An Empirical Comparison of Machine Learning and Deep Learning Models for Automated Fake News Detection by Yexin Tian, Shuo Xu, Yuchen Cao, Zhongyan Wang, Zijing Wei

“…Detecting fake news is a critical challenge in natural language processing (NLP), demanding solutions that balance accuracy, interpretability, and computational efficiency. …”
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Driver drowsiness shield (DDSH): a real-time driver drowsiness detection system by Archita Bhanja, Dibyajyoti Parhi, Dipankar Gajendra, Kreetish Sinha, Arup Kumar Sahoo

“…This paper aims to develop an advanced real-time drowsiness detection system using deep learning algorithms. For this purpose, we utilized an eye image dataset from the MRL Eye Dataset and performed extensive feature engineering and preprocessing to prepare the data for analysis. …”
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RSWD-YOLO: A Walnut Detection Method Based on UAV Remote Sensing Images by Yansong Wang, Xuanxi Yang, Haoyu Wang, Huihua Wang, Zaiqing Chen, Lijun Yun

“…These modifications result in the RSWD-YOLO network (RSWD means remote sensing walnut detection; YOLO means ‘You Only Look Once’, and it is the specific abbreviation used for a series of object detection algorithms), which is specifically designed for walnut detection. …”
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Construction of a risk prediction model for postoperative deep vein thrombosis in colorectal cancer patients based on machine learning algorithms by Xin Liu, Xingming Shu, Yejiang Zhou, Yifan Jiang

“…We employed the Synthetic Minority Oversampling Technique to address imbalanced data and split the dataset into training and validation sets in a 7:3 ratio. Feature selection was performed using Random Forest (RF), XGBoost, and Least Absolute Shrinkage and Selection Operator algorithms (LASSO). …”
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High-Precision Stored-Grain Insect Pest Detection Method Based on PDA-YOLO by Fuyan Sun, Zhizhong Guan, Zongwang Lyu, Shanshan Liu

“…To address these limitations, we proposed PDA-YOLO, an improved stored-grain insect pest detection algorithm based on YOLO11n which integrates three key modules: PoolFormer_C3k2 (PF_C3k2) for efficient local feature extraction, Attention-based Intra-Scale Feature Interaction (AIFI) for enhanced global context awareness, and Dynamic Multi-scale Aware Edge (DMAE) for precise boundary detection of small targets. …”
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Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology by Qinghai He, Zhiyuan Liu, Xiaoli Li, Yong He, Zhi Lin

“…Firstly, a quantitative relationship between HSI data of tea and their pigment contents was developed based on regression analysis, and the results showed that exceptional prediction performance was achieved by the partial least squares regression (PLSR) algorithm combined with the feature band algorithm of competitive adaptive reweighting (CARS), and the R_p² values of detection models of chlorophyll a, chlorophyll b and carotenoids were 0.90465, 0.92068 and 0.62666, respectively. …”
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Improved YOLOv8n Method for the High-Precision Detection of Cotton Diseases and Pests by Jiakuan Huang, Wei Huang

“…These findings provide compelling evidence of the superiority of the proposed algorithm. Compared to other advanced mainstream algorithms, it exhibits higher accuracy and recall, indicating that the improved algorithm performs better in the task of cotton pest and disease detection.…”
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SCL-YOLOv11: A Lightweight Object Detection Network for Low-Illumination Environments by Shulong Zhuo, Hao Bai, Lifeng Jiang, Xiaojian Zhou, Xu Duan, Yiqun Ma, Zihan Zhou

“…Next, Star Blocks from the StarNet framework are employed to optimize the C3k2 module in the Neck stage, thereby improving the localization accuracy of deep features without increasing network complexity. Meanwhile, the Minimum Point Distance based IoU(MPDIoU) loss function is adopted to mitigate gradient explosion risks while enhancing detection precision. …”
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An IoT-Enabled Wearable Device for Fetal Movement Detection Using Accelerometer and Gyroscope Sensors by Atcharawan Rattanasak, Talit Jumphoo, Wongsathon Pathonsuwan, Kasidit Kokkhunthod, Khwanjit Orkweha, Khomdet Phapatanaburi, Pattama Tongdee, Porntip Nimkuntod, Monthippa Uthansakul, Peerapong Uthansakul

“…This study evaluated ten signal extraction methods, six machine learning algorithms, and four feature selection techniques to enhance classification performance. …”
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Enabling Predication of the Deep Learning Algorithms for Low-Dose CT Scan Image Denoising Models: A Systematic Literature Review by Muhammad Zubair, Helmi B. Md Rais, Fasee Ullah, Qasem Al-Tashi, Muhammad Faheem, Arfat Ahmad Khan

“…Eliminating these noises and artifacts while preserving critical features poses a significant challenge. Traditional CT denoising algorithms struggle with edge blurring and high computational costs, often generating artifacts in flat regions as noise levels increase. …”
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AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images by Binyao Wang, Ya’nan Zhou, Weiwei Zhu, Li Feng, Jinke He, Tianjun Wu, Jiancheng Luo, Xin Zhang

“…Extensive experiments on a custom-built dataset validate AAMS-YOLO's effectiveness, demonstrating notable enhancements over the baseline in mAP0.5 (2.6%) and mAP0.5:0.95 (2.2%) and surpassing other state-of-the-art algorithms. The proposed model excels in detecting small and densely overlapping objects through advanced feature fusion and multi-scale processing strategies.…”
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Application of Machine Learning Techniques for Bearing Fault Diagnosis by Sarra Eddai, Nabih Feki, Ahmed Ghorbel, Abdelkhalak El Hami, Mohamed Haddar

“…This investigation provides a comprehensive analysis of the Case Western Reserve University (CWRU) dataset, data preprocessing procedures, feature extraction techniques, and machine learning algorithms utilized for fault detection. …”
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Surface Defect Detection for Small Samples of Particleboard Based on Improved Proximal Policy Optimization by Haifei Xia, Haiyan Zhou, Mingao Zhang, Qingyi Zhang, Chenlong Fan, Yutu Yang, Shuang Xi, Ying Liu

“…The proposed method is based on the proximal policy optimization (PPO) algorithm of the Actor-Critic framework, and defect detection is achieved by performing a series of scaling and translation operations on the mask. …”
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Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps by Chen Ji, Wenyang Xu, Xiangtian Zheng, Yasmeen Ahmed, Saad Ahmed Jamal, Fakhar Imam, Mohammed Saleh Ali Muthanna, Maha Ibrahim, Sajid Ullah, Dmitry E. Kucher

“…This study illustrates the efficacy of attention-based segmentation algorithms for resilient oceanographic applications.…”
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GEB-YOLO: Optimized YOLOv7 Model for Surface Defect Detection on Aluminum Profiles by Zihao Xu, Jinran Hu, Xingyi Xiao, Yujian Xu

“…In this paper, the GEB-YOLO is proposed based on the YOLOv7 algorithm. First, the global attention mechanism (GAM) is introduced, highlighting defect features. …”
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Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground‐Glass Nodules Based on AI‐Extracted Radiomic Features by Guozhen Yang, Yuanheng Huang, Huiguo Chen, Weibin Wu, Yonghui Wu, Kai Zhang, Xiaojun Li, Jiannan Xu, Jian Zhang

“…An independent cohort of 210 patients (118 with preinvasive lesions, 92 with IAC) from the Tianhe Campus served as external validation. Nineteen radiomic features were extracted and filtered using Boruta and LASSO algorithms. …”
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YOLO-SD: A Real-Time Crew Safety Detection and Early Warning Approach by Xinwei Lin, Shengzheng Wang, Zhen Sun, Min Zhang

“…Furthermore, features from different scales are extracted to get the final detection results. …”
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Simultaneous vehicular location and velocity detection towards 6G integrated communication and sensing by Bingpeng ZHOU, Shanshan MA

“…Joint vehicle location and velocity estimation algorithm was proposed for 6G millimeter wave integrated communication and sensing (ICAS), which was challenging due to random channel fading and multipath interference.A novel receiver state sensing (RSS) algorithm was proposed to simultaneously estimate vehicle (receiver) location and velocity, in conjunction with reflection channel estimation.Moreover, a novel multi-carrier Doppler calibration scheme was developed to reduce the disturbance of Doppler effect on RSS performance via frequency shift compensation.The impact of system parameters, e.g., signal bandwidth, the number of transceiver antenna and sub-carriers, on RSS performance was evaluated as well.It was verified by simulations that the proposed RSS algorithm outperformed state-of-the-art baseline methods due to the employed problem-specific system design.In addition, it was shown by simulations that the achieved RSS error was reduced with the increasing of sub-carrier frequency, the number of transceiver antennas and sub-carriers, and it was increased with the increasing distance between base station and vehicle receiver.Particularly, the vehicle location estimation accuracy was increasing with system bandwidth, but it was invariant with central carrier frequency, since only baseband features of received signal were employed for vehicle localization.In contrast, the vehicle velocity detection accuracy was increasing with central carrier frequency, yet it was insensitive to system bandwidth, given the fixed number of sub-carriers.…”
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Enhanced YOLOv8-based pavement crack detection: A high-precision approach. by ZuXuan Zhang, HongLi Zhang, TongJia Zhang

“…Experimental results show that the Precision, Recall, F1-score, mAP50 and FPS of the YOLOv8-DGS algorithm in pavement crack detection are 91.6%, 90%, 90.8%, 92.4% and 85 frames, respectively. …”
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Face Detection Method based on Lightweight Network and Weak Semantic Segmentation Attention Mechanism by Xiaoyan Wu

“…K-means++ algorithm is employed to perform clustering analysis on YOLOv4 model prior frames in this paper, and smaller size prior frames are set to capture small face information to solve the missing detection problem of small face targets in scenes. …”
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