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Premalatha, K.
- Enhanced Matrix Bloom Filter for Weak Password Identification
Abstract Views :153 |
PDF Views:2
Authors
Affiliations
1 Anna University of Technology, Coimbatore, TN, IN
2 Sri Krishna College of Engineering and Technology, Coimbatore, TN, IN
3 Bannari Amman Institute of Technology, Erode, TN, IN
1 Anna University of Technology, Coimbatore, TN, IN
2 Sri Krishna College of Engineering and Technology, Coimbatore, TN, IN
3 Bannari Amman Institute of Technology, Erode, TN, IN
Source
Networking and Communication Engineering, Vol 3, No 5 (2011), Pagination: 297-305Abstract
A single weak password exposes the entire network to an external threat. Password hacking is one of the most critical and commonly exploited for network security threats. A Bloom Filter (BF) is a simple space-efficient randomized data structure for representing a set in order to support membership queries. This compact representation is the payoff for allowing a small rate of false positives in membership queries; that is, queries might incorrectly recognize an element as member of the set. Matrix Bloom Filter (MBF) uses matrix representation of BFs to represent a data set. The false positive rate of MBF increases when the data set size increases. The proposed Enhanced Matrix Bloom Filter (EMBF) dynamically creates another bloom filter for the row which exceeds the given threshold value. This paper presents the identification of weak password using Enhanced Matrix Bloom Filter. It reduces the false positive rate if the word set size dynamically increases. The results of the experiment are examined on weak passwords and demonstrate the performance of EMBF and BF.Keywords
Bloom Filter, False Positive Rate, Hash Function, Matrix Bloom Filter, Weak Password.
- A Survey on Association Rule Mining
Abstract Views :226 |
PDF Views:1
Authors
Affiliations
1 Department of CSE, Bannari Amman Institute of Technology, Sathyamangalam, IN
1 Department of CSE, Bannari Amman Institute of Technology, Sathyamangalam, IN
Source
Data Mining and Knowledge Engineering, Vol 3, No 4 (2011), Pagination: 231-236Abstract
Association rule mining is a popular and well researched method to discover interesting relations between the itemsets in large databases. Association rules show attributes value conditions that occur frequently together in a given dataset. Mining Association rules from the databases has the overhead in generating interesting rules, which includes rare itemsets, mining interesting rules from large databases and generation of strong associations. This review concentrates on improving the performance of Apriori, generating interesting Association rules using large databases, Quantitative Association rule mining and optimizing the Association rules. It also states various techniques used in Association rule generation process.Keywords
Association Rule Mining, Support, Confidence, Alternate Measures, Particle Swarm Optimization, Quantitative Associations.- Diverse Depiction of Particle Swarm Optimization for Document Clustering
Abstract Views :156 |
PDF Views:0
Authors
Affiliations
1 Bannari Amman Institute of Technology, Tamil Nadu, IN
1 Bannari Amman Institute of Technology, Tamil Nadu, IN
Source
ICTACT Journal on Soft Computing, Vol 1, No 3 (2011), Pagination: 125-130Abstract
Document clustering algorithms play an important task towards the goal of organizing huge amounts of documents into a small number of significant clusters. Traditional clustering algorithms will search only a small sub-set of possible clustering and as a result, there is no guarantee that the solution found will be optimal. This paper presents different representation of particle in Particle Swarm Optimization (PSO) for document clustering. Experiments results are examined with document corpus. It demonstrates that the Discrete PSO algorithm statistically outperforms the Binary PSO and Simple PSO for document Clustering.Keywords
Particle Swarm Optimization, Document Clustering, Inertia Weight, Constriction Factor, Swarm Intelligence.- A Survey on Feature Selection Methods in Microarray Gene Expression Data for Cancer Classification
Abstract Views :195 |
PDF Views:2
Authors
Affiliations
1 School of Information Technology and Engineering, VIT University, Vellore, IN
2 Department of CSE, Bannari Amman Institute of Technology, Sathyamangalam, IN
3 Department of ECE, K.S. Rangasamy College of Technology, Tiruchengode, IN
1 School of Information Technology and Engineering, VIT University, Vellore, IN
2 Department of CSE, Bannari Amman Institute of Technology, Sathyamangalam, IN
3 Department of ECE, K.S. Rangasamy College of Technology, Tiruchengode, IN
Source
Research Journal of Pharmacy and Technology, Vol 10, No 5 (2017), Pagination: 1395-1401Abstract
Microarray technology is commonly used in the study of disease diagnosis using gene expression levels. It not only received the attention of the research community but also has a wide range of applications. The success of microarray technology depends on the precision of measurement, the usage of tools in data mining, analytical methods and statistical modeling. The feature selection methods are used to find an informative representation, by removing noisy and irrelevant features which would improve the classification performance. There exist several works in the literature to select the significant features from the microarray. This paper reviews the feature selection methods used to select significant genes from the microarray gene expression data for cancer classification.Keywords
Microarray, Feature Selection, Gene Expression, Cancer Classification, Gene Selection.- IOT Based Accident Prevention and Emergency Services
Abstract Views :420 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore-641049, Tamilnadu, IN
1 Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore-641049, Tamilnadu, IN
Source
Research Journal of Engineering and Technology, Vol 8, No 4 (2017), Pagination: 369-372Abstract
In this accelerated world, many technologies have been evolved for each and every second to improve human life style. There have been massive advancements in automobile technologies and still to come. Though advancements are made for the comfort of people, there are lot of accidents taking place because of increased vehicle density, violation of rules and carelessness. During night travel many drivers feel drowsy, they fall asleep unknowingly which leads to accident. To prevent this, sensor is used to detect whether the driver is dozy or not. If the driver is dozy the driver is alarmed through a buzzer and the speed of the car is drastically reduced. Hence, reduces the risk of major accidents. If accident occurs due to other reasons like violating the traffic rules then the accident is detected by a vibration sensor and the current global position of the vehicle is sent to nearest ambulance server by the use of Internet of Things (IoT) and ambulance can reach the accident spot immediately which in turn saves any human lives.Keywords
IOT, Traffic Density, Accident Prevention, Global Positioning System, Automatic Emergency Services.References
- Dinges D, Mallis M, Maislin G, et al. Evaluation of techniques for ocular measurement as an index of fatigue and the basis for alertness management. NHTSA, Washington DC, Tech. Rep. DOT HS 808762. 1998.
- Ji Q, Zhu Z, Lan P. Real-time nonintrusive monitors a prediction of driver fatigue. IEEE Transactions on Vehicular Technology.2004; 53(4); 1052-1068.
- Lenne M, Triggs T, Redman J.Time of day variations in driving performance. Accident Analysis & Prevention. 1997;29 (4);431-437.
- S.P. Bhumkar, V.V. Deotare, R.V. Babar. Accident Avoidance and Detection on Highways. International Journal of Engineering Trends and Technology (IJETT).2012; 3(2);247-252; ISSN: 2231-5381.
- Katore Koshal P., Bhambare Rajesh R. Vehicle Accident Prevention System using GSM and GPS Technique. International Journal of Computer Trends and Technology (IJCTT).2015; 29 (2); 92-96; ISSN: 2231-2803.
- D.B. Rane, Gaikwad Pooja Rajesh, Devkate Rashmi Rajendra and Futane Manjiree Suresh Accident Avoidance Using Eye Blink Detection. International Journal of Informative & Futuristic Research. 2015, 2 (6); 1880-1887.
- N. B. Gohil Embedded Based Vehicle Speed Control System Using Wireless Technology. International Journal of Advance Engineering and Research Development. 2015; 2 (11); 2348-6406.
- Boopathi K. Govindaraju, M. Sangeetha, M. Jagadeeshraja and M. Dhanasu Real Time based smart vehicle monitoring and alert using GSM and GPS. International Journal of Advanced Research in Computer and Communication Engineering. 2014; 3(11).
- Particle Swarm Optimization for Triclustering High Dimensional Microarray Gene Expression Data
Abstract Views :169 |
PDF Views:0
Authors
Affiliations
1 Anna University, Chennai, IN
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, IN
1 Anna University, Chennai, IN
2 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, IN
Source
Research Journal of Pharmacy and Technology, Vol 12, No 5 (2019), Pagination: 2222-2228Abstract
The study of high dimensional microarray gene expression data represents the large computational challenge due to its huge volume of the data. Many clustering techniques are applied to extract the coexpressed genes over the samples. Biclustering improved the traditional clustering by grouping the genes that similarly expressed over only a subset of samples. However, to cluster the high dimensional data with three dimensions such as genes, samples and time points, Triclustering technique is employed for grouping the coexpressed genes over a subset of samples under a subset of time points which imposes huge computational burden. In this paper, Particle Swarm Optimization technique is applied to extract the triclusters from the high dimensional data with objective function as Mean Square Residue. The algorithm is applied to three real life microarray gene expression data and the performance of the work is analyzed using the objective function. The biological significances of the extracted triclusters from all the three datasets are also analyzed. The biological significance analysis are also compared with other triclustering algorithms and the proposed work outperforms the other algorithms.Keywords
Particle Swarm Optimization, Triclustering, High Dimensional Data, Microarray Gene Expression Data, Mean Square Residue.References
- Gunavathi C, Premalatha K, Sivasubramanian K. A Survey on Feature Selection Methods in Microarray Gene Expression Data for Cancer Classification. Research J. Pharm. and Tech. 2017; 10(5): 1395-1401.
- Nallakaruppan M. K, P. Ilango, N. Deepa, Anand Muthukumarappan. Clustering of Wireless Sensor Network Data. Research J. Pharm. and Tech. 2017; 10(1): 73-82.
- Senthil Kumar T, Boselin Prabhu S R, S. Sophia. The Impact of Clustering Mechanism in Dense Wireless Sensor Network. Research J. Engineering and Tech. 7(1): 19-23.
- Meenakshi K, Safa M, Karthick T, Sivaranjani N. A Novel Study of Machine Learning Algorithms for Classifying Health Care Data. Research J. Pharm. and Tech. 2017; 10(5): 1429-1432.
- Rakesh Kumar Soni, Shrikant B. Burje. Tumor Detection Algorithm Using Boundary Based Approach. Int. J. Tech. 1(2): 101-106.
- B. Lokapriya Nandan, Velin Marita Sequeira, Kanika Verma, K. Ramanathan. Exploring beta-tubulin-protein interactome using computational techniques. Research J. Pharm. and Tech. 8(12): 1679-1684.
- Rajeshri Lanjewar, Tripti Sharma. A Determining Approach for Customer Behavior Analysis Using K-Mean Clustering. Int. J. Tech. 2011; 1(2): 125-129.
- Santanu Halder, Abul Hasnat, Debotosh Bhattacharjee, Mita Nasipuri. A Novel Low Space Image Storing and Reconstruction Method by K-Means Clustering Algorithm. Int. J. Tech. 2014; 4(1): 186-196.
- Cheng Y and Church GM. Biclustering of expression data. Proceedings of International Conference on Intelligent Systems Molecular Biology, 2000; 93-103.
- Kumaran U, Neelu Khare, A Sai Suraj. Privacy Preserving in Data Mining Technical: A Review. Research J. Pharm. and Tech 2016; 9(11): 2023-2026.
- Zhao L and Zaki MJ. TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data. Proceedings of the 2005 ACM SIGMOD international conference on Management of data. 2015; 694–705.
- Feng J, Barbano PE and Mishra B. Time-frequency feature detection for timecourse microarray data. Proceedings of the ACM Symposium on Applied Computing. 2004; 128-132.
- Zhao L and Zaki MJ. TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data. Proceedings of the 2005 ACM SIGMOD international conference on Management of data. 2015; 694–705.
- Jiang H. Zhou S, Guan J and Zheng Y. gTRICLUSTER: A More General and Effective 3D Clustering Algorithm for Gene-Sample-Time Microarray Data. Data Mining for Biomedical Applications.2006; 3916: 48–59.
- Bhar A, Haubrock M, Mukhopadhyay A, Maulik U, Bandyopadhyay S and Wingender E. δ-TRIMAX: Extracting triclusters and analyzing coregulation in time series gene expression data. Springer. 2012; 7534: 165–177.
- Aviles DG, Escudero CR, Alvarez FM and Riquelme JC. TriGen: A genetic algorithm to mine triclusters in temporal gene expression data. Neurocomputing. 2014; 132: 42–53.
- Bhar A, Haubrock M, Mukhopadhyay A, Maulik U, Bandyopadhyay S and Wingender E. Multiobjective triclustering of time-series transcriptome data reveals key genes of biological processes. BMC Bioinformatics. 2015; 16.
- G. Manikandan. A Comparative Analysis on the Applicability of Various Mutation Types for Achieving Privacy in medical Data Mining. Research J. Pharm. and Tech. 2017; 10(8): 2451-2455.
- Kennedy J and Eberhart RC. Particle Swarm Optimization. Proceedings of the 1995 IEEE International Conference on Neural Networks. 1995; 1942-1948.
- Kennedy J and Eberhart RC. Swarm Intelligence. Academic Press, London. 2001.
- Eberhart RC, Simpson P and Dobbins R. Computational Intelligence PC Tools, Academic Press. 1996.
- Eberhart, R., Kennedy, J., “A new optimizer using particle swarm theory”, MHS’95. Proceedings of the Sixth International Symposium on MicroMachine and Human Science. pp. 39–43, 1995.
- Shi Y, Eberhart R. A modified particle swarm optimizer. 1998 IEEE International Conference on Evolutionary Computation Proceedings, IEEE World Congress on Computational Intelligence. 1998; 69–73.
- Swathypriyadharsini P and Premalatha k. TrioCuckoo: A Multi Objective Cuckoo Search Algorithm for Triclustering Microarray Gene Expression Data. Journal of Information Science and Engineering. 2018; 34(6): 1617-1631.
- Bhar, A., Haubrock, M., Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S. and Wingender, E., “Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell,” Algorithms Mol Biol., Vol. 8, 2013.
- Huang DW, Sherman BT and Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nature Protocols. 2009; 4(1): 44-57.