Jordan University of Science and Technology

About: Jordan University of Science and Technology is a education organization based out in Irbid, Irbid, Jordan. It is known for research contribution in the topics: Population & Health care. The organization has 7582 authors who have published 13166 publications receiving 298158 citations. The organization is also known as: JUST.

Efficacy and Safety of White Willow Bark (Salix alba) Extracts.

Abstract: Willow bark extract has been used for thousands of years as an anti-inflammatory, antipyretic, and analgesic. In spite of its long history of use, relatively few human and animal studies have been published that confirm anecdotal observations. A small number of clinical studies have been conducted that support the use of willow bark extracts in chronic lower back and joint pain and osteoarthritis. Willow bark extracts also are widely used in sports performance and weight loss products presumably because of anti-inflammatory and analgesic activities, although no human studies have been published that specifically and directly document beneficial effects. In recent years, various in vitro and animal studies have demonstrated that the anti-inflammatory activity of willow bark extract is associated with down regulation of the inflammatory mediators tumor necrosis factor-α and nuclear factor-kappa B. Although willow bark extracts are generally standardized to salicin, other ingredients in the extracts including other salicylates as well as polyphenols, and flavonoids may also play prominent roles in the therapeutic actions. Adverse effects appear to be minimal as compared to non-steroidal anti-inflammatory drugs including aspirin. The primary cause for concern may relate to allergic reactions in salicylate-sensitive individuals.

An Improved Apriori Algorithm for Association Rules

Abstract: There are several mining algorithms of association rules. One of the most popular algorithms is Apriori that is used to extract frequent itemsets from large database and getting the association rule for discovering the knowledge. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time for scanning the whole database searching on the frequent itemsets, and presents an improvement on Apriori by reducing that wasted time depending on scanning only some transactions. The paper shows by experimental results with several groups of transactions, and with several values of minimum support that applied on the original Apriori and our implemented improved Apriori that our improved Apriori reduces the time consumed by 67.38% in comparison with the original Apriori, and makes the Apriori algorithm more efficient and less time consuming.

Position of the mental foramen in a northern regional Jordanian population

Abstract: The mental foramen is frequently encountered in a number of maxillofacial surgical procedures. Its position has been shown to vary according to race. The aim was to study the position, shape, and appearance of the mental foramen, as seen on panoramic radiographs of Jordanians, and to compare our findings with international values. Panoramic radiographs were randomly selected from the records of dental patients attending three dental services, and were analyzed according to patients’ age and gender, and the mental foramina’s anterior–posterior and superior–inferior positions, shape, appearance, and symmetry. There were 860 cases (1,720 sides) with a female-to-male ratio of 1:1.4, and mean age of 24. The most frequent anterior–posterior position was in the area between the long axes of first and second mandibular premolar teeth. With advancing age, there was an increase in the frequency of more posterior positioning. The anterior–posterior position was asymmetrical in 33% of cases. The most frequent superior–inferior position was below the level of apices of mandibular premolar teeth roots. With advancing age there was an increase in the frequency of more inferior positioning. The superior–inferior position was asymmetrical in 14% of cases. The majority of foramina were round in shape, and the most frequent appearance was the continuous type. Accessory mental foramina were seen in 10% of the cases. The position of the mental foramen on panoramic radiographs in this selected group of Jordanians is most commonly below and between the mandibular premolar teeth, and the most frequent appearance was the continuous type. These results are similar to previous findings in Caucasian populations.

K -Means clustering and neural network for object detecting and identifying abnormality of brain tumor

Abstract: Brain tumor diagnosis is a challenging and difficult process in view of the assortment of conceivable shapes, regions, and image intensities. The pathological detection and identification of brain tumor and comparison among normal and abnormal tissues need grouped scientific techniques for features extraction, displaying, and measurement of the disease images. Our study shows an improved automated brain tumor segmentation and identification approach using ANN from MR images without human mediation by applying the best attributes toward preparatory brain tumor case revelation. To obtain the exact district region of brain tumor from MR images, we propose a brain tumor segmentation technique that has three noteworthy improvement focuses. To begin with, K-means clustering will be utilized as a part of the principal organization in the process of improving the MR image to be marked in the districts regions in light of their gray scale. Second, ANN is utilized to choose the correct object in view of training phase. Third, texture feature of brain tumor area will be extracted to the division stage. With respect to the brain tumor identification, the grayscale features are utilized to analyze and diagnose the brain tumor to differentiate the benign and malignant cases. According to the study results demonstrated that: (1) enhancement adaptive strategy was utilized as post-processing in brain tumor identification; (2) identify and build an assessment foundation of automated segmentation and identification for brain tumor cases; (3) highlight the methods based on region growing method and K-means clustering technique to select the best region; and (4) evaluate the proficiency of the foreseen outcomes by comparing ANN and SVM segmentation outcomes, and brain tumor cases classification. The ANN approach classifier recorded accuracy of 94.07% with line assumption (brain tumor cases classification) and sensitivity of 90.09% and specificity of 96.78%.

An efficient employment of internet of multimedia things in smart and future agriculture

Abstract: Efficiently managing the irrigation process has become necessary to utilize water stocks due to the lack of water resources worldwide. Parched plant leads into hard breathing process, which would result in yellowing leaves and sprinkles in the soil. In this work, yellowing leaves and sprinkles in the soil have been observed using multimedia sensors to detect the level of plant thirstiness in smart farming. We modified the IoT concepts to draw an inspiration towards the perspective vision of ’Internet of Multimedia Things’ (IoMT). This research focuses on the smart employment of internet of Multimedia sensors in smart farming to optimize the irrigation process. The concepts of image processing work with IOT sensors and machine learning methods to make the irrigation decision. sensors reading have been used as training data set indicating the thirstiness of the plants, and machine learning techniques including the state-of-the-art deep learning were used in the next phase to find the optimal decision. The conducted experiments in this research are promising and could be considered in any smart irrigation system. The experimental results showed that the use of deep learning proves to be superior in the Internet of Multimedia Things environment.