Exploiting an antivirus interface

We propose a technique for defeating signature-based malware detectors by exploiting information disclosed by antivirus interfaces. This information is leveraged to reverse engineer relevant details of the detector's underlying signature database, revealing binary obfuscations that suffice to conceal malware from the detector. Experiments with real malware and antivirus interfaces on Windows operating systems justify the effectiveness of our approach.     

Data mining-based materialized view and index selection in data warehouses

Materialized views and indexes are physical structures for accelerating data access that are casually used in data warehouses. However, these data structures generate some maintenance overhead. They also share the same storage space. Most existing studies about materialized view and index selection consider these structures separately. In this paper, we adopt the opposite stance and couple materialized view and index selection to take view–index interactions into account and achieve efficient storage space sharing. Candidate materialized views and indexes are selected through a data mining process. We also exploit cost models that evaluate the respective benefit of indexing and view materialization, and help select a relevant configuration of indexes and materialized views among the candidates. Experimental results show that our strategy performs better than an independent selection of materialized views and indexes.     

Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg–Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg–Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error.     

Classification rule discovery for the aviation incidents resulted in fatality

Data mining methods have been successfully applied to different fields. Aviation industry is one of them. There is a large amount of knowledge and data accumulation in aviation industry. These data could be stored in the form of pilot reports, maintenance reports, incident reports or delay reports. This paper explains the data mining application on the incident reports of the Federal Aviation Administration (FAA) Accident/Incident Data System database, contains incident data records for all categories of civil aviation between the years of 2000 and 2006. In this study, we applied data mining methods on the incident reports. Moreover rough sets concept is used to reduce the attributes of data set. The purpose of this application is to find out the effective attributes in order to reduce the number of the fatality in the incidents. The categorization tools and decision trees are used to find the relations and rules about the incidents resulted in fatality. For this purpose data-mining analysis is conducted. As a result some rules about the fatality are obtained and also the parameters that affect the fatality in the incident have determined. The rules found are tested in terms of their accuracy and reliability, and these results are seen to be meaningful.     

Multi-objective rule mining using a chaotic particle swarm optimization algorithm

In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm.     

Research intelligence involving information retrieval – An example of conferences and journals

This paper reports a work that was intended to reveal the connection between topics investigated by conference papers and journal papers. This work selected hundreds of papers in data mining and information retrieval from well-known databases and showed that the topics covered by conference papers in a year often leads to similar topics covered by journal papers in the subsequent year and vice versa. This study used some existing algorithms and combination of these algorithms to proposed a new detective procedure for the researchers to detect the new trend and get the academic intelligence from conferences and journals.The goal of this research is fourfold: First, the research investigates if the conference papers’ themes lead the journal papers’. Second, the research examines how the new research themes can be identified from the conference papers. Third, the research looks at a specific area such as information retrieval and data mining as an illustration. Fourth, the research studies any inconsistencies of the correlation between the conference papers and the journal papers.This study explores the connections between the academic publications. The methodologies of information retrieval and data mining can be exploited to discover the relationships between published papers among all topics. By discovering the connections between conference papers and journal papers, researchers can improve the effectiveness of their research by identifying academic intelligence.This study discusses how conference papers and journal papers are related. The topics of conference papers are identified to determine whether they represent new trend discussed in journal papers. An automatic examination procedure based on information retrieval and data mining is also proposed to minimize the time and human resources required to predict further research developments. This study develops a new procedure and collects a dataset to verify those problems. Analytical results demonstrate that the conference papers submitted to journals papers are similar each year. Conference papers certainly affect the journal papers published over three years. About 87.23% of data points from papers published in 1991–2007 support our assumption. The research is intended to help researchers identify new trend in their research fields, and focus on the urgent topics. This is particularly valuable for new researchers in their field, or those who wish to perform cross-domain studies.     

Toward breast cancer survivability prediction models through improving training space

Due to the difficulties of outlier and skewed data, the prediction of breast cancer survivability has presented many challenges in the field of data mining and pattern precognition, especially in medical research. To solve these problems, we have proposed a hybrid approach to generating higher quality data sets in the creation of improved breast cancer survival prediction models. This approach comprises two main steps: (1) utilization of an outlier filtering approach based on C-Support Vector Classification (C-SVC) to identify and eliminate outlier instances; and (2) application of an over-sampling approach using over-sampling with replacement to increase the number of instances in the minority class. In order to assess the capability and effectiveness of the proposed approach, several measurement methods including basic performance (e.g., accuracy, sensitivity, and specificity), Area Under the receiver operating characteristic Curve (AUC) and F-measure were utilized. Moreover, a 10-fold cross-validation method was used to reduce the bias and variance of the results of breast cancer survivability prediction models. Results have indicated that the proposed approach leads to improving the performance of breast cancer survivability prediction models by up to 28.34% due to the improved training data space.     

Effective solution for unhandled exception in decision tree induction algorithms

This paper deals with some improvements to rule induction algorithms in order to resolve the tie that appear in special cases during the rule generation procedure for specific training data sets. These improvements are demonstrated by experimental results on various data sets. The tie occurs in decision tree induction algorithm when the class prediction at a leaf node cannot be determined by majority voting. When there is a conflict in the leaf node, we need to find the source and the solution to the problem. In this paper, we propose to calculate the Influence factor for each attribute and an update procedure to the decision tree has been suggested to deal with the problem and provide subsequent rectification steps.     

Data mining for adaptive learning sequence in English language instruction

The purpose of this paper is to propose an adaptive system analysis for optimizing learning sequences. The analysis employs a decision tree algorithm, based on students’ profiles, to discover the most adaptive learning sequences for a particular teaching content. The profiles were created on the basis of pretesting and posttesting, and from a set of five student characteristics: gender, personality type, cognitive style, learning style, and the students’ grades from the previous semester. This paper address the problem of adhering to a fixed learning sequence in the traditional method of teaching English, and recommend a rule for setting up an optimal learning sequence for facilitating students’ learning processes and for maximizing their learning outcome. By using the technique proposed in this paper, teachers will be able both to lower the cost of teaching and to achieve an optimally adaptive learning sequence for students. The results show that the power of the adaptive learning sequence lies in the way it takes into account students’ personal characteristics and performance; for this reason, it constitutes an important innovation in the field of Teaching English as a Second Language (TESL).     

ECMail:基于实体发现、查找和管理的中文邮件管理系统

随着个人用户的数据和信息级数增长,个人信息管理的研究成为热点.电子邮件作为个人信息的重要载体在个人信息业务中占据着重要的地位.随着个人信息的增加,用户在对邮件进行查询时经常遇到遗忘关键字的困扰,对此普通的邮件工具很难为用户组织和管理个人信息提供帮助.提出了一个基于实体发现、查找和管理的中文邮件管理系统--ECMail,有效改善了以上问题,同时对关键技术-中文分词、实体挖掘和实体关联管理的实现提出了自己的想法和处理机制,最终达到了提高用户邮件管理效率的目的.