Deblurring Method of Face Recognition AI Technology Based on Deep Learning by Weilong Li, Jie Li, Junhui Zhou

“…As a common method of deep learning, a convolutional neural network (CNN) shows excellent performance in face recognition. …”
Get full text

Performance Comparison of ResNet50, VGG16, and MobileNetV2 for Brain Tumor Classification on MRI Images by Muhammad Bayu Kurniawan, Ema Utami

“…Overall, this study confirms that VGG16 is the most efficient and effective model for MRI-based brain tumor classification.…”
Get full text

CNN Performance Improvement for Classifying Stunted Facial Images Using Early Stopping Approach by Yunidar Yunidar, Y Yusni, N Nasaruddin, Fitri Arnia

“…The main aim of this research is to identify the CNN model that is most effective in differentiating facial images of stunted children from normal children. …”
Get full text

Protein homodimers structure prediction based on deep neural network by A. Y. Hadarovich, A. A. Kalinouski, A. V. Tuzikov

“…Homodimers (complexes which consist of two identical proteins) are the most common type of protein complexes in nature but there is still no universal algorithm to predict their 3D structures. …”
Get full text

An Investigation on Prediction of Infrastructure Asset Defect with CNN and ViT Algorithms by Nam Lethanh, Tu Anh Trinh, Mir Tahmid Hossain

“…Convolutional Neural Networks (CNNs) have been demonstrated to be one of the most powerful methods for image recognition, being applied in many fields, including civil and structural health monitoring in infrastructure asset management. …”
Get full text

EM-COGLOAD: An investigation into age and cognitive load detection using eye tracking and deep learning by Gabriella Miles, Melvyn Smith, Nancy Zook, Wenhao Zhang

“…Alzheimer’s Disease is the most prevalent neurodegenerative disease, and is a leading cause of disability among the elderly. …”
Get full text

LEST: Large-Scale LiDAR Semantic Segmentation With Deployment-Friendly Transformer Architecture by Chuanyu Luo, Nuo Cheng, Sikun Ma, Han Li, Xiaohan Li, Shengguang Lei, Pu Li

“…Large-scale LiDAR-based point cloud semantic segmentation is a critical challenge for autonomous driving perception. Most state-of-the-art LiDAR semantic segmentation methods rely on complex operators, such as sparse 3D convolutions or KdTree structures, which hinder their deployment on modern embedded devices. …”
Get full text

A Spectral Interpretable Bearing Fault Diagnosis Framework Powered by Large Language Models by Panfeng Bao, Wenjun Yi, Yue Zhu, Yufeng Shen, Haotian Peng

“…A channel attention-augmented convolutional neural network provides an initial fault type prediction. …”
Get full text

Evaluation of Deep Learning Models for Polymetallic Nodule Detection and Segmentation in Seafloor Imagery by Gabriel Loureiro, André Dias, José Almeida, Alfredo Martins, Eduardo Silva

“…The initial results suggest that transformer-based methods perform better in most evaluation metrics, but at the cost of higher computational resources. …”
Get full text

Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data by Juan Pablo Carvajal-Dossman¹, Laura Guio, Danilo García-Orjuela, Jennifer J. Guzmán-Porras, Kelly Garces, Andres Naranjo, Silvia Juliana Maradei-Anaya, Jorge Duitama

“…Abstract Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. …”
Get full text

Exploration of genetic algorithms to build a balanced neutron spectra dataset useful to train unfolding techniques based on artificial neural networks by Bouhadida Maha, Hmede Rodayna, Brovchenko Mariya, Monange Wilfried, Vinchon Thibaut, Ducasse Quentin, Trompier François

“…We propose a comparison of two methods of building large dataset where the most adequate solution is obtained using a dynamic genetic algorithm (GA). …”
Get full text

A Multi-Task Spatiotemporal Graph Neural Network for Transient Stability and State Prediction in Power Systems by Shuaibo Wang, Xinyuan Xiang, Jie Zhang, Zhuohang Liang, Shufang Li, Peilin Zhong, Jie Zeng, Chenguang Wang

“…While AI has shown great potential, most existing AI-based approaches focus on single tasks, such as either stability assessments or state prediction, limiting their practical applicability. …”
Get full text

GNSS–VTEC prediction based on CNN–GRU neural network model during high solar activities by T. Y. Yang, J. Y. Lu, Y. Y. Yang, Y. H. Hao, M. Wang, J. Y. Li, G. C. Wei

“…The performance of the CNN–GRU model is compared with the most used empirical models, IRI and NeQuick, and two artificial intelligence models, GRU and SVM. …”
Get full text

Dynamic graph attention network based on multi-scale frequency domain features for motion imagery decoding in hemiplegic patients by Yinan Wang, Yinan Wang, Lizhou Gong, Yang Zhao, Yewei Yu, Hanxu Liu, Xiao Yang

“…MFF-DANet employs convolutional kernels of various scales to extract feature information across multiple frequency bands, followed by a channel attention-based average pooling operation to retain the most critical frequency domain features. …”
Get full text

Deep Learning-Based Pulmonary Nodule Screening: A Narrative Review by Abhishek Mahajan, Ujjwal Agarwal, Rajat Agrawal, Aditi Venkatesh, Shreya Shukla, K S. S. Bharadwaj, M L. V. Apparao, Vivek Pawar, Vivek Poonia

“…Given its capacity to generate three-dimensional pictures, computed tomography is the most effective means of detecting lung nodules with more excellent resolution of detected nodules. …”
Get full text

Advanced Hydro-Informatic Modeling Through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction by Shahariar Hossain Mahir, Md Tanjum An Tashrif, Md Ahsan Karim, Dipanjali Kundu, Anichur Rahman, Md. Amir Hamza, Fahmid Al Farid, Abu Saleh Musa Miah, Sarina Mansor

“…Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylhet, Bangladesh where transboundary water flows and climate change have increased the risk of disasters. …”
Get full text

Gait Recognition With Wearable Sensors Using Modified Residual Block-Based Lightweight CNN by Md. Al Mehedi Hasan, Fuad Al Abir, Md. Al Siam, Jungpil Shin

“…Deep learning-based networks have recently emerged as a promising technique in gait recognition, yielding better performance than template matching and traditional machine learning methods. However, most recent studies have focused on improving gait detection accuracy while neglecting model complexity in the deep learning domain, making them unsuitable for low-power wearable devices. …”
Get full text

Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis by Jia Xu, Yan Wang, Renyi Xu, Hailin Wang, Xinzhi Zhou

“…The framework is built upon a serial multi-scale convolutional prototype learning (SMCPL) network, enhanced with an efficient channel attention (ECA) mechanism to extract the most critical fault features. …”
Get full text

Systematic Review on Automation of Central Tire Inflation System Based on Terrain Conditions by Carl Luis C. Ledesma, Charlothe John I. Tablizo, Marites B. Tabanao, Emmanuel A. Salcedo, Emmy Grace T. Requillo, John Paul T. Cruz

“…ResNet-18 was used as the most appropriate CNN model to classify the terrain conditions on a gathered local dataset. …”
Get full text

Automatic Classification of Red Blood Cell Morphology Based on Quantitative Phase Imaging by Mengduo Jiang, Meng Shao, Xiao Yang, Linna He, Tao Peng, Tao Wang, Zeyu Ke, Zixin Wang, Shu Fang, Yuxin Mao, Xilin Ouyang, Gang Zhao, Jinhua Zhou

“…Compared with the traditional convolutional neural network, the developed method showed a lower misclassification rate and less processing time, especially for RBCs with more discocytes. …”
Get full text