Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS by Mengyuan Zhao, Beibei Cui, Yuehao Yu, Xiaoyi Zhang, Jiaxin Xu, Fengzheng Shi, Liang Zhao

“…Next, based on the YOLO v10 algorithm, this paper removes redundant detection layers to enhance the model’s ability to capture specific features and further reduce the number of parameters. …”
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Pavement pothole detection system based on deep learning and binocular vision by Tian Guan, Jianyuan Cai, Yu Wang, Wei Yang, Xiaobo Chang, Yi Han

“…The experimental results show that the model has better accuracy than the basic model and can effectively detect road potholes. In addition, we replaced the ordinary convolution in the CenterNet feature extraction network with pyramid convolution with multiple receptive fields, and designed a feature fusion module in the same network to fuse low-level and high-level features related to holes, thus establishing a PF-CenterNet that combines pyramid convolution with feature fusion to detect areas containing road potholes. …”
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A Universal Tire Detection Method Based on Improved YOLOv8 by Chi Guo, Mingxia Chen, Junjie Wu, Haipeng Hu, Luobing Huang, Junjie Li

“…To address the above problems, this paper proposes a lightweight YOLOv8n-SOI algorithm for tire defect detection. First, a similarity-based attention mechanism (SimAM) was introduced to the C2f block of the backbone network to improve the ability to extract the shape features of irregular tire defects in complicated backdrops. …”
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Predictive machine learning algorithm for COPD exacerbations using a digital inhaler with integrated sensors by Michael Reich, Njira Lugogo, Laurie D Snyder, Megan L Neely, Guilherme Safioti, Randall Brown, Michael DePietro, Roy Pleasants, Thomas Li, Lena Granovsky

“…Purpose By using data obtained with digital inhalers, machine learning models have the potential to detect early signs of deterioration and predict impending exacerbations of chronic obstructive pulmonary disease (COPD) for individual patients. …”
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Underground personnel detection and tracking using improved YOLOv7 and DeepSORT by Weiqiang FAN, Xuejin WANG, Yinghui ZHANG, Xiaoyu LI

“…Based on this, an improved YOLOv7 and DeepSORT underground personnel detection and tracking algorithm is proposed. First, in order to be able to extract more critical underground personnel image features and improve the model's adaptability in the complex scene of coal mine underground, the SimAM attention mechanism is incorporated into the Neck module of YOLOv7, and the improved YOLOv7 model is used to detect underground personnel targets. …”
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Early Sweet Potato Plant Detection Method Based on YOLOv8s (ESPPD-YOLO): A Model for Early Sweet Potato Plant Detection in a Complex Field Environment by Kang Xu, Wenbin Sun, Dongquan Chen, Yiren Qing, Jiejie Xing, Ranbing Yang

“…Aiming at the problems of low detection accuracy of sweet potato plants and the complex of target detection models in natural environments, an improved algorithm based on YOLOv8s is proposed, which can accurately identify early sweet potato plants. …”
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Prediction of Parkinson Disease Using Long-Term, Short-Term Acoustic Features Based on Machine Learning by Mehdi Rashidi, Serena Arima, Andrea Claudio Stetco, Chiara Coppola, Debora Musarò, Marco Greco, Marina Damato, Filomena My, Angela Lupo, Marta Lorenzo, Antonio Danieli, Giuseppe Maruccio, Alberto Argentiero, Andrea Buccoliero, Marcello Dorian Donzella, Michele Maffia

“…<b>Conclusions:</b> This study highlights the potential of combining advanced acoustic analysis with ML algorithms to develop non-invasive and reliable tools for early PD detection, offering substantial benefits for the healthcare sector.…”
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Classification of Leaf Diseases in Oil Palm Plants with Haar Wavelet Transform Features Based on Machine Learning by Jusman Yessi, Maulana Alfinto, Lubis Julnila Husna

“…This study aims to design a system to classify the types of leaf diseases of oil palm plants using texture feature extraction (Haar Wavelet Algorithm) and machine learning-based classification algorithms (Cubic SVM, Medium Gaussian SVM, Quadratic SVM, Cosine KNN, Fine KNN, and Weighted KNN). …”
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ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning by Raiyan Jahangir, Muhammad Nazrul Islam, Md. Shofiqul Islam, Md. Motaharul Islam

“…Regular monitoring is essential for effective management, as early detection and timely treatment greatly improve survival outcomes. …”
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Improving Vertebral Fracture Detection in C-Spine CT Images Using Bayesian Probability-Based Ensemble Learning by Abhishek Kumar Pandey, Kedarnath Senapati, Ioannis K. Argyros, G. P. Pateel

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Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis by Qianqian Zhao, Yijie Li, Chunliu Zhao, Ran Dong, Jiaxin Tian, Ze Zhang, Lin Huang, Jingwen Huang, Junhai Yan, Zhitao Yang, Jiangnan Ruan, Ping Wang, Li Yu, Jieming Qu, Min Zhou

“…Least absolute shrinkage and selection operator (LASSO) regression with 5-fold cross-validation was used to select the most predictive features. Twelve machine learning algorithms were independently trained. …”
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Exploring Feature Selection with Deep Learning for Kidney Tissue Microarray Classification Using Infrared Spectral Imaging by Zachary Caterer, Jordan Langlois, Connor McKeown, Mikayla Hady, Samuel Stumo, Suman Setty, Michael Walsh, Rahul Gomes

“…In this study, we propose a deep-learning-based framework for automating classification in kidney tumor tissue microarrays (TMAs) using an IR dataset. Feature selection algorithms reduce data dimensionality, followed by a deep learning classification approach. …”
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Facial Beauty Prediction Combining Dual-Branch Feature Fusion With a Stacked Broad Learning System by Junying Gan, Hantian Chen, Wenchao Xu, Huicong Li, Zhenxin Zhuang, Zhen Chen

“…Facial beauty prediction (FBP) is a key computer vision task that uses algorithms to assess facial attractiveness. Current models rely on single feature extraction, such as using a single convolutional neural network to extract local feature, failing to capture other potentially more important information contained within facial data and limiting feature diversity. …”
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Enhancing Cross-Modal Camera Image and LiDAR Data Registration Using Feature-Based Matching by Jennifer Leahy, Shabnam Jabari, Derek Lichti, Abbas Salehitangrizi

“…Various LiDAR feature layers, including intensity, bearing angle, depth, and different weighted combinations, are used to find correspondence with camera images utilizing state-of-the-art deep learning matching algorithms, i.e., SuperGlue and LoFTR. …”
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Function encoding based approach for App clone detection in cloud environment by Jia YANG, Cai FU, Lansheng HAN, Hongwei LU, Jingliang LIU

“…An efficient function-based encoding scheme in the cloud environment for detecting the cloned Apps was designed,called Pentagon.Firstly,a basic block feature extraction method was proposed.Secondly,a monotonic encoding algorithm for the App function was designed,which encoded the function based on the control flow graph structure and basic block attributes.Finally,a three-party libraries filtering method was proposed by using an efficient clustering algorithm based on the function feature.Experiments verified the effectiveness of the proposed scheme.The average search time is close to 79 ms,and the clone detection accuracy achieves 97.6%.…”
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Fault detection and diagnosis method for heterogeneous wireless network based on GAN by Xiaorong ZHU, Peipei ZHANG

“…Aiming at the problem that in the process of network fault detection and diagnosis,how to train the precise fault diagnosis and detection model based on small data volume,a fault diagnosis and detection algorithm based on generative adversarial networks (GAN) for heterogeneous wireless networks was proposed.Firstly,the common network fault sources in heterogeneous wireless network environment was analyzed,and a large number of reliable data sets was obtained based on a small amount of network fault samples through GAN algorithm.Then,the extreme gradient boosting (XGBoost) algorithm was used to select the optimal feature combination of input parameters in the fault detection stage and completed fault diagnosis and detection based on these data.Simulation results show that the algorithm can achieve more accurate and efficient fault detection and diagnosis for heterogeneous wireless networks,with an accuracy of 98.18%.…”
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Fault detection and diagnosis method for heterogeneous wireless network based on GAN by Xiaorong ZHU, Peipei ZHANG

“…Aiming at the problem that in the process of network fault detection and diagnosis,how to train the precise fault diagnosis and detection model based on small data volume,a fault diagnosis and detection algorithm based on generative adversarial networks (GAN) for heterogeneous wireless networks was proposed.Firstly,the common network fault sources in heterogeneous wireless network environment was analyzed,and a large number of reliable data sets was obtained based on a small amount of network fault samples through GAN algorithm.Then,the extreme gradient boosting (XGBoost) algorithm was used to select the optimal feature combination of input parameters in the fault detection stage and completed fault diagnosis and detection based on these data.Simulation results show that the algorithm can achieve more accurate and efficient fault detection and diagnosis for heterogeneous wireless networks,with an accuracy of 98.18%.…”
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Statistically Bounding Detection Latency in Low-Duty-Cycled Sensor Networks by Yanmin Zhu

“…A distinctive feature of this algorithm is that it ensures that the detection delay of any event occurring anywhere in the sensing field is statistically bounded. …”
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Detection of multiple pesticide residues on the surface of broccoli based on hyperspectral imaging by GUI Jiangsheng, GU Min, WU Zixian, BAO Xiao’an

“…To increase efficiency of the model and reduce the redundancy of the hyperspectral image, using the principal component analysis (PCA) algorithm and successive projection algorithm (SPA) for feature extraction. …”
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Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis. by Mwenge Mulenga, Arutchelvan Rajamanikam, Suresh Kumar, Saharuddin Bin Muhammad, Subha Bhassu, Chandramathi Samudid, Aznul Qalid Md Sabri, Manjeevan Seera, Christopher Ifeanyi Eke

“…This innovative approach markedly enhances the Area Under the Curve (AUC) performance of the Deep Neural Network (DNN) algorithm in colorectal cancer (CRC) detection using gut microbiome data, elevating it from 0.800 to 0.923. …”
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