Random Forest Classification Algorithm Research Articles

Comparison between Sentinel-2 and WorldView-3 sensors in mapping wetland vegetation communities of the Grassland Biome of South Africa, for monitoring under climate change

Monitoring changes in the areal extent and geographic distribution of wetland vegetation has become more critical considering the impact of anthropogenic and climate changes. We compared the capabilities of the optical space-borne sensors Sentinel-2 and WorldView-3 (WV3) to distinguish between wetland and terrestrial vegetation for improved reporting to the Sustainable Development Goal (SDG) sub-indicator 6.6.1a, and also map different wetland vegetation communities for two catchments in the Grassland Biome of South Africa. Ground truthing of vegetation communities was conducted between 2016 and 2018. A Random Forest classification algorithm was used with a 100-fold cross-validation to assess mean accuracies using all combinations of bands, a digital elevation model generated from fine-scale contours, spectral vegetation indices (VIs) and above-ground biomass (AGB). Five and eight wetland vegetation classes were mapped for Hogsback and Tevredenpan, respectively, of a total of 13 classes for each of the sites. Wetland and terrestrial vegetation were found to be highly separable, with overall accuracies (OAs) attaining 91–99% and individual user's accuracies 88–99% for both sensors and study areas. Even though the wetland vegetation communities consisted of a mosaic of smaller communities, monodominant species and plant functional type classes, they were found to be highly separable across sensors and study areas. The highest average OA of 83% for Hogsback's wetland vegetation communities was achieved using WV3 bands with elevation, AGB and the VIs, while the Sentinel-2 bands, elevation, AGB and VIs attained an average OA of 78%. For Tevredenpan, the use of the Sentinel-2 bands and elevation achieved the highest mean OA of 79% for the classification of wetland vegetation communities, while the WV3 (in this case the short-wave infrared bands were not available owing to shortage of funding) maximized at 74%. The inclusion of elevation data and spectral indices in the classification scenarios of wetland vegetation communities increased the OA by 4–17%. Omitting the red-edge and shortwave infrared bands for classification of vegetation classes resulted in a varied response across sensors and study areas, but decreased the OA by 4.8–7.3% when using the Sentinel-2 sensors. These results show promise for improved reporting and monitoring of the extent and types of palustrine wetlands in the Grassland Biome of South Africa using freely-available Sentinel-2 data.

Brain tumor segmentation and classification using hybrid deep CNN with LuNetClassifier

Brain tumour detection is essential for improving patient survival and prospects. This research work necessitates a physical examination with magnetic resonance imaging (MRI). As a result, computational algorithms are required for more accurate tumour diagnosis. Moreover, evaluating shape, boundaries, volume, size, segmentation, tumour detection, and classification remains difficult. To resolve these problems, hybrid deep convolutional neural network (DCNN) with enhanced LuNet classifier algorithm has been proposed for brain tumour detection. The main intention of the proposed approach is to locate the tumor and classify brain tumors as Glioma or Meningioma. For preprocessing, a Laplacian Gaussian filter (LOG) is used. A Fuzzy C Means with Gaussian mixture model (FCM-GMM) algorithm has been proposed for segmentation. To begin, use the extended LuNet algorithm to divide the data. A VGG16 extraction feature yields thirteen categorical features. Overall, the proposed method attempts to improve the performance of classifiers. The proposed LuNet classifiers are an excellent deep learning technique because it has low computational complexity, are inexpensive, and are simple to use even for those with little training experience. The simulated outcomes of the proposed algorithm compared to other conventional algorithms like SVM, Decision tree, Random forest, Alexnet, Resnet-50 and Googlenet classifier algorithm. The introduced hybrid approach achieves 99.7% accuracy. When compared to other existing algorithms, the proposed method outperforms them.

Intermediate-severity disturbance impacts in a mixedwood forest: A multi-scale analysis

Mixed Quercus-Pinus stands are increasingly desired by forest managers to achieve a range of objectives, including biodiversity enhancement and resilience to global change. To create and perpetuate desired mixed Quercus-Pinus stand composition and structure, quantitative information on natural disturbance impacts on stand development and succession is necessary. Wind is the most common natural canopy disturbance in eastern North America, and impacts vary in severity and spatiotemporal scales. Intermediate-severity disturbance (ISD; events that occur along a classification gradient between frequent gap-scale and infrequent, stand-replacing events) is hypothesized to be an important driver of mixed Quercus-Pinus creation and maintenance. Our overarching goal was to quantify patterns of intermediate-severity wind disturbance in a mixed Quercus-Pinus echinata Mill. forest on the Cumberland Plateau in Tennessee, USA. Specifically, our objectives were to: 1) quantify post-disturbance species composition, stand structure, and residual tree spatial patterns, 2) infer individual-tree, neighborhood, and site-specific impacts on tree mortality, and 3) describe frequency, size, shape, and spatial distribution of ISD-created canopy openings. We inventoried plots in Pinus echinata-dominated, disturbance-impacted portions of the forest. To infer individual-tree and spatially-explicit neighborhood characteristics that influenced survival probability, we applied a random forest classification algorithm. To document changes in tree spatial distribution before and after disturbance, we used spatial point pattern analysis. To characterize forest-scale disturbance patterns, we classified canopy gaps from high-resolution orthoimagery. The most important predictors of survival were basal area, taxonomic group, and distance to nearest neighbor. Pre-disturbance trees and residuals were spatially randomly distributed. Most detected openings were 50–150 m2, and the opening size-frequency distribution exhibited a reverse J-shape. Openings were spatially clustered within the forest at distances < 200 m and complex in shape. We provide quantitative recommendations on the frequency, size, shape, and spatial distribution of silvicultural entries patterned after natural disturbance. To emulate natural disturbance patterns, we recommend patch seedtree harvests with reserves or patch clearcuts with reserves, and these harvest-created openings should be concentrated (i.e., clustered) in portions of the stand in which P. echinata is most dominant and/or most competitive. Site preparation with prescribed fire, herbicide, and/or mechanical thinning may also be necessary to prepare the seedbed and reduce hardwood competition to favor Pinus establishment in openings.

Early detection of pine wilt disease tree candidates using time-series of spectral signatures.

Pine wilt disease (PWD), caused by pine wood nematode (PWN), poses a tremendous threat to global pine forests because it can result in rapid and widespread infestations within months, leading to large-scale tree mortality. Therefore, the implementation of preventive measures relies on early detection of PWD. Unmanned aerial vehicle (UAV)-based hyperspectral images (HSI) can detect tree-level changes and are thus an effective tool for forest change detection. However, previous studies mainly used single-date UAV-based HSI data, which could not monitor the temporal changes of disease distribution and determine the optimal detection period. To achieve these purposes, multi-temporal data is required. In this study, Pinus koraiensis stands were surveyed in the field from May to October during an outbreak of PWD. Concurrently, multi-temporal UAV-based red, green, and blue bands (RGB) and HSI data were also obtained. During the survey, 59 trees were confirmed to be infested with PWD, and 59 non-infested trees were used as control. Spectral features of each tree crown, such as spectral reflectance, first and second-order spectral derivatives, and vegetation indices (VIs), were analyzed to identify those useful for early monitoring of PWD. The Random Forest (RF) classification algorithm was used to examine the separability between the two groups of trees (control and infested trees). The results showed that: (1) the responses of the tree crown spectral features to PWD infestation could be detected before symptoms were noticeable in RGB data and field surveys; (2) the spectral derivatives were the most discriminable variables, followed by spectral reflectance and VIs; (3) based on the HSI data from July to October, the two groups of trees were successfully separated using the RF classifier, with an overall classification accuracy of 0.75–0.95. Our results illustrate the potential of UAV-based HSI for PWD early monitoring.

Application of Random Forest Algorithm on Tornado Detection

Tornadoes are highly destructive small-scale extreme weather processes in the troposphere. The weather radar is one of the most effective remote sensing devices for the monitoring and early warning of tornadoes. The existing tornado detection algorithms based on radar data are unsupervised and have strict multi-altitude constraints, such as the tornado detection algorithm based on tornado vortex signatures (TDA-TVS), which may lead to high false alarm rates, and the performance of the detection algorithm is greatly affected by the radar data quality control algorithm. A novel TDA-RF algorithm based on the random forest (RF) classification algorithm is proposed for real-time tornado identification of the S-band China new generation of Doppler weather radar (CINRAD-SA). The TDA-RF algorithm uses velocity features to identify tornadoes and adds features related to reflectivity and velocity spectrum width in radar level-II data. Historical CINRAD-SA tornado data from 2006–2015 are used to construct the tornado dataset and train the TDA-RF model. The performance of TDA-RF is evaluated using CINRAD-SA data from five tornadoes of 2016–2020 with enhanced Fujita(EF) scale ratings ranging from EF0 to EF4 and distances from 10 to 130 km to the radar. TDA-RF performs well overall with the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) of 71%, 29%, and 55%, respectively. Moreover, the TDA-RF improves POD and CSI, and reduces FAR compared to the TDA-TVS. The maximum tornado early-warning time of TDA-RF is 17 min, and the average is 6 min; TDA-RF can provide classification probability according to the tornado generation and development process to facilitate tracking ability.

Application of Random Forest Classifier Algorithm in Indonesian Sign Language System (Sibi) Detection System

Sign language is a language that prioritizes body movements and facial expressions as symbols to communicate with each other. Deaf and speech-impaired groups are the main users of sign language. One type of sign language in Indonesia is SIBI (Indonesian Sign Language System). SIBI (Indonesian Sign Language System) is a form of spoken language that is converted into sign language. Sign language is not only used as a means of communication and interaction between deaf and mute people, but also with normal people. However, until now there is still a communication gap between people who are deaf or mute and normal people. Then made a system design that can detect the sign language symbol SIBI (Indonesian Sign Language System). This system is made using the Random Forest Classifier algorithm with the help of MediaPipe Holistic and OpenCV and is made using the Python programming language. In this study, a dataset was created which was limited to 10 (ten) classes of symbols representing words in the SIBI (Indonesian Sign System) with a total of 8734 data lines which were then pre-processed by dividing the dataset into 70% training data and 30% test data. This study contains stages such as making detection, creating datasets, training classification models, and testing. The test is carried out by calculating the accuracy using the Confusion Matrix and then getting the Accuracy rate of 98.6%, Precision of 98.6%, and Recall of 98.66%. With the creation of the SIBI (Indonesian Language Sign System) detection system, it is hoped that it can reduce the gap between people who are deaf or speech impaired and normal people in communicating. Contribute to knowledge about symbol detection in SIBI (Indonesian Sign System) using the Random Forest Classifier algorithm as well as MediaPipe Holistic and OpenCV. As well as preserving and popularizing SIBI (Indonesian Sign System) as a sign language in Indonesia.

Physical Activity among U.S. Preschool-Aged Children: Application of Machine Learning Physical Activity Classification to the 2012 National Health and Nutrition Examination Survey National Youth Fitness Survey.

Early childhood is an important development period for establishing healthy physical activity (PA) habits. The objective of this study was to evaluate PA levels in a representative sample of U.S. preschool-aged children. The study sample included 301 participants (149 girls, 3–5 years of age) in the 2012 U.S. National Health and Examination Survey National Youth Fitness Survey. Participants were asked to wear an ActiGraph accelerometer on their wrist for 7 days. A machine learning random forest classification algorithm was applied to accelerometer data to estimate daily time spent in moderate- and vigorous-intensity PA (MVPA; the sum of minutes spent in running, walking, and other moderate- and vigorous-intensity PA) and total PA (the sum of MVPA and light-intensity PA). We estimated that U.S. preschool-aged children engaged in 28 min/day of MVPA and 361 min/day of total PA, on average. MVPA and total PA levels were not significantly different between males and females. This study revealed that U.S. preschool-aged children engage in lower levels of MVPA and higher levels of total PA than the minimum recommended by the World Health Organization.

Orman Zararlılarının Verdiği Zararın Google Earth Engine Kullanılarak İzlenmesi

Forests tend to disappear for various reasons. Insects have problems such as very high reproduction and spread rates, unpredictable distribution directions, and inability to intervene quickly in the fight against insects. For this reason, harmful insects are at the beginning of many factors that cause forest loss. For the study, Bursa-İnegöl Tahtaköprü location, which was affected by the red-tailed beech caterpillar (Calliteara pudibunda) affecting beech trees, was determined as the study area. This study was carried out on the Google Earth Engine (GEE) interface. For the study area, using Sentinel-2A and Landsat-8 satellite data for the period 2017-2021, 6 different plant indexes; NDVI, EVI, SAVI, RVI, TVI, NPCRI were calculated and it was determined that the most affected period from pests was October 2019. These indices were also calculated for October 2019, and classification was made for four different data combinations in 5 different classes (diseased, healthy, road, power line, settlement) using the Random Forest Classification Algorithm and Support Vector Machines methods, which are machine learning-based classification methods. The classification result was compared for Landsat-8 and Sentine-2A, and the best result was the combination including all plant indices for Sentinel-2A satellite data, and the overall accuracy was calculated as 98.48 and the kappa coefficient as 97.68.

Improving the Land Cover Mapping Accuracy of the Sentinel-2 Imagery on Google Earth Engine

Accurate land cover information is necessary for successful monitoring, planning and management of the land cover features. Thanks to free-access satellite images, studies have focused on the creation of more accurate thematic maps, which have been used as a base data in many applications. The cloud-based Google Earth Engine (GEE) service makes it easier to access, store and process these satellite images. This study aims to improve the accuracy of a land cover map produced with the Sentinel-2 satellite image. For this purpose, as the very first step, study site was classified using only traditional bands of the Sentinel-2 data. To improve the classification accuracy, Sentinel-1 Synthetic Aperture Radar (SAR) data, Advanced Land Observing Satellite (ALOS) World 3D data, various spectral indices and gray-level co-occurrence matrix (GLCM) features were added to the traditional bands of the Sentinel-2 data, leading to a multi-source classification process. In this study, where the city center of Trabzon was selected as the study area, the accuracy of the land cover map produced using the Random Forest (RF) classification algorithm was increased from 83.51% to 92.78% with the auxiliary data used.

Machine Learning Models for Identification and Prediction of Toxic Organic Compounds Using Daphnia magna Transcriptomic Profiles.

A wide range of environmental factors heavily impact aquatic ecosystems, in turn, affecting human health. Toxic organic compounds resulting from anthropogenic activity are a source of pollution in aquatic ecosystems. To evaluate these contaminants, current approaches mainly rely on acute and chronic toxicity tests, but cannot provide explicit insights into the causes of toxicity. As an alternative, genome-wide gene expression systems allow the identification of contaminants causing toxicity by monitoring the organisms’ response to toxic substances. In this study, we selected 22 toxic organic compounds, classified as pesticides, herbicides, or industrial chemicals, that induce environmental problems in aquatic ecosystems and affect human-health. To identify toxic organic compounds using gene expression data from Daphnia magna, we evaluated the performance of three machine learning based feature-ranking algorithms (Learning Vector Quantization, Random Forest, and Support Vector Machines with a Linear kernel), and nine classifiers (Linear Discriminant Analysis, Classification And Regression Trees, K-nearest neighbors, Support Vector Machines with a Linear kernel, Random Forest, Boosted C5.0, Gradient Boosting Machine, eXtreme Gradient Boosting with tree, and eXtreme Gradient Boosting with DART booster). Our analysis revealed that a combination of feature selection based on feature-ranking and a random forest classification algorithm had the best model performance, with an accuracy of 95.7%. This is a preliminary study to establish a model for the monitoring of aquatic toxic substances by machine learning. This model could be an effective tool to manage contaminants and toxic organic compounds in aquatic systems.

Learning Analytics: Analysis of Methods for Online Assessment

Assessment is a fundamental part of teaching and learning. With the advent of online learning platforms, the concept of assessment has changed. In the classical teaching methodology, the assessment is performed by an assessor, while in an online learning environment, the assessment can also take place automatically. The main purpose of this paper is to carry out a study on Learning Analytics, focusing in particular on the study and development of methodologies useful for the evaluation of learners. The goal of this work is to define an effective learning model that uses Educational Data to predict the outcome of a learning process. Supervised statistical learning techniques were studied and developed for the analysis of the OULAD benchmark dataset. The evaluation of the learning process of learners was performed by making binary predictions about passing or failing a course and using features related to the learner’s intermediate performance as well as the interactions with the e-learning platform. The Random Forest classification algorithm and other ensemble strategies were used to perform the task. The performance of the models trained on the OULAD dataset was excellent, showing an accuracy of 95% in predicting the students’ learning assessment.

P15.03.A The value of 7 Tesla MR spectroscopic imaging for improved preoperative determination of the tumor grade and IDH status in gliomas: preliminary data

Abstract Introduction A new generation of MR spectroscopic imaging (MRSI) methods using 7T scanners have demonstrated the capability to resolve more neuro- and oncometabolites at higher resolutions than clinical routine MRSI. In a cohort of glioma patients, we explored the automated preoperative and noninvasive classification of IDH-mutation status and tumor grade based on 7T MRSI. Methods This retrospective study included 36 patients (15 female) with histologically confirmed diffusely infiltrating glioma WHO grade 2-4 (9 grade 2, 9 grade 3 and 18 grade 4) and known IDH status (21 IDH1-mut, 15 IDH-wt) with an available 7T MRSI scan of sufficient data quality. The 3D MRSI scan had a 3.4 mm isotropic resolution and 15 minutes acquisition time. 12 spectral components were classified voxel-wise, including choline, glutamine and glycine. Within a tumor segmentation based on routine 3T imaging, we used a random forest algorithm for the voxel-wise classification of IDH mutation and grade (into low or high grade). Training used the leave-one-out cross validation method (i.e., for every patient data set, the other 35 datasets were used as training set) and feature selection out of the available combinations for metabolite ratios (e.g., glutamine to choline). The resulting voxel classifications were aggregated into a mean probability per patient that was the base for receiver-operator characteristic (ROC) curves both for grade and IDH status. Results The classification algorithm obtained an area under the curve (AUC) for IDH determination of 0.85 (e.g., 75% sensitivity and 95% specificity). For grade determination, the AUC was 0.88 (e.g., 87% sensitivity and 89% specificity). In comparison, the AUC per voxel would have resulted in an AUC of 0.66 for both. Further, classification by individual metabolite ratios resulted in lower AUCs in all cases. Conclusions According to our preliminary data, preoperative 7T MRSI is capable to determine the correct glioma grade and IDH status with high sensitivity and specificity by leveraging the extended metabolic panel width and voxel amount. By increasing this cohort in future, we intend to confirm our initial results and we also plan to extend classification to more molecular-pathological features (e.g., TERT). Thus, even a voxel-wise classification of tumor microenvironments could be attempted. Further improvements in 7T MRSI methodology such as absolute instead of relative quantification would also aid these attempts. In summary, 7T MRSI has shown its potential for improved preoperative characterization of gliomas.

Predictors Using Machine Learning of Complete Peroneal Nerve Palsy Recovery After Multiligamentous Knee Injury: A Multicenter Retrospective Cohort Study.

Background:Peroneal nerve (PN) palsy is one of the most debilitating sequelae of multiligamentous knee injuries (MLKIs). There is limited research on recovery from complete PN palsy.Purpose/Hypothesis:The purpose of this study was to characterize PN injuries and develop a predictive model of complete PN recovery after MLKI using machine learning. It was hypothesized that elevated body mass index (BMI) would be predictive of lower likelihood of recovery.Study Design:Case-control study; Level of evidence, 3.Methods:The authors conducted a retrospective review of patients seen at 2 urban hospital systems for treatment of MLKI with associated complete PN palsy, defined as the presence of complete foot drop with or without sensory deficits on physical examination. Recovery was defined as the complete resolution of foot drop. A random forest (RF) classifier algorithm was used to identify demographic, injury, treatment, and postoperative variables that were significant predictors of recovery from complete PN palsy. Validity of the RF model was assessed using overall accuracy, F1 score, and area under the receiver operating characteristic curve (AUC).Results:Overall, 16 patients with MLKI with associated complete PN palsy were included in the cohort. Among them, 75% (12/16) had documented knee dislocation requiring reduction. Complete recovery occurred in 4 patients (25%). Nerve contusions on magnetic resonance imaging were more common among patients without PN recovery, but there were no other significant differences between recovery and nonrecovery groups. The RF model found that older age, increasing BMI, and male sex were predictive of worse likelihood of PN recovery. The model was found to have good validity, with a classification accuracy of 75%, F1 score of 0.86, and AUC of 0.64.Conclusion:The RF model in this study found that increasing age, BMI, and male sex were predictive of decreased likelihood of nerve recovery. While further study of machine learning models with larger patient data sets is required to identify the most superior model, these findings present an opportunity for orthopaedic surgeons to better identify, counsel, and treat patients with MLKIs and concomitant complete PN palsy.

An Improved Retrieval Method for Porphyra Cultivation Area Based on Suspended Sediment Concentration

The rapid expansion of Porphyra farming in China lends considerable urgency to developing a satellite remote sensing retrieval method to monitor its cultivation, in order to promote sustainable economic development and protective utilization of ecosystem-oriented marine natural resources. For medium-resolution satellite imagery such as HY-1C images, pixel-by-pixel techniques are appropriate; however, many factors affect the retrieval accuracy of the Porphyra cultivation area. In coastal regions, Porphyra and suspended sediment radiate a similar spectrum, which inevitably causes errors in the identification of the Porphyra. To improve the overall retrieval accuracy of Porphyra cultivation area from medium-resolution HY-1C images, we considered suspended sediment concentration (SSC) as an independent variable and constructed a new model in conjunction with high-resolution Sentinel-2 satellite images using a linear regression method in Haizhou Bay, China. A comparative analysis was performed with a traditional random forest classification algorithm and pixel-based dichotomy model in different SSC seawater. The results showed that the new model expressed the best ability to supervise Porphyra cultivation area, and its overall relative error and root mean square deviation, whether in area or in validation sample points, were the lowest among the models. The experiment was performed by removing the SSC variable while using the same processes as in the new model, and the results indicate that the SSC played an important role in new model, which is suitable to be applied to coastal seawater containing more suspended sediment, as in the HY-1C coastal zone image. The application of the new model on temporal change in the retrieved results was indirectly verified as effective. This study provides an effective method to exactly extract Porphyra cultivation area in the coastal sea using medium-resolution HY-1C satellite imagery.

Clinical Implication of Machine Learning Based Cardiovascular Disease Prediction Using IBM Auto AI Service

Abstract: Cardio vascular diseases are the number one cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict mortality by heart failure. In this project, a model is built using the Random Forest Classifier Algorithm using AutoAI and a web application is created using Node Red Application and it showcases the prediction of heart failure in a web based format. The usage of IBM cloud environment for implementing the Machine learning Model using IBM Auto AI and and Node Red Flows are created for the display of Web Application Structure. The complete project explains the coordination among the Auto AI and Node red in the Cloud Platform.

Test and Analysis of Vegetation Coverage in Open-Pit Phosphate Mining Area around Dianchi Lake Using UAV-VDVI.

This work aimed to detect the vegetation coverage and evaluate the benefits of afforestation and ecological protection. Unmanned aerial vehicle (UAV) aerial survey was adopted to obtain the images of tailings area at Ma’anshan near the Dianchi Lake estuary, so as to construct a high-resolution Digital Orthophoto Map (DOM) and high-density Dense Image Matching (DIM) point cloud. Firstly, the optimal scale was selected for segmentation by considering the terrain. Secondly, the visible-band difference vegetation index (VDVI) of the classified vegetation information of the tail mining area was determined from the index gray histogram, ground class error analysis, and the qualitative and quantitative analysis of the bimodal index. Then, the vegetation information was extracted by combining the random forest (RF) classification algorithm. Finally, the extracted two-dimensional (2D) vegetation information was mapped to the three-dimensional (3D) point cloud, and the redundant data was eliminated. Fractional vegetation cover (FVC) was counted in the way of surface to point and human–machine combination. The experimental results showed that the vegetation information extracted from the 2D image was mapped to the 3D point cloud in the form of surface to point, and the redundant bare ground information was eliminated. The statistical FVC was 36.06%. The field survey suggested that the vegetation information in the turf dam area adjacent to the open phosphate deposit accumulation area research area was sparse. Relevant measures should be taken in the subsequent mining to avoid ecological damage caused by expanded phosphate mining. In general, applying UAV measurement technology and related 2D and 3D products to detect the vegetation coverage in an open phosphate mine area was of practical significance and unique technical advantages.

Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients.

The diagnosis based on clinical assessment of pediatric bipolar disorder (PBD) may sometimes lead to misdiagnosis in clinical practice. For the past several years, machine learning (ML) methods were introduced for the classification of bipolar disorder (BD), which were helpful in the diagnosis of BD. In this study, brain cortical thickness and subcortical volume of 33 PBD-I patients and 19 age-sex matched healthy controls (HCs) were extracted from the magnetic resonance imaging (MRI) data and set as features for classification. The dimensionality reduced feature subset, which was filtered by Lasso or f_classif, was sent to the six classifiers (logistic regression (LR), support vector machine (SVM), random forest classifier, naïve Bayes, k-nearest neighbor, and AdaBoost algorithm), and the classifiers were trained and tested. Among all the classifiers, the top two classifiers with the highest accuracy were LR (84.19%) and SVM (82.80%). Feature selection was performed in the six algorithms to obtain the most important variables including the right middle temporal gyrus and bilateral pallidum, which is consistent with structural and functional anomalous changes in these brain regions in PBD patients. These findings take the computer-aided diagnosis of BD a step forward.

Extraction of Mountain Grasslands in Yunnan, China, from Sentinel-2 Data during the Optimal Phenological Period Using Feature Optimization

The timely and accurate mapping of the spatial distribution of grasslands is crucial for maintaining grassland habitats and ensuring the sustainable utilization of resources. We used Google Earth Engine (GEE) and Sentinel-2 data for mountain grassland extraction in Yunnan, China. The differences in the normalized vegetation index in the time-series data of different ground objects were compared. February to March, during grassland senescence, was the optimum phenological stage for grassland extraction. The spectral, textural of Sentinel-2, and topographic features of the Shuttle Radar Topography Mission (SRTM) were used for the classification. The features were optimized using the recursive feature elimination (RFE) feature importance selection algorithm. The overall accuracy of the random forest (RF) classification algorithm was 91.2%, the producer’s accuracy of grassland was 96.7%, and the user’s accuracy of grassland was 89.4%, exceeding that of the cart classification (Cart), support vector machine (SVM), and minimum distance classification (MDC). The SWIR1 and elevation were the most important features. The results show that Yunnan has abundant grassland resources, accounting for 18.99% of the land area; most grasslands are located in the northwest at altitudes above 3200 m and in the Yuanjiang River regions. This study provides a new approach for feature optimization and grassland extraction in mountainous areas, as well as essential data for the further investigation, evaluation, protection, and utilization of grassland resources.

Using Machine Learning Classifiers and A Virtual Voice Assistant for Common Tasks, An Employee Performance Evaluation Model is Used

This study focuses on a conversational voice assistant for businesses that facilitates staff productivity and efficiency gains by speeding up routine, time-consuming operations. By using straightforward voice commands, they can manage conference rooms, arrange meetings, and adjust work hours thanks to speech recognition and TTS technologies. The necessity of employee performance analysis is also taken into account in this study, and this is done with the aid of machine learning classification models, specifically L1 and L2 Regularization, Random Forest, and Gradient Boost. The experimental results reveal that the Random Forest Classification Algorithm yields the highest accuracy, F1 Score, Train and Test AUC-ROC, with values of 97.08 percent, 98.27 percent, 99.82 percent, and 98.41 percent, respectively.

Exploring Feature Selection and Classification Algorithms For Cardiac Arrhythmia Disease Prediction

Cardiac Arrhythmia is the disease in which heartbeats abnormally due to which death of a person may occur if not diagnosed on time. Timely and accurate detection of cardiac arrhythmia can save the life of the patient. In this study fourteen classification algorithms and six feature selection algorithms are explored to find the best combination which can accurately detect cardiac arrhythmia. On the features selected through feature selection techniques fourteen classification algorithms are applied to classify cardiac arrhythmia. The random forest algorithm for feature selection and random forest classification algorithm found best among all the models applied with an accuracy of 86.57%, precision 79.12%, recall 79.12%, and f1-score 79.12%.