Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics by Thawirasm Jungrungrueang, Sawrawit Chairat, Kasidach Rasitanon, Praopim Limsakul, Krit Charupanit

“…We also employed a convolutional neural network model, enhanced with these dynamic features, to differentiate between dementia subtypes. …”
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Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer by Jalaleddin Mohamed, Necmi Serkan Tezel, Javad Rahebi, Raheleh Ghadami

“…<b>Conclusions:</b> The deep learning-Aquila Optimizer (DL-AO) framework offers a highly efficient and accurate approach for melanoma detection, making it suitable for deployment in resource-constrained environments such as mobile and edge computing platforms. …”
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Enhanced convolutional neural networks for defect detection in fiber-reinforced composites: a hyperparameter optimization approach by Pankaj Beldar, Rasika Chandramore, Sarika Jagtap, Dipali Bendale

“…This study presents an automated defect classification framework using convolutional neural networks (CNNs) designed to identify these five defect types from scanning electron microscopy (SEM) images. …”
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Macro and mesoscopic mechanical behavior of concrete with actual aggregate segmented by hybrid Transformers and convolutional neural networks by Dong Wang, Junxing Zheng, Jichen Zhong, Lin Gao, Shuling Huang, Jiajia Zheng

“…This study presents a deep learning-based numerical framework aimed at investigating both macro- and mesoscopic mechanical behavior of concrete, focusing on the actual aggregate morphology and distribution in 2D cross-sections. …”
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A novel intelligent fault diagnosis method for gearbox based on multi-dimensional attention denoising convolution by Wei Liu, Zeqiao Zhang, Zhiwei Ye, Qiyi He

“…Abstract In the field of intelligent fault diagnosis, particularly concerning rotating machinery, convolutional neural networks (CNNs) face significant challenges when applied to real industrial vibration data. …”
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Improving Fire and Smoke Detection with You Only Look Once 11 and Multi-Scale Convolutional Attention by Yuxuan Li, Lisha Nie, Fangrong Zhou, Yun Liu, Haoyu Fu, Nan Chen, Qinling Dai, Leiguang Wang

“…You Only Look Once (YOLO), as an efficient deep learning object detection framework, can rapidly locate and identify fire and smoke objects in visual images. …”
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Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis by Lin Zhou, Zhengzhi Zhu, Hongbo Gao, Chunyu Wang, Muhammad Attique Khan, Mati Ullah, Siffat Ullah Khan

“…The MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi‐omics adjacency matrix, thereby illuminating potential associations among diverse samples. …”
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A Multilevel Multimodal Hybrid Mamba-Large Strip Convolution Network for Remote Sensing Semantic Segmentation by Lingyu Yan, Qingyang Feng, Jing Wang, Jinshan Cao, Xiaoxiao Feng, Xing Tang

“…Specifically, this paper introduces a new multistage Mamba multimodal fusion framework (FMB) for UHR remote sensing image segmentation. …”
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DEDSWIN-Net: Dual Encoder Dilated Convolution and Swin Transformer Network for the Classification of Liver CT Images by Jyoshna Allenki, Hemant Kumar Soni

“…To resolve this concern, this paper suggests a novel dual encoder deep learning structure named DEDSWIN-Net to mitigate this problem. The proposed framework consists of four components: a dilated convolution-based encoder, a transformer-based encoder, a multi-scale multiple feature fusion decoder (MSMFD), and a DL training model. …”
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SNet: A novel convolutional neural network architecture for advanced endoscopic image classification of gastrointestinal disorders by Samra Siddiqui, Junaid A. Khan, Tallha Akram, Meshal Alharbi, Jaehyuk Cha, Dina A. AlHammadi

“…To enable the exhaustive evaluation of proposed framework across different datasets, the model has undergone training on a very complex HyperKvasir dataset, and later tested on Kvasir v1 and v2 datasets. …”
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Use of a convolutional neural network for direct detection of acid-fast bacilli from clinical specimens by Paul English, Muir J. Morrison, Blaine Mathison, Elizabeth Enrico, Ryan Shean, Brendan O'Fallon, Deven Rupp, Katie Knight, Alexandra Rangel, Jeffrey Gilivary, Amanda Vance, Haleina Hatch, Leo Lin, David P. Ng, Salika M. Shakir

“…We present the development of an artificial intelligence computer vision process using a deep convolutional neural network to detect acid-fast bacilli (AFB) from Kinyoun acid-fast stained slides. …”
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Advancements and outlooks in utilizing Convolutional Neural Networks for plant disease severity assessment: A comprehensive review by Douglas Leite, Alisson Brito, Gregorio Faccioli

“…Once limited to disease detection, Convolutional Neural Networks (CNNs) applications now exhibit automatic severity calculation and classification solutions. …”
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A Dual-Branch Network of Strip Convolution and Swin Transformer for Multimodal Remote Sensing Image Registration by Kunpeng Mu, Wenqing Wang, Han Liu, Lili Liang, Shuang Zhang

“…However, current advanced registration frameworks are unable to accurately register large-scale rigid distortions, such as rotation or scaling, that occur in multi-source remote sensing images. …”
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An intelligent framework for crop health surveillance and disease management. by Yasser M Ayid, Yasser Fouad, Mourad Kaddes, Heba M El-Hoseny

“…Additionally, environmental parameters such as temperature, humidity, and water levels are continuously monitored to aid in informed decision-making. The proposed framework incorporates Convolutional Neural Network (CNN), MobileNet-1, MobileNet-2, Residual Network (ResNet-50), and ResNet-50 with InceptionV3 to ensure precise disease identification and improved agricultural productivity.…”
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A Crowd Counting Framework Combining with Crowd Location by Jin Zhang, Sheng Chen, Sen Tian, Wenan Gong, Guoshan Cai, Ying Wang

“…In this paper, a new framework is proposed to resolve the problem. The proposed framework includes two parts. …”
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Clinical Applicability of Machine Learning Models for Binary and Multi-Class Electrocardiogram Classification by Daniel Nasef, Demarcus Nasef, Kennette James Basco, Alana Singh, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma

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A densely connected framework for cancer subtype classification by Yu Li, Denggao Zheng, Kaijie Sun, Chi Qin, Yuchen Duan, Qingqing Zhou, Yunxia Yin, Hongxing Kan, Jili Hu

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CLSTM-MT (a Combination of 2-Conv CNN and BiLSTM Under the Mean Teacher Collaborative Learning Framework): Encryption Traffic Classification Based on CLSTM (a Combination of 2-Conv... by Xiaozong Qiu, Guohua Yan, Lihua Yin

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Blockchain-Based Smart Monitoring Framework for Defense Industry by Abdullah Alqahtani, Shtwai Alsubai, Abed Alanazi, Munish Bhatia

“…Conspicuously, the current research presents a comprehensive framework based on the IoT-empowered Digital Twin technology for assessing the national integrity of defense personnel. …”
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MTAGCN: Multi-Task Graph-Guided Convolutional Network with Attention Mechanism for Intelligent Fault Diagnosis of Rotating Machinery by Bo Wang, Shuai Zhao

“…To overcome this issue, a graph convolutional network (GCN)-based fault diagnosis framework is introduced, which not only captures structural characteristics but also enhances diagnostic effectiveness. …”
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