This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental

In this work, the optical properties of Cu₂S with different thickness
(1400, 2400, 4400) Ǻ have been prepared by chemical spray pyrolys
is method onto clean glass substrate heated at 283 ^oC ±2. The effect
of thickness on the optical properties of Cu2S has been studied. It
was found that the optical properties of the electronic transitions on
fundamental absorption edge were direct allowed and the value of the
optical energy gap of Cu₂S (Eg) for direct transition decreased from
(2.4-2.1) eV with increasing of the thickness from (1400 - 4400)Ǻ
respectively. Also it was found that the absorption coefficient is
increased with increasing of thicknesses. The optical constants such<

 Additive aluminum powder to the polystyrene to prepare the composites Polystyrene– Aluminum.The samples were prepared by using mechanical  compressed method at low pressure and a temperature 120°C.         Measurements  of absorbance and reflectance spectra were carried out by UV-Visible  spectrophotometer , the effect of additive aluminum  on the optical band  gap Eop and  optical constants ( refractive index n, extinction coefficient k ,dielectric constant ε and optical conductivity σop)  were studied for the prepared composites .         Results showed a decrease in the Eop with increasing  perc

  The Influence of annealing temperature on the optical properties of (CuInSe2) thin films was studied. Thermal evaporation in vacuum technique has been used for films deposited on glass substrates, these films were annealed in vacuum at (100C°, 200C°) for (2 hours). The optical properties were studied in the range (300-900) nm. The obtained results revealed a reduction in energy band gap with annealing temperature . optical parameters such as reflectance, refractive index, extinction coefficient, real and imaginary parts of the dielectric constant, skin depth and optical conductivity are investigated before and after annealing. It was found that all these parameters were affected by annealing temperature.
 

Tin Oxide (SnO2) films have been deposited by spray pyrolysis technique at different substrate temperatures. The effects of substrate temperature on the structural, optical and electrical properties of SnO2 films have been investigated. The XRD result shows a polycrystalline structure for SnO2 films at substrate temperature of 673K. The thickness of the deposited film was of the order of 200 nm measured by Toulansky method. The energy gap increases from 2.58eV to 3.59 eV when substrate temperature increases from 473K to 673K .Electrical conductivity is 4.8*10-7(.cm)-1 for sample deposited at 473K while it increases to 8.7*10-3 when the film is deposited at 673K