Hybrid CNN-RF Model for Accurate Casting Defect Forecasting

Banerjee, Deepak; Sharma, Neha; Upadhyay, Deepak; Singh, Vijay

doi:10.1109/apcit62007.2024.10673686

2024 Asia Pacific Conference on Innovation in Technology (APCIT) 2024

DOI: 10.1109/apcit62007.2024.10673686

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Hybrid CNN-RF Model for Accurate Casting Defect Forecasting

Deepak Banerjee,

Neha Sharma,

Deepak Upadhyay

et al.

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Arabic Opinion Classification of Customer Service Conversations Using Data Augmentation and Artificial Intelligence

Al-Mutawa,

Al-Aama

2024

BDCC

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Customer satisfaction is not just a significant factor but a cornerstone for smart cities and their organizations that offer services to people. It enhances the organization’s reputation and profitability and drastically raises the chances of returning customers. Unfortunately, customer support service through online chat is often not rated by customers to help improve the service. This study employs artificial intelligence and data augmentation to predict customer satisfaction ratings from conversations by analyzing the responses of customers and service providers. For the study, the authors obtained actual conversations between customers and real agents from the call center database of Jeddah Municipality that were rated by customers on a scale of 1–5. They trained and tested five prediction models with approaches based on logistic regression, random forest, and ensemble-based deep learning, and fine-tuned two pre-trained recent models: ArabicT5 and SaudiBERT. Then, they repeated training and testing models after applying a data augmentation technique using the generative artificial intelligence, GPT-4, to improve the unbalance in customer conversation data. The study found that the ensemble-based deep learning approach best predicts the five-, three-, and two-class classifications. Moreover, data augmentation improved accuracy using the ensemble-based deep learning model with a 1.69% increase and the logistic regression model with a 3.84% increase. This study contributes to the advancement of Arabic opinion mining, as it is the first to report the performance of determining customer satisfaction levels using Arabic conversation data. The implications of this study are significant, as the findings can be applied to improve customer service in various organizations.

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Arabic Opinion Classification of Customer Service Conversations Using Data Augmentation and Artificial Intelligence

Al-Mutawa,

Al-Aama

2024

BDCC

View full text Add to dashboard Cite

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Hybrid CNN-RF Model for Accurate Casting Defect Forecasting

Cited by 1 publication

References 15 publications

Arabic Opinion Classification of Customer Service Conversations Using Data Augmentation and Artificial Intelligence

Arabic Opinion Classification of Customer Service Conversations Using Data Augmentation and Artificial Intelligence

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