Consumer recommendation prediction in online reviews using Cuckoo optimized machine learning models

Jain, Praphula Kumar; Yekun, Ephrem Admasu; Pamula, Rajendra; Srivastava, Gautam

doi:10.1016/j.compeleceng.2021.107397

Cited by 33 publications

(9 citation statements)

References 11 publications

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“…Ma et al (2017) proposed the collaborative filtering approach for identifying the customer’s preferences in online shopping. Jain et al (2021) proposed the cuckoo optimized technique for predicting consumer recommendation from online reviews. It combines the importance and need of the customer through the aspect level opinion mining.…”

Section: Literature Surveymentioning

confidence: 99%

RETRACTED: Customer’s opinion mining from online reviews using intelligent rules with machine learning techniques

Sabena

SaiRamesh

et al. 2022

Concurrent Engineering

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In the field of marketing, many surveys were conducted to analyze the customer satisfaction on products in their online purchases. But the real view of customers about the product is mirrored in the customer’s online reviews (COR) given by them, while they purchase the product online. This paper is the one for analyzing and distinguishing the real view about the customer satisfaction by reviewing their opinions for the product which they buy. As a part of opinion mining, the polarity of the specific word is extracted and classifies the review as positive or negative using Naïve Bayes classifier. And this creates a genuine view about the product from the customer point of view. The real opinion about the customer view on online shopping is going to be distinguished according to the intelligent rules generated based on the hypothesis. Intelligent rules help to classify the reviews by extracting the real opinion of the customer based on the feature they specified for the product which is purchased by the consumer. This kind of feature-based review classification supports the purchase of new users when they approach online shopping. This work also projects the customer view about which feature they really need and also feel good, from their review representation.

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Section: Literature Surveymentioning

confidence: 99%

RETRACTED: Customer’s opinion mining from online reviews using intelligent rules with machine learning techniques

Sabena

SaiRamesh

et al. 2022

Concurrent Engineering

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“…Simanta Shekhar Sarmah [5] explained that the deep learning modified neural network (DLMNN) achieved higher data security, however, the disease detection rate had to be enhanced. e prediction based on machine learning using optimization was developed by [17]. e developed method obtained better accuracy in prediction with minimal computational cost, however, the slow convergence is considered to be the drawback of the method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…e accuracy of the deep learning techniques can be further enhanced by using the optimization algorithms such as cuckoo optimization [17,20], particle swarm optimization [18], crow search algorithm [24], and so on.…”

Section: Introductionmentioning

confidence: 99%

Smart Healthcare: Disease Prediction Using the Cuckoo-Enabled Deep Classifier in IoT Framework

Kumar¹,

Reddy²,

Mohammad³

et al. 2022

Scientific Programming

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The Internet of Things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Presently, disease detection is performed using MRI images, X-rays, CT scans, and so on for diagnosing the diseases. The manual detection process is found to be time-consuming and may result in detection errors that affect the diagnosis. Hence, there is a need for an automatic system for which the deep learning methods gain a major interest. Hence, the idea to combine deep learning and disease prediction to effectively predict the disease is initiated. In this research, the deep learning method is combined with deep learning for the effective prediction of diseases, where the IoT network is employed in the data collection from the patients. The proposed cuckoo-based deep convolutional long-short term memory (deep convLSTM) classifier is employed for disease prediction, where the cuckoo search optimization is utilized for tuning the deep convLSTM classifier. The proposed method is compared with the conventional methods, and it achieved a training percentage of 97.591%, 95.874%, and 97.094%, respectively, for accuracy, sensitivity, and specificity. The comparative analysis proved that the proposed method obtained higher accuracy than other methods.

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“…Jain et al ( 2021a , b , c , d , ) a proposed a Cuckoo Search-eXtreme gradient boosting model and optimized the model to recommend airlines. They also (2021 b) proposed a sparse self-attentive network-based aspect-aware model that can effectively predict consumer recommendation decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of agricultural exports based on deep learning and text mining

Hsu

2022

J Supercomput

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Agricultural exports are an important source of economic profit for many countries. Accurate predictions of a country’s agricultural exports month on month are key to understanding a country’s domestic use and export figures and facilitate advance planning of export, import, and domestic use figures and the resulting necessary adjustments of production and marketing. This study proposes a novel method for predicting the rise and fall of agricultural exports, called agricultural exports time series-long short-term memory (AETS-LSTM). The method applies Jieba word segmentation and Word2Vec to train word vectors and uses TF-IDF and word cloud to learn news-related keywords and finally obtain keyword vectors. This research explores whether the purchasing managers’ index (PMI) of each industry can effectively use the AETS-LSTM model to predict the rise and fall of agricultural exports. Research results show that the inclusion of keyword vectors in the PMI values of the finance and insurance industries has a relative impact on the prediction of the rise and fall of agricultural exports, which can improve the prediction accuracy for the rise and fall of agricultural exports by 82.61%. The proposed method achieves improved prediction ability for the chemical/biological/medical, transportation equipment, wholesale, finance and insurance, food and textiles, basic materials, education/professional, science/technical, information/communications/broadcasting, transportation and storage, retail, and electrical and machinery equipment categories, while its performance for the electrical and optical categories shows improved prediction by combining keyword vectors, and its accuracy for the accommodation and food service, and construction and real estate industries remained unchanged. Therefore, the proposed method offers improved prediction capacity for agricultural exports month on month, allowing agribusiness operators and policy makers to evaluate and adjust domestic and foreign production and sales.

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Consumer recommendation prediction in online reviews using Cuckoo optimized machine learning models

Cited by 33 publications

References 11 publications

RETRACTED: Customer’s opinion mining from online reviews using intelligent rules with machine learning techniques

RETRACTED: Customer’s opinion mining from online reviews using intelligent rules with machine learning techniques

Smart Healthcare: Disease Prediction Using the Cuckoo-Enabled Deep Classifier in IoT Framework

Analysis of agricultural exports based on deep learning and text mining

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