With significant experience driving strategic change initiatives, I work with all levels of management and stakeholders to design and deliver solutions for business problems. I have a keen interest in people, processes and communication, and bring a well-rounded and analytical profile developed over a wide range of roles including process design, vendor management, presentation design and delivery, communications, strategy and operations. I enjoy decision-making and work well in environments of uncertainty with minimal management oversight, and working in large scale global teams.
In recent years, big data, machine learning and AI applications have attracted huge investment from all businesses globally. After seeing a lot of success certain sectors such as advertising/marketing, consumer goods, aviation etc., the banking and financial industry is looking to replicate similar success with these technologies.One of the critical pieces of the puzzle is DATA. Our financial institutions have got arguably the most crucial data about their customers – the data about our money. Apart from health and medical data, there is no other data in the world that is more valuable and highly protected and regulated. Every country has different laws and rules about what organisations can do with financial data.This creates a unique hurdle for Big Data, Machine Learning and AI applications – especially at a global scale. How to create a truly global data set of financial data that can be utilised by these applications without compromising on data security, data governance and data regulations?
M Shamim Kaiser received the Ph. D. degree in Telecommunication Engineering from the Asian Institute of Technology, Thailand in 2010. He is currently working as a Professor at the Institute of Information Technology of Jahangirnagar University, Dhaka, Bangladesh. He is the Associate Editor of the IEEE Access. His current research interests include Data analytic, Machine Learning, Wireless Network, Signal processing, WSN, Big data, and Cyber Security. Dr. Kaiser is a Life Member of Bangladesh Electronic Society; Bangladesh Physical Society. He is also a senior member of IEEE, USA, and IEICE, Japan, and volunteer of the IEEE Bangladesh Section. He is the founding Chapter Chair of the IEEE Computer Society Bangladesh Chapter.
The eHealthcare system is a contemporary healthcare practice demanding prognostic, preventive, person-specific healthcare services aided by computing, computational intelligence, and communication. Integrating the Internet of Things to the e-Healthcare system not only enables interoperability and P2P/M2M communication but also facilitates health condition monitoring remotely and disease tracking using pervasive sensing nodes. On the other hand, the machine learning—especially its deep architecture—has gotten huge attention and utilized in many applications such as smart home/city, self-driving cars, and translation services and more mainstream impact from these techniques will appear soon. In healthcare, the first big use case is for image analysis in radiology and pathology departments because of its ability to handle huge dataset which is beyond the scope of human capability, and then reliably convert these analyses into clinical insights that aid physicians in planning and providing personalized healthcare, ultimately leading to better outcomes, lower risk, and lower costs of care. Thanks to the 5G wireless network that enables controlling devices remotely in applications where real-time network performance is critical. The challenges and future research related to the Internet of healthcare things will be discussed in the light of Machine Learning, 5G Network, and Distributed Security.
Dr. Parikshit N. Mahalle obtained his B.E degree in Computer Science and Engineering from Sant Gadge Baba Amravati University, Amravati, India and M.E. degree in Computer Engineering from Savitribai Phule Pune University, Pune, India. He completed his Ph.D in Computer Science and Engineering specialization in Wireless Communication from Aalborg University, Aalborg, Denmark. He was Post Doc Researcher at CMI, Aalborg University, Copenhagen, Denmark. Currently, he is working as Professor and Head in the Department of Computer Engineering at STES’s Smt. Kashibai Navale College of Engineering, Pune, India. He has more than 20 years of teaching and research experience. He is serving as a subject expert in Computer Engineering, Research and Recognition Committee at several universities like SPPU (Pune), SGBU (Amravati). He is a senior member IEEE, ACM member, Life member CSI and Life member ISTE. Also, he is a member of IEEE transaction on Information Forensics and Security, IEEE Internet of Things Journal. He is a reviewer for IGI Global – International Journal of Rough Sets and Data Analysis (IJRSDA), Associate Editor for IGI Global - International Journal of Synthetic Emotions (IJSE), Inderscience International Journal of Grid and Utility Computing (IJGUC). He is a Member-Editorial Review Board for IGI Global – International Journal of Ambient Computing and Intelligence (IJACI). He is also working as an Associate Editor for IGI Global - International Journal of Synthetic Emotions (IJSE). He has also remained a technical program committee member for International conferences and symposium like IEEE ICC, IEEE INDICON, IEEE GCWSN, IEEE ICCUBEA, etc.
Due to large number of devices connected to Internet and in the sequel emergence of IoT, data generated and posted on the cloud is increasing at faster rate. This data is varied in size, variety, velocity and complexity resulting into the Big data and key challenge is to process it. This talk focuses on challenges of big data management essentially in the context of IoT. Data science and machine learning techniques will play key role in drawing good insights from this Big data. Data science workflow and the role of data scientist in data management in IoT is the main objective of this session.
Nilanjan Dey, is an Assistant Professor in the Department of Information Technology at Techno International New Town (Formerly known as Techno India College of Technology), Kolkata, India. He is a visiting fellow of the University of Reading, UK. He was an honorary Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012-2015). He was awarded his PhD. from Jadavpur University in 2015. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence, IGI Global. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing, Springer Nature, Series Co-Editor of Advances in Ubiquitous Sensing Applications for Healthcare, Elsevier, Series Editor of Computational Intelligence in Engineering Problem Solving and Intelligent Signal processing and data analysis, CRC. He has authored/edited more than 75 books with Springer, Elsevier, Wiley, CRC Press, and published more than 300 peer-reviewed research papers. His main research interests include Medical Imaging, Machine learning, Computer-Aided Diagnosis, Data Mining, etc. He is the Indian Ambassador of the International Federation for Information Processing (IFIP) – Young ICT Group.
Abstract: Advancement in medical imaging modalities result in huge varieties of images engaged in different management phases, namely prognosis, diagnosis, and treatment. In clinical practice, imaging has reserved a vital role to assist physicians and medical experts in decision-making. However, the counterpart that the physician faces is the complexity to deal with a large amount of data and image contents. Mainly, the interpretation is based on the physician’s observations, which is tedious, subject to error, and highly dependent on the skill and experience of the clinicians. Accordingly, emerging demand for automated tools has become essential for detecting, quantifying and classifying the disease for an accurate diagnosis.
Computer-aided Diagnosis (CAD) is an emergent research area that aims to meet the physicians’ demands, to speed up the diagnostic process, to reduce diagnostic errors, and to improve the quantitative evaluation. It is based mainly on medical images that provide direct visualization of the bodies and information ranging from functional activities, anatomical information, to the cellular and molecular expressions. Recently, varieties of Computer-aided Detection and diagnosis procedures have been established to assist the automated interpretation of the medical images to attain an accurate and reliable diagnosis.
This talk provides a state-of-the-art sight in medical imaging applied to CAD. Besides traditional machine learning, Deep learning is the fastest-growing field in machine learning and is widespread used in disease detection. Recent research shows that deep learning can increase disease detection accuracy significantly. The talk emphasizes on the CAD ability to improve the diagnostic accuracy and different future directions using traditional and as well as deep learning techniques as an opening that gathers the clinicians and engineers for an accurate diagnosis.
I am passionate about AI and the positive changes this technology is bringing to society. My research area includes designing, implementing and troubleshooting Deep Neural networks and Architectures. Over the course of my career, I have been involved in all aspects of Problem Solving , Algorithm Design , Understanding Business Acumen , Competitive Analysis , Building Teams , Intellectual Property , Patents and Technology Development.
The field of computer vision is seeing lot of traction today. Many researchers & startups are modifying and improving Convolutional Neural Networks undertaking performance, usecase, type of data to fit in and many more. So , it is important to know some of the most important developments in Convolutional neural network and the thought behind all these developments. Knowing which will help to build the intuition for future improvement in the field.