Recent technological developments allow for passive acoustic monitoring of Orthoptera (grasshoppers and crickets) in research and conservation across a broad range of temporal and... by David Bennett, Henning Nissen, Marc Andre Maschke, Heinrich Reck, Tim Diekötter

Get full text

Unveiling the secrets of neural network scaling for ECG classification by Byeong Tak Lee, Joon-myoung Kwon, Yong-Yeon Jo

“…Finally, we explore why scaling hyperparameters affects ECG and computer vision differently. Our findings suggest that the inherent periodicity of the ECG signals plays a crucial role in this difference.…”
Get full text

Research on Unsupervised Domain Adaptive Bearing Fault Diagnosis Method Based on Migration Learning Using MSACNN-IJMMD-DANN by Xiaoxu Li, Jiahao Wang, Jianqiang Wang, Jixuan Wang, Qinghua Li, Xuelian Yu, Jiaming Chen

“…To address the problems of feature extraction, cost of obtaining labeled samples, and large differences in domain distribution in bearing fault diagnosis on variable operating conditions, an unsupervised domain-adaptive bearing fault diagnosis method based on migration learning using MSACNN-IJMMD-DANN (multi-scale and attention-based convolutional neural network, MSACNN, improved joint maximum mean discrepancy, IJMMD, domain adversarial neural network, DANN) is proposed. …”
Get full text

Artificial Intelligence based Multi-sensor COVID-19 Screening Framework by Rakesh Chandra-Joshi, Malay Kishore-Dutta, Carlos M. Travieso

“…The deep learning model will extract the features from the input images and based on that test images will be classified into different categories. Similarly, cough sound and short talk can be trained on a convolutional neural network and after proper training, input voice samples can be differentiated into different categories. …”
Get full text

Analysis of Deep Learning Techniques for Vehicle Detection and Reidentification Using Data from Multiple Drones and Public Datasets by FELIPE P.A. EUPHRÁSIO, RAFAEL M. DE ANDRADE, ELCIO H. SHIGUEMORI, LIANGRID L. SILVA, MOISÉS JOSÉ S. FREITAS, NATHAN AUGUSTO Z. XAVIER, ARGEMIRO S.S. SOBRINHO

“…Abstract The detection and re-identification of vehicles in dynamic environments, such as highways monitored by a swarm of drones, presents significant challenges, particularly due to the variability of images captured from different angles and under various conditions. This scenario necessitates the development of suitable methods that integrate appropriate computational techniques, such as convolutional neural networks (CNN) to address the diversity of drone captures and improve accuracy in detection and re-identification. …”
Get full text

Comparison of classical, xgboost and neural network methods for parameter estimation in epidemic processes on random graphs by Ágnes Backhausz, Edit Bognár, Villő Csiszár, Damján Tárkányi, András Zempléni

“…The main goal of this paper is to quantitatively compare the performance of classical methods to XGBoost and convolutional neural networks in a parameter estimation problem for SIR epidemic spread. …”
Get full text

Identification of Eye Diseases Through Deep Learning by Elena Acevedo, Dinora Orantes, Marco Acevedo, Ricardo Carreño

“…The Canny filter was also applied to obtain the edges that allow the difference between the analyzed diseases. Once the images were pre-processed, a convolutional neural network of our own design was applied to perform the classification task. …”
Get full text

Research on Identification Method of Transformer Windings’ Loose Vibration Spectrum Considering a Multi-Load Current Condition by Jin Fang, Xudong Deng, Yuancan Xia, Chen Wu, Yuehua Li, Xin Li, Kaixin Chen, Fan Wang, Zhanlong Zhang

Get full text

Dynamic Gesture Recognition and Interaction of Monocular Camera Based on Deep Learning by SUNBo wen, YU Feng

“…In the front and back movement of gestures, the area of different gestures is compensated for and adjusted by the size of gesture images, so as to reduce the interference caused by the area changes caused by different gestures.…”
Get full text

Automated Seizure Detection through EEG Analysis and Deep Learning Technique by Srinivas Nowduri, M. Madhusudhana Subramanyam

“…However, one of the challenges of automatic seizure detection using EEG analysis is extracting optimal features that can distinguish between different states of epilepsy. To address this issue, this research proposes a new approach for automatically identifying epileptic seizures using a deep convolutional network. …”
Get full text

IsVoNet8: A Proposed Deep Learning Model for Classification of Some Fish Species by Özge Zencir Tanır, İsmail Akgül, Volkan Kaya

“…In this study, a new convolutional neural network model classifying 8 different belonging to 6 families (Mullidae, Sparidae, Carangidae, Serranidae, Clupeidae, Salmonidae) fish species using deep learning methods was proposed. …”
Get full text

A Lightweight GCT-EEGNet for EEG-Based Individual Recognition Under Diverse Brain Conditions by Laila Alshehri, Muhammad Hussain

“…However, existing EEG-based biometric systems employ deep neural networks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which face challenges such as high parameter complexity, limiting their practical application. …”
Get full text

Cascaded Feature Fusion Grasping Network for Real-Time Robotic Systems by Hao Li, Lixin Zheng

“…The network employs innovative structural designs, including depth-wise separable convolutions to reduce parameters and enhance computational efficiency; convolutional block attention modules to augment the model’s ability to focus on key features; multi-scale dilated convolution to expand the receptive field and capture multi-scale information; and bidirectional feature pyramid modules to achieve effective fusion and information flow of features at different levels. …”
Get full text

Action Recognition with 3D Residual Attention and Cross Entropy by Yuhao Ouyang, Xiangqian Li

“…Additionally, the integration of Fast Fourier Convolution (FFC) enhances the network’s capability to effectively capture temporal and spatial features. …”
Get full text

A Multi-Scale Feature Extraction Algorithm for Chinese Herbal Medicine Image Classification by Wenbin Dai, Yuxin Ma, Yan Fan, Jun Ma

“…FACNBlock utilizes a multi-scale representation module, using convolutions and atrous convolutions of varying sizes to generate and fuse multi-scale feature maps. …”
Get full text

Analysis and training of a traffic sign recognition neural network model by A. U. Mentsiev, T. G. Aigumov, E. M. Abdulmukminova

“…The purpose of the research is to develop and train a neural network model based on convolutional neural networks for effective recognition of road signs in images.Method. …”
Get full text

Underwater Acoustic Signal LOFAR Spectrogram Denoising Based on Enhanced Simulation by Tianxiang He, Sheng Feng, Jie Yang, Kun Yu, Junlin Zhou, Duanbing Chen

“…Furthermore, the experiments demonstrate that the proposed convolutional denoising model has transferability and generalization, making it suitable for denoising underwater acoustic signal in different marine areas.…”
Get full text

HyperKAN: Kolmogorov–Arnold Networks Make Hyperspectral Image Classifiers Smarter by Nikita Firsov, Evgeny Myasnikov, Valeriy Lobanov, Roman Khabibullin, Nikolay Kazanskiy, Svetlana Khonina, Muhammad A. Butt, Artem Nikonorov

“…Specifically, six cutting-edge neural networks were modified, including 1D (1DCNN), 2D (2DCNN), and 3D convolutional networks (two different 3DCNNs, NM3DCNN), as well as transformer (SSFTT). …”
Get full text

Wavelet-Driven Multi-Band Feature Fusion for RGB-T Salient Object Detection by Jianxun Zhao, Xin Wen, Yu He, Xiaowei Yang, Kechen Song

Get full text

DroneSilient (drone + resilient): an anti-drone system by Meghna Manoj Nair, Harini Sriraman, Gadiparthy Harika Sai, V. Pattabiraman

“…In this study, we present the DroneSilient System, a novel anti-drone system that combines different parts. The DroneSilient system includes components that connect to RF identification technology and image-capture technology. …”
Get full text