Discrimination of Golgi Proteins Through Efficient Exploitation of Hybrid Feature Spaces Coupled With SMOTE and Ensemble of Support Vector Machine

Tahir, Muhammad; Khan, Fazlullah; Rahmani, Mohammad Khalid Imam; Hoang, Vinh Truong

doi:10.1109/access.2020.3037343

Cited by 3 publications

(3 citation statements)

References 38 publications

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“…The fitness function utilized is often just the predictor performance [ 49 ]. When an algorithm is used for feature selection, it is often in cohorts with a classifier such as, but not limited to, Naïve Bayes (NB), k -nearest neighbors (k-NN), support vector machines (SVM) [ 50 ], decision trees, logistic regression (LR), random forest (RF) [ 51 ], or multilayer perceptron networks (MLP).…”

Section: Genetic Algorithms In Cancer Researchmentioning

confidence: 99%

Cancer Detection and Prediction Using Genetic Algorithms

Bhandari

Tripathy

Jawad

et al. 2022

Computational Intelligence and Neuroscience

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Cancer is a wide category of diseases that is caused by the abnormal, uncontrollable growth of cells, and it is the second leading cause of death globally. Screening, early diagnosis, and prediction of recurrence give patients the best possible chance for successful treatment. However, these tests can be expensive and invasive and the results have to be interpreted by experts. Genetic algorithms (GAs) are metaheuristics that belong to the class of evolutionary algorithms. GAs can find the optimal or near-optimal solutions in huge, difficult search spaces and are widely used for search and optimization. This makes them ideal for detecting cancer by creating models to interpret the results of tests, especially noninvasive. In this article, we have comprehensively reviewed the existing literature, analyzed them critically, provided a comparative analysis of the state-of-the-art techniques, and identified the future challenges in the development of such techniques by medical professionals.

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Section: Genetic Algorithms In Cancer Researchmentioning

confidence: 99%

Cancer Detection and Prediction Using Genetic Algorithms

Bhandari

Tripathy

Jawad

et al. 2022

Computational Intelligence and Neuroscience

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“…Besides the finance domain, SMOTE and its variations have found extensive application in other fields dealing with highly imbalanced datasets. In bio-informatics, SMOTE has been used to discriminate Golgi proteins Tahir et al (2020) and predict binding hot spots in protein-RNA interactions Zhou et al (2022). In medical diagnosis, SMOTE and its variations have been employed for diagnosing cervical cancer Abdoh et al (2018) and prostate cancer Abraham and Nair (2018).…”

Section: Related Workmentioning

confidence: 99%

Enhancing Financial Fraud Detection through Addressing Class Imbalance Using Hybrid SMOTE-GAN Techniques

Cheah,

Yang,

Lee

2023

IJFS

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The class imbalance problem in finance fraud datasets often leads to biased prediction towards the nonfraud class, resulting in poor performance in the fraud class. This study explores the effects of utilizing the Synthetic Minority Oversampling TEchnique (SMOTE), a Generative Adversarial Network (GAN), and their combinations to address the class imbalance issue. Their effectiveness was evaluated using a Feed-forward Neural Network (FNN), Convolutional Neural Network (CNN), and their hybrid (FNN+CNN). This study found that regardless of the data generation techniques applied, the classifier’s hyperparameters can affect classification performance. The comparisons of various data generation techniques demonstrated the effectiveness of the hybrid SMOTE and GAN, including SMOTified-GAN, SMOTE+GAN, and GANified-SMOTE, compared with SMOTE and GAN. The SMOTified-GAN and the proposed GANified-SMOTE were able to perform equally well across different amounts of generated fraud samples.

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“…These metrics used are listed as follows: a) Accuracy: The supreme instinctive performance measure is accuracy and it is calculated as the ratio of correctly predicted observations to the total number of observations considered [46,47].…”

Section: Performance Metricsmentioning

confidence: 99%

Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

Rahmani¹,

Taranum²,

Nikhat³

et al. 2022

Computer Systems Science and Engineering

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The COVID-19 pandemic is a virus that has disastrous effects on human lives globally; still spreading like wildfire causing huge losses to humanity and economies. There is a need to follow few constraints like social distancing norms, personal hygiene, and masking up to effectively control the virus spread. The proposal is to detect the face frame and confirm the faces are properly covered with masks. By applying the concepts of Deep learning, the results obtained for mask detection are found to be effective. The system is trained using 4500 images to accurately judge and justify its accuracy. The aim is to develop an algorithm to automatically detect a mask, but the approach does not facilitate the percentage of improper usage. Accuracy levels are as low as 50% if the mask is improperly covered and an alert is raised for improper placement. It can be used at traffic places and social gatherings for the prevention of virus transmission. It works by first locating the region of interest by creating a frame boundary, then facial points are picked up to detect and concentrate on specific features. The training on the input images is performed using different epochs until the artificial face mask detection dataset is created. The system is implemented using Tensor-Flow with OpenCV and Python using a Jupyter Notebook simulation environment. The training dataset used is collected from a set of diverse open-source datasets with filtered images available at Kaggle Medical Mask Dataset by Mikolaj Witkowski, Kera, and Prajna Bhandary. To simulate MobilNetV2 classifier is used to load and pre-process the image dataset for building a fully connected head. The objective is to assess the accuracy of the identification, measuring the efficiency and effectiveness of algorithms for precision, recall, and F1 score.

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Discrimination of Golgi Proteins Through Efficient Exploitation of Hybrid Feature Spaces Coupled With SMOTE and Ensemble of Support Vector Machine

Cited by 3 publications

References 38 publications

Cancer Detection and Prediction Using Genetic Algorithms

Cancer Detection and Prediction Using Genetic Algorithms

Enhancing Financial Fraud Detection through Addressing Class Imbalance Using Hybrid SMOTE-GAN Techniques

Automatic Real-Time Medical Mask Detection Using Deep Learning to Fight COVID-19

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