Journal of Intelligent & Fuzzy Systems - Volume 46, issue 1

Authors: Khatun, Jasminara | Amanathulla, Sk | Pal, Madhumangal

Article Type: Research Article

Abstract: In the realm of handling imprecise information, picture fuzzy cubic sets have emerged as a more versatile tool compared to cubic sets, cubic intuitionistic fuzzy sets, and similar models. These sets offer better adaptability, precision and compatibility with the system than existing fuzzy models. This paper extends the concept of picture fuzzy cubic sets to the domain of graph theory, introducing the novel concept of picture fuzzy cubic graphs that surpasses previous results in terms of generality. The paper explores various essential operations, including composition, the Cartesian product, P -join, R -join, P -union, R -union of picture fuzzy cubic …graphs. It also investigates the order and degree of picture fuzzy cubic graphs. Furthermore, this work presents two practical applications of picture fuzzy cubic graphs. The first application involves computing the impact of other companies on a specific company and the second application focuses on evaluating the overall impact within a group of companies. Show more

Keywords: Picture fuzzy cubic set, picture fuzzy cubic graph, R-union, R-join

DOI: 10.3233/JIFS-232523

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2981-2998, 2024

Authors: Mutar, Emad Kareem

Article Type: Research Article

Abstract: In reliability analysis, the structure-function is a commonly used mathematical representation of the studied system. A signature vector is used for systems with independently and identically distributed (i.i.d.) component lifetimes. Each element in the signature represents the probability that the failure of the corresponding component will fail the entire system. This paper aims to provide a comprehensive understanding of assessing the performance of two complex systems for optimal communication design. The study compares two systems with the same components using signatures, expected cost rate, survival signature, and sensitivity to determine which system is preferred. It also provides several sufficient conditions …for comparing the lifetimes of two systems based on the usual stochastic order. The results are applied to two communication systems that have the same components. The mathematical properties presented in the study have been proven to enable efficient weighting of the optimal design. Show more

Keywords: Coherent system, signature, survival signature, sensitivity, stochastic order

DOI: 10.3233/JIFS-234456

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 2999-3011, 2024

Authors: Peng, Yaxin | Yang, Keni | Zhao, Fangrong | Shen, Chaomin | Zhang, Yangchun

Article Type: Research Article

Abstract: Domain adaptation solves the challenge of inadequate labeled samples in the target domain by leveraging the knowledge learned from the labeled source domain. Most existing approaches aim to reduce the domain shift by performing some coarse alignments such as domain-wise alignment and class-wise alignment. To circumvent the limitation, we propose a coarse-to-fine unsupervised domain adaptation method based on metric learning, which can fully utilize more geometric structure and sample-wise information to obtain a finer alignment. The main advantages of our approach lie in four aspects: (1) it employs a structure-preserving algorithm to automatically select the optimal subspace dimension on the …Grassmannian manifold; (2) based on coarse distribution alignment using maximum mean discrepancy, it utilizes the smooth triplet loss to leverage the supervision information of samples to improve the discrimination of data; (3) it introduces structure regularization to preserve the geometry of samples; (4) it designs a graph-based sample reweighting method to adjust the weight of each source domain sample in the cross-domain task. Extensive experiments on several public datasets demonstrate that our method achieves remarkable superiority over several competitive methods (more than 1.5% improvement of the average classification accuracy over the best baseline). Show more

Keywords: Domain adaptation, metric learning, triplet loss, structure regularization, sample reweighting

DOI: 10.3233/JIFS-235912

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 3013-3027, 2024

Authors: Zekrifa, Djabeur Mohamed Seifeddine | Saravanakumar, R. | Nair, Sruthi | Pachiappan, Krishnagandhi | Vetrithangam, D. | Kalavathi Devi, T. | Ganesan, T. | Rajendiran, M. | Rukmani Devi, S.

Article Type: Research Article

Abstract: The increasing need for effective energy storage solutions has led to the prominence of lithium-ion batteries as a crucial technology across multiple industries. The proficient administration of these batteries is imperative in order to guarantee maximum efficiency, prolong their longevity, and uphold safety measures. This study presents a novel methodology for enhancing battery management systems (BMS) through the integration of cloud-based solutions, artificial intelligence (AI), and machine learning approaches. In this study, we present a conceptual framework that utilises cloud computing to augment the practical functionalities of battery management systems (BMS) specifically in the context of lithium-ion batteries. The incorporation …of cloud computing facilitates the implementation of scalable data storage, remote monitoring, and processing resources, hence enabling the execution of real-time analysis and decision-making processes. By leveraging the capabilities of machine learning and artificial intelligence, our methodology focuses on addressing crucial battery metrics, including the state of charge (SoC) and state of health (SoH). Through the ongoing collection and analysis of data obtained from battery systems that are deployed in real-world settings, the framework iteratively improves its predictive models, hence facilitating precise assessment of battery states. Ensuring safety is a crucial element in the management of batteries. The solution we propose utilises anomaly detection algorithms driven by artificial intelligence to detect potential safety issues, facilitating prompt responses and mitigating dangerous circumstances. In order to showcase the efficacy of our methodology, we offer practical implementations in several industries, encompassing the integration of renewable energy, use of electric vehicles, and optimisation of industrial processes. Through the utilisation of cloud-based machine learning techniques, we are able to enhance the efficiency of energy storage and consumption, while simultaneously enhancing the dependability and security of battery systems. This study highlights the potential of the proposed framework to revolutionise battery management paradigms, thereby guaranteeing secure and efficient energy prospects for a sustainable future. Show more

Keywords: Battery management system, state of health, state of charge, artificial intelligence, machine learning, cloud-based solutions

DOI: 10.3233/JIFS-236391

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 3029-3043, 2024