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An Artificial Neural Network (ANN) is a statistical data modeling tool inspired by the functionality and the structure of the biological nervous system. An ANN consists of processing elements known as neurons that are interconnected to each other and work in unison to answer a particular problem. Neural networks can be used in places where detecting trends and extracting patterns are too complex to...
In recent years, data mining techniques has been used widely to address the problem of huge data sets stored on huge, heterogeneous data warehouses and may be located in different sites. Rapid growth of data arise in many areas like healthcare, social media, science, Internet…etc. Multi-agent technology has improved the processing of such huge data by the use of the agent technology along with data...
In this paper, support vector machine (SVM) and mixed gravitational search algorithm (MGSA) are utilized to detect the breast cancer tumors in mammography images. Sech template matching method is used to segment images and extract the regions of interest (ROIs). Gray-level co-occurrence matrix (GLCM) is used to extract features. The mixed GSA is used for optimization of the classifier parameters and...
Detection of mitotic cells in histology images is an important but challenging process due to the resemblance of mitotic cells with other non-mitotic cells and also due to the different appearance of mitotic cells undergoing different phases of the division process. In this paper, we present an algorithm for classification of mitotic cells into its four different phases using eigenphase nuclei images...
Mining provides useful information from the huge volume of the data stored in repositories. The present study focus on implementing five different algorithms using the data mining WEKA. The algorithm in the study includes Naive Bayes, Zero R, One R, J48 and Random Tree algorithm. All these well-known familiar algorithms are used in classification rule mining techniques. Datasets are collected from...
Breast cancer is leading cause of death and also one of the most invasive types of cancers among women in worldwide. It happens when cells in the breast start to develop uncontrollably or spread throughout the body. Early detection and effective diagnosis is the only rescue to lessen breast cancer fatality. Accurate classification of breast tumor is an important task in medical diagnosis. Soft computing...
Breast cancer is one of the most dangerous cancers in the world especially in the Arab countries and Egypt. Due to the large spreading of the disease, automatic recognition systems can help physicians to classify the tumors as benign or malignant. However, performing a lot of pathological analysis consumes time and money. In this paper, we propose an algorithm for decreasing the number of features...
In developed countries death of women due to breast cancer has become regular. Data mining techniques are used to provide the analysis for the classification and prediction algorithms. The algorithms used here are Naïve Bayes classification algorithm and Naïve Bayes prediction algorithm. The algorithms are used to classify and predict whether the tumour is either benign or malignant. The data used...
Binary classification is a process of classifying the elements of a data set into two groups on the basis of a classification rule. It is useful and widely applied in many fields: Information Technology, Business, Medical Diagnosis, Finance, and so on. The problems of the previous works do not specify clearly which classifier utilizes to minimize which type of false, False Positive (FP) or False Negative...
In this paper, we described the practical application of data mining methods for estimation of survival rate and disease relapse for breast cancer patients. A comparative study of prominent machine learning models was carried out and according to the achieved results we concluded that the classifiers obviously learn some of the concepts of breast cancer survivability and recurrence. These algorithms...
Breast cancers are traditionally known to be one of the major causes of death among women. The need for early detection of breast cancer is highlighted by the fact that incidence rates for breast cancer is one of the highest among all cancers according to the American Cancer Society which quotes a morbidity of 230 000 and a mortality of 40 000 according to the latest figures gathered for the American...
Hierarchical fuzzy aggregation network (HFAN) is a fine multilayer information fusion system that carries out multi-criteria aggregation. It can be regarded as a functional classifier for dealing with decision-making problems. The HFAN comprises fuzzy aggregation operators built from adjusted parameters (γ) and associated weights (δ). In this paper, we adopt soft computing techniques (e.g., PSO and...
Data mining is now one of the most active field of research. Extracting those nuggets of information is becoming crucial and one of its important technique is classification. It helps to group the data in some predefined classes. Various techniques for classification exists which classifies the data using different algorithms. Each algorithm has its own area of best and worst performance. This paper...
Feature selection (FS) is an important technique in data mining to remove noise, irrelevant and redundant data. The paper introduces the ensemble approach using FS and without using FS tested on a standard medical dataset in order to compare the accuracy and time of both. This system uses best first search FS algorithm to reduce the noise in the dataset. The ensemble technique is a combination of...
Classification of Haberman's Survival information is useful to find out the patients survival probability after a breast cancer surgery. Dataset has been collected from a standard benchmark UCI machine learning repository. A study at the hospital named University of Chicago's Billings was conducted between the year 1958 and 1970 to identify the cancer patients who had undergone surgery for breast...
In this paper, we address the epidemiology and morphology questions of breast cancer with special focus on different cell features created by lesions. In addition, we provide an insight into feature extraction and classification schemes in the image analysis pipeline. Based on our conducted research work, a novel feature extraction approach, a modification of Distance Transform on Curved Space (DTOCS),...
For autonomous robots, the Fuzzy C-means algorithm (FCM) is used in the tasks like self-position estimation, path planning and environment navigation. This paper proposes a suspect point recheck method for fuzzy clustering algorithm. First, the proposed method works as the typical FCM to obtain an original clustering result. Then the method classifies all the data points into normal points and suspect...
Feature selection or variable reduction is a fundamental problem in data mining, refers to the process of identifying the few most important features for application of a learning algorithm. The best subset contains the minimum number of dimensions retaining a suitably high accuracy on classifier in representing the original features. The objective of the proposed approach is to reduce the number...
In this paper, a novel classification algorithm, ELMDF (Extreme Learning Machine based on Data Field), is proposed to solve the problem of estimating the number of hidden layer neurons in typical ELM. For constructing ELMDF, a new theory based on data field, FMDF (Fundamental Matrix of Data Field) is proposed in this paper. The breast cancer cell image dataset, and the genome dataset are used to test...
In this paper, an algorithm is presented for extracting fuzzy rules from the Breast Cancer dataset. To extract fuzzy rules, an imitation based evolutionary algorithm is used called Krill Herd (KH). The KH algorithm is converted to a binary algorithm here, and is used for the classification problem with innovation, named Binary Krill Herd-based Fuzzy Rule Miner (BKH-FRM). Choosing the best krill and...
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