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All Journal International Journal of Electrical and Computer Engineering Geosfera Indonesia
Huqqani, Ilyas Ahmad
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Computer Science & IT Earth & Planetary Sciences Education Electrical & Electronics Engineering Environmental Science

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Biometric classification system for dorsal finger creases utilizing multi-block circular shift combination local binary pattern Riaz, Imran; Ali, Ahmad Nazri; Ibrahim, Haidi; Huqqani, Ilyas Ahmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5234-5243

Abstract

In recent years, there has been a growing interest in biometric recognition based on finger dorsal patterns, making it a significant area of research. This paper introduces a biometric classification system that utilizes dorsal finger middle creases. The viability of this trait is assessed through the implementation of a new method known as multi block circular shift combination local binary pattern (MBCSC-LBP). The MBCSC-LBP approach involves dividing the image into multiple blocks to enhance robustness and capture both local and global information, thereby extracting discriminative features. These features from each block are then concatenated to form a comprehensive feature vector. To evaluate the accuracy of the proposed MBCSC-LBP feature extractor, a support vector machine (SVM) with a linear kernel is utilized. The classification accuracy achieved by this method is 96.22% indicating a promising performance.
Landslide Hazard Analysis Using a Multilayered Approach Based on Various Input Data Configurations Huqqani, Ilyas Ahmad; Tien, Tay Lea; Mohamad-Saleh, Junita
Geosfera Indonesia Vol. 6 No. 1 (2021): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v6i1.23347

Abstract

Landslide is a natural disaster that occurs mostly in hill areas. Landslide hazard mapping is used to classify the prone areas to mitigate the risk of landslide hazards. This paper aims to compare spatial landslide prediction performance using an artificial neural network (ANN) model based on different data input configurations, different numbers of hidden neurons, and two types of normalization techniques on the data set of Penang Island, Malaysia. The data set involves twelve landslide influencing factors in which five factors are in continuous values, while the remaining seven are in categorical/discrete values. These factors are considered in three different configurations, i.e., original (OR), frequency ratio (FR), and mixed-type (MT) data, which act as an input to train the ANN model separately. A significant effect on the final output is the number of hidden neurons in the hidden layer. In addition, three data configurations are processed using two different normalization methods, i.e., mean-standard deviation (Mean-SD) and Min-Max. The landslide causative data often consist of correlated information caused by overlapping of input instances. Therefore, the principal component analysis (PCA) technique is used to eliminate the correlated information. The area under the receiver of characteristics (ROC) curve, i.e., AUC is also applied to verify the produced landslide hazard maps. The best result of AUC for both Mean-SD and Min-Max with PCA schemes are 96.72% and 96.38%, respectively. The results show that Mean-SD with PCA of MT data configuration yields the best validation accuracy, AUC, and lowest AIC at 100 number of hidden neurons. MT data configuration with the Mean-SD normalization and PCA scheme is more robust and stable in the MLP model's training for landslide prediction. Keywords: Landslide; ANN; Hidden Neurons; Normalization; PCA; ROC; Hazard map Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License
Co-Authors Ali, Ahmad Nazri Ibrahim, Haidi Mohamad-Saleh, Junita Riaz, Imran Tien, Tay Lea