A lightweight multi-path convolutional neural network architecture using optimal features selection for multiclass classification of brain tumor using magnetic resonance images by Amreen Batool, Yung-Cheol Byun

“…Therefore, a lightweight Multi -path Convolutional Neural Network (M-CNN) is introduced to extract features using varying convolutional filters at each convolutional layer. …”
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Multimodal feature fusion-based graph convolutional networks for Alzheimer's disease stage classification using F-18 florbetaben brain PET images and clinical indicators. by Gyu-Bin Lee, Young-Jin Jeong, Do-Young Kang, Hyun-Jin Yun, Min Yoon

“…Alzheimer's disease (AD), the most prevalent degenerative brain disease associated with dementia, requires early diagnosis to alleviate worsening of symptoms through appropriate management and treatment. …”
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Custom YOLO Object Detection Model for COVID-19 Diagnosis by Noor Najah Ali, Aseel Hameed, Asanka G. Perera, Ali Al_Naji

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PGCF: Perception graph collaborative filtering for recommendation by Caihong Mu, Keyang Zhang, Jiashen Luo, Yi Liu

“…In addition, most GCN-based CF studies pay insufficient attention to the loss function and they simply select the Bayesian personalized ranking (BPR) loss function to train the model. …”
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Single Transit Detection in Kepler with Machine Learning and Onboard Spacecraft Diagnostics by Matthew T. Hansen, Jason A. Dittmann

“…We conclude that KOI-1271.02 has a radius of 5.32 ± 0.20 R _⊕ and most likely a mass of ${28.94}_{-0.47}^{0.23}$ M _⊕ . …”
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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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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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Enhanced Intrusion Detection Using Conditional-Tabular-Generative-Adversarial-Network-Augmented Data and a Convolutional Neural Network: A Robust Approach to Addressing Imbalanced... by Shridhar Allagi, Toralkar Pawan, Wai Yie Leong

“…Models built from training data may fail to prevent or classify intrusions accurately if the dataset is imbalanced. Most researchers employ SMOTE to balance the dataset. …”
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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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A comparative analysis on the use of a cellular automata Markov chain versus a convolutional LSTM model in forecasting urban growth using sentinel 2A images by Reda Yaagoubi, Charaf-Eddine Lakber, Yehia Miky

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A Novel 3D Convolutional Neural Network-Based Deep Learning Model for Spatiotemporal Feature Mapping for Video Analysis: Feasibility Study for Gastrointestinal Endoscopic Video Cla... by Mrinal Kanti Dhar, Mou Deb, Poonguzhali Elangovan, Keerthy Gopalakrishnan, Divyanshi Sood, Avneet Kaur, Charmy Parikh, Swetha Rapolu, Gianeshwaree Alias Rachna Panjwani, Rabiah Aslam Ansari, Naghmeh Asadimanesh, Shiva Sankari Karuppiah, Scott A. Helgeson, Venkata S. Akshintala, Shivaram P. Arunachalam

“…To reduce computational complexity, a (2 + 1)D convolution is used in place of full 3D convolution. …”
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MCANet: An Unsupervised Multi-Constraint Cascaded Attention Network for Accurate and Smooth Brain Medical Image Registration by Min Huang, Haoyu Wang, Guanyu Ren

“…The brain is one of the most important and complex organs of the human body, and it is very challenging to perform accurate and fast registration on it. …”
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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

“…The global positioning system (GPS) is the most common method for drone positioning, but the GPS is not always precise or available. …”
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AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation by Jie Li, Changchun Yang

“…However, most existing models fail to distinguish the importance of different nodes, which limits their performance. …”
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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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FORECASTING STOCK PRICES FOR MARITIME SHIPPING COMPANY IN COVID-19 PERIOD USING MULTIVARIATE MULTI-STEP MULTI-STEP CONVOLUTIONAL NEURAL NETWORK - BIDIRECTIONAL LONG SHORT-TERM MEMO... by Ahmad GHAREEB, Mihai Daniel ROMAN

“…This study is intended to propose a predictive method based on Multivariate Multi-step convolutional neural network - Bidirectional Long Short-Term Memory (Multivariate Multi-step CNN-BiLSTM) networks in order to forecast the prices of three of the most prominent stocks of big organizations operating in maritime transport. …”
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