Preferred Language
Articles
/
hhddLJEBVTCNdQwCBpOY
5G passive optical network employing all optical-OFDM_Hybrid SSMF/FSO
...Show More Authors

In this paper, a new 5G Passive Optical Network (5G-PON) employing all-optical orthogonal frequency division multiplexing (AO-OFDM) is proposed in hybrid bidirectional standard single mode fiber (SSMF)/free space optical (FSO). Additionally, an optical frequency generator (OFG) source is utilized. The proposed model is simulated using VPI photonics software. Analytical modeling and simulations have been conducted for a new approach to generate OFG by cascaded two-frequency modulators and one electro-absorption modulator. A sinusoidal RF signal source is utilized to drive all these modulators. The results reveal that 64 optical multiplexed carriers with a frequency spacing of 30 GHz are generated. These optical carriers have power variations of dB. Moreover, the center wavelength of the generated OFG can be tuned from 1300 nm for upstream transmission to 1577 nm for downstream transmission in the proposed 5G-PON. The proposed network achieves 960 Gbps and 10 Gbps for the downstream and upstream directions, respectively, under different turbulence effects. Furthermore, when 32 AO-OFDM channels are used, the simulation results show that the proposed model can achieve a SSMF length and FSO propagation ranges of 20 km and 2 km, respectively, with bit error rate (BER) ( ).

Scopus Clarivate Crossref
View Publication
Publication Date
Mon Jan 01 2024
Journal Name
Baghdad Science Journal
Artificial Neural Network and Latent Semantic Analysis for Adverse Drug Reaction Detection
...Show More Authors

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

... Show More
View Publication Preview PDF
Scopus (4)
Crossref (8)
Scopus Crossref
Publication Date
Thu Feb 01 2018
Journal Name
Journal Of Engineering
A Realistic Aggregate Load Representation for A Distribution Substation in Baghdad Network
...Show More Authors

Electrical distribution system loads are permanently not fixed and alter in value and nature with time. Therefore, accurate consumer load data and models are required for performing system planning, system operation, and analysis studies. Moreover, realistic consumer load data are vital for load management, services, and billing purposes. In this work, a realistic aggregate electric load model is developed and proposed for a sample operative substation in Baghdad distribution network. The model involves aggregation of hundreds of thousands of individual components devices such as motors, appliances, and lighting fixtures. Sana’a substation in Al-kadhimiya area supplies mainly residential grade loads. Measurement-based

... Show More
View Publication Preview PDF
Publication Date
Mon Mar 11 2019
Journal Name
Baghdad Science Journal
Analysing Iraqi Railways Network by Applying Specific Criteria Using the GIS Techniques
...Show More Authors

The railways network is one of the huge infrastructure projects. Therefore, dealing with these projects such as analyzing and developing should be done using appropriate tools, i.e. GIS tools. Because, traditional methods will consume resources, time, money and the results maybe not accurate. In this research, the train stations in all of Iraq’s provinces were studied and analyzed using network analysis, which is one of the most powerful techniques within GIS. A free trial copy of ArcGIS®10.2 software was used in this research in order to achieve the aim of this study. The analysis of current train stations has been done depending on the road network, because people used roads to reach those train stations. The data layers for this st

... Show More
View Publication Preview PDF
Scopus Clarivate Crossref
Publication Date
Mon Feb 20 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Text Encryption Algorithm Based on Chaotic Neural Network and Random Key Generator
...Show More Authors

This work presents a symmetric cryptography coupled with Chaotic NN , the encryption algorithm process the data as a blocks and it consists of multilevel( coding of character, generates array of keys (weights),coding of text and chaotic NN ) , also the decryption process consists of multilevel (generates array of keys (weights),chaotic NN, decoding of text and decoding of character).Chaotic neural network is used as a part of the proposed system with modifying on it ,the keys that are used in chaotic sequence are formed by proposed key generation algorithm .The proposed algorithm appears efficiency during the execution time where it can encryption and decryption long messages by short time and small memory (chaotic NN offer capacity of m

... Show More
View Publication Preview PDF
Publication Date
Sat Oct 30 2021
Journal Name
Iraqi Journal Of Science
Diagnosis and Classification of Type II Diabetes based on Multilayer Neural Network
...Show More Authors

     Diabetes is considered by the World Health Organization (WHO) as a main health problem globally. In recent years, the incidence of Type II diabetes mellitus was increased significantly due to metabolic disorders caused by malfunction in insulin secretion. It might result in various diseases, such as kidney failure, stroke, heart attacks, nerve damage, and damage in eye retina. Therefore, early diagnosis and classification of Type II diabetes is significant to help physician assessments.

The proposed model is based on Multilayer Neural Network using a dataset of Iraqi diabetes patients obtained from the Specialized Center for Endocrine Glands and Diabetes Diseases. The investigation includes 282 samples, o

... Show More
View Publication Preview PDF
Scopus (6)
Crossref (1)
Scopus Crossref
Publication Date
Fri Jul 26 2019
Journal Name
Dental Materials Journal
Semi-interpenetrating network composites reinforced with Kevlar fibers for dental post fabrication
...Show More Authors

View Publication
Scopus (14)
Crossref (11)
Scopus Clarivate Crossref
Publication Date
Sun Jun 20 2021
Journal Name
Baghdad Science Journal
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

... Show More
View Publication Preview PDF
Scopus (15)
Crossref (1)
Scopus Clarivate Crossref
Publication Date
Tue Jun 20 2023
Journal Name
Baghdad Science Journal
Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network
...Show More Authors

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

... Show More
View Publication Preview PDF
Scopus (13)
Crossref (11)
Scopus Crossref
Publication Date
Tue Apr 30 2024
Journal Name
Iraqi Journal Of Science
Crescent Moon Visibility: A New Criterion using Deep learned Artificial Neural-Network
...Show More Authors

     Many authors investigated the problem of the early visibility of the new crescent moon after the conjunction and proposed many criteria addressing this issue in the literature. This article presented a proposed criterion for early crescent moon sighting based on a deep-learned pattern recognizer artificial neural network (ANN) performance. Moon sight datasets were collected from various sources and used to learn the ANN. The new criterion relied on the crescent width and the arc of vision from the edge of the crescent bright limb. The result of that criterion was a control value indicating the moon's visibility condition, which separated the datasets into four regions: invisible, telescope only, probably visible, and certai

... Show More
Preview PDF
Scopus Crossref
Publication Date
Thu Oct 31 2024
Journal Name
Iraqi Geological Journal
Artificial Neural Network Application to Permeability Prediction from Nuclear Magnetic Resonance Log
...Show More Authors

Reservoir permeability plays a crucial role in characterizing reservoirs and predicting the present and future production of hydrocarbon reservoirs. Data logging is a good tool for assessing the entire oil well section's continuous permeability curve. Nuclear magnetic resonance logging measurements are minimally influenced by lithology and offer significant benefits in interpreting permeability. The Schlumberger-Doll-Research model utilizes nuclear magnetic resonance logging, which accurately estimates permeability values. The approach of this investigation is to apply artificial neural networks and core data to predict permeability in wells without a nuclear magnetic resonance log. The Schlumberger-Doll-Research permeability is use

... Show More
View Publication Preview PDF
Crossref