Frequent Item mining is the significance of data mining techniques to define patterns from the Bi... more Frequent Item mining is the significance of data mining techniques to define patterns from the Big datasets. Frequent Itemset Mining is one of the predictable data mining problems in most of the data mining applications. It comprises very large reckonings and Input/output traffic capacity. Also resources like single processor’s memory and CPU are very incomplete, which lowers the functioning of algorithm. In this exploration broadsheet aims to present a EFPGSID (Enhanced Frequent Pattern Growth Skewed intermediate data blocks), a parallel Frequent item mining algorithm based on the Spark RDD (Resilient Distributed Datasets) framework—a specially-considered in-memory parallel computing framework to backing load blanching algorithms and interactive data mining. The outcomes shown that the performance of the new system is effective compared with other FiDoop-DP mining algorithms. As the Investigational results show, Enhanced-FP-Growth with load balancing strategy clearly outperforms FP...
ABSTRACTIn many practical applications like biometrics, video surveillance and human computer int... more ABSTRACTIn many practical applications like biometrics, video surveillance and human computer interaction, face recognition plays a major role. The previous works focused on recognizing and enhancing the biometric systems based on the facial components of the system. In this work, we are going to build Integrated Expressional and Color Invariant Facial Recognition scheme for human biometric recognition suited to different security provisioning public participation areas.At first, the features of the face are identified and processed using bayes classifier with RGB and HSV color bands. Second, psychological emotional variance are identified and linked with the respective human facial expression based on the facial action code system. Finally, an integrated expressional and color invariant facial recognition is proposed for varied conditions of illumination, pose, transformation, etc. These conditions on color invariant model are suited to easy and more efficient biometric recognition...
Artificial Intelligent Systems and Machine Learning, 2013
Image segmentation is one of the key techniques in image understanding and computer vision. The w... more Image segmentation is one of the key techniques in image understanding and computer vision. The work of image segmentation is to divide an image into a number of non-overlapping regions, which have same characteristics such as gray level, color, tone, texture, etc. A various clustering based methods have been proposed for image segmentation. This paper discusses about categories of Fuzzy clustering algorithm and its problem in each algorithm. The proposed system present an improved Fuzzy C-means (FCM) algorithm for image segmentation by introducing a trade-off weighted fuzzy factor and a kernel metric. The trade-off weighted fuzzy factor depends on the both space distance of all neighboring pixels and their gray-level difference. This new algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhancement its robustness to noise and outliers, this new system introduce a kernel distance measure to its objective function. The new algorithm deter...
Face recognition plays an important vision task having many practical applications such as biomet... more Face recognition plays an important vision task having many practical applications such as biometrics, video surveillance, image retrieval, and human computer interaction. Most recently facial expression recognition has been focused for biometric facial recognition system in various confidential and high secured operational areas. Information for biometric representation and recognition are available in image space, scale and orientation. Combinatorial analysis of space, scale and orientation provide enriched features for more accurate biometric facial recognition. Position, spatial frequency and orientation selectivity properties of facial feature components play major role in visual perception. There are various methods have discussed for Facial Expression Recognition scheme for human biometric recognition. In this work we analyze current Facial Recognition schemes and provide an overview of the emerging Facial Expression Recognition methods and related research work done in this ...
Segmentation is the process of partitioning a digital image into multiple segments. Image segment... more Segmentation is the process of partitioning a digital image into multiple segments. Image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. According to the advanced medical pictures are not invariably delineated victimisation the constant quantity technique with previous likelihood, resulting in the distinction between the particular physical model and also the basic hypothesis of the model, specifically the matter of “model mismatch”, the strategy of medical image segmentation supported the multi-modal operate optimisation is projected during this paper. It projected a density model of the statistic orthogonal polynomials for image knowledge, the novel Particle Swarm Optimisation (PSO) technique is employed to resolve the multi-modal operate optimisation downside. On the idea of the heuristic optimisation search, the novel technique was prospering in multi-modal operate optimisatio...
Data Mining is the extraction of hidden predictive information from large databases. Clustering i... more Data Mining is the extraction of hidden predictive information from large databases. Clustering is one of the popular data mining techniques. Clustering on uncertain data, one of the essential tasks in mining uncertain data, posts significant challenges on both modeling similarity between uncertain objects and developing efficient computational methods. The previous methods extend traditional partitioning clustering methods. Such methods cannot handle uncertain objects that are geometrically indistinguishable, such as products with the same mean but very different variances in customer ratings. Surprisingly, probability distributions, which are essential characteristics of uncertain objects, have not been considered in measuring similarity between uncertain objects. In Existing method to use the well-known Kullback-Leibler divergence to measure similarity between uncertain objects in both the continuous and discrete cases, and integrate it into partitioning and density-based cluster...
Standardization of the crop regulation practices is vital for spike and corm yield maximization i... more Standardization of the crop regulation practices is vital for spike and corm yield maximization in gladiolus (Gladiolus hybrids Hort.). Hence, a field experiment was conducted in factorial randomized block design with three replications at Annamalai University, Annamalai Nagar, during 2019 with the objective to maximize the spike and corm yield of gladiolus by growth regulators and leaf regulation practices done after spike harvest. The gladiolus cv. Sarala was tested with 15 treatment combinations comprising growth regulator treatments of corms viz., 100ppm GA3(G1), 150ppm GA3(G2), 100ppm IAA(G3), 150ppm IAA (G4), and Control(G5)and leaf regulation practicesviz.,harvesting spike leaving all leaves (L1), harvesting spike with three leaves (L2), and clipping leaves at 20cm above the base (L3). The results revealed that the growth regulator treatments, given to corms significantly influenced the growth, flowering, and spike yield of gladiolus. Corm soaking treatment of GA3@150 ppm evi...
Frequent pattern mining has been an important subject matter in data mining from many years. Many... more Frequent pattern mining has been an important subject matter in data mining from many years. Many efficient algorithms have been designed for finding frequent search patterns in transactional database .Discovering frequent itemsets is the computationally intensive step in the task of mining association rules. A large number of candidate itemsets generation is one of the main challenge in mining. The objective of frequent pattern mining is to find frequently appearing subsets in a given sequence of sets. Frequent pattern mining comes across as a sub-problem in various other fields of data mining such as association rules discovery, classification, market analysis, clustering, web mining, etc. Various methods and algorithms have been proposed for mining frequent pattern.This paper presents comparative study on frequent mining techniques – Apriori and FP-Growth. [2]
Interest and examining activities in habitual face recognition have increased drastically over th... more Interest and examining activities in habitual face recognition have increased drastically over the pa st few years. Faces represent composite, multi-dimensional, signi fica t visual motivation and mounting a computation al model for face recognition. For most of the face recognition techn iques, solution depends on the feature extraction r epresentation and matching. These lessons are summarized by reflectin g the facial expression recognition in general and typically, lack in providing the particular aspect with minimal cost. This, in turn, developed a technique named Color Co mponent Feature Identification using the Bayes Classifier. The mode l is associated with RGB and HSV color bands along with its corresponding facial feature components. Performanc e of Color Component Feature Identification using t he Bayesian Classifier (CCFI-BC) technique reliably segments th e facial color depending on the texture and identif ies the features. These regions are further combined with RGB an...
Frequent Item mining is the significance of data mining techniques to define patterns from the Bi... more Frequent Item mining is the significance of data mining techniques to define patterns from the Big datasets. Frequent Itemset Mining is one of the predictable data mining problems in most of the data mining applications. It comprises very large reckonings and Input/output traffic capacity. Also resources like single processor’s memory and CPU are very incomplete, which lowers the functioning of algorithm. In this exploration broadsheet aims to present a EFPGSID (Enhanced Frequent Pattern Growth Skewed intermediate data blocks), a parallel Frequent item mining algorithm based on the Spark RDD (Resilient Distributed Datasets) framework—a specially-considered in-memory parallel computing framework to backing load blanching algorithms and interactive data mining. The outcomes shown that the performance of the new system is effective compared with other FiDoop-DP mining algorithms. As the Investigational results show, Enhanced-FP-Growth with load balancing strategy clearly outperforms FP...
ABSTRACTIn many practical applications like biometrics, video surveillance and human computer int... more ABSTRACTIn many practical applications like biometrics, video surveillance and human computer interaction, face recognition plays a major role. The previous works focused on recognizing and enhancing the biometric systems based on the facial components of the system. In this work, we are going to build Integrated Expressional and Color Invariant Facial Recognition scheme for human biometric recognition suited to different security provisioning public participation areas.At first, the features of the face are identified and processed using bayes classifier with RGB and HSV color bands. Second, psychological emotional variance are identified and linked with the respective human facial expression based on the facial action code system. Finally, an integrated expressional and color invariant facial recognition is proposed for varied conditions of illumination, pose, transformation, etc. These conditions on color invariant model are suited to easy and more efficient biometric recognition...
Artificial Intelligent Systems and Machine Learning, 2013
Image segmentation is one of the key techniques in image understanding and computer vision. The w... more Image segmentation is one of the key techniques in image understanding and computer vision. The work of image segmentation is to divide an image into a number of non-overlapping regions, which have same characteristics such as gray level, color, tone, texture, etc. A various clustering based methods have been proposed for image segmentation. This paper discusses about categories of Fuzzy clustering algorithm and its problem in each algorithm. The proposed system present an improved Fuzzy C-means (FCM) algorithm for image segmentation by introducing a trade-off weighted fuzzy factor and a kernel metric. The trade-off weighted fuzzy factor depends on the both space distance of all neighboring pixels and their gray-level difference. This new algorithm can accurately estimate the damping extent of neighboring pixels. In order to further enhancement its robustness to noise and outliers, this new system introduce a kernel distance measure to its objective function. The new algorithm deter...
Face recognition plays an important vision task having many practical applications such as biomet... more Face recognition plays an important vision task having many practical applications such as biometrics, video surveillance, image retrieval, and human computer interaction. Most recently facial expression recognition has been focused for biometric facial recognition system in various confidential and high secured operational areas. Information for biometric representation and recognition are available in image space, scale and orientation. Combinatorial analysis of space, scale and orientation provide enriched features for more accurate biometric facial recognition. Position, spatial frequency and orientation selectivity properties of facial feature components play major role in visual perception. There are various methods have discussed for Facial Expression Recognition scheme for human biometric recognition. In this work we analyze current Facial Recognition schemes and provide an overview of the emerging Facial Expression Recognition methods and related research work done in this ...
Segmentation is the process of partitioning a digital image into multiple segments. Image segment... more Segmentation is the process of partitioning a digital image into multiple segments. Image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain visual characteristics. According to the advanced medical pictures are not invariably delineated victimisation the constant quantity technique with previous likelihood, resulting in the distinction between the particular physical model and also the basic hypothesis of the model, specifically the matter of “model mismatch”, the strategy of medical image segmentation supported the multi-modal operate optimisation is projected during this paper. It projected a density model of the statistic orthogonal polynomials for image knowledge, the novel Particle Swarm Optimisation (PSO) technique is employed to resolve the multi-modal operate optimisation downside. On the idea of the heuristic optimisation search, the novel technique was prospering in multi-modal operate optimisatio...
Data Mining is the extraction of hidden predictive information from large databases. Clustering i... more Data Mining is the extraction of hidden predictive information from large databases. Clustering is one of the popular data mining techniques. Clustering on uncertain data, one of the essential tasks in mining uncertain data, posts significant challenges on both modeling similarity between uncertain objects and developing efficient computational methods. The previous methods extend traditional partitioning clustering methods. Such methods cannot handle uncertain objects that are geometrically indistinguishable, such as products with the same mean but very different variances in customer ratings. Surprisingly, probability distributions, which are essential characteristics of uncertain objects, have not been considered in measuring similarity between uncertain objects. In Existing method to use the well-known Kullback-Leibler divergence to measure similarity between uncertain objects in both the continuous and discrete cases, and integrate it into partitioning and density-based cluster...
Standardization of the crop regulation practices is vital for spike and corm yield maximization i... more Standardization of the crop regulation practices is vital for spike and corm yield maximization in gladiolus (Gladiolus hybrids Hort.). Hence, a field experiment was conducted in factorial randomized block design with three replications at Annamalai University, Annamalai Nagar, during 2019 with the objective to maximize the spike and corm yield of gladiolus by growth regulators and leaf regulation practices done after spike harvest. The gladiolus cv. Sarala was tested with 15 treatment combinations comprising growth regulator treatments of corms viz., 100ppm GA3(G1), 150ppm GA3(G2), 100ppm IAA(G3), 150ppm IAA (G4), and Control(G5)and leaf regulation practicesviz.,harvesting spike leaving all leaves (L1), harvesting spike with three leaves (L2), and clipping leaves at 20cm above the base (L3). The results revealed that the growth regulator treatments, given to corms significantly influenced the growth, flowering, and spike yield of gladiolus. Corm soaking treatment of GA3@150 ppm evi...
Frequent pattern mining has been an important subject matter in data mining from many years. Many... more Frequent pattern mining has been an important subject matter in data mining from many years. Many efficient algorithms have been designed for finding frequent search patterns in transactional database .Discovering frequent itemsets is the computationally intensive step in the task of mining association rules. A large number of candidate itemsets generation is one of the main challenge in mining. The objective of frequent pattern mining is to find frequently appearing subsets in a given sequence of sets. Frequent pattern mining comes across as a sub-problem in various other fields of data mining such as association rules discovery, classification, market analysis, clustering, web mining, etc. Various methods and algorithms have been proposed for mining frequent pattern.This paper presents comparative study on frequent mining techniques – Apriori and FP-Growth. [2]
Interest and examining activities in habitual face recognition have increased drastically over th... more Interest and examining activities in habitual face recognition have increased drastically over the pa st few years. Faces represent composite, multi-dimensional, signi fica t visual motivation and mounting a computation al model for face recognition. For most of the face recognition techn iques, solution depends on the feature extraction r epresentation and matching. These lessons are summarized by reflectin g the facial expression recognition in general and typically, lack in providing the particular aspect with minimal cost. This, in turn, developed a technique named Color Co mponent Feature Identification using the Bayes Classifier. The mode l is associated with RGB and HSV color bands along with its corresponding facial feature components. Performanc e of Color Component Feature Identification using t he Bayesian Classifier (CCFI-BC) technique reliably segments th e facial color depending on the texture and identif ies the features. These regions are further combined with RGB an...
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Papers by Mary Shyla