Highly Robust Synthetic Aperture Radar Target Recognition Method Based on Simulation Data Training by Liping Hu, Canming Yao, Jian Huang, Jinfan Liu, Guanyong Wang

“…Sufficient synthetic aperture radar (SAR) data is the key element in achieving excellent target recognition performance for most deep learning algorithms. It is unrealistic to obtain sufficient SAR data from the actual measurements, so SAR simulation based on electromagnetic scattering modeling has become an effective way to obtain sufficient samples. …”
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Hollows on Mercury: Creation and Analysis of a Global Reference Catalog With Deep Learning by Valentin T. Bickel, Ariel N. Deutsch, David T. Blewett

“…Abstract Hollows are geologically young depressions on Mercury, most likely associated with the loss of volatile species. …”
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Software Defect Prediction Based On Deep Learning Algorithms : A Systematic Literature Review by Akhlas Hasan, Shayma Mohi-Aldeen

“…The top three DL algorithms used in building SDP models and used in predicting software bugs were convolutional neural network (CNN), long-short-term memory (LSTM), and bidirectional LSTM. …”
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Region-Focusing Data Augmentation via Salient Region Activation and Bitplane Recombination for Target Detection by Huan Zhang, Xiaolin Han, Weidong Sun

“…As the performance of a convolutional neural network is logarithmically proportional to the amount of training data, data augmentation has attracted increasing attention in recent years. …”
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Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics by Wei Chen, Boqiang Liu, Suting Peng, Jiawei Sun, Xu Qiao

“…Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. …”
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Impact of Pretrained Deep Neural Networks for Tomato Leaf Disease Prediction by Mohamed Bouni, Badr Hssina, Khadija Douzi, Samira Douzi

“…This article identifies tomato leaf disease using a deep convolutional neural network (CNN) and transfer learning. …”
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Robot Ground Media Classification Based on Hilbert–Huang Transform and Attention-Based Spatiotemporal Coupled Network by Jixiang Niu, Han Li, Zhenxiong Liu, Wei Liu, Hejun Xu

“…Next, the instantaneous frequencies of the two most important channels were extracted using the HHT and added to the original dataset to expand the feature dimension. …”
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LayerFold: A Python library to reduce the depth of neural networks by Giommaria Pilo, Nour Hezbri, André Pereira e Ferreira, Victor Quétu, Enzo Tartaglione

“…We address typical cases, from fully connected to convolutional layers, discussing constraints and prospective challenges. …”
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Artificial Neural Networks for Image Processing in Precision Agriculture: A Systematic Literature Review on Mango, Apple, Lemon, and Coffee Crops by Christian Unigarro, Jorge Hernandez, Hector Florez

“…These specific crops were selected due to their diversity in color and size, providing a representative sample for analyzing the most commonly employed ANN methods in agriculture, especially for fruit ripening, damage, pest detection, and harvest prediction. …”
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Integrating Color and Contour Analysis with Deep Learning for Robust Fire and Smoke Detection by Abror Shavkatovich Buriboev, Akmal Abduvaitov, Heung Seok Jeon

“…Experiments show that the suggested model outperforms both conventional techniques and the most advanced YOLO-based methods, achieving accuracy (0.989) and recall (0.983). …”
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Optimized aspect level sentiment analysis of tweet data using deep learning and rule-based techniques by S. N. Enemuo, O. N. Akande, M. O. Lawrence, I. C. Saidu

“…Towards coming up with a localized sentiment analysis system, this research explored the use of rules and optimized Convolutional Neural Network (CNN) Deep Learning (DL) technique for aspect-level sentiment analysis of users’ tweets. …”
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CSL-SFNet for Cooperative Spectrum Sensing in Cognitive Satellite Network with GEO and LEO Satellites by Kai Yang, Shengbo Hu, Xin Zhang, Tingting Yan, Manqin Zhu

“…Cooperative spectrum sensing (CSS) has become the key technology for solving the above problems in recent years. However, most of the current CSS techniques are model-driven. …”
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Analysis of Internet Marketing Forecast Model Based on Parallel K-Means Algorithm by Xiaolei Chen, Sikun Ge

“…Secondly, the weights in the K-means algorithm are mostly only applicable to target detection tasks. …”
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Human Action Recognition Based on The Skeletal Pairwise Dissimilarity by E.E. Surkov, O.S. Seredin, A.V. Kopylov

“…SE-block allows to detect inter-channel dependencies and selecting the most important features. Additionally, we prepare a data for training, determine an optimal hyperparameters of the neural network model. …”
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Identification of temporal anomalies of spectrograms of vibration measurements of a turbine generator rotor using a recurrent neural network autoencoder by V. P. Kulagin, D. A. Akimov, S. A. Pavelyev, E. O. Guryanova

“…An experiment based on the homostatic method of checking the signal with Hamming windows, in the frequency, time and modulation domains and common initial data, allows one to determine the most promising signal characteristics for identification. …”
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Automated liver segmentation from CT images using modified ResUNet by R.V. Manjunath, Yashaswini Gowda N, H.M. Manu

“…The Segmentation of liver from computed tomography (CT) images is a critical task in medical research particularly when employing deep learning methods. Most doctors prefer to use Computed tomography images for liver disease identification hence automatic liver segmentation plays a decisive role. …”
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Performance Enhancement of EEG Signatures for Person Authentication Using CNN BiLSTM Method by Ashish Ranjan Mishra, Rakesh Kumar, Rajkumar Saini

“… Despite their vulnerability to competent forgers, signatures are one of the most widely used user verification methods. Recent research has revealed that EEG signals are harder to reproduce and give superior biometric information. …”
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Damage Detection and Identification on Elevator Systems Using Deep Learning Algorithms and Multibody Dynamics Models by Josef Koutsoupakis, Dimitrios Giagopoulos, Panagiotis Seventekidis, Georgios Karyofyllas, Amalia Giannakoula

“…For this purpose, multiple signal analysis methods have been developed to help identify anomalies in a system, through quantities such as vibrations or deformations in its critical components. In most applications, however, these data may be scarce or inexistent, hindering the overall process. …”
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Evaluation and prediction of coal seam mining mode: Coefficient of Variation-TOPSIS and CNN-NGO methods by Haixiong Li, Fei Wang

“…Specifically, in the practical application case of the WJZ 15206 working face, the model successfully predicted the high mining height as the most likely mining method, with a prediction index of 2.4265, close to the normalized output value of 2 for large mining height. …”
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Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model by Yanwen Shao, Tsz Kin Wan, Kei Hang Katie Chan

“…Under the metric of explained variance scores (EVS), the prediction performances were the most accurate for Germany (0.864), Italy (0.860) and the United States (0.766). …”
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