PFVnet, a feature enhancement network for low recognition coal and rock images by Cai Han, Zhenwen Liu, Shenglei Zhao, Yubo Li, Yanwei Duan, Xinzhou Yang, Chuanbo Hao

“…Based on this, the convolution operation was combined with the Vision Transformer network and the deep convolution algorithm was applied to design a parallel hybrid vision network model, PFVnet. …”
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Supervised Learning of Depth Estimation From Imperfect Rectified Stereo Laparoscopic Images of Liver Surface by Chongan Zhang, Ziyi Jin, Xiaoyue Liu, Yuelong Liang, Xiao Liang, Peng Wang, Xiujun Cai, Xuesong Ye

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Local fractional Elzaki transform and its application to local fractional differential equations by Mountassir Hamdi Cherif, Djelloul Ziane

“…The objective of our work is to couple the Elzaki transform method and the local fractional derivative which is called local fractional Elzaki transform, where we have provided important results of this transformation as local fractional Laplace-Elzaki duality, Elzaki transform of the local fractional derivative and the local fractional integral and the local fractional convolution, also we have presented the properties of some special functions with the local fractional derivative sense. …”
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Brief Study On A New Family of Analytic Functions by Jamiu Hamzat, Gangadharan Murugusundaramoorthy, Matthew Oluwayemi

“…The authors introduced a new class of analytic functions in this study by means of convolution principle and obtain its relations with some well-known subclasses of analytic univalent functions in geometric functions theory in the open unit disk $\mathbb{D}$. …”
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Analogues of torsion-free and curvature-free connections with a torsion non-tensor and a curvature non-tensor by Polyakova K. V.

“…A cha­racteristic of a curvature which is a convolution of a deformation tensor and a torsion, is considered. …”
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High-Frequency Workpiece Image Recognition Model Based on Hybrid Attention Mechanism by Jiaqi Deng, Chenglong Sun, Xin Liu, Gang Du, Liangzhong Jiang, Xu Yang

“…High-frequency workpieces are specialized items characterized by complex internal textures and minimal variance in properties. …”
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RUL Prediction of Rolling Bearings Based on Fruit Fly Optimization Algorithm Optimized CNN-LSTM Neural Network by Jiaping Shen, Haiting Zhou, Muda Jin, Zhongping Jin, Qiang Wang, Yanchun Mu, Zhiming Hong

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Flexible Polymer-Based Electrodes for Detecting Depression-Related Theta Oscillations in the Medial Prefrontal Cortex by Rui Sun, Shunuo Shang, Qunchen Yuan, Ping Wang, Liujing Zhuang

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Deep learning with data transformation improves cancer risk prediction in oral precancerous conditions by John Adeoye, Yuxiong Su

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Detecting Vietnamese fake news by Duc Vinh Vo, Phuc Do

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Detecting Vietnamese fake news by Duc Vinh Vo, Phuc Do

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A security data detection and management method in digital library network based on deep learning by Diyin Zhu, Yihang Wei, Jiali Cai, Jingwen Wang, Zhongshan Chen

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Volleyball technical action recognition based on CNN-LSTM by Zhigang Zhang, Yong Tian, Jinchong Qi

“…In response to the accuracy and efficiency issues of traditional camera technology in the special environment of volleyball sports, this study first uses millimeter wave radar to collect point cloud spatial data of athletes, and then extracts human spatial features from the data through convolutional neural networks to raise the resolution and accuracy of the data. …”
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YOLO-GML: An object edge enhancement detection model for UAV aerial images in complex environments. by Zhihao Zheng, Jianguang Zhao, Jingjing Fan

“…Finally, we propose a Lightweight layered Shared Convolutional BN(LLSCB) Detection Head based on LSCD, so that the detection heads share the convolutional layer, and the BN is calculated independently, which improves the detection accuracy and reduces the number of parameters. …”
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HUMAN EMOTION RECOGNITION SYSTEM USING DEEP LEARNING ALGORITHMS by Kateryna Yuvchenko, Valentyn Yesilevskyi, Olena Sereda

“…The work examines emotions as a special type of mental processes that express a person’s experience of his attitude to the surrounding world and himself. …”
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Review of Research on Trajectory Prediction of Road Pedestrian Behavior by YANG Zhiyong, GUO Jieru, GUO Zihang, ZHANG Ruixiang, ZHOU Yu

“…Based on these data, this paper systematically reviews both physics-based and data-driven prediction approaches, with a focus on the development of statistical models, deep learning, and reinforcement learning models. Special emphasis is placed on deep learning methods, categorized by network architecture into sequential models, convolutional neural networks, graph convolutional networks,  generative adversarial networks, etc. …”
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Untrained perceptual loss for image denoising of line-like structures in MR images. by Elisabeth Pfaehler, Daniel Pflugfelder, Hanno Scharr

“…In particular, we investigate if the special characteristics of these datasets (connectivity, sparsity) benefit from the use of special loss functions for network training. …”
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Inpainting of damaged temple murals using edge- and line-guided diffusion patch GAN by G. Sumathi, M. Uma Devi

“…The WSFN uses the original image, a line drawing, and an edge map to capture mural details, which are then texturally inpainted in the SCN using gated convolution for enhanced results. Special attention is given to globally extending the receptive field for large-area inpainting. …”
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Research on Formation of Microsatellite Communication with Genetic Algorithm by Guoqiang Wu, Yuguang Bai, Zhaowei Sun

“…The correct capability of (512, 256) LDPC code is better than (2, 1, 7) convolution code, distinctively. The design system can satisfy the communication requirements of microsatellites. …”
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Vehicle Traffic Estimation Using Deep Learning by Meetkumar Patel, Daniel Silver

“…Thus, we design and develop a machine learning approach which can predict vehicular traffic density and flowrate up to two days in the future based on the weather, calendar and special events data. First, Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) networks are utilized to predict the number of new vehicles and the total number of vehicles in images captured by a Nova Scotia Webcams (NS Webcams) video camera. …”
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