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Background: Mental health issues are increasingly prominent worldwide, posing significant threats to patients and deeply affecting their families and social relationships. Traditional diagnostic methods are subjective and delayed, indicating the need for an objective and effective early diagnosis method. Methods: To this end, this paper proposes a lightweight detection method for multi-mental disorders with fewer data sources, aiming to improve diagnostic procedures and enable early patient detection. First, the proposed method takes Electroencephalography (EEG) signals as sources, acquires brain rhythms through Discrete Wavelet Decomposition (DWT), and extracts their approximate entropy, fuzzy entropy, permutation entropy, and sample entropy to establish the entropy-based matrix. Then, six kinds of conventional machine learning classifiers, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Naive Bayes (NB), Generalized Additive Model (GAM), Linear Discriminant Analysis (LDA), and Decision Tree (DT), are adopted for the entropy-based matrix to achieve the detection task. Their performances are assessed by accuracy, sensitivity, specificity, and F1-score. Concerning these experiments, three public datasets of schizophrenia, epilepsy, and depression are utilized for method validation. Results: The analysis of the results from these datasets identifies the representative single-channel signals (schizophrenia: O1, epilepsy: F3, depression: O2), satisfying classification accuracies (88.10%, 75.47%, and 89.92%, respectively) with minimal input. Conclusions: Such performances are impressive when considering fewer data sources as a concern, which also improves the interpretability of the entropy features in EEG, providing a reliable detection approach for multi-mental disorders and advancing insights into their underlying mechanisms and pathological states. Full article

Multi-objective combinatorial optimization problems (MOCOPs) are designed to identify solution sets that optimally balance multiple competing objectives. Addressing the challenges inherent in applying deep reinforcement learning (DRL) to solve MOCOPs, such as model non-convergence, lengthy training periods, and insufficient diversity of solutions, this study introduces a novel multi-objective combinatorial optimization algorithm based on DRL. The proposed algorithm employs a uniform weight decomposition method to simplify complex multi-objective scenarios into single-objective problems and uses asynchronous advantage actor–critic (A3C) instead of conventional REINFORCE methods for model training. This approach effectively reduces variance and prevents the entrapment in local optima. Furthermore, the algorithm incorporates an architecture based on graph transformer networks (GTNs), which extends to edge feature representations, thus accurately capturing the topological features of graph structures and the latent inter-node relationships. By integrating a weight vector layer at the encoding stage, the algorithm can flexibly manage issues involving arbitrary weights. Experimental evaluations on the bi-objective traveling salesman problem demonstrate that this algorithm significantly outperforms recent similar efforts in terms of training efficiency and solution diversity. Full article

Fractional vegetation cover (FVC) is a crucial indicator for measuring the growth of surface vegetation. The changes and predictions of FVC significantly impact biodiversity conservation, ecosystem health and stability, and climate change response and prediction. Southwest China (SWC) is characterized by complex topography, diverse climate types, and rich vegetation types. This study first analyzed the spatiotemporal variation of FVC at various timescales in SWC from 2000 to 2020 using FVC values derived from pixel dichotomy model. Next, we constructed four machine learning models—light gradient boosting machine (LightGBM), support vector regression (SVR), k-nearest neighbor (KNN), and ridge regression (RR)—along with a weighted average heterogeneous ensemble model (WAHEM) to predict growing-season FVC in SWC from 2000 to 2023. Finally, the performance of the different ML models was comprehensively evaluated using tenfold cross-validation and multiple performance metrics. The results indicated that the overall FVC in SWC predominantly increased from 2000 to 2020. Over the 21 years, the FVC spatial distribution in SWC generally showed a high east and low west pattern, with extremely low FVC in the western plateau of Tibet and higher FVC in parts of eastern Sichuan, Chongqing, Guizhou, and Yunnan. The determination coefficient R² scores from tenfold cross-validation for the four ML models indicated that LightGBM had the strongest predictive ability whereas RR had the weakest. WAHEM and LightGBM models performed the best overall in the training, validation, and test sets, with RR performing the worst. The predicted spatial change trends were consistent with the MODIS-MOD13A3-FVC and FY3D-MERSI-FVC, although the predicted FVC values were slightly higher but closer to the MODIS-MOD13A3-FVC. The feature importance scores from the LightGBM model indicated that digital elevation model (DEM) had the most significant influence on FVC among the six input features. In contrast, soil surface water retention capacity (SSWRC) was the most influential climate factor. The results of this study provided valuable insights and references for monitoring and predicting the vegetation cover in regions with complex topography, diverse climate types, and rich vegetation. Additionally, they offered guidance for selecting remote sensing products for vegetation cover and optimizing different ML models. Full article

Existing dehazing methods deal with real-world haze images with difficulty, especially scenes with thick haze. One of the main reasons is lacking real-world pair data and robust priors. To improve dehazing ability in real-world scenes, we propose a semi-supervised codebook learning dehazing method. The codebook is used as a strong prior to guide the hazy image recovery process. However, the following two issues arise when the codebook is applied to the image dehazing task: (1) Latent space features obtained from the coding of degraded hazy images suffer from matching errors when nearest-neighbour matching is performed. (2) Maintaining a good balance of image recovery quality and fidelity for heavily degraded dense hazy images is difficult. To reduce the nearest-neighbor matching error rate in the vector quantization stage of VQGAN, we designed the unit dual-attention residual transformer module (UDART) to correct the latent space features. The UDART can make the latent features obtained from the encoding stage closer to those of the corresponding clear image. To balance the quality and fidelity of the dehazing result, we design a haze density guided weight adaptive module (HDGWA), which can adaptively adjust the multi-scale skip connection weights according to haze density. In addition, we use mean teacher, a semi-supervised learning strategy, to bridge the domain gap between synthetic and real-world data and enhance the model generalization in real-world scenes. Comparative experiments show that our method achieves improvements of 0.003, 2.646, and 0.019 over the second-best method for the no-reference metrics FADE, MUSIQ, and DBCNN, respectively, on the real-world dataset URHI. Full article

Abiotic stress factors can be detected using visible and near-infrared spectral signatures. Previous work demonstrated the potential of this technology in crop monitoring, although a large majority used vegetation indices, which did not consider the complete spectral information. This work explored the capabilities of spectral information for abiotic stress detection using supervised machine learning techniques such as support vector machine (SVM), random forest (RF), and neural network (NN). This study used avocados grown under various water treatments, maize submitted to nitrogen deficiency, and common beans under phosphorous restriction. The spectral characterization of the crops subjected to abiotic stress was studied on the visible to near-infrared (450 to 900 nm) spectrum, identifying discriminative bands and spectral ranges. Then, the advantages of using an integrated approach based on machine learning to detect abiotic stress in crops were demonstrated. Instead of relying on vegetation indices, the proposed approach used several spectral features obtained by analyzing the discriminative signature shape, applying a spectral subset band selection algorithm based on similarity, and using the minimum redundancy maximum relevance (MRMR), F-test and chi-square test ranks for feature selection. The results showed that supervised classifiers applied to the spectral features outperform the accuracies obtained from vegetation indices. The best common bean results were obtained using SVM with accuracies up to 91%; for maize and avocado, NN obtained 90% and 82%, respectively. It is noted that detection accuracy depends on various factors, such as crop type, genotype, and level of stress. Full article

The onset of the COVID-19 pandemic has compelled a swift transformation in higher-education methodologies, particularly in the domain of course modality. This study highlights the potential for artificial intelligence and machine learning to improve decision-making in advanced engineering education. We focus on the potential for large existing datasets to align institutional decisions with student and faculty preferences in the face of rapid changes in instructional approaches prompted by the COVID-19 pandemic. To ascertain the preferences of students and instructors regarding class modalities across various courses, we utilized the Cognitive Process-Embedded Systems and e-learning conceptual framework. This framework effectively delineates the task execution process within the scope of technology-enhanced learning environments for both students and instructors. This study was conducted in seven Iranian universities and their STEM departments, examining their preferences for different learning styles. After analyzing the variables by different feature selection methods, we used three ML methods—decision trees, support vector machines, and random forest—for comparative analysis. The results demonstrated the high performance of the RF model in predicting curriculum style preferences, making it a powerful decision-making tool in the evolving post-COVID-19 educational landscape. This study not only demonstrates the effectiveness of ML in predicting educational preferences but also contributes to understanding the role of self-regulated learning in educational policy and decision-making in higher education. Full article

Proteins perform different biological functions through binding with various molecules which are mediated by a few key residues and accurate prediction of such protein binding residues (PBRs) is crucial for understanding cellular processes and for designing new drugs. Many computational prediction approaches have been proposed to identify PBRs with sequence-based features. However, these approaches face two main challenges: (1) these methods only concatenate residue feature vectors with a simple sliding window strategy, and (2) it is challenging to find a uniform sliding window size suitable for learning embeddings across different types of PBRs. In this study, we propose one novel framework that could apply multiple types of PBRs Prediciton task through Multi-scale Sequence-based Feature Fusion (PMSFF) strategy. Firstly, PMSFF employs a pre-trained language model named ProtT5, to encode amino acid residues in protein sequences. Then, it generates multi-scale residue embeddings by applying multi-size windows to capture effective neighboring residues and multi-size kernels to learn information across different scales. Additionally, the proposed model treats protein sequences as sentences, employing a bidirectional GRU to learn global context. We also collect benchmark datasets encompassing various PBRs types and evaluate our PMSFF approach to these datasets. Compared with state-of-the-art methods, PMSFF demonstrates superior performance on most PBRs prediction tasks. Full article

This paper introduces a novel network, DDFE-Transformer (Data-Driven Feature Extraction-Transformer), for fault diagnosis using acoustic emission signals. The DDFE-Transformer network integrates two primary modules: the DDFE module, focusing on noise reduction and feature enhancement, and the Transformer module. The DDFE module employs two techniques: the Wavelet Kernel Network (WKN) for noise reduction and the Convolutional Block Attention Module (CBAM) for feature enhancement. The wavelet function in the WKN reduces noise, while the attention mechanism in the CBAM enhances features. The Transformer module then processes the feature vectors and sends the results to the softmax layer for classification. To validate the proposed method’s efficacy, experiments were conducted using acoustic emission datasets from NASA Ames Research Center and the University of California, Berkeley. The results were compared using the four key metrics obtained through confusion matrix analysis. Experimental results show that the proposed method performs excellently in fault diagnosis using acoustic emission signals, achieving a high average accuracy of 99.84% and outperforming several baseline models, such as CNN, CNN-LSTM, CNN-GRU, VGG19, and ZFNet. The best-performing model, VGG19, only achieved an accuracy of 88.61%. Additionally, the findings suggest that integrating noise reduction and feature enhancement in a single framework significantly improves the network’s classification accuracy and robustness when analyzing acoustic emission signals. Full article

With the increasing scale and complexity of software, the advantages of using neural networks for static vulnerability detection are becoming increasingly prominent. Before inputting into a neural network, the source code needs to undergo word embedding, transforming discrete high-dimensional text data into low-dimensional continuous vectors suitable for training in neural networks. However, analysis has revealed that different implementation ideas by code writers for the same functionality can lead to varied code implementation methods. Embedding different code texts into vectors results in distinctions that can reduce the robustness of a model. To address this issue, this paper explores the impact of converting source code into different forms on word embedding and finds that a TAC (Three-Address Code) can significantly eliminate noise caused by different code implementation approaches. Given the excellent capability of a GNN (Graph Neural Network) in handling non-Euclidean space data and complex features, this paper subsequently employs a GNN to learn and classify vulnerabilities by capturing the implicit syntactic structure information in a TAC. Based on this, this paper introduces TACSan, a novel static vulnerability detection system based on a GNN designed to detect vulnerabilities in C/C++ programs. TACSan transforms the preprocessed source code into a TAC representation, adds control and data edges to create a graph structure, and then inputs it into the GNN for training. Comparative testing and evaluation of TACSan against other renowned static analysis tools, such as VulDeePecker and Devign, demonstrate that TACSan’s detection capabilities not only exceed those methods but also achieve substantial enhancements in accuracy and F1 score. Full article

In most re-identification approaches, embedding vectors are compared to identify the best match for a given query. However, this comparison does not take into account whether the encoded information in the embedding vectors was extracted reliably from the input images. We propose the first attempt that illustrates how all three types of uncertainty, namely model uncertainty (also known as epistemic uncertainty), data uncertainty (also known as aleatoric uncertainty), and distributional uncertainty, can be estimated for embedding vectors. We provide evidence that we do indeed estimate these types of uncertainty, and that each type has its own value for improving re-identification performance. In particular, while the few state-of-the-art approaches that employ uncertainty for re-identification during inference utilize only data uncertainty to improve single-shot re-identification performance, we demonstrate that the estimated model uncertainty vector can be utilized to modify the feature vector. We explore the best method for utilizing the estimated model uncertainty based on the Market-1501 dataset and demonstrate that we are able to further enhance the performance above the already strong baseline UAL. Additionally, we show that the estimated distributional uncertainty resembles the degree to which the current sample is out-of-distribution. To illustrate this, we divide the distractor set of the Market-1501 dataset into four classes, each representing a different degree of out-of-distribution. By computing a score based on the estimated distributional uncertainty vector, we are able to correctly order the four distractor classes and to differentiate them from an in-distribution set to a significant extent. Full article

In recent years, machine learning (ML) has been extensively used for the quantitative prediction of mineral resources. However, the accuracy of prediction models is often influenced by data quality, feature selection, and algorithm limitations. This research investigates the benefits of data-driven feature optimization techniques in enhancing model accuracy. Using the Lhasa region in Tibet as the study area, this research applies ensemble learning methods, such as random forest and gradient boosting tree techniques, to optimize 43 feature variables encompassing geology, geochemistry, and geophysics. The optimized feature variables are then input into a support vector machine (SVM) model to generate a prospectivity map. The performance characteristics of the SVM, RF_SVM, and GBDT_SVM models are evaluated using ROC curves. The results indicate that the feature-optimized GBDT_SVM model achieves superior classification accuracy and prediction effectiveness, demonstrating that feature optimization is a necessary step for mineral prospectivity mapping, as it can significantly improve the performance of mineral prospectivity prediction. Full article

Sleep apnea (SA) is a prevalent disorder characterized by recurrent events of nocturnal apnea. Polysomnography (PSG) represents the gold standard for SA diagnosis. This laboratory-based procedure is complex and costly, and less cumbersome wearable devices have been proposed for SA detection and monitoring. A novel textile multi-sensor monitoring belt recording electrocardiogram (ECG) and breathing frequency (BF) measured by thorax excursion was developed and tested in a sleep laboratory for validation purposes. The aim of the current study was to evaluate the diagnostic performance of ECG-derived heart rate variability and BF-derived breathing rate variability and their combination for the detection of sleep apnea in a population of patients with a suspicion of SA. Fifty-one patients with a suspicion of SA were recruited in the sleep laboratory of the Cantonal Hospital St. Gallen. Patients were equipped with the monitoring belt and underwent a single overnight laboratory-based PSG. In addition, some patients further tested the monitoring belt at home. The ECG and BF signals from the belt were compared to PSG signals using the Bland-Altman methodology. Heart rate and breathing rate variability analyses were performed. Features derived from these analyses were used to build a support vector machine (SVM) classifier for the prediction of SA severity. Model performance was assessed using receiver operating characteristics (ROC) curves. Patients included 35 males and 16 females with a median age of 49 years (range: 21 to 65) and a median apnea-hypopnea index (AHI) of 33 (IQR: 16 to 58). Belt-derived data provided ECG and BF signals with a low bias and in good agreement with PSG-derived signals. The combined ECG and BF signals improved the classification accuracy for SA (area under the ROC curve: 0.98; sensitivity and specificity greater than 90%) compared to single parameter classification based on either ECG or BF alone. This novel wearable device combining ECG and BF provided accurate signals in good agreement with the gold standard PSG. Due to its unobtrusive nature, it is potentially interesting for multi-night assessments and home-based patient follow-up. Full article

In order to accurately obtain the seedling emergence rate of breeding sunflower and to assess the quality of sowing as well as the merit of sunflower varieties, a method of extracting the sunflower seedling emergence rate using multi-source remote sensing information from unmanned aerial vehicles is proposed. Visible and multispectral images of sunflower seedlings were acquired using a UAV. The thresholding method was used to segment the excess green image of the visible image into vegetation and non-vegetation, to obtain the center point of the vegetation to generate a buffer, and to mask the visible image to achieve weed removal. The components of color models such as the hue–saturation value (HSV), green-relative color space (YCbCr), cyan-magenta-yellow-black (CMYK), and CIELAB color space (L*A*B) models were compared and analyzed. The A component of the L*A*B model was preferred for the optimization of K-means clustering to segment sunflower seedlings and mulch using the genetic algorithm, and the segmentation accuracy was improved by 4.6% compared with the K-means clustering algorithm. All told, 10 geometric features of sunflower seedlings were extracted using segmented images, and 10 vegetation indices and 48 texture features of sunflower seedlings were calculated based on multispectral images. The Pearson’s correlation coefficient method was used to filter the three types of features, and the geometric feature set, the vegetation index set, the texture feature set, and the preferred feature set were constructed. The construction of a sunflower plant number estimation model using the crested porcupine optimizer–support vector machine is proposed and compared with the sunflower plant number estimation models constructed based on decision tree regression, BP neural network, and support vector machine regression. The results show that the accuracy of the model based on the preferred feature set is higher than that of the other three feature sets, indicating that feature screening can improve the accuracy and stability of models; assessed using the CPO-SVR model, the accuracy of the preferred feature set was the highest, with an R² of 0.94, an RMSE of 5.16, and an MAE of 3.03. Compared to the SVR model, the value of the R² is improved by 3.3%, the RMSE decreased by 18.3%, and the MAE decreased by 18.1%. The results of the study can be cost-effective, accurate, and reliable in terms of obtaining the seedling emergence rate of sunflower field breeding. Full article

This study included 468 patients and aimed to use multimodal ultrasound radiomic technology to predict the malignancy of TI-RADS 4-5 thyroid nodules. First, radiomic features are extracted from conventional two-dimensional ultrasound (transverse ultrasound and longitudinal ultrasound), strain elastography (SE), and shear-wave-imaging (SWE) images. Next, the least absolute shrinkage and selection operator (LASSO) is used to screen out features related to malignant tumors. Finally, a support vector machine (SVM) is used to predict the malignancy of thyroid nodules. The Shapley additive explanation (SHAP) method was used to intuitively analyze the specific contributions of radiomic features to the model’s prediction. Our proposed model has AUCs of 0.971 and 0.856 in the training and testing sets, respectively. Our proposed model has a higher prediction accuracy compared to those of models with other modal combinations. In the external validation set, the AUC of the model is 0.779, which proves that the model has good generalization ability. Moreover, SHAP analysis was used to examine the overall impacts of various radiomic features on model predictions and local explanations for individual patient evaluations. Our proposed multimodal ultrasound radiomic model can effectively integrate different data collected using multiple ultrasound sensors and has good diagnostic performance for TI-RADS 4-5 thyroid nodules. Full article

Wild horse crossing events are a major concern for highway safety in rural and suburban areas in many states of the United States. This paper provides a practical and real-time approach to detecting wild horses crossing highways using 3D light detection and ranging (LiDAR) technology. The developed LiDAR data processing procedure includes background filtering, object clustering, object tracking, and object classification. Considering that the background information collected by LiDAR may change over time, an automatic background filtering method that updates the background in real-time has been developed to subtract the background effectively over time. After a standard object clustering and a fast object tracking method, eight features were extracted from the clustering group, including a feature developed to specifically identify wild horses, and a vertical point distribution was used to describe the objects. The classification results of the four classifiers were compared, and the experiments showed that the support vector machine (SVM) had more reliable results. The field test results showed that the developed method could accurately detect a wild horse within the detection range of LiDAR. The wild horse crossing information can warn drivers about the risks of wild horse–vehicle collisions in real-time. Full article