Automatic classification of MR brain tumor images using KNN, ANN, SVM and CNN

A brain tumor classification system has been designed and developed. This work presents a new approach to the automated classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumors based on first order statistics and gray level co-occurrence matrix, in magnetic resonance images. The magnetic resonance feature image used for the tumor detection consists of T2-weighted magnetic resonance images for each axial slice through the head. To remove the unwanted noises in the magnetic resonance image, median filtering is used. First order statistics and gray level co-occurrence matrix-based features are extracted. Finally, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks are used to classify the brain tumor images. The application of the proposed method for tracking tumor is demon­strated to help pathologists distinguish its type of tumor. A classification with an accuracy of 89%, 90%, 91% and 95% has been obtained by, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks

Using Decision Trees for comparing different consistency models

[EN] One technique used to improve highway safety from the point of view of the infrastructure is to examine the consistency of the design. Design consistency refers to if highway geometry is conformance to driver expectancy. When the consistency of the road is inadequate, the more likely it is that drivers will be startled and a crash will occur. The consistency, based on operating speed, has been calculated in Spanish two-lane rural highways. This consistency has been evaluated using a local method, to measure the consistency of each element of the road and using a global method, to measure the consistency of a segment of the road. Different models of consistency have been compared using Decision Trees (DTs). DTs are a Data Mining Techniques which can be used to solve classification problems. The results show that DTs are a suitable technique to compare consistence models and they permit to establish limits between the different models analyzed.Support from Spanish Ministry of Economy and Competitiveness (Research Project TRA2012-37823), co-funded with FEDER, is gratefully acknowledged.Griselda López wishes to express her acknowledgement to the regional ministry of Economy, Innovation and Science of the regional government of Andalusia (Spain) for their scholarship to train teachers and researchers in Deficit AreasGarach Morcillo, L.; Calvo Poyo, FJ.; López-Maldonado, G. (2014). Using Decision Trees for comparing different consistency models. Procedia - Social and Behavioral Sciences. 160:332-341. https://doi.org/10.1016/j.sbspro.2014.12.145S33234116

Automatic Rice Leaf Diseases Detection Using SVM

Rice leaf diseases can be detected and recognized automatically. This proposed method, combines super pixels, expectation maximization algorithm, and pyramid of histograms of orientation gradients, to recognize rice diseases. In the proposed method first, pre-processing is performed. Second simple linear iterative clustering is used to divide a diseased leaf image into a number of compact regions, which can dramatically accelerate the convergence speed of the expectation maximization algorithm that is adopted to segment the diseased leaf regions and obtain the lesion image. Third, the pyramid of histograms of orientation gradients features are extracted from the segmented lesion image. In the fourth stage extracted features are reduced using principal component analysis Finally, support vector machine is used to classify and recognize different rice diseases. A database of rice diseased leaf images, is taken to conduct the experiment and the results show that the proposed method is effective and feasible for recognizing rice diseases

Invasive weed optimization with stacked long short term memory for PDF malware detection and classification

Due to high versatility and widespread adoption, PDF documents are widely exploited for launching attacks by cyber criminals. PDFs have been conventionally utilized as an effective method for spreading malware. Automated detection and classification of PDF malware are essential to accomplish security. Latest developments of artificial intelligence (AI) and deep learning (DL) models pave a way for automated detection of PDF malware. In this view, this article develops an Invasive Weed Optimization with Stacked Long Short Term Memory (IWO-S-LSTM) technique for PDF malware detection and classification. The presented IWO-S-LSTM model focuses on the recognition and classification of different kinds of malware that exist in PDF documents. The proposed IWO-S-LSTM model initially undergoes pre-processing in two stages namely categorical encoding and null value removal. Besides, autoencoder (AE) based outlier detection approach is presented to remove the existence of outliers. In addition, S-LSTM model is utilized to detect and classify PDF malware. Finally, IWO algorithm is applied to fine tune the hyperparameters involved in the S-LSTM model. To determine the enhanced outcomes of the IWO-S-LSTM model, a series of simulations were executed on two benchmark datasets. The experimental outcomes outperformed the promising performance of the IWO-S-LSTM technique on the other approaches