Minimizing the limitations of GL analyser of fusion based email classification

In the last decade, the Internet email has become one of the primary method of communication used by everyone for the exchange of ideas and information. However, in recent years, along with the rapid growth of the Internet and email, there has been a dramatic growth in spam. Classifications algorithms have been successfully used to filter spam, but with a certain amount of false positive trade-offs. This problem is mainly caused by the dynamic nature of spam content, spam delivery strategies, as well as the diversification of the classification algorithms. This paper presents an approach of email classification to overcome the burden of analyzing technique of GL (grey list) analyser as further refinements of our previous multi-classifier based email classification [10]. In this approach, we introduce a “majority voting grey list (MVGL)” analyzing technique with two different variations which will analyze only the product of GL emails. Our empirical evidence proofs the improvements of this approach, in terms of complexity and cost, compared to existing GL analyser. This approach also overcomes the limitation of human interaction of existing analyzing technique.

Multi-tier phishing email classification with an impact of classifier rescheduling

Protecting user's mailbox from infiltration of phishing email is a significant research issue now a day. Many researches are going on filtering phishing using classification based algorithms and achieve substantial performance. It has been studied and investigated with different classification algorithms and observed that the outputs of the classifiers vary from one another with same corpora. This paper presents the impact of classifier rescheduling of multi-tier classification of phishing email to observe the best scheduling in the classification process. In our method, the features of phishing email will be extracted and classified in a sequential fashion by using the multi-tier classification and the outputs will be sent to the decision fusion process. Empirical evidence proofs that the impact of rescheduling of classifiers among the tiers gives diverse outcomes in terms of accuracy as well as number of false positive instances.

Height and construction costs of residential high-rise buildings in Shanghai

A widely recognized theme of construction economics suggests that the cost of construction per square meter increases as building height rises. However, over a number of years, research conducted regarding the height and cost issue has established a classic relationship between the two factors which can be represented by a U-shaped curve. This paper describes the study of the height-cost relationship of high-rise residential buildings in Shanghai in terms of the total construction cost and elemental costs while considering the context and commonality of buildings. This research was developed as an extension of the previous work, which examined data for buildings in Hong Kong. Initial findings indicate that the curves illustrating the relationships between height and cost of residential buildings in Shanghai and Hong Kong exhibit different profiles. The dissimilarities indicate that different sets of criteria should be applied in the judgment of height that affects cost in different locations. In terms of elemental costs, the findings suggest that there are differences in the way these costs react to changes in the building height.

Email classification using data reduction method

Classifying user emails correctly from penetration of spam is an important research issue for anti-spam researchers. This paper has presented an effective and efficient email classification technique based on data filtering method. In our testing we have introduced an innovative filtering technique using instance selection method (ISM) to reduce the pointless data instances from training model and then classify the test data. The objective of ISM is to identify which instances (examples, patterns) in email corpora should be selected as representatives of the entire dataset, without significant loss of information. We have used WEKA interface in our integrated classification model and tested diverse classification algorithms. Our empirical studies show significant performance in terms of classification accuracy with reduction of false positive instances.

The economic burden of ill health due to diet, physical inactivity, smoking, alcohol and obesity in the UK : an update to 2006-07 NHS costs

Background: Estimates of the economic cost of risk factors for chronic disease to the NHS provide evidence for prioritization of resources for prevention and public health. Previous comparable estimates of the economic costs of poor diet, physical inactivity, smoking, alcohol and overweight/obesity were based on economic data from 1992–93.

Methods: Diseases associated with poor diet, physical inactivity, smoking, alcohol and overweight/obesity were identified. Risk factor-specific population attributable fractions for these diseases were applied to disease-specific estimates of the economic cost to the NHS in the UK in 2006–07.

Results: In 2006–07, poor diet-related ill health cost the NHS in the UK £5.8 billion. The cost of physical inactivity was £0.9 billion. Smoking cost was £3.3 billion, alcohol cost £3.3 billion, overweight and obesity cost £5.1 billion.

Conclusion: The estimates of the economic cost of risk factors for chronic disease presented here are based on recent financial data and are directly comparable. They suggest that poor diet is a behavioural risk factor that has the highest impact on the budget of the NHS, followed by alcohol consumption, smoking and physical inactivity.

Analysing the relationship between new housing supply and residential construction costs with the regional heterogeneities

New housing supply in Australia has been experiencing a low rate of increase in conjunction with a dramatic increase in residential construction costs since the 1990s. This study aims to estimate the relationship between new housing supply and residential construction costs with the regional heterogeneities. Based on a panel error correction model, it can be identified that there is a causal link as well as a significant correlation between new housing supply and construction costs in the Australian sub-national housing construction markets. The model developed in this research assists policy makers to better understand the nature of the supply side of the housing sector and then enact appropriate policies to improve the new housing supply in Australia.

Phishing email feature selection approach

Phishing emails are more dynamic and cause high risk of significant data, brand and financial loss to average computer user and organizations. To address this problem, we propose a hybrid feature selection approach based on combination of content-based and behavior-based. Our proposed hybrid features selections are able to achieve 93% accuracy rate as compared to other approaches. In addition, we successfully tested the quality of our proposed behavior-based feature using the Information Gain, Gain Ratio and Symmetrical Uncertainty.

New housing supply and residential construction costs in Australia : a panel ECM approach

The supply of new housing in Australia has been experiencing a low increase rate since the 1990s in conjunction with an increasingly strong housing demand. On the contrary, residential construction costs across Australia?s states maintained dramatic increases simultaneously. Economic theory suggests that new housing supply is correlated to the costs of residential constructions. However, few empirical studies have focused on examining this relationship for Australian housing markets. To comprehensively investigate the relationship between the supply of new housing and residential construction costs a function for new housing supply considering the effects of regional heterogeneities is introduced in this study. By estimating a panel error correction model (ECM) applicable for quantifying the correlation with regional heterogeneities, this research identifies that a causal link and a strong correlation exist in between new housing supply and residential construction costs in Australia.

The level of representative underwriting and the underwriting costs of REIT SEOs

Purpose – The purpose of this paper is to investigate factors influencing the underwriting discount for US Real Estate Investment Trust (REIT) Seasoned Equity Offerings (SEOs).

Design/methodology/approach – The study provides new evidence on determinants of underwriting discounts with a comprehensive dataset of 783 US REIT SEOs from 1996 until June 2010. Ordinary least squares regressions are performed to estimate the effect of the level of representative underwriting along with other potential factors on underwriting discounts.

Findings – The study complements the well-documented notion of the economies of scale in SEO underwriting discounts. The equally (value) weighted underwriting discounts averaged 4.21 per cent (4.10 per cent) with a declining trend over time. The findings of this study show the statistically and economically significant negative effect of the level of representative underwriting on the underwriting discounts, as well as the significance of the structure of underwriting syndicate in determining the underwriting discounts. The findings suggest that issuers can minimize the costs of raising secondary equity capital by optimally allocating the underwriting business among the underwriters.

Originality/value – This paper adds to the international REIT SEO literature by exploring new evidence behind underwriting discounts. The study includes data before and after the REIT Modernization Act 1999 and during the recent global financial crisis period.

Hybrid feature selection for phishing email detection

Phishing emails are more active than ever before and putting the average computer user and organizations at risk of significant data, brand and financial loss. Through an analysis of a number of phishing and ham email collected, this paper focused on fundamental attacker behavior which could be extracted from email header. It also put forward a hybrid feature selection approach based on combination of content-based and behavior-based. The approach could mine the attacker behavior based on email header. On a publicly available test corpus, our hybrid features selections are able to achieve 96% accuracy rate. In addition, we successfully tested the quality of our proposed behavior-based feature using the information gain.

Detecting unwanted email using VAT

Spam or unwanted email is one of the potential issues of Internet security and classifying user emails correctly from penetration of spam is an important research issue for anti-spam researchers. In this paper we present an effective and efficient spam classification technique using clustering approach to categorize the features. In our clustering technique we use VAT (Visual Assessment and clustering Tendency) approach into our training model to categorize the extracted features and then pass the information into classification engine. We have used WEKA (www.cs.waikato.ac.nz/ml/weka/) interface to classify the data using different classification algorithms, including tree-based classifiers, nearest neighbor algorithms, statistical algorithms and AdaBoosts. Our empirical performance shows that we can achieve detection rate over 97%.