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It is necessary to convert to automation in a tomato hydroponic greenhouse because of the aging of farmers, the reduction in agricultural workers as a proportion of the population, COVID-19, and so on. In particular, agricultural robots are attractive as one of the ways for automation conversion in a hydroponic greenhouse. However, to develop agricultural robots, crop monitoring techniques will be necessary. In this study, therefore, we aimed to develop a maturity classification model for tomatoes using both support vector classifier (SVC) and snapshot-type hyperspectral imaging (VIS: 460–600 nm (16 bands) and Red-NIR: 600–860 nm (15 bands)). The spectral data, a total of 258 tomatoes harvested in January and February 2022, was obtained from the tomatoes’ surfaces. Spectral data that has a relationship with the maturity stages of tomatoes was selected by correlation analysis. In addition, the four different spectral data were prepared, such as VIS data (16 bands), Red-NIR data (15 bands), combination data of VIS and Red-NIR (31 bands), and selected spectral data (6 bands). These data were trained by SVC, respectively, and we evaluated the performance of trained classification models. As a result, the SVC based on VIS data achieved a classification accuracy of 79% and an F1-score of 88% to classify the tomato maturity into six stages (Green, Breaker, Turning, Pink, Light-red, and Red). In addition, the developed model was tested in a hydroponic greenhouse and was able to classify the maturity stages with a classification accuracy of 75% and an F1-score of 86%.

Abstract This study evaluates the effectiveness of various machine learning algorithms in predicting startup success and explores the performance improvement achieved by applying Principal Component Analysis (PCA) to the models. By analyzing logistic regression, support vector classifier (SVC), XGBoost, and other supervised learning algorithms, the study demonstrates that PCA enhances the generalization performance of most models. Notably, Support Vector Classifier (SVC) showed an accuracy of 0.78, precision of 0.83, recall of 0.73, and F1 score of 0.74 without PCA, but performance significantly improved with PCA, recording an accuracy of 0.90, precision of 0.90, recall of 0.89, and F1 score of 0.89. Academically, this research contributes to the literature by examining how dimension reduction can boost the accuracy of machine learning models for startup success prediction, providing a valuable intersection of machine learning and venture capital studies. Practically, it offers investors AI-driven decision-making tools to enhance the precision of investment evaluations and better identify startups with high growth potential. Despite its contributions, this study is limited by the specific dataset used, suggesting that future research could explore various datasets and alternative dimension reduction techniques. Future studies could also assess real-time data application and incorporate deep learning models to improve predictive performance in startup success evaluation.

Biomarkers have already been proposed as powerful classification features for use in the training of neural network-based and other machine learning and artificial intelligence-based prognostic models in the scientific field of personalized nutrition. In this paper, we construct and study cascaded SVM-based classifiers for automated metabolic syndrome diagnosis. Specifically, using blood exams, we achieve an average accuracy of about 84% in correctly classifying body mass index. Similarly, cascaded SVM-based classifiers achieve a 74% accuracy in correctly classifying systolic blood pressure. Next, we propose and implement a system that achieves an 84% accuracy in metabolic syndrome prediction. The proposed system relies not only on prediction of the body mass index but also on prediction from blood exams of total cholesterol, triglycerides and glucose. For the aim of self-completeness of the paper, the key concepts with regard to metabolic syndrome are summarized, and a review of previous related work is included. Finally, conclusions are drawn and indications for related future research are outlined.

ObjectiveThis study sought to explore the utility of machine learning models in predicting insomnia severity based on Traditional Chinese Medicine (TCM) constitution classifications, with an aim to discuss the potential applications of such models in the treatment and prevention of insomnia.MethodsWe analyzed a dataset of 165 insomnia patients from the Shanghai Minhang District Integrated Traditional Chinese and Western Medicine Hospital. TCM constitution was assessed using a standardized Constitution in Chinese Medicine (CCM) scale. Sleep quality, or insomnia severity, was evaluated using the Spiegel Sleep Questionnaire (SSQ). Machine learning models, including Random Forest Classifier (RFC), Support Vector Classifier (SVC), and K-Nearest Neighbors (KNN), were utilized. These models were optimized using Grid Search algorithm and were trained and tested on stratified patient data, with the TCM constitution classifications serving as primary predictors.ResultsThe RFC outperformed others, achieving a weighted average accuracy, precision, recall, and F1-score of 0.91, 0.94, 0.92, and 0.92 respectively, it also effectively classified the severity of insomnia with high area under receiver operating characteristic curve (AUC-ROC) values. Feature importance analysis demonstrated the Damp-heat constitution as the most influential predictor, followed by Yang-deficiency, Qi-depression, Qi-deficiency, and Blood-stasis constitutions.ConclusionThe results demonstrate the potent utility of machine learning, specifically RFC, coupled with TCM constitution classifications in predicting insomnia severity. Notably, the constitution classifications such as Damp-heat and Yang-deficiency emerged as crucial determinants, emphasizing its potential in guiding targeted insomnia treatments. This approach enables the development of more personalized and efficient interventions, thereby enhancing patient outcomes.

Purpose Stress is widespread in the modern world. It is a complex fusion of psychological and physiological tension that leads to various health issues, such as heart disease, high blood pressure, and widespread anxiety. Although monitoring emotions, especially stress, is critically challenging, however, to tackle this challenge head-on, advancements in machine learning have paved the way for unraveling the complexities of human emotions and detecting early signs of stress. Methods In this exploratory study, we introduce an innovative framework built on a Sequential Feature Extractor (SFE), which collaborates seamlessly with k-Nearest Neighbor (KNN), linear Support Vector Classifier (SVC), Support Vector Machine (SVM), and Logistic Regression (LR). The model identifies seven crucial features in this context through refined preprocessing methods. Results The SFE + KNN model stands out by leveraging its attributes, displaying remarkable precision and an F1-Score of 88.00% when detecting stress. Furthermore, concerning individual emotions, this model excels in various ways. The SFE + SVM methodology accurately identifies Transient emotions at a rate of 94.00% and flags Baseline emotions with a perfect score of 100.00%. Amusement is deftly grasped with 79.00% accuracy using SFE + LR. Meanwhile, the SFE + SVC approach astutely recognizes Stress at 84.00% and Meditation at 92.00%. These results underscore the model’s capability to untangle the complex tapestry of human sentiments and stress responses successfully. Conclusions The study utilizes the publicly available WESAD Dataset and achieves impressive accuracy levels in detecting stress and various emotions. The approach taken in this study contributes to understanding human emotional experiences and coping mechanisms, leading to improved resilience and emotional intelligence.

The characteristics of soil moisture content (SMC) distribution in an area are necessarily analyzed for the design and construction of sponge cities. Combining remote sensing data with experimental data, this paper establishes a machine learning model to reveal the characteristics of SMC. Taking Beijing as an example, the SMC distribution was obtained and the characteristics were analyzed after training and validating. When comparing different machine learning methods, it can be concluded that the support vector classifier (SVC) method trained with remote sensing and grayscale data can achieve the highest accuracy (76.69%). The calculation results show that the districts with the highest and lowest SMC value are Xicheng District (19.94%) and Daxing District (11.04%), respectively, in Beijing. The mean SMC value of Beijing is 15.65%. The SMC distribution characteristic in Beijing shows that the soil in the west and north are relatively wet, while the soil in the east and south are relatively dry. Therefore, it is suggested that the timely monitoring of the SMC of vegetation covered areas at the north and west should be carried out. Water conservation facilities also need to be established with the development of city constructions in the south and east areas.

Parkinson’s disease is caused by tumors, a progressive nervous system disorder that affects development. Stiffness or slow movement is the basic sign of this problem. There is no cure for Parkinson's disease, but some drugs can improve the condition, and sometimes brain surgery can help patients improve their condition. Using machine learning strategies, we developed a priori model to identify patients affected by Parkinson’s disease. By controlling the importance of features, we recognize the most significant indicators of patients who belong to this disease-related estimate. The model-based logic strategies we use include logistic regression (LR), k nearest neighbors (k-NN), support vector classifier (SVC), gradient boosting classifier (GBC), and random forest classifier (RF). The estimated reliability, like the ROC curve and confusion matrix, is five-fold cross-validation. We construct another model that depends on the ensemble method and utilization of majority voting, weighted average, bagging, Ada_boost and Gradient_boosting. The model is also recognized in the five-fold cross-validation and confusion matrix, precision; recall rate and F1 score. The correlation matrix is also drawn to show whether these features are related to each other. Our findings indicate that, compared with different methods, machine learning can provide more reliable clinical outcome assessments for patients with Parkinson’s disease. Among the five algorithms, the higher accuracy fluctuates in the middle of 70–95%. Among them, SVC obtains 93.83% accuracy from the five basic classifiers, and Bagging obtains 73.28% accuracy from the ensemble technique.