A content-based image retrieval (CBIR) is a technique used to retrieve images from an image database. However, the CBIR process suffers from less accuracy to retrieve images from an extensive image database and ensure the privacy of images. This paper aims to address the issues of accuracy utilizing deep learning techniques as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon, Kim, Kim, and Song (CKKS). To achieve these aims, a system has been proposed, namely RCNN_CKKS, that includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a

This article aims to provide a bibliometric analysis of intellectual capital research published in the Scopus database from 1956 to 2020 to trace the development of scientific activities that can pave the way for future studies by shedding light on the gaps in the field. The analysis focuses on 638 intellectual capital-related papers published in the Scopus database over 60 years, drawing upon a bibliometric analysis using VOSviewer. This paper highlights the mainstream of the current research in the intellectual capital field, based on the Scopus database, by presenting a detailed bibliometric analysis of the trend and development of intellectual capital research in the past six decades, including journals, authors, countries, inst

The green production of iron oxide nanoparticles (FeONPs) due to its numerous biotechnological uses has attracted a lot of attention and clean and eco-friendly approaches in the medical field.

A liquid-solid chromatography of Bovine Serum Albumin (BSA) on (diethylaminoethyl-cellulose) DEAE-cellulose adsorbent is worked experimentally, to study the effect of changing the influent concentration of (0.125, 0.25, 0.5, and 1 mg/ml) at constant volumetric flow rate Q=1ml/min. And the effect of changing the volumetric flow rate (1, 3, 5, and 10 ml/min) at constant influent concentration of Co=0.125mg/ml. By using a glass column of (1.5cm) I.D and (50cm) length, packed with adsorbent of DEAE-cellulose of height (7cm). The influent is introduced in to the column using peristaltic pump and the effluent concentration is investigated using UV-spectrophotometer at 30oC and 280nm wavelength. A spread (steeper) break-through curve is gained

The present study examines the extraction of lead (Pb), cadmium (Cd) and nickel (Ni) from   a contaminated soil by washing process. Ethylenediaminetetraacetic acid disodium salt (Na₂EDTA) and hydrochloric acid (HCl) solution were used as extractants.  Soil washing is one of the most suitable in-situ/ ex-situ remediation method in removing heavy metals. Soil was artificially contaminated with 500 mg/kg (Pb , Cd and Ni ).  A set of batch experiments were carried out at different conditions of  extractant concentration , contact time, pH and agitation speed. The results  showed  that the  maximum removal efficiencies  of (Cd, Pb  and Ni ) were (97, 88 and 24 )&nbs

With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect

Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a