A Smartphone App Based Model for Classification of Users and Reviews (A Case Study for Tourism Application)

Sahoo, Ramesh K.; Sethi, Srinivas; Udgata, Siba K.

doi:10.1007/978-981-33-6081-5_30

Cited by 3 publications

(8 citation statements)

References 10 publications

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“…A reliable trust management scheme based on a mathematical approach has been discussed in [68] to categorize secondary users into honest, malicious, or suspicious based on spectrum sensing reputation in the cognitive radio network. Based on this concept, authors in [6] proposed a mathematical model with static weights factor for ensuring data integrity by filtering out fake and invalid reviews, isolating genuine reviews, and categorizing various users into Malicious, Suspicious, and Honest categories. But in this model, a range has been used by using the estimated rating of a location; if the review given by a user is placed in that range, the review will be considered genuine or otherwise fake for detecting and eliminating fake reviews from the dataset to ensure data integrity.…”

Section: E Comparative Analysis For Data Integritymentioning

confidence: 99%

“…Therefore it also helps to identify and isolate genuine reviews by detecting fake reviews and honest users to maintain the integrity of data in the MCS environment. In this section, dynamic values of weight factor for Incentive, Activeness, and Reliability level of the user have been considered instead of fixed values, that is considered in [6]. W 1 , W 2 and W 3 are the weight factors for the Reliability level (R u ), Activeness level (A u ), and Incentive level (I u ), respectively, and it is computed using the given equation in Eq.22 to Eq.24.…”

Section: J Estimation Of Honesty Level Of Usermentioning

confidence: 99%

“…The user's honesty level (H u ) can be computed using dynamic weight factors and different performance metrics such as Incentive, Activeness, and Reliability level of the user placed in Eq.25 [6].…”

Section: J Estimation Of Honesty Level Of Usermentioning

confidence: 99%

“…Finally, the user's honesty level has been estimated using the user's reliability, incentive, and activeness levels and the associated dynamic weight factor that reflects the trustworthiness of users. Figure 5 Provides a visual comparison among mathematical models with static weight factor [6], Fuzzy based mathematical model With Static Weight Factor Model [7], and the proposed model in which no. of users(in %age) represented by the x-axis and user type by the y-axis.…”

Section: A Categorization Of Usermentioning

confidence: 99%

“…Deep learning models [2], [4], different supervised, unsupervised, and semi-supervised machine learning algorithms [5], [3] are also proposed for detecting the fake reviews and to help the user in proper decision making. A mathematical approach-based fake review detection framework has been presented in [6] to identify fake reviews for the isolation of genuine reviews. Further, it categorized the users into suspicious, honest, and malicious.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Data Integrity in Mobile Crowdsensing Environment with Machine Learning and Cost-Benefit Analysis

Sahoo¹,

Pradhan²,

Sethi³

et al. 2023

IJCDS

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Mobile Crowdsensing (MCS) is a major source of a vast dataset containing heterogeneous types of data collected from various sources and stored in the local or remote server. Proper analysis of MCS data helps in better decision-making. However, MCS data suffers from data integrity issues, such as validity, accuracy, and reliability, that affect decision-making. Therefore, ensuring data integrity in the MCS environment is essential as it is a major source of a huge dataset. The proposed work considers user review data collection and analysis using a mobile application developed for the purpose. To ensure the data integrity, identification of fake and invalid reviews in the dataset need to be determined. This work proposes two approaches to solve data integrity issues. The first approach is to detect and eliminate fake/ invalid reviews from the dataset. The second is to identify the sources of fake/ invalid reviews and block them to protect the dataset from future fake reviews. Machine learning (ML) models are proposed to solve these issues and to ensure data integrity by filtering out fake reviews from real-time data sets. The proposed model uses data fuzzification over a purely mathematical model that categorizes users or customers as honest, suspicious, or malicious and their reviews/ feedback as genuine or fake using ratings provided by the user in the MCS Environment. Using the developed mobile application, user can give feedback about the desired location through various devices, which is stored in a cloud platform. The dataset can be analyzed through a fuzzy logic-based mathematical model followed by an ML algorithm and cost-benefit analysis to detect genuine reviews for maintaining data integrity. Further accuracy of the proposed models is compared with popular ML algorithms such as Naive Bayes (NB), Bayes Net(BN), Support Vector Machine(SVM), Decision Tree(J48), and Random Forest(RF). Initially, it achieves 99.79% of accuracy using the Random Forest algorithm that has been enhanced to 100% using cost-benefit analysis in cross-validation mode.

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Section: E Comparative Analysis For Data Integritymentioning

confidence: 99%

Section: J Estimation Of Honesty Level Of Usermentioning

confidence: 99%

Section: J Estimation Of Honesty Level Of Usermentioning

confidence: 99%

Section: A Categorization Of Usermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enhancing Data Integrity in Mobile Crowdsensing Environment with Machine Learning and Cost-Benefit Analysis

Sahoo¹,

Pradhan²,

Sethi³

et al. 2023

IJCDS

View full text Add to dashboard Cite

show abstract

Ensuring Data Integrity in Mobile Crowdsensing Environment Using Fuzzy Logic

Sahoo

Pradhan

Sethi

2022

Lecture Notes in Networks and Systems

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Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

2022

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During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.

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A Smartphone App Based Model for Classification of Users and Reviews (A Case Study for Tourism Application)

Cited by 3 publications

References 10 publications

Enhancing Data Integrity in Mobile Crowdsensing Environment with Machine Learning and Cost-Benefit Analysis

Enhancing Data Integrity in Mobile Crowdsensing Environment with Machine Learning and Cost-Benefit Analysis

Ensuring Data Integrity in Mobile Crowdsensing Environment Using Fuzzy Logic

Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

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