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International Conference on Graphic and Image Processing (ICGIP)

作者： Lin, Hui Long, JianFeng Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9780819489326

With the development of information technology, video surveillance is widely used in security monitoring and identity recognition. For most of pure face tracking algorithms are hard to specify the initial location and scale of face automatically, this paper proposes a fast and robust method to detect and track face by combining adaboost with camshift algorithm. At first, the location and scale of face is specified by adaboost algorithm based on Haar-like features and it will be conveyed to the initial search window automatically. Then, we apply camshift algorithm to track face. The experimental results based on OpenCV software yield good results, even in some special circumstances, such as light changing and face rapid movement. Besides, by drawing out the tracking trajectory of face movement, some abnormal behavior events can be analyzed.

关键词： Video surveillance adaboost algorithm camshift algorithm OpenCV face detection and tracking

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A Novel adaboost Based algorithm for Processing Defect Big Data 6

A Novel Adaboost Based Algorithm for Processing Defect Big D...

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2017 IEEE 6th Data Driven Control and Learning Systems Conference (DDCLS’17)

作者： Yinlei Wen Huaguang Zhang Jinhai Liu Fangming Li College of Information Science and Engineering Northeastern University

ISBN: (纸本)9781509054626

In the practice applications of defect detecting, large amounts of data need to be analyzed. In this paper, a new analysis method is developed based on adaboost algorithm. By using neural networks with a fixed structure, a series of models are built which may be not accurate. Error rates of the models are computed to gain and adjust the weights of every model. A higher accurate model is built by the models and weights. Compared with traditional neural network method, this adaboost based method does not need to adjust the node numbers of neural networks. In addition, it remains accuracy and reduces ***, an example is given to demonstrate the effectiveness and advantages of the methods.

关键词： Big data adaboost algorithm Neural network Defect detection

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Research on the Strength Prediction Method of Coal and Rock Mass Based on the Signal While Drilling in a Coal Mine

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4427-4427页

作者： Yang, Zheng Liu, Hongtao Ding, Ziwei China Univ Min & Technol Beijing Sch Energy & Min Engn Beijing 100083 Peoples R China Shaanxi Xiaobaodang Min Co Ltd Yulin 719000 Peoples R China Xian Univ Sci & Technol Coll Energy Engn Xian 710054 Peoples R China

To study the response relationship between drilling signal and rock mass geomechanical parameters, accurately and quickly perceive and predict the strength of coal and rock mass, guide the optimization of drilling control parameters and the design of the support scheme, and improve the efficiency of roadway excavation, the prediction of rock uniaxial compressive strength based on drilling signal was carried out. Based on the 112,206 return air chute in the Xiaobaodang No.1 Coal Mine as the engineering background, through the drilling data obtained from the roof anchor cable support, data processing, and feature selection, this paper establishes a coal and rock mass strength prediction model based on the adaboost integrated algorithm, optimizes the hyperparameter of the model, and analyzes and evaluates the prediction results. The results show that in the adaboost integration model, the R2 of SVM is the highest, 0.972, and the values of RMSE, MAE, MAPE, and other error indicators are the lowest. The prediction accuracies of the SVM model, tree model, and linear model are 98.8%, 85.4%, and 75.6%, respectively. The experimental results show that the adaboost integrated algorithm using a based learning machine has higher prediction accuracy. At the same time, compared with the current advanced model, it further verifies the effectiveness of the model in the coal mine.

关键词： tunnel excavation while drilling signal signal acquisition signal denoising adaboost algorithm uniaxial compressive strength

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Enhancing autonomous vehicle performance with ensemble weighted support vector-based optimization in cloud

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第3期28卷 1-18页

作者： Sethuraman, Ravikumar Venkataramani, Kiruthika Devi, Malu Manickam Yamuna Subbaraj, Surendran JAIN Deemed To Be Univ Fac Engn & Technol Dept Comp Sci & Engn Jain Global Campus Bengaluru 562112 Karnataka India Vellore Inst Technol Sch Elect Engn Chennai Campus Chennai Tamil Nadu India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vijayawada Andhra Pradesh India Tagore Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India

In autonomous vehicles, cloud computing plays a major task in the development and operation. Meanwhile, conventional cloud computing methods face several challenges such as data storage, scalability, and cost efficiency in autonomous driving systems. To overcome these complexities, this study proposes the Modified Ensemble Weighted Support vector-based Adaptive Crossover Black Widow algorithm for cloud computing in autonomous vehicles. This study employs a Support Vector Machine for classification tasks and a Random Forest for preprocessing and feature selection. Also, this study utilizes an ensemble approach to enhance the classification performance and adaboost algorithm to enable real-time object detection. This real-time detection capability is important for ensuring the safety and efficiency of autonomous vehicles in various environments. Additionally, this study employs Gradient boosting to enhance the reliability and Adaptive crossover-based Black Widow Optimization to increase the efficiency of the proposed method. Validation of this method is conducted using the datasets namely the Karlsruhe Institute of Technology and Toyota Technological Institute dataset and Common Objects in Context 2017 dataset. The results demonstrate the proposed method achieved high efficiency with specificity, accuracy, precision, recall, and F1-score of 97.82%, 98.63%, 97.93%, 97.84%, and 97.85% respectively. Also, this study ensures more effective cloud computing solutions in autonomous vehicle systems.

关键词： Cloud computing Support vector machine Random forest adaboost algorithm Gradient boosting Adaptive crossover based Black Widow Optimization

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Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems

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APPLIED SCIENCES-BASEL 2025年第2期15卷 887-887页

作者： Pecolt, Sebastian Blazejewski, Andrzej Krolikowski, Tomasz Maciejewski, Igor Gierula, Kacper Glowinski, Sebastian Koszalin Univ Technol Fac Mech Engn & Power Engn Sniadeckich 2 PL-75453 Koszalin Poland Slupsk Pomeranian Univ Inst Hlth Sci Westerplatte 64 PL-76200 Slupsk Poland

Featured Application The Raspberry Pi-based facial recognition system offers practical applications in access control for homes, businesses, and industrial sites, providing an affordable and reliable security solution. Its portability makes it ideal for surveillance in resource-limited settings. Additionally, it can enhance consumer electronics by enabling personalized smart home experiences. With further development, the system could also support demographic analysis in public spaces, contributing to informed decision *** Facial recognition technology has significantly advanced in recent years, with promising applications in fields ranging from security to consumer electronics. Its importance extends beyond convenience, offering enhanced security measures for sensitive areas and seamless user experiences in everyday devices. This study focuses on the development and validation of a facial recognition system utilizing a Haar cascade classifier and the adaboost machine learning algorithm. The system leverages characteristic facial features-distinct, measurable attributes used to identify and differentiate faces within images. A biometric facial recognition system was implemented on a Raspberry Pi microcomputer, capable of detecting and identifying faces using a self-contained reference image database. Verification involved selecting the similarity threshold, a critical factor influencing the balance between accuracy, security, and user experience in biometric systems. Testing under various environmental conditions, facial expressions, and user demographics confirmed the system's accuracy and efficiency, achieving an average recognition time of 10.5 s under different lighting conditions, such as daylight, artificial light, and low-light scenarios. It is shown that the system's accuracy and scalability can be enhanced through testing with larger databases, hardware upgrades like higher-resolution cameras, and advanced deep learning algorithms to address cha

关键词： adaboost algorithm biometric features facial recognition Haar classifier machine learning security system

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Classifying zircon: A machine-learning approach using zircon geochemistry

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GONDWANA RESEARCH 2025年 137卷 227-233页

作者： Kong, Jintao Yu, Hongru Sun, Junyi Zhang, Huan Zhang, Miaomiao Xia, Zhi PetroChina Coalbed Methane Co Ltd Linfen Branch Linfen 042300 Peoples R China Jilin Univ Coll Earth Sci Changchun 130000 Peoples R China Accenture Melbourne 3000 Australia South China Normal Univ Sch Geog Guangzhou 510630 Peoples R China

This study presented a novel, rapid, and accurate method for determining zircon origin via a comprehensive analysis of a dataset containing 27,818 zircon trace element sets. This method integrated back propagation neural networks with the adaboost algorithm. The optimal classifier characterized as a linear combination of a two-layer neural network model, comprised 100 base classifiers and 400 hidden neurons. It was rigorously trained over 1000 iterations, which resulted in an unbiased error rate of 8.31%. To facilitate practical application, the classifier was integrated into a macro-enabled Excel spreadsheet. (c) 2024 International Association for Gondwana Research. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： adaboost algorithm Back Propagation Neural Networks Machine learning Zircon origin

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Predicting the risk of a high proportion of three/multiple pronuclei (3PN/ MPN) zygotes in individual IVF cycles using comparative machine learning algorithms

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EUROPEAN JOURNAL OF OBSTETRICS & GYNECOLOGY AND REPRODUCTIVE BIOLOGY 2025年 306卷 139-146页

作者： Hong, Pingping Lu, Yaxin Li, Haiyang Liu, Zifeng Ou, Jianpin Li, Tao Shu, Yimin Sun Yat Sen Univ Affiliated Hosp 3 Reprod Med Ctr 6 East Longkou Rd Guangzhou 510630 Peoples R China Sun Yat Sen Univ Affiliated Hosp 3 Ctr Big Data & Artificial Intelligence 600 Tianhe Rd Guangzhou Guangdong Peoples R China Univ Kansas Med Ctr Ctr Adv Reprod Med Dept Obstet & Gynecol Overland Pk KS 66211 USA

Background: The majority of machine learning applications in assisted reproduction have been focused on predicting the likelihood of pregnancy. In the present study, we aim to investigate which machine learning models are most effective in predicting the occurrence of a high proportion (>30 %) of 3PN/MPN zygotes in individual IVF cycles. Methods: Eight machine learning algorithms were trained and compared, including the adaboost and Gaussian NB. Data from IVF cycles carried out from September 2015 to September 2019 were used as a training set. Cycle data from October 2019 to June 2020 were used as a validation set to verify the training model. Cycles with a 3PN/MPN zygote proportion higher than 30 % were classified as high 3PN/MPN zygote proportion cycles. Results: The adaboost algorithm was the best model for model construction and external validation. In both the training and validation sets, age, basal FSH, FSH and E2 level on the day of Gonadotrophin (GN) stimulation, and FSH and LH levels on the day of HCG were statistically higher in patients with 3PN/MPN > 30 % than in patients with 3PN/MPN <= 30 %;AFC, AMH, E2 level on HCG day and total number of oocytes were lower in patients with 3PN/MPN > 30 % than in patients with 3PN/MPN <= 30 %. The top five predictors were the number of oocytes retrieved, age, male factor infertility, AFC, and total days of GN stimulation. Conclusion: By applying a suitable machine learning algorithm, we can potentially predict the risk of a high proportion of 3PN/MPN zygotes in individual IVF cycles before insemination and avoid polyspermy fertilization by ICSI fertilization method.

关键词： Three/multiple pronuclei (3PN/MPN) zygotes Machine learning algorithms adaboost algorithm IVF Abnormal fertilization

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Flood susceptibility mapping through geoinformatics and ensemble learning methods, with an emphasis on the adaboost-Decision Tree algorithm, in Mazandaran, Iran

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EARTH SCIENCE INFORMATICS 2024年第2期17卷 1433-1457页

作者： Jahanbani, Maryam Vahidnia, Mohammad H. Aghamohammadi, Hossein Azizi, Zahra Islamic Azad Univ Sci & Res Branch Fac Nat Resources & Environm Dept Remote Sensing & GIS Tehran Iran

Floods, as natural disasters, impose significant human and financial burdens, necessitating stringent mitigation measures. The recurrent annual incidence of floods precipitates considerable economic setbacks and tragic human casualties. In the realm of disaster management, flood susceptibility mapping has evolved into an indispensable instrument for preemptive intervention. In recent years, the amalgamation of machine learning (ML) methodologies and geographic information systems (GIS) has demonstrated remarkable promise in the realm of flood susceptibility mapping. Nonetheless, the inherent limitations of standalone ML models have constrained their predictive efficacy. Several shortcomings are evident in prior research. These include the failure to utilize contemporary ensemble approaches capable of enhancing performance and the limited exploration of diverse classifier combinations, which are instrumental in augmenting reliability. Simultaneously, there is an absence of current and up-to-date flood susceptibility maps on recent floods within the study area. Hence, this study endeavors to enhance the precision of flood susceptibility mapping, within the Haraz-Neka River basin across Mazandaran province, by harnessing an ensemble of ML models. The research methodology encompassed several pivotal phases. Initially, data about 240 flood sites were meticulously compiled. Subsequently, 70% of this dataset was allocated for training and cartographic elucidations, whereas the remaining 30%, selected at random, served to validate the resultant maps. The analytical framework incorporated a spectrum of influential parameters, encompassing Elevation, Slope, Aspect, Rainfall, land use, Vegetation Differentiation Index (NDVI), Soil Hydrology Groups, Proximity to the River, Distance from Landslides, Topographic Wetness Index (TWI), Stream Power Index (SPI), and Sediment Transport Index (STI) for spatial modeling. The results undeniably highlight the superior performance of the e

关键词： Flood susceptibility map Spatial modeling Ensemble machine learning adaboost algorithm Bagging Disaster risk reduction Decision tree classifier

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Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on adaboost model

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BMC PSYCHOLOGY 2024年第1期12卷 1页

作者： Zhou, Yiwei Zhang, Zejie Li, Qin Mao, Guangyun Zhou, Zumu Univ Shanghai Sci & Technol Business Sch Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Sch Intelligent Emergency Management Shanghai 200093 Peoples R China Univ Shanghai Sci & Technol Smart Urban Mobil Inst Shanghai 200093 Peoples R China Wenzhou Ctr Dis Control & Prevent Wenzhou 325000 Peoples R China Wenzhou Med Univ Kangning Hosp Zhejiang Prov Clin Res Ctr Mental Disorders Wenzhou 325007 Peoples R China Wenzhou Med Univ Sch Publ Hlth Dept Prevent Med Wenzhou 325035 Peoples R China

Objectives COVID-19 epidemics often lead to elevated levels of depression. To accurately identify and predict depression levels in home-quarantined individuals during a COVID-19 epidemic, this study constructed a depression prediction model based on multiple machine learning algorithms and validated its effectiveness. Methods A cross-sectional method was used to examine the depression status of individuals quarantined at home during the epidemic via the network. Characteristics included variables on sociodemographics, COVID-19 and its prevention and control measures, impact on life, work, health and economy after the city was sealed off, and PHQ-9 scale scores. The home-quarantined subjects were randomly divided into training set and validation set according to the ratio of 7:3, and the performance of different machine learning models were compared by 10-fold cross-validation, and the model algorithm with the best performance was selected from 15 models to construct and validate the depression prediction model for home-quarantined subjects. The validity of different models was compared based on accuracy, precision, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC), and the best model suitable for the data framework of this study was identified. Results The prevalence of depression among home-quarantined individuals during the epidemic was 31.66% (202/638), and the constructed adaboost depression prediction model had an ACC of 0.7917, an accuracy of 0.7180, and an AUC of 0.7803, which was better than the other 15 models on the combination of various performance measures. In the validation sets, the AUC was greater than 0.83. Conclusions The adaboost machine learning algorithm developed in this study can be used to construct a depression prediction model for home-quarantined individuals that has better machine learning performance, as well as high effectiveness, robustness, and generalizability.

关键词： COVID-19 Machine learning Home-quarantined individuals adaboost algorithm

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A DEPTH CASCADE FACE DETECTION algorithm BASED ON adaboost 5

A DEPTH CASCADE FACE DETECTION ALGORITHM BASED ON ADABOOST

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2016 5th IEEE International Conference on Network Infrastructure and Digital Content(IC-NIDC 2016)

作者： Wenxiang Yu Jiapeng Xiu Chen Liu Zhengqiu Yang Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509012473

In this paper,a face detection algorithm based on adaboost algorithm combined with Haar classifier depth cascade about OpenCV is *** order to solve the problem that the training depth of adaboost algorithm is not enough and the Haar classifier is not accurate because of the lack of depth cascade *** the face feature extraction,adaboost algorithm is used to train the face samples to train the strong *** using OpenCV Haar classifier to depth classification for these strong classifier,it equal to cascade the adaboost algorithm,using high efficiency characteristic matrix and integral image method to accelerate the extraction rate of image feature values of the Haar-like.

关键词： Face detection adaboost algorithm Haar classifier Characteristic matrix Integral image

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