Application of Decision Tree (M5Tree) Algorithm for Multicrop Yield Prediction of the Semi-Arid Region of Maharashtra, India by Kalpesh Borse, Prasit Agnihotri

“…Modern artificial intelligence algorithms have shown to be highly useful tools for accurately predicting agricultural production. …”
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Enhanced CO₂ Emissions Prediction Using Temporal Fusion Transformer Optimized by Football Optimization Algorithm by Amel Ali Alhussan, Marwa Metwally

Subjects: “…carbon dioxide emissions prediction…”
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WCN24-657 ARTIFICIAL INTELLIGENCE IN PREDICTING PROGRESSION IN ADVANCED CKD: FROM ALGORITHM DEVELOPMENT TO REAL-WORLD DEPLOYMENT by Ricardo Aguilar Grajeda, Priyanka Arya, Chin-Chi Kuo, Mandar Gori, Suresha Venkataraya, Joel Fernandes, Sanjay Singh, Yu-Ting Lin, Hsiu-Yin Chiang, Yi-Chun Chan, Min-Yen Wu, Zi-Han Lin, Arsh Kumar Jain

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A Genetic algorithm aided hyper parameter optimization based ensemble model for respiratory disease prediction with Explainable AI. by Balraj Preet Kaur, Harpreet Singh, Rahul Hans, Sanjeev Kumar Sharma, Chetna Sharma, Md Mehedi Hassan

“…Moreover, among all the hyperparameter-optimized algorithms, adaboost algorithm outperformed all the other hyperparameter-optimized algorithms. …”
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Efficient Recovery of Linear Predicted Coefficients Based on Adaptive Steepest Descent Algorithm in Signal Compression for End-to-End Communications by Abel Kamagara, Abbas Kagudde, Baris Atakan

“…Herein, the steepest descent algorithm is applied at the receiver to decode the affected linear predicted coefficients. …”
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Prediction of the Punching Load Strength of SCS Slabs with Stud-Bolt Shear Connectors Using Numerical Modeling and GEP Algorithm by Mehdi Yousefi, Mohammad Golmohammadi, Seyed Hashem Khatibi, Majid Yaghoobi

“…Finally, using the experimental setup and gene expression programming (GEP) algorithm, several numerical models were planned to predict the maximum strength of the slabs and a simple relation was proposed. …”
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An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithm by Yuan Jin, Yunliang Lai, Azadeh Noori Hoshyar, Nisreen Innab, Meshal Shutaywi, Wejdan Deebani, A. Swathi

“…Although recent studies propose comprehensive automated diagnostic systems, these systems tend to focus on one aspect, such as feature selection, prioritization, or predictive accuracy. A more complete approach that considers all of these factors can improve the efficiency of a cardiac prediction system. …”
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Comparison of Support Vector Machine and Decision Tree Algorithm Performance with Undersampling Approach in Predicting Heart Disease Based on Lifestyle by Gusti Ayu Putu Febriyanti, Anna Baita

“…This study evaluates the performance of two machine learning algorithms, namely Support Vector Machine (SVM) and Decision Tree (DT), in predicting heart disease risk by applying undersampling techniques to handle data imbalance. …”
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A Prediction of the Shooting Trajectory for a Tuna Purse Seine Using the Double Deep Q-Network (DDQN) Algorithm by Daeyeon Cho, Jihoon Lee

“…This study proposes a method for predicting shooting trajectories using the Double Deep Q-Network (DDQN) algorithm. …”
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TCN-LSTM-MHSA model optimized by improved slime mould algorithm for stress prediction of roadway anchor bolts (cables) by QI Junyan, CHE Yuhao, WANG Lei, YUAN Ruifu

Subjects: “…anchor bolt (cable) stress prediction…”
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Deep learning algorithms enable MRI-based scapular morphology analysis with values comparable to CT-based assessments by Hanspeter Hess, Alexandra Oswald, J. Tomás Rojas, Alexandre Lädermann, Matthias A. Zumstein, Kate Gerber

“…A deep learning-based segmentation network was trained with paired CT derived scapula segmentations. An algorithm to fuse multi-plane segmentations was developed to generated high-resolution 3D models of the scapula on which morphological landmark- and axes were predicted using a second deep learning network for morphological analysis. …”
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Reference frame list optimization algorithm in video coding by quality enhancement of the nearest picture by Junyan HUO, Ruipeng QIU, Yanzhuo MA, Fuzheng YANG

“…Interframe prediction is a key module in video coding, which uses the samples in the reference frames to predict those in the current picture, thus helps to represent the complex video by transmitting a small amount of the prediction residual.In lossy video coding, the qualities of reference frames are affected by the quantization distortion, which lead to poor prediction accuracy and performance degradation.Targeted at the low latency video services, a reference frame list optimization algorithm was proposed, which enhanced the quality of the nearest reference frame by a deep learning-based convolutional neural network, and integrated the enhanced reference frame into the reference frame list to improve the accuracy of interframe prediction.Compared with H.265/HEVC reference software HM16.22, the proposed algorithm provides BD-rate savings of 9.06%, 14.92% and 13.19% for Y, Cb and Cr components, respectively.…”
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Research on Predicting Joint Rotation Angles Through Mechanomyography Signals and the Broad Learning System by Yu Bai, Xiaorong Guan, Huibin Li, Shi Cheng, Rui Zhang, Long He

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Development and validation of a nomogram for predicting lung cancer based on acoustic–clinical features by Zhou Lu, Zhou Lu, Jiaojiao Sha, Xunxia Zhu, Xiaoyong Shen, Xiaoyu Chen, Xin Tan, Rouyan Pan, Shuyi Zhang, Shi Liu, Tao Jiang, Jiatuo Xu

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A deep learning based prediction model for effective elastic properties of porous materials by Chang Liu, Ran Guo, Yangming Su

“…Furthermore, a neural network-based machine learning algorithm is established to predict the mechanical properties of porous materials. …”
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Predicting over-the-counter antibiotic use in rural Pune, India, using machine learning methods by Pravin Arun Sawant, Sakshi Shantanu Hiralkar, Yogita Purushottam Hulsurkar, Mugdha Sharad Phutane, Uma Satish Mahajan, Abhay Machindra Kudale

“…METHODS The features of OTC antibiotic use were selected using stepwise logistic, lasso, random forest, XGBoost, and Boruta algorithms. Regression and tree-based models with all confirmed and tentatively important features were built to predict the use of OTC antibiotics. …”
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An interpretable predictive model for bank customers’ income using the eXtreme Gradient Boosting algorithm and the SHAP method: a case study of an Anonymous Chilean Bank by Patricio Salas, Patricio Sáez, Vicente Marchant

“…Feature reduction is accomplished through the implementation of Boruta and BorutaSHAP, ensuring that no predictive power is lost throughout the process. …”
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Construction of a risk prediction model for postoperative deep vein thrombosis in colorectal cancer patients based on machine learning algorithms by Xin Liu, Xingming Shu, Yejiang Zhou, Yifan Jiang

“…Moreover, the SHAP method identified age and preoperative prealbumin as the primary determinants influencing ML model predictions. Finally, the study employed LIME for more precise prediction and interpretation of individual predictions.ConclusionThe machine learning algorithms effectively predicted postoperative lower limb deep vein thrombosis in colorectal cancer patients. …”
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Development and Internal Validation of Machine Learning Algorithms for Predicting Subsequent Contralateral Slipped Capital Femoral Epiphysis in Patients With Unilateral Slips by David P. VanEenenaam, Jr., BS, Carter Hall, BS, Daniel A. Maranho, MD, PhD, Christopher J. DeFrancesco, MD, Eduardo N. Novais, MD, Wudbhav N. Sankar, MD

“…Machine learning (ML) algorithms can be leveraged to identify complex, nonlinear patterns in data and allow for more accurate predictions on which patients may need a prophylactic pin. …”
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Energy consumption reduction for an ultra-low-cost artificial pancreas using an event-trigger MPC strategy by Jhon E. Goez-Mora, Pablo S. Rivadeneira

“…This work aims to evaluate the performance of an impulsive Model Predictive Control (MPC) strategy with an event-triggered (ET) approach implemented in an artificial pancreas (AP) system to determine if insulin injection is necessary, avoiding the execution of the control algorithm and the use of hardware associated to that in each sampling period. …”
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