Densenet Model for Binary Glaucoma Classification Performance Assessment with Texture Feature by Wildan Jameel Hadi, Amal Sufiuh Ajrash, Sahar Muneam Salman, Mays jalal jasim, Mina Taha Ibrahim

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MLCRP: ML-Based GPU Cache Performance Modeling Featured With Reuse Profiles by Minjung Cho, Eui-Young Chung

“…Finally, we propose a method to extract RP features from real GPU application traces, enabling the trained model to predict cache performance. …”
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Feature Clustering Analysis Using Reference Model towards Rolling Bearing Performance Degradation Assessment by Xiaoxi Ding, Liming Wang, Wenbin Huang, Qingbo He, Yimin Shao

“…Along with the working time going, this new monitored chart picked by FCA aims to describe the feature clustering distribution transition by a series of reference models. …”
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Performance Analysis of Eye Movement Event Detection Neural Network Models with Different Feature Combinations by Birtukan Adamu Birawo, Pawel Kasprowski

“…Various combinations of these features have been used as input to the networks. The performance of the proposed method was evaluated across all feature combinations and compared to state-of-the-art feature sets. …”
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Research on the performance of the SegFormer model with fusion of edge feature extraction for metal corrosion detection by Bingnan Yan, Conghui Wang, Xiaolong Hao

“…In this paper, a SegFormer metal corrosion detection method based on parallel extraction of edge features is proposed. Firstly, to solve the boundary ambiguity problem of metal corrosion images, an edge-feature extraction module (EEM) is introduced to construct a spatial branch of the network to assist the model in extracting shallow details and edge information from the images. …”
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Using Permutation-Based Feature Importance for Improved Machine Learning Model Performance at Reduced Costs by Adam Khan, Asad Ali, Jahangir Khan, Fasee Ullah, Muhammad Faheem

“…These selected features were used to retrain the ML models without hyperparameters (default settings) to determine whether similar performance could be achieved at low computational cost. …”
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Role of Feature Diversity in the Performance of Hybrid Models—An Investigation of Brain Tumor Classification from Brain MRI Scans by Subhash Chand Gupta, Shripal Vijayvargiya, Vandana Bhattacharjee

“…Our proposed hybrid models are built by the fusion of feature vectors. …”
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How can selection of biologically inspired features improve the performance of a robust object recognition model? by Masoud Ghodrati, Seyed-Mahdi Khaligh-Razavi, Reza Ebrahimpour, Karim Rajaei, Mohammad Pooyan

“…These patches are selected indiscriminately from different positions of an image and this can lead to the extraction of non-discriminating patches which eventually may reduce the performance. In the proposed model we used an evolutionary algorithm approach to select a set of informative patches. …”
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PHYSICS-DRIVEN FEATURE CREATION TO IMPROVE MACHINE LEARNING MODELS PERFORMANCE FOR OIL PRODUCTION RATE PREDICTION by Eghbal Motaei, Seyed Mehdi Tabatabai, Tarek Ganat, Ahmad Khanifar, Sulaiman Dzaiy, Timur Chis

“…The same models were then built using physics-driven features, and their performance metrics were compared. …”
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An interpretable and adaptable data-driven model for performance prediction in thermal plants by G. Prokhorskii, M. Preißinger, S. Rudra, E. Eder

“…To safely operate complex industrial systems such as thermal power plants, establishing reliable monitoring tools is paramount for better understanding the underlying processes. Data-driven models are a useful aid for monitoring and control of thermal power plants, but they require an effective feature selection to allow for an accurate, computationally efficient, and interpretable model. …”
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Performance Comparison of Prediction of Hydraulic Jump Length Under Multiple Neural Network Models by Ziyuan Xu, Zirui Liu, Yingzi Peng

“…Furthermore, we compared the performance of PINNs with other data-driven models such as Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), and Transformers. …”
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Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories by Huan Lin, Weiye Yu, Zhan Lian

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Optimization of multi-objective feature regression models for designing performance assessment methods in college and university educational reform by Fengjun Qi, Zhenping Liu, Wenzheng Zhang, Zhenjie Sun

“…Multi-objective regression offers a promising approach to optimizing the construction of performance evaluation index systems. However, conventional regression models often rely on a shared input space for all targets, neglecting the fact that distinct and complex feature sets may influence each target. …”
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Modeling Dual-Task Performance: Identifying Key Predictors Using Artificial Neural Networks by Arash Mohammadzadeh Gonabadi, Farahnaz Fallahtafti, Judith Heselton, Sara A. Myers, Ka-Chun Siu, Julie Blaskewicz Boron

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Prediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost method by Rencheng Fang, Tao Zhou, Baohua Yu, Zhigang Li, Long Ma, Tao Luo, Yongcai Zhang, Xinqi Liu

“…Based on this motivation, this paper proposes an improved Binary Snake Optimizer (MBSO) as a wrapped feature selection model, taking the Mat and Por student achievement data in the UCI database as an example, and comparing the MBSO feature selection model with other feature methods, the MBSO is able to select features with strong correlation to the students and the average number of student features selected reaches a minimum of 7.90 and 7.10, which greatly reduces the complexity of student achievement prediction. …”
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Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson’s disease classification by Sameer Bhat, Piotr Szczuko

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Predictive performance and uncertainty analysis of ensemble models in gully erosion susceptibility assessment by Congtan Liu, Haoming Fan, Yixuan Wang

“…This study aims to identify the optimal feature datasets and to quantify the uncertainty associated with gully erosion prediction models by developing a novel methodological framework based on ensembles of the three machine learning models: Random Forest (RF), Convolutional Neural Network (CNN), and Transformer models. …”
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Intelligent Modelling Techniques for Enhanced Thermal Comfort and Energy Optimisation in Residential Buildings by Shamaila Iram, Hafiz Muhammad Athar Farid, Abduljelil Adeola Akande, Hafiz Muhammad Shakeel

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AI-Based Performance Classification of Multi-Level System-of-Systems via Hypergraph Modeling by Jun Jiang, Abdeslem Smahi, Yiwen Chen, Othman Lakhal, Rochdi Merzouki

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Modelling Match Outcome in Australian Football: Improved accuracy with large databases by Young C., Luo W., Gastin P., Tran J., Dwyer D.

“…The purpose of this study was to evaluate several methodological opportunities, to enhance the accuracy of this type of modelling. Specifically, we evaluated the potential benefits of 1) modelling match outcome using an increased number of seasons and PIs compared with previous reports, 2) how to identify eras where technical performance characteristics were stable and 3) the application of a novel feature selection method. …”
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