Nasir A. Yakub

Work place: Department of Computer and Communications Engineering, Abubakar Tafawa Balewa University, Bauchi

E-mail: aynasir@gmail.com

Website:

Research Interests: Image Compression, Embedded System, Computer Vision, Image Manipulation, Image Processing

Biography

Nasir A. Yakub is currently a Ph.D. Student, at the Laboratory of Computer Science, Robotics and Microelectronics (LIRMM), Universite de Montpellier, France. He received a B.Eng. Computer Engineering from Bayero University Kano (BUK), Kano city, Nigeria, and degree of Master of Engineering (Electrical-Computer and Microelectronic system) from Universiti Teknologi Malaysia (UTM), Skudai, Johor Bahru, Malaysia. He is currently a lecturer in the Department of Computer and Communications Engineering, Abubakar Tafawa Balewa University Bauchi (ATBU), Bauchi, Nigeria. His research interest includes Computer and Robotic Vision, Image processing and Embedded systems.

Author Articles
A Survey of Data Mining Techniques for Indoor Localization

By Usman S. Toro Nasir A. Yakub Aliyu B. Dala Murtala A. Baba Kabiru I. Jahun Usman I. Bature Abbas M. Hassan

DOI: https://doi.org/10.5815/ijem.2021.06.03, Pub. Date: 8 Dec. 2021

The important need for suitable indoor positioning systems has recently seen an exponential rise with location-based services emerging in many sectors of human life. This has led to adopting techniques to mine location data to discover useful insights to improve the accuracy of the various indoor positioning systems. Although indoor positioning has been reviewed in some literary works, an in-depth survey of how data mining could improve the performance of indoor localization systems is still lacking. This paper surveys data mining techniques such as Na¨─▒ve Bayes, Regression, K-Means, K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Expectation Maximization (EM), Neural Networks (NN), and Deep Learning (DL) including how they were used to improve the accuracy of indoor positing systems using various supporting technologies such as WiFi, Bluetooth, Radio Frequency Identification (RFID), Visible Light Communication (VLC), and indoor localization techniques such as Received Signal Strength Index (RSSI), Channel State Information (CSI), fingerprinting, and Time of Flight (ToF). Additionally, we present some of the challenges of existing indoor positioning systems that employ data mining while highlighting areas of future research that could be exploited in addressing those challenges.

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