Jiawen Li^1,2, Yuesheng Huang¹, Yayi Lu¹, Leijun Wang^1,*, Yongqi Ren¹, Rongjun Chen¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1581-1599, 2024, DOI:10.32604/cmc.2024.052666 - 18 July 2024

Abstract In the context of the accelerated pace of daily life and the development of e-commerce, online shopping is a mainstream way for consumers to access products and services. To understand their emotional expressions in facing different shopping experience scenarios, this paper presents a sentiment analysis method that combines the e-commerce review keyword-generated image with a hybrid machine learning-based model, in which the Word2Vec-TextRank is used to extract keywords that act as the inputs for generating the related images by generative Artificial Intelligence (AI). Subsequently, a hybrid Convolutional Neural Network and Support Vector Machine (CNN-SVM) model… More >

Adil Hussain¹, Kashif Naseer Qureshi^2,*, Khalid Javeed³, Musaed Alhussein⁴

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2513-2528, 2023, DOI:10.32604/csse.2023.040305 - 28 July 2023

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 More >

Sumin Xie¹, Ling Liu^2,*, 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 - 06 February 2023

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… More >

Dong Huang^1,*, Sean Xu²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5319-5329, 2023, DOI:10.32604/cmc.2023.033798 - 28 December 2022

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 More >

Bilal Chandio¹, Asadullah Shaikh², Maheen Bakhtyar¹, Mesfer Alrizq², Junaid Baber¹, Adel Sulaiman^2,*, Adel Rajab², Waheed Noor³

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1263-1287, 2022, DOI:10.32604/cmes.2022.019535 - 19 April 2022

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… More >

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 - 15 April 2022

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 More >

Honghao Gao^1,2, Jung Yoon Kim^2,*, Yuyu Yin³

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 19-21, 2022, DOI:10.32604/cmes.2022.019665 - 24 January 2022

Abstract This article has no abstract. More >

Ling-Jing Kao¹, Chih-Chou Chiu^1,*, 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 - 04 January 2022

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… More >

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 - 02 December 2021

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… More >

Abdullah Ali Salamai^1,*, 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 - 11 October 2021

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… More >