Preferred Language
Articles
/
ijs-7779
Epidemiological Complex Networks: A Survey
...Show More Authors

     In this review paper, several research studies were surveyed to assist future researchers to identify available techniques in the field of infectious disease modeling across complex networks. Infectious disease modelling is becoming increasingly important because of the microbes and viruses that threaten people’s lives and societies in all respects. It has long been a focus of research in many domains, including mathematical biology, physics, computer science, engineering, economics, and the social sciences, to properly represent and analyze spreading processes. This survey first presents a brief overview of previous literature and some graphs and equations to clarify the modeling in complex networks, the detection of societies and their medical information, the identification of nodes, the method of communication with individuals and their spread, the analysis of their transmission through complex networks, and the detection of mathematical methods over the past century. Secondly, the types of epidemiological models and complex networks and the extent of their impact on humans are presented.

Scopus Crossref
View Publication Preview PDF
Quick Preview PDF
Publication Date
Mon Mar 11 2019
Journal Name
Baghdad Science Journal
Solving Mixed Volterra - Fredholm Integral Equation (MVFIE) by Designing Neural Network
...Show More Authors

       In this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency and the accuracy of our method.

         

... Show More
View Publication Preview PDF
Scopus (2)
Scopus Clarivate Crossref
Publication Date
Sun Feb 27 2022
Journal Name
Iraqi Journal Of Science
Efficient Hybrid DCT-Wiener Algorithm Based Deep Learning Approach For Semantic Shape Segmentation
...Show More Authors

    Semantic segmentation is effective in numerous object classification tasks such as autonomous vehicles and scene understanding. With the advent in the deep learning domain, lots of efforts are seen in applying deep learning algorithms for semantic segmentation. Most of the algorithms gain the required accuracy while compromising on their storage and computational requirements. The work showcases the implementation of Convolutional Neural Network (CNN) using Discrete Cosine Transform (DCT), where DCT exhibit exceptional energy compaction properties. The proposed Adaptive Weight Wiener Filter (AWWF) rearranges the DCT coefficients by truncating the high frequency coefficients. AWWF-DCT model reinstate the convolutional l

... Show More
View Publication Preview PDF
Scopus (1)
Crossref (1)
Scopus Crossref
Publication Date
Sun Jun 30 2013
Journal Name
Al-khwarizmi Engineering Journal
Estimated Outlet Temperatures in Shell-and-Tube Heat Exchanger Using Artificial Neural Network Approach Based on Practical Data
...Show More Authors

The objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.

... Show More
View Publication Preview PDF
Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
Performance Assessment of Solar-Transformer-Consumption System Using Neural Network Approach
...Show More Authors

Solar energy is one of the immeasurable renewable energy in power generation for a green, clean and healthier environment. The silicon-layer solar panels absorb sun energy and converts it into electricity by off-grid inverter. Electricity is transferred either from this inverter or from transformer, consumed by consumption unit(s) available for residential or economic purposes. The artificial neural network is the foundation of artificial intelligence and solves many complex problems which are difficult by statistical methods or by humans. In view of this, the purpose of this work is to assess the performance of the Solar - Transformer - Consumption (STC) system. The system may be in complete breakdown situation due to failure of both so

... Show More
View Publication Preview PDF
Scopus (5)
Crossref (2)
Scopus Clarivate Crossref
Publication Date
Thu Jun 01 2023
Journal Name
Baghdad Science Journal
Comparison of Faster R-CNN and YOLOv5 for Overlapping Objects Recognition
...Show More Authors

Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and

... Show More
View Publication Preview PDF
Scopus (6)
Crossref (2)
Scopus Crossref
Publication Date
Mon Dec 20 2021
Journal Name
Baghdad Science Journal
Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation
...Show More Authors

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

... Show More
View Publication Preview PDF
Scopus (5)
Crossref (1)
Scopus Clarivate Crossref
Publication Date
Fri Sep 15 2023
Journal Name
Al-academy
The recepient’s beauty in the algerian theatre and the recepient’s hypothetical bet , reading in stop theater by m’hammed ben gataf
...Show More Authors

Meditationsuggetsthat the theatrical world and the receiptindicatethattherie a thread connection or relationshipbetweenthembecausetheateris an art thatfailsinto the abyss of the humanfield ,receivingis a practice thatisreffered to by corners of society and islocated in the context of thisfield. Thus, the relationshipbetweenalgeriantheater and the recepientissanctified by the nature of theaterwhichisbased on a social basis thatbuildsitsexistance and continuityfrom the recepient.Its impossible to imagine a real theaterthatwillbeestablishedunlessit’sassociatedwith an audience so one hand never claps to makesound. This connectionis the first phenomenonthatcanbecome accessible to all people and itremains the second phenomenonthatrequires th

... Show More
View Publication Preview PDF
Crossref
Publication Date
Mon Jan 30 2023
Journal Name
Iraqi Journal Of Science
Prediction of Well Logs Data and Estimation of Petrophysical Parameters of Mishrif Formation, Nasiriya Field, South of Iraq Using Artificial Neural Network (ANN)
...Show More Authors

    Petrophysical properties including volume of shale, porosity and water saturation are significance parameters for petroleum companies in evaluating the reservoirs and determining the hydrocarbon zones. These can be achieved through conventional petrophysical calculations from the well logs data such as gamma ray, sonic, neutron, density and deep resistivity. The well logging operations of the targeted limestone Mishrif reservoirs in Ns-X Well, Nasiriya Oilfield, south of Iraq could not be done due to some problems related to the well condition. The gamma ray log was the only recorded log through the cased borehole. Therefore, evaluating the reservoirs and estimating the perforation zones has not performed and the drilled well was

... Show More
View Publication Preview PDF
Scopus (1)
Crossref (1)
Scopus Crossref
Publication Date
Sat Jun 06 2020
Journal Name
Journal Of The College Of Education For Women
Image classification with Deep Convolutional Neural Network Using Tensorflow and Transfer of Learning
...Show More Authors

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

... Show More
View Publication Preview PDF
Crossref
Publication Date
Sun Jun 01 2014
Journal Name
Baghdad Science Journal
Classification of fetal abnormalities based on CTG signal
...Show More Authors

The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was t

... Show More
View Publication Preview PDF
Crossref