Journal of Intelligent & Fuzzy Systems - Volume 44, issue 1

Authors: Xu, Yunjian | Guo, Aiyin

Article Type: Research Article

Abstract: The orthophotos of Pinus tabulaeformis and seabuckthorn were collected by UAV, these images were used as test images, and the performance of six image segmentation algorithms were qualitatively analyzed and quantitatively compared such as fuzzy pixel clustering and watershed algorithms. The error rate, relative final measurement accuracy, and running time are used as evaluation criteria. The experimental results show that the segmentation algorithms’ performance of the affected forest image is closely related to the image-capturing height and noise. Finally, the guiding suggestions for the application of the orthophoto segmentation algorithm are given from unmanned aerial vehicles in the affected forest …area. Show more

Keywords: Forest diseases and insect pests, Unmanned aerial vehicle (UAV) orthographic image, fuzzy pixel clustering, watershed algorithm

DOI: 10.3233/JIFS-221403

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1269-1281, 2023

Authors: Pandurangan, Raji | Jayaseelan, Samuel Manoharan | Rajalingam, Suresh | Angelo, Kandavalli Michael

Article Type: Research Article

Abstract: The traffic signal recognition model plays a significant role in the intelligent transportation model, as traffic signals aid the drivers to driving the more professional with awareness. The primary goal of this paper is to proposea model that works for the recognition and detection of traffic signals. This work proposes the pre-processing and segmentation approach applying machine learning techniques are occurred recent trends of study. Initially, the median filter & histogram equalization technique is utilized for pre-processing the traffic signal images, and also information of the figures being increased. The contrast of the figures upgraded, and information about the color …shape of traffic signals are applied by the model. To localize the traffic signal in the obtained image, then this region of interest in traffic signal figures are extracted. The traffic signal recognition and classification experiments are managed depending on the German Traffic Signal Recognition Benchmark-(GTSRB). Various machine learning techniques such as Support Vector Machine (SVM), Extreme Learning Machine (ELM), Linear Discriminant Analysis (LDA), Principal Component Analysis (PCA), Convolutional neural network (CNN)- General Regression Neural Network (GRNN) is used for the classification process. Finally, the obtained results will be compare in terms of the performance metrics like accuracy, F1 score, kappa score, jaccard score, sensitivity, specificity, recall, and precision. The result shows that CNN-GRNN with ML techniques by attaining 99.41% accuracy compare to other intelligent methods. In this proposed technique is used for detecting and classifying various categories of traffic signals to improve the accuracy and effectiveness of the system. Show more

Keywords: Traffic signal images, traffic signs, median filter, gabor filter, forecasting

DOI: 10.3233/JIFS-221720

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1283-1303, 2023

Authors: Jin, Hui | Li, Jun-qing

Article Type: Research Article

Abstract: With the emphasis of the exhaust gas emission of the transportation vehicles, the mode of picking up and delivering products simultaneously has become a challenging issue in the vehicle routing problem (VRP). To remedy this issue, we investigate a special VRP with realistic constraints including product classification, pickup-delivery, and time window (PC-VRPSPDTW). Then, a hybrid algorithm combining tabu search and artificial immune algorithm (TS-AIA) is proposed. In the proposed algorithm, the earliest time and residual capacity (ETRC) heuristic is designed to generate the initial population. Then, two metaheuristics including variable neighborhood search and large neighborhood search are cooperated to balance …the exploration and exploitation abilities. Besides, a new crossover operator is designed to increase the population diversity. Finally, a series of comparative experiments on the extension version of the Solomon’s benchmarks are performed to verify the effectiveness of the proposed algorithm. Show more

Keywords: Product classification, pickup and delivery, hybrid complementary metaheuristic, tabu search, artificial immune algorithm

DOI: 10.3233/JIFS-222118

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1305-1322, 2023

Authors: Chen, Tuantuan | Xin, Delin | Zhang, Zhongwen | Chen, Hu | Jiang, Qiangqiang

Article Type: Research Article

Abstract: Mineshaft development mode design and decision-making is a complex system theory problem with significant implications for the mine’s investment cost, operational safety, and production efficiency. Because of the many factors that influence mine shaft development mode decision-making, various decision-makers have different worries and inclinations, resulting in greater subjectivity and uncertainty in the decision-making process. The concept of a specialty chain was born out of a belief in group decision-making. By merging and assessing the decision-making information of different groups in the same specialty chain, a systematic decision-making index database of the mine shaft development model was created. To elucidate the …correlation model and hierarchical link between the decision-making indexes, the Interpretative Structure Model (ISM) was applied. The multilevel decision-making index system of mine shaft development mode was established. The decision-making group structure was optimized. The relative importance of the Analytical Hierarchy Process (AHP) was modified to determine the scale. A collaborative weight determination method of multiple decision-making groups was established to reduce the influence of individual subjective consciousness on decision-making results. The ISM-GAHP-FCA decision-making model of mine shaft development mode was built in conjunction with Fuzzy Comprehensive Analysis (FCA) to increase fuzzy decision-making information’s integration and analysis ability. The decision-making outcomes from the analysis of 10 typical mine shaft types in China are adaptable to the actual situation. The model can effectively express the hierarchy, significance, and fuzziness of mine shaft development mode decision-making indexes and limit the interference of decision-maker subjectivity on decision-making results. Show more

Keywords: Mine development mode, mineshaft, interpretive structural model, group decision making, analytic hierarchy process, fuzzy comprehensive evaluation

DOI: 10.3233/JIFS-212119

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1323-1336, 2023

Authors: Li, Jingmin | Xu, Shuzhen

Article Type: Research Article

Abstract: As an important basic industry of national economy, the iron and steel industry has provided an important raw material guarantee for a long time. However there are a large number of hazard sources which are prone to safety accidents in the production process. Then safety evaluation in the production system is highly needed to effectively prevent the occurrence of accidents in iron and steel enterprises. Hence we introduce a method based on deep learning model to evaluate safety of the enterprises. Firstly, the risk factors and casualties in production process are investigated, and a set of safety evaluation index system …is constructed.Secondly, since deep neural network model has the characteristics of strong feature extraction ability and simple model structure, we design a safety evaluation model based on deep neural network. The 25-dimensional evaluation index value is the input of the network, and the network output corresponds to five risk levels. On this basis, the optimization algorithm of deep neural network model is designed to explore the mapping relationship between risk characteristics and safety level. Tensorflow deep learning framework is used to build the evaluation model, classification loss function and network optimization method are designed to train the model. Finally, through experiments, the optimal model structure is determined by comparing the influence of different parameter optimization strategies, different hidden layer structures, and different activation functions on the safety evaluation performance. A three hidden layer structure with the Adam back propagation algorithm and LeakyRelu activation function is adopted to obtain higher accuracy and faster convergence rate. The experiments show that our evaluation model provides an operational method for evaluating the safety management status of iron and steel enterprises. Show more

Keywords: Iron and steel enterprises, safety assessment, neural network, optimization algorithm, deep learning

DOI: 10.3233/JIFS-220246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1337-1348, 2023

Authors: Soumya, T.V. | Sabu, M.K.

Article Type: Research Article

Abstract: The cogent area, Probabilistic rough sets, offers methods that are used to trisect the data into positive, negative and boundary regions for optimum (α, β) pairs. These basic methods generate three regions based on a single quality, including cost, entropy, impurity, correlation and variance, thereby the best (α, β) pair is generated. The optimization of multiple qualities has significance in real-life applications; however, experiments rarely discussed the optimization of different criteria together in probabilistic rough sets. This probe conducts multi-objective optimization of uncertainty, impurity and correlation, to determine a trisection at optimal (α, β) pairs. For that, this work proposes …a hybrid method that involves Weighted Sum and Artificial Bee Colony Algorithm to optimize the thresholds. The results are compared with the Information-theoretic rough sets and Game-theoretic rough sets. The proposed method outperforms regarding optimal qualities, multiple optimum thresholds, minimal size of boundary regions, and better evaluation results. By attesting the study on experimental data sets, optimal (α, β) pairs are obtained at which the uncertainty and impurity are minima. Moreover, the correlation at this threshold is reasonable. From the application viewpoint, it reduces the cost of further analysis by generating the minimum delayed decision and maximizes the benefit with optimal decisions by considering multiple optimized qualities simultaneously. Show more

Keywords: Multi-objective optimization, probabilistic rough sets, artificial bee colony algorithm, entropy, gini index

DOI: 10.3233/JIFS-221359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1349-1367, 2023

Authors: Xu, Wei | Mao, Jun-Jun | Zhu, Meng-Meng

Article Type: Research Article

Abstract:  The group decision-making problem usually involves decision makers (DMs) from different professional backgrounds, which leads to a considerable point, that it is the fact that there will be a certain difference in the professional cognition, risk preference and other hidden inherent factors of these DMs to the objective things that need to be evaluated. To improve the reasonability of decision-making, these hidden inherent preference (HIP) of DMs should be determined and eliminated prior to decision making. As a special form of fuzzy set, q-rung orthopair fuzzy numbers (q-ROFNs) is a useful tool to process uncertain information in decision making problems. …Hence, under the environment of q-ROFNs, the determination of HIP based on distance from average score is proposed and a risk model is established to eliminate the HIP by analyzing the possible impact. Meanwhile, a dominant function is proposed, which extends the comparison method between q-ROFNs and an integrated decision-making method is provided. Finally, considering the application background of double carbon economy, an example by selecting the best design of electric vehicles charging station (EVCS) is conducted to illustrate the proposed method, and the feasibility and efficiency are verified. Show more

Keywords: group decision-making, q-ROFNs, hidden inherent preference, risk model, double carbon economy

DOI: 10.3233/JIFS-221702

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1369-1384, 2023

Authors: Al Ghour, Samer

Article Type: Research Article

Abstract: In this paper, we introduce soft somewhat ω-continuous soft mappings and soft somewhat ω-open soft mappings as two new classes of soft mappings. We characterize these two concepts. Also, we prove that the class of soft somewhat ω-continuous (resp. soft somewhat ω-open) soft mappings contains the class of soft somewhat continuous (resp. soft somewhat open) soft mappings. Moreover, we obtain some sufficient conditions for the composition of two soft somewhat ω-continuous (resp. soft somewhat ω-open) soft mappings to be a soft somewhat ω-continuous (resp. a soft somewhat ω-open) soft mapping. Furthermore, we introduce some sufficient conditions for restricting a soft …somewhat ω-continuous (resp. soft somewhat ω-open) soft mapping to being a soft somewhat ω-continuous (resp. soft somewhat ω-open) soft mapping. In addition to these, we introduce extension theorems regarding soft somewhat ω-continuity and soft somewhat ω-openness. Finally, we investigate the correspondences between the novel notions in soft topology and their general topological analogs. Show more

Keywords: Soft somewhat continuous soft mapping, soft ω-continuous soft mappings, soft somewhat open soft mapping, soft generated soft topological space

DOI: 10.3233/JIFS-222098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1385-1396, 2023

Authors: Li, Fang | Lu, Weihua | Yang, Xiyang | Guo, Chong

Article Type: Research Article

Abstract: In the existing short-term forecasting methods of time series, two challenges are faced: capture the associations of data and avoid cumulative errors. For tackling these challenges, the fuzzy information granule based model catches our attention. The rule used in this model is fuzzy association rule (FAR), in which the FAR is constructed from a premise granule to a consequent granule at consecutive time periods, and then it describes the short-association in data. However, in real time series, another association, the association between a premise granule and a consequent granule at non-consecutive time periods, frequently exists, especially in periodical and seasonal …time series. While the existing FAR can’t express such association. To describe it, the fuzzy long-association rule (FLAR) is proposed in this study. This kind of rule reflects the influence of an antecedent trend on a consequent trend, where these trends are described by fuzzy information granules at non-consecutive time periods. Thus, the FLAR can describe the long-association in data. Correspondingly, the existing FAR is called as fuzzy short-association rule (FSAR). Combining the existing FSAR with FLAR, a novel short-term forecasting model is presented. This model makes forecasting at granular level, and then it reduces the cumulative errors in short-term prediction. Note that the prediction results of this model are calculated from the available FARs selected by the k-medoids clustering based rule selection algorithm, therefore they are logical and accurate. The better forecasting performance of this model has been verified by comparing it with existing models in experiments. Show more

Keywords: Trend fuzzy information granule, fuzzy long-association rule, long-association, k-medoids clustering based rule selection algorithm, short-term forecasting

DOI: 10.3233/JIFS-222721

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1397-1411, 2023

Authors: Amini, Mohammad | Targhi, Alireza Tavakoli | Hosseinzadeh, Mehdi | Farivar, Faezeh | Bidaki, Reza

Article Type: Research Article

Abstract: Handwriting problems, also known as dysgraphia, are defined as a disorder or difficulty in producing written language associated with writing mechanics. The occurrence of handwriting problems among elementary students varies from 10 to 34%. With negative impacts on educational performance, handwriting problems cause low self-confidence and disappointment in the students. In this research, a pen-tablet was employed to sample children’s handwriting, which revealed online features of handwriting such as kinematic and temporal features as well as wrist and hand angles and pen pressure on the surface. This digitizer could also extract the online handwriting features when the pen was not …in contact with the surface. Such features are called in-air features. The purpose of this study was to propose a method for diagnosing dysgraphia along with an evaluation of the impact of in-air features on the diagnosis of this disorder. A rich dataset (OHF-1) of online handwriting features of dysgraphic and non-dysgraphic students was prepared. After the extraction of a huge set of features and choosing a feature selection method, three machine learning methods, i.e. SVM, Random Forest and AdaBoost were compared and with the SVM method, an accuracy of 85.7% in diagnosing dysgraphia was achieved, when both in-air and on-surface features were included. However, while using purely in-air data or merely on-surface features, accuracies of 80.9% and 71.4% were achieved, respectively. Our findings showed that in-air features had a significant amount of information related to the diagnosis of dysgraphia. Consequently, they might serve as a significant part of the dysgraphia diagnosis. Show more

Keywords: Handwriting Analysis, Identification of Dysgraphia, In-Air Analysis, Machine Learning, Online Handwriting Features

DOI: 10.3233/JIFS-221708

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 1, pp. 1413-1424, 2023