Predicting the Evolution of the Supercontinuum Generation With CNN-LSTM Model by Yi Feng, Ruiyuan Liu, Xinyue Chang, Xiangzhen Huang, Yuan He, Ning Li, Tiantian Zhou, Chujun Zhao

“…We propose a hybrid deep learning model, namely convolutional neural network–long short-term memory (CNN-LSTM) approach to investigate the evolution of the supercontinuum (SC) generation numerically. …”
Get full text

Implementation of Machine Vision Methods for Cattle Detection and Activity Monitoring by Roman Bumbálek, Tomáš Zoubek, Jean de Dieu Marcel Ufitikirezi, Sandra Nicole Umurungi, Radim Stehlík, Zbyněk Havelka, Radim Kuneš, Petr Bartoš

“…For training time, the fastest was 7:47:17, with a difference of 1:02:00 between the fastest and slowest times. …”
Get full text

Intrinsic factors influence a physiological measure in a forest bird community: adults and females have higher H/L ratios than juveniles and males by Finja Strehmann, Markus Vogelbacher, Clara Guckenbiehl, Yvonne R. Schumm, Juan F. Masello, Petra Quillfeldt, Nikolaus Korfhage, Hicham Bellafkir, Markus Mühling, Bernd Freisleben, Nina Farwig, Dana G. Schabo, Sascha Rösner

“…As physiological measure, we used the heterophil to lymphocyte (H/L) ratio of individuals belonging to different species in the forest bird community, which was assessed using a novel deep learning approach based on convolutional neural networks (CNNs) applied to whole blood smear scans. …”
Get full text

OriLoc: Unlimited-FoV and Orientation-Free Cross-View Geolocalization by Boni Hu, Haowei Li, Shuhui Bu, Lin Chen, Pengcheng Han

“…Additionally, we develop an orientation estimation module using convolution-based sliding windows to assess similarity between satellite-view and query embeddings. …”
Get full text

Simulation and Identification of the Habitat of Antarctic Krill Based on Vessel Position Data and Integrated Species Distribution Model: A Case Study of Pumping-Suction Beam Trawl... by Heng Zhang, Yuyan Sun, Hanji Zhu, Delong Xiang, Jianhua Wang, Famou Zhang, Sisi Huang, Yang Li

“…In addition, factors such as sea surface height (SSH), sea surface temperature (SST), sea ice concentration (SIC), and sea surface salinity (SSS) have impacts on the habitat distribution to varying degrees, and each factor exhibits different suitability response characteristics in different seasons and sub-regions. …”
Get full text

Hybrid Wavelet-Attention Model for Detecting Changes in High-Resolution Remote Sensing Images by Lhuqita Fazry, MGS M. Luthfi Ramadhan, Alif Wicaksana Ramadhan, Muhammad Febrian Rachmadi, Aprinaldi Jasa Mantau, Lukito Edi Nugroho, Chi-Hung Chi, Wisnu Jatmiko

“…Change detection is a remote sensing task for detecting a change from two satellite images in the same area, while being taken at different times. Change detection is one of the most difficult remote sensing tasks because the change to be detected (real-change) is mixed with apparent changes (pseudo-change) due to differences in the two images, such as brightness, humidity, seasonal differences, etc. …”
Get full text

EST-STFM: An Efficient Deep-Learning-Based Spatiotemporal Fusion Method for Remote Sensing Images by Qiyuan Zhang, Xiaodan Zhang, Chen Quan, Tong Zhao, Wei Huo, Yuanchen Huang

“…By integrating images with different spatial and temporal characteristics, it is possible to generate remote sensing data with enhanced detail and frequency. …”
Get full text

Detection of microfibres in wastewater sludge with deep learning by Félix Martí-Pérez, Ana Domínguez-Rodríquez, Carlos Monserrat, Cèsar Ferri, María-José Luján-Facundo, Eva Ferrer-Polonio, Amparo Bes-Piá, José-Antonio Mendoza-Roca

“…By leveraging convolutional neural networks (CNNs), we developed a robust system for accurately identifying and localising microfibres. …”
Get full text

Energy-Efficient on-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing by Flor Ortiz, Nicolas Skatchkovsky, Eva Lagunas, Wallace A. Martins, Geoffrey Eappen, Saed Daoud, Osvaldo Simeone, Bipin Rajendran, Symeon Chatzinotas

“…To benchmark the performance of the proposed model, we implement conventional Convolutional Neural Networks (CNN) on a Xilinx Versal VCK5000, and provide a detailed comparison of accuracy, precision, recall, and energy efficiency for different traffic demands. …”
Get full text

Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…First, investigated the effects of different global information extraction methods on the experimental results; second, analyzed the effects of different modules on the network effects; third, explored the impact of different scales on network performance, sequential cascade structure, and rationalization of hierarchical feature fusion; and fourth, verified the robustness of the enhancement modules designed by testing them on different backbone networks. …”
Get full text

Laparoscopic Suture Gestures Recognition via Machine Learning: A Method for Validation of Kinematic Features Selection by Juan M. Herrera-Lopez, Alvaro Galan-Cuenca, Antonio J. Reina, Isabel Garcia-Morales, Victor F. Munoz

“…Research in this task has utilized both image data, mainly using Deep Learning and Convolutional Neural Networks, and kinematic data extracted from the surgeons’ instruments, processing kinematic sequences with Markov models, Recurrent Neural Networks and even unsupervised learning techniques. …”
Get full text

Bitemporal Remote Sensing Change Detection With State-Space Models by Lukun Wang, Qihang Sun, Jiaming Pei, Muhammad Attique Khan, Maryam M. Al Dabel, Yasser D. Al-Otaibi, Ali Kashif Bashir

“…Change detection in very-high-resolution remote sensing images has gained significant attention, particularly with the rise of deep learning techniques such as convolutional neural networks and Transformers. The Mamba structure, successful in computer vision, has been applied to this domain, enhancing computational efficiency. …”
Get full text

FTIR-Based Microplastic Classification: A Comprehensive Study on Normalization and ML Techniques by Octavio Villegas-Camacho, Iván Francisco-Valencia, Roberto Alejo-Eleuterio, Everardo Efrén Granda-Gutiérrez, Sonia Martínez-Gallegos, Daniel Villanueva-Vásquez

“…Furthermore, the impact of different normalization techniques (Min-Max, Max-Abs, Sum of Squares, and Z-Score) on classification accuracy was evaluated. …”
Get full text

Tongue-LiteSAM: A Lightweight Model for Tongue Image Segmentation With Zero-Shot by Daiqing Tan, Hao Zang, Xinyue Zhang, Han Gao, Ji Wang, Zaijian Wang, Xing Zhai, Huixia Li, Yan Tang, Aiqing Han

“…Additionally, data perturbation techniques were employed to enhance the zero-shot segmentation capability of the model and ensure robust performance across different data sources. Results: Experiments conducted on six distinct tongue image datasets demonstrated that the Tongue-LiteSAM model outperformed traditional convolutional neural network-based models and transformers, the original SAM model, and other related improved models in tongue image segmentation tasks. …”
Get full text

Multiscale implicit frequency selective network for single-image dehazing by Zhibo Wang, Jia Jia, Jeongik Min

“…As hazy and clear images considerably differ in high-frequency components, we introduce an implicit frequency selection module to amplify high-frequency components of features and generate candidate feature maps. …”
Get full text

Review of image classification based on deep learning by Fu SU, Qin LV, Renze LUO

“…In recent years,deep learning performed superior in the field of computer vision to traditional machine learning technology.Indeed,image classification issue drew great attention as a prominent research topic.For traditional image classification method,huge volume of image data was of difficulty to process and the requirements for the operation accuracy and speed of image classification could not be met.However,deep learning-based image classification method broke through the bottleneck and became the mainstream method to finish these classification tasks.The research significance and current development status of image classification was introduced in detail.Also,besides the structure,advantages and limitations of the convolutional neural networks,the most important deep learning methods,such as auto-encoders,deep belief networks and deep Boltzmann machines image classification were concretely analyzed.Furthermore,the differences and performance on common datasets of these methods were compared and analyzed.In the end,the shortcomings of deep learning methods in the field of image classification and the possible future research directions were discussed.…”
Get full text

Quality-Aware PPG-Based Blood Pressure Classification for Energy-Efficient Trustworthy BP Monitoring Devices With Reduced False Alarms by Yalagala Sivanjaneyulu, M. Sabarimalai Manikandan, Srinivas Boppu, Linga Reddy Cenkeramaddi

“…In this paper, we present four SQA methods and nine machine learning (ML) based BP classification models, including logistic regression, decision tree, random forest, multilayer perceptron, k-nearest neighbours, XGBoost, AdaBoost, Bagged Tree, and one-dimensional convolutional neural network (1D-CNN). Four SQA methods are based on the average magnitude difference function (AMDF/(SQA-M1)) features and the AMDF features with the total number of zero-crossings present in raw/original (SQA-M2), derivative (SQA-M3), and smoothed derivative (SQA-M4) PPG segment features. …”
Get full text

Leveraging Synthetic Data to Develop a Machine Learning Model for Voiding Flow Rate Prediction From Audio Signals by Marcos Lazaro Alvarez, Alfonso Bahillo, Laura Arjona, Diogo Marcelo Nogueira, Elsa Ferreira Gomes, Alipio M. Jorge

“…This study trains four different machine learning (ML) models (random forest, gradient boosting, support vector machine and convolutional neural network) using both regression and classification approaches to predict and categorize the voiding flow rate from sound events. …”
Get full text

Prediction of vasopressor needs in hypotensive emergency department patients using serial arterial blood pressure data with deep learning by Yeongho Choi, Ki Hong Kim, Yoonjic Kim, Dong Hyun Choi, Yoon Ha Joo, Sae Won Choi, Kyoung Jun Song, Sang Do Shin

“…We developed prediction models using convolutional neural networks (CNNs) and long short‐term memory (LSTM) networks. …”
Get full text

Beef Traceability Between China and Argentina Based on Various Machine Learning Models by Xiaomeng Xiang, Chaomin Zhao, Runhe Zhang, Jing Zeng, Liangzi Wang, Shuran Zhang, Diego Cristos, Bing Liu, Siyan Xu, Xionghai Yi

“…The classification accuracy of the PLS-DA model built on these results was 98.8%, while the prediction accuracy was 94.12% for the convolutional neural network (CNN) and 82.35% for the Random Forest algorithm. …”
Get full text