Effective Machine Learning Algorithm Research Articles

Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review

Predicting hospital length of stay is critical for efficient hospital management, enabling proactive resource allocation, the optimization of bed availability, and optimal patient care. This paper explores the potential of machine learning algorithms to revolutionize hospital length-of-stay predictions, contributing to healthcare efficiency and patient care. The main objective is to identify the most effective machine learning algorithm for building a predictive model capable of predicting hospital length of stay. The bibliographic search of the existing literature on machine learning algorithms applied to hospital length of stay predictions highlighted the most relevant papers within this area of research. The papers were analyzed in terms of model types and metrics that contributed to the considerable impact on healthcare decision making. We also discuss the challenges and limitations of machine learning algorithms for predicting length of stay, and the importance of data quality and ethical considerations.

E-Healthcare System for Stroke Management Using IoT and Machine Learning

Machine learning is one of the hottest topics in e-healthcare and has a big social impact. It is challenging to build and develop an efficient and successful e-healthcare system without machine learning. Machine learning's primary function is to analyse healthcare data, which is gathered from various sources and can be both homogeneous and heterogeneous. Therefore, creating an effective machine learning algorithm that functions with both homogeneous and heterogeneous data is a difficult task. On the other hand, it is noted that a sizable number of health-related products, such as fitness bands, smart watches, and sensors, are being developed, and the majority of people use these items to track their own health. Each person's health data is also gathered by these devices, and a machine learning algorithm is integrated into the data to identify any strange patterns or behaviours. These gadgets capture any aberrant behaviour or pattern, and an alarm message is sent to the individual. Thus, machine learning (ML) presents a promising technique for cutting healthcare device costs and describing doctor-patient relationships at a higher level.

A Bond-Based Machine Learning Model for Molecular Polarizabilities and A Priori Raman Spectra.

The use of machine learning (ML) algorithms in molecular simulations has become commonplace in recent years. There now exists, for instance, a multitude of ML force field algorithms that have enabled simulations approaching ab initio level accuracy at time scales and system sizes that significantly exceed what is otherwise possible with traditional methods. Far fewer algorithms exist for predicting rotationally equivariant, tensorial properties such as the electric polarizability. Here, we introduce a kernel ridge regression algorithm for machine learning of the polarizability tensor. This algorithm is based on the bond polarizability model and allows prediction of the tensor components at the cost similar to that of scalar quantities. We subsequently show the utility of this algorithm by simulating gas phase Raman spectra of biphenyl and malonaldehyde using classical molecular dynamics simulations of these systems performed with the recently developed MACE-OFF23 potential. The calculated spectra are shown to agree very well with the experiments and thus confirm the expediency of our algorithm as well as the accuracy of the used force field. More generally, this work demonstrates the potential of physics-informed approaches to yield simple yet effective machine learning algorithms for molecular properties.

Boosting Algorithms for the Accident Severity Classification

<div><i>Background:</i> Road accident severity estimation is a critical aspect of road safety analysis and traffic management. Accurate severity estimation contributes to the formulation of effective road safety policies. Knowledge of the potential consequences of certain behaviors or conditions can contribute to safer driving practices. Identifying patterns of high-severity accidents allows for targeted improvements in terms of overall road safety. <i>Objective:</i> This study focuses on analyzing road accidents by utilizing real data, i.e., US road accidents open database called “CRSS.” It employs advanced machine learning models such as boosting algorithms such as LGBM, XGBoost, and CatBoost to predict accident severity classification based on various parameters. The study also aims to contribute to road safety by providing predictive insights for stakeholders, functional safety engineering community, and policymakers using KABCO classification systems. The article includes sections covering theoretical methodology, data analysis, model development, evaluation, performance metrics, and implications for improving road safety measures by comparing the performance of different boosting algorithms on the CRSS dataset. This study aims to identify the most effective machine learning algorithm to integrate into our product line in the near future, enabling accurate prediction of both accident severity and occurrence. <i>Results and Conclusions:</i> This study addresses challenges in evaluating performance metrics for different severity classes within unbalanced datasets, emphasizing the impact of dominant classes like Class O (O = no apparent injury) on overall accuracy. The investigation reveals the limitations and conservatism associated with imbalanced data in boosting models, hinting at a potential ceiling in their performance around 80%. Comparative analysis of algorithms, including CatBoost, XGBoost, and LGBM, demonstrates comparable performance even in the case of applying KNN algorithm for pre-processing, based on various metrics, especially accuracy, <i>F</i><sub>1</sub>-score, ROC-AUC, and PR-AUC for all severity classes. XGBoost with KNN algorithm did not show any significant performance improvement compared to the XGBoost without KNN algorithm. The study includes performance metrics, such as <i>F</i><sub>1</sub>-score, CM upper triangle, ROC-AUC, and PR-AUC applied to an accident analysis case study. Future work directions involve extending the application of CatBoost, XGBoost, and other algorithms to diverse datasets, exploring the capabilities of deep neural networks, refining dataset preparation for accuracy improvement, and creating unified tools for hazard analysis and risk assessment.</div>

A proposed technique for predicting heart disease using machine learning algorithms and an explainable AI method

One of the critical issues in medical data analysis is accurately predicting a patient’s risk of heart disease, which is vital for early intervention and reducing mortality rates. Early detection allows for timely treatment and continuous monitoring by healthcare providers, which is essential but often limited by the inability of medical professionals to provide constant patient supervision. Early detection of cardiac problems and continuous patient monitoring by physicians can help reduce death rates. Doctors cannot constantly have contact with patients, and heart disease detection is not always accurate. By offering a more solid foundation for prediction and decision-making based on data provided by healthcare sectors worldwide, machine learning (ML) could help physicians with the prediction and detection of HD. This study aims to use different feature selection strategies to produce an accurate ML algorithm for early heart disease prediction. We have chosen features using chi-square, ANOVA, and mutual information methods. The three feature groups chosen were SF-1, SF-2, and SF-3. The study employed ten machine learning algorithms to determine the most accurate technique and feature subset fit. The classification algorithms used include support vector machines (SVM), XGBoost, bagging, decision trees (DT), and random forests (RF). We evaluated the proposed heart disease prediction technique using a private dataset, a public dataset, and different cross-validation methods. We used the Synthetic Minority Oversampling Technique (SMOTE) to eliminate inconsistent data and discover the machine learning algorithm that achieves the most accurate heart disease predictions. Healthcare providers might identify early-stage heart disease quickly and cheaply with the proposed method. We have used the most effective ML algorithm to create a mobile app that instantly predicts heart disease based on the input symptoms. The experimental results demonstrated that the XGBoost algorithm performed optimally when applied to the combined datasets and the SF-2 feature subset. It had 97.57% accuracy, 96.61% sensitivity, 90.48% specificity, 95.00% precision, a 92.68% F1 score, and a 98% AUC. We have developed an explainable AI method based on SHAP approaches to understand how the system makes its final predictions.

A CT-based machine learning model for using clinical-radiomics to predict malignant cerebral edema after stroke: a two-center study.

This research aimed to create a machine learning model for clinical-radiomics that utilizes unenhanced computed tomography images to assess the likelihood of malignant cerebral edema (MCE) in individuals suffering from acute ischemic stroke (AIS). The research included 179 consecutive patients with AIS from two different hospitals. These patients were randomly assigned to training (n = 143) and validation (n = 36) sets with an 8:2 ratio. Using 3DSlicer software, the radiomics features of regions impacted by infarction were derived from unenhanced CT scans. The radiomics features linked to MCE were pinpointed through a consistency test, Student's t test and the least absolute shrinkage and selection operator (LASSO) method for selecting features. Clinical parameters associated with MCE were also identified. Subsequently, machine learning models were constructed based on clinical, radiomics, and clinical-radiomics. Ultimately, the efficacy of these models was evaluated by measuring the operating characteristics of the subjects through their area under the curve (AUCs). Logistic regression (LR) was found to be the most effective machine learning algorithm, for forecasting the MCE. In the training and validation cohorts, the AUCs of clinical model were 0.836 and 0.773, respectively, for differentiating MCE patients; the AUCs of radiomics model were 0.849 and 0.818, respectively; the AUCs of clinical and radiomics model were 0.912 and 0.916, respectively. This model can assist in predicting MCE after acute ischemic stroke and can provide guidance for clinical treatment and prognostic assessment.

Comparison of Machine Learning Algorithms in Public Sentiment Analysis of TAPERA Policy

The rapid development of information technology has changed the way people interact and express their opinions on public policies, including the People's Housing Savings (Tapera) policy in Indonesia. People now primarily express their views openly on social media platforms like Twitter, generating a substantial amount of text data for analysis to understand public sentiment. However, the main challenge in this sentiment analysis is determining the most effective machine learning algorithm for classifying public opinion with high accuracy. This study aims to compare the performance of three machine learning algorithms, namely Naïve Bayes, Support Vector Machine, and Random Forest, in analyzing public sentiment towards the Tapera policy. This study analyzes public comment data obtained from Twitter. We measure the accuracy of each algorithm to determine its optimal performance in sentiment classification. The research method consists of several stages, starting with data collection, text preprocessing to clean and prepare data, and then applying the three algorithms to analyze sentiment. The results showed that Naïve Bayes had the highest accuracy of 69.17%, followed by Support Vector Machine with an accuracy of 68.42%, and Random Forest with an accuracy of 66.17%. This shows that Naïve Bayes is the most effective algorithm to use in sentiment analysis of public comments related to the Tapera policy, especially in the context of complex text data from social media. The conclusion of this study is that Naïve Bayes is superior in classifying public sentiment towards the Tapera policy compared to Support Vector Machine and Random Forest. As a result, this study makes a significant contribution to selecting the most appropriate machine learning algorithm for public sentiment analysis towards public policy, which in turn can help the government understand and respond to public perceptions more effectively.

Decoding machine learning in nursing research: A scoping review of effective algorithms.

The rapid evolution of artificial intelligence (AI) technology has revolutionized healthcare, particularly through the integration of AI into health information systems. This transformation has significantly impacted the roles of nurses and nurse practitioners, prompting extensive research to assess the effectiveness of AI-integrated systems. This scoping review focuses on machine learning (ML) used in nursing, specifically investigating ML algorithms, model evaluation methods, areas of focus related to nursing, and the most effective ML algorithms. The scoping review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. A structured search was performed across seven databases according to PRISMA-ScR: PubMed, EMBASE, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI). Twenty-six articles published between 2019 and 2023 met the inclusion and exclusion criteria, and 46% of studies were conducted in the US. The average MERSQI score was 12.2, indicative of moderate- to high-quality studies. The most used ML algorithm was Random Forest. The four second-most used were logistic regression, least absolute shrinkage and selection operator, decision tree, and support vector machine. Most ML models were evaluated by calculating sensitivity (recall)/specificity, accuracy, receiver operating characteristic (ROC), area under the ROC (AUROC), and positive/negative prediction value (precision). Half of the studies focused on nursing staff or students and hospital readmission or emergency department visits. Only 11 articles reported the most effective ML algorithm(s). The scoping review provides insights into the current status of ML research in nursing and recognition of its significance in nursing research, confirming the benefits of ML in healthcare. Recommendations include incorporating experimental designs in research studies to optimize the use of ML models across various nursing domains. The scoping review demonstrates substantial clinical relevance of ML applications for nurses, nurse practitioners, administrators, and researchers. The integration of ML into healthcare systems and its impact on nursing practices have important implications for patient care, resource management, and the evolution of nursing research.

Machine learning thermal comfort prediction models based on occupant demographic characteristics

This study aims to investigate the predictive occupant demographic characteristics of thermal sensation (TS) and thermal satisfaction (TSa) as well as to find the most effective machine learning (ML) algorithms for predicting TS and TSa. To achieve this, a survey campaign was carried out in three mixed-mode buildings to develop TS and TSa prediction models by using six ML algorithms (Logistic Regression, Naïve Bayes, Decision Tree (DT), Random Forest (RF), K-Nearest Neighborhood (KNN) and Support Vector Machine). The prediction models were developed based on six demographic characteristics (gender, age, thermal history, education level, income, occupation). The results show that gender, age, and thermal history are significant predictors of both TS and TSa. Education level, income, and occupation were not significant predictors of TS, but were significant predictors of TSa. The study also found that RF and KNN are the most effective ML algorithms for predicting TS, while DT and RF are the most effective ML algorithms for predicting TSa. The study found that the accuracy of TS prediction models ranges from 83% to 99%, with neutral being the most correctly classified scale. The accuracy of TSa prediction models ranges from 84% to 97%, with dissatisfaction being the most common misclassification.

Enhancing Loan Prediction Accuracy: A Comparative Analysis of Machine Learning Algorithms with XAI Integration

The contemporary financial landscape necessitates loan recommendation systems that offer both accuracy and transparency. Conventional assessment methodologies often suffer from limitations in efficiency and transparency, leading to potential risks for both lenders and borrowers. This research proposes the development of a novel loan recommendation system that leverages the power of machine learning (ML) and Explainable Artificial Intelligence (XAI). The paper delves into the processes of data collection, preprocessing, model training, evaluation, and subsequent integration into a web application using the Flask framework. The employed datasets encompass a variety of loan types, with the study aiming to identify the most effective ML algorithms from a selection that includes XGBoost, CatBoost, Random Forest, Gradient Boosting, and Logistic Regression. To enhance the system's transparency, Explainable AI methods, such as LIME, are incorporated. The culmination of this research is a web application that facilitates personalized predictions regarding loan eligibility, accompanied by clear explanations. Index terms - Loan Recommendation System, Machine Learning, Explainable AI, XGBoost, CatBoost, Random Forest, Gradient Boost, Logistic Regression,LIME.

High-efficiency scattering field modeling in metallic components: a machine-learning-inspired approach

We present a highly efficient method for characterizing the scattering field distribution of surface plasmon polaritons in metallic components by combining the eXtended Pseudospectral Frequency-Domain (X-PSFD) method with an iterative, machine-learning-inspired procedure. Shifting away from traditional matrix operations, we utilize the “Adam” optimizer—an effective and swift machine learning algorithm—to solve the scattering field distribution. Our method encompasses the derivation of the associated cost function and gradient differentiation of the field, leveraging spectral accuracy at Legendre collocation points in the Helmholtz equation. We refine the total-field/scattered-field (TF/SF) formulation within the X-PSFD framework for optimized incident field management and employ Chebyshev–Lagrange interpolation polynomials for rapid, accurate computation of broad-band results. To ensure global accuracy, we introduce unique physical boundary conditions at subdomain interfaces. Demonstrating our method’s robustness and computational efficiency, we model perfect electric conductors (PECs) and silver nanocylinders, and we apply our approach to analyze the excited electric field on subtly distorted metallic surfaces, particularly plasmonic structures, thereby validating its wide-ranging effectiveness.

DEPRESSION DETECTION BY ANALYZING SOCIAL MEDIA POST OF USER: A REVIEW

In contemporary times, identifying depression at an early stage has become a critical issue within the field of psychology. Mental health problems, notably depression, afflict a significant portion of the global population, with an estimated 300 million individuals currently affected. Leveraging the vast amount of user-generated content on social media platforms, researchers are exploring ways to utilize this data to identify potential mental health issues in users. Depression remains a pressing concern in society and a topic of ongoing research globally. Despite numerous studies attempting to understand various mood disorders such as depression, anxiety, and stress using activity logs from devices like smartphones, accurately predicting depressive episodes remains challenging. Social media analysis has emerged as a popular approach to tackle this challenge. In this paper, we propose a system for assessing depression and detecting suicidal ideation by predicting suicidal tendencies based on the severity of depression. Our study aims to develop effective machine learning algorithms for identifying potential signs of depression in individuals through their social media posts. To achieve this, we trained and tested classifiers to discern whether a person exhibits signs of depression using features extracted from their social media activities. Various machine learning algorithms were employed to train and classify users into different levels of depression on a scale ranging from 0 to 100%. Data in the form of posts were collected and categorized into depressed or non-depressed using machine learning algorithms. Our primary contribution lies in exploring a range of features and their effectiveness in detecting the degree of depression. We aim to build a deep learning model capable of categorizing users with depression by learning from individual-level labels to infer post-level labels. By amalgamating the probabilities of post label categories, we can create temporal posting profiles to classify users with depression. Our findings reveal distinct variations in posting behaviors between depressed and non-depressed users, as indicated by the combined probabilities of post label categories. In our study, we utilized natural language processing (NLP) techniques, employing the BERT algorithm, to efficiently identify potential depression indicators in social media content. This approach offers a more accessible and effective means of detecting depression. Keywords— Machine Learning, NLP, BERT Algorithm, Depression, Classification, Social Media Post.

A Two-stage Method for Damage Detection in Z24 Bridge Based on K-nearest Neighbor and Artificial Neural Network

In this paper, we propose an effective approach for identifying damages in the Z24 bridge, a large-scale bridge in Switzerland. The dataset of the Z24 bridge is evaluated as a benchmark, reflecting the behavior of a real structure that has been used for numerous studies. However, most of the previous studies have only addressed the issues of updating the model or estimating the location and severity of damages. The core idea behind our proposed approach is to leverage the strengths of two effective Machine Learning (ML) algorithms: K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN), to assess both the location and severity of damages in the Z24 bridge. First, we employ KNN, an unsupervised learning algorithm, for pinpointing the damage location. This strategy proves highly efficient, significantly reducing computation time by circumventing the need for a loss function during KNN training. By adopting this approach, KNN effectively mitigates the risk of encountering local minima in the ANN optimization process. Subsequently, we deploy ANN to determine the damage severity. When compared to previous studies on the Z24 bridge, our proposed method (KNN-ANN) exhibits promising results. Furthermore, our results illustrate that KNN-ANN consistently outperforms traditional ANN methodologies.

P066 Predictive Modeling of Infection Risk in Lupus Nephritis using Multiple Machine Learning Algorithms

Abstract Background/Aims The incidence of infections is considerably higher among patients with lupus nephritis (LN), and these infections significantly contribute to mortality rates. This study analysed the characteristics of peripheral blood lymphocyte subsets in LN patients, aiming at using machine learning (ML) methods to explore the risk factors of infection and establish a most effective ML algorithm to predict the occurrence of co-infection in LN. Methods This retrospective study encompassed LN patients, consisting of 111 non-infected individuals and 72 infected individuals. Additionally, 206 healthy controls (HCs) matched age and sex were recruited. The patient’s basic information, infection site, pathogen type, clinical manifestation, patient medication, and auxiliary laboratory indexes were recorded. Eight ML methods were compared to establish a corresponding model through a training group, and then verify the results in a test group. We trained the ML models, including Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Random Forest (RF), Ada boost (Ada), Extreme Gradient Boosting (XGB), after selecting variables for LN infection prediction in the training set, and further evaluated potential predictors of infection. Results The levels of T, B, helper T, suppressor T, Natural killer cells in the infected patients were significantly decreased when compared with both non-infected LN patients and HCs. The number of regulatory T cells in LN was significantly lower than HCs, and the infected patients had the fewest regulatory T cells among all these groups. Regarding localization of infection, the respiratory tract was most frequently involved (69.4%), followed by gastrointestinal (20.8%) and urinary tract infection (12.5%). Concerning pathogens, bacterial infection was more common. Viral included Epstein Barr virus, cytomegalovirus, and respiratory syncytial virus. In training group, the AUC values, accuracy and precision of DT, RF, Ada, and XGB were 1, 100%, and 100%, respectively. As for the modeled effects of predicting LN co-infection in test groups by the eight algorithms, the three algorithms with the highest AUC values were SVM, RF and XGB, with values of 0.85, 0.81 and 0.76, respectively. Among the algorithms, XGB algorithm has the highest accuracy (80%) and precision (84.62%), thus it is recommended to be used for prediction in clinical practice. In addition, the top four weighting factors for infection identified of LN by the XGB in ML were T, red blood cell, hospital length of stay, and lymphocyte%. Conclusion Both innate and adaptive immune systems in LN patients are disturbed, and monitoring the lymphocyte subsets may provide a reference for the prevention and treatment of infection. Our research found that the XGB algorithm performs better than other models with the most accuracy in prediction, which may be one of the preferred algorithms in LN patients with co-infection for future research. Disclosure J. Zhang: None. P. Cai: None. J. Liu: None. Q. Xie: None.

Using Artificial Intelligence Algorithms to Detect Hate Speech in Social Media Posts

Detecting hate speech on social media is of great importance to prevent negative impacts on people and communities and to remove such content. However, detecting hate speech is a complex and challenging process due to linguistic and cultural diversity. Therefore, it is important to develop powerful and effective machine learning algorithms. Since detecting such content using traditional methods can be time-consuming and costly, it is stated that artificial intelligence-based machine learning algorithms have great potential in this regard. The aim of this study is to evaluate the performance of artificial intelligence-based machine learning algorithms used in detecting posts containing hate speech on social media. The study focuses on the problem of detecting and managing hate speech on social media platforms. In this study, we will compare the performances of different algorithms and determine the most suitable methods. Additionally, the effects of the dataset and feature extraction methods on algorithm performance will be analyzed. Algorithms are often based on natural language processing techniques and try to detect hate speech by learning features in texts. The performance of these algorithms can vary depending on factors such as language, culture, the attributes they use, and the training dataset, so a comprehensive analysis is required. In the research, the performance of the algorithms used in detecting hate speech was compared with the dataset and feature extraction methods. In this process, the algorithms' linguistic and cross-cultural effectiveness, feature selection and representation, false positive and false negative rates, and overall accuracy will be analyzed.

Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study.

Atrial fibrillation (AF) represents a hazardous cardiac arrhythmia that significantly elevates the risk of stroke and heart failure. Despite its severity, its diagnosis largely relies on the proficiency of health care professionals. At present, the real-time identification of paroxysmal AF is hindered by the lack of automated techniques. Consequently, a highly effective machine learning algorithm specifically designed for AF detection could offer substantial clinical benefits. We hypothesized that machine learning algorithms have the potential to identify and extract features of AF with a high degree of accuracy, given the intricate and distinctive patterns present in electrocardiogram (ECG) recordings of AF. This study aims to develop a clinically valuable machine learning algorithm that can accurately detect AF and compare different leads' performances of AF detection. We used 12-lead ECG recordings sourced from the 2020 PhysioNet Challenge data sets. The Welch method was used to extract power spectral features of the 12-lead ECGs within a frequency range of 0.083 to 24.92 Hz. Subsequently, various machine learning techniques were evaluated and optimized to classify sinus rhythm (SR) and AF based on these power spectral features. Furthermore, we compared the effects of different frequency subbands and different lead selections on machine learning performances. The light gradient boosting machine (LightGBM) was found to be the most effective in classifying AF and SR, achieving an average F1-score of 0.988 across all ECG leads. Among the frequency subbands, the 0.083 to 4.92 Hz range yielded the highest F1-score of 0.985. In interlead comparisons, aVR had the highest performance (F1=0.993), with minimal differences observed between leads. In conclusion, this study successfully used machine learning methodologies, particularly the LightGBM model, to differentiate SR and AF based on power spectral features derived from 12-lead ECGs. The performance marked by an average F1-score of 0.988 and minimal interlead variation underscores the potential of machine learning algorithms to bolster real-time AF detection. This advancement could significantly improve patient care in intensive care units as well as facilitate remote monitoring through wearable devices, ultimately enhancing clinical outcomes.

Predicting political attitudes from web tracking data: a machine learning approach

ABSTRACT Anecdotal evidence suggests that the surge of populism and subsequent political polarization might make voters’ political preferences more detectable from digital trace data. This potential scenario could expose voters to the risk of being targeted and easily influenced by political actors. This study investigates the linkage between over 19,000,000 website visits, tracked from 1,003 users in Germany, and their survey responses to explore whether website choices can accurately predict political attitudes across five dimensions: Immigration, democracy, issues (such as climate and the European Union), populism, and trust. Our findings indicate a limited ability to identify political attitudes from individuals’ website visits. Our most effective machine learning algorithm predicted interest in politics and attitudes toward democracy but with dependency on model parameters. Although website categories exhibited suggestive patterns, they only marginally distinguished between individuals with anti- or pro-immigration attitudes, as well as those with populist or mainstream attitudes. This further confirm the reliability of surveys in measuring attitudes compared to digital trace data and, from a normative perspective, suggests that the potential to extract sensitive political information from online behavioral data, which could be utilized for microtargeting, remains limited.

Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation.

Machine learning (ML) techniques offer a promising avenue for improving the integration of remote sensing data into mathematical crop models, thereby enhancing crop growth prediction accuracy. A critical variable for this integration is the leaf area index (LAI), which can be accurately assessed using proximal or remote sensing data based on plant canopies. This study aimed to (1) develop a machine learning-based method for estimating the LAI in rice and soybean crops using proximal sensing data and (2) evaluate the performance of a Remote Sensing-Integrated Crop Model (RSCM) when integrated with the ML algorithms. To achieve these objectives, we analyzed rice and soybean datasets to identify the most effective ML algorithms for modeling the relationship between LAI and vegetation indices derived from canopy reflectance measurements. Our analyses employed a variety of ML regression models, including ridge, lasso, support vector machine, random forest, and extra trees. Among these, the extra trees regression model demonstrated the best performance, achieving test scores of 0.86 and 0.89 for rice and soybean crops, respectively. This model closely replicated observed LAI values under different nitrogen treatments, achieving Nash-Sutcliffe efficiencies of 0.93 for rice and 0.97 for soybean. Our findings show that incorporating ML techniques into RSCM effectively captures seasonal LAI variations across diverse field management practices, offering significant potential for improving crop growth and productivity monitoring.

Comparison of Support Vector Machine and Random Forest Method on Static Analysis Windows Portable Executable (PE) Malware Detection

Malware has emerged as a significant concern for computer system security, as it spreads rapidly and adversely affects system performance. Detecting malware has become crucial, and one of the methods utilized is Machine Learning classification, which learns the characteristics of an application without executing it. In this study, the author evaluates the efficacy of malware detection in the static analysis of Windows Portable Executable (PE) files using the Support Vector Machine (SVM) and Random Forest algorithms. The author employs a dataset containing both malware-related PE files and safe applications to train the SVM and Random Forest models to classify PE files as either malware or safe. The objective is to determine the most effective machine learning algorithm for malware detection in PE files. The research compares the performance of both algorithms to identify the superior one for malware detection. The results indicate that the Random Forest algorithm achieves an impressive accuracy of 98.53%, while the SVM algorithm performs slightly lower with an accuracy of 97.14%.

Carpal tunnel syndrome prediction with machine learning algorithms using anthropometric and strength-based measurement.

Carpal tunnel syndrome (CTS) stands as the most prevalent upper extremity entrapment neuropathy, with a multifaceted etiology encompassing various risk factors. This study aimed to investigate whether anthropometric measurements of the hand, grip strength, and pinch strength could serve as predictive indicators for CTS through machine learning techniques. Enrollment encompassed patients exhibiting CTS symptoms (n = 56) and asymptomatic healthy controls (n = 56), with confirmation via electrophysiological assessments. Anthropometric measurements of the hand were obtained using a digital caliper, grip strength was gauged via a digital handgrip dynamometer, and pinch strengths were assessed using a pinchmeter. A comprehensive analysis was conducted employing four most common and effective machine learning algorithms, integrating thorough parameter tuning and cross-validation procedures. Additionally, the outcomes of variable importance were presented. Among the diverse algorithms, Random Forests (accuracy of 89.474%, F1-score of 0.905, and kappa value of 0.789) and XGBoost (accuracy of 86.842%, F1-score of 0.878, and kappa value of 0.736) emerged as the top-performing choices based on distinct classification metrics. In addition, using variable importance calculations specific to these models, the most important variables were found to be wrist circumference, hand width, hand grip strength, tip pinch, key pinch, and middle finger length. The findings of this study demonstrated that wrist circumference, hand width, hand grip strength, tip pinch, key pinch, and middle finger length can be utilized as reliable indicators of CTS. Also, the model developed herein, along with the identified crucial variables, could serve as an informative guide for healthcare professionals, enhancing precision and efficacy in CTS prediction.