Prof. Mohd Hafiz Yusoff
Albukhary International University, Malaysia
Title: Big Data Technology to reduce poverty: A case study in Malaysia.
Abstract: The limited availability of data on poverty and inequality poses major challenges to the monitoring of Government Agencies twin goals – ending extreme poverty and boosting shared prosperity. According to a recently completed study, for nearly one hundred countries at most two poverty estimates are available over the past decade. Worse still, for around half of them there was either one or no poverty estimate available. Increasing the frequency of data on poverty is critical to effectively monitoring the Agencies twin goals.
Against this background, the science of “Big Data” is often looked to as providing a potential solution. The rapidly increasing volumes of raw data and the accompanying improvement of computer science have enabled us to fill other kinds of data gaps in ways that we could not even have dreamt of in the past.
In this research project, the approach and strategy to produce the meaningful data of poverty will be presented. This research project hopefully will give more impact to poverty alleviation programs.
Prof. DIPAK KUMAR JANA
Haldia Institute of Technology, India
Title: Adaptive Neuro-Fuzzy Inference System, Artificial Neural Network and Internal Type-2 Fuzzy Logic techniques for Quality of Polypropylene
Abstract: The polypropylene (PP) is a versatile thermoplastic resin available in a wide range of formulations for engineering applications. In this paper presents a comparative approach to predict the quality of polypropylene in petrochemical plants using Adaptive Neuro-Fuzzy Inference System (ANFIS), artificial neural network (ANN), and internal type-2 fuzzy logic (IT2FLC). A model is constructed based on a large number of data collected from a renowned petrochemical plant in India and used to predict the polypropylene quality through the proposed approach. The quality of polypropylene depends on the output parameter the xylene solubility of the PP and H2, donor flow, pressure and temperature of polymerization reactors have been considered as input parameters. The main parameters mentioned were analyzed in an ANFIS, ANN, IT2FIC models for comparison of testing parameters. The selected ANN model is the Feed-Forward Back Propagation Levenberg–Marquardt algorithm with performance function MSE with 3 numbers of layers and 20 neurons. Comparisons were made for quality assessments of PP and deeply analyzed and presented. Results showed that the calculated parameters and the estimated values of the selected ANN were well matched. The production level of the ANN model is meaning that the testing installation and works are done well. The measured values on prediction indicators are well matched with the values estimated by the ANN showing that the robust estimations were performed.
To be added...