SmartRipen: LSTM-GRU feature selection& XGBoost-CNN for fruit ripeness detection by Archana Ganesh Said, Bharti Joshi

“…Artificial ripening, frequently performed with the use of calcium carbide, speeds up the process but harms fruit and its nutritional value. …”
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Analyzing the influence of manufactured sand and fly ash on concrete strength through experimental and machine learning methods by S Sathvik, Solomon Oyebisi, Rakesh Kumar, Pshtiwan Shakor, Olutosin Adejonwo, Adithya Tantri, V Suma

“…Furthermore, machine learning (ML) techniques were engaged to predict the compressive strength of the concrete samples using Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Gaussian Process Regression (GPR). …”
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Machine learning to predict postdialysis fatigue in patients undergoing hemodialysis by Yuhan Zhang, Jue Guo, Na Yang, Xiangyun Li, Yuxiang Liu, Meiqin Yan, Peng Shen

“…However, accurate and efficient models for predicting postdialysis fatigue (PDF) in this population are still needed because PDF is surprisingly prevalent.Aims This study aimed to explore the potential of ML models for predicting PDF in patients undergoing HD.Data sources A total of 1,281 Chinese patients undergoing HD from six tertiary hospitals (65.26% male, mean age = 54.48 years).Design Cross-sectional study.Methods Seven ML models were compared: Logistic regression (LR), Decision tree (DT), Random forests (RF), LightGBM (LGBM), CatBoost, XGBoost (XGB), and Gradient boosting tree (GBT), to predict the PDF and identify variables with predictive value based on the best-performing model among Chinese patients undergoing HD. …”
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Machine Learning to Retrieve Gap-Free Land Surface Temperature from Infrared Atmospheric Sounding Interferometer Observations by Fabio Della Rocca, Pamela Pasquariello, Guido Masiello, Carmine Serio, Italia De Feis

“…We address this problem by using machine learning techniques, i.e., Gradient Boosting, Random Forest, Gaussian Process Regression, Neural Network, and Stacked Regression. …”
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Surface-engineered chitosan-coated nanostructured lipid carriers for intranasal delivery of Oxcarbazepine and Vitamin E oil in epilepsy management by Farhan Haider, Eman Aldosari, Rabea Parveen, Sanjula Baboota, Azka Gull, Saba Khan, Javed Ali

“…Results In the present work, CS OXC-NLC was fabricated using melt emulsification process. Central Composite Rotatable Design has been utilized to optimize formulation. …”
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Testing the Applicability and Transferability of Data-Driven Geospatial Models for Predicting Soil Erosion in Vineyards by Tünde Takáts, László Pásztor, Mátyás Árvai, Gáspár Albert, János Mészáros

“…Soil loss was formerly modeled by USLE, thus providing non-observation-based reference datasets for the calibration of parcel-specific prediction models using various ML methods (Random Forest, eXtreme Gradient Boosting, Regularized Support Vector Machine with Linear Kernel), which is a well-established approach in digital soil mapping (DSM). …”
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Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds by Vittoria Asti, Michela Ablondi, Arnaud Molle, Andrea Zanotti, Matteo Vasini, Alberto Sabbioni

“…Most of the Italian breeds must adapt to modern market demands, and gait traits are suitable phenotypes to help this process. Inertial measurement unit (IMU) technology can be used to objectively assess them. …”
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Intelligent Prediction and Numerical Simulation of Landslide Prediction in Open-Pit Mines Based on Multi-Source Data Fusion and Machine Learning by Li Qing, Linfeng Xu, Juehao Huang, Xiaodong Fu, Jian Chen

“…Based on these comparisons, a high-precision random forest and slope boosting coupled method is developed to enhance the landslide prediction accuracy. …”
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Hepato-protective and repro-protective power of immulant (echinacea purpurea) against chlorpyrifos toxicity via apoptotic genes signaling pathway by Ahlam G. Khalifa, Fadwa A. Elroby, Asmaa K. Abdelghany

“…Abstract Background In agro practices, insecticides are applied to control insects, thereby boosting yields of crops or storage effectiveness, and minimizing monetary losses. …”
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Machine learning-enhanced fully coupled fluid–solid interaction models for proppant dynamics in hydraulic fractures by Dennis Delali Kwesi Wayo, Sonny Irawan, Lei Wang, Leonardo Goliatt

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Benchmarking Variants of Recursive Feature Elimination: Insights from Predictive Tasks in Education and Healthcare by Okan Bulut, Bin Tan, Elisabetta Mazzullo, Ali Syed

“…For example, while RFE wrapped with tree-based models such as Random Forest and Extreme Gradient Boosting (XGBoost) yields strong predictive performance, these methods tend to retain large feature sets and incur high computational costs. …”
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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

“…Python software was used to process the data, and machine learning techniques such as Random Forest, Decision Tree, Logistic Regression, Extreme Gradient Boosting, and Gaussian were applied. …”
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Explaining the Understanding of Elderly Patients with Type 2 Diabetes Regarding Information Therapy Related to Self-care: A Qualitative Study by Masoud Koorki, Alireza Isfandyari–Moghaddam, Atefeh Zarei, Sahar Khoshravesh, Erfan Ayubi

“…This method plays an important role in empowering patients to make informed health decisions and participate in the recovery process. In this regard, the present study was conducted to explain the understanding of elderly people with type 2 diabetes regarding information therapy related to self-care. …”
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IL-17A in gastric carcinogenesis: good or bad? by Weidong Li, Xiaodong Huang, Xiaowen Han, Jiayi Zhang, Lei Gao, Hao Chen, Hao Chen

“…While IL-17 is related to inflammatory processes that may lead to cancer, its specific influence on gastric cancer development and therapy needs to be completely understood. …”
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Near Real‐Time Mapping of All‐Sky Land Surface Temperature From GOES‐R Using Machine Learning by Sadegh Ranjbar, Danielle Losos, Sophie Hoffman, Shiva Arabi, Ankur Desai, Paul C. Stoy

“…Abstract Land surface temperature (LST) is crucial for understanding earth system processes. We expanded the Advanced Baseline Imager Live Imaging of Vegetated Ecosystems (ALIVE) framework to estimate LST in near‐real‐time for both cloudy and clear sky conditions at a five‐minute resolution. …”
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Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

“…<b>Method:</b> This study addresses these issues by employing boosting algorithms, for enhanced classification accuracy. …”
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Optimal rainwater harvesting locations for arid and semi-arid regions by using MCDM-based GIS techniques by Waqed H. Hassan, Karrar Mahdi, Zahraa K. Kadhim

“…The Analytic Hierarchy Process determined tier weights based on seven factors. …”
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The Quest for the Best Explanation: Comparing Models and XAI Methods in Air Quality Modeling Tasks by Thomas Tasioulis, Evangelos Bagkis, Theodosios Kassandros, Kostas Karatzas

“…Overall, the findings demonstrate the large potential of directly incorporating explainability methods during model development for equal and more transparent modeling processes.…”
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Anion-mediated approach to overcome oxidation in ether electrolytes for high-voltage sodium-ion batteries by Xingyu Wang, Qi Fan, Ziheng Liu, Xinyue Zhu, Mei Yang, Zhiyuan Guo, Yuting Chen, Liuqi Wang, Yu Jing, Hui Xia

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Impact of Zirconia and Lanthanum Promoters on Multiwalled CNT Generation in Dry Reforming of Methane by Wan Nabilah Manan, Wan Nor Roslam Wan Isahak, Zahira Yaakob, Salma Samidin

“…This discovery represents a breakthrough in catalyst development, providing a dual advantage of reducing carbon deposition and boosting H₂ production in the Dry Reforming of Methane (DRM) process. …”
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