Burnout Risk Profiles in Psychology Students: An Exploratory Study with Machine Learning by M. Graça Pereira, Martim Santos, Renata Magalhães, Cláudia Rodrigues, Odete Araújo, Dalila Durães

“…The accuracy of the three machine learning models—Random Forest, XGBoost, and Support Vector Machine—was 95.06%, 93.82%, and 97.53%, respectively. …”
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Scalable machine learning framework for predicting critical links in urban networks by Nourhan Bachir, Chamseddine Zaki, Hassan Harb, Roland Billen

“…The framework integrates structural, functional, and newly proposed features, offering a comprehensive representation of road network dynamics. …”
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Evolutionary sparse learning reveals the shared genetic basis of convergent traits by John B. Allard, Sudip Sharma, Ravi Patel, Maxwell Sanderford, Koichiro Tamura, Slobodan Vucetic, Glenn S. Gerhard, Sudhir Kumar

“…Genes contributing to genetic models for echolocation were highly enriched for functional categories related to hearing, sound perception, and deafness, a pattern that has eluded previous efforts applying standard molecular evolutionary approaches. …”
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Crochet increases attention through a requiring motor skill learning by Davide Rossi Sebastiano, Cristina Muscio, Dunja Duran, Deborah Bonfoco, Sara Dotta, Paola Anversa, Elisa Pellencin, Pietro Tiraboschi, Elisa Visani

“…Abstract In this study, we compared the effects promoted by a brief single session of crochet in a group of skilled knitters (CRO) and a control group (CRT) on the Attentional Network Test (ANT) and the whole brain Functional Connectivity (FC) revealed by Magnetoencephalography (MEG). …”
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Optimizing Time-Sensitive Software-Defined Wireless Networks With Reinforcement Learning by Hyeontae Joo, Sangmin Lee, Seunghwan Lee, Hwangnam Kim

“…Meanwhile, software-defined networking (SDN) has successfully presented its efficiencies in ensuring quality of service for network traffic to accommodate many functions of network control and management. In this regard, we propose a traffic engineering solution based on reinforcement learning (RL) to implement TSN links with SDN over a wireless network, then optimize the quality of TSN links, and protect background traffic from excessive resource allocation for TSN-enabled but SDN-supported traffic. …”
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Machine learning in additive manufacturing——NiTi alloy’s transformation behavior by Lidong Gu, Kongyuan Yang, Hongchang Ding, Zezhou Xu, Chunling Mao, Panpan Li, Zhenglei Yu, Yunting Guo, Luquan Ren

“…The laser powder bed fused NiTi alloys (LPBF-NiTi) demonstrate shape memory functionality and superelasticity as a result of their distinctive phase transition characteristics. …”
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Machine learning technology in the classification of glaucoma severity using fundus photographs by Sukhumal Thanapaisal, Passawut Uttakit, Worapon Ittharat, Pukkapol Suvannachart, Pawasoot Supasai, Pattarawit Polpinit, Prapassara Sirikarn, Panawit Hanpinitsak

“…Abstract This study evaluates the performance of a machine learning model in classifying glaucoma severity using color fundus photographs. …”
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Boosting proactive motor control via statistical learning with brain stimulation by Giulia Ellena, Federica Contò, Michele Tosi, Lorella Battelli

“…Results suggest a dominant role for the right hemisphere in mediating attentional learning effects, with implications for understanding lateralized functions in adaptation of the motor control.…”
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Neural mechanisms of credit assignment for delayed outcomes during contingent learning by Phillip P Witkowski, Lindsay JH Rondot, Zeb Kurth-Nelson, Mona M Garvert, Raymond J Dolan, Timothy EJ Behrens, Erie Boorman

“…Here, we leverage multivariate pattern analyses of functional magnetic resonance imaging (fMRI) data and an adaptive learning task to shed light on the underlying neural mechanisms of such specific credit assignment. …”
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Dynamic ensemble-based machine learning models for predicting pest populations by Ankit Kumar Singh, Md Yeasin, Ranjit Kumar Paul, A. K. Paul, Anita Sarkar

“…This study introduces a dynamic ensemble model with absolute log error (ALE) and logistic error functions using four machine learning models—artificial neural networks (ANNs), support vector regression (SVR), k-nearest neighbors (kNN), and random forests (RF). …”
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Devising a Breast Cancer Diagnosis Protocol through Machine Learning by Tooba Mujtaba, Saif Ullah Hashmi, Usama Bin Imtiaz, Sheikh Jameel Fathima Nusra

“…It is categorized based on receptors, including the estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2), which are the focus of the present research We analyzed gene expression from data obtained from a functional genomics repository called Array Express. …”
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Detection of high-risk diseases in poultry feces through transfer learning by Abdulkadir Tasdelen, Yenal Arslan

“…Early diagnosis of infectious and high-risk diseases such as Coccidiosis, Salmonellosis, and Newcastle disease, coupled with taking necessary precautions, will contribute to the efficient functioning of global economies supply chain. This study aims to detect high-risk Coccidiosis, Salmonellosis, and Newcastle diseases in poultry through transfer learning using poultry feces. …”
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Adaptive Q-Learning Grey Wolf Optimizer for UAV Path Planning by Golam Moktader Nayeem, Mingyu Fan, Golam Moktader Daiyan

“…However, standard GWO has several limitations, such as premature convergence, susceptibility to local minima, and unsuitability for dynamic environments due to its lack of adaptive learning. We propose a Q-learning-based GWO algorithm to address these issues in this study. …”
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Leveraging deep learning for robust EEG analysis in mental health monitoring by Zixiang Liu, Juan Zhao

“…Conventional methods for EEG-based mental health evaluation often depend on manually crafted features or basic machine learning approaches, like support vector classifiers or superficial neural networks. …”
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Investigating AI Chatbots’ Role in Online Learning and Digital Agency Development by Irina Engeness, Magnus Nohr, Trine Fossland

“…Based on data from 46 students, collected through structured questionnaires and follow-up interviews, the findings show that chatbots functioned as interactive learning partners, helping students clarify complex concepts, generate learning resources, and engage in reflection—thereby supporting their PDC. …”
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Physiological Responses on Krabi-Krabong Skill Learning in Students of Buriram Province by Amorntheap Wandee, Ban Chabairam, Suchart Homjan

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Generative deep learning for predicting ultrahigh lattice thermal conductivity materials by Liben Guo, Yuanbin Liu, Zekun Chen, Hongao Yang, Davide Donadio, Bingyang Cao

“…The recent development of generative models and machine learning (ML) holds great promise for predicting new functional materials. …”
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Integrating artificial intelligence within South African higher learning institutions by Phumzile D. Mogoale, Agnieta Pretorius, Refilwe C. Mogase, Mmatshuene A. Segooa

“…Background: Artificial intelligence (AI) technology is transforming education through personalised learning and creates dynamic, adaptive learning environments that cater to each student’s unique strengths and challenges. …”
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Android Malware Category and Family Identification Using Parallel Machine Learning by Ahmed Hashem El Fiky, Mohamed Ashraf Madkour, Ayman El Shenawy

“…This paper proposes an effective systematic and functional parallel machine-learning model for the dynamic detection of Android malware categories and families.  …”
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Predictive athlete performance modeling with machine learning and biometric data integration by Qin Jianjun, Haytham F. Isleem, Walaa J. K. Almoghayer, Mohammad Khishe

“…Abstract The Purpose of this study is to propose a new integrative framework for athletic performance prediction based on state-of-the-art machine learning analysis and biometric data biometric scanning. …”
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