UNESCO Laureate Prof Sir Bashiru Aremu
@crownintl.education
Vice Chancellor and World Acclaimed Distinguished Professor Emeritus
Crown University Intl Chartered Inc
RESEARCH INTERESTS
General Examination of Biometric Technology Security System, Mathematical Theorem and Formulae as a bedrock of Computer Science
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
M. Pushpa Rani, Bashiru Aremu, and Xavier Fernando
Springer Nature Singapore
Binay Kumar Pandey, Digvijay Pandey, Vinay Kumar Nassa, Shaji George, Bashiru Aremu, Pankaj Dadeech, and Ankur Gupta
Springer Nature Singapore
J. Michael Antony Sylvia, M. Pushpa Rani, and Bashiru Aremu
IEEE
Due to technological advancements, the Internet of Things (IoT) has been extensively used in a number of environments during this period. The IoT is being utilized successfully, particularly in the field of weather monitoring. As a result, IoT weather sensors generate massive amounts of weather data on a regular basis. This research aims to efficiently analyze the massive amounts of data generated by IoT climate sensors to develop an effective early flood forecasting system. Numerous methods for forecasting floods using historical data have been invented. However, all of these methodologies are becoming inefficient as a result of climate change and the volume of the data. This research provides a methodology for extracting strongly correlated weather features in order to reduce the error in weather forecasts caused by data volume and climate change. The Feed-Forward Artificial Neural Network (FFANN) is used to forecast early rainfall and floods. Furthermore, Chennai has been chosen as the study area for this research. Finally, two experiments are conducted to demonstrate this early flood forecasting system's prediction accuracy and training efficiency. The experimental results demonstrate that the proposed flood forecasting system outperforms recently developed systems in terms of accuracy and training efficiency.
Keshab Nath, R Dhanalakshmi, V. Vijayakumar, Bashiru Aremu, K. Hemant Kumar Reddy, and Gao Xiao-Zhi
IOS Press
Detection of densely interconnected nodes also called modules or communities in static or dynamic networks has become a key approach to comprehend the topology, functions and organizations of the networks. Over the years, numerous methods have been proposed to detect the accurate community structure in the networks. State-of-the-art approaches only focus on finding non-overlapping and overlapping communities in a network. However, many networks are known to possess a hidden or embedded structure, where communities are recursively grouped into a hierarchical structure. Here, we reinvent such sub-communities within a community, which can be redefined based on nodes similarity. We term those derived communities as hidden or hierarchical communities. In this work, we present a method called Hidden Community based on Neighborhood Similarity Computation (HCNC) to uncover undetected groups of communities that embedded within a community. HCNC can detect hidden communities irrespective of density variation within the community. We define a new similarity measure based on the degree of a node and it’s adjacent nodes degree. We evaluate the efficiency of HCNC by comparing it with several well-known community detectors through various real-world and synthetic networks. Results show that HCNC has better performance in comparison to the candidate community detectors concerning various statistical measures. The most intriguing findings of HCNC is that it became the first research work to report the presence of hidden communities in Les Miserables, Karate and Polbooks networks.