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Semantic segmentation involves extracting meaningful information from images or input from a video or recording frames. It is the way to perform the extraction by checking pixels by pixel using a classification approach. It gives us more accurate and fine details from the data we need for further evaluation. Formerly, we had a few techniques based on some unsupervised learning perspectives or some conventional ways to do some image processing tasks. With the advent of time, techniques are improving, and we now have more improved and efficient methods for segmentation. Image segmentation is slightly simpler than semantic segmentation because of the technical perspective as semantic segmentation is pixels based. After that, the detected part based on the label will be masked and refer to the masked objects based on the classes we have defined with a relevant class name and the designated color. In this paper, we have reviewed almost all the supervised and unsupervised learning algorithms from scratch to advanced and more efficient algorithms that have been done for semantic segmentation. As far as deep learning is concerned, we have many techniques already developed until now. We have studied around 120 papers in this research area. We have concluded how deep learning is helping in solving the critical issues of semantic segmentation and gives us more efficient results. We have reviewed and comprehensively studied different surveys on semantic segmentation, specifically using deep learning.

Machine learning is widely accepted as an accurate statistical approach for malware detection to cope with the rising uncertainty risk and complexity of modern intrusions. Not only has machine learning security been asked, but it has also been challenged in the past. However, it has been identified that machine learning contains intrinsic weaknesses that may be exploited to avoid detection during testing. So, look at it another way, machine learning can become an intelligence system bottleneck. We use the related attack methodology to classify different types of attacks using learning-based malware detection techniques in this research by evaluating attackers with unique abilities and talents. After this, to carefully identify the security of Drebin, Android malware detection has been performed. We implemented and did a set of comparable malware detection using the linear SVM and other relevant techniques, including Sec-SVM, Reduced SVM, Reduced Sec-SVM, Na ̈ıve Bayes, Random Forest Classifier, and some deep neural networks. The main agenda of this paper is the presentation of a scalable and straightforward securelearning methodology that reduces the effect of adversarial attacks. In the presence of an attack, the detection accuracy is only a bit worsened. Finally, we evaluate that our robust technique may be accurately adapted to additional intrusion prevention tasks.

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