Preferred Language
Articles
/
bsj-6213
Arabic Speech Classification Method Based on Padding and Deep Learning Neural Network
...Show More Authors

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 evaluate the pronunciation of the Arabic alphabet. Voice data from six school children are recorded and used to test the performance of the proposed method. The padding technique has been used to augment the voice data before feeding the data to the CNN structure to developed the classification model. In addition, three other feature extraction techniques have been introduced to enable the comparison of the proposed method which employs padding technique. The performance of the proposed method with padding technique is at par with the spectrogram but better than mel-spectrogram and mel-frequency cepstral coefficients. Results also show that the proposed method was able to distinguish the Arabic alphabets that are difficult to pronounce. The proposed method with padding technique may be extended to address other voice pronunciation ability other than the Arabic alphabets.

Scopus Clarivate Crossref
View Publication Preview PDF
Quick Preview PDF
Publication Date
Mon Jul 15 2024
Journal Name
2024 46th Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (embc)
Automatic COVID-19 Detection from Chest X-ray using Deep MobileNet Convolutional Neural Network
...Show More Authors

View Publication
Scopus (1)
Scopus Crossref
Publication Date
Wed Mar 24 2021
Journal Name
Ieee Access
Smart IoT Network Based Convolutional Recurrent Neural Network With Element-Wise Prediction System
...Show More Authors

An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to

... Show More
Scopus (12)
Crossref (12)
Scopus Clarivate Crossref
Publication Date
Sun Feb 03 2019
Journal Name
Journal Of The College Of Education For Women
Solution of the electromechanical machining problem using the collocation method based on Bernstein polynomials
...Show More Authors

0

View Publication Preview PDF
Publication Date
Sun Jan 14 2018
Journal Name
Journal Of Engineering
A Nonlinear MIMO-PID Neural Controller Design for Vehicle Lateral Dynamics model based on Modified Elman Neural Network
...Show More Authors

This paper presents a new design of a nonlinear multi-input multi-output PID neural controller of the active brake steering force and the active front steering angle for a 2-DOF vehicle model based on modified Elman recurrent neural. The goal of this work is to achieve the stability and to improve the vehicle dynamic’s performance through achieving the desired yaw rate and reducing the lateral velocity of the vehicle in a minimum time period for preventing the vehicle from slipping out the road curvature by using two active control actions: the front steering angle and the brake steering force. Bacterial forging optimization algorithm is used to adjust the parameters weights of the proposed controller. Simulation resul

... Show More
View Publication Preview PDF
Publication Date
Mon Apr 03 2023
Journal Name
International Journal Of Online And Biomedical Engineering (ijoe)
An Integrated Grasshopper Optimization Algorithm with Artificial Neural Network for Trusted Nodes Classification Problem
...Show More Authors

Wireless Body Area Network (WBAN) is a tool that improves real-time patient health observation in hospitals, asylums, especially at home. WBAN has grown popularity in recent years due to its critical role and vast range of medical applications. Due to the sensitive nature of the patient information being transmitted through the WBAN network, security is of paramount importance. To guarantee the safe movement of data between sensor nodes and various WBAN networks, a high level of security is required in a WBAN network. This research introduces a novel technique named Integrated Grasshopper Optimization Algorithm with Artificial Neural Network (IGO-ANN) for distinguishing between trusted nodes in WBAN networks by means of a classifica

... Show More
View Publication
Scopus Clarivate Crossref
Publication Date
Fri Sep 01 2023
Journal Name
Journal Of Engineering
Dual Stages of Speech Enhancement Algorithm Based on Super Gaussian Speech Models
...Show More Authors

Various speech enhancement Algorithms (SEA) have been developed in the last few decades. Each algorithm has its advantages and disadvantages because the speech signal is affected by environmental situations. Distortion of speech results in the loss of important features that make this signal challenging to understand. SEA aims to improve the intelligibility and quality of speech that different types of noise have degraded. In most applications, quality improvement is highly desirable as it can reduce listener fatigue, especially when the listener is exposed to high noise levels for extended periods (e.g., manufacturing). SEA reduces or suppresses the background noise to some degree, sometimes called noise suppression alg

... Show More
View Publication Preview PDF
Crossref (4)
Crossref
Publication Date
Thu Dec 28 2017
Journal Name
Al-khwarizmi Engineering Journal
Simulation Study of Mass Transfer Coefficient in Slurry Bubble Column Reactor Using Neural Network
...Show More Authors

 

The objective of this study was to develop neural network algorithm, (Multilayer Perceptron), based correlations for the prediction overall volumetric mass-transfer coefficient (kLa), in slurry bubble column for gas-liquid-solid systems. The Multilayer Perceptron is a novel technique based on the feature generation approach using back propagation neural network. Measurements of overall volumetric mass transfer coefficient were made with the air - Water, air - Glycerin and air - Alcohol systems as the liquid phase in bubble column of 0.15 m diameter. For operation with gas velocity in the range 0-20 cm/sec, the overall volumetric mass transfer coefficient was found to decrease w

... Show More
View Publication Preview PDF
Publication Date
Wed Dec 01 2021
Journal Name
Computers & Electrical Engineering
Utilizing different types of deep learning models for classification of series arc in photovoltaics systems
...Show More Authors

View Publication
Crossref (12)
Crossref
Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
Self-Localization of Guide Robots Through Image Classification
...Show More Authors

The field of autonomous robotic systems has advanced tremendously in the last few years, allowing them to perform complicated tasks in various contexts. One of the most important and useful applications of guide robots is the support of the blind. The successful implementation of this study requires a more accurate and powerful self-localization system for guide robots in indoor environments. This paper proposes a self-localization system for guide robots.  To successfully implement this study, images were collected from the perspective of a robot inside a room, and a deep learning system such as a convolutional neural network (CNN) was used. An image-based self-localization guide robot image-classification system delivers a more accura

... Show More
View Publication Preview PDF
Scopus (3)
Crossref (1)
Scopus Crossref
Publication Date
Fri Mar 01 2024
Journal Name
Baghdad Science Journal
Exploring the Challenges of Diagnosing Thyroid Disease with Imbalanced Data and Machine Learning: A Systematic Literature Review
...Show More Authors

Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise

... Show More
View Publication Preview PDF
Scopus (2)
Crossref (1)
Scopus Crossref