Journal of Software Engineering & Intelligent Systems

ISSN: 2518-8739 (Online)
August 2022 | VOLUME. 7  ISSUE. 2
Title:

CHOICE OF METRICS IN ACHIEVING SOFTWARE QUALITY ASSURANCE

Authors:

Ikenna Caesar Nwandu, Juliet N. Odii, Euphemia C. Nwokorie, Stanley A. Okolie, Charles O. Ikerionwu

Abstract:

The quality of software indicates how much the software performs its functions intended to bring maximum satisfaction to the user. The issue of software quality assurance remains a check-point for producing competent software products with less defects, capable of fulfilling the objective of satisfying the user. This study is geared towards this direction by analyzing the dependency of software quality life cycle on software quality assurance. It also analyzed the basic concepts of software quality assurance, with which it deduced that software quality activities have to be a continuous process throughout software development in order to assure quality of software. This study further proposed a logic for choosing metrics during the evaluation stages. It concluded by positing that the choice of metrics should largely depend on the desired software properties and/or attributes, with the assurance that quality guidelines are not compromised.

Keywords: software quality; software development; software metrics; quality assurance; quality engineering;
Available: CHOICE OF METRICS IN ACHIEVING SOFTWARE QUALITY ASSURANCE by Ikenna Caesar Nwandu, Juliet N. Odii, Euphemia C. Nwokorie, Stanley A. Okolie, Charles O. Ikerionwu will be available under cc by-nc 4.0 License on 31st August 2022. Permissions beyond the scope of this license may be available at JSEIS.
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Title:

A NOVEL CONVOLUTIONAL NEURAL NETWORK FOR COVID-19 DETECTION AND CLASSIFICATION USING CHEST X-RAY IMAGES

Authors:

Muhammad Talha Nafees, Irshadullah Khan, Muhammad Rizwan, Maazullah, Muhammad Irfanullah Khan, Muhammad Farhan

Abstract:

The early and rapid diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), the main cause of fatal pandemic coronavirus disease 2019 (COVID-19), with the analysis of patients chest X-ray (CXR) images has life-saving importance for both patients and medical professionals. In this research a very simple novel and robust deep-learning convolutional neural network (CNN) model with less number of trainable-parameters is proposed to assist the radiologists and physicians in the early detection of COVID-19 patients. It also helps to classify patients into COVID-19, pneumonia and normal on the bases of analysis of augmented X-ray images. This augmented dataset contains 4803 COVID-19 from 686 publicly available chest X-ray images along with 5000 normal and 5000 pneumonia samples. These images are divided into 80% training and 20 % validation. The proposed CNN model is trained on training dataset and then tested on validation dataset. This model has a promising performance with a mean accuracy of 92.29%, precision of 99.96%, Specificity of 99.85 along with Sensitivity value of 85.92%. The result can further be improved if more data of expert radiologist is publically available.

Keywords: Coronavirus; COVID-19; deep learning; customized CNN model; classification; detection; medical image processing; CXR, Chest-X-rays;
Available: A NOVEL CONVOLUTIONAL NEURAL NETWORK FOR COVID-19 DETECTION AND CLASSIFICATION USING CHEST X-RAY IMAGES by Muhammad Talha Nafees, Irshadullah Khan, Muhammad Rizwan, Maazullah, Muhammad Irfanullah Khan, Muhammad Farhan will be available under cc by-nc 4.0 License on 31st August 2022. Permissions beyond the scope of this license may be available at JSEIS.
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Title:

ROBOTIC SOFTWARE ARCHITECTURE AND RESEARCH TRENDS: A PROGRESSIVE VIEW

Authors:

Sana Amin, Muhammad Imran Babar

Abstract:

Robotic software is an arrangement of coded directions and guidelines that tells mechanical devices and electronic framework, referred together as a robot, to facilitate and perform different daily life functions. Numerous robotic software frameworks, approaches and systems have been proposed to make the programming and development process of robots easier. Robotic software is an Artificial Intelligence based framework that keeps running on a host device as opposed to an independent machine. Robotic software system incorporates the expert framework and virtual assistants. Computer software always faces many issues and complexities while to handle these complexities robotic software framework is used. Robotic Software Architecture (RSA) describes specific rules and regulations for designers and developers to get the expected results. This research focuses on the competitive analysis of RSA in terms of challenges, evolution and lessons for better visibility and knowledge management of high-level design of robotic software system. The main purpose of this research study is to explore and identify the existing RSA approaches and to find out the key issues of robotic environment.

Keywords: robotics; robotic software architecture; architectural styles; taxonomies; robot operating system;
Available: ROBOTIC SOFTWARE ARCHITECTURE AND RESEARCH TRENDS: A PROGRESSIVE VIEW by Sana Amin, Muhammad Imran Babar will be available under cc by-nc 4.0 License on 31st August 2022. Permissions beyond the scope of this license may be available at JSEIS.
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