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

       Remote sensing is a source of up-to-date information. The present study relied on various approaches for gathering information, including descriptive, quantitative and quantitative analytical processes. Particularly,  we conducted the analysis of the satellite data ETM + of the satellite Landsat7 and the digital models of Digital Elevation Model of SRTM using ArcGIS9.2. The model depends on primary mathematical equations and  constitutes an essential base for GIS applications that rely on data, computer, and software, performing the processes of data entry, analysis and processing. This paper deals with the geomorphological characteristics of a selected study area in Kirkuk province. The cha

      Background: Left ventricular function and volumes have major diagnostic and prognostic importance in patients with various cardiac diseases, such as ischemic heart disease which is a life-threatening heart disease condition characterized by systolic dysfunction and a decrease in cardiac output. 

According to left ventricular ejection fraction, the degree of ischemic heart disease was classified as mild, moderate, and severe. To determine cardiac function and hemodynamics, the echocardiography technique is used, which is a noninvasive diagnostic method.

Patients and Methods: The study included 216 patients between 25 and 75 years old; 121 males and 95 females; 265 normal individuals (age range: 25 to 75 years ol

Image is an important digital information that used in many internet of things (IoT) applications such as transport, healthcare, agriculture, military, vehicles and wildlife. etc. Also, any image has very important characteristic such as large size, strong correlation and huge redundancy, therefore, encrypting it by using single key Advanced Encryption Standard (AES) through IoT communication technologies makes it vulnerable to many threats, thus, the pixels that have the same values will be encrypted to another pixels that have same values when they use the same key. The contribution of this work is to increase the security of transferred image. This paper proposed multiple key AES algorithm (MECCAES) to improve the security of the tran

Enhancement of the performance for hybrid solar air conditioning system was presented in this paper. The refrigerant temperature leaving the condenser was controlled using three-way valve, this valve was installed after the compressor to regulate refrigerant flow rate towards the solar system. A control system using data logger, sensors and computer was proposed to set the opening valve ratio. The function of control program using LabVIEW software is to obtain a minimum refrigerant temperature from the condenser outlet to enhance the overall COP of the unit by increasing the degree of subcooled refrigerant. A variable load electrical heater with coiled pipe was used instead of the solar collector and the storage tank to simulate the sola

Detection and classification of animals is a major challenge that is facing the researchers. There are five classes of vertebrate animals, namely the Mammals, Amphibians, Reptiles, Birds, and Fish, and each type includes many thousands of different animals. In this paper, we propose a new model based on the training of deep convolutional neural networks (CNN) to detect and classify two classes of vertebrate animals (Mammals and Reptiles). Deep CNNs are the state of the art in image recognition and are known for their high learning capacity, accuracy, and robustness to typical object recognition challenges. The dataset of this system contains 6000 images, including 4800 images for training. The proposed algorithm was tested by using 1200

A non-destructive assay (NDA) for radioactive waste drum has been studied
using a local manufacturing gamma scanning system. The gamma system has been
designed and implemented using scanning system contains a high efficiency
portable HPGe detector for characterization and surveying the radioactive waste
drums at Al-Tuwaitha site- Baghdad. To achieve identification with nonhomogenous
radioactive waste drum, six parallel plastic pipes (2cm in diameter)
were inserted inside the cement type Portland contain radioactive sources and
located at different distances from the outer diameter of the drum. The efficiency
calibration is measured by conventional technique, using five miscellaneous radio
nuclides with drum. Th

     Bacillus subtilis, an isolate of bacillus genus, was obtained from the laboratories of Ministry of Science and Technology. The best efficient Bacillus subtilis isolate in cellulose and semi-cellulose hydrolysis was treated with Dielectric-barrier Discharge (DBD). Atmospheric cold plasma technique (non-thermal) was used by exposing them at different times (2, 3, 4 and 5 mins) separately as a first stage, and then 60 seconds after any treatment separately as a second stage. After 48 hours, the difference between the plasma source and the sample was fixed at 0.5 cm. The results showed a variation in the growth of the isolate according to the exposure time by the appearance of culture turbidity and the estimation o