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. …”
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Supercontinuum Generation in Suspended Core Fibers Based on Intelligent Algorithms by Meiqian Jing, Tigang Ning

“…This study presents a reverse-optimization framework for supercontinuum (SC) generation in Ge₂₀Sb₁₅Se₆₅ suspended-core fibers (SCFs), integrating neural network modeling with the Nutcracker Optimization Algorithm to co-design optimal fiber structures and pump pulse parameters. …”
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Financial engineering and the digital economy: The implementations of machine learning algorithms by Pingping Fu, Honghao Yang, Wenhao Qian, ELsiddig Idriss Mohamed, Wafa Ali J. Almohri, Huda M. Alshanbari

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Finding Random Integer Ideal Flow Network Signature Algorithms by Kardi Teknomo, Erna Budhiarti Nababan, Indriati Njoto Bisono, Resmana Lim

“… We propose a Random Integer Ideal Flow Network (IFN) Signature Algorithm that generates integral flow assignments in strongly connected directed graphs under uncertainty. …”
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Extended Blahut–Arimoto Algorithm for Semantic Rate-Distortion Function by Yuxin Han, Yang Liu, Yaping Sun, Kai Niu, Nan Ma, Shuguang Cui, Ping Zhang

“…Furthermore, by considering the semantic knowledge base (SKB) as a specific instance of synonymous mapping, the EBA algorithm provides a theoretical approach for analyzing and predicting the SKB size. …”
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Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems by Salam Fraihat, Qusai Shambour, Mohammed Azmi Al-Betar, Sharif Naser Makhadmeh

“…The VAE-MCRS model utilizes the latent features generated by the VAE in conjunction with user–item interactions to enhance recommendation accuracy and predict ratings for unrated items. Experiments carried out using the Yahoo! …”
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Application of the metaheuristic algorithms to quantify the GSI based on the RMR classification by Pouya Koureh Davoodi, Farnusch Hajizadeh, Mohammad Rezaei

“…This study addresses this challenge by analyzing data from fourteen different rock types and employing three metaheuristic optimization algorithms, namely Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Grey Wolf Optimization (GWO), to develop predictive models for quantifying GSI based on the RMR. …”
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Design of a Prediction Model to Predict Students’ Performance Using Educational Data Mining and Machine Learning by Jayasree R, Sheela Selvakumari

“…Initially, there was inadequate study of the various prediction techniques to select the ones that would best predict students’ success in educational environments. …”
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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. …”
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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. …”
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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. …”
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A Quantitative Evaluation Method Based on Back Analysis and the Double-Strength Reduction Optimization Method for Tunnel Stability by Jinglai Sun, Fan Wang, Xinling Wang, Xu Wu

“…Compared with the traditional method, the proposed back analysis method can reduce errors in the predicted performance, and unlike the SRM, the ODSRM can avoid overestimating the safety factor with the same reduction factor. …”
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CPO-VMD Combined With Multiscale Permutation Entropy for Noise Reduction in GNSS Vertical Time Series in Mining Areas by Xu Yang, Xinxin Yao, Xinjian Fang, Xuexiang Yu, Yi Wu, Shicheng Xie

“…The method uses the CPO algorithm to optimize the key parameters of the VMD, determines the high-frequency components with MPE values higher than a set threshold as noise components and removes them, and then reconstructs the remaining components in order to obtain the noise-reduced time series. …”
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PCA-FSA-MLR Model and Its Application in Runoff Forecast by GUO Cunwen, CUI Dongwen

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A Novel Method for Noise Reduction and Jump Correction of Maglev Gyroscope Rotor Signals Under Instantaneous Perturbations by Di Liu, Zhen Shi, Chenxi Zou, Ziyi Yang, Jifan Li

“…To solve this problem, we propose a novel noise reduction algorithm that integrates Moving Average Filtering with Autoregressive Integrated Moving Average (MAF-ARIMA), based on the noise characteristics of the rotor jump signal. …”
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Mining privacy-preserving association rules using transaction hewer allocator and facile hash algorithm in multi-cloud environments by D. Dhinakaran, S. Gopalakrishnan, D. Selvaraj, M.S. Girija, G. Prabaharan

“…The complexities involved in the mining of frequent itemsets led us to introduce the Apriori with Tid Reduction (ATid) algorithm considering scalability and computational operational improvements to the mining process due to the Tid Reduction concept. …”
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Evolutionary Algorithms for the Optimal Design of Robotic Cells: A Dual Approximation for Space and Time by Raúl-Alberto Sánchez-Sosa, Ernesto Chavero-Navarrete

“…In response, this study presents a dual approach to optimize both spatial design and traversal time in robotic cells, using bioinspired evolutionary algorithms. Initially, a genetic algorithm is employed to optimize the layout of the cell elements, reducing space usage and avoiding interferences between workstations. …”
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Interior Radiation Noise Reduction Method Based on Multiframe Processing in Infrared Focal Plane Arrays Imaging System by Yiyang Li, Weiqi Jin, Zhihao Liu

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Robust Design Optimization of Viscoelastic Damped Composite Structures Integrating Model Order Reduction and Generalized Stochastic Collocation by Tianyu Wang, Chao Xu, Teng Li

“…Pareto optimal solutions are determined by combining the proposed MOR and gSC approaches with a well-established Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, which accounts for robustness in handling design variables, objectives, and constraints. …”
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Mahalanobis distance–based kernel supervised machine learning in spectral dimensionality reduction for hyperspectral imaging remote sensing by Jing Liu, Yulong Qiao

“…Spectral dimensionality reduction is a crucial step for hyperspectral image classification in practical applications. …”
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