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 using different tools and techniques. However, this paper presents a comprehensive review of the methods and techniques used to detect brain tumor through MRI image segmentation. Lastly, the paper concludes with a concise discussion and provides a direction toward the upcoming trend of more advanced research studies on brain image segmentation and Tumor detection.
Heart sound is an electric signal affected by some factors during the signal's recording process, which adds unwanted information to the signal. Recently, many studies have been interested in noise removal and signal recovery problems. The first step in signal processing is noise removal; many filters are used and proposed for treating this problem. Here, the Hankel matrix is implemented from a given signal and tries to clean the signal by overcoming unwanted information from the Hankel matrix. The first step is detecting unwanted information by defining a binary operator. This operator is defined under some threshold. The unwanted information replaces by zero, and the wanted information keeping in the estimated matrix. The resulting matrix
... Show MoreCommunity detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem. In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a
... Show MoreUntil recently, researchers have utilized and applied various techniques for intrusion detection system (IDS), including DNA encoding and clustering that are widely used for this purpose. In addition to the other two major techniques for detection are anomaly and misuse detection, where anomaly detection is done based on user behavior, while misuse detection is done based on known attacks signatures. However, both techniques have some drawbacks, such as a high false alarm rate. Therefore, hybrid IDS takes advantage of combining the strength of both techniques to overcome their limitations. In this paper, a hybrid IDS is proposed based on the DNA encoding and clustering method. The proposed DNA encoding is done based on the UNSW-NB15
... Show MoreSoftware-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr
... Show MoreTo determine the important pathogenic role of celiac disease in triggering several autoimmune disease, thirty patients with Multiple Sclerosis of ages (22-55) years have been investigated and compared with 25 healthy individuals. All the studied groups were carried out to measure anti-tissue transglutaminase antibodies IgA IgG by ELISA test, anti-reticulin antibodies IgA and IgG, and anti-endomysial antibodies IgA and IgG by IFAT. There was a significant elevation in the concentration of anti-tissue transglutaminase antibodies IgA and IgG compared to control groups (P≤0.05), there was 4(13.33%) positive results for anti-reticulin antibodies IgA and IgG , 3(10%) positive results for anti-endomysial antibodies IgA and IgG . There were 4 pos
... Show MoreTo determine the important pathogenic role of celiac disease in triggering several autoimmune disease, thirty patients with Multiple Sclerosis of ages (22-55) years have been investigated and compared with 25 healthy individuals. All the studied groups were carried out to measure anti-tissue transglutaminase antibodies IgA IgG by ELISA test, anti-reticulin antibodies IgA and IgG, and anti-endomysial antibodies IgA and IgG by IFAT. There was a significant elevation in the concentration of anti-tissue transglutaminase antibodies IgA and IgG compared to control groups (P≤0.05), there was 4(13.33%) positive results for anti-reticulin antibodies IgA and IgG , 3(10%) positive results for anti-endomysial antibodies IgA and IgG . There were 4 pos
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