Usman I. Bature

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

E-mail: 4070usa@gmail.com

Website:

Research Interests: Embedded System

Biography

Usman Isyaku Bature is currently a PhD. Candidate in the Department of Electrical Engineering, Universiti of Teknologi Petronas (UTP) Malaysia. 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), Nigeria. His research interest includes Image processing, Nano-material, Embedded systems and Biomedical 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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Off-line Handwritten Signature Verification System: Artificial Neural Network Approach

By N. M. Tahir Adam N. Ausat Usman I. Bature Kamal A. Abubakar Ibrahim Gambo

DOI: https://doi.org/10.5815/ijisa.2021.01.04, Pub. Date: 8 Feb. 2021

Nowadays, it is evident that signature is commonly used for personal verification, this justifies the necessity for an Automatic Verification System (AVS). Based on the application, verification could either be achieved Offline or Online. An online system uses the signature’s dynamic information; such information is captured at the instant the signature is generated. An offline system, on the other hand, uses an image (the signature is scanned). In this paper, some set of simple shaped geometric features are used in achieving offline Verification of signatures. These features include Baseline Slant Angle (BSA), Aspect Ratio (AR), and Normalized Area (NA), Center of Gravity as well as the line’s Slope that joins the Center of Gravities of the signature’s image two splits. Before the features extraction, a signature preprocessing is necessary to segregate its parts as well as to eliminate any available spurious noise. Primarily, System training is achieved via a signature record which was acquired from personalities whose signatures had to be validated through the system. An average signature is acquired for each subject as a result of incorporating the aforementioned features which were derived from a sample set of the subject’s true signatures. Therefore, a signature functions as the prototype for authentication against a requested test signature. The similarity measure within the feature space between the two signatures is determined by Euclidian distance. If the Euclidian distance is lower than a set threshold (i.e. analogous to the minimum acceptable degree of similarity), the test signature is certified as that of the claiming subject otherwise detected as a forgery. Details on the stated features, pre-processing, implementation, and the results are presented in this work.

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