A Critical analysis on Neural Networks and Deep Learning Based Techniques for the Cloud Computing System and its Impact on Industrial Management

Kanade, Shantanu; Surya, S.; Kanade, Anuradha; Sreenivasulu, K; Ajitha, E.; Ray, Samrat

doi:10.1109/icacite53722.2022.9823663

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…The management team had different strategies, which led to indecision. The inability to reach a consensus hindered decision-making processes, leaving Byju unable to exploit market opportunities or address pressing issues (Kanade et al 2022). The work environment was toxic because of rivalries and power struggles.…”

Section: Internal Conflictsmentioning

confidence: 99%

A case study of BYJU’S failure

Samrat Ray,

Kaviya Jain,

Piyush Birru

et al. 2024

World J. Adv. Res. Rev.

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Once an education IT pioneer, BYJU'S, failure convoluted startup workers, investors. Byju failed due to financial mismanagement. Bad decisions and inconsistent financing prevented the corporation from paying its obligations. Company disagreements and leadership instability hurt BYJU'S operations. Due to negative press, BYJU'S brand suffers, its reputation suffered from data breaches, false advertising, and forced purchases. When the economy is terrible, earning more is tougher. These issues must be addressed for the future of technology. Businesses must be frugal, transparent, and financially aware. Customise marketing and market segmentation to accommodate everyone. Openness to new ideas and flexibility are needed to compete in a fast-changing market. Innovation, perseverance, and morality help Ed-tech overcome problems and capture opportunities. Businesses may learn from BYJU'S errors to grow sustainably, establish trust, and lead in the fast-paced, competitive educational technology business. Ed-tech must innovate after Byju failed. The company couldn't innovate; therefore, it struggled to compete and survive. Edtech companies must be inventive, customer-focused, and adaptable to compete in a competitive, ever-changing business. Learning from setbacks and encouraging resilience and creativity may help ed-tech companies thrive.

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Section: Internal Conflictsmentioning

confidence: 99%

A case study of BYJU’S failure

Samrat Ray,

Kaviya Jain,

Piyush Birru

et al. 2024

World J. Adv. Res. Rev.

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“…On the basis of SBM model, Tone learned the advantages of super efficient DEA model and redefined the super efficient SBM model by integrating the advantages of the two models. Compared with the general SBM model, the super efficient SBM model can give priority to comparing and distinguishing the efficient DMU in the frontier [38][39][40][41][42] . However, both the SBM model containing unexpected output and the traditional DEA model have a common defect, that is, when evaluating the same type of DUM, multiple decision -making units will be in the forefront of production, that is, multiple efficiency values are 1.…”

Section: Super Efficient Sbm Model Based On Unexpected Outputmentioning

confidence: 99%

Research on the spatial-temporal evolution pattern of China's industrial carbon emission efficiency-based on the super-efficiency SBM model

Samrat Ray

2023

World J. Adv. Res. Rev.

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This paper uses the super efficiency SBM model based on unexpected output to calculate the industrial carbon emission efficiency of 30 provinces in China from 2005 to 2019, and uses spatial autocorrelation and other methods to conduct an empirical analysis on the dynamic evolution process and temporal and spatial differences of carbon emission efficiency. The results show that China's industrial carbon emission efficiency is still in the low efficiency stage, with an average value of 0.34. In the study period, although the overall trend is rising, the spatial and temporal differences between provinces are obvious. The overall Moran's I index is subject to the impact of social policy environment on the time path, showing an overall "M" type fluctuation trend, and its spatial difference characteristics change in stages. At the same time, there is a significant global spatial agglomeration effect across the country, and there is a spatial dependency between provinces. The agglomeration trend is significant, forming a cluster with the eastern coastal area as the high value center, and the northwest inland area as the low value center. The Getis OrdGI * index reveals that the local spatial agglomeration characteristics of China's industrial carbon emission efficiency always maintain the pattern of "hot in the east and cold in the west", which echoes the overall spatial evolution pattern of China. The hot spots and sub hot spots of China's industrial carbon emission efficiency are mainly concentrated in the southeast coastal areas with relatively superior economic development, and present a "semi enclosed" pattern. Cold spot area and sub cold spot area gradually evolved into a "suppression" pattern dominated by provinces in northwest China, comprehensively suppressing hot spots. This paper conducts an all-round evaluation of China's industrial carbon emission efficiency from the perspective of regional differentiation, and deeply studies the regional differences and spatial evolution pattern of carbon emission efficien cy in various provinces. It is crucial to achieve the goal of carbon emission reduction in China, and has important practical value and practical significance.

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A Critical analysis on Neural Networks and Deep Learning Based Techniques for the Cloud Computing System and its Impact on Industrial Management

Cited by 2 publications

References 13 publications

A case study of BYJU’S failure

A case study of BYJU’S failure

Research on the spatial-temporal evolution pattern of China's industrial carbon emission efficiency-based on the super-efficiency SBM model

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