Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D CNNs have shown improved accuracy in the classification of ASD compared to traditional machine learning algorithms, on all these datasets with higher accuracy of 99.45%, 98.66%, and 90% for Autistic Spectrum Disorder Screening in Data for Adults, Children, and Adolescents respectively as they are better suited for the analysis of time series data commonly used in the diagnosis of this disorder
Attacking a transferred data over a network is frequently happened millions time a day. To address this problem, a secure scheme is proposed which is securing a transferred data over a network. The proposed scheme uses two techniques to guarantee a secure transferring for a message. The message is encrypted as a first step, and then it is hided in a video cover. The proposed encrypting technique is RC4 stream cipher algorithm in order to increase the message's confidentiality, as well as improving the least significant bit embedding algorithm (LSB) by adding an additional layer of security. The improvement of the LSB method comes by replacing the adopted sequential selection by a random selection manner of the frames and the pixels wit
... Show MoreNew series of metal ions complexes have been prepared from the new ligand [2,2′‐(5,5‐dimethylcyclohexane‐1,3‐diylidene)bis(azan‐1‐yl‐1‐ylidene)dibenzoic acid] derived from 5,5‐dimethylcyclohexane‐1,3‐dione and 2‐aminobenzoic acid. Accordingly, mono‐nuclear Ni(II), Pd(II), Re (II), and Pt(IV) complexes were prepared by the reaction of previous ligand with NiCl2.6H2O, PdCl2, ReCl5, and H2PtCl6.6H2O, respectively. The compounds have been characterized by Fourier‐transform infrared (FTIR), ultraviolet–visible (UV–vis), mass, H
Since the COVID-19 pandemic alarm was made by the severe acute respiratory syndrome (SARS)-coronavirus (CoV) 2, several institutions and agencies have pursued to clarify the viral virulence and infectivity. The fast propagation of this virus leads to an unprecedented rise in the number of cases worldwide. COVID-19 virus is exceptionally contagious that spreads through droplets, respiratory secretions, and direct contact. The enveloped, single-stranded RNA virus has a specific envelop region called (S) region encoding (S protein) that specifically binds to the host cell receptor. Viral infection requires receptors' participation on the host cell membrane's surface, a key- step for the viral invasion of susceptible cells.
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... Show MoreThe ligand 2-[1-(1H-indol-3-yl)ethylimino) methyl]naphthalene-1-ol, derived from 1-hydroxy-2-naphthaldehyde and 2-(1H-indol-3-yl)ethylamine, was used to produce a new sequence of metal ions complexes. Thus ligand reactions with NiCl2.6H2O, PdCl2, FeCl3.6H2O and H2PtCl6.6H2O were sequentially made to collect mono-nuclear Ni(II), Pd(II), Fe (III), and Pt(IV). (IR or FTIR), Ultraviolet Reflective (UV–visible), Mass Spectra analysis, Bohr-magnetic (B.M.), metal content, chloride content and molar conductivity have been the defining features of the composites. The Fe(III) and Pt(IV) complexes have octahedral geometries, while the Ni(II) complex has tetra
... Show MoreCommunity detection is useful for better understanding the structure of complex networks. It aids in the extraction of the required information from such networks and has a vital role in different fields that range from healthcare to regional geography, economics, human interactions, and mobility. The method for detecting the structure of communities involves the partitioning of complex networks into groups of nodes, with extensive connections within community and sparse connections with other communities. In the literature, two main measures, namely the Modularity (Q) and Normalized Mutual Information (NMI) have been used for evaluating the validation and quality of the detected community structures. Although many optimization algo
... Show MoreThe research involved a rapid, automated and highly accurate developed CFIA/MZ technique for estimation of phenylephrine hydrochloride (PHE) in pure, dosage forms and biological sample. This method is based on oxidative coupling reaction of 2,4-dinitrophenylhydrazine (DNPH) with PHE in existence of sodium periodate as oxidizing agent in alkaline medium to form a red colored product at ʎmax )520 nm (. A flow rate of 4.3 mL.min-1 using distilled water as a carrier, the method of FIA proved to be as a sensitive and economic analytical tool for estimation of PHE.
Within the concentration range of 5-300 μg.mL-1, a calibration curve was rectilinear, where the detection limit was 3.252 μg.mL
Researches in the field of evaluation of industrial products emotionally are internationally new and non-existing in the Arabic speaking countries, which is considered the crux of the problem in the current research, in addition to the need of the designers and design students to know how to measure the emotional responses for the industrial product in order to get benefit from them in their designs. The research objective is to get a tool that uses emojis in measuring the emotional responses for the products. The researcher designed an emotional verbal wheel and emojis wheel. The sample of the research consisted of (7) chairs different in design and use, and the respondents were (89) students. The most important results are:
1- Desi
DAIRMD Professor Hayder R. Al-Hamamy, **Professor Adil A. Noaimi, **Dr. Ihsan A. Al-Turfy, IOSR Journal of Dental and Medical Sciences (IOSR-JDMS), 2015