Modelling on Car-Sharing Serial Prediction Based on Machine Learning and Deep Learning by Nihad Brahimi, Huaping Zhang, Lin Dai, Jianzi Zhang

“…To achieve that, various machine learning models, namely vector autoregression (VAR), support vector regression (SVR), eXtreme gradient boosting (XGBoost), k-nearest neighbors (kNN), and deep learning models specifically long short-time memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), CNN-LSTM, and multilayer perceptron (MLP), were performed on different kinds of features. …”
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Hybrid Deep Learning Models for Sentiment Analysis by Cach N. Dang, María N. Moreno-García, Fernando De la Prieta

“…Hybrid deep sentiment analysis learning models that combine long short-term memory (LSTM) networks, convolutional neural networks (CNN), and support vector machines (SVM) are built and tested on eight textual tweets and review datasets of different domains. …”
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Digital processing of images with defects due to double beam attenuation in indirect X-ray fluorescence mapping by Luisa Dutra Silva, Lucas da Costa Souza, Davi Ferreira Oliveira, Marcelino José Anjos, Elicardo Alves de Souza Gonçalves

“…This algorithm showed the characteristics of the convolution matrix K needed to solve the problem. The results show the real possibility of using the equipment for this adapted measurement and a methodology that can be expanded to different situations.…”
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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

“…By comparing the training processes and test results of different models, it was found that the ResNet-34 model performed the best, achieving an accuracy of 92.8% with no signs of overfitting. …”
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Inferring Mechanical Properties of Wire Rods via Transfer Learning Using Pre-Trained Neural Networks by Adriany A. F. Eduardo, Gustavo A. S. Martinez, Ted W. Grant, Lucas B. S. Da Silva, Wei-Liang Qian

“…Firstly, different possible architectures are compared, particularly between multi-output and multi-label convolutional neural networks (CNNs). …”
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Daily soil temperature prediction using hybrid deep learning and SHAP for sustainable soil management by Meysam Alizamir, Kaywan Othman Ahmed, Salim Heddam, Sungwon Kim, Jeong Eun Lee

“…Based on the results of this study, various deep learning models demonstrated optimal performance for soil temperature prediction at different depths across the two stations. At Chamchamal station, the hybrid deep learning model that combines bidirectional gated recurrent unit (BiGRU) and convolutional neural network (CNN), denoted as BiGRU-CNN achieved the best result for the 05 cm depth (RMSE = 1.298°C), while the hybrid model based on gated recurrent unit (GRU) and convolutional neural network (CNN), referred to as GRU–CNN yielded the best performance at 10 cm (RMSE = 1.333°C). …”
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Detection of <i>Aspergillus flavus</i> in Figs by Means of Hyperspectral Images and Deep Learning Algorithms by Cristian Cruz-Carrasco, Josefa Díaz-Álvarez, Francisco Chávez de la O, Abel Sánchez-Venegas, Juan Villegas Cortez

“…The images were taken after inoculation of the microtoxin using 3 different concentrations, related to three different classes and healthy figs (healthy controls). …”
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An Integrated Lightweight Neural Network Design and FPGA-Accelerated Edge Computing for Chili Pepper Variety and Origin Identification via an E-Nose by Ziyu Guo, Yong Yin, Haolin Gu, Guihua Peng, Xueya Wang, Ju Chen, Jia Yan

“…The system uses the AIRSENSE PEN3 e-nose from Germany to collect gas data from thirteen different varieties of chili peppers and two specific varieties of chili peppers originating from seven different regions. …”
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YOLO Network-Based Extraction of Target Geometry From Time-Frequency Representation by Jingyuan Han, Kunyi Guo, Xinqing Sheng

“…Compared with the existing network model, the ability to extract geometric parameters is enhanced by improving the network structure, integrating the attention mechanism, and introducing deformable convolution operators. The attributed scattering center (ASC) models for targets are established to generate the TFRs. …”
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Comparative Study of the Performance of Two Treatment Planning Systems Using Tests from the MPPG 5b Guidelines, TECDOC 1583 and Venselaar's Confidence Limits by Carlos Vega D'Espaux, Federico Lorenzo, Franco La Paz Mastandrea, Nicolás Larragueta

“… This work presents a comprehensive comparative study of the performance of two Treatment Planning Systems (TPS) in radiotherapy: the Eclipse™ TPS by Varian Medical Systems, which utilizes the Analytical Anisotropic Algorithm (AAA), and the MIRS TPS by Nuclemed, which uses the Convolution and Superposition (CS) Algorithm. The evaluation of both systems was conducted following different methods, including point measurements, dose profiles, and measurements on phantoms with heterogeneities. …”
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Quantum Integral Inequalities with Respect to Raina’s Function via Coordinated Generalized Ψ-Convex Functions with Applications by Saima Rashid, Saad Ihsan Butt, Shazia Kanwal, Hijaz Ahmad, Miao-Kun Wang

“…This novel framework is the convolution of quantum calculus, convexity, and special functions. …”
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STMSF: Swin Transformer with Multi-Scale Fusion for Remote Sensing Scene Classification by Yingtao Duan, Chao Song, Yifan Zhang, Puyu Cheng, Shaohui Mei

“…Emerging vision transformers (ViTs) are more powerful in modeling long-range dependences of features than conventional deep convolution neural networks (CNNs). Thus, they outperform CNNs in several computer vision tasks. …”
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Research on YOLOv3 model compression strategy for UAV deployment by Fei Xu, Litao Huang, Xiaoyang Gao, Tingting Yu, Leyi Zhang

“…In this paper, we tend to compress YOLOv3 model in different aspects to achieve load availability at the edge. …”
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Salient object detection with non-local feature enhancement and edge reconstruction by Tao Xu, Jingyao Jiang, Lei Cai, Haojie Chai, Hanjun Ma

“…It aggregates various image details from different branches to better capture and enhance edge information, thereby generating saliency maps with more exact edges. …”
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Multi‐data classification detection in smart grid under false data injection attack based on Inception network by H. Pan, H. Yang, C. N. Na, J. Y. Jin

“…Abstract During operation, the smart grid is subject to different false data injection attacks (FDIA). If the different kinds of FDIAs and typical failures have been detected, the system operator can develop various defenses to protect the smart grid in multiple categories. …”
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Research on graph-based heterogeneous data integration method by HUANG Yuezhen, YANG Fen, TIAN Feng, ZHANG Chengye, LI Yuchan

“…The table names and field names were regarded as different types of entities in the graph. Then, input the constructed graph into the graph neural network, and the vector representation of each node in the graph was obtained through graph convolution. …”
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Comparative evaluation of deep learning and machine learning techniques for sentiment analysis of electronic product review data by Nagelli Archana, Saleena B., Prakash B.

“…The Naïve Bayes, support vector machine, decision tree, convolution neural network, long short term memory, recursive neural networks, and recurrent neural networks were used on the dataset after applying different data preprocessing. …”
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A multimodal deep learning model with differential evolution-based optimized features for classification of power quality disturbances by Md Nurul Islam

“…Detection and classification of the different power quality disturbances has become one of the current priority research topics for last few years. …”
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Research on delay upper bound analysis based on network calculus for LEO satellite network by WANG Huizi, SUN Lei, WANG Jianquan, LIN Shangjing, WANG Zhuoqun, SUN Kewen

“…Next, a traffic model was formulated based on leaky bucket model, considering the worst-case impact of interference from different priorities, and then the service curve for a satellite node was established. …”
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Urban Traffic Flow Forecasting Based on Graph Structure Learning by Guangyu Huo, Yong Zhang, Yimei Lv, Hao Ren, Baocai Yin

“…At the same time, the temporal convolution network captures the temporal correlation between a single time series. …”
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