A Kp‐Driven Machine Learning Model Predicting the Ultraviolet Emission Auroral Oval by Huiting Feng, Dedong Wang, Yuri Y. Shprits, Artem Smirnov, Deyu Guo, Yoshizumi Miyoshi, Stefano Bianco, Shangchun Teng, Run Shi, Su Zhou, Yongliang Zhang

“…Based on the data spanning from 2005 to 2016 obtained from DMSP/SSUSI, we explore several machine learning algorithms, such as KNN, RF, and XGBoost, to construct an auroral oval prediction model. …”
Get full text

A Hypergraph powered approach to Phenotype-driven Gene Prioritization and Rare Disease Prediction by Shrinithi Natarajan, Niveditha Kundapuram, Nisarga Bhaskar, Sai Sailaja Policharla, Bhaskarjyoti Das

“…The proposed method outperforms existing state-of-the-art tools such as Phenomizer and GCN, in terms of both prediction accuracy and processing speed. Notably, it captures 50% of causal genes within the top 10 predictions and 85% within the top 100 predictions and the algorithm maintains a high accuracy rate of 98.09% for the top-ranked gene. …”
Get full text

Fall prediction in a quiet standing balance test via machine learning: Is it possible? by Juliana Pennone, Natasha Fioretto Aguero, Daniel Marczuk Martini, Luis Mochizuki, Alexandre Alarcon do Passo Suaide

“…Machine learning is a computer-science area that uses statistics and optimization methods in a large amount of data to make outcome predictions. Thus, to assess the performance of machine learning algorithms in classify participants by age, number of falls and falls frequency based on features extracted from a public database of stabilometric assessments. 163 participants (116 women and 47 men) between 18 and 85 years old, 44.0 to 75.9 kg mass, 140.0 to 189.8 cm tall, and 17.2 to 31.9 kg/m2 body mass index. …”
Get full text

Analysis and Prediction of Grouting Reinforcement Performance of Broken Rock Considering Joint Morphology Characteristics by Guanglin Liang, Linchong Huang, Chengyong Cao

“…Furthermore, multiple machine learning algorithms are employed to construct a robust predictive model. …”
Get full text

The current status and future directions of artificial intelligence in the prediction, diagnosis, and treatment of liver diseases by Bo Gao, Wendu Duan

“…With the rapid progress of artificial intelligence (AI) technology, its applications in the medical field, particularly in the prediction, diagnosis, and treatment of liver diseases, have drawn increasing attention. …”
Get full text

Predicting Credit Card Fraud using Supervised Machine Learning Methods: Comparative Analysis by Güner Altan, Metin Recep Zafer

Get full text

Myoelectric signal and machine learning computing in gait pattern recognition for flat fall prediction by Shuo Zhang, Biao Chen, Chaoyang Chen, Maximillian Hovorka, Jin Qi, Jie Hu, Gui Yin, Marie Acosta, Ruby Bautista, Hussein F. Darwiche, Bryan E. Little, Carlos Palacio, John Hovorka

Get full text

NDVI Prediction with RGB UAV Imagery Utilizing Advanced Machine Learning Regression Models by I. Aydin, U. G. Sefercik

“…In the literature, RGB camera-based NDVI prediction studies involving machine learning and deep learning algorithms have focused on the correlation of the results with the reference data (R²) or the model accuracy of the algorithms used. …”
Get full text

Development and Validation of a Clinical Risk Model for Predicting Malignancy in Patients with Thyroid Nodules by Shiva Borzouei, Ali Safdari, Erfan Ayubi

“…The purpose of the current study was to develop and validate a clinical risk model to predict malignancy in patients with thyroid nodules.   …”
Get full text

Constructing a fall risk prediction model for hospitalized patients using machine learning by Cheng-Wei Kang, Zhao-Kui Yan, Jia-Liang Tian, Xiao-Bing Pu, Li-Xue Wu

“…Abstract Study objectives This study aimed to identify the risk factors associated with falls in hospitalized patients, develop a predictive risk model using machine learning algorithms, and evaluate the validity of the model’s predictions. …”
Get full text

Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers with Oversampling and Feature Augmentation by B. Shamreen Ahamed, Meenakshi S. Arya, Auxilia Osvin V. Nancy

“…There are many algorithms that have played a critical role in the prediction of diseases. …”
Get full text

Predicting the academic achievement of students using black hole optimization and Gaussian process regression by Yanyu Chen, Xiaolin Yao

“…This study uses a combination of black hole optimization (BHO) and Gaussian process regression (GPR) algorithms to predict students’ academic success in higher education. …”
Get full text

Parametric BIM and Machine Learning for Solar Radiation Prediction in Smart Growth Urban Developments by Seongchan Kim, Jong Bum Kim

“…The simulation results were then used to create ML models for context-specific solar radiation prediction. For ML model creation, four algorithms were compared and tested with several data diagnosis techniques. …”
Get full text

Deep Learning-Based Network Security Data Sampling and Anomaly Prediction in Future Network by Lan Liu, Jun Lin, Pengcheng Wang, Langzhou Liu, Rongfu Zhou

“…Then, through offline and real-time analyses, network security abnormal events are predicted in the future network. With the comparison of various algorithms and the adjustment of hyperparameters, the data characteristics and classification algorithms corresponding to different network security attacks are found. …”
Get full text

Prediction of adverse drug reactions based on pharmacogenomics combination features: a preliminary study by Mingxiu He, Mingxiu He, Yiyang Shi, Fangfang Han, Fangfang Han, Fangfang Han, Yongming Cai, Yongming Cai, Yongming Cai

“…We proposed a novel deep learning architecture, DGANet, based on the constructed features for ADR prediction. The algorithm uses Convolutional Neural Networks (CNN) and cross-features to learn the latent drug-gene-ADR associations for ADRs prediction.Results and DiscussionThe performance of DGANet was compared to three state-of-the-art algorithms with different genomic features. …”
Get full text

Soft-computing models for predicting plastic viscosity and interface yield stress of fresh concrete by Waleed Bin Inqiad, Muhammad Faisal Javed, Deema Mohammed Alsekait, Naseer Muhammad Khan, Majid Khan, Fahid Aslam, Diaa Salama Abd Elminaam

“…The comparison of results revealed that XGB is the most accurate algorithm to predict plastic viscosity (training $$\:{R}^{2}=0.959$$ , testing $$\:{R}^{2}=0.947$$ ) and interface yield stress (training $$\:{R}^{2}=0.925$$ , testing $$\:{R}^{2}=0.965$$ ). …”
Get full text

Comprehensive Evaluation of Bankruptcy Prediction in Taiwanese Firms Using Multiple Machine Learning Models by Hung V. Pham, Tuan Chu, Tuan M. Le, Hieu M. Tran, Huong T.K. Tran, Khanh N. Yen, Son V. T. Dao

“…This study developed an advanced bankruptcy prediction model using Support Vector Machines (SVM), Random Forest (RF), and Artificial Neural Network (ANN) algorithms based on datasets from the UCI machine learning repository. …”
Get full text

Predicting adolescent psychopathology from early life factors: A machine learning tutorial by Faizaan Siddique, Brian K. Lee

“…We provide a didactic example of machine learning for risk prediction in this study by determining whether early life factors could be useful for predicting adolescent psychopathology. …”
Get full text

Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data by Cong Liu, Lin Wang, Xuetong Fu, Junzhe Zhang, Ran Wang, Xiaofeng Wang, Nan Chai, Longfeng Guan, Qingshan Chen, Zhongchen Zhang

“…Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). …”
Get full text

A Comparison of the Performance of Ensemble Tree and Neural Networks for The Prediction of Traffic Accident Duration by Hüseyin Korkmaz, Mehmet Ali Ertürk, Mehmet Adak

“…Statistical tests and machine learning algorithms were applied to the extracted data set and prediction of traffic accident duration was performed. …”
Get full text