An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins

Jain, Shruti; Salau, Ayodeji Olalekan

doi:10.1080/23311916.2019.1599537

Cited by 44 publications

(16 citation statements)

References 18 publications

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“…Arrangement is sorted as regulated and unsupervised characterization. The SVM classifier goes under the class of regulated machine learning and is seen to under perform at the premise of number-crunching ideas and factual hypothesis [25,26]. SVM classifier can characterize both straight and non-direct order data.…”

Section: Materials and Methodologymentioning

confidence: 99%

Detection of glaucoma using two dimensional tensor empirical wavelet transform

Jain

Salau

2019

SN Appl. Sci.

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Retinal image analysis is extensively used in the medical field for the detection of abnormalities in the eye. Amongst the several abnormalities in the eye, glaucoma has been identified as the second major cause of blindness. The glaucoma disease are of various types, out of which close angle glaucoma causes more pain and has the most effect on eye vision in comparison with the open angle glaucoma. In this paper, a futuristic computer aided classification method is designed and differentiated in terms of its sensitivity, specificity, and classification accuracy. The proposed method is used to detect glaucoma eye fundus from normal eye images using tensor based empirical wavelet transform (tensor based EWT). The method was tested on samples obtained from the online database; Medical image group Analysis, and an accuracy of 98.7% detection rate was achieved. The tensor based EWT yields 22% improvement in comparison with the littlewood EWT. The performance of the Tensor based EWT technique was compared with existing techniques and was found to outperform existing results.

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Section: Materials and Methodologymentioning

confidence: 99%

Detection of glaucoma using two dimensional tensor empirical wavelet transform

Jain

Salau

2019

SN Appl. Sci.

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“…The SVM is implemented with a search grid using a sigmoid gamma function kernel for training, while the k -NN is implemented with number of neighbours of five and uniformly weighted functions for training [ 38 ]. There are several techniques for k -NN, such as KD-tree [ 39 ], fast library for approximate nearest neighbours (FLANN) [ 40 ], cover tree [ 41 ], and semi-convex hull tree [ 42 ]. However, this study implements k -NN using the brute-force, because of its competitive performance prior to neighbour search for small data samples.…”

Section: Experimental Validationmentioning

confidence: 99%

“…This consideration is important for our application since the COVID-19 image datasets are quite small (i.e., without augmentation). Furthermore, our choice is reinforced by the fact that brute-force k -NN exhibit high performance as presented in several image processing applications [ 34 , 42 , 43 ].…”

Section: Experimental Validationmentioning

confidence: 99%

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Sedik

Iliyasu

El-Rahiem

et al. 2020

Viruses

172

125

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This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

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“…Feature selection was applied in the cell apoptosis/survival dataset to achieve a good result by dividing it into three main categories, namely: wrapper method (WM), filtering method (FM), and Embedded Method (EM). After applying the feature selection (FS) algorithm, seven different marker proteins were obtained [42].…”

Section: Feature Selection-based Classification Algorithmsmentioning

confidence: 99%

Prediction-Based Model for Student Dropouts using Modified Mutated Firefly Algorithm

Gamao¹

2019

IJATCSE

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Academic database is considered as the heart and soul of every higher education institutions. This database contains a vast amount of useful information that is useful for analysis. Algorithms for machine learning play a significant role in mining academic databases and have been proven to be effective when applied in the academic field. Prediction models are made using relevant classification algorithms for dropout analysis. The success of the prediction model depends on the performance of the feature selection algorithm used for dimensionality reduction. The study utilized the Modified Mutated Firefly Algorithm (MMFA) as a dimensionality reduction strategy to enhance the accuracy of the prediction model for dropout analysis. The results of the simulation revealed that the Decision Tree (DT) classifier outperformed the Naïve Bayesian using the three UCI datasets. After the test of benchmark datasets, a students' cumulative dataset was used to come up with a predictive model for dropout analysis of Davao del Norte State College, Davao del Norte, Philippines. The results of the experiment confirmed that the MMFA+DT obtained an accuracy rate of 95.82%, while MMFA+NB only has 92.85% using 10-fold cross-validation.

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An image feature selection approach for dimensionality reduction based on kNN and SVM for AkT proteins

Cited by 44 publications

References 18 publications

Detection of glaucoma using two dimensional tensor empirical wavelet transform

Detection of glaucoma using two dimensional tensor empirical wavelet transform

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections

Prediction-Based Model for Student Dropouts using Modified Mutated Firefly Algorithm

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