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Suseendran, G.
- Lung Cancer Image Segmentation Using Rough Set Theory
Abstract Views :267 |
PDF Views:147
Authors
Affiliations
1 School of Computing Science, Vels University, Chennai, IN
1 School of Computing Science, Vels University, Chennai, IN
Source
Indian Journal of Medicine and Healthcare, Vol 4, No 6 (2015), Pagination: 1-8Abstract
Background/Objectives: Lung cancer seems to be the common cause of death among people through the world. Early detection of lung cancer can increase the chance of survival among people. An attempt is made to segment the CT image of lung cancer using Rough K-Means clustering, which is one of the most important unsupervised learning methods in machine learning.
Methods/Statistical analysis: The necessary CT images are collected from Mitra Scan centre, Salem for this study. The proposed method is compared with the bench mark K-means algorithm in order to achieve the efficiency. Findings: the performance of proposed Rough Set technique is compared with existing Clustering (k means) work which shows its efficiency level of segmented image portion and the prediction rate is better than its counterpart.
Improvements/Applications: The proposed technique predicts the early symptoms of the disease with segmented region of image matched to the similar patterns of diseased portions of trained patient images.
Keywords
Computed Tomography (CT) Image, Segmentation, Rough K-Means, Clustering.Full Text
- Aggregated K Means Clustering and Decision Tree Algorithm for Spirometry Data
Abstract Views :178 |
PDF Views:0
Authors
K. Rohini
1,
G. Suseendran
1
Affiliations
1 Department of Information and Technology, School of Computing Sciences, Vels University, P.V. Vaithiyalingam Road, Pallavaram, Chennai - 600117, Tamil Nadu, IN
1 Department of Information and Technology, School of Computing Sciences, Vels University, P.V. Vaithiyalingam Road, Pallavaram, Chennai - 600117, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 44 (2016), Pagination:Abstract
Objectives: The present research work generally focuses on predicting diseases from the lung disease test by using data mining techniques for spirometry data. Methods/Statistical Analysis: Spirometry is used to create baseline lung function, check out dyspnea, disclose pulmonary disease, watching effects of therapies used to treat respiratory disease, calculate respiratory impairment, evaluate operative risk, and performs surveillance for occupational-relevant lung diseases. Pulmonary function tests are used to find out lung capacity, based on which the many of the lung diseases can be identified. In this research work, a combination of k-means clustering algorithm and Decision tree algorithm was developed. From the results investigation, it is known that the proposed aggregated k-means algorithm and decision tree algorithm for spirometry data is better which compared to other algorithms such as Genetic algorithm, classifier training algorithm, and neural network based classification algorithms. Findings: Existing algorithms are unable to handle noisy data and also with Failure occurrence for a nonlinear data set. It should not classify the data set based on their input attributes. Prediction is not possible for existing system. Applications/Improvement: Spirometry data which is used to predict the lung capacity using Aggregated K-means and Decision tree algorithm. Our proposed approach is evaluated for each dataset accordingly.Keywords
Decision Tree, Pulmonary Function Test Means, Spirometry Data.Full Text
- Incremental Quality Based Reverse Ranking for Spatial Data
Abstract Views :198 |
PDF Views:0
Authors
Affiliations
1 School of Computing Sciences, Vels University, Chennai - 600 117, Tamil Nadu, IN
1 School of Computing Sciences, Vels University, Chennai - 600 117, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 1 (2016), Pagination:Abstract
Background/Objectives: The scope of this proposed work is to minimize the search time and complexity in spatial database. Methods: Improves the query processing in spatial database by using the existing R Tree, IR Tree and Reverse Ranking. A comparative analysis is made between the existing methods and the proposed method Incremental Quality Reverse Ranking (IQRR). The proposed method effectively evaluates to find the top-k spatial objects in multiple query processing. Findings: To evaluate the performance of the proposed approach, a comparative study has been performed in this work. The R tree and IR tree are compared with the proposed work namely Incremental Quality Reverse Ranking (IQRR). The evaluation parameters are radius, time, location, directions and number of dams. Applications: A spatial preference query ranks objects (e.g. Dams) based on the qualities of features (irrigation, water supply, flood control, hydro electricity, navigation, recreation, and pollution control) in their spatial neighborhood. In future, according to the user specification, it may be developed for any spatial network application. This application can be deployed in the cloud server and cloud will provide a service to the user.Keywords
Spatial Databases, Query Processing, Indexing Structures, R-tree, IR-treeFull Text
- Secure Intrusion-Detection System in Mobile Adhoc Networks
Abstract Views :162 |
PDF Views:0
Authors
Affiliations
1 School of Computing Science, Vels University, IN
1 School of Computing Science, Vels University, IN
Source
Indian Journal of Science and Technology, Vol 9, No 19 (2016), Pagination:Abstract
Objectives: This paper proposes the new idea of intrusion detection system to improve the security in mobile adhoc networks. Methods/Analysis: Intrusion is defined as form of undesirable hobby occurred in community that's affecting the integrity and confidentiality of community. The present intrusion detection method superior Adaptive Acknowledgement Scheme (EAACK) takes longer time for encrypting facts and signature length is also large which creates network overhead. Findings: In proposed technique intrusion detection method is carried out via the use of superior Encryption preferred (AES) and routing via on demand Distance Vector (AODV) protocol. The proposed technique continues security alongside development in performance of MANET like PDR and end to stop delay. The proposed method calls for less time for encrypt and decrypt the records so it overcomes the hassle of EAACK. Novelty/Improvement: The overarching interruption discovery method EAACK builds overhead inside of the group, so in future depictions will attempt to lessen the group overhead. Experimenting with the execution of proposed artistic creations in genuine group environment as opposed to programming recreation.Keywords
AES, AODV, EAACK, MANET, PDR.Full Text
- Effectuation of Secure Authorized Deduplication in Hybrid Cloud
Abstract Views :182 |
PDF Views:0
Authors
Affiliations
1 Department of Information Technology, School of Computing Sciences, Vels University, Chennai – 600117, Tamil Nadu, IN
1 Department of Information Technology, School of Computing Sciences, Vels University, Chennai – 600117, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 25 (2016), Pagination:Abstract
Objectives: Different user can access the same data repeatedly and trying to store it in the memory of the cloud server. Due to this there is a problem of maintaining the storage space and bandwidth. The main purpose of this study is how the data is secured, whether the authorized person is accessing the data or not and finally to check whether same data is repeatedly stored in the memory to avoid duplication of the data. Methods: In deduplication, to guard the confidentiality of sensitive information, it's encrypted/decrypted by the planned convergent coding technique before outsourcing for higher protection of knowledge security. Findings: The convergent encryption method and open authorization protocol and deduplication are combined together and check the data for deduplication in a secured way. The possibilities of using other algorithms are also considered for further implementation.Keywords
Authorized Duplicate Check, Confidentiality, Deduplication, Hybrid Cloud, Open AuthorizationFull Text
- Survey on Heart Disease Prediction System Based on Data Mining Techniques
Abstract Views :365 |
PDF Views:0
Authors
Affiliations
1 Department of Information and Technology, School of Computing Sciences, Vels University, Chennai, IN
1 Department of Information and Technology, School of Computing Sciences, Vels University, Chennai, IN
Source
Indian Journal of Innovations and Developments, Vol 6, No 1 (2017), Pagination: 1-9Abstract
Objectives: To be familiar with the kinds of coronary illness, and information mining procedures to fight them.
Methods/Statistical analysis: To handle this, data mining concepts and techniques used were discussed to discover hidden patterns from medical domain.
Findings: The purpose of predictions in data mining is to discover trends in patient data through patterns generation to improve the health strategy. The algorithms presented here are with a specific end goal to anticipate the coronary illness which includes some constraint.
Keywords
Data Mining, CVD Diseases, Disease Prediction.Full Text
References
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- Predicting Lung Disease Severity Evaluation and Comparison of Hybird Decision Tree Algorithm
Abstract Views :405 |
PDF Views:0
Authors
K. Rohini
1,
G. Suseendran
1
Affiliations
1 Department of Information Technology, School of Computing Sciences, Vels University, Chennai, IN
1 Department of Information Technology, School of Computing Sciences, Vels University, Chennai, IN
Source
Indian Journal of Innovations and Developments, Vol 6, No 1 (2017), Pagination: 1-10Abstract
Objective: To focus on classification algorithms to arrive better prediction model for Lung Disease Severity.
Methods/Statistical analysis: In therapeutic analyses, the part of information mining methodologies is being expanded. Especially Classification calculations are exceptionally useful in arranging the information, which is critical for basic leadership prepare for therapeutic experts. In this paper the analysis is done in the WEKA apparatus on the spiro informational index.
Findings: The paper embarks to make relative assessment of classifiers, for example, J48, Random forest and proposed Hybird Decision Tree(HDT) Algorithm with regards to Spiro dataset to amplify genuine positive rate and limit false positive rate of defaulters as opposed to accomplishing just higher grouping exactness utilizing WEKA instrument. The tests comes about appeared in this paper are about grouping exactness, affectability and specificity.
Application/Improvements: The outcomes created on this dataset likewise demonstrate that the productivity and exactness of J48 is superior to anything other choice tree classifiers. J48 develops purge branches, it is the most urgent stride for govern era in J48. In more often than not this approach over fits the preparation cases with boisterous information. The proposed Hybird Decision Tree (HDT) Algorithm demonstrates great exactness in less time.
Keywords
Decision Tree, Pulmonary Function Test Means, Spirometry Data, Hybird Decision Tree Algorithm, J48 Algorithm.Full Text
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