Journal of Intelligent & Fuzzy Systems - Volume 46, issue 3

Authors: Xiao, Yanjun | Yin, Shanshan | Ren, Guoqing | Liu, Weiling

Article Type: Research Article

Abstract: The Flexible Job Shop Scheduling Problem (FJSP) is an extension of the classical Job Shop Scheduling Problem (JSP). The research objective of the traditional FJSP mainly considers the completion time, but ignores the energy consumption of the manufacturing system. In this paper, a mathematical model of the energy-efficient flexible job shop scheduling problem is constructed. The optimization objectives are completion time, delay time, and total equipment energy consumption. To solve the model, an improved non-dominated sorting genetic algorithm (CT-NSGA-II) is proposed to obtain the optimal scheduling solution. First, the heuristic rules of GLR were used to generate the initial population …with good quality and diversity. Second, different crossover and variation operators are designed for the process sequencing and equipment selection parts to enhance the diversity of the evolutionary population. The sparsity theory is introduced to find sparse solutions and three neighborhood structures are designed to perform local search on sparse solutions to improve the uniformity of the optimal solution set distribution. Finally, a competitive selection strategy based on the bidding mechanism is proposed for the Pareto optimal solution set to obtain a better scheduling scheme. The experimental results show that the proposed improved algorithm is feasible and effective in the FJSP problem considering energy consumption, and the algorithm has some application value in improving the efficiency of smart shop operation. Show more

Keywords: Flexible job shop scheduling, energy consumption, non-dominated sorting genetic algorithm, sparsity theory, neighborhood search

DOI: 10.3233/JIFS-233337

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5493-5520, 2024

Authors: Gao, Yuan

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Abstract: This article has been retracted.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-234951

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5521-5532, 2024

Authors: Yu, Zhiqiang | Wang, Ting | Liu, Shihu | Tan, Xuewen

Article Type: Research Article

Abstract: As the typical distant language pair, Chinese and Vietnamese vary widely in syntactic structure, which significantly influences the performance of Chinese-Vietnamese machine translation. To address this problem, we present a simple approach with a pre-reordering model for closing syntactic gaps of the Chinese-Vietnamese language pair. Specifically, we first propose an algorithm for recognizing the modifier inverse, one of the most representative syntactic different in Chinese-Vietnamese language pair. Then we pre-train a pre-reordering model based on the former recognition algorithm and incorporate it into the attention-based translation framework for syntactic different reordering. We conduct empirical studies on Chinese-Vietnamese neural machine translation …task, the results show that our approach achieves average improvement of 2.75 BLEU points in translation quality over the baseline model. In addition, the translation fluency can be significantly improved by over 2.44 RIBES points. Show more

Keywords: Neural machine translation, linguistic difference, Chinese-Vietnamese

DOI: 10.3233/JIFS-233762

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5533-5544, 2024

Authors: He, Hongxuan | Wang, Pei | Lu, Jiakuan

Article Type: Research Article

Abstract: Fuzzy β-covering(Fβ-C) plays a key role in processing real-valued data sets and covering plays an important role in the topological spaces. Thus they have attracted much attention. But the relationship between Fβ-C and topology has not been studied. This inspires the research of Fβ-C from the perspective of topology. In this paper, we construct Fβ-C rough continuous and homeomorphism mappings by using Fβ-C operator. We not only obtain some equivalent descriptions of the mappings but also profoundly reveal the relationship of two Fβ-C approximation spaces. We give the classification method of Fβ-C approximation spaces with the help of homeomorphism mapping, …propose a new method to construct topology induced by Fβ-C operator and investigate the properties in the topological spaces further. Finally, we obtain the necessary and sufficient conditions for Fβ-C operators to be topological closure operators. Show more

Keywords: Fuzzy β-covering, Fuzzy β-covering mapping, Fuzzy β-covering operator, Topology

DOI: 10.3233/JIFS-231117

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5545-5553, 2024

Authors: Aurangzeb, Khursheed

Article Type: Research Article

Abstract: Background: Due to rapid progress in the fields of artificial intelligence, machine learning and deep learning, the power grids are transforming into Smart Grids (SG) which are versatile, reliable, intelligent and stable. The power consumption of the energy users is varying throughout the day as well as in different days of the week. Power consumption forecasting is of vital importance for the sustainable management and operation of SG. Methodology: In this work, the aim is to apply clustering for dividing a smart residential community into several group of similar profile energy user, which will be effective for developing …and training representative deep neural network (DNN) models for power load forecasting of users in respective groups. The DNN models is composed of convolutional neural network (CNN) followed by LSTM layers for feature extraction and sequence learning respectively. The DNN For experimentation, the Smart Grid Smart City (SGSC) project database is used and its energy users are grouped into various clusters. Results: The residential community is divided into four groups of customers based on the chosen criterion where Group 1, 2, 3 and 4 contains 14 percent, 22 percent, 19 percent and 45 percent users respectively. Almost half of the population (45 percent) of the considered residential community exhibits less than 23 outliers in their electricity consumption patterns. The rest of the population is divided into three groups, where specialized deep learning models developed and trained for respective groups are able to achieve higher forecasting accuracy. The results of our proposed approach will assist researchers and utility companies by requiring fewer specialized deep-learning models for accurate forecasting of users who belong to various groups of similar-profile energy consumption. Show more

Keywords: Smart community, smart grids, power load forecasting, sustainable systems, outliers, machine learning, deep learning, data analytic, clustering, power consumption, consumption behavior

DOI: 10.3233/JIFS-235873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5555-5573, 2024

Authors: Natarajan, Ezhilarasan | Augustin, Felix

Article Type: Research Article

Abstract: Tuberculosis (TB) stands as the second leading global infectious cause of death, following closely behind the impact of COVID-19. The standard approach to diagnose TB involves skin tests, but these tests can yield inaccurate results due to limited access to healthcare and insufficient diagnostic resources. To enhance diagnostic accuracy, this study introduces a novel approach employing a Bipolar Fuzzy Utility Matrix Inference System (BFUMIS) and a Bipolar Mamdani Fuzzy Inference System (BMFIS) to assess TB disease levels. By considering factors associated with the causation of TB, the study devises suitable membership functions for bipolar fuzzy sets (BFS) using both triangular …and trapezoidal fuzzy numbers. Using a point factor scale, the study clusters the rules systematically and assesses the level of uncertainty within these grouped rules by utilizing bipolar triangular fuzzy numbers (BTFN). To handle the BTFN, this study proposes converting bipolar triangular fuzzy into bipolar crisp score (CBTFBCS) algorithm as a defuzzification method. The optimal bipolar fuzzy utility sets (BFUS) are determined from the bipolar fuzzy utility matrix to identify patients’ TB disease levels. These sets play a pivotal role in characterizing the severity of TB disease levels in patients. Additionally, rigorous validation of the utility framework is accomplished through measures of bipolar fuzzy satisfactory factors and sensitivity analyses. Furthermore, the study introduces the BMFIS, which presents a novel perspective on the conventional fuzzy inference system. This innovative system integrates the Mamdani fuzzy inference system (MFIS) into a bipolar fuzzy context, enriching the diagnostic process with enhanced insights. To demonstrate the efficacy of the proposed methods, extensive validation is carried out using actual clinical data. The performance metrics used in this validation effectively demonstrate the superiority of the proposed approach. Show more

Keywords: Bipolar triangular fuzzy number, pulmonary tuberculosis, bipolar fuzzy utility matrix, bipolar Mamdani fuzzy inference system, performance measures

DOI: 10.3233/JIFS-233682

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5575-5607, 2024

Authors: Zhang, Xing-Xian | Liu, Wenli | Wang, Xu | Zuo, Wenjin | Wang, Ying-Ming | Sun, Licheng

Article Type: Research Article

Abstract: Efficiency is a relative measure that allows assessment across different ranges. Evaluating the performance of decision-making units (DMUs) from an optimistic perspective yields the best relative efficiency (optimistic efficiency), which establishes an efficiency frontier. Conversely, evaluating from a pessimistic perspective produces the worst relative efficiency (pessimistic efficiency) and creates an inefficiency frontier. This study examines the efficiency of DMUs in two scenarios and proposes models for adjustment coefficient. The pessimistic and optimistic efficiencies are adjusted to the lower and upper bounds of the DMUs based on the adjustment coefficient, enabling determination of efficiency intervals for all DMUs, as well as …evaluation and ranking. A Hurwicz criterion-based approach is introduced and applied to compare and rank the interval efficiencies of DMUs. Two numerical examples are examined using the proposed DEA adjustment coefficient models to demonstrate its potential application and validity. Show more

Keywords: Data envelopment analysis, interval efficiency, adjustment coefficient model, ranking

DOI: 10.3233/JIFS-233051

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5609-5621, 2024

Authors: Pang, Kuo | Lu, Yifan | Xu, Lixian | Yan, Wei | Zou, Li | Lu, Mingyu

Article Type: Research Article

Abstract: The research of object-oriented concept is one of the basic contents of formal concept analysis. To overcome the complexity of computing object-oriented concept, this paper proposes an Object-oriented Concept Acquisition model (OCA) based on attribute topology. The object-oriented attribute topology is first proposed to visualize the coupling relationship between attributes. Second, inspired by rough set theory, object-oriented attribute topology is transformed into rough object-oriented attribute topology. Furthermore, based on the weights of the edges in the rough object-oriented attribute topology, object-oriented concepts are obtained by finding reachable paths. Finally, examples and experiments are used to demonstrate the effectiveness of our …proposed method. Show more

Keywords: Formal concept analysis, object-oriented concept, rough object-oriented attribute topology, object-oriented concept acquisition

DOI: 10.3233/JIFS-233062

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5623-5633, 2024

Authors: Sharmila, V. | Ezhumalai, P.

Article Type: Research Article

Abstract: The global incidence of skin cancer has been rising, resulting in increased mortality and morbidity if left untreated. Accurate diagnosis of skin malignancies is crucial for early intervention through excision. While various innovative medical imaging techniques, such as dermoscopy, have improved the way we examine skin cancers, the progress in medical imaging for identifying skin lesions has not kept pace. Skin lesions exhibit diverse visual features, including variations in size, shape, boundaries, and artifacts, necessitating an efficient image-processing approach to assist dermatologists in decision-making. In this research, we propose an automated skin lesion classifier called GreyNet, which utilizes optimized convolutional …neural networks (CNNs) or shift-invariant networks (SIN). GreyNet comprises three components: (i) a trained fully deep CNN for semantic segmentation, relating input images to manually labeled standard scans; (ii) an enhanced dense CNN with global information exchange and adaptive feature salvaging module to accurately classify each pixel in histopathological scans as benign or malignant; and (iii) a binary grey wolf optimizer (BGWO) to improve the classification process by optimizing the network’s hyperparameters. We evaluate the performance of GreyNet in terms of lesion segmentation and classification on the HAM10000 database. Extensive empirical results demonstrate that GreyNet outperforms existing lesion segmentation methods, achieving improved dice similarity score, volume error, and average processing time of 1.008±0.009, 0.903±0.009%, and 0.079±0.010 s, respectively. Moreover, GreyNet surpasses other skin melanoma classification models, exhibiting improved accuracy, precision, specificity, sensitivity, false negative rate, false positive rate, and Jaccard similarity score (JSS) of 96.5%, 97%, 96.2%, 92.1%, 3.8%, 3%, and 89.5%, respectively. Based on our experimental analysis, we conclude that GreyNet is an efficient tool to aid dermatologists in identifying skin melanoma. Show more

Keywords: Classification, convolution neural networks, optimization, semantic segmentation, skin cancer, super-resolution

DOI: 10.3233/JIFS-232325

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5635-5653, 2024

Authors: Gu, Xiaohong

Article Type: Research Article

Abstract: Hand-drawn is one of the few visual descriptors that can directly represent visual content, and has significant research in the area of computer vision. Aiming at the problem of sparse features in the realm of hand-drawn image retrieval, hand-drawn images, and the easy deformation of hand-drawn images, this paper proposes a feature extraction method of grid resource sharing collaborative algorithm, which can be obtained utilizing precisely extracted semantic characteristics from hand-drawn images through computer multimedia-aided design Efficient and accurate retrieval results. First, the fundamental framework for obtaining semantic features is algorithm; then the attention model mechanism is the grid resource …sharing collaborative introduced in the process of supervised training, and the attention structure block is introduced after the convolutional neural network’s bottom layer. To locate effective semantic features, In order to accomplish high-precision retrieval, the attention structure block combines channel attention structure and spatial attention structure to build the attention structure block. The last feature descriptor is then created by combining various semantic feature levels. The proposed strategy is practical and efficient, as demonstrated by the experimental findings on the comparison database Flickr15k. In addition, in the task of hand-drawn image classification, the proposed attention mechanism greatly improves the classification accuracy. Show more

Keywords: Hand-painted retrieval, grid resource sharing collaborative algorithm, computer-aided, hand-painted classification

DOI: 10.3233/JIFS-233701

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 5655-5666, 2024