Comparative performance analysis of Naive Bayes and SVM classifier for oral X-ray images

Karthick, G. S.; Harikumar, R.

doi:10.1109/ecs.2017.8067843

Cited by 19 publications

(9 citation statements)

References 10 publications

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“…However, performance needs to be checked on large scale datasets using evaluations of confusion matrix and K-fold CV. A study by [83] discussed the classification of oral images of tooth diseases. Overall, 72 X-ray images are used with five classes comprising of 5 dental impairments.…”

Section: B Classification Based Approaches Used For Load Balancingmentioning

confidence: 99%

“…Further, hybridized features are being combined in a way to take maximum advantage from the proposed algorithm. In the same way, machine learning algorithms are being hybridized with load balancing algorithms for accuracy purposes such as SVM and PSO for audio file classifications [23], K-Nearest Neighbor (K-NN) and ACO for datasets classification [24], SVM with PSO used for video classifications [25], Decision Trees (DT) and SVM for text classifications [26], Naïve Bayes and SVM for image classifications [27].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

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Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These approaches often adopt multi-objective QoS metrics, such as reduced SLA violations, reduced makespan, high throughput, low overload, low energy consumption, high optimization, minimum migrations, and higher response time. The cloud applications are generally computation-intensive and can grow exponentially in memory with the increase in size if no proper effective and efficient load balancing technique is adopted resulting in poor quality solutions. To provide a better load balancing solution in cloud computing, with extensive data, a new hybrid model is being proposed that performs classification on the number of files present in the cloud using file type formatting. The classification is performed using Support Vector Machine (SVM) considering various file formats such as audio, video, text maps, and images in the cloud. The resultant data class provides high classification accuracy which is further fed into a metaheuristic algorithm namely Ant Colony Optimization (ACO) using File Type Formatting FTF for better load balancing in the cloud. Frequently used QoS metrics, such as SLA violations, migration time, throughput time, overhead time, and optimization time are evaluated in the cloud environment and comparative analysis is performed with recent metaheuristics, such as Ant Colony Optimization-Particle Swarm Optimization (ACOPS), Chaotic Particle Swarm Optimization (CPSO), Q-learning Modified Particle Swarm Optimization (QMPSO), Cat Swarm Optimization (CSO) and D-ACOELB. The proposed algorithm outperforms them and provides good performance with scalability and robustness.

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Section: B Classification Based Approaches Used For Load Balancingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

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“…For given instance, the Naive Bayesian (NB) Classifier, regards that all attributes are independent of each other [4]. Under this assumption, we can approximate ( | ) as formula (1).…”

Section: Nb Classifiermentioning

confidence: 99%

The Research of Postoperative Life Expectancy of Lung Cancer Based on Semi-Naive Bayesian

Hui¹,

Zhou²,

Jiang³

et al. 2017

International Symposium on Computer Science and Artificial Intelligence(ISCSAI)

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Since lung cancer has a high fatality rate, it is really necessary to help surgeons predict the postoperative life expectancy of patients. In this paper, we are committed to discuss some semi-Naive Bayesian classifiers that will effectively predict the postoperative survival time of patients with lung cancer through combining the idea of information gain with assemble. The classifier with the best performance evaluation will be selected for helping surgeons to better judge their methods of treating patients.

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“…Along with the development of information technology, it is possible to identify fruit maturity with the computers help [3] [4]. Identification can be done by classifying the image of tomatoes with various methods such as K-Nearest Neighbor (KNN) [4] [5], Random Forest [6], Support Vector Machine(SVM) [7] [8], Naïve Bayes [7] [8] [9] [10] [11] [12] [13], etc. In this study, identification of maturity in a set of tomatoes using the Naïve Bayes algorithm.…”

Section: Introductionmentioning

confidence: 99%

Tomato Maturity Classification using Naive Bayes Algorithm and Histogram Feature Extraction

Kusuma

Setiadi

Putra

2018

JAIS

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Tomatoes have nutritional content that is very beneficial for human health and is one source of vitamins and minerals. Tomato classification plays an important role in many ways related to the distribution and sales of tomatoes. Classification can be done on images by extracting features and then classifying them with certain methods. This research proposes a classification technique using feature histogram extraction and Naïve Bayes Classifier. Histogram feature extractions are widely used and play a role in the classification results. Naïve Bayes is proposed because it has high accuracy and high computational speed when applied to a large number of databases, is robust to isolated noise points, and only requires small training data to estimate the parameters needed for classification. The proposed classification is divided into three classes, namely raw, mature and rotten. Based on the results of the experiment using 75 training data and 25 testing data obtained 76% accuracy

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Comparative performance analysis of Naive Bayes and SVM classifier for oral X-ray images

Cited by 19 publications

References 10 publications

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

The Research of Postoperative Life Expectancy of Lung Cancer Based on Semi-Naive Bayesian

Tomato Maturity Classification using Naive Bayes Algorithm and Histogram Feature Extraction

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