Landslide is one of the disasters which cause property damages, infrastructure destruction, injury and death. This paper presents the analysis of landslide hazard mapping of Penang Island Malaysia using bivariate statistical methods. Bivariate statistical methods are simple approach which are capable to produce good results in short computational time. In this study, three bivariate statistical methods, i.e. Frequency Ratio (FR), Information Value (IV) and Modified Information Value (MIV) are used to generate the landslide hazard maps of Penang Island. These bivariate statistical methods are computed using MATLAB tool. Landslide hazard map is categorized into 4 levels of hazard. The accuracy of each method and effectiveness in predicating landslides are validated and determined by using Receiver of Characteristics curve. The accuracies of FR, IV and MIV methods are 79.58%, 79.14% and 79.37% respectively.

Latent fingerprints are of decisive influence when it comes to identifying suspects, as these are usually encountered at crime scenes. Also, these serve as a compelling evidence in a court of law. Some of the formidable challenges with latent fingerprints are deficient ridge information and poor ridge clarity due to background noise and non-linear distortions. An effective fingerprint enhancement scheme is suggested to meliorate the clarity and continuity of ridges and valleys in a latent fingerprint image. The proposed method deals with both local (minutia or singular points, ridge termination, bifurcation, broken ridges, short ridges, core and delta) and global features (ridge orientation and ridge frequency), while retaining the true ridge-valley structures and removing noise at the same time. The proposed design establishes scalability, accessibility and flexibility.

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

Biometric system identifies individuals by utilizing their behavioral or physical traits. A multimodal biometric system is one of the best choices to overcome the numerous problems including vulnerability to environmental impacts, sensitivity to spoof attacks, non-universality, and intra-class variations problems associated with unimodal biometric system. Hand based biometrics is a swiftly growing field of research. This paper proposes a multimodal biometric system based on hand modalities, which combines the extracted information of dorsal finger crease and finger knuckle print. Dorsal finger crease with a small image dimension is a newly explored biometric trait with a highly distinctive pattern and can be used as a reliable identifier in biometric system. To the best of our knowledge, it is the first time to integrate the dorsal finger crease with finger knuckle print for more secure and better recognition performance of the proposed multimodal biometric system. Additionally, we developed a dorsal finger image acquisition device to construct a novel dataset, USM-FCKP, which simultaneously captures dorsal finger crease and finger knuckle print images. For feature extraction, an efficient texture descriptor, Circular Shift Combination Local Binary Pattern (CSC-LBP) is applied, and compared its performance with other LBP variants, including Basic-LBP, Uniform-LBP, Orthogonal Difference-LBP, and Symmetric Inline Matrix-LBP. Furthermore, feature-level fusion is performed, and a Support Vector Machine (SVM) classifier is employed to evaluate the effectiveness of the proposed dataset and multimodal biometric system. Experimental results demonstrate that the proposed multimodal approach achieves superior recognition performance compared to unimodal methods.