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 manifestations of climate change are increasing with the days: sudden rains and floods, lakes that evaporate, rivers that experience unprecedentedly low water levels, and successive droughts such as the Tigris, Euphrates, Rhine, and Lape rivers. At the same time, energy consumption is increasing, and there is no way to stop the warming of the Earth's atmosphere despite the many conferences and growing interest in environmental problems. An aspect that has not received sufficient attention is the tremendous heat produced by human activities. This work links four elements in the built environment that are known for their high energy consumption (houses, supermarkets, greenhouses, and asphalt roads) according t

<abstract><p>Many variations of the algebraic Riccati equation (ARE) have been used to study nonlinear system stability in the control domain in great detail. Taking the quaternion nonsymmetric ARE (QNARE) as a generalized version of ARE, the time-varying QNARE (TQNARE) is introduced. This brings us to the main objective of this work: finding the TQNARE solution. The zeroing neural network (ZNN) technique, which has demonstrated a high degree of effectiveness in handling time-varying problems, is used to do this. Specifically, the TQNARE can be solved using the high order ZNN (HZNN) design, which is a member of the family of ZNN models that correlate to hyperpower iterative techniques. As a result, a novel

In this study, industrial fiber and polymer mixtures were used for high-speed impact (ballistic) applications where the effects of polymer (epoxy), polymeric
mixture (epoxy + unsaturated polyester), synthetic rubber (polyurethane), Kevlar fiber, polyethylene fiber (ultra High molecular weight) and carbon fiber.
Four successive systems of samples were prepared. the first system component made of (epoxy and 2% graphene and 20 layer of fiber), then ballistic test was
applied, the sample was successful in the test from a distance of 7 m. or more than, by using a pistol personally Glock, Caliber of 9 * 19 mm. The second
system was consisting of (epoxy, 2% graphene, 36 layers of fiber and one layer of hard rubber), it was succeeded

Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

Compensation is one of the most discussed topics in the arena of civil law that requires research About solutions to the damages that arise from the promotion of extremist ideas, which were imposed by the developments taking place in society and the increasing escalation of accidents and their increasing risks, which now threaten individuals and their property on a daily basis in large numbers, as the injured party always seeks to require quick compensation from the person responsible for the damage that satisfies his desires and removes the effects of the damage caused, The importance of compensation increases if the violation affects a person’s physical integrity or his right to life, which is the highest right recognized for humans in

problem of the research is the decline of the role of urban space with time as an influential system in societal relations. The research aims to define indicators for achieving social interaction in the city, and to determine indicators for achieving integration in the urban space, and to study the relationship between the integration of urban space and community interaction over time. the research assumed that by distinguishing the social interaction space from the urban space and developing urban spaces in order to be truly interactive spaces, this will help us achieve social interaction and build a positive relationship between them, which enables us to achieve integration within the urban spaces leading to social interaction. Because

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod