A Machine-Learning-Based Approach to Informing Student Admission Decisions by Tuo Liu, Cosima Schenk, Stephan Braun, Andreas Frey

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A comprehensive review of predictive analytics models for mental illness using machine learning algorithms by Md. Monirul Islam, Shahriar Hassan, Sharmin Akter, Ferdaus Anam Jibon, Md. Sahidullah

“…Our emotional, psychological, and social well-being are all parts of our mental health, influencing our thoughts, emotions, and behaviors. Mental health also influences how we respond to stress, interact with others, and make good or bad decisions. …”
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Comprehensive Machine Learning Model for Cervical Cancer Prediction and Risk Factor Identification by null Mahendra, Mila Desi Anasanti

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Application of Artificial Neural Networks to Predict the Use of Mobile Learning by University Students by Alejandro Valencia-Arias, Julián Alberto Uribe-Gómez, Evelyn Flores-Siapo, Lucia Palacios-Moya, Ada Gallegos, Ezequiel Martínez Rojas

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Hierarchical Behavior Planning Framework for Networked MSV Systems in Inland Waterway Traffic Separation Schemes by Wei Tao, Lizheng Wang, Jian Tan, Man Zhu, Yuanqiao Wen

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Effects of implementation leadership of head nurses on nurses’ evidence-based pain management practices behavior by Lingyu Li, Quanru Wang, Ying Cao, Ling Liao, Binjie Yang, Min Tan

“…The scores for head nurses’ IL were (M = 2.88, SD = 0.74) and nurses’ EBPMP behavior was (M = 3.98, SD = 0.76). The results of the HLM analysis showed that IL of head nurses had a positive predictive effect on nurses’ EBPMP behavior (p < 0.001); Nurses’ attitudes towards pain management and participation in pain education and training had a positive predictive effect on nurses’ EBPMP behavior (p < 0.001). …”
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The digital age students: Exploring leadership, freedom, and ethical online behavior: A quantitative study by Mohamed Mekheimer, Walid Mohamed Abdelhalim

“…This study investigates the complex interplay between leadership skills, personal freedom, and digital responsibility among 1226 university students in Egypt, addressing a critical need to understand student-led online behavior. Employing validated questionnaires and a quantitative, cross-sectional design, we examined the predictive relationships among these constructs. …”
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Discretized representations in V1 predict suboptimal orientation discrimination by Julien Corbo, O. Batuhan Erkat, John McClure, Hussein Khdour, Pierre-Olivier Polack

“…The relative neuronal activity at those discrete domains accurately predicted the probabilities of the animals’ decisions. …”
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The Relationship between Social Deficits and Personal Skills with Behavioral Empowerment in Yazd High School Students by Meimanat Abedini Baltork, Mohammad Rezapour

“…Conclusion: The social deficits are positively correlated with studentschr('39') behavioral empowerment and individual skills can be predictive of studentschr('39') behavioral empowerment.…”
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Spatio-temporal prediction of terrorist attacks based on GCN-LSTM by Yingjie Du, Ning Ding, Hongyu Lv

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Inherent-attribute-aware dual-graph autoencoder for rating prediction by Yangtao Zhou, Qingshan Li, Hua Chu, Jianan Li, Lejia Yang, Biaobiao Wei, Luqiao Wang, Wanqiang Yang

“…However, existing methods still have two significant limitations: i) External attributes are often unavailable in the real world due to privacy issues, leading to low quality of representations; and ii) existing methods lack considering complex associations in users' rating behaviors during the encoding process. To meet these challenges, this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder, named IADGAE, for rating prediction. …”
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Brain systems for probabilistic and dynamic prediction: computational specificity and integration. by Jill X O'Reilly, Saad Jbabdi, Matthew F S Rushworth, Timothy E J Behrens

“…A computational approach to functional specialization suggests that brain systems can be characterized in terms of the types of computations they perform, rather than their sensory or behavioral domains. We contrasted the neural systems associated with two computationally distinct forms of predictive model: a reinforcement-learning model of the environment obtained through experience with discrete events, and continuous dynamic forward modeling. …”
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Can behavioral features reveal lying in an online personality questionnaire? The impact of mouse dynamics and speech by Eduard Kuric, Peter Demcak, Peter Smrecek, Stefan Benus

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Investigating the effect of knowledge, attitude, and social marketing mixes on the behavior of consumption herbal medicines by patients by Mahdi Amraei, Effat Jahanbani Veshareh, Kambiz Ahmadi Angali, Amin Torabipour

“…Based on linear regression analysis, attitude, knowledge and place as the three main predictive factors had a positive effect on patients' consumption behavior. …”
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Leveraging Degradation Events for Enhanced Remaining Useful Life Prediction by Zeeshan Abbas, Muhammad Sharif, Musrat Hussain, Naeem Hussain, Mehboob Hussain, Naveed Ahmad Khan

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Handgrip strength predicts leg power but not cardiorespiratory fitness in children by Salem Alotaibi, Rizwan Qaisar, Rizwan Qaisar, Rizwan Qaisar, M. Azhar Hussain, M. Azhar Hussain, Osama Aljuhani, Maha H. Alhussain, Diogo Luís Marques, Diogo Luís Marques, Daniel Almeida Marinho, Daniel Almeida Marinho, Shaea A. Alkahtani

“…PurposeThe current study examined the ability of handgrip strength (HGS) to predict leg muscle power, cardiorespiratory fitness, and movement behaviors in children.MethodsOne hundred eighteen male children aged 10–13 years from primary and middle schools in Saudi Arabia were recruited for this cross-sectional study. …”
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Quantitative method for network security situation based on attack prediction by Hao HU, Run-guo YE, Hong-qi ZHANG, Ying-jie YANG, Yu-ling LIU

“…To predict the attack behaviors accurately and comprehensively as well as to quantify the threat of attack,a quantitative method for network security situation based on attack prediction was proposed.By fusing the situation factors of attacker,defender and network environment,the capability of attacker and the exploitability rate of vulnerability were evaluated utilizing the real-time detected attack events,and the expected time-cost for attack-defense were further calculated.Then an attack prediction algorithm based on the dynamic Bayesian attack graph was designed to infer the follow-up attack actions.At last,the attack threat was quantified as the security risk situation from two levels of the hosts and the overall network.Experimental analysis indicates that the proposed method is suitable for the real adversarial network environment,and is able to predict the occurrence time of attack accurately and quantify the attack threat reasonably.…”
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Quantitative method for network security situation based on attack prediction by Hao HU, Run-guo YE, Hong-qi ZHANG, Ying-jie YANG, Yu-ling LIU

“…To predict the attack behaviors accurately and comprehensively as well as to quantify the threat of attack,a quantitative method for network security situation based on attack prediction was proposed.By fusing the situation factors of attacker,defender and network environment,the capability of attacker and the exploitability rate of vulnerability were evaluated utilizing the real-time detected attack events,and the expected time-cost for attack-defense were further calculated.Then an attack prediction algorithm based on the dynamic Bayesian attack graph was designed to infer the follow-up attack actions.At last,the attack threat was quantified as the security risk situation from two levels of the hosts and the overall network.Experimental analysis indicates that the proposed method is suitable for the real adversarial network environment,and is able to predict the occurrence time of attack accurately and quantify the attack threat reasonably.…”
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Multiclass Sparse Bayesian Regression for fMRI-Based Prediction by Vincent Michel, Evelyn Eger, Christine Keribin, Bertrand Thirion

“…Inverse inference has recently become a popular approach for analyzing neuroimaging data, by quantifying the amount of information contained in brain images on perceptual, cognitive, and behavioral parameters. As it outlines brain regions that convey information for an accurate prediction of the parameter of interest, it allows to understand how the corresponding information is encoded in the brain. …”
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A study on the effectiveness of machine learning models for hepatitis prediction by Popy Khatun, Shafeel Umam, Rubaiya Binte Razzak, Iffat Binta Shamsuddin, Nahid Salma

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