Associate Professor, Faculty of Information Science and Technology, Multimedia University
Dr. Shohel has more than 22 years meritorious working experience and he holds a challenging career which combines research, versatile administration and excellent teaching.
His core research interest is in the area of Biometrics, big data, cloud computing, information security, image and signal processing, pattern recognition and classification. He has published over 50 research papers in international peer-reviewed journals and international conference proceedings as a result of his research work. His research works have been published by high ranked peer- reviewed journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), International Journal of Pattern Recognition and Artificial Intelligence (IJPRI), Expert Systems with Applications, Discrete Dynamics in Nature and Society (DDNS) as well as several peer- reviewed International journals. Several of his findings have been presented in a number of well recognized IEEE conferences as well. He has been appointed technical paper reviewer for Journal of Pattern Recognition Letters, IEEE Transaction on Neural Networks, IEEE Transactions on Automation Science and Engineering, Journal of Computer Methods and Programs in Biomedicine and International Journal of Computer Theory and Engineering. He has also been invited to review technical papers for several international conferences. In recognition of his professional contribution, he has obtained recognition as a member of IEEE Computer Society, IEEE Communication Society and International Association of Computer Science and Information Technology (IACSIT).
Dr. Shohel has been a member of Multimedia University since 2001 and now he serves as an Associate Professor and the Chairperson of Industrial Training programme of the Faculty of Information Science and Technology.
He received his doctor of philosophy from Multimedia University in Engineering, specializing in hand signature verification, and holds masters of information technology degree from the University Kebangsaan Malaysia, specializing in industrial computing. He received his Bachelor of Science degree in Agricultural science from Bangladesh Agricultural University.
Keynote Title: Big Data: Trends, Challenges & Opportunities
Every day, approximately 2.5 quintillion bytes of data are created. These data come from digital pictures, videos, posts to social media sites, intelligent sensors, purchase transaction records, cell phone GPS signals, to name a few. This is known as Big Data. There is no doubt that Big Data and especially what we do with it has the potential to become a driving force for innovation and value creation.
Innovations in technology and greater affordability of digital devices have presided over today’s Age of Big Data, an umbrella term for the explosion in the quantity and diversity of high frequency digital data. These data hold the potential as yet largely untapped to allow decision makers to track development progress, improve social protection, and understand where existing policies and programmes require adjustment.
Turning Big Data—call logs, mobile-banking transactions, online user-generated content such as blog posts and Tweets, online searches, satellite images, etc. into actionable information requires using computational techniques to unveil trends and patterns within and between these extremely large socioeconomic datasets. New insights gleaned from such data mining should complement official statistics, survey data, and information generated by Early Warning Systems, adding depth and nuances on human behaviours and experiences and doing so in real time, thereby narrowing both information and time gaps. The promise of data-driven decision-making is now being recognized broadly, and there is growing enthusiasm for the notion of “Big Data.’’ There is currently a wide gap between its potential and its realization.
Heterogeneity, scale, timeliness, complexity, and privacy problems with Big Data impede progress at all phases of the pipeline that can create value from data. A large amount of data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Transforming such content into a structured format for later analysis is a major challenge. The value of data explodes when it can be linked with other data, thus data integration is a major creator of value. Since most data is directly generated in digital format today, we have the opportunity and the challenge both to influence the creation to facilitate later linkage and to automatically link previously created data. Data analysis, organization, retrieval, and modeling are other foundational challenges. Data analysis is a clear bottleneck in many applications, both due to lack of scalability of the underlying algorithms and due to the complexity of the data that needs to be analyzed. Finally, presentation of the results and its interpretation by non-technical domain experts is crucial to extracting actionable knowledge.
The many novel challenges and opportunities associated with Big Data necessitate rethinking many aspects of these data management platforms, while retaining other desirable aspects. It should be point out that the appropriate investment in Big Data will lead to a new wave of fundamental technological advances that will be embodied in the next generations of Big Data management and analysis platforms, products, and systems. Thus, we should believe that these research problems are not only timely, but also have the potential to create huge economic value in the world economy for years to come. However, they are also hard, requiring us to rethink data analysis systems in fundamental ways. A major investment in Big Data, properly directed, can result not only in major scientific advances, but also lay the foundation for the next generation of advances in science, medicine, and business.