Predicting Restroom Dirtiness Based on Water Droplet Volume Using the LightGBM Algorithm by Sumio Kurose, Hironori Moriwaki, Tadao Matsunaga, Sang-Seok Lee

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Evaluating soiling effects to optimize solar photovoltaic performance using machine learning algorithms by Muhammad Faizan Tahir, Anthony Tzes, Tarek H.M. El-Fouly, Mohamed Shawky El Moursi, Nauman Ali Larik

“…Additionally, machine learning algorithms such as artificial neural networks, support vector machines, regression trees, ensemble of regression trees, Gaussian process regression, efficient linear regression, and kernel methods are employed to predict power reduction due to soiling and soiling losses across various soiling percentages. …”
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Development of immune-derived molecular markers for preeclampsia based on multiple machine learning algorithms by Zhichao Wang, Long Cheng, Guanghui Li, Huiyan Cheng

“…Several machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), bagged trees, and random forest (RF), were used to select immune-related signaling genes closely associated with the occurrence of PE. …”
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Association between serum hypertriglyceridemia and hematological indices: data mining approaches by Somayeh Ghiasi Hafezi, Amin Mansoori, Alireza Kooshki, Marzieh Hosseini, Sahar Ghoflchi, Mark Ghamsary, Gordon Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…Machine learning methodologies, specifically logistic regression, decision tree, and random forest algorithms, were utilized for data analysis in the investigation of individuals with normal and high TG levels. …”
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CABLE NEWS NETWORK (CNN) ARTICLES CLASSIFICATION USING RANDOM FOREST ALGORITHM WITH HYPERPARAMETER OPTIMIZATION by Dewi Retno Sari Saputro, Krisna Sidiq

“…The data is in form of text and has large amounts so good handling is needed to avoid overfitting and underfitting. Random forest is proper to apply to the data because the algorithm works very well on large amounts of data. …”
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The Construction and Empirical Analysis of the Company’s Financial Early Warning Model Based on Data Mining Algorithms by Aiqun Wang, Hongxiang Yu

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Study on the factors influencing the impaired abilities of daily living in middle-aged and older adult arthritis patients based on binary logistic regression and categorical decisi... by Bao-xuan Zhang, Bao-xuan Zhang, Bao-xuan Zhang, Jin-ping Luo, Jin-ping Luo, Jin-ping Luo, Jia-ying Sun, Jia-ying Sun, Jia-ying Sun, Ming-hui Geng, Ming-hui Geng, Ming-hui Geng, Yi-fan Mou, Yi-fan Mou, Yi-fan Mou, Nan-nan Cheng, Nan-nan Cheng, Nan-nan Cheng, Zhao-xuan Wang, Zhao-xuan Wang, Zhao-xuan Wang, Wen-qiang Yin, Wen-qiang Yin, Wen-qiang Yin, Zhong-ming Chen, Zhong-ming Chen, Zhong-ming Chen, Dong-ping Ma, Dong-ping Ma, Dong-ping Ma

“…Variables with significant differences in univariate analysis were included in binary logistic regression model and decision tree model based on the E-CHAID algorithm to explore the factors associated with impaired ADL in middle-aged and older adult arthritis patients in China.ResultsThe results of the logistic regression model indicated that sex, place of residence, age, education level, falls, Internet usage, depressive symptoms, pain, self-rated health, and number of comorbid chronic diseases were the influencing factors for impaired ADL. …”
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Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors by Michael Reich, Njira Lugogo, Laurie D Snyder, Megan L Neely, Guilherme Safioti, Randall Brown, Michael DePietro, Roy Pleasants, Thomas Li, Lena Granovsky

“…This analysis aimed to determine if a machine learning algorithm capable of predicting impending exacerbations could be developed using data from an integrated digital inhaler.Patients and methods A 12-week, open-label clinical study enrolled patients (≥40 years old) with COPD to use ProAir Digihaler, a digital dry powder inhaler with integrated sensors, to deliver their reliever medication (albuterol, 90 µg/dose; 1–2 inhalations every 4 hours, as needed). …”
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Hypoglossal Nerve Stimulation Respiratory Lead Migration: MAUDE Database Review and Case Report by Emily A. Commesso, Marcus F. Paoletti, Eric J. Kezirian

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Penentuan Jalur Diagnostik Penyakit Berbasis Konsep Pembelajaran Mesin: Studi kasus Penyakit Hepatitis C by Jimmy Tjen, Valentino Pratama

“…Based on the experiment, the distance correlation-based classification tree algorithm outperforms the classical classification tree algorithm by around 3% while using only 7 features instead of 12 as in the classical algorithm. …”
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Theoretical knowledge enhanced genetic algorithm for mine ventilation system optimization considering main fan adjustment by Wentian Shang, Jinzhang Jia

“…Comparative analysis with four other algorithms shows that, although this algorithm has a longer runtime due to the need to identify the minimum spanning tree during iterations, its ability to reduce problem dimensionality and improve population quality results in more stable and superior convergence performance, especially for large-scale mine ventilation systems. …”
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Utilizing machine learning algorithms for predicting Anxiety-Depression Comorbidity Syndrome in Gastroenterology Inpatients (ADCS-GI) by Min Tan, Jinjin Zhao, Yushun Tao, Uroosa Sehar, Yan Yan, Qian Zou, Qing Liu, Long Xu, Zeyang Xia, Lijuan Feng, Jing Xiong

“…Results Among eight machine learning algorithms, the Decision Tree and K-nearest neighbors models demonstrated an accuracy exceeding 81% and a sensitivity in the same range for detecting ADCS in patients. …”
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Class Balancing for Soil Data: Predictive Modeling Approach for Crop Recommendation Using Machine Learning Algorithms by Sapkal Kranti G., Kadam Avinash B.

“…Several classification algorithms, including Support Vector Classifier (SVC), Logistic Regression, Decision Tree, Random Forest, and XGBoost, were employed to predict soil characteristics. …”
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Predictive prioritization of genes significantly associated with biotic and abiotic stresses in maize using machine learning algorithms by Anjan Kumar Pradhan, Prasad Gandham, Kanniah Rajasekaran, Niranjan Baisakh

“…Improved performances of the algorithms via feature selection from the raw gene features identified 235 unique genes as top candidate genes across all models for all stresses. …”
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Comparative Analysis of Machine Learning Algorithms for Predicting Patient Admission in Emergency Departments Using EHR Data by Ahmad Abdul Chamid, Ratih Nindyasari, Muhammad Imam Ghozali

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Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment by Dimple Dimple, Jitendra Rajput, Nadhir Al-Ansari, Ahmed Elbeltagi

“…As a result, machine learning algorithms can improve irrigation water quality characteristics, which is critical for farmers and crop management in various irrigation procedures. …”
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3D spatial evolutionary particle swarm algorithm based emergency communication spatial deployment optimization by Yumin Chen, Xicheng Tan, Jinguang Jiang, Xiaoliang Meng, Zeenat Khadim Hussain, Jianguang Tu, Huaming Wang, You Wan, Zongyao Sha

“…By considering the spatial distribution of buildings and trees, the algorithm can achieve optimal data transmission quality by using a given number of ANET nodes. …”
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The application of a recommendation algorithm to the selection of places and the volume of planting of forest plantations, taking into account various characteristics by Korzhova Maria, Andreeva Kristina, Zolotarev Daniil, Zolnikov Vladimir

“…The process includes data collection and analysis, on the basis of which an algorithm is developed for selecting tree species and planting volume.…”
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Identifying determinants of malnutrition in under-five children in Bangladesh: insights from the BDHS-2022 cross-sectional study by Tanzila Tamanna, Shohel Mahmud, Nahid Salma, Md. Musharraf Hossain, Md. Rezaul Karim

“…Descriptive statistics were conducted to summarize the key characteristics of the dataset. Boruta algorithm was employed to identify important features related to malnutrition which were then used to evaluate several machine learning models, including K-Nearest Neighbors (KNN), Neural Networks (NN), Classification and Regression Trees (CART), XGBoost (XGBM), Support Vector Machines (SVM), and Random Forest (RF), in addition to the traditional logistic regression (LR) model. …”
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Modeling habitat suitability for endangered herb (Salvia leriifolia Benth) using Innovative hybrid machine learning algorithms by Emran Dastres, Hamidreza Rabiei-Dastjerdi, Hassan Esmaeili, Mahdis Amiri, Ali Sonboli, Mohammad Hossein Mirjalili

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