Modeling of Exhaust Gas Temperature at the Turbine Outlet Using Neural Networks and a Physical Expansion Model by Alessandro Brusa, Alice Grossi, Mirco Lenzi, Fenil Panalal Shethia, Nicolò Cavina, Ioannis Kitsopanidis

“…Traditional physical modeling approaches, based on thermodynamic and fluid dynamics features of gas expansion, can be used for this purpose. …”
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Study of kinetic performance effect on wheel and rail wear using computer simulation by Nikolay V. Limarenko, Victor P. Zharov, Yury V Panov, Boris G Shapoval

“…The purpose of the research is to obtain mathematic models that are able to assess quantitatively the effect of the inductor mass on the parameters featuring the inductor performance quality. …”
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Application of Machine Learning Techniques for Bearing Fault Diagnosis by Sarra Eddai, Nabih Feki, Ahmed Ghorbel, Abdelkhalak El Hami, Mohamed Haddar

“…The methodology employed encompasses a systematic approach, including data preprocessing, feature extraction, and model development. This research employs advanced machine learning techniques, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, and Naive Bayes algorithms, in conjunction with time-domain and frequency-domain feature extraction methods. …”
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Spatiotemporal Land Use Change Detection Through Automated Sampling and Multi-Feature Composite Analysis: A Case Study of the Ebinur Lake Basin by Yi Yang, Liang Zhao, Ya Guo, Shihua Liu, Xiang Qin, Yixiao Li, Xiaoqiong Jiang

“…In addition, an object-oriented Gradient Boosting Decision Tree (GBDT) model was employed to perform accurate land use classification. …”
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Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison by Farag M. A. Altalbawy, Ahmad Alkhayyat, Ramdevsinh Jhala, Anupam Yadav, T. Ramachandran, Aman Shankhyan, A. Karthikeyan, Dhirendra Nath Thatoi, Vladimir Vladimirovich Sinitsin

“…In a comparable manner, the KNN approach exhibited outstanding predictive performance, also attaining an R2 value of 0.998. MLP, although slightly lower in performance compared to the other models, still proves to be a reliable predictor of temperature values, with an R-squared score of 0.984. …”
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Verbalization of the concept of “hearing” in Slavic languages by Liudmyla Pedchenko

“…The main Slavic name for the ability to hear – слух – is a verbal formation that implements the nominative model: ‘action’ > ‘ability to perform an action’. …”
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Individual Identification of Holstein Cows from Top-View RGB and Depth Images Based on Improved PointNet++ and ConvNeXt by Kaixuan Zhao, Jinjin Wang, Yinan Chen, Junrui Sun, Ruihong Zhang

“…Finally, body pattern image classification based on the improved ConvNeXt network model was performed for individual cow identification. …”
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UGS-M3F: unified gated swin transformer with multi-feature fully fusion for retinal blood vessel segmentation by Ibtissam Bakkouri, Siham Bakkouri

“…To overcome these challenges, the Unified Gated Swin Transformer with Multi-Feature Full Fusion (UGS-M3F) model has been developed as a powerful deep learning framework tailored for retinal vessel segmentation. …”
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Effectiveness of Engineering Service Organizations in Global Projects Execution by Aleksei Stepin

“…Highlighted the importance to consider several specific features of Engineering Service companies in order to maintain the high efficiency of the global Engineering Services model. …”
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Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury by Yukun Wang, Xuebo Chen

“…Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. …”
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ANN-SVM-IP: An Innovative Method for Rapidly and Efficiently Detecting and Classifying of External Defects of Apple Fruits by Nashaat M. Hussain Hassan, Mohamed M. Hassan Mahmoud, Mohamed A. Ismeil, M. Mourad Mabrook, A. A. Donkol, A. M. Mabrouk

“…Therefore, as the results demonstrate, the integration of traditional machine learning techniques (artificial neural networks and support vector machines with image preprocessing) provides a cost-effective and efficient approach for detecting and classifying external defects in apples. The proposed model focuses on speed and scalability by relying on the combination of traditional machine learning techniques, which are characterized by low cost, high performance, and simplicity of implementation, which was confirmed by the results obtained (99.4% for separating different defects, 97% for defect extraction, and a speed of 3.29 milliseconds, sufficient to complete defect identification and classification for each frame). …”
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Enhancing sarcasm detection in sentiment analysis for cyberspace safety using advanced deep learning techniques by Raghu Dhumpati, Archana Sasi, Shaik Johny Basha, Suryanarayana Vadhri, P. Saroja, Swathi Alluri, Ramesh Vatambeti

“…Standard NLP-based auxiliary features were extracted, and an embedded CNN model refined these features into feature vectors. …”
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Prediction of Neoadjuvant Chemoradiotherapy Sensitivity in Patients With Esophageal Squamous Cell Carcinoma Using CT-Based Radiomics Combined With Clinical Features by Xindi Li, Jigang Dong, Baosheng Li, Ouyang Aimei, Yahong Sun, Xia Wu, Wenjuan Liu, Ruobing Li, Zhongyuan Li, Yu Yang

“…To assess the performance of this combined model, we utilized calibration curves and receiver operating characteristic (ROC) curves. …”
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Research on an hourly heat load forecasting model for district heating systems based on heterogeneous model fusion by Tieliu Jiang, Wenyue Liu, Jianqing Lin, Xu Jin, Zhongyan Liu

“…Diebold-Mariano test confirms that its predictive performance is significantly superior to the comparative models (p < 0.05). …”
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Refined prognostication of pathological complete response in breast cancer using radiomic features and optimized InceptionV3 with DCE-MRI by Satyabrata Pattanayak, Tripty Singh, Rishabh Kumar

“…These evaluation metrics provided a robust assessment of the model’s performance and ensured the reliability of our findings. …”
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Space Target Recognition of Radar Cross-Section Sequence Based on Transformer by Meng Xie, Weiwei Wu, Chenchen Fu, Sujunjie Sun

“…We then integrate period and size features into a unified feature set and introduce a cross-attention-based multi-feature interaction module to fuse physical and statistical features, learning dependencies between them to enhance target recognition accuracy. …”
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D4Care: A Deep Dynamic Memory-Driven Cross-Modal Feature Representation Network for Clinical Outcome Prediction by Binyue Chen, Guohua Liu

“…Besides, we use gating mechanisms and dynamic memory components to enable the model to learn feature information of different historical-current patterns, further improving the model’s performance. …”
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OCTNet: A Modified Multi-Scale Attention Feature Fusion Network with InceptionV3 for Retinal OCT Image Classification by Irshad Khalil, Asif Mehmood, Hyunchul Kim, Jungsuk Kim

“…Traditional approaches rely on machine learning for feature extraction, while deep learning methods utilize data-driven classification model training. …”
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A Two-Phase Feature Selection Framework for Intrusion Detection System: Balancing Relevance and Computational Efficiency (2P-FSID) by C. Rajathi, Rukmani Panjanathan

“…However, high-dimensional data necessitates the need for effective feature selection. This study proposes the Two-Phase Feature Selection framework for Intrusion Detection (2P-FSID) to enhance model performance and interpretability. …”
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TFF-Net: A Feature Fusion Graph Neural Network-Based Vehicle Type Recognition Approach for Low-Light Conditions by Huizhi Xu, Wenting Tan, Yamei Li, Yue Tian

“…Accurate vehicle type recognition in low-light environments remains a critical challenge for intelligent transportation systems (ITSs). To address the performance degradation caused by insufficient lighting, complex backgrounds, and light interference, this paper proposes a Twin-Stream Feature Fusion Graph Neural Network (TFF-Net) model. …”
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