Dr. MOHD SHAHID HUSAIN
@cas.edu.om
Assistant Professor IT Department
College of Applied Sciences, University of Technology & Applied Sciences, Oman
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
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
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.
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
Mohammed Siddique, Tasneem Ahmed, and Mohammad Shahid Husain
Springer Science and Business Media LLC
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.
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.
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.
Syed Adnan Afaq, Mohd. Shahid Husain, Almustapha Bello, and Halima Sadia
CRC Press
Mohammad Shahid Husain, Mohammad Zunnun Khan, and Tamanna Siddiqui
Auerbach Publications
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.
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.
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.
Manish Madhava Tripathi, Mohammad Haroon, Zunnun Khan, and Mohammad Shahid Husain
Springer International Publishing
Mohd Shahid Husain
International Journal of Intelligent Systems and Applications in Engineering
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.
M H Adnan, M F Hassan, I A Aziz, O Nurika, and M S Husain
IOP Publishing
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
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.
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.
Mohd Shahid Husain and M. Akheela Khanum
ACM Press
The most significant phase in the development of a quality software project is Requirement engineering. The objective of the software requirement engineering is the elicitation of the requirements of the clients and their analysis. In general the requirements are expressed in natural languages which are ambiguous in nature. Ambiguity means the same word or sentence can be interpreted differently by different persons.
The Word Sense Disambiguation (WSD) system assigns the correct meaning to the words having multiple interpretations, depending on the context of use. In this paper, we propose a framework, for removing ambiguities in an SRS (Software Requirement Specifications) document in an efficient way. This framework uses the WordNet and the concept of Association rule mining for assigning the correct interpretation of a word in given context.