This paper presents the implementation of vehicles classification using extremegradientboost (XG boost) algorithm to improve the accuracy in the vehicle classification with respect to the shapes and features. Genera...
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ISBN:
(纸本)9781665411202
This paper presents the implementation of vehicles classification using extremegradientboost (XG boost) algorithm to improve the accuracy in the vehicle classification with respect to the shapes and features. Generally, Classification based on different parameter such as hefty, classes, structures, extracting features, segmenting the images and semantic classification are being challenge to incorporate in Machine Learning. In order to overcome this barrier, XG boostalgorithm has been implemented to achieve the high performance vehicle classification from the large scale surveillance dataset. The experimental results shows that the accuracy is improved in the vehicle classification with standard resolution image.
The power grid operators collect the data on a smart grid via the phasor measurement unit (PMU), which is placed at several points on the bus. However, the data at the PMU is prone to bad data;to classify that data, a...
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The power grid operators collect the data on a smart grid via the phasor measurement unit (PMU), which is placed at several points on the bus. However, the data at the PMU is prone to bad data;to classify that data, a detection scheme must be executed in the server system. The events in PMU data were subjected to several transmission conditions in the distribution system, and detecting those conditions is the major need for research. Hence to cope with that, a multi-level classification of synchrophasor's data is proposed in this paper. An agglomerative clustering-based extremegradientboosting (AXGboost) classifier is proposed for a multi-level classification. The puzzle optimization method updates the probability of a data line point to the agglomerative cluster detector. The model is analyzed on IEEE 14 and 39 bus system PMU data for classification and evaluation of a model. As a result, the classification accuracy of 99.49% is obtained for IEEE 14 bus, while the accuracy for IEEE 39 bus is 99.74%, which is comparably higher than the 14 bus system. The obtained result on both systems is compared with state-of-the-art techniques such as isolation forest classifier, weighting and imbalance bagging and PMUNET.
Vehicle classification (VC) is a prominent research domain within image processing and machine learning (ML) for identifying vehicle volumes and traffic rule violations. In developed countries, nearly 40% of daily acc...
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Vehicle classification (VC) is a prominent research domain within image processing and machine learning (ML) for identifying vehicle volumes and traffic rule violations. In developed countries, nearly 40% of daily accidents are fatal, while in developing countries, the figure rises to 70%. Traditionally, vehicle detection and classification have been performed manually by experts, which is difficult, time-consuming, and prone to errors. Furthermore, incorrect detection and classification can result in hazardous situations. This highlights the need for more reliable techniques to identify and classify vehicles accurately and practically. In existing applications, numerous automated methods have been proposed. However, employing deep and machine learning algorithms on complex datasets of vehicle images has failed to achieve accuracy in various climate conditions and has been time-consuming. This paper presents an accurate, robust, real-time system to classify vehicles from onsite roads. The proposed system utilizes a random wavelet transform for pre-processing, edge and region-based segmentation for feature extraction, an embedded method for feature selection, and the XGboostalgorithm for VC. The proposed work classifies vehicles under complex weather, illumination, color, and occlusion conditions over 10 datasets, including a novel dataset named SRM2KTR, containing 75,436 vehicle images on an FPGA platform. The results show 98.81% accuracy, outperforming the state-of-the-art (98%). The system was demonstrated with four different classifiers, classifying images in 0.16 ns with an average accuracy of 97.79%. The system exhibits high accuracy, rapid identification time, and robustness in practical use.
Cancer is a disease linked to the untamed and rapid division of cells in the body. Cancer detection through conventional methods like complete blood count is a tedious and time-consuming task prone to human errors. Th...
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Cancer is a disease linked to the untamed and rapid division of cells in the body. Cancer detection through conventional methods like complete blood count is a tedious and time-consuming task prone to human errors. The introduction of image processing techniques and computer-aided diagnostics is beneficial to this field as the results obtained by utilizing these methods are quick and accurate. The proposed method in this paper uses a design Convolutional Leaky RELU with Catboost and XGboost (CLR-CXG) to segment the images and extract the important features that help in classification. The binary classification algorithm and gradientboosting algorithm Catboost (Categorical boost) and XGboost (extremegradientboost) are implemented individually. Moreover, Convolutional Leaky RELU with Catboost (CLRC) is designed to decrease bias and provide high accuracy, while Convolutional Leaky RELU with XGboost (CLRXG) is designed for classification or regression prediction problems which will increase the speed of executing the algorithm and improve its performance. Thus the CLR-CXG classifies the test images into Acute Lymphoblastic Leukemia (ALL) or Multiple Myeloma (MM). Finally, the CLRC algorithm achieved 100% accuracy in classifying cancer cells, and the recorded run time is 10s. Moreover, the CLRXG algorithm has gained an accuracy of 97.12% for classifying cancer cells and 12 s for executing the process.
Wind energy has become a significant component of the new trend, with wind turbines being frequently used to generate electricity. In recent decades, a number of big and small wind farms have been built in many nation...
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Wind energy has become a significant component of the new trend, with wind turbines being frequently used to generate electricity. In recent decades, a number of big and small wind farms have been built in many nations as a part of the global push to increase the production of hygienic energy from sources of renewable energy. Failure of wind turbines will adversely affect the power production. In recent years, various analysis is conducted to understand the failure of turbines. Here, machine learning model approach is used to guess the failure of wind turbines. This model will help the engineering team to forecast the failure of turbines. Based on that, business activity and planning can be made to prevent power production loss. A model has been designed using Random Forest algorithm and extreme gradient boost algorithm (XGboost). On comparing Bias and Scores in the Receiver Operating Characteristic curve of both the algorithm, Random Forest algorithm gives the best result when compared to XGboostalgorithm. Copyright (C) 2022 Elsevier Ltd. All rights reserved.
Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for ...
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Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging extremegradientboosting (XGboost) and the Multi-Objective Optimization Genetic algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGboost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGboost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGboost and Mob-GA individually for vehicle classification.
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