Diabetes is one of the major health issues. In diabetes patient one serious problem experience is the Diabetic Retinopathy (DR) and visual deficiency and is vascular disease of retina. Hence prediction of DR from pati...
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ISBN:
(纸本)9781538678084
Diabetes is one of the major health issues. In diabetes patient one serious problem experience is the Diabetic Retinopathy (DR) and visual deficiency and is vascular disease of retina. Hence prediction of DR from patient eye retina becomes wry crucial at early stage to cure. We focuses on presenting an empirical method in this research to collect required data and then developing several models to predict the chance of diabetic retinopathy. Here we use diabetic eye retina image dataset as input for prediction and evaluation. There are many techniques and algorithms that help to diagnose DR in retinal fundus images. We utilized some datamining techniques such as Support vector machine (SVM), naive bayes and Local binary pattern (LBP) to extract image features and analyze image dataset.
Computer-aided diagnosis (CAD), a field of medical analysis, is rapidly advancing in a large range and is becoming more complex. Computer-aided recently, there has been a lot of interest in diagnostics for the reason ...
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Facial expression recognition is one of the technological capabilities in identifying a face image to follow up on research conducted by psychologists. The recognition of facial expressions is very important to know t...
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ISBN:
(纸本)9781728146102
Facial expression recognition is one of the technological capabilities in identifying a face image to follow up on research conducted by psychologists. The recognition of facial expressions is very important to know the emotions of someone who is experiencing it. In this study two datasets were used, namely the FER2013 and CK + datasets. The FER2013dataset and CK+ are datasets designed to identify facial expressions. At the feature extraction stage, it uses the Histogram of Oriented Gradient (HOG) feature dan Local Binary pattern (LBP) feature. Whereas in the classification stage, the Extreme learningmachine (ELM) classifier is used. The greatest accuracy by using HOG feature is 63.86% for the FER2013dataset and 99.79% for the CK + dataset with sigmoid as an activation function. And the greatest accuracy by using LBP feature is 55.11% for the FER2013dataset and 98.72% for the CK + dataset with RBF as an activation function.
This paper presents the basic approach of multiclass classification for handwritten digit recognition using Support Vector machine and a comparative accuracy analysis for three well known kernel functions (linear, RBF...
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ISBN:
(纸本)9781538623077
This paper presents the basic approach of multiclass classification for handwritten digit recognition using Support Vector machine and a comparative accuracy analysis for three well known kernel functions (linear, RBF and polynomial) and feature vectors corresponding to different cell sizes. However, the process of digit recognition includes several basic steps such as preprocessing, feature extraction and classification. Among them, feature extraction is the fundamental step for digit classification and recognition as accurate and distinguishable feature plays an important role to enhance the performance of a classifier. Histogram of Oriented Gradient (HOG) feature extraction technique has been used here. Therefore, for various cell sizes, the experimental results show around 98-100% accuracy for trained data and 91-97% accuracy for test set data according to various kernel functions. The target of this paper is to select a kernel function best suited for a particular resolution of image.
With the increasing complexity of business environment, the importance of data analysis in business decision-making has become increasingly prominent. As a powerful data analysis tool, machinelearning algorithm has b...
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This work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus and patternrecognition, relies also o...
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ISBN:
(纸本)9783642030697
This work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus and patternrecognition, relies also on general knowledge and application context for understanding visual content in conceptual terms. Our work is an attempt to imitate this behavior by devising an evidence driven probabilistic, inference framework using ontologies and bayesian networks. Experiments conducted for two different image analysis, tasks showed improvement performance, compared to the case where computer vision techniques act isolated from any type of knowledge or context.
Financial information extraction from big financial reports is a tedious task. This paper speaks about page-wise feature generation and applying learning algorithms for identifying financial information (balance sheet...
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ISBN:
(纸本)9781509036967
Financial information extraction from big financial reports is a tedious task. This paper speaks about page-wise feature generation and applying learning algorithms for identifying financial information (balance sheets, cash flows, and income statements) in Form 10-K or annual reports of companies. Balance sheets, cash flows, and income statements have some structure in them and are semi-structured information. This approach employs selection of unigrams and bigrams based on frequency of occurrence and expert advice, generation of page wise features, and applying learning models for identifying patterns of specific financial information. Different supervised learning models are applied yielding results with very high accuracy (greater than 99%).
Sparse subspace learning has been demonstrated to be effective in datamining and machinelearning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the view of...
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ISBN:
(纸本)9781509048472
Sparse subspace learning has been demonstrated to be effective in datamining and machinelearning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the view of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l(2,1)-norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, we attempt to solve our problem by l(1)-norm error function which is resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Particularly, differ from conventional non-negative updating rules, we design a novel multiplicative update rule to iteratively solve the feature weight matrix, and we validate its non-negativity. Comparative experiments on various original datasets with and without malicious pollution demonstrate performance superiority of our model.
This paper presents a data preprocessing procedure to select support vector (SV) candidates. We select decision boundary region vectors (BRVs) as SV candidates. Without the need to use the decision boundary, BRVs can ...
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ISBN:
(数字)9783540734994
ISBN:
(纸本)9783540734987
This paper presents a data preprocessing procedure to select support vector (SV) candidates. We select decision boundary region vectors (BRVs) as SV candidates. Without the need to use the decision boundary, BRVs can be selected based on a vector's nearest neighbor of opposite class (NNO). To speed up the process, two spatial approximation sample hierarchical (SASH) trees are used for estimating the BRVs. Empirical results show that our data selection procedure can reduce a full dataset to the number of SVs or only slightly higher. Training with the selected subset gives performance comparable to that of the full dataset. For large datasets, overall time spent in selecting and training on the smaller dataset is significantly lower than the time used in training on the full dataset.
datamining is vast area that co-relates diverse branches i.e Statistics, data Base, machinelearning and Artificial intelligence. Various applications are accessible in various areas. Churning of the Customer is the ...
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ISBN:
(纸本)9781538662274
datamining is vast area that co-relates diverse branches i.e Statistics, data Base, machinelearning and Artificial intelligence. Various applications are accessible in various areas. Churning of the Customer is the behavior when client never again needs to stay with his association with the company. Customer Churn Management is assuming essential job in client management. Nowadays different telecommunication companies are concentrating on distinguishing high esteemed and potential churning clients to expand benefit and share market. It is comprehended that making new clients are costlier than to holding existing client. There is a current issue that customer leave the organization because of obscure reasons. In our investigation, we predict churn behavior of the client by utilizing diverse datamining methods. It will in the long run help in breaking down client's behavior and characterize whether it is a churning client or not. We utilize online accessible data set available at Kaggle repository and for forecasting of Customer behavior we utilized different algorithms while we achieved 99.8% accuracy level using Bagging Algorithms.
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