2010 IEEE 2nd International Advance Computing Conference (IACC), 2010
Privacy has in recent times become an astounding akin to an oxymoron. It can either be embellishe... more Privacy has in recent times become an astounding akin to an oxymoron. It can either be embellished or marred with technology; confiscating more consideration in many data mining applications. We are focusing on information safety measures in order to preserve the individual's privacy, so that no personal information can be gained by the hacker from the data. Under the modern
Proceedings of the International Conference on Advances in Computing, Communication and Control - ICAC3 '09, 2009
There is a need for a CAPTCHA as a result of the abuse of automated 'bot... more There is a need for a CAPTCHA as a result of the abuse of automated 'bots' [6]. The problem of spamming resulted as the bots intruded into the internet [3]. However, there were many solutions provided for stopping these bots from entering the internet. The best and most feasible solution proposed till now has been the CAPTCHA or the fuzzy
2011 International Conference on Computational Intelligence and Communication Networks, 2011
Distributed Denial of Service (DDoS) is a type of attack in the application layer initiated from ... more Distributed Denial of Service (DDoS) is a type of attack in the application layer initiated from the various hosts to a single web server. The aim of this attack is to consume all the resources of the targeted system by exploiting the vulnerability. We proposed a mathematical model called Recurrence Quantification Analysis (RQA) for detecting the DDoS attacks by computing entropy and determinism of selected packet attributes. To detect the anomalies and check the performance we considered the live traffic traces from the network and various RQA parameters like entropy, laminarity and determinism were used to determine the uncertainty or randomness in the dataset.
2011 International Conference on Computational Intelligence and Communication Networks, 2011
Nowadays, health care is one of the most important subjects in our life. Increasing level of pati... more Nowadays, health care is one of the most important subjects in our life. Increasing level of patients demand across the world obliges health care services in a more flexible and uniform manner. So that patients can get useful health services in the home environment without going to the health center personally. The e-healthcare is new kinds of health care system
International Journal of Bioinformatics Research and Applications, 2012
Medical diagnosis processes vary in the degree to which they attempt to deal with different compl... more Medical diagnosis processes vary in the degree to which they attempt to deal with different complicating aspects of diagnosis such as relative importance of symptoms, varied symptom pattern and the relation between diseases themselves. Rough set approach has two major advantages over the other methods. First, it can handle different types of data such as categorical, numerical etc. Secondly, it does not make any assumption like probability distribution function in stochastic modeling or membership grade function in fuzzy set theory. It involves pattern recognition through logical computational rules rather than approximating them through smooth mathematical functional forms. In this paper we use rough set theory as a data mining tool to derive useful patterns and rules for kidney cancer faulty diagnosis. In particular, the historical data of twenty five research hospitals and medical college is used for validation and the results show the practical viability of the proposed approach.
Chronic Kidney Disease (CKD) is usually characterized by a gradual loss of the functioning which ... more Chronic Kidney Disease (CKD) is usually characterized by a gradual loss of the functioning which the kidney does over time due to various factors. Early prediction and treatment save the kidney and halts the progress of CKD. CKD disease is being viewed as global public health issue for the past decade. The greatest threat for this deadly disease is developing countries where getting therapy is very expensive. The importance of predicting individuals who are at risk of CKD as well as applying clustering techniques cannot be underestimated since these can modify the progression of the disease. Identifying the silent killer disease early offers best opportunities for implementing possible strategies for lessening the probability of kidney loss. Neuro-fuzzy algorithm is applied to determine the risk of CKD in patients. Predictions done using neuro-fuzzy gave an accuracy of 97 percent. Using selected features, prediction for CKD disease is done so as to identify the risk. The results of the prediction are clustered to identify the percentage of patients with a high risk of having kidney disease who have a higher probability of being diabetic. Using hierarchical clustering three clusters formed show that there is a strong relationship between chronic kidney and diabetes.
The primary objective of this paper is to develop and propose a model using the concepts from Rou... more The primary objective of this paper is to develop and propose a model using the concepts from Rough Set Theory to cluster the patients in the diabetic dataset. The model to be developed incorporates Rough Clustering of the dataset, and from the clusters formed, compute the accuracy on the testing data. Rough Clustering will help splitting the data into clusters of patients that suffer from Diabetes Mellitus and the ones which do not. As a result, the patients suffering from Diabetes Mellitus will be clustered together and will provide us with the average values of the features used in the model for data clustering. The results obtained will provide more depth in the field of rough clustering for diabetes as the number of studies done on diabetes using rough set theory are few to none.
An intelligent predictive model using deep learning is proposed to predict the patient risk facto... more An intelligent predictive model using deep learning is proposed to predict the patient risk factor and severity of diabetics using conditional data set. The model involves deep learning in the form of a deep neural network which helps to apply predictive analytics on the diabetes data set to obtain optimal results. The existing predictive models is used to predict the severity and the risk factor of the diabetics based on the data which is processed. In our case Firstly, a feature selection algorithm is run for the selection process. Secondly, the deep learning model has a deep neural network which employs a Restricted Boltzmann Machine (RBM) as a basic unit to analyse the data by assigning weights to the each branch of the neural network. This deep neural network, coded on python, will help to obtain numeric results on the severity and the risk factor of the diabetics in the data set. At the end, a comparative study is done between the implementation of this model on type 1 diabetes mellitus, Pima Indians diabetes and the Rough set theory model. The results add value to additional reports because the number of studies done on diabetes using a deep learning model is few to none. This will help to predict diabetes with much more precision as shown by the results obtained.
The present age of internet and the rising of business have resulted into many folds of increase ... more The present age of internet and the rising of business have resulted into many folds of increase in the volume of data which are to be used for various applications on a day to day basis. Therefore, it is an obvious challenge to reduce the dataset and find useful information pertaining to the interest of the organisation. But another challenge lies in hiding sensitive information in order to provide privacy. Thus, attribute reduction and privacy preservation are two major challenges in privacy preserving data mining. In this paper, we propose a sensitive rule hiding model to hide sensitive fuzzy association rules. Proposed model uses rough set on intuitionistic fuzzy approximation spaces with ordering to reduce the dataset dimensionality. We use triangular and trapezoidal membership function to get the fuzzified information system. Finally, decreasing the support of right hand side of the rule is used to hide sensitive fuzzy association rules.
Healthcare Organizations have seen an alarming rise in cyber-attacks in the recent years. One way... more Healthcare Organizations have seen an alarming rise in cyber-attacks in the recent years. One way a hacker could get control was by breaking into a medical network to gain access over the active medical devices that patients rely on for their survival. Our network model proposes a low-interaction and a medium-interaction honeypot based intrusion detection system using Dionaea and Kippo SSH to secure our internal network and study the activities of the intruders. We also look at a possible Metasploit attack and Brute force attack logged by Dionaea and Kippo SSH which prepares the Malware Analysis report of the suspicious file downloaded.
Lower back pain can occur due to various reasons involving any body part such as the interconnect... more Lower back pain can occur due to various reasons involving any body part such as the interconnected network of spinal cord, nerves, bones, discs or tendons in the lumbar spine. Understanding and knowing about the origins of this disorder and getting a diagnostic treatment that determines the underlying reason, is the primary step in achieving an effective and efficient cure. Despite of heavy spending on resources such as time and money, devoted to lower back pain research methodologies, fruitful management remains a significant goal and lower back pain continues to be a cause of considerable concern on the primary care setting. One of the reason for this, could be the degrees of importance for researching into specific domain like these, are regularly developed by researchers and funding bodies, with less consideration of the needs of primary care practitioners. This study aims to determine the research priorities of primary care practitioners who manage low back pain on a day-today basis and to identify whether a person is abnormal or normal using collected physical spine data of 381 patients with 12 parameters. This study also identifies the degree of importance of each parameter used in the classification and ranks those parameters accordingly.
2010 IEEE 2nd International Advance Computing Conference (IACC), 2010
Privacy has in recent times become an astounding akin to an oxymoron. It can either be embellishe... more Privacy has in recent times become an astounding akin to an oxymoron. It can either be embellished or marred with technology; confiscating more consideration in many data mining applications. We are focusing on information safety measures in order to preserve the individual's privacy, so that no personal information can be gained by the hacker from the data. Under the modern
Proceedings of the International Conference on Advances in Computing, Communication and Control - ICAC3 '09, 2009
There is a need for a CAPTCHA as a result of the abuse of automated 'bot... more There is a need for a CAPTCHA as a result of the abuse of automated 'bots' [6]. The problem of spamming resulted as the bots intruded into the internet [3]. However, there were many solutions provided for stopping these bots from entering the internet. The best and most feasible solution proposed till now has been the CAPTCHA or the fuzzy
2011 International Conference on Computational Intelligence and Communication Networks, 2011
Distributed Denial of Service (DDoS) is a type of attack in the application layer initiated from ... more Distributed Denial of Service (DDoS) is a type of attack in the application layer initiated from the various hosts to a single web server. The aim of this attack is to consume all the resources of the targeted system by exploiting the vulnerability. We proposed a mathematical model called Recurrence Quantification Analysis (RQA) for detecting the DDoS attacks by computing entropy and determinism of selected packet attributes. To detect the anomalies and check the performance we considered the live traffic traces from the network and various RQA parameters like entropy, laminarity and determinism were used to determine the uncertainty or randomness in the dataset.
2011 International Conference on Computational Intelligence and Communication Networks, 2011
Nowadays, health care is one of the most important subjects in our life. Increasing level of pati... more Nowadays, health care is one of the most important subjects in our life. Increasing level of patients demand across the world obliges health care services in a more flexible and uniform manner. So that patients can get useful health services in the home environment without going to the health center personally. The e-healthcare is new kinds of health care system
International Journal of Bioinformatics Research and Applications, 2012
Medical diagnosis processes vary in the degree to which they attempt to deal with different compl... more Medical diagnosis processes vary in the degree to which they attempt to deal with different complicating aspects of diagnosis such as relative importance of symptoms, varied symptom pattern and the relation between diseases themselves. Rough set approach has two major advantages over the other methods. First, it can handle different types of data such as categorical, numerical etc. Secondly, it does not make any assumption like probability distribution function in stochastic modeling or membership grade function in fuzzy set theory. It involves pattern recognition through logical computational rules rather than approximating them through smooth mathematical functional forms. In this paper we use rough set theory as a data mining tool to derive useful patterns and rules for kidney cancer faulty diagnosis. In particular, the historical data of twenty five research hospitals and medical college is used for validation and the results show the practical viability of the proposed approach.
Chronic Kidney Disease (CKD) is usually characterized by a gradual loss of the functioning which ... more Chronic Kidney Disease (CKD) is usually characterized by a gradual loss of the functioning which the kidney does over time due to various factors. Early prediction and treatment save the kidney and halts the progress of CKD. CKD disease is being viewed as global public health issue for the past decade. The greatest threat for this deadly disease is developing countries where getting therapy is very expensive. The importance of predicting individuals who are at risk of CKD as well as applying clustering techniques cannot be underestimated since these can modify the progression of the disease. Identifying the silent killer disease early offers best opportunities for implementing possible strategies for lessening the probability of kidney loss. Neuro-fuzzy algorithm is applied to determine the risk of CKD in patients. Predictions done using neuro-fuzzy gave an accuracy of 97 percent. Using selected features, prediction for CKD disease is done so as to identify the risk. The results of the prediction are clustered to identify the percentage of patients with a high risk of having kidney disease who have a higher probability of being diabetic. Using hierarchical clustering three clusters formed show that there is a strong relationship between chronic kidney and diabetes.
The primary objective of this paper is to develop and propose a model using the concepts from Rou... more The primary objective of this paper is to develop and propose a model using the concepts from Rough Set Theory to cluster the patients in the diabetic dataset. The model to be developed incorporates Rough Clustering of the dataset, and from the clusters formed, compute the accuracy on the testing data. Rough Clustering will help splitting the data into clusters of patients that suffer from Diabetes Mellitus and the ones which do not. As a result, the patients suffering from Diabetes Mellitus will be clustered together and will provide us with the average values of the features used in the model for data clustering. The results obtained will provide more depth in the field of rough clustering for diabetes as the number of studies done on diabetes using rough set theory are few to none.
An intelligent predictive model using deep learning is proposed to predict the patient risk facto... more An intelligent predictive model using deep learning is proposed to predict the patient risk factor and severity of diabetics using conditional data set. The model involves deep learning in the form of a deep neural network which helps to apply predictive analytics on the diabetes data set to obtain optimal results. The existing predictive models is used to predict the severity and the risk factor of the diabetics based on the data which is processed. In our case Firstly, a feature selection algorithm is run for the selection process. Secondly, the deep learning model has a deep neural network which employs a Restricted Boltzmann Machine (RBM) as a basic unit to analyse the data by assigning weights to the each branch of the neural network. This deep neural network, coded on python, will help to obtain numeric results on the severity and the risk factor of the diabetics in the data set. At the end, a comparative study is done between the implementation of this model on type 1 diabetes mellitus, Pima Indians diabetes and the Rough set theory model. The results add value to additional reports because the number of studies done on diabetes using a deep learning model is few to none. This will help to predict diabetes with much more precision as shown by the results obtained.
The present age of internet and the rising of business have resulted into many folds of increase ... more The present age of internet and the rising of business have resulted into many folds of increase in the volume of data which are to be used for various applications on a day to day basis. Therefore, it is an obvious challenge to reduce the dataset and find useful information pertaining to the interest of the organisation. But another challenge lies in hiding sensitive information in order to provide privacy. Thus, attribute reduction and privacy preservation are two major challenges in privacy preserving data mining. In this paper, we propose a sensitive rule hiding model to hide sensitive fuzzy association rules. Proposed model uses rough set on intuitionistic fuzzy approximation spaces with ordering to reduce the dataset dimensionality. We use triangular and trapezoidal membership function to get the fuzzified information system. Finally, decreasing the support of right hand side of the rule is used to hide sensitive fuzzy association rules.
Healthcare Organizations have seen an alarming rise in cyber-attacks in the recent years. One way... more Healthcare Organizations have seen an alarming rise in cyber-attacks in the recent years. One way a hacker could get control was by breaking into a medical network to gain access over the active medical devices that patients rely on for their survival. Our network model proposes a low-interaction and a medium-interaction honeypot based intrusion detection system using Dionaea and Kippo SSH to secure our internal network and study the activities of the intruders. We also look at a possible Metasploit attack and Brute force attack logged by Dionaea and Kippo SSH which prepares the Malware Analysis report of the suspicious file downloaded.
Lower back pain can occur due to various reasons involving any body part such as the interconnect... more Lower back pain can occur due to various reasons involving any body part such as the interconnected network of spinal cord, nerves, bones, discs or tendons in the lumbar spine. Understanding and knowing about the origins of this disorder and getting a diagnostic treatment that determines the underlying reason, is the primary step in achieving an effective and efficient cure. Despite of heavy spending on resources such as time and money, devoted to lower back pain research methodologies, fruitful management remains a significant goal and lower back pain continues to be a cause of considerable concern on the primary care setting. One of the reason for this, could be the degrees of importance for researching into specific domain like these, are regularly developed by researchers and funding bodies, with less consideration of the needs of primary care practitioners. This study aims to determine the research priorities of primary care practitioners who manage low back pain on a day-today basis and to identify whether a person is abnormal or normal using collected physical spine data of 381 patients with 12 parameters. This study also identifies the degree of importance of each parameter used in the classification and ranks those parameters accordingly.
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Papers by N.Ch.Sriman Narayana Iyengar