A deep ensemble framework for human essential gene prediction by integrating multi-omics data by Xue Zhang, Weijia Xiao, Brent Cochran, Wangxin Xiao

“…More than 200 features from these biological data are extracted/learned which are integrated together to train a series of cost-sensitive deep neural networks. …”
Get full text

Data-driven discovery of the design rules for considering the curing deformation and the application on double-double composites by Yizhuo Gui, Hongwei Song, Jinglei Yang, Cheng Qiu

“…Two specific machine learning (ML) models were built. One is combined model of convolutional neural networks (CNN) and principle component analysis (PCA) technique for connecting the layup sequences and their corresponding PID. …”
Get full text

Analysis of Students' Statistical Literacy Ability Based on Learning Anxiety : A Phenomenological Study in High School Students by Fadhilah Rahmania, Ishaq Nuriadin

“…The subjects of this study are 7 students of MAN 2 East Jakarta who were selected based on purposive sampling techniques from the aspect of learning anxiety. The data analysis used is an interactive model from Miles and Huberman which has four main stages, namely data collection, data reduction, data presentation, and conclusion drawing. …”
Get full text

Predicting nighttime black ice using atmospheric data for efficient winter road maintenance patrols by Jinhwan Jang

“…One potential solution is to forecast nighttime black ice using atmospheric data. In this context, the present study investigates machine learning techniques, including Random Forest, CatBoost, and Deep Neural Networks, for forecasting nighttime icing on rural highways in Korea. …”
Get full text

Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems by Tarana Khan, Urfi Khan, Adnan Khan, Calahan Mollan, Inga Morkvenaite-Vilkonciene, Vijitashwa Pandey

“…A Digital twin (DT) enables the acquisition and subsequent analysis of large amounts of process data. Various machine learning (ML) algorithms exist for analysis and prediction that can be used in this scenario. …”
Get full text

Combining machine learning and dynamic system techniques to early detection of respiratory outbreaks in routinely collected primary healthcare records by Dérick G. F. Borges, Eluã R. Coutinho, Thiago Cerqueira-Silva, Malú Grave, Adriano O. Vasconcelos, Luiz Landau, Alvaro L. G. A. Coutinho, Pablo Ivan P. Ramos, Manoel Barral-Netto, Suani T. R. Pinho, Marcos E. Barreto, Roberto F. S. Andrade

“…Our validation approach comprised (i) a comparative analysis across Brazilian capitals; (ii) an analysis of warning signs for the COVID-19 period; and (iii) a comparison with related surveillance methods (namely EARS C1, C2, C3) based on real and synthetic labeled data. …”
Get full text

Variogram modelling optimisation using genetic algorithm and machine learning linear regression: application for Sequential Gaussian Simulations mapping by André William Boroh, Alpha Baster Kenfack Fokem, Martin Luther Mfenjou, Firmin Dimitry Hamat, Fritz Mbounja Besseme

“…The objective of this study is to develop an advanced approach to variogram modelling by integrating genetic algorithms (GA) with machine learning-based linear regression, aiming to improve the accuracy and efficiency of geostatistical analysis, particularly in mineral exploration. …”
Get full text

Advanced Credit Card Fraud Detection: An Ensemble Learning Using Random Under Sampling and Two-Stage Thresholding by Ibrahim Almubark

“…The well-known Synthetic Minority Over-sampling Technique (SMOTE) was used for over-sampling. Data was utilized to train the model and subsequently generate predictions by utilizing testing data following the pre-processing of the dataset. …”
Get full text

Well-log lithofacies classification based on machine learning for Chang-7 member in Longdong area of Ordos Basin by SHEN Li,WANG Caizhi,NING Congqian,LIU Yingming,WANG Hao

“…After comparing the application effects of different unbalanced data classification algorithm in the region, it’s found that bagging algorithm of ensemble learning notably improved the classification performance of all lithofacies by combining multiple classifiers. …”
Get full text

A comprehensive survey on secure healthcare data processing with homomorphic encryption: attacks and defenses by Chian Hui Lee, King Hann Lim, Sivaraman Eswaran

“…It demonstrates HE’s versatility in securing electronic health records (EHRs), enabling privacy-preserving genomic data analysis, protecting medical imaging, facilitating privacy-preserving machine learning (ML), supporting secure federated learning, ensuring confidentiality in clinical trials, and enhancing remote monitoring and telehealth services. …”
Get full text

Advances in weed identification using hyperspectral imaging: A comprehensive review of platform sensors and deep learning techniques by Bright Mensah, Nitin Rai, Kelvin Betitame, Xin Sun

“…Techniques like image calibration, standard normal variate, multiplicative scatter correction, Savitsky-Golay smoothing, derivatives, and features selection are among the most used techniques, (d) traditional machine learning models namely support vector machines (SVM), partial least square discriminant analysis (PLS-DA), maximum likelihood classifiers (MLC), and random forest (RF) are the widely employed classifiers for weed identification, (e) the application of deep learning technique, namely convolutional neural networks (CNNs) are limited, but its application demonstrated superior performance accuracies compared to traditional machine learning models. …”
Get full text

Developing an explainable deep learning module based on the LSTM framework for flood prediction by Zhi Zhang, Dagang Wang, Yiwen Mei, Jinxin Zhu, Xusha Xiao

Get full text

On Conditional Independence Graph Learning From Multi-Attribute Gaussian Dependent Time Series by Jitendra K. Tugnait

“…In this paper we provide a unified theoretical analysis of multi-attribute graph learning for dependent time series using a penalized log-likelihood objective function formulated in the frequency domain using the discrete Fourier transform of the time-domain data. …”
Get full text

Ensemble stacked model for enhanced identification of sentiments from IMDB reviews by Komal Azim, Alishba Tahir, Mobeen Shahroz, Hanen Karamti, Annia Almeyda Vazquez, Angel Rojas Vistorte, Imran Ashraf

“…The Internet Movie Database (IMDB) movie reviews and Urdu tweets are examined in this study using Urdu sentiment analysis. The Urdu hack library was used to preprocess the Urdu data, which includes preprocessing operations including normalizing individual letters, merging them, including spaces, etc. concerning punctuation. …”
Get full text

Application of Machine Learning Techniques to Classify Twitter Sentiments Using Vectorization Techniques by Manjog Padhy, Umar Muhammad Modibbo, Rasmita Rautray, Subhranshu Sekhar Tripathy, Sujit Bebortta

“…Traditional Twitter Sentiment Analysis (TSA) faces challenges due to rule-based or dictionary algorithms, dealing with feature selection, ambiguity, sparse data, and language variations. …”
Get full text

GENDER-SPECIFIC PREDICTORS OF VAULT PERFORMANCE IN GYMNASTICS: A MACHINE LEARNING APPROACH by Dušan Đorđević, Janez Vodičar, Robi Kreft, Edvard Kolar, Miloš Paunović, Saša Veličković, Miha Marinšek

“…Data were collected from 27 national-level gymnasts (17 female, 10 male) during the Slovenian Cup competition. …”
Get full text

Vibration Diagnostic Methods from Methodsof Obtaining Data to Processing It Using Modern Means by Anton O. Zhuravlev, Alexey O. Polyakov, Denis A. Andrikov

“…The purpose of the review is to determine the existing methods of vibration diagnostics, determine their properties and compare them. As a result of the analysis, it was found that the most developing direction in the field of vibration signal research is a combination of wavelet transformation and neural network learning.…”
Get full text

RETRACTED: Simplicial Complex-Enhanced Manifold Embedding of Spatiotemporal Data for Structural Health Monitoring by Nan Xu, Zhiming Zhang, Yongming Liu

Get full text

YOLOv8m for Automated Pepper Variety Identification: Improving Accuracy with Data Augmentation by Madalena de Oliveira Barbosa, Fernanda Pereira Leite Aguiar, Suely dos Santos Sousa, Luana dos Santos Cordeiro, Irenilza de Alencar Nääs, Marcelo Tsuguio Okano

“…Comparative analysis reveals that training with the augmented dataset yielded significant improvements across key performance indicators, particularly in box precision, recall, and mean average precision (mAP50 and mAP95), underscoring the effectiveness of data augmentation. …”
Get full text

Using Machine Learning on Macroeconomic, Technical, and Sentiment Indicators for Stock Market Forecasting by Michalis Patsiarikas, George Papageorgiou, Christos Tjortjis

Get full text