A Novel Framework Based on Deep Learning and ANOVA Feature Selection Method for Diagnosis of COVID-19 Cases from Chest X-Ray Images

Selection Method ◽

Rt Pcr ◽

X Ray ◽

Negative Results ◽

Difficulty Breathing ◽

The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the proposed method. This method reached 98.72% accuracy for two-class classification (COVID-19, healthy) and 92% accuracy for three-class classification (COVID-19, healthy, pneumonia).

A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images

10.1101/2021.10.10.21264809 ◽

2021 ◽

Author(s):

Hamid Nasiri ◽

Seyyed Ali Alavi

Keyword(s):

Feature Selection ◽

Predictive Accuracy ◽

False Negative ◽

Selection Method ◽

Rt Pcr ◽

X Ray ◽

Negative Results ◽

Difficulty Breathing ◽

The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the proposed method. This method reached 98.72% accuracy for two-class classification (COVID-19, healthy) and 92% accuracy for three-class classification (COVID-19, healthy, pneumonia).

A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images

10.36227/techrxiv.16713244 ◽

2021 ◽

Author(s):

Hamid Nasiri ◽

Seyyed Ali Alavi

Keyword(s):

Feature Selection ◽

Predictive Accuracy ◽

False Negative ◽

Selection Method ◽

Rt Pcr ◽

X Ray ◽

Negative Results ◽

Difficulty Breathing ◽

The new coronavirus (known as COVID-19) was first identified in Wuhan and quickly spread worldwide, wreaking havoc on the economy and people's everyday lives. Fever, cough, sore throat, headache, exhaustion, muscular aches, and difficulty breathing are all typical symptoms of COVID-19. A reliable detection technique is needed to identify affected individuals and care for them in the early stages of COVID-19 and reduce the virus's transmission. The most accessible method for COVID-19 identification is RT-PCR; however, due to its time commitment and false-negative results, alternative options must be sought. Indeed, compared to RT-PCR, chest CT scans and chest X-ray images provide superior results. Because of the scarcity and high cost of CT scan equipment, X-ray images are preferable for screening. In this paper, a pre-trained network, DenseNet169, was employed to extract features from X-ray images. Features were chosen by a feature selection method (ANOVA) to reduce computations and time complexity while overcoming the curse of dimensionality to improve predictive accuracy. Finally, selected features were classified by XGBoost. The ChestX-ray8 dataset, which was employed to train and evaluate the proposed method. This method reached 98.72% accuracy for two-class classification (COVID-19, healthy) and 92% accuracy for three-class classification (COVID-19, healthy, pneumonia).

Towards Optimization of Malware Detection using Chi-square Feature Selection on Ensemble Classifiers

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.d2359.0410421 ◽

2021 ◽

Vol 10 (4) ◽

pp. 254-262

Author(s):

*Fadare Oluwaseun Gbenga ◽

Adetunmbi Adebayo Olusola ◽

(Mrs) Oyinloye Oghenerukevwe Eloho ◽

Mogaji Stephen Alaba

Keyword(s):

Feature Selection ◽

Malware Detection ◽

Ensemble Methods ◽

Nearest Neighbors ◽

Selection Method ◽

Gradient Boosting ◽

K Nearest Neighbors ◽

Chi Square ◽

Extreme Gradient Boosting

The multiplication of malware variations is probably the greatest problem in PC security and the protection of information in form of source code against unauthorized access is a central issue in computer security. In recent times, machine learning has been extensively researched for malware detection and ensemble technique has been established to be highly effective in terms of detection accuracy. This paper proposes a framework that combines combining the exploit of both Chi-square as the feature selection method and eight ensemble learning classifiers on five base learners- K-Nearest Neighbors, Naïve Bayes, Support Vector Machine, Decision Trees, and Logistic Regression. K-Nearest Neighbors returns the highest accuracy of 95.37%, 87.89% on chi-square, and without feature selection respectively. Extreme Gradient Boosting Classifier ensemble accuracy is the highest with 97.407%, 91.72% with Chi-square as feature selection, and ensemble methods without feature selection respectively. Extreme Gradient Boosting Classifier and Random Forest are leading in the seven evaluative measures of chi-square as a feature selection method and ensemble methods without feature selection respectively. The study results show that the tree-based ensemble model is compelling for malware classification.

IoT Botnet Attack Detection Based on Optimized Extreme Gradient Boosting and Feature Selection

Sensors ◽

10.3390/s20216336 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6336 ◽

Cited By ~ 1

Author(s):

Mnahi Alqahtani ◽

Hassan Mathkour ◽

Mohamed Maher Ben Ismail

Keyword(s):

Feature Selection ◽

Large Scale ◽

Selection Method ◽

Attack Detection ◽

Gradient Boosting ◽

Fisher Score ◽

Detection Approach ◽

Extreme Gradient Boosting ◽

Iot Devices

Nowadays, Internet of Things (IoT) technology has various network applications and has attracted the interest of many research and industrial communities. Particularly, the number of vulnerable or unprotected IoT devices has drastically increased, along with the amount of suspicious activity, such as IoT botnet and large-scale cyber-attacks. In order to address this security issue, researchers have deployed machine and deep learning methods to detect attacks targeting compromised IoT devices. Despite these efforts, developing an efficient and effective attack detection approach for resource-constrained IoT devices remains a challenging task for the security research community. In this paper, we propose an efficient and effective IoT botnet attack detection approach. The proposed approach relies on a Fisher-score-based feature selection method along with a genetic-based extreme gradient boosting (GXGBoost) model in order to determine the most relevant features and to detect IoT botnet attacks. The Fisher score is a representative filter-based feature selection method used to determine significant features and discard irrelevant features through the minimization of intra-class distance and the maximization of inter-class distance. On the other hand, GXGBoost is an optimal and effective model, used to classify the IoT botnet attacks. Several experiments were conducted on a public botnet dataset of IoT devices. The evaluation results obtained using holdout and 10-fold cross-validation techniques showed that the proposed approach had a high detection rate using only three out of the 115 data traffic features and improved the overall performance of the IoT botnet attack detection process.

A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-021-03491-4 ◽

2021 ◽

Author(s):

Pratik Bhowal ◽

Subhankar Sen ◽

Ram Sarkar

Keyword(s):

Feature Selection ◽

Selection Method ◽

Coalition Game ◽

X Ray ◽

Wheat Kernel Variety Identification Based on a Large Near-Infrared Spectral Dataset and a Novel Deep Learning-Based Feature Selection Method

Frontiers in Plant Science ◽

10.3389/fpls.2020.575810 ◽

2020 ◽

Vol 11 ◽

Author(s):

Lei Zhou ◽

Chu Zhang ◽

Mohamed Farag Taha ◽

Xinhua Wei ◽

Yong He ◽

...

Keyword(s):

Feature Selection ◽

Deep Learning ◽

Near Infrared ◽

Selection Method ◽

Variety Identification ◽

Wheat Kernel ◽

Infrared Spectral

A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection

Applied Sciences ◽

10.3390/app9030437 ◽

2019 ◽

Vol 9 (3) ◽

pp. 437 ◽

Cited By ~ 9

Author(s):

Shen Su ◽

Yanbin Sun ◽

Xiangsong Gao ◽

Jing Qiu ◽

Zhihong Tian

Keyword(s):

Feature Selection ◽

Anomaly Detection ◽

False Negative ◽

Window Size ◽

Selection Method ◽

Sensor Data ◽

Data Correlation ◽

The Right ◽

Sensor Clustering

Selecting the right features for further data analysis is important in the process of equipment anomaly detection, especially when the origin data source involves high dimensional data with a low value density. However, existing researches failed to capture the fact that the sensor data are usually correlated (e.g., duplicated deployed sensors), and the correlations would be broken when anomalies occur with happen to the monitored equipment. In this paper, we propose to capture such sensor data correlation changes to improve the performance of IoT (Internet of Things) equipment anomaly detection. In our feature selection method, we first cluster correlated sensors together to recognize the duplicated deployed sensors according to sensor data correlations, and we monitor the data correlation changes in real time to select the sensors with correlation changes as the representative features for anomaly detection. To that end, (1) we conducted curve alignment for the sensor clustering; (2) we discuss the appropriate window size for data correlation calculation; (3) and adopted MCFS (Multi-Cluster Feature Selection) into our method to adapt to the online feature selection scenario. According to the experiment evaluation derived from real IoT equipment, we prove that our method manages to reduce the false negative of IoT equipment anomaly detection of 30% with almost the same level of false positive.

Finding COVID-19 from Chest X-rays using Deep Learning on a Small Dataset

10.36227/techrxiv.12083964.v2 ◽

2020 ◽

Cited By ~ 2

Author(s):

Lawrence Hall ◽

Dmitry Goldgof ◽

Rahul Paul ◽

Gregory M. Goldgof

Keyword(s):

Deep Learning ◽

Bacterial Pneumonia ◽

False Negative ◽

False Negative Rate ◽

X Rays ◽

Test Results ◽

X Ray ◽

Chest X Ray ◽

Small Dataset ◽

Children Under 5

Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and provide images for diagnosis quickly. This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease. We have obtained 122 chest X-rays of COVID-19 and over 4,000 chest X-rays of viral and bacterial pneumonia. Unfortunately, we missed the fact that the chest X-rays of viral and bacterial pneumonia came from children under 5 years old. So, this work shows that you can tell kids with pneumonia from COVID-19 adult cases which is not anyone's goal. However, data from adult chest X-rays of other causes of lung disease is needed to see if you can tell adult diseases apart.