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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S³OvA: A Reformable TinyML Solution for Self-Adaptive IoT-based Systems
The proliferation of IoT-based systems necessitates intelligent edge devices capable of autonomously adapting to dynamic environments while operating under highly constrained resource conditions. However, current TinyML solutions rely predominantly on static models that cannot evolve after deployment, which limits their effectiveness in real-world scenarios where environmental conditions and data distributions continuously change. To address this limitation, this paper presents S³OvA (SVM, SMO, Syed with OvA), a reformable TinyML solution that enables progressive model updating directly on microcontrollers (MCUs) through hybrid offline/online methodologies. It integrates Support Vector Machines (SVM) with Sequential Minimal Optimisation (SMO) for efficient training, Syed et al.’s incremental learning method for dynamic updating, and the One-vs-All (OvA) strategy for multiclass problems. In addition, it implements linear, polynomial, and RBF kernels while preserving computational efficiency on resource-constrained platforms. Consequently, experimental validation on the ESP32-S Module demonstrates its ability to achieve accuracy improvements of up to 20 points through incremental learning, while preserving memory stability throughout adaptive cycles. At the same time, the evaluation reveals operational limits with empirically determined constraints on the one hand, and validates performance within binary classification, multiclass extension, and adaptive scenarios on the other. In parallel, memory analysis confirms implementation efficiency through stable heap usage and controlled resource progression. Thus, S³OvA bridges the critical gap between static TinyML deployments and the dynamic requirements of IoT-based systems, enabling local intelligence that simultaneously reduces latency, preserves data privacy, and enhances energy autonomy. This reformable solution therefore establishes a new paradigm for self-adaptive edge computing, where distributed intelligence emerges through continuous sensor-level learning, fundamentally transforming IoT-based systems from passive data collectors into autonomous decision-making entities.
Copyright (c) 2026 Mohamed Maoui, Rohallah Benaboud
Intelligent Walrus Optimizer Fused Feedforward Neural Network (IntWO-FFNet) for Embedded Perception and Decision-Making in Industrial Robots
Intelligent industrial robots rely significantly on accurate vision and autonomous decision-making to do high-performance tasks. Embedded systems, compact, real-time computer units, have become critical for delivering these capabilities, especially in resource-constrained industrial environments. Despite their advantages, embedded systems meet obstacles such as high computational cost, overfitting, and inadequate parameter tuning, which impede real-time performance and generalizability in dynamic industrial environments. The purpose of this research is to develop an embedded neural network framework that has been tuned using metaheuristic algorithms to increase the precision of robotic vision and the effectiveness of decision-making while considering available resources. Multimodal data (vision, force, and proximity) are acquired from industrial environments. Raw data is cleaned and normalized using min-max scaling. Principal Component Analysis (PCA) is used to extract statistical and geographical characteristics, reducing dimensionality. This research proposes an Intelligent Walrus Optimizer Fused Feed-forward Neural Network (IntWO-FFNet) to enhance the accuracy, efficiency, and adaptability of industrial robots by enabling intelligent perception and decision-making on embedded systems. An FFNet is used on the embedded system to identify environmental inputs and forecast task-specific behaviors. The FFNet is fine-tuned with IntWO to improve learning rates, weight initialization, and hidden layer configurations for less error and faster convergence. The proposed method was implemented using Python 3.10.1. The proposed IntWO-FFNet approach performs better than multimodal baseline architectures, achieving superior results, with accuracy ranging from 95% to 99%. Integrating neural networks with optimization approaches into embedded systems dramatically improves real-time robotic perception and decision-making, providing intelligent automation aligned with industrial robots. The dataset contains 10,214 multimodal samples across five task classes (pick, place, weld, idle, interaction) and five industrial object types. All experiments were executed on a simulated embedded environment (Raspberry-Pi–equivalent ARM setup) using Python 3.10.1, with evaluation performed using stratified train–validation–test splits. Performance was benchmarked against the BIIRCS baseline model, where the proposed IntWO-FFNet achieved 96.3% accuracy, 9.8% RMSE, and 97.5% task-coverage rate.
DeepFM-MOTSO: A Deep Factorization Machine Framework Optimized by Multi-Objective Tuna Swarm for Online Advertising Fraud Detection
Online advertising fraud is still a significant problem that inflates marketing expenses and distorts campaign performance. In order to improve fraud detection accuracy in the face of extreme class imbalance, this study proposes a hybrid DeepFM–MOTSO framework that combines a Deep Factorization Machine (DeepFM) with a Multi-Objective Tuna Swarm Optimizer (MOTSO). While MOTSO simultaneously optimizes several goals—maximizing F1-score, precision, recall, and AUC, as well as minimizing loss—for balanced classification, the model captures both low-order feature interactions through the FM component and high-order nonlinear representations via deep neural layers. A real-world advertising dataset with roughly 2,043 records that included contextual and behavioral features like session_duration, click_interval, impression_count, and device_type was used to test the method. With an accuracy of 0.952, precision of 0.214, recall of 0.179, F1-score of 0.195, and AUC of 0.864, the experimental results demonstrate that the proposed DeepFM–MOTSO outperformed all comparative baselines, indicating superior capability in identifying minority-class fraudulent instances. The findings confirm that multi-objective optimization effectively improves model convergence, stability, and real-time adaptability for intelligent online advertising fraud detection.
Underwater Target Recognition and Path Planning Using Otsu-KMeans Segmentation and EM-PF-SLAM with Enhanced A-JPS Algorithm
Autonomous Underwater Vehicles (AUVs) face significant challenges in obstacle recognition and path planning due to sonar image noise and uncertain underwater environments. This study proposes a robust target recognition and obstacle avoidance framework integrating forward-looking sonar image processing, probabilistic SLAM, and a fast global path planner. Sonar images are first segmented using Otsu thresholding and refined through K-means clustering to extract obstacle features. These features are then fused with odometry and inertial data using an Expectation-Maximization (EM) based data association module and Particle Filter (PF) for posterior state estimation, enabling accurate SLAM under sonar uncertainty. For navigation, an improved A*-JPS algorithm is applied to achieve time-efficient path planning. Experiments were conducted on the publicly available SeaNet Dataset, which contains diverse acoustic scenes from Northwest Pacific environments. Tests were run on a platform with an Intel i5-10210U CPU and 16 GB RAM, using evaluation metrics including recognition accuraacy, RMSE, absolute trajectory error (ATE), loop closure recall, and path smoothness. Results show that the proposed Otsu-K-means sonar segmentation achieves 99.3% recognition accuracy, outperforming standalone methods by over 25%. The EM-PF-SLAM system achieves an ATE of 0.42 m and a loop closure recall of 95.3%, reducing localization uncertainty by over 40% compared to PF-only baselines. The hybrid A*-JPS planner reduces path planning time to 0.12 s and achieves a smoothness score of 0.85. These findings highlight the method’s suitability for real-time, high-precision AUV operations in complex acoustic environments.
Energy-Aware Task Scheduling in Cloud Environments Using Digital Twin Architecture with CNN-LSTM and Multi-Agent Deep Reinforcement Learning
The increasing complexity of dispersed cloud networks necessitates smart solutions for energy sustainability and stringent Quality of Service (QoS) maintenance. Existing scheduling approaches, primarily single-agent Deep Reinforcement Learning (DRL), suffer from significant performance and scalability issues in large-scale, dynamic environments. This study proposes a novel, smart, energy-conscious paradigm for task scheduling that integrates a Digital Twin (DT) architecture with Multi-Agent Deep Reinforcement Learning (MADRL). The DT provides a faithful, up-to-the-minute representation of the network, utilizing a hybrid Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) model to forecast future workload demands and server power consumption trends. Using these reliable forecasts, MADRL systems develop a dynamic, decentralized scheduling strategy where each server acts as a cooperative agent. The primary objective is to solve a multi-objective optimization problem that minimizes overall energy consumption (via consolidation and dynamic server shutdown) while adhering to QoS requirements (limiting task latency and increasing throughput). Experimental evaluations, comparing our framework against state-of-the-art single-agent DRL baselines (A3C and DDPG), demonstrate superior performance. Our strategy achieves a significant reduction in response time and operational energy cost, proving the viability of this integrated AI and DT technology for efficiently and scalably managing resource allocation in modern distributed cloud environments.
Spatiotemporal Electric Energy Efficiency Evaluation via Hybrid Graph Neural Network and Transformer Architecture
Traditional power efficiency assessment mechanisms face limitations in processing spatiotemporal data. Therefore, we combined deep learning models to construct a power resource efficiency evaluation method that integrates spatiotemporal information. Regarding the model, we combined graph neural networks to process spatial information. Transformers were used to process the features implicit in continuous information, and a hybrid GNN-Transformer approach was employed to achieve dual modeling of temporal and spatial features. The model employed an attention mechanism to capture the characteristics of key time nodes and critical loads in both temporal and spatial data. This paper evaluates the model on two real-world power system datasets: (1) hourly load and day-ahead price data from the US PJM grid (2018–2023; ~43,800 samples across 13 control areas); and (2) 15-minute wind, solar, and load data from a Chinese provincial grid (2020–2022; ~105,000 samples). Both datasets span diverse weather and seasonal conditions, enabling robust assessment of the model’s generalization. Experimental results showed that our proposed model and several baseline models were evaluated on real-world power grid datasets. The proposed hybrid GNN-Transformer model aggregates spatial dependencies in the power grid topology through graph convolutional networks (GCNs) and utilizes a multi-head self-attention mechanism to model long-term temporal dynamics. On the PJM dataset, this method reduces the mean squared error (MSE) from 0.1089 to 0.0823 on the LSTM baseline, a relative reduction of 24.4%, which is significantly better than existing methods.
Application of BiLSTM CRF Model Based on Hierarchical Attention for Implicit Emotion Recognition in Literary Texts
In recent years, emotion recognition in textual narratives has become an important area of affective computing and natural language understanding. Unlike explicit emotional expressions, implicit emotions are concealed within linguistic context, narrative tone, and metaphorical cues, posing significant challenges for traditional machine learning approaches. To address this issue, this study proposes a Hierarchical Attention-based BiLSTM-CRF model for Implicit Emotion Recognition in literary texts. The model integrates a hierarchical attention mechanism to capture both sentence-level and document-level semantic dependencies, while the BiLSTM-CRF architecture effectively encodes sequential context and label inter-dependencies. The proposed model was evaluated on a multi-class emotion dataset containing seven emotion categories such as Joy, Sadness, Anger, Fear, Disgust, Surprise, and Neutral. Experimental results demonstrate that the proposed model yields superior performance compared with conventional deep learning baselines, including HDEL, BERT-BiLSTM-CRF, GT-BiLSTM, and Hierarchical Attention Networks. Specifically, the model achieves an overall Accuracy of 0.92, Precision of 0.91, Recall of 0.90, F1-Score of 0.90, and ROC-AUC of 0.95, indicating enhanced ability to identify subtle emotional implications within complex literary expressions. These findings highlight the effectiveness of combining hierarchical attention with sequence labeling for advanced emotion cognition and provide a valuable framework for future studies on literary emotion analysis and intelligent human–computer interaction.
T-BiLSTM-CCO: A Transformer-BiLSTM framework with Cuckoo Catfish Optimization for multilingual image description localization
Image description localization benefits from integrating textual and visual cues. However, conventional neural machine translation models often overlook fine-grained grounding. This research introduces aNeural Machine Translation Model (NMT) named Transformer integrated Bidirectional Long Short-Term Memory Network-tuned with Cuckoo catfish optimizer (TBiLSTM-CCO) to enhance translation accuracy while preserving image-caption semantic congruence in multilingual captioning. Multilingual image-caption pairs were gathered from the Multi30 and MS-COCO datasets, each containing aligned captions in multiple languages. Visual features are extracted using ResNet, producing high-dimensional semantic embeddings representing objects and scenes. The proposed hybrid architecture combines the CCO with a Transformer-enhanced Bi-LSTM network. This configuration enhances sequence modeling and attention capabilities, optimally tuning parameters to improve translation fidelity, multimodal feature fusion, and localized visual alignment during description generation. The proposed T-Bi-LSTM-CCO achieves higher BLEU-1 to BLEU-4 scores of 0.90, 0.89,0.87, and 0.85 values than the existing methods. Python was used to conduct experiments on the MS-COCO and Multi30k datasets, utilizing Word2Vec for text embeddings and ResNet-152 for visual features. The CCO was trained for over 50 epochs. The hybrid T-Bi-LSTM-CCO-model consistently achieved better alignment between descriptions and image regions, validating its effectiveness for multimodal translation and grounding tasks. The multimodal NMT framework combines Deep Learning (DL) and visual features, improving translation quality and image-description localization, showing robustness for real-world multilingual applications.
A New Energy Vehicle Autonomous Driving Decision Control Method Integrating Neural Networks and Reinforcement Learning
Autonomous driving has emerged as a capable technology to improve the energy efficiency and safety of New Energy Vehicles (NEVs) by enabling intelligent driving decisions. Conventional energy-efficient autonomous driving methods are often limited to longitudinal velocity planning and fixed-route strategies, which fail to achieve optimal adaptability in dynamic traffic conditions. To overcome these challenges, this research proposes an autonomous driving decision control method integrating Neural Networks (NNs) with Reinforcement Learning (RL). In the proposed framework, convolutional neural networks (CNNs) extract spatial-temporal features from multi-sensor inputs, including camera, Light Detection and Ranging (LiDAR), and radar data, to capture lane geometry, vehicle distances, and obstacle dynamics. The data were preprocessed using Min-Max normalization to scale all features to a consistent range. These features form the state space for a Capuchin Search-driven Scalable Deep Q-Network (CapSA-SDQN)-based RL agent, which simultaneously manages lane-changing and vehicle-following behaviors and optimizes decision control in complex and dynamic traffic environments. The decision control method further incorporates a rule-based safety checker that is embedded downstream to guarantee safe maneuver execution. The strategy is trained and tested in the simulation platform under diverse traffic scenarios, including congested urban intersections, highway merging, and dynamic overtaking. Results show that the proposed CapSA-SDQN approach achieves significant improvements in average speed (46.72) and reduced speed standard deviation (2.15). The findings demonstrate that integrating neural networks with reinforcement learning provides a scalable solution for NEV autonomous driving in real-world environments.
A Hybrid Deep Learning Architecture for Network Security Situation Awareness and Pre-Alarm in Large-Scale Data Environments Using LSTM, Autoencoder, and GNN
In the large-scale data environment, network security situational awareness (NSSA) often faces the problems of poor real-time performance and low early warning accuracy. Therefore, this article proposes an intelligent early warning model that integrates multi-source heterogeneous data. This model constructs a distributed processing architecture based on Spark+Flink, which integrates a hybrid analysis mechanism of long-term memory network (LSTM), self-encoder and graph neural network (GNN) to efficiently detect abnormal behaviors and infer attack paths. At the same time, a situation scoring mechanism with dynamic weight adjustment is designed, and a time decay suppression strategy is introduced to optimize the alarm output and reduce the false alarm rate. The experiment was conducted on CICIDS2017 public data set and a real log of a provincial government cloud, and evaluated by F1-score, AUC, response delay and effective alarm compression ratio. The results show that the F1-score of this model is 0.93 on CICIDS2017, which is significantly better than the traditional method. In the real government cloud environment, the system throughput is up to 183,000 pieces/second, the average response delay is controlled within 300ms, and the number of effective alarms is reduced by over 65%. This study verifies the feasibility and superiority of the proposed model in high concurrency scenarios, and provides a practical basis for building an intelligent and extensible network security defense system.
KG-GAN: Knowledge Graph-Constrained GAN for Culturally Faithful Virtual Scene Synthesis
This paper presents a knowledge-guided generative adversarial network (KG-GAN) for culturally faithful virtual scene construction and immersive experience optimization. A domain knowledge graph (KG) encodes traditional cultural entities and relations as graph embeddings, which condition the generator together with textual prompts. The semantic consistency discriminator calculates text-image cosine similarity based on CLIP cross-modal embedding, jointly scoring lexical semantic consistency and symbol reconstruction accuracy. The generator employs AdaIN for knowledge-conditional modulation, and multi-scale training and PWC-Net optical flow regularization collaboratively optimize local details and inter-frame stability. To balance global realism and local cultural details., we adopt a multi-scale training schedule and an attention controller that dynamically allocates textures and color palettes to culture-critical regions. Temporal stability is further improved with a lightweight consistency loss.Experiments on a curated cultural corpus and human-corrected pairs report cultural semantic fidelity of 89.7%–92.1%, relation compliance of 86.9%–88.6%, inter-frame SSIM under fast transitions of 0.782, and optical-flow smoothness of 0.751. A vocational Chinese-language classroom case study shows higher cultural expressiveness, learner engagement, and motion comfort compared with diffusion-based and CLIP-guided baselines, with ablations confirming the contributions of KG constraints, the semantic discriminator, and attention control. The framework is plug-and-play for XR/education/museums, and we provide KG schemas, code, and evaluation scripts to support reproducible deployment.
Joint Cultural and Grammatical Correction of Japanese Translations via a GAN-BiLSTM Framework with Policy Gradient Optimization
Japanese translations often exhibit grammatical deviations and cultural inconsistencies. This study proposes an automatic correction framework that integrates a Generative Adversarial Network with a Bi-LSTM encoder–decoder augmented by embedded cultural vectors to generate correction candidates. A convolutional discriminator jointly evaluates grammaticality and cultural appropriateness under a Wasserstein adversarial objective, while policy gradient optimization refines the generator using discriminator-based rewards. Semantic consistency is preserved through back-translation constraints. Experiments on 60,000 culturally annotated Japanese–Chinese sentence pairs from news, business, and academic domains, split into training, validation, and testing sets, compare the proposed method with RNN, Transformer, and GAN-only baselines. The model achieves 86.2 percent grammatical accuracy, a cultural alignment score of 4.52, a fluency score of 4.10, and a perplexity of 45.6, consistently outperforming baselines across sentence lengths and cultural load levels. The results demonstrate unified optimization of grammatical correction, cultural alignment, and semantic preservation in Japanese translation post-editing.
SSO-FFNN: A Scalable Seeker Optimization-Enhanced Feedforward Neural Network for Predicting Anxiety Levels in College Students
This study aims to predict anxiety among college students using optimization-enhanced neural network models instead of conventional machine learning. The student anxiety and depression dataset from Kaggle (6,982 records) was analyzed through comprehensive text preprocessing (tokenization, stop-word removal, TF-IDF feature extraction, and SMOTE balancing), exploratory data analysis, and a set of baseline models including ANN, RNN, ES-ANN + LOF, Harmony-Search ANN, and GA-BP ANN. To address the limitations of earlier approaches, a new SSO-FFNN (Scalable Seeker Optimization-Enhanced Feedforward Neural Network) framework was introduced, where the SSO algorithm optimizes network weights and biases to avoid local minima and improve convergence speed. Results show that the proposed SSO-FFNN achieved 97 % accuracy, an F1-score of 0.84, and a ROC-AUC of 0.97, outperforming all baseline models. These findings highlight the potential of optimization-driven neural networks for early anxiety detection and timely preventive counselling in academic settings.
Copyright (c) 2026 Wenjing Ding, Longhua Chen
Att-BiLSTM-GAN: A Temporal Coherence-Preserving GAN Framework for Dynamic Art Video Stylization
Dynamic art video generation has emerged as a significant research area in computer vision and digital creativity, enabling the transformation of ordinary videos into visually compelling artistic content. However, existing methods often struggle to maintain temporal coherence, preserve motion integrity, and ensure faithful style transfer across consecutive frames, leading to artifacts such as flickering and content leakage. Specifically, an Attention-enriched Bidirectional Long Short-Term Memory integrated with Generative Adversarial Networks (Att-BiLSTM-GAN) method is introduced for dynamic art video generation. Dynamic action painting styles data are collected, featuring bold brushstrokes, splashes, and dynamic motion-like artistic patterns. Histogram Equalization (HE) is applied during preprocessing to normalize illumination and enhance contrast, while a Visual Geometry Group 16 (VGG16)-based encoder extracted spatial and textural features from content and style references. GAN-based style transfer techniques are applied to impose artistic attributes from reference artworks, preserving both global style patterns and local texture details. The incorporation of BiLSTM reduces temporal distortions by modeling frame-to-frame dependencies, while adaptive style loss functions balance stylistic richness. Experimental evaluations are conducted on the Dynamic Action Painting Styles dataset, with state-of-the-art baselines such as StyleMaster, SRCNN, VDSR, SRResNet, EDSR, TT-VSR, and baseline GAN models, the proposed Att-BiLSTM-GAN achieved superior results with PSNR (36.21), SSIM (0.96), CSD-Score (0.94), CLIP-Text (0.88), Motion Smooth (0.91), and FID (35.42), confirming significant performance gains and improved temporal coherence across all evaluation metrics. This research highlights the potential of combining sequential learning with style transfer for generating high-quality dynamic art videos.
The Application of Adaptive Reinforcement Learning in Dynamic Pricing Strategies
Dynamic pricing is crucial for maximizing revenue and maintaining competitiveness in markets with fluctuating demand, perishable goods, and diverse customer preferences. Preferences in purchasing differ significantly, and traditional methods, such as rule-based algorithms and statistical scale forecasting, struggle to adapt to rapidly changing market conditions, competitive maneuvers, and evolving consumer strategies. Behavior shifts in the real world, yet these approaches frequently fail to react effectively, leading to sub-optimal pricing and decreased profitability. Profitability in dynamic markets is often hindered because past research, while applying machine learning techniques to pricing improvement, has produced models that adapt slowly to real-time changes, depend heavily on historical data, and struggle to handle multi-agent scenarios. Scenarios involving fast-changing and unpredictable environments ultimately impact overall profitability. Profitability in a dynamic marketplace is enhanced through an Adaptive Reinforcement Learning (ARL)-based pricing framework that utilizes Q-Learning and Deep Q-Networks (DQN) for real-time optimization in response to changing market conditions, competition, and inventory levels. Inventory challenges are addressed by utilizing a curated dataset that has been enhanced through feature engineering, transformation, and systematic cleaning, providing reliable inputs for training. Training strength is validated by benchmarking against fixed, rule-based models and cost-plus in controlled experimentation. Experiments highlight a reward anatomical structure that balances income, profit, efficiency, justice, and customer retention, moving beyond income-only goals. Goals are achieved by modeling pricing as a Markov-based Decision Process, where ARL agents continuously refine policies through interaction with the environment. When compared to baseline approaches, the ARL-based model's accuracy in revenue and price optimization decreased by less than 20%, indicating that it can adapt and optimize pricing techniques in intricate, cutthroat markets.
SGWO: A Modified Grey Wolf Optimizer for Fog–Cloud Workflow Scheduling with Emphasis on Makespan and Cost Efficiency
The rapid deployment of IoT applications creates several challenges. To address these challenges, a new paradigm called Fog Computing extends Cloud Computing services to the network edge, which reduces latency, conserves energy, and saves bandwidth. One of the major issues in enhancing the IoT application's performance is the tasks' scheduling, specifically, how to allocate them to different resources distributed across Fog-Cloud Computing layers. In this paper, a scheduling algorithm named SGWO is proposed, which adapts the standard Grey Wolf Optimizer (GWO) algorithm by including an improvement aimed at optimizing the balance between the exploration and exploitation phases. This adaptation enables the efficient scheduling of dependent tasks, modeled as general workflows, by allocating them efficiently across a hybrid topology composed of fog and cloud nodes with the dual objectives of minimizing makespan and reducing resource utilization costs. A series of experiments were conducted in a static simulation environment using diverse workflow datasets under various topology configurations. The results demonstrate that SGWO converges quickly and delivers better performance in terms of both makespan and cost, achieving a fitness value higher than those obtained by PSO, SQGA, GA, MHEFT, and FCFS algorithms by 43.3\%, 50.8\%, 55.4\%, 54.5\%, and 57.7\%, respectively, thereby confirming its effectiveness in scheduling for this type of infrastructure.
Copyright (c) 2026 Raouf Belmahdi, Djamila Mechta, Saad Harous
Defense Against False Data Injection and Fault Tolerance Optimization for Resource Scheduling in Software-Defined Satellite-Terrestrial Networks Using SFT-LoRA
With the widespread application of Satellite-Terrestrial Integrated Networks (STINs) in key fields such as disaster monitoring, military communications, and the Internet of Things, their resource scheduling systems are facing severe security threats. This paper proposes a Software-Defined Flexible Topology SFT training mechanism that combines spatiotemporal feature analysis and a dynamic fault-tolerant scheduling optimization model. Firstly, in the detection layer, a cross-layer feature extractor based on bidirectional LSTM-CNN is designed, and SFT training mechanism is adopted to realize real-time recognition of injected data; Secondly, at the fault-tolerant layer, build an elastic resource reallocation strategy, predict task demand fluctuations through sliding windows, and dynamically adjust LoRA weight priority and node redundancy; The models were trained and validated on a satellite-terrestrial simulation dataset generated with NS-3 and OpenStack, including 100 nodes, 12 typical attack types, and over 12,000 samples. The training process integrated bidirectional LSTM layers (128 units), CNN filters, and reinforcement learning with PPO for adaptive scheduling. Finally, the experiment is based on NS-3 simulation platform and OpenStack cloud environment, and the injected attack samples cover 12 typical attack modes. Results show that the proposed framework outperforms traditional IDS and a defenseless baseline, achieving a 98.5% FDI detection rate with a false alarm rate below 1.2%. Moreover, the task completion rate after attacks increases to 96.3%, scheduling delay is reduced by 21.7%, and resource utilization is improved by 18.4%. This framework provides an effective solution for resource scheduling security in highly dynamic and weakly connected environments.
DBERA: A Dynamic Bayesian Network Framework for Real-Time Entrepreneurial Risk Assessment
Entrepreneurial ventures are inherently exposed to multiple risks arising from financial uncertainty, competitive dynamics, and individual decision-making behaviors. A Bayesian network-based model provides a probabilistic framework for capturing and analyzing these interdependent risk factors. Traditional methods often rely on static financial ratios, subjective judgment, or linear risk assessment models that fail to adapt to dynamic market conditions and complex interrelationships among variables. This limits their ability to provide accurate, context-aware risk evaluation for entrepreneurs. To address these limitations, the Dynamic Bayesian-Economic Risk Analyzer (DBERA) is proposed, which integrates market volatility, competitor signals, and personal investment patterns into a unified Bayesian framework. Continuously updated conditional probability distributions enable DBERA's context-aware, real-time risk assessment for entrepreneurs. Unlike static models, it adjusts to market dynamics and uncovers new causal linkages between indicators. The model was tested using simulated and real datasets using scenario-based Monte Carlo simulations. Validation measures, including sensitivity analysis, probabilistic inference consistency, and prediction accuracy, were used to assess resilience across varied situations. Scenario planning may help politicians, bankers, and business owners assess their approaches to managing uncertainty and identify success factors. DBERA considerably improves forecast accuracy and decision-making help. The prediction power increases by 83% for competition intensity, 95% for investment pattern impact, 93% for finance availability, 96.1% for operational capacity, 97.3% for innovation index contribution, and 82.5 for environmental risk exposure. These discoveries demonstrate that DBERA is a versatile tool for reducing risk and achieving entrepreneurial success.
ELCNN-BiLSTM: A Hybrid Deep Learning Model for Timeliness Prediction in Cross-Border E-Commerce Logistics
This paper suggests a Bidirectional LSTM (ELCNN-BiLSTM) model, which is an E-Commerce Logistics CNN-based model of intelligent decision-making and timeliness prediction in the cross-border logistics processes. The data to be used in the study was gathered within a global e-commerce logistics system of a cosmetics retailer with 1,25, 000 delivery records that have 24 spatio-temporal and operational characteristics, such as the shipment weight, their origin, destination, the distance of the route, the time in customs, and the time in the warehouse. The formulated task is a binary classification task to forecast whether a delivery is on-time or delayed, with an on-time threshold parameter being delivery within +/-1 day of the expected arrival. Preprocessing was performed on the basis of alignment of the timestamps, normalisation, treatment of 3.4% missing values by interpolation, and resampling to address a 1:1.7 ratio of the class imbalance with SMOTE. The ELCNN-BiLSTM architecture proposed is a combination of spatial feature extraction with convolutional layers and long-term temporal dependencies with the bidirectional LSTM layers that allow adaptive acquisition of logistics patterns on routes and time zones. Benchmarking of the model was done against LSTM, CNN-LSTM, Att-GRU, and XGBoost baselines on measurements of Accuracy, Precision, Recall, F1-Score, PR-AUC and ROC-AUC. Findings reveal that the suggested model has a better predictive power with 95.4 % accuracy, 93.9 % precision and 96.3 % of ROC-AUC, which are significantly higher than other approaches in all the most important measures. The ability and robustness of the model to generalise and statistical significance tests were statistically proven through statistical tests and k-fold cross-validation. The study presents a powerful data-oriented model of predicting supply chain delays in real-time, which can improve the reliability of supply chains and help to make intelligent decisions in the global e-commerce business.
Automated Pneumonia Detection Using Dual-Architecture Deep Learning with GhostNet and Mo-bileNetV2
Pneumonia remains a significant global health concern, especially in regions with limited medical resources, underscoring the need for accurate, efficient, and interpretable diagnostic solutions. This study proposes a dual-architecture deep learning framework combining GhostNet and MobileNetV2 for automated pneumonia detection using chest X-ray images. The model leverages GhostNet’s efficient feature extraction and MobileNetV2’s lightweight precision, integrated via a custom concatenation layer to enhance performance while maintaining computational efficiency. Training was conducted on a publicly available pediatric chest X-ray dataset comprising 5,872 images from the Guangzhou Women and Children’s Medical Center. A patient-level split of 70% for training, 15% for validation, and 15% for testing was used, ensuring no data leakage across subsets. Although cross-validation was not applied, generalizability was assessed on an external adult dataset from Indiana University (Open-i), with the model achieving 85% test accuracy and 87% validation accuracy. On the internal test set, the proposed method attained 97.45% accuracy, 99.82% precision, 96.74% recall, and a 98.25% F1-score, outperforming established models such as ResNet50, VGG16, and EfficientNet. Training and validation loss curves showed minimal divergence, and Grad-CAM visualizations offered interpretability by highlighting salient lung regions influencing predictions. The lightweight and adaptable nature of the model makes it particularly suitable for real-world deployment in resource-constrained healthcare environments. Future work will focus on expanding the dataset, adopting k-fold cross-validation, integrating continual learning strategies, conducting subgroup and fairness analyses, and exploring explainable AI tools to further enhance clinical applicability and trust.
Copyright (c) 2026 MEENU meenu, AMRENDRA CHAUDHARY CHAUDHARY, Tushant Kumar, Adarsh Kumar
BEACON-AI: A CNN- BiLSTM -Attention Framework for Real-Time Multimodal Student Behavior Analysis and Academic Early Warning
Monitoring students' behavior and engagement in real time is essential to improve learning outcomes and reduce academic risk. As a result of their reliance on static academic records and manual observations, traditional early warning systems often fail to identify behavioral markers predictive of declining involvement. BEACON-AI is a real-time deep learning platform proposed in this work for multimodal student behavior analysis and academic risk prediction. The system utilizes physiological, behavioral, and environmental data. These data are obtained from 30 days of wearable Internet of Things sensor records on Kaggle. These recordings include facial expressions, posture, heart rate, movement, and classroom context. To capture temporal and contextual patterns, the data preprocessing included normalization, one-hot encoding, and segmentation into sliding time-series windows, among other steps. BEACON-AI uses a CNN-BiLSTM-Attention hybrid model to forecast attention, engagement, and inactivity at multiple levels. With accuracy rates of 94.2% for attention detection, 92.5% for engagement categorization, and over 85% for early disengagement prediction, the system outperformed baseline models primarily used in academic settings. To demonstrate a high level of dependability in identifying behavioral changes, task-wise accuracy, recall, and F1 Scores were specifically measured. It is possible to comprehend the results of the attention mechanism since it highlights the most critical behavioral contributions. The overall value of combining multimodal behavioral and physiological data for educational interventions is demonstrated by BEACON-AI, which provides real-time, scalable behavioral monitoring and proactive academic early warning.
A Hybrid Approach of Metaheuristic Algorithms and Group Method of Data Handling to Predict Source Code Testability
Testability allows developers to improve software by enabling effective testing throughout the development process. The purpose of this paper is a study based on the prediction of software testability using machine learning (ML) algorithms. Based on experimental data, a group method of data handling (GMDH) algorithm has been proposed to forecast the testability of source code. Metaheuristic algorithms like Ant Colony Optimization (ACO), Firefly Algorithm (FA), and Colliding Bodies Optimization (CBO) have been used to optimize the GMDH parameters in order to enhance and raise the forecasting accuracy. Metaheuristics enhance GMDH, forming diverse hybrids. Comparing eight indicators, CBO achieves top efficiency in software testability prediction with R: 0.674, RMSE: 0.208, MAE: 0.167, and RAE: 1.020. Also, with the results obtained from other indicators, it is determined that it has the best performance. Hence, the CBO algorithm applied to the GMDH model to predict testability is selected as the best method.
Using multi-channel priority scheduling mechanism to improve the transmission efficiency of Store-Forward processing structure under RISC-V architecture
In RISC-V processor design, the Store-Forward processing architecture is used to coordinate data storage and loading operations to avoid collisions. However, as the demand for multi-core and parallel tasks increases, traditional methods face high transmission latency and low bandwidth utilization problems, affecting the overall system performance. Therefore, we propose a multi-channel priority scheduling mechanism, which dynamically adjusts the priorities of multiple data streams to optimize resource allocation, reduce congestion, and improve transmission efficiency. A RISC-V test platform is built based on the Gem5 simulator, and the performance of standard scheduling and multi-channel priority scheduling under data-intensive workloads is compared. The experimental results show that after adopting the new mechanism, the average transmission delay is reduced from 60 nanoseconds to 48 nanoseconds, which is a reduction of 20%. At the same time, the peak throughput increased from 5GB per second to 6.5 GB, an increase of 30%. In 1000 random data transmission tasks, the scheduling mechanism reduces the processing completion time to 80 milliseconds, which reduces the waiting time by 20% compared to the benchmark of 100 milliseconds. These data indicate that the mechanism effectively mitigates the congestion problem and optimizes resource utilization. Through multi-channel priority scheduling, the transmission efficiency of the Store-Forward processing structure is significantly improved in the RISC-V environment, providing a feasible solution for high-performance computing applications.
Copyright (c) 2026 Boling Chen, Jiacheng Fu, Dengbin Liao, Junbing Pan, Xiaoying Mo, Yiting Huang
BIAW-DL-SA: Blockchain-Integrated Audio Watermarking and Deep Learning for Ownership Verification and Forgery Detection in AI-Generated Music
Copyright ownership and audio content recognition are pressing concerns as generative AI is increasingly used in music composition. AI-generated music authenticity and creator rights are now a major issue. Existing ownership verification methods lack robust, tamper-proof processes and often fail to detect deep learning model forgeries. Blockchain-Integrated Audio Watermarking with Deep Learning-Based Source Attribution (BIAW-DL-SA) embeds imperceptible watermarks using a convolutional autoencoder and registers metadata on a decentralized blockchain ledger for transparent, tamper-resistant verification. Fake or Real (FoR), Deepfake Voice Recognition (DFVR), Open Dataset Synthetic Speech (ODSS), Copy-Move Forgery Detection (CMFD), and CVoiceFake were used in evaluations. BIAW-DL-SA outperforms AIR-Fund, AICORE, and ROYAL-AI-M in attribution precision and ownership verification accuracy, reaching 95% with more registered tracks. BIAW-DL-SA maintains sub-120 ms latency even under severe request load, while competitor platforms experience latency growth. These findings prove BIAW-DL-SA is scalable, real-time, and tamper-resistant for music platforms, artists, and copyright organizations. The method improves AI-generated music copyright protection.
PCA-Optimized SVM Framework for Detecting False Data Injection Attacks in Battery Energy Storage Systems
False Data Injection Attacks (FDIAs) pose a significant threat to the reliability of Battery Energy Storage Systems (BESS) in smart grids. This paper proposes a PCA-optimized Support Vector Machine (SVM) detection framework specifically designed for BESS. First, we model the impact of FDIAs on BESS operations, demonstrating how such attacks can manipulate state-of-charge, power output, and grid frequency to disrupt system efficiency and stability. Key operational indicators, including voltage, current, and power, are used for an extensive feature extraction process, after which Principal Component Analysis (PCA) is applied to select the most informative features and improve classification efficiency. The proposed framework is evaluated through comprehensive simulations, achieving a detection accuracy of 98.5%, precision of 97.8%, recall of 98.2%, and an F1-score of 98.0%. The confusion matrix and ROC curve confirm the robustness of the model, showing minimal false positives and false negatives. These results demonstrate that the proposed PCA-optimized SVM framework provides an effective and computationally efficient solution for mitigating FDIAs in BESS and strengthens the security foundation of future smart grids.
Bridge Defect Detection via CNNs, Multimodal Data Fusion, and Predictive Maintenance Models
Bridge structures are affected by weathering, load, and natural disasters over long-term use, leading to defects such as cracks, corrosion, and deformation. These defects directly threaten the safety and stability of bridges. Traditional inspection methods rely on manual processes, which are costly, inefficient, and prone to subjective errors. This paper proposes a new bridge defect detection method combining machine vision with big data analytics to improve accuracy and efficiency. High-resolution cameras and drones collect image data, which are analyzed using a VGG-based Convolutional Neural Network (CNN) with attention mechanisms to automatically detect and classify defects like cracks, spalling, and corrosion. The multimodal fusion strategy, utilizing graph neural networks, integrates image data with historical detection records, environmental conditions, and load information to predict the service life and maintenance needs of bridges. In experiments, this method achieves an accuracy of 95.8%, an F1 score of 93.5%, and a mean absolute error of ±10%. The accuracy of this method is approximately 30% higher than traditional techniques, and the data analysis reveals that bridges in high-humidity areas experience 23% higher corrosion rates than those in dry conditions. These results confirm the potential of the method for predictive maintenance and enhanced decision support in bridge management.
Copyright (c) 2026 Yongfei Shen, Jun Xu, Yan Dang, Yafei Liu, Xiaoning Wang
Accurate Alignment Control and Energy Transmission Optimization of Dynamic Wireless Charging Mechanical Platform
The potential of dynamic wireless power transfer (DWPT) to link electric vehicles to charging stations without the need for physical connections has attracted a lot of interest. Two of the most crucial characteristics for real-world uses of wireless dynamic charging are stable output power and good system efficiency. Nevertheless, researchers face a significant obstacle in their pursuit of more efficient power transmission. The literature explores several methods to optimize power transmission efficiency. The control method, compensation capacitance, magnetic coupler design, and power electronic circuit are all part of the research. Additionally, whether the wireless charging system is static or dynamic determines the sort of inquiry that is conducted. An efficiency optimization strategy, namely an ANN, is suggested for low-speed dynamic charging application situations to regulate the output voltage and increase system efficiency. It is possible to create a stronger magnetic field devoid of dead spots by overlapping two DD coils. As the receiver travels along the track, an optimal current control mechanism is suggested to dynamically modify the direction and ratio of the two transmitter currents. This technology may maximize the dynamic charging system's efficiency while regulating the output power to the appropriate value simultaneously. In order to verify how well the suggested approach works, a 600 W model is built. The suggested technique achieves system efficiency for charging dynamically of 88. 3% to 90. 4%, according to the testing data. When both DD coils are activated to the same current, the suggested technique improves efficiency by a maximum of 2. 3%. Arranging the coils coaxially is necessary for best power while transmission efficiency when establishing a wireless power transfer mechanism for an electric car. Coil angular as well as lateral misalignments are common in wireless charging systems. This research presents a novel alignment method for EV wireless charging that addresses the issue of the receiver and transmitter coil misalignment. Coil design and analysis with mutual inductance along with effectiveness as key considerations is the focus of the research. Ansys Maxwell is used to represent wireless coils that have angular misalignment. By using angular motion to align the coils, the suggested practical EV system successfully decreases misalignment that occurs while two-wheelers are parked. To do this, just turn the transmitter coil so it faces the right way. To further aid automated alignment, tiny sensing coils are used to detect misalignment. In order to charge batteries in both constant current as well as constant voltage modes, a power management mechanism is also required. For maximum capacity use and extended lifetime of lithium-ion batteries, it is essential to charge them effectively using both CC and CV modes. At 160 mm apart, the created system may increase the wireless charging system's efficiency to 98. 3% using a 24 V, 16 Ah Lithium-Ion Phosphate (LiFePO4) batteries.
A YOLO-GAN Integrated Framework for Real-Time Opera Stage Performance Assistance and Motion Capture Feedback
The system offers technical support for stage performance innovation and digital preservation of traditional dance, enabling real-time interaction between performers and stage effects. Experiments validate its utility in enhancing performance immersion and heritage documentation. The proposed system combines remote sensing images with high spatial and temporal resolution and a convolutional neural network (CNN) for feature extraction and change detection of urban boundaries. An improved YOLO model is adopted to enhance the accuracy and real-time performance of detecting buildings and urban areas. Real-time motion capture technology (200 Hz sampling) tracks performer movements, providing 3D pose data for stage effect synchronization. This enhances detection of dynamic actions, with 98.7% joint precision in skeletal tracking. The improved YOLO model achieves 52.6% recognition accuracy in dynamic stage environments, with a 73.4% enhancement in detection speed compared to baseline models. In complex backgrounds, accuracy reaches 88.9%, while simpler scenarios yield 45.2%. This study presents a real-time stage performance assistance system integrating YOLO, motion capture, and GAN technologies. The system leverages YOLO for high-speed performer detection (82.5% accuracy in dynamic scenes) and motion capture for 3D pose tracking (98.7% joint precision). A Pix2Pix GAN generates adaptive stage backgrounds (realism score 4.3/5), enabling interactive feedback for lighting and sound effects. Experiments show 12 ms response latency and 85.6% system stability in live performances, demonstrating its utility for digital protection of dance intangible cultural heritage.
Hybrid AdaBoost and CatBoost Models Enhanced with HGS and CGO for Non-Stationary Runoff Forecasting
This study presents a hybrid forecasting model for non-stationary daily runoff series, integrating state-of-the-art machine learning algorithms, CatBoost, AdaBoost, and HGBoost, with metaheuristic optimization techniques, HGS and CGO. The model was evaluated using a dataset consisting of 29085 from Russian River Basin dataset. Performance was assessed using key metrics such as RMSE (6.79), R² (0.9549), and error scatter. Results show that the hybrid AdaBoost-HGS model outperforms other approaches, achieving the highest R² of 0.9549 and the lowest error, followed by the AdaBoost-CGO model. Although the CatBoost-HGS model showed promise, it was less effective than the other schemes. Furthermore, the study highlights the faster convergence and efficiency of AdaBoost-based models in terms of both prediction performance and computational time. These findings underscore the potential of hybrid ML models in enhancing runoff prediction accuracy and efficiency, which is crucial for applications in flood control and water resource management.
KorvexChecker: A BERT-Based Verification Framework for LLM Outputs in Turkish
Large Language Models (LLMs) have demonstrated exceptional capabilities in generating human-like text, yet their outputs often suffer from critical issues such as hallucination, ethical violations, and lack of meaningfulness. These challenges can result in misinformation, offensive language, and unreliable outputs, especially in niche languages like Turkish. To address these concerns, we introduce KorvexChecker, a modular and multi-functional tool for validating LLM outputs in terms of ethical compliance, meaningfulness, and hallucination detection. KorvexChecker employs a combination of fine-tuned BERT models for sentiment analysis, ethical violation detection, and text classification to identify whether a given text requires further verification. The tool integrates various external verification resources, such as Web Search API for factual accuracy and journal database for academical document validation. Furthermore, Zemberek, a Turkish NLP library, is utilized for preprocessing, normalization, and segmentation of Turkish text. The system was evaluated on a custom-labeled dataset of 7552 Turkish samples, divided into training, validation, and test sets. Experimental results demonstrate that the BERT-based classifiers achieved an average F1-score of 93.3% across multiple tasks, significantly outperforming both rule-based methods and traditional machine learning baselines. A small-scale user test showed the framework to be responsive and effective in real-time scenarios, with an average inference latency of 800 milliseconds per query. By leveraging both advanced machine learning techniques and external validation mechanisms, KorvexChecker offers a robust framework for ensuring the trustworthiness of AI-generated text, with potential applications in academia, media, and content moderation.
Copyright (c) 2026 Mehmet Göl, Savaş Öztürk, Uğur Dursun