Iterative improved learning algorithm for petrographic image classification accuracy enhancement

Abstract

<p>Rock image classification using image processing has been practiced to assist trained geologists in decision making. However, the study of microstructures of rocks and their use in geological investigations offer challenges in the areas of Image processing and Pattern Classification due to the stochastic nature of the mineral textures that is revealed at the microscopic level. Locally relevant Igneous Rock Microstructure images were classified from Volcanic and Plutonic Rock subtypes. The imaging method used mineral grain size as the key physical feature of classification. Three algorithms, namely Radial Basis Function (RBF) Support Vector Machine classifier; Improved (RBF) Support Vector Machine classifier; and AdaBoost algorithm with Improved RBF Support Vector Machine algorithm as base classifier, were used as a base classifier in a novel ‘Iterative Improved Learning (IIL)’ approach. Implementing the IIL approach in the chosen algorithm resulted in accurately classified images that were added to the training set to enhance the ‘breadth and depth’ of the learning knowledge. The algorithm iterated through all available classifier approaches and compared the inter-classifier performance and knowledge of the misclassified images accumulated during the execution of all algorithms.</p>

Detecting malicious URLs using binary classification through adaboost algorithm

“…The amazing majority of these peculiarities can be found as binary properties.The Malware classification chart was shown in Table 1. A prototype has been developed in Java platform using the AdaBoost algorithm [14,15]. The screenshot below is attached as a reference.…”

“…Classification algorithms are then required to determine the line between the decision sets. The authors classified the undiscovered malicious Web sites by employing the characteristics of the network address [14][15]. The motive [16][17] of this research is to analyze malicious websites of good ones from their URL features.…”

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“…This algorithm does not seem to impact business-critical operational infrastructure but still provides value via crossselling. A feedback loop mechanism will be required to gauge the algorithm's performance and to make tweaks over time to pursue desired outcomes (Marathe et al, 2019).…”

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

“…The MRI is considered to be more accurate to assess the level of cancer infiltration than computed tomography [17]- [19]. The registration of biomedical images has many approaches, gold standard uses region-of-interest markers, and other methods include correlation of geometrical characteristics [20], [21]. Intensity-based methods are more worked in recent years to quantify correlations between an image with the intensity values (color or gray level).…”