Bidirectional Conservative–Dissipative Transitions in a Five-Dimensional Fractional Chaotic System by Yiming Wang, Fengjiao Gao, Mingqing Zhu

“…To validate the system’s physical realizability, a signal processing platform based on Digital Signal Processing (DSP) is implemented. …”
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BIM log mining framework using deep learning for productivity assessment in construction facilities by Ali Akbar, Younghee Chang, Jinwoo Song, Seojoon Lee, Sanghyeon Park, Jinhyun Bae, Soonwook Kwon

“…The cross-validation identified XGBoost as the top-performing architecture (R2 = 0.97 ± 0.01), demonstrating the effectiveness of gradient boosting on the engineered tabular features. The framework incorporates an integrated interface with visualization and natural language processing for enhanced insight generation and accessibility. …”
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Understanding students’ sentiment from feedback with a new feature selection and semantics networks by Tran Anh Tuan, Dao Thi Thanh Loan, Nichnan Kittiphattanabawon

“…In this study, we propose the Student Sentiment from Feedback (SSF) framework, which includes four main procedures: pre-processing, feature selection, classification, and theme finding. …”
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Domain Adaptation for Pedestrian Detection Based on Prediction Consistency by Yu Li-ping, Tang Huan-ling, An Zhi-yong

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Effect of acid pretreatments with various acid types on gelling properties and identification characteristics of pigskin gelatin by Zi-Xuan Yang, Xiao-Mei Sha, Hui Wang, Ting Fang, Sheng Shu, Zong-Cai Tu

“…A further comparison of this work with prior studied revealed that 8 were detected under different extraction and processing conditions. These common characteristic peptides could be used as the foundation for pigskin gelatin traceability, boosting accuracy.…”
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Smart agriculture: utilizing machine learning and deep learning for drought stress identification in crops by Tariq Ali, Saif Ur Rehman, Shamshair Ali, Khalid Mahmood, Silvia Aparicio Obregon, Rubén Calderón Iglesias, Tahir Khurshaid, Imran Ashraf

“…Notably, the accuracy of SVM was 82%, while gradient boosting and RNN showed 96%, and 94%, respectively and LSTM obtained an astounding 97% accuracy. …”
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Methodological Validation of Machine Learning Models for Non-Technical Loss Detection in Electric Power Systems: A Case Study in an Ecuadorian Electricity Distributor by Carlos Arias-Marín, Antonio Barragán-Escandón, Marco Toledo-Orozco, Xavier Serrano-Guerrero

“…Hyperparameter optimization was performed by using grid search, and the models were validated by using cross-validation techniques, finding that the ensemble methods Categorical Boosting (CGB), Light Gradient Boosting Machine (LGB) and Extreme Gradient Boosting (EGB) are the most suitable for identifying losses, achieving high performance and reasonable computational cost. …”
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Securing the economic management and service infrastructure of banks via the use of artificial intelligence (MO-ILSTM) by Xintong Wu

“…The capacity of LSTM to identify long-range relationships in sequential data is restricted. By boosting data processing and decision-making in service infrastructure or economic management amid market volatility, MO-ILSTM aims to increase long-range dependency capture. …”
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Deep learning for property prediction of natural fiber polymer composites by Ivan P. Malashin, Dmitry Martysyuk, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Vadim Tynchenko

“…Best DNN model architecture (four hidden layers (128–64–32–16 neurons), ReLU activation, 20% dropout, a batch size of 64, and the AdamW optimizer with a learning rate of $$10^{-3}$$ ) obtained through hyperparameter optimization using Optuna, delivered the best performance (R $$^2$$ up to 0.89) and MAE reductions of 9–12% compared to gradient boosting, driven by the DNN’s ability to capture nonlinear synergies between fiber-matrix interactions, surface treatments, and processing parameters while aligning architectural complexity with multiscale material behavior.…”
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Pre-Trained Language Model Ensemble for Arabic Fake News Detection by Lama Al-Zahrani, Maha Al-Yahya

“…Various ensemble approaches, including a weighted-average ensemble, hard voting, and soft voting, were evaluated to determine the most effective techniques for boosting learning models and improving prediction accuracies. …”
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A Unified Surrogate Framework for Data-Driven Reliability Analysis of Mechanical Systems from Low to Multi-DOF by Lun Shao, Alexandre Saidi, Abdel-Malek Zine, Mohamed Ichchou

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SUSTAINABILITY, COMPETITIVITY AND FUTURE PERSPECTIVES FOR RURAL DEVELOPMENT TOWARDS BIOECONOMY - TULCEA COUNTY CASE STUDY by Steliana RODINO

“…Agriculture is well represented and it may become a cornerstone for food processing industry, boosting the economic progress. …”
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Ensemble prediction modeling of flotation recovery based on machine learning by Guichun He, Mengfei Liu, Hongyu Zhao, Kaiqi Huang

“…With the rise of artificial intelligence (AI) in mineral processing, predicting the flotation indexes has attracted significant research attention. …”
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DNA sequence classification for diabetes mellitus using NuSVC and XGBoost: A comparative. by Said A Salloum, Khaled Mohammad Alomari, Ayham Salloum

“…XGBoost was trained with up to 300 boosting rounds, and performance was evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and log loss. …”
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Feature Selection Using Pearson Correlation for Ultra-Wideband Ranging Classification by Gita Indah Hapsari, Rendy Munadi, Bayu Erfianto, Indrarini Dyah Irawati

“…The Random Forest and Gradient Boosting models exhibit superior performance, maintaining classification accuracy above 90%. …”
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Eco‐Friendly Synthesis of Selenium Nanoparticles via Sternbergia candida: Enhancing Antioxidant Defense and Mitigating Salt Stress in Pepper (Capsicum annuum L.) Plants by Senem Kanber, Mahmut Yildiztekin, Mehmet Firat Baran

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A comprehensive approach to Queue Waiting Time Prediction using Tree-Based Ensembles with Data Balancing and Explainable AI by Tapodhir Karmakar Taton, Bipin Saha, Md. Johirul Islam, Shaikh Khaled Mostaque

“…The following regression models have been used to assess the performance: Random Forest (RF), Extra Trees (ET), Gradient Boosting (GBR), Histogram-Based Gradient Boosting (HGBR), Voting (VR) and Ridge Regression. …”
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Achieving Excellence in Cyber Fraud Detection: A Hybrid ML+DL Ensemble Approach for Credit Cards by Eyad Btoush, Xujuan Zhou, Raj Gururajan, Ka Ching Chan, Omar Alsodi

“…The hybrid model leverages ML techniques including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Logistic Regression (LR) alongside DL techniques such as Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM) with attention mechanisms. …”
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Egg freshness during storage: the effect of laying hen age and shelf life prediction using a novel hybrid modeling method by Yifeng Lu, Jing Li, Zihao He, Linyun Chen, Huixin Tian, Chen Xu, Xinglian Xu, Minyi Han

“…A novel hybrid model combining BP-ANN, cuckoo search and adaptive boosting (CS-BP-AdaBoost) was proposed for predicting the remaining egg shelf life, with the input being Haugh unit, yolk index, air cell depth, albumen pH, hen age, and breed. …”
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Concrete Crack Detection and Segregation: A Feature Fusion, Crack Isolation, and Explainable AI-Based Approach by Reshma Ahmed Swarna, Muhammad Minoar Hossain, Mst. Rokeya Khatun, Mohammad Motiur Rahman, Arslan Munir

“…For classification, XGB, random forest (RF), adaptive boosting (AdaBoost), and category boosting (CatBoost) are tested. …”
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