The Nexus between the Machine Learning Techniques and Software Project Estimation

Miah, Md. Shelim; Pasupuleti, Mahesh Babu; Adusumalli, Harshini Priya

doi:10.18034/gdeb.v10i1.627

Cited by 5 publications

(2 citation statements)

References 11 publications

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“…At the same time, the infusion of mathematics provides the necessary theoretical foundation and framework for problem formulation (Karniadakis & Kirby II, 2003). Moreover, domain-specific expertise ensures the relevance and applicability of computational solutions to real-world challenges (Smith & Karr, 2009;Miah et al, 2021).…”

Section: The Scope Of Computational Sciencementioning

confidence: 99%

Interdisciplinary Nature of Computational Science Cases in Business Studies

Husen,

Chowdhury

2023

Glob. discl. econ. bus.

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The intricate confluence of computational science and business studies heralds an unprecedented era in the academic and industrial landscape. This paper comprehensively explores this interdisciplinary nexus, delving deep into the transformative potential that computational methodologies bring to business arenas. From leveraging vast datasets in business analytics to pioneering financial models, computational tools are reshaping the very paradigms of business decision-making. However, these advancements are not without challenges. Ethical considerations, over-reliance on models, and the essentiality of human insight remain critical discussions. Moreover, the paper underscores the need for further collaborative research, emphasizing the importance of a symbiotic relationship between computational scientists and business professionals. As the digital age progresses, integrating computational science into business studies becomes beneficial and imperative for organizations seeking innovative solutions and sustained growth in an increasingly complex market environment.

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Section: The Scope Of Computational Sciencementioning

confidence: 99%

Interdisciplinary Nature of Computational Science Cases in Business Studies

Husen,

Chowdhury

2023

Glob. discl. econ. bus.

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“…Frey (2019) propose IaC as a best practice to speed up the DevOps lifecycle. Eric Wright also published a book on "DevOps Automation with Terraform and VMware" (Miah et al, 2021). Meanwhile, Jagdish Patni et al have overcome the drawbacks associated with the manual infrastructure deployment and management approach by implementing a 3-tier architecture using Azure ARM templates, Pulumi, and Terraform.…”

Section: Literature Overviewmentioning

confidence: 99%

Enterprise SaaS Workloads on New-Generation Infrastructure-as-Code (IaC) on Multi-Cloud Platforms

Achar

2021

Glob. discl. econ. bus.

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Cloud Computing has become the primary model used by DevOps practitioners and researchers to provision infrastructure in minimal time. But recently, the traditional method of using a single cloud provider has fallen out of favor due to several limitations regarding performance, compliance rules, geographical reach, and vendor lock-in. To address these issues, industry and academia are implementing multiple clouds (i.e., multi-cloud). However, managing the infrastructure provisioning of enterprise SaaS applications faces several challenges, such as configuration drift and the heterogeneity of cloud providers. This has seen Infrastructure-as-Code (IaC) technologies being used to automate the deployment of SaaS applications. IaC facilitates the rapid deployment of new versions of application infrastructures without degrading quality or stability. Therefore, this work presents a vision of uniformly managing the infrastructure provisioning of enterprise SaaS applications that utilize multiple cloud providers. Hence, we introduce an initial design for the IaC-based Multi-Cloud Deployment pattern and discuss how it addresses the relative challenges.

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The Reputation of Machine Learning in Wireless Sensor Networks and Vehicular Ad Hoc Networks

Pasupuleti¹,

Adusumalli²

2021

Asian bus. rev.

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It's difficult to deal with the dynamic nature of VANETs and WSNs in a way that makes sense. Machine learning (ML) is a preferred method for dealing with this kind of dynamicity. It is possible to define machine learning (ML) as a way of dealing with heterogeneous data in order to get the most out of a network without involving humans in the process or teaching it anything. Several techniques for WSN and VANETs based on ML are covered in this study, which provides a fast overview of the main ML ideas. Open difficulties and challenges in quickly changing networks, as well as diverse algorithms in relation to ML models and methodologies, are also covered in the following sections. We've provided a list of some of the most popular machine learning (ML) approaches for you to consider. As a starting point for further research, this article provides an overview of the various ML techniques and their difficulties. This paper's comparative examination of current state-of-the-art ML applications in WSN and VANETs is outstanding.

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The Nexus between the Machine Learning Techniques and Software Project Estimation

Cited by 5 publications

References 11 publications

Interdisciplinary Nature of Computational Science Cases in Business Studies

Interdisciplinary Nature of Computational Science Cases in Business Studies

Enterprise SaaS Workloads on New-Generation Infrastructure-as-Code (IaC) on Multi-Cloud Platforms

The Reputation of Machine Learning in Wireless Sensor Networks and Vehicular Ad Hoc Networks

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