Maldroid- attribute selection analysis for malware classification

Android is the most dominant operating system in the mobile market and the number of Android users is increasing year by year. Malware authors use android market as a hub for malicious apps and spread malware to users with the intention to threaten privacy; and this has remained undetected due to the weakness in signature-based detection. A major problem with malware detection is the existence of numerous features in malware code and the need to look at the relevant features in malware analysis. As a result, applying any security solution in malware analysis is considered inefficient because mobile devices have limited resources in terms of its memory, processor and storage. Hence, the objective of this paper is to find the most effective and efficient attribute selection and classification algorithm in malware detection. Moreover, in order to get the best combination between attribute selection and classification algorithm, eight attributes selection and seven categories machine learning algorithm are applied in this study. The experiment evaluated 8000 real data samples and the result showed that InfoGainEval and KNN algorithm are the most selected in attribute selection and classification process

Adaboost-multilayer perceptron to predict the student’s performance in software engineering

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students

Efficient feature selection analysis for accuracy malware classification

Android is designed for mobile devices and its open-source software. The growth and popularity of android platform are high compared to another platform. Due to its glory, the number of malware has been increasing exponentially. Android system used a permission mechanism to allow users and developers to manage their access to private information, system resources, and data storage required by Android applications (apps). It became an advantage to an attacker to violent the data. This paper proposes a novel framework for Android malware detection. Our framework used three major methods for effective feature representation on malware detection and used this method to classify malware and benign. The result demonstrates that the Random forest is with 23 features is more accurate detection than the other machine learning algorithm

Adaboost-multilayer perceptron to predict the student’s performance in software engineering

Software Engineering (SE) course is one of the backbones of today's computer technology sophistication. Effective theoretical and practical learning of this course is essential to computer students. However, there are many students fail in this course. There are many aspects that influence a student's performance. Currently, student performance analysis methods just focus on historical achievement and assessment methods given in the class. Need more research to predict student's performance to overcome the problem of student failing. The objective of this research is to perform a prediction for student's performance in the SE using enhanced Multilayer Perceptron (MLP) machine learning classification with Adaboost. This research also investigates the requirements of each student before registering in this course. This research achieved 87.76 percent accuracy in classifying the performance of SE students

Malware visualizer: A web apps malware family classification with machine learning

Within the past few years, malware has been a serious threat to the security and privacy of all mobile phone users. Due to the popularity of smartphones, primarily Android, this makes them a very viable target for spreading malware. Many solutions in the past have proven to be ineffective and result many false positives. Other than that, most of the solution focuses on the android apk file, instead of visualizing the apk into image-based form. The objective of this project is to build a web apps to classify malware by transforming the apk file into image-based representation. This project uses three classification algorithm which are Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). The web apps is developed using Python with help of Streamlit with is a Python library for building datadriven web apps. The dataset contains 25 malware classes ranging from Trojan Horses to Spyware and 1 legitimate application class

Customer purchasing decision: an empirical study among Malaysian hypermarket shoppers / Mohd Najmie Osman...[et al.]

A report by Malaysian Retail Association stated that the estimation on Malaysia Retail Industry Quarterly Growth Rate 2017 was 1.5 % to 5.5% and at the average whole year by 3.9%. In Malaysia, the first hypermarket was Makro Cash & Carry outlet, a company owned by HSV Holding from the Netherland in 1993. This was followed by other hypermarkets such as Carrefour, Giant, Tesco, AEON Big and Mydin. The objective of the research was to identify the variables that would aid in developing better understanding of the dynamics hypermarket customer purchasing decision. Location, price, promotion, and variety of products represent the independent variables. 100 questionnaires were distributed to respondents that shopped at a particular hypermarket by using purposive sampling method. The data were statistically analyzed for reliability, correlation and multiple regressions. Based on the findings, it was discovered that price, promotion and variety of products have direct influence on customer purchasing decision. The results would help the industry in improving their action by emphasizing on the three independent variables in managing the complex issues of hypermarket customer purchasing decision. In conclusion, this study was carried out to gain a better understanding of the factors that influence customers in the process of making their purchasing decision at hypermarket

Comparison of two classification models for sex estimation based on bone length of hispanic population

One of the essential factors of conducting a forensic investigation is to determine sex. Although multiple studies have been conducted using hand bone, the studies using the Hispanic population are minimal. The purpose of this study is to develop the Discriminant Function Analysis (DFA) and Artificial Neural Network (ANN) model for sex estimation based on the Hispanic population using left-hand bone. The samples used are subjects ranged between age groups of infants and 18 years old which comprised of 91 females and 92 males. For the input, the length of nineteen bones from the subjects’ left hand is measured in centimeters and then normalized to become input for both models. The DFA model is chosen as a benchmark in this study to be compared with the ANN model based on accuracy percentage. The chosen DFA model is due to the widely used in estimating sex based on quantitative input. For the results, the DFA model produces a 72.7% accuracy percentage while the ANN produces 83.8%. Thus, the ANN model is selected to be the most ideal model in estimating sex compared to the DFA model

Polymerase chain reaction detection of Pasteurella multocida type B:2 in mice following oral inoculation

Haemorrhagic Septicaemia (HS) is an acute, fatal, septicaemic disease of cattle and water buffaloes caused by Pasteurella multocida, serotype B:2 in tropical countries. The limitations associated with accurate predictions of mortality, survival levels and the detection of the presence of the organism from various organs of infected animals. Hence, this study used mouse model to evaluate the pattern of mortality and bacterial recovery from organs. Twenty-four mice were randomly divided into two groups. Infected group were inoculated orally with 109 colony forming unit of P. multocida type B, the group 2 were negative controls. The mice were observed for 5 days post-inoculation. At necropsy, visceral organs of dead animals were subjected for the confirmation using Polymerase Chain Reaction (PCR). The results showed that mortality rate was significantly different (p<0.05) between the infected and control groups. Within infected group, highly significant difference (p<0.05) was observed where 12.5% of the mortality rate was recorded within 24 h and 62.5% within 48 h post-infection. The survival rate, in infected group, was found to be around 25%. In diagnosis, P. multocida type B was detected from all organs of animals that did not survive. In contrast, P. multocida type B was neither recovered nor detected from the organs of mice which survived until the end of the experimental period (120 h). The results of this study indicated that manipulation of the organism in experimental animals provided clear information of the incidence of the disease in the field

Acute phase protein profiles in calves following infection with whole cell, lipopolysaccharides, and outer membrane protein extracted from Pasteurella multocida type B:2

Acute Phase Protein (APP) investigations of serum or plasma following natural or experimental infection frequently reveal substantial alterations in the APPs, several of which are of veterinary importance in the assessment of herd health. The present study of the experimental nature was conducted to evaluate the acute phase protein responses; haptoglobin, Serum Amyloid A (SAA) and serum albumin in relation to infection with Pasteurella multocida type B and its immunogens; lipopolysaccharide (LPS) and Outer Membrane Protein (OMP) in calves. Eight clinically healthy, non-pregnant and non-lactating Brangus cross calves weighing 150±50 kg were used in this study. The calves (n = 8) were divided into 4 groups of 2 calves in each group. The control group was inoculated with sterile Phosphate Buffered Saline (PBS) whereas group 2 were inoculated with wild-type P. multocida type B:2 and group 3 and 4 were inoculated with LPS and OMP respectively. Blood samples were collected via jugular vein-puncture at 3 h intervals for APPs analysis. APPs were quantified by commercially available ELISA methods. Moribund animals were euthanized while the surviving animals were killed after 48 h. The results revealed that there were statistically significant differences (p0.05) with mean levels of 32.677±1.556 and 36.185±2.239 U L-1, respectively. While P. multocida group (22.193±2.727 U L-1) showed statistically significant difference (p<0.05) than the negative control group (34.233±6.900 U L-1). In conclusion, the findings of this study indicated that APPs; SAA and haptoglobin are sensitive biomarkers to explore host response in relation to Haemorrhagic Septicaemia infections in clinical settings

Acute phase protein profile and clinico-pathological changes in mice associated with the infection of Pasteurella multocida type B and the bacterial lipopolysaccharide and outer membrane protein immunogens

Haemorrhagic Septicaemia (HS) is a killer disease of cattle and buffalo of economic importance in Asia and Africa. There is insufficient information on the responses of Balb c mice as animal model in respect of immunogens and Acute Phase Proteins (APP) profiles. Therefore, the present study aims to evaluate the acute phase protein profiles in mice associated with the infection of Pasteurella multocida type B and the bacterial lipopolysaccharide and outer membrane protein immunogens. Two hundred healthy Balb/c mice of 8-10 weeks old were used in this study. They were divided into four equal groups of 50 mice each. Mice of group 1 were inoculated intra-peritoneal with 1.0 mL sterile Phosphate Buffered Saline (PBS) pH 7, group 2 were inoculated with 1.0 mL of 109 colony forming unit (cfu) of P. multocida B: 2. Mice of groups 3 and 4 were inoculated intra-peritoneal with 1.0 mL of LPS and 1.0 mL of OMP, respectively. Acute phase proteins analysis were done using two sites Enzyme Linked Immunoassay (ELISA) highly sensitive test kits. The data was analyzed using SPSS. Haptoglobin concentration increased significantly in group 3 and 4 (p<0.05) following inoculation with immunogens compared to control group. Mice in group 3 and 4 showed significantly (p<0.000) 3 times higher concentrations of SAA and significantly (p<0.037) 1.3 times increased concentrations of SAA, respectively compared to the control group. There was no significant changes in the concentrations of fibrinogen in group 2 (p = 0.177), group 3 (p = 0.088) and group 4 (p = 0.359). C-reactive protein in groups 2 and 3 showed significantly (p<0.05) higher levels than the control group. Albumin showed significant increase (p<0.05) in group 2 compared to the control group. There were significant changes in the concentrations of acute phase proteins and clinical responses post inoculation with immunogens indicating adverse pro-inflammatory reactions in mice in the present study