Determining the face of wearing a mask from not wearing a mask from visual data such as video and still, images have been a fascinating research topic in recent decades due to the spread of the Corona pandemic, which has changed the features of the entire world and forced people to wear a mask as a way to prevent the pandemic that has calmed the entire world, and it has played an important role. Intelligent development based on artificial intelligence and computers has a very important role in the issue of safety from the pandemic, as the Topic of face recognition and identifying people who wear the mask or not in the introduction and deep education was the most prominent in this topic. Using deep learning techniques and the YOLO (”You only look once”) neural network algorithm, which is an efficient real-time object identification algorithm, an intelligent system was developed in this thesis to distinguish which faces are wearing a mask and who is not wearing a wrong mask. The proposed system was developed based on data preparation, preprocessing, and adding a multi-layer neural network, followed by extracting the detection algorithm to improve the accuracy of the system. Two global data sets were used to train and test the proposed system and worked on it in three models, where the first contains the AIZOO data set, the second contains the MoLa RGB CovSurv data set, and the third model contains a combined data set for the two in order to provide cases that are difficult to identify and the accuracy results that were obtained. obtained from the merging datasets showed that the face mask (0.953) and the face recognition system were the most accurate in detecting them (0.916).
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 med
... Show More
(9)
This paper presents a new algorithm in an important research field which is the semantic word similarity estimation. A new feature-based algorithm is proposed for measuring the word semantic similarity for the Arabic language. It is a highly systematic language where its words exhibit elegant and rigorous logic. The score of sematic similarity between two Arabic words is calculated as a function of their common and total taxonomical features. An Arabic knowledge source is employed for extracting the taxonomical features as a set of all concepts that subsumed the concepts containing the compared words. The previously developed Arabic word benchmark datasets are used for optimizing and evaluating the proposed algorithm. In this paper,
... Show More
Improving students’ use of argumentation is front and center in the increasing emphasis on scientific practice in K-12 Science and STEM programs. We explore the construct validity of scenario-based assessments of claim-evidence-reasoning (CER) and the structure of the CER construct with respect to a learning progression framework. We also seek to understand how middle school students progress. Establishing the purpose of an argument is a competency that a majority of middle school students meet, whereas quantitative reasoning is the most difficult, and the Rasch model indicates that the competencies form a unidimensional hierarchy of skills. We also find no evidence of differential item functioning between different scenarios, suggesting
... Show MoreThis paper presents the Extended State Observer (ESO) based repetitive control (RC) for piezoelectric actuator (PEA) based nano-positioning systems. The system stability is proved using Linear Matrix Inequalities (LMIs), which guarantees the asymptotic stability of the system. The ESObased RC used in this paper has the ability to eliminate periodic disturbances, aperiodic disturbances and model uncertainties. Moreover, ESO can be tuned using only two parameters and the model free approach of ESO-based RC, makes it an ideal solution to overcome the challenges of nano-positioning system control. Different types of periodic and aperiodic disturbances are used in simulation to demonstrate the effectiveness of the algorithm. The comparison studi
... Show More
(5)
(4)
Electromyogram (EMG)-based Pattern Recognition (PR) systems for upper-limb prosthesis control provide promising ways to enable an intuitive control of the prostheses with multiple degrees of freedom and fast reaction times. However, the lack of robustness of the PR systems may limit their usability. In this paper, a novel adaptive time windowing framework is proposed to enhance the performance of the PR systems by focusing on their windowing and classification steps. The proposed framework estimates the output probabilities of each class and outputs a movement only if a decision with a probability above a certain threshold is achieved. Otherwise (i.e., all probability values are below the threshold), the window size of the EMG signa
... Show More
(25)
Objectives Bromelain is a potent proteolytic enzyme that has a unique functionality makes it valuable for various therapeutic purposes. This study aimed to develop three novel formulations based on bromelain to be used as chemomechanical caries removal agents. Methods The novel agents were prepared using different concentrations of bromelain (10–40 wt. %), with and without 0.1–0.3 wt. % chloramine T or 0.5–1.5 wt. % chlorhexidine (CHX). Based on the enzymatic activity test, three formulations were selected; 30 % bromelain (F1), 30 % bromelain-0.1 % chloramine (F2) and 30 % bromelain-1.5 % CHX (F3). The assessments included molecular docking, Fourier-transform infrared spectroscopy (FTIR), viscosity and pH measurements. The efficiency
... Show More
(8)
(7)
Objectives: Bromelain is a potent proteolytic enzyme that has a unique functionality makes it valuable for various therapeutic purposes. This study aimed to develop three novel formulations based on bromelain to be used as chemomechanical caries removal agents. Methods: The novel agents were prepared using different concentrations of bromelain (10–40 wt. %), with and without 0.1–0.3 wt. % chloramine T or 0.5–1.5 wt. % chlorhexidine (CHX). Based on the enzymatic activity test, three formulations were selected; 30 % bromelain (F1), 30 % bromelain-0.1 % chloramine (F2) and 30 % bromelain-1.5 % CHX (F3). The assessments included molecular docking, Fourier-transform infrared spectroscopy (FTIR), viscosity and pH measurements. The efficie
... Show More
(8)
(7)
Products’ quality inspection is an important stage in every production route, in which the quality of the produced goods is estimated and compared with the desired specifications. With traditional inspection, the process rely on manual methods that generates various costs and large time consumption. On the contrary, today’s inspection systems that use modern techniques like computer vision, are more accurate and efficient. However, the amount of work needed to build a computer vision system based on classic techniques is relatively large, due to the issue of manually selecting and extracting features from digital images, which also produces labor costs for the system engineers. In this research, we pr
... Show More
(2)
(1)