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  • Open Access

    ARTICLE

    Data Analytics for the Identification of Fake Reviews Using Supervised Learning

    Saleh Nagi Alsubari1, Sachin N. Deshmukh1, Ahmed Abdullah Alqarni2, Nizar Alsharif3, Theyazn H. H. Aldhyani4,*, Fawaz Waselallah Alsaade5, Osamah I. Khalaf6

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3189-3204, 2022, DOI:10.32604/cmc.2022.019625 - 27 September 2021

    Abstract Fake reviews, also known as deceptive opinions, are used to mislead people and have gained more importance recently. This is due to the rapid increase in online marketing transactions, such as selling and purchasing. E-commerce provides a facility for customers to post reviews and comment about the product or service when purchased. New customers usually go through the posted reviews or comments on the website before making a purchase decision. However, the current challenge is how new individuals can distinguish truthful reviews from fake ones, which later deceive customers, inflict losses, and tarnish the reputation… More >

  • Open Access

    ARTICLE

    Big Data Analytics with OENN Based Clinical Decision Support System

    Thejovathi Murari1, L. Prathiba2, Kranthi Kumar Singamaneni3,*, D. Venu4, Vinay Kumar Nassa5, Rachna Kohar6, Satyajit Sidheshwar Uparkar7

    Intelligent Automation & Soft Computing, Vol.31, No.2, pp. 1241-1256, 2022, DOI:10.32604/iasc.2022.020203 - 22 September 2021

    Abstract In recent times, big data analytics using Machine Learning (ML) possesses several merits for assimilation and validation of massive quantity of complicated healthcare data. ML models are found to be scalable and flexible over conventional statistical tools, which makes them suitable for risk stratification, diagnosis, classification and survival prediction. In spite of these benefits, the utilization of ML in healthcare sector faces challenges which necessitate massive training data, data preprocessing, model training and parameter optimization based on the clinical problem. To resolve these issues, this paper presents new Big Data Analytics with Optimal Elman Neural… More >

  • Open Access

    ARTICLE

    Design and Experimentation of Causal Relationship Discovery among Features of Healthcare Datasets

    Y. Sreeraman*, S. Lakshmana Pandian

    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 539-557, 2021, DOI:10.32604/iasc.2021.017256 - 16 June 2021

    Abstract Causal relationships in a data play vital role in decision making. Identification of causal association in data is one of the important areas of research in data analytics. Simple correlations between data variables reveal the degree of linear relationship. Partial correlation explains the association between two variables within the control of other related variables. Partial association test explains the causality in data. In this paper a couple of causal relationship discovery strategies are proposed using the design of partial association tree that makes use of partial association test among variables. These decision trees are different… More >

  • Open Access

    ARTICLE

    An Optimal Big Data Analytics with Concept Drift Detection on High-Dimensional Streaming Data

    Romany F. Mansour1,*, Shaha Al-Otaibi2, Amal Al-Rasheed2, Hanan Aljuaid3, Irina V. Pustokhina4, Denis A. Pustokhin5

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2843-2858, 2021, DOI:10.32604/cmc.2021.016626 - 06 May 2021

    Abstract Big data streams started becoming ubiquitous in recent years, thanks to rapid generation of massive volumes of data by different applications. It is challenging to apply existing data mining tools and techniques directly in these big data streams. At the same time, streaming data from several applications results in two major problems such as class imbalance and concept drift. The current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection (MOMBD-CDD) method on High-Dimensional Streaming Data. The presented MOMBD-CDD model has different operational stages such as pre-processing, CDD, and… More >

  • Open Access

    ARTICLE

    Sentiment Analysis for Arabic Social Media News Polarity

    Adnan A. Hnaif1,*, Emran Kanan2, Tarek Kanan1

    Intelligent Automation & Soft Computing, Vol.28, No.1, pp. 107-119, 2021, DOI:10.32604/iasc.2021.015939 - 17 March 2021

    Abstract In recent years, the use of social media has rapidly increased and developed significant influence on its users. In the study of the behavior, reactions, approval, and interactions of social media users, detecting the polarity (positive, negative, neutral) of news posts is of considerable importance. This proposed research aims to collect data from Arabic social media pages, with the posts comprising the main unit in the dataset, and to build a corpus of manually-processed data for training and testing. Applying Natural Language Processing to the data is crucial for the computer to understand and easily manipulate More >

  • Open Access

    ARTICLE

    Economic Shocks of Covid-19: Can Big Data Analytics Help Connect the Dots

    Hakimah Yaacob, Qaisar Ali*, Nur Anissa Sarbini, Abdul Nasir Rani, Zaki Zaini, Nurul Nabilah Ali, Norliza Mahalle

    Intelligent Automation & Soft Computing, Vol.27, No.3, pp. 653-668, 2021, DOI:10.32604/iasc.2021.015442 - 01 March 2021

    Abstract Since the beginning of the Covid-19 pandemic, big data analytics (BDA) remains a signatory medium in the battle against it. Governments and policymakers alike are yet to leverage on this scalable technology in an attempt to curb the economic effects of Covid-19. The primary objective of this study is to leverage on BDA to identify economic shocks, and propose a strategic solution for economic recovery in ASEAN member states (AMS). The findings of this study suggest that BDA techniques, frameworks, and architectures are effective tools in predicting and tracking economic shocks, as well as in… More >

  • Open Access

    ARTICLE

    Live Data Analytics with IoT Intelligence-Sensing System in Public Transportation for COVID-19 Pandemic

    Abdullah Alamri1,*, Sultan Alamri2

    Intelligent Automation & Soft Computing, Vol.27, No.2, pp. 441-452, 2021, DOI:10.32604/iasc.2021.015198 - 18 January 2021

    Abstract The COVID-19 pandemic has presented an unprecedented challenge to the entire world. It is a humanitarian crisis on a global scale. The virus continues to spread throughout nations, putting health systems under enormous pressure in the battle to save lives. With this growing crisis, companies and researchers worldwide are searching for ways to overcome the challenges associated with this virus. Also, the transport sector will play a critical role in revitalizing economies while simultaneously containing the spread of COVID-19. As the virus is still circulating, the only solution is to redesign public transportation to make More >

  • Open Access

    ARTICLE

    A Model to Create Organizational Value with Big Data Analytics

    Ali Mirarab1,∗, Seyedeh Leili Mirtaheri2,†, Seyed Amir Asghari3,‡

    Computer Systems Science and Engineering, Vol.35, No.2, pp. 69-79, 2020, DOI:10.32604/csse.2020.35.069

    Abstract Value creation is a major factor not only in the sustainability of organizations but also in the maximization of profit, customer retention, business goals fulfillment, and revenue. When the value is intended to be created from Big Data scenarios, value creation entails being understood over a broader range of complexity. A question that arises here is how organizations can use this massive quantity of data and create business value? The present study seeks to provide a model for creating organizational value using Big Data Analytics (BDA). To this end, after reviewing the related literature and… More >

  • Open Access

    ARTICLE

    A Scalable Approach for Fraud Detection in Online E-Commerce Transactions with Big Data Analytics

    Hangjun Zhou1,2,*, Guang Sun1,3, Sha Fu1, Wangdong Jiang1, Juan Xue1

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 179-192, 2019, DOI:10.32604/cmc.2019.05214

    Abstract With the rapid development of mobile Internet and finance technology, online e-commerce transactions have been increasing and expanding very fast, which globally brings a lot of convenience and availability to our life, but meanwhile, chances of committing frauds also come in all shapes and sizes. Moreover, fraud detection in online e-commerce transactions is not totally the same to that in the existing areas due to the massive amounts of data generated in e-commerce, which makes the fraudulent transactions more covertly scattered with genuine transactions than before. In this article, a novel scalable and comprehensive approach More >

  • Open Access

    ARTICLE

    Quantitative Analysis of Crime Incidents in Chicago Using Data Analytics Techniques

    Daniel Rivera Ruiz1,*, Alisha Sawant1

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 389-396, 2019, DOI:10.32604/cmc.2019.06433

    Abstract In this paper we aim to identify certain social factors that influence, and thus can be used to predict, the occurrence of crimes. The factors under consideration for this analytic are social demographics such as age, sex, poverty, etc., train ridership, traffic density and the number of business licenses per community area in Chicago, IL. A factor will be considered pertinent if there is high correlation between it and the number of crimes of a particular type in that community area. More >

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