A new model for study of quality attributes to components based development approach

Jaffar, Rafid Nabil; Hussain, Alaa Abd Al Muhsen; Chiad, Wisam

doi:10.21533/pen.v7i3.686

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Birçok durumda insanlar görevlerini makinalardan daha iyi yapabilseler de, makinalardan daha yavaştırlar ve kolayca yorulabilirler. Bunun yanısıra, insanlar hata bulmakta zorlanırlar, gözlem yapmayı devam ettirmekte zorlanırlar, eğitilmelerine ihtiyaç vardır ve yeteneklerini geliştirmeleri zaman alır (Jaffar et al, 2019). Ayrıca birçok durum da vardır ki, en iyi uzmanlar dahil muayene esnasında zorlanırlar veya tedirgin olurlar.…”

Section: Kalite Kontrolde İşgücü Ve Yapay Zekaunclassified

Kali̇te Ölçümünde İşgücü Planlamasi Ve Yapay Görme Si̇stemleri̇

ÖZCAN,

SÜMEN,

ERKASAP

2023

Uluslararası Akademik Yönetim Bilimleri Dergisi

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Günümüzde Moore yasasına uygun şekilde gücü hızla artıp ucuzlayan bilgisayarlar yapay görme sistemlerinin yaygınlaşmasının önünü açmıştır. Bununla beraber yapay görme sistemlerinin yazılımlar, kameralar, ışık sistemleri gibi başka bileşenler gerektirmesi ve bu bileşenlerin entegrasyonu ile eğitim çalışmaları konuyu ciddi bir yatırım konumuna getirmektedir. Kuşkusuz yapay görme sistemlerinin endüstride ve iç lojistik uygulamalarda kalitesizlik maliyetlerini azaltan çok önemli bir araç olması yatırımları teşvik etmektedir. Ancak ekonomiklik ilkesine uyması da vazgeçilemez koşuldur. Dolayısı ile bu sistemlerin kullanıldıkları süre boyunca götürülerinin üstünde getiri sağlamalarına dikkat edilmelidir. Bu çalışmada kalite iyileştirmeleri sağlamak amaçlı kullanılması düşünülen yapay görme sistemlerinin yatırım karlılığının anlaşılmasını kestirecek bir model tanıtılmaktadır. Ülkemiz genelinde yürüttüğümüz araştırmalar ile EMVA (European Machine Vision Association) ile yaptığımız görüşmeler çerçevesinde temin edilen veriler benzer bir modelin bulunmaması nedeniyle kimi karsız yatırımların yapılmakta olduğu, kimi karlı olabilecek yatırım fırsatlarının kaçırıldığı gerçeklerini ortaya çıkartmıştır. Model, kalitesizlik maliyetlerini, yapay görme sisteminin tahmini bedelini, kalitesizlik maliyetlerinde yapay görme sistemi sayesinde sağlanabilecek tasarruf tutarını veri olarak almakta ve işgücü maliyetleri açısından da bir öneri geliştirmektedir.

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Section: Kalite Kontrolde İşgücü Ve Yapay Zekaunclassified

Kali̇te Ölçümünde İşgücü Planlamasi Ve Yapay Görme Si̇stemleri̇

ÖZCAN,

SÜMEN,

ERKASAP

2023

Uluslararası Akademik Yönetim Bilimleri Dergisi

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“…This is known as the availability. [10]. which are self-explaining, robust, fault tolerant, adaptive, self-optimizing, deductive, learning, cooperative, autonomous, and agile [13] There has been increasing effort for industrial applications of artificial intelligence (AI) systems.…”

Section: Primarily Literature Reviewmentioning

confidence: 99%

Development Metrics for Intelligent Systems

Aljafare,

Marhoob

2023

jast

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The existence of metrics for Intelligent systems is very important in order to evaluate, manage, and classify them. Intelligent information systems (IIS) have become a significant part of our daily lives, institutions, and ongoing business, and it has become necessary to have metrics for Intelligent systems in order to measure the success of organizations in using Intelligent systems. In this study, We will choose a group of metrics for intelligent systems, work to develop them, and then determine their source and method of mitigation. It defines the phrase "intelligent systems metrics" and goes over the key metrics and guidelines that can be used to define IISM as well as which metrics have the biggest influence. The significance of each metric in gauging various intelligent systems will be determined, and when these metrics have been reviewed, the effects of each criterion on IIS will be clear.

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“…Agile approaches have recently introduced one of the most recently mentioned concepts in the software industry, it is the concept of: DevOps. Dating back to 2009 [28]- [30], DevOps can be defined as a set of practices that tries to bridge the gap between develop and operations covering all the elements that help in the implementation of high-quality software [31], [32]. DevOps emphasizes the utilization of human expertise, technology, and streamlined processes to foster a culture of innovation and ongoing collaboration throughout the software development lifecycle.…”

Section: Introductionmentioning

confidence: 99%

DevOps Ontology - An ontology to support the understanding of DevOps in the academy and the software industry

Pardo¹,

Orozco-Garcés²,

Guerrero³

2023

PEN

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Currently, the degree of knowledge about what DevOps really means and what it entails is still limited. This can result in an informal and even incorrect implementation in many cases. Although several proposals related to DevOps adoption can be found, confusion is not uncommon and terminology conflict between the proposals is still evident. This article proposes DevOps Ontology, a semi-formal ontology that proposes a generic, consistent, and clear language to enable the dissemination of information related to implementing DevOps in software development. The ontology presented in this article facilitates the understanding of DevOps by identifying the relationships between software process elements and the agile principles/values that may be related to them. The DevOps Ontology has been defined considering the following aspects: the REFSENO formalism that uses the representation in UML was used and the language OWL language using Prótegé and HermiT Reasoner to evaluate the consistency of its structure. Likewise, it was satisfactorily evaluated in three application cases: a theoretical validation; instantiation of the continuous integration and deployment practices proposed by the company GitLab. Furthermore, a mobile app was created to retrieve information from the DevOps Ontology using the SPARQL protocol and RDF language. The app also evaluated the Ontology's proficiency in responding to knowledge-based questions using SPARQL. The results showed that DevOps Ontology is consistent, complete, and concise, i.e.: to say: the consistency could be observed in the ability to be able to infer knowledge from the ontology, ensuring that the ontology is complete by checking for any incompleteness and verifying that all necessary definitions and inferences are well-established. Additionally, the ontology was assessed for conciseness to ensure that it doesn't contain redundant or unnecessary definitions. Furthermore, it has the potential for improvement by incorporating new concepts and relationships as needed. The newly suggested ontology creates a set of terms that provide a systematic and structured approach to organizing the existing knowledge in the field. This helps to minimize the confusion, inconsistency, and heterogeneity of the terminologies and concepts in the area of interest.

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A new model for study of quality attributes to components based development approach

Cited by 4 publications

References 6 publications

Kali̇te Ölçümünde İşgücü Planlamasi Ve Yapay Görme Si̇stemleri̇

Kali̇te Ölçümünde İşgücü Planlamasi Ve Yapay Görme Si̇stemleri̇

Development Metrics for Intelligent Systems

DevOps Ontology - An ontology to support the understanding of DevOps in the academy and the software industry

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