Asian Journal of Research in Computer Science

The growth of regularly generated data from many financial activities has significant implications for every corner of financial modelling. This study has investigated the utilization of these continuous growing data by a means of an automated process. The automated process can be developed by using Machine learning based techniques that analyze the data and gain experience from the underlying data. Different important domains of financial fields such as Credit card fraud detection, bankruptcy detection, loan default prediction, investment prediction, marketing and many more can be modelled by implementing machine learning methods. Among several machine learning based techniques, the use of parametric and non-parametric based methods are approached by this research. Two parametric models namely Logistic Regression, Gaussian Naive Bayes models and two non-parametric methods such as Random Forest, Decision Tree are implemented in this paper. All the mentioned models are developed and implemented in the field of Credit card fraud detection, bankruptcy detection, loan default prediction. In each of the aforementioned cases, the comparative study among the classification techniques is drawn and the best model is identified. The performance of each classifier on each considered domain is evaluated by various performance metrics such as accuracy, F1-score and mean squared error. In the credit card fraud detection model the decision tree classifier performs the best with an accuracy of 99.1% and, in the loan default prediction and bankruptcy detection model, the random forest classifier gives the best accuracy of  97% and 96.84% respectively.

This work is aimed at adding an extra layer of security to the login process of an electronic examination system as security has been identified as one of the critical success factors in the management of such exams. It proposes to secure the login process of an e-exam system through authentication and encryption to control access and avoid impersonation. A model of the e-exam system with Quick Response (QR) code generation capability was designed where a student’s matriculation number is accepted as input which is then converted into a two-dimensional bar code using a QR generator. Outputs from the QR code generator are then secured by encryption using MD5 and SHA-224 encryption algorithms. MD5 algorithm produces a 32-bit hash value which is further encrypted using SHA-224 that produces a resulting 56-bit hash value that is then saved in the password column of the user table in the database. This research resulted in a secure and web-based electronic examination authentication system implemented and tested on a client-server architecture. Performance evaluation of the developed system revealed that it is fast and effective, capable of  authenticating students in an average of 0.624 seconds when the smartphone flashlight is off, and 0.318 seconds with flashlight turned on and consequently, resistant to brute force attacks. This paper fulfils an identified need to develop an electronic exam system that not only secures the question bank but equally ensures the security of the login process as well as the login details using a combination of two security techniques.

Nowadays, common diseases like malaria, typhoid and cholera become more dangerous problems for people living in this world. The objective is how it can avoid the queue of patients in hospital. In this article, the author has proposed a model of expert systems using the knowledge of physician and other health professionals. The rule based expert system XPerMal useful for patients infected with common diseases and this system will give an answer as similar to a doctor or medical expert and also this system is very useful in rural areas where we have young medical experts or have no medical expert. The reasoning strategy is a key element in many medical tasks. It is well known that developing countries face a shortage of medical expertise in the medical sciences. Patients also find a huge queue in hospitals. Because of this, they are unable to provide good medical services to their inhabitants. The knowledge is acquired from literature review and human experts in the specific field and is used as a basis for analysis, diagnosis and decision-making. Knowledge is represented by an integrated formalism that combines rules and facts.

Due to the rapid development in information technology, Big Data has become one of its prominent feature that had a great impact on other technologies dealing with data such as machine learning technologies. K-mean is one of the most important machine learning algorithms. The algorithm was first developed as a clustering technology dealing with relational databases. However, the advent of Big Data has highly effected its performance. Therefore, many researchers have proposed several approaches to improve K-mean accuracy in Big Data environment. In this paper, we introduce a literature review about different technologies proposed for k-mean algorithm development in Big Data. We demonstrate a comparison between them according to several criteria, including the proposed algorithm, the database used, Big Data tools, and k-mean applications. This paper helps researchers to see the most important challenges and trends of the k-mean algorithm in the Big Data environment.

Mobile malware is malicious software that targets mobile phones or wireless-enabled Personal digital assistants (PDA), by causing the collapse of the system and loss or leakage of confidential information. As wireless phones and PDA networks have become more and more common and have grown in complexity, it has become increasingly difficult to ensure their safety and security against electronic attacks in the form of viruses or other malware. Android is now the world's most popular OS. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices, they are incorporated in many aspects of our everyday lives. In today’s digital world most of the anti-malware tools are signature based which is ineffective to detect advanced unknown malware viz. Android OS, which is the most prevalent operating system (OS), has enjoyed immense popularity for smart phones over the past few years. Seizing this opportunity, cybercrime will occur in the form of piracy and malware. Traditional detection does not suffice to combat newly created advanced malware. So, there is a need for smart malware detection systems to reduce malicious activities risk. The present paper includes a thorough comparison that summarizes and analyses the various detection techniques.