From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation by Muhammad Waqas Ahmed, Muhammad Adnan, Muhammad Ahmed, Davy Janssens, Geert Wets, Afzal Ahmed, Wim Ectors

“…The process involves matching each pixel of the input frame with a georeferenced orthomosaic using a feature-matching algorithm. Subsequently, a tracking-enabled YOLOv8 object detection model is applied to the frame to detect vehicles and their trajectories. …”
Get full text

Monitoring and Analyzing Driver Physiological States Based on Automotive Electronic Identification and Multimodal Biometric Recognition Methods by Shengpei Zhou, Nanfeng Zhang, Qin Duan, Xiaosong Liu, Jinchao Xiao, Li Wang, Jingfeng Yang

Get full text

A Novel Equivariant Self-Supervised Vector Network for Three-Dimensional Point Clouds by Kedi Shen, Jieyu Zhao, Min Xie

Get full text

Enhancing deep convolutional neural network models for orange quality classification using MobileNetV2 and data augmentation techniques by Phan Thi Huong, Lam Thanh Hien, Nguyen Minh Son, Huynh Cao Tuan, Thanh Q. Nguyen

Get full text

Small traffic sign recognition method based on improved YOLOv7 by Bo Meng, Weida Shi

“…Abstract As autonomous and assisted driving technologies progress rapidly, the significance of traffic sign recognition intensifies. Currently, the detection accuracy of algorithms for traffic sign recognition remains suboptimal, particularly when identifying small traffic signs amid complex backgrounds and under inadequate lighting, leading frequently to errors in detection. …”
Get full text

An intelligent IDS using bagging based fuzzy CNN for secured communication in vehicular networks by M. Anand, S. Muthurajkumar

“…Hence, an efficient Feature Selection Algorithm named Weightage and Ranking Based Feature Selection Algorithm and a Bagging based Fuzzy Convolutional Neural Network classification algorithm with Adam optimizer are proposed in this article which are used to identify the attacks more effectively using bagging with fuzzy inference in the deep convolutional neural network classifier. …”
Get full text

A Mountain Summit Recognition Method Based on Improved Faster R-CNN by Yueping Kong, Yun Wang, Song Guo, Jiajing Wang

“…Traditional summit detection methods operate on handcrafted features extracted from digital elevation model (DEM) data and apply parametric detection algorithms to locate mountain summits. …”
Get full text

A Rapid Identification Method for Cottonseed Varieties Based on Near-Infrared Spectral and Generative Adversarial Networks by Qingxu Li, Hao Li, Renhao Liu, Xiaofeng Dong, Hongzhou Zhang, Wanhuai Zhou

“…Feature wavelengths were extracted using Bootstrap Soft Shrinkage (BOSS) and Competitive Adaptive Reweighted Sampling (CARS) algorithms. …”
Get full text

A Study of Deep Learning Models for Audio Classification of Infant Crying in a Baby Monitoring System by Denisa Maria Herlea, Bogdan Iancu, Eugen-Richard Ardelean

“…This paper presents a comprehensive evaluation of deep learning models for infant cry detection, analyzing the performance of various architectures on spectrogram and MFCC feature representations. …”
Get full text

An unsupervised underwater image enhancement method based on generative adversarial networks with edge extraction by Yanfei Jia, Ziyang Wang, Liquan Zhao

“…Obtaining such paired datasets in natural conditions is challenging, leading to performance issues in these algorithms. To address this issue, we propose an unsupervised generative adversarial network with edge detection for enhancing underwater images without needing paired data. …”
Get full text

Bagging Vs. Boosting in Ensemble Machine Learning? An Integrated Application to Fraud Risk Analysis in the Insurance Sector by Ruixing Ming, Osama Mohamad, Nisreen Innab, Mohamed Hanafy

“…Notably, the combination of the Gradient Boosting Machine (GBM) algorithm with NCR re-sampling and GBMVI feature selection emerges as the most effective configuration, offering superior fraud detection capabilities. …”
Get full text

Analyzing mental stress in Indian students through advanced machine learning and wearable technologies by Shruti Gedam, Sandip Dutta, Ritesh Jha

“…Univariate feature analysis found that XGBoost regularly demonstrated good accuracy, showing its dependability for detecting mental stress. …”
Get full text

Development of IIOT-Based Pd-Maas Using RNN-LSTM Model with Jelly Fish Optimization in the Indian Ship Building Industry by PNV Srinivasa Rao, PVY Jayasree

“…The study focuses on the optimization of predictive maintenance as a service on the industrial Internet of Things by machine learning algorithms. The main contribution of the study is the use of optimization techniques for feature selection and RNN-LSTM for improved accuracy.   …”
Get full text

Method for long-term coherent-noncoherent signal accumulation with non-zero higher derivatives range to radar target by S. V. Kozlov, Van Cuong Le

“…Using the theories of ordinal statistics and the method of moments, a method for calculating the probability of correct detection is developed. Estimates of processing losses are made in comparison with coherent and incoherent accumulation algorithms for a signal reflected from a point target, for the case when there is no range and frequency migration. …”
Get full text

The Impact of a Deep Learning Self-Adaptive Colour Restoration Pipeline for Deep Underwater Images in 3D Reconstruction by M. Vlachos, D. Skarlatos, S. Demesticha

“…Evaluation was performed using established feature detection algorithms, such as SIFT and SURF, applied to multiple underwater datasets capturing diverse imaging conditions. …”
Get full text

Research on Methods for the Recognition of Ship Lights and the Autonomous Determination of the Types of Approaching Vessels by Xiangyu Gao, Yuelin Zhao

“…The bidirectional feature pyramid network (BiFPN) is adopted to enhance multi-scale feature fusion. …”
Get full text

Malware prediction technique based on program gene by Da XIAO, Bohan LIU, Baojiang CUI, Xiaochen WANG, Suoxing ZHANG

“…With the development of Internet technology,malicious programs have risen explosively.In the face of executable files without source,the current mainstream malware detection uses feature detection based on similarity,with lack of analysis of malicious sources.To resolve this status,the definition of program gene was raised,a generic method of extracting program gene was designed,and a malicious program prediction method was proposed based on program gene.Utilizing machine learning and deep-learning algorithms,the forecasting system has good prediction ability,with the accuracy rate of 99.3% in the deep-learning model,which validates the role of program gene theory in the field of malicious program analysis.…”
Get full text

Toward automatic and reliable evaluation of human gastric motility using magnetically controlled capsule endoscope and deep learning by Xueshen Li, Yu Gan, David Duan, Xiao Yang

“…Abstract In this paper, we develop a combination of algorithms, including camera motion detector (CMD), deep learning models, class activation mapping (CAM), and periodical feature detector for the purpose of evaluating human gastric motility by detecting the presence of gastric peristalsis and measuring the period of gastric peristalsis. …”
Get full text

LittleFaceNet: A Small-Sized Face Recognition Method Based on RetinaFace and AdaFace by Zhengwei Ren, Xinyu Liu, Jing Xu, Yongsheng Zhang, Ming Fang

“…Currently, although there are many target detection algorithms, they all require a large amount of data for training. …”
Get full text

Clustering and classification of early knee osteoarthritis using machine-learning analysis of step-up and down test kinematics in recreational table tennis players by Ui-jae Hwang, Kyu Sung Chung, Sung-min Ha

“…Unsupervised learning (Louvain clustering) was used to identify distinct movement patterns, whereas supervised learning algorithms were employed to classify EOA status. The feature importance was assessed using feature permutation importance (FPI). …”
Get full text