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Fire Detection Methods Based on Various Color Spaces and Gaussian Mixture Models

100%

Munshi A.

tom Vol. 15, no 3

197--214

Fire disasters are very serious problems that may cause damages to ecological systems, infrastructure, properties, and even a threat to human lives; therefore, detecting fires at their earliest stage is of importance. Inspired by the technological advancements in artificial intelligence and image processing in solving problems in different applications, this encourages adopting those technologies in reducing the damage and harm caused by fire. This study attempts to propose an intelligent fire detection method by investigating three approaches to detect fire based on three different color models: RGB, YCbCr, and HSV are presented. The RGB method is applied based on the relationship among the red, green and blue values of pixels in images. In the YCbCr color model, image processing and machine learning techniques are used for morphological processing and automatic recognition of fire images. Whereas for the HSV supervised machine learning techniques are adopted, namely decision rule and Gaussian mixture model (GMM). Further, the expectation maximization (EM) algorithm is deployed for the GMM parameters estimation. The three proposed models were tested on two data sets, one of which contains fire images, the other consists of non-fire images with some having fire-like colors to test the efficiency of the proposed methods. The experimental results showed that the overall accuracies on two data sets for the RGB, YCbCr, and HSV methods were satisfactory and were efficient in detecting outdoor and indoor fires.

Network Intrusion Detection Using Machine Learning Techniques

51%

Almutairi Y. S. , Alhazmi B. , Munshi A.

tom Vol. 16, no 3

193--206

Intrusion detection systems (IDS) are essential for the protection of advanced communication networks. These systems were primarily designed to identify particular patterns, signatures, and rule violations. Machine Learning and Deep Learning approaches have been used in recent years in the field of network intrusion detection to provide promising alternatives. These approaches can discriminate between normal and anomalous patterns. In this paper, the NSL-KDD (Network Security Laboratory Knowledge Discovery and Data Mining) benchmark data set has been used to evaluate Network Intrusion Detection Systems (NIDS) by using different machine learning algorithms such as Support Vector Machine, J48, Random Forest, and Naïve Bytes with both binary and multi-class classification. The results of the application of those techniques are discussed in details and outperformed previous works.