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Co-Authors
- N. Boomathi
- P. Sivasubramanian
- S. Raguraman
- Arulanand Natarajan
- S. Subramanian
- D. Sasikala
- S. Logeswari
- A. M. Natarajan
- C. Gunavathi
- K. Sivasubramanian
- J. J. Nandhini
- P. Swathypriyadharsini
- G. Priyatharsini
- S. V. Krishnamoorthy
- N. Sathiah
- K. Senguttuvan
- T. GAVYA
- K. PREMALATHA
- C. CHINNAIAH
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Journals
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Premalatha, K.
- Safety of Botanical and Microbial Pesticide Mixtures to Trichogramma chilonis Ishii (Hymenoptera: Trichogrammatidae)
Abstract Views :219 |
PDF Views:123
Authors
Affiliations
1 Department of Agricultural Entomology, Agricultural College and Research Institute, Madurai, 625 104, Tamil Nadu, IN
1 Department of Agricultural Entomology, Agricultural College and Research Institute, Madurai, 625 104, Tamil Nadu, IN
Source
Journal of Biological Control, Vol 19, No 1 (2005), Pagination: 23-29Abstract
Laboratory experiments were conducted to study the effects of botanical (neem, sweet-flag and pongamia) and microbial pesticide mixtures (HaNPV, Bacillus thuringiensis (=Spicturin) and spinosad) on adult mortality and adult emergence of the egg parasitoid, Trichogramma chilonis. In contact toxicity test, neem + sweet-flag and neem + sweet-flag + pongamia at 0.12 and 0.18 per cent concentrations registered up to 33.89 per cent mortality of adult parasitoids while the same concentrations had 79.58 to 81.00 per cent adult emergence. Experiments with mixtures of microbial pesticides revealed that spinosad at 75 g a. i./ha and its combination with HaNPV or Spicturin or with both registered cent per cent adult mortality in 24 hours. Also, spinosad or its combinations registered poor adult emergence of this parasitoid, which ranged from 13.08 to 13.83 while HaNPV and Spicturin registered adult emergence of 93.33 and 93.58 per cent. The possibilities of using the botanical mixtures or microbial pesticide mixtures along with this parasitoid are discussed.Keywords
Botanical Insecticides, Ha Npv, Spicturin, Spinosad, Trichogramma chilonis.Full Text
- Enhanced Matrix Bloom Filter for Weak Password Identification
Abstract Views :150 |
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 :214 |
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 :150 |
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 :185 |
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 :412 |
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
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- Particle Swarm Optimization for Triclustering High Dimensional Microarray Gene Expression Data
Abstract Views :154 |
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.
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- 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.
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- Beauveria bassiana as an effective IPM component against cotton stem weevil Pempherulus affinis Faust
Abstract Views :300 |
PDF Views:108
Authors
Affiliations
1 Department of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore – 641 003, Tamil Nadu, IN
1 Department of Agricultural Entomology, Tamil Nadu Agricultural University, Coimbatore – 641 003, Tamil Nadu, IN
Source
Journal of Biological Control, Vol 35, No 3 (2021), Pagination: 205-208Abstract
The Integrated Pest Management (IPM) in reduction of cotton stem weevil in ecofriendly manner is attaining importance in recent days. In present study, three treatments including IPM module 1, IPM module 2 and farmers’ practice were imposed against cotton stem weevil in a field trial. Among the three treatments, IPM module 2 which included basal application of FYM 25 t/ha and 250 kg/ha of neem cake, seed treatment with Beauveria bassania @10g/kg of seed, border crop with Hibiscus cannabinus, soil drenching with Imidacloprid 17.80 SL @ 25 g a.i./ha (125 ml/ha) at 15 DAS and placement of cotton stem bits (25 kg/500box/ha) + Hibiscus cannabinus stem bits (25 kg/500box/ha) + Chlorpyriphos dust 1.5 DP (2.5 kg/500box/ha) @ 30 DAS followed by earthing up @ 30 DAS recorded least stem weevil infestation of 13.21% with a yield of 1642.75 kg/ha. It was followed by IPM module 1 (21.78%) and farmers’ practice (33.56%) with yield of 1456.25 kg/ha and 1588.25 kg/ha, respectively. The mean survival of plants was also higher in IPM module 2 (94.28%) followed by farmers’ practice (88.57%) and IPM module 1 (80.00%).
Keywords
Cotton stem weevil, farmers’ practice, IPM module, per cent damage, plant survivalFull Text
- Laboratory assessment on compatibility of entomopathogenic fungus, Beauveria bassiana Balsamo-vuillemin with imidacloprid 48% FS for bhendi (Abelmoschus esculentus L. moench) seed treatment
Abstract Views :112 |
PDF Views:75
Authors
Affiliations
1 Department of Entomology, Agricultural College and Research Institute, Madurai – 625104, Tamil Nadu, India., IN
2 Department of Entomology, Agricultural College and Research Institute, Madurai – 625104, Tamil Nadu, India ., IN
3 Department of Plant Pathology, Agricultural College and Research Institute, Madurai – 625104, Tamil Nadu, India ., IN
1 Department of Entomology, Agricultural College and Research Institute, Madurai – 625104, Tamil Nadu, India., IN
2 Department of Entomology, Agricultural College and Research Institute, Madurai – 625104, Tamil Nadu, India ., IN
3 Department of Plant Pathology, Agricultural College and Research Institute, Madurai – 625104, Tamil Nadu, India ., IN
Source
Journal of Biological Control, Vol 36, No 2 & 3 (2022), Pagination: 143 - 150Abstract
Beauveria bassiana (Balsamo-Vuillemin), an effective entomopathogenic fungi is well positioned in the biological control of insect pests for more than ten decades around the world. Its potential can be attributed to the fungus’s entry through several parts of the insect and its mode of action. But sometimes a virulent strain of B. bassiana may become ineffective because of xenobiotics and environmental factors. To enhance the efficacy of B. bassiana, which is necessary for placing it in Integrated Pest Management, the Colony Forming Unit (CFU) of B. bassiana should be compatible with xenobiotics used in crop production. The compatible concentrations of imidacloprid 48% FS (500 ppm) for B. bassiana was studied in the laboratory condition and results revealed low per cent growth inhibition (21.25%) and maximum radial growth (1.46 cm) at 15 Days After Inoculation (DAI). The inhibition of colony growth was reduced by treating the bhendi seeds with B. bassiana and imidacloprid 48 % FS at a time interval of four hours which showed high mean colony growth (51.12).Keywords
Beauveria bassiana, compatibility, imidacloprid 48% FS, seed treatmentFull Text
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