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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Deep Learning for Cardiovascular Disease Detection by Shivan H. Hussein, Najdavan A. Kako

“… Despite improvements, cardiovascular diseases (CVD) remain the most significant killer globally, accounting for around 17.9 million lives annually. …”
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Learning Dynamic Spatial-Temporal Dependence in Traffic Forecasting by Chaoyu Ren, Yuezhu Li

“…Specifically, we designed a dynamic graph convolution module to model local and global spatial connections in terms of both road distance and adaptive correlation. …”
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CNN-Based Image Segmentation Approach in Brain Tumor Classification: A Review by Nurul Huda, Ku Ruhana Ku-Mahamud

“…This study explores the application of Convolutional Neural Networks (CNNs) for brain tumor segmentation, leveraging their ability to automatically extract hierarchical features from medical images. …”
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Comparison of Doubling the Size of Image Algorithms by S. E. Vaganov, S. I. Khashin

“…According to the results of numerical experiments, the most accurate among the reviewed algorithms is the 17-point interpolation method, slightly worse is Lanczos convolution interpolation with the parameter a=3 (see the table at the end)…”
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Using Nearest-Neighbor Distributions to Quantify Machine Learning of Materials’ Microstructures by Jeffrey M. Rickman, Katayun Barmak, Matthew J. Patrick, Godfred Adomako Mensah

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A Spatiotemporal Data Cube Approach to Classification of Variable Stars: A Catalog of Candidate Variable Stars from the TESS Full-frame Image Raw Data by Harry Qiang, Marina Kounkel, Sally Bass, Ryan Lingg, Logan Sizemore, Dylan Carroll, Brian Hutchinson, Keivan G. Stassun

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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

“…The CNN model involves two convolution layers, max-pooling, ReLU as the activation layer, and a dense layer. …”
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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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A Novel Lightweight U-Shaped Network for Crack Detection at Pixel Level by Zhong Luo, Xinle Li, Yanfeng Zheng

“…Cracks are the most prevalent form of damage on pavement surfaces. …”
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YOLO: A Competitive Analysis of Modern Object Detection Algorithms for Road Defects Detection Using Drone Images by Amit Hasan Sadhin, Siti Zaiton Mohd Hashim, Hussein Samma, Nurulaqilla Khamis

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3L-YOLO: A Lightweight Low-Light Object Detection Algorithm by Zhenqi Han, Zhen Yue, Lizhuang Liu

“…First, we introduce switchable atrous convolution (SAConv) into the C2f module of YOLOv8n, improving the model’s ability to efficiently capture global contextual information. …”
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A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion by Qi Wang, Guanghu Xu, Donglin Jing

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Defending Data Poisoning Attack Through Watermarked Friendly Noise Luminosity Activated Dense Layered UNet for Classification of Lung Disease by M. Shyamala Devi, S. Priya

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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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Application of a modified set of GoogLeNet and ResNet-18 convolutional neural networks towards the identification of environmentally derived-MPLs in the Yadkin-pee dee river basin by Wesley Allen Williams, Anirudh Arunprasad, Shyam Aravamudhan

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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

“…Extensive studies have fully proved the effectiveness of collaborative filtering (CF) recommendation models based on graph convolutional networks (GCNs). As an advanced interaction encoder, however, GCN-based CF models do not differentiate neighboring nodes, which will lead to suboptimal recommendation performance. …”
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