Resul Das

Effective diagnosis of heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS base software 9.1.3 for diagnosing of the heart disease. A neural networks ensemble method is in the centre of the proposed system. This ensemble based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with the proposed tool. We obtained 89.01% classification accuracy from the experiments made on the data taken from Cleveland heart disease database. We also obtained 80.95% and 95.91% sensitivity and specificity values, respectively, in heart disease diagnosis.

A comparison of multiple classification methods for diagnosis of Parkinson disease

In this paper, different types of classification methods are compared for effective diagnosis of Parkinsons diseases. The reliable diagnosis of Parkinsons disease is notoriously difficult to achieve with misdiagnosis reported to be as high as 25% of cases. The approaches described in this paper purpose to efficiently distinguish healthy individuals. Four independent classification schemas were applied and a comparative study was carried out. These are Neural Networks, DMneural, Regression and Decision Tree respectively. Various evaluation methods were employed for calculating the performance score of the classifiers. According to the application scores, neural networks classifier yields the best results. The overall classification score for neural network is 92.9%. Moreover, we compared our results with the result that was obtained by kernel support vector machines [Singh, N., Pillay, V., & Choonara, Y. E. (2007). Advances in the treatment of Parkinsons disease. Progress in Neurobiology, 81, 29-44]. To the best of our knowledge, our correct classification score is the highest so far.

Diagnosis of valvular heart disease through neural networks ensembles

In the last decades, several tools and various methodologies have been proposed by the researchers for developing effective medical decision support systems. Moreover, new methodologies and new tools are continued to develop and represent day by day. Diagnosing of the valvular heart disease is one of the important issue and many researchers investigated to develop intelligent medical decision support systems to improve the ability of the physicians. In this paper, we introduce a methodology which uses SAS Base Software 9.1.3 for diagnosing of the valvular heart disease. A neural networks ensemble method is in the centre of the proposed system. The ensemble-based methods creates new models by combining the posterior probabilities or the predicted values from multiple predecessor models. So, more effective models can be created. We performed experiments with proposed tool. We obtained 97.4% classification accuracy from the experiments made on data set containing 215 samples. We also obtained 100% and 96% sensitivity and specificity values, respectively, in valvular heart disease diagnosis.

Creating meaningful data from web logs for improving the impressiveness of a website by using path analysis method

Web usage mining is to analyze web log files to discover user accessing patterns of web pages. In order to effectively manage and report on a website, it is necessary to get feedback about activity on the web servers. The aim of this study is to help the web designer and web administrator to improve the impressiveness of a website by determining occurred link connections on the website. Therefore, web log files are pre-processed and then path analysis technique is used to investigate the URL information concerning access to electronic sources. The proposed methodology is applied to the web log files in the web server of Firat University. The results and findings of this experimental study can be used by the web designer in order to plan the upgrading and enhancement to the website.

Evaluation of ensemble methods for diagnosing of valvular heart disease

In this work, we investigate the use of ensemble learning for improving classifiers which is one of the important directions in the current research of machine learning, in which bagging, boosting and random subspace are three powerful and popular representatives. Researchers have so far shown the efficacies of ensemble methods in many practical classification problems. However, for valvular heart disease detection, there are almost no studies investigating their feasibilities. Thus, in this study, we evaluate the performance of three popular ensemble methods for the diagnosis of the valvular heart disorders. To evaluate the performance of investigated ensemble methodology, a comparative study is realized by using a data set containing 215 samples. Moreover, to achieve a comprehensive comparison, we consider the previous results reported by earlier methods (Comak, Arslan, & Turkoglu, 2007; Sengur, 2008a,b; Sengur & Turkoglu, 2008; Turkoglu, Arslan, & Ilkay, 2002, 2003; Uguz, Arslan, & Turkoglu, 2007). Experimental results suggest the feasibilities of ensemble classification methods, and we also derive some valuable conclusions on the performance of ensemble methods for valvular heart disease detection.

Performance analysis of classification algorithms on early detection of liver disease

In this research UCI Indian Liver Patient Dataset (ILPD) used.Boosted C5.0 and CHAID algorithms are used to identify liver disease risk factors.This research shows females have more chance of liver disease than males.Common risk factors of liver disease were extracted by data mining.This research produced quite simple rules. The human liver is one of the major organs in the body and liver disease can cause many problems in human life. Fast and accurate prediction of liver disease allows early and effective treatments. In this regard, various data mining techniques help in better prediction of this disease. Because of the importance of liver disease and increase the number of people who suffer from this disease, we studied on liver disease through using two well-known methods in data mining area.In this paper, novel decision tree based algorithms is used which leads to considering more factors in general and predictions with high accuracy compared to other studies in liver disease. In this application, 583 UCI instances of liver disease dataset from the UCI repository are considered. This dataset consists of 416 records of liver disease and 167 records of healthy liver. This dataset is analyzed by two algorithms named Boosted C5.0 and CHAID algorithms. Until now there is no work in the literature that uses boosted C5.0 and CHAID for creating the rules in liver disease. Our results show that in both algorithms, the DB, ALB, SGPT, TB and A/G factors have a significant impact on predicting liver disease which according to the rules generated by both algorithms important ranges are DB=10.900-1.200, ALB 4.00-4.300, SGPT=34-37, TB=0.600-1.200 (by boosted C5.0), A/G=1.180-1.390, as well as in the Boosted C5.0 algorithm, Alkphos, SGOT and Age have significant impact in prediction of liver disease. By comparing the performance of these algorithms, it becomes clear that C5.0 algorithm via Boosting technique has an accuracy of 93.75% and this result reveals that it has a better performance than the CHAID algorithm which is 65.00%. Another important achievement of this paper is about the ability of both algorithms to produce rules in one class for liver disease. The results of our assessment show that Boosted C5.0 and CHAID algorithms are capable to produce rules for liver disease. Our results also show that boosted C5.0 considers the gender in liver disease, a factor which is missing in many other studies. Meanwhile, using the rules generated in boosted C5.0 algorithm, we obtained the important result about low susceptibility of female to liver disease than male. This factor is missing in other studies of liver disease. Therefore, our proposed computer-aided diagnostic methods as an expert and intelligent system have impressive impact on liver disease detection. Based on obtained results, we observed that our model had better performance compared to existing methods in the literature.

Common network attack types and defense mechanisms

For every organization having a well secured network is the primary requirement to reach their goals. A network is said to be secure if it can protect itself from sophisticated attacks. Due to the rapid increase in the number of network users, security becomes the main challenge in the area of network field. Most security related threats target the layers of the OSI reference model. Sophisticated attack types such as Distributed Denial of Service (DDoS), Man-in-the-Middle and IP spoofing attacks are used to attack these layers. In this paper, we analyze most of the attack types that cause serious problems in computer networks and defense techniques to stop or prevent these attacks.

Real time face recognition and tracking system

Nowadays that security comes into more prominence every day, it is necessary for people to keep more passwords in their mind and carry more cards with themselves. Such implementations however, are becoming less secure and practical, thus leading to an increasing interest in techniques related to biometrics systems. Biometrics systems are the systems which store physical properties of people in electronic environment and enable them to be recognized by the stored electronic information when needed. Biometrics is the identification of human. It works on the principle of identification of physical properties of a person which he or she cannot alter, are distinctive from others, can be used for identification, and are in his or her possession only. Extensive studies are conducted on biometrics techniques such as fingerprint, hand, face, iris, retina and voice recognition. Some systems have been developed, tested, and results have been obtained. Face recognition systems are among the most important subjects in biometrics systems. These systems, which are very important for security in particular, have been widely used and developed in many countries. This study aims to achieve face recognition successfully by detecting human face in real time, based on Principal Component Analysis (PCA) algorithm and comparing the result with pre-recorded face samples.

Analysis of attack types on TCP/IP based networks via exploiting protocols

The packets of the communication protocols contain header information and the actual data (payload) to be transmitted. In this study, types of attacks carried out by changing the header information that is used to transmit payload, or exploiting such processes as connection establishing or termination in TCP/IP network will be discuss.

A steganography application for secure data communication

Information security is the process of protecting information and information systems from unauthorized access, use, disclosure, disruption, modification or destruction. Nowadays, apart from the military and institutional information security, individual information security is very important. If individual information security is ignored, it can cause intangible damage and monetary loss. In this study, a steganography application was developed to increase personal information security. This application is intended to provide a more secure way of communication for emails which play an important role in personal data transfer. Thus, Steganography, which is one of the communication hiding techniques, is used. And also existing steganography methods are examined and a new application has been developed to be used for personal information security. Finally, this application was applied to an electronic mail communication.