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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Interpretable Machine Learning Framework for Early-Stage Potato Yield Forecasting Using Climatic Features in Bangladesh
Abstract: Bangladesh, one of the world’s major potato-producing countries, faces significant yield variability driven by changing climatic conditions, with profound implications for agricultural productivity and food security. However, the specific impact of meteorological factors on potato production has not been thoroughly investigated. The present paper aims to provide a robust quantitative framework for assessing the relative effects of agro-climatic variables on potato yield formation. Weather variables that were statistically significantly related to the mortality count were first selected using Analysis of Variance (ANOVA) with F-regression feature ranking and Individual Weather Importance ranking through Random Forest. Pearson and Spearman’s correlation analyses were additionally performed to assess the strength and direction of these relationships, along with bivariate kernel density estimation (KDE) used to enlarge climatic optima toward yield maximization. For testing predictive robustness, climatic variability was taken into account, and three machine learning algorithms —RF (Random Forest), SVR (Support Vector Regression), and KNN (K-Nearest Neighbors) —were investigated. The RF model was the most accurate and generalizable, yielding almost perfect results (R² = 0.999) and minimal forecast errors (MAPE = 0.70%, MAE = 0.0803, RMSE = 0.1014). The developed framework enhances the understanding of climate–yield relationships and provides practical insights for science-based agricultural management and adaptation to climatic stress. These results have immediate implications for middle-income agrarian economies, such as Bangladesh, in strengthening resilience to climatic variability through sustainable food security.
Copyright (c) 2026 MD Jiabul Hoque, Nor Hidayati Binti Abdul Aziz, Sumayea Benta Hasan, Md. Khaliluzzaman
ATT-LCNN: An Attention-Enhanced Lightweight CNN for Acoustic Signal-Based Municipal Pipeline Leak Detection
This study focuses on the recognition of acoustic signals from municipal pipeline leaks. An original end-to-end recognition algorithm (ATT-LCNN) based on the fusion of an attention mechanism and an improved lightweight convolutional neural network (CNN) is proposed, targeting the bottlenecks of traditional methods in minor leak detection and poor generalization. The algorithm employs a preprocessing flow of "DC component removal → improved Kalman filtering → signal normalization → segmented windowing", constructs a multi-dimensional fusion feature set across time, frequency, and time-frequency domains, and embeds an improved SE-Net attention module with depth-wise separable convolution for lightweight and high-accuracy feature learning. Experiments are conducted on a self-built simulated pipeline dataset (1250 samples, covering 3 pipe diameters, 3 leakage levels, 5 propagation distances, 5 noise intensities, with 750 leak samples and 500 non-leak samples), with 5- fold cross-validation to ensure statistical robustness. Performance is benchmarked against traditional machine learning methods (SVM, Random Forest) and standard deep learning models (vanilla CNN, LSTM). The results show that the ATT-LCNN algorithm achieves a 98.7% detection rate (recall), 98.2% precision, 98.4% F1-score for minor leaks, maintains an overall accuracy of over 97.2% at a propagation distance of 20m, and reaches 97.8% accuracy under 60 dB strong noise conditions. The model has only 1.2M parameters, with a single-sample inference time of 2.3ms on CPU, meeting embedded deployment requirements. The repeated detection accuracy fluctuation range is only 0.8%, with stable performance across various pipe diameters and materials. Compared with baseline methods, ATT-LCNN improves minor leak detection accuracy by 6.3%-12.1% under high-noise conditions, balancing recognition accuracy, anti-interference capability, and real-time performance, providing technical support for intelligent leak detection in municipal pipeline networks.
Copyright (c) 2026 Mingjie Liang, Cuimei Li
AI-Blockchain Integrated Trusted Interaction Mechanism... Informatica 50 (2026) 411–426 411 AI-Blockchain Integrated Trusted Interaction Mechanism for Enhanced Network Security
This paper proposes an AI–blockchain integrated trusted interaction mechanism for network security. The mechanism combines a deep-learning anomaly detector with a permissioned blockchain and smart contracts to provide real-time threat analysis, decentralized authentication and tamper-resistant trust management. The AI module is a two-branch neural network that processes network-traffic features and interaction-behavior graphs and outputs an anomaly score for each event. The blockchain layer adopts a consortium architecture with PBFT-style consensus, and smart contracts implement identity verification, trust update rules and access control.Experiments are conducted on labeled network-traffic traces and an enterprise-like testbed. We compare the proposed mechanism with an AI-only baseline (deep model with centralized control) and a blockchain-only baseline (rule-based smart contracts without AI) using detection-level metrics—accuracy, precision, recall, F1-score, false positive rate (FPR) and false negative rate (FNR)—and system-level metrics—response time and throughput. Under low load, the system reaches 180 ms response time and 5000 requests/s throughput; under high load, response time is 300 ms and throughput is 4200 requests/s. Compared with the strongest baseline, throughput increases by 42.8% and FPR is reduced by 60.9%, while FNR remains low across diverse attack scenarios. These results confirm that the mechanism supports trusted interaction in dynamic network environments.
Adaptive UAV Inspection and Path Planning for Distribution Networks Using Multi-Agent Deep Reinforcement Learning
Efficient inspection of distribution networks is crucial for ensuring the stable operation of the power system. To address the limitations of existing methods in complex environments, this paper proposes an adaptive UAV inspection path planning strategy based on multi-agent deep reinforcement learning, specifically employing the Multi-Agent Actor-Attention-Critic (MAAC) algorithm. This method constructs a reinforcement learning environment with a designed reward function to enable UAVs to collaboratively learn optimal inspection paths. Simulation experiments conducted on the AirSim platform demonstrate the proposed method's superior performance compared to Enhanced Particle Swarm Optimization (EPSO) and Double Deep Q-Network (DDQN). The MAAC-based model achieved a higher cumulative discounted reward (4.56 vs. 2.21 for EPSO and 4.37 for DDQN) and a reduced average running time (812.85 seconds vs. 833.45 and 923.41 seconds, respectively), validating its advantages in both solution quality and computational efficiency. The results indicate strong adaptability and high computational efficiency, offering significant potential for practical UAV inspection applications. Future work will focus on integrating advanced techniques to further improve robustness and learning efficiency.
Copyright (c) 2026 Tingting Yang, Xin Wang
Predicting Loess Collapsibility Using Grey Relational Analysis and Transformer Networks Zeliang Chen Lanzhou New Area Urban Construction Engineering Co., Ltd., Lanzhou 73000, China
Subgrade and pavement diseases caused by loess collapsibility seriously affect the safety and durability of road engineering. Precise prediction of the collapsibility coefficient plays a pivotal role in engineering prevention and control in loess regions. To address the issues where loess collapsibility is influenced by the coupling effect of multiple factors and traditional forecasting approaches lack adequate generalization capability, this study presents a predictive model integrating grey relational analysis and Transformer network, based on the NCE7# Highway project in Lanzhou New Area. Firstly, indoor experiments were conducted to analyze the correlation between various indicators (such as sampling depth and compaction state) and the collapsibility coefficient. Grey relational degree was used to screen out saturation (0.79), natural density (0.78), void ratio (0.71), and compression coefficient (0.71) as core indicators, eliminating weakly correlated redundant information. Subsequently, an optimized Transformer model was constructed based on these core indicators, which captures the deep coupling relationships among indicators through the self-attention mechanism. Based on indoor experimental data, comparative verification was carried out with RF, BP, and SVM models. The results demonstrate that the Transformer model achieves an R² of 0.976 and an MSE of 0.0009 on the training set, and an R² of 0.953 and an MSE of 0.0015 on the test set. Compared with traditional models, the MSE is reduced by 57.1%– 67.9% and the R ² is improved by 5.7% – 8.9%. The model stabilizes after only 42 epochs, with a generalization performance decay of merely 8.3%, significantly outperforming the decay rate of over 35% observed in traditional models, effectively mitigating the overfitting problem. In summary, this study accurately screens core indicators through grey relational degree and combines the powerful feature extraction capability of the Transformer network to realize high-precision and stable prediction of the loess collapsibility coefficient. It provides a scientific basis and efficient technical support for the formulation of foundation treatment schemes, disease prevention and control, and service life extension of road engineering in loess areas.
Development of a Federated Learning and Knowledge Graph-Based Personalized E-Commerce Recommendation System Using PTB-MFA
E-commerce platforms require accurate and personalized recommendation systems while ensuring user privacy and scalability. Traditional centralized recommendation models face challenges related to privacy risks, data heterogeneity, and limited personalization. To address these limitations, this study proposes a Painting Training-Based Optimization with Modified Federated Averaging (PTB- MFA) framework that integrates federated learning with knowledge graph embeddings for privacy- preserving personalized recommendation. The proposed method captures complex semantic relationships among users, products, and categories while enabling adaptive client-level personalization. Experiments conducted on an e-commerce personalized recommendation dataset demonstrate that PTB-MFA outperforms existing methods, achieving improvements of 16.9% in Click-Through Rate, 19.9% in user engagement, and 6.37% in Mean Average Precision. The results confirm that the proposed framework effectively balances personalization accuracy, privacy preservation, and computational efficiency for next-generation e-commerce systems.
Copyright (c) 2026 Li Jianmin, Yang Jinmei
IoT Privacy Protection Scheme Based on Hyperledger Fabric
The increasing use of Internet of Things devices has exacerbated the contradiction between data sharing and privacy protection, while existing blockchain based solutions still have significant limitations in scalability, computational efficiency, and access control. Therefore, this study proposes a Hyperledger Fabric privacy protection framework for the Internet of Things, and constructs a smart contract system that includes device management, policy management, and access control. By combining advanced encryption standard algorithms, interstellar file system storage, and Shamir secret sharing mechanism, secure storage and flexible sharing of data have been achieved. The experiment showed that the throughput of the research scheme was stable between 370 TPS and 420 TPS on both the training and testing sets, with an average delay controlled between 50 ms-72 ms, which was significantly better than other comparative schemes. The utilization rate of the central processing unit in terms of resource overhead was maintained at around 50%, with a minimum memory consumption of 505 MB. In addition, in terms of decryption performance, the decryption success rate of this scheme under various attacks was between 98% and 99%, with an average decryption cost of the lowest 45 ms. The research scheme balances performance and efficiency while ensuring privacy and security, demonstrating its application advantages in Internet of Things privacy protection scenarios.
Copyright (c) 2026 Weiyan Feng, Li Zhang, Xin Zhao
Adaptive Warehouse Task Allocation Using Stacked Surrogate Models and Active Learning with Simulation-Based Validation
Modern warehouse operations face increasing order volumes, volatile demand, and shrinking lead times, challenging conventional static task dispatching policies such as FIFO or priority-based picking. This study proposes a closed-loop intelligent warehouse management framework that integrates task-level feature engineering, lightweight policy simulation, stacked surrogate modelling, and active learning retraining to enable adaptive, real-time decision support. The surrogate model combines an attention-enhanced TensorFlow multilayer perceptron, a LightGBM classifier, and a logistic regression meta-learner to predict optimal dispatching policies for dynamically generated warehouse tasks. Experiments were conducted using a publicly available logistics warehouse dataset comprising over 3,000 inventory records, from which realistic task batches were generated and evaluated across multiple dispatching strategies. Simulation-based validation was performed using a SimPy discrete-event model capturing contention between automated guided vehicles and human pickers under varying workload and resource conditions. The stacked surrogate achieved validation accuracy exceeding 98% with balanced precision and recall across all policy classes. Simulation results showed stable throughput, reduced average waiting times, and balanced resource utilisation compared with FIFO baselines. Additional robustness analyses demonstrated consistent model behaviour under demand variability and resource disturbances. The integration of an active learning loop enables continuous recalibration of the surrogate model using observed simulation KPIs, ensuring adaptability to changing operational contexts. The proposed framework provides a computationally efficient and interpretable approach for adaptive warehouse task allocation and demonstrates broader applicability to intelligent decision-making in cyber-physical systems aligned with Industry 4.0 principles.
Adaptive Navigation for Robots Based on Object Relationship Reasoning and Reinforcement Learning
This study focuses on the deep integration of object relationship reasoning and reinforcement learning, proposing an adaptive navigation framework for robots. The core research contents include designing a dynamic object relationship graph model, quantifying relationship strength through spatial, motion, and functional features to achieve real-time modeling and updating of environmental relationships, constructing a reinforcement learning system that integrates relationship features, optimizing the state space and two-layer reward mechanism to improve the semantic rationality of decision-making, and building a closed-loop adaptive mechanism of "perception-reasoning-learning-decision-making" to dynamically adjust the learning rate and exploration rate, ensuring adaptability to complex environments. Experimental results show that the framework achieves navigation success rates of 96.7% and 92.5% in indoor dynamic scenes and outdoor semi-structured scenes, respectively, with collision rates of only 1.7% and 3.3%. Compared with traditional algorithms such as PPO and DQN+SLAM, the success rate improved by 8.3%-13.4%, the collision rate reduced by 4.9%-9.1%, and the average path length shortened by 3.7%-14.4%. Despite sensor noise interference and high-density obstacle environments, the framework maintains good robustness, with a stable decision response time of 32.6ms, meeting real-time navigation requirements and providing an effective solution for robot navigation in highly dynamic and interactive scenarios.
Copyright (c) 2026 Jianguo Zhang, Ziran Jia, Wenxing Sun, Duanjiao Li, Yun Chen, Junwen Yao, Jianming Liu, Yongchao Liang, Ning Ding
Fusion of Improved LSTM with Graph Attention Networks for Malicious IoT Traffic Detection
In the rapid development of the Internet of Things, malicious traffic is highly mixed with normal business traffic, and exhibits strong long-term and short-term temporal dependencies and suddenness. Traditional detection methods have shortcomings in identifying covert attacks and reducing false positives. To this end, this study proposes an Internet of Things malicious traffic detection method that integrates an improved Long Short-Term Memory (LSTM) network with a Multi-Feature Graph Attention Network (MFGAT). In the temporal modeling stage, an attention mechanism and residual connections are introduced to enhance the representation of critical time-slice features, while lightweight gating and parameter compression strategies are employed to reduce model complexity. In the spatial modeling stage, a graph attention mechanism is utilized to weight the relationships among traffic nodes, enabling collaborative enhancement of multi-dimensional traffic features. The experimental results show that the area under the curve of the proposed method reaches 0.96, with an average accuracy of 0.91. The ablation experiment shows that after introducing improvement measures, the F1 value increases from 0.87 to 0.92, the parameter counts decreases from 5.13M to 4.23M, and the single sample inference delay is shortened from 7.64ms to 5.61ms. In complex scenarios, this method maintains an F1 score above 0.80 under highly imbalanced class distributions. conditions, with an average detection delay of only about 30ms under sudden attacks, and maintains long-term stability through rolling updates in concept drift scenarios. The proposed method not only outperforms existing methods in detection accuracy and robustness, but also has the advantages of lightweight and real-time performance, providing a feasible solution for efficient identification of malicious traffic in the Internet of Things environment.
Credit Evaluation of Supply Chain Partners Using Ensemble Learning and Conditional GANs
Driven by the deep integration of the industrial internet and digital finance, supply chain risk control is shifting towards dynamic data intelligence. However, extreme sample class imbalances and data feature heterogeneity in real business scenarios severely restrict the accuracy of credit assessment. To overcome the identification blind spots of sparse risk samples, the study proposes an enterprise supply chain partner credit evaluation model combining Conditional Generative Adversarial Networks (CGAN) and Ensemble Learning (EL). First, an improved CGAN reconstructs the true distribution of default samples to alleviate data skew. Then, a Tree-structured Parzen Estimator (TPE) based Bayesian Optimization (BO) algorithm adaptively tunes a two-layer Stacking ensemble framework (including XGBoost, LightGBM, and CatBoost) for deep heterogeneous feature mining. Results show that the model exhibits excellent comprehensive classification performance, with an F1 score of 0.949 and a Kolmokolov-Smirnov statistic of 0.62, significantly outperforming benchmark models such as Attentive Interpretable Tabular Learning (TabNet). In credit simulation, the model achieves a 94.5% default rejection rate while maintaining high credit coverage, avoiding huge bad debt losses. The study effectively solves the problem of capturing minute risks in high-dimensional heterogeneous spaces, providing a scientific basis with high economic value for financial institutions to optimize credit resource allocation and reduce decision-making uncertainty.
IoT and Multi-Source Data-Driven Intelligent Warehouse Optimization Using KMA-RDTC
Warehouse operations have developed over the decades through the enhanced Internet of Things (IoT) technology. The traditional operation is perceived as inconsistent and operationally inefficient, which are the main deficiencies in traditional inventory processes. The research fills the gaps by introducing a smart warehouse optimization tool on IoT and multi-source data to optimize and refine inventory accuracy, reduce operational expenses, and optimize the use of resources. Data from 2500 real-time warehouse operations were pre-processed using min-max normalization, and dimensionality reduction was applied via Principal Component Analysis (PCA). The Komodo Mlipir Algorithm-tuned Random Decision Tree Classifier (KMA-RDTC) was proposed to integrate the RDTC with the KMA for optimizing warehouse operations. The RDTC provides robust classification and anomaly detection for inventory data and demand forecasting, while the KMA optimizes RDTC hyperparameters using exploration-exploitation strategies, enabling efficient real-time anomaly detection and forecasting, and enhancing predictive performance, operational accuracy, resource utilization, and decision-making in intelligent warehouse environments. Through experimental testing implemented in Python, it demonstrated superior predictive reliability over traditional Machine Learning (ML) methods by providing accuracy (0.985), prediction accuracy (0.958), sensitivity (0.934), specificity (0.962), Root Mean Squared Error (RMSE) (0.10), and Mean Absolute Error (MAE) (0.07) with Python implementation. These results indicate accurate anomaly detection, efficient resource utilization, and high operational precision. The framework provides a comprehensive design for intelligent warehouse operations, enabling scalable, data-driven, and sustainable logistics aligned with Industry 4.0 principles.
Copyright (c) 2026 Jiping Liu, Chenxiao Liu
ADRL-VPP: An Adaptive Deep Reinforcement Learning Framework for Load Regulation in Virtual Power Plants
Virtual Power Plants (VPPs) combine renewable energy sources with storage units to provide an integrated power generation system that improves grid stability and energy efficiency. Traditional load-balancing solutions struggle to respond to fluctuations in generation, electricity costs, and demand, frequently resulting in power outages and grid instability. To solve these issues, this study proposes an adaptive deep reinforcement learning system, ADRL-VPP (Adaptive Deep Reinforcement Learning for Virtual Power Plant Load Management), which enhances load management while ensuring stable, efficient VPP operation. Experiments were carried out using the VPP_LoadReg_Dataset, which included 10,000 samples of solar, wind generation, storage, load, time, weather, and electricity price variations. ADRL-VPP was evaluated against conventional backstepping and fuzzy adaptive controllers as baselines. The model uses a deep Q-network (DQN) with ε-greedy exploration and adaptive learning rate optimisation to discover optimal actions, including increasing, decreasing, saving, or doing nothing. The experimental results demonstrate that ADRL-VPP outperformed the baseline controllers, achieving 92% accuracy, 90% precision, 91% recall, 90.5% F1-score, and a Grid Stability Improvement Index (GSII) of 0.87. Overall, ADRL-VPP provides a strong, intelligent solution for dynamic load regulation in VPPs, demonstrating the promise of deep reinforcement learning for sustainable, adaptable power management.
Copyright (c) 2026 Cong Zhang, Jin Wang, Lu Lin, Xiaoyi Wang, Yuxuan Li, Yue Ban
Abnormal Traffic Detection in Industrial Control Networks Using a CNN-LSTM Fusion Model
This paper used a combination of two neural network models, convolutional neural network (CNN) and long short-term memory (LSTM), to detect abnormal traffic in industrial control networks. The performance of the support vector machine (SVM), traditional back-propagation neural network (BPNN), gated recurrent unit, and the CNN-LSTM algorithms were compared using the natural gas pipeline dataset from the University of Mississippi and the public KDDCUP99 dataset. Moreover, ablation experiments were conducted on the proposed algorithm. Finally, the performance of the four algorithms was evaluated in a laboratory-built industrial control network. The results showed that the CNN-LSTM algorithm was highly effective in detecting abnormal traffic. For the natural gas pipeline dataset, this algorithm achieved an accuracy of 0.998 ± 0.014, a false alarm rate of 0.010 ± 0.011, and a precision of 0.994 ± 0.012. For the KDDCUP99 dataset, its accuracy, false alarm rate, and precision were 0.995 ± 0.011, 0.004 ± 0.013, and 0.997 ± 0.011, respectively. Moreover, both the CNN and LSTM parts contributed to the overall performance.
Comparative Analysis of Transformer-Based and Neural Network Models for Emotion Detection in Tweets
This study assesses the effectiveness of transformer-based and neural network models for detecting emotions in tweets. Five models are evaluated: two transformer-based frameworks (DistilBERT and ALBERT), two neural network architectures (CNN and 3CNN-3LSTM), and a hybrid model (3CNN- 3LSTM-GloVe 300x). The models are evaluated based on accuracy, precision, recall, and F1-score. The findings indicate that ALBERT attains the maximum accuracy at 86.38%, succeeded by DistilBERT with an accuracy of 84.35%. The 3CNN-3LSTM model exhibits an accuracy of 83.79%, whilst the CNN model demonstrates the lowest performance at 65.37%. The hybrid 3CNN-3LSTM-GloVe 300x model exhibits a performance of 75.61%. The results demonstrate that transformer-based models surpass neural network models in emotion recognition, especially in recognizing subtle emotional expressions. Nonetheless, transformer-based models demonstrate increased computational expenses, highlighting the necessity for optimization in real-time applications. This study enhances the domain of emotion detection by a comparative comparison of diverse models, emphasizing the benefits of transformers while acknowledging the computational difficulties. The results indicate significant implications for marketing, mental health, and digital communication, highlighting the need for further enhancement of transformer models for effective implementation.
Copyright (c) 2026 Bin Zhang, Xi Yang, Chen Zhang, Tangsen Huang
API Interception-Based GPU Virtualization for Containerized HPC Workloads in Cloud Environments
In recent years, with the development of cloud-native technologies such as containers and Kubernetes, high-performance computing (HPC) tasks using GPUs have gradually migrated to container cloud environments, introducing new challenges in fine-grained GPU resource management. Currently, the native Kubernetes framework lacks support for allocating fractional GPU resources to containers, permitting only exclusive access to entire physical GPUs, which results in low cluster-wide GPU utilization. To enable efficient GPU sharing for HPC tasks in containerized environments, GPU virtualization — allocating precise amounts of GPU compute and memory resources to different containers with isolation guarantees — becomes essential. However, current GPU virtualization technologies remain in their infancy and exhibit the following limitations: (1) NVIDIA GPUs enforce strict closed-source policies at the driver and lower layers, forcing existing solutions to rely on reverse engineering approaches for virtualization; (2) current implementations fail to address GPU idle cycles during HPC task execution, leading to computational resource wastage. To address these issues, this paper proposes a GPU virtualization system for container cloud environments. The key contributions include: (a) profiling the workflow and invocation mechanisms of CUDA-based HPC tasks (e.g., deep learning training) to characterize their GPU memory and compute usage patterns; (b) developing formal models for HPC resource utilization processes; and (c) implementing a resource isolation and quota mechanism via API interception and forwarding. Experimental results demonstrate that our system achieves superior virtualization efficiency with lower overhead. The proposed adaptive elastic GPU allocation method yields 37% higher average GPU utilization and 26% greater cluster throughput under heavy loads compared to static allocation in KubeShare.
Copyright (c) 2026 Ming Lu, Heng Wu, Rongzhou Luo
Digital Twin-Based Multimodal Data Fusion for Health Monitoring in Smart Elderly Care Platforms
In the smart elderly care mobile service platform, multimodal sensor data is redundant due to differences in format, accuracy, etc., and the lack of effective analysis makes it difficult to extract feature components, which affects the fusion effect. A new method is proposed for this: first, real-time health data is collected through platform integrated sensors such as electrocardiograms, and then the raw data is redundantly processed to generate standardized datasets in a unified format. Next, a digital twin model is constructed, and feature component extraction techniques are used to analyze the data distribution structure. The dynamic correlation of the quantified data is calculated by associating distribution features to complete the fusion. The experiments have proved that under the application of this method, when the elderly are in a state of hypertension, the blood pressure fusion value significantly increases to nearly 1. When the heart rate is too fast, the pulse fusion value and electrocardiogram fusion value reach approximately 0.8 and 0.7 respectively. The average accuracy rate of identifying various abnormal health conditions is over 85%. The overall standard deviation is 0.004, indicating that the degree of dispersion of the fused data is smaller. In terms of data fusion error, it is below 0.15% under all data volume conditions, which is conducive to promoting the development of the elderly care industry.
Copyright (c) 2026 Zhe Li, Jinhao Liang, Yu Zhang
An Agent-Based Simulation Framework for Clinical Management Integrating Fuzzy Logic-Based Patient Satisfaction Evaluation
Efficient clinical management relies on coordinated operational workflows and reliable mechanisms for assessing patient experience. This study proposes an agent-based simulation framework for clinical management that integrates a fuzzy logic-based Patient Satisfaction Evaluation Agent (PSEA) within a multi-agent system composed of twelve specialized agents. The framework was implemented using the Mesa simulation platform and evaluated through simulation-based experiments involving 100 patients over 100 simulated time steps. Prior to its integration into the multi-agent system, the fuzzy satisfaction model embedded within the PSEA was independently designed and empirically validated using real survey data collected from 80 patients. The satisfaction scores produced by the fuzzy inference system showed strong agreement with patient-reported evaluations, achieving a correlation coefficient of 0.76 and a mean absolute error of 1.12. At the system level, simulation-derived indicators reveal a balance between adaptive coordination and operational stability. The multi-agent system performed an average of 3.68 ± 1.79 resource reallocation decisions per time step, while 12 out of 100 simulated time steps involved no reallocation, reflecting the emergence of stable operational phases.Overall, the results demonstrate that combining agent-based simulation with a pre-validated fuzzy satisfaction assessment provides a coherent and interpretable framework for supporting coordinated and patient-centered clinical management. Future work will focus on interoperability through standards such as HL7 and FHIR.
Copyright (c) 2026 Amadou DIABAGATE, Yazid Hambally Yacouba, Awa FOFANA, Abdellah AZMANI, Adama COULIBALY
A Hybrid AI Framework Combining Causal Nonmonotonic Reasoning with VMD-GNN and IGWO-DBN for Decision-Making under Big Data
With the deep integration of big data and Artificial Intelligence, data-driven predictive models face key challenges such as missing causality and rigid reasoning. Traditional approaches rely on statistical correlations, which struggle to handle uncertainty, conflicting evidence, and sparse data in dynamic environments, resulting in poor interpretability of decision-making. This paper proposes an Artificial Intelligence decision-making model enhanced by causal nonmonotonic reasoning. This model combines Variational Mode Decomposition and Graph Neural Network to process dynamic temporal data and graph structures, and introduces an improved Grey Wolf Optimizer to optimize the parameters of Deep Belief Network, thus constructing a decision framework that integrates dynamic causal discovery, revocable rule inference, and counterfactual evaluation. The experimental results on the MIMIC-III medical dataset and IEEE-CIS financial dataset show that the proposed model achieves an average processing error as low as 0.02, which is superior to the three baseline models Graph Attention Network- Recurrent Neural Network, Particle Swarm Optimization-Ant Colony Optimization, and Genetic Algorithm-Long Short-Term Memory Network. In financial volatility prediction, the model achieved a root mean square error of 0.12, a coefficient of determination of 0.92, and a peak-valley capture rate of 0.89. It also maintained a robust recall rate ranging from 0.8 to 0.98 across confidence intervals of 0 to 0.6, demonstrating the best overall performance. These results indicate that the proposed model shows strong accuracy and adaptability in complex decision-making scenarios. It effectively addresses the problems of causal blindness and rigid reasoning in current Artificial Intelligence systems, offering a new approach to trustworthy Artificial Intelligence and promoting intelligent and efficient development in key fields such as healthcare and finance.
Copyright (c) 2026 Hui Li, Chao Xv, Ruijuan Liu
STC-RL: A Spatiotemporal Control Framework for Intelligent Environmental Regulation in Automated Warehousing Using IoT and Deep Learning
Intelligent warehousing environments require precise, energy-efficient control of temperature, humidity, and other environmental parameters. To address this, we propose STC-RL, a novel deep reinforcement learning framework that integrates Transformer-based temporal modeling, 3D CNN-based spatial field reconstruction, and graph neural network (GNN)-enhanced anomaly detection within a continuous- action reinforcement learning policy. Specifically, the Transformer captures long-range temporal dependencies from multi-sensor time series, while the 3D CNN generates a spatial thermal-humidity field via bilinear interpolation of sensor coordinates. The GNN encodes physical sensor topology to detect equipment failures through residual-based anomaly scoring. The RL agent operates in a continuous action space (e.g., setpoint temperature, humidifier output) and optimizes a multi-objective reward balancing environmental deviation, energy consumption, switching frequency, and anomaly alerts. Experimental results on a real 2,100 m² cold-chain warehouse show that STC-RL reduces energy consumption by 13.1%, achieves an average AUC-ROC of 0.936 for anomaly detection, and lowers temperature/humidity prediction RMSE to 1.02°C / 4.15%, outperforming six baselines. The system also cuts food spoilage by 66.7% and improves temperature compliance to 97.6%.
Gold Price Forecasting with a Hybrid Queuing Search Algorithm and Extreme Gradient Boosting Regressor Model
Gold is a conventional haven metal in wealth management and risk diversification tactics since its price trends mostly display signs of future political and economic trends. The forecasting of future prices of gold in the process of regression analysis is extremely challenging, considering their unpredictability and volatility. A hybrid model for a machine learning model is formed in this study by combining the Extreme Gradient Boosting Regressor (XGBR) with optimization techniques Mayfly algorithm (MA) and Queuing Search Algorithm (QSA). The hybrid model is developed in this study by using daily data points from 2011 to 2023, based on the variables Exponential Moving Average (EMA), Simple Moving Average (SMA), On- Bal Volume (OBV), Average True Range (ATR), along with Open, High, Low, volume, and Close patterns. 5-fold Cross-Validation is applied to test the performance of the model on 80% of the data used for training and 20% data used for testing. The QSA-XGBR model gives a better R² statistic with a value of 0.998 on the training data and 0.997 on the test data compared to other models, including Decision Tree (DT), Transformer, iTransformer, LSTM, XGBR, MA-XGBR, and Bi-LSTM with regard to accuracy. The SHAP value of each variable explains the importance of the variable. The variables with the highest contribution to the model are the Low variable, the High variable, and the Open variable. Additionally, this research assesses the generalization of the QSA-XGBR model by applying it to silver and crude oil, demonstrating its robust performance across multiple asset classes. Finally, this study demonstrated the significance of the improvement in forecast accuracy gained through the proposed model through the Diebold-Mariano test. For analysts and investors, the QSA-XGBR model provides insightful information that could be improved by macroeconomic variables and testing under volatile circumstances.
Swarm-Optimized Ensemble Learning for Intrusion Detection using CICIDS2018 and UNSW-NB15
This paper presents a comprehensive framework for enhancing the accuracy of intrusion detection systems (IDS) by combining multiple machine learning classifiers with swarm-based optimization algorithms. We use different classification models (Logistic Regression, Decision Tree, Extra Trees, Random Forest, and XGBoost) for evaluating the impact of the proposed approach on two benchmark cybersecurity datasets CICIDS2018 and UNSW-NB15. To address shortcomings pertaining to the detection precision and model stability, three metaheuristic optimization algorithms i.e., Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and Bat Algorithm are employed for feature selection and hyperparameter optimization. Empirical results indicate that the proposed CRF-CAPS obtains significant performance improvements in all evaluation criteria and can achieve as large as 7.5% of accuracy improvement over baseline models. The best accuracy of 97.6% for the improved model based on UNSW-NB15 and 90.9% for the CICIDS2018. In addition, the optimization resulted in a decrease in the inference time of many models, which enables real-time operation. These findings demonstrate the efficacy of hybrid optimization to narrow the performance gaps observed in the recent IDSII literature. The proposed model achieves higher overall performance than recent IDS studies between 2020 and 2025, which showed accuracy in the range of 84–91%. In addition, the swarm-based optimization could reduce features by around 30%, which translated into significant improvement in inference speed and model efficiency.
Copyright (c) 2026 Wafa Kareem Abdullah, Idress Mohammed Husien
Topology and Detailing in Personalized 3D Garment Modeling Through Neural Implicit Representations and Constraining GANs
Current 3D clothing modeling methods often face the problems of discontinuous geometric expression, poor style adaptability and lack of high-quality generation mechanism when dealing with complex topological structure and personalized design requirements. In the aspect of 3D geometric reconstruction, an implicit coding method based on the signed distance function is introduced to realize a high-precision representation of the garment surface in continuous space, and the multi-resolution feature fusion strategy is used to enhance the modeling ability of details of the model. A topology-aware adversarial generation network architecture is constructed, graph convolution is introduced to extract clothing topological structure features, and the generation process is optimized by combining physical constraints to enhance the network's understanding of geometric continuity and physical rationality. In personalized modeling, the mapping mechanism of human morphological parameters to implicit space is systematically constructed, and a conditional generation control method of dynamically deformable topological structure is proposed. The methodology integrates three core components: (1) an Signed Distance Function (SDF)-based implicit encoder with multi-resolution feature fusion, trained on 12,500 garment models to achieve sub-millimeter geometric precision; (2) a topology-aware Generative Adversarial Network (GAN) with graph convolution for structural feature extraction, optimized via 200,000 training iterations; and (3) a conditional control module mapping human parameters (e.g., bust, waist circumference) to garment topology, validated across 34 diverse body shapes. Statistical analysis shows significant improvements (p<0.01) in wrinkle detail capture (67.5% vs. 55.2% for traditional methods) and topological integrity (56.78% recall, 95% CI [52.3%, 61.2%]).
Copyright (c) 2026 Fengyi Liu, Jie Ren, Siru Liu
Ensemble Feature Fusion of VGG16, ResNet50, and Vision Transformer for Pneumonia Detection in Chest X-ray Images
This study proposes a novel heterogeneous ensemble deep learning architecture for pneumonia classifica- tion from chest X-ray images by integrating pretrained convolutional neural networks(CNN), VGG16 and ResNet50 with a fine-tuned vision transformer (ViT). The model employs a feature-level fusion strategy that concatenates deep local spatial features extracted by the CNN backbones and feeds them into the ViT to capture global contextual relationships via self-attention. This design effectively addresses the limitations of standalone CNN and ViT models by synergistically combining their complementary strengths. Extensive ablation studies and experimental evaluations demonstrate that the ensemble model significantly outper- forms individual CNN and ViT baseline models, achieving an accuracy of 98.5%, precision of 98.7%, recall of 98.3%, F1-score of 98.5%, and an area under the receiver operating characteristic (AUC-ROC) curve of 0.99 on the pneumonia X-ray dataset. The architecture balances detailed local feature extraction and holistic global context modelling, offering a robust and efficient solution for medical image classification.
Copyright (c) 2026 Deepa A B, Varghese Paul
Research on System Software Fault Data Analysis and Diagnosis Improvement Based on ISOA-SVM
With rapid development of information technology, the complexity and scale of system software continue to expand, and software failure has become a key factor affecting system stability and reliability. Aiming at this problem, this study puts forward an improved system software fault data analysis and diagnosis method based on ISOA-SVM (Support Vector Machine with Improved Seagull Optimization Algorithm). An efficient fault diagnosis model is constructed by deeply analyzing the characteristics of software fault data and combining the advantages of ISOA-SVM in dealing with high-dimensional and nonlinear data. Experimental results show that compared with the traditional SVM method, ISOA-SVM improves fault diagnosis accuracy by 15.3% and shortens the fault detection time by 20.7%. In addition, this study also explores the influence of different parameter configurations on model performance. Further, it improves diagnostic efficiency and accuracy of model by optimizing the parameter combination. The results show that fault diagnosis method based on ISOA-SVM has obvious advantages in improving the stability and reliability of system software and provides strong support for the fault diagnosis of large-scale and complex system software in the future.
Elevator Fault Prediction and Adaptive Maintenance Using Nonlinear IoT with CNN-LSTM and BP Neural Networks
With the acceleration of urbanization, elevators, as an indispensable vertical transportation tool for high- rise buildings, have attracted increasing attention for their safety and reliability. However, the traditional elevator maintenance method often relies on regular maintenance and post-maintenance, and it is difficult to effectively prevent the occurrence of failures. Therefore, this study proposes an intelligent elevator fault prediction and adaptive maintenance algorithm based on nonlinear IoT (Internet of Things), aiming to improve the level of elevator operation and maintenance through advanced technology. In this study, a nonlinear IoT platform was constructed, and sensors were used to collect elevator operation data in real time, covering multi-dimensional information such as speed, acceleration, and door opening and closing status, and the dataset was derived from 500 commercial elevators in multiple cities in China, and a standardized dataset containing 1 million records was formed after preprocessing. At the level of algorithm construction, CNN-LSTM and BP neural network fusion models are adopted, the former uses convolutional neural network (CNN) to extract local features of data, combined with long short-term memory network (LSTM) to process time series information, and the latter is used to optimize the parameters of the error backpropagation optimization model, revealing the potential characteristics of faults through in-depth mining of elevator operation data. The designed intelligent fault prediction algorithm can accurately identify abnormal patterns in elevator operation and predict potential faults. In order to verify the effectiveness of the algorithm, the traditional fault prediction method was used as the baseline for comparative experiments, and the prediction accuracy of the proposed algorithm was 95% after the double-tailed t-test, which was highly statistically significant compared with the 80% accuracy of the baseline method (P <0.01 ） . At the same time, the adaptive maintenance algorithm can automatically adjust the maintenance strategy based on the prediction results, and the actual application verification shows that after the use of adaptive maintenance, the elevator maintenance cost is reduced by 30% and the downtime is reduced by 50%. The intelligent elevator fault prediction and adaptive maintenance algorithm based on nonlinear Internet of Things proposed in this study not only significantly improves the accuracy of elevator fault prediction, but also realizes the intelligence and automation of maintenance strategies, which provides a strong guarantee for the safe and efficient operation of elevators.
TAU-PSO: A Transferable Attention U-Net with Particle Swarm Optimization for Optic Cup-Disc Segmentation in Fundus Images
The retina is impacted by diabetic retinopathy, a disorder frequently observed in people diagnosed with diabetes mellitus for an extended period. This condition can ultimately lead to vision loss as a result of pathological retinal microvascular leakage. Early diagnosis of any optic disc abnormalities is crucial for ophthalmologists to treat their patients effectively. Segmenting the optic disc to screen for glaucoma using the optic cup-to-disc ratio is also essential. Several retinal diseases can be significantly helped by automated screening of fundus images utilizing computational approaches. To enhance the efficiency of current annotated samples without necessitating thousands of examples of training for Optic Disc (OD) and Optic Cup (OC) Segmentation, we present the lightweight network known as the Transferable Attention U-Net (TAU-Net) model with Particle Swarm Optimization (PSO) for hyperparameter tuning. The model employs attention modules and two adversarial domain discriminators (feature and attention discriminators) to learn domain-invariant representations across DRISHTI-GS1, RIM-ONE, and REFUGE datasets, while structured dropout blocks reduce overfitting. Experimental results show that our method achieves superior segmentation performance compared to several state-of-the-art approaches. Specifically, for the optic cup, the TAU-PSO model attains 98.2% accuracy, 96.6% precision, 97.2% recall, and 96.4% Dice score, while for the optic disc, it achieves 97.1% accuracy, 96.0% precision, 96.5% recall, and 96.8% Dice score. Compared to baseline methods such as NFN+, SDU-Net, and DU-Net, our approach demonstrates consistent improvements of 2–4% across key metrics, highlighting the effectiveness of combining PSO-based hyperparameter optimization with domain-adversarial learning. The proposed TAU- PSO framework provides a robust and transferable solution for OD–OC segmentation under cross-dataset conditions, offering a reliable tool for early diagnosis of diabetic macular edema.
Copyright (c) 2026 Saba Sheiba, Dr. Neelakantappa M