Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither no

Dust is a frequent contributor to health risks and changes in the climate, one of the most dangerous issues facing people today. Desertification, drought, agricultural practices, and sand and dust storms from neighboring regions bring on this issue. Deep learning (DL) long short-term memory (LSTM) based regression was a proposed solution to increase the forecasting accuracy of dust and monitoring. The proposed system has two parts to detect and monitor the dust; at the first step, the LSTM and dense layers are used to build a system using to detect the dust, while at the second step, the proposed Wireless Sensor Networks (WSN) and Internet of Things (IoT) model is used as a forecasting and monitoring model. The experiment DL system

This paper deals with the design and implementation of an ECG system. The proposed system gives a new concept of ECG signal manipulation, storing, and editing. It consists mainly of hardware circuits and the related software. The hardware includes the circuits of ECG signals capturing, and system interfaces. The software is written using Visual Basic languages, to perform the task of identification of the ECG signal. The main advantage of the system is to provide a reported ECG recording on a personal computer, so that it can be stored and processed at any time as required. This system was tested for different ECG signals, some of them are abnormal and the other is normal, and the results show that the system has a good quality of diagno

The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.

Digital tampering identification, which detects picture modification, is a significant area of image analysis studies. This area has grown with time with exceptional precision employing machine learning and deep learning-based strategies during the last five years. Synthesis and reinforcement-based learning techniques must now evolve to keep with the research. However, before doing any experimentation, a scientist must first comprehend the current state of the art in that domain. Diverse paths, associated outcomes, and analysis lay the groundwork for successful experimentation and superior results. Before starting with experiments, universal image forensics approaches must be thoroughly researched. As a result, this review of variou

CdO films were deposited on substrates from glass, Silicon and Porous silicon by thermal chemical spray pyrolysis technique with different thicknesses (130 and 438.46) nm. Measurements of X-ray diffraction of CdO thin film proved that the structure of the Polycrystalline is cubic lattice, and its crystallite size is located within nano scale range where the perfect orientation is (200). The results show that the surface’s roughness and the root mean square increased with increasing the thickness of prepared films. The UV-Visible measurements show that the CdO films with different thicknesses possess an allowed direct transition with band gap (4) eV. AFM measurement revealed that the silicon porosity located in nano range. Cadmium oxide f

The Internet of Things (IoT) is an expanding domain that can revolutionize different industries. Nevertheless, security is among the multiple challenges that it encounters. A major threat in the IoT environment is spoofing attacks, a type of cyber threat in which malicious actors masquerade as legitimate entities. This research aims to develop an effective technique for detecting spoofing attacks for IoT security by utilizing feature-importance methods. The suggested methodology involves three stages: preprocessing, selection of important features, and classification. The feature importance determines the most significant characteristics that play a role in detecting spoofing attacks. This is achieved via two techniques: decision tr

A simple, fast, selective of a new flow injection analysis method coupled with potentiometric detection was used to determine vitamin B1 in pharmaceutical formulations via the prepared new selective membranes. Two electrodes were constructed for the determination of vitamin B1 based on the ion-pair vitamin B1-phosphotungestic acid (B1-PTA) in a poly (vinyl chloride) supported with a plasticized di-butyl phthalate (DBPH) and di-butyl phosphate (DBP). Applications of these ion selective electrodes for the determination of vitamin B1 in the pharmaceutical preparations for batch and flow injection systems were described. The ion selective membrane exhibited a near-Nernstian slope values 56.88 and 58.53 mV / decade, with the linear dy

The uses of traditional plant extract in the treatment of various diseases have been flourished. The present study, IJSR, Call for Papers, Online Journal

This study aimed to assess orthodontic postgraduate students’ use of social media during the COVID-19 lockdown. Ninety-four postgraduate students (67 master’s students and 27 doctoral students) were enrolled in the study and asked to fill in an online questionnaire by answering questions regarding their use of social media during the COVID-19 lockdown. The frequency distributions and percentages were calculated using SPSS software. The results showed that 99% of the students used social media. The most frequently used type of social media was Facebook, 94%, followed by YouTube, 78%, and Instagram, 65%, while Twitter and Linkedin were used less, and no one used Blogger. About 63% of the students used elements of social media to l