838 research outputs found
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Expert system for tool wear monitoring in blanking
A description is given of a simple yet powerful expert system created using the CRYSTAL shell which is able to monitor the potential and functional failures of the tool and the monitoring equipment. The techniques of feature extraction, selection and classification using the Bayesian rule are presented. Finally supervised learning, necessary when new situations are encountered, is also discussed
Implementation of Convolutional Neural Networks for Batik Image Dataset
One method of image recognition that can be used is a convolutional neural network (CNN). However, the training model of CNN is not an easy thing; it takes tuning parameters that take a long time in the training process. This research will do Batik pattern recognition by using CNN. From the experiment that we conducted, the result shows that the feature extraction, selection, and reduction give the accuracy more significant than raw image dataset. The feature selection and reduction also can improve the execution time. Parameters value that gave best accuracy are: epoch = 200, batch_size = 20, optimizer = adam, learning_rate = 0.01, network weight initialization = lecun_uniform, neuron activation function = linear
COLOR HISTOGRAM BASED MEDICAL IMAGE RETRIEVAL SYSTEM
This paper aims to focus on the feature extraction, selection and database creation of brain images for image retrieval which will aid for computer assisted diagnosis. The impact of content-based access to medical images is frequently reported but existing systems are designed for only a particular context of diagnosis. But, our concept of image retrieval in medical applications aims at a general structure for semantic content analysis that is suitable for numerous applications in case-based reasoning. By using the features, the database created for comparison. The color histogram is used to measure the similarity between the stored database image and the query image. The image which is more similar to the query image is retrieved as the resultant image. If the quer
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection
Recently, Deep Learning has been showing promising results in various
Artificial Intelligence applications like image recognition, natural language
processing, language modeling, neural machine translation, etc. Although, in
general, it is computationally more expensive as compared to classical machine
learning techniques, their results are found to be more effective in some
cases. Therefore, in this paper, we investigated and compared one of the Deep
Learning Architecture called Deep Neural Network (DNN) with the classical
Random Forest (RF) machine learning algorithm for the malware classification.
We studied the performance of the classical RF and DNN with 2, 4 & 7 layers
architectures with the four different feature sets, and found that irrespective
of the features inputs, the classical RF accuracy outperforms the DNN.Comment: 11 Pages, 1 figur
Unsupervised Hybrid Feature Extraction Selection for High-Dimensional Non-Gaussian Data Clustering with Variational Inference
Clustering has been a subject of extensive research in data mining, pattern recognition, and other areas for several decades. The main goal is to assign samples, which are typically non-Gaussian and expressed as points in high-dimensional feature spaces, to one of a number of clusters. It is well known that in such high-dimensional settings, the existence of irrelevant features generally compromises modeling capabilities. In this paper, we propose a variational inference framework for unsupervised non-Gaussian feature selection, in the context of finite generalized Dirichlet (GD) mixture-based clustering. Under the proposed principled variational framework, we simultaneously estimate, in a closed form, all the involved parameters and determine the complexity (i.e., both model an feature selection) of the GD mixture. Extensive simulations using synthetic data along with an analysis of real-world data and human action videos demonstrate that our variational approach achieves better results than comparable techniques
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization
Existing Android malware detection approaches use a variety of features such
as security sensitive APIs, system calls, control-flow structures and
information flows in conjunction with Machine Learning classifiers to achieve
accurate detection. Each of these feature sets provides a unique semantic
perspective (or view) of apps' behaviours with inherent strengths and
limitations. Meaning, some views are more amenable to detect certain attacks
but may not be suitable to characterise several other attacks. Most of the
existing malware detection approaches use only one (or a selected few) of the
aforementioned feature sets which prevent them from detecting a vast majority
of attacks. Addressing this limitation, we propose MKLDroid, a unified
framework that systematically integrates multiple views of apps for performing
comprehensive malware detection and malicious code localisation. The rationale
is that, while a malware app can disguise itself in some views, disguising in
every view while maintaining malicious intent will be much harder.
MKLDroid uses a graph kernel to capture structural and contextual information
from apps' dependency graphs and identify malice code patterns in each view.
Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted
combination of the views which yields the best detection accuracy. Besides
multi-view learning, MKLDroid's unique and salient trait is its ability to
locate fine-grained malice code portions in dependency graphs (e.g.,
methods/classes). Through our large-scale experiments on several datasets
(incl. wild apps), we demonstrate that MKLDroid outperforms three
state-of-the-art techniques consistently, in terms of accuracy while
maintaining comparable efficiency. In our malicious code localisation
experiments on a dataset of repackaged malware, MKLDroid was able to identify
all the malice classes with 94% average recall
Analysis and Classification of Breast Cancer Disease Via Different Datasets and Classifier Models
Nowadays, Tumour is one of the important reasons of human death worldwide, producing about 9.6 million people in 2018. BC (breast cancer) is the common reason for cancer deaths in females. BC is a type of cancer that can be treated when detected early. The main motive of this analysis is to detect cancer early in life using ML (machine learning) techniques. The features of the people included in the WDBC (Wisconsin diagnostic breast cancer) and Coimbra BC datasets were classified by SVOF-KNN, KNN, and Naïve Bayes techniques. The pre-processing data phase was applied to the datasets before classification. After the data pre-processing steps, three classification methods were applied to the data. Specificity and Sensitivity rates were used to calculate the success of the techniques. As an outcome of the BC diagnosis classification, the SVOF-KNN technique was found with a 91 percent specificity rate and 90 percent sensitivity rate. When the outcomes attained from feature extraction and selection are calculated. It is seen that feature extraction, selection, and data pre-processing techniques improve the specificity and sensitivity rate of the detection system
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