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Mr. Rao Faizan Ali
Universiti Teknologi PETRONAS

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0 Business Process Management
0 Information Management
0 Information Security
0 Information Security Management
0 Information System

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Information Security
Information Security Policy Compliance

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Short Biography

received a bachelor's degree in computer science from COMSATS University Islamabad, Pakistan, and the M.Phil. Degree in computer science from the University of Management and Technology, Lahore, Pakistan. He has 07 years' experience in teaching and research. He has been with various information security positions in financial, consulting, academia, and government sectors. He is pursuing a Ph.D. degree with University Technology PETRONAS, Malaysia. He is currently working as a research officer in the Department of Computer and Information Sciences, University Technology PETRONAS, Perak Malaysia. He has published in reputed conferences and journals.

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Journal article
Published: 16 August 2021 in Journal of Biomolecular Structure and Dynamics
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Lysine glutarylation is a post-translation modification which plays an important regulatory role in a variety of physiological and enzymatic processes including mitochondrial functions and metabolic processes both in eukaryotic and prokaryotic cells. This post-translational modification influences chromatin structure and thereby results in global regulation of transcription, defects in cell-cycle progression, DNA damage repair, and telomere silencing. To better understand the mechanism of lysine glutarylation, its identification in a protein is necessary, however, experimental methods are time-consuming and labor-intensive. Herein, we propose a new computational prediction approach to supplement experimental methods for identification of lysine glutarylation site prediction by deep neural networks and Chou’s Pseudo Amino Acid Composition (PseAAC). We employed well-known deep neural networks for feature representation learning and classification of peptide sequences. Our approach opts raw pseudo amino acid compositions and obsoletes the need to separately perform costly and cumbersome feature extraction and selection. Among the developed deep learning-based predictors, the standard neural network-based predictor demonstrated highest scores in terms of accuracy and all other performance evaluation measures and outperforms majority of previously reported predictors without requiring expensive feature extraction process. iGluK-Deep:Computational Identification of lysine glutarylationsites using deep neural networks with general Pseudo Amino Acid Compositions Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P.D.D Dominic Communicated by Ramaswamy H. Sarma

ACS Style

Sheraz Naseer; Rao Faizan Ali; Yaser Daanial Khan; P. D. D. Dominic. iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions. Journal of Biomolecular Structure and Dynamics 2021, 1 -14.

AMA Style

Sheraz Naseer, Rao Faizan Ali, Yaser Daanial Khan, P. D. D. Dominic. iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions. Journal of Biomolecular Structure and Dynamics. 2021; ():1-14.

Chicago/Turabian Style

Sheraz Naseer; Rao Faizan Ali; Yaser Daanial Khan; P. D. D. Dominic. 2021. "iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions." Journal of Biomolecular Structure and Dynamics , no. : 1-14.

Journal article
Published: 27 July 2021 in Sustainability
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Big data is rapidly being seen as a new frontier for improving organizational performance. However, it is still in its early phases of implementation in developing countries’ healthcare organizations. As data-driven insights become critical competitive advantages, it is critical to ascertain which elements influence an organization’s decision to adopt big data. The aim of this study is to propose and empirically test a theoretical framework based on technology–organization–environment (TOE) factors to identify the level of readiness of big data adoption in developing countries’ healthcare organizations. The framework empirically tested 302 Malaysian healthcare employees. The structural equation modeling was used to analyze the collected data. The results of the study demonstrated that technology, organization, and environment factors can significantly contribute towards big data adoption in healthcare organizations. However, the complexity of technology factors has shown less support for the notion. For technology practitioners, this study showed how to enhance big data adoption in healthcare organizations through TOE factors.

ACS Style

Ebrahim Ghaleb; P. Dominic; Suliman Fati; Amgad Muneer; Rao Ali. The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees. Sustainability 2021, 13, 8379 .

AMA Style

Ebrahim Ghaleb, P. Dominic, Suliman Fati, Amgad Muneer, Rao Ali. The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees. Sustainability. 2021; 13 (15):8379.

Chicago/Turabian Style

Ebrahim Ghaleb; P. Dominic; Suliman Fati; Amgad Muneer; Rao Ali. 2021. "The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees." Sustainability 13, no. 15: 8379.

Journal article
Published: 13 May 2021 in IEEE Access
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In biological systems, Nitration is a crucial post-translational modification which occurs on various amino acids. Nitration of Tyrosine is regarded as nitorsative stress biomarker resulting in the formation of peroxynitrite and other reactive and harmful nitrogen species. NitroTyrosine is closely related to Carcinogenesis, tumor growth progression and other major pathological conditions including systemic autoimmune diseases, inflammation, neurodegeneration and cardiovascular disorders. Additionally, the alteration in Nitrotyrosine profile occurs well before appearance of any symptoms of aforementioned diseases making nitrotyrosine a biomarker and potential target for early prognosis of aforementioned diseases. The wet lab identification of potential nitrotyrosine sites is laborious, time-taking and costly due to challenges of in vitro, ex vivo and in vivo identification processes. To supplement wet lab identification of nitrotyrosine, we proposed, implemented and evaluated a different approach to develop tyrosine nitration site predictors using pseudo amino acid compositions (PseAAC) and deep neural networks (DNNs). Proposed approach does not require any feature extraction and uses DNNs for learning a feature representation of peptide sequences and classification thereof. Validation of proposed approach is done using well-known model evaluation measures. Among different deep neural networks, convolutional neural network-based predictor achieved best scores on independent dataset with accuracy of 87.2%, matthew’s correlation coefficient score of 0.74 and AuC score of 0.91 which outperforms the previous reported scores of Nitrotyrosine predictors.

ACS Style

Sheraz Naseer; Rao Faizan Ali; Suliman Mohamed Fati; Amgad Muneer. iNitroY-Deep: Computational Identification of Nitrotyrosine Sites to Supplement Carcinogenesis Studies Using Deep Learning. IEEE Access 2021, 9, 73624 -73640.

AMA Style

Sheraz Naseer, Rao Faizan Ali, Suliman Mohamed Fati, Amgad Muneer. iNitroY-Deep: Computational Identification of Nitrotyrosine Sites to Supplement Carcinogenesis Studies Using Deep Learning. IEEE Access. 2021; 9 (99):73624-73640.

Chicago/Turabian Style

Sheraz Naseer; Rao Faizan Ali; Suliman Mohamed Fati; Amgad Muneer. 2021. "iNitroY-Deep: Computational Identification of Nitrotyrosine Sites to Supplement Carcinogenesis Studies Using Deep Learning." IEEE Access 9, no. 99: 73624-73640.

Review
Published: 09 April 2021 in Applied Sciences
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A grave concern to an organization’s information security is employees’ behavior when they do not value information security policy compliance (ISPC). Most ISPC studies evaluate compliance and noncompliance behaviors separately. However, the literature lacks a comprehensive understanding of the factors that transform the employees’ behavior from noncompliance to compliance. Therefore, we conducted a systematic literature review (SLR), highlighting the studies done concerning information security behavior (ISB) towards ISPC in multiple settings: research frameworks, research designs, and research methodologies over the last decade. We found that ISPC research focused more on compliance behaviors than noncompliance behaviors. Value conflicts, security-related stress, and neutralization, among many other factors, provided significant evidence towards noncompliance. At the same time, internal/external and protection motivations proved positively significant towards compliance behaviors. Employees perceive internal and external motivations from their social circle, management behaviors, and organizational culture to adopt security-aware behaviors. Deterrence techniques, management behaviors, culture, and information security awareness play a vital role in transforming employees’ noncompliance into compliance behaviors. This SLR’s motivation is to synthesize the literature on ISPC and ISB, identifying the behavioral transformation process from noncompliance to compliance. This SLR contributes to information system security literature by providing a behavior transformation process model based on the existing ISPC literature.

ACS Style

Rao Ali; P. Dominic; Syed Ali; Mobashar Rehman; Abid Sohail. Information Security Behavior and Information Security Policy Compliance: A Systematic Literature Review for Identifying the Transformation Process from Noncompliance to Compliance. Applied Sciences 2021, 11, 3383 .

AMA Style

Rao Ali, P. Dominic, Syed Ali, Mobashar Rehman, Abid Sohail. Information Security Behavior and Information Security Policy Compliance: A Systematic Literature Review for Identifying the Transformation Process from Noncompliance to Compliance. Applied Sciences. 2021; 11 (8):3383.

Chicago/Turabian Style

Rao Ali; P. Dominic; Syed Ali; Mobashar Rehman; Abid Sohail. 2021. "Information Security Behavior and Information Security Policy Compliance: A Systematic Literature Review for Identifying the Transformation Process from Noncompliance to Compliance." Applied Sciences 11, no. 8: 3383.

Journal article
Published: 29 March 2021 in Symmetry
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Amidation is an important post translational modification where a peptide ends with an amide group (–NH2) rather than carboxyl group (–COOH). These amidated peptides are less sensitive to proteolytic degradation with extended half-life in the bloodstream. Amides are used in different industries like pharmaceuticals, natural products, and biologically active compounds. The in-vivo, ex-vivo, and in-vitro identification of amidation sites is a costly and time-consuming but important task to study the physiochemical properties of amidated peptides. A less costly and efficient alternative is to supplement wet lab experiments with accurate computational models. Hence, an urgent need exists for efficient and accurate computational models to easily identify amidated sites in peptides. In this study, we present a new predictor, based on deep neural networks (DNN) and Pseudo Amino Acid Compositions (PseAAC), to learn efficient, task-specific, and effective representations for valine amidation site identification. Well-known DNN architectures are used in this contribution to learn peptide sequence representations and classify peptide chains. Of all the different DNN based predictors developed in this study, Convolutional neural network-based model showed the best performance surpassing all other DNN based models and reported literature contributions. The proposed model will supplement in-vivo methods and help scientists to determine valine amidation very efficiently and accurately, which in turn will enhance understanding of the valine amidation in different biological processes.

ACS Style

Sheraz Naseer; Rao Ali; Amgad Muneer; Suliman Fati. iAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions. Symmetry 2021, 13, 560 .

AMA Style

Sheraz Naseer, Rao Ali, Amgad Muneer, Suliman Fati. iAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions. Symmetry. 2021; 13 (4):560.

Chicago/Turabian Style

Sheraz Naseer; Rao Ali; Amgad Muneer; Suliman Fati. 2021. "iAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions." Symmetry 13, no. 4: 560.

Journal article
Published: 05 March 2021 in Sustainability
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The advancement of information communication technology in healthcare institutions has increased information security breaches. Scholars and industry practitioners have reported that most security breaches are due to negligence towards organizational information security policy compliance (ISPC) by healthcare employees such as nurses. There is, however, a lack of understanding of the factors that ensure ISPC among nurses, especially in developing countries such as Malaysia. This paper develops and examines a research framework that draws upon the factors of organizational climate of information security (OCIS) and social bond theory to enhance ISPC among nurses. A questionnaire was adopted in which responses were obtained from 241 nurses employed in 30 hospitals in Malaysia. The findings from the study demonstrated that the ISPC among nurses is enhanced through OCIS factors. The influence on ISPC was even more significant when examined by the mediating effect of the social bond. It implies that influential OCIS factors reinforce social bonds among nurses and eventually increase the ISPC. For information security practitioners, the study findings emphasize the prevalence of socio-active information security culture in healthcare organizations to enhance ISP compliance among nurses.

ACS Style

Ke Dong; Rao Ali; P. Dominic; Syed Ali. The Effect of Organizational Information Security Climate on Information Security Policy Compliance: The Mediating Effect of Social Bonding towards Healthcare Nurses. Sustainability 2021, 13, 2800 .

AMA Style

Ke Dong, Rao Ali, P. Dominic, Syed Ali. The Effect of Organizational Information Security Climate on Information Security Policy Compliance: The Mediating Effect of Social Bonding towards Healthcare Nurses. Sustainability. 2021; 13 (5):2800.

Chicago/Turabian Style

Ke Dong; Rao Ali; P. Dominic; Syed Ali. 2021. "The Effect of Organizational Information Security Climate on Information Security Policy Compliance: The Mediating Effect of Social Bonding towards Healthcare Nurses." Sustainability 13, no. 5: 2800.

Journal article
Published: 16 November 2020 in Symmetry
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Oil and Gas organizations are dependent on their IT infrastructure, which is a small part of their industrial automation infrastructure, to function effectively. The oil and gas (O&G) organizations industrial automation infrastructure landscape is complex. To perform focused and effective studies, Industrial systems infrastructure is divided into functional levels by The Instrumentation, Systems and Automation Society (ISA) Standard ANSI/ISA-95:2005. This research focuses on the ISA-95:2005 level-4 IT infrastructure to address network anomaly detection problem for ensuring the security and reliability of Oil and Gas resource planning, process planning and operations management. Anomaly detectors try to recognize patterns of anomalous behaviors from network traffic and their performance is heavily dependent on extraction time and quality of network traffic features or representations used to train the detector. Creating efficient representations from large volumes of network traffic to develop anomaly detection models is a time and resource intensive task. In this study we propose, implement and evaluate use of Deep learning to learn effective Network data representations from raw network traffic to develop data driven anomaly detection systems. Proposed methodology provides an automated and cost effective replacement of feature extraction which is otherwise a time and resource intensive task for developing data driven anomaly detectors. The ISCX-2012 dataset is used to represent ISA-95 level-4 network traffic because the O&G network traffic at this level is not much different than normal internet traffic. We trained four representation learning models using popular deep neural network architectures to extract deep representations from ISCX 2012 traffic flows. A total of sixty anomaly detectors were trained by authors using twelve conventional Machine Learning algorithms to compare the performance of aforementioned deep representations with that of a human-engineered handcrafted network data representation. The comparisons were performed using well known model evaluation parameters. Results showed that deep representations are a promising feature in engineering replacement to develop anomaly detection models for IT infrastructure security. In our future research, we intend to investigate the effectiveness of deep representations, extracted using ISA-95:2005 Level 2-3 traffic comprising of SCADA systems, for anomaly detection in critical O&G systems.

ACS Style

Sheraz Naseer; Rao Faizan Ali; P.D.D Dominic; Yasir Saleem. Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures. Symmetry 2020, 12, 1882 .

AMA Style

Sheraz Naseer, Rao Faizan Ali, P.D.D Dominic, Yasir Saleem. Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures. Symmetry. 2020; 12 (11):1882.

Chicago/Turabian Style

Sheraz Naseer; Rao Faizan Ali; P.D.D Dominic; Yasir Saleem. 2020. "Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures." Symmetry 12, no. 11: 1882.

Journal article
Published: 16 October 2020 in Sustainability
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Information security attacks on oil and gas (O&G) organizations have increased since the last decade. From 2015 to 2019, almost 70 percent of O&G organizations faced at least one significant security breach worldwide. Research has shown that 43 percent of security attacks on O&G organizations occur due to the non-compliant behavior of O&G employees towards information security policy. The existing literature provides multiple solutions for technical security controls of O&G organizations. However, there are very few studies available that address behavioral security controls, specifically for O&G organizations of developing countries. The purpose of this study is to provide a comprehensive framework for information security policy compliance (ISPC) for the O&G sector. A mixed-method approach is used to develop the research framework. Semi-structured interviews from O&G specialists refined the developed framework. Based on qualitative study a survey questionnaire was developed. To evaluate the research framework, structural equation modeling was applied to a sample of 254 managers/executives from 150 Malaysian O&G organizations. The obtained test results confirmed the proposed research model, according to which good social bonding among employees plays a critical role in improving ISPC. However, there was less support for the notion that all organizational governance factors significantly improve the social bonding of Malaysian O&G organizations employees. This paper contributes to the current information system (IS) literature by exploring the interrelationships among organizational governance, social bonding, and information security policy compliance (ISPC) in Malaysian O&G organizations.

ACS Style

Rao Ali; P.D.D. Dominic; Kashif Ali. Organizational Governance, Social Bonds and Information Security Policy Compliance: A Perspective towards Oil and Gas Employees. Sustainability 2020, 12, 8576 .

AMA Style

Rao Ali, P.D.D. Dominic, Kashif Ali. Organizational Governance, Social Bonds and Information Security Policy Compliance: A Perspective towards Oil and Gas Employees. Sustainability. 2020; 12 (20):8576.

Chicago/Turabian Style

Rao Ali; P.D.D. Dominic; Kashif Ali. 2020. "Organizational Governance, Social Bonds and Information Security Policy Compliance: A Perspective towards Oil and Gas Employees." Sustainability 12, no. 20: 8576.

Journal article
Published: 31 January 2020
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ACS Style

Rao Faizan Ali. Information Security Policy And Compliance In Oil And Gas Organizations-A Pilot Study. 2020, 1 .

AMA Style

Rao Faizan Ali. Information Security Policy And Compliance In Oil And Gas Organizations-A Pilot Study. . 2020; ():1.

Chicago/Turabian Style

Rao Faizan Ali. 2020. "Information Security Policy And Compliance In Oil And Gas Organizations-A Pilot Study." , no. : 1.

Journal article
Published: 13 March 2019 in IEEE Access
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ACS Style

Khurram Shahzad; Rao Muhammad Adeel Nawab; Adnan Abid; Kareem Sharif; Faizan Ali; Faisal Aslam; Arslaan Mazhar; Rao Faizan Ali. A Process Model Collection and Gold Standard Correspondences for Process Model Matching. IEEE Access 2019, 7, 30708 -30723.

AMA Style

Khurram Shahzad, Rao Muhammad Adeel Nawab, Adnan Abid, Kareem Sharif, Faizan Ali, Faisal Aslam, Arslaan Mazhar, Rao Faizan Ali. A Process Model Collection and Gold Standard Correspondences for Process Model Matching. IEEE Access. 2019; 7 ():30708-30723.

Chicago/Turabian Style

Khurram Shahzad; Rao Muhammad Adeel Nawab; Adnan Abid; Kareem Sharif; Faizan Ali; Faisal Aslam; Arslaan Mazhar; Rao Faizan Ali. 2019. "A Process Model Collection and Gold Standard Correspondences for Process Model Matching." IEEE Access 7, no. : 30708-30723.

Conference paper
Published: 01 August 2016 in 2016 Sixth International Conference on Innovative Computing Technology (INTECH)
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The advancements in Business Process Management Systems (BPMS) have placed process models at the center of enterprise information systems. Due to the significant importance of process models, organizations are maintaining more and more process models to explicitly represent the flow of their business operations. The sheer number has led to the development of repositories to efficiently manage these collections. These repositories provide techniques for storing process models and searching relevant models against a given query process model. Searching involves comparing the query model with each source process model in the collection to compute the degree of similarity between query-source process model pair. While several techniques have been developed for that purpose, a direct comparison of these approaches have rarely been made. A key reason to that is, the absence of a freely available benchmark collection of process models that contains examples of process models with diverse features. To overcome that problem, we have employed a systematic and rigorous protocol to generate a diversified collection of process models, and compare it with the famous SAP's process model collection to establish the superiority of our collection. Further, we have applied a baseline approach to establish that the variants of process models (that we have generated) are significantly different from each other. It is pertinent to mention that our collection is freely available and we contend that the proposed collection will be useful in making a direct comparison of existing techniques and developing, evaluating and analyzing new techniques for process matching.

ACS Style

Khurram Shahzad; Kareem Shareef; Rao Faizan Ali; Rao Muhammad Adeel Nawab; Adnan Abid. Generating process model collection with diverse label and structural features. 2016 Sixth International Conference on Innovative Computing Technology (INTECH) 2016, 644 -649.

AMA Style

Khurram Shahzad, Kareem Shareef, Rao Faizan Ali, Rao Muhammad Adeel Nawab, Adnan Abid. Generating process model collection with diverse label and structural features. 2016 Sixth International Conference on Innovative Computing Technology (INTECH). 2016; ():644-649.

Chicago/Turabian Style

Khurram Shahzad; Kareem Shareef; Rao Faizan Ali; Rao Muhammad Adeel Nawab; Adnan Abid. 2016. "Generating process model collection with diverse label and structural features." 2016 Sixth International Conference on Innovative Computing Technology (INTECH) , no. : 644-649.

Journal article
Published: 19 March 2014
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ACS Style

Rao Faizan Ali. A Publicly Available RGB-D Data Set of Muslim Prayer Postures Recorded Using Microsoft Kinect for Windows. 2014, 1 .

AMA Style

Rao Faizan Ali. A Publicly Available RGB-D Data Set of Muslim Prayer Postures Recorded Using Microsoft Kinect for Windows. . 2014; ():1.

Chicago/Turabian Style

Rao Faizan Ali. 2014. "A Publicly Available RGB-D Data Set of Muslim Prayer Postures Recorded Using Microsoft Kinect for Windows." , no. : 1.