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Wind power prediction is essential for ensuring the stability and efficient operation of modern power systems, particularly as renewable energy integration continues to expand. This paper presents a comprehensive review of machine learning techniques applied to wind power prediction, emphasizing their advantages over traditional physical and statistical models. Machine learning methods, especially deep learning approaches such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ensemble learning techniques like XGBoost, excel in addressing the nonlinearity and complexity of wind power data. The review also explores critical aspects such as data preprocessing, feature selection strategies, and model optimization techniques, which significantly enhance prediction accuracy and robustness. Challenges such as data acquisition difficulties, complex terrain influences, and sensor quality issues are examined in depth, with proposed solutions discussed. Additionally, the paper highlights future research directions, including the potential of multi-model fusion, emerging deep learning technologies like Transformers, and the integration of smart sensors and IoT technologies to develop intelligent, automated, and reliable prediction systems. By addressing existing challenges and leveraging advanced machine learning techniques, this work provides valuable insights into the current state of wind power prediction research and offers strategic guidance for enhancing the applicability and reliability of prediction models in practical scenarios. [ABSTRACT FROM AUTHOR]

Model optimization is a problem of great concern and challenge for developing an image classification model. In image classification, selecting the appropriate hyperparameters can substantially boost the model's ability to learn intricate patterns and features from complex image data. Hyperparameter optimization helps to prevent overfitting by finding the right balance between complexity and generalization of a model. The ensemble genetic algorithm and convolutional neural network (EGACNN) are proposed to enhance image classification by fine-tuning hyperparameters. The convolutional neural network (CNN) model is combined with a genetic algorithm GA) using stacking based on the Modified National Institute of Standards and Technology (MNIST) dataset to enhance efficiency and prediction rate on image classification. The GA optimizes the number of layers, kernel size, learning rates, dropout rates, and batch sizes of the CNN model to improve the accuracy and performance of the model. The objective of this research is to improve the CNN-based image classification system by utilizing the advantages of ensemble learning and GA. The highest accuracy is obtained using the proposed EGACNN model which is 99.91% and the ensemble CNN and spiking neural network (CSNN) model shows an accuracy of 99.68%. The ensemble approaches like EGACNN and CSNN tends to be more effective as compared to CNN, RNN, AlexNet, ResNet, and VGG models. The hyperparameter optimization of deep learning classification models reduces human efforts and produces better prediction results. Performance comparison with existing approaches also shows the superior performance of the proposed model. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The existing detection algorithms are unable to achieve a suitable balance between detection accuracy and inference speed. As the accuracy of the algorithm increases, its complexity also rises, resulting in a decrease in detection speed, which undermines its practicality. This issue is particularly evident in the context of surface defect detection in industrial parts, where low contrast, small target features, difficult feature extraction, and low real-time detection efficiency are prominent challenges. This study proposes a novel method for steel defect detection based on the YOLO v8 algorithm, which improves detection accuracy while maintaining low computational complexity. Firstly, the global background and edge information are adaptively extracted via the MSA-SPPF module in order to obtain a more comprehensive feature representation. Furthermore, the anti-interference ability of the model is enhanced through the deformability of attention and the large convolution kernel characteristics. Secondly, the design of Dynamic Conv and C2f-OREPA enables the model to efficiently reduce the demand for computational resources while maintaining high performance. It is further proposed that the RepHead detection head approximates the multi-branch structure of the original training by a single convolution operation. This approach not only enriches the feature representation but also maintains an efficient inference process. The effectiveness of the improved MRP-YOLO algorithm is verified using the NEU-DET industrial surface defect dataset. The experimental results demonstrate that the mAP of the MRP-YOLO algorithm reaches 75.6%, which is 2.2% higher than that of the YOLOv8n algorithm, while the FLOPs are only 2.3 G higher. It indicates that the detection accuracy is significantly improved with a limited increase in computational complexity. [ABSTRACT FROM AUTHOR]

This paper explores the application of the YOLOv5s algorithm integrated with the DeepSORT tracking detection algorithm in vehicle target detection, leveraging its advantages in data processing, loss function, network structure, and training strategy. Regarding detection frame regression, adopting Focal-EIOU can improve vehicle detection accuracy by precisely measuring overlap and better handle complex scenarios, enhancing the overall performance. The CoordConv convolution layer with more spatial position information is employed to enhance the original network structure's convolution layer and improve vehicle positioning accuracy. The principle and effectiveness of the Shuffle Attention mechanism are analyzed and added to the YOLOv5s network structure to enhance training, improve detection accuracy and running speed. And the DeepSORT tracking detection algorithm is designed to achieve high-speed operation and high-accuracy matching in target tracking, enabling efficient and reliable tracking of objects. Simultaneously, the network structure is optimized to enhance algorithmic speed and performance. To meet the requirements of vehicle detection in practical transportation systems, real-world vehicle images are collected as a dataset for model training to achieve accurate vehicle detection. The results show that the accuracy rate P of the improved YOLOv5s algorithm is increased by 0.484%, and mAP_0.5:0.95 reaches 92.221%, with an increase of 1.747%. [ABSTRACT FROM AUTHOR]

The rapid evolution of deep learning has led to significant achievements in computer vision, primarily driven by complex convolutional neural networks (CNNs). However, the increasing depth and parameter count of these networks often result in overfitting and elevated computational demands. Knowledge distillation (KD) has emerged as a promising technique to address these issues by transferring knowledge from a large, well-trained teacher model to a more compact student model. This paper introduces a novel knowledge distillation method that simplifies the distillation process and narrows the performance gap between teacher and student models without relying on intricate knowledge representations. Our approach leverages a unique teacher network architecture designed to enhance the efficiency and effectiveness of knowledge transfer. Additionally, we introduce a streamlined teacher network architecture that transfers knowledge effectively through a simplified distillation process, enabling the student model to achieve high accuracy with reduced computational demands. Comprehensive experiments conducted on the CIFAR-10 dataset demonstrate that our proposed model achieves superior performance compared to traditional KD methods and established architectures such as ResNet and VGG networks. The proposed method not only maintains high accuracy but also significantly reduces training and validation losses. Key findings highlight the optimal hyperparameter settings (temperature T = 15.0 and smoothing factor α = 0.7), which yield the highest validation accuracy and lowest loss values. This research contributes to the theoretical and practical advancements in knowledge distillation, providing a robust framework for future applications and research in neural network compression and optimization. The simplicity and efficiency of our approach pave the way for more accessible and scalable solutions in deep learning model deployment. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To address the issues of low processing efficiency, poor forming accuracy, and internal damage in glass material processing, this study proposes the use of flame jet forming. However, the mechanism of flame jet processing requires further elucidation. This research investigates the relationship between the indentation morphology on the glass surface and the inlet velocity of the flame jet. A theoretical model was established through mathematical analysis to reflect this relationship. The model's accuracy was validated using numerical simulation methods. By comparing experimental data with theoretical model results, surface tension was incorporated, and the model was iteratively optimized using MATLAB R2024a. The final optimized model demonstrated an absolute error range of 0.009 to 0.069 mm. This study confirms the feasibility of flame jet processing and enriches the understanding of its mechanism, providing a novel, efficient, and precise method for processing glass materials. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the increasing amount of devices connected to the huge net known as the internet, it is not surprising the corresponding growth of cyber attacks. The era of the Internet of Things (IoT) has proved to be an environment in which malicious activities have been able to proliferate with great comfort. Due to this kind of threat, having powerful machine learning-based classifiers is almost a critical need nowadays. Besides, to properly face new and previously unseen attacks unsupervised learners need to be involved. An unsupervised network threat detector is provided in this work. The proposed approach is based on three steps. The main one is a novel anomaly score, which relies on modeling the tails of the empirical distributions and on an interpretation of the well-known Bayes theorem. A step for an unsupervised feature selection and another one for data reduction are also considered, both aimed at gaining robustness of the procedure. All three steps are in a completely unsupervised way, facilitating an ad hoc scenario deploying. The whole method showed good performance reaching 98.44 % and 98.14 % in the F1-score over different datasets. The obtained results are competitive with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Yoga has become an essential part of modern life, and hence, there has been a tremendous demand for self-training yoga platforms for trainer-less yoga practice. Robust and efficient recognition of yoga poses in video stream is the first requirement of such systems. However, the existing techniques for yoga pose recognition are compute-intensive and may fail in complex real-world conditions. These issues pose serious limitations on their practical applicability. To this end, this paper examines state-of-the-art deep learning techniques to implement a robust and compute-efficient system for yoga pose recognition in real-time on a resource-constrained embedded platform. The first technique uses a hybrid CNN & LSTM model, while the other three (3DCNN Model1, 3DCNN Model2, and 3DCNN Model3) employ the Sports1M pre-trained 3DCNN model named C3D. We assessed the performance of the designed architectures on a publicly available yoga pose database by applying four well-known metrics, namely recognition accuracy, precision, recall, and F1-score. On three database splits, the designed hybrid CNN & LSTM, 3DCNN Model1, 3DCNN Model2, and 3DCNN Model3 achieved mean recognition accuracy of 98.80%, 99.07% 98.19%, and 98.43%, respectively. Also, on one of the splits, the best-performing model achieved the highest recognition accuracy of 99.65% and, thus, surpassed the baseline accuracy of 99.38%. Also, the optimal model runs at a frame rate of 31 FPS on an Nvidia GPU-enabled desktop, much better than the previous best of 3 FPS. Finally, to evaluate the model's efficiency on embedded systems, we optimized it using TensorRT SDK and deployed it on an Nvidia Xavier embedded platform. The optimized model runs at 8 FPS on the resource-constrained embedded platform, demonstrating its suitability for real-world applications. A working demo of the developed system is available at https://youtu.be/at1GJ8Nxx38, and the source codes are available at https://github.com/sumeetssaurav/Yoga-Pose-Classification. [ABSTRACT FROM AUTHOR]

With the accelerated advancements in artificial intelligence and the increasing emphasis on sustainable supply chain management, the integration of multimodal artificial intelligence (AI) into green supply chains has emerged as a critical research frontier. This study delves into the synergistic potential and challenges of combining multimodal AI, which leverages diverse data types such as text, images, and numerical data, to enhance decision-making processes in green supply chains. Through the meticulous design of a data strategy and model framework, this research establishes a sophisticated and efficient data processing and model training pipeline. The experimental results reveal that the comprehensive analysis and fusion of multimodal data significantly improve the prediction accuracy of key supply chain metrics, with observed increases in accuracy and recall rates by 12.4% and 9.8%, respectively. Additionally, the model's limitations are critically assessed, and targeted improvement strategies are proposed. The practical implications of this study are profound, offering actionable insights for the application of multimodal AI in real-world energy sector scenarios. The findings underscore the potential of this technology to optimize operations, reduce environmental impact, and drive sustainable growth in the energy industry. [ABSTRACT FROM AUTHOR]

In the dynamic operation of a boom sprayer, the boom oscillation will cause the detection value of the boom height to fluctuate greatly, resulting in failures of the control system. Based on the previously developed static boom height detection model for the entire wheat growth cycle, this study aimed to optimize the model to reduce the impact of boom oscillation on the accuracy of boom height detection. Three ultrasonic sensors were installed on each section boom of a three-section boom sprayer, and dynamic boom height detection tests were conducted at vehicle speeds of 4 to 8 km/h across six growth stages of winter wheat in Beijing, a total detection area within a single fixed operational row of approximately 14 ha. The test results showed that as vehicle speed increased, boom oscillations intensified across all sections. By setting the boom oscillation correction parameters, the detecting value of each section of boom height is corrected. The results show that the fluctuation and deviation degree of the boom height-detecting value are obviously reduced, and the correction effect is obvious. Further analysis of the detecting value of the boom height after the correction shows that the previously established detection model still maintains high detection accuracy under dynamic conditions; that is, the detection position of the ultrasonic sensor does not downward shift. This paper provides a low-cost technical method that can be directly applied to the dynamic detection of boom height. [ABSTRACT FROM AUTHOR]

The multi-scale object detection algorithm based on deep learning is a common method for safety monitoring in current power operation scenarios, which is of great significance to ensuring the safety of power operations. For certain power applications with high real-time requirements, the computational complexity of deep learning models may become a bottleneck. Deploying deep learning models requires high-performance hardware support, such as GPUs, which might not be easily achievable in some power field environments. According to the characteristics of common safety monitoring tasks in power operation scenarios, this paper proposes an automatic structured pruning method for multi-scale object detection algorithms. This method is designed for the safety monitoring of single and fixed-scale targets, effectively reducing the complexity of inference calculations and improving the frames per second (FPS) of real-time object detection without compromising accuracy. Furthermore, the proposed method can adaptively perform automatic structured pruning for targets of different scales. Experiments conducted using the YOLOv5 model demonstrate that the proposed method improves inference speed by approximately 20% for safety monitoring tasks without reducing detection accuracy. [ABSTRACT FROM AUTHOR]

Purpose: Forecasting outpatient volume during a significant security crisis can provide reasonable decision-making references for hospital managers to prevent sudden outbreaks and dispatch medical resources on time. Based on the background of standard hospital operation and Coronavirus disease (COVID-19) periods, this paper constructs a hybrid grey model to forecast the outpatient volume to provide foresight decision support for hospital decision-makers. Design/methodology/approach: This paper proposes an improved hybrid grey model for two stages. In the non-COVID-19 stage, the Aquila Optimizer (AO) is selected to optimize the modeling parameters. Fourier correction is applied to revise the stochastic disturbance. In the COVID-19 stage, this model adds the COVID-19 impact factor to improve the grey model forecasting results based on the dummy variables. The cycle of the dummy variables modifies the COVID-19 factor. Findings: This paper tests the hybrid grey model on a large Chinese hospital in Jiangsu. The fitting MAPE is 2.48%, and the RMSE is 16463.69 in the training group. The test MAPE is 1.91%, and the RMSE is 9354.93 in the test group. The results of both groups are better than those of the comparative models. Originality/value: The two-stage hybrid grey model can solve traditional hospitals' seasonal outpatient volume forecasting and provide future policy formulation references for sudden large-scale epidemics. [ABSTRACT FROM AUTHOR]

Introduction: Mechanical damage significantly reduces the market value of fruits, making the early detection of such damage a critical aspect of agricultural management. This study focuses on the early detection of mechanical damage in blueberries (variety: Sapphire) through a non-destructive method. Methods: The proposed method integrates hyperspectral image fusion with a multi-strategy improved support vector machine (SVM) model. Initially, spectral features and image features were extracted from the hyperspectral information using the successive projections algorithm (SPA) and Grey Level Co-occurrence Matrix (GLCM), respectively. Different models including SVM, RF (Random Forest), and PLS-DA (Partial Least Squares Discriminant Analysis) were developed based on the extracted features. To refine the SVM model, its hyperparameters were optimized using a multi-strategy improved Beluga Whale Optimization (BWO) algorithm. Results: The SVM model, upon optimization with the multi-strategy improved BWO algorithm, demonstrated superior performance, achieving the highest classification accuracy among the models tested. The optimized SVM model achieved a classification accuracy of 95.00% on the test set. Discussion: The integration of hyperspectral image information through feature fusion proved highly efficient for the early detection of bruising in blueberries. However, the effectiveness of this technology is contingent upon specific conditions in the detection environment, such as light intensity and temperature. The high accuracy of the optimized SVM model underscores its potential utility in post-harvest assessment of blueberries for early detection of bruising. Despite these promising results, further studies are needed to validate the model under varying environmental conditions and to explore its applicability to other fruit varieties. [ABSTRACT FROM AUTHOR]

In the study, the structural parameters of Zichang (ZC) coking coal from northern Shaanxi Province were examined. A theoretical calculation was employed to build a molecular structure model for ZC coal, as well as applying principles of quantum chemistry, the prediction of NMR spectrogram and density for the model was achieved, and the molecular chemical formula was C199H155O36N3. The molecular structure optimization and annealing kinetics calculations are based on molecular mechanics (MM) and molecular dynamics (MD). Subsequently, a representative simplified model was constructed using the aromatic structure as the fundamental unit. On this foundation, the electrostatic potential (ESP), atomic charge distribution, and energy level orbitals were analyzed for this simplified model. The outcomes of this research can serve as an essential guide for determining the reaction order of the active categories during the low-temperature oxidation process for ZC coking coal. [ABSTRACT FROM AUTHOR]

Phishing and spam have been a cybersecurity threat with the majority of breaches resulting from these types of social engineering attacks. Therefore, detection has been a long‐standing challenge for both academic and industry researcher. New and innovative approaches are required to keep up with the growing sophistication of threat actors. One such illumination which has vast potential are large language models (LLM). LLM emerged and already demonstrated their potential to transform society and provide new and innovative approaches to solve well‐established challenges. Phishing and spam have caused financial hardships and lost time and resources to email users all over the world and frequently serve as an entry point for ransomware threat actors. While detection approaches exist, especially heuristic‐based approaches, LLMs offer the potential to venture into a new unexplored area for understanding and solving this challenge. LLMs have rapidly altered the landscape from business, consumers, and throughout academia and demonstrate transformational potential to profoundly impact the society. Based on this, applying these new and innovative approaches to email detection is a rational next step in academic research. In this work, we present IPSDM, an improved phishing spam detection model based on fine‐tuning the BERT family of models to specifically detect phishing and spam emails. We demonstrate our fine‐tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets. Moreover, IPSDM consistently outperforms the baseline models in terms of classification accuracy, precision, recall, and F1‐score, while concurrently mitigating overfitting concerns. [ABSTRACT FROM AUTHOR]

In Reunion Island, most of household wastes (HW) are landfilled. Residual household wastes (RHW) represent 44.6% of HW produced and are totally landfilled. Indeed, significant environmental burdens are caused by current waste management system. Hence, a new system that will ensure the energy recovery of waste was implemented. With the aim of integrating gasification into the waste management system in Reunion Island, a study of gasification of RHW through modeling is suggested in this work. Indeed, a 3-step methodology was adopted, the first step is waste characterization, 3 mixtures of waste have been established based on local context and data from the last waste characterisation campaign in Reunion. The second step is the modeling of a stratified downdraft reactor using an optimized steady-state equilibrium-based and kinetic model. Optimization consists of using correction factors to modify the equilibrium constants of the methanation and water gas shift reactions. The models were validated against extensive experimental data from literature. In addition, the effect of equivalence ratio and moisture content was studied in the steady-state equilibrium model. Furthermore, a sensitivity analysis of the kinetic model was conducted by investigating the effect of solid residence time and reduction temperature on the composition of producer gas. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has inaugurated new approaches to implement fault diagnosis of roller bearings, which is essential to deal with the current industrial big data era. Unfortunately, many existing deep learning models, particularly convolutional neural network (CNN) models, have the following drawbacks. Single-channel convolution neural network in pooling layer has the problem of data loss. The feature information extracted by CNN is not integrated due to lack of feature learning ability, which will induce unsatisfactory diagnostic results and poor generalization ability. To deal with the above issues, a dual-flow convolutional neural network (DFCNN) is proposed for intelligent diagnosis the faults of roller bearings. The multi-channel convolutional neural network is used for feature extraction to solve the problem of data loss in the pooling layer. More comprehensive fault features can be extracted by multiple feature fusion after the input of two dimensions. Network parameters are adjusted using model optimization. The proposed method is demonstrated by the bearing experiment and collected vibration data including different types of faults. The results show that the accuracy rate is more superior than other six different neural network, and it is suitable for roller bearing fault diagnosis. [ABSTRACT FROM AUTHOR]

Introduction: Mechanical damage significantly reduces the market value of fruits, making the early detection of such damage a critical aspect of agricultural management. This study focuses on the early detection of mechanical damage in blueberries (variety: Sapphire) through a non-destructive method. Methods: The proposed method integrates hyperspectral image fusion with amultistrategy improved support vector machine (SVM) model. Initially, spectral features and image features were extracted from the hyperspectral information using the successive projections algorithm (SPA) and Grey Level Co-occurrence Matrix (GLCM), respectively. Different models including SVM, RF (Random Forest), and PLS-DA (Partial Least Squares Discriminant Analysis) were developed based on the extracted features. To refine the SVM model, its hyperparameters were optimized using a multi-strategy improved Beluga Whale Optimization (BWO) algorithm. Results: The SVM model, upon optimization with the multi-strategy improved BWO algorithm, demonstrated superior performance, achieving the highest classification accuracy among the models tested. The optimized SVM model achieved a classification accuracy of 95.00% on the test set. Discussion: The integration of hyperspectral image information through feature fusion proved highly efficient for the early detection of bruising in blueberries. However, the effectiveness of this technology is contingent upon specific conditions in the detection environment, such as light intensity and temperature. The high accuracy of the optimized SVM model underscores its potential utility in post-harvest assessment of blueberries for early detection of bruising. Despite these promising results, further studies are needed to validate the model under varying environmental conditions and to explore its applicability to other fruit varieties. [ABSTRACT FROM AUTHOR]

Facial keypoint detection technology faces significant challenges under conditions such as occlusion, extreme angles, and other demanding environments. Previous research has largely relied on deep learning regression methods using the face's overall global template. However, these methods lack robustness in difficult conditions, leading to instability in detecting facial keypoints. To address this challenge, we propose a joint optimization approach that combines regression with heatmaps, emphasizing the importance of local apparent features. Furthermore, to mitigate the reduced learning capacity resulting from model pruning, we integrate external supervision signals through knowledge distillation into our method. This strategy fosters the development of efficient, effective, and lightweight facial keypoint detection technology. Experimental results on the CelebA, 300W, and AFLW datasets demonstrate that our proposed method significantly improves the robustness of facial keypoint detection. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Agrometeorology is the property of Editorial Board of Chinese Journal of Agrometeorology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Drainage & Irrigation Machinery Engineering / Paiguan Jixie Gongcheng Xuebao is the property of Editorial Department of Drainage & Irrigation Machinery Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aims to optimize the performance of the bagged tree in an ensemble classifier for predicting diabetes prevalence in women. The study used a dataset of 1,888 women with six features: age, BMI, glucose level, insulin level, blood pressure, and pregnancy status. The dataset was divided into training and testing sets with a 70:30 ratio. The bagged tree ensemble classifier was used for the analysis, and five-fold cross-validation was applied. The study found that using all features during training resulted in a 92.3% training accuracy and a 99.5% testing accuracy. However, applying optimization techniques such as feature selection, parameter tuning, and a maximum number of splits improved model performance. Feature selection optimized the accuracy performance by 0.2%, while parameter tuning improved the test accuracy by 0.2%. Moreover, decreasing the maximum number of splits from 1322 to 800 or 600 resulted in an optimized model with 0.1% higher validation accuracy. Finally, the optimized bagged tree models were evaluated using various performance metrics, including accuracy, precision, recall, and F1 score. The study found that Model 1, which used 800 maximum number of splits and 50 learners, outperformed Model 2 in terms of recall and F1 score, while Model 2, which used 600 maximum number of splits and 50 learners, had a higher precision score. The study concludes that optimization techniques can significantly improve the performance of the bagged tree in predicting diabetes prevalence in women. [ABSTRACT FROM AUTHOR]

Most existing cellular automata (CA) models impose strict requirements on the number and spatial distribution of samples. This makes it a challenge to capture spatial heterogeneity in urban dynamics and meet the modeling needs of large and complex geographic areas. This paper presents a CA model based on geographically optimal similarity (GOS) transition rules and similarly sized neighborhoods (SSN). By comparing the similarity in geographical configuration between samples and predicted points, the model enables a comprehensive characterization of the driving mechanism behind urban expansion and its self-organizing scope. This helps to mitigate the impact of sample selection and assumptions about spatial stationarity on simulation results. The performance of GOS-SSN-CA simulation was tested by taking the urban expansion in the Changsha-Zhuzhou-Xiangtan urban agglomeration in China as an example. The results show that GOS can derive more accurate and reliable urban transition rules with fewer samples, thereby significantly reducing spatial prediction errors compared with logistic regression. Moreover, SSN selects different neighborhood sizes to represent the difference between the local self-organizing range and surrounding cells, thus further improving the simulation accuracy and restricting urban expansion morphology. Overall, GOS-SSN-CA effectively characterizes the geographical similarity of urban expansion, improves simulation accuracy while constraining the urban expansion form, and enhances the practical application value of CA. [ABSTRACT FROM AUTHOR]

The purpose of the article is to present a new approach to geographic data processing using deep neural networks, which provides more efficient, accurate and automated analysis. The methodology includes the use of deep neural networks for automatic identification of characteristics of geographic objects without prior manual processing. Findings include numerical data in the form of tables and graphs demonstrating the performance improvement of GeoDNN compared to traditional methods. This article presents an improved approach to geographic data processing based on the use of deep neural networks (GeoDNN). The proposed technique provides a more efficient and automated analysis of geodata. GeoDNN uses deep neural networks to automatically identify the characteristics of geographic objects without prior manual processing. This avoids human errors and increases the accuracy of the analysis, and self-learning mechanisms ensure continuous improvement using new data. GeoDNN is characterized by high performance when processing large volumes of geodata due to its optimized structure and parallel computations. The GeoDNN architecture is described in detail, including flowcharts, mathematical formulas, and algorithms. Processes of data preparation, their normalization and augmentation, as well as model training with parameters, optimization methods and loss functions are considered. Originality is the application of deep neural networks to automate the processing of geographic data, which allows to avoid human errors, increases the accuracy of the analysis and ensures constant improvement with the use of new data. The practical value of GeoDNN lies in its ability to automate the analysis of geodata, which significantly reduces processing time and reduces the need for manual labor. This makes the system efficient for use in real-world applications such as automating land parcel analysis. [ABSTRACT FROM AUTHOR]

As vehicles with high accident and casualty rates within the road transportation system, engineering vehicles have been receiving much attention and emphasis in terms of safety. Accurate analyses and evaluations of risk factors in vehicle operation are imperative for enhancing the management level of engineering vehicles. This study explores the differences between various types of drivers by analyzing the driving characteristics of professional drivers. The evaluation index system is developed and quantified by integrating factors related to engineering vehicle drivers, road environment, and industry management. Additionally, the risk assessment model is developed using the error backpropagation algorithm. The optimal model is determined by comparing the number of nodes in different hidden layers, the activation function, and regularization optimization. The prediction accuracy of this model's coefficient of determination is 0.912, indicating that the model has validity. This study is conducive to improving the safety level of engineering vehicle operation in order to reduce the rate of vehicle traffic accidents, the severity of accidents, and the consequences of losses. It also has practical application value in safeguarding social security. [ABSTRACT FROM AUTHOR]

Because of the vast ocean area and the large amount of high-resolution image data, ship detection and data processing have become more difficult. These difficulties can be solved using the artificial intelligence interpretation method. The efficient and accurate detection ability of ship target detection has been widely recognized with the increasing application of deep learning technology. It is widely used in the practice of ship target detection. Firstly, we set up a data set concerning ship targets by collecting and training a large number of images. Then, we improved the YOLO v5 algorithm. The feature specify module (FSM) is used in the improved algorithm. The improved YOLO v5 algorithm was applied to ship detection practice under the framework of Anaconda. Finally, the training results were optimized, and the false alarm rate was reduced. The detection rate was improved. According to the statistics pertaining to experimental results with other algorithm models, the improved YOLO v5 algorithm can effectively suppress conflicting information, and the detection ability of ship details is improved. This work has accumulated valuable experience for related follow-up research. [ABSTRACT FROM AUTHOR]

A regression model represents the relationship between explanatory and response variables. In real life, explanatory variables often affect a response variable with a certain time lag, rather than immediately. For example, the marriage rate affects the birth rate with a time lag of 1 to 2 years. Although deep learning models have been successfully used to model various relationships, most of them do not consider the time lags between explanatory and response variables. Therefore, in this paper, we propose an extension of deep learning models, which automatically finds the time lags between explanatory and response variables. The proposed method finds out which of the past values of the explanatory variables minimize the error of the model, and uses the found values to determine the time lag between each explanatory variable and response variables. After determining the time lags between explanatory and response variables, the proposed method trains the deep learning model again by reflecting these time lags. Through various experiments applying the proposed method to a few deep learning models, we confirm that the proposed method can find a more accurate model whose error is reduced by more than 60% compared to the original model. [ABSTRACT FROM AUTHOR]

The objective of multimodal sentiment analysis is to extract and integrate feature information from text, image, and audio data accurately, in order to identify the emotional state of the speaker. While multimodal fusion schemes have made some progress in this research field, previous studies still lack adequate approaches for handling inter-modal information consistency and the fusion of different categorical features within a single modality. This study aims to effectively extract sentiment coherence information among video, audio, and text and consequently proposes a multimodal sentiment analysis method named joint chain interactive attention (VAE-JCIA, Video Audio Essay–Joint Chain Interactive Attention). In this approach, a 3D CNN is employed for extracting facial features from video, a Conformer is employed for extracting audio features, and a Funnel-Transformer is employed for extracting text features. Furthermore, the joint attention mechanism is utilized to identify key regions where sentiment information remains consistent across video, audio, and text. This process acquires reinforcing features that encapsulate information regarding consistency among the other two modalities. Inter-modal feature interactions are addressed through chained interactive attention, and multimodal feature fusion is employed to efficiently perform emotion classification. The method is experimentally validated on the CMU-MOSEI dataset and the IEMOCAP dataset. The experimental results demonstrate that the proposed method significantly enhances the performance of the multimodal sentiment analysis model. [ABSTRACT FROM AUTHOR]

With the advancement of artificial intelligence, deep learning has become instrumental in land cover classification. While there has been a notable emphasis on refining model structures to improve classification accuracy, it is imperative to also emphasize the pivotal role of data-driven optimization techniques. This paper presents an in-depth investigation into optimizing multi-class land cover classification using high-resolution multispectral images from Worldview3. We explore various optimization strategies, including refined sampling strategies, data band combinations, loss functions, and model enhancements. Our optimizations led to a substantial increase in the Mean Intersection over Union (mIoU) classification accuracy, improving from a baseline of 0.520 to a final accuracy of 0.709, which represents a 35.2% enhancement. Specifically, by optimizing the classic semantic segmentation network in four key aspects, we improved the mIoU by 15.5%. Further improvements through changes in data combinations, sampling methods, and loss functions led to an overall 17.2% increase in mIoU. The proposed model optimization methods enabled the OUNet to outperform the baseline model by providing more precise edge detection and feature representation, while reducing the model parameters scale. Experimental evidence shows that in the application of multi-class land surface classification, increasing the quantity and diversity of samples, avoiding data imbalance issues, is equally valuable for improving overall classification accuracy as it is for enhancing model performance. [ABSTRACT FROM AUTHOR]

Existing direct and inverse kinematic models of planar parallel robots assume that the robot's active joints are all at the bases. However, this approach becomes excessively complex when modeling a planar parallel robot in which the active joints are within one single kinematic chain. To address this problem, our article unveils an alternative for a 3RRR symmetric planar robot modeling technique for the derivation of the robot workspace and the analysis of its direct and inverse kinematics. The workspace was defined using a system of inequalities, and the direct and inverse kinematics models were generated using vectorial analysis and an optimized geometrical approach, respectively. The resulting models are systematically presented and validated. Two final model renditions are delivered supplying a thorough equation analysis and an applicability discussion based on the importance of the robot's mobile platform orientation. The advantages of this model are discussed in comparison to the traditional modeling approach: whereas conventional techniques require the solution of complex eighth-degree polynomials for the analysis of the active joint configuration of these robots, these models provide an efficient back-of-the-envelope analysis approach that requires the solution of a simple second-degree polynomial. [ABSTRACT FROM AUTHOR]

A mutation operator in a real-coded genetic algorithm is developed and applied for efficient bridge-model optimization. A mutation operator that changes uniformly or dynamically with a crossover operator is proposed to address optimization problems. The performance of the combined genetic operators was verified using a variety of available test problems based on the convergence and search speed of the global optimal solution. It is shown that the genetic algorithm proposed in this study yields relatively better results than the available algorithms and is more effective in constrained optimization problems. The performance of the proposed genetic algorithm is also verified through a sample study using a field load test for the model optimization of an existing bridge. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a progressive neurodegenerative condition. It causes cognitive impairment and memory loss in individuals. Healthcare professionals face challenges in detecting AD in its initial stages. In this study, the author proposed a novel integrated approach, combining LeViT, EfficientNet B7, and Dartbooster XGBoost (DXB) models to detect AD using magnetic resonance imaging (MRI). The proposed model leverages the strength of improved LeViT and EfficientNet B7 models in extracting high-level features capturing complex patterns associated with AD. A feature fusion technique was employed to select crucial features. The author fine-tuned the DXB using the Bayesian optimization hyperband (BOHB) algorithm to predict AD using the extracted features. Two public datasets were used in this study. The proposed model was trained using the Open Access Series of Imaging Studies (OASIS) Alzheimer's dataset containing 86,390 MRI images. The Alzheimer's dataset was used to evaluate the generalization capability of the proposed model. The proposed model obtained an average generalization accuracy of 99.8% with limited computational power. The findings highlighted the exceptional performance of the proposed model in predicting the multiple types of AD. The recommended integrated feature extraction approach has supported the proposed model to outperform the state-of-the-art AD detection models. The proposed model can assist healthcare professionals in offering customized treatment for individuals with AD. The effectiveness of the proposed model can be improved by generalizing it to diverse datasets. [ABSTRACT FROM AUTHOR]

The optimization problem of the experimental appraisal capability evaluation model can essentially be seen as a multi-attribute decision-making problem for decision optimization. The Analytic Hierarchy Process model and the Analytic Hierarchy Network model are the fundamental models for solving this problem. The Fuzzy Analytic Hierarchy Process model adds fuzziness to the basic model, while the Double Fuzzy Analytic Hierarchy Process model and Double Fuzzy Analytic Hierarchy Network model make greater use of fuzzy information, enhancing the applicability of the model. However, researchers have encountered issues such as the abuse of the Analytic Hierarchy Process model, failure to fully utilize fuzzy advantages, and mismatched problem characteristics in the process of evaluating experimental identification capabilities. To address the above issues, a method for selecting an experimental identification capability evaluation model is proposed based on an improved plant growth algorithm, which includes a target layer, a criterion layer, a key factor layer, and a scheme layer. The research content has important reference value for evaluating experimental identification capabilities. [ABSTRACT FROM AUTHOR]

A good image editing model should learn to map the relationships between styles from different domains, cater to the high quality and diversity of the generated images, and be highly scalable across different domains. At the same time, given the importance of multi-device deployments, especially of models on lightweight devices, lightweight optimization of models is an essential and critical task. Based on these key points, a new approach to optimizing and improving existing base models, namely OMGD-StarGAN, is proposed by combining PatchGAN discriminators and DynamicD dynamic training strategies, ResNet style generators, and modulated convolution, based on online multi-granularity knowledge distillation algorithms and StarGAN v2. A comparison of various experiments is conducted, and the experimental results show that the proposed model reduces the computational cost while improving the quality and diversity of the generated images. [ABSTRACT FROM AUTHOR]

In recent years, genomic selection has been widely used in plant breeding to increase genetic gain. Selections are based on breeding values of each genotype estimated using genome-wide markers. The present study developed genomic prediction models for grain protein content (GPC) and test weight (TW) in a diverse panel of 170 spring wheat lines phenotyped in five environments. Five prediction models (GBLUP, RRBLUP, EGBLUP, RF, RKHS) were investigated. The population was genotyped for genome-wide markers with the Infinium iSelect 90 K SNP assay. Environmental variation was adjusted by calculating BLUPs across environments using the complete random effect GxE model. Both GPC and TW showed high heritability of 0.867 and 0.854, respectively. When using the five-fold cross-validation scheme in the five statistical models, we found that the EGBLUP model had the highest mean prediction accuracy (0.743) for GPC, while the RRBLUP model showed the highest mean prediction accuracy (0.650) for TW. Testing various proportions of the training population indicated that a minimum of 100 genotypes were required to train the model for optimum accuracy. Testing the prediction across environments showed that BLUPs outperformed 80% of the tested environments, even though at least one of the environments had higher prediction accuracies for each trait. Thus, the optimized GS model for GPC and TW has the potential to predict trait values accurately. Implementing GS would aid breeding through accurate early generation selection of superior lines, leading to higher genetic gain per breeding cycle. [ABSTRACT FROM AUTHOR]

Micro-expression has emerged to be a feasible strategy in affective estimation due to its great reliability in emotion detection. Recent years have witnessed that deep learning methods were successfully applied to the micro-expression recognition field. In visual data, micro-expression only exists in regions such as eyebrows and mouth, etc, which leads to its imbalanced distribution. Therefore, it's difficult for networks to distinguish the above micro-expression description with weak intensity when extracting feature maps. To tackle such issues, we propose a novel lightweight attention model, LAM, to improve the network recognition performance. LAM is enabled to calculate the correlation between the feature maps (channel dimension) and the correlation within the feature maps (spatial dimension), thus helping the network to focus on micro-expression information. Additionally, cooperated with residual block at various scales in Resnet, LAM can adaptively compute and update the feature maps in each network layer. Technically, coping with small datasets, we build LAM without adding obvious parameters, while a straightforward but efficient strategy that transfer facial expression knowledge is utilized together. Extensive experimental evaluations on two benchmarks (CASME II and SAMM) and post-hoc feature visualizations demonstrate the effectiveness of our proposed network with LAM. [ABSTRACT FROM AUTHOR]

To identify promising sustainable fuels, e.g., to select novel synthetic fuels with the greatest impact on minimizing global warming, new methods for rapid and economical technical fuel assessment are urgently needed. Here, numerical models that are capable of predicting technical key data quickly and without experimental setup are necessary. One method is the use of chemical kinetic models, which are able to predict the technical key parameters related to combustion behavior. For a rapid technical fuel assessment, these chemical kinetic models need to be validated for new fuel components and for different temperature and pressure ranges. This work presents a new approach to extend the existing semi-detailed chemical kinetic models. For the application of the approach, the semi-detailed reaction mechanism DLR Concise was selected and extended for the low temperature combustion modeling of n-heptane and isooctane. The open-source software reaction mechanism generator (RMG) was used for this extension. Furthermore, an optimization of the merged chemical kinetic model with the linear transformation model (linTM) was conducted in order to improve the reproducibility of ignition delay times. The improvement of the predictive performance of ignition delay times at low temperatures for both species was successfully demonstrated. Therefore, this approach can be used to quickly add new species or reaction pathways to an existing semi-detailed reaction mechanism to enable a model-based technical fuel assessment for the early identification of promising fuels. [ABSTRACT FROM AUTHOR]

Particle size ratio, tablet thickness, and compaction pressure are three important tableting parameters that affect the obtained tablet spectrum. In this study, the single-factor test and response surface analysis (RSM) were adopted to examine how these three parameters and their interactions affect the analytical measurements performed over terahertz time-domain spectroscopy (THz-TDS). Tablet samples were composed of a different percent (wt-%) of acetylsalicylic acid within microcrystalline cellulose. Results indicate that change in absorption coefficient was found to be more obvious for a larger particle size ratio. Thinner tablets were sensitive to absorption coefficient effects created as the transmitted THz radiation propagates through the tablet interfaces. Compression pressure has a limited effect on the absorption coefficient. The particle size ratio had the largest effect on the coefficient of correlation of the quantitative regression model, followed by the compaction pressure, and the tablet thickness. In this study, the optimized tableting parameters were determined: tablet thickness of 0.48 mm, particle size ratio of about 1:1, and compaction pressure of 30 MPa. Under this condition, the R2 of the quantitative regression model of acetylsalicylic acid content was 0.971 and 0.998 at the range of acetylsalicylic acid mass fraction between 10 and 90 wt-%, 1 and 30 wt-%, respectively. This study provides a method to effectively reduce the scattering effect of spectral detection of tablets by improving the sample's properties, to provide a reference for enhancing the reliability and accuracy of quantitative measurements. [ABSTRACT FROM AUTHOR]

To create a supervised machine learning algorithm aimed at predicting an optimal cerebrospinal fluid (CSF) dilution when determining virus specific antibody indices to reduce the need for repeated tests. The CatBoost model was trained, optimized, and tested on a dataset with five input variables: albumin quotient, immunoglobulin G (IgG) in CSF, IgG quotient (QIgG), intrathecal synthesis (ITS) and limes quotient (LIM IgG). Albumin and IgG concentrations in CSF and serum were performed by immunonephelometry on Atellica NEPH 630 (Siemens Healthineers, Erlangen, Germany) and ITS and LIM IgG were calculated according to Reiber. Concentrations of IgG antibodies to measles, rubella, varicella zoster and herpes simplex 1/2 viruses were analysed in CSF and serum by ELISA (Euroimmun, Lübeck, Germany). Optimal CSF dilution was defined for each virus and used as a classification variable while the standard operating procedure was set to start at 2×-dilution of CSF. The dataset included 571 samples with the imbalanced distribution of the optimal CSF dilutions: 2× dilution n=440, 3× dilution n=109, 4× dilution n=22. The optimized CatBoost model achieved an area under the curve (AUC) score of 0.971, and a test accuracy of 0.900. The model falsely classified 14 (9.9 %) samples of the testing set but reduced the need for repeated testing compared to the standard protocol by 42 %. The output of the CatBoost model is mostly dependant on the QIgG, ITS and CSF IgG variables. An accurate algorithm was achieved for predicting the optimal CSF dilution, which reduces the number of test repeats. [ABSTRACT FROM AUTHOR]

Sleep is a complex process divided into different stages, and a decrease in sleep quality can lead to adverse health-related effects. Therefore, diagnosing and treating sleep-related conditions is crucial. The Cyclic Alternating Pattern (CAP) is an indicator of sleep instability and can assist in assessing sleep-related disorders such as sleep apnea. However, manually detecting CAP-related events is time-consuming and challenging. Therefore, automatic detection is needed. Despite their usually higher performance, the utilization of deep learning solutions may result in models that lack interpretability. Addressing this issue can be achieved through the implementation of feature-based analysis. Nevertheless, it becomes necessary to identify which features can better highlight the patterns associated with CAP. Such is the purpose of this work, where 98 features were computed from the patient's electroencephalographic signals and used to train a neural network to identify the CAP activation phases. Feature selection and model tuning with a genetic algorithm were also employed to improve the classification results. The proposed method's performance was found to be among the best state-of-the-art works that use more complex models. [ABSTRACT FROM AUTHOR]

The settlement monitoring and control of high-speed railway (HSR) underline engineering is the basis of realizing the high smoothness construction and high stability operation of railway track. Bridge engineering accounts for more than 50% of the total mileage in the HSR projects that has been established in China. Therefore, it is of great significance for the smooth construction of HSR project and the operation safety of HSR network to carry out settlement monitoring and prediction, so as to realize the rapid and accurate judgment of settlement deformation trend. In order to establish a settlement prediction model that conforms to the actual settlement deformation of HSR piers, the relevant research was carried out based on the autoregressive (AR) model and linear hypothesis theory. Firstly, on the basis of classical AR model, combined with the actual situation of modeling, considering the influence of model variable errors on AR model modeling, the modeling and solution method of autoregressive EIV model for HSR pier settlement prediction considering the model variable errors was derived. Then, the optimum prediction model selection method for HSR pier settlement based on linear hypothesis theory was deduced. Finally, a prediction method of HSR pier settlement based on autoregressive EIV model was designed. The research results show that, the algorithm proposed can be used to establish the optimum settlement prediction model that conforms to the actual settlement deformation of HSR pier, which can objectively and accurately predict the settlement deformation trend of HSR pier, and provide a scientific basis for the construction and operation decision-making. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning technology, its application in the field of agricultural product quality assessment, especially in the quality classification and classification of tobacco leaves, has been widely concerned. As an important cash crop, the accurate evaluation of tobacco leaf quality is very important for the development of the entire tobacco industry. The traditional tobacco leaf quality assessment method relies on manual experience, which is not only inefficient, but also easy to be affected by subjective factors. This study, based on deep learning theory, aimed at the limitations of traditional methods in tobacco quality assessment and investigated in detail the deep learning method for tobacco quality classification and classification based on images. The empirical research on data acquisition, preprocessing, model construction, training, verification, testing, and optimization was carried out systematically. The results showed that the optimized deep learning model performed well in tobacco quality grading and classification tasks, and had higher accuracy, recall rate, and F1 scores than the existing manual methods. At the same time, a series of model optimization strategies were proposed, which laid a foundation for the further development and application of deep learning models in the agricultural field. In addition, this study also exposed issues such as computational resources and interpretability of deep learning models in practical applications, which would become the direction of future research. [ABSTRACT FROM AUTHOR]

Urban sewer pipes are a vital infrastructure in modern cities, and their defects must be detected in time to prevent potential malfunctioning. In recent years, to relieve the manual efforts by human experts, models based on deep learning have been introduced to automatically identify potential defects.However, these models are insufficient in terms of dataset complexity,model versatility and performance. Our work addresses these issues with amulti-stage defectdetectionarchitectureusinga compositebackboneSwinTransformer.Themodelbasedon this architecture is trained using a more comprehensive dataset containingmore classes of defects. By ablation studies on the modules of combined backbone Swin Transformer, multi-stage detector, test-time data augmentation and model fusion, it is revealed that they all contribute to the improvement of detection accuracy from different aspects. The model incorporating all these modules achieves the mean Average Precision (mAP) of 78.6% at an Intersection over Union (IoU) threshold of 0.5. This represents an improvement of 14.1% over the ResNet50 Faster Region-based Convolutional Neural Network (R-CNN) model and a 6.7% improvement over You Only Look Once version 6 (YOLOv6)-large, the highest in the YOLO methods. In addition, for other defect detection models for sewer pipes, although direct comparison with themis infeasible due to the unavailability of their private datasets, our results are obtained from a more comprehensive dataset and have superior generalization capabilities. [ABSTRACT FROM AUTHOR]

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