Sleep quality and associated factors among adult patients with epilepsy attending follow-up care at referral hospitals in Amhara region, Ethiopia. by Sintayehu Simie Tsega, Birhaneselassie Gebeyehu Yazew, Kennean Mekonnen

“…In the multivariable binary logistic regression, being unable to read and write [AOR = 3.16, 95%CI: 1.53, 6.51], taking polytherapy treatment [AOR = 2.10, 95% CI: 1.37, 3.21], poor medication adherence [AOR = 2.53, 95%CI: 1.02, 6.23] and having poor support [AOR = 2.72, 95%CI: 1.53, 4.82] and moderate social support [AOR = 1.89, 95%CI: 1.05, 3.41] were significantly associated with higher odds of poor sleep quality.…”
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Domestic Tourists’ Willingness to Pay for Natural and Cultural Heritage Sites in Pokhara Valley, Nepal by Arjun K. Thapa, Kumar Bhattarai

“…The study was based on cross-sectional data collected from 130 domestic tourists visiting three purposively selected fee-paying sites in Pokhara. …”
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Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials by Khuram Rashid, Fatima Rafique, Zunaira Naseem, Fahad K. Alqahtani, Idrees Zafar, Minkwan Ju

“…In the initial phase, three machine learning models—Decision Tree, Random Forest, and Multi-layer Perceptron—were developed and trained on a dataset of 1030 records to predict sustainable concrete's compressive strength accurately. …”
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Novel approximate adaptive carry lookahead adder for error resilient applications with generic method for error analysis by Viraj Joshi, Pravin Mane

“…In comparison to ETA-I, ETA-II and GeAr, proposed method has shown improvement in delay by 9%, 17.9% and 21.3% respectively. Error analysis is done for proposed adder using random probabilistic method and generic analysis method. …”
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Predicting avalanche danger in northern Norway using statistical models by K.-U. Eiselt, R. G. Graversen, R. G. Graversen

“…Random forest (RF) models are trained and optimised for a binary case and for a four-level case. …”
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Discovering the key symptoms for identifying patterns in functional dyspepsia patients: A doctor's decision and machine learning by Da-Eun Yoon, Heeyoung Moon, In-Seon Lee, Younbyoung Chae

“…Implicit importance was assessed by feature importance from the random forest classification models, which classify the three pattern for general differentiation and perform binary classification for specific types. …”
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Improved detection of microbiome-disease associations via population structure-aware generalized linear mixed effects models (microSLAM). by Miriam Goldman, Chunyu Zhao, Katherine S Pollard

“…Traits can be quantitative or binary (such as case/control). MicroSLAM is fit in three steps for each species. …”
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Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neura... by Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, Syoko Ueno, Takeshi Kubo, Tomohiro Kuroda, Kaori Togashi

“…For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. …”
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Magnitude and factors associated with chronic liver disease among adults (≥ 18 Years) attending gastroenterology and hepatology clinics in selected public hospitals, West Arsi Zone... by Beresa Lema Gage, Debela Gela, Teshome Habte Wurjine, Teshome Habte

“…A total of 384 adult participants were selected using systematic random sampling. Data were collected through structured interviews and medical record reviews. …”
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The Work Engagement Among Nurses in an Urban-Based Tertiary Hospital by Ampan Vimonvattana, Nontawat Benjakul

“…Participants were selected through simple random sampling. They completed an online survey including demographic data and the Utrecht Work Engagement Scale (UWES), which assesses three dimensions of engagement: vigor, dedication, and absorption. …”
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A Fast Image Encryption Scheme Based on a Four-Dimensional Variable-Parameter Hyperchaotic Map and Cyclic Shift Strategy by Guidong Zhang, Yanhao Zhao, Yanpei Zheng, Yulin Shen, Jun Huang

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MINIMIZING THE MULTILEVEL REPRESENTATIONS OF SYSTEMS OF BOOLEAN FUNCTIONS BASED ON SHANNON DECOMPOSITION by P. N. Bibilo, Y. Y. Lankevich

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A Robust Filter Design for Uncertain Singular Systems with Unreliable Channels by Ching-Min Lee, Meng-Hsuan Hsieh

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Optimizing Cardiovascular Risk Assessment with a Soft Voting Classifier Ensemble by Ammar Oad, Zulfikar Ahmed Maher, Imtiaz Hussain Koondhar, Karishima Kumari, Hammad Bacha

“…The accuracy precision recall and F1_score value is provided by the suggested ensemble method with 70.9% 72.3% 68.6%, 70.1% and Random Forest gives 71.5%, 72.2%, 70.3%, and 71.2%. …”
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The association between sugar-sweetened beverage consumption, muscle strength, and psychological symptoms among Chinese adolescents: a multicenter cross-sectional survey by Yanjie Zhou, Chunhua Xue, Gulnur Ahmat, Huijuan Lou, Yun Liu, Li Ma

“…The present study may provide theoretical support and assistance for the prevention and intervention of psychological symptoms in Chinese adolescents.MethodsIn this study, 42,832 adolescents aged 12–17 years in mainland China were assessed cross-sectionally for SSB consumption, standing long jump reflecting muscle strength, psychological symptoms, and related covariates using a three-stage stratified whole-cluster random sampling method. …”
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Multi-Class Decoding of Attended Speaker Direction Using Electroencephalogram and Audio Spatial Spectrum by Yuanming Zhang, Jing Lu, Fei Chen, Haoliang Du, Xia Gao, Zhibin Lin

“…Prior research on directional focus decoding, a.k.a. selective Auditory Attention Decoding (sAAD), has primarily focused on binary “left-right” tasks. However, decoding of the attended speaker’s precise direction is desired. …”
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Determinants of the Use of Hermetic Storage Bags for Maize Storage among Smallholder Farmers in Northwest Ethiopia by Tigist Kefale Mekonen, Biruk Yazie Wubetie

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Genetic clustering algorithm by M. A. Anfyorov

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Comparison of Machine Learning Models to Predict Nighttime Crash Severity: A Case Study in Tyler, Texas, USA by Raja Daoud, Matthew Vechione, Okan Gurbuz, Prabha Sundaravadivel, Chi Tian

“…Then, seven machine learning techniques, namely binary logistic regression, k-nearest neighbors, naïve Bayes, random forest, artificial neural network, Extreme Gradient Boosting (XGBoost), and a Long Short-Term Memory (LSTM) model, were all applied to the unseen test data. …”
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Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand by Guangmei Yang, Guangdong Wang, Leping Wan, Xinle Wang, Yan He

“…To improve computational efficiency, we used three algorithms to develop prediction models, including Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Light Gradient Boosting Machine (LightGBM) algorithms. …”
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