Single Transit Detection in Kepler with Machine Learning and Onboard Spacecraft Diagnostics by Matthew T. Hansen, Jason A. Dittmann

“…We present a novel technique using an ensemble of convolutional neural networks incorporating the onboard spacecraft diagnostics of Kepler to classify transits within a light curve. …”
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CME Arrival Time Prediction via Fusion of Physical Parameters and Image Features by Yufeng Zhong, Dong Zhao, Xin Huang, Long Xu

“…Coronal mass ejections (CMEs) are among the most intense phenomena in the Sun–Earth system, often resulting in space environment effects and consequential geomagnetic disturbances. …”
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Solar Radio Burst Detection Based on Deformable DETR by Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan, Hao Wu

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Convolution neural network–based Alzheimer's disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonan... by Shaik Basheera, M Satya Sai Ram

“…Existing deep learning systems work on raw magnetic resonance imaging (MRI) images and cortical surface as an input to the convolution neural network (CNN) to perform classification of AD. …”
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Joint classification and regression with deep multi task learning model using conventional based patch extraction for brain disease diagnosis by Padmapriya K., Ezhumalai Periyathambi

“…Magnetic resonance imaging (MRI) is increasingly used in clinical score prediction and computer-aided brain disease (BD) diagnosis due to its outstanding correlation. Most modern collaborative learning methods require manually created feature representations for MR images. …”
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A Two-Stage Method for Diagnosing COVID-19, Leveraging CNN, and Transfer Learning on CT Scan Images by Touba torabipour, Abolfazl Gandomi, Mohammad Ghanimi

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Vision-Based American Sign Language Classification Approach via Deep Learning by Nelly Elsayed

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MSRD-Unet: Multiscale Residual Dilated U-Net for Medical Image Segmentation by Muna Khalaf, Ban N. Dhannoon

“…The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. …”
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A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion by Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu

“…Convolutional neural networks have achieved excellent results in image denoising; however, there are still some problems: (1) The majority of single-branch models cannot fully exploit the image features and often suffer from the loss of information. (2) Most of the deep CNNs have inadequate edge feature extraction and saturated performance problems. …”
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Improving Malaria diagnosis through interpretable customized CNNs architectures by Md. Faysal Ahamed, Md Nahiduzzaman, Golam Mahmud, Fariya Bintay Shafi, Mohamed Arselene Ayari, Amith Khandakar, M. Abdullah-Al-Wadud, S. M. Riazul Islam

“…To address these challenges, we employed several customized convolutional neural networks (CNNs), including Parallel convolutional neural network (PCNN), Soft Attention Parallel Convolutional Neural Networks (SPCNN), and Soft Attention after Functional Block Parallel Convolutional Neural Networks (SFPCNN), to improve the effectiveness of malaria diagnosis. …”
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M<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>Convformer: Multiscale Masked Hybrid Convolution-Transformer Network for Hyperspectral Image Super-Res... by Shuo Wang, Boneng Shi, Ninglian Wang, Yuzhu Zhang, Yan Zhu

“…This work focuses on the single hyperspectral image super-resolution problem and develops a multiscale masked hybrid convolution-transformer framework. The starting point of this work is an attempt to add a random mask to the input signal to reduce the redundancy of the original features, which the model combines with multiscale representation inference to improve its learning and generalization capabilities. …”
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Assessment of using transfer learning with different classifiers in hypodontia diagnosis by Tansel Uyar, Didem Sakaryalı Uyar

“…Abstract Background Hypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imaging is the most common method of diagnosis. However, in recent years, artificial intelligence-based decision support systems have been employed to make highly accurate diagnoses. …”
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An innovative methodology for segmenting vessel like structures using artificial intelligence and image processing by Reynaldo Villarreal, Sindy Chamorro-Solano, Steffen Cantillo, Roberto Pestana-Nobles, Sair Arquez, Yolanda Vega-Sampayo, Leonardo Pacheco-Londoño, Jheifer Paez, Nataly Galan-Freyle, Cristian Ayala, Paola Amar

“…In this study, an algorithm incorporating modules based on Efficient Sub-Pixel Convolutional Neural Network for image super-resolution, U-Net based Neural baseline for image segmentation, and image binarization for masking was developed. …”
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RESEARCH ON DEEP NEURAL NETWORK LEARNING BASED ON IMPROVED BP ALGORITHM by HUANG Pei

“…Deep learning can make the computing model that contains a number of processing layers to learn the data that contains many levels of abstract representation.This kind of learning way in the most advanced speech recognition,visual object recognition,object detection and many other areas,such as biology,genetics and medicine brought significant improvement.Deep learning can find the complex structure of large data,and the convolution neural network as one of the important models of the depth study in the processing of voice,image,video and text,and other aspects of a new breakthrough.It is the use of BP algorithm to guide the machine how to get the error before the layer to adjust the parameters of this layer,so that these parameters are more conducive to the calculation of the model.In view of the shortcomings of traditional BP algorithm,a fast BP algorithm is proposed,which has the disadvantages of slow convergence speed and often falls into local minimum points.The improved convolutional neural network is used to validate the data set MNIST,English character recognition and medical image.The simulation results show the effectiveness of the proposed algorithm.…”
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Assessing Impact of Seasonal Lighting Variation on Visual Positioning of Drones by Che-Cheng Chang, Bo-Yu Liu, Bo-Ren Chen, Po-Ting Wu

“…For visual-based positioning, convolutional neural networks (CNNs) are often used to match geometric features in drone positioning. …”
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AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation by Jie Li, Changchun Yang

“…In contemporary society, individuals are inundated with a vast amount of redundant information, and recommendation systems have undoubtedly opened up new avenues for managing irrelevant data. Graph convolutional networks (GCNs) have demonstrated remarkable performance in the field of recommendation systems by iteratively performing node convolutions to capture information from neighboring nodes, thereby enhancing recommendation efficacy. …”
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Model for non-periodic time series prediction by CAO Jianwen, WEI Xingbao, YANG Yi, LI Caihong, ZHAO Wenqing

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KERATOCONUS DETECTION USING DEEP LEARNING by Younis Abbosh, Shatha Ali, Dia Ali, Iman Jasim

“…The eye is considered as one of the most complicated organs of the human body, with various ailments that can impact it. …”
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Deep learning model for early acute lymphoblastic leukemia detection using microscopic images by Vatsala Anand, Prabhnoor Bachhal, Deepika Koundal, Arvind Dhaka

“…The design of the deep optimized CNN model consisted of five convolutional blocks with thirteen convolutional layers and five max pool layers. …”
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DFANet: A Deep Feature Attention Network for Building Change Detection in Remote Sensing Imagery by Peigeng Lu, Haiyong Ding, Xiang Tian

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