Machine Learning-Based Modeling of Hot Carrier Injection in 40 nm CMOS Transistors by Xhesila Xhafa, Ali Dogus Gungordu, Mustafa Berke Yelten

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Development and validation of machine learning models for MASLD: based on multiple potential screening indicators by Hao Chen, Jingjing Zhang, Xueqin Chen, Ling Luo, Wenjiao Dong, Yongjie Wang, Jiyu Zhou, Canjin Chen, Wenhao Wang, Wenbin Zhang, Zhiyi Zhang, Yongguang Cai, Danli Kong, Yuanlin Ding

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Machine learning regression models and associated variables for predicting preoperative anxiety in paediatric patients by Abdul Rahman, Javed Khan Sheikh, Mohammad Sarwar Raeen

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Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review by Arantxa Ortega-Leon, Daniel Urda, Ignacio J. Turias, Simón P. Lubián-López, Simón P. Lubián-López, Isabel Benavente-Fernández, Isabel Benavente-Fernández, Isabel Benavente-Fernández

Subjects: “…machine learning…”
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Development of Microbiology Plantation-Based Multimodal Segmentation for Smart Garden Using Machine Learning by S. Prasath, Ramkumar Govindaraj, Ram Subbiah, Sulaiman Ali Alharbi, Hesham S. Almoallim, S. Priya, Begna Dejene Mulugeta

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Transforming Cardiovascular Risk Prediction: A Review of Machine Learning and Artificial Intelligence Innovations by Dimitrios-Ioannis Kasartzian, Thomas Tsiampalis

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Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles by Yu Sun, Shuhuai Qin, Yingli Li, Naimul Hasan, Yan Vivian Li, Jiangguo Liu

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A Mini-Review of Machine Learning in Big Data Analytics: Applications, Challenges, and Prospects by Isaac Kofi Nti, Juanita Ahia Quarcoo, Justice Aning, Godfred Kusi Fosu

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Transformer-generated atomic embeddings to enhance prediction accuracy of crystal properties with machine learning by Luozhijie Jin, Zijian Du, Le Shu, Yan Cen, Yuanfeng Xu, Yongfeng Mei, Hao Zhang

“…Abstract Accelerating the discovery of novel crystal materials by machine learning is crucial for advancing various technologies from clean energy to information processing. …”
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Advances in Architectures, Big Data, and Machine Learning Techniques for Complex Internet of Things Systems by David Gil, Magnus Johnsson, Higinio Mora, Julian Szymanski

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Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis by Jingyang Wang, Haiyang Bian, Jiangshan Tan, Jun Zhu, Lulu Wang, Wei Xu, Lei Wei, Xuegong Zhang, Yanmin Yang

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Blood Vessel Segmentation and Classification of Diabetic Retinopathy with Machine Learning-Based Ensemble Model by Cihan Akyel, Bünyamin Ciylan

“…In this study, the noise of blood vessels in fundus images was eliminated using the LinkNet-RCB7 model, and diabetic retinopathy was categorized into five classes using a machine learning-based ensemble model. Artificial intelligence-based classification training using images as input takes a long time and requires high resource requirements such as Random Access Memory (RAM) and Graphics Processing Unit (GPU). …”
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Prediction of Ultimate Load of Rectangular CFST Columns Using Interpretable Machine Learning Method by Tien-Thinh Le, Hieu Chi Phan

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The Application of Differing Machine Learning Algorithms and Their Related Performance in Detecting Skin Cancers and Melanomas by Suboh Alkhushayni, Du’a Al-zaleq, Luwis Andradi, Patrick Flynn

“…When comparing different existing implementations of machine learning against public datasets and several we seek to create, we attempted to create a more accurate model that can be readily adapted to use in clinical settings. …”
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A Topological Approach to Enhancing Consistency in Machine Learning via Recurrent Neural Networks by Muhammed Adil Yatkin, Mihkel Kõrgesaar, Ümit Işlak

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Identification and validation of key autophagy-related genes in lupus nephritis by bioinformatics and machine learning. by Su Zhang, Weitao Hu, Yelin Tang, Xiaoqing Chen

“…Differentially expressed autophagy-related genes (DE-ARGs) among DEGs, key module genes and autophagy-related genes (ARGs) were obtained by venn plot, and subjected to protein-protein interaction network construction. Two machine learning methods were applied to identify signature genes. …”
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Tunnel squeezing prediction based on partially missing dataset and optimized machine learning models by Peng Guan, Guangzhao Ou, Feng Liang, Weibang Luo, Qingyong Wang, Chengyuan Pei, Xuan Che

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Rapid Detection of Hybrid Maize Parental Lines Using Stacking Ensemble Machine Learning by M. Aqil, M. Azrai, M. J. Mejaya, N. A. Subekti, F. Tabri, N. N. Andayani, Rahma Wati, S. Panikkai, S. Suwardi, Z. Bunyamin, E. Roy, M. Muslimin, M. Yasin, E. Prakasa

“…The integration of the model with machine learnings (logistic regression, SVM, random forest, and KNNs) enables rapid recognition of off-type plants even though it is operated by personnel with limited skills of seed technology on ideotype recognition. …”
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Identifying effective immune biomarkers in alopecia areata diagnosis based on machine learning methods by Qingde Zhou, Lan Lan, Wei Wang, Xinchang Xu

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Age group classification based on optical measurement of brain pulsation using machine learning by Martti Ilvesmäki, Hany Ferdinando, Kai Noponen, Tapio Seppänen, Vesa Korhonen, Vesa Kiviniemi, Teemu Myllylä

“…In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups. …”
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