Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma by Tao Shen, Tingting Dong, Haiyang Wang, Yi Ding, Jianuo Zhang, Xinyi Zhu, Yeping Ding, Wen Cai, Yalan Wei, Qiao Wang, Sufen Wang, Feiyun Jiang, Bin Tang

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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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Automated Measurement of Air Bubbles Dispersion in Ice Cream Using Machine Learning Methods by Igor A. Korolev

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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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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

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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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Consumer opinion on the use of machine learning in healthcare settings: A qualitative systematic review by Jacqueline H Stephens, Celine Northcott, Brianna F Poirier, Trent Lewis

“…Introduction Given the increasing number of artificial intelligence and machine learning (AI/ML) tools in healthcare, we aimed to gain an understanding of consumer perspectives on the use of AI/ML tools for healthcare diagnostics. …”
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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 Machine Learning Assisted Phase Behavior Modelling of Surfactant–Oil–Water Systems by Daulet Magzymov, Meruyert Makhatova, Zhassulan Dairov, Murat Syzdykov

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Predicting the Adsorption Efficiency Using Machine Learning Framework on a Carbon-Activated Nanomaterial by Kalapala Prasad, V. Ravi Kumar, R. Suresh Kumar, A. S. Rajesh, Anjani Kumar Rai, Essam A. Al-Ammar, Saikh Mohammad Wabaidur, Amjad Iqbal, Dawit Kefyalew

“…The adsorption effectiveness of paracetamol on carbon-activated nanoparticle was calculated using experimental results. Thus, by using machine learning framework, the adsorption efficiency of paracetamol on a carbon-activated nanomaterial was predicted.…”
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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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Less Is More: Machine Learning-Based Shortened Sleep Questionnaires for Efficient Clinical Practice by Eui Min Jeong, Jae Kyoung Kim

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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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