In recent years, there has been a significant increase in research demonstrating the new and diverse uses of non-thermal food processing technologies, including more efficient mixing and blending processes, faster energy and mass transfer, lower temperature and selective extraction, reduced thermal and concentration gradients, reduced equipment size, faster response to extraction control, faster start-up, increased production, and a reduction in the number of steps in preparation and processing. Applications of ultrasound technology have indicated that this technology has a promising and significant future in the food industry and preservation, and there is a wide scope for its use due to the higher purity of final products and the

KE Sharquie, SA Al-Mashhadani, AA Noaimi, AA Hasan, Journal of Cutaneous and Aesthetic Surgery, 2012 - Cited by 19

With the high usage of computers and networks in the current time, the amount of security threats is increased. The study of intrusion detection systems (IDS) has received much attention throughout the computer science field. The main objective of this study is to examine the existing literature on various approaches for Intrusion Detection. This paper presents an overview of different intrusion detection systems and a detailed analysis of multiple techniques for these systems, including their advantages and disadvantages. These techniques include artificial neural networks, bio-inspired computing, evolutionary techniques, machine learning, and pattern recognition.

Most Internet-tomography problems such as shared congestion detection depend on network measurements. Usually, such measurements are carried out in multiple locations inside the network and relied on local clocks. These clocks usually skewed with time making these measurements unsynchronized and thereby degrading the performance of most techniques. Recently, shared congestion detection has become an important issue in many computer networked applications such as multimedia streaming and
peer-to-peer file sharing. One of the most powerful techniques that employed in literature is based on Discrete Wavelet Transform (DWT) with cross-correlation operation to determine the state of the congestion. Wavelet transform is used as a de-noisin

The advent of UNHCR reports has given rise to the uniqueness of its distinctive way of image representation and using semiotic features. So, there are a lot of researches that have investigated UNHCR reports, but no research has examined images in UNHCR reports of displaced Iraqis from a multimodal discourse perspective. The present study suggests that the images are, like language, rich in many potential meanings and are governed by clearly visual grammar structures that can be employed to decode these multiple meanings. Seven images are examined in terms of their representational, interactional and compositional aspects. Depending on the results, this study concludes that the findings support the visual grammar theory and highlight the va

Data of multispectral satellite image (Landsat- 5 and Landsat-7) was used to monitoring the case of study area in the agricultural (extension and plant density), using ArcGIS program by the method of analysis (Soil adjusted vegetative Index). The data covers the selected area at west of Baghdad Government with a part of the Anbar and Karbala Government. Satellite image taken during the years 1990, 2001 and 2007. The scene of Satellite Image is consists of seven of spectral band for each satellite, Landsat-5(TM) thematic mapper for the year 1990, as well as satellite Landsat-7 (ETM+) Enhancement thematic mapper for the year 2001 and 2007. The results showed that in the period from 1990 to 2001 decreased land area exposed (bare) and increased

This research describes a new model inspired by Mobilenetv2 that was trained on a very diverse dataset. The goal is to enable fire detection in open areas to replace physical sensor-based fire detectors and reduce false alarms of fires, to achieve the lowest losses in open areas via deep learning. A diverse fire dataset was created that combines images and videos from several sources. In addition, another self-made data set was taken from the farms of the holy shrine of Al-Hussainiya in the city of Karbala. After that, the model was trained with the collected dataset. The test accuracy of the fire dataset that was trained with the new model reached 98.87%.