Dr. MOHD SHAHID HUSAIN

@cas.edu.om

Assistant Professor IT Department
College of Applied Sciences, University of Technology & Applied Sciences, Oman



                          

https://researchid.co/mshusain

Dr. Mohd. Shahid Husain is a Research professional and Faculty member with 14 years of teaching & research experience. He is currently working as Assistant Professor in College of Applied Sciences, UTAS, Oman.
His area of interest includes Artificial Intelligence, Information Retrieval, Data Mining, Web mining, Sentiment Analysis and Computer Networks & Security.
He has published 4 books, 10 book chapters & more than 30 research papers in Journals/conferences of international repute. He was involved with many sponsored projects as PI/Co-PI. Currently he is involved in ongoing project sponsored by CAS, MoHE. He is also contributing his knowledge and experience as member of Editorial Board/Advisory committee and TPC in various international Journals/Conferences of repute. He is active member of different professional bodies including ACM, IEEE young professionals, IEEE-TCII, ISTE, CSTA, IACSIT.

EDUCATION

PhD (Computer Science & Engineering)
M. Tech. (Information Technology)
B. Tech. (Information Technology)

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence

30

Scopus Publications

830

Scholar Citations

14

Scholar h-index

23

Scholar i10-index

Scopus Publications


  • A Deep Learning-based Approach to Predict the Flood Patterns Using Sentinel-1A Time Series Images
    Mohammed Siddique, Tasneem Ahmed, and Mohammad Shahid Husain
    Springer Science and Business Media LLC

  • Enhanced mechanism to prioritize the cloud data privacy factors using AHP and TOPSIS: a hybrid approach
    Mohammad Zunnun Khan, Mohd Shoaib, Mohd Shahid Husain, Khair Ul Nisa, and Mohammad. Tabrez Quasim
    Springer Science and Business Media LLC
    AbstractCloud computing is a new paradigm in this new cyber era. Nowadays, most organizations are showing more reliability in this environment. The increasing reliability of the Cloud also makes it vulnerable. As vulnerability increases, there will be a greater need for privacy in terms of data, and utilizing secure services is highly recommended. So, data on the Cloud must have some privacy mechanisms to ensure personal and organizational privacy. So, for this, we must have an authentic way to increase the trust and reliability of the organization and individuals The authors have tried to create a way to rank things that uses the Analytical Hieratical Process (AHP) and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Based on the result and comparison, produce some hidden advantages named cost, benefit, risk and opportunity-based outcomes of the result.In this paper, we are developing a cloud data privacy model; for this, we have done an intensive literature review by including Privacy factors such as Access Control, Authentication, Authorization, Trustworthiness, Confidentiality, Integrity, and Availability. Based on that review, we have chosen a few parameters that affect cloud data privacy in all the phases of the data life cycle. Most of the already available methods must be revised per the industry’s current trends. Here, we will use Analytical Hieratical Process and Technique for Order Preference by Similarity to the Ideal Solution method to prove that our claim is better than other cloud data privacy models. In this paper, the author has selected the weights of the individual cloud data privacy criteria and further calculated the rank of individual data privacy criteria using the AHP method and subsequently utilized the final weights as input of the TOPSIS method to rank the cloud data privacy criteria.

  • Network analysis in a peer-to-peer energy trading model using blockchain and machine learning
    Saurabh Shukla, Shahid Hussain, Reyazur Rashid Irshad, Ahmed Abdu Alattab, Subhasis Thakur, John G. Breslin, M Fadzil Hassan, Satheesh Abimannan, Shahid Husain, and Syed Muslim Jameel
    Elsevier BV

  • A service-categorized security scheme with physical unclonable functions for internet of vehicles
    Nadhir Ben Halima, Ala Saleh Alluhaidan, Mohammad Zunnun Khan, Mohd Shahid Husain, and Mohammad Ayoub Khan
    Springer Science and Business Media LLC
    AbstractIn smart cities, communication and information exchange for the Internet of Vehicles rely on open and closed infrastructures along the roadside. Secure communications rely on the sender and receiver devices having self-sustaining authentication methods. The perquisites of the authentication methods are to grip communication without being falsified by an adversary or unidentified third parties. This article introduces the Service-Categorized Security Scheme (SCSS) with a physically unclonable function (PUF) for handling sensitive guidance/communication information. The vehicle-side authentication, access control, and service demands are governed using service-based PUF factors such as digital signatures, passwords, etc. To prevent anonymous third parties and adversaries, the PUF operates over compromised and uncompromised communication devices. Device-specific keys generated by PUFs based on intrinsic physical variances help identify between compromised and uncompromised devices, while keys generated by uncompromised devices conform to their expected profiles In the service-sharing process, mutual authentication using synchronized keys is used for security and service verification. The synchronized keys are integrated with the PUF for monitoring de-synchronization and individual operation. This decision is made using federated learning from the external service provider and the communicator of the vehicle. Through the learning process, a de-synchronization occurrence at the service provider and vehicle is identified as the reason for disconnecting the session. As a result, any suspicious activity that contradicts service security is identified, and the information of the communicating vehicle is secured. The proposed scheme is analyzed using the metrics authentication time, adversary detection ratio, complexity, de-synchronization time, and successful sessions.

  • An internet of things enabled machine learning model for Energy Theft Prevention System (ETPS) in Smart Cities
    Mohammad Tabrez Quasim, Khair ul Nisa, Mohammad Zunnun Khan, Mohammad Shahid Husain, Shadab Alam, Mohammed Shuaib, Mohammad Meraj, and Monir Abdullah
    Springer Science and Business Media LLC
    AbstractEnergy theft is a significant problem that needs to be addressed for effective energy management in smart cities. Smart meters are highly utilized in smart cities that help in monitoring the energy utilization level and provide information to the users. However, it is not able to detect energy theft or over-usage. Therefore, we have proposed a multi-objective diagnosing structure named an Energy Theft Prevention System (ETPS) to detect energy theft. The proposed system utilizes a combination of machine learning techniques Gated Recurrent Unit (GRU), Grey Wolf Optimization (GWO), Deep Recurrent Convolutional Neural Network (DDRCNN), and Long Short-Term Memory (LSTM). The statistical validation has been performed using the simple moving average (SMA) method. The results obtained from the simulation have been compared with the existing technique in terms of delivery ratio, throughput, delay, overhead, energy conversation, and network lifetime. The result shows that the proposed system is more effective than existing systems.

  • Flood Monitoring and Early Warning Systems – An IoT Based Perspective
    Dr Tasneem Ahmed, Mohammed Siddique, and Mohammad Shahid Husain
    European Alliance for Innovation n.o.
    One of the most frequently occurring calamities around the world is the flood. For flood prone areas or countries, an essential part of their governance is flood management. The necessity to continuously review and analyse the adverse or ambient environmental conditions in real-time demands developing a monitoring system so that floods could be detected beforehand. This paper discusses different Internet of Things (IoT) based techniques and applications implemented for efficient flood monitoring and an early warning system and it is observed that in future, the combination of IoT and Synthetic Aperture Radar (SAR) data may be helpful to develop robust and secure flood monitoring and early warning system that provides effective and efficient mapping during natural disasters. The emerging technology in the discipline of computing is IoT, an embedded system that enables devices to gather real-time data to further store it in the computational devices using Wireless Sensor Networks (WSN) for further processing. The IoT based projects that can help collect data from sensors are an added advantage for researchers to explore in providing better services to people. These systems can be integrated with cloud computing and analyzing platforms. Researchers recently have focussed on mathematical modeling based flood prediction schemes rather than physical parametric based flood prediction. The new methodologies explore the algorithmic approaches. There have been many systems proposed based on analog technology to web-based and now using mobile applications. Further, alert systems have been designed using web-based applications that gather processed data by Arduino Uno Microcontroller which is received from ultrasonic and rain sensors. Additionally, the machine learning based embedded systems can measure different atmospheric conditions such as temperature, moisture, and rains to forecast floods by analyzing varying trends in climatic changes.

  • A critical analysis of cyber threats and their global impact
    Syed Adnan Afaq, Mohd. Shahid Husain, Almustapha Bello, and Halima Sadia
    CRC Press

  • Big Data Concepts, Technologies and Applications
    Mohammad Shahid Husain, Mohammad Zunnun Khan, and Tamanna Siddiqui
    Auerbach Publications


  • The Rise of Deepfake Technology: Issues, Challenges, and Countermeasures
    Mohd Akbar, Mohd Suaib, and Mohd Shahid Hussain
    IGI Global
    Deepfake technology is an emerging technology prevailing in today's digital world. It is used to create fake videos by exploiting some of the artificial intelligence (AI) based techniques and deep learning methodology. The facial expressions and motion effects are primarily used to train and manipulate the seed frame of someone to generate the desired morphed video frames that mimic as if they are real. Deepfake technology is used to make a highly realistic fake video that can be widely used to spread the wrong information or fake news by regarding any celebrity or political leader which is not created by them. Due to the high impact of social media, these fake videos can reach millions of views within an hour and create a negative impact on our society. This chapter includes the crucial points on methodology, approach, and counter applications pertinent to deep-fake technology highlighting the issues, challenges, and counter measures to be adopted. Through observations and analysis, the chapter will conclude with profound findings and establishes the future directions of this technology.

  • Preface


  • An Integrated Image Classification Approach to Detect the Flood Prone Areas using Sentinel-1 Images


  • An Empirical Approach to Monitor the Flood-Prone Regions of North India Using Sentinel-1 Images
    Mohammed Siddique, Tasneem Ahmed, and Mohd Shahid Husain
    International Association for Educators and Researchers (IAER)
    Floods in India is among the perilous natural disasters with a high impact on its economic sectors. One of the critical factors to handle such hazardous events is monitoring the affected areas and changes in flood patterns. Flood management is a very complex issue, largely owing to the growing population and investments in flood-affected regions. Satellite images especially Synthetic Aperture Radar (SAR) images are very useful and effective because SAR images are acquired day and night in all types of weather conditions. This research analyzes a combination of machine learning algorithms implemented on Sentinel-1A (SAR) data using supervised classification techniques to monitor the flooded areas in the North Indian region. Random Forest (RF) and the K-nearest neighbour (KNN) classification is applied to classify the different land covers such as water bodies, land, vegetation, and bare soil land covers. The outcomes of the presented work depict that the SAR data provides efficient information that helps in monitoring the flooded extents and the analysis shows that Sentinel-1 images are quite effective to detect changes in flood patterns in urban, vegetation, and regular water areas of the selected regions. The distribution of flooded areas was 16.6% and 16.8% in the respective region which is consistent with the resultant images of the proposed approach using RF and KNN classifiers. The obtained results indicate that both classifiers used in the work generate higher classification accuracy. These classifiers define the potential of multi-polarimetric SAR data in the classification of flood-affected areas. For a thorough evaluation and comparison, the RF and KNN are utilized as benchmarked classifiers. The classification accuracies based on the investigated results from the three SAR images can be improved by incorporating spatial and polarimetric features. In the future, the deep-learning classification techniques using ensemble strategies are expected to achieve an increased accuracy level with an overall classification strategy of urban and vegetation mapping.

  • Random Forest Based Flood Monitoring Using Sentinel-1 Images: A Case Study of Flood Prone Regions of North-East India
    Mohammed Siddique, Tasneem Ahmed, and Mohd Shahid Husain
    IEEE
    The use of SAR satellite images will be very helpful in flood monitoring as the acquisition of synthetic aperture radar (SAR) images is possible day-night in all weather conditions and are very sensitive to water bodies and the changes in their behaviour. The usage of SAR (like Sentinal-1) images is an added advantage in handling the rescue operations and damage assessments based on images acquired before flood, flood at peak, and after flood effects. This paper discusses flood mapping and results in two different case studies. This is covered in phase-1 with RGB composite images of the cities of Gorakhpur and Ayodhya and phase-2 to analyse the flood situation using an accuracy assessment of Basti city based on the supervised classification method on SAR data. In this paper, random forest classification (RF) technique has been used to identify the flood prone areas by using Sentinel-1 satellite images and interpreted the changes detected for rescue operations. Sentinel-1 images are classified as Crisis image and Archive image, and further analysed to identify the flood prone areas (water bodies due to flood), permanent water bodies, urban (Built-up area), and vegetation.

  • Security in Digital Healthcare System
    Manish Madhava Tripathi, Mohammad Haroon, Zunnun Khan, and Mohammad Shahid Husain
    Springer International Publishing

  • Preface


  • Exploiting Artificial Immune System to Optimize Association Rules for Word Sense Disambiguation
    Mohd Shahid Husain
    International Journal of Intelligent Systems and Applications in Engineering

  • Cloud Computing in E-Governance: Indian Perspective
    Mohd. Shahid Husain and M. Akheela Khanum
    IGI Global
    Cloud Computing is becoming a rapidly accepted and deployed paradigm both by individuals and organizations alike. The government of various countries is also moving its services to cloud to offer better and just in time services to the users. This chapter explores the basic concepts of Cloud Computing, which includes the main features of Cloud Computing, the cloud deployment models, the services offered by the cloud, motivations behind adoption of cloud by organizations, in general and by the Government, in particular. We also lay an insight into the various Cloud Computing initiatives taken by the Government of India to facilitate its citizens with easy access to information/services.


  • Modified ISR hyper-heuristic for tuning automatic genetic clustering chromosome size
    M H Adnan, M F Hassan, I A Aziz, O Nurika, and M S Husain
    IOP Publishing

  • A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Educationx
    Muhamad Hariz Muhamad Adnan, Shamsul Arrieya Ariffin, Hafizul Fahri Hanafi, Mohd Shahid Husain, and Ismail Yusuf Panessai
    UiTM Press, Universiti Teknologi MARA
    Recently, the promotion of Science, technology, engineering and mathematics (STEM) education has become the highlight due to the shortage in the STEM workforce. Surprisingly, the enrolment rates in STEM degrees are still low in many countries. Social media has been identified as one of the main platforms that can help to increase prospective students’ interest in STEM and also Technical and Vocational Education and Training (TVET) subjects. However, very little research has been done for the higher education institutions in Malaysia in leveraging social media and social media analytics effectively to increase the students’ interests and awareness of STEM and TVET disciplines. Therefore, this paper aims to propose a framework to increase prospective students’ interest in STEM and TVET using social media and big data analytics. The objectives of this study are to explore various social media applications in education and study these applications towards increasing students’ interests and propose a suitable framework for Malaysian higher education institutions. The framework is proposed by following the theory synthesis methodology. Four main components of the framework have been proposed, namely social media, role model or mentoring, massive open online courses and big data analytics. Each component is significant and requires a considerable amount of time to develop. The suggested framework is anticipated to benefit higher education institutions with a significant gain of the number of students, revenues and positive reputations.
  
 Keywords: Social media, Social media analytics, STEM, E-learning, Education
  


  • Cloud computing in E-governance: Indian perspective
    Mohd. Shahid Husain and M. Akheela Khanum
    IGI Global
    Cloud Computing is becoming a rapidly accepted and deployed paradigm both by individuals and organizations alike. The government of various countries is also moving its services to cloud to offer better and just in time services to the users. This chapter explores the basic concepts of Cloud Computing, which includes the main features of Cloud Computing, the cloud deployment models, the services offered by the cloud, motivations behind adoption of cloud by organizations, in general and by the Government, in particular. We also lay an insight into the various Cloud Computing initiatives taken by the Government of India to facilitate its citizens with easy access to information/services.

  • Big data on E-government
    Mohd. Shahid Husain and Neha Khan
    IGI Global
    All aspects of big data need to be thoroughly investigated, with emphasis on e-governance, needs, challenges and its framework. This chapters recognizes that e-governance needs big data to be reliable, fast and efficient. Another principle is that the trust of a citizen is the main concern. The extraction of meaningful data from large variety of data is a critical issue in big data hence new approaches must be developed. This chapter basically discusses the key concepts of veracity in big data on e-governance. Its main aim is to provide the comprehensive overview big data in e-governance. E-government is still struggling to move advanced level of development. Current e-government applications handle only structured data and sharing between the applications is also difficult.

RECENT SCHOLAR PUBLICATIONS

  • Cyberology An Optimized Approach to the Cyber-World
    MS Husain, M Faisal, H Sadia, T Ahmed, S Shukla, A Kaleem
     2025

  • Exploring the Cybersecurity Landscape Through Cyber Forensics
    MS Husain
     2025

  • A deep learning-based approach to predict the flood patterns using Sentinel-1A time series images
    M Siddique, T Ahmed, MS Husain
    Journal of the Indian Society of Remote Sensing 52 (12), 2753-2767 2024

  • Network analysis in a peer-to-peer energy trading model using blockchain and machine learning
    S Shukla, S Hussain, RR Irshad, AA Alattab, S Thakur, JG Breslin, ...
    Computer Standards & Interfaces 88, 103799 2024

  • Enhanced mechanism to prioritize the cloud data privacy factors using AHP and TOPSIS: a hybrid approach
    MZ Khan, M Shoaib, MS Husain, KU Nisa, MT Quasim
    Journal of Cloud Computing 13 ((42)) 2024

  • A service-categorized security scheme with physical unclonable functions for internet of vehicles
    NB Halima, AS Alluhaidan, MZ Khan, MS Husain, MA Khan
    Journal of Big Data 10 (1), 178 2023

  • An internet of things enabled machine learning model for Energy Theft Prevention System (ETPS) in Smart Cities
    MS Husain, MZ Khan, MT Quasim, K Nisa, S Alam, M Shuaib, M Meraj, ...
    Journal of Cloud Computing 12 2023

  • Big Data Concepts, Technologies, and Applications
    MS Husain, MZ Khan, T Siddiqui
    CRC, Taylor & Francis group 2023

  • Advances in Cyberology and the Advent of the Next-Gen Information Revolution
    MS Husain, M Faisal, H Sadia, T Ahmad, S Shukla
    IGI Global. 2023

  • Flood Monitoring and Early Warning Systems--An IoT Based Perspective.
    M Siddique, T Ahmed, MS Husain
    EAI endorsed transactions on internet of things 9 (2) 2023

  • The Rise of Deepfake Technology: Issues, Challenges, and Countermeasures
    M Akbar, M Suaib, MS Hussain
    Advances in Cyberology and the Advent of the Next-Gen Information Revolution 2023

  • A Critical Analysis of Cyber Threats and Their Global Impact
    H Afaq, S. A., Husain, M. S., Bello, A., & Sadia
    Computational Intelligent Security in Wireless Communications, 201-220 2023

  • An Integrated Image Classification Approach to Detect the Flood Prone Areas using Sentinel-1 Images
    M Siddique, T Ahmed, MS Husain
    2023 10th International Conference on Computing for Sustainable Global 2023

  • An Empirical Approach to Monitor the Flood-Prone Regions of North India Using Sentinel-1 Images
    M Siddique, T Ahmed, MS Husain
    Annals of Emerging Technologies in Computing (AETiC) 6 (4), 1-14 2022

  • RANDOM FOREST BASED FLOOD MONITORING USING SENTINEL-1 IMAGES: A CASE STUDY OF FLOOD PRONE REGIONS OF NORTH-EAST INDIA
    M Siddique, T Ahmed, MS Husain
    IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing 2022

  • An Intelligent Approach to Automatic Query Formation from Plain Text using Artificial Intelligence
    M Akbar, MS Hussain, M Suaib
    International Journal of Computer and Information Technology (2279-0764) 11 (3) 2022

  • Analysis of Deep-Fake Technology Impacting Digital World Credibility: A Comprehensive Literature Review
    M Akbar, M Suaib, MS Hussain
    International Journal of Computer and Information Technology (2279-0764) 11 (2) 2022

  • Security in Digital Healthcare System
    MM Tripathi, M Haroon, Z Khan, MS Husain
    Pervasive Healthcare: A Compendium of Critical Factors for Success, 217-231 2022

  • Pervasive Healthcare: A Compendium of Critical Factors for Success
    MS Husain, MHBM Adnan, MZ Khan, S Shukla, FU Khan
    Springer International Publishing AG 2022

  • Study of computational techniques to deal with ambiguity in SRS documents
    MS Husain
    Computational Intelligence in Software Modeling 1, 107 2022

MOST CITED SCHOLAR PUBLICATIONS

  • Methodological study of opinion mining and sentiment analysis techniques
    PK Singh, MS Husain
    International Journal on Soft Computing 5 (1), 11 2014
    Citations: 143

  • Critical Concepts, Standards, and Techniques in Cyber Forensics
    MS Husain, MZ Khan
    IGI Global 2019
    Citations: 81

  • Pervasive Healthcare: A Compendium of Critical Factors for Success
    MS Husain, MHBM Adnan, MZ Khan, S Shukla, FU Khan
    Springer International Publishing AG 2022
    Citations: 53

  • Advances in Cyberology and the Advent of the Next-Gen Information Revolution
    MS Husain, M Faisal, H Sadia, T Ahmad, S Shukla
    IGI Global. 2023
    Citations: 52

  • An Unsupervised Approach to Develop a Stemmer
    MS Husain
    International Journal on Natural Language Computing (IJNLC) 1 (2), 15-23 2012
    Citations: 35

  • Application of artificial intelligence in fighting against cyber crimes: a review
    MZ Siddiqui, S Yadav, MS Husain
    Int. J. Adv. Res. Comput. Sci 9 (2), 118-122 2018
    Citations: 29

  • Analysis of mental state of users using social media to predict depression! a survey
    A Khan, MS Husain, A Khan
    International Journal of Advanced Research in Computer Science 9 (2), 100-106 2018
    Citations: 27

  • Different technique of load balancing in distributed system: A review paper
    HM M Shahid Husain, Khan Riyaz
    Global Conference on Communication Technologies (GCCT), 2015, 371-375 2015
    Citations: 25

  • A Compendium Over Cloud Computing Cryptographic Algorithms and Security Issues
    Neha Mishra, M Shahid Husain, Jitesh P Tripathi
    BIJIT - BVICAM’s International Journal of Information Technology 7 (1), 810-814 2015
    Citations: 25

  • Network analysis in a peer-to-peer energy trading model using blockchain and machine learning
    S Shukla, S Hussain, RR Irshad, AA Alattab, S Thakur, JG Breslin, ...
    Computer Standards & Interfaces 88, 103799 2024
    Citations: 19

  • Big Data Classification using Evolutionary Techniques: A Survey
    N Khan, M S Husain, M R Beg
    IEEE International Conference on Engineering and Technology (ICETECH), 243-247 2015
    Citations: 17

  • Analytical study of feature extraction techniques in opinion mining
    PK Singh, MS Husain
    Computer Science 85 2013
    Citations: 17

  • Security in Digital Healthcare System
    MM Tripathi, M Haroon, Z Khan, MS Husain
    Pervasive Healthcare: A Compendium of Critical Factors for Success, 217-231 2022
    Citations: 15

  • A language Independent Approach to develop Urdu stemmer
    MS Husain, F Ahamad, S Khalid
    Advances in Computing and Information Technology: Proceedings of the Second 2013
    Citations: 14

  • Word Sense Ambiguity: A Survey
    MS Husain, MR Beg
    International Journal of Computer & Information Technology 2 (6), 1161-1168 2013
    Citations: 13

  • Nature inspired approach for intrusion detection systems
    MS Husain
    Design and analysis of security protocol for communication, 171-182 2020
    Citations: 12

  • An internet of things enabled machine learning model for Energy Theft Prevention System (ETPS) in Smart Cities
    MS Husain, MZ Khan, MT Quasim, K Nisa, S Alam, M Shuaib, M Meraj, ...
    Journal of Cloud Computing 12 2023
    Citations: 11

  • A Social Media Analytics Framework to Increase Prospective Students’ Interests in STEM and TVET Education
    MHM Adnan, SA Ariffin, HF Hanafi, MS Husain, IY Panessai
    Asian Journal of University Education 16 (4), 82-90 2021
    Citations: 11

  • Introduction to Email, Web, and Message Forensics
    MZ Khan, MS Husain, M Shoaib
    Critical Concepts, Standards, and Techniques in Cyber Forensics, 174-186 2020
    Citations: 11

  • Analysis of DDOS attack detection and prevention in cloud environment: A review
    A Verma, M Arif, MS Husain
    International Journal of Advanced Research in Computer Science 9 (0), 107-113 2018
    Citations: 11