Explainable machine learning to predict the cost of capital by Niklas Bussmann, Paolo Giudici, Paolo Giudici, Alessandra Tanda, Alessandra Tanda, Ellen Pei-Yi Yu

“…Our findings pave the way for future investigations on the impact of ESG and country factors in predicting the cost of capital.…”
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Ephemeral gullies. A spatial and temporal analysis of their characteristics, importance and prediction by Jeroen Nachtergaele, Jean Poesen, Gerard Govers

“…This study, therefore, aimed at:1) describing spatial and temporal variations in ephemeral gully characteristics, in three contrasting environments;2) extending the existing studies on the importance of ephemeral gully erosion in space and time by using high-altitude stereo aerial photos (HASAP) to assess ephemeral gully volumes;3) improving ephemeral gully prediction, through the development of both empirical relationships to directly predict ephemeral gully volumes and process-oriented relationships to be built in physically-based erosion models;4) evaluating the medium to long-term evolution of an (ephemeral) gully.…”
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Development and validation of machine learning classifiers for predicting treatment-needed retinopathy of prematurity by Nasser Shoeibi, Majid Abrishami, Seyedeh Maryam Hosseini, Mohammad-Reza Ansari-Astaneh, Razieh Farrahi, Bahareh Gharib, Fatemeh Neghabi, Mojtaba Abrishami, Mehdi Sakhaee, Mehrdad Motamed Shariati

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Development and validation of a gut motility based model for predicting bowel preparation quality by Hongjiao Wu, Rui Wu, Yunyun Zhang, Xinyi Lu, Wei Zhao, Biyun Xu, Jun Liu, Nina Zhang

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Prediction of postpartum depression in women: development and validation of multiple machine learning models by Weijing Qi, Yongjian Wang, Yipeng Wang, Sha Huang, Cong Li, Haoyu Jin, Jinfan Zuo, Xuefei Cui, Ziqi Wei, Qing Guo, Jie Hu

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DeepPredict: a state-of-the-art web server for protein secondary structure and relative solvent accessibility prediction by Wafa Alanazi, Wafa Alanazi, Di Meng, Gianluca Pollastri

Subjects: “…protein structure prediction…”
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Models based on dietary nutrients predicting all-cause and cardiovascular mortality in people with diabetes by Fang Wang, Yukang Mao, Jinyu Sun, Jiaming Yang, Li Xiao, Qingxia Huang, Chenchen Wei, Zhongshan Gou, Kerui Zhang

“…The study aims to establish models predicting long-term mortality and explore dietary nutrients associated with reduced long-term events to guide daily dietary decisions in people with DM. …”
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Comparative Analysis of Machine Learning Models for Predicting Contaminant Concentration Distributions in Hospital Wards by Chonggang Zhou, Yunfei Ding

“…Four common machine learning models—multiple linear regression (MLR), support vector regression (SVR), backpropagation (BP) neural network, and convolutional neural network (CNN)—were employed to predict the distribution of contaminants within the wards. …”
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Mathematical models of contact patterns between age groups for predicting the spread of infectious diseases by Sara Y. Del Valle, J. M. Hyman, Nakul Chitnis

“…We apply the analysis to the spread of a smallpox-like disease, derive the formula for the reproduction number, $\Re_{0}$, and based on this threshold parameter, show the level of human behavioral change required to control the epidemic.We analyze how different mixing patterns can affect the disease prevalence, the cumulative number of new infections, and the final epidemic size.Our analysis indicates that the combination of residual immunity and behavioral changes during a smallpox-like disease outbreak can play a key role in halting infectious disease spread; and that realistic mixing patterns must be included in the epidemic model for the predictions to accurately reflect reality.…”
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Choice of machine learning models for predicting the development of psychological disorders in people with hypothireosis and hyperthireosis by Нурал Гулієв

“…The article solves the problem of choosing the best models for predicting the occurrence of psychological disorders in people with endocrinological problems. …”
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Comparison of the effects of three time series models in predicting the trend of erythrocyte blood demand by QIU Yajuan, ZHANG Jianping, LUO Jia, LI Peilin, LUO Mengzhuo, LI Qiongying, LIU Ge, LEI Qing, LIAO Kai

Subjects: “…arima model…”
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Predicting congenital syphilis cases: A performance evaluation of different machine learning models. by Igor Vitor Teixeira, Morgana Thalita da Silva Leite, Flávio Leandro de Morais Melo, Élisson da Silva Rocha, Sara Sadok, Ana Sofia Pessoa da Costa Carrarine, Marília Santana, Cristina Pinheiro Rodrigues, Ana Maria de Lima Oliveira, Keduly Vieira Gadelha, Cleber Matos de Morais, Judith Kelner, Patricia Takako Endo

“…<h4>Objective</h4>The main goal of this work is to evaluate the performance of different machine learning models on predicting undesirable outcomes of congenital syphilis in order to assist resources allocation and optimize the healthcare actions, especially in a constrained health environment.…”
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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

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Interpreting machine learning models based on SHAP values in predicting suspended sediment concentration by Houda Lamane, Latifa Mouhir, Rachid Moussadek, Bouamar Baghdad, Ozgur Kisi, Ali El Bilali

“…The results revealed that all implemented models are efficient in SSC prediction with NSE, RMSE, and r varying from 0.53 to 0.86, 1.20–2.55 g/L, and 0.83–0.91 g/L respectively. …”
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Development of Regression Models considering Time-Lag and Aerosols for Predicting Heating Loads in Buildings by Hong Soo Lim, Gon Kim

“…In addition, the study develops different prediction models for buildings of different sizes. …”
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Computational models based on machine learning and validation for predicting ionic liquids viscosity in mixtures by Bader Huwaimel, Jowaher Alanazi, Muteb Alanazi, Tareq Nafea Alharby, Farhan Alshammari

“…Abstract This research article presents a thorough and all-encompassing examination of predictive models utilized in the estimation of viscosity for ionic liquid solutions. …”
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Development of data-driven machine learning models and their potential role in predicting dengue outbreak by Bushra Mazhar, Nazish Mazhar Ali, Farkhanda Manzoor, Muhammad Kamran Khan, Muhammad Nasir, Muhammad Ramzan

“…The current article endeavors to present an overview of predicting dengue outbreaks through data-based machine-learning models. …”
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Systematic review of prognostic models for predicting recurrence and survival in patients with treated oropharyngeal cancer by Janine Dretzke, Hisham Mehanna, David J Moore, Malcolm J Price, Ahmad K Abou-Foul, Katie Scandrett, Esther Albon, Bethany Hillier, Paul Nankivell

“…The Prediction model Risk Of Bias ASsessment Tool was used to assess risk of bias (RoB). …”
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Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach by Marko Martinović, Kristian Dokic, Dalibor Pudić

“…These observations emphasize the need to match model selection with data structure, performance objectives, and practical resource constraints when predicting and improving innovation outcomes at the firm level.…”
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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

“…To get increased insights into the model prediction process, shapely and individual conditional expectation analyses were carried out on the XGB algorithm which highlighted that water, cement, and time after mixing are the most influential parameters to estimate both fresh properties of concrete. …”
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