Search Results (553)

A portion of individuals diagnosed with primary central nervous system lymphomas (PCNSL) may experience early relapse or refractory (R/R) disease following treatment. This research explored the potential of MRI-based radiomics in forecasting R/R cases in PCNSL. Forty-six patients with pathologically confirmed PCNSL diagnosed between January 2008 and December 2020 were included in this study. Only patients who underwent pretreatment brain MRIs and complete postoperative follow-up MRIs were included. Pretreatment contrast-enhanced T1WI, T2WI, and T2 FLAIR imaging were analyzed. A total of 107 radiomic features, including 14 shape-based, 18 first-order statistical, and 75 texture features, were extracted from each sequence. Predictive models were then built using five different machine learning algorithms to predict R/R in PCNSL. Of the included 46 PCNSL patients, 20 (20/46, 43.5%) patients were found to have R/R. In the R/R group, the median scores in predictive models such as support vector machine, k-nearest neighbors, linear discriminant analysis, naïve Bayes, and decision trees were significantly higher, while the apparent diffusion coefficient values were notably lower compared to those without R/R (p < 0.05). The support vector machine model exhibited the highest performance, achieving an overall prediction accuracy of 83%, a precision rate of 80%, and an AUC of 0.78. Additionally, when analyzing tumor progression, patients with elevated support vector machine and naïve Bayes scores demonstrated a significantly reduced progression-free survival (p < 0.05). These findings suggest that preoperative MRI-based radiomics may provide critical insights for treatment strategies in PCNSL. Full article

Fault diagnosis is crucial to guarantee safe operation and extend the operating time while preventing the thermal runaway of the lithium-ion battery. This study presents a data-driven thermal runaway diagnosis framework where Bayesian optimization techniques are applied to optimize the hyperparameter of various machine learning techniques. We use different machine learning models such as support vector machine, naive Bayes, decision tree ensemble, and multi-layer perceptron to estimate a high likelihood of causes of thermal runaway by using the experimental measurements of open-source battery failure data. We analyze different evaluation metrics, including the prediction accuracy, confusion metrics, and receiver operating characteristic curves of different models. An experimental evaluation shows that the classification accuracy of the decision tree ensemble outperforms that of other models. Furthermore, the decision tree ensemble provides robust prediction accuracy even with the strictly limited dataset. Full article

Background and Objectives: Intra/postpartum hemorrhage stands as a significant obstetric emergency, ranking among the top five leading causes of maternal mortality. The aim of this study was to assess the predictive performance of four machine learning algorithms for the prediction of postpartum and intrapartum hemorrhage. Materials and Methods: A prospective multicenter study was conducted, involving 203 patients with or without intra/postpartum hemorrhage within the initial 24 h postpartum. The participants were categorized into two groups: those with intra/postpartum hemorrhage (PPH) and those without PPH (control group). The PPH group was further stratified into four classes following the Advanced Trauma Life Support guidelines. Clinical data collected from these patients was included in four machine learning-based algorithms whose predictive performance was assessed. Results: The Naïve Bayes (NB) algorithm exhibited the highest accuracy in predicting PPH, boasting a sensitivity of 96.3% and an accuracy of 98.6%, with a false negative rate of 3.7%. Following closely were the Decision Tree (DT) and Random Forest (RF) algorithms, each achieving sensitivities exceeding 94% with a false negative rate of 5.9%. Regarding severity classification I, the NB and Support Vector Machine (SVM) algorithms demonstrated superior predictive capabilities, achieving a sensitivity of 96.4%, an accuracy of 92.1%, and a false negative rate of 3.6%. The most severe manifestations of HPP were most accurately predicted by the NB algorithm, with a sensitivity of 89.3%, an accuracy of 82.4%, and a false negative rate of 10.7%. Conclusions: The NB algorithm demonstrated the highest accuracy in predicting PPH. A notable discrepancy in algorithm performance was observed between mild and severe forms, with the NB and SVM algorithms displaying superior sensitivity and lower rates of false negatives, particularly for mild forms. Full article

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

(1) Background: Globally, the kidney donor shortage has made the allocation process critical for patients awaiting a kidney transplant. Adopting Machine Learning (ML) models for donor–recipient matching can potentially improve kidney allocation processes when compared with traditional points-based systems. (2) Methods: This study developed an ML-based approach for donor–recipient matching. A comprehensive evaluation was conducted using ten widely used classifiers (logistic regression, decision tree, random forest, support vector machine, gradient boosting, boost, CatBoost, LightGBM, naive Bayes, and neural networks) across three experimental scenarios to ensure a robust approach. The first scenario used the original dataset, the second used a merged version of the dataset, and the last scenario used a hierarchical architecture model. Additionally, a custom ranking algorithm was designed to identify the most suitable recipients. Finally, the ML-based donor–recipient matching model was integrated into a web-based platform called Nephron. (3) Results: The gradient boost model was the top performer, achieving a remarkable and consistent accuracy rate of 98% across the three experimental scenarios. Furthermore, the custom ranking algorithm outperformed the conventional cosine and Jaccard similarity methods in identifying the most suitable recipients. Importantly, the platform not only facilitated efficient patient selection and prioritisation for kidney allocation but can be flexibly adapted for other solid organ allocation systems built on similar criteria. (4) Conclusions: This study proposes an ML-based approach to optimize donor-recipient matching within the kidney allocation process. Successful implementation of this methodology demonstrates significant potential to enhance both efficiency and fairness in kidney transplantation. Full article

Crop classification plays a pivotal role in agricultural remote sensing, offering critical insights into planting areas, growth monitoring, and yield evaluation. Leveraging the power of Google Earth Engine, this paper centers on the agricultural landscape of Krishna District as its study region. It explores the efficacy of multiple machine learning approaches, specifically Random Forest (RF), Classification and Regression Tree (CART), Naive Bayes, and Support Vector Machine (SVM), in composition of Sentinel-1 and Sentinel-2 satellite imagery for crop categorization. By meticulously assessing and contrasting the evaluations of these four classification methods, the results highlight the efficacy of RF. The overall accuracy (OA) regarding RF classification reaches 0.86, surpassing the results obtained by Naive Bayes (OA = 0.68), CART (OA = 0.63), and SVM (OA = 0.78). This scalable and straightforward classification methodology harnesses the advantages of cloud-based platforms for data handling and analysis. The timely and precise identification in crop typing holds immense importance for monitoring alterations in harvest patterns, estimating yields, and issuing crop safety alerts in the Krishna District and beyond. This paper contributes to the agricultural geospatial sensing domain by providing an innovative approach for accurate crop classification, with broad applications in precision farming and crop management. Full article

Context: We are advancing our efforts in agriculture by creating a crop prediction system with the help of machine learning. Our goal is to build an ML model that can estimate the properties of a crop. It will push ahead in agriculture by developing a predictive tool for crops using machine learning in agriculture in terms of both time and money. Our farmers can understand easily and analyze what best they are going to farm. Objective: The main theme of this project is to support farmers in yielding a good crop by making a robust model. By identifying the significant role of technology in advanced farming practices, we aim to create a solution that helps farmers make informed decisions about crop selections and agricultural practices. Utilizing data analytics and AI-driven insights enhances productivity and efficiency. Our final goal is to encourage farmers with the tools and knowledge they need to grow in an increasingly complex agricultural landscape. Methods: To complete this model, we collected data from different sources like the data of weather, humidity, pH value, temperature, nitrogen, phosphorous, and potassium values, and rainfall in mm. We implemented it through ML algorithms like GNB (Gaussian Naïve Bayes), SVM (Support Vector Machine), RF (Random Forest), and DT (Decision Tree). Result: The GNB classifier achieves an accuracy of 99%, surpassing others. Full article

This paper presents a novel methodology for detecting faults in wind turbine blades using computational learning techniques. The study evaluates two models: the first employs logistic regression, which outperformed neural networks, decision trees, and the naive Bayes method, demonstrating its effectiveness in identifying fault-related patterns. The second model leverages clustering and achieves superior performance in terms of precision and data segmentation. The results indicate that clustering may better capture the underlying data characteristics compared to supervised methods. The proposed methodology offers a new approach to early fault detection in wind turbine blades, highlighting the potential of integrating different computational learning techniques to enhance system reliability. The use of accessible tools like Orange Data Mining underscores the practical application of these advanced solutions within the wind energy sector. Future work will focus on combining these methods to improve detection accuracy further and extend the application of these techniques to other critical components in energy infrastructure. Full article

The tiger longicorn beetle, Xylotrechus chinensis Chevrolat (Coleoptera: Cerambycidae), has posed a significant threat to mulberry trees in Greece since its invasion in 2017, which may be associated with global warming. Detection typically relies on observing adult emergence holes on the bark or dried branches, indicating severe damage. Addressing pest threats linked to global warming requires efficient, targeted solutions. Remote sensing provides valuable, swift information on vegetation health, and combining these data with machine learning techniques enables early detection of pest infestations. This study utilized airborne multispectral data to detect infestations by X. chinensis in mulberry trees. Variables such as mean NDVI, mean NDRE, mean EVI, and tree crown area were calculated and used in machine learning models, alongside data on adult emergence holes and temperature. Trees were classified into two categories, infested and healthy, based on X. chinensis infestation. Evaluated models included Random Forest, Decision Tree, Gradient Boosting, Multi-Layer Perceptron, K-Nearest Neighbors, and Naïve Bayes. Random Forest proved to be the most effective predictive model, achieving the highest scores in accuracy (0.86), precision (0.84), recall (0.81), and F-score (0.82), with Gradient Boosting performing slightly lower. This study highlights the potential of combining remote sensing and machine learning for early pest detection, promoting timely interventions, and reducing environmental impacts. Full article

High student dropout rates are a critical issue in Somaliland, significantly impeding educational progress and socioeconomic development. This study leveraged data from the 2022 National Education Accessibility Survey (NEAS) to predict student dropout rates using supervised machine learning techniques. Various algorithms, including logistic regression (LR), probit regression (PR), naïve Bayes (NB), decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were employed to analyze the survey data. The analysis revealed school dropout rate of 12.67%. Key predictors of dropout included student’s grade, age, school type, household income, and type of housing. Logistic regression and probit regression models highlighted age and student’s grade as critical predictors, while naïve Bayes and random forest models underscored the significance of household income and housing type. Among the models, random forest demonstrated the highest accuracy at 95.00%, indicating its effectiveness in predicting dropout rates. The findings from this study provide valuable insights for educational policymakers and stakeholders in Somaliland. By identifying and understanding the key factors influencing dropout rates, targeted interventions can be designed to enhance student retention and improve educational outcomes. The dominant role of demographic and educational factors, particularly age and student’s grade, underscores the necessity for focused strategies to reduce dropout rates and promote inclusive education in Somaliland. Full article

LiDAR-unmanned aerial system (LiDAR-UAS) technology can accurately and efficiently obtain detailed and accurate three-dimensional spatial information of objects. The classification of objects in estuarine areas is highly important for management, planning, and ecosystem protection. Owing to the presence of slopes in estuarine areas, distinguishing between dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes is difficult. In addition, the imbalance in the number of point clouds also poses a challenge for accurate classification directly from point cloud data. A multifeature-assisted and multilayer fused neural network (MLF-PointNet++) is proposed for LiDAR-UAS point cloud classification in estuarine areas. First, the 3D shape features that characterize the geometric characteristics of targets and the visible-band difference vegetation index (VDVI) that can characterize vegetation distribution are used as auxiliary features to enhance the distinguishability of dense vegetation (lawns and trees) on slopes and the ground at the tops of slopes. Second, to enhance the extraction of target spatial information and contextual relationships, the feature vectors output by different layers of set abstraction in the PointNet++ model are fused to form a combined feature vector that integrates low and high-level information. Finally, the focal loss function is adopted as the loss function in the MLF-PointNet++ model to reduce the effect of imbalance in the number of point clouds in each category on the classification accuracy. A classification evaluation was conducted using LiDAR-UAS data from the Moshui River estuarine area in Qingdao, China. The experimental results revealed that MLF-PointNet++ had an overall accuracy (OA), mean intersection over union (mIOU), kappa coefficient, precision, recall, and F₁-score of 0.976, 0.913, 0.960, 0.953, 0.953, and 0.953, respectively, for object classification in the three representative areas, which were better than the corresponding values for the classification methods of random forest, BP neural network, Naive Bayes, PointNet, PointNet++, and RandLA-Net. The study results provide effective methodological support for the classification of objects in estuarine areas and offer a scientific basis for the sustainable development of these areas. Full article

This work conducts a rigorous examination of the economic influence of tourism in Saudi Arabia, with a particular focus on predicting tourist spending patterns and classifying spending behaviors during the COVID-19 pandemic period and its implications for sustainable development. Utilizing authentic datasets obtained from the Saudi Tourism Authority for the years 2015 to 2021, the research employs a variety of machine learning (ML) algorithms, including Decision Trees, Random Forests, K-Neighbors Classifiers, Gaussian Naive Bayes, and Support Vector Classifiers, all meticulously fine-tuned to optimize model performance. Additionally, the ARIMA model is expertly adjusted to forecast the economic landscape of tourism from 2022 to 2030, providing a robust predictive framework for future trends. The research framework is comprehensive, encompassing diligent data collection and purification, exploratory data analysis (EDA), and extensive calibration of ML algorithms through hyperparameter tuning. This thorough process tailors the predictive models to the unique dynamics of Saudi Arabia’s tourism industry, resulting in robust forecasts and insights. The findings reveal the growth trajectory of the tourism sector, highlighted by nearly 965,073 thousand tourist visits and 7,335,538 thousand overnights, with an aggregate tourist expenditure of SAR 2,246,491 million. These figures, coupled with an average expenditure of SAR 89,443 per trip and SAR 9198 per night, form a solid statistical basis for the employed predictive models. Furthermore, this research expands on how ML and AI innovations contribute to sustainable tourism practices, addressing key aspects such as resource management, economic resilience, and environmental stewardship. By integrating predictive analytics and AI-driven operational efficiencies, the study provides strategic insights for future planning and decision-making, aiming to support stakeholders in developing resilient and sustainable strategies for the tourism sector. This approach not only enhances the capacity for navigating economic complexities in a post-pandemic context, but also reinforces Saudi Arabia’s position as a premier tourism destination, with a strong emphasis on sustainability leading into 2030 and beyond. Full article

Background: The global pharmaceutical industry is crucial for providing medications but faces challenges in distributing products safely, especially in tropical and remote areas. Pharmaceuticals require careful transport control to maintain quality; therefore, manufacturers must adopt optimal distribution strategies to ensure product quality throughout the supply chain. The current research focused on creating a model to assess risk levels and predict risk categorization (low, moderate, and high) associated with thermal mapping across pharmaceutical transportation pathways. Methods: Data from a company for pharmaceutical logistics in Brazil were used. The data had 85,261 instances and six attributes (season, origin, destination, route, temperature, and temperature excursion). The dataset consisted of critical destinations, including the shipment time, cargo temperature, and route information. The classification algorithms (CART-Decision Tree, NB-Naive Bayes, and MP-Multilayer Perceptron) were used to build up a model of rules for predicting risk levels in thermal mapping routes; Results: The MP model presented the best performance, indicating a better application probability. The machine learning model is the basis for an automated risk prediction for routes of pharmaceutical transportation; Conclusions: the developed MP model might automatically predict risk during the distribution of pharmaceutical products, which might lead to optimizing time and costs. Full article

This study examines the performance of various machine learning (ML) models in predicting Interstitial Glucose (IG) levels using data from wrist-worn wearable sensors. The insights from these predictions can aid in understanding metabolic syndromes and disease states. A public dataset comprising information from the Empatica E4 smart watch, the Dexcom Continuous Glucose Monitor (CGM) measuring IG, and a food log was utilized. The raw data were processed into features, which were then used to train different ML models. This study evaluates the performance of decision tree (DT), support vector machine (SVM), Random Forest (RF), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), lasso cross-validation (LassoCV), Ridge, Elastic Net, and XGBoost models. For classification, IG labels were categorized into high, standard, and low, and the performance of the ML models was assessed using accuracy (40–78%), precision (41–78%), recall (39–77%), F1-score (0.31–0.77), and receiver operating characteristic (ROC) curves. Regression models predicting IG values were evaluated based on R-squared values (−7.84–0.84), mean absolute error (5.54–60.84 mg/dL), root mean square error (9.04–68.07 mg/dL), and visual methods like residual and QQ plots. To assess whether the differences between models were statistically significant, the Friedman test was carried out and was interpreted using the Nemenyi post hoc test. Tree-based models, particularly RF and DT, demonstrated superior accuracy for classification tasks in comparison to other models. For regression, the RF model achieved the lowest RMSE of 9.04 mg/dL with an R-squared value of 0.84, while the GNB model performed the worst, with an RMSE of 68.07 mg/dL. A SHAP analysis identified time from midnight as the most significant predictor. Partial dependence plots revealed complex feature interactions in the RF model, contrasting with the simpler interactions captured by LDA. Full article

In the paper, personalized results are presented in the methodology for monitoring information security based on voice authentication. Integration of sound preprocessing and Machine Learning techniques for feature extraction, training, and validation of classification models has been implemented. The objects of research are staked mixed-test voice profiles. Classifies were selected with quantitative evaluation under a threshold of 90.00% by Naive Bayes and Discriminant Analysis. Significantly improved accuracy to approximate levels of 96.0% was established at Decision Tree synthesis. Strongly satisfactory performance indices were reached at the diagnosis of voice profiles using Feed-Forward and Probabilistic Neural Networks, respectively, 98.00% and 100.00%. Full article