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Recurrent Stroke Prediction using Machine Learning Algorithms with Clinical Public Datasets: An Empirical Performance Evaluation
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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 algorithms implementation in the recurrent stroke prediction models. This research aims to investigate and compare the performance of machine learning algorithms using recurrent stroke clinical public datasets. In this study, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Bayesian Rule List (BRL) are used and compared their performance in the domain of recurrent stroke prediction model. The result of the empirical experiments shows that ANN scores the highest accuracy at 80.00%, follows by BRL with 75.91% and SVM with 60.45%.

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Publication Date
Wed Mar 23 2022
Journal Name
Journal Of Educational And Psychological Researches
The level of evaluation of the educational counselors ’performance as a predictor of their level of satisfaction with their evaluation
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The current research aims to reveal the level of satisfaction of the mentors with the evaluation of their performance according to gender (male - female) and to formulate the predictive equation for the level of performance (dependent variable) from knowing the level of satisfaction with the evaluation (independent variable). (16 paragraphs) contains alternatives to the answer that measures the level of satisfaction (weak, medium, and high) (1,2,3), that is, with a hypothetical average of (32). It consisted of 100 educational counselors consisting of 45 males and 55 females, the results of the research concluded that the level of satisfaction with performance is below the mean when compared with the hypothetical average of the scale of s

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Publication Date
Thu Aug 01 2024
Journal Name
Advances In Science And Technology Research Journal
Power Predicting for Power Take-Off Shaft of a Disc Maize Silage Harvester Using Machine Learning
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Publication Date
Sun Mar 31 2024
Journal Name
Iraqi Geological Journal
Permeability Prediction and Facies Distribution for Yamama Reservoir in Faihaa Oil Field: Role of Machine Learning and Cluster Analysis Approach
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Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

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Publication Date
Thu Feb 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
Using financial ratio to compare performance of the commercial and the Islamic banks Listed on Palestine Exchange
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        The research aimed to compare the performance of the commercial and the  Islamic banks listed in the Palestinian's Stock Exchange .To achieve the objectives of the study we selected all  the commercial and the Islamic banks listed in the Palestinian Stock Exchange  to obtain the necessary data for the analysis process during the period of (2009-2013) .the comparison based on the performance indicators ( liquidity rate, profitability rate ,the activity rate and the market rate).

        a statistical method was used to analyze the date to find the performance differences between the commercial banks,

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Publication Date
Fri Mar 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Peak to Average Power Ratio Reduction of OFDM Signals Using Clipping and Iterative Processing Methods
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One of the serious problems in any wireless communication system using multi carrier modulation technique like Orthogonal Frequency Division Multiplexing (OFDM) is its Peak to Average Power Ratio (PAPR).It limits the transmission power due to the limitation of dynamic range of Analog to Digital Converter and Digital to Analog Converter (ADC/DAC) and power amplifiers at the transmitter, which in turn sets the limit over maximum achievable rate.

        This issue is especially important for mobile terminals to sustain longer battery life time. Therefore reducing PAPR can be regarded as an important issue to realize efficient and affordable mobile communication services.

   

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Publication Date
Sun Dec 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
Evoluation of service system in the general eduction Baghdad hospital using Q GERT
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That the essential contribution of this research is a description of how complex systems analysis service of the properties of the queue in Baghdad Teaching Hospital using a technique network is techniques method (Q - GERT) an acronym of the words:

Queuing theory _ Graphical Evaluation and Review Technique

Any method of assessment and review chart where you will be see the movement flow of patients within the system and after using this portal will be represented system in the form of planned network probabilistic analysis and knowledge of statistical distributions appropriate for times of arrival and departure were using the program ready (Win QSB) and simulatio

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Publication Date
Fri Apr 01 2022
Journal Name
Baghdad Science Journal
Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering
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Among the metaheuristic algorithms, population-based algorithms are an explorative search algorithm superior to the local search algorithm in terms of exploring the search space to find globally optimal solutions. However, the primary downside of such algorithms is their low exploitative capability, which prevents the expansion of the search space neighborhood for more optimal solutions. The firefly algorithm (FA) is a population-based algorithm that has been widely used in clustering problems. However, FA is limited in terms of its premature convergence when no neighborhood search strategies are employed to improve the quality of clustering solutions in the neighborhood region and exploring the global regions in the search space. On the

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Non-linear support vector machine classification models using kernel tricks with applications
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The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

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Publication Date
Thu Aug 13 2020
Journal Name
International Journal Of Innovation, Creativity And Change
Aspects Affecting the Public Service Motivation: Empirical Study in Some Public Service Organizations in Iraq
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Public service motivation in public service organisations is one of the main problems that many developing countries have begun to experience, including Iraq. The public agenda is increasingly complex with reduced financial resources. In addition, these organisations have increased responsibilities with respect to the achievement of public goals and objectives as they are responsible for providing key services to members of the community (education, health and social work). This research is based on the degree of public service motivation among those working in health education and social work in public organisations, and aims to show the extent of the difference in the public service motivation of these employees according to di

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Publication Date
Mon Mar 11 2019
Journal Name
Baghdad Science Journal
Developing Load Balancing for IoT - Cloud Computing Based on Advanced Firefly and Weighted Round Robin Algorithms
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The evolution of the Internet of things (IoT) led to connect billions of heterogeneous physical devices together to improve the quality of human life by collecting data from their environment. However, there is a need to store huge data in big storage and high computational capabilities.   Cloud computing can be used to store big data.  The data of IoT devices is transferred using two types of protocols: Message Queuing Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP). This paper aims to make a high performance and more reliable system through efficient use of resources. Thus, load balancing in cloud computing is used to dynamically distribute the workload across nodes to avoid overloading any individual r

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