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
Alzheimer’s disease (AD) is an age-related progressive and neurodegenerative disorder, which is characterized by loss of memory and cognitive decline. It is the main cause of disability among older people. The rapid increase in the number of people living with AD and other forms of dementia due to the aging population represents a major challenge to health and social care systems worldwide. Degeneration of brain cells due to AD starts many years before the clinical manifestations become clear. Early diagnosis of AD will contribute to the development of effective treatments that could slow, stop, or prevent significant cognitive decline. Consequently, early diagnosis of AD may also be valuable in detecting patients with dementia who have n
... Show MoreBeen Antkhav three isolates of soil classified as follows: Bacillus G3 consists of spores, G12, G27 led Pal NTG treatment to kill part of the cells of the three isolates varying degrees treatment also led to mutations urged resistance to streptomycin and rifampicin and double mutations
Ankylosing spondylitis is a complex debilitating disease because its pathogenesis is not clear. This study aims at detecting some pathogenesis factors that lead to induce the disease. Chlamydia pneumoniae is one of these pathogenesis factors which acts as a triggering factor for the disease. The study groups included forty Iraqi Ankylosing spondylitis patients and forty healthy persons as a control group. Immunological and molecular examinations were done to detect Chlamydia. pneumoniae in AS group. The immunological results were performed by Enzyme-Linked Immunosorbent Assay (ELISA) to detect anti-IgG and anti-IgM antibodies of C. pneumoniae revealed that five of forty AS patients' samples (12.5%) were positive for anti-IgG and IgM C. pneu
... Show More<abstract><p>Many variations of the algebraic Riccati equation (ARE) have been used to study nonlinear system stability in the control domain in great detail. Taking the quaternion nonsymmetric ARE (QNARE) as a generalized version of ARE, the time-varying QNARE (TQNARE) is introduced. This brings us to the main objective of this work: finding the TQNARE solution. The zeroing neural network (ZNN) technique, which has demonstrated a high degree of effectiveness in handling time-varying problems, is used to do this. Specifically, the TQNARE can be solved using the high order ZNN (HZNN) design, which is a member of the family of ZNN models that correlate to hyperpower iterative techniques. As a result, a novel
... Show MoreIn modern hydraulic control systems, the trend in hydraulic power applications is to improve efficiency and performance. “Proportional valve” is generally applied to pressure, flow and directional-control valves which continuously convert a variable input signal into a smooth and proportional hydraulic output signal. It creates a variable resistance (orifice) upstream and downstream of a hydraulic actuator, and is meter in/meter out circuit and hence pressure drop, and power losses are inevitable. If velocity (position) feedback is used, flow pattern control is possible. Without aforementioned flow pattern, control is very “loose” and relies on “visual” feed back by the operator. At this point, we should examine how this valv
... Show MoreThe research illustrated that the theory of constraints is "A group of concepts and basics that aim at helping the management in order to determine the difficulties and how to overcome these difficulties through determining the necessary change and how these change can be done efficiently and effectively ".
The research showed that the theory of constraint including its tools calls for magnifying throughput, constricting the product cycle , determining the optimal production mix , utilization of scare resources and canceling the idle time , that achieving throughput increasing then increasing profit .
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