High throughput computational screening and interpretable machine learning for iodine capture of metal-organic frameworks by Haoyi Tan, Yukun Teng, Guangcun Shan

“…Initially, the relationship between the structural characteristics of MOF materials (including density, surface area and pore features) and their adsorption properties was explored, with the aim of identifying the optimal structural parameters for iodine capture. …”
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Monitoring water quality parameters using multi-source data-driven machine learning models by Yubo Zhao, Mo Chen, Jinyu He, Yanping Ma

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Influence of Printing Parameters on the Morphological Characteristics of Plasma Directed Energy-Deposited Stainless Steel by Luis Segovia-Guerrero, Antonio José Gil-Mena, Nuria Baladés, David L. Sales, Carlota Fonollá, María de la Mata, María de Nicolás-Morillas

“…Moreover, advanced 3D scanning and computational analysis were used to assess the key morphological features, including bead width and height. …”
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LatentResNet: An Optimized Underwater Fish Classification Model with a Low Computational Cost by Muhab Hariri, Ercan Avsar, Ahmet Aydın

“…This paper presents LatentResNet, a computationally lightweight deep learning model involving two key innovations: (i) using the encoder from the proposed LiteAE, a lightweight autoencoder for image reconstruction, as input to the model to reduce the spatial dimension of the data and (ii) integrating a DeepResNet architecture with lightweight feature extraction components to refine encoder-extracted features. …”
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A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes by Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada

“…This study presents a comparative analysis of hyper-parameter optimization methods used in developing predictive models for patients at risk of heart failure readmission and mortality. …”
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SDES-YOLO: A high-precision and lightweight model for fall detection in complex environments by Xiangqian Huang, Xiaoming Li, Limengzi Yuan, Zhao Jiang, Hongwei Jin, Wanghao Wu, Ru Cai, Meilian Zheng, Hongpeng Bai

“…With only 2.9M parameters and 7.2 GFLOPs of computation, SDES-YOLO achieves an mAP@0.5 of 85.1%, representing a 3.41% improvement over YOLOv8n, while reducing parameter count and computation by 1.33% and 11.11%, respectively. …”
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A Futuristic Approach to Security in Cloud Data Centers Using a Hybrid Algorithm by Dipankar Chatterjee, Mostaque Md. Morshedur Hassan, Nazrul Islam, Asmita Ray, Munsifa Firdaus Khan Barbhuyan

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Parameter Prediction for Metaheuristic Algorithms Solving Routing Problem Instances Using Machine Learning by Tomás Barros-Everett, Elizabeth Montero, Nicolás Rojas-Morales

“…Tuning the parameters of a metaheuristic is a computationally costly task. …”
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MEAC: A Multi-Scale Edge-Aware Convolution Module for Robust Infrared Small-Target Detection by Jinlong Hu, Tian Zhang, Ming Zhao

“…Traditional convolutional neural networks (CNNs) struggle to detect such weak, low-contrast objects due to their limited receptive fields and insufficient feature extraction capabilities. To overcome these limitations, we propose a Multi-Scale Edge-Aware Convolution (MEAC) module that enhances feature representation for small infrared targets without increasing parameter count or computational cost. …”
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Forecasting loan, deferred rate and customer segmentation in banking industry: A computational intelligence approach by Mahtab Vasheghani, Ebrahim Nazari Farokhi, Behrooz Dolatshah

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Noise Reduction with Recursive Filtering for More Accurate Parameter Identification of Electrochemical Sources and Interfaces by Mitar Simić, Milan Medić, Milan Radovanović, Vladimir Risojević, Patricio Bulić

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Investigation of the Carbon Dioxide Centrifugal Compressor Working Process using Computational Fluid Dynamics Methods by Fateeva E.S., Danilishin A.M., Kozhukhov Y.V., Kazantsev R.A.

“…The most significant results are the describing the features of the carbon dioxide centrifugal compressor working process using both the built-in real gas models in Ansys CFX and imported tables of real gas properties and the demonstration of satisfactory agreement between simulation and experiment. …”
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THE METHOD OF JITTER DETERMINING IN THE TELECOMMUNICATION NETWORK OF A COMPUTER SYSTEM ON A SPECIAL SOFTWARE PLATFORM by Mykhailo Mozhaiev, Nina Kuchuk, Maksym Usatenko

“…Relevance of the study: When choosing a platform, the quality criteria for computer system service depend significantly on the parameters of the basic telecommunications network. …”
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Rehabilitation and Motion Symmetry Analysis With a TACX Smart Cycling Trainer Using Computational Intelligence by Hana Charvatova, Daniel Martynek, Alexandra Molcanova, Ales Prochazka

“…The classification of spectral features evaluated separately for the left and right legs pointed the classification accuracy of 94.5% for accelerometric data and 99.1% for gyrometric data estimated by the use of the two layer neural network and the symmetry coefficient of 1.05 for the slope of 8%. …”
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A Deep Learning based Optimization Model for Based Computer Interface of Wheelchair Directional Control by Zaid Raad Saber Zubair

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Computational Screening of Heterocyclic L-Type Calcium Channel Blockers for Potential Anti-Parkinson’s Activity by Thomas Kurian, Rani Sebastian

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GELSTATS: A Computer Program for Population Genetics Analyses Using VNTR Multilocus Probe Data by Steven H. Rogstad, Stephan Pelikan

“…A jackknife test for heterozygosity differences between groups is also computed. Examples of GELSTATS analyses illustrate some features of the program.…”
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Enhancement of rime algorithm using quadratic interpolation learning for parameters identification of photovoltaic models by Shazly A. Mohamed, Abdullah M. Shaheen, Mohammed H. Alqahtani, Badr M. Al Faiya

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Intelligent Recognition and Parameter Estimation of Radar Active Jamming Based on Oriented Object Detection by Jiawei Lu, Yiduo Guo, Weike Feng, Xiaowei Hu, Jian Gong, Yu Zhang

“…Second, for the five ISRJ types, a post-processing algorithm based on boxes fusion is designed to further extract features for secondary recognition. Finally, by integrating the detection box information and secondary recognition results, parameters of different ISRJ are estimated. …”
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Bivariate Epanechnikov-exponential distribution: statistical properties, reliability measures, and applications to computer science data by H. M. Barakat, M. A. Alawady, I. A. Husseiny, M. Nagy, A. H. Mansi, M. O. Mohamed

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