Classification of Diseases in Oil Palm Leaves Using the GoogLeNet Model by Asmah Indrawati, Abdul Rahman, Erwin Pane, Muhathir

“…Oil palm productivity is closely related to the conditions of its leaves, which play a vital role in photosynthesis. This research employed a comprehensive dataset of 1,230 images, consisting of 410 showing leaves, another 410 depicting bagworm infestations, and an additional 410 displaying caterpillar infestations. …”
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A practical evaluation of AutoML tools for binary, multiclass, and multilabel classification by Marcelo V. C. Aragão, Augusto G. Afonso, Rafaela C. Ferraz, Rairon G. Ferreira, Sávio G. Leite, Felipe A. P. de Figueiredo, Samuel B. Mafra

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Smart grid stability prediction using Adaptive Aquila Optimizer and ensemble stacked BiLSTM by Safwan Mahmood Al-Selwi, Mohd Fadzil Hassan, Said Jadid Abdulkadir, Mohammed Gamal Ragab, Alawi Alqushaibi, Ebrahim Hamid Sumiea

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Rainfall Forecasting Using a BiLSTM Model Optimized by an Improved Whale Migration Algorithm and Variational Mode Decomposition by Yueqiao Yang, Shichuang Li, Ting Zhou, Liang Zhao, Xiao Shi, Boni Du

“…Secondly, IWMA is utilized to globally optimize multiple hyperparameters of the BiLSTM model, enhancing its ability to capture complex nonlinear relationships and long-term dependencies. …”
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Reliable estimation via hybrid gradient boosting machine for mud loss volume in drilling operations by Xiaozhi Lu, Farag M. A. Altalbawy, Tarak Vora, R. Manjunatha, Debasish Shit, Shirin Shomurotova, Akshay Kumar, Atreyi Pramanik, Ajay Sharma, Raed H. C. Alfilh, Samim Sherzod, Mohammad Mahtab Alam

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Optimizing electric vehicle energy consumption prediction through machine learning and ensemble approaches by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adeeb Noor, Sufyan Ali Memon

“…This research makes three key advances: (1) systematic comparison of four hyperparameter optimization methods (GridSearchCV, RandomizedSearchCV, Optuna, PSO) for KNN regression, (2) development of a stacking hybrid ensemble combining KNN with tree-based models, and (3) comprehensive validation on real-world data with novel temporal feature engineering. …”
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An improved performance model for artificial intelligence-based diabetes prediction by Ugwu Hillary Okwudili, Oparaku Ogbonna Ukachukwu, V. C. Chijindu, Michael Okechukwu Ezea, Buhari Ishaq

“…Abstract This research study addresses the pressing issue of diabetes prediction using advanced machine learning techniques, presenting the development of an ensemble model that significantly outperforms existing methods by 1.6% using area under the curve as the primary performance metric. …”
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A case study on canola (Brassica napus L.) potential yield prediction using remote sensing imagery and advanced data analytics by Nitin Rai, Harsh Pathak, Maria Villamil Mahecha, Dennis R. Buckmaster, Yanbo Huang, Paul Overby, Xin Sun

“…The promising performance of all the models coupled with a comprehensive hyperparameter tuning approaches suggests its applicability in predicting canola yield in real field conditions.…”
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Genetic Algorithms Applied to Optimize Neural Network Training in Reference Evapotranspiration Estimation by Eluã Ramos Coutinho, Jonni G.F. Madeira, Robson Mariano da Silva, Angel Ramon Sanchez Delgado, Alvaro L.G.A. Coutinho

“…Consequently, determining information that can minimize water consumption, such as evapotranspiration, is increasingly necessary. This research evaluates the capacity of Genetic Algorithms (GAs) in training and fine-tuning the parameters of Artificial Neural Networks (ANNs) (MLP-GA) to obtain daily values of reference evapotranspiration (ETo) in accordance with the Penman-Monteith FAO-56 method. …”
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Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging by Youngjin Han, Inwhee Joe

“…Predicting survival outcomes in critical accidents has been a focal point in machine learning research. This study addresses several limitations of existing methods, including insufficient management of data imbalance, lack of emphasis on hyperparameter tuning, and proneness to overfitting. …”
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Analyzing infant cry to detect birth asphyxia using a hybrid CNN and feature extraction approach by Samrat Kumar Dey, Khandaker Mohammad Mohi Uddin, Arpita Howlader, Md. Mahbubur Rahman, Hafiz Md. Hasan Babu, Nitish Biswas, Umme Raihan Siddiqi, Badhan Mazumder

“…Despite the importance of early asphyxia detection, existing methods are often delayed and not always effective. This research addresses the need for a faster, more accurate approach to detecting infant asphyxia using machine learning (ML) and deep learning (DL) techniques. …”
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NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat by Kamrunnahar Khan Bristy, Dip Ghosh, Md. Abul Hashem

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Prediction Method of Lithology Log Based on XGBoost Algorithm by CHU Qingjun, GE Yunlong, TONG Maosong, WANG Yan, AN Lyuxing, YU Chuanwu, JIA Xin

“…Noise is added to the samples to improve the robustness of the model. The 8 hyperparameters of the XGBoost algorithm are optimized using grid search. …”
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A Geomechanical Modeling Method for Shale Oil Reservoir Cluster Well Area Based on GridSearchCV by HUANG Lei, QI Yin, CHEN Weihua, DU Xianfei, MA Bing, TANG Jizhou

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TAL-SRX: an intelligent typing evaluation method for KASP primers based on multi-model fusion by Xiaojing Chen, Xiaojing Chen, Jingchao Fan, Jingchao Fan, Shen Yan, Longyu Huang, Longyu Huang, Longyu Huang, Guomin Zhou, Guomin Zhou, Jianhua Zhang, Jianhua Zhang

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AutoML: A systematic review on automated machine learning with neural architecture search by Imrus Salehin, Md. Shamiul Islam, Pritom Saha, S.M. Noman, Azra Tuni, Md. Mehedi Hasan, Md. Abu Baten

“…Additionally, we delve into several noteworthy research directions in NAS methods including one/two-stage NAS, one-shot NAS and joint hyperparameter with architecture optimization. …”
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Optimizing mRNA Vaccine Degradation Prediction via Penalized Dropout Approaches by Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Latifah Munirah Kamarudin

“…These methodologies extend beyond mRNA vaccine research, demonstrating versatility across diverse machine learning domains. …”
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Robust-tuning machine learning algorithms for precise prediction of permeability impairment due to CaCO3 deposition by Mohammad Javad Khodabakhshi, Masoud Bijani, Masoud Hasani

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Enhanced Fault Localization in Multi-Terminal HVDC Systems Using Improved Gaussian Process Regression by Abha Pragati, Manohar Mishra, Pritam Bhowmik, Josep M. Guerrero, Debadatta Amaresh Gadanayak

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Combination of Conditioning Factors for Generation of Landslide Susceptibility Maps by Extreme Gradient Boosting in Cuenca, Ecuador by Esteban Bravo-López, Tomás Fernández, Chester Sellers, Jorge Delgado-García

“…In this study, a specific Machine Learning (ML) method was further analyzed due to the good results obtained in the previous stage of this research. The algorithm implemented is Extreme Gradient Boosting (XGBoost), which was used to evaluate the susceptibility to landslides recorded in the city of Cuenca (Ecuador) and its surroundings, generating the respective Landslide Susceptibility Maps (LSM). …”
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