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Automating Financial Audits with Random Forests and Real-Time Stream Processing: A Case Study on Efficiency and Risk Detection
In the current complex economic environment, enterprises are increasingly in need of efficient, accurate and real-time financial audits. Traditional audit methods are difficult to cope with the challenges brought by massive data and dynamic risks. This paper explores the automation method of financial audits based on artificial intelligence in depth, aiming to improve audit efficiency and risk identification capabilities. The study introduces the random forest algorithm, constructs 100 decision trees, self-samples data from the training set, and randomly selects features at each node for splitting to reduce the overfitting risk of a single decision tree and improve the generalization ability of the model. At the same time, with the help of real-time data processing platforms such as Kafka and Blink, real-time collection, processing and analysis of financial data are achieved to ensure the timeliness and dynamism of the audit process. After a series of steps, including extracting 500 features from multi-source data, dividing the data set containing 5,000 records into 70% training set and 30% test set, the model is trained and evaluated. The results show that this method has achieved remarkable results, with audit efficiency increased by 30%, risk detection accuracy increased to 90%, audit coverage enhanced, and error detection rate, data processing speed, accuracy and risk identification rate optimized. In addition, the average adoption rate of audit recommendations reached 87%, the average effectiveness of corrective measures was 91%, the audit satisfaction rate was about 90%, the average error rate after improvement was reduced by 47%, and the average efficiency was increased by more than 50%. These achievements provide strong technical support for corporate financial management and promote the intelligent transformation of financial auditing.
Graph Neural Network-Based User Preference Model for Social Network Access Control
The popularity and deepening of social networks have increased the risk of personal information leakage for users. To enhance the security of social networks, this study constructed an access control model based on the preferences of social network users. This model utilizes graph neural networks to generate access control strategies based on user preferences, and introduces a multi-layer attention mechanism to optimize the graph neural network. To better capture user preference information, the study sets the learning rate to 0.0001. The experimental results demonstrated that in the Twitter dataset, the accuracy of the proposed model reached 95.7% and the F1 score reached 96.2%, which were significantly higher than those of other models. These results indicated that the model could more accurately classify access control in social networks and reduce false positives. The area under the receiver operation characteristic curve of the proposed model was 0.982, which was higher than other models. The decision time was 13.77 seconds, significantly lower than other models. This indicated that the model could more effectively distinguish different types of user access requests and provide more reliable guarantees for secure access to social networks. The user's preferred social network access control model based on graph neural networks has superior performance, effectively ensuring the information security of social network users and laying the foundation for further development of access control technology
Fusion of Deep Convolutional Neural Networks and Brain Visual Cognition for Enhanced Image Classification
The brain visual system is one of the core centers for human perception of external information. How to establish the brain visual cognitive system to classify and process image information is a key matter in the area of human-computer connection. In order to improve the accuracy of computer vision image classification, a fusion intelligent computing model based on deep convolutional neural network and brain visual cognition is proposed. This model simulates the visual processing mechanism of the human brain and uses brain computer interface technology to extract electroencephalogram signals, thereby achieving efficient classification and processing of image information. When designing an image classification model based on DCNN, a long short-term memory network structure is introduced to extract time series features of electroencephalogram signals. In order to enhance the classification accuracy of the model, attention mechanism and occlusion independent neural response methods are also applied to improve the accuracy of capturing the correlation information between brain response and image features. The results show that the prediction accuracy of the research model reaches 93.54% and 94.03% in the V4 visual region and L0 visual region, respectively. The highest accuracy on facial visual images reaches 95.46%, while the lowest accuracy on animal visual images is 91.57%. By introducing the long short-term memory module, the loss value of the model decreases from 0.26 to 0.21, with a reduction of 19.23%. In addition, ablation experiments show that by introducing attention mechanisms and occlusion independent neural responses, the final classification accuracy is improved to 93.94%. In summary, the research on the fusion intelligent computing model grounded on deep convolutional neural networks and brain visual cognition effectively improves the accuracy of image classification and demonstrated its potential in the field of intelligent computing.
Research on Optimization Method of Landscape Architecture Planning and Design Based on Two-Dimensional Fractal Graph Generation Algorithm
The development of modern mathematical theory, especially two-dimensional fractal graph algorithm, provides a possibility for large-scale landscape data processing. Landscape digital identification technology is an innovative technology based on digital landscape technology and computer identification of experimental data. It is an important artificial intelligence technology, which includes three steps: landscape acquisition, landscape processing and landscape identification. The characteristics of the scene in the landscape picture can be collected by special instruments, such as cameras, etc., and then the collected data can be processed by two-dimensional fractal graph algorithm, and finally realice the automatic identification of the landscape. For images with significant boundary characteristics, we can extract the boundary of the región quickly and accurately, so as to realice the segmentation of the region. However, when the edge features of the image are not good enough, there is little color difference between the background and the region, or there is some interference, the result will be very bad. In this paper, based on the two-dimensional fractal graph generation algorithm, a series of optimization of landscape architecture planning and design. The accuracy of landscape prime number can reflect whether specific types of landscape pictures can be correctly identified and divided. 200 Pictures are divided into six categories, namely Water scene, landscape scene, living scene, sky scene, architecture and transportation then exact ratios of two-dimensional fractal graph network -8s, two-dimensional fractal graph network - 16s, two-dimensional fractal graph network -32s and two-dimensional fractal graph network -32s. It reached the best level in pixel accuracy, average accuracy, average IU, etc., and the pixel accuracy has reached as high as 100%, average accuracy has reached 100%, average accuracy has reached 100%. When compared to the recommended algorithm, the 2D fractal graph generation algorithm has the highest accuracy (94.52%), precision (93.34%), and recall (94.18%) in the classification process.
Enhanced COVID-19 Detection Through Combined Image Enhancement and Deep Learning Techniques
The rapid spread of COVID-19 has highlighted the need for automated patient data analysis to enable faster and more accurate diagnosis. Using pre-trained deep learning models on X-ray images has shown potential for effective COVID-19 detection. However, the performance of these models is highly dependent on the quality and quantity of training data. To address these challenges, enhancing the visual quality of X-ray images is critical for reliable virus detection. This study evaluates and combines three image enhancement techniques—Histogram Equalization, Contrast-Limited Adaptive Histogram Equalization (CLAHE), and Gamma Correction—to determine the optimal approach for improving detection accuracy. A dataset comprising 125 chest X-ray images from COVID-19-positive patients and 500 images from non-COVID-19 cases was used. The images were preprocessed using the enhancement techniques, and the enhanced datasets were employed to train ResNet50 and DenseNet201 models. Simulation results demonstrate that enhanced images consistently yield higher detection accuracy than unenhanced images. Among the techniques tested, combining Histogram Equalization, CLAHE, and Gamma Correction with the DenseNet201 model achieved the highest performance, attaining a remarkable accuracy of 99.03%. This outperforms previous methods, including the DarkCovidNet model, which achieved an accuracy of 98.08% on the same dataset.
Enhancing Predictive Capabilities for Cyber Physical Systems Through Supervised Learning
The rapid advancement and proliferation of Cyber-Physical Systems (CPS) have led to an exponential increase in the volume of data generated continuously. Efficient classification of this streaming data is crucial for predicting system behaviors and enabling proactive decision-making. This research aims to extract actionable knowledge from the continuous data streams of CPS and predict their behavior using advanced supervised learning algorithms. The predictions facilitate timely interventions and necessary actions within the interconnected physical network. The background of this work lies in the intersection of CPS, machine learning, and data stream mining. Traditional batch processing methods are inadequate for real-time analysis of CPS data due to their inherent latency and computational inefficiency. This research employs state-of-the-art techniques for real-time data processing, including incremental learning, sliding window models, and ensemble methods tailored for streaming data. Our approach differs from existing works by focusing on a comprehensive framework that integrates real-time data ingestion, preprocessing, feature extraction, and model updating in a seamless pipeline. Unlike previous studies that often rely on static datasets and offline analysis, our method ensures continuous learning and adaptation to evolving data patterns. Comparative analysis with existing techniques demonstrates superior performance in terms of accuracy, latency, and scalability. Specifically, our models achieved an average classification accuracy of 92%, with a precision of 90%, recall of 89%, and an F1 score of 89.5%. These metrics indicate significant improvements over traditional batch processing methods, which typically lag in responsiveness and adaptability. This research provides a robust and efficient solution for the realtime classification of streaming data from CPS, enhancing the system's ability to predict behaviors and take necessary actions promptly.
A Comparative Study of Deep Learning Algorithms for Detecting Fungal Infection Skin Diseases
Many people place a high value on the health of their skin, frequently spending large sums of money on skincare products. Fungal infections are one of the most common skin conditions that can damage a person's self-esteem. When dealing with skin health issues, seeking advice from a knowledgeable dermatologist is essential. Deep learning is a contemporary technique that saves doctors time and helps them spot diseases early. Two deep learning algorithms that are useful in identifying patterns of skin illnesses are Mask R-CNN and YOLOv5. This paper explores using Mask R-CNN and YOLOv5 to recognize skin illnesses caused by fungal infections, going through several processing phases. The research results show that the YOLOv5 strategy performed best in accuracy, recall, precision, F1-Score, and AUC. This algorithm shows great potential and warrants further investigation in practical applications.
A Unified Trace Meta-Model for Alignment and Synchronization of BPMN and UML Models
Organizations often face information system (IS) failures due to misalignment with business goals. Business process models (BPMs) play a crucial role in addressing this issue but are often developed independently of IS models (ISMs), resulting in non-interoperable systems. This paper proposes a traceability method to link BPMs and ISMs, bridging the gap between business and software domains. We introduce a unified trace meta-model integrating BPMN elements with UML constructs (use cases and class diagrams) via traceability links. This meta-model is instantiated as the BPMNTraceISM diagram, ensuring seamless integration through bidirectional transformation models. To validate our approach, we developed a graphical editor for BPMNTraceISM diagrams and implemented transformations using the ATLAS Transformation Language (ATL). A case study on a loan approval process demonstrates the method’s effectiveness in aligning BPMN and UML elements, improving interoperability and model alignment across domains.
A Review of Machine Learning Techniques in the Medical Domain
We have witnessed a rapid exponential growth of all types of data in all domains specifically in the medical domain. The utilization of machine learning techniques has made significant strides across various domains, with deep learning achieving notable success in recent years. Lately, deep learning has gained increasing attention in the medical field. While deep learning excels at automatically learning discriminative features from raw data, it is still challenging to achieve high performance without a huge amount of data and some handcrafted steps. To address these challenges, deep learning has been incorporated with other new trends and domain knowledge to enhance deep learning's capabilities and improve performance covering the ever-growing needs. Transfer learning utilizes knowledge from natural images, curriculum learning integrates domain-specific knowledge, active learning selects the most informative samples to reduce reliance on labeled data, and federated learning enables collaborative training across organizations while ensuring data privacy. In this review paper, these new trends incorporated with deep learning have been investigated and presented as applications in the medical domain by investigating articles that have applied these trends and published in highly reputable journals in the Science Direct database in recent years.
Vision Transformer-Based Framework for AI-Generated Image Detection in Interior Design
Increasingly, images generated by artificial intelligence (AI) are being used within interior design as a source of authenticity and ethical use. Based on limited Convolutional Neural Network (CNN) capabilities in data descriptive processes, including long-range dependencies and global patterns, this study examines how Vision Transformer (ViT) can be utilized in detecting AI-generated interior design images. We finetuned and evaluated four ViT models, ViT-B16, ViT-B32, ViT-L16, and ViT-L32, on 1,000 samples per class dataset. Accuracy, precision, recall, F1-score, and computational efficiency were used to assess performance. Results show that models with smaller patch sizes (i.e., 16×16) perform better than larger ones (i.e., 32×32). It was found that ViT-B16 and ViT-L16 had the highest accuracy (96.25%) and F1- score (0.9625) in identifying minor inconsistencies in the AI-generated images. ViT-B32 and ViT-L32 enjoy better computational efficiency based on lower classification performance (80.00% and 81.25% accuracy, respectively, for ViT-B32 and ViT-L32). The best tradeoff between accuracy and resource efficiency turns out to be ViT-B16. However, computational costs were higher with ViT — ViT-L16, although just as accurate. Computationally, ViT-B32 and ViT-L32 were also efficient, which was more appropriate for realtime applications with lower accuracy than speed. Through this work, we contribute a domain-specific deep learning framework for AI-generated image detection in interior design to increase authenticity verification. Future work will address improving computational efficiency and generalizing the model across all (or most) generative models and design styles.
Efficient Logistics Path Optimization and Scheduling Using Deep Reinforcement Learning and Convolutional Neural Networks
With the rapid development of e-commerce and online shopping, the logistics industry is facing unprecedented challenges. Traditional logistics path - planning methods, such as SPA, HA, GA, etc., struggle to cope with the complex and ever-changing logistics environment. To address this issue, this study proposes an innovative model that combines Deep reinforcement learning (DRL) with a Convolutional neural network (CNN) to achieve efficient logistics path optimization. In this research, a detailed analysis and pre-processing of the public datasets, the City Logistics Dataset (CLDS) and the Traffic Status Dataset (TSDS), were carried out to construct a model capable of effectively handling diverse logistics environments. Six baseline methods, namely the classic shortest path algorithm (SPA), heuristic algorithm (HA), genetic algorithm (GA), rule-based method (RBM), traditional deep reinforcement learning method (TDRM), and the most advanced deep learning method (ADLM), were selected for comparison. The experimental results indicate that the proposed model performs excellently across various environments. For instance, in suburban areas, it achieves a path length of 180 kilometers, a completion time of 120 minutes, a punctuality rate of 92%, and a dispatch success rate of 95%. In urban settings, the path length is 200 kilometers, the completion time is 150 minutes, the punctuality rate is 90%, and the dispatch success rate is 93%. On highways, it reaches a path length of 170 kilometers, a completion time of 110 minutes, a punctuality rate of 93%, and a dispatch success rate of 95%. Compared with the baseline methods, the model shows significant improvements in key metrics such as path length, completion time, punctuality, and dispatch success rate. Additionally, it outperforms them in terms of computation time and robustness scores, demonstrating great potential for practical applications.
Design and Application of Improved Genetic Algorithm for Optimizing the Location of Computer Network Nodes
The rapid development of computer technology has made network stability and node positioning accuracy important challenges in optimizing computer network design. This study proposes an optimization method based on the Improved Genetic Algorithm (IGA) to improve the positioning accuracy and stability of network nodes. Firstly, by combining the characteristics of the centroid algorithm and the Approximate Point in Triangulation Test (APIT) algorithm, preliminary optimization of node positions is carried out. Subsequently, an IGA is utilized for further optimization, dynamically adjusting the crossover probability and mutation probability to balance global and local search capabilities and avoid the algorithm falling into local optima. The experimental results showed that IGA achieved significant performance improvement in node localization. Compared with the centroid algorithm, the maximum error of IGA has been reduced by 19% and the overall average error has been reduced by 8.8%. Compared with APIT, IGA has reduced the maximum error by 7% and the overall average error by 3.8%. Regarding fitness values, IGA exhibited faster convergence speed, achieving optimal results with only 75 iterations, surpassing traditional genetic algorithms and APIT algorithms. The node coverage rate reached 98.6%, far higher than the 85.3% of the centroid algorithm and 90.5% of the APIT algorithm. These results demonstrate that IGA has higher accuracy, stability, and computational efficiency in complex network environments, providing an efficient and reliable solution for optimizing the design of computer network nodes.
Optimization of Emergency Material Logistics Supply Chain Path Based on Improved Ant Colony Algorithm
Path selection is a critical challenge in emergency logistics management, particularly under realistic disaster-related conditions. This study addresses the problem of optimizing logistics transportation during major epidemics, considering constraints such as vehicle load, volume, and maximum travel distance per delivery. The goal is to minimize costs related to distribution trips, time, early/late penalties, and fixed vehicle expenses. By framing the problem as a generalized Traveling Salesman Problem, we developed an Improved Ant Colony Algorithm (IACA) to reduce the longest distribution path. Simulation data from Pudong, Shanghai lockdown zones revealed that IACA outperformed the traditional ACO algorithm, achieving a 30% cost reduction and higher accuracy (R² = 0.98). Additionally, experiments on gate assignment and TSP demonstrated the algorithm's superior optimization ability and stability. Overall, IACA enhances delivery route efficiency, lowers costs and energy consumption, and improves emergency logistics performance, proving to be a robust and reliable solution.
Application Method and Least Squares Support Vector Machine Analysis of a Heat Pipe Network Leakage Monitoring System Using an Inspection Robot
With the maturity of the Internet and big data technAology, heat supply intelligence has become a development trend, and the traditional heat pipe network management mode is gradually transitioning to an "intelligent heat pipe network". It has become a hot spot for research and development at home and abroad. Combining big data technology, inspection robot control, heat pipe network leakage warning and data monitoring, scientific monitoring and evaluation of the energy-saving operation of heat pipe networks, and intelligent operation of heat pipes have become the current development trend. Whether in terms of the economic benefits of energy-saving operation of heat pipe networks or the social benefits of realizing intelligent operation and management of heat pipe networks, the study of a lateral leakage monitoring system for heat pipe networks is of great significance. This paper examines a technique for implementing a lateral leakage monitoring system for heat pipe networks using an inspection robot control system, which includes a real-time tracking module utilizing LSSVM (Least Squares Support Vector Machine) optimization to improve detection accuracy. The monitoring module can acquire, store, visualize, and send sensor data and video data; the user-defined interface module receives and parses XML user files from the server and generates user-defined interfaces and logic, thus realizing the humancomputer interaction function. The experimental findings show that enhancing the weight factor and radial basis kernel function parameters of the LSSVM with the gravitational search technique resulted in an outstanding classification accuracy of 99.99% with a classification time of only 55.938 seconds, surpassing other optimization techniques.
Biometric-Based Secure Encryption Key Generation Using Convolutional Neural Networks and Particle Swarm Optimization
With the rapid expansion of computer networks and information technology, ensuring secure data transmission is increasingly vital—especially for image data, which often contains sensitive information. This research presents a biometric-based encryption system that uses fingerprint recognition and deep learning to generate strong, random encryption keys. Two convolutional neural networks (CNNs) are employed: one to verify identity based on a user’s ID and another to extract fingerprint features for key generation. These keys are optimized using Particle Swarm Optimization (PSO), enhancing their randomness and resistance to brute-force attacks. The system generates keys in real-time, eliminating the need for storage and minimizing the risk of theft or leakage. To further improve security, encryption keys are automatically updated after every ten messages, with different keys generated from multiple fingerprints of the same individual. Testing with the SOCOFing dataset (6,000 original and 49,270 synthetic images) achieved 99.75% identity verification and 99.83% classification accuracy. Performance metrics—entropy of 7.89, correlation factor of 0.00628, and zero repetition—demonstrate high robustness. This approach offers a secure, adaptive, and personalized encryption method ideal for sensitive domains like finance and healthcare.
Metaheuristic-Enhanced SVR Models for California Bearing Ratio Prediction in Geotechnical Engineering
Soil resistance characteristics, particularly the California Bearing Ratio (CBR), play a pivotal role in pavement and subgrade design. However, conventional laboratory-based CBR testing is often time-consuming, labor-intensive, and costly. This study presents a novel machine learning framework that combines Support Vector Regression (SVR) with three recent metaheuristic optimization algorithms—Dingo Optimization Algorithm (DOA), Alibaba and the Forty Thieves Optimization (AFT), and Adaptive Opposition Slime Mold Algorithm (AOSMA)—to predict CBR values efficiently and accurately. A dataset consisting of 220 soil samples with eight geotechnical input parameters was used to develop and evaluate the hybrid models. The predictive performance of each model was assessed using multiple evaluation metrics, including R², RMSE, MSE, RSR, and WAPE. Results indicate that the SVR–AFT (SVAF) hybrid model outperformed the others, achieving an R² of 0.9968 and an RMSE of 0.7946 in the testing phase, demonstrating high generalization ability and predictive precision. The integration of SVR with metaheuristic algorithms significantly enhances model robustness and accuracy, offering a practical and cost-effective alternative to empirical CBR testing methods. This work highlights the potential of hybrid AI models in solving complex geotechnical prediction problems and contributes to the growing body of research at the intersection of civil engineering and artificial intelligence.
A Cutting-Edge Bio-Inspired Computational Framework for Advanced Virtual Reality Classification through Sophisticated Predictive Methodologies
Virtual reality (VR) enables the simulation of a wide variety of complex environments, from tiny biological structures to entirely imaginary worlds. These simulations create new possibilities for learning, training, and interaction that go beyond the limits of the physical world. However, virtual reality (VR) realizes this imaginary world, so it is not just a dream. VR works through the invocation of many of the senses. It creates realistic simulations through the creation of immersive settings that combine the real and the imagined, thereby affording special hands-on learning possibilities in a variety of subjects. This study investigates the effectiveness of combining Histogram Gradient Boosting Classification (HGBC) with Decision Tree Classification (DTC), the Ebola Optimization Search (EOS), and the Differential Squirrel Search Algorithm (DSSA) to predict VR outcomes. By integrating these advanced predictive and optimization techniques, the approach aims to enhance accuracy. Research will be conducted to ascertain the possible uses of VR, enhance user experience, and assess the impact on industries related to training, education, healthcare, and entertainment. In the evaluation phase, HGDS attained the highest accuracy of 0.967 in the test phase, making it the top-performing hybrid model, while DTEO showed the lowest accuracy of 0.907, identifying it as the weakest model.
GWO-RF: A Grey Wolf Optimized Random Forest Model for Predicting Employee Turnover
This study proposes an employee turnover prediction model (GWO-RF) that combines Grey Wolf Optimization (GWO) algorithm with Improved Random Forest (LPRF). The model optimizes node splitting strategy by combining C4.5 information gain rate and CART Gini coefficient (constraint condition α+β=1) through linear programming. The model is based on 12,365 employee data (15 features, including structured indicators such as workload and salary-to-position ratio), and uses 7:2:1 data segmentation and SMOTE to handle class imbalance. Moreover, its key parameters include GWO population size of 50, number of iterations of 100, number of random forest decision trees of 50-200, and maximum depth of 5-15. The test set results show that the model has an AUC of 0.923±0.008 and an F1-score of 0.871. At the business level, the retention rate of high-risk employees increases by 41.9% (p<0.01), and the cost of single intervention decreases by 54.3%. The innovation of the model is that the LPR node splitting algorithm solves the overfitting problem of traditional random forests (increasing the accuracy of the validation set by 12.6%), but the prediction accuracy for new employees who have been employed for less than 3 months is low (AUC 0.782). Therefore, in the future, it is necessary to enhance the real-time time series modeling capabilities.
Comparative Analysis of Machine Learning Models for Water Quality Prediction Using Regional Monitoring Data
This study investigates the comparative performance of four labelical machine learning algorithms—Decision Tree, Support Vector Machine (SVM), Random Forest, and Neural Network—on water quality prediction tasks using a dataset comprising 1,000 real-time sensor data points from five distinct geographic regions. The dataset includes critical water parameters such as pH, ammonia nitrogen, dissolved oxygen, total phosphorus, COD, and BOD. Preprocessing steps include missing value imputation, outlier removal using boxplot analysis, normalization, and correlation-based feature selection. Each model is tuned through grid search for optimal performance. Experimental results show that the Neural Network achieved the lowest mean squared error (MSE = 0.047) and highest coefficient of determination (R² = 0.976), outperforming the other models. The Random Forest showed superior robustness to overfitting, while SVM offered strong results on high-dimensional subsets. Decision Trees, although less accurate (MSE = 0.130), provided high interpretability. This comparison provides practical guidance for selecting machine learning models in environmental monitoring systems, where trade-offs between accuracy, interpretability, and computational cost are essential.
A GAN-Based Framework for Synthetic Financial Data Generation, Risk Forecasting, and Portfolio Optimization under Uncertainty
This article proposes a financial risk dynamic prediction and decision optimization model based on Generative Adversarial Network (GAN). The model generates synthetic financial data, trains a risk prediction model, and optimizes financial decisions based on predicted risks. Simulation results show that the proposed method outperforms traditional machine learning models, achieving a mean absolute error (MAE) of 0.012 and a mean squared error (MSE) of 0.002, indicating high prediction accuracy. The model achieves an average risk of 4.5% and an average return of 8.2%, surpassing conventional algorithms. With a recommended portfolio allocation of 65% equities, 30% bonds, and 5% cash, it optimizes investment decisions by maximizing returns while minimizing risks. Overall, the proposed approach provides a novel and effective solution for financial risk prediction and decision optimization, demonstrating superior performance over existing methods.
GridRiskNet: A Two-Stage Hybrid Model for Project Investment Risk Management of Power Grid Enterprises Using Big Data Mining
To enhance the power grid enterprise's ability to comprehensively perceive and dynamically assess investment risks in engineering projects, this study proposes a risk management model called GridRiskNet based on big data mining. This model integrates structured, unstructured, and spatiotemporal data and realizes intelligent identification of project risk probability distributions and potential impact ranges by constructing a two-stage hybrid modeling architecture. In the first stage, the model uses eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) to extract static and dynamic features in parallel. In the second stage, it introduces Graph Attention Recurrent Neural Network (GA-RNN) to model risk propagation paths under the power grid topology. Meanwhile, this study combines Spatio-Temporal Graph Convolutional Network (ST-GCN) to improve the coupling expression of meteorological and text features. The experiment uses multi-source public data for verification, such as power infrastructure data from the U.S. Energy Information Administration, meteorological observation data from the National Oceanic and Atmospheric Administration, and power grid topology data from OpenStreetMap. The results show that GridRiskNet performs excellently in risk prediction stability and regional propagation modeling. Among them, the risk principal component analysis projection score in 2023 reached 7.779. This indicates that cost overruns, climate pressure, and equipment technology risks together form a high-risk cluster, with cost overruns increasing by 269% compared with 2018. In the State-of-the-Art comparison, GridRiskNet achieves an F1-score of 0.892, a Receiver Operating Characteristic - Area Under Curve of 0.962, a Risk Impact Radius error of approximately 4.8 km, and a Risk Entropy of 0.89; these are comprehensively better than existing methods. Moreover, the model has good cross-modal feature fusion and risk transmission mechanism identification capabilities, and can effectively characterize the spatiotemporal coupling risk features in complex power grid projects. Overall, this system can provide power grid enterprises with structured and interpretable risk index outputs and regional early warning support. Thus, it helps to improve the investment safety and operational and maintenance resilience of projects.
Real-Time Motion Recognition in Special Training Systems Based on the Optimized BBO-KNN Method of Motion Morphology
The traditional sports training boxing system has problems with insufficient accuracy and poor real-time performance in high similarity action classification, and lacks adaptability to individual action differences. This article constructs a sports training system based on dynamic weight optimization KNN (BBO-KNN), aiming to improve the accuracy and real-time performance of complex action recognition, and provide technical support for personalized training. In response to the problems of insufficient accuracy (high FP rate), poor real-time performance (delay>1s), and lack of individual adaptability in high similarity action classification of traditional sports training systems, this study proposes a KNN model based on dynamic weight optimization (BBO-KNN). The model performance is optimized by fusing proprietary datasets with public datasets and using 5-fold cross validation (training/testing ratio 7:3). The experimental results validate that BBO-KNN significantly outperforms benchmark models such as LSTM (94.50%) and SVM (89.30%) in accuracy (96.20% ± 0.3%). The system performs highly similar actions such as running ↔ The FP rate of jumping has decreased to 1.6%, and the global FP rate is 1.39%.and robustness (noise interference fluctuation ± 1.2%). The classification error distribution shows its stability advantage, and the confusion matrix highlights the accurate recognition of highly similar actions (such as running → jumping). Research has shown that the BBO-KNN model effectively solves the real-time and robustness problems of motion recognition through dynamic weight optimization. In the future, it can be extended to complex movements such as gymnastics by combining visual data and adapting to individual style differences through incremental learning.
Improved DenseNet-DCGAN for Enhanced Digital Restoration of Embroidery Cultural Heritage
At present, embroidery image restoration technology still has deficiencies in terms of color uniformity and detail restoration. To address these issues, the study improves the densely connected convolutional network and the deep convolutional generative adversarial network through spatial pyramid pooling, and proposes a novel method for embroidery image classification and restoration. The experimental results showed that the research method largely restored the details and colors of the original image and effectively addressed the uneven color issue. The average prediction accuracy, recall rate, and specificity of the image classification model on Suzhou embroidery, Hunan embroidery, Guangdong embroidery, and Shu embroidery reached 96.3%, 98.5%, and 99.4%, respectively. The structural similarity index of the image restoration model has reached 0.99. The restored image was almost indistinguishable to the naked eye in terms of details, texture, and color. The research method has significant advantages in classifying embroidery images and high-quality restoration tasks, and can provide reliable technical support for the digital protection and intelligent restoration of traditional embroidery cultural relics.
Enhanced Prediction of Tropical Tree Biomass Using Ensemble Models
The present paper aims to propose a novel model to investigate its utility in evaluating the beneficial effects of tropical forest biomass. To address the multiplicity of variables, as well as the complexity and nonlinear relationships between them, five Machine Learning (ML) models, namely Gradient Boosting (GB), Extra Trees (ET), XGB, ElasticNet, and Poisson Regression, were employed to concurrently predict both the below-ground and above-ground tree biomass (BGB and AGB, respectively), as well as the total biomass (TB = BGB + AGB). Since the results of the aforementioned models were not entirely satisfactory, an additional model called the Stacking Ensemble (SE) was introduced. Each model can have its parameters optimized by Grid Search with cross-validation to make sure that there is generalization and consistent performance. The data collected were based on 175 trees from 27 ecoregional plots located in the Central Highlands ecoregion of Vietnam. The dataset was processed to investigate the proposed model's ability to predict tree biomass. The study's findings revealed that the proposed method demonstrated strong and efficient predictive capabilities for biomass estimation in forest ecoregions. The Stacking model showed the most significant improvements in the highest R 2 (0.968) and VAF (0.971), and the lowest errors, and MDAPE (23.081 percent), which means that it has a strong ability to predict and minimal bias. However, STD (105.763) was marginally higher; nevertheless, the error and strength of this variation exceeded this variance. Thus, incorporating a Stacking Ensemble (SE) model strengthens the ML approach in predicting forest tree biomass amounts.
HematoFusion: A Weighted Residual-Vision Transformer Ensemble for Automated Classification of Haematologic Disorders in Microscopic Blood Images
Haematologic malignancies pose a significant global challenge, with 1.34 million new cases reported in 2019 and leukemia claiming 311,594 lives in 2020. Early diagnosis of these blood disorders increases survival chances by enabling prompt treatment, yet their complexity and variable cellular morphology hinder accurate detection. Advances in Medical Imaging and AI, particularly Image Classification, offer solutions by analyzing blood samples for subtle morphological patterns. This study advances the field by introducing a novel data set for the classification of red blood cells and using open-source data for the classification of leukemia and lymphoma (each covering 29,363; 16,811; and 1,436 images, respectively). We fine-tuned multiple AI models, including EfficientNetB3, ResNet50V2, and a pretrained Vision Transformer (ViT), and combined their strengths into a weighted ensemble framework. Evaluated across various metrics (including accuracy, precision, recall, etc.), the proposed HematoFusion model excelled, achieving 96% accuracy in the morphology of red blood cells, 99% in Leukemia, and 96% in Lymphoma, surpassing most existing models in terms of accuracy while covering a wider range of haematologic disorders. These findings demonstrate the potential of integrated AI frameworks to improve haematologic diagnostics with precision and reliability.
Deep Learning and Rule-Based Hybrid Model for Enhanced English Composition Scoring Using Attention Mechanisms and Graph Convolutional Networks
Through the profound exploration conducted on AI technology in the field of education, early automatic scoring systems for English compositions have problems such as high misjudgment rate and low efficiency. To improve the efficiency, accuracy, and stability of the English composition grading model, a deep learning and manual rule-based English composition grading model was designed. The research extracted sequence features by introducing attention mechanisms, enhancing contextual correlation analysis, and aggregating global features through graph convolutional networks to extract high-order semantic relationships. Finally, a visual manual scoring rule was designed, which integrated deep semantic features and manual rule features through the Wide&Deep architecture to jointly optimize the scoring results. The experiment outcomes indicated that the accuracy recall curve area of the research method was 92.3%. In practical application testing, the highest group stability index of the research method was 0.07 in June. When faced with 600 concurrent requests, the average response time of the research method reached a stable value of 3.4 seconds. The outcomes above demonstrated that the English essay scoring model, which combines deep learning with manual rules as proposed by the research, exhibited excellent accuracy, speed, and stability. It effectively addressed the issues of a high misjudgment rate and low efficiency found in traditional scoring systems, thereby enhancing the model's reliability.
Integrating DDPG and QPSO for Multi-Objective Optimization in High Proportion Renewable Energy Power Dispatch Systems
This study proposes a novel dispatch optimization model that integrates deep deterministic policy gradient (DDPG) and quantum particle swarm optimization (QPSO) to address the challenges posed by high proportions of renewable energy in power systems. The proposed multi-objective optimization framework considers system cost reduction, supply-demand balance, and dynamic adaptability to renewable energy fluctuations. The experimental results on the IEEE 30-bus and 118-bus systems demonstrated significant improvements. This method reduced total system costs by 13.6% and 11.4%, respectively. It also increased supply reliability to 97.1% and achieved an energy utilization rate of 94.85%. Additionally, it minimized frequency deviation to 1.25 Hz. The optimization time was also improved, achieving a reduction of 58.3 seconds in efficiency. The research results have important practical application value in improving power system economy, enhancing system reliability, and dynamic adaptability. It can provide efficient and reliable technical support for power dispatch planning, load management, and real-time control under high percentage renewable energy scenarios.