The electrical activity of the heart and the electrocardiogram (ECG) signal are fundamentally related. In the study that has been published, the ECG signal has been examined and used for a number of applications. The monitoring of heart rate and the analysis of heart rhythm patterns, the detection and diagnosis of cardiac diseases, the identification of emotional states, and the use of biometric identification methods are a few examples of applications in the field. Several various phases may be involved in the analysis of electrocardiogram (ECG) data, depending on the type of study being done. Preprocessing, feature extraction, feature selection, feature modification, and classification are frequently included in these stages. Every stage must be finished in order for the analysis to go smoothly. Additionally, accurate success measures and the creation of an acceptable ECG signal database are prerequisites for the analysis of electrocardiogram (ECG) signals. Identification and diagnosis of various cardiac illnesses depend heavily on the ECG segmentation and feature extraction procedure. Electrocardiogram (ECG) signals are frequently obtained for a variety of purposes, including the diagnosis of cardiovascular conditions, the identification of arrhythmias, the provision of physiological feedback, the detection of sleep apnea, routine patient monitoring, the prediction of sudden cardiac arrest, and the creation of systems for identifying vital signs, emotional states, and physical activities. The ECG has been widely used for the diagnosis and prognosis of a variety of heart diseases. Currently, a range of cardiac diseases can be accurately identified by computerized automated reports, which can then generate an automated report. This academic paper aims to provide an overview of the most important problems associated with using deep learning and machine learning to diagnose diseases based on electrocardiography, as well as a review of research on these techniques and methods and a discussion of the major data sets used by researchers.
During recent years, there has been an increasing interest in the investigation of the cytokines roles in pathogenesis of cancer, thus the study aimed at evaluating the level of tumor necrosis factor-alpha(TNF-?) in sera of Iraqi multiple myeloma (MM) patients. Beta 2-microglobulion (?2-m) was assessed to determine if there was any association between this cytokine and the level of ?2- m, as the latter is related to the stage of the disease. In addition, the age and gender were also taken into consideration. Furthermore, we investigated the relationship between IgG and TNF-? in sera of patients. 49 Iraqi patients (27 males and 22 females).The patients were also divided into two groups: the first group included (17) patients who were
... Show MoreThis study is planned with the aim of constructing models that can be used to forecast trip production in the Al-Karada region in Baghdad city incorporating the socioeconomic features, through the use of various statistical approaches to the modeling of trip generation, such as artificial neural network (ANN) and multiple linear regression (MLR). The research region was split into 11 zones to accomplish the study aim. Forms were issued based on the needed sample size of 1,170. Only 1,050 forms with responses were received, giving a response rate of 89.74% for the research region. The collected data were processed using the ANN technique in MATLAB v20. The same database was utilized to
Construction projects are characterized as projects with multi phases and activities, complex, unique, and have many different parties and stakeholders. Risks could appear at one or more of the construction project stages and may affect the achievement of project objectives. Therefore, one of the key elements in the planning phase of any project is the risk management process (RMP). This study attempts to understand the terminology of risk in general, risk management, and response to risk in particular. This study is mainly a review of thirty-eight studies that have been published between 1997 and 2020 that demonstrate the importance of the crucial phase of risk response from the risk management process and its impact on
... Show MoreThe main aim of this research is to introduce financing cost optimization and different financing alternatives. There are many studies about financing cost optimization. All previous studies considering the cost of financing have many shortcomings, some considered only one source of financing as a credit line without taking into account different financing alternatives. Having only one funding alternative powers, restricts contractors and leads to a very specific financing model. Although it is beneficial for the contractor to use a long-term loan to minimize interest charges and prevent a substantial withdrawal from his credit line, none of the existing financial-based planning models have considered long-term loans in
... Show MoreGenetic algorithms (GA) are a helpful instrument for planning and controlling the activities of a project. It is based on the technique of survival of the fittest and natural selection. GA has been used in different sectors of construction and building however that is rarely documented. This research aimed to examine the utilisation of genetic algorithms in construction project management. For this purpose, the research focused on the benefits and challenges of genetic algorithms, and the extent to which genetic algorithms is utilised in construction project management. Results showed that GA provides an ability of generating near optimal solutions which can be adopted to reduce complexity in project management and resolve difficult problem
... Show MoreZainab M. Al-Bahrani Department of Oral Diagnosis, College of Dentistry, University of Baghdad, Baghdad, Iraq.Corresponding author: Zainab M. Al-Bahra...