Predicting CO2 adsorption in KOH-activated biochar using advanced machine learning techniques by Raouf Hassan, Alireza Baghban

“…We employed a comprehensive suite of machine learning methods, like convolutional neural networks, random forests, artificial neural networks, linear regression, ridge and lasso regressions, elastic net, support vector machines, decision trees, gradient boosting machines, k-nearest neighbors, light gradient boosting machines, extreme gradient boosting, CatBoost, and Gaussian process, to build predictive models. …”
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High-entropy alloy enables multi-path electron synergism and lattice oxygen activation for enhanced oxygen evolution activity by Tao Zhang, Hui-Feng Zhao, Zheng-Jie Chen, Qun Yang, Niu Gao, Li Li, Na Luo, Jian Zheng, Shi-Da Bao, Jing Peng, Xu Peng, Xin-Wang Liu, Hai-Bin Yu

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Predicting the heat capacity of strontium-praseodymium oxysilicate SrPr4(SiO4)3O using machine learning, deep learning, and hybrid models by Amir Hossein Sheikhshoaei, Ali Khoshsima, Davood Zabihzadeh

“…In this study, the capability of five advanced machine learning models, including Random Forest (RF), Gradient Boosting (GBoost), Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Decision Tree (DT) models, and three deep learning models, TabNet, Deep Belief Network (DBN), and Deep Neural Network (DNN) was investigated. …”
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A Nanostructured Ru‐Mn‐Nb Alloy with Oxygen‐Enriched Boundaries for Ampere‐Level Hydrogen Evolution by Jie Li, Xue Wang, Jun Yu, Kai Xu, Zhe Jia, Hongkun Li, Lei Ren, Yiyuan Yang, Keke Chang, Yangyang Li, Xiangfa Liu, Jian Lu, Sida Liu

“…Moreover, oxygen‐rich interfaces in Ru62Mn12Nb21O5 enhanced charge transfer and the kinetics of water dissociation as well as optimized hydrogen adsorption/desorption processes, thus boosting HER performance. The crystal–crystal heterostructure and oxygen‐rich interfaces in Ru62Mn12Nb21O5 are induced by its dual‐phase nanocrystalline structure, which represents a new structural design for enhancing the performance of catalysts for sustainable energy development.…”
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Machine learning framework for oxytetracycline removal using nanostructured cupric oxide supported on magnetic chitosan alginate biocomposite by Hassan Rasoulzadeh, Hossein Azarpira, Mojtaba Pourakbar, Amir Sheikhmohammadi, Alieh Rezagholizade-shirvan

“…Consequently, the Tikhonov model outperforms the Yandex Boosting model due to its higher accuracy and lower error rates, whereas Yandex Boosting, despite strong training performance, suffers from overfitting, leading to inferior testing performance. …”
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Improving Diagnostic Performance for Head and Neck Tumors with Simple Diffusion Kurtosis Imaging and Machine Learning Bi-Parameter Analysis by Suzuka Yoshida, Masahiro Kuroda, Yoshihide Nakamura, Yuka Fukumura, Yuki Nakamitsu, Wlla E. Al-Hammad, Kazuhiro Kuroda, Yudai Shimizu, Yoshinori Tanabe, Masataka Oita, Irfan Sugianto, Majd Barham, Nouha Tekiki, Nurul N. Kamaruddin, Miki Hisatomi, Yoshinobu Yanagi, Junichi Asaumi

“…<b>Results:</b> Bi-parameter analysis with gradient boosting achieved the best diagnostic performance, with an AUC of 0.81. …”
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Advanced graph embedding for intelligent heating, ventilation, and air conditioning optimization: An ensemble learning-based recommender system by Shouliang Lai, Xiyu Yi, Peiling Zhou, Lu Peng, Wentao Liu, Shi Sun, Binrong Huang

“…This study introduces a robust and scalable software architecture designed for real-time data ingestion, processing, and user interaction within a smart building setting. …”
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Random Reflectance: A New Hyperspectral Data Preprocessing Method for Improving the Accuracy of Machine Learning Algorithms by Pavel A. Dmitriev, Anastasiya A. Dmitrieva, Boris L. Kozlovsky

“…Hyperspectral plant phenotyping is a method that has a wide range of applications in various fields, including agriculture, forestry, food processing, medicine and plant breeding. It can be used to obtain a large amount of spectral and spatial information about an object. …”
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Using Supervised Machine Learning Algorithms to Predict Bovine Leukemia Virus Seropositivity in Florida Beef Cattle: A 10‐Year Retrospective Study by Ameer A. Megahed, Y. Reddy Bommineni, Michael Short, João H. J. Bittar

“…Methods Logistic regression (LR), decision tree (DT), gradient boosting (GB), random forest (RF), neural network (NN), and support vector machine (SVM) were used. …”
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Machine Learning-Based Classification of Suspension Droplet-Solid Wall Impacts for Control of Droplet Fragmentation by Mikhail Vulf, Dmitry Zharikov, Dmitry Kolomenskiy, Dmitry Eskin, Pavel Osinenko

“…We tested ten models, including Logistic Regression, KNN, SVM, ensemble methods (CatBoost, XGBoost, LightGBM, Random Forest, AdaBoost), and neural networks (MLP, TabNet). …”
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Machine Learning-Based Cerrado Land Cover Classification Using PlanetScope Imagery by Thanan Rodrigues, Frederico Takahashi, Arthur Dias, Taline Lima, Enner Alcântara

“…Three machine learning methods were evaluated: Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). A post-processing process was applied to avoid misclassification of forest in areas of savanna. …”
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Innovative approaches for skin disease identification in machine learning: A comprehensive study by Kuldeep Vayadande, Amol A. Bhosle, Rajendra G. Pawar, Deepali J. Joshi, Preeti A. Bailke, Om Lohade

“…This study intends to promote a broader knowledge of machine learning's potential to transform the diagnosis and treatment of skin disorders, eventually increasing patient outcomes and boosting the provision of healthcare services, by putting light on the field's developing developments in dermatology.…”
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OA−ICOS−Based Oxygen and Carbon Dioxide Sensors for Field Applications in Gas Reflux Chicken Coops by Weijia Li, Guanyu Lin, Jianing Wang, Jifeng Li, Yulai Sun, Depu Yao, Xiaogang Yan, Zhibin Ban

“…To improve the instrument’s response speed, the miniaturization of the cavity and structural optimization were implemented, achieving a rapid response time of merely 6.22 s, addressing the stringent requirements for quick responsiveness in poultry respiration thermometry research. A signal processing model tailored for on−site applications was designed, boosting the system’s signal−to−noise ratio by 4.7 times under complex environmental noise conditions. …”
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Laser Texturing to Improve Wear Resistance of 65Mn Steel Rotary Tiller Blades: Effects of Scanning Speed by Heng Xiao, Dongyan Yang, Yiding Ou, Junlan Zhang, Yue Hu, Lei Ma

“…The results show that laser processing treatment significantly improves the wear resistance of 65Mn steel blades through the lubrication effect due to the wear debris capturing ability of the laser-processed micro-pits. …”
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Lightweight coal mine conveyor belt foreign object detection based on improved Yolov8n by Jierui Ling, Zhibo Fu, Xinpeng Yuan

“…Thirdly, to enhance the algorithm’s focus on key features, a Large Separable Kernel Attention mechanism (LSKA) is utilized to improve the original SPPF, thereby boosting the overall performance of the algorithm. …”
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Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait by Anam Naz, Hikmat Ullah Khan, Tariq Alsahfi, Mousa Alhajlah, Bader Alshemaimri, Ali Daud

“…In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. …”
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Multi-Domain Controversial Text Detection Based on a Machine Learning and Deep Learning Stacked Ensemble by Jiadi Liu, Zhuodong Liu, Qiaoqi Li, Weihao Kong, Xiangyu Li

“…Secondly, we design a two-tier stacked ensemble architecture, which not only combines the strengths of multiple machine learning algorithms, e.g., gradient-boosted decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost), with deep learning models, e.g., gated recurrent unit (GRU) and long short-term memory (LSTM), but also implements the support vector machine (SVM) for efficient meta-learning. …”
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Predicting Workplace Hazard, Stress and Burnout Among Public Health Inspectors: An AI-Driven Analysis in the Context of Climate Change by Ioannis Adamopoulos, Antonios Valamontes, Panagiotis Tsirkas, George Dounias

“…Real-time environmental data improved hazard detection, while Natural Language Processing (NLP)-based text mining identified stress-related indicators in worker reports. …”
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Physical activity interventions to improve cognition in first-episode psychosis: What we know so far by Cinzia Perlini, Maria Gloria Rossetti, Francesca Girelli, Marcella Bellani

“…Patients with first-episode psychosis (FEP) exhibit deficits in processing speed, short-term memory, attention, working memory, and executive functioning, which respond poorly to psychotropic drugs. …”
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Deep Learning-Based Prediction of Pitch Response for Floating Offshore Wind Turbines by Ruifeng Chen, Ke Zhang, Min Luo, Ye An, Lixiang Guo

“…The comprehensive framework, which encompasses feature selection, data processing, deep learning model construction, and interpretation, demonstrates significant potential for addressing a broad range of engineering problems through deep learning methodologies.…”
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