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Epidemiological Complex Networks: A Survey
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     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.

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Publication Date
Thu Jul 01 2021
Journal Name
Diyala Journal For Pure Science
Survey of Air Quality and Health Risk Assessment Around East Baghdad Oil Field, Iraq
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Air pollution evaluation of the operational processes in the East Baghdad oil field was carried out. The analysis was carried out by ICP-MS technique. Total Suspended Particles (TSP) air load was higher than Iraqi Standards and world international allowable limits of World Health Organization. The mean concentrations of gases carbon monoxide, carbon dioxide, sulfur dioxide, in the air were within national and world standards, while the mean concentration of nitrogen dioxide was higher than standard limits. The air of the study area is considered a good quality for CO, CO2 and NO2 with no health effect, while it is hazardous for TSP that have serious risk for people with respiratory disease. The mean concentrations of Cd, Cr, Cu and

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Publication Date
Thu Dec 30 2021
Journal Name
Gsc Biological And Pharmaceutical Sciences
Survey and revision of leaf miners to some plants from different localities of Iraq
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This investigation showed (31) species belonging to (15) genera under (five) families and two orders. The leafminers Dipter families (Agromozidae, Anthomyiidae, Drosophilidae), Agromyzid flies is the highest level of investigated many host plants, but other families have lowest host plants. The synonyms of species were provided from GBIF scarlet's. The date and localities of sampling collection were recorded.

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Publication Date
Wed Jan 30 2019
Journal Name
Journal Of The College Of Education For Women
Contribution of Iraqi women in development: from the perspective faculty members to the complex Jadiriyah/University of Baghdad
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The researchers in this study, which aimed to find out Degree contribution of Iraqi
women in development from the perspective faculty members of the complex Jadriya
University in Baghdad, and the the impact of demographic variables (gender, college, place of
living, school level,) for its contribution.
The study population consisted of some faculty members Jadiriyah compound in
Baghdad's University for the academic year 20012/2013. This study followed a descriptive
approach the field, and the analytical method, the study sample consisted of (250) teaching all
of College of Science for girls and education for girls, and to achieve objective of the study
was the development of a questionnaire, was sure of its sincer

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Publication Date
Sat Nov 01 2014
Journal Name
Our Dermatology Online
Topical erythromycin-zinc acetate complex lotion versus topical erythromycin gel in treatment of mild to moderate acne vulgaris
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HR Al-Hamamy, KE Sharquie, AA Noaimi, WN Hussein, Our Dermatology Online, 2014 - Cited by 6

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Publication Date
Sun Feb 25 2024
Journal Name
Baghdad Science Journal
An Improved Cuckoo Search Algorithm for Maximizing the Coverage Range of Wireless Sensor Networks
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The issue of increasing the range covered by a wireless sensor network with restricted sensors is addressed utilizing improved CS employing the PSO algorithm and opposition-based learning (ICS-PSO-OBL). At first, the iteration is carried out by updating the old solution dimension by dimension to achieve independent updating across the dimensions in the high-dimensional optimization problem. The PSO operator is then incorporated to lessen the preference random walk stage's imbalance between exploration and exploitation ability. Exceptional individuals are selected from the population using OBL to boost the chance of finding the optimal solution based on the fitness value. The ICS-PSO-OBL is used to maximize coverage in WSN by converting r

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Publication Date
Thu Feb 09 2023
Journal Name
Artificial Intelligence Review
Community detection model for dynamic networks based on hidden Markov model and evolutionary algorithm
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Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over

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Publication Date
Wed Jun 30 2021
Journal Name
International Journal Of Intelligent Engineering And Systems
Promising Gains of 5G Networks with Enhancing Energy Efficiency Using Improved Linear Precoding Schemes
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Publication Date
Thu Dec 01 2022
Journal Name
Baghdad Science Journal
Distributed Heuristic Algorithm for Migration and Replication of Self-organized Services in Future Networks
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Nowadays, the mobile communication networks have become a consistent part of our everyday life by transforming huge amount of data through communicating devices, that leads to new challenges. According to the Cisco Networking Index, more than 29.3 billion networked devices will be connected to the network during the year 2023. It is obvious that the existing infrastructures in current networks will not be able to support all the generated data due to the bandwidth limits, processing and transmission overhead. To cope with these issues, future mobile communication networks must achieve high requirements to reduce the amount of transferred data, decrease latency and computation costs. One of the essential challenging tasks in this subject

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Publication Date
Mon Dec 28 2020
Journal Name
International Journal Of Psychosocial Rehabilitation
Predicting the Sporting Achievement in the Pole Vault for Men Using Artificial Neural Networks
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The physical sports sector in Iraq suffers from the problem of achieving sports achievements in individual and team games in various Asian and international competitions, for many reasons, including the lack of exploitation of modern, accurate and flexible technologies and means, especially in the field of information technology, especially the technology of artificial neural networks. The main goal of this study is to build an intelligent mathematical model to predict sport achievement in pole vaulting for men, the methodology of the research included the use of five variables as inputs to the neural network, which are Avarage of Speed (m/sec in Before distance 05 meters latest and Distance 05 meters latest, The maximum speed achieved in t

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Publication Date
Mon Feb 01 2021
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Comparative study of logistic regression and artificial neural networks on predicting breast cancer cytology
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<p>Currently, breast cancer is one of the most common cancers and a main reason of women death worldwide particularly in<strong> </strong>developing countries such as Iraq. our work aims to predict the type of tumor whether benign or malignant through models that were built using logistic regression and neural networks and we hope it will help doctors in detecting the type of breast tumor. Four models were set using binary logistic regression and two different types of artificial neural networks namely multilayer perceptron MLP and radial basis function RBF. Evaluation of validated and trained models was done using several performance metrics like accuracy, sensitivity, specificity, and AUC (area under receiver ope

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