Confimizer: A Novel Algorithm to Optimize Cloud Resource by Confidentiality-Cost Trade-Off Using BiLSTM Network

Achar, Sandesh; Faruqui, Nuruzzaman; Bodepudi, Anusha; Reddy, Manjunath

doi:10.1109/access.2023.3305506

Cited by 7 publications

(1 citation statement)

References 26 publications

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“…It has been observed from the literature review that Machine Learning and Deep Learning approaches are evaluated using four evaluation metrics [41]. These are: accuracy, precision, recall, specificity, and F1-score [42]. It has been further observed that recall and sensitivity have been used interchangeably [43].…”

Section: Evaluation Metricsmentioning

confidence: 99%

DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media

Hossain,

Faruqui,

Mahmud

et al. 2023

Applied Sciences

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SocialMedia Marketing (SMM) has become a mainstream promotional scheme. Almost every business promotes itself through social media, and an educational institution is no different. The users’ responses to social media posts are crucial to a successful promotional campaign. An adverse reaction leaves a long-term negative impact on the audience, and the conversion rate falls. This is why selecting the content to share on social media is one of the most effective decisions behind the success of a campaign. This paper proposes a Data-Driven Promotional Management System (DPMS) for universities to guide the selection of appropriate content to promote on social media, which is more likely to obtain positive user reactions. The main objective of DPMS is to make effective decisions for Social Media Marketing (SMM). The novel DPMS uses a well-engineered and optimized BiLSTM network, classifying users’ sentiments about different university divisions, with a stunning accuracy of 98.66%. The average precision, recall, specificity, and F1-score of the DPMS are 98.12%, 98.24%, 99.39%, and 98.18%, respectively. This innovative Promotional Management System (PMS) increases the positive impression by 68.75%, reduces the adverse reaction by 31.25%, and increases the conversion rate by 18%. In a nutshell, the proposed DPMS is the first promotional management system for universities. It demonstrates significant potential for improving the brand value of universities and for increasing the intake rate.

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Section: Evaluation Metricsmentioning

confidence: 99%

DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media

Hossain,

Faruqui,

Mahmud

et al. 2023

Applied Sciences

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PersoNet: A Novel Framework for Personality Classification-Based Apt Customer Service Agent Selection

Sandra,

Prabowo,

Gaol

et al. 2024

IEEE Access

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Personality classification has garnered significant interest in psychology, computational social science, and Machine Learning (ML) due to its wide-ranging applications. This paper presents PersoNet, an innovative framework developed to identify personality types using the Myers-Briggs Type Indicator (MBTI), aimed at enhancing customer service experiences by matching customers with suitable support agents. PersoNet employs a Bidirectional Long Short-Term Memory (BiLSTM) neural network architecture and has achieved an impressive classification accuracy of over 93.98%. Our extensive experiments with the MBTI dataset reveal that the BiLSTM architecture effectively captures both temporal dependencies and semantic subtleties in textual data, contributing to this high level of accuracy. Consequently, PersoNet can accurately select customer service agents who match customer personalities, achieving a Customer Satisfaction Rate (CSR) of over 97.82%-a notable improvement of 20.25% in CSR based on our experimental data. These results establish PersoNet as a cutting-edge tool in personality classification, surpassing existing methods in both accuracy and computational efficiency and markedly enhancing customer service quality.

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Deep-Hill: An Innovative Cloud Resource Optimization Algorithm by Predicting SaaS Instance Configuration Using Deep Learning

Abouelyazid

2024

IEEE Access

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Confimizer: A Novel Algorithm to Optimize Cloud Resource by Confidentiality-Cost Trade-Off Using BiLSTM Network

Cited by 7 publications

References 26 publications

DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media

DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media

PersoNet: A Novel Framework for Personality Classification-Based Apt Customer Service Agent Selection

Deep-Hill: An Innovative Cloud Resource Optimization Algorithm by Predicting SaaS Instance Configuration Using Deep Learning

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