Journal of Intelligent & Fuzzy Systems - Volume 43, issue 1

Authors: Zhong, Ying | Huang, Chenze | Li, Qi

Article Type: Research Article

Abstract: With the rapid growth of data scale, the problems of collaborative filtering recommendation algorithm are more and more obvious, such as data sparsity, cold start, scalability, and the change of user interest over time. About the existing problems, we introduce the fuzzy clustering and propose a collaborative filtering algorithm based on fuzzy C-means clustering. The algorithm performs fuzzy clustering on the item attribute information to make items belonging to different categories in different membership degree, increases the data density, effectively reduces the data sparsity, and solves the issue that the inaccuracy of similarity leads to the low recommendation accuracy. Meanwhile, …the algorithm introduces the time weight function. Different evaluation times give different time weight values, and recently evaluated items are more representative of the user current interest, so we give a higher weight value, and early evaluated items have less effect on the user current interest, thus the weight value are relatively lower. The experimental results show that our algorithm can effectively alleviate the data sparsity problem and time migration of users preferences, thus achieve better performance. Show more

Keywords: Recommender systems, collaborative filtering, data sparsity, interest migration

DOI: 10.3233/JIFS-212216

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 309-323, 2022

Authors: Xiao, Feng | Liu, Lu | Han, Jiayu | Guo, Degui | Wang, Shang | Cui, Hai | Peng, Tao

Article Type: Research Article

Abstract: Time series forecasting (TSF) is significant for many applications, therefore the exploration and study for this problem has been proceeding. With the advances of computing power, deep neural networks (DNNs) have shown powerful performance on many machine learning tasks when considerable amounts of data can be used. However, sufficient data may be unavailable in some scenarios, which leads to performance degradation or even not working of DNN-based models. In this paper, we focus on few-shot time series forecasting task and propose to employ meta-learning to alleviate the problems caused by insufficient training data. Therefore, we propose a meta-learning-based prediction mechanism …for few-shot time series forecasting task, which mainly consists of meta-training and meta-testing. The meta-training phase uses first-order model-agnostic meta-learning algorithm (MAML) as a core component to conduct cross-task training, and thus our method also inherits the advantages of the MAML, i.e., model-agnostic, in the sense that our method is compatible with any model trained with gradient descent. In the meta-testing phase, the DNN-based models are fine-tuned by the small number of time series data from an unseen task in the meta-training phase. We design two groups of comparison models to validate the effectiveness of our method. The first group, as the baseline models, is trained directly on specific time series dataset from target task. The second group, as comparison models, is trained by our proposed method. Also, we conduct data sensitivity study to validate the robustness of our method. The experimental results indicate the second group models outperform the first in different degrees in terms of prediction accuracy and convergence speed, and our method has strong robustness for forecast horizons and data scales. Show more

Keywords: Time series forecasting, meta-learning, few-shot learning

DOI: 10.3233/JIFS-212228

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 325-341, 2022

Authors: Liu, Shulin | Jiang, Rui

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-212229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 343-354, 2022

Authors: Gondere, Mesay Samuel | Schmidt-Thieme, Lars | Sharma, Durga Prasad | Scholz, Randolf

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-212233

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 355-364, 2022

Authors: Meziane, Mohammed El-Amine

Article Type: Research Article

Abstract: The new wave of industry 4.0 has made battery-based automated guided vehicles (AGVs) an essential tool for material handling in manufacturing systems. However, many challenges related to battery management and machines and AGVs energy consumption. To handle these challenges an efficient battery management strategy is designed. The proposed approach supports multispeed operating modes for machines and AGVs, which offers a high flexibility to the manufacturing system. The aim of the proposed approach is to keep the minimal residual electric charge above the critical level, while enhancing the global performance of the manufacturing system. As a consequence, it increases the AGVs …production hours and guarantees batteries safety. The developed approach can bring economic benefits for industry 4.0, by increasing the productivity and avoiding AGVs batteries damage. Extended literature benchmark instances related to the manufacturing 4.0 are used to evaluate the efficiency of the suggested approach. Show more

Keywords: Automated guided vehicle, battery management, industry 4.0, sustainability, multi-speed operating mode

DOI: 10.3233/JIFS-212242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 365-381, 2022

Authors: Hussain, Muhammad | Alotaibi, Fouziah | Qazi, Emad-ul-Haq | AboAlSamh, Hatim A.

Article Type: Research Article

Abstract: The face is a dominant biometric for recognizing a person. However, face recognition becomes challenging when there are severe changes in lighting conditions, i.e., illumination variations, which have been shown to have a more severe effect on recognition performance than the inherent differences between individuals. Most of the existing methods for tackling the problem of illumination variation assume that illumination lies in the large-scale component of a facial image; as such, the large-scale component is discarded, and features are extracted from small-scale components. Recently, it has been shown that large-scale component is also important; in addition, small-scale component contains detrimental …noise features. Keeping this in view, we introduce a method for illumination invariant face recognition that exploits large-scale and small-scale components by discarding the illumination artifacts and detrimental noise using ContourletDS. After discarding the unwanted components, local and global features are extracted using a convolutional neural network (CNN) model; we examined three widely employed CNN models: VGG-16, GoogLeNet, and ResNet152. To reduce the dimensions of local and global features and fuse them, we employ linear discriminant analysis (LDA). Finally, ridge regression is used for recognition. The method was evaluated on three benchmark datasets; it achieved accuracies of 99.7%, 100%, and 79.76% on Extended Yale B, AR, and M-PIE, respectively. The comparison reveals that it outperforms the state-of-the-art methods. Show more

Keywords: Face recognition, deep learning, convolutional neural network (CNN)

DOI: 10.3233/JIFS-212254

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 383-396, 2022

Authors: Zhang, Xuewu | Jing, Wenfeng

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-212257

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 397-407, 2022

Authors: Wang, Jianhua | Zhu, Kai | Peng, Yongtao | Zhu, Kang

Article Type: Research Article

Abstract: Due to the fact that the real manufacturing processes are often constrained by many kinds of resources and the trend that the energy consumption of factories is regulated more and more strictly, this paper studies the energy-efficient multi-resource flexible job shop scheduling problem (EE-MRFJSP). The goal is to minimize the energy consumption and completion time for all of the jobs’ production. Firstly, a general mathematic model for EE-MRFJSP is set up, in which the unit energy consumptions of the main resource’s different states are varied, and a constraint formula to ensure no crossover working periods for any resource is included. …Then, a non-dominated sorting teaching-learning-based optimization(NSTLBO) algorithm is proposed to solving the problem, the details of NSTLBO include the real encoding method, Giffler Thompson rule for decoding, non-dominated sorting rule to rank the pareto sets and crowding distance of solution for maintaining the population’s diversity, and the traditional two evolving stages: teacher education and student mutual study. Finally, comparative experiments are made based on some new designed instances, and the results verify our proposed NSTLBO algorithm can effectively solve the EE-MMFJSP, and has obvious advantages by comparing with NSGA-II, NRGA, and MOPSO. Show more

Keywords: Scheduling, energy-efficient, multi-resource constraint, flexible job shop, NSTLBO

DOI: 10.3233/JIFS-212258

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 409-423, 2022

Authors: Li, Mingxia | Chen, Kebing | Liu, Baoxiang

Article Type: Research Article

Abstract: The substitutability between products or the intensity of market competition is the key parameter affecting the supplier’s pricing decision. However, the parameter cannot be accurately measured in real life. This paper provides a method based on prior information to solve this issue. First, compared to classical concept lattice theory, the interval concept lattice theory can deal with uncertain information more accurately. It is used to extract the objects within the interval parameters [α , β ], and then interval concepts and lattice structure are built. Second, based on the interval concepts and lattice structure, the association rule mining algorithm is …designed to further extract the association rules under different interval parameters. Third, to obtain the effective association degree between two objects, the rule optimization algorithm is put forward by comparing the update of rules. Finally, the association degree can indirectly reflect the substitutability between products. Then the price of a new product can be determined. Our paper provides some implication on pricing for suppliers in competitive supply chain. Show more

Keywords: Pricing decision, formal context, interval concept lattice structure, optimization and mining of association rule

DOI: 10.3233/JIFS-212265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 425-435, 2022

Authors: Srifi, Mehdi | Oussous, Ahmed | Ait Lahcen, Ayoub | Mouline, Salma

Article Type: Research Article

Abstract: In the era of big data, recommender systems (RSs) have become growing essential tools. They represent important machine learning solutions that mainly contribute to keeping users engaged with personalized content in e-platforms. Several RSs have been proposed in the literature, and most of them have focused on English content. However, for content in other languages like Arabic, very restricted works have been done to develop RSs. In recent times, the Arabic content on the Web has increased significantly because of the growing number of Arabic web users. This highlights the need for building RSs for Arabic content. To better handle …this challenge, we decided to provide the research community with a novel deep learning (DL)-based RS devoted to Arabic content. The main goal of the proposed RS is to predict user preferences from textual reviews written in the Arabic language. This is achieved by combining two independent DL techniques into one system: a convolutional neural network (CNN)-text processor for representing users and items; and a neural network, in particular, a multi-layer perceptron (MLP) to estimate interactions between user-item pairs. Extensive experiments on four large-scale Arabic datasets demonstrate that our proposed system can achieve better prediction accuracy than other state-of-the-art alternatives. Notably, it improves the MSE between 0.84% and 16.96%, and the MAE between 0.14% and 13.71%. This work is the first attempt designed to deal with a large volume of data in the Arabic context, opening up new research possibilities for future developments of Arabic RSs. Show more

Keywords: Arabic, recommender systems, user reviews, natural language processing, deep learning

DOI: 10.3233/JIFS-212274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 437-449, 2022