Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting

This article applies Machine Learning techniques to solve Intrusion Detection problems within computer networks. Due to complex and dynamic nature of computer networks and hacking techniques, detecting malicious activities remains a challenging task for security experts, that is, currently available defense systems suffer from low detection capability and high number of false alarms. To overcome such performance limitations, we propose a novel Machine Learning algorithm, namely Boosted Subspace Probabilistic Neural Network (BSPNN), which integrates an adaptive boosting technique and a semi parametric neural network to obtain good tradeoff between accuracy and generality. As the result, learning bias and generalization variance can be significantly minimized. Substantial experiments on KDD 99 intrusion benchmark indicate that our model outperforms other state of the art learning algorithms, with significantly improved detection accuracy, minimal false alarms and relatively small computational complexity.Comment: 9 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact Factor 0.423, http://sites.google.com/site/ijcsis

Discriminant Subspace Analysis for Uncertain Situation in Facial Recognition

Facial analysis and recognition have received substential attention from researchers in biometrics, pattern recognition, and computer vision communities. They have a large number of applications, such as security, communication, and entertainment. Although a great deal of efforts has been devoted to automated face recognition systems, it still remains a challenging uncertainty problem. This is because human facial appearance has potentially of very large intra-subject variations of head pose, illumination, facial expression, occlusion due to other objects or accessories, facial hair and aging. These misleading variations may cause classifiers to degrade generalization performance

Spam Recognition using Linear Regression and Radial Basis Function Neural Network

Spamming is the abuse of electronic messaging systems to send unsolicited bulk messages. It is becoming a serious problem for organizations and individual email users due to the growing popularity and low cost of electronic mails. Unlike other web threats such as hacking and Internet worms which directly damage our information assets, spam could harm the computer networks in an indirect way ranging from network problems like increased server load, decreased network performance and viruses to personnel issues like lost employee time, phishing scams, and offensive content. Though a large amount of research has been conducted in this area to prevent spamming from undermining the usability of email, currently existing filtering methods\u27 performance still suffers from extensive computation (with large volume of emails received) and unreliable predictive capability (due to highly dynamic nature of emails). In this chapter, we discuss the challenging problems of Spam Recognition and then propose an anti-spam filtering framework; in which appropriate dimension reduction schemes and powerful classification models are employed. In particular, Principal Component Analysis transforms data to a lower dimensional space which is subsequently used to train an Artificial Neural Network based classifier. A cost-sensitive empirical analysis with a publicly available email corpus, namely Ling-Spam, suggests that our spam recognition framework outperforms other state- of-the-art learning methods in terms of spam detection capability. In the case of extremely high misclassification cost, while other methods\u27 performance deteriorates significantly as the cost factor increases, our model still remains stable accuracy with low computation cost

Adolescence is an opportunity for farm injury prevention: A call for better age-based data disaggregation

Injury is a leading cause of mortality and injury-related morbidity, which can have lifelong impacts on physical and mental health, as well as on an individual’s and family’s economic livelihood. Transport and unintentional injuries are the leading cause of death for adolescents 10–24 years of age, with more lives lost than communicable or non-communicable diseases, nutritional or maternal health causes or selfharm. Predominantly, in the injury prevention arena, there is a tendency to focus on young (especially under 5 years) children and therefore, despite the persistently high injury burden among adolescents, there has been limited research on, and evaluation of, the prevention of injury-related harms among adolescents

A Systematic Review: Types of Feedback Provision in Enhancing English Language in Online Learning Environment

Feedback is a significant part of learning system and framework. Since instructors and students are physically separated in online platform, feedback becomes compulsory tools to be implemented in assisting the process of teaching and learning. Therefore, this systematic review paper aims to find out which forms of English communication skills being focussed on online learning, to explore the varieties of feedback provision that are used to enhance English communication skills in online learning and lastly to find out if the feedback implementation in online learning has affected students positively, especially in English language. Despite the fact that feedback plays a significant role in assisting students, there have been few studies that examine the progress made so far as reported in the literature, and which type of feedback has actually substantial in improving and enhancing the learners’ communication skills especially in English language. Varieties of feedback discovered; and all intended to aid and enhance the communication skills in online learning, especially in English language. When conducting this systematic literature review study, PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommended procedures were used, and a total of 30 articles were found by utilising online databases such as Scopus, IEEEXplore Digital Library and etc, as according to the review selection guideline. Feedback provision, communication skills, online learning, student, and English language were employed as search keywords. The results were analysed in light of the three focuses described. Interestingly, it is revealed that the feedback utilization has positively affecting the students especially in English language learning via online learning