Balancing Predictive Performance and Interpretability in Machine Learning: A Scoring System and an Empirical Study in Traffic Prediction by Fabian Obster, Monica I. Ciolacu, Andreas Humpe

“…This paper investigates the empirical relationship between predictive performance, often called predictive power, and interpretability of various Machine Learning algorithms, focusing on bicycle traffic data from four cities. …”
Get full text

Predictive reward-prediction errors of climbing fiber inputs integrate modular reinforcement learning with supervised learning. by Huu Hoang, Shinichiro Tsutsumi, Masanori Matsuzaki, Masanobu Kano, Keisuke Toyama, Kazuo Kitamura, Mitsuo Kawato

“…In this study, we investigated the cerebellum's role in executing reinforcement learning algorithms, with a particular emphasis on essential reward-prediction errors. …”
Get full text

Utilizing LSTM-GRU for IOT-Based Water Level Prediction Using Multi-Variable Rainfall Time Series Data by Indrastanti Ratna Widiasari, Rissal Efendi

Get full text

Enhanced clustering techniques for lithofacies and electrofacies identification in post-salt carbonate and siliciclastic reservoirs: a case study from the campos basin, Brazil by Abel Carrasquilla, Herson Rocha

Subjects: Get full text

Automation system for finding parking places by О. Napadii, N. Tsopa, Ye. Batrak

Subjects: Get full text

Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach by Vito Santamato, Caterina Tricase, Nicola Faccilongo, Massimo Iacoviello, Agostino Marengo

Subjects: Get full text

A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making by Michal Pruski, Simone Willis, Kathleen Withers

Subjects: Get full text

Half-hourly electricity price prediction model with explainable-decomposition hybrid deep learning approach by Sujan Ghimire, Ravinesh C. Deo, Konstantin Hopf, Hangyue Liu, David Casillas-Pérez, Andreas Helwig, Salvin S. Prasad, Jorge Pérez-Aracil, Prabal Datta Barua, Sancho Salcedo-Sanz

“…Input features are identified using the Partial Autocorrelation Function , and models are optimized using the Optuna algorithm. The final prediction combines the trend, seasonal, and residual components’ predictions. …”
Get full text

Clinical Prediction Models Based on Traditional Methods and Machine Learning for Predicting First Stroke: Status and Prospects by ZHANG Zijiao, DING Shunjing, ZHAO Di, LIANG Jun, LEI Jianbo

“…In recent years, advancements in big data and artificial intelligence technologies have opened new avenues for stroke risk prediction. This article reviews the current research status of traditional methods and machine learning models in predicting first-ever stroke risk and outlines future development trends from three perspectives: First, emphasis should be placed on technological innovation by incorporating advanced algorithms such as deep learning and large models to further enhance the accuracy of predictive models. …”
Get full text

Integrating proximal geophysical sensing and machine learning for digital soil mapping: Spatial prediction and model evaluation using a small dataset by Danilo César de Mello, Gustavo Vieira Veloso, Murilo Ferre de Mello, Marcos Guedes de Lana, Isabelle de Angeli Oliveira, Fellipe Alcantara de Oliveira Mello, Rafael Gomes Siqueira, Lucas Carvalho Gomes, Elpídio Inácio Fernandes-Filho, Carlos Ernesto Gonçalves Reynaud Schaefer, Márcio Rocha Francelino, Emilson Pereira Leite, Tiago Osório Ferreira, José Alexandre Melo Demattê

“…However, studies focusing the spatial prediction of geophysical data remain limited. In this research, we aimed to model and predict the spatial distribution of soil geophysical properties using parent material and terrain attributes with machine learning algorithms. …”
Get full text

Enhancing Manufacturing Precision: Leveraging Motor Currents Data of Computer Numerical Control Machines for Geometrical Accuracy Prediction Through Machine Learning by Lucijano Berus, Jernej Hernavs, David Potocnik, Kristijan Sket, Mirko Ficko

“…Different machine learning algorithms, such as Random Forest (RF), k-nearest neighbors (k-NN), and Decision Trees (DT), were used for predictive modeling. …”
Get full text

Prediction-Based Distributed State Estimation Control for IoT-Enabled Train Positioning Systems by L. Ponnarasi, P. B. Pankajavalli, Y. Lim, R. Sakthivel, Sultan Alfarhood, Mejdl Safran

Subjects: Get full text

Assessment and Dynamic Prediction of Green Space Ecological Service Value in Guangzhou City, China by Zhefan Li, Zhaokang Zhou, Zhenhua Liu, Jiahe Si, Jiaming Ou

Subjects: Get full text

Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm by Hongjun Fan, Xiaoqing Zhao, Zongjun Wang, Zheqing Zhang, Ao Chang

“…Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. …”
Get full text

Crop Type Classification by DESIS Hyperspectral Imagery and Machine Learning Algorithms by Nizom Farmonov, Khilola Amankulova, Jozsef Szatmari, Alireza Sharifi, Dariush Abbasi-Moghadam, Seyed Mahdi Mirhoseini Nejad, Laszlo Mucsi

“…However, precise and continuous spectral signatures, important for large-area crop growth monitoring and early prediction of yield production with cutting-edge algorithms, can be only provided via hyperspectral imaging. …”
Get full text

Predicting the Magnitude of Earthquakes Using Grammatical Evolution by Constantina Kopitsa, Ioannis G. Tsoulos, Vasileios Charilogis

Get full text

A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors by Huiling Qin, Mudassar Rehman, Muhammad Farhan Hanif, Muhammad Yousaf Bhatti, Muhammad Kamran Siddiqui, Mohamed Abubakar Fiidow

Get full text

Classification of imbalanced travel mode choice dataset with SMOTE and prediction using interpretable machine learning by Mujahid Ali

“…Therefore, the current study used the Synthetic Minority Oversampling Technique (SMOTE) to balance the TMC dataset and used several ML algorithms such as random forest (RF), decision tree (DT), Extreme Gradient Boosting (XGBoost), K-nearest Neighbors (KNN), and logistic regression for the prediction of TMC. …”
Get full text

Algorithm for selecting predictors and prognosis of atrial fibrillation in patients with coronary artery disease after coronary artery bypass grafting by B. I. Geltser, K. I. Shakhgeldyan, V. Yu. Rublev, B. O. Shcheglov, E. A. Kokarev

“…These values in best model based on multivariate LR were lower (0,75; 0,7; 0,68 and 0,7, respectively).Conclusion. The developed algorithm for selecting predictors made it possible to verify significant predictive ranges and weight coefficients characterizing their influence on PAF development. …”
Get full text

Prospects of Predicting the Polar Motion Based on the Results of the Second Earth Orientation Parameters Prediction Comparison Campaign by Tomasz Kur, Justyna Śliwińska‐Bronowicz, Malgorzata Wińska, Henryk Dobslaw, Jolanta Nastula, Aleksander Partyka, Santiago Belda, Christian Bizouard, Dale Boggs, Sara Bruni, Lue Chen, Mike Chin, Sujata Dhar, Robert Dill, PengShuo Duan, Jose M. Ferrandiz, Junyang Gou, Richard Gross, Sonia Guessoum, Songtao Han, Robert Heinkelmann, ChengLi Huang, Christopher Irrgang, Jacek Kudrys, Jia Li, Marcin Ligas, Lintao Liu, Weitao Lu, Volker Mayer, Wei Miao, Maciej Michalczak, Sadegh Modiri, Michiel Otten, Todd Ratcliff, Shrishail Raut, Jan Saynisch‐Wagner, Matthias Schartner, Erik Schoenemann, Harald Schuh, M. Kiani Shahvandi, Benedikt Soja, Xiaoqing Su, Daniela Thaller, Maik Thomas, Guocheng Wang, Yuanwei Wu, CanCan Xu, Xueqing Xu, Xinyu Yang, Xin Zhao, Zhijin Zhou

“…Abstract Growing interest in Earth Orientation Parameters (EOP) resulted in various approaches to the EOP prediction algorithms, as well as in the exploitation of distinct input data, including the observed EOP values from various operational data centers and modeled effective angular momentum functions. …”
Get full text