Numerical modeling of the long-term poromechanical performance of a deep enhanced geothermal system in northern Québec by Saeed Vadiee, Biao Li, Jasmin Raymond, Mafalda M. Miranda

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AAGP integrates physicochemical and compositional features for machine learning-based prediction of anti-aging peptides by Saptashwa Datta, Jen-Chieh Yu, Yi-Hsiang Lin, Yun-Chen Cheng, Ching-Tai Chen

“…Peptides were encoded using 4,305 features, followed by adaptive feature selection with a heuristic algorithm on both datasets. …”
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Student academic performance prediction via hypergraph and TabNet by Yisheng Yang, Shiyu Zhao, Sufang An, Xiaoyong Li, Yong Zhang

“…This method first processes and extracts usable behavior features from collected multi-source campus behavior data; secondly, it utilizes K-Nearest Neighbors (KNN) to construct a hypergraph to describe the higher-order associations among students; then, it uses hypergraph convolution to aggregate neighborhood features to learn sample embedding representations; finally, the academic performance of students are predicted by the TabNet model. …”
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CHMMConvScaleNet: a hybrid convolutional neural network and continuous hidden Markov model with multi-scale features for sleep posture detection by Dikun Hu, Weidong Gao, Kai Keng Ang, Mengjiao Hu, Rong Huang, Gang Chuai, Xiaoyan Li

“…To optimize performance, a Continuous Hidden Markov Model (CHMM) with rollover features is presented. …”
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ID-insensitive deepfake detection model based on multi-attention mechanism by Yuncan Sheng, Zhengrui Zou, Zongxuan Yu, Mengxue Pang, Wei Ou, Wenbao Han

“…Specifically, the proposed multi-attention deepfake detection model consists of the following three parts: (1) Texture Feature Enhancement: We employ CondenseNet to enhance texture features efficiently, preserving subtle details and ensuring feature integrity; (2) Multi-Scale Artifact Detection: We introduce an artifact detection module that identifies potentially manipulated regions, enabling localized detection and minimizing the impact of identity information. (3) Multi-Attention Mechanism: By generating multiple attention maps, our model prioritizes different regions of the input image, fusing both texture and local features to improve classification performance. …”
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Decoupled Model-Free Adaptive Control with Prediction Features Experimentally Applied to a Three-Tank System Following Time-Varying Trajectories by Soheil Salighe, Nehal Trivedi, Fateme Bakhshande, Dirk Söffker

“…In this paper, the performance of three model-free control approaches on a multi-input, multi-output (MIMO) nonlinear system with constant and time-varying references is compared. …”
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Occlusion Vehicle Target Recognition Method Based on Component Model by Haorui Han, Hanshan Li

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Deepfake Face Detection and Adversarial Attack Defense Method Based on Multi-Feature Decision Fusion by Shanzhong Lei, Junfang Song, Feiyang Feng, Zhuyang Yan, Aixin Wang

“…This model comprises two key modules: one for extracting temporal features between video frames and another for spatial features within frames. …”
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DFF-ResNet: An Insect Pest Recognition Model Based on Residual Networks by Wenjie Liu, Guoqing Wu, Fuji Ren, Xin Kang

“…In this paper, we proposed a feature fusion residual block to perform the insect pest recognition task. …”
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Highly efficient stacking ensemble learning model for automated keratoconus screening by Zahra J. Muhsin, Rami Qahwaji, Ibrahim Ghafir, Mo’ath AlShawabkeh, Muawyah Al Bdour, Saif Aldeen AlRyalat, Majid Al-Taee

“…Results The pre-processing and feature selection techniques reduced the model's parameters to just 6.33% of the original dataset, improving classification performance, and cutting over 85% of the training time. …”
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Predicting customer subscription in bank telemarketing campaigns using ensemble learning models by Michael Peter, Hawa Mofi, Said Likoko, Julius Sabas, Ramadhani Mbura, Neema Mduma

“…We recommend the integration of advanced balancing techniques and real-time prediction systems to further enhance model performance and adaptability. Future work could explore deep learning models and interpretability techniques to gain deeper insights into customer behavior patterns. …”
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An Intelligent Smart Dynamic Feature Analysis Based Approach by Utilizing Deep Learning to Improve the Breast Cancer Detection by Seong-O Shim, Lal Hussain, Eesa Alsolami, Monagi H. Alkinani

“…The model was also trained using Quantization aware training (QAT) to enable efficient deployment on low-resource devices without significant performance degradation. …”
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Optimizing Large Railway Vision Models for Efficient In-Context Learning by Xuemei Zhan, Xubo Wu, Hua Ma

“…Large railway vision models (LRVMs) have exhibited remarkable performance in tackling diverse railway-related vision-based tasks, attributed to their capacity for in-context learning (ICL). …”
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Steel Surface Defect Detection Algorithm Based on Improved YOLOv8 Modeling by Miao Peng, Sue Bai, Yang Lu

“…Overall, the proposed model demonstrates superior recognition performance.…”
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Developing the new diagnostic model by integrating bioinformatics and machine learning for osteoarthritis by Jian Du, Tian Zhou, Wei Zhang, Wei Peng

“…Subsequently, protein-protein interaction (PPI) network analysis and machine learning were employed to identify the most relevant potential feature genes of OA, and ANN diagnostic model and receiver operating characteristic (ROC) curve were constructed to evaluate the diagnostic performance of the model. …”
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Understanding Software Defect Prediction Through eXplainable Neural Additive Models by Ruiqi He, Yong Li, Chi Sun

“…Experimental evaluations on six software projects demonstrate that XNAMs outperform existing models in prediction performance while offering clear explanations of feature contributions, ensuring high transparency and practical applicability.…”
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On the Deployment of Edge AI Models for Surface Electromyography-Based Hand Gesture Recognition by Andres Gomez-Bautista, Diego Mendez, Catalina Alvarado-Rojas, Ivan F. Mondragon, Julian D. Colorado

“…Results: The findings of this study demonstrate that by assigning relative importance to features and removing redundant or superfluous information, it is possible to enhance the system’s execution by up to 31% while preserving the model’s performance at a comparable level. …”
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Economic Evaluation of the Investment in Sensor Equipment Based on Data Valuation in Prediction Model by Jeong-Gi Lee, Deok-Joo Lee

“…Our approach leverages cooperative game theory and integrates explainable AI (XAI) techniques for feature valuation to assess the impact of each sensor within prediction models. …”
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Features of the morphofunctional state of parotid salivary glands in six-month-old rats with experimentally induced fetal macrosomia by O. V. Garmash, H. I. Gubina-Vakulyk, David Vondrášek

“…The paper aims at studying the features of the morphofunctional state of parotid gland tissue in six-month-old rats born with induced macrosomia in its different variations. …”
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Research on rock strength prediction model based on machine learning algorithm by Xiang Ding, Mengyun Dong, Wanqing Shen

“…By selecting different features, the optimal feature combination for predicting rock compressive strength was obtained, and the optimal parameters for different models were obtained through the Sparrow Search Algorithm (SSA). …”
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