Research on the Wetland Vegetation Classification Method Based on Cross-Satellite Hyperspectral Images by Min Yang, Jing Qin, Xiaodan Wang, Yanfeng Gu

“…Furthermore, discrepancies in data distribution between training and test sets result in a notable decline in model performance, impeding model sharing across satellite hyperspectral sensors and hindering the interpretation of wetland scenes. …”
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The relationship between dietary vitamin B1 and stroke: a machine learning analysis of NHANES data by Shihan Guo, Shihan Guo, Xu Jiao, Xu Jiao, Mingfei Li, Zhuo Li, Yun Lu

“…Additionally, the Least Absolute Shrinkage and Selection Operator (LASSO) was utilized for feature selection. Eight machine learning methods were employed to construct predictive models and evaluate their performance. …”
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Exploration of the Ignition Delay Time of RP-3 Fuel Using the Artificial Bee Colony Algorithm in a Machine Learning Framework by Wenbo Liu, Zhirui Liu, Hongan Ma

“…Based on 30 independent resampling trials, the CGD-ABC-BP model with a three-hidden-layer structure of [21 17 19] achieved strong performance on internal test data: R² = 0.994 ± 0.001, MAE = 0.04 ± 0.015, MAPE = 1.4 ± 0.05%, and RMSE = 0.07 ± 0.01. …”
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Predicting responsiveness to fixed-dose methylene blue in adult patients with septic shock using interpretable machine learning: a retrospective study by Shasha Xue, Li Li, Zhuolun Liu, Feng Lyu, Fan Wu, Panxiao Shi, Yongmin Zhang, Lina Zhang, Zhaoxin Qian

“…The SVM model trained on the ML dataset demonstrated the best predictive performance, with an area under the curve (AUC) of 0.74 (95% CI 0.62–0.84), 76% accuracy, 36% sensitivity, and 94% specificity. …”
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Wind-Concerned Sea Ice Detection and Concentration Retrieval From GNSS-R Data Using a Modified Convolutional Neural Network by Wei Ban, Linhu Zhang, Xiaohong Zhang, Han Nie, Xiaoli Chen, Xuejing Chen

“…The model is based on convolutional layers for the feature extraction from delay-Doppler maps, along with fully connected layers for fusing the flattened feature map and wind speed parameters. …”
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TOOL WEAR STATE MONITORING BASED ON WAVELET PACKET BP＿ADABOOST ALGORITHM by ZHU Xiang, XIE Feng

“…Aiming at the problems of less tool wear state data,low diagnostic efficiency,high maintenance cost and lack of effective diagnostic methods during CNC machine tool processing,A method of extracting features by wavelet packet analysis and kernel principal component analysis,and using BPAda Boost algorithm to diagnose tool wear state is proposed.The tool vibration signal and the cutting force signal are collected by installing an acceleration sensor on the machined workpiece of the numerical control machine tool and a force gauge on the workbench; Then the wavelet packet decomposition is performed on the signal to pass the signal through the low-pass filter and the high-pass filter of different dimensions,so that the conditional selection can be performed to form the energy value corresponding to the different frequency bands. …”
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Internet of Things Assisted Plant Disease Detection and Crop Management Using Deep Learning for Sustainable Agriculture by Eman A. Al-Shahari, Ghadah Aldehim, Mohammed Aljebreen, Jehad Saad Alqurni, Ahmed S. Salama, Sitelbanat Abdelbagi

“…In addition, the Densely Connected Networks (DenseNet201) model is deployed for feature extraction. In addition, the SHO technique is exploited for optimum hyperparameter tuning of the DenseNet201 model. …”
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An Innovative Stepwise C‐Means Clustering Approach for Classification of Adolescent Idiopathic Scoliosis by Jiale Gong, Zifang Zhang, Yunzhang Cheng, Liang Cheng, Yating Dong, Lin Sha, Qin Fan, Jian Chen, Chaomeng Wu, Wenyuan Sui, Yaqing Zhang, Fuyun Liu, Weiming Hu, Wenqing Wei, Junlin Yang

“…A total of 102 features were extracted per model, and dimensionality reduction yielded 30 final features by the Stacked Autoencoder method. …”
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Forest aboveground carbon storage estimation and uncertainty analysis by coupled multi-source remote sensing data in Liaoning Province by Hancong Fu, Hengqian Zhao, Ge Liu, Yujiao Zhang, Xiadan Huangfu, Jinbao Jiang

“…Owing to the difficulty of one-to-one matching between spaceborne LiDAR spots and satellite image pixels, geographical correlation was performed to extract the average pixel value of multiple pixels covered by the light spot based on an area-weighting method as the model’s input features. …”
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Automated brain tumor recognition using equilibrium optimizer with deep learning approach on MRI images by Mahmoud Ragab, Iyad Katib, Sanaa A. Sharaf, Hassan A. Alterazi, Alanoud Subahi, Sana G. Alattas, Sami Saeed Binyamin, Jaber Alyami

“…Besides, the squeeze-excitation ResNet (SE-ResNet50) model is applied to derive feature vectors, and its parameters are fine-tuned by the design of the EO model. …”
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A hybrid framework: singular value decomposition and kernel ridge regression optimized using mathematical-based fine-tuning for enhancing river water level forecasting by Iman Ahmadianfar, Aitazaz Ahsan Farooque, Mumtaz Ali, Mehdi Jamei, Mozhdeh Jamei, Zaher Mundher Yaseen

“…Statistical criteria and data visualization tools indicates that the L-SKRidge model has superior efficiency in both the Brook (achieving R = 0.970 and RMSE = 0.051) and Dunk (with R = 0.958 and RMSE = 0.039) Rivers, surpassing the performance of other hybrid and standalone frameworks. …”
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Hybrid transformer and convolution iteratively optimized pyramid network for brain large deformation image registration by Xinxin Cui, Yuee Zhou, Caihong Wei, Guodong Suo, Fengqing Jin, Jianlan Yang

“…However, there are two main limitations in existing research: one is that it over-focuses on the fusion of multi-layer deformation sub-fields on the decoding path, while ignoring the impact of feature encoders on network performance; the other is the lack of specialized design for the characteristics of feature maps at different scales. …”
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Enhanced Response of ZnO Nanorod-Based Flexible MEAs for Recording Ischemia-Induced Neural Activity in Acute Brain Slices by José Ignacio Del Río De Vicente, Valeria Marchetti, Ivano Lucarini, Elena Palmieri, Davide Polese, Luca Montaina, Francesco Maita, Jan Kriska, Jana Tureckova, Miroslava Anderova, Luca Maiolo

“…Acute mouse brain slices were tested under two ischemic models: oxygen–glucose deprivation (OGD) and hyperkalemia. …”
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Finger drawing on smartphone screens enables early Parkinson's disease detection through hybrid 1D-CNN and BiGRU deep learning architecture. by Zhaohui Zhu, E Wu, Pengfei Leng, Jiajun Sun, Mingming Ma, Zhigeng Pan

“…<h4>Results</h4>The proposed model demonstrated robust diagnostic performance, achieving a cross-validation accuracy of 87.93% for spiral drawings (89.64% sensitivity, 86.33% specificity). …”
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1D Convolutional Neural Network-Based Hierarchical Classification of Eye Movements Using Noncontact Electrooculography by Hyo Won Son, Tae Mu Lee, Sang Hyuk Kim, Hyun Jae Baek

“…A hierarchical classification model based on a 1D convolutional neural network (CNN) was proposed for signal analysis, and K-fold cross-validation was used to train and validate the model. …”
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Predicting Mortality in Hospitalized COVID-19 Patients in Zambia: An Application of Machine Learning by Clyde Mulenga, Patrick Kaonga, Raymond Hamoonga, Mazyanga Lucy Mazaba, Freeman Chabala, Patrick Musonda

“…The best-performing model was the XGB which had an accuracy of 92.3%, recall of 94.2%, F1-Score of 92.4%, and ROC_AUC of 97.5%. …”
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DEDANet: Mountainous Cropland Extraction From Remote Sensing Imagery With Detail Enhancement and Distance Attenuation by Liang Huang, Zixuan Zhang, You Yu, Bo-Hui Tang

“…Into the encoding stage, introduced is a detail enhancement convolution module that amplifies high-frequency edge information; through a multibranch feature extraction pathway fusing five distinct convolutional types, the model significantly enhances its sensitivity and representational capacity for irregular cropland boundaries. …”
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Barlow Twins deep neural network for advanced 1D drug–target interaction prediction by Maximilian G. Schuh, Davide Boldini, Annkathrin I. Bohne, Stephan A. Sieber

“…We also propose the use of an influence method to investigate how the model reaches its decision based on individual training samples. …”
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Peramalan Penjualan Produk Menggunakan Extreme Gradient Boosting (XGBoost) dan Kerangka Kerja CRISP-DM untuk Pengoptimalan Manajemen Persediaan (Studi Kasus: UB Mart) by Raihan Winurputra, Dian Eka Ratnawati

“…The forecasting process follows the main phases of the CRISP-DM framework: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The Modeling phase involves testing feature selection to train the model that can deliver best performance. …”
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Supervised Learning-Based Fault Classification in Industrial Rotating Equipment Using Multi-Sensor Data by Aziz Kubilay Ovacıklı, Mert Yagcioglu, Sevgi Demircioglu, Tugberk Kocatekin, Sibel Birtane

“…Feature importance analysis has revealed how specific domain characteristics, such as vibration velocity and ultrasound levels, contribute significantly to performance and enabled the detection of multiple faults simultaneously. …”
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