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

Recent population studies have shown that placenta accreta spectrum (PAS) disorders remain undiagnosed before delivery in half to two-thirds of cases. In a series from specialist diagnostic units in the USA, around one-third of cases of PAS disorders were not diagnosed during pregnancy. Maternal

This study investigates the feasibility of a mobile robot navigating and discovering its location in unknown environments, followed by the creation of maps of these navigated environments for future use. First, a real mobile robot named TurtleBot3 Burger was used to achieve the simultaneous localization and mapping (SLAM) technique for a complex environment with 12 obstacles of different sizes based on the Rviz library, which is built on the robot operating system (ROS) booted in Linux. It is possible to control the robot and perform this process remotely by using an Amazon Elastic Compute Cloud (Amazon EC2) instance service. Then, the map to the Amazon Simple Storage Service (Amazon S3) cloud was uploaded. This provides a database

The aim of this paper is to present a weak form of -light functions by using -open set which is -light function, and to offer new concepts of disconnected spaces and totally disconnected spaces. The relation between them have been studied. Also, a new form of -totally disconnected and inversely -totally disconnected function have been defined, some examples and facts was submitted.

The rise of Industry 4.0 and smart manufacturing has highlighted the importance of utilizing intelligent manufacturing techniques, tools, and methods, including predictive maintenance. This feature allows for the early identification of potential issues with machinery, preventing them from reaching critical stages. This paper proposes an intelligent predictive maintenance system for industrial equipment monitoring. The system integrates Industrial IoT, MQTT messaging and machine learning algorithms. Vibration, current and temperature sensors collect real-time data from electrical motors which is analyzed using five ML models to detect anomalies and predict failures, enabling proactive maintenance. The MQTT protocol is used for efficient com

This paper considers the nonlinear homogeneous fractional Burger's equation as a type of nonlinear fractional partial differential equations (FPDE). Our goal in this paper is to show that an initial value problem (IVP) can be modified with a second initial condition when (α ∈ ( 1,2 ]) as the velocity of the movement, and the obtained solution agrees with the nature of the wave with space and time for the problem. The Caputo fractional derivative is used in all the fractional derivatives. Also, the algorithm of the Laplace transform decomposition method (LTDM) for fractional PDEs is presented. The approximate solution converges to the exact solution in Theorem 1. Also, a numerical simulation is made to confirm the theoretical resu

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD