Predicting photodegradation rate constants of water pollutants on TiO2 using graph neural network and combined experimental-graph features by Mahia V. Solout, Jahan B. Ghasemi

“…Three GNN models were developed: Graph Convolutional Network (GCN), Graph Attention Network (GAT), and a combined GAT-GCN model. …”
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AFHNet: Attention-Free Hybrid Network for Salient Object Detection in Underwater Images by Qian Tang, Zhen Wang, Xuqi Wang, Shan-Wen Zhang

“…The Global Feature Enhancement Module (GFEM) improves feature representation by fusing local and global features, while Multilevel Feature Fusion with Multitask Learning (MFF-ML) enhances detail preservation and feature diversity. …”
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Golden eagle optimized CONV-LSTM and non-negativity-constrained autoencoder to support spatial and temporal features in cancer drug response prediction by Wesam Ibrahim Hajim, Suhaila Zainudin, Kauthar Mohd Daud, Khattab Alheeti

“…The classification model, Golden Eagle Optimization-based Convolutional Long Short-Term Memory neural networks (GEO-Conv-LSTM), is assembled by integrating Convolutional Neural Network CNN and LSTM models, with parameter tuning performed by the GEO algorithm. …”
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AMFEF-DETR: An End-to-End Adaptive Multi-Scale Feature Extraction and Fusion Object Detection Network Based on UAV Aerial Images by Sen Wang, Huiping Jiang, Jixiang Yang, Xuan Ma, Jiamin Chen

“…Specifically, to extract target features from complex backgrounds more accurately, we propose an adaptive backbone network, FADC-ResNet, which dynamically adjusts dilation rates and performs adaptive frequency awareness. …”
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Revolutionizing educational decision-making: a robust machine learning mechanism for predicting student performance by Muhammad Nadeem Gul, Waseem Abbasi, Muhammad Yaqoob Wani

“…A supervised machine learning approach, specifically the random forest algorithm, was employed to build the prediction model. Redundant features were eliminated to reduce complexity and computational cost. …”
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Reconfigurable mmWave Planar Phased Array Featuring Wide Elevation and Full Azimuth Spatial Coverage for 5G Vehicular Application by Saiful Islam, Hanvit Kim, Tuyen Dinh Nguyen, Sunwoo Kim, Hyoungsuk Yoo

“…In each mode, the radiation performances is verified with and without the car model. …”
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Performance Analysis in Production Systems with Uncertain Data: A Stochastic Data Envelopment Analysis Approach by Seyedeh Fatemeh Bagheri, Alireza Amirteimoori, Sohrab Kordrostami, Mansour Soufi

“…The problem of determining an optimal benchmark to inefficient decision-making units (DMUs) is an important issue in the field of performance analysis. Previous methods for determining the projection points of inefficient DMUs have only focused on one objective and other features have been ignored. …”
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Open-source Flux Transport (OFT). I. HipFT–High-performance Flux Transport by Ronald M. Caplan, Miko M. Stulajter, Jon A. Linker, Cooper Downs, Lisa A. Upton, Bibhuti Kumar Jha, Raphael Attie, Charles N. Arge, Carl J. Henney

“…HipFT is designed to enable users to write extensions easily, enhancing its flexibility and adaptability. We describe HipFT’s model features, validations of its numerical methods, performance of its parallel and GPU-accelerated code implementation, analysis/postprocessing options, and example use cases.…”
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Multilevel and multidimensional features of socioeconomic status and cognitive function in older adulthood: a cross-sectional analysis of the IGNITE studyResearch in context by Jermon A. Drake, Lu Wan, Chaeryon Kang, Peter J. Gianaros, Patricio Solis-Urra, Haiqing Huang, Rebecca Reed, Charles H. Hillman, Eric D. Vidoni, Jeffrey M. Burns, Arthur F. Kramer, Edward McAuley, George Grove, Kirk I. Erickson, Lauren E. Oberlin

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N2GNet tracks gait performance from subthalamic neural signals in Parkinson’s disease by Jin Woo Choi, Chuyi Cui, Kevin B. Wilkins, Helen M. Bronte-Stewart

“…In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs). …”
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Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography. by MinJae Woo, Linglin Zhang, Beatrice Brown-Mulry, InChan Hwang, Judy Wawira Gichoya, Aimilia Gastounioti, Imon Banerjee, Laleh Seyyed-Kalantari, Hari Trivedi

“…This study evaluates a deep learning model for classifying normal versus potentially abnormal regions of interest (ROIs) on mammography, aiming to identify imaging, pathologic, and demographic characteristics that may induce suboptimal model performance in certain patient subgroups. …”
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Based on Regular Expression Matching of Evaluation of the Task Performance in WSN: A Queue Theory Approach by Jie Wang, Kai Cui, Kuanjiu Zhou, Yanshuo Yu

“…Finally, based on the queuing model, the sensor networks of task scheduling dynamic performance are evaluated. …”
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A Comparative Study of Deep Learning’s Performance Methods for News Article using Word Representations by Iman Saladin B. Azhar, Winda Kurnia Sari, Naretha Kawadha Pasemah Gumay

“…This research evaluates the effectiveness of three deep learning techniques Convolutional Neural Network (CNN), Deep Neural Network (DNN), and Long Short-Term Memory (LSTM) for online news classification using 300-dimensional GloVe word representations. The CNN model utilizes convolutional and pooling layers to extract local features, the DNN relies on dense layers to learn abstract representations, while the LSTM excels at capturing long-term dependencies between words. …”
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Segmentation Performance and Mapping of Dunes in Multi-Source Remote Sensing Images Using Deep Learning by Pengyu Zhao, Jiale An, Jianghua Zheng, Wanqiang Han, Nigela Tuerxun, Bochao Cui, Xuemi Zhao

“…However, the diversity and complexity of the dune morphology present significant challenges when using traditional classification methods, particularly in feature extraction, model parameter optimization, and large-scale mapping. …”
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Rectified Tangent Activation (RTA): A Novel Activation Function for Enhanced Deep Learning Performance by Gaurav Kumar Pandey, Sumit Srivastava

“…In deep learning, activation functions (AFs) influence a model’s performance, convergence rate, and generalization capability. …”
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A New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit Performance by Aihua Zhang, Chen Chen, Hamid Reza Karimi

“…And then the decision parameters of the kernel parameters determine the input signal to map to the feature space deduced that a well plant model by discarding redundant features. …”
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Predefined-Performance Sliding-Mode Tracking Control of Uncertain AUVs via Adaptive Disturbance Observer by Yuhang Guo, Zijun Gao, Yuhang Hu, Zhankui Song

“…In this paper, a sliding-mode control strategy incorporating prescribed features was systematically designed, resolving the dual challenges of trajectory tracking precision maintenance and disturbance attenuation for an AUV subjected to dynamic model inaccuracies and disturbances. …”
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The Synthesis of Polycarboxylate Dispersants Containing Benzenesulfonic Acid Groups and Their Performance in Promoting Coal Particle Dispersion by Lin Li, Zhisen Li, Shuo Yang, Chuandong Ma, Wenqi Zhang, Meng He, Xiaofang You

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Sex Differences and the Relationship Between Athlete Anthropometrics and Long Jump Performance at National Elite Level by Godwin Chinedu Uzomba, Philip X. Fuchs, Cristina Cortis, Andrea Fusco

“…Conclusions: This study enhances the understanding of key anthropometric features influencing long jump performance at an elite level. …”
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Real-world therapeutic performance of pirfenidone for connective tissue disease-associated interstitial lung diseases by Xueting Yuan, Chen Yu, Shengyun Liu, Qiang Shu, Xinwang Duan, Lin Tang, Liying Peng, Shuang Zhou, Chanyuan Wu, Jiuliang Zhao, Dong Xu, Lan Song, Hui Huang, Mengtao Li, Yanhong Wang, Qian Wang, Xiaofeng Zeng

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