Chorioamnionitis and its associated factors among women admitted to the maternity unit of Public Hospitals in Addis Ababa, Ethiopia. by Tadios Niguss Derese, Mekdes Dereje Wondafrash, Abel Melese Teka, Hiwot Soboksa Mideksa, Lidia Dagne Mario, Tsegaye Gebreyes Hundie, Robel Bayou Tilahun, Abel Abebe Demie

“…Significance was set at p < 0.05 with a 95% confidence interval, and data were presented in tables and graphs.A total number of 356 patient's data were analysed and the proportion of patients with Chorioamnionitis was found to be 21.3%. After adjustment for possible confounders on multi-variable binary logistic regression analysis age < 25 years [AOR = 0.26, 95% CI (0.09-0.72)], having premature rupture of membrane [AOR = 2.24, 95% CI (1.05-4.78)], duration of labor < 12 hour [AOR = 0.10, 95% CI (0.04-0.24)], and having urinary tract infection [AOR = 3.54, 95% CI (1.72-7.27)] were the significant variable associated with Chorioamnionitis. …”
Get full text

#GAMEADDICTED: A Machine Learning Framework for Digital Game Addiction Detection and Early Intervention by Esra Kahya Ozyirmidokuz, Bekir Asim Celik, Eduard Alexandru Stoica

“…After extensive preprocessing, we trained and evaluated six machine learning models: XGBoost, Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Decision Trees, and Random Forests. XGBoost consistently outperformed all other models, achieving the highest accuracy of 93.06% on the binary-labeled dataset and 92.49% on the expanded dataset. …”
Get full text

Predicting suicidal behavior outcomes: an analysis of key factors and machine learning models by Mohammad Bazrafshan, Kourosh Sayehmiri

“…In the second phase, missing data were removed, and the dataset was standardized. Five binary classification algorithms were utilized, including Random Forest, Logistic Regression, and Decision Trees, with hyperparameters optimized using the area under the receiver operating characteristic curve (AUC) and F1 score metrics. …”
Get full text

Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts by Renao Jiang, Longfei Dai, Xinjian Xu, Zhen Zhang

Get full text

H∞ ILC Design for Discrete Linear Systems with Packet Dropouts and Iteration-Varying Disturbances by Bu Xuhui, Zhang Hongwei, Song YunZhong, Yu Fashan

Get full text

hdpGLM: An R Package to Estimate Heterogeneous Effects in Generalized Linear Models Using Hierarchical Dirichlet Process by Diogo Ferrari

Get full text

Research on mechanisms for optimizing the risk resistance capability of hypernetworks by Lei Chen, Xiujuan Ma, Fuxiang Ma, Yalan Li

Get full text

Machine learning-based classification of valvular heart disease using cardiovascular risk factors by Muhammad Usman Aslam, Songhua Xu, Sajid Hussain, Muhammad Waqas, Nafiu Lukman Abiodun

Get full text

Investigation of factors affecting crash severity of rear-end crashes with high collision speeds in work zones: A South Carolina case study by Mahyar Madarshahian, Jason Hawkins, Nathan Huynh, Chowdhury K.A. Siddiqui

Get full text

Incidence‐data‐based species richness estimation via a Beta‐Binomial model by Chun‐Huo Chiu

Get full text

CO2 CAPTURE MODELING IN AQUEOUS SOLUTION OF METHYLDIETHANOLAMINE AND HEXYLAMINE by CHAKIB BOUALLOU

“…The aim of this work is to establish a thermodynamic model using Non-Random Two-Liquid (NRTL) Electrolyte Aspen PlusTM model, which allows to simulate the CO2 capture by an aqueous mixture of methyldiethanolamine (MDEA) and hexylamine (HA) with mass concentration of MDEA 37 wt % + HA 3 wt %. …”
Get full text

Predicting Student Performance and Enhancing Learning Outcomes: A Data-Driven Approach Using Educational Data Mining Techniques by Athanasios Angeioplastis, John Aliprantis, Markos Konstantakis, Alkiviadis Tsimpiris

“…Five machine learning algorithms—k-nearest neighbors, random forest, logistic regression, decision trees, and neural networks—were applied to identify correlations between courses and predict grades. …”
Get full text

Hypertensive Patients’ Knowledge of Risk Factors and Warning Signs of Stroke at Felege Hiwot Referral Hospital, Northwest Ethiopia: A Cross-Sectional Study by Addisu Taye Abate, Netsanet Bayu, Tesfamichael G. Mariam

“…Out of the total 284 selected hypertensive patients, 278 of them responded completely with a response rate of 97.9 %. Of these, more than three fourths, 214 (77%) and 201 (72.3%), of them did not identify any risk factors and warning signs of stroke, respectively, with an overall proportion of only 18.3% of them having good knowledge towards stroke. …”
Get full text

Optimization of Inversion Probability Tunable Sequence Phase Modulation for SBS Suppression in Fiber System by Qianhe Shao, Yifeng Yang, He Wang, Haowei Xia, Jingshuai Ma, Chenxiang Qian, Junqing Meng

“…Compared to the pseudo-random binary sequence (PRBS) case, this scheme increases the output power by 700 W at 10 GHz FWHM optical linewidth.…”
Get full text

Increasing proportion of mildly aged population in rural mitigates farmland abandonment in the farming-pastoral ecotone of northern China. by Yuling Jin, Guoliang Zhang, Xin Chen, Yi Zhou, Yukai Wei, Sicheng Mao, Haile Zhao, Wenting Liu, Zhihua Pan, Pingli An

“…The dynamic changes in farmland abandonment from 2000 to 2020 were systematically explored using a trajectory-based land use change detection approach. Binary logit regression models were employed to analyze the driving mechanism of the current farmland abandonment based on 1,195 questionnaires, and then random forest models were used to predict future farmland abandonment trends under various scenarios. …”
Get full text

Hidden semi-Markov models to segment reading phases from eye movements by Brice Olivier, Anne Guérin-Dugué, Jean-Baptiste Durand

Get full text

Knowledge and associated factors towards trachoma in rural Lemo district, Southern Ethiopia, 2021 by Mikias Mered Tilahun, Biruk Lelisa Eticha, Mulunesh Girma Shobiso, Merkineh Markos Lorato

“…Seven kebeles were selected using a simple random sampling method. Then, a systematic random sampling procedure with a five-interval size was applied to select the household.Our study assessed the association between the outcome variable and explanatory variables using binary and multivariate logistic regressions. …”
Get full text

Transformer-Driven Affective State Recognition from Wearable Physiological Data in Everyday Contexts by Fang Li, Dan Zhang

Get full text

Degeneralization Algorithm for Generation of Büchi Automata Based on Contented Situation by Laixiang Shan, Jun Qin, Mingshi Chen, Zheng Qin

Get full text

Design and Implementation of Stream Cipher Using Neural Network by Siddeq Ameen, Mazin Othman, Safwan Hasoon, Moyed Al-Razaq

Get full text