Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze medical images with favorable results. It can help save lives faster and rectify some medical errors. In this study, we look at the most up-to-date methodologies for medical image analytics that use convolutional neural networks on MRI images. There are several approaches to diagnosing and classifying brain cancers. Inside the brain, irregular cells grow so that a brain tumor appears. The size of the tumor and the part of the brain affected impact the symptoms.
High vehicular mobility causes frequent changes in the density of vehicles, discontinuity in inter-vehicle communication, and constraints for routing protocols in vehicular ad hoc networks (VANETs). The routing must avoid forwarding packets through segments with low network density and high scale of network disconnections that may result in packet loss, delays, and increased communication overhead in route recovery. Therefore, both traffic and segment status must be considered. This paper presents real-time intersection-based segment aware routing (RTISAR), an intersection-based segment aware algorithm for geographic routing in VANETs. This routing algorithm provides an optimal route for forwarding the data packets toward their destination
... Show MoreFree Space Optical (FSO) technology offers highly directional, high bandwidth communication channels. This technology can provide fiber-like data rate over short distances. In order to improve security associated with data transmission in FSO networks, a secure communication method based on chaotic technique is presented. In this paper, we have turned our focus on a specific class of piece wise linear one-dimensional chaotic maps. Simulation results indicate that this approach has the advantage of possessing excellent correlation property. In this paper we examine the security vulnerabilities of single FSO links and propose a solution to this problem by implementing the chaotic signal generator “reconfigurable tent map”. As synchronizat
... Show MoreThe aim of this paper is to design a PID controller based on an on-line tuning bat optimization algorithm for the step-down DC/DC buck converter system which is used in the battery operation of the mobile applications. In this paper, the bat optimization algorithm has been utilized to obtain the optimal parameters of the PID controller as a simple and fast on-line tuning technique to get the best control action for the system. The simulation results using (Matlab Package) show the robustness and the effectiveness of the proposed control system in terms of obtaining a suitable voltage control action as a smooth and unsaturated state of the buck converter input voltage of ( ) volt that will stabilize the buck converter sys
... Show MoreSteganography is defined as hiding confidential information in some other chosen media without leaving any clear evidence of changing the media's features. Most traditional hiding methods hide the message directly in the covered media like (text, image, audio, and video). Some hiding techniques leave a negative effect on the cover image, so sometimes the change in the carrier medium can be detected by human and machine. The purpose of suggesting hiding information is to make this change undetectable. The current research focuses on using complex method to prevent the detection of hiding information by human and machine based on spiral search method, the Structural Similarity Index Metrics measures are used to get the accuracy and quality
... Show MoreThe influx of data in bioinformatics is primarily in the form of DNA, RNA, and protein sequences. This condition places a significant burden on scientists and computers. Some genomics studies depend on clustering techniques to group similarly expressed genes into one cluster. Clustering is a type of unsupervised learning that can be used to divide unknown cluster data into clusters. The k-means and fuzzy c-means (FCM) algorithms are examples of algorithms that can be used for clustering. Consequently, clustering is a common approach that divides an input space into several homogeneous zones; it can be achieved using a variety of algorithms. This study used three models to cluster a brain tumor dataset. The first model uses FCM, whic
... Show MoreClassification of imbalanced data is an important issue. Many algorithms have been developed for classification, such as Back Propagation (BP) neural networks, decision tree, Bayesian networks etc., and have been used repeatedly in many fields. These algorithms speak of the problem of imbalanced data, where there are situations that belong to more classes than others. Imbalanced data result in poor performance and bias to a class without other classes. In this paper, we proposed three techniques based on the Over-Sampling (O.S.) technique for processing imbalanced dataset and redistributing it and converting it into balanced dataset. These techniques are (Improved Synthetic Minority Over-Sampling Technique (Improved SMOTE), Border
... Show MoreGeographic Information Systems (GIS) are obtaining a significant role in handling strategic applications in which data are organized as records of multiple layers in a database. Furthermore, GIS provide multi-functions like data collection, analysis, and presentation. Geographic information systems have assured their competence in diverse fields of study via handling various problems for numerous applications. However, handling a large volume of data in the GIS remains an important issue. The biggest obstacle is designing a spatial decision-making framework focused on GIS that manages a broad range of specific data to achieve the right performance. It is very useful to support decision-makers by providing GIS-based decision support syste
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