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Surveys are commonly used to facilitate research in epidemiology, health, and the social and behavioral sciences. Often, these surveys are not simple random samples, and respondents are given weights reflecting their probability of selection into the survey. We show that using survey weights can be beneficial for evaluating the quality of predictive models when splitting data into training and test sets. In particular, we characterize model assessment statistics, such as sensitivity and specificity, as finite population quantities and compute survey-weighted estimates of these quantities with test data comprising a random subset of the original data. Using simulations with data from the National Survey on Drug Use and Health and the National Comorbidity Survey, we show that unweighted metrics estimated with sample test data can misrepresent population performance, but weighted metrics appropriately adjust for the complex sampling design. We also show that this conclusion holds for models trained using upsampling for mitigating class imbalance. The results suggest that weighted metrics should be used when evaluating performance on test data derived from complex surveys. [ABSTRACT FROM AUTHOR]

Uncommon genetic illnesses pose significant challenges for detection due to their low occurrence and intricate genetic makeup. Traditional targeted genetic testing methods are often limited, missing rare or unidentified genetic variations. This project proposes a novel strategy that leverages Whole-Genome Sequencing (WGS) data and Random Forest (RF) analysis to overcome these limitations. WGS provides a comprehensive view of an individual's genetic profile, capturing a wide array of genetic variations that targeted approaches might overlook. By employing the RF method, which excels at handling complex datasets and detecting non-linear interactions, this project aims to uncover intricate links between rare genetic diseases and gene variations. The Swedish Genome Reference dataset will serve as the foundation for this research. RF analysis will be applied to this extensive dataset to identify patterns and connections that might reveal new genetic markers and previously unknown risk factors for these illnesses. This approach allows for exploring vast genetic datasets to detect structures and associations, providing deeper insights into the genetic underpinnings of rare diseases. Combining WGS with RF analysis offers a powerful tool for discovering genetic indicators and risk factors contributing to rare genetic disorders, achieving an accuracy rate of 97 %. This innovative approach can significantly enhance understanding, diagnosis, and treatment of these conditions. By highlighting the value of advanced computational techniques and comprehensive WGS databases, the project aims to pave the way for more personalized and specialized medical treatments, ultimately improving patient outcomes for those affected by rare genetic diseases. [ABSTRACT FROM AUTHOR]

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

Due to a series of problems in the diagnosis of liver disease, the mortality rate of liver disease patients is very high. Therefore, it is necessary for doctors and researchers to find a more effective non-invasive diagnostic method to meet clinical needs. We analyzed data from 416 patients with liver disease and 167 patients without liver disease from northeastern Andhra Pradesh, India. On the basis of considering age, gender and other basic data of patients, this paper uses total bilirubin and other clinical data as parameters to build a diagnostic model. In this paper, the accuracy of artificial intelligence method Random Forest (RF) and Support Vector Machine (SVM) model in the diagnosis of liver patients was compared. The results show that the support vector machine model based on Gaussian kernel function is more excellent in diagnostic accuracy, that is, SVM method is more suitable for the diagnosis of liver diseases. [ABSTRACT FROM AUTHOR]

Intensity measures based on energy have proven to be robust indicators of damage for a variety of structural types. This article presents a modified ground-motion model (GMM) incorporating a deep neural network to predict elastic input energy spectra for both horizontal and vertical ground motions, considering the pulselike ground motions. The newly developed model employs six predictor variables, that is, moment magnitude MW, fault mechanism F, rupture distance Rrup, logarithmic rupture distance ln(Rrup), rupture directivity term Idir, and logarithmic shear-wave velocity ln(VS30)as inputs. A subset of records, sourced from the recently updated Next Generation Attenuation-West2 Project database constituted by 2745 ground motions from 97 earthquakes, have been employed in the development of the model. The performance of the developed model remains within the prescribed error range. In addition, the proposed model is compared against currently used GMMs. The predicted spectra obtained from the present study are in good agreement with those given by other literature, and the standard deviations of residuals have been reduced by ~20% and are more stable. Observations from these results indicate that the newly proposed model generates improved predictions. [ABSTRACT FROM AUTHOR]

Metamaterial absorbers (MMAs) have tremendous potential for controlling and modulating Terahertz electromagnetic (EM) waves. It is challenging to design MMAs for optimal performance using conventional methods due to their time-consuming and computationally demanding nature. Machine learning (ML) regressor approaches have emerged as potential tools for optimizing the predictive modeling of MMAs in recent years. This article examined different regression techniques, such as Decision Tree, k-Nearest Neighbors, Random Forest, Extra Trees (ET), Extreme Gradient Boosting, Bagging, and Categorical Boosting. The primary goal is to assess the effectiveness of each regressor technique in forecasting the performance of MMAs using various performance metrics. The study focuses on a representative spectrum of MMAs, chosen for its key features that are common across a wide range of MMAs. The extra tree Regressor outperforms other models in the comparative analysis due to its capacity to integrate multiple decision trees and include randomness into the feature selection process, resulting in improved predictive skills. The ET regressor forecasts MMA performance remarkably well for a 70 % test size, with a Root Mean Squared Error of 0.0251, a Mean Absolute Error of 0.0074, a Mean Squared Error of 0.006, and an Adjusted R-squared of 0.9873. These findings can assist researchers in optimizing metamaterial absorber designs for Terahertz applications using ML regressor techniques and provide insights into how these methods can be generalized to other spectra with similar characteristics. [ABSTRACT FROM AUTHOR]

In the rapidly field of mobile networks, efficient spectrum management is critical to meet the growing demand for data services and optimize resource allocation. Traditional spectrum management techniques often face challenges in handling dynamic and complex network environments. This study addresses these challenges by proposing an ensemble machine learning algorithm combining Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) classifiers for effective spectrum management in mobile networks. The proposed ensemble model leverages the strengths of each individual algorithm, combining SVM's robustness in high-dimensional spaces, RF's capability in handling large datasets with higher accuracy, and DT's efficiency in rule-based decision making. To evaluate the performance of the ensemble model, a dataset representing spectrum usage patterns in a dense urban mobile network environment was utilized. The model was trained to predict spectrum occupancy and allocate resources dynamically to minimize interference and maximize network throughput. The experimental results demonstrate a significant improvement in prediction accuracy and resource allocation efficiency. The ensemble model achieved an accuracy of 95.6%, surpassing individual classifiers-SVM at 92.3%, RF at 93.1%, and DT at 89.7%. Additionally, the ensemble approach reduced network interference by 18% and increased overall throughput by 23% compared to traditional spectrum management methods. The findings suggest that the proposed ensemble machine learning model provides a more accurate and efficient solution for spectrum management in mobile networks, potentially leading to enhanced service quality and network performance. [ABSTRACT FROM AUTHOR]

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

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

The selection of an optimal dataset is crucial for successful remote sensing analysis. The PRISMA hyperspectral sensor (with 240 spectral bands) and Landsat OLI-2 (boasting high dynamic resolution) offer robust data for various remote sensing applications, anticipating their increased demand in the coming years. However, despite their potential, we have not identified a rigorous evaluation of both datasets in geological applications utilizing Machine Learning Algorithms. Consequently, we conduct a comprehensive analysis using Random Forest, a widely-recommended machine learning algorithm, and employ K-fold cross-validation (with K = 2, 5, 10) with grid-search hyperparameter tuning for enhanced performance. Toward this aim, diverse image-processing approaches, including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA), were applied to enhance feature selection and extraction. Subsequently, to ensure better performance of the RF algorithm, this study utilized well-distributed points instead of polygons to represent each target, thereby mitigating the effects of spatial autocorrelation. Our results reveal dataset-hyperparameter dependencies, with PRISMA mainly influenced by max_depth and Landsat 9 by max_features. Employing grid-search optimally balances dataset characteristics and data splitting (folds), generating accurate lithological maps across all K values. Notably, a significant hyperparameter shift at K = 10 produces the best lithological maps. Fieldwork and petrographic investigations validate the lithological maps, indicating PRISMA's slight superiority over Landsat OLI-2. Despite this, given the dataset nature and band count difference, we still advocate Landsat 9 as a potent multispectral input for future applications due to its superior radiometric resolution. [ABSTRACT FROM AUTHOR]

The study aims to report empirical findings of quantitative research investigating what variables are significant leading proxies of fraudulent financial reporting (FFR) and the performance of the fraud detection model. The paper used financial and non-financial proxies as indicators to detect FFR with the panel data of 2235 observations of listed companies on the Vietnamese Stock Exchange from 2014 to 2020. Based on the materiality principle in auditing, the study divided the profit variance ratio into four material fraud thresholds of over 5%, 10%, 20%, and 50%. Two data mining techniques were employed: random forest for the classification model and an artificial neural network for building the best fraud prediction model. The findings show that the average accuracy of the prediction results of the random forest algorithm (RFA) reaches 91% for a material fraud threshold of 5%; when the materiality of fraud increases to above 50% of profit variance, the predictability is 98%. The average prediction accuracy of an artificial neural network (ANN) for the training set is 99%, and the test set is 97% at different fraud thresholds. These results confirm that RFA and ANN give a high accuracy in predicting fraud, and the determinants of firms committing to FFR are proxies of financial stability, followed by cash in the business and the nature of the industry. Notably, the three most important proxies related to FFR include return on total assets, return on equity, and EBT on total assets. The findings have practical implications: to identify fraudulent firms, creditors, analysts, and other stakeholders should use financial and non-financial ratios and employ data mining techniques instead of traditional fraud detection methods. [ABSTRACT FROM AUTHOR]

Currently, the rapid development of China's social economy has put forward higher requirements for the power supply chain, and the research on the power material price prediction model helps to ensure the adaptability and flexibility of the supply chain and provides assistance to the management of power materials. However, the time discontinuity of power material procurement data, the existing research methods to meet the demand for forecasting data in discontinuous time, this paper starts from the raw materials of electric power equipment, using Granger causality test to screen the factors affecting the price of raw materials and the lead time, and predicts the price of raw materials through the two models of linear regression and Random Forest, and calculates the numerical relationship between the raw material price and the purchase price of electric power materials, so as to utilize the predicted price of raw materials to forecast the price of electric power materials and to predict the price of electric power materials. Finally, it is found that the linear regression model is more favorable for the prediction of power equipment procurement price, and it predicts the future procurement price of 110kV power cables, and provides procurement strategies based on the prediction results. [ABSTRACT FROM AUTHOR]

The primary goal of this study is to compare the analysis results of sentiment analysis using three different machine learning models: Naïve Bayes, Support Vector Machine (SVM), and Random Forest. The raw dataset used for this study is sourced from the Google Play Scraper API, which is then preprocessed to ensure quality and accuracy of feature extraction. Once preprocessed, the machine divides the dataset for training and testing using the 80:20 rule. The results of this comparison provide insights into the strengths and weaknesses of each algorithm in the context of sentiment analysis of user reviews. This study aims to inform practitioners about the most effective techniques for extracting actionable insights from user-generated content on digital platforms. The evaluation shows that the Naïve Bayes model achieved the highest accuracy of 81%, followed by the SVM model with 80%, and the Random Forest model with 76%. These findings highlight the Naïve Bayes model as the most accurate for sentiment analysis in this context, with all models demonstrating robust performance. [ABSTRACT FROM AUTHOR]

This study investigates the effectiveness of four machine learning models for predicting house prices: Linear Regression (LR), Artificial Neural Networks (ANNs), Random Forest Regressor, and Support Vector Regression (SVR). As the demand for housing continues to rise due to population growth, the housing business has become one with high long-term potential. This is evidenced by an upward trend in house prices in Indonesia and other regions. For instance, in the United States, new single-family home sales climbed 8.8% to 693,000 units (SAAR) in March 2024, marking the fastest pace since September 2023 and an 8.3% year-over-year increase. Property values are influenced by various internal factors, such as area and number of rooms, as well as external factors like inflation rates and government policies. Linear Regression (LR) is known for its simplicity and ease of interpretation, providing high accuracy with low computational cost. In contrast, Artificial Neural Networks (ANNs) excel at capturing non-linear relationships, making them suitable for more complex datasets. Random Forest Regressor, an ensemble learning method, enhances predictive performance by averaging multiple decision trees and controlling overfitting, while SVR is effective in high-dimensional spaces and can model non-linear relationships using kernel functions. We employ the Boston housing dataset, a widely used benchmark for house price prediction tasks, to compare these models. The dataset is split into 70% for training and 30% for testing. The performance of the models is evaluated using Mean Squared Error (MSE), R-squared, and Mean Absolute Error (MAE). The results show that Linear Regression achieves an MSE of 0.0106, R-squared of 0.67, and MAE of 0.075. The Artificial Neural Network achieves an MSE of 0.0046, R-squared of 0.86, and MAE of 0.047. The Random Forest Regressor achieves an MSE of 0.0060, R-squared of 0.81, and MAE of 0.050, while the SVR achieves an MSE of 0.0054, R-squared of 0.83, and MAE of 0.056. By comparing these models, we find that the Artificial Neural Network (ANN) achieves the highest accuracy, followed by SVR, Random Forest Regressor, and Linear Regression. These findings provide valuable insights for buyers, sellers, and developers in the housing market, highlighting the potential of advanced machine learning models in accurately predicting house prices. [ABSTRACT FROM AUTHOR]

This research tries to detect mental illness using sentiment analysis on Reddit data, as well as comparing the performance of the k-Nearest Neighbors (k-NN), Random Forest, and Neural Network models. Using text post data from the pre-pandemic and post-pandemic periods, we concluded that the Random Forest model had the highest overall performance with an F1 Score, accuracy, recall and precision of 80.6%, making it quite effective in detecting depression. Even though the Neural Network model shows slightly lower accuracy, namely 79%, in fact this model has the lowest error rate, namely 0.06496. The k-NN model showed the lowest accuracy and higher error rate. These findings highlight the potential of sentiment analysis and machine learning in identifying mental health issues on social media and suggest that better models can improve early detection and intervention efforts. [ABSTRACT FROM AUTHOR]

In recent years, social media platforms have emerged as powerful channels for political discourse and public opinion expression. Twitter has become a dynamic space where individuals share views, opinions, and sentiments on various topics, including significant political events such as elections. The 2024 Presidential Election in Indonesia is a good moment to represents public sentiment. The study aims to the explore sentiment expression on social media, considering diverse perspectives, language nuances, and specific cultural contexts within the Indonesian political landscape. To determine sentence sentiment, both standard sentiment analysis and ABSA are considered, with a preference for ABSA due to its ability to identify aspects within a sentence and provide more detailed sentiment analysis. Therefore, this research proposes a solution by applying a BERT-based deep learning model. The model's performance is evaluated using standard metrics, including confusion matrix, precision, recall, F1-score, and accuracy. Comparative assessments are made with machine learning models, such as Random Forest, Naïve Bayes, and Support Vector Machine. The results indicate that, with a 50:50 aspect ratio, BERT, Random Forest, Naïve Bayes, and Support Vector Machine achieve comparable accuracies. However, when the aspect ratio shifts to 60:40, BERT outperforms other models. Notably, in scenarios with a larger amount of training data, Support Vector Machine demonstrates slightly superior performance in predicting sentiment from Twitter data. [ABSTRACT FROM AUTHOR]

Breast cancer cases are an alarming problem for several countries and are among other female diseases which are in the top list of population ill-health. To overcome this obstacle, it is crucial to develop reliable predictive models with high accuracy to maximize impact on early detection of breast cancer. Since we want to contrast two different models in studying patient death from breast cancer, our goal is to evaluate these models for their accuracy. The Cox model outperforms the Survival Random Forest in terms of survival probability, with a C-index of 0.757 and an AUC of 0.8561 compared to the Survival Random Forest's C-index and AUC of 0.5837. Furthermore, the Cox model's superiority is proved by its feature selection score of 46.72174, whereas the Survival Random Forest has no similar metrics. Additionally, Random Forest shows superior performance with higher precision in identifying survival probabilities with perfect specificity (100%), precision (100%), and overall accuracy (96.8%), versus 91.3%, 71.4%, and 83.9% for Naive Bayes. Random Forest is a more reliable solution for accurately anticipating both positive and negative cases due of its adaptability and zero false positive rate. [ABSTRACT FROM AUTHOR]

The stocks of nickel mining companies in Indonesia are one of the most influential stocks in the global economy. Understanding its price movement is quite complicated, it requires an advanced technique to process it. This research provides investors with better information regarding nickel mining companies and prediction techniques using prediction models, allowing investors to create more successful strategies with lower investment risks. Thus, allowing small investors to compete with institutional players. This study focuses on comparing the accuracy of two stock prediction models, i.e. Bidirectional Long Short-Term Memory (BiLSTM) and Random Forest (RF), in predicting the stock prices of a nickel mining company in Indonesia, Antam (Aneka Tambang) using a dataset from Yahoo Finance with several forecast time frame as the comparison. The evaluation metrics we use to compare the accuracy of the two models are Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The results of this study showed that the BiLSTM model has a lower error compared to the RF model, although in terms of training time and computational resource usage, the RF model shows a much better efficiency than the BiLSTM model. The RMSE shows that the BiLSTM model error is 0.0568633, while the RF model error is 0.0746966 for the short forecast time frame. For the long forecast time frame, the BiLSTM model error is 0.0753416, while the RF model error is 0.1549706. The RF model's performance deteriorates significantly as the forecast time frame lengthens, indicating a strong inverse correlation between the model's accuracy and the length of the forecast time window. The results demonstrate the exceptional capability of the BiLSTM model in making the right decision in predicting the stock price of nickel mining companies. [ABSTRACT FROM AUTHOR]

Credit risk is a problem of the inability of debtor to pay bills that can cause losses to both parties concerned. In order to avoid losses caused by bad loans, the loan provider have to conduct an analysis on the debtor. The loan providers are required to be objective and accurate in evaluating credit application. During the credit application, the debtor has to submit data such as personal data, monthly income, employment history, and other data. The data is then proceeded to generate credit score, based on the score the credit application will be accepted and rejected. The screening process must be done quickly and accurately. The machine learning approach is suitable for overcoming the problem of timing and accuracy of predictions and can predict factors that affect loan approval with feature importance. This research aims to identify the features that have a high feature importance with a machine learning approach using the Random Forest Algorithm. The stages of the research method used are data understanding, feature extraction, data pre-processing, exploratory data analysis, modeling, and insight. The metrics used to evaluate the model performance is recall, this model produces recall score of 0.9091. Based on research, there are some most important factors that affect loan approval, repeat_user, mobile_likelihood, monthly_salary, industry, year_of_service [ABSTRACT FROM AUTHOR]

The reduction of maternal mortality is part of the steps to realize the actualization of the human right to health, which is stated in the United Nations Sustainable Development Goals (SDG). As our contribution, this research proposes some machine learning decision tree-based models such as Decision Tree (DT), ensemble bagging with DT, AdaBoost with DT, and Random Forest, which may inspire parties who have health authority to implement machine learning approaches into their Internet of Things (IoT) system-based systems for supporting health professionals to predict the risk of pregnancy for mothers. The dataset used was the Maternal Health Risk dataset gathered from Kaggle. The result showed that the best machine learning DT-based model was Random Forest with 50 DT models inside, which achieved the best overall accuracy (82%) and best recall on predicting low-risk and high-risk classes with 85% and 88%, respectively, even though that model achieved 73% recall on predicting medium-risk. [ABSTRACT FROM AUTHOR]

Software-defined networking (SDN) is an approach to network management allowing to enhance the performance of the network and making it more flexible. The centralized architecture of SDN makes it vulnerable to cyberattacks, especially distributed denial of service (DDoS) attacks. Existing research investigates the detection of DDoS attacks separately on the control plane and data plane. However, there is a need for efficient and accurate detection of these attacks using features obtained from both control and data planes. Therefore, we present a mechanism for identifying DDoS attacks using entropy, multiple feature selection mechanisms, and deep learning. Initially, we use entropy on the control plane to detect anomalous activity and identify suspicious switches. Next, we capture traffic on the suspicious switches to detect DDoS attacks. To detect these attacks, we utilize multi-layer perceptron (MLP) deep learning models, convolutional neural network (CNN), and the long short-term memory (LSTM) approach. An InSDN dataset is used to train the model and test data are generated using Mininet emulation and the Ryu controller. The results reveal that LSTM outperforms MLP and CNN, achieving an accuracy of 99.83%. [ABSTRACT FROM AUTHOR]

The aims of this study were to examine the relationship between lamb and ewe behaviors in postnatal 3-hour and lamb survival using machine learning (ML) algorithms and to determine the best ML classifier to predict lamb survival. The research data consisted of postnatal 3-hour behavior records of 43 Kivircik ewes and their 65 lambs, along with preweaning survival information of lambs. The prediction of lamb survival was performed on three datasets containing different features using decision tree, support vector machine (SVM), multilayer perceptron, logistic regression, random forest (RF), K-nearest neighbors, and boosting (B) ML algorithms. The accuracy, precision, recall, and F1 score values of the RF algorithm were 0.931, and the area under curve value was 0.966 for dataset 1, which included parity of dam, birth type and sex of lamb, and birth weight predictors, as well as postnatal lamb and ewe behaviors as features. In dataset 2, which includes principal component scores instead of lamb and ewe behaviors, the RF approach made classification with an accuracy of 0.909. In dataset 3, which includes the maternal behavior score as a feature, the RF and SVM algorithms showed similar performance (0.909 accuracy). These results indicate that, by using lamb and ewe behaviors in the postnatal 3-hour with ML methods, it is possible to classify lambs as either surviving or dying before weaning with high accuracy. In addition, it was determined that the ML algorithm that best adapted to the current study data was the RF classifier. • Relationship between lamb and ewe behaviors in postnatal 3-hour and lamb survival. • Using machine learning to classify lambs as either surviving or dying before weaning. • The best performance was obtained using random forest with an accuracy of 0.931. • Sucking durations during 150 and 180 minutes were the most crucial features of survival. [ABSTRACT FROM AUTHOR]

Millions of individuals throughout the world suffer with diabetes, a chronic condition that if unchecked can have catastrophic health repercussions. In order to forecast diabetes risk and aid healthcare professionals in managing or preventing the condition, machine learning algorithms have become increasingly effective. The goal of our work is to inspect the achievement of machine learning techniques in predicting diabetes. The dataset used in previous study consists of demographic and clinical data of patients who have been diagnosed with diabetes and those who have not. Different classification and Neural Network algorithms, such logistic regression, Artificial Neural Network, XGBoost Random Forest, Voting Classifier and Naïve bays were employed to forecast the occurrence of diabetic in patients. The findings of the study indicate that these machine learning algorithms achieved significant accuracy rates in diabetes prediction. Among the algorithms utilized, the Random Forest algorithm achieved the best accuracy rate of 86.5The study also discovered that a range of parameters, such as hypertension, age, body weight, and levels of glucose, were valid markers of diabetes. For individuals who have a greater chance of acquiring diabetes, these factors can help medical experts act early and provide unique treatment strategies. [ABSTRACT FROM AUTHOR]

At present, online education platforms are very popular. However, these platforms also have some drawbacks, including that they cannot be completely based on the needs of students, many students ignore the needs of students in order to adapt to the platform, do not adopt a more personalized learning style, and do not fully realize the individualized teaching. There are many factors at play, such as shifts in learning and teaching patterns, online platforms, hardware limitations of the network, and more. However, whether this model can finally satisfy teachers and students, and how the teaching effect is, has become a problem of concern. We propose a random forest-based method to evaluate college online courses. This method not only focuses on a single influencing factor, but also takes into account the special needs of students and teachers, and formulates different evaluation criteria, so that the system has not only universal applicability. The experimental results show that this method can improve the proficiency and level of teachers' online teaching, and can effectively combine several courses taught by students to form a personalized teaching method. [ABSTRACT FROM AUTHOR]

Management Information Systems stands to gain from the application of a framework that allows for the classification of previous and ongoing re-search as well as the generation of potential research hypotheses. Since the beginning of the era of global informatization, advances in computer com-munication technology have been making steady progress toward maturity. These advancements are now utilized extensively in information manage-ment systems and play an extremely significant part in the overall domain of information management. Processing of data, making predictions, and decid-ing between options are all significantly aided by advancements in computer communication technology. The information management system is signifi-cantly improved as a result of its incorporation. The information manage-ment system will involve data communication, making it susceptible to harmful assaults and tampering on the part of unauthorized parties. This re-search primarily employs the Random Forest(RF) algorithm in order to de-termine whether or not the data is attacked when it is being transferred from one computer to another over the course of the computer communication process. The findings indicate that our algorithm is able to accurately deter-mine whether or not the data are subjected to an attack during the computer communication process of the information management system. [ABSTRACT FROM AUTHOR]

One of the most dangerous threats in WSNs is botnet attacks, in which attackers use mutual communications between IoT devices to launch large-scale malicious activities. In this regard, developments in the field of effective and reliable means of defence against this type of threat, in particular, reliable methods for detecting, identifying, and countering botnet attacks, are becoming increasingly important and relevant. This paper presents a comprehensive study that applies machine learning techniques, namely Random Forest and XGBoost, to identify botnet attacks on IoT effectively. These algorithms are analyzed, compared, and shown to be highly effective in detecting complex patterns indicative of botnet activity, thus achieving a significant improvement in IoT security. The conducted research aims to make a useful contribution to the problem of securing WSNs and IoT in general. The results of the study demonstrated high accuracy in detecting attacks with an accuracy of 99.18% for XGBoost and Random Forest showed an accuracy of 99.21%. Thus, it was shown that the significance of applying machine learning techniques such as Random Forest and XGBoost can be one of the key approaches in combating botnet attacks and securing the IoT. The results of the work emphasize the promising application of machine learning techniques for effective defense against cyber threats and highlight the importance of further. [ABSTRACT FROM AUTHOR]

Objective: To investigate the environmental and social aspects of poverty contributing to malaria incidence in Indonesia from 2016 to 2020. Methods: Random forest regression was used to analyse the independent variables contributing to malaria incidence. Environmental conditions were extracted from remotely sensed data, including vegetation, land temperature, soil moisture, precipitation, and elevation. In contrast, the social aspects of poverty were obtained from government statistical reports. Results: From 2016 to 2020, the contribution of each environmental and social aspect of poverty to malaria incidence fluctuated annually. Generally, the top three essential variables were people aged 15 years and above, experiencing poverty (variable importance/VI=32.0%), people experiencing poverty who work in the agricultural sector (VI=14.4%), and precipitation (VI=9.8%). It was followed by people experiencing poverty who are unemployed (VI=9.2%), land temperature (VI=5.2%), people experiencing poverty who have low education (VI=8.0%), soil moisture (VI=7.4%), elevation (VI=6.0%), and vegetation (VI=3.8%). Conclusions: Poverty and variables related to climate have become the crucial determinants of malaria in Indonesia. The government must strengthen malaria surveillance through climate change mitigation and adaptation programs and accelerate poverty alleviation programs to support malaria elimination. [ABSTRACT FROM AUTHOR]

Falls detection approaches struggle with both Big Data scalability and upholding individual privacy, this research work proposed a novel approach for posture recognition followed by fall detection, taking advantage of the synergy between Random Forests and Uniform Local Binary Patterns (uLBP) histograms for an accurate and fast posture identification while respecting privacy. Additionally, it referred to deep recurrent neural networks distributed on a Hadoop and Spark platform for time series analysis in fall detection. This combination of methods allowed us to achieve acceptable real-time monitoring precision. This study, therefore addressed two objectives simultaneously: efficiency and scalability in posture recognition using Random Forests and uLBP, and fall detection relying on the recurrent neural network (RNN) for time series processing. The suggested solution is designed for home telemonitoring, where scalability and effective data management are supported through Hadoop/Spark. The integration of these technologies promotes reliable detection without any privacy violation, paving the way for a wider adoption of home monitoring systems for an increasing population of dependent individuals. [ABSTRACT FROM AUTHOR]

We test the predictive performance of different ensemble methods for forecasting systemic risk in Russia for the period 2008-2024. In contrast to the existing research on machine learning ensemble techniques, we find that conventional random forest works better for the Russian data. Based on this model, we additionally conduct variable importance analysis. We identify that the first three lags of the exchange rate as well as the level of non-performing loans are of utmost importance for systemic risk in the national financial sector. [ABSTRACT FROM AUTHOR]

Structural damage detection (SDD) remains highly challenging, due to the difficulty in selecting the optimal damage features from a vast amount of information. In this study, a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD. Signal datasets were obtained by numerical experiments and vibration experiments, respectively. Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage. Results indicated a 5% to 10% improvement in detection accuracy compared to using original datasets without feature selection, demonstrating the feasibility of this method. The proposed method, based on tree model and classification, addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring. [ABSTRACT FROM AUTHOR]

One of the most dangerous threats in WSNs is botnet attacks, in which attackers use mutual communications between IoT devices to launch large-scale malicious activities. In this regard, developments in the field of effective and reliable means of defence against this type of threat, in particular, reliable methods for detecting, identifying, and countering botnet attacks, are becoming increasingly important and relevant. This paper presents a comprehensive study that applies machine learning techniques, namely Random Forest and XGBoost, to identify botnet attacks on IoT effectively. These algorithms are analyzed, compared, and shown to be highly effective in detecting complex patterns indicative of botnet activity, thus achieving a significant improvement in IoT security. The conducted research aims to make a useful contribution to the problem of securing WSNs and IoT in general. The results of the study demonstrated high accuracy in detecting attacks with an accuracy of 99.18% for XGBoost and Random Forest showed an accuracy of 99.21%. Thus, it was shown that the significance of applying machine learning techniques such as Random Forest and XGBoost can be one of the key approaches in combating botnet attacks and securing the IoT. The results of the work emphasize the promising application of machine learning techniques for effective defense against cyber threats and highlight the importance of further. [ABSTRACT FROM AUTHOR]

This paper presents a system that is able to detect physical intrusion in a specific space based on temperature and humidity change. This specific space was housing hardware components important for information security management infrastructure. Presented system is able to predict that two spaces are connected and that there is a physical breach in protected space. The presented prediction approach involves identifying patterns in historical data, where the subsequent outcomes are already known in advance, and validating these patterns using more recent data. System is implemented using k-Nearest Neighbours, Random Forest, and Support Vector Machine algorithms in Python programming language on Raspberry Pi. Real observed data to predict if specific temperature and humidity indicates intrusion were used. This approach can be used to detect intrusions in the room or in other closed space. More specifically thermal equilibrium phenomenon between two spaces after barrier between them are opened was monitored. Through process of supervised learning using labelled data, system was able to detect intrusion by using k-nearest neighbours, random forest, and support vector machine with different accuracy. Presented model shows better results using k-nearest neighbours and support vector machine with accuracy of 100% compared to random forest with accuracy of 95%. The system is low cost because of cheap Raspberry Pi controller and sensors. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study was to develop a predictive model for the sexual experiences of adolescents using the random forest method and to identify the "variable importance." Methods: The study utilized data from the 2019 to 2021 Korea Youth Risk Behavior Web-based Survey, which included 86,595 man and 80,504 woman participants. The number of independent variables stood at 44. SPSS was used to conduct Rao-Scott χ² tests and complex sample t-tests. Modeling was performed using the random forest algorithm in Python. Performance evaluation of each model included assessments of precision, recall, F1-score, receiver operating characteristics curve, and area under the curve calculations derived from the confusion matrix. Results: The prevalence of sexual experiences initially decreased during the COVID-19 pandemic, but later increased. "Variable importance" for predicting sexual experiences, ranked in the top six, included week and weekday sedentary time and internet usage time, followed by ease of cigarette purchase, age at first alcohol consumption, smoking initiation, breakfast consumption, and difficulty purchasing alcohol. Conclusion: Education and support programs for promoting adolescent sexual health, based on the top-ranking important variables, should be integrated with health behavior intervention programs addressing internet usage, smoking, and alcohol consumption. We recommend active utilization of the random forest analysis method to develop high-performance predictive models for effective disease prevention, treatment, and nursing care. [ABSTRACT FROM AUTHOR]

The accurate detection and quantification of light precipitation is problematic, particularly in the Arctic region. Satellite and ground-based observations of light precipitation are frequently underestimated at high latitudes. Remote sensing and in-situ observations from the Iqaluit, NU supersite (64oN, 69oW) were integrated to train, develop, and validate a random forest (RF) model that can diagnose precipitation type and other weather element occurrences. Observations from multiple lidars, optical disdrometers, traditional precipitation gauges and meteorological aerodrome (METAR) reports from 2015–2020 were integrated and used in the RF model development. The model was trained at Iqaluit, validated over different time periods, and applied to another region (Whitehorse, YT; 61oN, 135oW). Results indicate the importance of accurate visibility observations to train the model. Overall, the RF model was capable of distinguishing precipitation types and demonstrated the potential to be used at all sites/networks where similar automated and cost-effective instruments are already deployed (e.g. radar sites, airports with ceilometers, etc.). This would reduce the dependency on METARs while improving weather element occurrence accuracy. [Traduit par la rédaction] Une méthode d'apprentissage automatique pour intégrer les observations des supersites de l'Arctique et diagnostiquer la présence d'éléments météorologiques. La détection et la quantification précises des précipitations légères posent problème, en particulier dans la région arctique. Les observations satellitaires et terrestres des précipitations légères sont souvent sous-estimées aux hautes latitudes. La télédétection et les observations in situ du supersite d'Iqaluit, au Nunavut (64oN, 69oW) ont été intégrées pour former, établir et valider un modèle de forêt aléatoire (FA) qui peut diagnostiquer le type de précipitations et d'autres occurrences d'éléments météorologiques. Des observations provenant de multiples lidars, de disdromètres optiques, de pluviomètres traditionnels et de rapports d'aérodromes météorologiques (METAR) de 2015 à 2020 ont été intégrées et utilisées dans l'élaboration du modèle de forêt aléatoire. Le modèle a été mis à l'essai à Iqaluit, validé sur différentes périodes et appliqué à une autre région (Whitehorse, Yukon; 61oN, 135oW). Les résultats dénotent l'importance d'observations précises de la visibilité pour mettre à l'essai le modèle. Dans l'ensemble, le modèle FA a été capable de distinguer les types de précipitations et a montré qu'il pouvait être utilisé sur tous les sites/réseaux où des instruments automatisés et rentables similaires sont déjà déployés (p. ex. sites radar, aéroports dotés de céilomètres, etc.). Cela permettrait de réduire la dépendance à l'égard des METAR tout en améliorant la précision de l'occurrence des éléments météorologiques. [ABSTRACT FROM AUTHOR]

Deep learning techniques have transformed image processing by providing powerful tools for extracting detailed patterns and features from large amounts of data. This paper provides a unique hybrid methodology for hyperspectral image (HSI) analysis that integrates Random Forest (RF) feature selection with Convolutional Neural Networks (CNNs) for classification in the domain of HSI classification. The study leverages CNNs' inherent automatic hierarchical feature extraction ability and RF's effectiveness in recognizing and conserving essential features. This study thoroughly validated the proposed approach using the Indian Pines dataset, a prominent HSI dataset of dimensions 145x145 pixels and 200 spectral bands collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The combination of RF-driven dimensionality reduction and CNN-based classification yields a robust model with an accuracy rate of 99.35%, demonstrating its efficacy in categorizing HSIs with varying object scales. [ABSTRACT FROM AUTHOR]

Background and study aims: The present study was undertaken to design a new machine learning (ML) model that can predict the presence of viremia in hepatitis C virus (HCV) antibody (anti-HCV) seropositive cases. This retrospective study was conducted between January 2012-January 2022 with 812 patients who were referred for anti-HCV positivity and were examined for HCV ribonucleic acid (HCV RNA). Models were constructed with 11 features with a predictor (presence and absence of viremia) to predict HCV viremia. To build an optimal model, this current study also examined and compared the three classifier data mining approaches: RF, SVM and XGBoost. The highest performance was achieved with XGBoost (90%), which was followed by RF (89%), SVM Linear (85%) and SVM Radial (83%) algorithms, respectively. The four most important key features contributing to the models were: alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB) and anti-HCV levels, respectively, while "ALB" was replaced by the "AGE" only in the XGBoost model. This study has shown that XGBoost and RF based ML models, incorporating anti-HCV levels and routine laboratory tests (ALT, AST, ALB), and age are capable of providing HCV viremia diagnosis with 90% and 89% accuracy, respectively. These findings highlight the potential of ML models in the early diagnosis of HCV viremia, which may be helpful in optimizing HCV elimination programs. [ABSTRACT FROM AUTHOR]

Rock physics plays an important role in the oil, gas, and water industries by providing essential data for reservoir management. This study focuses on predicting Shear Wave Velocity (Vs), a key geophysical parameter, using the Deep Belief Network (DBN) and Random Forest (RF) algorithms. The dataset is divided for training, validation, and testing, revealing that the DBN model surpasses the RF and empirical models in accuracy. With a low RMSE value of 0.0398 km/s in testing and an R2 of 0.9775, the DBN model demonstrates precision in Vs prediction. The analysis identifies specific parameters—Measure Depth (MD), Caliper (CALL), Gamma Ray (GR), Shallow Electrical Resistivity (RES-S), Medium Electrical Resistivity (RES-M), Deep Electrical Resistivity (RES-D), Compressional Wave Velocity (DCT), Neutron Porosity (NPHI), and Photoelectric Coefficient Index (PEF)—as significantly influencing Vs, while CALI and GR have a relatively lesser effect. The results of the Vs prediction indicated that the DBN algorithm excels in deep feature extraction, unsupervised pretraining, and efficient handling of complex data. Ultimately, the DBN algorithm proves to be a superior and time-saving alternative for Vs prediction and analysis in subsurface reservoirs. • Rock physics aids oil, gas, and water industries through crucial reservoir data. • Deep Belief Network (DBN) excels in Vs prediction with superior performance. • DBN model achieves remarkable precision with a low RMSE of 0.0398 km/s. • Correlation analysis identifies key parameters influencing shear wave velocity (Vs). • DBN algorithm proves a time-saving, reliable alternative for Vs prediction in subsurface reservoirs. [ABSTRACT FROM AUTHOR]

Calcium flux has been successfully verified to play an important role in the malignant proliferation and progression of brain tumors, which can serve as an important diagnosis guide. However, clinical diagnosis based on calcium information remains challenging because of the highly complex and heterogeneous features in calcium signals. Here we propose a calcium feature-based tumor diagnosis and treatment guidance platform (CA-TDT-GP) using random forest analysis framework for the efficient prediction of complex tumor behaviors for clinical therapy guidance. Multiple important features associated with brain tumor biological malignancy were screened out through comprehensive feature importance analysis. It provided useful guidance for understanding the biological process and the selection of drugs of brain tumors. Further clinical validation confirmed the accurate prediction of tumor biological characteristics by the model, with a coefficient of determination of over 0.86 in the same cohort of patients and over 0.77 for the new cohort of patients. We further verified the clinical malignant assessment by this model, which performed a 100% prediction match with diagnosed WHO grades, indicating great potential of the platform for clinical guidance. This promising model provides a new diagnostic and therapeutic tool for brain tumor research and preclinical treatment. [ABSTRACT FROM AUTHOR]

This study used parallel factor method (PARAFAC), self-organizing map (SOM), and random forest models to study the dissolved organic matter (DOM) sources and characteristics in rivers with varying non-point source inputs. The artificial canal Cihuai New River (CH) and the Gouqu (GQ) which are heavily polluted by agricultural surface sources were selected as the study objects. The PARAFAC model resolved four chemical components. C1 comprises two peaks, C1 (T1) (UVC fulvic acid) and C1 (T2) (humic-like acid). C2 includes two peaks, C2 (T1) (tyrosine-like protein) and C2 (T2) (tryptophan-like protein). C3 has two peaks, C3 (T1) (humic-like) and C3 (T2) (UVA fulvic acid). C4 is identified as humic-like fulvic acid. The SOM model shows that the degree of humification in the GQ, which is influenced by agricultural non-point source pollution, is higher than that of the unaffected CH. The primary source of humic substances in the river is agricultural non-point source pollution. CH is influenced by surrounding human activities and the eutrophication of water bodies, resulting in a higher level of autochthonous characteristics and biological activity in DOM. Random Forest model indicated that the C3 was the most sensitive (R2 = 0.88) to river’s changes and therefore it is a good indicator of river’s water quality. And NH4+ has a strong driving effect on the water quality of both rivers. Principal Component Analysis (PCA) reveals that the agricultural river DOM (GQ) is mainly composed of humic substances, while the artificial river DOM (CH) is predominantly derived from autochthonous sources. The combination of PARAFAC, SOM, and random forest methods helps overcome the limitations of traditional approaches and provides a scientific basis for the management of river water quality pollution.Highlights: Differences in DOM spectra of rivers with different surface source intensities. Analyzing three-dimensional fluorescence data using machine learning. Combine PARAFAC with self-organizing maps and random forest models to complement the limitations of traditional analytical methods. [ABSTRACT FROM AUTHOR]

This article focuses on identifying the directions of changes in the decision-making process of purchasing package travels and the motives determining this purchase, as well as the impact of these motives on the affective, behavioral and global loyalty of travel agency customers during the COVID-19 pandemic. To achieve the research goal, a literature review and field research were conducted. In the case of secondary sources, the content analysis method was used to examine them, while data from primary sources (N = 1,508) were collected using an indirect survey technique (CAWI). The data analysis was carried out using machine learning -- the variable importance method and the random forest algorithm. The obtained results allowed us to conclude that during the period of threat of the COVID-19 virus pandemic, tourist packages had been purchased less frequently, and buyers either had limited their trips to domestic trips or had adapted their travel destinations to the changing pandemic situation. The most important motivators that determined the choice of a travel agency during the COVID-19 pandemic were an attractive offer, a wide selection of package travels, previous positive experiences and trust in the organiser. It was also confirmed that in the face of the pandemic threat, buyers of package travels had been loyal to travel agencies. The use of machine learning allowed for more in-depth analyses and identification of motives that had a key impact on the development of buyer loyalty during the pandemic. The factors identified in the study encouraging buyers of package travels to maintain long-term relationships with their suppliers are belief in the value of the travel agency's offer, trust in the travel agency, individual approach travel agency employees, efficient service in the travel agency, and a wide selection of package travels. [ABSTRACT FROM AUTHOR]

Machine learning applications in the medical field has proliferated in the past decade. One of the main usages of machine learning in medical field is the detection of diseases from text and image data. In this work, a new hybrid multi-layer algorithm (MLHA) based on machine learning algorithms is proposed to predict diabetes. The algorithm consists of two layers. The first layer consists of three different machine learning algorithms; SVM, random forest and K-mean, that work in parallel on the dataset. The output of this layer is fed into the second layer that consists of only one algorithm; XGBoost, that is trained from the output data of the first layer. We trained the proposed model with two different datasets and its accuracy has been compared with three different common well-known algorithms; feed forward back propagation neural network, logistic regression and random forest. Our results show that the accuracy of MLHA reached 86.5% for the first dataset and approximately 84.6% for the second dataset. This reported accuracy outperformed all the classical models. [ABSTRACT FROM AUTHOR]

Oral cancer (OC)is frequently occurring cancer that affects various areas within the oral cavity. Despite the advancement of sophisticated diagnostic and therapeutic techniques, the morbidity and mortality rates associated with oral cancer continue to be concerning. OC can affect a person's quality of life. Beforehand identification of oral cancer can greatly reduce the need for aggressive treatment. In recent times machine learning ways has shown promising results in the identification of oral cancer. In this study, we have applied machine learning techniques such as SVM, XG Boost, and Random Forest (RF) in the stage classification of oral cancer. Our dataset contains a collection of oral images obtained from different levels of oral cancer patients from SMS hospital. We extracted various features from these images, such as color, texture, and shape, and used them as input data in our machine-learning model. We have calculated various measures like accuracy, precision, and F1 score and found that accuracy Random Forest have outperformed than SVM and XG Boost by achieving accuracy as 87.1%. [ABSTRACT FROM AUTHOR]

The present study uses the benchmark UNSW-NB15 datasets to detect network anomalies using the proposed deep learning-based Convolutional Neural Network (CNN) model. Several studies have already worked on network anomaly detection with some limits. However, earlier research has used old datasets and obtained limited accuracy. In addition, previous studies could not detect the latest malicious activities. Therefore, in the present study, firstly, we implemented the machine learning-based random forest method to diminish the dimensionality of the dataset and pick the most notable features. In this case, the random forest method has set only fifteen features from the forty-one and reduced the complexity of the data. Subsequently, the CNN model was trained and tested on the UNSW-NB15 dataset with 99.00% testing accuracy. The performance of the offered CNN model was assessed using precision, recall and the f1-values. The present study's outcome can be used in real-time malicious activity detection. [ABSTRACT FROM AUTHOR]

The growth of digital and social media has led to an exponential increase in the availability of music and its lyrics. It has been observed that objectionable content is becoming a huge problem in the age of the digital era. This research aims to develop a system for automatically detecting lyrics that may contain offensive or controversial themes, such as hate speech, sexism, racism, and violence, by generating labeled data using weak supervision. In this study, we generated labels using heuristic rules using the Snorkel framework, and the performance of various machine learning classifiers, namely, Logistic Regression, Random Forest, and Support Vector Machine, were evaluated on label-generated data. The results show that DistilBert performs better than frequency-based methods (TF-IDF and CountVectorizer) by 3-5% in F1 score, and the results are even more impressive on SMOTE augmented dataset, where Logistic Regression and SVM on Distilbert vectorized data performed similarly and has exhibited 6-8% of F1 score gain over frequency based methods. In addition, it asserts that the proposed method can be used to detect offensive lyrics in other languages, given the same type of data used in this study. Our work will help music industry professionals and digital content monitoring systems for hate and offensive content to adopt the methodology and approaches used in our work. [ABSTRACT FROM AUTHOR]

This paper presents an approach for model free gait recognition using supervised learning. The method involves utilizing gait entropy images (GEnIs) as features to represent gait information. These GEnI are generated by calculating entropy for individual pixels within silhouettes throughout a full gait cycle. To enhance the robustness of gait recognition, we introduce the inclusion of view angle information, which corresponds to the perspective from which the gait sequence is captured. This additional feature is combined with the GEnI in order to create a comprehensive representation. In scenarios such as real-time CCTV footage, the view angles are frequently unknown. To address this, we propose a two phase algorithm. In the first phase, we predict the view angle of a gait sequence. This prediction is achieved by training a random forest classifier. Once the predicted angle feature is obtained, it is integrated with the GEnI feature. Subsequently, in the second phase, another random forest classifier is trained for gait recognition based on this combined feature set. The capabilty of the proposed approach is assessed through experiments performed on public dataset CASIA-B. Experimental outcomes and comparison with the state-of-the-art provide substantial evidence supporting the effectiveness of our approach. In a cross-walking scenario, we attain accuracies of 95.91% for regular walking, 62.21% for walking while carrying a bag, and 27.64% for walking while wearing a coat. [ABSTRACT FROM AUTHOR]