Age estimation was used in forensic anthropology to help in the identification of individual remains and living person. However, the estimation methods tend to be unique and applicable only to a certain population. This paper analyzed age estimation using twelve regression models carried out on X-ray images of the left hand taken from an Asian data set for subjects under the age of 19. All the nineteen bones of the left hand were measured using free image software and the statistical analysis were performed using SPSS. There are two methods to determine age in this study which are single bone method and all bones method. For single bone method, S-curve regression model was found to have the highest R-square value using second metacarpal for males, and third proximal phalanx for females. For age estimation using single bone, fifth metacarpal from males and fifth proximal phalanx from females can be used due to the lowest mean square error (MSE) value. To conclude, multiple linear regressions is the best techniques for age estimation in cases where all bones are available, but if not, S-curve regression can be used using single bone method.
Sex estimation is used in forensic anthropology to assist the identification of individual remains. However, the estimation techniques tend to be unique and applicable only to a certain population. This paper analyzed sex estimation on living individual child below 19 years old using the length of 19 bones of left hand applied for three classification techniques, which were Discriminant Function Analysis (DFA), Support Vector Machine (SVM) and Artificial Neural Network (ANN) multilayer perceptron. These techniques were carried out on X-ray images of the left hand taken from an Asian population data set. All the 19 bones of the left hand were measured using Free Image software, and all the techniques were performed using MATLAB. The group of age "16-19" years old and "7-9" years old were the groups that could be used for sex estimation with as their average of accuracy percentage was above 80%. ANN model was the best classification technique with the highest average of accuracy percentage in the two groups of age compared to other classification techniques. The results show that each classification technique has the best accuracy percentage on each different group of age.
Android is a free open-source operating system (OS), which allows an in-depth understanding of its architecture. Therefore, many manufacturers are utilizing this OS to produce mobile devices (smartphones, smartwatch, and smart glasses) in different brands, including Google Pixel, Motorola, Samsung, and Sony. Notably, the employment of OS leads to a rapid increase in the number of Android users. However, unethical authors tend to develop malware in the devices for wealth, fame, or private purposes. Although practitioners conduct intrusion detection analyses, such as static analysis, there is an inadequate number of review articles discussing the research efforts on this type of analysis. Therefore, this study discusses the articles published from 2009 until 2019 and analyses the steps in the static analysis (reverse engineer, features, and classification) with taxonomy. Following that, the research issue in static analysis is also highlighted. Overall, this study serves as the guidance for novice security practitioners and expert researchers in the proposal of novel research to detect malware through static analysis.