Research on herd sheep facial recognition based on multi-dimensional feature information fusion technology in complex environment by Fu Zhang, Fu Zhang, Xiaopeng Zhao, Shunqing Wang, Yubo Qiu, Sanling Fu, Yakun Zhang

“…A transfer learning strategy was employed for weight pre-training, and performance was evaluated using FPS, model weight, mean average precision (mAP), and test set accuracy. …”
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U + LSTM-F: A data-driven growth process model of rice seedlings by Xin Tian, Weifan Cao, Shaowen Liu, Buyue Zhang, Junshuo Wei, Zheng Ma, Rui Gao, Zhongbin Su, Shoutian Dong

“…Additionally, an attention mechanism is introduced to enhance model performance, and the model's effectiveness is evaluated using multiple quantitative metrics. …”
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A Comparative Study of Machine Learning Models for Short-Term Load Forecasting by Etna Vianita, Henri Tantyoko

“…The results showed that MLP with lag features achieved the best performance (RMSE: 57.63, MAE: 34.54, MAPE: 0.22), highlighting its ability to model nonlinear and sequential dependencies. …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical Information System (GIS) and Machine Learning (ML) models in Baitarani River Basin, Odisha by Abhijeet Das

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A two-stage model for enhanced mango leaf disease detection using an innovative handcrafted spatial feature extraction method and knowledge distillation process by Mohammad Manzurul Islam, Mst. Nasrat Jahan Niva, Abdullahi Chowdhury, Saleh Masum, Rifat Ara Shams, Taskeed Jabid, Md. Sawkat Ali, Md. Mostofa Kamal Rasel, Muhammad Firoz Mridha

“…In the second stage, we introduce a Knowledge Distillation (KD) process to further enhance model performance by transferring knowledge from a larger teacher model to a smaller student model. …”
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Weaning performance prediction in lactating sows using machine learning, for precision nutrition and intelligent feeding by Jiayi Su, Xiangfeng Kong, Wenliang Wang, Qian Xie, Chengming Wang, Bie Tan, Jing Wang

“…The shapley additive explanations (SHAP) heatmap used for feature importance analysis revealed that, although the key predictors of weaning performance varied across models, this study newly identified lactation duration, birth litter weight, parity, and backfat thickness on the 7th day of lactation (L.d7BF) as consistently important features across different models. …”
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Image data-driven intelligent recognition of permafrost strength and feature visualization based analysis by Zhaoming YAO, Xun WANG, Hang WEI, Xiaolong WANG

“…In addition, the model’s performance under different disturbance conditions was studied by simulating typical interference scenarios and analyzing their impact on the model’s predictive performance, providing a basis for future improvements in data augmentation strategies and model optimization. …”
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INTEGRAL EQUATIONS FOR MODELING CYLINDRICAL MIRROR ANTENNAS by V. I. Nefedov, D. N. Trefilov, A. N. Dementiev, V. V. Vetrova, S. M. Kolesnikov, A. V. Shpak

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Thermodynamic Model of a Gas Turbine Considering Atmospheric Conditions and Position of the IGVs by Tarik Boushaki, Kacem Mansouri

“…Only nominal data are required, and some additional data are needed to calibrate the model on the turbine under study. A key feature of this model is the development of an innovative relationship that allows direct calculation of the mass flow of air entering the turbine and, thus, the performances of the turbine according to atmospheric conditions (such as pressure, temperature, and relative humidity) and the position of the compressor inlet guide vanes (IGV). …”
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Comprehensive framework for thyroid disorder diagnosis: Integrating advanced feature selection, genetic algorithms, and machine learning for enhanced accuracy and other performance... by Ankur Kumar, Sanjay Dhanka, Abhinav Sharma, Anchal Sharma, Surita Maini, Mochammad Fahlevi, Fazla Rabby, Mohammed Aljuaid, Rohit Bansal

“…In this case, the performance metrics used for model evaluation are accuracy, F1 Score, sensitivity, specificity, precision, and Cohen's Kappa with 80% of the dataset to train the model and the rest 20% used to test it. …”
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Mapping Urban Tree Species by Integrating Canopy Height Model with Multi-Temporal Sentinel-2 Data by Yang Yao, Xiaoke Wang, Haiming Qin, Weimin Wang, Weiqi Zhou

“…The species-specific F1 accuracy of a tree varies under different models and feature combinations, which underscores the need for tailored model tuning and an increase in overall model performance. …”
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Multimodal radiomics model with triple -timepoint contrast-enhanced ultrasound for precise diagnosis of C-TIRADS 4 thyroid nodules by Linlin Shao, Lili Zhang, Lifang Liu, Fangfang Sun, Hongyu Li, Tongfeng Liu, Feng Hu, Lirong Zhao

“…When CEUS radiomic features were combined with US features, the diagnostic performance of the CEUS radiomics model was comparable to that of the US+CEUS radiomics model (AUC: 0.813 vs. 0.829, P=0.005). …”
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High-Performance Real-Time Human Activity Recognition Using Machine Learning by Pardhu Thottempudi, Biswaranjan Acharya, Fernando Moreira

“…The system utilizes wearable sensors (accelerometers and gyroscopes) integrated with the kit to enable seamless data acquisition and processing. Our model achieves outstanding performance in classifying dynamic activities, including walking, walking upstairs, and walking downstairs, with high precision and recall, demonstrating its reliability and robustness. …”
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Multiscale Information Fusion Based on Large Model Inspired Bacterial Detection by Zongduo Liu, Yan Huang, Jian Wang, Genji Yuan, Junjie Pang

“…In this study, we present EagleEyeNet, a novel multi-scale information fusion model designed to address these challenges. EagleEyeNet leverages large models as teacher networks in a knowledge distillation framework, significantly improving detection performance. …”
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Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed by Rukai Wang, Ximin Yuan, Fuchang Tian, Minghui Liu, Xiujie Wang, Xiaobin Li, Minrui Wu

“…The optimized loss function further improves the prediction performance, resulting in a significant improvement in the accuracy of flood peak prediction, with a reduction of 0.26% in the relative error of the peak prediction by the GWN model. …”
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Bitemporal Remote Sensing Change Detection With State-Space Models by Lukun Wang, Qihang Sun, Jiaming Pei, Muhammad Attique Khan, Maryam M. Al Dabel, Yasser D. Al-Otaibi, Ali Kashif Bashir

“…To address the lack of fine-grained details in current models, we propose a multibranch patch attention module, which captures both local and global features by partitioning data into smaller patches. …”
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Predicting the diabetic foot in the population of type 2 diabetes mellitus from tongue images and clinical information using multi-modal deep learning by Zhikui Tian, Dongjun Wang, Xuan Sun, Chuan Cui, Hongwu Wang

“…According to the results, the model had better performance to distinguish between T2DM and DF, and by comparing the performance of the model with and without tongue images, it was found that the model with tongue images performed better.…”
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PD-Net: Parkinson’s Disease Detection Through Fusion of Two Spectral Features Using Attention-Based Hybrid Deep Neural Network by Munira Islam, Khadija Akter, Md. Azad Hossain, M. Ali Akber Dewan

“…Additionally, the merging of a multi-head attention mechanism significantly enhances the model’s ability to concentrate on essential details, hence improving its overall performance. …”
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Identification of Rice Varieties Using Machine Learning Algorithms by Murat Koklu, İlkay Çınar

“…With these models, performance measurement values were obtained for feature sets of 12, 16, 90 and 106. …”
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Automated Detection of Gastrointestinal Diseases Using Resnet50*-Based Explainable Deep Feature Engineering Model with Endoscopy Images by Veysel Yusuf Cambay, Prabal Datta Barua, Abdul Hafeez Baig, Sengul Dogan, Mehmet Baygin, Turker Tuncer, U. R. Acharya

“…This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. The novelty of this work is the development of ResNet50*, a new variant of the ResNet model, featuring convolution-based residual blocks and a pooling-based attention mechanism similar to PoolFormer. …”
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