Impact of Colour Space Transformation on Smoke Detection Accuracy using RESNET50

Mohannad Taha · Mohamed Safaa

Detecting smoke that precedes fire is a vital matter since it will detect fire incidents in a very early stage since these incidents have very high catastrophic effects on people's lives as well as industrial matters. In order to produce a more reliable detection system, in this article, we dove deeper to examine the effect of colour conversion of the captured footage to enhance the detection percentage using a pre-trained CNN model (ResNet50) that was altered to do a binary classification and was trained on a dataset that consists of smoke and non-smoke scenario images. We examined the system using the footage's original status (RGB) and also tested four colour spaces (HSV, YCbCr, LAB, and grayscale). The testing results showed that HSV had the highest accuracy of 92.1% and the lowest errors during training and testing. Regarding accuracy, the order after HSV was RGB, YCbCr, LAB, and finally, grayscale. Grayscale was the lowest in the testing results, with 85.4%. These results indicate that colour spaces do affect the detection quality and using them would improve the quality of smoke detection systems.

Enhancing Malware Detection Through Machine Learning Techniques

Zeina S. Jassim · Mohamad M. Kassir

Malware detection is important to computer network security since it is the principal attack vector against modern enterprises. As a result, firms must remove viruses from computer systems. Using artificial intelligence, namely machine learning techniques, to function in real-time with an IT system is the ideal solution to this problem. This issue has yet to be fixed, but it is still significant because a lack of processing power and memory constrains these features. The most popular method for evaluating systems and intrusion detection models is using the Application Program Interface (API) calls via the KDD-CUP99 data set to give this solution. KDD-CUP99 has more than three hundred thousand samples, each with 54 features. However, the data set attributes were designed and chosen to provide us with a high malware detection rate. The quality of this data was lowered to produce results. To get the desired results, the attributes of this data were reduced. Data transformation and purification are used in this process. Inaccurate, unnecessary, duplicated, or missing information is eliminated by data cleansing. Data cleaning eliminates inaccurate, excessive, redundant, or lacking information. By comparing this study to earlier research that employed lengthy sequences of software interface (API) calls with the same machine-learning classifiers, data transformation includes discretization, which transforms the continuous process of discretizing continuous data into discrete forms is a type of data transformation. Using more advanced algorithms to do the task at hand with the best precision and the least expense increases accuracy and performance. The data set was divided into two categories using a Support Vector Machine (SVM), Decision Tree (DT), and Iterative Dichotomiser 3 (ID3). The findings revealed that little previous research uses a five-class classification strategy for malware detection. The accuracy of several works is comparable to the accuracy acquired in the proposed work.