This page has only limited features, please log in for full access.

Unclaimed
Azhar Imran
School of Software Engineering, Beijing University of Technology, Beijing, 100124, P. R. China

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 30 November 2020 in IEEE Access
Reads 0
Downloads 0

The idea of a shared economy becomes one of the companies as an enterprise type. Especially with the advanced development of digital smart devices and the internet, several forms of the mutual economy have been advanced in accord with the need for sharing of separate income. Shareable commodity and digital content are also seeking to utilize. When digital content is used as a sharing economy, various possible threats may arise in the course of transactions, the potential for theft, alteration, and hacking of contents. This paper presents a comprehensive overview of the security and privacy of Blockchain. Blockchain promise transparent, tamper-proof and secure systems that can enable novel solutions, especially when combined with smart contracts. In this research, we proposed a content protection and transaction method using Blockchain Ethereum Technology. The encryption algorithm is incorporated in proposed system to make transparent transactions and it is also implemented on content itself to prevent from smart forgery and hacking. The experimental results signify that the proposed method has strong potential to enhance transactions transparency by minimizing the security threats in digital content transactions.

ACS Style

Umair Khan; Zhang Yong An; Azhar Imran. A Blockchain Ethereum Technology-Enabled Digital Content: Development of Trading and Sharing Economy Data. IEEE Access 2020, 8, 217045 -217056.

AMA Style

Umair Khan, Zhang Yong An, Azhar Imran. A Blockchain Ethereum Technology-Enabled Digital Content: Development of Trading and Sharing Economy Data. IEEE Access. 2020; 8 (99):217045-217056.

Chicago/Turabian Style

Umair Khan; Zhang Yong An; Azhar Imran. 2020. "A Blockchain Ethereum Technology-Enabled Digital Content: Development of Trading and Sharing Economy Data." IEEE Access 8, no. 99: 217045-217056.

Journal article
Published: 02 September 2020 in IEEE Access
Reads 0
Downloads 0

Patients with breast cancer are prone to serious health-related complications with higher mortality. The primary reason might be a misinterpretation of radiologists in recognizing suspicious lesions due to technical issues in imaging qualities and heterogeneous breast densities which increases the false- (positive and negative) ratio. Early intervention is significant in establishing an up-to-date prognosis process which can successfully mitigate complications of disease with higher recovery. The manual screening of breast abnormalities through traditional machine learning schemes misinterpret the inconsistent featureextraction process which poses a problem, i.e., patients being called-back for biopsies to eliminates the suspicions. However, several deep learning-based methods have been developed for reliable breast cancer prognosis and classification but very few of them provided a comprehensive overview of lesions segmentation. This research focusses on providing benefits and risks of breast multi-imaging modalities, segmentation schemes, feature extraction, classification of breast abnormalities through state-of-the-art deep learning approaches. This research also explores various well-known databases using "Breast Cancer" keyword to present a comprehensive survey on existing diagnostic schemes to open-up new research challenges for radiologists and researchers to intervene as early as possible to develop an efficient and reliable breast cancer prognosis system using prominent deep learning schemes.

ACS Style

Tariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Azhar Imran; Khalil Ur Rehman. A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities. IEEE Access 2020, 8, 165779 -165809.

AMA Style

Tariq Mahmood, Jianqiang Li, Yan Pei, Faheem Akhtar, Azhar Imran, Khalil Ur Rehman. A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities. IEEE Access. 2020; 8 (99):165779-165809.

Chicago/Turabian Style

Tariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Azhar Imran; Khalil Ur Rehman. 2020. "A Brief Survey on Breast Cancer Diagnostic With Deep Learning Schemes Using Multi-Image Modalities." IEEE Access 8, no. 99: 165779-165809.

Research article
Published: 17 August 2020 in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization
Reads 0
Downloads 0

Cataract is the most prevalent cause of blindness worldwide, which accounts for more than 51% of overall blindness. The early detection of cataract can salvage impaired vision leading to blindness. Most of the existing cataract classification systems are based on traditional machine learning methods with hand-engineered features. The manual extraction of retinal features is generally a time-taking process and requires professional ophthalmologists. Convolutional neural network (CNN) is a widely accepted model for automatic feature extraction, but it necessitates a larger dataset to evade overfitting problems. Contrarily, classification using SVM has great generalisation power to elucidate small-sample dataset. Therefore, we proposed a hybrid model by integrating deep learning models and SVM for 4-class cataract classification. The transfer learning-based models (AlexNet, VGGNet, ResNet) are employed for automatic feature extraction and SVM performs as a recogniser. The proposed architecture evaluated on 8030 retinal images with strong feature extraction and classification techniques has achieved 95.65% of accuracy. The results of this study have verified that the proposed method outperforms conventional methods and can provide a reference for other retinal diseases.

ACS Style

Azhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Ji-Jiang Yang; Yanping Dang. Automated identification of cataract severity using retinal fundus images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 2020, 8, 691 -698.

AMA Style

Azhar Imran, Jianqiang Li, Yan Pei, Faheem Akhtar, Ji-Jiang Yang, Yanping Dang. Automated identification of cataract severity using retinal fundus images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization. 2020; 8 (6):691-698.

Chicago/Turabian Style

Azhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Ji-Jiang Yang; Yanping Dang. 2020. "Automated identification of cataract severity using retinal fundus images." Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization 8, no. 6: 691-698.

Journal article
Published: 14 August 2020 in IEEE Access
Reads 0
Downloads 0

Electronic health records are used to extract patient’s information instantly and remotely, which can help to keep track of patients’ due dates for checkups, immunizations, and to monitor health performance. The Health Insurance Portability and Accountability Act (HIPAA) in the USA protects the patient data confidentiality, but it can be used if data is re-identified using ’HIPAA Safe Harbor’ technique. Usually, this re-identification is performed manually, which is very laborious and time captivating exertion. Various techniques have been proposed for automatic extraction of useful information, and accurate diagnosis of diseases. Most of these methods are based on Machine Learning and Deep Learning Methods, while the auxiliary diagnosis is performed using Rule-based methods. This review focuses on recently published papers, which are categorized into Rule-Based Methods, Machine Learning (ML) Methods, and Deep Learning (DL) Methods. Particularly, ML methods are further categorized into Support Vector Machine Methods (SVM), Bayes Methods, and Decision Tree Methods (DT). DL methods are decomposed into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Network (DBN) and Autoencoders (AE) methods. The objective of this survey paper is to highlight both the strong and weak points of various proposed techniques in the disease diagnosis. Moreover, we present advantage, disadvantage, focused disease, dataset employed, and publication year of each category.

ACS Style

Jahanzaib Latif; Chuangbai Xiao; Shanshan Tu; Sadaqat Ur Rehman; Azhar Imran; Anas Bilal. Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review. IEEE Access 2020, 8, 150489 -150513.

AMA Style

Jahanzaib Latif, Chuangbai Xiao, Shanshan Tu, Sadaqat Ur Rehman, Azhar Imran, Anas Bilal. Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review. IEEE Access. 2020; 8 (99):150489-150513.

Chicago/Turabian Style

Jahanzaib Latif; Chuangbai Xiao; Shanshan Tu; Sadaqat Ur Rehman; Azhar Imran; Anas Bilal. 2020. "Implementation and Use of Disease Diagnosis Systems for Electronic Medical Records Based on Machine Learning: A Complete Review." IEEE Access 8, no. 99: 150489-150513.

Article
Published: 10 June 2020 in Multimedia Tools and Applications
Reads 0
Downloads 0

In recent years, a rapid rise in the incidence of Large for gestational age (LGA) neonate is reported, and health care professionals employed themselves to discover the cause. Utmost of the previous studies were cohort or observational studies that employed simple linear or multivariate regression models, and very few of them employed machine learning (ML) schemes. Therefore, this research proposes to use 1 expert-driven and 7 automated feature selection schemes with well-known ML classifiers using 10 and 30 folds cross-validation. The induced results were compared with existing baselines, and Wilcoxon signed-rank test and the Friedman test were also introduced to verify the results. The ranked 20 features of the proposed expert-driven feature selection scheme outperformed amongst 7 automated feature selection schemes with a prediction precision, accuracy, and AUC scores of 0.94606, 0.84529, and 0.86492, respectively. Out of twenty features, eleven features were found similar to twenty ranked features of the automated feature selection schemes subsets. The classification results of the extracted features were utmost identical to the results of twenty features subset proposed by the expert-driven feature selection scheme. Therefore, we suggest pediatricians to refresh LGA diagnosis process with the proposed scheme because of its practical usage and maximum expert involvement.

ACS Style

Faheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Bo Liu. Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data. Multimedia Tools and Applications 2020, 79, 34047 -34077.

AMA Style

Faheem Akhtar, Jianqiang Li, Yan Pei, Azhar Imran, Asif Rajput, Muhammad Azeem, Bo Liu. Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data. Multimedia Tools and Applications. 2020; 79 (45-46):34047-34077.

Chicago/Turabian Style

Faheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Bo Liu. 2020. "Diagnosis of large-for-gestational-age infants using a semi-supervised feature learned from expert and data." Multimedia Tools and Applications 79, no. 45-46: 34047-34077.

Conference paper
Published: 15 May 2020 in IOP Conference Series: Earth and Environmental Science
Reads 0
Downloads 0

The rapid and accurate identification of tomato diseases is the basis of crop disease control. In order to achieve accurate identification of tomato diseases, this paper first explores the impact of model depth and the presence or absence of mixup data enhancement on the ResNet model. Experimental results show that using the data set enhanced by mixup can effectively make the model more robust. At the same time, ResNet34 depth model recognition accuracy is higher. Considering the differences in classification accuracy and calculation speed between ResNet and SE-ResNet, the paper chooses the SE-ResNet model as the basis of the network structure. We tuned the model and built a SE-ResNet network that is more suitable for tomato disease identification. The experimental results show that the accuracy of training SE-ResNet using datasets such as mixup is 88.83%. Our model can effectively identify various tomato diseases and the severity of each tomato disease.

ACS Style

Tao Zhang; Xiankun Zhu; Yiqing Liu; Kun Zhang; Azhar Imran. Deep Learning Based Classification for Tomato Diseases Recognition. IOP Conference Series: Earth and Environmental Science 2020, 474, 1 .

AMA Style

Tao Zhang, Xiankun Zhu, Yiqing Liu, Kun Zhang, Azhar Imran. Deep Learning Based Classification for Tomato Diseases Recognition. IOP Conference Series: Earth and Environmental Science. 2020; 474 ():1.

Chicago/Turabian Style

Tao Zhang; Xiankun Zhu; Yiqing Liu; Kun Zhang; Azhar Imran. 2020. "Deep Learning Based Classification for Tomato Diseases Recognition." IOP Conference Series: Earth and Environmental Science 474, no. : 1.

Conference paper
Published: 26 February 2020 in Lecture Notes in Electrical Engineering
Reads 0
Downloads 0

Cataract is one of the prevailing cause of blindness in the industrial world that accounts for more than 50% of blindness. The early detection of cataract can protect serious threats of visual impairment. Most of the existing work is based on manual extraction of features, but this paper aims at automatic detection of a cataract into its different grades using deep convolutional neural network integrated with data augmentation techniques. The Gaussian-scale space theory and the general data augmentation settings are used to improve the dataset in terms of quality and quantity, which lead to overcome the issues of the unbalanced dataset. The training and testing of the proposed model are performed on both the original dataset and the augmented dataset. The model accuracy of the convolutional neural network with augmented dataset presented in this paper is 0.9691, which shows an optimal performance compared with the original dataset, and other methods.

ACS Style

Azhar Imran; Jianqiang Li; Yan Pei; Fawaz Mokbal; Ji-Jiang Yang; Qing Wang. Enhanced Intelligence Using Collective Data Augmentation for CNN Based Cataract Detection. Lecture Notes in Electrical Engineering 2020, 148 -160.

AMA Style

Azhar Imran, Jianqiang Li, Yan Pei, Fawaz Mokbal, Ji-Jiang Yang, Qing Wang. Enhanced Intelligence Using Collective Data Augmentation for CNN Based Cataract Detection. Lecture Notes in Electrical Engineering. 2020; ():148-160.

Chicago/Turabian Style

Azhar Imran; Jianqiang Li; Yan Pei; Fawaz Mokbal; Ji-Jiang Yang; Qing Wang. 2020. "Enhanced Intelligence Using Collective Data Augmentation for CNN Based Cataract Detection." Lecture Notes in Electrical Engineering , no. : 148-160.

Conference paper
Published: 01 January 2020 in 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
Reads 0
Downloads 0

Social media such as Facebook and Twitter has gained strong attention for sharing information, worldwide connectivity and brand marketing all over the globe. The prevalent use of social networking sites has produced exceptional amounts of data. The mining of these social media applications has its potential to excerpt illegal activities which may be helpful for individuals, business and customers. The mining of data obtained from Facebook and Twitter, can be used to predict emotions of stakeholder and its analysis can provide very valuable information regarding behavioral inclinations of the writer. The automatic sentiment analysis to detect the emotional content in textual data has been widely used in many research fields. Most of the existing sentiment analysis techniques are tailored for English Language. This paper presents Urdu based antisocial behavior detection (ASB). We aim in particular to establish antisocial behavior detection method and defining its emotional state. We are intended to introduce a sentiment analysis based behavioral model that describes emotions related to antisocial behavior. In addition to describing the negative emotional state, our model will also use the concept of behavioral tendencies and evidence to predict the possible behavior of social media activists based on input text. We will outline the design of behavior detection systems based on social media posts. The learning algorithm learn about emotions from social media posts in the Roman Urdu language to predict user’s behavior regarding any specific post. The results of this study has verified that our method has outperformed state-of-the-art methods in terms of accuracy. A bilingual or multilingual ASB approach can be made in future.

ACS Style

Muhammad Sohail; Azhar Imran; Hameed Ur Rehman; Muhammad Salman. Anti-Social Behavior Detection in Urdu Language Posts of Social Media. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2020, 1 -7.

AMA Style

Muhammad Sohail, Azhar Imran, Hameed Ur Rehman, Muhammad Salman. Anti-Social Behavior Detection in Urdu Language Posts of Social Media. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). 2020; ():1-7.

Chicago/Turabian Style

Muhammad Sohail; Azhar Imran; Hameed Ur Rehman; Muhammad Salman. 2020. "Anti-Social Behavior Detection in Urdu Language Posts of Social Media." 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) , no. : 1-7.

Conference paper
Published: 01 January 2020 in 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
Reads 0
Downloads 0

Developers usually copy and paste code during software development that results in similar patches of code spread in different files. Such type of code clones cause difficulty in making changes and result in bugs. In this study, we want to analyze the extent of the use of code cloning in the development of mobile applications and their effect on post-release bugs. This information should help developers and designers to develop and design better applications and software that present their results to the best advantage.

ACS Style

Muhammad Zubair Azeem; Azhar Imran; Faheem Akhtar; Ahsan Wajahat; Jahanzaib Latif; Suhail Ahmed Memon. Effects of Code Cloning in Mobile Applications. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2020, 1 -6.

AMA Style

Muhammad Zubair Azeem, Azhar Imran, Faheem Akhtar, Ahsan Wajahat, Jahanzaib Latif, Suhail Ahmed Memon. Effects of Code Cloning in Mobile Applications. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). 2020; ():1-6.

Chicago/Turabian Style

Muhammad Zubair Azeem; Azhar Imran; Faheem Akhtar; Ahsan Wajahat; Jahanzaib Latif; Suhail Ahmed Memon. 2020. "Effects of Code Cloning in Mobile Applications." 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) , no. : 1-6.

Conference paper
Published: 01 December 2019 in 2019 IEEE Symposium Series on Computational Intelligence (SSCI)
Reads 0
Downloads 0

A cataract is the prevailing cause of visual impairment in the modern world. The detection of cataract at early stages can lessen the risk of blindness. This study presents an automated system for cataract detection and grading based on retinal images. The system is comprised of image acquisition, preprocessing, feature extraction, classifier building, and cataract detection and grading. The preprocessing steps such as green channel extraction, histogram equalization, and top-bottom hat transformation are used to improve the quality of retinal images. The wavelet and texture features are extracted from the fundus image for building a classifier. A combination of SOM (Self-Organizing Maps) and RBF (Radial Basis Function) neural network has been taken to obtain better prediction accuracy of cataract. SOM-RBF neural network is evaluated on Tongren dataset with 8030 subjects categorized into four classes: Normal, Mild, Mature, and Severe. The proposed method achieved 95.3% and 91.7% of accuracy for cataract detection and grading tasks, respectively. The experimental results indicate that the proposed method performs better than the traditional RBF and other baseline methods.

ACS Style

Azhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Ji-Jiang Yang; Qing Wang. Cataract Detection and Grading with Retinal Images Using SOM-RBF Neural Network. 2019 IEEE Symposium Series on Computational Intelligence (SSCI) 2019, 2626 -2632.

AMA Style

Azhar Imran, Jianqiang Li, Yan Pei, Faheem Akhtar, Ji-Jiang Yang, Qing Wang. Cataract Detection and Grading with Retinal Images Using SOM-RBF Neural Network. 2019 IEEE Symposium Series on Computational Intelligence (SSCI). 2019; ():2626-2632.

Chicago/Turabian Style

Azhar Imran; Jianqiang Li; Yan Pei; Faheem Akhtar; Ji-Jiang Yang; Qing Wang. 2019. "Cataract Detection and Grading with Retinal Images Using SOM-RBF Neural Network." 2019 IEEE Symposium Series on Computational Intelligence (SSCI) , no. : 2626-2632.

Journal article
Published: 14 October 2019 in Applied Sciences
Reads 0
Downloads 0

An accurate and efficient Large-for-Gestational-Age (LGA) classification system isdeveloped to classify a fetus as LGA or non-LGA, which has the potential to assist paediatricians andexperts in establishing a state-of-the-art LGA prognosis process. The performance of the proposedscheme is validated by using LGA dataset collected from the National Pre-Pregnancy and ExaminationProgram of China (2010–2013). A master feature vector is created to establish primarily datapre-processing, which includes a features’ discretization process and the entertainment of missingvalues and data imbalance issues. A principal feature vector is formed using GridSearch-basedRecursive Feature Elimination with Cross-Validation (RFECV) + Information Gain (IG) featureselection scheme followed by stacking to select, rank, and extract significant features from the LGAdataset. Based on the proposed scheme, different features subset are identified and provided tofour different machine learning (ML) classifiers. The proposed GridSearch-based RFECV+IG featureselection scheme with stacking using SVM (linear kernel) best suits the said classification processfollowed by SVM (RBF kernel) and LR classifiers. The Decision Tree (DT) classifier is not suggestedbecause of its low performance. The highest prediction precision, recall, accuracy, Area Underthe Curve (AUC), specificity, and F1 scores of 0.92, 0.87, 0.92, 0.95, 0.95, and 0.89 are achievedwith SVM (linear kernel) classifier using top ten principal features subset, which is, in fact higherthan the baselines methods. Moreover, almost every classification scheme best performed with tenprincipal feature subsets. Therefore, the proposed scheme has the potential to establish an efficientLGA prognosis process using gestational parameters, which can assist paediatricians and experts toimprove the health of a newborn using computer aided-diagnostic system.

ACS Style

Faheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Qing Wang; Li; Pei; Wang. Diagnosis and Prediction of Large-For-Gestational-Age Fetus Using the Stacked GeneralizationMethod. Applied Sciences 2019, 9, 4317 .

AMA Style

Faheem Akhtar, Jianqiang Li, Yan Pei, Azhar Imran, Asif Rajput, Muhammad Azeem, Qing Wang, Li, Pei, Wang. Diagnosis and Prediction of Large-For-Gestational-Age Fetus Using the Stacked GeneralizationMethod. Applied Sciences. 2019; 9 (20):4317.

Chicago/Turabian Style

Faheem Akhtar; Jianqiang Li; Yan Pei; Azhar Imran; Asif Rajput; Muhammad Azeem; Qing Wang; Li; Pei; Wang. 2019. "Diagnosis and Prediction of Large-For-Gestational-Age Fetus Using the Stacked GeneralizationMethod." Applied Sciences 9, no. 20: 4317.

Journal article
Published: 16 August 2019 in IEEE Access
Reads 0
Downloads 0

The blood vessels are the primary anatomical structure that can be visible in retinal images. The segmentation of retinal blood vessels has been accepted worldwide for the diagnosis of both cardiovascular (CVD) and retinal diseases. Thus, it requires an appropriate vessel segmentation method for automatic detection of retinal diseases such as diabetic retinopathy and cataract. The detection of retinal diseases using computer-aided diagnosis (CAD) can help people to avoid the risks of visual impairment and save medical resources. This survey presents a comparative analysis of various machine learning and deep learning-based methods for automated blood vessel segmentation in retinal images. This paper briefly describes fundus photography, publicly available retinal databases, pre-processing and post-processing techniques for retinal vessels segmentation. A comprehensive review of the state of the art supervised and unsupervised blood vessel segmentation methodologies are presented in this paper. The objective of this study is to establish a professional structure to familiarize an individual with up-to-date vessel segmentation techniques. Moreover, we compared these approaches to the dataset, evaluation metrics, pre-processing and post-processing steps, feature extraction, segmentation methods, and induced results.

ACS Style

Azhar Imran; Jianqiang Li; Yan Pei; Ji-Jiang Yang; Qing Wang. Comparative Analysis of Vessel Segmentation Techniques in Retinal Images. IEEE Access 2019, 7, 114862 -114887.

AMA Style

Azhar Imran, Jianqiang Li, Yan Pei, Ji-Jiang Yang, Qing Wang. Comparative Analysis of Vessel Segmentation Techniques in Retinal Images. IEEE Access. 2019; 7 ():114862-114887.

Chicago/Turabian Style

Azhar Imran; Jianqiang Li; Yan Pei; Ji-Jiang Yang; Qing Wang. 2019. "Comparative Analysis of Vessel Segmentation Techniques in Retinal Images." IEEE Access 7, no. : 114862-114887.

Journal article
Published: 08 July 2019 in IEEE Access
Reads 0
Downloads 0

Dynamic web applications play a vital role in providing resources manipulation and interaction among clients and servers. The features presently supported by browsers have raised business opportunities, by supplying high interactivity in web-based services, like web banking, e-commerce, social networking, forums, and at the same time, these features have brought serious risks and increased vulnerabilities in web applications that enable Cyber-attacks to be executed. One of the common high-risk cyber-attack of web application vulnerabilities is Cross-Site Scripting (XSS). Nowadays, XSS is still dramatically increasing and considered as one of the most severe threats for organizations, users, and developers. If the ploy is successful, the victim is at the mercy of the cybercriminals. In this research, a robust artificial neural network-based multilayer perceptron (MLP) scheme integrated with the dynamic feature extractor is proposed for XSS attack detection. The detection scheme adopts a large real-world dataset, the dynamic features extraction mechanism, and MLP model, which successfully surpassed several tests on an employed unique dataset under careful experimentation, and achieved promising and state-of-the-art results with accuracy, detection probabilities, false positive rate, and AUC-ROC scores of 99.32%, 98.35%, 0.3%, and 90.02% respectively. Therefore, it has the potentials to be applied for XSS based attack detection in either the client-side or the server-side.

ACS Style

Fawaz Mahiuob Mohammed Mokbal; Wang Dan; Azhar Imran; Lin Jiuchuan; Faheem Akhtar; Wang Xiaoxi. MLPXSS: An Integrated XSS-Based Attack Detection Scheme in Web Applications Using Multilayer Perceptron Technique. IEEE Access 2019, 7, 100567 -100580.

AMA Style

Fawaz Mahiuob Mohammed Mokbal, Wang Dan, Azhar Imran, Lin Jiuchuan, Faheem Akhtar, Wang Xiaoxi. MLPXSS: An Integrated XSS-Based Attack Detection Scheme in Web Applications Using Multilayer Perceptron Technique. IEEE Access. 2019; 7 (99):100567-100580.

Chicago/Turabian Style

Fawaz Mahiuob Mohammed Mokbal; Wang Dan; Azhar Imran; Lin Jiuchuan; Faheem Akhtar; Wang Xiaoxi. 2019. "MLPXSS: An Integrated XSS-Based Attack Detection Scheme in Web Applications Using Multilayer Perceptron Technique." IEEE Access 7, no. 99: 100567-100580.

Conference paper
Published: 19 May 2019 in Lecture Notes in Electrical Engineering
Reads 0
Downloads 0

Infants with gestational weight above the 90th percentile of same gestational age are termed as Large for gestational age (LGA). LGA suffers from serious complications during and after the antepartum period because they don’t get earlier identification of the disease. Earlier recognition of LGA infant could slow progression and prevent further complication of the disease. In Medical science prevention and mitigation of disease requires examination of certain biochemical indicators (BI). Machine Learning (ML) has been evolved and envisioned as a tool to predict LGA infants with most deterministic characteristics. This study aims to identify most deterministic BI for LGA prediction with minimal computational overhead. To the best of my knowledge, this is the first time a study is carried out to identify most deterministic BI associated with LGA and to develop LGA prediction model using advanced ML techniques in the Chinese population. To develop an effective LGA prediction model, we used Information Gain (IG) an entropy-based feature selection method to filter out most deterministic BI for early identification of the disease. Finally, to verify the idea of applying IG, four widely used ML classifiers were used considering Precision and AUC as a performance metrics. The drastic improvement in precision from 33 to 71% validates our idea of applying IG to mine the most deterministic BI for early prediction of LGA.

ACS Style

Faheem Akhtar; Jianqiang Li; Yu Guan; Azhar Imran; Muhammad Azeem. Monitoring Bio-Chemical Indicators Using Machine Learning Techniques for an Effective Large for Gestational Age Prediction Model with Reduced Computational Overhead. Lecture Notes in Electrical Engineering 2019, 130 -137.

AMA Style

Faheem Akhtar, Jianqiang Li, Yu Guan, Azhar Imran, Muhammad Azeem. Monitoring Bio-Chemical Indicators Using Machine Learning Techniques for an Effective Large for Gestational Age Prediction Model with Reduced Computational Overhead. Lecture Notes in Electrical Engineering. 2019; ():130-137.

Chicago/Turabian Style

Faheem Akhtar; Jianqiang Li; Yu Guan; Azhar Imran; Muhammad Azeem. 2019. "Monitoring Bio-Chemical Indicators Using Machine Learning Techniques for an Effective Large for Gestational Age Prediction Model with Reduced Computational Overhead." Lecture Notes in Electrical Engineering , no. : 130-137.

E literature review
Published: 10 May 2019 in International Journal of Crowd Science
Reads 0
Downloads 0

Purpose Simulation is a well-known technique for using computers to imitate or simulate the operations of various kinds of real-world facilities or processes. The facility or process of interest is usually called a system, and to study it scientifically, we often have to make a set of assumptions about how it works. These assumptions, which usually take the form of mathematical or logical relationships, constitute a model that is used to gain some understanding of how the corresponding system behaves, and the quality of these understandings essentially depends on the credibility of given assumptions or models, known as VV&A (verification, validation and accreditation). The main purpose of this paper is to present an in-depth theoretical review and analysis for the application of VV&A in large-scale simulations. Design/methodology/approach After summarizing the VV&A of related research studies, the standards, frameworks, techniques, methods and tools have been discussed according to the characteristics of large-scale simulations (such as crowd network simulations). Findings The contributions of this paper will be useful for both academics and practitioners for formulating VV&A in large-scale simulations (such as crowd network simulations). Originality/value This paper will help researchers to provide support of a recommendation for formulating VV&A in large-scale simulations (such as crowd network simulations).

ACS Style

Yanan Wang; Jianqiang Li; Sun Hongbo; Yuan Li; Faheem Akhtar; Azhar Imran. A survey on VV&A of large-scale simulations. International Journal of Crowd Science 2019, 3, 63 -86.

AMA Style

Yanan Wang, Jianqiang Li, Sun Hongbo, Yuan Li, Faheem Akhtar, Azhar Imran. A survey on VV&A of large-scale simulations. International Journal of Crowd Science. 2019; 3 (1):63-86.

Chicago/Turabian Style

Yanan Wang; Jianqiang Li; Sun Hongbo; Yuan Li; Faheem Akhtar; Azhar Imran. 2019. "A survey on VV&A of large-scale simulations." International Journal of Crowd Science 3, no. 1: 63-86.

Review
Published: 01 January 2019 in 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
Reads 0
Downloads 0

Machine and deep learning algorithms are rapidly growing in dynamic research of medical imaging. Currently, substantial efforts are developed for the enrichment of medical imaging applications using these algorithms to diagnose the errors in disease diagnostic systems which may result in extremely ambiguous medical treatments. Machine and deep learning algorithms are important ways in medical imaging to predict the symptoms of early disease. Deep learning techniques, in specific convolutional networks, have promptly developed a methodology of special for investigating medical images. It uses the supervised or unsupervised algorithms using some specific standard dataset to indicate the predictions. We survey image classification, object detection, pattern recognition, reasoning etc. concepts in medical imaging. These are used to improve the accuracy by extracting the meaningful patterns for the specific disease in medical imaging. These ways also indorse the decision-making procedure. The major aim of this survey is to highlight the machine learning and deep learning techniques used in medical images. We intended to provide an outline for researchers to know the existing techniques carried out for medical imaging, highlight the advantages and drawbacks of these algorithms, and to discuss the future directions. For the study of multi-dimensional medical data, machine and deep learning provide a commendable technique for creation of classification and automatic decision making. This paper provides a survey of medical imaging in the machine and deep learning methods to analyze distinctive diseases. It carries consideration concerning the suite of these algorithms which can be used for the investigation of diseases and automatic decision-making.

ACS Style

Jahanzaib Latif; Chuangbai Xiao; Azhar Imran; Shanshan Tu. Medical Imaging using Machine Learning and Deep Learning Algorithms: A Review. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2019, 1 -5.

AMA Style

Jahanzaib Latif, Chuangbai Xiao, Azhar Imran, Shanshan Tu. Medical Imaging using Machine Learning and Deep Learning Algorithms: A Review. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). 2019; ():1-5.

Chicago/Turabian Style

Jahanzaib Latif; Chuangbai Xiao; Azhar Imran; Shanshan Tu. 2019. "Medical Imaging using Machine Learning and Deep Learning Algorithms: A Review." 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) , no. : 1-5.

Conference paper
Published: 01 January 2019 in 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
Reads 0
Downloads 0

Nowadays, computers are used everywhere to carry out daily routine tasks. The input devices i.e. keyboard or mouse are used to feed input to computers. The surveillance of input devices is much important as monitoring the users logging activity. A keylogger also referred as a keystroke logger, is a software or hardware device which monitors every keystroke typed by a user. Keylogger runs in the background that user cannot identify its presence. It can be used as monitoring software for parents to keep an eye on children activity on computers and for the owner to monitor their employees. A keylogger (which can be either spyware or software) is a kind of surveillance software that has the ability to store every keystroke in a log file. It is very dangerous for those systems which use their system for daily transaction purpose i.e. Online Banking Systems. A keylogger is a tool, made to save all the keystroke generated through the machine which sanctions hackers to steal sensitive information without user's intention. Privileged also relies on the access for both implementation and placement by Kernel keylogger, the entire message transmitted from the keyboard drivers, while the programmer simply relies on kernel level facilities that interrupt. This certainly needs a large power and expertise for real and error-free execution. However, it has been observed that 90% of the current keyloggers are running in userspace so they do not need any permission for execution. Our aim is focused on detecting userspace keylogger. Our intention is to forbid userspace keylogger from stealing confidential data and information. For this purpose, we use a strategy which is clearly based on detection manner techniques for userspace keyloggers, an essential category of malware packages. We intend to achieve this goal by matching I/O of all processes with some simulated activity of the user, and we assert detection in case the two are highly correlated. The rationale behind this is that the more powerful stream of keystrokes, the more I/O operations are required by the keylogger to log the keystrokes into the file.

ACS Style

Ahsan Wajahat; Azhar Imran; Jahanzaib Latif; Ahsan Nazir; Anas Bilal. A Novel Approach of Unprivileged Keylogger Detection. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2019, 1 -6.

AMA Style

Ahsan Wajahat, Azhar Imran, Jahanzaib Latif, Ahsan Nazir, Anas Bilal. A Novel Approach of Unprivileged Keylogger Detection. 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). 2019; ():1-6.

Chicago/Turabian Style

Ahsan Wajahat; Azhar Imran; Jahanzaib Latif; Ahsan Nazir; Anas Bilal. 2019. "A Novel Approach of Unprivileged Keylogger Detection." 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) , no. : 1-6.

Conference paper
Published: 01 October 2018 in 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Reads 0
Downloads 0

Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is considered the most common cause of blindness. Early diagnosis and treatment can reduce the suffering of patients and prevent visual impairment from turning into blindness. Recently, cataract diagnosis applying pattern recognition is in a rising period. For retinal fundus images, the task is usually cataract classification. However, it needs complex manual processing, which demands dexterous people and time taking exertion. Besides, it faces the challenge of effective interpretability and dependability. In this paper, we develop a deep-learning algorithm to intuitively identify cataract attributes to solve these limitations. Our model, is a 18(50)-layer convolutional neural network that inputs retinal image in G channel and outputs the prediction with heatmap. The heatmap localizes the areas where most indicative of different levels of cataract. Furthermore, we extend the training strategy for the corresponding task, which aims at improving the performance of the network. Comparing with other methods in cataract classification, we succeeded to achieve state of the art accuracy of proposed method on detection and grading task. Most importantly, our model provides a compelling reason via localizing the areas revealing cataract in the image.

ACS Style

Jianqiang Li; Xi Xu; Yu Guan; Azhar Imran; Bo Liu; Li Zhang; Ji-Jiang Yang; Qing Wang; Liyang Xie. Automatic Cataract Diagnosis by Image-Based Interpretability. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 2018, 3964 -3969.

AMA Style

Jianqiang Li, Xi Xu, Yu Guan, Azhar Imran, Bo Liu, Li Zhang, Ji-Jiang Yang, Qing Wang, Liyang Xie. Automatic Cataract Diagnosis by Image-Based Interpretability. 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2018; ():3964-3969.

Chicago/Turabian Style

Jianqiang Li; Xi Xu; Yu Guan; Azhar Imran; Bo Liu; Li Zhang; Ji-Jiang Yang; Qing Wang; Liyang Xie. 2018. "Automatic Cataract Diagnosis by Image-Based Interpretability." 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) , no. : 3964-3969.

Conference paper
Published: 01 July 2018 in 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)
Reads 0
Downloads 0

Retinal fundus image can perceive deep-seated blood vessels in the human body in a non-invasive manner. Retinal blood vessels are the primary anatomical structure that can be visible in the fundus image, while changes in the structural feature of retinal blood vessels cannot only reflect all sort of pathological changes but also serve as an important evidence for diagnosing cataract and other diseases. Automatic fundus image processing and analyzing in the computer has a significant effect on the auxiliary medical diagnosis. Moreover, the blood vessels extracted can be used as a feature for the classification of cataract fundus images. Most of the blood vessel extraction methods often used a heuristic feature set that are usually be extracted manually. For the limitations of current methods, we propose to use deep learning to identify blood vessels, which can perform automatic feature learning. We collected the dataset containing fundus images of 5620 patients for the extraction of blood vessels. We then performed Preprocessing by extracting green channel components and histogram equalization. We also present FCN structure in the fusion of dual sources in which preprocessed grayscale image and the edge information processed by the Sobel operators are used as an input. We also document that FCN enhance the richness of the input features and improve the accuracy. It can be concluded that the proposed method achieves the optimal accuracy for recognizing blood vessels of patients with cataract. Moreover, the accuracy of extracting normal fundus vessels reaches 94.91%. Furthermore, we are intended to use this proposed method for the vascular identification of other medical images.

ACS Style

Jianqiang Li; Qidong Hu; Azhar Imran; Li Zhang; Ji-Jiang Yang; Qing Wang. Vessel Recognition of Retinal Fundus Images Based on Fully Convolutional Network. 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) 2018, 2, 413 -418.

AMA Style

Jianqiang Li, Qidong Hu, Azhar Imran, Li Zhang, Ji-Jiang Yang, Qing Wang. Vessel Recognition of Retinal Fundus Images Based on Fully Convolutional Network. 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). 2018; 2 ():413-418.

Chicago/Turabian Style

Jianqiang Li; Qidong Hu; Azhar Imran; Li Zhang; Ji-Jiang Yang; Qing Wang. 2018. "Vessel Recognition of Retinal Fundus Images Based on Fully Convolutional Network." 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) 2, no. : 413-418.

Journal article
Published: 08 March 2018 in International Journal of Education and Management Engineering
Reads 0
Downloads 0
ACS Style

Azhar Imran; Muhammad Faiyaz; Faheem Akhtar. An Enhanced Approach for Quantitative Prediction of Personality in Facebook Posts. International Journal of Education and Management Engineering 2018, 8, 8 -19.

AMA Style

Azhar Imran, Muhammad Faiyaz, Faheem Akhtar. An Enhanced Approach for Quantitative Prediction of Personality in Facebook Posts. International Journal of Education and Management Engineering. 2018; 8 (2):8-19.

Chicago/Turabian Style

Azhar Imran; Muhammad Faiyaz; Faheem Akhtar. 2018. "An Enhanced Approach for Quantitative Prediction of Personality in Facebook Posts." International Journal of Education and Management Engineering 8, no. 2: 8-19.