Automated brain tumor detection is becoming a highly considerable medical diagnosis research.In recent medical diagnoses, detection and classification are highly considered to employ machine learning and deep learning techniques. Nevertheless, the accuracy and performance of current models need to be improved for suitable treatments. In this paper, an improvement in deep convolutional learning is ensured by adopting enhanced optimization algorithms, Thus, Deep Convolutional Neural Network (DCNN) based on improved Harris Hawks Optimization (HHO), called G-HHO has been considered. This hybridization features Grey Wolf Optimization (GWO) and HHO to give better results, limiting the convergence rate and enhancing performance.Moreover, Otsu thresholding is adopted to segment the tumor portion that emphasizes brain tumor detection. Experimental studies are conducted to validate the performance of the suggested method on a total number of 2073 augmented MRI images. The technique's performance was ensured by comparing it with the nine existing algorithms on huge augmented MRI images in terms of accuracy, precision, recall, f-measure, execution time, and memory usage. The performance comparison shows that the DCNN-G-HHO is much more successful than existing methods, especially on a scoring accuracy of 97%. Additionally, the statistical performance analysis indicates that the suggested approach is faster and utilizes less memory at identifying and categorizing brain tumor cancers on the MR images. The implementation of this validation is conducted on the Python platform. The relevant codes for the proposed approach are available at: https://github.com/bryarahassan/DCNN-G-HHO.
Nowadays, in most of the fields, task automation is area of interest and research due to that manual execution of a task is error prone, time consuming, involving more human resources and focus concerning. In the area of Computer laboratory administration, the old fashioned administration cannot run with today's growth, where the Operating System (OS) and required applications are installed on all the machines one by one. Therefore, a framework for automating Lab administration in regards of Operating Systems and Application installations will be proposed in this research. Affordability, simplicity, usability are taken into major consideration. All the parts of the framework are implemented and illustrated in detail which promotes a great enhancement in the area of Computer Lab Administration.
In a world dominated by technology people expect fast swift and efficient services, and for governments this means that citizens and companies expect public services to keep pace with this development and be fast and free of routines. Therefore, most of the developed countries became to adopt the e-Government concept where it enables this improvement and utilize information and communication technology (ICT) to serve the citizens. Basically, the purpose of this research is to provide Kurdistan Regional Government (KRG) organisations and the public sector with a means to comprehend what is essential from a digital communication framework perspective to support delivery of an online public service and identify the components required to achieve this goal along with a high level definition of these components. This paper outcomes the establishment of a high-tech government communication infrastructure and applications via investigating the current and future ICT demands for KRG government organisations, conducting two surveys, and interviewing the stakeholders and clients. It also produces a set of recommendation and suggestions and approaches for designing an efficient framework that mediates information securely among KRG organisations and facilitate collaboration and integration among them.
Machine Learning (ML) is a part of Artificial intelligence (AI) that designs and produces systems, which is capable of developing and learning from experiences automatically without making them programmable. ML concentrates on the computer program improvement, which has the ability to access and utilize data for learning from itself. There are different algorithms in ML field, but the most important questions that arise are: Which technique should be utilized on a dataset? and How to investigate ML algorithm? This paper presents the answer for the mentioned questions. Besides, investigation and checking algorithms for a data set will be addressed. In addition, it illustrates choosing the provided test options and metrics assessment. Finally, researchers will be able to conduct this research work on their datasets to select an appropriate model for their datasets.
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