With the high usage of computers and networks in the current time, the amount of security threats is increased. The study of intrusion detection systems (IDS) has received much attention throughout the computer science field. The main objective of this study is to examine the existing literature on various approaches for Intrusion Detection. This paper presents an overview of different intrusion detection systems and a detailed analysis of multiple techniques for these systems, including their advantages and disadvantages. These techniques include artificial neural networks, bio-inspired computing, evolutionary techniques, machine learning, and pattern recognition.

Corona virus sickness has become a big public health issue in 2019. Because of its contact-transparent characteristics, it is rapidly spreading. The use of a face mask is among the most efficient methods for preventing the transmission of the Covid-19 virus. Wearing the face mask alone can cut the chance of catching the virus by over 70\%. Consequently, World Health Organization (WHO) advised wearing masks in crowded places as precautionary measures. Because of the incorrect use of facial masks, illnesses have spread rapidly in some locations. To solve this challenge, we needed a reliable mask monitoring system. Numerous government entities are attempting to make wearing a face mask mandatory; this process can be facilitated by using face m

Although text document images authentication is difficult due to the binary nature and clear separation between the background and foreground but it is getting higher demand for many applications. Most previous researches in this field depend on insertion watermark in the document, the drawback in these techniques lie in the fact that changing pixel values in a binary document could introduce irregularities that are very visually noticeable. In this paper, a new method is proposed for object-based text document authentication, in which I propose a different approach where a text document is signed by shifting individual words slightly left or right from their original positions to make the center of gravity for each line fall in with the m

In this work a fragile watermarking scheme is presented. This scheme is applied to digital color images in spatial domain. The image is divided into blocks, and each block has its authentication mark embedded in it, we would be able to insure which parts of the image are authentic and which parts have been modified. This authentication carries out without need to exist the original image. The results show the quality of the watermarked image is remaining very good and the watermark survived some type of unintended modification such as familiar compression software like WINRAR and ZIP

Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing

Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by usin

The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when

In this paper, a literature survey was introduced to study of enhancing the hazy images , because most of the images captured in outdoor images have low contrast, color distortion, and limited visual because the weather conditions such as haze and that leads to decrease the quality of images capture. This study is of great importance in many applications such as surveillance, detection, remote sensing, aerial image, recognition, radar, etc. The published researches on haze removal are divided into several divisions, some of which depend on enhancement the image, some of which depend on the physical model of deformation, and some of them depend on the number of images used and are divided into single-image and multiple images dehazing model