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  • Open Access


    An Enhanced Intelligent Intrusion Detection System to Secure E-Commerce Communication Systems

    Adil Hussain1, Kashif Naseer Qureshi2,*, Khalid Javeed3, Musaed Alhussein4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2513-2528, 2023, DOI:10.32604/csse.2023.040305

    Abstract Information and communication technologies are spreading rapidly due to their fast proliferation in many fields. The number of Internet users has led to a spike in cyber-attack incidents. E-commerce applications, such as online banking, marketing, trading, and other online businesses, play an integral role in our lives. Network Intrusion Detection System (NIDS) is essential to protect the network from unauthorized access and against other cyber-attacks. The existing NIDS systems are based on the Backward Oracle Matching (BOM) algorithm, which minimizes the false alarm rate and causes of high packet drop ratio. This paper discussed the existing NIDS systems and different… More >

  • Open Access


    Enhanced E-commerce Fraud Prediction Based on a Convolutional Neural Network Model

    Sumin Xie1, Ling Liu2,*, Guang Sun2, Bin Pan2, Lin Lang2, Peng Guo3

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1107-1117, 2023, DOI:10.32604/cmc.2023.034917

    Abstract The rapidly escalating sophistication of e-commerce fraud in recent years has led to an increasing reliance on fraud detection methods based on machine learning. However, fraud detection methods based on conventional machine learning approaches suffer from several problems, including an excessively high number of network parameters, which decreases the efficiency and increases the difficulty of training the network, while simultaneously leading to network overfitting. In addition, the sparsity of positive fraud incidents relative to the overwhelming proportion of negative incidents leads to detection failures in trained networks. The present work addresses these issues by proposing a convolutional neural network (CNN)… More >

  • Open Access


    A Transaction Frequency Based Trust for E-Commerce

    Dong Huang1,*, Sean Xu2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5319-5329, 2023, DOI:10.32604/cmc.2023.033798

    Abstract Most traditional trust computing models in E-commerce do not take the transaction frequency among participating entities into consideration, which makes it easy for one party of the transaction to obtain a high trust value in a short time, and brings many disadvantages, uncertainties and even attacks. To solve this problem, a transaction frequency based trust is proposed in this study. The proposed method is composed of two parts. The first part is built on the classic Bayes analysis based trust models which are ease of computing for the E-commerce system. The second part is the transaction frequency module which can… More >

  • Open Access


    Sentiment Analysis of Roman Urdu on E-Commerce Reviews Using Machine Learning

    Bilal Chandio1, Asadullah Shaikh2, Maheen Bakhtyar1, Mesfer Alrizq2, Junaid Baber1, Adel Sulaiman2,*, Adel Rajab2, Waheed Noor3

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1263-1287, 2022, DOI:10.32604/cmes.2022.019535

    Abstract Sentiment analysis task has widely been studied for various languages such as English and French. However, Roman Urdu sentiment analysis yet requires more attention from peer-researchers due to the lack of Off-the-Shelf Natural Language Processing (NLP) solutions. The primary objective of this study is to investigate the diverse machine learning methods for the sentiment analysis of Roman Urdu data which is very informal in nature and needs to be lexically normalized. To mitigate this challenge, we propose a fine-tuned Support Vector Machine (SVM) powered by Roman Urdu Stemmer. In our proposed scheme, the corpus data is initially cleaned to remove… More >

  • Open Access


    A Secure E-commerce Environment Using Multi-agent System

    Farah Tawfiq Abdul Hussien*, Abdul Monem S. Rahma, Hala Bahjat Abdul Wahab

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 499-514, 2022, DOI:10.32604/iasc.2022.025091

    Abstract Providing security for the customers in the e-commerce system is an essential issue. Providing security for each single online customer at the same time is considered a time consuming process. For a huge websites such task may cause several problems including response delay, losing the customer orders and system deadlock or crash, in which reduce system performance. This paper aims to provide a new prototype structure of multi agent system that solve the problem of providing security and avoid the problems that may reduce system performance. This is done by creating a software agent which is settled into the customer… More >

  • Open Access


    Introduction to the Special Issue on “Innovation and Application of Intelligent Processing of Data, Information and Knowledge in E-Commerce”

    Honghao Gao1,2, Jung Yoon Kim2,*, Yuyu Yin3

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 19-21, 2022, DOI:10.32604/cmes.2022.019665

    Abstract This article has no abstract. More >

  • Open Access


    Inter-Purchase Time Prediction Based on Deep Learning

    Ling-Jing Kao1, Chih-Chou Chiu1,*, Yu-Fan Lin2, Heong Kam Weng1

    Computer Systems Science and Engineering, Vol.42, No.2, pp. 493-508, 2022, DOI:10.32604/csse.2022.022166

    Abstract Inter-purchase time is a critical factor for predicting customer churn. Improving the prediction accuracy can exploit consumer’s preference and allow businesses to learn about product or pricing plan weak points, operation issues, as well as customer expectations to proactively reduce reasons for churn. Although remarkable progress has been made, classic statistical models are difficult to capture behavioral characteristics in transaction data because transaction data are dependent and short-, medium-, and long-term data are likely to interfere with each other sequentially. Different from literature, this study proposed a hybrid inter-purchase time prediction model for customers of on-line retailers. Moreover, the analysis… More >

  • Open Access


    Enhancement of E-commerce Service by Designing Last Mile Delivery Platform

    Ali Alkhalifah*, Fadwa Alorini, Reef Alturki

    Computer Systems Science and Engineering, Vol.42, No.1, pp. 49-67, 2022, DOI:10.32604/csse.2022.021326

    Abstract The revolution of technology and the rapid evolution of the digital world had a significant effect on the development and expansion of e-commerce. Last mile delivery, for which different app-based delivery services have recently emerged, is a new area of research that is not thoroughly addressed. Delivery service is one of the supporting platforms of e-commerce. One of the delivery issues is that many customers experience difficulties in communicating and coordinating with the logistics companies responsible for the delivery service. This challenge is emphasized in this study which introduces a new system to facilitate communication and coordination between customers and… More >

  • Open Access


    Forecasting E-Commerce Adoption Based on Bidirectional Recurrent Neural Networks

    Abdullah Ali Salamai1,*, Ather Abdulrahman Ageeli1, El-Sayed M. El-kenawy2

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5091-5106, 2022, DOI:10.32604/cmc.2022.021268

    Abstract E-commerce refers to a system that allows individuals to purchase and sell things online. The primary goal of e-commerce is to offer customers the convenience of not going to a physical store to make a purchase. They will purchase the item online and have it delivered to their home within a few days. The goal of this research was to develop machine learning algorithms that might predict e-commerce platform sales. A case study has been designed in this paper based on a proposed continuous Stochastic Fractal Search (SFS) based on a Guided Whale Optimization Algorithm (WOA) to optimize the parameter… More >

  • Open Access


    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

    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 of companies. The present paper… More >

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