Distinguishing Resting State From Motor Imagery Swallowing Using EEG and Deep Learning Models by Sevgi Gokce Aslan, Bulent Yilmaz

“…Additionally, we explored the utilization and potential contributions of deep learning models, particularly Convolutional Neural Networks (CNNs), in EEG-based classification processes. …”
Get full text

Elevator Running Fault Monitoring Method Based on Vibration Signal by Mingxing Jia, Xiongfei Gao, Hongru Li, Hali Pang

“…Because the elevator fault monitoring field has less fault information, it is different from the large sample situation in the field of face recognition. …”
Get full text

State of Charge Estimation in Li-Ion Batteries Using a Parallel LSTM-Based Approach: The Impact of Modeling Based on Operating States by Osman Ozer, Hayri Arabaci

“…Nevertheless, the current-voltage behavior of Li-ion cells varies significantly under different operating conditions, such as charging, discharging, and idle states. …”
Get full text

Using Deep Learning to Predict Sentiments: Case Study in Tourism by C. A. Martín, J. M. Torres, R. M. Aguilar, S. Diaz

“…We develop and compare various classifiers based on convolutional neural networks (CNN) and long short-term memory networks (LSTM). …”
Get full text

Cyberattack Monitoring Architectures for Resilient Operation of Connected and Automated Vehicles by Zulqarnain H. Khattak, Brian L. Smith, Michael D. Fontaine

“…The monitoring system detected three different emulated cyberattacks with high accuracy. …”
Get full text

Damage Detection Method for Road Ancillary Facilities Integrating Attention Mechanism by Shuang Yang, Huiqin Wang, Ke Wang, Nan Guo

“…The model first introduces the D-GhostNet V3Conv module, replacing the standard convolutional layers, significantly enhancing feature extraction capabilities while reducing computational costs. …”
Get full text

Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN by Nivea Kesav, M.G. Jibukumar

“…The field of image processing has experienced remarkable growth in the area of biomedical applications with the invention of different techniques in deep learning. Brain tumor classification and detection is a subject of prime importance where Convolutional Neural Networks (CNN) find application. …”
Get full text

Gait Phase Recognition in Multi-Task Scenarios Based on sEMG Signals by Xin Shi, Xiaheng Zhang, Pengjie Qin, Liangwen Huang, Yaqin Zhu, Zixiang Yang

“…The method proposed in this paper is significantly different compared to other methods (<i>p</i> < 0.001). …”
Get full text

Impact of the STFT Window Size on Classification of Grain-Oriented Electrical Steels from Barkhausen Noise Time–Frequency Spectrograms via Deep CNNs by Michal Maciusowicz, Grzegorz Psuj

“…Depending on the material to be examined, a signal with different characteristics can be observed. Frequently, a signal with multi-phase Barkhausen activity characteristics is obtained, like in the case of grain-oriented electrical steels. …”
Get full text

DECTNet: A detail enhanced CNN-Transformer network for single-image deraining by Liping Wang, Guangwei Gao

“…Recently, Convolutional Neural Networks (CNN) and Transformers have been widely adopted in image restoration tasks. …”
Get full text

An interpretable framework for gastric cancer classification using multi-channel attention mechanisms and transfer learning approach on histopathology images by Muhammad Zubair, Muhammad Owais, Taimur Hassan, Malika Bendechache, Muzammil Hussain, Irfan Hussain, Naoufel Werghi

“…The proposed framework uses three different attention mechanism channels and convolutional neural networks to extract multichannel features during the classification process. …”
Get full text

Advancements in detecting Deepfakes: AI algorithms and future prospects − a review by Laishram Hemanta Singh, Panem Charanarur, Naveen Kumar Chaudhary

“…Innovative AI techniques, such as deep learning, transfer learning, Long Short-Term Memory (LSTM) networks, and Convolutional Neural Networks (CNNs), have been developed to effectively detect and combat Deepfakes. …”
Get full text

Symbol Detection and Channel Estimation for Space Optical Communications Using Neural Network and Autoencoder by Abdelrahman Elfikky, Zouheir Rezki

“…Additionally, with no fading and for both perfect and imperfect CSI with different code rates and fading channels, the proposed AE-based detection outperforms both benchmark learning frameworks and most popular convolutional codes.…”
Get full text

Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy Conditions by Mauro Larrat, Claudomiro Sales

“…This study evaluates different machine learning algorithms in detecting and identifying drones using radar data from a 60 GHz millimeter-wave sensor. …”
Get full text

Study on Quality Assessment Methods for Enhanced Resolution Graph-Based Reconstructed Images in 3D Capacitance Tomography by Robert Banasiak, Mateusz Bujnowicz, Anna Fabijańska

“…However, given the recent advancements in Graph Convolutional Neural Networks (GCNs) for improving ECT image reconstruction, reliable Quality Assessment methods are essential for comparing the performance of different GCN models. …”
Get full text

Optimal Feature Selection and Classification for Parkinson’s Disease Using Deep Learning and Dynamic Bag of Features Optimization by Aarti, Swathi Gowroju, Mst Ismat Ara Begum, A. S. M. Sanwar Hosen

“…The framework’s adaptability to different datasets further highlights its versatility and potential for further medical applications. …”
Get full text

Studying Forgetting in Faster R-CNN for Online Object Detection: Analysis Scenarios, Localization in the Architecture, and Mitigation by Baptiste Wagner, Denis Pellerin, Sylvain Huet

“…In this context, the widely used architecture Faster R-CNN (Region Convolutional Neural Network) faces catastrophic forgetting: the acquisition of new knowledge leads to the loss of previously learned information. …”
Get full text

Replay-Based Incremental Learning Framework for Gesture Recognition Overcoming the Time-Varying Characteristics of sEMG Signals by Xingguo Zhang, Tengfei Li, Maoxun Sun, Lei Zhang, Cheng Zhang, Yue Zhang

“…This study proposes an incremental learning framework based on densely connected convolutional networks (DenseNet) to capture non-synchronous data features and overcome catastrophic forgetting by constructing replay datasets that store data with different time spans and jointly participate in model training. …”
Get full text

Analysis of the Influence of Image Resolution in Traffic Lane Detection Using the CARLA Simulation Environment by Aron Csato, Florin Mariasiu, Gergely Csiki

“…The aim of this study is to show the influence of image resolution in traffic lane detection using a virtual dataset from virtual simulation environment (CARLA) combined with a real dataset (TuSimple), considering four performance parameters: Mean Intersection over Union (mIoU), F1 precision score, Inference time, and processed frames per second (FPS). By using a convolutional neural network (U-Net) specifically designed for image segmentation tasks, the impact of different input image resolutions (512 × 256, 640 × 320, and 1024 × 512) on the efficiency of traffic line detection and on computational efficiency was analyzed and presented. …”
Get full text

How to detect occluded crosswalks in overview images? Comparing three methods in a heavily occluded area by Yuanyuan Zhang, Joseph Luttrell, IV, Chaoyang Zhang

“…Deep learning models based on convolutional neural networks (CNNs) with the VGG16 architecture were trained using 16 815 images to automatically detect crosswalks from both aerial and street view images. …”
Get full text