• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.107-112
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• 2024

Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques

• Journal of radiological science and technology
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• v.47 no.4
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• pp.263-270
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• 2024

The objective of this study was to evaluate the accuracy and impact of leaf open time (LOT) and pitch using various machine learning models on EBT film-based delivery quality assurance (DQA) performed on 211 patients of helical tomotherapy (HT). We randomly selected passed (n=191) and failed (n=20) DQA measurements to evaluate the accuracy of the k-nearest neighbor (KNN), support vector machine (SVM), naive Bayes (NB) and logistic regression (LR) models using scale-dependent metrics such as the coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). We evaluated the performance of the four prediction models in terms of the accuracy, precision, sensitivity, and F1-score using a confusion matrix, finding the NB and LR models to achieve optimal results. The results of this study are expected to reduce the workload of medical physicists and dosimetrists by predicting DQA results according to LOT and pitch in advance.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.94-99
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• 2002

KS and ISO have proposed several evaluation methods of conventional machine tools. Even though the accuracy of the tools can be evaluated with these methods, there are still no proper evaluation methods of high speed machining. Because it is hard to evaluate characteristics of high speed machining such as decrease of cutting temperature, cutting force, and reduced machining time. Therefore, new evaluation method for high speed machine should be developed. In this paper, several shapes of model have been proposed to evaluate cutting accuracy of high speed machine.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.1
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• pp.78-84
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• 2014

The rotation of a spindle unit must be accurate for high-quality machining and to improve the quality of the machine tools.Therefore, the proper measurement of the rotation accuracy and ensuring a proper analysis are very important. Separate processes are necessary because spindle errors and roundness errors associated with the test balls can both factor into the measured rotation error values. We used three methods to discern test ball errors and analyzed which could be deemed as the most proper technique in a test of the rotation accuracy of the main spindle of a machine tool.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.4
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• pp.97-103
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• 2001

The study on the high-speed machine tool is very important for the improvement of productivity since it can shortens cutting and non-cutting time. Especially, high speed of feed drive system is the major research field. In the industries of the advanced countries, the feed drive systems at the speed of 60 m/min have been already developed based on the high lead ball screws. In this study, a high speed feed drive system at the speed of 60 m/ min has been developed, and its movements characteris-tics are investigated. As the movement characteristics, positioning accuracy, angular accuracy, straightness and micro step-response are measured. Thermal characteristics of the system is also discussed. For measuring the movement characteris-tics, a laser interferometer, a memory-based Hi-coder and a cooling device are used. The experimental results confirm that the movement characteristics and the thermal behavior of the system are satisfactory in the aspect of accuracy and stability.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1998.03a
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• pp.263-268
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• 1998

Development of high speed feed drive system has been a major issue for the past few decades in machine tool industries. The reduction of the tool change time as well as rapid travel time can enhance the productivity. However, the high speed feed drive system generates more heat in nature, which leads thermal expansion that has adverse effects on the accuracy of machined parts. The detail of the model proposed is described in the paper together with the experimental methodologies using a proposed compact measurement system to examine the validity of the proposed approach. The results showed the machining accuracy could be maintained to better than $\pm$5${\mu}{\textrm}{m}$ while using this T-18 sensor

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.3
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• pp.51-51
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• 1998

It is very important to test linear cycle positioning accuracy of Machining centers as it affect all other machines machined by them in industries. For example, if the linear positioning accuracy of each axes directions is bad, the size of works will be wrong and the change-ability will be bad in the assembly of machine parts. In this paper, measuring systems are organized to measure linear displacements of table or spindle of machine center using laser interferometer, magnescale and tick pulses comming out from computer in order to get data at constant time intervals from the sensors. And each set of data gotten from test is expressed to a plots by computer treatment and the results of linear positioning error motion is estimated to numerics by statistical treatments.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.3 s.96
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• pp.215-221
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• 1999

The object of the thermal error compensation system in machine tools is improving the accuracy of a machine tool through real time error compensation. The accuracy of the machine tool totally depends on the accuracy of thermal error model. A thermal error model can be obtained by appropriate combination of temperature variables. The proposed method for optimal variable selection in the thermal error model is based on correlation grouping and successive regression analysis. Collinearity matter is improved with the correlation grouping and the judgment function which minimizes residual mean square is used. The linear model is more robust against measurement noises than an engineering judgement model that includes the higher order terms of variables. The proposed method is more effective for the applications in real time error compensation because of the reduction in computational time, sufficient model accuracy, and the robustness.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.23-30
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• 2021

Sentiment Analysis has become very important field of research because posting of reviews is becoming a trend. Supervised, unsupervised and semi supervised machine learning methods done lot of work to mine this data. Feature engineering is complex and technical part of machine learning. Deep learning is a new trend, where this laborious work can be done automatically. Many researchers have done many works on Deep learning Convolutional Neural Network (CNN) and Long Shor Term Memory (LSTM) Neural Network. These requires high processing speed and memory. Here author suggested two models simple & bidirectional deep leaning, which can work on text data with normal processing speed. At end both models are compared and found bidirectional model is best, because simple model achieve 50% accuracy and bidirectional deep learning model achieve 99% accuracy on trained data while 78% accuracy on test data. But this is based on 10-epochs and 40-batch size. This accuracy can also be increased by making different attempts on epochs and batch size.

• Journal of the Korean Society for Precision Engineering
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• v.13 no.3
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• pp.56-62
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• 1996

This paper presented a new measuring system to improve circular motion accuracy by using two-dimensonal probe and master ring for NC machine tools. This measuring system reduced the circular motion error conspicuously by eliminating the influence of the acceleration/deceleration range and compensating the friction force whose influences were significant while measuring the motion. Experimental results show that this system had enough accuracy to measure a circular motion for NC machine tools, compared with the circular test method and the r .theta. method.