DAO-LGBM: dual annealing optimization with light gradient boosting machine for advocates prediction in online customer engagement

Abu-Salih, Bilal; Alotaibi, Salihah; Abukhurma, Ruba; Almiani, Muder; Aljaafari, Mohammed

doi:10.1007/s10586-023-04220-6

Cited by 3 publications

(1 citation statement)

References 63 publications

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“…In the broader context of natural language processing, unraveling the subtleties of sarcasm enriches language models, making them more attuned to the intricacies of human expression. Hence, the significance of sarcasm detection extends far beyond its surface interpretation, permeating various facets of digital communication and information processing [8].…”

Section: Introductionmentioning

confidence: 99%

Decoding sarcasm: unveiling nuances in newspaper headlines

2024

IJECE

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This study navigates the intricate landscape of sarcasm detection within the condensed confines of newspaper titles, addressing the nuanced challenge of decoding layered meanings. Leveraging natural language processing (NLP) techniques, we explore the efficacy of various machine learning models—linear regression, support vector machines (SVM), random forest, na¨ıve Bayes multinomial, and gaussian na¨ıve Bayes—tailored for sarcasm detection. Our investigation aims to provide insights into sarcasm within the succinct framework of newspaper titles, offering a comparative analysis of the selected models. We highlight the varied strengths and weaknesses of these models. Random forest exhibits superior performance, achieving a remarkable 94% accuracy in accurately identifying sarcasm in text. It is closely trailed by SVM with 90% accuracy and logistic regression with 83% accuracy.

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Section: Introductionmentioning

confidence: 99%

Decoding sarcasm: unveiling nuances in newspaper headlines

2024

IJECE

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Product Helpfulness Detection With Novel Transformer Based BERT Embedding and Class Probability Features

Ishtiaq,

Munir,

Raza

et al. 2024

IEEE Access

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Nowadays global market products are readily accessible worldwide, and a vast array of reviews across numerous platforms are posted daily in several categories, making it challenging for customers to stay informed about their product interests. To make informed decisions regarding product quality, users require access to reviews and ratings. Owners and managers must analyze customer ratings and the underlying emotional content of reviews to enhance the product's quality, cost, customer service, and environmental impact. The primary aim of our proposed research is to accurately predict product helpfulness through customer reviews using the Large Language Model (LLM), thereby assisting customers in saving time and money. We employed a benchmark dataset, the Amazon Fine Food Reviews, to develop numerous advanced machine-learning techniques. We introduced a novel transformer approach BERF (BERT Random Forest) for feature engineering to enhance the value of user evaluations for Amazon's gourmet food products. The BERF method utilizes BERT embeddings and class probability features derived from product helpfulness online reviews textual data. We have balanced the dataset using the Synthetic Minority Oversampling TEchnique (SMOTE) approach. Our comprehensive study results demonstrated that the Light Gradient Boosting Machine (LGBM) strategy outperformed existing state-of-the-art approaches, achieving an accuracy of 98%. The performance of each method is confirmed using a k-fold method and further improved through hyperparameter optimization. Our innovative study employing a transformer model has significantly enhanced the utility of customer reviews, substantially reducing online product scams and preventing wasted time and money.

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Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Madni,

Raza,

Sehar

et al. 2024

IEEE Access

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Driver drowsiness detection is a critical field of research within automotive safety, aimed at identifying signs of fatigue in drivers to prevent accidents. Drowsiness impairs a driver's reaction time, decision-making ability, and overall alertness, significantly increasing the risk of collisions. Nowadays, the challenge is to detect drowsiness using physiological signals, which often require direct contact with the driver's body. This can be uncomfortable and distracting. This study aimed at detecting driver drowsiness through eye movement behavior imagery of the driver. We utilized a standard image dataset based on the eye movement behavior of drivers to conduct this research experiment. We proposed a novel transfer learning-based features generation which combined the strengths of the Visual Geometry Group (VGG-16) and Light Gradient-Boosting Machine (LGBM) methods. The proposed VGLG (VGG16-LGBM) approach first extracts spatial features from input eye image data and then generates salient transfer features using LGBM. Experimental evaluations reveal that the k-neighbors classifier outperformed the state-of-the-art approach with a high-performance accuracy of 99%. The computational complexity analysis shows that the proposed approach detects driver drowsiness in 0.00829 seconds. We have enhanced the performance through hyperparameter tuning and validations using k-fold validation. This research has the potential to revolutionize driver drowsiness detection, aiming to prevent road accidents and save precious lives.

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DAO-LGBM: dual annealing optimization with light gradient boosting machine for advocates prediction in online customer engagement

Cited by 3 publications

References 63 publications

Decoding sarcasm: unveiling nuances in newspaper headlines

Decoding sarcasm: unveiling nuances in newspaper headlines

Product Helpfulness Detection With Novel Transformer Based BERT Embedding and Class Probability Features

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

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