The 1st National Conference on Trends and Innovations in Information Technology (TIIT'16) is going to be organized by the department of Information Technology on 24-26 February 2016 at Quaid-e-Awam University of Engineering, Science and Technology,Nawabshah. The Conference is expected to provide an excellent forum for sharing knowledge and results in theory, methodology and applications of Information Technology. The Conference encourages innovative research contributions in all the major fields of Information Technology covering theoretical and practical aspects. Among others, the key goal of the conference is to provide a platform to researchers and practitioners from both academia as well as industry to meet and share cutting-edge developments in IT.

Keynote Speakers:

Prof. Dr. Hameedullah Kazi
Pro-vice chancellor, Engineering and Management Sciences,
Isra Unversity, Hyderabad, Sindh, Paksitan

Title: Robustness in Intelligent Tutoring Systems and Knowledge Acquisition Bottleneck

Abstract: An intelligent tutoring system is a software system that teaches students by providing a problem scenario related to a particular domain, together with a workspace to solve the problem. Various strategies have been suggested to address of robustness and knowledge acquisition by expanding the plausible solution space of tutoring systems. The robustness can be increased by adding a large base of solutions into the system. However, that will lead to the classical AI problem of knowledge acquisition bottleneck. We discuss some relevant approaches that make use of existing knowledge sources with an attempt to alleviate the burden of knowledge acquisition. Furthermore, we also describe how these solutions can be benefited by some domains more than others. We also discuss a variety of ways in which such techniques can be evaluated and what kind of performance metrics can be employed.

Engr. Dr. Abdul Rehman Abbasi
Principal Engineer and Head (MS Program)
Karachi Institute of Power Engineering
Pakistan Atomic Energy Commission, Karachi, Pakistan

Title: Information Technology for Complex Industrial Process Management

Abstract: Efficient and productive industrial process management heavily relies on timely and coordinated information retrieval and implementation. Especially, a complex industrial process such as a nuclear power facility may be benefited in managing plant configuration, maintenance and operation related issues in the most effective and safe manner.

In this talk, requirements of the nuclear power facility during design, construction, operation and maintenance phases are elaborated. Use of information technology (IT) as a method to automate and ensure the timely and effective capture, processing and distribution of key nuclear power plant information to support during these phases will be discussed. Finally, trends and technological challenges in this hi-tech industry are presented.

Dr. Haroon Rashid
Associate Professor and Head of the Electrical Engineering Department
Bahria University,
Karachi, Pakistan

Title:Challenges and Opportunities in Infrastructure Support for Electric Vehicles and Smart Grid

Abstract: The world is moving towards fuel efficiency and optimization. Smart Grid (SG) along with battery electric vehicle (BEV) appear to have great future together. There are many issues to be addressed for the successful development of this technology since Smart Grid is still in developmental stage and attracting tremendous efforts from governments, electric utilities and researchers. It is therefore, desirable to develop a vehicle to smart grid test-bed that could analyze, and demonstrate various novel Smart Grid solutions, namely demand response, real-time pricing, and congestion management. Moreover, automotive telematics can be served by 5.9 GHz dedicated short range radio (DSRC) which is to be connected to Next Generation Networks. Various models and strategies are proposed for Intelligent Energy Systems which will leads the current power infra-structure towards SMART GRID in two phases i.e., Smart metering and Smart Grid apps. However several other domains like Implementing security and seamless connectivity in EV telematics, cyber security, cellular connectivity and integration of DSRC are still need to be unveiled. This talk will cover aforementioned features, issues and some possible solutions that might be real candidate as key to address these problems.

Dr. Yasir Arafat Malakani
Associate Professor,
Institute of Mathematics & Computer Science,
University of Sindh, Jamshoro, Paksitan

Service Discovery in Pervasive Computing Environments: Trends and Issues

Abstract: Mark Weiser gave the vision of Pervasive computing in 1991. In Pervasive computing environments, devices are spread around us, whereby they are interconnected with each other through either wireless or wired connectivity and provide services to its users seamlessly without requiring continuous attention from them. This makes service discovery as one of the essential requirements of the Pervasive computing environments. Since, these environments are composed of heterogeneous devices/services, most of the time pervasive applications need to discover the devices and services based on some contextual information, such as device or user’s location, user’s preferences, etc. Consequently, traditional service discovery mechanisms do not fulfill all the requirements of Pervasive computing applications. In this talk, we review the state-of-the-art on service discovery in general and examine their suitability in Pervasive computing environments. We also discuss some of the open research issues and challenges that need to be addressed by the device or service discovery systems/protocols to meet the requirements of Pervasive computing environments.

Dr. Qurat-ul-ain Nizamani
Associate Professor,
Institute of Mathematics & Computer Science (IMCS),
University of Sindh, Jamshoro, Pakistan

Gamification: Concepts and Applications

Abstract: Gamification is the emerging field which has recently received much attention from the researchers. Though, the term ‘gamification’ was introduced in 2002 by Nick Pelling, the field became actually alive in 2010. Gamification aims at utilizing the game elements and design constructs in non-gaming contexts such as in education, finance, health, etc. The inspiration for gamification comes from the fact that humans perform better when they have motivation and competitiveness. Socializing nature of humans has suggested to researchers that human computer interaction can be made fun oriented and challenging in order to keep the participants engaged in computer related activities. To serve this purpose, video game playing elements can be used which keep the participants involved in computer related applications. For instance, to provide competitive environment to the users of the application, different levels of achievement are identified. Once a user completes a level, he can be awarded badges, rewards, virtual currency, etc. Further, to make the competition more strong, performance of individuals can be made visible to other competitors as well. Recently, many e-commerce giants have introduced applications based on the idea of gamification such as Yahoo, ebay, Aldo, Nike, etc. This talk will focus on discussing the preliminary concepts, applications, and promising areas of research where gamification can be successfully employed.

Dr. Imtiaz Ali Korejo
Associate Professor
Institute of Mathematics and Computer Science,
University of Sindh, Jamshoro, Pakistan

Title: A Hybrid Genetic Algorithm for Vehicle Routing Problem

Abstract: The Vehicle Routing Problem (VRP) has become an important and difficult issue in current field of combinatorial optimization. This problem is closely related with many real world applications including mail delivery, school bus routing, solid waste collection, oil distribution tankers, parcel pick-up delivery and many. Specific situations and constraints are satisfied for the solution of the VRP by using different approaches of several heuristics. Solutions of VRP are generated by using genetic algorithms; these solutions may occur either feasible or infeasible in whole search space. However, feasible search space is smaller than the whole search space. In order to find the solution that does not violate any constraint, such solution belongs to feasible solution. The conventional genetic algorithm schemes incorporate additional repair and improvement methods, which are designed for a specific constraint to retain the generated solutions in the feasible search space. The integration of GAs with random insertion and adaptive mutation approach is used to improve the performance of VRP on different problems. The objective of random insertion is to introduce initial solutions and to reconstruct the existing ones. Three different approaches are evaluated: simple GAs, random insertion GAs, and random insertion with adaptive mutation GAs. These schemes have different properties, simple GAs usually stuck on local optimum for the VRP problems. Random insertion aims to maintain the diversity of population in order to avoid the premature convergence. Random insertion and adaptive mutation maintain the population diversity and help out to find the optimum solution. This research proposes hybrid genetic algorithm based on a random insertion heuristics and adaptive mutation for the vehicle routing problem with constraints. The suggested technique enhances the maintained diversity and transfers the knowledge. The random insertion and adaptive mutation approaches have been incorporated with genetic algorithms to improve its performance for VRP. The proposed scheme is not only applied to a particular problem but it can be applied to different problems. The suggested algorithms can also be applied for the complex vehicle routing problem.

Dr. Muhammad Wasim Ashraf
Assistant Professor
Department of Electronics
G.C University Lahore, Pakistan

Title: Simulation, Modeling and Development of MEMS based Energy Harvester

Abstract: Energy crisis is world major problem today. Micro and nano electromechanical system (MEMS and NEMS) based energy harvesting devices can be used as one of the alternative source to fulfill the needs of energy at small scale. MEMS/NEMS based devices and systems are gaining popularity from last few years because of small size, light weight, low cost, ease of fabrication and operation, accuracy, high efficiency and more reliability. Design of MEMS energy harvester is very important. Here, MEMS based energy harvester simulation and development has been presented. The structural simulation has been performed in ANSYS parametric design language. The development of energy harvester is based on synthesis of well-aligned ZnO nanorods on Copper (Cu) sheet by using hydrothermal method at low temperature that leads cost effective Cu electrode which could enable energy harvest from the walking motions. First, the seed layer of gold (Au) has been deposited by plasma sputtering. Then, the growth process of nanorods was carried out in a sealed chemical bath. The longer and bigger nanorods were produced a surface with larger contact area and higher roughness. The larger contact area improves the absorption rate of incident light and the rougher surface strengthens the scattering effect. The surfaces were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), and energy dispersive X-ray spectroscopy (EDX). Then, high output nano energy generators were developed and tested. The maximum output voltage of 1.8 V and maximum output current of 148 nA were achieved.

Dr. Irshad Ali
Assistant Professor,
Karakoram International University, Skardu, Pakistan

Multiple Human Tracking in High-Density Crowds

Abstract: As public concern about crime and terrorist activity increases, the importance of public security is growing, and video surveillance systems are increasingly widespread tools for monitoring, management, and law enforcement in public areas. At the same time, video surveillance systems have become a popular research area in computer vision. Many algorithms exist to detect and track people in video streams. However, human detection and tracking in high density crowds, where object occlusion is very high, is still an unsolved problem. Typical approaches such as background modeling and body part-based pedestrian detection fail when most of the scene is in motion and most body parts of most of the pedestrians are occluded. In this talk, I will disscuss the main challenges and latest research in human tracking in high density crowds.

Dr. Muhammad Mohsin Nazir
Associate Professor
Department of Computer Science
Lahore College for Women University,
Jail Road, Lahore, Pakistan

Title:Nano Networking: Introducing OSI Layers & Geographical Routing at Nano Scale

Abstract: Nano networking technology is an evolving field covering the communication domain within nano sized devices and it is trying to give comprehensive set of tools to the engineering community. Advanced nano devices are being regularly made and transmit all the available information at nano scale. The combination of existing networks (wired/wireless) combined with these nano machines can range from nano scale to a micro/macro level. Wireless networking at nano scale must have the capability for multi-hoping based communication within interconnected devices. The source and destination addressing itself is a difficult task to carry communication at nano scale, even with multiple sources and destinations. The existing techniques in traditional wireless communication such as geographical routing, geo-casting, hierarchical, multipath, power-aware, and hybrid routing can be altered to cater the technical inherent features existing at nano sized communication devices. Geo-routing based technique can provide the solution in a form that by using geo-routing the source has to send the message to the destination location instead of using network address. With the routing technique at nano scale a node can send its data without knowing the knowledge about route and network topology. Many techniques have also been developed for interconnection between nano nodes like static routing, but these techniques do not address the issues related to dynamic network environment. This talk work will discuss introduction of dynamic routing technique in nano networks with application of geo-routing to overcome the difficulties of communication in Nano networks.

Dr. Waheed Iqbal
Assistant Professor,
PUCIT, University of Punjab,
Lahore, Pakistan

Title:Real-Time Big Data Processing

Abstract:The demand to process Big Data in real time is increasing for various reasons including fraud detection, system performance monitoring, predictive analytics, and log processing. Big data is challenging to process in real time mainly due to its volume, velocity, and variety. In this talk, we discuss various tools and services (Kafka, Spark, Cassendra, Storm, and Hadoop) are available to process Big Data in real time. We will also explain some practical examples of processing Big Data in real-time.

Dr. Waheed Noor
Department of Computer Science & Information Technology,
University of Balochistan, Quetta, Pakistan

Title:Predictive Optimization: Beyond Supervised and Unsupervised Learning

Abstract:Predictive models use a variety of statistical and machine learning techniques to make predictions about the future based on current and historical data. These predictions are expressed as scores or probabilities that represent the likelihood of a particular event, behavior, or opportunity that will take place in future. Broadly, these predictive models are based on either supervised learning or unsupervised learning methods depending on the problem and the historical data. In supervised learning, learning algorithm is presented with a set of classified examples and then it is expected to learn the concept of classifying unseen (future) examples by predicting class labels. On the other hand, unsupervised learning algorithm is presented with a set of examples without labels/classes then it is expected to learn the hidden patterns or grouping in the data to classify unseen (future) examples. However, there is a broad class of problems, which we call predictive optimization, often faced in direct marketing, credit scoring, bid pricing, item response, network admission control, financial aid for education and health care, where optimization of revenue, cost, or risk based on some control or decision variable. For instance, imagine a company wanting to maximize the revenue it obtains from a specific product or service. When the company is able to offer the product or service at a variable discount across a group of potential customers, it should offer to each customer the discount that maximizes expected revenue. In these problems, response probabilities or scores are directly used to find the optimal decision. Researchers from machine learning and data mining have developed many models to exploit predictive models for making or recommending optimal decisions; often predictive models are based on supervised learning scheme. Such predictive models are suboptimal for predictive optimization. Moreover, historical data for such problems are often sparse and incomplete, while all decisions other than the one historically made are censored. Such predictive optimization problems are in dire need of attention for the development of machine learning and data mining techniques that are inline with the specification of such problems.

Dr. Junaid Baber
Department of Computer Science & Information Technology,
University of Balochistan, Quetta, Pakistan

Title:Automatic Image Segmentation for Large Collections

Abstract:Image segmentation is one of the most significant tasks in computer vision. Since automatic techniques are hard for this purpose, a number of interactive techniques are used for image segmentation. The result of these techniques largely depends on users feedback. It is difficult to get good interactions for large databases. On the other hand, automatic image segmentation is becoming a significant objective in computer vision and image analysis. We propose an automatic approach to detect foreground. We are applying Maximal Similarity Based Region Merging (MSRM) technique for region merging and using image boundary to identify foreground regions. The results confirm the effectiveness of the approach. This approach reveals its effectiveness especially to extract multiple objects from background.

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  • Conference program has been uploaded
  • New dates for the conference are 24-26 February 2016
  • Early registration deadline is extended to 1st January 2016
  • Submission is closed
  • Paper submission date has been extended to 8th November 2015
  • All accepted papers will be invited for publication in HEC recognized X/Y-category journal

  • Paper submission is open