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Sumathi, S.
- Molecular Typing and Mapping of MHC Class II-DRB3 Gene in Indian River Buffaloes (Bubalus bubalis)
Abstract Views :366 |
PDF Views:103
Authors
Affiliations
1 Dept. of Animal Genetics and Breeding, Madras Veterinary College, Tamil Nadu Veterinary & Animal Sciences University, Chennai - 600 007, IN
1 Dept. of Animal Genetics and Breeding, Madras Veterinary College, Tamil Nadu Veterinary & Animal Sciences University, Chennai - 600 007, IN
Source
Indian Journal of Science and Technology, Vol 3, No 5 (2010), Pagination: 557-560Abstract
The present study was undertaken with the objectives of characterizing Bubu-MHC loci by PCR and genotyping MHC loci for allelic variation. The PCR product of second exon of the Bubu-MHC-DRB3 gene (304 bp) exhibited genetic polymorphism while digesting with HaeIII enzyme resulting in three restriction fragment patterns in Murrah, four in Surti and three in Murrah graded buffaloes. In all the three genetic groups, the pattern `b' (82, 222 bp) was frequently observed. The restriction fragment analysis with RsaI revealed five patterns in Murrah and three in Surti. The pattern `s' (67, 93, 144 bp) with a frequency of 0.4444 and `l' (67, 237 bp) with a frequency of 0.5000 were observed. Microsatellite typing revealed nine alleles ranging from 160 to 212 bp at the second intron. In Murrah, the allele 190 bp was observed exclusively. In Surti, alleles 192 and 212 bp were observed more frequently. RsaI enzyme revealed more polymorphic patterns of DRB3 than HaeIII. Microsatellite typing provided certain breed-specific alleles. This gene was physically localized to chromosome 2p following tyramide signal amplification in between bands 15-22 using (cDNA probes derived from Bos taurus cattle) fluorescence in situ hybridization.Keywords
Bubu-MHC, PCR-RFLP, FISH, BuffaloesFull Text
References
- Ahmed S and Othman EO (2006) The characterisation of HaeIII patterns in second exon of buffalo MHC class II DRB gene. Biotechnol. 5, 514-516.
- Aravindakshan TV, Mahalinga Nainar A and Sivaselvam S.N. (2000) Polymorphism in exon 2 of the Bubu-MHC-DRB3 gene in Indian buffalo (Bubalus bubalis var. indicus) detected by PCR-RFLP. Anim. Sci. 70, 221-226.
- Chowdhary P, De la sene C, Harbit I, Eriksson l and Gustavsson I (1995) FISH on metaphase and interphase chromosomes demonstrates the physical order of the genes for GPI, CRC and LIPE in pigs. Cytogenet. Cell Genet. 71, 175-178.
- Ellegren H, Davies CJ and Andersson l (1993) Strong association between polymorphisms in an intronic microsatellite and in the coding sequence of the BoLA-DRB3 typing gene: implications for microsatellite stability and PCR-based DRB3 typing. Anim. Genet. 24, 269-275.
- Fries R, Hediger R and Stranzinger G (1986) Tentative chromosomal localization of the bovine major histocompatibility complex by in situ hybridization. Anim. Genet. 17, 287-294.
- Iannuzzi L (1994) Standard karyotype of river buffalo (Bubalus bubalis L., 2n=50). Cytogenet. Cell Genet. 67, 102-113.
- Iannuzzi I, Gallagher DS, Womack JE, Di meo GP, Skow IC and Ferrara L (1993) Chromosomal localization of the major histocompatibility complex in cattle and river buffalo by in situ hybridization. Hereditas. 118, 187-190.
- Montgomery GW and Sise JA (1990) Extraction of DNA from sheep white blood cells. NZL. J. Agri. Res. 33, 437-441.
- Sigurdardottir S, Borsch C, Gustafsson K and Andersson l (1991) Cloning and sequence analysis of 14 DRB alleles of the bovine major histocompatibility complex by using the polymerase chain reaction. Anim. Genet. 22, 199-209.
- Van Eijk MJT, Haynes JAS and Lewin HA (1992) Extensive polymorphism of the BoLA-DRB3 gene distinguished by PCR-RFLP. Anim. Genet. 23, 483-496.
- Van Haeringen WA, Gwakisa PS, Mikko S, Erythorsdottir E, Holm LE, Olsaker I, Outteridge P and Andersson P (1999) Heterozygosity excess at the cattle DRB locus revealed by large scale genotyping of two closely linked microsatellites. Anim. Genet. 30, 169-176.
- Face Recognition - Multi Algorithm Approach using Average Half Face
Abstract Views :322 |
PDF Views:74
Authors
Affiliations
1 Research Scholar, Department of ECE, Sathyabama University, Chennai-600 096, IN
2 Professor & Head Department of ECE, St.Peter’s College of Engineering & Technology, Chennai-600 054, IN
3 Department of ECE, Prathyusha Institute of Technology and Management, Chennai-600 096, IN
1 Research Scholar, Department of ECE, Sathyabama University, Chennai-600 096, IN
2 Professor & Head Department of ECE, St.Peter’s College of Engineering & Technology, Chennai-600 054, IN
3 Department of ECE, Prathyusha Institute of Technology and Management, Chennai-600 096, IN
Source
Indian Journal of Innovations and Developments, Vol 1, No 12 (2012), Pagination: 811-815Abstract
Face recognition has received much attention in recent years due to its many applications such as human computer interface, video surveillance and face image database management. It is a challenging technique due to under different lighting conditions, facial expressions and changes in head pose. Single class of feature is not enough to capture all the available information in face. Multi algorithm approach of face recognition improves the accuracy using feature level fusion. This paper proposes an efficient technique for identification of an individual by using Average Half Face (AHF). We propose feature fusion technique using Principal component Analysis (PCA) and Discrete Wavelet Transform (DWT). For classification, distance classifier is used. The proposed method was tested using the cropped extended Yale B database, where the images vary in illumination and expression. High recognition performance has been obtained by fusion of PCA and Wavelet features at feature level for average half face compared to full face.Keywords
Face Recognition, PCA, Wavelet, Multi Algorithm, Average Half Face.Full Text
References
- Soyuj Kumar Sahoo, Tarun Choubisa, SR Mahadeva Prasanna (2012). Multimodal Biometric Person Authentication: A Re view, IETE Technical Review vol 29 issue 1, pp.54-75.
- Rao RM, and Bopardikar AS, (1998).Wavelet Transforms-Introduction to theory and Applications, Addison Wesley Longman.
- Turk M, and Pentland A, (1991). Eigenfaces for recognition, J. Cognitive Neurosci., vol. 13, no. 1, pp. 71–86.
- Penev PS and Atick JJ (1996). Local Feature Analysis: A General statistical theory for object representation, Computational Neuroscience laboratory, The Rockfeller University, USA.
- Duda RO, Hart, PE, (1973). Pattern classification and scene analysis, Wiley, New York.
- Ramesha K and Raja KB, (2011). Gram-Schmidt Orthogonalization Based Face Recognition Using Dwt, International Journal of Engineering Science and Technology, pp 494-503.
- Ergun Gumus, Niyazi Kilic, Ahmet Sertbas, Osman N. Ucan, (2010).Evaluation of face recognition techniques using PCA, wavelets and SVM”, Expert Systems with Applications, pp 6404–6408.
- Nandini C, and RaviKumar C, (2007). Multi- Biometrics Approach for Facial Recognition, IEEE International Conference on Computational Intelligence and Multimedia Applications, pp 417-422.
- Sumatra Kar and Swati Hiremath, (2006). A Multi-Algorithmic Face Recognition System, IEEE International Conference on Advanced Computing and Communications, pp 321-326.
- Marcialis GL, and Roli F, (2002). Fusion of LDA and PCA for Face Verification, Proceedings of the Workshop on Biometric Authentication, Springer LNCS 2359, Copenhagen Denmark.
- Arun Ross and Rohin Govindarajan. Feature Level Fusion in Biometric Systems.
- Josh Harguess and Shalini Gupta, (2008). 3D Face Recognition with the Average Half Face”, IEEE International Conference on Pattern Recognition, pp 1-4.
- Wankou Yang and Changyin Sun, (2011). A multi-manifold discriminant analysis method for image feature extraction, Journal of Pattern Recognition, pp 1-9.
- Josh Harguess and Aggarwal JK, (2009). A Case for the Average-Half-Face in 2D and 3D for Face Recognition, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp7-12.
- Wei Chen and Tongfeng Sun, (2009). Face Detection Based on Half Face-template, IEEE 9th International Conference on Electronic Measurement & Instruments, pp 54-57.
- Sumathi and Ranihemamalini R, (2011). “Efficient Identification System Using wavelet transform and Average Half-face”, CIIT International Journal of Digital Image Processing, Vol 3,No 20,.ISSN-0974-9691,pp 1259-1263.
- Sumathi and Ranihemamalini R, (2012). “Multi-biometric Authentication using DWT and Score Level Fusion”, European Journal of Scientific Research, Vol.80 No.2 ISSN 1450-216X, pp.213-223.
- Kittler J, Hatef M, Duin R, and Matas J, (1998). On combining classifiers, IEEE Transaction on Pattern Anal. Mach. Intell., vol. 20, no. 3, pp. 226–239.
- Cover TM, and Hart PE, (1967). Nearest neighbor pattern Classifiers, IEEE Trans. Information Theory, Vol. 13,pp 21-27.
- Lin SH, Kung SY and Lin LJ, (1997). Face Recognition / Detection by probabilistic decision based Neural Network, IEEE Trans.Neural Networks,vol 8,no 1,pp 114-132,Jan.1997.
- Nefian AV and Hayes MH III, (1998). Hidden Markov Models for Face Recognition, Proc IEEE int’l Conf. Acoustic,Speech and Signal Processing,pp.2721-2724.
- Xiaoyang Tan and Bill Triggs, (2010). Enhanced Local Texture Feature Sets for Face Recognition under Difficult Lighting Conditions, IEEE Transactions on Image Processing, pp1635-1650.
- Yale FaceDatabase: http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html
- Comparative Study on Socio-Economic, Somatic and Dietary Status of Elderly People Living in Old Age Home and Community Set Up
Abstract Views :225 |
PDF Views:2
Authors
Affiliations
1 Staff Training Unit, University of Agricultural Sciences, Hebbal, IN
2 Department of Statistics, GKVK campus, UAS, Bangalore, IN
1 Staff Training Unit, University of Agricultural Sciences, Hebbal, IN
2 Department of Statistics, GKVK campus, UAS, Bangalore, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 52, No 3 (2015), Pagination: 318-329Abstract
Aging is a natural process. Older people are at a risk of malnutrition, not only because of food insecurity, but also due to various social, physiological and health changes with aging. However, very few studies have been done in developing countries to identify and manage their health care needs. Hence, the present study was taken up to assess the socio-economic, somatic and dietary status of elderly people living in old age home and in community set-up in a small town Virudhunagar, Tamil Nadu with a total of 100 subjects (50 from old age home and 50 community dwelling). A questionnaire was developed to record personal data, socio-economic status and dietary pattern. Maximum percentage of elders in the community life belonged to the age group 66-75 years (44%) whereas, majority of elders (44%) in old age home were in the age group of 76-95 years. The anthropometric measurements were recorded using standard procedures. Majority of the respondents from both old age home (75% M and 42.3% F) and community life (65.2% M and 48.2% F) were found to be in normal BMI range. Dietary intake was based on 24 hour dietary recall method. The percentage adequacy for macro and micronutrient intake was better among community life than old age home respondents except for beta carotene. The overall nutritional status of the community life respondents was better than that of old age home. The study recommended regular assessment of nutritional status of elderly population in maintaining their health status.Keywords
Aging, Malnutrition, Health Care Needs, Anthropometric, Macro and Micronutrients.
References
- Anonymous. India Demographic profile 2013: cited in http://www.indexmundi.com/india/demographics_profile.html
- Blackburn, G.L., Bistrian, B.R. and Maini, B.S. Nutritional and metabolic assessment of hospitalized patients, J. Parenter. Enter. Nutr., 1977, 1, 11 - 22.
- Langiano, E ., Di Russo, C., Atrei, P., Ferrara, M., Allegretti, V., Verdicchio, I. and De Vito, E. Nutritional status of elderly institutionalized subjects in a health district in Frosinone (Italy), 2009, 65, 17-28.
- Beck, A.M., Ovesen, L. and Osler, M. The 'Mini Nutritional Assessment' (MNA) and the'Determine your Nutritional Health' (NSI Checklist) as predictor of morbidity and mortality in an elderly Danish population. Br. J. Nutr., 1999, 81, 31-36.
- Ramesh Bhat, Ganaraja, B., Bhagyalakshmi Meenu, S., Vinodini, A. and Nayanatara, A.K. A study of prevalence of obesity and an assessment of nutritional status in elderly South Indian population, Int. J. App. Bio. Pharm. Tech., 2012, 3, 10-14.
- Swati Verma and Sakshi Sharma, Prevalence of obesity among the urban geriatrics. Sparkle N Spice, Ann. Maga. Cum. J. IHM PUSA, 2011, 8, 1-9.
- Marais, M.L., Marais, D. and Labadarios, D. Assessment of nutritional status of older people in homes for the aged in the Somerset West area, S. Afr. J. Clin. Nutr, 2007, 20, 102-108.
- Shabayek, M.M. and Saleh, S.I., Nutritional status of institutionalized and free-living elderly in Alexandria. J. Egypt. Pub. Health. Assoc., 2000, 75, 437-59.
- ICMR, Nutrient Requirement and Recommended Dietary Allowances for Indians. NIN, Hyderabad, India, 2010.
- Effect of Processing on In Vitro Starch and Protein Digestibility of White and Brown Ragi
Abstract Views :172 |
PDF Views:0
Authors
Hima Bindu
1,
S. Sumathi
1
Affiliations
1 Department of Foods and Nutrition, Post Graduate and Research Center, A N G R Agricultural University, Rajendranagar, Hyderabad, IN
1 Department of Foods and Nutrition, Post Graduate and Research Center, A N G R Agricultural University, Rajendranagar, Hyderabad, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 47, No 5 (2010), Pagination: 200-206Abstract
In Indian diet, the major carbohydrate is starch which is mainly derived from the cereais such as rice, wheat, ragi and jowar. Diet containing two or more cereals compared to a diet containing any one cereal may be of therapeutic value. Ragi based products such as malted weaning food; porridge and malt are the familiar products with the Indian population.- Soft Computing Techniques based Recursive Error Correcting Output Code for Multi-Class Pattern Classification
Abstract Views :141 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore, IN
2 Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, IN
1 Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore, IN
2 Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, IN
Source
Fuzzy Systems, Vol 3, No 1 (2011), Pagination: 13-20Abstract
The pattern recognition methods like speech recognition, text classification, image recognition results in the solving of multi-class problems. This can be achieved by means of classifying multi-class problems into several two class problems using the Soft Computing techniques such as Neural Networks and Support Vector Machines. The best code matrix for a given problem cannot be designed taking into account only the features of the code matrix, viz., overall classifier accuracy, minimum hamming distance and margin of classification, but also the features of the problem, viz., attributes, samples and classes are to be considered. Conventionally, code matrix is designed based on either the features of the problem or the features of the code matrix. The proposed work, focused on designing a new code matrix based on both the features of the problem and code matrix. In order to improve the accuracy and reduce computation time, the generation of feature dependent code matrix through an evolutionary algorithm is proposed. This model aims at developing a hybrid version of Recursive Error Correcting Output Code with Biogeography Based Optimization to achieve maximum classification accuracy and minimum computational time.Keywords
Biogeography Based Optimization, C5.0 Binary Search Tree, Radial Basis Function Neural Network, Recursive Error Correcting Output Codes, Support Vector Machine.- Efficient Identification System Using Wavelet Transform and Average Half-Face
Abstract Views :139 |
PDF Views:2
Authors
Affiliations
1 Sri Sai Ram Engineering College, Chennai-44, Tamilnadu, IN
2 Electronics & Instrumentation Engineering Department, St. Peter's University, Chennai-54, Tamilnadu, IN
1 Sri Sai Ram Engineering College, Chennai-44, Tamilnadu, IN
2 Electronics & Instrumentation Engineering Department, St. Peter's University, Chennai-54, Tamilnadu, IN
Source
Digital Image Processing, Vol 3, No 20 (2011), Pagination: 1259-1263Abstract
Face recognition based on biometrics is one of the most hot and challengeable technologies. This paper proposes an efficient technique for identification of an individual. The person identification is done by face recognition using an average half face as a feature. Discrete Wavelet Transform (DWT) is used for feature extraction and Support Vector Machine (SVM) is proposed for classification. The proposed system consists of three phases: (i) Preprocessing, (ii) Feature extraction and (iii) Classification. The proposed method was tested using the cropped extended Yale database, where the images vary in illumination and expression. The experiment was demonstrated with various thresholds. Better results were obtained for a threshold of 0.5. The proposed system shows a high degree of success in identifying the individual with reduced computation time and memory storage saving of 31%.Keywords
Average Half Face, Discrete Wavelet Transform, Face Recognition, Support Vector Machine.- An Optimization Approach to Digital Image Watermarking Based on GA and PSO
Abstract Views :153 |
PDF Views:3
Authors
P. Surekha
1,
S. Sumathi
1
Affiliations
1 Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore – 641 004, IN
1 Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore – 641 004, IN
Source
Digital Image Processing, Vol 2, No 9 (2010), Pagination: 319-329Abstract
The increasing effect of illegal exploitation and imitation of digital images in the field of image processing has led to the urgent development in the growth of copyright protection methods. Digital watermarking has proved best in protecting illegal authentication of data. In this paper, we propose a hybrid digital image watermarking scheme based on computational intelligence paradigms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The watermark image is embedded into the host image using Discrete Wavelet Transform (DWT). During the extraction process, GA, and PSO are applied to improve the robustness, and fidelity of the watermarked image by evaluating the fitness function. The perceptual transparency and the robustness of the watermarked and the extracted images are evaluated by applying filtering attacks, additive noise, rotation, scaling and JPEG compression attacks to the watermarked image. From the simulation results the performance of the Particle Swarm Optimization technique is proved best based on the computed robustness and transparency measures along with the evaluated parameters like elapsed time, computation time and fitness value. The performance of proposed scheme was evaluated with a set of 50 textures images taken from online resources of Tampere University of Technology, Finland and the entire algorithm for different stages was simulated using MATLAB R2008b.Keywords
DWT, Genetic Algorithm, Particle Swarm Optimization, Robustness and Transparency.- A Novel Algorithm for Quick QRS Complex Detection in ECG Based On Discrete Wavelet Transform
Abstract Views :168 |
PDF Views:3
Authors
Affiliations
1 Department of Electrical Engineering, V.M.K.V.Engineering College, Salem, IN
2 Department of Electrical Engineering, K. S. R. College of Technology, and Tiruchengodu, IN
1 Department of Electrical Engineering, V.M.K.V.Engineering College, Salem, IN
2 Department of Electrical Engineering, K. S. R. College of Technology, and Tiruchengodu, IN
Source
Digital Image Processing, Vol 1, No 2 (2009), Pagination: 73-77Abstract
This paper presents an algorithm based on the discrete wavelet transform, for feature extraction from the ElectroCardioGraph (ECG) signal and recognition of abnormal heart beats. Wavelets provide simultaneous time and frequency information. The new algorithm detects the R waves as well as Premature Ventricular Contraction (PVC) waves in the ECG signal. The wavelet transform decomposes the ECG signal into a set of frequency band. By using wavelet decomposition, we reduced the amount of data necessary to be processed by the algorithm to less than ten percent of the original data. The adaptive threshold algorithm is implemented with a value greater than that of R waves and less than the value of PVC. For the standard 24 hour Massachusetts Institute of Technology/Beth Isrel Hospital (MIT-BIH) arrhythmia database, this algorithm correctly detects 99.4 percent of the QRS complexes.
Keywords
ECG, QRS Complex Detection, Premature Ventricular Contraction, Discrete Wavelet Transform, Cubic Spline Wavelet.- Boolean Algebraic Algorithm for Mining Association Rules from Large Database
Abstract Views :177 |
PDF Views:3
Authors
S. Sumathi
1,
R. Lawrance
2
Affiliations
1 Department of Computer Science, Ayya Nadar Janaki Ammal College, Sivakasi, IN
2 Ayya Nadar Janaki Ammal College, Sivakasi, IN
1 Department of Computer Science, Ayya Nadar Janaki Ammal College, Sivakasi, IN
2 Ayya Nadar Janaki Ammal College, Sivakasi, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 7 (2012), Pagination: 361-364Abstract
In the earlier days, the association rule mining is used for Market Basket analysis to find the regularity in purchasing behavior of customer. Association Rule Mining (ARM) is one of the functionalities in Data Mining, to find the relationships among the items in a particular set of itemsets. There are huge numbers of algorithms to find relationships among the items. In this paper we introduce a new Boolean algebraic algorithm for finding frequent itemsets and deriving the association rules in a large transaction database. It has two phases. In the first phase, it finds the frequent itemsets. In the second phase, by using the Boolean AND and XOR operator, it derives the association rules from the founded frequent itemset in first phase. This algorithm mines the association rules efficiently than Apriori.Keywords
Association Rule Mining, Boolean Algebra, Data Mining, Frequent Item Set Mining.- Palatal Rugae and Dental Work Information-Tools of Human Identification
Abstract Views :134 |
PDF Views:3
Authors
Affiliations
1 Sri Sairam Engineering College, Chennai-44, IN
1 Sri Sairam Engineering College, Chennai-44, IN
Source
Biometrics and Bioinformatics, Vol 4, No 7 (2012), Pagination: 318-323Abstract
Dental biometrics is used in forensic dentistry to identify or verify persons. This paper presents a method for human identification based on dental work information along with palatal rugae recognition which is considered to be unique for each person just like the finger prints. The proposed method works with three main processing steps: segmentation (feature extraction), creation of a dental code, and matching. In the segmentation step, seed points of the dental works are detected by thresholding. The final segmentation is obtained with a snake (active contour) algorithm. The dental code is defined from the position (upper or lower), the size of the dental works, and distance between neighboring dental works. The matching stage is performed with the Edit distance (Levenshtein distance). The palatal rugae is also considered. By superimposing the rugae patterns of unknown with those in database the identification is made easier.
- Abduction of Newborn Infants Using Footprint Recognition
Abstract Views :160 |
PDF Views:3
Authors
Affiliations
1 Sri Sairam Engineering College, Chennai-44, IN
1 Sri Sairam Engineering College, Chennai-44, IN
Source
Biometrics and Bioinformatics, Vol 4, No 7 (2012), Pagination: 324-329Abstract
Identify or authenticate the person using different biometric traits. Whenever sensational crimes such as abduction of infants or baby switching occurs, need for biometric identification technique increases. For this issue here we propose a novel online newborn personal authentication system based on footprint recognition. A newborn footprint database is established to examine the performance of the system and promising experimental results demonstrate the effectiveness of the proposed system. The entire process can be implemented in software. For the software implementation, there are two steps one is image acquisition which is done by capturing the newborn footprint images using a digital camera and the other is preprocessing the obtained image which involves various stages like orientation, normalization, scale normalization, ROI extraction and recognized by ordinal code and hamming distance. These experiments were conducted on a personal computer using Matlab R2009a. This method represents a reliable, expeditious and cost efficient method for establishing probable personal identity which is thus encouraged by Federal Bureau of Investigation (FBI).
Keywords
Abduction, Newborn, Biometric, Footprint Recognition, ROI, Ordinal Code, Hamming Distance FBI.- Application of Particle Swarm Optimization for Solving Multi-Depot Vehicle Routing Problems
Abstract Views :223 |
PDF Views:4
Authors
P. Surekha
1,
S. Sumathi
1
Affiliations
1 Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore-641004, IN
1 Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore-641004, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 3, No 11 (2011), Pagination: 677-686Abstract
The Multi-Depot Vehicle Routing Problem (MDVRP), an extension of classical VRP, is a NP-hard problem for simultaneously determining the routes for several vehicles from multiple depots to a set of customers and then return to the same depot. The objective of the problem is to find routes for vehicles to service all the customers at a minimal cost in terms of number of routes and total travel distance, without violating the capacity and travel time constraints of the vehicles. The solution to the MDVRP, in this paper, is obtained through Particle Swarm Optimization (PSO). The customers are grouped based on distance to their nearest depots and then routed with Clarke and Wright saving method. Further the routes are scheduled and optimized using POS. A set of five different Cordeau’s benchmark instances (p01, p02, p03, p04, p06) from the online resource of University of Malaga, Spain were experimented using MATLAB R2008b software. The results were evaluated in terms of depot’s route length, optimal route, optimal distance, computational time, average distance, and number of vehicles. Comparison of the experimental results with state-of-the-art techniques shows that the performance of PSO is feasible and effective for solving the multi-depot vehicle routing problem.Keywords
MDVRP, Particle Swarm Optimization, Optimal Route, Scheduling, Clarke and Wright Saving Method.- Planning, Scheduling and Optimizing Job Shop Scheduling Problem Using Genetic Algorithm
Abstract Views :176 |
PDF Views:3
Authors
Affiliations
1 Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore-641004, IN
2 Cognizant Technology Solutions, Chennai, IN
1 Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore-641004, IN
2 Cognizant Technology Solutions, Chennai, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 3, No 1 (2011), Pagination: 68-73Abstract
Evolutionary algorithms are having a leading focus in solving several optimization problems. Job-shop scheduling problem (JSSP) is one among the common NP-hard combinatorial optimization problems used to allocate machines for a set of jobs over time and hence optimizing the processing time, waiting time, completion time, and makespan. In this paper an eminent approach based on the paradigm of evolutionary computation for solving job shop scheduling problem is proposed. The solution to the problem is alienated into three phases; planning, scheduling and optimization. Initially, the jobs are scheduled, in which the machines and jobs with respect to levels are planned. Scheduling is optimized using Genetic Algorithm (GA), which is a powerful search technique, built on a model of the biological evolution. Like natural evolution GA deal with a population of individuals rather than a single solution and fuzzy interface is applied for planning and scheduling of jobs. The Fisher and Thompson 10×10 instance (FT10) problem is selected as the experiment problem and the algorithm is simulated using the MATLAB R2008B software.Keywords
Job Shop Scheduling Problem, Genetic Algorithm, Fuzzy Logic, FT10, Makespan.- Genetic Algorithm and Ant Colony Optimization for Optimizing Combinatorial Fuzzy Job Shop Scheduling Problems
Abstract Views :161 |
PDF Views:7
Authors
P. Surekha
1,
S. Sumathi
1
Affiliations
1 Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore-641004, IN
1 Electrical and Electronics Engineering Department, PSG College of Technology, Coimbatore-641004, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 2, No 9 (2010), Pagination: 223-231Abstract
In this paper, we present a genetic algorithm and ant colony optimization algorithm for solving the Job-shop Scheduling Problem (JSSP). The genetic algorithm generates the initial population, selects the individuals for reproduction creating new individuals. Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ants, used to solve this combinatorial optimization problem. In JSSP ants move from one machine (nest) to another machine (food source) depending upon the job flow, thereby optimizing the sequence of jobs. The sequence of jobs is scheduled using Fuzzy logic and optimized using GA and ACO. The makespan, completion time, makespan efficiency, algorithmic efficiency and the elapsed time for the genetic algorithm and the ant colony algorithm are evaluated and compared. Computational results of these optimization algorithms are compared by analyzing the JSSP benchmark instances, FT10 and the ABZ10 problems.Keywords
Job Shop Scheduling Problem, Makespan, Planning, Scheduling, Genetic Algorithm, Ant Colony Optimization.- Evolutionary Algorithms for Load Frequency Control in Two Area Interconnected Power System
Abstract Views :169 |
PDF Views:4
Authors
Affiliations
1 Information Technology Department, PSG College of Technology, Coimbatore-641004, Tamilnadu, IN
2 Electrical and Electronics Engg. Department, PSG College of Technology, Coimbatore-641004, Tamilnadu, IN
1 Information Technology Department, PSG College of Technology, Coimbatore-641004, Tamilnadu, IN
2 Electrical and Electronics Engg. Department, PSG College of Technology, Coimbatore-641004, Tamilnadu, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 2, No 9 (2010), Pagination: 232-242Abstract
A Power system is an interconnected electric network which makes available, electric power generated by the generating plants to the loads through transmission lines. Modern power systems are large, highly complex and organized in the form of regional grids, which are interconnected to facilitate power transfer between areas via tie-lines. Interconnections make the system more reliable, since the power can be borrowed from the neighboring area. It is the responsibility of power generating system to ensure that adequate power is delivered to the load, both reliably and economically. Any electrical system must be maintained at the desired operating level characterized by nominal frequency and voltage profile. Hence, a Power System Control is required to maintain a continuous balance between power generation and load demand. The quality of power supply is affected due to continuous and random changes in load during the operation of the power system. Load Frequency Controller (LFC) play an important role in maintaining constant frequency in order to ensure the reliability of electric power. In order improve the performance and stability of this control loop, PID controllers are normally used. But these fixed gain controllers fail to perform under varying load conditions and hence provide poor dynamic characteristics. Also the conventional PSO based PID controllers will have large settling time, overshoot and oscillations. In order to achieve better dynamic performance, system stability and sustainable utilization of generating systems, PID gains must be well tuned. In this paper, Evolutionary Algorithms (EA) like, Enhanced Particle Swarm Optimization (EPSO), Multi Objective Particle Swarm Optimization (MOPSO), and Stochastic Particle Swarm Optimization (SPSO) are proposed to overcome the premature convergence problem in a standard PSO. Simulation results demonstrate that the proposed controller adapt themselves appropriate to varying loads and hence provide better performance characteristics with respect to settling time, oscillations and overshoot.Keywords
Load Frequency Control (LFC), Evolutionary Algorithm (EA), Enhanced Particle Swarm Optimization (EPSO), Multi Objective Particle Swarm Optimization (MOPSO), and Stochastic Particle Swarm Optimization (SPSO).- Biochemical Characterization of Isolates of Alternaria helianthi (Hansf.) Tubaki and Nishihara Causing Sunflower Blight
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Authors
Affiliations
1 Department of Plant Pathology, College of Agriculture, Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad (Telangana), IN
2 Department of Plant Pathology, College of Agriculture, Rajendranagar, Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad (Telangana), IN
3 Department of Plant Pathology, Indian Institute of Oilseeds Research, Rajendranagar, Hyderabad (Telangana), IN
4 Department of Biochemistry, College of Agriculture, Rajendranagar, Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad (Telangana), IN
1 Department of Plant Pathology, College of Agriculture, Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad (Telangana), IN
2 Department of Plant Pathology, College of Agriculture, Rajendranagar, Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad (Telangana), IN
3 Department of Plant Pathology, Indian Institute of Oilseeds Research, Rajendranagar, Hyderabad (Telangana), IN
4 Department of Biochemistry, College of Agriculture, Rajendranagar, Acharya N.G. Ranga Agricultural University, Rajendranagar, Hyderabad (Telangana), IN
Source
International Journal of Plant Sciences, Vol 11, No 2 (2016), Pagination: 249-254Abstract
A pure culture of 25 isolates of Alternaria helianthi were collected from IIOR, Rajendranagar, Hyderabad and biochemical nature was tested under in vitro. The isolates were characterized based on production of total sugars, total proteins, total free amino acids and phytotoxins. The estimation of all parameters reflected significant variation among all. The isolate Ah-25 produced maximum concentration of total sugar (13.28 mg), while minimum concentration was noticed in Ah-13 (3.10 mg). Similarly, the total proteins content was found highest in the isolate Ah-25 (21.43 mg) and lowest with the isolate Ah-15 (9.53 mg). Among the isolates, the total free amino acids ranged between 5.67 mg (Ah-15) to 21.24 mg (Ah-21). The phytotoxicity of the crude toxin was tested by adopting detached leaf technique at different concentrations. None of the tested isolates have produced symptoms at 50 ppm concentration. However, the typical symptoms of necrotic lesions were observed at 100 ppm with nine isolates (Ah-1, Ah-2, Ah-4, Ah-7, Ah-12, Ah-17, Ah-21, Ah-24 and Ah-25). Among the remaining isolates Ah-3, Ah-5, Ah-6, Ah-9, Ah-10, Ah-11, Ah-16, Ah-18 and Ah-23 showed necrotic symptoms at 200 ppm toxin concentration. Whereas the isolates Ah-8, Ah-13, Ah-14, Ah-15, Ah-19, Ah-20 and Ah-22 resulted in symptom development at 500 ppm concentration. Further, the strains were found to vary in their biochemical composition between all the isolates under the study.Keywords
Sunflower, Alternaria helianthi, Phytotoxins.References
- Allen, S.J., Brown, J.F. and Kochman, J.K. (1983). Production of inoculums and field assessement of Alternaria helianthi on sunflower. Pl Dis., 67 : 665-668.
- Amaresh, V.S. and Nargund, V.B. (1999). Isolation of toxin from Alternaria helianthi causing blight in sunflower. J. Mycol. & Pl. Pathol., 35(2) : 367-369.
- Balasubramanyam, N. and Kolte, S.J. (1980). Effect of different intensities of Alternaria blight on yield and oil content of sunflower. J. Agril. Sci., 94 : 749-751.
- Bhaskaran, R. and Kandaswamy, T.K. (1978). Production of toxic metabolite by Alternaria helianthi in vitro and in vivo. Madras Agril. J., 65(12) : 801-804.
- CMIE (2009). Executive Summary- GDP Growth. Centre for Monitoring Indian Economy (CMIE) Pvt. Ltd., Mumbai, M.S. (INDIA).
- Dater, V.V. (1994). Investigation on purple blotch of onion in India. Acta Horti., 358 : 259-263.
- Khurana, Anil, Mehta, Naresh and Sangwan, M.S. (2005). Variation in biochemical contents of Alternaria brassicae isolates. J. Mycol. & Pl. Pathol., 35:2-10.
- Kolte, S.J., Balasubramanyam, N., Tiwari, A.N. and Awasti, R.P. (1979). Field performance of fungicides in the control of Alternaria blight of sunflower. Indian J. Agril. Sci., 44 : 555-559.
- Lipps, P.E. and Herr, L.J. (1986). Reaction of Helianthus, annuus and Helianthus, tuberosus plant introductions to Alternaria helianthi. Pl. Dis., 70: 831-835.
- Maiero, M., Bean, G.A. and Timothy, J.N. (1991). Toxin production by Alternaria solani and its related phytotoxicity to tomato breeding lines. Phytopathol., 81 : 1030-1033.
- Mathur, S.J., Prasad, N., Agrawat, J.M. and Chippa, H.P. (1978). Estimation of losses due to Alternaria blight of sunflower. Indian J. Mycol. & Pl. Pathol., 8 : 15.
- Mayee, C.D. and Datar, V.V. (1986). Phytopathometry, Technical Bulletin-I. Marathwada Agricultural University, Parbhani. 46.
- Morris, J.B., Yang, S.M. and Wilson, L. (1983). Reaction of Helianthus, species to plant introductions to Alternaria helianthi. Pl. Dis., 70 : 831-835.
- Reddy, P.C. and Gupta, B.M. (1977). Disease loss appraisal due to leaf blight of sunflower incited by Alternaria helianthi. Indian Phytopathol., 30 : 569-570.
- Robeson, D.J. and Strobel, G.A. (1985). Deoxyradicinin and 3epideoxyradicinol production by the sunflower pathogen Alternaria helianthi. Ann. Applied Biol., 107 : 409-415.
- Sadasivan, T.S. and Manickam, A. (1996). Biochemical methods for agricultural sciences. 2nd ed. Wiley Estern Limited. New Delhi and Coimbatore. 126.
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- Hybrid Evolutionary Algorithms for Frequency and Voltage Control in Power Generating System
Abstract Views :139 |
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Authors
Affiliations
1 Department of Information Technology, PSG College of Technology, Tamil Nadu, IN
2 Department of Electrical and Electronics Engineering, PSG College of Technology, Tamil Nadu, IN
1 Department of Information Technology, PSG College of Technology, Tamil Nadu, IN
2 Department of Electrical and Electronics Engineering, PSG College of Technology, Tamil Nadu, IN
Source
ICTACT Journal on Soft Computing, Vol 1, No 2 (2010), Pagination: 88-97Abstract
Power generating system has the responsibility to ensure that adequate power is delivered to the load, both reliably and economically. Any electrical system must be maintained at the desired operating level characterized by nominal frequency and voltage profile. But the ability of the power system to track the load is limited due to physical and technical consideration. Hence, a Power System Control is required to maintain a continuous balance between power generation and load demand. The quality of power supply is affected due to continuous and random changes in load during the operation of the power system. Load Frequency Controller (LFC) and Automatic Voltage Regulator (AVR) play an important role in maintaining constant frequency and voltage in order to ensure the reliability of electric power. The fixed gain PID controllers used for this application fail to perform under varying load conditions and hence provide poor dynamic characteristics with large settling time, overshoot and oscillations. In this paper, Evolutionary Algorithms (EA) like, Enhanced Particle Swarm Optimization (EPSO), Multi Objective Particle Swarm Optimization (MOPSO), and Stochastic Particle Swarm Optimization (SPSO) are proposed to overcome the premature convergence problem in a standard PSO. These algorithms reduce transient oscillations and also increase the computational efficiency. Simulation results demonstrate that the proposed controller adapt themselves appropriate to varying loads and hence provide better performance characteristics with respect to settling time, oscillations and overshoot.Keywords
Load Frequency Control (LFC), Automatic Voltage Regulator (AVR), Evolutionary Algorithm (EA), Enhanced Particle Swarm Optimization (EPSO), Multi Objective Particle Swarm Optimization (MOPSO), and Stochastic Particle Swarm Optimization (SPSO).- Implementation of Genetic Algorithm for a DWT Based Image Watermarking Scheme
Abstract Views :147 |
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Authors
P. Surekha
1,
S. Sumathi
1
Affiliations
1 Department of Electrical and Electronics Engineering, PSG College of Technology, Tamil Nadu, IN
1 Department of Electrical and Electronics Engineering, PSG College of Technology, Tamil Nadu, IN
Source
ICTACT Journal on Soft Computing, Vol 2, No 1 (2011), Pagination: 244-252Abstract
This paper proposes a new optimization method for digital images in the Discrete Wavelet Transform (DWT) domain. Digital image watermarking has proved its efficiency in protecting illegal authentication of data. The amplification factor of the watermark is the significant parameter that helps in improving the perceptual transparency and robustness against attacks. The tradeoff between the transparency and robustness is considered as an optimization problem and is solved by applying Genetic Algorithm. The experimental results of this approach prove to be secure and robust to filtering attacks, additive noise, rotation, scaling, cropping and JPEG compression. The Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and computational time are evaluated for a set of images obtained from the Tampere University of Technology, Finland using the MATLAB R2008b software.Keywords
DWT, Genetic Algorithm, Robustness, Transparency, PSNR, MSE, Computational Time.- Phytochemical Studies and in Vitro Anti-Inflammatory Activity of Melia azedarach (L) Flower
Abstract Views :158 |
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Authors
Affiliations
1 Department of Biochemistry, Dharamapuram Gnanambigai Govt. Arts College, Mayiladuthurai 609001, IN
1 Department of Biochemistry, Dharamapuram Gnanambigai Govt. Arts College, Mayiladuthurai 609001, IN
Source
Research Journal of Pharmacognosy and Phytochemistry, Vol 6, No 1 (2014), Pagination: 19-21Abstract
Physiochemical parameters such as colour, taste, odour, ash values, moisture content were determined. Carbohydrate and protein content were also determined. Various colour change were observed when treated with different chemical reagents and Preliminary phytochemical screening showed the presence of alkaloids, flavonoids, terpenoids, anthraquinonens, tannins, saponins and cardic glycosides. The aqueous extracts of the flower of Melia azedarach (L) were studied for in vitro anti-inflammatory activity by HRBC membrane stabilization method. The flower extract exhibited membrane stabilization effect by inhibiting hypotonicity induced lysis of erythrocyte membrane. The aqueous extract shows significant anti inflammatory activity at the concentration of 800 μg/ml which is comparable to the standard drug (Diclofenac sodium 200μg/ml)The anti inflammatory activity of the extracts were concentration dependent, with the increasing concentration the activity is also increased.Keywords
Phytochemicals, Melia azedarach, Anti Inflammatory.- Sentiment Analysis and Aspect Classification on Hotel Reviews
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Data Mining and Knowledge Engineering, Vol 10, No 7 (2018), Pagination: 156-159Abstract
Analyzing the sentiment such as attitude, emotion and opinion will help to know about the individual opinion or perspective regarding a product or service. Sentiment analysis can be viewed as natural language processing task that aims to process and analyse the opinions, emotions and sentiments which are expressed in unstructured data. Sentiment analysis on English language has become important field of research area with many commercial applications such as hotels reviews, movie reviews, etc.
Hotels can be benefitted by using this sentiment analysis on hotel reviews to know whether they meet the customer requirement or not. Further aspect classification helps them know much deeper about the quality and service of the hotel. For the aspect classification purpose a machine learning model is built using suitable algorithm. The ultimate focus of this paper is to find the algorithm which gives best accuracy for our dataset.
Keywords
Sentiment Analysis, Aspect Classification, Polarity.- Brain Tumour Segmentation Strategies Utilizing Mean Shift Clustering and Content based Active Contour Segmentation
Abstract Views :200 |
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Authors
Affiliations
1 Department of Electrical Engineering, PSG College of Technology, IN
1 Department of Electrical Engineering, PSG College of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 4 (2019), Pagination: 2002-2008Abstract
This paper proposes an automatic brain tumor segmentation using Mean shift clustering and content based active contour segmentation. In diagnosis of the disease medical imaging has more advantages. Many people suffer from brain tumor, it is a serious and dangerous disease. A brain tumor occurs when abnormal cells form in the brain. A proper diagnosis of brain tumor is provided by the medical imaging. The detection of tumor from brain is an important and difficult task in the medical field. One essential part in detecting the tumor is image segmentation. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. In view of high amount information in MRI pictures, tumor segmentation and classification are hard. The image segmentation is performed on different dataset of MRI cerebrum tumor pictures. The segmentation gives an automatic brain tumor recognition method to build the exactness, yields with decline in the analysis time. The image segmentation technique includes image acquisition, image preprocessing, denoising, and finally the feature extraction. The input image is pre-processed using wiener filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it undergoes segmentation process, where Mean Shift Clustering and Content based active segmentation techniques are used. Finally, the features are extracted from the segmented image using gray level co-occurrence matrix (GLCM). The image segmentation is implemented using MATLAB software. Finally, the tumor is segmented and energy, contrast, correlation, homogeneity is extracted, and comparison results are analyzed.Keywords
Edge Adaptive Total Variation Denoising (EATVD), Gray Level Cooccurrence Matrix (GLCM), Magnetic Resonance Imaging (MRI), Convolution Neural Network (CNN), Artificial Neural Network (ANN), Support Vector Machine (SVM).References
- Ramesh Babu Vallabhaneni and V. Rajesh, “Brain Tumor Detection using Mean Shift Clustering and GLCM Features with Edge Adaptive Total Variation Denoising Technique”, Alexandria Engineering Journal, Vol. 57, No. 4, pp. 2387-2392, 2018.
- T. Chithambaram and K. Perumal, “Brain tumor Segmentation using Genetic Algorithm and ANN Techniques”, Proceedings of IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, pp. 970-982, 2017.
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- P. Priyadarsni, B. Nandhini, A.R. Catherine, K. Sahana and K. Sundaravadivu, “Soft-Computing Assisted Tool to Extract Tumor Section from Brain MR Images”, Proceedings of IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, pp. 2776-2780, 2017.
- Megha Kadam and Avinash Dhole, “Brain Tumor Detection using GLCM with the Help of KSVM”, International Journal of Engineering and Technical Research, Vol. 7, No. 2, pp. 2454-4698, 2017.
- G.B. Praveen and Anita Agrawal, “Multistage Classification and Segmentation of Brain Tumor”, IEEE International Conference on Computing for Sustainable Global Development, pp. 1628-1632, 2016.
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- K. Machhale, H.B. Nandpuru, V. Kapur and L. Kosta, “MRI Brain Cancer Classification using Hybrid Classifier (SVM-KNN)”, Proceedings of International Conference on Industrial Instrumentation and Control, pp. 60-65, 2015.
- P. Sangeetha, “Brain Tumor Classification using PNN and Clustering”, Proceedings of International Conference on Innovations in Engineering and Technology, Vol .3, No. 3, pp. 796-803, 2014.
- Snehal Basutkar, Aparna Davkhar, Bharat Mahajan and Moresh Mukhedkar, “Brain Tumor Detection using Segmentation”, International Journal of Advance Engineering and Research Development, Vol. 3, No. 3, pp. 682-687, 2016.
- D. Haritha, “Brain Tumor Segmentation”, International Journal of Advanced Technology in Engineering and Science, Vol. 4, No. 3, pp. 265-270, 2016.
- Shubhangi S. Veer and Pradeep M. Patil, “Brain Tumor Segmentation using GLCM”, International Journal of Emerging Technologies and Engineering, Vol. 2, No. 9, pp. 131-135, 2015.
- Nameirakpam Dhanachandra, Khumanthem Manglem and Yambem JinaChanu, “Image Segmentation using K-means Clustering Algorithm and Subtractive Clustering Algorithm”, Proceedings of 11th International MultiConference on Information Processing, pp. 764-771, 2015.
- Shweta Jain, “Brain Cancer Classification using GLCM Based Feature Extraction in Artificial Neural Network”, International Journal of Computer Science and Engineering Technology, Vol. 4, No. 7, pp. 966-970, 2013.
- S. Goswami and L.K.P. Bhaiya, “A Hybrid Neuro-Fuzzy Approach for Brain Abnormality Detection using GLCM Based Feature Extraction”, Proceedings of International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, pp. 1-7, 2013.
- A.R. Mathew, P.B. Anto and N.K. Thara, “Brain Tumor Segmentation and Classification using DWT, Gabour Wavelet and GLCM”, Proceedings of International Conference on Intelligent Computing, Instrumentation and Control Technologies, pp. 1744-1750, 2017.
- W. Xu, X. Yue, Y. Chen and M. Reformat, “Ensemble of Active Contour-based Image Segmentation”, Proceedings of IEEE International Conference on Image Processing, pp. 86-90, 2017.
- Q. Sun and H. Tian, “Interactive Image Segmentation using Power Watershed and Active Contour Model”, Proceedings of 3rd IEEE International Conference on Network Infrastructure and Digital Content, pp. 401-405, 2012.
- Improvement on the Characteristics of Transformer Oil using Nanofluids
Abstract Views :230 |
PDF Views:96
Authors
S. Sumathi
1,
R. Rajesh
2
Affiliations
1 Department of Electrical and Electronics Engineering, Anna University, Regional Campus, Coimbatore 641 046, IN
2 Department of Instrumentation and Control Engineering, Sri Krishna College of Technology, Coimbatore 641 042, IN
1 Department of Electrical and Electronics Engineering, Anna University, Regional Campus, Coimbatore 641 046, IN
2 Department of Instrumentation and Control Engineering, Sri Krishna College of Technology, Coimbatore 641 042, IN
Source
Current Science, Vol 118, No 1 (2020), Pagination: 29-33Abstract
Transformers play an important role in transmission and distribution systems. Even to this day, 75% of high-voltage transformer failure is the outcome of improper dielectric insulation. The reliable operation and ageing characteristics of the transformers mainly depend on the insulation material. Mineral oil has been used as an insulation and coolant for almost a century in power transformers. Due to the development of extra high voltage and to cope with the increasing demand in voltage level, a nanofluids-based transformer oil is proposed here. In this work, nanoparticles such as aluminium oxide, molybdenum disulphide and titanium dioxide are used with transformer oil to analyse the various critical characteristics like dielectric strength, acidity, interfacial tension, viscosity, flash point and fire point of the power transformer. The observed results show that the proposed nanofluids-based transformer oil provides better performance than the normal transformer oil.Full Text
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- Performance Analysis of SVM and Deep Learning with CNN for Brain Tumor Detection and Classification
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Authors
Affiliations
1 Department of Electrical and Electronics Engineering, PSG College of Technology, IN
1 Department of Electrical and Electronics Engineering, PSG College of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 3 (2020), Pagination: 2145-2152Abstract
A brain tumor occurs when abnormal cells form within the brain. In diagnosis of the disease medical imaging has many advantages. Many people suffer from brain tumor, it is a serious and dangerous disease. A proper diagnosis of brain tumor is provided by the medical imaging. The detection and classification of tumor from brain is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. Tumor detection and classification are very hard because of high quantity of data in MRI images. One essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision, yields with decrease in the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification include image acquisition, image preprocessing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using wiener filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co-occurrence Matrix (GLCM) features are used. In the last stage images are classified either as tumorous or non-tumorous. Classification is done using Support Vector Machine (SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy of these classifier, CNN would provide high accuracy. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using PSGIMSR (PSG Hospitals, Coimbatore) dataset and implemented using MATLAB software.Keywords
Wiener filter, Edge Adaptive Total Variation Denoising, Gray Level Co-occurrence Matrix, Support Vector Machine, Convolutional Neural Network.References
- Ramesh Babu Vallabhaneni and V. Rajesh, “Brain Tumor Detection using Mean Shift Clustering and GLCM Features with Edge Adaptive Total Variation Denoising Technique”, ARPN Journal of Engineering and Applied Sciences, Vol. 12, No. 3, pp. 666-671, 2018.
- T. Chithambaram and K. Perumal, “Brain Tumor Segmentation using Genetic Algorithm and ANN Techniques”, Proceedings of IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, pp. 1-6, 2018.
- T. Chithambaram and K. Perumal, “Brain Tumor Detection and Segmentation in MRI Images using Neural Network”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 7, No 3, pp. 1-12, 2017, pp. 155-164.
- Megha Kadam and Avinash Dhole, “Brain Tumor Detection using GLCM with the help of KSVM”, International Journal of Engineering and Technical Research, Vol.7, No. 2, pp. 31-39, 2017.
- G.B. Praveen and Anita Agrawal, “Multistage Classification and Segmentation of Brain Tumor”, Proceedings of IEEE International Conference on Computing for Sustainable Global Development, pp. 1-7, 2016.
- D. Haritha, “Brain Tumour Segmentation”, International Journal of Advanced Technology in Engineering and Science, Vol. 4, No. 3, pp. 265-270, 2016.
- S. Goswami and L.K.P. Bhaiya, “A Hybrid Neuro-Fuzzy Approach for Brain Abnormality Detection using GLCM Based Feature Extraction”, Proceedings of International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, pp. 1-7, 2013.
- A.R. Matthew, A. Prasad and P.B. Anto, “A Review on Feature Extraction Techniques for Tumour Detection and Classification from Brain MRI”, Proceedings of International Conference on Intelligent Computing, Instrumentation and Control Technologies, pp. 1766-1771, 2017.
- W. Xu, X. Yue, Y. Chen and M. Reformat, “Ensemble of Active Contour based Image Segmentation”, Proceedings of IEEE International Conference on Image Processing, pp. 86-90, 2017.
- D.R. Byreddy and M. Raghunadh, “An Application of Geometric Active Contour in Bio-Medical Engineering”, Proceedings of International Conference on Circuits, Systems, Communication and Information Technology Applications, pp. 322-326, 2014.
- Ruchi D. Deshmukh and Chaya Jadhav, “Study of Different Brain Tumor MRI Image Segmentation Techniques”, International Journal of Computer Science Engineering and Technology, Vol. 4, No. 4, pp. 133-136, 2014.
- A.R. Kavitha, L. Chitra and R. Kanaga, “Brain Tumor Segmentation using Genetic Algorithm with SVM Classifier”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 5, No. 3, pp. 1468-1471, 2016.
- R.J. Ramteke and Y. Khachane Monali, “Automatic Medical Image Classification and Abnormality Detection Using K-Nearest Neighbor”, International Journal of Advanced Computer Research, Vol. 2, No. 4, pp. 190-196, 2012.
- Aurav Gupta and Vinay Singh, “Brain Tumor Segmentation and Classification using FCM and Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 5, pp. 792-796, 2017.
- N. Vani, A. Sowmya and N. Jayamma, “Brain Tumor Classification using Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 7, pp. 1724-1729, 2017.
- S.U Aswathy, G. Glan Devadhas and S.S. Kumar, “MRI Brain Tumor Segmentation using Genetic Algorithm with SVM Classifier”, Proceedings of National Symposium on Antenna Signal Processing and Interdisciplinary Research, pp. 22-26, 2017.
- Narmada M. Balasooriya and Ruwan D. Nawarathna, “A Sophisticated Convolutional Neural Network Model for Brain Tumor Classification”, Proceedings of IEEE International Conference on Industrial and Information Systems, pp. 1-5, 2017.
- P. Chinmayi and L. Agilandeeswari, “An Efficient Deep Learning Neural Network based Brain Tumor Detection System”, International Journal of Pure and Applied Mathematics, Vol. 117, No. 17, pp. 1-12, 2017.
- Ketan Machhale and Hari Babu Nandpuru, “MRI Brain Cancer Classification using Hybrid Classifier (SVM-KNN)”, Proceedings of International Conference on Industrial Instrumentation and Control, pp. 1-7, 2015.
- M.F.B. Othman, N.B. Abdullah and N.F.B. Kamal, “MRI Brain Classification using Support Vector Machine”, Proceedings of 4th International Conference on Modelling, Simulation and Applied Optimization, pp. 1-4, 2011.
- Comparative Analysis of Genetic Algorithm - Support Vector Machine and Deep Learning with Convolutional Neural Network for Brain Tumor Detection and Classification
Abstract Views :151 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, PSG College of Technology, IN
1 Department of Electrical and Electronics Engineering, PSG College of Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 4 (2020), Pagination: 2159-2168Abstract
A brain tumor occurs when abnormal cells form within the brain. Many people suffer from brain tumor, and it is a serious and dangerous disease. The detection and classification of brain tumor is an important and difficult task in the medical field. The brain tumor detection technique in the MRI images is very significant in many symptomatic and cure applications. But, as the data in MRI images are of high-quality, tumor detection and classification are very hard in this process. Medical imaging plays a major role in properly diagnosing the disease, wherein an essential part in detecting the tumor is image segmentation. The segmentation provides an automatic brain tumor detection technique in order to increase the precision of the yields while also decreasing the diagnosis time. The goal is to detect the tumor from the MRI images and extract the features from the segmented tumor and finally classify it. The image detection and classification process includes image acquisition, image pre-processing, denoising, image segmentation, feature extraction and classification. The input image is pre-processed using Weiner and median filtering and the noise is removed using Edge Adaptive Total Variation Denoising (EATVD) technique. Once the noise is removed from the image, it is used for segmentation process, where Mean Shift Clustering is used. The segmented tumor undergoes features extraction stage, where Gray Level Co-occurrence Matrix (GLCM) features are used. In the last stage, images are classified either as tumorous or non-tumorous. Classification is done using Genetic Algorithm Support Vector Machine (GA-SVM), Deep Learning with Convolutional Neural Network (CNN). Early detection of the tumor region can be achieved without much time lapse in the calculation by using this efficient classifier model. This system presents a prototype for detecting objects based on GA-SVM that classifies images and assesses whether the image is cancerous. While comparing the accuracy and computational time of these classifiers, CNN would provide high accuracy and GA-SVM with lesser simulation time. The simulation results obtained for brain tumor detection and analysis are done with minimum computational time and with reasonable accuracy. This proposed system is tested using SPL dataset, which consists of 20 cases with 40 image samples of T2 FLAIR weighted MRI image and implemented using MATLAB software.Keywords
Wiener filter, Edge Adaptive Total Variation Denoising, Gray Level Co-occurrence Matrix, Genetic Algorithm Support Vector Machine, Convolutional Neural Network.References
- Ramesh Babu Vallabhaneni and V. Rajesh, “Brain Tumor Detection using Mean Shift Clustering and GLCM Features with Edge Adaptive Total Variation Denoising Technique”, Alexandria Engineering Journal, Vol. 57, No. 4, pp. 2387-2392, 2018.
- T. Chithambaram and K. Perumal, “Brain Tumor Segmentation using Genetic Algorithm and ANN Techniques”, Proceedings of IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, pp. 1-7, 2018.
- T. Chithambaram and K. Perumal, “Brain Tumor Detection and Segmentation in MRI Images using Neural Network”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 7, No 3, pp. 155-164, 2017.
- Megha Kadam and Avinash Dhole, “Brain Tumor Detection using GLCM with the Help of KSVM”, International Journal of Engineering and Technical Research, Vol. 7, No. 2, pp. 1-12, 2017.
- G.B. Praveen and Anita Agrawal, “Multistage Classification and Segmentation of Brain Tumor”, Proceedings of IEEE International Conference on Computing for Sustainable Global Development, pp. 132-138, 2016.
- D. Haritha, “Brain Tumour Segmentation”, International Journal of Advanced Technology in Engineering and Science, Vol. 4, No. 3, pp. 265-270, 2016.
- S. Goswami and L.K.P. Bhaiya, “A Hybrid Neuro-Fuzzy Approach for Brain Abnormality Detection using GLCM based Feature Extraction”, Proceedings of International Conference on Emerging Trends in Communication, Control, Signal Processing and Computing Applications, pp. 1-7, 2013.
- A.R. Matthew, A. Prasad and P.B. Anto, “A Review on Feature Extraction Techniques for Tumour Detection and Classification from Brain MRI”, Proceedings of International Conference on Intelligent Computing, Instrumentation and Control Technologies, pp. 1766-1771, 2017.
- W. Xu, X. Yue, Y. Chen and M. Reformat, “Ensemble of Active Contour-based Image Segmentation”, Proceedings of IEEE International Conference on Image Processing, pp. 86-90, 2017.
- D.R. Byreddy and M. Raghunadh, “An Application of Geometric Active Contour in Bio-Medical Engineering”, Proceedings of International Conference on Circuits, Systems, Communication and Information Technology Applications, pp. 322-326, 2014.
- Ruchi D. Deshmukh and Prof. Chaya Jadhav, “Study of Different Brain Tumor MRI Image Segmentation Techniques”, International Journal of Computer Science Engineering and Technology, Vol. 4, No. 4, pp. 133-136, 2014.
- A.R. Kavitha, L. Chitra and R. Kanaga, “Brain Tumor Segmentation using Genetic Algorithm with SVM Classifier”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 5, No. 3, pp. 1468-1471, 2016.
- R.J. Ramteke and Y. Khachane Monali, “Automatic Medical Image Classification and Abnormality Detection Using K-Nearest Neighbor”, International Journal of Advanced Computer Research, Vol. 2, No. 4, pp. 190-196, 2012.
- Aurav Gupta and Vinay Singh, “Brain Tumor Segmentation and Classification using FCM and Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 5, pp. 792-796, 2017.
- N. Vani, A. Sowmya and N. Jayamma, “Brain Tumor Classification using Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 7, pp. 1724-1729, 2017.
- S.U. Aswathy, G. Glan Devadhas and S.S. Kumar, “MRI Brain Tumor Segmentation using Genetic Algorithm with SVM Classifier”, Proceedings of National Symposium on Antenna Signal Processing and Interdisciplinary Research, pp. 22-26, 2017.
- Narmada M. Balasooriya and Ruwan D. Nawarathna, “A Sophisticated Convolutional Neural Network Model for Brain Tumor Classification”, Proceedings of IEEE International Conference on Industrial and Information Systems, pp. 1-12, 2017.
- P. Chinmayi and L. Agilandeeswari, M. Prabhukumar and K. Muralibabu, “An Efficient Deep Learning Neural Network based Brain Tumor Detection System”, International Journal of Pure and Applied Mathematics, Vol. 117, No. 17, pp. 151-160, 2017.
- Ketan Machhale, Hari Babu Nandpuru, Vivek Kapur and Laxmi Kosta, “MRI Brain Cancer Classification using Hybrid Classifier (SVM-KNN)”, Proceedings of International Conference on Industrial Instrumentation and Control, pp. 1-9, 2015.
- M.F.B. Othman, N.B. Abdullah and N.F.B. Kamal, “MRI Brain Classification using Support Vector Machine”, Proceedings of 4th International Conference on Modeling, Simulation and Applied Optimization, pp. 1-4, 2011.
- V.K. Sachdeva, I. Gupta, N. Khandelwal and C.K. Ahuja, “Multiclass Brain Tumor Classification using GA-SVM”, Proceedings of International Conference on Developments in E-Systems Engineering, pp. 182-187, 2011.
- Ahmed Kharrat, Karim Gasmi , Mohamed Ben Messaoud, Nacera Benamrane and Abid Mohamed, “A Hybrid Approach for Automatic Classification of Brain MRI using Genetic Algorithm and Support Vector Machine”, Leonardo Journal of Sciences, Vol. 17, pp. 71-82, 2010.
- A Comprehensive Review on Role of Nutrition in Management of Breast Cancer
Abstract Views :78 |
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Authors
K. Suganya
1,
S. Sumathi
1
Affiliations
1 Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore - 641 043, Tamil Nadu, IN
1 Department of Biochemistry, Biotechnology and Bioinformatics, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore - 641 043, Tamil Nadu, IN
Source
The Indian Journal of Nutrition and Dietetics, Vol 59, No 4 (2022), Pagination: 506-523Abstract
Breast cancer (BC) is the most frequent malignancy in women and the second most common cause of cancer worldwide. There’s a lot of evidence that lifestyle factors including food, body weight, and physical activity are linked to a higher risk of breast cancer. Several bioactive food ingredients, including both essential and non-essential nutrients, can change gene expression profiles. Consequently, nutrigenomics provides information on the effects of consumed nutrients and other food components on gene regulation and transcription factors, i.e., diet-gene interaction, to find dietetic components that are beneficial or damaging to one’s health. Biological processes such as epigenetics, transcriptomics, and proteomics influence nutritional genomics (nutrigenomics), which is the junction of health, food, and genomics. As a result, it will help to determine unique nutritional requirements based on a person’s genetic composition (personalized diet), and also the link between diet and chronic diseases such as cancer, opening up new avenues for a better understanding of the impacts of breast cancer and its management. Chemotherapy or radiotherapy patients with BC experience a variety of symptoms that influence their quality of life. According to research studies on nutritional therapy during BC treatment, nutritional counseling and supplementation with certain dietary elements may be useful in reducing drug-induced side effects and increasing therapeutic efficacy. As a result, nutritional control in BC patients may be considered a critical component of a multimodal treatment strategy. The goal of this review is to give a summary of the existing research on the association between dietary variables and BC.Keywords
Breast Cancer, Lifestyle, Diet, Nutrigenomics, Personalized MedicineReferences
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