This document describes a study that uses handwriting analysis to identify psychological personality traits using support vector machines (SVM). Handwriting samples were collected and preprocessed by removing noise and segmenting lines. Features like slope, shape, and edge histograms were extracted. SVM with radial basis function kernel was used for classification. Analysis of single lines achieved 95% accuracy while multiple lines achieved 91% accuracy in identifying traits like cheerfulness and weariness. The methodology was also applied to analyze handwriting of celebrities and compare the results to analyses by graphologists. The study aims to automate handwriting analysis using machine learning techniques.
A CHINESE CHARACTER RECOGNITION METHOD BASED ON POPULATION MATRIX AND RELATIO...Teady Matius
A Chinese character has many different forms, information in its features will have many variations. Therefore, it needs a relational database to store many variations of their features. The use of the relational database to store the sets of features enables the use of distance measurements methods while measuring the sets of feature that owned by a Chinese character to recognize a Chinese character image inputted. The feature used in this thesis is the pixel population matrix. The sets of the features are stored and queried by using the relational database.
This paper discusses about how to recognize the Chinese character image and Chinese radical image by using relational database and pixel population matrix.
Textural Feature Extraction of Natural Objects for Image ClassificationCSCJournals
The field of digital image processing has been growing in scope in the recent years. A digital image is represented as a two-dimensional array of pixels, where each pixel has the intensity and location information. Analysis of digital images involves extraction of meaningful information from them, based on certain requirements. Digital Image Analysis requires the extraction of features, transforms the data in the high-dimensional space to a space of fewer dimensions. Feature vectors are n-dimensional vectors of numerical features used to represent an object. We have used Haralick features to classify various images using different classification algorithms like Support Vector Machines (SVM), Logistic Classifier, Random Forests Multi Layer Perception and Naïve Bayes Classifier. Then we used cross validation to assess how well a classifier works for a generalized data set, as compared to the classifications obtained during training.
Comparison on PCA ICA and LDA in Face Recognitionijdmtaiir
Face recognition is used in wide range of application.
In recent years, face recognition has become one of the most
successful applications in image analysis and understanding.
Different statistical method and research groups reported a
contradictory result when comparing principal component
analysis (PCA) algorithm, independent component analysis
(ICA) algorithm, and linear discriminant analysis (LDA)
algorithm that has been proposed in recent years. The goal of
this paper is to compare and analyze the three algorithms and
conclude which is best. Feret Dataset is used for consistency
An Experiment with Sparse Field and Localized Region Based Active Contour Int...CSCJournals
This paper discusses various experiments conducted on different types of Level Sets interactive segmentation techniques using Matlab software, on select images. The objective is to assess the effectiveness on specific natural images, which have complex image composition in terms of intensity, colour mix, indistinct object boundary, low contrast, etc. Besides visual assessment, measures such as Jaccard Index, Dice Coefficient and Hausdorrf Distance have been computed to assess the accuracy of these techniques, between segmented and ground truth images. This paper particularly discusses Sparse Field Matrix and Localized Region Based Active Contours, both based on Level Sets. These techniques were not found to be effective where object boundary is not very distinct and/or has low contrast with background. Also, the techniques were ineffective on such images where foreground object stretches up to the image boundary.
Performance Evaluation of Basic Segmented Algorithms for Brain Tumor DetectionIOSR Journals
In the field of computers segmentation of image plays a very important role. By this method the required
portion of object is traced from the image. In medical image segmentation, clustering is very famous
method . By clustering, an image is divided into a number of various groups or can also be called as clusters.
There are various methods of clustering and thresholding which have been proposed in this paper such as otsu
, region growing , K Means , fuzzy c means and Hierarchical self organizing mapping algorithm. Fuzzy c-means
(FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method
(developed by Dunn in 1973 and improved by Bezdek in 1981) is frequently used in pattern recognition. As
process of fuzzy c mean is too slow, this drawback is then removed. In this paper by experimental analysis and
performance parameters the segmentation of hierarchical self organizing mapping method is done in a better
way as compared to other algorithms. The various parameters used for the evaluation of the performance are as follows: segmentation accuracy (Sa) , area (A), rand index (Ri),and global consistency error (Gce)
A CHINESE CHARACTER RECOGNITION METHOD BASED ON POPULATION MATRIX AND RELATIO...Teady Matius
A Chinese character has many different forms, information in its features will have many variations. Therefore, it needs a relational database to store many variations of their features. The use of the relational database to store the sets of features enables the use of distance measurements methods while measuring the sets of feature that owned by a Chinese character to recognize a Chinese character image inputted. The feature used in this thesis is the pixel population matrix. The sets of the features are stored and queried by using the relational database.
This paper discusses about how to recognize the Chinese character image and Chinese radical image by using relational database and pixel population matrix.
Textural Feature Extraction of Natural Objects for Image ClassificationCSCJournals
The field of digital image processing has been growing in scope in the recent years. A digital image is represented as a two-dimensional array of pixels, where each pixel has the intensity and location information. Analysis of digital images involves extraction of meaningful information from them, based on certain requirements. Digital Image Analysis requires the extraction of features, transforms the data in the high-dimensional space to a space of fewer dimensions. Feature vectors are n-dimensional vectors of numerical features used to represent an object. We have used Haralick features to classify various images using different classification algorithms like Support Vector Machines (SVM), Logistic Classifier, Random Forests Multi Layer Perception and Naïve Bayes Classifier. Then we used cross validation to assess how well a classifier works for a generalized data set, as compared to the classifications obtained during training.
Comparison on PCA ICA and LDA in Face Recognitionijdmtaiir
Face recognition is used in wide range of application.
In recent years, face recognition has become one of the most
successful applications in image analysis and understanding.
Different statistical method and research groups reported a
contradictory result when comparing principal component
analysis (PCA) algorithm, independent component analysis
(ICA) algorithm, and linear discriminant analysis (LDA)
algorithm that has been proposed in recent years. The goal of
this paper is to compare and analyze the three algorithms and
conclude which is best. Feret Dataset is used for consistency
An Experiment with Sparse Field and Localized Region Based Active Contour Int...CSCJournals
This paper discusses various experiments conducted on different types of Level Sets interactive segmentation techniques using Matlab software, on select images. The objective is to assess the effectiveness on specific natural images, which have complex image composition in terms of intensity, colour mix, indistinct object boundary, low contrast, etc. Besides visual assessment, measures such as Jaccard Index, Dice Coefficient and Hausdorrf Distance have been computed to assess the accuracy of these techniques, between segmented and ground truth images. This paper particularly discusses Sparse Field Matrix and Localized Region Based Active Contours, both based on Level Sets. These techniques were not found to be effective where object boundary is not very distinct and/or has low contrast with background. Also, the techniques were ineffective on such images where foreground object stretches up to the image boundary.
Performance Evaluation of Basic Segmented Algorithms for Brain Tumor DetectionIOSR Journals
In the field of computers segmentation of image plays a very important role. By this method the required
portion of object is traced from the image. In medical image segmentation, clustering is very famous
method . By clustering, an image is divided into a number of various groups or can also be called as clusters.
There are various methods of clustering and thresholding which have been proposed in this paper such as otsu
, region growing , K Means , fuzzy c means and Hierarchical self organizing mapping algorithm. Fuzzy c-means
(FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method
(developed by Dunn in 1973 and improved by Bezdek in 1981) is frequently used in pattern recognition. As
process of fuzzy c mean is too slow, this drawback is then removed. In this paper by experimental analysis and
performance parameters the segmentation of hierarchical self organizing mapping method is done in a better
way as compared to other algorithms. The various parameters used for the evaluation of the performance are as follows: segmentation accuracy (Sa) , area (A), rand index (Ri),and global consistency error (Gce)
Colour Image Segmentation Using Soft Rough Fuzzy-C-Means and Multi Class SVM ijcisjournal
Color image segmentation algorithms in the literature segment an image on the basis of color, texture, and
also as a fusion of both color and texture. In this paper, a color image segmentation algorithm is proposed
by extracting both texture and color features and applying them to the One-Against-All Multi Class Support
Vector Machine classifier for segmentation. A novel Power Law Descriptor (PLD) is used for extracting
the textural features and homogeneity model is used for obtaining the color features. The Multi Class SVM
is trained using the samples obtained from Soft Rough Fuzzy-C-Means (SRFCM) clustering. Fuzzy set
based membership functions capably handle the problem of overlapping clusters. The lower and upper
approximation concepts of rough sets deal well with uncertainty, vagueness, and incompleteness in data.
Parameterization tools are not a prerequisite in defining Soft set theory. The goodness aspects of soft sets,
rough sets and fuzzy sets are incorporated in the proposed algorithm to achieve improved segmentation
performance. The Power Law Descriptor used for texture feature extraction has the advantage of being
dealt in the spatial domain thereby reducing computational complexity. The proposed algorithm is
comparable and achieved better performance compared with the state of the art algorithms found in the
literature.
Comparision of methods for combination of multiple classifiers that predict b...IJERA Editor
Predictive analysis include techniques fromdata mining that analyze current and historical data and make
predictions about the future. Predictive analytics is used in actuarial science, financial services, retail, travel,
healthcare, insurance, pharmaceuticals, marketing, telecommunications and other fields.Predicting patterns can
be considered as a classification problem and combining the different classifiers gives better results. We will
study and compare three methods used to combine multiple classifiers. Bayesian networks perform
classification based on conditional probability. It is ineffective and easy to interpret as it assumes that the
predictors are independent. Tree augmented naïve Bayes (TAN) constructs a maximum weighted spanning tree
that maximizes the likelihood of the training data, to perform classification.This tree structure eliminates the
independent attribute assumption of naïve Bayesian networks. Behavior-knowledge space method works in two
phases and can provide very good performances if large and representative data sets are available.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
A new model for iris data set classification based on linear support vector m...IJECEIAES
Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
K-MEDOIDS CLUSTERING USING PARTITIONING AROUND MEDOIDS FOR PERFORMING FACE R...ijscmc
Face recognition is one of the most unobtrusive biometric techniques that can be used for access control as well as surveillance purposes. Various methods for implementing face recognition have been proposed with varying degrees of performance in different scenarios. The most common issue with effective facial biometric systems is high susceptibility of variations in the face owing to different factors like changes in pose, varying illumination, different expression, presence of outliers, noise etc. This paper explores a novel technique for face recognition by performing classification of the face images using unsupervised learning approach through K-Medoids clustering. Partitioning Around Medoids algorithm (PAM) has been used for performing K-Medoids clustering of the data. The results are suggestive of increased robustness to noise and outliers in comparison to other clustering methods. Therefore the technique can also be used to increase the overall robustness of a face recognition system and thereby increase its invariance and make it a reliably usable biometric modality
Parametric Comparison of K-means and Adaptive K-means Clustering Performance ...IJECEIAES
Image segmentation takes a major role for analyzing the area of interest in image processing. Many researchers have used different types of techniques for analyzing the image. One of the widely used techniques is K-means clustering. In this paper we use two algorithms K-means and the advance of K-means is called as adaptive K-means clustering. Both the algorithms are using in different types of image and got a successful result. By comparing the Time period, PSNR and RMSE value from the result of both algorithms we prove that the Adaptive K-means clustering algorithm gives a best result as compard to K-means clustering in image segmentation.
Content Based Image Retrieval (CBIR) is one of the
most active in the current research field of multimedia retrieval.
It retrieves the images from the large databases based on images
feature like color, texture and shape. In this paper, Image
retrieval based on multi feature fusion is achieved by color and
texture features as well as the similarity measures are
investigated. The work of color feature extraction is obtained by
using Quadratic Distance and texture features by using Pyramid
Structure Wavelet Transforms and Gray level co-occurrence
matrix. We are comparing all these methods for best image
retrieval
Blind Image Seperation Using Forward Difference Method (FDM)sipij
In this paper, blind image separation is performed, exploiting the property of sparseness to represent images. A new sparse representation called forward difference method is proposed. It is known that most of the independent component analysis (ICA) basis functions, extracted from images are sparse and gives unreliable sparseness measure. In the proposed method, the image mixture is first transformed to sparse images. These images are divided into blocks and for each block the sparseness measure ε0 norm is applied. The block having the most sparseness is considered to determine the separation matrix. The efficiency of the proposed method is compared with other sparse representation functions.
Combined cosine-linear regression model similarity with application to handwr...IJECEIAES
Abstract: the similarity or the distance measure have been used widely to calculate the similarity or dissimilarity between vector sequences, where the document images similarity is known as the domain that dealing with image information and both similarity/distance has been an important role for matching and pattern recognition. There are several types of similarity measure, we cover in this paper the survey of various distance measures used in the images matching and we explain the limitations associated with the existing distances. Then, we introduce the concept of the floating distance which describes the variation of the threshold’s selection for each word in decision making process, based on a combination of Linear Regression and cosine distance. Experiments are carried out on a handwritten Arabic image documents of Gallica library. These experiments show that the proposed floating distance outperforms the traditional distance in word spotting system.
Offline Character Recognition Using Monte Carlo Method and Neural Networkijaia
Human Machine interface are constantly gaining improvements because of increasing development of
computer tools. Handwritten Character Recognition do have various significant applications like form
scanning, verification, validation, or checks reading. Because of the importance of these applications
passionate research in the field of Off-Line handwritten character recognition is going on. The challenge in
recognising the handwritings lies in the nature of humans, having unique styles in terms of font, contours,
etc. This paper presents a novice approach to identify the offline characters; we call it as character divider
approach which can be used after pre-processing stage. We devise an innovative approach for feature
extraction known as vector contour. We also discuss the pros and cons including limitations, of our
approach
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Colour Image Segmentation Using Soft Rough Fuzzy-C-Means and Multi Class SVM ijcisjournal
Color image segmentation algorithms in the literature segment an image on the basis of color, texture, and
also as a fusion of both color and texture. In this paper, a color image segmentation algorithm is proposed
by extracting both texture and color features and applying them to the One-Against-All Multi Class Support
Vector Machine classifier for segmentation. A novel Power Law Descriptor (PLD) is used for extracting
the textural features and homogeneity model is used for obtaining the color features. The Multi Class SVM
is trained using the samples obtained from Soft Rough Fuzzy-C-Means (SRFCM) clustering. Fuzzy set
based membership functions capably handle the problem of overlapping clusters. The lower and upper
approximation concepts of rough sets deal well with uncertainty, vagueness, and incompleteness in data.
Parameterization tools are not a prerequisite in defining Soft set theory. The goodness aspects of soft sets,
rough sets and fuzzy sets are incorporated in the proposed algorithm to achieve improved segmentation
performance. The Power Law Descriptor used for texture feature extraction has the advantage of being
dealt in the spatial domain thereby reducing computational complexity. The proposed algorithm is
comparable and achieved better performance compared with the state of the art algorithms found in the
literature.
Comparision of methods for combination of multiple classifiers that predict b...IJERA Editor
Predictive analysis include techniques fromdata mining that analyze current and historical data and make
predictions about the future. Predictive analytics is used in actuarial science, financial services, retail, travel,
healthcare, insurance, pharmaceuticals, marketing, telecommunications and other fields.Predicting patterns can
be considered as a classification problem and combining the different classifiers gives better results. We will
study and compare three methods used to combine multiple classifiers. Bayesian networks perform
classification based on conditional probability. It is ineffective and easy to interpret as it assumes that the
predictors are independent. Tree augmented naïve Bayes (TAN) constructs a maximum weighted spanning tree
that maximizes the likelihood of the training data, to perform classification.This tree structure eliminates the
independent attribute assumption of naïve Bayesian networks. Behavior-knowledge space method works in two
phases and can provide very good performances if large and representative data sets are available.
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
A new model for iris data set classification based on linear support vector m...IJECEIAES
Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.
K-MEDOIDS CLUSTERING USING PARTITIONING AROUND MEDOIDS FOR PERFORMING FACE R...ijscmc
Face recognition is one of the most unobtrusive biometric techniques that can be used for access control as well as surveillance purposes. Various methods for implementing face recognition have been proposed with varying degrees of performance in different scenarios. The most common issue with effective facial biometric systems is high susceptibility of variations in the face owing to different factors like changes in pose, varying illumination, different expression, presence of outliers, noise etc. This paper explores a novel technique for face recognition by performing classification of the face images using unsupervised learning approach through K-Medoids clustering. Partitioning Around Medoids algorithm (PAM) has been used for performing K-Medoids clustering of the data. The results are suggestive of increased robustness to noise and outliers in comparison to other clustering methods. Therefore the technique can also be used to increase the overall robustness of a face recognition system and thereby increase its invariance and make it a reliably usable biometric modality
Parametric Comparison of K-means and Adaptive K-means Clustering Performance ...IJECEIAES
Image segmentation takes a major role for analyzing the area of interest in image processing. Many researchers have used different types of techniques for analyzing the image. One of the widely used techniques is K-means clustering. In this paper we use two algorithms K-means and the advance of K-means is called as adaptive K-means clustering. Both the algorithms are using in different types of image and got a successful result. By comparing the Time period, PSNR and RMSE value from the result of both algorithms we prove that the Adaptive K-means clustering algorithm gives a best result as compard to K-means clustering in image segmentation.
Content Based Image Retrieval (CBIR) is one of the
most active in the current research field of multimedia retrieval.
It retrieves the images from the large databases based on images
feature like color, texture and shape. In this paper, Image
retrieval based on multi feature fusion is achieved by color and
texture features as well as the similarity measures are
investigated. The work of color feature extraction is obtained by
using Quadratic Distance and texture features by using Pyramid
Structure Wavelet Transforms and Gray level co-occurrence
matrix. We are comparing all these methods for best image
retrieval
Blind Image Seperation Using Forward Difference Method (FDM)sipij
In this paper, blind image separation is performed, exploiting the property of sparseness to represent images. A new sparse representation called forward difference method is proposed. It is known that most of the independent component analysis (ICA) basis functions, extracted from images are sparse and gives unreliable sparseness measure. In the proposed method, the image mixture is first transformed to sparse images. These images are divided into blocks and for each block the sparseness measure ε0 norm is applied. The block having the most sparseness is considered to determine the separation matrix. The efficiency of the proposed method is compared with other sparse representation functions.
Combined cosine-linear regression model similarity with application to handwr...IJECEIAES
Abstract: the similarity or the distance measure have been used widely to calculate the similarity or dissimilarity between vector sequences, where the document images similarity is known as the domain that dealing with image information and both similarity/distance has been an important role for matching and pattern recognition. There are several types of similarity measure, we cover in this paper the survey of various distance measures used in the images matching and we explain the limitations associated with the existing distances. Then, we introduce the concept of the floating distance which describes the variation of the threshold’s selection for each word in decision making process, based on a combination of Linear Regression and cosine distance. Experiments are carried out on a handwritten Arabic image documents of Gallica library. These experiments show that the proposed floating distance outperforms the traditional distance in word spotting system.
Offline Character Recognition Using Monte Carlo Method and Neural Networkijaia
Human Machine interface are constantly gaining improvements because of increasing development of
computer tools. Handwritten Character Recognition do have various significant applications like form
scanning, verification, validation, or checks reading. Because of the importance of these applications
passionate research in the field of Off-Line handwritten character recognition is going on. The challenge in
recognising the handwritings lies in the nature of humans, having unique styles in terms of font, contours,
etc. This paper presents a novice approach to identify the offline characters; we call it as character divider
approach which can be used after pre-processing stage. We devise an innovative approach for feature
extraction known as vector contour. We also discuss the pros and cons including limitations, of our
approach
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
MEDICAL DIAGNOSIS CLASSIFICATION USING MIGRATION BASED DIFFERENTIAL EVOLUTION...cscpconf
Constructing a classification model is important in machine learning for a particular task. A
classification process involves assigning objects into predefined groups or classes based on a
number of observed attributes related to those objects. Artificial neural network is one of the
classification algorithms which, can be used in many application areas. This paper investigates
the potential of applying the feed forward neural network architecture for the classification of
medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and
applied to feed forward neural network to enhance the learning process and the network
learning is validated in terms of convergence rate and classification accuracy. In this paper,
MBDE algorithm with various migration policies is proposed for classification problems using
medical diagnosis.
Methodological Study Of Opinion Mining And Sentiment Analysis Techniques ijsc
Decision making both on individual and organizational level is always accompanied by the search of other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining and sentiment analysis are the formalization for studying and construing opinions and sentiments. The digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
Cursive Handwriting Segmentation Using Ideal Distance Approach IJECEIAES
Offline cursive handwriting becomes a major challenge due to the huge amount of handwriting varieties such as slant handwriting, space between words, the size and direction of the letter, the style of writing the letter and handwriting with contour similarity on some letters. There are some steps for recursive handwriting recognition. The steps are preprocessing, morphology, segmentation, features of letter extraction and recognition. Segmentation is a crucial process in handwriting recognition since the success of segmentation step will determine the success level of recognition. This paper proposes a segmentation algorithm that segment recursive handwriting into letters. These letters will form words using a method that determine the intersection cutting point of image recursive handwriting with an ideal image distance. The ideal distance of recursive handwriting image is an ideal distance segmentation point in order to avoid the cutting of other letter’s section. The width and height of images are used to determine the accurate segmentation point. There were 999 recursive handwriting input images taken from 25 researchers used for this study. The images used are the images obtained from preprocessing step. Those are the images with slope correction. This study used Support Vector Machine (SVM) to recognize recursive handwriting. The experiments show the proposed segmentation algorithm able to segment the image precisely and have 97% success recognizing the recursive handwriting.
An overlapping conscious relief-based feature subset selection methodIJECEIAES
Feature selection is considered as a fundamental prepossessing step in various data mining and machine learning based works. The quality of features is essential to achieve good classification performance and to have better data analysis experience. Among several feature selection methods, distance-based methods are gaining popularity because of their eligibility in capturing feature interdependency and relevancy with the endpoints. However, most of the distance-based methods only rank the features and ignore the class overlapping issues. Features with class overlapping data work as an obstacle during classification. Therefore, the objective of this research work is to propose a method named overlapping conscious MultiSURF (OMsurf) to handle data overlapping and select a subset of informative features discarding the noisy ones. Experimental results over 20 benchmark dataset demonstrates the superiority of OMsurf over six existing state-of-the-art methods.
A Novel Framework For Numerical Character Recognition With Zoning Distance Fe...IJERD Editor
Advancements of Computer technology has made every organization to implement the automatic processing systems for its activities. One of the examples is the recognition of handwritten characters, which has always been a challenging task in image processing and pattern recognition. In this paper we propose Zone based features for recognition of the handwritten characters. In this zoning approach a digit image is divided into 8x8 zones and centre pixel is computed for each zone. This procedure is sequentially repeated for entire zone. Finally features are extracted for classification and recognition.
Data mining is a process to extract information from a huge amount of data and transform it into an
understandable structure. Data mining provides the number of tasks to extract data from large databases such
as Classification, Clustering, Regression, Association rule mining. This paper provides the concept of
Classification. Classification is an important data mining technique based on machine learning which is used to
classify the each item on the bases of features of the item with respect to the predefined set of classes or groups.
This paper summarises various techniques that are implemented for the classification such as k-NN, Decision
Tree, Naïve Bayes, SVM, ANN and RF. The techniques are analyzed and compared on the basis of their
advantages and disadvantages
Methodological study of opinion mining and sentiment analysis techniquesijsc
Decision making both on individual and organizational level is always accompanied by the search of
other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum
discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated
content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining
and sentiment analysis are the formalization for studying and construing opinions and sentiments. The
digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is
an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
KNOWLEDGE BASED ANALYSIS OF VARIOUS STATISTICAL TOOLS IN DETECTING BREAST CANCERcscpconf
In this paper, we study the performance criterion of machine learning tools in classifying breast cancer. We compare the data mining tools such as Naïve Bayes, Support vector machines, Radial basis neural networks, Decision trees J48 and simple CART. We used both binary and multi class data sets namely WBC, WDBC and Breast tissue from UCI machine learning depositary. The experiments are conducted in WEKA. The aim of this research is to find out the best classifier with respect to accuracy, precision, sensitivity and specificity in detecting breast cancer
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
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Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Forklift Classes Overview by Intella PartsIntella Parts
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SVM Based Identification of Psychological Personality Using Handwritten Text
1. Syeda Asra. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 4, (Part - 4) April 2016, pp.89-94
www.ijera.com 89|P a g e
SVM Based Identification of Psychological Personality Using
Handwritten Text
Syeda Asra1,
Dr.Shubhangi2
(1
Department of Computer Science & Engineering, Appa Institute of Engineering & Technology, Kalaburagi,
India)
(2
Visvesvaraya Technological University, Regional Centre, Kalaburagi, India )
ABSTRACT
Identification of Personality is a complex process. To ease this process, a model is developed using cursive
handwriting. Area based, width based and height based thresholds are set for only character selection, word
selection and line selection. The rest is considered as noise. Followed by feature vector construction. Slope
feature using slope calculation, shape features and edge detection done using Sobel filter and direction
histogram is considered. Based on the direction of handwriting the analysis was done. Writing which rises to
the right shows optimism and cheerfulness. Sagging to the right shows physical or mental weariness. The lines
which are straight, reveals over-control to compensate for an inner fear of loss of control.The analysis was done
using single line and multiple lines. Simple techniques have provided good results. The results using single line
were 95% and multiple lines were 91%.The classification is done using SVM classifier.
Index Terms— Support Vector Machine (SVM), Personality, Human Behavior.
I. INTRODUCTION
Human behavior [1] refers to the range of
behaviors exhibited by humans and which are
influenced by culture, attitudes, emotions, values,
ethics, authority, rapport, hypnosis, persuasion,
coercion and genetics. Human behavior is
experienced throughout an individual’s entire
lifetime. It includes the way they act based on
different factors such as genetics, social norms, core
faith, and attitude. Behavior is impacted by certain
traits each individual has. The traits vary from
person to person and can produce different actions
or behavior from each person. Everyday conceptions
of personality trait [2][3] make two key
assumptions. First are stable over time. Most people
accept that an individuals’ behavior naturally varies
somewhat from occasion to occasion, but would
maintain the core consistency which defines
individual’s true nature like the unchangeable spots
of the leopard.
II. RELATED WORK
A variety of systems have been designed
and implemented. The previous and the most recent
system consisted of single line and only slope
features were considered based on line regression
[4]. The network was trained using ANN [5] and
SVM [6].The performances were compared using
ANN and SVM. The results with SVM were found
to be 97% and that of ANN was 85%.
III. PROPOSED METHODOLOGY
Trying to understand people’s inner
motivations is notoriously uncertain science.
Personality identification offers one of the few
routes into this world. Handwriting is brain
writing.Handwriting Analysis or Graphology [7] is a
scientific method of identifying, evaluating and
understanding personality through the strokes and
patterns revealed by handwriting. Accuracy of
handwriting depends on how skilled the analyst is.
Perhaps Human intervention in handwriting analysis
is effective but prone to fatigue and inaccurate.
Hence a system is developed to automate this
process.
Pre processing
Feature ExtractionPreprocessing
Line
Segmentation
Marphological
Operation
Binarization using
otsu’s method
SVM
Classify
Feature
Extraction
Templates
1. Image
Dilation
2.Measure the
properties of
image
1. Image
Dilation
2. Edge
Histogram
based
Features
1. Resize
2. Gray
Conversion
Knowledge Base
Query Image
Decision
1. Resize
2. Gray
Conversion
1. Image
Dilation
2. Edge
Histogram
based
Features
Training
Testing
Fig 1: Block diagram of proposed system
RESEARCH ARTICLE OPEN ACCESS
2. R.p Pathak.et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 4, (Part - 1) April 2016, pp.00-00
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3.1 Image Handwriting Acquisition and Database
Creation
Data samples of 500 in number were
collected from people belonging to different works
of life both equally from males & females. In order
to make the results time and mood invariant the
samples were collected in different days at different
points of time. A4 paper with black ball point pen
with a hard surface was used for writing .Three
paragraphs were given to write. The images were
scanned using laser jet scanner with a resolution of
2528x3507 pixels and 300dpi.
3.2 Noise Removal & Image Handwriting Pre
processing
A global threshold of the image is found
using Otsu’s method [8].The image is then
converted into binary image as shown in figure 2.
Morphological based operation, image dilation is
performed to bridge the gap between the characters
in the cursive handwriting as shown in figure 3.
Figure 2: Gray image converted into binary image
Figure3: Image Dilated
3.3 Feature Detection and Psychological
Representation
Three features were extracted relevant for
recognition, slope feature, shape feature and
direction of histogram.
3.3.1 Slope Feature
Slope feature was used to find the slope of the
line
3.3.2 Shape Feature
The shape feature was extracted using area,
perimeter, form factor, major axis, minor axis,
roundness compactness, density
3.3.3 Edge Orientation Histogram
The basic idea is to build a histogram [9]
with the directions of the gradients of the edges
(borders or contours). It is possible to detect
edges in an image but it in this we are interested in
the detection of the angles. This is possible
through Sobel operators [10].
IV. CLASSIFICATION
4.1 SVM’s Overview [11]
SVM classification is based on the idea of
decision hyper planes that determine decision
boundaries in input space or high dimensional
feature space. SVM constructs linear functions
(hyper planes either in input space or in feature
space) from a set of labelled training dataset. This
hyper plane will try to split the positive samples
from the negative samples. The linear separator is
commonly constructed with maximum distance
from the hyper plane to the closest negative and
positive samples. Intuitively, this causes correct
classification for training data which is near, but not
equal to the testing data. Throughout training phase
SVM takes a data matrix as input data and labels
each one of samples as either belonging to a given
class (positive) or not (negative).SVM treats each
sample in the matrix as a row in a input space or
high dimensional feature space, where the number
of attributes identifies the dimensionality of the
space. SVM learning algorithm determines the best
hyper plane which separates each positive and
negative training sample. The trained SVM can be
deployed to perform predictions about a test samples
(new) in the class. Nonlinear problems in SVM are
solved by mapping the n- dimensional input space
into a high dimensional feature space. Finally in this
high dimensional feature space a linear classifier is
constructed which acts as nonlinear classifier in
input space.
3. Syeda Asra. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 4, (Part - 4) April 2016, pp.89-94
www.ijera.com 91|P a g e
4.2 Radial Basis Function [12](RBF) and
Support Vector Machines (SVM) networks
The belief is that a classification problem cast
into a higher dimensional space becomes more
likely separable than in a lower dimensional
space.
RBF network can be used find a set weights for
a curve fitting problem. The weights are in
higher dimensional space than the original data.
Learning is equivalent to finding a surface in
high dimensional space that provides the best fit
to training data.
Hidden layers provide a set of functions that
constitute an arbitrary basis for input patterns
when they are expanded to the hidden space;
these functions are called radial basis functions.
Input layer + only one hidden layer responsible
for spanning hidden space to which input
vectors are mapped + a linear output layer.
The functions are nonlinear. → In a pattern
recognition task this often guarantees better
separation.
The functions map the samples from the input
space to hidden space, in which the same
samples are hopefully separable.
4.3 Radial Basis Function (RBF)
4.3.1 Gaussian functions
φ(r) = exp ( − r 2
/2σ 2
) for some σ > 0 and r ∈ R
(Eqn. 1)
Gaussian functions are probably the most used. In
general, the selection depends on the application.
Support vectors are a set of training samples
extracted by the algorithm used to find the
optimal plane.
The distance between the separating hyper
plane and the closest data point is called the
margin of separation (denoted ρ in the book).
The goal of a support vector machine is to find
the particular hyper plane that maximizes the
margin of separation.
Furthermore, the goal is to determine such a
separating hyper plane which is optimal
Optimality is defined here as di · (wT
xi + b) ≥ 1
for i = 1, . . . N training samples, where di = +1
or di = −1.
Also the weight vector w should minimize the
cost function: Θ(w) = 1 /2wT
w
Obtaining the optimal solution
The two optimality constraints can be expressed
in a form of single cost function.
(Eqn. 1)
Where αi are so called Lagrange multiplies.
The optimal solution is found at the saddle
point of J(·) (J is minimized with respect to w
and b and maximized with respect to α)
The saddle point is found by differentiating J
first with respect to w and then b and setting the
differentials equal to zero.
Input data is transformed from input space with
dimension m0 to a higher dimension feature
space with dimension m1.
The separating hyper plane i.e. the weights wj is
estimated in the feature space.
The inner-product kernel can be used to
construct the optimal hyper plane in the feature
space without having to consider the feature
space itself in explicit form.
Inner product kernel loosely speaking: It is a
function replacing activation function of a
single neuron network.
The inner product kernel is a function that is
used to find the optimal hyper plane for a
support vector machine network.
Inner product kernel is a function:
K(x, xi) = φ T
(x)φ(xi),
(Eqn. 3)
Where φ is a set of transformation function
φj(·), j = 1, . . . m1. m1 is the dimensionality of
the higher order space (original dimension
m0). xi is ith
training sample i.e. it is a known sample
unlike x.
Depending on how the kernel is defined
different learning machines can be built using
SVM. Such machines are polynomial learning
machines, RBF networks, and two-layer
perceptrons.
Figure 4: Architecture of Support Vector Machine
x = [x 1, x 2 . . . m0] ^T is input sample.
K (·, ·) are inner product kernels. I x 1, x 2 . . .
x m1 are examples of input data. I after
applying inner product kernels to input sample
x, the data sample is mapped from input space
to feature space.
An optimal hyper plane is searched for in the
feature space.
Note the indexing: input vector element is
denoted by x 1 whereas x 1 denotes training
sample 1.
4. R.p Pathak.et al. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 4, (Part - 1) April 2016, pp.00-00
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V. CO-RELATION BETWEEN
FEATURE DETECTION AND
PERSONALITY
5.1 Up-Hill Line(Cheerfulness)
Writing "uphill" reveals the applicant
worthy of further trait-match evaluation. He's
optimistic, ambitious and cheerful.Healthy mental
energy & can stay busy active & constantly on the
go.
5.2 Down –Hill Line (Weariness)
Writing ''downhill" may be caused by a
temporary depression, ill health, or physical fatigue.
If this applicant has special skills necessary in your
business, it would be wise to have him return on
another day and obtain another sample of his
writing. The first down slant sample could be just a
temporary discouragement from job-hunting. If the
second sample has the same down slanted
appearance, it is a warning of an ingrained
pessimism which keeps this applicant on the job-
seeking circuit. Negative, disillusionment, constant
disappointment.
5.3 Constant Line(Reveals over control to
compensate for an inner fear )
The writer of a firm, straight, even baseline
controls his moods, allowing him to go directly
toward his goals without getting side-tracked.
Reveals over control to compensate for an inner fear
Healthy mental energy & can stay busy active &
constantly on the go.
VI. RESULTS AND DISCUSSIONS
6.1 Analysis of Single Line in Handwritten Text
Line segmentation was done using
morphological operations. Figure shows a binary
image classified as Ideal. This indicates lines with
constant slope. Figure shows the line classified as
weariness. This indicates temporary depression and
disillusionment and pessimism. The last
classification is cheerfulness as shown in the figure.
This indicates people with healthy mental energy
and optimism.
Figure. 5: Single line classified as ideal
Fig 6: Line classified as weariness
Figure 9: Line classified as cheerfulness
6.2 Analysis of Multiple Lines in Handwritten
Text
The experimentation is carried for multiple
lines. The SVM RBF kernel used has given
outstanding results.
Fig. 8: Multiple lines considered for classification
Figure 9: Multiple lines classified as weariness
5. Syeda Asra. Int. Journal of Engineering Research and Applications www.ijera.com
ISSN: 2248-9622, Vol. 6, Issue 4, (Part - 4) April 2016, pp.89-94
www.ijera.com 93|P a g e
7 Handwriting Analysis of Celebrities and
Comparison with Graphologists
7.1 Hand Writing Analysis of Mr. Mittal (CEO of
Arcelor Mittal)
The figure shows the handwritten text of
famous personality Mr.Mittal. The network
classified his handwriting as Ideal. The
graphologists say he is controlled. Head rules his
heart.
Figure10: Handwriting Sample of Mr. Laxmi Mittal
Fig11: Image denoised and dilated
Figure 12:Mr.Mittals writing classified as ideal
VII. CONCLUSION
The current work is carried considered
single lines and multiple lines for better assessing
the personality trait. The training was done based on
SVM. The network was trained for unknown
samples of celebrities. The results were 94.4%.
Samples were collected from different people at
different point of time to make the network robust
and results invariant.
IX. FUTURE WORKS
This work can be extended for considering
other handwriting features such as margins, spaces
between characters and so on to better judge the
personality.
ACKNOWLEDGEMENTS
I would like to thank the Almighty above
all then my parents my husband my kids and my
guide for giving me encouragement in all kind of
endeavours directly or indirectly. I further extend
my thanks to the famous psychologist Gordon
Allport.
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6. R.p Pathak.et al. Int. Journal of Engineering Research and Applications www.ijera.com
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