Preferred Language
Articles
/
WxfV0IwBVTCNdQwC9wgF
A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal
...Show More Authors

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesion from five Macaca fasicularis monkeys. The proposed classifier is based on a CNN using filtered segmented EMG signals from the pre- and post-lesion periods as inputs, while the kNN is designed using four hand-crafted EMG features. The results suggest that the CNN provides a promising classification technique for TSCI, compared to conventional machine learning classification. The kNN with hand-crafted EMG features classified the pre- and post-lesion EMG data with an F-measure of 89.7% and 92.7% for the left- and right-side muscles, respectively, while the CNN with the EMG segments classified the data with an F-measure of 89.8% and 96.9% for the left- and right-side muscles, respectively. Finally, the proposed deep learning classification model (CNN), with its learning ability of high-level features using EMG segments as inputs, shows high potential and promising results for use as a TSCI classification system. Future studies can confirm this finding by considering more subjects.

Scopus Clarivate Crossref
View Publication
Publication Date
Tue Sep 01 2020
Journal Name
Journal Of Engineering
An Adaptive Digital Neural Network-Like-PID Control Law Design for Fuel Cell System Based on FPGA Technique
...Show More Authors

This paper proposes an on-line adaptive digital Proportional Integral Derivative (PID) control algorithm based on Field Programmable Gate Array (FPGA) for Proton Exchange Membrane Fuel Cell (PEMFC) Model. This research aims to design and implement Neural Network like a digital PID using FPGA in order to generate the best value of the hydrogen partial pressure action (PH2) to control the stack terminal output voltage of the (PEMFC) model during a variable load current applied. The on-line Particle Swarm Optimization (PSO) algorithm is used for finding and tuning the optimal value of the digital PID-NN controller (kp, ki, and kd) parameters that improve the dynamic behavior of the closed-loop digital control fue

... Show More
View Publication Preview PDF
Crossref (1)
Crossref
Publication Date
Sun Mar 04 2018
Journal Name
Journal Of The College Of Education For Women
Novel of (Ya Mariam), for Sinan Antwane : A Pragmatic Study
...Show More Authors

Pragmatics looked at literary discourse as an external subject that needed an actor and based on a communicative relationship, that is, it examines literary work by focusing on the author's intent, and attempting to monitor the pilgrim and logical dimension in the text. It focuses on the practical and communicative aspect of the language, which aims to convince the recipient and his or her emotional influence.And the novel (Yamrim) starting from the title (Sos) communication, and building dialogue and pilgrims based on two different logic completely, the logic of optimism and look at things with absolute objectivity, the logic of pessimism and closure and look at things with absolute passion. These two tendencies are embodied by the char

... Show More
View Publication Preview PDF
Publication Date
Thu Jun 06 2024
Journal Name
Journal Of Applied Engineering And Technological Science (jaets)
Deep Learning and Its Role in Diagnosing Heart Diseases Based on Electrocardiography (ECG)
...Show More Authors

Diagnosing heart disease has become a very important topic for researchers specializing in artificial intelligence, because intelligence is involved in most diseases, especially after the Corona pandemic, which forced the world to turn to intelligence. Therefore, the basic idea in this research was to shed light on the diagnosis of heart diseases by relying on deep learning of a pre-trained model (Efficient b3) under the premise of using the electrical signals of the electrocardiogram and resample the signal in order to introduce it to the neural network with only trimming processing operations because it is an electrical signal whose parameters cannot be changed. The data set (China Physiological Signal Challenge -cspsc2018) was ad

... Show More
View Publication
Scopus Crossref
Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
The Classification of Fetus Gender Based on Fuzzy C-Mean Using a Hybrid Filter
...Show More Authors
Abstract<p>This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT), (median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Lap</p> ... Show More
View Publication
Scopus (3)
Crossref (2)
Scopus Crossref
Publication Date
Sat May 01 2021
Journal Name
Journal Of Physics: Conference Series
The classification of fetus gender based on fuzzy C-mean using a hybrid filter
...Show More Authors

This paper proposes a new approach, of Clustering Ultrasound images using the Hybrid Filter (CUHF) to determine the gender of the fetus in the early stages. The possible advantage of CUHF, a better result can be achieved when fuzzy c-mean FCM returns incorrect clusters. The proposed approach is conducted in two steps. Firstly, a preprocessing step to decrease the noise presented in ultrasound images by applying the filters: Local Binary Pattern (LBP), median, median and discrete wavelet (DWT),(median, DWT & LBP) and (median & Laplacian) ML. Secondly, implementing Fuzzy C-Mean (FCM) for clustering the resulted images from the first step. Amongst those filters, Median & Laplace has recorded a better accuracy. Our experimental evaluation on re

... Show More
View Publication
Scopus (3)
Crossref (2)
Scopus Crossref
Publication Date
Thu Jun 29 2023
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Synthesis of Novel Porphyrin Derivatives and Investigate their Application in Sensitized Solar Cells
...Show More Authors

Solar energy has significant advantages compared to conventional sources such as coal and natural gas, including no emissions, no need for fuel, and the potential for installation in a wide range of locations with access to sunlight. In this investigation, heterocyclic derivatives were synthesized from several porphyrin derivatives (4,4',4",4"'-(porphyrin-5,10,15,20-tetrayl) tetra benzoic acid) compound (3), obtained by reaction Pyrrole with 4-formyl benzoic acid. Subsequently, porphyrin derivative-component amides 5a, 5b, and 5c were produced by reacting compound (3) with amine derivatives at a 1:4 molar ratio. These derivatives exhibited varying sensitivities for utilization in solar cells, with compound 5a displaying the highest power

... Show More
View Publication Preview PDF
Crossref
Publication Date
Sat Dec 01 2018
Journal Name
Al-nahrain Journal Of Science
Image Classification Using Bag of Visual Words (BoVW)
...Show More Authors

In this paper two main stages for image classification has been presented. Training stage consists of collecting images of interest, and apply BOVW on these images (features extraction and description using SIFT, and vocabulary generation), while testing stage classifies a new unlabeled image using nearest neighbor classification method for features descriptor. Supervised bag of visual words gives good result that are present clearly in the experimental part where unlabeled images are classified although small number of images are used in the training process.

View Publication Preview PDF
Crossref (23)
Crossref
Publication Date
Thu Apr 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Role of System Strategic Learning Smart In Sustainability Success of Managing Network e-Business
...Show More Authors

Purpose: Determining and identifying the relationships of smart strategic education systems and their potential effects on sustainable success in managing clouding electronic business networks according to green, economic and environmental logic based on vigilance and awareness of the strategic mind.

Design: Designing a hypothetical model that reveals the role and investigating audit and cloud electronic governance according to a philosophy that highlights smart strategic learning processes, identifying its assumptions in cloud spaces, choosing its tools, what it costs to devise expert minds, and strategic intelligence.

Methodology:

... Show More
View Publication Preview PDF
Crossref
Publication Date
Thu Sep 01 2022
Journal Name
Iraqi Journal Of Physics
Development and Assessment of Feed Forward Back Propagation Neural Network Models to Predict Sunshine Duration
...Show More Authors

         The duration of sunshine is one of the important indicators and one of the variables for measuring the amount of solar radiation collected in a particular area. Duration of solar brightness has been used to study atmospheric energy balance, sustainable development, ecosystem evolution and climate change. Predicting the average values of sunshine duration (SD) for Duhok city, Iraq on a daily basis using the approach of artificial neural network (ANN) is the focus of this paper. Many different ANN models with different input variables were used in the prediction processes. The daily average of the month, average temperature, maximum temperature, minimum temperature, relative humidity, wind direction, cloud level and atmosp

... Show More
View Publication Preview PDF
Crossref
Publication Date
Thu Aug 01 2024
Journal Name
Water Practice &amp; Technology
Artificial neural network and response surface methodology for modeling oil content in produced water from an Iraqi oil field
...Show More Authors
ABSTRACT<p>The majority of the environmental outputs from gas refineries are oily wastewater. This research reveals a novel combination of response surface methodology and artificial neural network to optimize and model oil content concentration in the oily wastewater. Response surface methodology based on central composite design shows a highly significant linear model with P value &lt;0.0001 and determination coefficient R2 equal to 0.747, R adjusted was 0.706, and R predicted 0.643. In addition from analysis of variance flow highly effective parameters from other and optimization results verification revealed minimum oily content with 8.5 ± 0.7 ppm when initial oil content 991 ppm, tempe</p> ... Show More
View Publication
Scopus (3)
Crossref (2)
Scopus Clarivate Crossref