A Policy Recommendations Framework To Resolve Global Software Development Issues

Hassan, Mujtaba; Hussain, Muzammil; Ayubi, Salah-u-din; Irfan, Maham

doi:10.1109/icic48496.2019.8966719

Cited by 43 publications

(11 citation statements)

References 42 publications

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“…Several approaches for predicting and detecting COVID19 were explored. Most ML-based tactics have been used to forecast the presence, trends, or expansion of COVID19, whereas most Deep Learning approaches have been used to detect COVID19, showing that this problem has a diverse set of solutions and methodologies [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48]. The suggested approach is innovative because, in previous analogous techniques, just a few algorithms are employed and results are compared, or the dataset utilized is confined to one nation or fewer, or the dataset is made up of a smaller number of observations [43][44][45][46][47][48].…”

Section: Related Workmentioning

confidence: 99%

FLOSSC: A Computer-Aided Tool for Detecting Corona Virus Infections through Chest X-Ray Images

Ahmad

Javed

Aftab

et al. 2021

2021 International Conference on Innovative Computing (ICIC)

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Artificial intelligence has radically altered the world, and it continues to progress at an alarming rate as time passes. AI applications include healthcare and medical solutions, illness diagnostics, agriculture, constructing security infrastructures, autonomous cars, intelligent systems, industrial production, robotics, and much more. COVID19 is a deadly virus that first appeared in China in 2019 and soon spread over the world. By 2020, the globe had witnessed a tremendous epidemic, with countless lives lost as a result of this dreadful virus, which has now become a severe health danger. Furthermore, in 2021, several nations will be infected with new Covid19 forms that are more deadly and spread quicker. The research describes the proposed methodology for diagnosing covid-19 and pneumonia from human chest X-ray images using transfer learning with Resnet-18 and VGG-16 neural networks. The focal loss function was also used to homogenize the imbalanced dataset, which included X-ray images of normal, pneumonia, and Covid-19 patients. The purpose is to assess the performance and accuracy of fine-tuned neural networks after including Binary Cross-Entropy (BCE) and Focal Loss (FL) functions. However, when we used our Resnet-18 and VGG-16 neural networks with BCE and FL functions, the VGG-16 with FL function outperformed all other models, with training and validation accuracy of 98.37 percent and 97.37 percent, correspondingly.

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Section: Related Workmentioning

confidence: 99%

FLOSSC: A Computer-Aided Tool for Detecting Corona Virus Infections through Chest X-Ray Images

Ahmad

Javed

Aftab

et al. 2021

2021 International Conference on Innovative Computing (ICIC)

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“…Much more work has done in the field of deep and machine learning for the past decade, and in the future, there is a chance that deep learning, machine learning, big data, and information retrieval will remain the most tempting areas for researchers in the medical and engineering fields [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ].…”

Section: Related Workmentioning

confidence: 99%

A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

et al. 2020

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The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

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“…The quality performs a fundamental role assuring that a product is stable and safe for use [Mishra and Otaiwi 2020]. The test reviewing process is a main step for software quality, the adoption of this method presents gains due to error removal in previous phases and time reduction in future maintenance processes [Hassan et al 2019]. The appliance of a quality process on huge software is still a challenge.…”

Section: Introductionmentioning

confidence: 99%

An Industry Case Study: Methodology Application to the Reviewing Process on Android Releases Homologation

Lancellotta¹,

Barbosa²,

Santos³

et al. 2022

Anais Estendidos Do XIII Congresso Brasileiro De Software: Teoria E Prática (CBSoft Estendido 2022)

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Quality is the main factor at any process, in the software context is not different. The Android Operational System has rigid quality standards. Device producers who choose to use Android, must comply with rules and contract requirements. The quality assurance concerning these rules on Android release homologation process is challenging, due to the great amount of test artifacts to be analyzed at the end of process. In this paper, we propose a methodology to improve the review step for Android releases homologation. As a preliminary result, we identified 52,84% of improvement using the proposed approach. In advance of this research, we intend to implement the automation and evaluate it’s impact in a context of software quality product in our company.

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A Policy Recommendations Framework To Resolve Global Software Development Issues

Cited by 43 publications

References 42 publications

FLOSSC: A Computer-Aided Tool for Detecting Corona Virus Infections through Chest X-Ray Images

FLOSSC: A Computer-Aided Tool for Detecting Corona Virus Infections through Chest X-Ray Images

A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

An Industry Case Study: Methodology Application to the Reviewing Process on Android Releases Homologation

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