Predicting cardiovascular disease and all-cause mortality using the lymphocyte-to-monocyte ratio: Insights from explainable machine learning models by Jichao Wu, Die Huang, Jiefang Li, Jingxing Yi, Yu Lei, Jun Yin

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Efficient Feature Selection and Hyperparameter Tuning for Improved Speech Signal-Based Parkinson’s Disease Diagnosis via Machine Learning Techniques by Deepak Painuli, Suyash Bhardwaj, Utku Kose

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Assessment of Machine Learning Algorithms in Short-term Forecasting of PM10 and PM2.5 Concentrations in Selected Polish Agglomerations by Bartosz Czernecki, Michał Marosz, Joanna Jędruszkiewicz

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Predicting hydropower generation: A comparative analysis of Machine learning models and optimization algorithms for enhanced forecasting accuracy and operational efficiency by Chunyang Wang, Chao Li, Yudong Feng, Shoufeng Wang

“…Optimizing hydropower generation is crucial for addressing economic and environmental concerns, though it requires comprehensive monitoring and understanding of energy conversion processes. Machine Learning techniques such as integrated Gradient Boosting and Categorical Gradient Boosting, optimized with Hunger Games search, Chaos game optimization, and Archimedes Optimization Algorithm algorithms, are used to forecast and optimize hydropower generation. …”
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Estimating the Pile Settlement Using a Machine Learning Technique Optimized by Henry's Gas Solubility Optimization and Particle Swarm Optimization by Saravana Kumar, Savarimuthu Robinson

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Analisis Perbandingan Algoritma Machine Learning dan Deep Learning untuk Klasifikasi Citra Sistem Isyarat Bahasa Indonesia (SIBI) by Mohammad Farid Naufal, Selvia Ferdiana Kusuma

“…Pada penelitian sebelumnya belum ada yang melakukan perbandingan algoritma klasifikasi machine learning dan deep learning untuk pengenalan SIBI. …”
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Multiple machine learning-based integrations of multi-omics data to identify molecular subtypes and construct a prognostic model for HNSCC by Xiaoqin Luo, Chao Li, Gang Qin

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Innovación en sueño by Laura Vigil, Toni Zapata, Andrea Grau, Marta Bonet, Montserrat Montaña, María Piñar

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Applications of Artificial Intelligence for Precision Agriculture by Yiannis Ampatzidis

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Development and validation of a deep learning system for detection of small bowel pathologies in capsule endoscopy: a pilot study in a Singapore institution by Bochao Jiang, Michael Dorosan, Justin Wen Hao Leong, Marcus Eng Hock Ong, Sean Shao Wei Lam, Tiing Leong Ang

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A novel aggregated coefficient ranking based feature selection strategy for enhancing the diagnosis of breast cancer classification using machine learning by E. Sreehari, L. D. Dhinesh Babu

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Machine learning analysis of pathological images to predict 1-year progression-free survival of immunotherapy in patients with small-cell lung cancer by Hirotaka Matsumoto, Hiroaki Akamatsu, Nobuyuki Yamamoto, Yuki Sato, Daichi Fujimoto, Yoshihiko Taniguchi, Motohiro Tamiya, Yasuhiro Koh, Junya Fukuoka, Hisashi Tanaka, Naoki Furuya, Ryota Shibaki, Tsukasa Nozawa, Akira Sano, Yuka Kitamura, Takashi Kijima, Toshihide Yokoyama, Satoru Miura, Akito Hata, Jun Sugisaka

“…The primary outcome was the mean area under the curve (AUC) of machine learning models in predicting the 1-year PFS.Results We analyzed 100,544 patches of pathological images from 78 patients. …”
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Optimization and modeling of sulfur removal from liquid fuel using carbon-based adsorbents through synergistic application of RSM and machine learning by Karim Maghfour Sarkarabad, Mohsen Shayanmehr, Ahad Ghaemi

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RETRACTED ARTICLE: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 by Lizhe Wang, Yu Wang, Yueyang Li, Li Zhou, Sihan Liu, Yongyi Cao, Yuzhi Li, Shenting Liu, Jiahui Du, Jin Wang, Ting Zhu

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Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models by RN Singh, P. Krishnan, C. Bharadwaj, Sonam Sah, B. Das

“…Yield reductions of 44-69% were observed in susceptible genotypes. Machine learning models were able to give accurate early yield predictions. …”
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Retraction Note: A prospective diagnostic model for breast cancer utilizing machine learning to examine the molecular immune infiltrate in HSPB6 by Lizhe Wang, Yu Wang, Yueyang Li, Li Zhou, Sihan Liu, Yongyi Cao, Yuzhi Li, Shenting Liu, Jiahui Du, Jin Wang, Ting Zhu

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Predicting child mortality determinants in Uttar Pradesh using Machine Learning: Insights from the National Family and Health Survey (2019–21) by Pinky Pandey, Sacheendra Shukla, Niraj Kumar Singh, Mukesh Kumar

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Predicting pregnancy loss and its determinants among reproductive-aged women using supervised machine learning algorithms in Sub-Saharan Africa by Tirualem Zeleke Yehuala, Sara Beyene Mengesha, Nebebe Demis Baykemagn

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Machine learning analysis of the relationships between traumatic childbirth experience with positive and negative fertility motivations in Iran in a community-based sample by Mahdieh Arian, Talat Khadivzadeh, Mahla Shafeei, Sedigheh Abdollahpour

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Novel Optimization Algorithms Usage to Model the Compressive Strength of Ultra-High-Performance Concrete in Machine Learning Technique: Support Vector Regression by Tianhua Zhou, Dorota Mozyrska

“…This study suggested a machine learning method for predicting the CS of UHPC including support vector regression (SVR). …”
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