evolving fraud strategies, large-scale datasets, and the need for high detection accuracy with minimal false alarms. This paper proposes the Bayesian Ensemble Risk-Aware Generative Adversarial Network (BE-RAGAN), a hybrid and scalable fraud detection framework that integrates Black-Scholes feature engineering, Variational Autoencoders (VAE), Nyström approximation-based Gaussian Processes (GP), Random Projection Trees (RPTree), and Gated Recurrent Units (GRU) with Bayesian Reliability Fusion. The framework is evaluated on the Kaggle Synthetic Financial Datasets for Fraud Detection, which contains 6 million highly imbalanced transactions. Comparative experiments demonstrate that the Bayesian Ensemble Risk- Aware GAN outperforms baseline models including DL Ensemble and UAAD-FDNet variants. BE- RAGAN achieves a sensitivity of 0.970, specificity of 0.984, AUC of 0.968, precision of 0.987, F1-score of 0.968, and recall of 0.867, surpassing the performance of competing methods across all key metrics. These results confirm the robustness, adaptability, and scalability of BE-RAGAN for large-scale and real-time fraud detection. In addition, the framework enhances transparency through Bayesian reliability-based confidence scores, supporting interpretability in fraud risk assessment.
Enhancing Library Recommendation Systems with Integrated PSO for Parameter Tuning in BERT-Derived Multimodal Contexts
This paper presents a novel multimodal recommendation system that integrates Particle Swarm Optimization (PSO) with Bidirectional Encoder Representations from Transformers (BERT) to address critical challenges in library reading promotion, including low recommendation accuracy and cold-start problems. The proposed system leverages multimodal data encompassing book text, cover images, and user behavior patterns. BERT facilitates deep semantic encoding of textual information, while PSO dynamically optimizes key hyperparameters including learning rate, batch size, and dropout rate, alongside multimodal fusion weights. Experimental validation was conducted using real-world data from a provincial public library. The PSO-BERT model demonstrated superior performance across all evaluated metrics: accuracy (0.831, +21.8% vs. collaborative filtering), recall (0.805), F1-score (0.818), and hit rate (0.852). User satisfaction surveys further confirmed significant improvements, with relevance and novelty scores reaching 8.6 and 7.9 points, representing increases of 21.1% and 33.9%, respectively, compared to traditional collaborative filtering. Ablation studies underscored the critical importance of multimodal feature integration, with the fused model maintaining F1-scores above 0.65, substantially outperforming unimodal configurations. The PSO-BERT integrated multimodal recommendation system exhibits substantial potential for enhancing recommendation accuracy, mitigating cold-start challenges, and improving user satisfaction in library reading promotion contexts.
Optimizing Animation Style Transfer with LS-GAN and GCN-ECANet for Increased Accuracy and Efficiency
Animation is a comprehensive art form that integrates painting, film, music, and other artistic styles. As art continues to evolve, animation style transfer has become a key research focus. However, existing transfer methods suffer from low accuracy, poor transfer quality, and long processing times. To address these issues, this study proposes an improved animation style transformation model. This model introduces the least squares method into the generative adversarial network to optimize its loss function, enhance model stability, and improve output quality. Simultaneously combining graph convolutional networks to optimize efficient channel attention networks and enhance their adaptability to complex scenes. The results show that the proposed model achieves an accuracy of 95.1% and a success rate of 86.9% in high-noise environments, with an average accuracy error as low as 0.0076%, significantly outperforming comparison models. Additionally, the model achieves a style transfer similarity of 0.971, a transfer image clarity of 94.8%, and a loss rate of only 0.195%, demonstrating its strong performance. These experimental results indicate that the proposed model exhibits high robustness and transfer stability while efficiently and accurately generating high-quality transferred images. It effectively addresses the issues in traditional animation style transfer models and provides a new method for animation style transfer research.
Hybrid MIRL and ACO-based Approach for Real-Time Path Planning in Visual SLAM
For autonomous robotic systems, real-time establishing paths in dynamic situations is still a major difficulty. Current Visual SLAM-integrated planners, including A\ and Dijkstra, frequently perform poorly in uncertain situations due to a lack of flexibility and collaborative intelligence. In order to improve navigation, this study presents MIRL-ACO-SLAM, a hybrid framework that combines Ant Colony Optimization (ACO) and Multi-Intelligence Reinforcement Learning (MIRL). Real-time spatial maps are created by the network using Visual SLAM (ORB-SLAM3), which allows agents to make decisions locally through reinforcement learning. Pheromone-based optimization guarantees global convergence to the best solutions. Pheromone-guided pruning and selective agent activation improve scalability on bigger maps while lowering computing costs. In contrast with traditional SLAM-based planners, MIRL-ACO-SLAM delivers 18.6% shorter route length, 24.3% quicker scheduling speed, with a 94% route completion rate in dynamic situations, according to empirical assessments conducted utilizing the Zurich MAV dataset. The suggested system opens the door for implementation in mission-critical applications like transportation and search-and-rescue by offering a dependable, scalable, and adaptable answer for real-time robotic navigation.
Optimizing LSTM Hyperparameters with Fish Swarm Optimization for Enhanced Power Load Prediction in Wireless Networks
Predicting power loads is an important part of managing the electricity system and provides a basic guarantee for the dependability and financial operation of state grid companies. In order to minimise energy production costs, it is advantageous to have an accurate prediction for efficient energy scheduling, which involves balancing power generation and demand. Many scholars have devoted their time and energy to creating trustworthy load forecasting models in the hopes of achieving the highest possible prediction accuracy. Utilising a variety of ML, DL, AI, and hybrid approaches allows for accurate power load prediction. An integrated model for power load prediction using the Fish Swarm Optimisation (FSO) algorithm and the Long Short-Term Memory (LSTM) neural network is presented in this research. By optimising the LSTM network's hyper parameters using FSO, this study speeds up model convergence and avoids becoming stuck in local optima, which reduces the effect of humans picking LSTM hyper parameters at random on prediction results. The proposed approach is evaluated using mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and R2 metrics and compared with LSTM, CNN, Attention-LSTM, AC-BiLSTM and LSTM-PSO approaches. The proposed LSTM-FSO significantly reduces MAE between 3.75% - 49.59%, MAPE between 9.98% - 39.65%, RMSE between 11.17% - 56.10% and increased R2 between 0.1% - 8.07% compared to other methods. The results of the experiments show that the suggested system has great potential for use and significantly beats competing solutions. Power load forecasting is shown to be more stable and reliable using the FSO algorithm with LSTM, according to the simulation findings.
Copyright (c) 2026 Shuai Gao , Hu Yue , Yujun Wang, Hao Hu , Meiying Zhu
Scalable Data Lake Architecture with Apache Spark for Predictive Maintenance and Optimization in Energy Storage Systems
The fast development of energy storage power stations in current smart grids has resulted in massive and complicated datasets that require effective management and analysis to maintain battery performance, optimize maintenance schedules, and reduce energy expenditures. Existing centralized systems suffer with scalability and latency, resulting in delayed analysis, lower prediction accuracy, and ineffective pricing schemes. This study presents DCDL-ESM (Distributed Computing over Data Lake for Energy Storage Management), a scalable architecture that combines a centralized data lake and an Apache Spark-based distributed computing framework. The system collects raw data from 100 energy storage power plants (a total of 2.8 TB over 12 months), performs distributed preprocessing, data purification, normalization, and trend analysis, and uses predictive modeling for maintenance scheduling and charge optimization. Validation was carried out using historical operational datasets (80% training, 20% testing split), resulting in a 42% reduction in processing time, 91.3% predictive maintenance accuracy, 24.7% energy cost savings, 38% improvement in CPU and memory utilization, and 31% storage efficiency gains through partitioning and compression. Results were tested for robustness using cross-validation and bias analysis. The suggested solution closes scalability and performance gaps while setting the framework for future integration of real-time analytics and edge computing to minimize latency and support autonomous control.
Copyright (c) 2026 Shaofeng Yu, Jianxu Zhong, Xin Yan, Jinpeng You, Zehan Cai
Evaluating and Securing Power Systems against Vulnerabilities Introduced by Large Language Models
An exciting new direction for improving operational efficiency and decision-making is the use of Large Language Modeling (LLMs) to contemporary power systems. Nevertheless, there may be unanticipated security risks associated with this move. Using LLMs to power networks may pose certain risks, which this paper examines. It stresses the need of doing research and developing remedies immediately. It is a challenging but vital job to secure large language models in a power monitoring context. Through the implementation of thorough security measures, the promotion of a security-conscious culture, and the continuous monitoring of new threats while technologies, we may maximize the benefits of LLMs while minimizing their hazards. It is our duty as information security experts to pioneer this new field and make sure that our security protocols adapt to the increasing sophistication of our AI systems. Security flaws in LLM that allow rapid injection attacks are among the most critical ones. These types of attacks take advantage of LLMs' fundamental features by deliberately feeding them data that will cause them to operate in an unexpected way or leak private information. Many LLMs have seen extensive usage with the introduction of commercially accessible systems like ChatGPT. One crucial area of cybersecurity that is seeing a rise in the use of LLMs is power monitoring systems. It is critical to safeguard these systems against cyber-attacks since they are essential for the stability of society and the nation's energy supply. In order to keep these systems resilient and reliable, it is essential to detect unexpected vulnerabilities, especially zero-day attacks. One potential way to improve these detection capabilities is via LLMs. It integrates power-system standards and threat intelligence with traditional anomaly detection, LLM-assisted reasoning over code/configs/logs, and protocol-aware telemetry. In order to uncover undisclosed power monitoring system weaknesses, we used models with LSTM with GRU. Since these models are masters of sequential data analysis, they are ideal for this job. Sequential data includes things like sensor readings, system logs, and network traffic. By identifying unusual activity that differs from typical system operation, LSTMs and GRUs are able to discover new, "zero-day" vulnerabilities, in contrast to conventional security technologies that depend on predetermined attack signatures. Here, we built an LLM model using TinyLlama Chat 1.1. The LLM takes the processed packet data and the extracted context as input and outputs a user-friendly summary of the packet file. Through the use of machine learning models, the program provides a concise, well-organized, and straightforward overview of the network's operations.
Copyright (c) 2026 Manpo Li, Xuerui Yang, Xiaochen Yang, Shugui Zhang
K-Nearest Neighbor-Based Detection and Mitigation of False Data Injection Attacks in Nonlinear Automatic Generation Control Systems
The security of Automatic Generation Control (AGC) systems is crucial for maintaining frequency stability in modern power grids. False Data Injection (FDI) attacks can compromise AGC operations by altering measurement data and misleading control actions. This paper proposes a K-Nearest Neighbor (KNN)-based detection and mitigation framework for FDI attacks, explicitly considering AGC nonlinearities such as the governor dead-band (GDB), generation rate constraint (GRC), and transportation time delay (TTD). The proposed non-parametric model detects abnormal data by analyzing feature distances without requiring extensive training data or computational resources. A two-area AGC test system is used for validation, and the method is evaluated in terms of detection accuracy, false positive rate (FPR), and computational efficiency. Simulation results demonstrate that the proposed approach achieves a detection accuracy of 95.2% and an FPR of 3.5% at k = 5, while reducing computation time by more than 80% compared to deep learning methods. These findings confirm that the KNN framework offers a lightweight and effective solution for real-time FDI attack detection and mitigation in nonlinear AGC systems.
Intelligent Detection of Transmission Line Hazards Using Video Image Analysis Techniques
A new approach to power line upkeep has emerged in recent years: automated inspections driven by computer vision. In order to keep power transmission dependable, safe, and sustainable, it is now necessary to use a large collection of pictures and videos. Recent studies have shown that deep learning approaches may significantly improve power line inspection operations. Manual inspection is the gold standard for transmission line safety detection, but it's slow, susceptible to human error, and constrained by inspection cycles, ambient conditions, and the level of expertise of the inspectors doing the checks. Identifying and alerting of transmission line abnormalities in real-time is challenging, and there are substantial constraints and safety dangers to consider. A novel solution that combines the intuitive image recognition benefits of video surveillance with the high-precision range and speed measurement capacities of modern radar detection technology has just emerged: lightning fusion technology. The two datasets are intelligently merged and analyzed via the use of sophisticated data processing methods. This article delves into a lightning vision fusion–based intelligent monitoring method for transmission lines and suggests a system that uses deep learning (DL) algorithms to automatically record and analyze changes in the surrounding environment of transmission lines, greatly enhancing the accuracy and timeliness of such monitoring. In addition to reducing the need for human involvement and operating expenses, the experimental findings demonstrate that this system successfully prevents missed and false alerts, offering a stronger technical assurance for the reliable and secure functioning of the power system.
Copyright (c) 2026 Yuanyuan Xing, Xu Shuang, Jiaxian Chen, Zhipeng Hu, Jiang Wei
A Hybrid CNN-GrabCut and GAN Architecture for Semantic Segmentation and Artistic Style Transfer in Image Generation
Driven by digital transformation and the growing demand for artistic innovation, the development of artistic image generation systems has gained wide attention. However, existing systems face several limitations, including low accuracy in semantic segmentation, unnatural fusion in style transfer, and weak originality in generated images. Therefore, an artistic image generation system model based on graph cutting method convolutional neural network generative adversarial network is proposed, which uses an improved convolutional neural network (CNN) combined with GrabCut algorithm for semantic segmentation. By introducing convolutional block attention module to optimize CNN, it can better capture global information and complex features. In terms of style transfer, the system uses a semantic guided GAN architecture to achieve precise adaptation and natural transition of style features. The study achieved a pixel accuracy of 98% on the PASCAL VOC dataset. With the increase of training data, its highest intersection to union ratio reached 92%, significantly higher than the comparison algorithm. In real-time testing, the system only occupies 710 MB of memory and has a response time of less than 62 ms, outperforming other models in performance. In addition, in the image quality test conducted on the ArtBench-10 dataset, the system achieved a peak signal-to-noise ratio of up to 54 dB and a structural similarity index of 92%. These results indicate that the proposed model delivers high accuracy and strong diversity in painting art design. It effectively solves current problems in segmentation precision and style fusion, offering new ideas for artistic creation and supporting the development of intelligent painting systems.
Automobile Fault Diagnosis Using Lightweight Cetacean Optimization and Multi-Scale Residual Neural Networks
With the advancement of automotive technologies, accurate and real-time fault diagnosis is essential for ensuring vehicle safety and reducing maintenance costs. This paper proposes an automobile fault diagnosis system based on the Lightweight Whale Optimization Algorithm (LWA) integrated with a Multi-Scale Residual Unit deep neural network. The proposed model is trained on a real-world automobile sensor dataset containing 2,000 labeled samples spanning four fault types: engine, brake system, battery, and transmission failures. The model demonstrates a diagnostic accuracy of 95.4%, outperforming baseline methods such as Support Vector Machine (90.1%), Random Forest (92.3%), and CNN-based models (94.2%). Additionally, the LWA achieves faster convergence and a 25% reduction in inference time compared to traditional MSRU models, with a response time under 2.5 seconds. The lightweight design also reduces model parameters by 64%, enabling real-time deployment in embedded vehicle systems. Experimental results show that the proposed method not only enhances diagnostic accuracy but also improves computational efficiency, offering a practical solution for intelligent automotive maintenance.