Muhammad Hasnain¹, Imran Ghani², Seung Ryul Jeong^3,*, Aitizaz Ali¹

Computer Systems Science and Engineering, Vol.40, No.1, pp. 327-339, 2022, DOI:10.32604/csse.2022.018300 - 26 August 2021

Abstract This paper presents a review of the ensemble learning models proposed for web services classification, selection, and composition. Web service is an evolutionary research area, and ensemble learning has become a hot spot to assess web services’ earlier mentioned aspects. The proposed research aims to review the state of art approaches performed on the interesting web services area. The literature on the research topic is examined using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) as a research method. The study reveals an increasing trend of using ensemble learning in the chosen papers More >

A. Sumathi^*, S. Meganathan

Computer Systems Science and Engineering, Vol.40, No.1, pp. 313-325, 2022, DOI:10.32604/csse.2022.017484 - 26 August 2021

Abstract Gestational Diabetes Mellitus (GDM) is an illness that represents a certain degree of glucose intolerance with onset or first recognition during pregnancy. In the past few decades, numerous investigations were conducted upon early identification of GDM. Machine Learning (ML) methods are found to be efficient prediction techniques with significant advantage over statistical models. In this view, the current research paper presents an ensemble of ML-based GDM prediction and classification models. The presented model involves three steps such as preprocessing, classification, and ensemble voting process. At first, the input medical data is preprocessed in four levels… More >

Shraddha R. Khonde^1,2,*, Venugopal Ulagamuthalvi¹

Computer Systems Science and Engineering, Vol.40, No.1, pp. 37-51, 2022, DOI:10.32604/csse.2022.017130 - 26 August 2021

Abstract Exchange of data in networks necessitates provision of security and confidentiality. Most networks compromised by intruders are those where the exchange of data is at high risk. The main objective of this paper is to present a solution for secure exchange of attack signatures between the nodes of a distributed network. Malicious activities are monitored and detected by the Intrusion Detection System (IDS) that operates with nodes connected to a distributed network. The IDS operates in two phases, where the first phase consists of detection of anomaly attacks using an ensemble of classifiers such as… More >

Jumana Alsubhi¹, Abdulrahman Gharawi¹, Mohammad Alahmadi^2,*

Journal of Information Hiding and Privacy Protection, Vol.3, No.4, pp. 193-200, 2021, DOI:10.32604/jihpp.2021.027871 - 22 March 2022

Abstract Nowadays, machine learning (ML) algorithms cannot succeed without the availability of an enormous amount of training data. The data could contain sensitive information, which needs to be protected. Membership inference attacks attempt to find out whether a target data point is used to train a certain ML model, which results in security and privacy implications. The leakage of membership information can vary from one machine-learning algorithm to another. In this paper, we conduct an empirical study to explore the performance of membership inference attacks against three different machine learning algorithms, namely, K-nearest neighbors, random forest, More >

Mingting Liu¹, Xiaozhang Liu^1,*, Anli Yan¹, Xiulai Li^1,2, Gengquan Xie¹, Xin Tang³

Journal of Information Hiding and Privacy Protection, Vol.3, No.4, pp. 181-192, 2021, DOI:10.32604/jihpp.2021.027385 - 22 March 2022

Abstract As machine learning moves into high-risk and sensitive applications such as medical care, autonomous driving, and financial planning, how to interpret the predictions of the black-box model becomes the key to whether people can trust machine learning decisions. Interpretability relies on providing users with additional information or explanations to improve model transparency and help users understand model decisions. However, these information inevitably leads to the dataset or model into the risk of privacy leaks. We propose a strategy to reduce model privacy leakage for instance interpretability techniques. The following is the specific operation process. Firstly,… More >

Pengzhi Xu^1,2, Zetian Mai^1,2, Yuhao Lin¹, Zhen Guo^1,2,*, Victor S. Sheng³

Journal of Information Hiding and Privacy Protection, Vol.3, No.4, pp. 165-179, 2021, DOI:10.32604/jihpp.2021.027280 - 22 March 2022

Abstract With the increase of software complexity, the security threats faced by the software are also increasing day by day. So people pay more and more attention to the mining of software vulnerabilities. Although source code has rich semantics and strong comprehensibility, source code vulnerability mining has been widely used and has achieved significant development. However, due to the protection of commercial interests and intellectual property rights, it is difficult to obtain source code. Therefore, the research on the vulnerability mining technology of binary code has strong practical value. Based on the investigation of related technologies,… More >

Yue Jiang, Xinyu Zhang, Wohuan Jia, Li Xu^*

Journal on Artificial Intelligence, Vol.3, No.4, pp. 163-169, 2021, DOI:10.32604/jai.2021.027590 - 07 February 2022

Abstract As a new type of knowledge sharing platform, the community question answer website realizes the acquisition and sharing of knowledge, and is loved and sought after by the majority of users. But for multi-answer questions, answer quality assessment becomes a challenge. The answer selection in CQA (Community Question Answer) was proposed as a challenge task in the SemEval competition, which gave a data set and proposed two subtasks. Task-A is to give a question (including short title and extended description) and its answers, and divide each answer into absolutely relevant (good), potentially relevant (potential) and… More >

Taifeng Pan^*

Journal of Quantum Computing, Vol.3, No.4, pp. 161-171, 2021, DOI:10.32604/jqc.2021.025373 - 10 January 2022

Abstract The rapid development of Internet of Things (IoT) technology has brought great convenience to people’s life. However, the security protection capability of IoT is weak and vulnerable. Therefore, more protection needs to be done for the security of IoT. The paper proposes an intrusion detection method for IoT based on multi GBDT feature reduction and hierarchical traffic detection model. Firstly, GBDT is used to filter the features of IoT traffic data sets BoT-IoT and UNSW-NB15 to reduce the traffic feature dimension. At the same time, in order to improve the reliability of feature filtering, this… More >

Muhammad Haris^1,*, Muhammad Noman Hasan¹ , Abdul Basit², Shiyin Qin¹

Journal of Quantum Computing, Vol.3, No.3, pp. 89-95, 2021, DOI:10.32604/jqc.2021.016390 - 21 December 2021

Abstract This article presents a multilayer hybrid classical-quantum classifier for predicting the lifetime of LiFePO4 batteries using early degradation data. The multilayer approach uses multiple variational quantum circuits in cascade, which allows more parameters to be used as weights in a single run hence increasing accuracy and provides faster cost function convergence for the optimizer. The proposed classifier predicts with an accuracy of 92.8% using data of the first four cycles. The effectiveness of the hybrid classifier is also presented by validating the performance using untrained data with an accuracy of 84%. We also demonstrate More >

Matthew Wilkinson, Javier Villarreal, Andrew Meade^*

CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.3, pp. 1373-1393, 2021, DOI:10.32604/cmes.2021.017883 - 25 November 2021

Abstract A meshless and matrix-free fluid dynamics solver (SOMA) is introduced that avoids the need for user generated and/or analyzed grids, volumes, and meshes. Incremental building of the approximation avoids creation and inversion of possibly dense block diagonal matrices and significantly reduces user interaction. Validation results are presented from the application of SOMA to subsonic, compressible, and turbulent flow over an adiabatic flat plate. More >