Machine Learning for Early Detection of Phishing URLs in Parked Domains: An Approach Applied to a Financial Institution by Jaqueline D. Duarte, Pedro Chagas Junior, Joao Paulo Javidi da Costa, Elena J. da Costa, Laerte Peotta de Melo, Rafael Rabelo Nunes, Carlos V. N. Gabriel Soares, Thiago Erivan da Cunha Silva

“…A Light Gradient Boosting Machine classifier achieved recall of 96.02% and accuracy of 97.28% on a balanced dataset, validated through 10-fold cross-validation. …”
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DeepBiteNet: A Lightweight Ensemble Framework for Multiclass Bug Bite Classification Using Image-Based Deep Learning by Doston Khasanov, Halimjon Khujamatov, Muksimova Shakhnoza, Mirjamol Abdullaev, Temur Toshtemirov, Shahzoda Anarova, Cheolwon Lee, Heung-Seok Jeon

“…Our model, optimized for mobile deployment with quantization and TensorFlow Lite, enables rapid on-client computation and eliminates reliance on cloud-based processing. <b>Conclusions</b>: Our work shows how ensemble learning, when carefully designed and combined with realistic data augmentation, can boost the reliability and usability of automatic insect bite diagnosis. …”
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Chaos-enhanced manganese electrolysis: nodule suppression and improved efficiency using controllable chaotic electrical signals by Jie Yang, Chunbiao Li, Qian Zhang, Zhihao Wu, Xin Zhang, Peiqiao Liu, Zuohua Liu, Changyuan Tao, Guocan Zheng, Yong Yang, Hanke Wei

“…In this study, we proposed a novel, universally applicable methodology for constructing an offset boosting function for chaotic systems. By integrating this approach with traditional techniques, a four-dimensional chaotic system with two-dimensional offset boosting was developed and successfully implemented by a real chaotic circuit for manganese metal electrolysis, replacing conventional DC. …”
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Ensemble Learning-Based Day-Ahead Power Forecasting of Distributed Photovoltaic Generation by Yijuan LIU, Yunlong CHEN, Jiyan LIU, Xuemei ZHANG, Xiaoyu WU, Weizheng KONG

“…The proposed model shows good adaptability in the test process and has the highest decision coefficient (R2) of 0.9819. …”
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A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images by Oguzhan Katar, Ozal Yildirim, Ru-San Tan, U Rajendra Acharya

“…<b>Results</b>: We set up 13 different training scenarios to test 4 different classifiers: support vector machine (SVM), logistic regression (LR), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost). …”
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Improving the quality of payment fraud detection by using a combined approach of transaction analysis by Світлана Гавриленко, Олексій Абдуллін

“…The best result is obtained with the model based on gradient boosting, which allows to process unbalanced data. …”
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Machine-Learning-Based Analysis of Internal Forces in Reinforced Concrete Conical and Cylindrical Tanks Under Hydrostatic Pressure Considering Material Nonlinearity by May Haggag, Mohamed K. Ismail, Ahmed Elansary

“…Four machine learning models—decision trees, random forests, gradient boosting, and extreme gradient boosting—were utilized to predict critical internal forces, including the maximum ring tension force, maximum meridional moment, and maximum meridional axial force. …”
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Algorithmic Classification of Psychiatric Disorder–Related Spontaneous Communication Using Large Language Model Embeddings: Algorithm Development and Validation by Ryan Allen Shewcraft, John Schwarz, Mariann Micsinai Balan

“…Psychiatric disorders have an impact on cognitive and emotional processes, which in turn affect the content and way individuals with these disorders communicate using language. …”
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Dynamic weighted ensemble model for predictive optimization in green sand casting: Advancing industry 4.0 manufacturing by Rajesh V․ Rajkolhe, Dr. Sanjay S․ Bhagwat, Dr. Priyanka V․ Deshmukh

“…This research presents an enhanced predictive model for green sand casting, designed to tackle the nonlinear complexities arising from interdependent process parameters. Casting defects substantially affect product quality and rejection rates, making accurate prediction vital. …”
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Revolutionizing agri-food technology: Development and validation of the Portable Intelligent Oil Recognition System (PIORS) by Montaser N.A. Ramadan, Mohammed A.H. Ali, Shin Yee Khoo, Layth Hamad, Mohammad Alkhedher

“…Several Machine learning models, including Support Vector Machines (SVM), AdaBoost, Random Forest, K-Nearest Neighbors (K-NN), Gradient-Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) were implemented and thoroughly validated to obtain correct categorization. …”
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Early and accurate nutrient deficiency detection in hydroponic crops using ensemble machine learning and hyperspectral imaging by Nagarajan S․, Maria Merin Antony, Murukeshan Vadakke Matham

“…In this context, this research presents and proposes different machine learning-based approaches that utilizes ensemble techniques such as Random Forest (RF), Bagging or Bootstrap Aggregating, Adaboost or Adaptive Boosting, and eXtreme Gradient Boosting (XGB) classifiers for early detection of nutrient deficiencies in hydroponic crops. …”
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Mining autonomous student patterns score on LMS within online higher education by Ricardo Ordoñez-Avila, Jaime Meza, Sebastian Ventura

“…The models evaluated showed minimal differences in RMSE ([0.5411 .. 0.6025]). The gradient boosting model achieved the best performance of R2 = 0.6693, MAE = 0.4041 and RMSE = 0.5411 with the Law online program data, as with the Psychology online program data, with an R2 = 0.6418, MAE = 0.4232 and RMSE = 0.6025, while the combination of both data sets reflected the best performance with the extreme gradient boosting (XGBoost) model with the values of R2 = 0.6294, MAE = 0.4295 and RMSE = 0.5985. …”
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Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adil Noor

“…This research examined the performance of eleven machine learning models for this purpose: Ridge Regression, Lasso Regression, K-Nearest Neighbors, Gradient Boosting, Support Vector Regression, Multi-Layer Perceptron, XGBoost, CatBoost, LightGBM, Gaussian Processes for Regression(GPR) and Extra Trees Regressor, considering real historical data from Colorado. …”
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Application of Pathomic Features for Differentiating Dysplastic Cells in Patients with Myelodysplastic Syndrome by Youngtaek Hong, Seri Jeong, Min-Jeong Park, Wonkeun Song, Nuri Lee

“…Through a feature selection process, 30 characteristics were further analyzed. …”
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Leveraging Explainable Artificial Intelligence (XAI) for Expert Interpretability in Predicting Rapid Kidney Enlargement Risks in Autosomal Dominant Polycystic Kidney Disease (ADPKD... by Latifa Dwiyanti, Hidetaka Nambo, Nur Hamid

“…We applied seven machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Tree, XGBoost, and Deep Neural Network (DNN)—to data from the Polycystic Kidney Disease Outcomes Consortium (PKDOC) database. …”
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DEVELOPMENT OF INSTITUTIONS OF FINANCIAL INNOVATION by Elena Kabachevskaja, Svetlana Kosenko, Ekaterina Novikova

“…Boosting innovation contributes to the development of financial institutions. …”
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Research on Optimized Algorithm for Deep Learning Based Recognition of Sediment Particles in Turbulent Flow by WANG Hao, YANG Feiqi, ZHANG Lei, WU Wei, XIE Haonan, ZHAO Lin

“…The YOLOv5 deep learning network structure is enhanced by improving convolutional blocks, incorporating attention mechanisms, and optimizing loss function processing, boosting the detector's overall performance in accurately capturing the motion of particles over short distances. …”
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Research and predictive analysis of pyrolysis characteristics of multi-source organic solid wastes by ZHANG Zihang, XING Bo, MA Zhongqing, HU Yanjun, ZHANG Zhixiao, YUAN Shizhen, LU Rufei, CHEN Yingquan, WANG Shurong*

“…Subsequently, the random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) algorithms were utilized to predict the high heating value (HHV) of organic solid waste, the distribution of fast pyrolysis products, and the thermogravimetric curves under various atmospheres. …”
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Deciphering Local Microstrain-Induced Optimization of Asymmetric Fe Single Atomic Sites for Efficient Oxygen Reduction by Peng Zhang, Siying Huang, Kuo Chen, Xiaoqi Liu, Yachao Xu, Yongming Chai, Yunqi Liu, Yuan Pan

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Mobile application based on KDD to predict high-crime areas and promote sustainability in citizen security in a district of Lima-Perú by Hugo Vega-Huerta, Javier Vilca Velasquez, Nicolas Anicama Espinoza, Gisella Luisa Elena Maquen-Niño, Luis Guerra-Grados, Jorge Pantoja-Collantes, Oscar Benito-Pacheco, Juan Carlos Lázaro-Guillermo, Adegundo Camara-Figueroa, Javier Cabrera-Díaz, Rubén Gil-Calvo, Frida López-Córdova

“…Machine learning algorithms, such as Random Forest and Gradient Boosting, were used to make these predictions. Visualization techniques, such as heat maps, were also used to represent crime events and facilitate their understanding by users. …”
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