Noise Elimination for Wide Field Electromagnetic Data via Improved Dung Beetle Optimized Gated Recurrent Unit by Zhongyuan Liu, Xian Zhang, Diquan Li, Shupeng Liu, Ke Cao

“…Experiments demonstrate that the optimization capacity of the IDBO algorithm is conspicuously superior to other intelligent optimization algorithms, and the IDBO-GRU algorithm surpasses the probabilistic neural network (PNN) and the GRU algorithm in the denoising accuracy of WFEM data. …”
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Multiscale Feature-Enhanced Water Body Detector of Truncated Gaussian Clutter in SAR Imagery by Bo Zhu, Yuli Xia, Yongsheng Zhou, Xiaoning Lv, Minqin Liu

“…Based on metrics of accuracy, <italic>F</italic>1, and mean of intersection over union, TGCFeWD achieves the best performance (92.4&#x0025;, 82.4&#x0025;, and 80.1&#x0025; for all data with five water body types) compared to several traditional methods, and even outperforms some neural-network-based methods in certain scenarios. The results are validated on the HISEA flooding dataset.…”
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Evaluation of machine learning-based regression techniques for prediction of diabetes levels fluctuations by Badriah Alkalifah, Muhammad Tariq Shaheen, Johrah Alotibi, Tahani Alsubait, Hosam Alhakami

“…To support this an Artificial Neural Network (ANN), Binary Decision Tree (BDT), Linear Regression (LR), Boosting Regression Tree Ensemble (BSTE), Linear Regression with Stochastic Gradient Descent (LRSGD), Stepwise (SW), Support Vector Machine (SVM), and Gaussian process regression (GPR) were investigated. …”
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Breast mass classification based on supervised contrastive learning and multi‐view consistency penalty on mammography by Lilei Sun, Jie Wen, Junqian Wang, Zheng Zhang, Yong Zhao, Guiying Zhang, Yong Xu

“…In this paper, A novel classification algorithm based on Convolutional Neural Network (CNN) is proposed to improve the diagnostic performance for breast cancer on mammography. …”
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The Short-Term Wind Power Forecasting by Utilizing Machine Learning and Hybrid Deep Learning Frameworks by Sunku V.S., Namboodiri V., Mukkamala R.

“…The objective is to develop an innovative deep learning (DL) model that integrates a convolutional neural network (CNN) with a gated recurrent unit (GRU) to enhance forecasting precision for day-ahead applications. …”
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DLBWE-Cys: a deep-learning-based tool for identifying cysteine S-carboxyethylation sites using binary-weight encoding by Zhengtao Luo, Zhengtao Luo, Zhengtao Luo, Qingyong Wang, Qingyong Wang, Qingyong Wang, Yingchun Xia, Yingchun Xia, Yingchun Xia, Xiaolei Zhu, Xiaolei Zhu, Xiaolei Zhu, Shuai Yang, Shuai Yang, Shuai Yang, Zhaochun Xu, Zhaochun Xu, Lichuan Gu, Lichuan Gu, Lichuan Gu

“…In this study, we developed a new deep learning model, DLBWE-Cys, which integrates CNN, BiLSTM, Bahdanau attention mechanisms, and a fully connected neural network (FNN), using Binary-Weight encoding specifically designed for the accurate identification of cysteine S-carboxyethylation sites. …”
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Hierarchical quantitative prediction of photovoltaic power generation depreciation expense based on matrix task prioritization considering uncertainty risk by Yinming Liu, Wengang Wang, Xiangyue Meng, Yuchen Zhang, Zhuyu Chen

“…According to the calculated risk value, a double-layer photovoltaic power generation cost planning model is constructed, the upper and lower objective functions of the model are determined, and the constraint conditions are designed; Obtain a cost planning objective function solution base on a matrix task prioritization method, and generating a prioritization table; Prediction of photovoltaic power generation depreciation expense based on long-short memory neural network for each solution in the sorting table. …”
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Enhancing paddy leaf disease diagnosis -a hybrid CNN model using simulated thermal imaging by Jagamohan Padhi, Kunal Mishra, Ashoka Kumar Ratha, Santi Kumari Behera, Prabira Kumar Sethy, Aziz Nanthaamornphong

“…Eighteen Convolutional Neural Network (CNN) models were evaluated using transfer learning, with statistical analysis via Duncan's multiple range test (DMRT) identifying Darknet53 as the best-performing model, achieving an accuracy of 95.79 %, sensitivity of 95.79 %, specificity of 95.93 %, and an F1 score of 0.96. …”
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Image Quality Assessment Based on Multi-Scale Representation and Shifting Transformer by Geng Fu, Ziyu Wang, Cuijuan Zhang, Zerong Qi, Mingzheng Hu, Shujun Fu, Yunfeng Zhang

“…Recently, transformer-based algorithms have excelled in computer vision, particularly in image classification, surpassing convolutional neural network (CNN) methods. To enhance IQA using transformers, we propose Swin-MIQT, a multi-scale spatial pooling transformer with shifted windows. …”
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An effective vessel segmentation method using SLOA-HGC by Zerui Liu, Junliang Du, Weisi Dai, Wenke Zhu, Ziqing Ye, Lin Li, Zewei Liu, Linan Hu, Lin Chen, Lixiang Sun

“…Our retinal blood vessel segmentation algorithm enhances microfine vessel extraction, improves edge texture clarity, and normalizes vessel distribution. It stabilizes neural network training for complex retinal vascular features. …”
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F10.7 Daily Forecast Using LSTM Combined With VMD Method by Yuhang Hao, Jianyong Lu, Guangshuai Peng, Ming Wang, Jingyuan Li, Guanchun Wei

“…The F10.7 sequence is decomposed into several intrinsic mode functions (IMF) by VMD, then the LSTM neural network is utilized to forecast each IMF. All IMF prediction results are aggregated to obtain the final F10.7 value. …”
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Prediction of the RFID Identification Rate Based on the Neighborhood Rough Set and Random Forest for Robot Application Scenarios by Hong-Gang Wang, Shan-Shan Wang, Ruo-Yu Pan, Sheng-Li Pang, Xiao-Song Liu, Zhi-Yong Luo, Sheng-Pei Zhou

“…Compared with BP neural network (BPNN) and other prediction models, NRS-RF has shorter prediction time and faster calculation speed. …”
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Modeling Canopy Height of Forest–Savanna Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data by Arifou Kombate, Guy Armel Fotso Kamga, Kalifa Goïta

“…We tested four methods: Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN). The RF algorithm obtained the best predictions using 98% relative height (RH98). …”
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Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data by Haytam Elyoussfi, Abdelghani Boudhar, Salwa Belaqziz, Mostafa Bousbaa, Karima Nifa, Bouchra Bargam, Abdelghani Chehbouni

“…The models evaluated include two ML approaches: Support Vector Regression (SVR) and eXtreme Gradient Boosting (XGBoost) and four DL models: 1-Dimensional Convolutional Neural Network (1D-CNN), Long Short-Term Memory Networks (LSTM), Gated Recurrent Unit (GRU), and Bi-directional Long Short-Term Memory Network (Bi-LSTM). …”
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Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Op... by Wei Xie, Jie-sheng Wang, Cheng Xing, Sha-Sha Guo, Meng-wei Guo, Ling-feng Zhu

“…Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. …”
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Analysis of tensile properties in tempered martensite steels with different cementite particle size distributions by Kengo Sawai, Keiya Sugiura, Toshio Ogawa, Ta-Te Chen, Fei Sun, Yoshitaka Adachi

“…We succeeded in developing image-based regression models with high accuracy using a convolutional neural network (CNN). Moreover, gradient-weighted class activation mapping (Grad-CAM) suggested that fine cementite particles and coarse and spheroidal cementite particles are the dominant factors for tensile strength and total elongation, respectively.…”
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Learning to Boost the Performance of Stable Nonlinear Systems by Luca Furieri, Clara Lucia Galimberti, Giancarlo Ferrari-Trecate

“…Our methods enable learning over specific classes of deep neural network performance-boosting controllers for stable nonlinear systems; crucially, we guarantee <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_{p}$</tex-math></inline-formula> closed-loop stability even if optimization is halted prematurely. …”
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QoE-Driven Big Data Management in Pervasive Edge Computing Environment by Qianyu Meng, Kun Wang, Xiaoming He, Minyi Guo

“…Then, with respect to accuracy, we propose a Tensor-Fast Convolutional Neural Network (TF-CNN) algorithm based on deep learning, which is suitable for high-dimensional big data analysis in the pervasive edge computing environment. …”
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ENHANCING TOMATO LEAF DISEASE DETECTION THROUGH MULTIMODAL FEATURE FUSION by Puja SARAF, Jayantrao PATIL, Rajnikant WAGH

“…We have performed a comparison of different classifiers like Support Vector Machine (SVM), XGBoost, Random Forest (RF), Naive Bayes (NB), Convolutional Neural Network (CNN) and proposed Ensemble method used in the classification task. …”
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Deep Learning-Based Post-Stroke Myoelectric Gesture Recognition: From Feature Construction to Network Design by Tianzhe Bao, Zhiyuan Lu, Ping Zhou

“…In this paper, we delve into the potential of deep learning (DL) for post-stroke hand gesture recognition by collecting the sEMG signals of eight chronic stroke subjects, focusing on three primary aspects: feature domains of sEMG (time, frequency, and wavelet), data structures (one or two-dimensional images), and neural network architectures (CNN, CNN-LSTM, and CNN-LSTM-Attention). …”
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