Automated Detection of Brain Tumor Cells Using Support Vector Machine

Birare, Geetanjali; Chakkarwar, V. A.

doi:10.1109/icccnt.2018.8494133

Cited by 18 publications

(5 citation statements)

References 12 publications

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“…SVM implemented in many classification fields, see Fig [8], [9], medics [10], [11], engineering [12], [13] etc. SVM gives good accuracy in many applied fields, so it become one of the popular classification methods.…”

Section: Support Vector Machinementioning

confidence: 99%

Machine Predictive Maintenance by Using Support Vector Machines

et al. 2023

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Predictive Maintenance (PdM) is an adoptable worth strategy when we deal with the maintenance business, due to a necessity of minimizing stop time into a minimum and reduce expenses. Recently, the research of PdM is now begin in utilizing the artificial intelligence by using the machine data itself and sensors. Data collected then analyzed and modelled so that the decision can be made for the near and next future. One of the popular artificial intelligences in handling such classification problem is Support Vector Machines (SVM). The purpose of the study is to detect machine failure by using the SVM model. The study is using database approach from the model of Machine Learning. The data collection comes from the sensors installed on the machine itself, so that it can predict the failure of machine function. The study also to test the performance and seek for the best parameter value for building a detection model of machine predictive maintenance The result shows based on dataset AI4I 2020 Predictive Maintenance, SVM is able to detect machine failure with the accuracy of 80%.

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Section: Support Vector Machinementioning

confidence: 99%

Machine Predictive Maintenance by Using Support Vector Machines

et al. 2023

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“…The development of artificial intelligence in the medical field is very rapid. Some of them are diabetes detection [1][2][3], brain detection [4][5][6], cancer [7,8], heart disease [9][10][11] and others. Disease detection using artificial intelligence has also been used by the medical team to be an early diagnosis in detecting an abnormal condition.…”

Section: Linear Discriminant Analysismentioning

confidence: 99%

Performance evaluation of linear discriminant analysis and support vector machines to classify cesarean section

Abdillah

Azwardi

Permana

et al. 2021

EEJET

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Currently the hospital is a place that is very vulnerable to the transmission of Covid-19, so giving birth in a hospital is very risky. In addition, the hospital currently only accepts cesarean deliveries, while mothers who can give birth vaginally are recommended to give birth in a midwife because the chances of being exposed to Covid-19 are much lower. In general, this study aims to examine the performance of the LDA-SVM method in predicting whether a prospective mother needs to undergo a C-section or simply give birth normally. The aims of this study are: 1) to determine the best parameters for building the detection model; 2) to determine the best accuracy from the model; 3) to compare the accuracies with the other methods. The data used in this study is the dataset of caesarian section. This data consists of the results of 80 pregnant women following C-section with the most important characteristics of labor problems in the clinical field. Based on the results of the experiments that have been carried out, several parameter values that provide the best results for building the detection model are obtained, namely σ (sigma) –5.9 for 70 % training data, σ=4, –6.1 and ‑6.6 for 80 % training data and σ=4 and 16 for 90 % training data. Besides, the results obtained show that the LDA-SVM method is able to classify the C-section method properly with an accuracy of up to 100 %. This research is also able to surpass the methods in previous studies. The results show that LDA-SVM for this case study generates an accuracy of 100.00 %. This method has great potential to be used by doctors used as an early detection to determine whether a mother needs to go through a C-section or simply give birth vaginally. So that mothers can prevent the transmission of Covid-19 in the hospital

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“…G. Birare and V. Chakkarwar [12] proposed a system that classifies tumorous MRI images as benign and malignant. The steps involved were pre-processing; used for improving different image parameters like noise removal, smoothing edges and removing unnecessary parts from background by applying adaptive contrast enhancement which is based on sigmoid function to get a clearer image.…”

Section: Figure 1 Digital Image Processing Systemmentioning

confidence: 99%