The Application of Machine Learning Techniques in Software Project Management- An Examination

Pasupuleti, Mahesh Babu

doi:10.18034/abcjar.v7i2.626

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…Instead of relying on intuition or traditional wisdom, marketers can make choices based on real-world user responses. Computational methods have paved the way for a more data-driven, analytical approach to marketing (Pasupuleti, 2018). The ability to predict consumer behavior, gauge sentiment, map consumer journeys, and empirically test marketing hypotheses, as seen in A/B testing, exemplifies the transformative impact of computational science on the marketing landscape.…”

Section: Real-world Example: A/b Testing In Marketing Campaignsmentioning

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: Real-world Example: A/b Testing In Marketing Campaignsmentioning

confidence: 99%

Interdisciplinary Nature of Computational Science Cases in Business Studies

Husen,

Chowdhury

2023

Glob. discl. econ. bus.

View full text Add to dashboard Cite

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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

Miah

Pasupuleti²,

Adusumalli³

2021

Glob. discl. econ. bus.

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Machine Learning is an application of artificial intelligence that allows computers to learn and develop without explicit programming. In other words, the goal of ML is to let computers learn on their own without human involvement and then alter their activities. ML also allows huge data processing. Project management planning and evaluation are vital in project execution. Project management is difficult without a realistic and logical plan. We give a complete overview of works on Machine Learning in Software Project Management. The first category contains software project management research articles. The third category includes research on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in production, and promotion of machine-learning project prediction. Our contribution also provides a broader viewpoint and context for future project risk management efforts. In conclusion, machine learning is more successful in reducing project failure probabilities, increasing output ratio for growth, and facilitating analysis on software fault prediction based on accuracy.

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The Application of Machine Learning Techniques in Software Project Management- An Examination

Cited by 4 publications

References 4 publications

Interdisciplinary Nature of Computational Science Cases in Business Studies

Interdisciplinary Nature of Computational Science Cases in Business Studies

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

The Nexus between the Machine Learning Techniques and Software Project Estimation

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