Multi-layer Decision methodology For ranking Z-numbers

The new concept of a – number has been recently introduced in decision making analysis. This concept is capable of effectively dealing with uncertainty in information about a decision. As this concept is relatively new in fuzzy sets, its underlying theoretical aspects have not been established yet. In this paper, a multi-layer methodology for ranking Z – numbers is proposed for the first time. This methodology consists of two layers: Z – number conversion as the first layer and fuzzy number ranking as the second layer. In this study, the conversion methodology of Z – numbers into fuzzy numbers is extended to conversion into standardised generalised fuzzy number so that the methodology is applicable to both positive and negative data values. The methodology is validated by means of thorough comparison with some established ranking methods for consistency purposes. This methodology is considered as a generic decision making procedure, especially when – numbers are applied to real decision making problems

The analysis level of optimism that influence investor’s risk tolerance in asset allocation

Investor’s risk of tolerance level has been widely categorized into three types, namely, risk averse, risk seeker and risk neutral. Nevertheless, in assessing the risk of a particular asset, investors that fall under the same risk tolerance classification may have different levels of optimism. It is thus beneficial to complement types of investor’s risk of tolerance with level of optimism. In this study, a fuzzy asset allocation model that satisfy heterogeneous investor’s risk of tolerance with regards to investor’s level of optimism is proposed. Enhancing Fuzzy Inferences System (FIS) with cooperation of optimism level, this study obtains a flexible fuzzy allocation model which is based on heterogeneous types of investor’s risk of tolerance combined with various level of optimism. Empirical evidence on 30 Malaysian shares employing the model developed shows that the proposed model successfully able to differentiate various combinations of investor’s risk of tolerance level and investor’s level of optimism. Furthermore, model is able to determine asset allocation and priority shares for each combination accordingly. In conclusion, it is shown that employing the proposed model allows investor to make beneficial investment decision according to his combined risk tolerance and level of optimism

A reliability based consistent fuzzy preference relations for risk assessment in oil and gas industry

In decision making, linguistic variables tend to be complex to handle but they make more sense than classical fuzzy numbers. Fuzziness is not sufficient enough to deal with information and degree of reliability of information is critical. Z-numbers is proposed to model the uncertainty produced by human judgment when eliciting information. Therefore, the implementation of z-numbers is taken into consideration, where it has more authority to describe the knowledge of human being and extensively used in the uncertain information development. This issue has motivated the authors to propose fuzzy multi criteria decision making methodology using z-numbers. The proposed methodology is demonstrated the capability to handle knowledge of human being and uncertain information for risk assessment in oil and gas industry. This assessment is due to periodic basis, which will give insights from the operational until the strategic level of decision making process that is capable of dealing with uncertainty in human judgment. The consistent fuzzy preference relations is developed to calculate the preference-weights of the criteria related based on the derived network structure and to resolve conflicts arising from differences in information and opinions provided by the decision makers. The proposed methodology is constructed without losing the generality of the consistent fuzzy preference relations under fuzzy environment

Integrated Generative Adversarial Networks and Deep Convolutional Neural Networks for Image Data Classification A Case Study for COVID-19

Convolutional Neural Networks (CNNs) have garnered significant utilisation within automated image classification systems. CNNs possess the ability to leverage the spatial and temporal correlations inherent in a dataset. This study delves into the use of cutting-edge deep learning for precise image data classification, focusing on overcoming the difficulties brought on by the COVID-19 pandemic. In order to improve the accuracy and robustness of COVID-19 image classification, the study introduces a novel methodology that combines the strength of Deep Convolutional Neural Networks (DCNNs) and Generative Adversarial Networks (GANs). This proposed study helps to mitigate the lack of labelled coronavirus (COVID-19) images, which has been a standard limitation in related research, and improves the model’s ability to distinguish between COVID-19-related patterns and healthy lung images. The study uses a thorough case study and uses a sizable dataset of chest X-ray images covering COVID-19 cases, other respiratory conditions, and healthy lung conditions. The integrated model outperforms conventional DCNN-based techniques in terms of classification accuracy after being trained on this dataset. To address the issues of an unbalanced dataset, GAN will produce synthetic pictures and extract deep features from every image. A thorough understanding of the model’s performance in real-world scenarios is also provided by the study’s meticulous evaluation of the model’s performance using a variety of metrics, including accuracy, precision, recall, and F1-score