Paint provides a shield that protects our structures, particularly steel structures, from corrosion and damage due to harsh environmental conditions such as sunlight and humidity. It is essential to ensure that paint ...
详细信息
ISBN:
(数字)9781665470698
ISBN:
(纸本)9781665470698
Paint provides a shield that protects our structures, particularly steel structures, from corrosion and damage due to harsh environmental conditions such as sunlight and humidity. It is essential to ensure that paint coating works effectively and remains in good structural conditions (without peeling or fracture). Assessing the paint condition is complex, especially on large structures such as bridges, and can be time-consuming and costly. The traditional visual inspection methods depend on the experience and evaluation of inspectors in the field and require hard labor and time to perform. This study aims to employ hyperspectral imaging and image classification approaches to develop a structural condition monitoring system that rapidly assesses paint conditions and degradation. Several classification algorithms, such as Decision Tree, Support Vector Machines, Logistic Regression, and Naive Bayes, were trained and tested. The results obtained showed that the Decision Tree classifier outperformed the rest of the classifiers, achieving a highly accurate assessment of the paint condition and degradation levels with a detection accuracy of 0.98.
An image classifier uses machine learning algorithm to recognize the images. A classifier allocates class labels to specific data points and classifies the images using supervised learning. It recognizes the target cl...
详细信息
ISBN:
(纸本)9781728158754
An image classifier uses machine learning algorithm to recognize the images. A classifier allocates class labels to specific data points and classifies the images using supervised learning. It recognizes the target classes using labelled sample images by training a Convolutional neural network model (CNN). This paper aims to design an image classifier for web application. Image classification will eliminate the need of manual search in the web application. CNN is an efficient algorithm to classify the images according to the category label. An image classification model has been designed using CNN to classify the images of fruits and vegetables. Accuracy of the model is based on the classification technique. For successful classification data pre-processing, data augmentation and feature extraction are involved. Positive results were obtained by the CNN model after testing it with the input images. The model can classify the 90483 images of 131 fruits and vegetables. This paper involves the description and methodology for designing the image classifier.
There are a wide variety of classification problems in the world,so the classification task has always been a fundamental and important machine learning ***,many scholars have focused on the accuracy and training spee...
详细信息
ISBN:
(数字)9789887581536
ISBN:
(纸本)9781665482561
There are a wide variety of classification problems in the world,so the classification task has always been a fundamental and important machine learning ***,many scholars have focused on the accuracy and training speed of classification models,while neglecting an important factor of classifier models:*** many cases,it is extremely difficult to collect labeled data,so the use of both labeled and unlabeled data to train classifiers that generate better semi-supervised learning is also the focus of many *** the above reasons,this paper proposes a new classifier,which is based on the axiomatic fuzzy set(AFS) *** not only realizes the interpretability of the fuzzy theory,but also uses the LogitBoost algorithm to verify the inconsistency of the axiomatic fuzzy set theory.
Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power system. Analysis of dissolved gases content in power trans...
详细信息
ISBN:
(纸本)9781424455379
Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power system. Analysis of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve classification problem of nonlinearity and small sample. However, SVM has rarely been applied to diagnosis transformer fault by analysis the dissolved gases content in power transformer. In this study, support vector machine is proposed to analysis dissolved gases content in power transformer oil, among which cross-validation is used to determine free parameters of support vector machine. The experimental data from the electric power company in Sichuan are used to illustrate the performance of proposed SVM model. The experimental results indicate that the proposed SVM model can achieve very good diagnosis accuracy under the circumstances of small sample. Consequently, the SVM model is a proper alternative for diagnosing power transformer fault.
Human activity recognition is an emerging field of ubiquitous and pervasive computing. Although recent smartphones have powerful resources, the execution of machine learning algorithms on a large amount of data is sti...
详细信息
Human activity recognition is an emerging field of ubiquitous and pervasive computing. Although recent smartphones have powerful resources, the execution of machine learning algorithms on a large amount of data is still a burden on smartphones. Three major factors including;classification algorithm, data feature, and smartphone position influence the recognition accuracy and time. In this paper, we present a comparative study of six classification algorithms, six data features, and four different positions that are most commonly used in the recognition process using smartphone accelerometer. This analysis can be used to select any specific classification algorithm, data feature, and smartphone position for human activity recognition in terms of accuracy and response time. The methodology we used is composed of two major components;a data collector, and a classifier. A set of eleven activities of daily living, four different positions for data collection and ten volunteers contributed to make it a worth-full comparative study. Results show that K-Nearest Neighbor and J48 algorithms performed well both in terms of time and accuracy irrespective of data features whereas the performance of other algorithms is dependent on the selected data features. Similarly, mean and mode features gave good results in terms of accuracy irrespective of the classification algorithm. A short version of the paper has already been presented at ICIS 2014.
The severe degradation of wireless network performance indicators in cells seriously affects user perception. Rapidly identifying small cell performance degradation based on fixed thresholds set by network optimizatio...
详细信息
ISBN:
(纸本)9781665455336
The severe degradation of wireless network performance indicators in cells seriously affects user perception. Rapidly identifying small cell performance degradation based on fixed thresholds set by network optimization personnel through business experience can lead to misjudgment and omission. An anomaly detection algorithm is used to remove abnormal indicator values to eliminate sudden fluctuations caused by sudden situations. Then, the DBSCAN clustering algorithm is used to calculate the dynamic threshold of the indicators, and a method for quantifying the degree of indicator degradation is proposed. Finally, combined with historical poor quality cell label data, the Lightgbm classification algorithm is used to identify cells with degraded wireless network performance indicators. To verify the effectiveness of this method, the experimental results show that the accuracy of identifying cells with degraded wireless network performance indicators using this method is higher than that of empirical rules and traditional classification algorithms.
(1)This paper realizes the network security assessment model based on hidden Markov. Firstly, the principle and basic problem of Hidden Markov Model are analyzed. Select the Snort software alarm as the input, convert ...
详细信息
ISBN:
(纸本)9781450353441
(1)This paper realizes the network security assessment model based on hidden Markov. Firstly, the principle and basic problem of Hidden Markov Model are analyzed. Select the Snort software alarm as the input, convert the alarm into the quantification value of the network security risk model, and quantify the network security in real time, then design and implement the whole Hidden Markov Model.
SCHOLAT is a free, massive and comprehensive academic social network platform, which aims to realize reliable data sharing. In this paper, a new conference information management system based on SCHOLAT is designed an...
详细信息
ISBN:
(纸本)9789811945496;9789811945489
SCHOLAT is a free, massive and comprehensive academic social network platform, which aims to realize reliable data sharing. In this paper, a new conference information management system based on SCHOLAT is designed and developed. In addition to containing typical functions of conference information management system, the system also provides individual services, including convenient online conference space, personalized domain, association with SCHOLAT ecosystem and customized access control. A hybrid classification algorithm for SCHOLAT user's academic emails is proposed to enhance the high quality services of the system. Benefit from convenient and helpful services, the conference system has grown rapidly in popularity since it was deployed and has been applied to lots of academic conferences up to now.
Decision tree is an important learning method in machine learning and data mining,this paper discusses the method of choosing the best attribute based on information entropy At analyzes the process and the characters ...
详细信息
ISBN:
(纸本)9783037851517
Decision tree is an important learning method in machine learning and data mining,this paper discusses the method of choosing the best attribute based on information entropy At analyzes the process and the characters of classification and the discovery knowledge based on decision tree about the application of decision tree on data mining. Through an instance, the paper shows the procedure of selecting the decision attribute in detail,finally it pointes out the developing trends of decision tree.
The life of humans living without liver tumors is one of the fundamental care of human livelihood. Therefore, for better care, detection of liver disease at a primitive phase is necessary. For medical experts, predict...
详细信息
ISBN:
(纸本)9781728158754
The life of humans living without liver tumors is one of the fundamental care of human livelihood. Therefore, for better care, detection of liver disease at a primitive phase is necessary. For medical experts, predicting the illness in the early stages due to subtle signs is a very difficult task Many, when it is too late, the signs become evident. The current work aims to augment the perceive nature of liver disease by means of machine learning methods to solve this epidemic. The key purpose of the present work focused on algorithms for classification of healthy people from liver datasets. Centered on their success variables, this research also aims to compare the classification algorithms and to provide prediction accuracy results.
暂无评论