High Performance Computing (HPC) Lab
FAST-NUCES houses an OpenStack based private cloud infrastructure and next generation provisioning frameworks like Kubernetes and Kubeflow. Dr. Muhammad Usman Awais is managing an HPC lab which provides access to the above mentioned infrastructures. Although the scale is not very large, it is good enough for the students to conduct research for their master's theses and Final Year Projects (FYPs). Following major research areas are being explored in the lab
- Distributed Machine Learning Algorithms
- Software Defined Networks in Cloud Infrastructures
- Modeling and Simulation over the Cloud.
Big Data Lab Establishment
A Big Data lab has been established in Lahore campus. The use of Big Data is becoming a crucial way for leading companies to outperform their peers. In most industries, established competitors and new entrants alike, leverage data-driven strategies to innovate, compete, and capture value. Big Data will help to create new growth opportunities, and also entirely new categories of companies, such as those that aggregate and analyze industry data.
Students of Dr. Usman Awais and Dr. Zareen Alamgir have successfully setup this lab and cluster. In guidance of Dr. Usman Awais an OpenStack Cloud infrastructure is also established. The deployed OpenStack cloud provides Platform as a Service (PaaS) infrastructure.The PaaS enables the students to analyze many different distributed computing technologies, including Data and Compute Clusters and Grids.The initial set up will be used by master thesis students, and students working on their Final Year Projects (FYP). It will allow the students to have a practical exposure to cloud related technologies. A personalized cloud dashboard can also be made accessible over the internet, in future.
Students can access this Hadoop master cluster from other Labs. Now "Big Data" students can access the Hadoop cluster for their assignments, without going to the Big Data Lab.
Following are the students who made considerable efforts to realize the plan of setting up these labs.
- Muhammad Naqeeb : L15-5024
- Amir Shahzad: L15-5025
- Muhammad Hassan: L14-5017
Current and Past Projects
· Fuzzy clustering of mixed mode data in Apache SPARK
· Exploiting review helpfulness rating on scalable recommendation systems using Apache Spark
· SP-CURE, a clustering algorithm developed on Apache Spark to cluster gigantic datasets
· Personalized User Tag recommender
· for Social Media Photos using Spark
· Tag Recommender for Spark, a system for recommending user tags for huge datasets.
· Social Event based recommendation system in distributed environment
· Distributed Typicality-based Recommendation System for Apache Spark
· Hybrid Recommendation System using Apache Spark Framework
· Keyword based personalized Hotel Recommender on Map Reduce.
· Opinion Mining on Big Data using MapReduce Framework.
· Deep Packet analysis in Software Defined Networks
· Distributed algorithms for Machine Learning (kubernetes operators are being used)
· FRSG to develop a Kubernetes operator
· Distributed Simulation using Docker ( Kubernetes will be included)
- Zareen Alamgir and Hassan Jamil, “Personalized recommender systems for big data using distributed Spark framework”, World Conference on Technology, Innovation and Entrepreneurship (WOCTINE), Istanbul University, Istanbul, Turkey, June 2019.
- Zareen Alamgir, “Generating recommendations for customers using bipartite graph”, World Conference on Technology, Innovation and Entrepreneurship (WOCTINE), Istanbul University, Istanbul, Turkey, June 2019.
- Zareen Alamgir, Saira Karim and Syed Husnine, “Linear algorithm for generating c-isolated bicliques”, International Journal of Computer Mathematics, Vol 94, Issue 8, pp 1574 – 1590, 2017.
- Noshaba Nasir, Kashif Zafar and Zareen Alamgir, “Sentiment Analysis of Social Media using MapReduce”, Women in Data Science, WinDS, Houston, Texas, USA, 2017.
To spur the research in the field of Machine Learning, a high fidelity server is made available for the faculty and students at FAST-NUCES. The server is a Dell PowerEdge R740xd2 series server. Using a freely available solution named Portainer.io, the server is accessible to the authorized users over the web. Meaning, users can access it while being at their home. Portainer.io is a docker based solution which allows many users to use the GPU server simultaneously, while allowing users to deploy a software stack of their own choice in their containerized environment. The IT department of FAST-NUCES with the assistance from Dr. Muhammad Usman Awais is able to make it 24/7 available for the users since the begnning of 2020.
Liberty lab aims to put together and apply the knowledge gained from diverse areas of expertise including robotics, embedded systems and artificial intelligence, to engineer out-of-the-box solutions that practically solve the problems faced by the local society. Research is done to make robots that can achieve autonomous behavior. Problems of localization, mapping, navigation and image processing in robots are explored. Control algorithms on ground and air robots are developed to improve the navigation capabilities of such robots. The goal is to make autonomous mobile and industrial robots, and induce learning and long-term autonomy in them. This lab achieves its aims via projects at both graduate and undergraduate levels.
- Spy Copter
- Personal Assistant for Elderly (PIE)
- Visual Guidance for Navigating UAV
- Multi-Hop DTN Based Phone to Phone Communication
- Obstacle detection and object recognition robot
- Bionic hand
- Object fetching Robot
- Home automation system
- Indoor Navigation Systems
Research Areas and Ongoing Projects
- Smart Student Monitoring System
- Mobility Modelling for Fully Autonomous Micro UAVs in Urban Traffic Surveillance Scenario
- Security Audit Tool for IOT devices
Machine Intelligence Group (MInG)
- MInG – Machine Intelligence Group was established in FAST- NUCES in 2004. The main objectives of this research group are to conduct research in cutting edge technologies and bridging the gap between academia and industry. The main research focus is on: Optimization techniques, Data science, Multi-agent systems, Expert systems, Machine learning, Block-chain technology, Internet of things, Healthcare solutions, Robotics, Machine vision, Intelligent transportation systems, Data mining, and other such areas in artificial intelligence domain. MInG has published more than hundred research articles in international conferences and journals. The research team consists of FYP, MS and PhD Thesis students, along with faculty members and industrial partners. The group has been working in cutting edge technologies to solve real world problems.
Co-founders consists of Dr. Rauf Baig, Dr. Kashif Zafar and Dr. Usman Shahid.
- Dynamic Route Planning using Computational Intelligence techniques
- Road Sign Detection using Deep Learning
- Cancer Classification using Microarray
- Stroke Detection using Machine Learning
- Dimensionality Reduction using Machine Learning
- Interactive Car Racing Tracks Generation using Evolutionary Techniques
- Neural Fuzzy Expert System for Taxation Department
- Knowledge Based Expert System for Stock Exchange
- H. Tufail, K. Zafar , A. R. Baig, “Relational database security using digital watermarking and evolutionary techniques”. Computational Intelligence, 35(4): 693-716 (2019)
- M. Naz, K. Zafar, A. Khan, “Ensemble Based Classification of Sentiments using Forest Optimization Algorithm” Data; MDPI, (ISSN 2306-5729); ISI-indexed 2019
- N. Saleem, K. Zafar, A. Fatima, “Enhanced Feature Subset Selection using Niche Based Bat Algorithm” Computation; MDPI, (2079-3197); ISI-indexed 2019
- A. Naseer, K. Zafar, “Meta Features-based Scale Invariant OCR Decision Making using LSTM-RNN’ ‘Computational and Mathematical Organization Theory' SPRINGER, 2018 (ISSN: 1572-9346- Impact Factor: 0.769).
- S. Javed, K. Zafar, “Player Profiling and Quality Assessment of Dynamic Car Racing Tracks using Entertainment Quantifier” Computational Intelligence; Wiley (ISSN: 1467-8640; SCI-E, IF: 0.964).
- U. Abdullah, A. Ligeza, K. Zafar, "Performance Evaluation of Rule-Based Expert Systems: An Example from Medical Billing Domain", Expert Systems; Wiley (ISSN: 1468-0394; SCI-E, IF: 1.15).
- N. Qamar, F. Batool, K. Zafar, “"Efficient Effort Estimation of Web based Projects using Neuro-Web" International Journal of Advanced and Applied Sciences; (ISSN: 2313-626X, ISI-indexed 2018)
- A. Naseer, K. Zafar, “Comparative Analysis of Raw Images and Meta Feature based Urdu OCR using CNN and LSTM” “International Journal of Advanced Computer Science and Applications” Vol.9, NO.1. (ISSN: 2156-5570, ISI-indexed 2018)
- H. Tufail, K. Zafar, A. R. Baig, “Digital Watermarking for Relational Database Security using mRMR based Binary Bat Algorithm” International Workshop on Privacy, Security and Trust in Computational Intelligence, IEEE TrustCom, August 2018, New York, USA
Research Center for Information Management and Cyber Security (CIMACS)
The Center for Information Management and Cyber Security (CIMACS), headed by Dr. Taimur Bakhshi is involved in undertaking research and development in the broad domains of information management, cyber security and computer networking. The center undertakes consultancy, auditing, installation and management work in the following avenues.
- Information Systems Security
- Software Defined Infrastructure
- Internet of Things (IoT) - Communication Stack Security
- Behavioural Profiling and Forensics
- Academic Programmes and Staff Training
Current & Past Projects
· User behaviour profiling
· Securing software defined networks
· Social engineering vulnerabilities
· Ransomware detection and mitigation
· Android platform security:
· Securing the Internet of Things (IoT)
· Fault-Tolerance in Software Defined Networks
· Forensic of Things
· IoT for Smart Cities
Further information about these projects is available on the center website: www.cimacs.org
- Hussein Oudah, Bogdan Ghita, Taimur Bakhshi, Abdulrahman Alruban, and David J. Walker, “Using Burstiness for Network Applications Classification,” Journal of Computer Networks and Communications, vol. 2019, Article ID 5758437, 10 pages, 2019.
- Hussein Oudah, Bogdan Ghita, Taimur Bakhshi, “A Novel Features Set for Internet Traffic Classification using Burstiness”. 5th International Conference on Information Systems Security and Privacy; Prague, Czech Republic; 02/2019.
- Taimur Bakhshi, “Securing Software Defined Networks: On Feasibility of Network Behaviour Profiling”. International Conference on Latest trends in Electrical Engineering & Computing Technologies, Karachi, Pakistan; 11/2019.
- Taimur Bakhshi, Ibrahim Nadir, “On MOS-Enabled Differentiated VoIP Provisioning in Campus Software Defined Networking”. 15th International Conference on Emerging Technologies 2019 (ICET’19), Peshawar, Pakistan; 12/2019.
- Taimur Bakhshi, Saman Shahid, “Securing Internet of Bio-Nano Things: ML-Enabled Parameter Profiling of Bio-Cyber Interfaces”. 22nd IEEE International Multi Topic Conference 2019, Islamabad, Pakistan; 11/2019.
- Sarah Tahir Bokhari, Taimur Bakhshi, Tehreem Aftab, Ibrahim Nadir, “Exploring Blockchain-Secured Data Validation in Smart Meter Readings”. 22nd IEEE International Multi Topic Conference 2019, Islamabad, Pakistan; 11/2019.
- Asif Ali, Kashif Zafar, Taimur Bakhshi, “On Nature-Inspired Dynamic Route Planning: Hammerhead Shark Optimization Algorithm”. 15th International Conference on Emerging Technologies 2019 (ICET’19), Peshawar, Pakistan; 12/2019.
Software Engineering Research Centre (SERC)
Software Research Engineering Centre is dedicated to conducting research and development in various facets of software engineering. The centre has been established to promote theoretical research in the software engineering area, resolve problems faced by the software industry, and help establish software engineering practices in the industry. Through the establishment of the SERC, the University seeks to help and support the local software industry in establishing and improving their processes and practices through continuous feedback and training. SERC aims to achieve these objectives by collecting the industry data to understand productivity, cost, and quality parameters. This will hopefully also help in developing more suitable process and lifecycle models for different types of projects being undertaken by our local industry in the offshore and distributed environment. In the past, this Centre has arranged many seminars and workshops in the software engineering domain to encourage and promote academia-industry collaboration. Current research focuses on areas such as empirical software engineering, software cost estimation, software project management, and requirements engineering.
- DISC – Design using integrated software chips
- Development and analysis of a new Object to Relational Mapping technique
- Mining code repositories for automatic detection of bad smells in the code
- An Empirical Study to Analyse the Impact of Testing and Code Inspections on Overall Productivity
- Small - Separated Modelling and Language for meta-modelling
- Humaira Aslam Chughtai, Zeeshan Ali Rana, People Profile Metrics for Improved Classification of Defect Prone Files in Open Source Projects, In Proceedings of the 3rd International Conference on Advancements in Computational Sciences (ICACS 2020), 17 - 19 February 2020, Lahore, Pakistan
- Shahzad Ali and Zeeshan Ali Rana, “Evaluating Performance of Software Defect Models Using Area Under Precision-Recall Curve (AUC-PR)”, 2nd International Conference on Advancements in Computational Sciences (ICACS 2019), February 2019, Lahore – Pakistan.
- Nosheen Qamar and Ali Afzal Malik, “Birds of a Feather Gel Together: Impact of Team Homogeneity on Software Quality and Team Productivity”, IEEE Access, Volume 7, Issue 1, July 2019.
- Nosheen Qamar and Ali Afzal Malik, “Evaluating the Impact of Pair Testing on Team Productivity and Test Case Quality – A Controlled Experiment”, Pakistan Journal of Engineering and Applied Sciences, Volume 25, July 2019.
Optimization and Data Science (OptiMi'nDS) research group, headed by Dr. Irfan Younas, carries out research and development in Optimization and Data Science related areas. Major research themes in our research group include Evolutionary Computation, Swarm Intelligence, Evolutionary Deep Learning, Multi/Many-objective Optimization, Artificial Intelligence, Data Science, Machine Learning, Natural Language Processing and Information Retrieval.
Solving large scale optimization problems have always been very challenging and demanding. Our research group carries out multi-disciplinary research into mathematical models and intelligent algorithms for a variety of real world optimization, specifically NP-hard problems. The NP-hard problems are very complex with intractably large and highly complex search spaces.
Our research group also works in area of Natural Language Processing (NLP) and Machine Learning. NLP addresses fundamental questions at the intersection of human languages and computer science. Understanding complex language utterances is also a crucial part of artificial intelligence. Applications of NLP are everywhere because people communicate almost everything in language: web search, advertisement, emails, customer service, language translation, radiology reports, etc.
Research Group Members:
· Dr. Irfan Younas (Faculty member)
· Dr. Maryam Bashir (Faculty member)
· Mr. Muhammad Amir Iqbal (Faculty member & PhD student)
· Mr. Shakeel Zafar (Faculty member & PhD student)
· Mr. Qamar Askari (PhD student)
· Ms. Maria Tamoor (PhD student)
· Ms. Sadia Marium (PhD student under co-supervision)
· Ms. Umber Nisar (PhD student)
· Mr. Asif Ameer (PhD student)
Past and Ongoing Projects:
· Designing novel Socio-inspired Optimization Algorithms for Global Optimization
· Developing Transfer Learning Based Classifier System for Image Classification
· Evolving Deep Neural Networks using Evolutionary Computation
· Large scale optimization of Assignment, Planning and Scheduling Problems
· Distributed Large Scale Many Objective Optimization
· Multi and Many Objective Optimization Algorithms
· Many Objective Optimization for IoT
· Learning Regular Expressions using Learning Classifier Systems
· Solving Large-scale Optimization Problems using Evolutionary Computation and Machine Learning
· Solving Classification and Learning Problems using Evolutionary Machine Learning
· Predicting Future News Events and Crimes using Data Science
- * means equal contribution.
- + means student under supervision.
- Saba Kanwal+, Irfan Younas, and Maryam Bashir. "Evolving Convolutional Autoencoders Using Multi-Objective Particle Swarm Optimization", Computers & Electrical Engineering 91 (2021): 107108: https://doi.org/10.1016/j.compeleceng.2021.107108. (Impact Factor = 2.663)
- Qamar Askari+, and Irfan Younas. "Political Optimizer Based Feedforward Neural Network for Classification and Function Approximation" Neural Processing Letters 53, no. 01, (2021): 429-458. (Impact Factor = 2.891)
- Shah Bano+, MaryamBashir, and Irfan Younas. "A Many-objective Memetic Generalized Differential Evolution Algorithm for DNA Sequence Design", IEEE ACESS 8 (2020): 222684-222699. (Impact Factor = 3.745)
- Qamar Askari+ , Irfan Younas, and Mehreen Saeed. "Political Optimizer: A novel socio-inspired meta-heuristic for global optimization." Knowledge-Based Systems (2020): 105709. (Impact Factor = 5.921)
- Qamar Askari+, Mehreen Saeed, and Irfan Younas. "Heap-based optimizer inspired by corporate rank hierarchy for global optimization." Expert Systems with Applications (2020): 113702. (Impact Factor = 5.452)
- Irfan Younas*, Uzman Perwaiz+*, and Adeem Ali Anwar+. "Many-objective BAT algorithm." Plos one 15, no. 6 (2020): e0234625. (Impact Factor = 2.740)
- Qamar Askari+, Irfan Younas, and Mehreen Saeed. "Critical evaluation of sine cosine algorithm and a few recommendations." In Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, pp. 319-320. 2020. (CORE rank A)
- Adeem Ali Anwar+, and Irfan Younas. "Optimization of Many Objective Pickup and Delivery Problem with Delay Time of Vehicle Using Memetic Decomposition Based Evolutionary Algorithm." International Journal on Artificial Intelligence Tools 29, no. 01 (2020): 2050003. (Impact Factor = 0.689)
- Hafiz Asadul Rehman+, Muhammad Iqbal, Irfan Younas, and Maryam Bashir. "Learning Regular Expressions Using XCS-Based Classifier System." International Journal of Pattern Recognition and Artificial Intelligence (2019): 2051011. (Impact Factor = 1.375)
- Irfan Younas, Farzad Kamrani, Maryam Bashir, and Johan Schubert. "Efficient genetic algorithms for optimal assignment of tasks to teams of agents." Neurocomputing 314 (2018): 409-42 (Impact Factor = 4.438)
- Carleton University, Ottawa, Ontario, Canada
- Higher Colleges of Technology, Fujairah, UAE