Corrigendum to “Development of an explainable machine learning model for Alzheimer’s disease prediction using clinical and behavioural features” [MethodsX 15 (2025) 103491] by Rajkumar Govindarajan, K. Thirunadanasikamani, Komal Kumar Napa, S. Sathya, J.Senthil Murugan, K.G.Chandi Priya

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A comparative performance analysis of machine learning models for compressive strength prediction in fly ash-based geopolymers concrete using reference data by Muhammad Kashif Anwar, Muhammad Ahmed Qurashi, Xingyi Zhu, Syyed Adnan Raheel Shah, Muhammad Usman Siddiq

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Impact of artificial intelligence on academic performance in medical education: A systematic review by Masomeh Kalantarion, Mehrsa Heidari, Nasrin Khajeali, Zahra Khorrami, Mitra Amini

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Open Government Data Topic Modeling and Taxonomy Development by Aljaž Ferencek, Mirjana Kljajić Borštnar

“…This research presents a structured approach to developing a taxonomy for open government data (OGD) impact areas using machine learning-driven topic modeling and iterative taxonomy refinement. …”
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Establishment of predictive models for postoperative delirium in elderly patients after knee/hip surgery based on total bilirubin concentration: machine learning algorithms by Shuhui Hua, Chuan Li, Yuanlong Wang, YiZhi Liang, Shanling Xu, Jian Kong, Hongyan Gong, Rui Dong, Yanan Lin, Xu Lin, Yanlin Bi, Bin Wang

“…Our research aims to delve into the connection between plasma bilirubin levels and postoperative delirium, with the goal of crafting ten machine learning (ML) models capable of predicting POD instances. …”
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ProLinker–Generator: Design of a PROTAC Linker Base on a Generation Model Using Transfer and Reinforcement Learning by Yanlin Luo, Danyang Song, Chengwei Zhang, An Su

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Decoding student cognitive abilities: a comparative study of explainable AI algorithms in educational data mining by Tianyue Niu, Ting Liu, Yiming Taclis Luo, Patrick Cheong-Iao Pang, Shuaishuai Huang, Ao Xiang

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Advancing Alzheimer’s disease risk prediction: development and validation of a machine learning-based preclinical screening model in a cross-sectional study by Yanfei Chen, Bing Wang, Yankai Shi, Wenhao Qi, Shihua Cao, Bingsheng Wang, Ruihan Xie, Jiani Yao, Xiajing Lou, Chaoqun Dong, Xiaohong Zhu, Danni He

“…This study aimed to develop a convenience, efficient prediction model for AD risk using machine learning techniques.Design and setting We conducted a cross-sectional study with participants aged 60 and older from the National Alzheimer’s Coordinating Center. …”
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Application of Machine Tool Thermal Error Compensation in Digital Twin-based System by MA Chi, LI Minging, LIU Jialan, HE Jialong, HUA Chunlei, WANG Liang

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Machine vision model using nail images for non-invasive detection of iron deficiency anemia in university students by Jorge Raul Navarro-Cabrera, Miguel Angel Valles-Coral, María Elena Farro-Roque, Nelly Reátegui-Lozano, Lolita Arévalo-Fasanando

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The integration of explainable AI in Educational Data Mining for student academic performance prediction and support system by Md. Mahmudul Islam, Farhad Hossain Sojib, Md. Fazle Hasan Mihad, Mahmudul Hasan, Mahfujur Rahman

“…In this study, we proposed an EDM system that integrates machine learning to classify student academic performance and explainable AI techniques for explainability. …”
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Enhancing e-learning through AI: advanced techniques for optimizing student performance by Rund Mahafdah, Seifeddine Bouallegue, Ridha Bouallegue

“…The practical results obtained by implementing machine learning and deep learning models, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), show substantial enhancements in forecasting different performance metrics. …”
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Detection of breast cancer using machine learning and explainable artificial intelligence by Tharunya Arravalli, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, D. Cenitta, Rajagopala Chadaga, K. S. Swathi

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Machine Learning Reveals Magmatic Fertility of Skarn-Type Tungsten Deposits by Rui-Chang Tan, Yong-Jun Shao, Yi-Qu Xiong, Zhi-Wei Fan, Hong-Fei Di, Zhao-Jun Wang, Kang-Qi Xu

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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

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Comparing the Effectiveness of Artificial Intelligence Models in Predicting Ovarian Cancer Survival: A Systematic Review by Farkhondeh Asadi, Milad Rahimi, Nahid Ramezanghorbani, Sohrab Almasi

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Random Oversampling-Based Diabetes Classification via Machine Learning Algorithms by G. R. Ashisha, X. Anitha Mary, E. Grace Mary Kanaga, J. Andrew, R. Jennifer Eunice

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Machine learning-assisted wearable sensing systems for speech recognition and interaction by Tao Liu, Mingyang Zhang, Zhihao Li, Hanjie Dou, Wangyang Zhang, Jiaqian Yang, Pengfan Wu, Dongxiao Li, Xiaojing Mu

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Predicting early cessation of exclusive breastfeeding using machine learning techniques. by Freja Marie Nejsum, Rikke Wiingreen, Andreas Kryger Jensen, Ellen Christine Leth Løkkegaard, Bo Mølholm Hansen

“…We aimed to develop and validate two models to predict cessation of exclusive breastfeeding within one month among infants born after 35 weeks gestation using machine learning techniques.…”
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Attention-driven hybrid deep learning and SVM model for early Alzheimer’s diagnosis using neuroimaging fusion by Arjun Kidavunil Paduvilan, Godlin Atlas Lawrence Livingston, Sampath Kumar Kuppuchamy, Rajesh Kumar Dhanaraj, Muthuvel Subramanian, Amal Al-Rasheed, Masresha Getahun, Ben Othman Soufiene

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