Related Papers
A Genetic Algorithm Based Feature Selection for Classification of Brain MRI Scan Images Using Random Forest Classifier
IJAERS Journal
— A brain tumour is a mass of tissue that is formed by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Magnetic Resonance Imaging is a useful imaging technique that is widely used by physicians to investigate different pathologies. After a long clinical research, it is proved to be harmless. Improvement in computing power has introduced Computer Aided Diagnosis (CAD) which can efficiently work in an automated environment. Diagnosis or classification accuracy of such a CAD system is associated with the selection of features. This paper proposes an enhanced brain MRI image classifier targeting two main objectives, the first is to achieve maximum classification accuracy and second is to minimize the number of features for classification. Feature selection is performed using Genetic Algorithm (GA) while classifiers used are Random forest Classifier.
Variability analysis of the hierarchical clustering algoritms and its implication on consensus clustering
IJAERS Journal
— Clustering is one of the most important unsupervised learning tools when no prior knowledge about the data set is available. Clustering algorithms aim to find underlying structure of the data sets taking into account clustering criteria, properties in the data and specific way of data comparison. In the literature many clustering algorithms have been proposed having a common goal which is, given a set of objects, grouping similar objects in the same cluster and dissimilar objects in different clusters. Hierarchical clustering algorithms are of great importance in data analysis providing knowledge about the data structure. Due to the graphical representation of the resultant partitions, through a dendrogram, may give more information than the clustering obtained by non hierarchical clustering algorithms. The use of different clustering methods for the same data set, or the use of the same clustering method but with different initializations (different parameters), can produce different clustering. So several studies have been concerned with validate the resulting clustering analyzing them in terms of stability / variability, and also, there has been an increasing interest on the problem of determining a consensus clustering. This work empirically analyzes the clustering variability delivered by hierarchical algorithms, and some consensus clustering techniques are also investigated. By the variability of hierarchical clustering, we select the most suitable consensus clustering technique existing in literature. Results on a range of synthetic and real data sets reveal significant differences of the variability of hierarchical clustering as well as different performances of the consensus clustering techniques.
International Journal of Advanced Engineering Research and Science
Innovation of System Biological Approach in Computational Drug Discovery
2016 •
Prashant Ankur Jain prashant.jain
Improved Classification of Breast Cancer Data using Hybrid Techniques
IJAERS Journal
Breast cancer is the second leading cancer for women in developed countries including India. Many new cancer detection and treatment approaches were developed. The most effective way to reduce breast cancer deaths is detect it earlier. The frequent occurrence of breast cancer and its serious consequences have attracted worldwide attention in recent years. Problems such as low rate of accuracy and poor self-adaptability still exist in traditional diagnosis. In order to solve these problems, an Ada Boost-SVM classification algorithm, Combined with k-means is proposed in this research for the early diagnosis of breast cancer. The effectiveness of the proposed methods are examined by calculating its accuracy, confusion matrix which give important clues to the physicians for early diagnosis of breast cancer.
International Journal of Advanced Engineering Research and Science
Citrus Fruit Quality Classification using Support Vector Machines
Glenda Botelho
Computational Intelligence and Decision Making Intelligent Systems, Control and Automation: Science and Engineering Volume 61, 2013, pp 407-416
A Tool for Biomedical – Documents Classification Using Support Vector Machines
2013 •
Paula Verissimo
International Journal of Advanced Engineering Research and Science
An Efficient Energy Savings Schemes using Adjacent Lossless Entropy Compression for WSN
2017 •
HN Suresh
Economic and multivariate analysis of banana production (Musa sp.) Cultivated in the semi- arid region of northeast Brazil
IJAERS Journal
— The objective of this work was to evaluate the economic viability and develop a multivariate analysis for banana crop data in the semi-arid region of Brazilian Northeastern. The study adopted a methodology widely disseminated in scientific research and income evaluations for the economic analysis. Through the economic indexes, we could evaluate the efficiency of the administrator and his workforce. Hierarchical clustering and Principal component analysis were used as multivariate analysis. The result of the economic analysis revealed that, on average, the total gross revenue from conventional management exceeded by 2.24 times the total gross income from agroecological management. However, the production costs of the conventional management exceeded by 2.15 times agroecological management. The multivariate analysis revealed that the variables are divided into three distinct groups formed between the equilibrium price and the profitability index, which do not correlate with each other or with the others, and by the subgroup formed by the return rate index, total operating profit and gross income, which are correlated with each other and not correlated with the others. We conclude that the multivariate analysis confirmed that the methodology used in the composition of all the economic indexes studied was correct.
Segmentation of Optic Disc in Fundus Images using Convolutional Neural Networks for Detection of Glaucoma
IJAERS Journal
— The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a difficult task due to various anatomical structures like blood vessel, optic cup, optic disc, macula and fovea. Blood vessel segmentation can assist in the detection of pathological changes which are possible indicators for arteriosclerosis, retinopathy, microaneurysms and macular degeneration. The segmentation of optic disc and optic cup from retinal images is used to calculate an important indicator, cup-to disc ratio(CDR) accurately to help the professionals in the detection of Glaucoma in fundus images.In this proposed work, an automated segmentation of anatomical structures in fundus images such as blood vessel and optic disc is done using Convolutional Neural Networks (CNN). A Convolutional Neural Network is a composite of multiple elementary processing units, each featuring several weighted inputs and one output, performing convolution of input signals with weights and transforming the outcome with some form of nonlinearity. The units are arranged in rectangular layers (grids), and their locations in a layer correspond to pixels in an input image. The spatial arrangement of units is the primary characteristics that makes CNNs suitable for processing visual information; the other features are local connectivity, parameter sharing and pooling of hidden units. The advantage of CNN is that it can be trained repeatedly so more features can be found. An average accuracy of 95.64% is determined in the classification of blood vessel or not. Optic cup is also segmented from the optic disc by Fuzzy C Means Clustering (FCM). This proposed algorithm is tested on a sample of hospital images and CDR value is determined. The obtained values of CDR is compared with the given values of the sample images and hence the performance of proposed system in which Convolutional Neural Networks for segmentation is employed, is excellent in automated detection of healthy and Glaucoma images. Keywords— Fundus images ,blood vessel segmentation ,global contrast normalization ,zero phase component analysis, convolutional neural networks,Optic cup and disc, FCM.
A Survey on the Project in title " Unsupervised Adaptive P300 BCI in the Framework of Chaotic Theory and Stochastic Theory "
IJAERS Journal
—In this paper we present a survey of work that has been done in the project " Unsupervised Adaptive P300 BCI in the framework of chaotic theory and stochastic theory " we summarised the following papers,.We developed a new pre-processing method for denoising P300-based brain-computer interface data that allows better performance with lower number of channels and blocks. The new denoising technique is based on a modified version of the spectral subtraction denoising and works on each temporal signal channel independently thus offering seamless integration with existing pre-processing and allowing low channel counts to be used. We also developed a novel approach for brain-computer interface data that requires no prior training. The proposed approach is based on interval type-2 fuzzy logic based classifier which is able to handle the users' uncertainties to produce better prediction accuracies than other competing classifiers such as BLDA or RFLDA. In addition, the generated type-2 fuzzy classifier is learnt from data via genetic algorithms to produce a small number of rules with a rule length of only one antecedent to maximize the transparency and interpretability for the normal clinician. We also employ a feature selection system based on an ensemble neural networks recursive feature selection which is able to find the effective time instances within the effective sensors in relation to given P300 event. The basic principle of this new class of techniques is that the trial with true activation signal within each block has to be different from the rest of the trials within that block. Hence, a measure that is sensitive to this dissimilarity can be used to make a decision based on a single block without any prior training. The new methods were verified using various experiments which were performed on standard data sets and using real-data sets obtained from real subjects experiments performed in the BCI lab in King Abdulaziz University. The results were compared to the classification results of the same data using previous methods. Enhanced performance in different experiments as quantitatively assessed using classification block accuracy as well as bit rate estimates was confirmed. It will be shown that the produced type-2 fuzzy logic based classifier will learn simple rules which are easy to understand explaining the events in question. In addition, the produced type-2 fuzzy logic classifier will be able to give better accuracies when compared to BLDA or RFLDA on various human subjects on the standard and real-world data sets. Keywords— Type-2 fuzzy logic systems, linguistic model generation, modeling perceptions, brain computer interfaces.
