Research Groups

Department of Software Engineering

 

Research Group Name

Members

Artificial Intelligence

(Multi-disciplinary)

Prof. Dr. Muhammad Shaheen (Head)

Dr. Shoaib Ahmed, A&M

Dr. Arif Jamal Malik

Ms. Asma Parveen

Mr. Muhammad Babar Yaqoob Khan

Ms. Noushin Mazhar

Ms. Bushra Qadir

Ms. Rabia Rauf

Ms. Salma Khan

Ms. Fauzia Khan

Mr. Aqib Rehman

Mr. Waqas Ahmed

Multimedia

(Multi-disciplinary)

Dr. Muhammad Ishtiaque ( Head )

Dr. Muhammed Habib

Dr. Sajid Ali Khan

Mr. M.Fahad Khan

Ms. Nida Tasneem

Mr. Ayyaz Mehmood

Mr. Amir Azad, A&M

Mr. Zain Shakeel, A&M

 

Future Networks Research Group

Dr. Shariq Hussain (Head)

Dr. Aaqif Afzaal Abbasi

Ms. Tehmina Karamat

Mr. Umar Mahmud

Mr. M. Humair Noor

Ms. Aamina Akbar

Ms. Amber Sarwar

Mr. Sherjeel Farooqui

Software Engineering

Dr. Maryam Kausar (Head)

Mr. Muhammad Aqeel Iqbal

Mr. Ejaz Gul

Mr. Waqas Ahmed

Mr. Aqib Rehman

Mr. Muhammad Asif

Signal Processing and Pattern Recognition

(Multi-disciplinary)

Mr. Adnan Khan

Mr. Muhammad Azam

Dr.Arif Jamal

Mr. Babar Yaqoob

Information Security

Dr. Imran Daud (Head)

Ms. Rana Khattak

Ms. Bushra Yaseen, A&M

Software Development

Mr. Muhammed Usman Khan (Head)

Mr. Muhammad Asif

Data Science

Dr. Shariq Bashir (Head)

Mr. Muhammad Sajid Qureshi

Mr. Malik Khizer Hayat

Mr. Zafar Mehmood

Mr. Saqib Nazir

Ms. Noushin Mazhar

 

Web TV

(Multi-disciplinary)

Dr. Shoaib, A&M (Head)

Mr. Mansoor Waheed, A&M

Mr. Arshad Ali, A&M

Mr. Sherjeel Farooqui

Mr. Muhammad Asif

 

FM Radio Automation

(Multi-disciplinary)

Prof. Dr. Muhammad Shaheen (Head)

Ms. Ummul Baneen, A&M

Mr. Muhammad Asif

 

 Artificial Intelligence Research Group

Artificial Intelligence group commits to addressing national/ international scientific and industrial problems through an interdisciplinary stance, spanning machine learning, data mining, evolutionary computing, big data, and remote sensing. The theme of this group is to exploit the problems especially of national interest and to seek efficient AI algorithms to solve them. The group aims to conduct research both in basic and applied paradigms. To work in a basic AI paradigm, synergies are to be explored in the dimensions of statistics, cognitive sciences, and engineering. The applied paradigm in the scenario of Pakistan specifically and the world in general delves into data mining algorithms and the interfaces between data mining and allied disciplines.

Current Project:

 Data Mining for Sustainable Energy Development

Energy plays a vital role in the economic, social and ecological development of a country. The role of energy in these three aspects defines its prominence in sustainability. To ensure sustainable energy development the consumption and production patterns may comply with the conditions of sustainability. This project is aimed at developing an automated intelligent geographic decision support system that ensures optimal energy development plans for Pakistan in order to cope with the current energy crisis.

The Commission on Sustainable Development at the World Summit in South Africa provided a list of indicators for sustainable energy development. These indicators evaluate energy development in all the three aspects of sustainability i.e. social, ecological and economic. The indicators are not specialized for renewable energy resources and all of them are not quantifiable. These indicators can help optimizing energy development in a country. Data mining/warehousing and artificial intelligence have the potential to extract hidden knowledge from large databases. 

The energy plan of Pakistan can be optimized in two phases. In the first part of the project, a compact list of quantifiable sustainability indicators for renewable and non-renewable energy will be prepared on the basis of data collected from the energy sector of the country as well as existing international databases. The database of these sustainability indicators will then be equipped with the energy scenarios of different world countries including Pakistan as the major one. Data mining and machine learning techniques will then be applied to these sustainability indicators’ databases to evaluate the existing energy network. Spatial network analysis is used to analyze data based on locations but the existing techniques of spatial analysis have certain limitations and can’t optimally be applied on huge databases. A few mathematically verified techniques of spatial data mining will be developed and implemented in the study for making specific and requirement-oriented network analysis. 

The project is divided into two phases. In the first phase, both spatial and nonspatial databases will be mapped on remotely sensed satellite images on which existing and newly developed data mining and artificial intelligence techniques will be applied to answer the following questions.

  • What are the energy network bottlenecks and hotspots?

  • What should be the optimal energy network design to avoid losses and to ensure optimal energy utilization?

  • What are the different patterns of failures and losses in the energy sector?

  • What changes should be made in the existing network to increase energy utilization with minimum losses?

In the second phase, machine learning/prediction techniques will be applied to the same GIS to determine potential sites for renewable energy. The energy sources for this project will be hydel power, solar and wind energy only. Because of exhaustive work required, the study will be conducted for only one city but the generalization of methodology will enable the rest of the regions to adopt the same.

Members List:

Prof. Dr. Muhammad Shaheen (Head)
Dr. Shoaib Ahmed, A&M
Dr. Arif Jamal Malik
Ms. Asma Parveen
Mr. Muhammad Babar Yaqoob Khan
Ms. Noushin Mazhar
Ms. Bushra Qadir
Ms. Rabia Rauf
Ms. Fauzia Khan
Mr. Aqib Rehman
Ms. Salma Khan

 

 Multimedia Research Research Group

The Multimedia Research Group was formed on October 2016. The aim of this research group is to conduct research in the area of digital imaging processing, image & video compression, multimedia forensics & security, multimedia system, and computer vision.  However, at a particular instant, the focal area of research for this research group is to work in the area of multimedia especially image and video compression, which is comparatively a new field of research in the country. The idea is to discover new knowledge in the field of multimedia and to disseminate it through the scientific publications/applications.

The Multimedia Research Group has eight members working in the following research areas:

  • Digital Imaging Processing

  • Image & Video Compression

  • Multimedia Forensics & Security

  • Multimedia System

  • Computer Vision

Members List:

Dr. Muhammad Ishtiaq ( Head )
Dr. Muhammed Habib
Mr. M. Fahad Khan
Dr. Sajid Ali Khan
Ms. Nida Tasneem 
Amir Azad, A&M
Zain Shakeel, A&M

 Future Networks Research Group

Realizing the needs and advances in the field of computer networks, Future Networks group was formed in October 2016 in order to bridge the gap by exploiting the potential of software engineering faculty and students. The idea is to explore new findings by focusing on the research challenges in the area of future networks.

The Future Network group mainly focuses on the research challenges in the areas of network security, mobile ad-hoc networks, wireless sensor networks, cloud computing, web services and the internet of things (IoT).

Current Funded Project: Energy-Aware Framework for Context-Awareness (EaFCa)

With the advent of smart, inexpensive and seamless devices the computing world has shifted towards pervasive computing. Pervasive computing vows to deliver better services and adapt them according to the users as well as the context of the situations. This leads to context awareness wherein activity is recognized and adapted based on the sensor information. This recognition process is mainly carried out on small devices that interact with both onboard and surrounding sensors. The recognition requires data exchange and processing that consume precious battery power. Hence energy awareness becomes essential in a context-aware environment.

Energy awareness is the mechanism through which algorithms consume less energy during their cycles. It starts by identifying frameworks and end into energy-conscious algorithms.

Output

Journal paper titled "Power Profiling of Context Aware Systems: A Contemporary Analysis and Framework for Power Conservation" published in Wireless Communications and Mobile Computing. 

Link: https://www.hindawi.com/journals/wcmc/2018/1347967/

Context-awareness in the ability of a system to recognize situations based on sensor data or current context. The aim is to facilitate service discovery, delivery and adaption for a user. Context is gathered and recognized in a smart personal space. An environment includes numerous personal spaces and fixed appliances that provide functionalities. This environment connects to remote services via a gateway thus creating an Internet-of-Thing (IoE) environment. A user’s smart personal space not only gathers data but also recognizes activities. This space is constrained by handheld, battery-operated devices. The effect of the recognition algorithm on power consumption must be established. This paper shows that the power consumption follows the time complexity of an algorithm.

Members List:

Dr. Shariq Hussain (Head)
Dr. Aaqif Afzaal Abbasi
Ms. Tehmina Karamat
Mr. Umar Mahmud
Mr. M. Humair Noor
Mr. Sherjeel Farooque

Ms. Amber Sarwar
Ms. Aamina Akbar

 Software Engineering Research Group

The Software Engineering research group was founded in October 2016. The aim of this research group is to investigate the scientific and technological knowledge in the field of computer software and to develop software systems/applications based on the empirical studies and theoretical results determined through such investigations. There are ten members in this group who are working along with the postgraduate students in the following areas:

  • Agile Scalability

  • Aspect-Oriented Programming

  • Automated Software Engineering

  • Global Software Engineering

  • Model-Based Testing

  • Pre-Requirement Specification Traceability

  • Priority Assignment of Bug Sheet

  • Requirement Validation

  • Requirement Visualization

  • Requirements Engineering

  • Software Testing

  • Usability Engineering

  • Information Security

  • Data Structures

  • Databases

  • Programming Languages

Members List:

Dr. Maryam Kausar (Head)
Mr. Muhammad Aqeel Iqbal
Mr. Ejaz Gul
Mr. Waqas Ahmed
Mr. Aqib Rehman

 Software Development Research Group

Software Development Group is formed to enhance the software development skills of the DSE students in a professional manner under the supervision of faculty members. The purpose of this group is to provide opportunities to students to work on independent projects or in liaison with other research groups’ projects of DSE. We warmly encourage the competent students of DSE to be the part of Software Development Group. It will provide a gateway to practice the learned skills in different domains of software engineering.

Members List:

Mr. Muhammed Usman Khan
Mr. Muhammad Asif

 Information Security Research Group

Security and Privacy have always been a core issue in the information technology-based systems. Therefore, considering the importance of this area, the ISRG focuses to study, analyze and propose state of the art solutions that help the information society from the possible threats. The faculty members, undergraduate and post-graduate students are the members of this research group. The research lines of this group are :

  • Cryptography
  • Data Privacy
  • Cyber Security
  • Security Management

Members List:

Dr. Imran Daud (Head)
Ms. Rana Khattak
Ms. Bushra Yaseen, A&M

 Signal Processing and Pattern Recognition Research Group

In many real-life situations, it is important to automatically classify a given signal based on its characteristics, e.g. detection of seizures in EEG signals. This problem of signal classification is usually solved by a combination of signal processing techniques and machine learning algorithms. The signal analysis methods like time-frequency analysis and wavelet transform are used in the first stage to extract features that can better separate one class of signals from others. The second stage employs machine learning/pattern recognition techniques for classifying the extracted features to achieve the final decision. This interdisciplinary research group combines signal processing expertise from the Electrical Engineering department and machine learning expertise from the computer science department to develop and efficiently implement algorithms for the classification of real-life signals such as biomedical signals. The focus research areas are:

  • Design of advanced signal analysis techniques for the analysis of non-stationary signals.
  • Extraction of features from joint time-frequency representations.
  • Application and enhancement of existing machine learning algorithms for the classification of non-stationary signals.
  • Real-time implementation of algorithms on hardware and software platforms such as GPUs/FPGAs/DSPs.

Group members:

Dr. Nabeel Ali Khan (Head)
Mr. Adnan Khan
Dr.Arif Jamal
Mr. Babar Yaqoob

Research Vacancy: Augmented Reality Developer & 3D Graphic Designer

As part of a research project at FUI, we are looking for an augmented reality developer and 3D graphic designer who have experience in using Unity 3D and has worked with AR Tools. Kindly send your CV at maryam.kausar@fui.edu.pk by 11th Dec 2019. Only shortlisted candidates will be called for interview. 

 

Last updated 5/12/2019