Research Article
A Method to Improve the Multiplicative Inconsistency Preserving the Preference Information of Every Element of an Intuitionistic Fuzzy Preference Relation
Hyonil Oh*
,
Gukchol Ri,
Yungil Kim,
Cholju Kim
Issue:
Volume 8, Issue 2, June 2024
Pages:
22-33
Received:
12 April 2024
Accepted:
23 May 2024
Published:
24 May 2024
Abstract: In general, almost intuitionistic fuzzy preference relations (IFPRs) provided by experts are multiplicutively inconsistent because of the complexity of a problem, lack of correct or sufficient knowledge about the problem domain, the ambiguity inherent in human thinking and so forth on. To solve this subject, we propose a method to improve the multiplicative inconsistency preserving the preference information of every element of an initial IFPR. For this, we formulate a formula that straightforwardly calculates the multiplicative consistent IFPR preserving the preference information of every element of the IFPR. Based on it, the necessary and sufficient results for the IFPR to be multiplicatively consistent are derived. By using the results, a consistency testing matrix and a consistency index that can select the most inconsistent elements in the IFPR are constructed and a method that revises them by a proper intuitionitic fuzzy numbers for improving inconsistency as well as preserving the initial preference information is proposed. Then, it is proved that the consistency index converges into zero. As a result, an acceptable consistent IFPR that preserves the preference information of every element and saves a lot of elements of the initial IFPR is constructed. In addition, this method needs a few calculations in comparison with previous methods to improve multiplicative inconsistency of IFPRs, because they calculate a multiplicative consisten IFPR by solving the optimal models constructed based on sufficient conditions for IFPRs to be mltiplicatively consistent. Finally, illustrative examples and comparative analysis are given to demonstrate the efficiency of the proposed method.
Abstract: In general, almost intuitionistic fuzzy preference relations (IFPRs) provided by experts are multiplicutively inconsistent because of the complexity of a problem, lack of correct or sufficient knowledge about the problem domain, the ambiguity inherent in human thinking and so forth on. To solve this subject, we propose a method to improve the multi...
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Research Article
Development of Pidgin English Hate Speech Classification System for Social Media
Issue:
Volume 8, Issue 2, June 2024
Pages:
34-44
Received:
16 March 2024
Accepted:
2 April 2024
Published:
14 June 2024
Abstract: With the widespread use of social media, people from all walks of life—individuals, friends, family, public and private organizations, business communities, states, and entire nations—are exchanging information in various formats, including text, messages, audio, video, cartons, and pictures. Social media also facilitates the distribution and propagation of hate speech, despite the immense benefits of knowledge sharing through these platforms. The purpose of this work was to construct a text-based, Pidgin English hate speech classification system (HSCS) in social media, taking into account the alarming rate at which hate speech is shared and propagated on social media, as well as the negative effects of hate speech on society. We used text data sets in Pidgin English that were taken from Twitter and Facebook (3,153). To train the Support Vector Machine (SVM) text classifier to identify hate speech in Pidgin English, 70% of the Pidgin English data set was annotated. The SVM classifier's performance was tested and assessed using the remaining thirty percent of the Pidgin English text data set. The test set findings' confusion matrix, as determined by the HSCS performance evaluation, was 62.04%, 64.42%, 0.7541, 0.6947, and 0.64 in terms of accuracy, precision, recall, F1-score, and Receiver Operating Characteristics (ROC) curve. When HSCS was compared to other Machine Learning (ML) classifiers, such as Logistic Regression (LR), Random Forest (RF), and Naive Bayes, the results showed that LR had accuracy and precision of 61.51% and 63.89%, RF had 54.88% and 50.65%, and Naive Bayes had 61.51% and 63.89%.
Abstract: With the widespread use of social media, people from all walks of life—individuals, friends, family, public and private organizations, business communities, states, and entire nations—are exchanging information in various formats, including text, messages, audio, video, cartons, and pictures. Social media also facilitates the distribution and propa...
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Research Article
The Need for Adaptive Access Control System at the Network Edge
Muhammad Bello Aliyu*,
Hassan Suru,
Danlami Gabi,
Muhammad Garba,
Musa Argungu
Issue:
Volume 8, Issue 2, June 2024
Pages:
45-55
Received:
17 April 2024
Accepted:
31 May 2024
Published:
14 June 2024
Abstract: The emergence of edge computing, characterized by its distributed nature and real-time processing, necessitates a paradigm shift in access control mechanisms. Traditional, static methods struggle to adapt to the dynamic and heterogeneous environment of edge computing. This research addresses this gap by proposing an Adaptive Risk-Based Access Control (ARBAC) model specifically designed for edge environments. The objective of this research is to develop a robust access control system that dynamically responds to the changing security landscape of edge computing. The proposed ARBAC model integrates real-time data on user context, resource sensitivity, action severity, and risk history to dynamically assess the security risk associated with each access request. This approach ensures a balance between robust security and user experience by tailoring access controls based on the specific context. The research builds upon the growing recognition of the limitations of traditional access control methods in edge environments. Existing literature highlights the need for adaptive and risk-based access control models to address the dynamic nature of edge computing. This research contributes to this evolving field by proposing an ARBAC model that leverages real-time information for contextually relevant access decisions. The proposed ARBAC model offers several advantages. By dynamically adjusting access controls based on risk levels, the model enhances security and ensures compliance with regulatory requirements. Additionally, it improves network performance by reducing load and facilitating faster access to resources. Furthermore, the model's scalability makes it suitable for managing access in large-scale edge deployments. In conclusion, this research proposes an ARBAC model that aligns with the dynamic nature of edge computing environments. By leveraging real-time data and contextual information, the model offers a robust and adaptable approach to access control, promoting security, compliance, performance, and scalability in edge computing. This research paves the way for further exploration and implementation of ARBAC systems, empowering organizations to effectively manage access control in the evolving landscape of edge computing and IoT.
Abstract: The emergence of edge computing, characterized by its distributed nature and real-time processing, necessitates a paradigm shift in access control mechanisms. Traditional, static methods struggle to adapt to the dynamic and heterogeneous environment of edge computing. This research addresses this gap by proposing an Adaptive Risk-Based Access Contr...
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