Change detection is a technology ascertaining the changes of
specific features within a certain time Interval. The use of remotely
sensed image to detect changes in land use and land cover is widely
preferred over other conventional survey techniques because this
method is very efficient for assessing the change or degrading trends
of a region. In this research two remotely sensed image of Baghdad
city gathered by landsat -7and landsat -8 ETM+ for two time period
2000 and 2014 have been used to detect the most important changes.
Registration and rectification the two original images are the first
preprocessing steps was applied in this paper. Change detection using
NDVI subtractive has been computed, subtrac

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

<p> Traditionally, wireless networks and optical fiber Networks are independent of each other. Wireless networks are designed to meet specific service requirements, while dealing with weak physical transmission, and maximize system resources to ensure cost effectiveness and satisfaction for the end user. In optical fiber networks, on the other hand, search efforts instead concentrated on simple low-cost, future-proofness against inheritance and high services and applications through optical transparency. The ultimate goal of providing access to information when needed, was considered significantly. Whatever form it is required, not only increases the requirement sees technology convergence of wireless and optical networks but

Technically, mobile P2P network system architecture can consider as a distributed architecture system (like a community), where the nodes or users can share all or some of their own software and hardware resources such as (applications store, processing time, storage, network bandwidth) with the other nodes (users) through Internet, and these resources can be accessible directly by the nodes in that system without the need of a central coordination node. The main structure of our proposed network architecture is that all the nodes are symmetric in their functions. In this work, the security issues of mobile P2P network system architecture such as (web threats, attacks and encryption) will be discussed deeply and then we prop

—Medical images have recently played a significant role in the diagnosis and detection of various diseases. Medical imaging can provide a means of direct visualization to observe through the human body and notice the small anatomical change and biological processes associated by different biological and physical parameters. To achieve a more accurate and reliable diagnosis, nowadays, varieties of computer aided detection (CAD) and computer-aided diagnosis (CADx) approaches have been established to help interpretation of the medical images. The CAD has become among the many major research subjects in diagnostic radiology and medical imaging. In this work we study the improvement in accuracy of detection of CAD system when comb