Journal of Intelligent & Fuzzy Systems - Volume 44, issue 5

Authors: Simin, Wang | Lulu, Qin | Chunmiao, Ma | Weiguo, Wu

Article Type: Research Article

Abstract: With the rapid development of cloud computing, there are more and more large-scale data centers, which makes the energy management of data centers more complex. In order to achieve better energy-saving effect, it is necessary to solve the problems of concurrent management and interdependence of IT, refrigeration, storage, and network equipment. Reinforcement learning learns by interacting with the environment, which is a good way to realize the independent management of the data center. In this paper, a overall energy consumption method for data center based on deep reinforcement learning is proposed to achieve collaborative energy saving of data center task …scheduling and refrigeration equipment. A new multi-agent architecture is proposed to separate the training process from the execution process, simplify the interaction process during system operation and improve the operation effect. In the deep learning stage, a hybrid deep Q network algorithm is proposed to optimize the joint action value function of the data center and obtain the optimal strategy. Experiments show that compared with other reinforcement learning methods, our method can not only reduce the energy consumption of the data center, but also reduce the frequency of hot spots. Show more

Keywords: Energy consumption, data center, job scheduling, cooling system, deep reinforcement learning, multi-agent system

DOI: 10.3233/JIFS-223769

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7333-7349, 2023

Authors: Hu, Wujin | Shao, Yi | Liu, Yefei

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-224539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7351-7365, 2023

Authors: Zhang, Yu | Liu, Fan | Hu, Yupeng | Li, Xiaoli | Dong, Xiangjun | Cheng, Zhiyong

Article Type: Research Article

Abstract: Cross-domain recommendation aims to alleviate the target domain’s data sparsity problem by leveraging source domain knowledge. Existing GCN-based approaches perform graph convolution operations in each domain separately. However, the direct effect of item feature and topological structure information in the source domain are neglected for user preference modeling in the target domain. In this paper, we propose a novel Dual Attentive Graph Convolutional Network for Cross-Domain Recommendation (DAG4CDR). Specifically, we integrate the source and target domain’s interaction data to construct a unified user-item bipartite graph and then perform GCN propagation on the graph to learn user and item embeddings. Over …the unified graph, the interaction data from both domains can be leveraged to learn user and item embeddings via information propagation. In the embedding aggregation phase, the messages passed from different items of two domains to users are weighted by a designed dual attention mechanism, which considers the contributions of different items from both node- and domain-level. We conducted extensive experiments to validate the effectiveness of our method on several publicly available datasets, and the results demonstrate the superiority of our model on preference modeling for both common and non-common users. Show more

Keywords: Cross-domain recommendation, graph convolutional network, attention mechanism

DOI: 10.3233/JIFS-222411

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7367-7378, 2023

Authors: Yousif, Majeed A. | Hamasalh, Faraidun K.

Article Type: Research Article

Abstract: In this paper, a novel numerical scheme is developed using a new construct by non-polynomial spline for solving the time fractional Generalize Fisher equation. The proposed models represent bacteria, epidemics, Brownian motion, kinetics of chemicals and fuzzy systems. The basic concept of the new approach is constructing a non-polynomial spline with different non-polynomial trigonometric and exponential functions to solve fractional differential equations. The investigated method is demonstrated theoretically to be unconditionally stable. Furthermore, the truncation error is analyzed to determine the or-der of convergence of the proposed technique. The presented method was tested in some examples and compared graphically with …analytical solutions for showing the applicability and effectiveness of the developed numerical scheme. In addition, the present method is compared by norm error with the cubic B-spline method to validate the efficiency and accuracy of the presented algorithm. The outcome of the study reveals that the developed construct is suitable and reliable for solving nonlinear fractional differential equations. Show more

Keywords: Non-polynomial spline, generalize fisher equation, truncation error, stability analysis

DOI: 10.3233/JIFS-222445

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7379-7389, 2023

Authors: Tuo, Meimei | Yang, Wenzhong

Article Type: Research Article

Abstract: In today’s big data era, there are a large number of unstructured information resources on the web. Natural language processing researchers have been working hard to figure out how to extract useful information from them. Entity Relation Extraction is a crucial step in Information Extraction and provides technical support for Knowledge Graphs, Intelligent Q&A systems and Intelligent Retrieval. In this paper, we present a comprehensive history of entity relation extraction and introduce the relation extraction methods based on Machine Mearning, the relation extraction methods based on Deep Learning and the relation extraction methods for open domains. Then we summarize the …characteristics and representative results of each type of method and introduce the common datasets and evaluation systems for entity relation extraction. Finally, we summarize current entity relation extraction methods and look forward to future technologies. Show more

Keywords: Information extraction, relation extraction, natural language processing, machine learning, deep learning

DOI: 10.3233/JIFS-223915

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7391-7405, 2023

Authors: Sun, Quan | Yang, Lichen | Li, Hongsheng | Sun, Guodong

Article Type: Research Article

Abstract: Aluminum electrolytic capacitor (AEC) is one of the most pivotal components that affect the reliability of power electronic systems. The electrolyte evaporation and dielectric degradation are the two main reasons for the parametric degradation of AEC. Remaining useful life (RUL) prediction for AEC is beneficial for obtaining the health state in advance and making reasonable maintenance strategies before the system suffers shutdown malfunction, which can increase the reliability and safety. In this paper, a hybrid machine learning (ML) model with GRU and PSO-SVR is proposed to realize the RUL prediction of AEC. The GRU is used for the recursive multi-step …prediction of AEC to model the times series of AEC, SVR optimized by PSO for hyper-parameters is applied for error compensation caused by recursive GRU. Finally, the proposed model is validated by two kinds of data sets with accelerated degradation experiments. Compared with the other methods, the results show that the proposed scheme can obtain greater prediction performance index of RUL under different prediction time points, which can support the technology of health management for power electronic system. Show more

Keywords: Aluminum electrolytic capacitor, remaining useful life, machine learning, error compensation

DOI: 10.3233/JIFS-220866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7407-7417, 2023

Authors: Dhumras, Himanshu | Bajaj, Rakesh Kumar

Article Type: Research Article

Abstract: Systematic assessment of insufficiencies and inexactness in the information along with parametrization of multi-sub attributes is one of the substantial features in the field of decision-making. In the present communication, a new way of defining Picture Fuzzy Hypersoft Set (PFHSS) has been presented which contains an additional capacity of accommodating the components of neutral membership (abstain) and refusal compared to Intuionistic Fuzzy Hypersoft Set (IFHSS). The main objective of the present study is to establish the novelty of PFHSS with some of the basic operations and introduce various important aggregation operators. Some of the important properties and operational laws related …to the introduced picture fuzzy hypersoft weighted average/ordered weighted average operator (PFHSWA/PFHSOWA) and weighted geometric/ordered weighted geometric operator (PFHSWG/PFHSOWG) have been proved in detail. On the basis of these aggregation operators and obtained results, a new algorithm for solving a decision-making problem, involving the multi-sub attributes and their parametrization in the shade of abstain and refusal feature, has been proposed. A numerical example of the selection process of employees for a company has been solved in order to suitably ensure and validate the implementation of the proposed methodology. Some of the advantageous features of the proposed notions and algorithm have been listed along with the comparative analysis in contrast with the existing literature. Finally, the efficacy of the proposed notion and methodology has been duly concluded with the scope for future work. Show more

Keywords: Picture fuzzy set, soft set, hypersoft set, aggregation operators, decision-making

DOI: 10.3233/JIFS-222437

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7419-7447, 2023

Authors: Shamia, D. | Balasamy, K. | Suganyadevi, S.

Article Type: Research Article

Abstract: Security, secrecy, and authenticity problems have arisen as a result of the widespread sharing of medical images in social media. Copyright protection for online photo sharing is becoming a must. In this research, a cutting-edge method for embedding encrypted watermarks into medical images is proposed. The proposed method makes use of fuzzy-based ROI selection and wavelet-transformation to accomplish this. In the first step of the process, a fuzzy search is performed on the original picture to locate relevant places using the center region of interest (RoI) and the radial line along the final intensity. The suggested method takes a digital …picture and divides it into 4×4 non-overlapping blocks, with the intent of selecting low information chunks for embedding in order to maximize invisibility. By changing the coefficients, a single watermark bit may be inserted into both the left and right singular SVD matrices. The absence of false positives means the suggested technique can successfully integrate a large amount of data. Watermarks are encrypted using a pseudorandom key before being embedded. Discrete wavelet transform saliency map, block mean method, and cosine functions are used to construct an adaptively-generated pseudo-random key from the cover picture. Images uploaded to social media platforms must have a high degree of invisibility and durability. These watermarking features, however, come with a price. The optimal scaling factor is used to strike a balance between the two in the proposed system. Furthermore, the suggested scheme’s higher performance is confirmed by comparison with the latest state-of-the-art systems. Show more

Keywords: Watermarking, key component, wavelet transform, Fuzzy ROI, encryption

DOI: 10.3233/JIFS-222618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7449-7457, 2023

Authors: Guo, Xiaobin | Chen, Ying | Zhuo, Quanxiu

Article Type: Research Article

Abstract:  In paper the generalized real eigenvalue and fuzzy eigenvector of a crisp real symmetric matrix with respect to another real symmetric matrix is studied. The original generalized fuzzy eigen problem is extended into a crisp generalized eigen problem of a real symmetric matrix with high orders using the arithmetic operation of LR fuzzy matrix and vector. Two cases are analysed: (a) the unknown eigenvalue λ is a non negative real number; (b) the unknown eigenvalue λ is a negative real number. Two computing models are established and an algorithm for finding the generalized fuzzy eigenvector of a real symmetric matrix …is derived. Moreover, a sufficient condition for the existence of a strong generalized fuzzy eigenvector is given. Some numerical examples are shown to illustrated our proposed method. Show more

Keywords: Fuzzy numbers, fuzzy eigenvectors, matrix computation, fuzzy linear systems

DOI: 10.3233/JIFS-222641

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7459-7467, 2023

Authors: Li, Xiaoning | Yu, Qiancheng | Yang, Yufan | Tang, Chen | Wang, Jinyun

Article Type: Research Article

Abstract: This paper proposes an evolutionary ensemble model based on a Genetic Algorithm (GAEEM) to predict the transmission trend of infectious diseases based on ensemble again and prediction again. The model utilizes the strong global optimization capability of GA for tuning the ensemble structure. Compared with the traditional ensemble learning model, GAEEM has three main advantages: 1) It is set to address the problems of information leakage in the traditional Stacking strategy and overfitting in the Blending strategy. 2) It uses a GA to optimize the combination of base learners and determine the sub. 3) The feature dimension of the data …used in this layer is extended based on the optimal base learner combination prediction information data, which can reduce the risk of underfitting and increase prediction accuracy. The experimental results show that the R2 performance of the model in the six cities data set is higher than all the comparison models by 0.18 on average. The MAE and MSE are lower than 42.98 and 42,689.72 on average. The fitting performance is more stable in each data set and shows good generalization, which can predict the epidemic spread trend of each city more accurately. Show more

Keywords: Evolutionary ensemble, genetic algorithm, ensemble strategy, epidemics transmission prediction

DOI: 10.3233/JIFS-222683

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7469-7481, 2023