Improving Software Development Process using Data Traceability Management

Rahmaoui, Othmane; Souali, Kamal; Ouzzif, Mohammad

doi:10.3991/ijes.v7i1.10113

Cited by 2 publications

(2 citation statements)

References 4 publications

(4 reference statements)

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“…A single respondent stated that the Solution Maintenance Plan artifact is in place, which may be a warning about whether this artifact is actually implemented in the agency. There are several works in the literature [3,[51][52][53][54] which assert the importance of organizations having a well-defined software development process, especially in relation to government agencies, which require maturity level 3 for an organization to be able to provide software development services to any agency of the Brazilian Government. Regarding the analysis of the basic ICT processes, we can conclude that only the ICT People Management process and its respective artifacts (Talent Bank (Skills and Competencies) and Role and Responsibility Definition Form) are not implemented in the agency.…”

Section: Basic Ict Processesmentioning

confidence: 99%

Information and Communication Technology (ICT) Governance Processes: A Case Study

et al. 2020

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Information and Communication Technology (ICT) Governance is increasingly necessary and present in organizations aiming to improve the maturity of their ICT processes. This paper presents an analysis of the ICT Governance processes of a Brazilian Federal Public Administration agency. To assess the maturity of the ICT Governance processes, we surveyed and diagnosed the processes performed by the agency and organized a series of meetings/discussions to assist in the improvement and modeling of the processes related to the ICT Contract Planning process. As a result, we proposed improvements and identified the maturity level of the existing ICT processes, also assessing the awareness of employees of the General Coordination of Information Technology regarding these processes. Our findings reveal that the agency still needs to implement the following processes: (1) ICT People Management; (2) Business Process Modeling (Automated/to Automate); (3) Change Management; (4) Execution Monitoring of the ICT Projects and Services Portfolio; and (5) ICT Service Continuity Management. We also identified several artifacts that need to be implemented by the agency in different processes and collected survey participants’ suggestions about new processes to improve the maturity in ICT Governance.

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Section: Basic Ict Processesmentioning

confidence: 99%

Information and Communication Technology (ICT) Governance Processes: A Case Study

et al. 2020

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“…Artificial Intelligence (AI) techniques, particularly Machine Learning (ML) [4], have been widely used in medical diagnosis and treatment systems to automate the classification of patients and the prediction of cardiovascular diseases. These techniques have been applied to a wide range of domains, beyond healthcare [5]- [7], including: Medical image segmentation [8], [9], COVID-19 Disease [10], Natural Language Processing (NLP) [11], Image processing [12], Industrial automation, Predictive modelling and Diagnosis of Neurological disorders [13]. With the help of ML, we can process large datasets, identify patterns, and make predictions that would be difficult or impossible to do manually [14].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Machine Learning Algorithms for Heart Disease Classification and Prediction

El-Ibrahimi,

Terrada,

El Gannour

et al. 2023

Int. J. Onl. Eng.

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According to the World Health Organization (WHO), cardiovascular disease is one of the leading causes of death worldwide. Thus, the prevention of this kind of illness is considered as a huge human health challenge. Additionally, the diagnostic process often involves a combination of clinical examination, laboratory tests, and other diagnostic procedures, which can be complex and time-consuming. However, advances in medical technology and research have led to improved methods for diagnosing heart disease, which can help to improve patient outcomes. Furthermore, Machine Learning (ML) methods have shown promise in helping to improve the diagnosis of heart disease. Each method requires specific parameters to produce good results. In this paper, we propose a diagnosis support system based on optimized Machine Learning algorithms, which is Artificial Neural Network (ANN), Support Vector Machine (SVM), K_Nearest Neighbour (KNN), Naive Bayes (NB), and Decision Tree (DT) to analyze the major cardiovascular risk factors, such as age, gender, high blood pressure, etc. To train and validate the ML models, a medical dataset of 558 patients with atherosclerosis is used. In this work, we achieved a 96.67% as promising accuracy level for the atherosclerosis prediction with ANN.

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Improving Software Development Process using Data Traceability Management

Cited by 2 publications

References 4 publications

Information and Communication Technology (ICT) Governance Processes: A Case Study

Information and Communication Technology (ICT) Governance Processes: A Case Study

Optimizing Machine Learning Algorithms for Heart Disease Classification and Prediction

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