Suchergebnisse

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.

Getting knowledge from raw data has delivered beneficial information in several domains. The prevalent utilizing of social media produced extraordinary quantities of social information. Simply, social media delivers an available podium for employers for sharing information. Data Mining has ability to present applicable designs that can be useful for employers, commercial, and customers. Data of social media are strident, massive, formless, and dynamic in the natural case, so modern encounters grow. Investigation methods of data mining utilized via social networks is the purpose of the study, accepting investigation plans on the basis of criteria, and by selecting a number of papers to serve as the foundation for this article. Afterward a watchful evaluation of these papers, it has beeniscovered that numerous data extraction approaches were utilized with social media data to report a number of various research goals in several fields of industrial and service. Though, implementations of data mining are still raw and require more work via industry and academic world to prepare the work sufficiently. Bring this analysis to a close. Data mining is the most important rule for uncovering hidden data in large datasets, especially in social network analysis, and it demonstrates the most important social media technology.

Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the most effective parameter, particularly when Age<49.5. Whereas Ki67 appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimum error rate, and the test maximum accuracy for K_value selection with an accuracy of 86.24%. Where the distance metric has been assigned using the Euclidean approach. From previous models, it seems that Breast Cancer Grade2 is the most prevalent type. For the future perspective, a comparative study could be performed to compare the supervised and unsupervised data mining algorithms.

This paper investigated an Iraqi dataset from Korek Telecom Company as Call Detail Recorded (CDRs) for six months falling between Sep. 2020-Feb. 2021. This data covers 18 governorates, and it falls within the period of COVID-19. The Gravity algorithm was applied into two levels of abstraction in deriving the results as the macroscopic and mesoscopic levels respectively. The goal of this study was to reveal the strength and weakness of people migration in-between the Iraqi cities. Thus, it has been clear that the relationship between each city with the others is based on and of mobile people. However, the COVID-19 effects on the people's migration needed to be explored. Whereas the main function of the gravity model is to clarify the migration flows through modeling spatial interaction. This was implemented using Python scripting language. It is concluded that the gravity model has a powerful ability to analyze the movement of people between cities. According to the mean of result between governorates, showing that the highest attraction was between Babil and Anbar governorates amounted to , while the lowest attraction was between Wasit and Thi-Qar governorates with , and the others ranged between .

This review explores the Knowledge Discovery Database (KDD) approach, which supports the bioinformatics domain to progress efficiently, and illustrate their relationship with data mining. Thus, it is important to extract advantages of Data Mining (DM) strategy management such as effectively stressing its role in cost control, which is the principle of competitive intelligence, and the role of it in information management. As well as, its ability to discover hidden knowledge. However, there are many challenges such as inaccurate, hand-written data, and analyzing a large amount of variant information for extracting useful knowledge by using DM strategies. These strategies are successfully applied in several applications as data warehouses, predictive analytics, business intelligence, bioinformatics, and decision support systems. There are many DM techniques that are applied for disease diagnostics and treatment, for example cancer diseases that are investigated using multi-layer perception, Naïve Bayes, Decision Tree, Simple Logistic, K-Nearest Neighbor. As will be explored in this paper. Consequently, for future perspectives there is research in progress for real Iraqi data of Breast Cancer using Data Mining techniques, specifically the Tree decision and K-nearest algorithms.

Twitter popularity has increasingly grown in the last few years, influencing life's social, political, and business aspects. People would leave their tweets on social media about an event, and simultaneously inquire to see other people's experiences and whether they had a positive/negative opinion about that event. Sentiment Analysis can be used to obtain this categorization. Product reviews, events, and other topics from all users that comprise unstructured text comments are gathered and categorized as good, harmful, or neutral using sentiment analysis. Such issues are called polarity classifications. This study aims to use Twitter data about OK cuisine reviews obtained from the Amazon website and compare the effectiveness of three commonly used supervised learning classifiers, Naive Bayes, Logistic Regression, and Support Vector Machine. This is achieved by using two method of feature selection involving count Vectorizer and Term-Frequency-Inverse Data Frequency. The findings showed that the support vector machine classifier had achieved the highest accuracy of 91%, by feature selection: Count Vectorizer. But it is time consuming. For both accuracy and execution time concentrates, logistic regression is recommended.

The interesting new sources of data for official statistics are cell phone data. Electronic media has defined the way of research human behavior rapidly over the last decade. As data storage and sensing technology progressed, electronic records now cover a diverse variety of human activities from localized data (phone) to open source contributions on Wikipedia and the Open Area Map. Electronic records now encompass the numerous fields of activity. The ad hoc vehicle network is a research community-based wireless technology for the implementation of intelligent transport applications. It is necessary to estimate migration flows and predict future trends to understand the causes and effects of migration and to enforce policies to deliver certain services. Several studies have exposed in this review with their datasets such as Credit card records (CCRs) provide deep insight into buying behavior; Call Details Records (CDRs) present new possibilities for under-implemented human mobility. Therefore, various forms of transportation and other travel behavior, various travel-related events such as "Home-Tour-Work -Tour-Tour" and the corresponding travel-related motifs have also been distinguished by the inclusion of land use details in the GIS data. This review also investigated migration trips residence between cities and inside a single city. The review concluded clear results for the adoption of the mentioned data, for example, mobile phone data (CDRs), because it is very useful as it provides real big data or real time big data without additional cost and is available in telecommunications companies, from which it is possible to analyze the movement of communities and deduce the activity of a particular city. In the future, there is a tendency to use this type of data from Korek Telecom Company in Iraq to the flow migration of Iraq governorates by using the gravity model. As well as, an attempt to study the conditions of cities and the movement of individuals in urban places to clarify the needs of the city in its need for new improvements.

This study investigates data set as satellite images of type multispectral Landsat-7, which are observed for AL_Nasiriya city, it is located in southern of Iraq, and situated along the banks of the Euphrates River. These raw data are thermal bands of satellite images, they are taken as thermal images. These images are processed and examined using ENVI 5.3 program. Consequently, the emitted Hydrocarbon is extracted, and the black body algorithm is employed. As well as, the raster calculations are performed using ArcGIS, where gas and oil features are sorted. The results are estimate and determine the oil and gas fields in the city. This study uncovers, and estimates several unexplored oil and gas fields. Whereas, the real oil and gas exploration is high costly regarding to actual existed ones in proportional to human and equipment. For future, it is intended to perform domain oil and gas exploration in order to compare between the presented results of this study with the actual existed ones.

Nowadays, almost everyone is glued to their phones. It turns out that the fear of being without your phone has a fancy name: nomophobia. Researchers can now analyze our phone usage using data mining techniques to determine how much we rely on them. They can monitor everything from screen time and social media activity to email habits and app addiction. This information assists us in understanding the impact of technology on our daily lives and may even lead to new interventions or treatment options for those who suffer from nomophobia. Nomophobia, like addiction, progresses through multiple aspects such as initiation, affirmation, need, and dependency. It also manifests in a variety of ways, including socially, physiologically, and physically. The study goal is to look into the nomophobia patterns of the Iraqi academic population (professors, students, and employees) at the University of Baghdad. A descriptive, cross-sectional survey design was used to collect data between 17th October, 2021, and 1st October, 2022. The sample for this study consists of 305 participants. A sociodemographic data sheet, Internet usage profiles, and a nomophobia questionnaire are used to collect information. Thus, data mining techniques have been used to analyze the collected data, hence the concluded results emphasize that there are two major patterns (students group that are annoying during inability to find information on a mobile phone, inability to use it, and inability to check it, and panic when they consume out the credits or hit the monthly data limit, awkward because they couldn't check their notifications for updates from their connections and online networks, subsequently they would feel weird because they would not know what to do). They exhibit nomophobia, and all the examined individuals have acceptable impacts ofnomophobia.

This paper is intended to apply data mining techniques for real Iraqi biochemical dataset to discover hidden patterns within tests relationships. It is worth noting that preprocessing steps take remarkable efforts to handle this type of data, since it is pure data set with so many null values reaching a ratio of 94.8%, then it becomes 0% after achieving these steps. However, in order to apply Classification And Regression Tree (CART) algorithm, several tests were assumed as classes, because of the dataset was unlabeled. Which then enabled discovery of patterns of tests relationships, that consequently, extends its impact on patients' health, since it will assist in determining test values by performing only relevant tests. Therefore decreases the number of tests for patients.

Education and lifelong learning are necessary components of daily city life for urban communities to encourage sustainable and positive communities. The study attempts to analyze the actual school distribution patterns and densities in Baghdad, the Iraqi capital. The significance of this study is that it is associated with one of the essential aspects of humanity: the improvement and affluence of schooling; it impacts school attendance limitations and educational evolution. The education process has been inextricably tied to students' timely and orderly entrance to their schools. Hence the decision maker and planner are concerned by this. The statistics examined elementary and high schools, and the investigated data are related to the Ministry of Education that was available in 2004. Regarding spatial analysis and results assessment, this work employs Microsoft Excel techniques, ArcGIS, C# simulator, and the buffer methodology. It has depicted the school densities and assessed the distances between them using spatial analytic techniques. The study reveals that the school distribution is uneven, and there is a disparity among neighbors in the spatial distribution. Also, the in-between distances of observed schools formulate four significant patterns. At the same time, this study offers important information about the spatial distribution of schools as a significant influencing indicator. In the future, it will be beneficial to investigate the student's geographical accessibility to their residence and the transportation to/from these schools.