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%.
Background: Tear of MCL of the knee is a frequent problem among knee ligaments injuries.Injuries to the MCL are usually caused by contact on the outside of the knee and are accompanied by sharp pain on the inside of the knee. Contrary to most other knee ligaments the medial collateral ligament (MCL) has an excellent ability to heal, being fairly large and well vascularised structure. The vast majority of isolated medial ligament injuries heal without significant long-term problems
Objectives: is to compare between the early clinical examination, and assessment under general anesthesia (GA), and to find out the best methods to assess the MCL tear especially in suspected cases.
Type
... Show MoreAnalyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col
... Show MoreThe research dealt with the subject of measuring the competitive performance of the National Insurance Company and some of its branches (Basra, Ninwa, Kirkuk and Babil), Depending on the Revenue Growth Index at the activity level, and the Revealed Comparative Advantage Index RCAIAt the branch level,To measure the competitiveness of the company And some branches, As the problem of research in the lack of adoption by some companies in the insurance service sector on scientific indicators to measure their competitive performance, The aims of the research is to measure the competitiveness of the National Insurance Company, as well as the competitiveness of its branches according to the scientific method, One of the main Conclusions of the re
... Show MoreThe expanding use of multi-processor supercomputers has made a significant impact on the speed and size of many problems. The adaptation of standard Message Passing Interface protocol (MPI) has enabled programmers to write portable and efficient codes across a wide variety of parallel architectures. Sorting is one of the most common operations performed by a computer. Because sorted data are easier to manipulate than randomly ordered data, many algorithms require sorted data. Sorting is of additional importance to parallel computing because of its close relation to the task of routing data among processes, which is an essential part of many parallel algorithms. In this paper, sequential sorting algorithms, the parallel implementation of man
... Show MoreBy optimizing the efficiency of a modular simulation model of the PV module structure by genetic algorithm, under several weather conditions, as a portion of recognizing the ideal plan of a Near Zero Energy Household (NZEH), an ideal life cycle cost can be performed. The optimum design from combinations of NZEH-variable designs, are construction positioning, window-to-wall proportion, and glazing categories, which will help maximize the energy created by photovoltaic panels. Comprehensive simulation technique and modeling are utilized in the solar module I-V and for P-V output power. Both of them are constructed on the famous five-parameter model. In addition, the efficiency of the PV panel is established by the genetic algorithm
... Show MoreEco-friendly concrete is produced using the waste of many industries. It reduces the fears concerning energy utilization, raw materials, and mass-produced cost of common concrete. Several stress-strain models documented in the literature can be utilized to estimate the ultimate strength of concrete components reinforced with fibers. Unfortunately, there is a lack of data on how non-metallic fibers, such as polypropylene (PP), affect the properties of concrete, especially eco-friendly concrete. This study presents a novel approach to modeling the stress-strain behavior of eco-friendly polypropylene fiber-reinforced concrete (PFRC) using meta-heuristic particle swarm optimization (PSO) employing 26 PFRC various mixtures. The cement was partia
... Show MoreThe complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra
... Show MoreThe current research seeks to achieve several objectives, including knowing the extent of the audit directorate of the Ministry of Construction, Housing and General Municipalities of the International Standard (ISO19011:2018) regarding determining the efficiency and evaluation of auditors and diagnosing the gap between requirements and application and knowing the reasons for not applying some of the items in the standard, starting from the problem, The field raised the following question (Does the audit directorate determine the efficiency and evaluation of auditors according to the standard ISO19011:2018?), and the importance of research lies in determining the return that can be achieved by the directorate through its application of stand
... Show MoreThe study aimed to identify the use of the electronic concept maps method in learning some of the skills of the floor exercises in the artistic gymnastics for third graders ,as well as to identify the best group between the two research groups (experimental And the officer to learn and retain some of the skills of the floor exercises in the artistic gymnastics of the research subject , and the experimental method was used and included the sample research on students of the collage of Physical Education and Sports Sciences/University of Baghdad, third grade, and has selected (10) Students for each group of The experimental and controlling groups randomly by lottery and after the completion of the period of implementation of the experiment wh
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