Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

A field experiment was carried out during two winter season 2013, 2014 at the field of the Department of Field Crops, College of Agriculture, University of Baghdad, to study the effect of seeds soaking with Gibberellic acid and foliar with Abscisic Acid on the growth, yield, and content of Anise oil seeds using factorial experiment within RCBD design with three replicates. The seeds was treated within GA3 were soaked with two concentrations of  30, and 60 mg. litter-1 in addition to without soaking and the code has been B0 , B1 , B2 overlapped these transactions with two concentrations of Abscisic Acid  3, and 6 mg. litter-1 in addition to without foliar A0 , A1 , A2The seeds to be treated  with GA3 are soaked for 24 hours prior

The continuous advancement in the use of the IoT has greatly transformed industries, though at the same time it has made the IoT network vulnerable to highly advanced cybercrimes. There are several limitations with traditional security measures for IoT; the protection of distributed and adaptive IoT systems requires new approaches. This research presents novel threat intelligence for IoT networks based on deep learning, which maintains compliance with IEEE standards. Interweaving artificial intelligence with standardization frameworks is the goal of the study and, thus, improves the identification, protection, and reduction of cyber threats impacting IoT environments. The study is systematic and begins by examining IoT-specific thre

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%.

Abstract

To evaluate the effectiveness of Phenolic Compound of Salvia Frigida on induced atopic dermatitis (AD) of mice. Forty mice were included in the study, divided in to four groups (10 mice/group): apparently healthy, induced AD without treatment, induced AD treated with tacrolimus 0.1% ointment, and induced AD treated with Phenolic Compound of Salvia Frigida cream 5%. Examination of histopathology was done and skin homogenates levels also measured. Levels of WBC, Eosinophil, skin tissue homogenate of IL-13 and IL-4, serum IgE, and histopathological scores were significantly increased among induced non treated AD group in comparison with control group. Comparisons of non-treated induced AD

Success in selecting the best among the sources of supply is one of the most important factors in the efficiency of the procurement activity in the company, because the proper selection of the source of the supply significantly affect the achievement of what is desired by the factors of quality, quantity, price and service, and the ability of the competent supplier to meet everything associated with this Factors of commitments, hereby supporting the procurement function's efforts to fully discharge its responsibilities, and in view of adopting of Al-Furat company quality management system by applying the standards of ISO (9001: 2015) and purpose of getting the on-demand benefits from the application of international standards reg

Autoría: Jehan Faris Yousif. Localización: Opción: Revista de Ciencias Humanas y Sociales. Nº. 89, 2019. Artículo de Revista en Dialnet.

The aim of the present study is to examine the effectiveness of a proposed unite in voluntary work in enhancing critical thinking skills and the attitudes towards responsible citizenship among eighth grade female students in the Sultanate of Oman. In order to collect the study data, the researchers employed a quasi-experimental research design with twenty female students from Al-Sideeqah bint Al-Sideeq for basic education school. The research data were collected via a critical thinking test that consisted of twenty-five items and a scale of twenty items under three different dimensions, which aimed to measure students' attitudes towards responsible citizenship. The researchers implemented these two instruments as pre- and post the experi

To date, comprehensive reviews and discussions of the strengths and limitations of Remote Sensing (RS) standalone and combination approaches, and Deep Learning (DL)-based RS datasets in archaeology have been limited. The objective of this paper is, therefore, to review and critically discuss existing studies that have applied these advanced approaches in archaeology, with a specific focus on digital preservation and object detection. RS standalone approaches including range-based and image-based modelling (e.g., laser scanning and SfM photogrammetry) have several disadvantages in terms of spatial resolution, penetrations, textures, colours, and accuracy. These limitations have led some archaeological studies to fuse/integrate multip