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
Due to the continuing demand for larger bandwidth, the optical transport becoming general in the access network. Using optical fiber technologies, the communications infrastructure becomes powerful, providing very high speeds to transfer a high capacity of data. Existing telecommunications infrastructures is currently widely used Passive Optical Network that apply Wavelength Division Multiplexing (WDM) and is awaited to play an important role in the future Internet supporting a large diversity of services and next generation networks. This paper presents a design of WDM-PON network, the simulation and analysis of transmission parameters in the Optisystem 7.0 environment for bidirectional traffic. The simulation shows the behavior of optical
... Show MoreThis paper proposes a new method Object Detection in Skin Cancer Image, the minimum
spanning tree Detection descriptor (MST). This ObjectDetection descriptor builds on the
structure of the minimum spanning tree constructed on the targettraining set of Skin Cancer
Images only. The Skin Cancer Image Detection of test objects relies on their distances to the
closest edge of thattree. Our experimentsshow that the Minimum Spanning Tree (MST) performs
especially well in case of Fogginessimage problems and in highNoisespaces for Skin Cancer
Image.
The proposed method of Object Detection Skin Cancer Image wasimplemented and tested on
different Skin Cancer Images. We obtained very good results . The experiment showed that
The objective of this study was to evaluate the alteration in levels of gonado trophins hormones i.e.,Leutizing (LH),Follicular(FSH) in sera of patients with thyroid disorders and molecular binding study of (LH ,FSH) with their antibodies The study was conducted at the specialized center for endocrinology and diabetes from January / 2009 to March / 2010.Two hundreds and twenty three Iraqi subjects, 109 patients with thyroid disorders at age range between (40-50) years and 114 healthy individuals as control group were included in this study.The majority of patients were female with hyperthyroidism and (49.54 % ) were at age range between(40 - 50) years. The levels of hormones(LH,FSH.tri iodothyronine(T3).thyroxine(T4), thy
... Show MoreA modification to cascaded single-stage distributed amplifier (CSSDA) design by using active inductor is proposed. This modification is shown to render the amplifier suitable for high gain operation in small on-chip area. Microwave office program simulation of the Novel design approach shows that it has performance compatible with the conventional distributed amplifiers but with smaller area. The CSSDA is suitable for optical and satellite communication systems.
Objective: Detection the presumptive prevalence of silent celiac disease in patients with type 1 diabetes mellitus with determination of which gender more likely to be affected.
Methods: One hundred twenty asymptomatic patients [75 male , 45 female] with type 1 diabetes mellitus with mean age ± SD of 11.25 ± 2.85 year where included in the study . All subjects were serologically screened for the presence of anti-tissue transglutaminase IgA antibodies (anti-tTG antibodies) by Enzyme-Linked Immunosorbent Assay (ELISA) & total IgA was also measured for all using radial immunodiffusion plate . Anti-tissue transglutaminase IgG was selectively done for patients who were expressing negative anti-tissue transglutaminase IgA with low tot
Objective: Detection the presumptive prevalence of
silent celiac disease in patients with type 1 diabetes
mellitus with determination of which gender more
likely to be affected.
Methods: One hundred twenty asymptomatic patients
[75 male , 45 female] with type 1 diabetes mellitus
with mean age ± SD of 11.25 ± 2.85 year where
included in the study . All subjects were serologically
screened for the presence of anti-tissue transglutaminase
IgA antibodies (anti-tTG antibodies) by Enzyme-
Linked Immunosorbent Assay (ELISA) & total IgA
was also measured for all using radial
immunodiffusion plate . Anti-tissue transglutaminase
IgG was selectively done for patients who were
expressing negative anti-
Introduction The Hybrid Gamma Camera (HGC) is being developed to enhance the localisation of radiopharmaceutical uptake in targeted tissues during surgical procedures such as sentinel lymph node (SLN) biopsy. Purpose To assess the capability of the HGC, a lymph-node-contrast (LNC) phantom was constructed for an evaluative study simulating medical scenarios of varying radioactivity concentration and SLN size. Materials and methods The phantom was constructed using two methyl methacrylate PMMA plates (8 mm thick). The SLNs were simulated by drilling circular wells of diameters ranging between 10 mm and 2.5 mm (16 wells in total) in one plate. These simulated SLNs were placed underneath scattering material with thicknesses ranging between 5 mm
... Show MoreIn this paper we reported the microfabrication of three-dimensional structures using two-photon polymerization (2PP) in a mixture of MEH-PPV and an acrylic resin. Femtosecond laser operating at 800nm was employed for the two-photon polymerization processes. As a first step in this project we obtained the better composition in order to fabricate microstructers of MEH-PPV in the resin via two-photon polymerzation. Acknowledgement:This research is support by Mazur Group, Harvrad Universirt.
This paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett
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