Journal of Intelligent & Fuzzy Systems - Volume 42, issue 6

Authors: Yu, Hui | Li, Jun-qing | Chen, Xiao-Long | Zhang, Wei-meng

Article Type: Research Article

Abstract:  During recent years, the outpatient scheduling problem has attracted much attention from both academic and medical fields. This paper considers the outpatient scheduling problem as an extension of the flexible job shop scheduling problem (FJSP), where each patient is considered as one job. Two realistic constraints, i.e., switching and preparation times of patients are considered simultaneously. To solve the outpatient scheduling problem, a hybrid imperialist competitive algorithm (HICA) is proposed. In the proposed algorithm, first, the mutation strategy with different mutation probabilities is utilized to generate feasible and efficient solutions. Then, the diversified assimilation strategy is developed. The enhanced global …search heuristic, which includes the simulated annealing (SA) algorithm and estimation of distribution algorithm (EDA), is adopted in the assimilation strategy to improve the global search ability of the algorithm.?Moreover, four kinds of neighborhood search strategies are introduced to?generate new?promising?solutions.?Finally, the empires invasion strategy?is?proposed to?increase the diversity of the population. To verify the performance of the proposed HICA, four efficient algorithms, including imperialist competitive algorithm, improved genetic algorithm, EDA, and modified artificial immune algorithm, are selected for detailed comparisons. The simulation results confirm that the proposed algorithm can solve the outpatient scheduling problem with high efficiency. Show more

Keywords: Flexible job shop scheduling, outpatient scheduling, hybrid imperialist competitive algorithm, neighborhood search strategies

DOI: 10.3233/JIFS-212024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5523-5536, 2022

Authors: Aggarwal, Eshika | Mohanty, B.K.

Article Type: Research Article

Abstract: An outranking procedure for Multi-Attribute Decision-Making (MADM) problems is introduced in our work that acts as a decision-aid in recommending the products to the buyers. The buyer’s product assessment is taken as Interval-Valued Intuitionistic Fuzzy Sets (IVIFS) in each attribute. The confidence level that is implicit in the buyer’s product rating is explicated in the proposed work using fuzzy entropy. As the confidence level of the buyer on the product assessment is for both satisfaction and reluctance, it is suitably distributed in membership and non-membership parts of IVIFS. Our work generates a dominance matrix that represents partial or full dominance …of one product over another after scoring the products that are unified with buyer’s confidence. The proposed work suggests the product ranking after ascertaining the buyer’s flexibility. An algorithm is written in our work to validate the procedure developed. We have compared our work with other similar works to highlight the benefits of the proposed work. A numerical example is illustrated to highlight the procedure developed. Show more

Keywords: Interval-valued Intuitionistic fuzzy sets, confidence, partial dominance matrix, outranking, flexibility behaviour

DOI: 10.3233/JIFS-212026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5537-5551, 2022

Authors: Zhang, Min | Yang, Haijie | Li, Pengfei | Jiang, Ming

Article Type: Research Article

Abstract: Human pose estimation is still a challenging task in computer vision, especially in the case of camera view transformation, joints occlusions and overlapping, the task will be of ever-increasing difficulty to achieve success. Most existing methods pass the input through a network, which typically consists of high-to-low resolution sub-networks that are connected in series. Still, during the up-sampling process, the spatial relationships and details might be lost. This paper designs a parallel atrous convolutional network with body structure constraints (PAC-BCNet) to address the problem. Among the mentioned techniques, the parallel atrous convolution (PAC) is constructed to deal with scale changes …by connecting multiple different atrous convolution sub-networks in parallel. And it is used to extract features from different scales without reducing the resolution. Besides, the body structure constraints (BC), which enhance the correlation between each keypoint, are constructed to obtain better spatial relationships of the body by designing keypoints constraints sets and improving the loss function. In this work, a comparative experiment of the serial atrous convolution, the parallel atrous convolution, the ablation study with and without body structure constraints are conducted, which reasonably proves the effectiveness of the approach. The model is evaluated on two widely used human pose estimation benchmarks (MPII and LSP). The method achieves better performance on both datasets. Show more

Keywords: Computer vision, human pose estimation, parallel atrous convolution, body structure constraints

DOI: 10.3233/JIFS-212061

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5553-5563, 2022

Authors: Huang, Rui-Lu | Deng, Min-hui | Li, Yong-yi | Wang, Jian-qiang | Li, Jun-Bo

Article Type: Research Article

Abstract: With the attention of people to environmental and health issues, health-care waste (HCW) management has become one of the focus of researchers. The selection of appropriate HCW treatment technology is vital to the survival and development of human beings. In the assessment process of HCW disposal alternative, the evaluation information given by decision makers (DMs) often has uncertainty and ambiguity. The expression, transformation and integration of this information need to be further studied. We develop an applicable decision support framework of HCW treatment technology to provide reference for relevant staff. Firstly, the evaluation information of DMs is represented by interval …2-tuple linguistic term sets (ITLTs). To effectively express qualitative information, the cloud model theory is used to process the linguistic information, a novel concept of interval 2-tuple linguistic integrated cloud (ITLIC) is proposed, and the relevant operations, distance measure and possibility degree of ITLICs are defined. Moreover, a weighted Heronian mean (HM) operator based ITLIC is presented to fuse cloud information. Secondly, the HCW treatment technology decision support model based on the BWM and PROMETHEE is established. Finally, the proposed model is demonstrated through an empirical example, and the effectiveness and feasibility of the model is verified by comparison with extant methods. Show more

Keywords: Cloud model theory, decision support framework, interval 2-tuple linguistic information, health-care waste management

DOI: 10.3233/JIFS-212065

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5565-5590, 2022

Authors: Priyadharshini, A. | Chitra, S.

Article Type: Research Article

Abstract: Lung cancer is one of the most commonly occurring diseases that ranked in the top of the present survey. Advancements in the medical field enable non-invasive methods of computerised diagnosis procedures and detection processes. Deep learning methods are already in evaluation by keeping the deep analysis on improving segmentation accuracy and prediction accuracy etc. The classification of tumour type depends on the quality of segmentation work and feature mappings. In this paper, we developed a robust model that classifies the types of tumours with improved accuracy but is also capable of detecting the early stages of cancer by detecting the …unique hidden points of the image intensity in the lung images, etc. The system is comprised of a novel relative convergence technique for feature extraction technique to extract the infected area and its characteristic pixels to evaluate a unique feature mapping vector. The MSB feature mapping vectors are analysed with Hybrid Regress Fuzzy Net. The final result on whether a tumour is present in the CT image or normal depends on the three individual decisions made by the three algorithms mentioned. The accuracy of each algorithm is also considered for the probable decision-making. The performance measure of the entire proposed Hybrid Regress Net is evaluated through Accuracy, Precision, Recall and F1Score etc. Show more

Keywords: Lung tumor detection, nero-fuzzy logic, Image processing, medical imaging, machine learning

DOI: 10.3233/JIFS-212071

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5591-5604, 2022

Authors: Sanjay, Chintakindi | Alsamhan, Ali | Abidi, Mustufa Haider

Article Type: Research Article

Abstract: Manufacturing companies are focusing on continuous process development to thrive in today’s quality-conscious market. It is particularly relevant to investigate machining processes for advanced materials such as superalloys. Drilling is a major operation that is used in the majority of manufacturing processes. Hence, this research work is focused on investigating the drilling performance of the Monel K500. The output responses under consideration are metal removal rate (MRR), surface roughness, and tool wear. Various contemporary techniques were utilized in this work, namely machine learning methods, artificial neural networks, principal component analysis, and grey relation analysis using uncoated, coated, and HSS (high-speed …steel) drills. After annealing, the softened material can be easily machined to increase the MRR and decrease tool wear and surface roughness. The experimental results show that, after annealing, the surface roughness values for HSS drills have been reduced by 23.86%, uncoated drills by 27.29%, and coated drills by 29.27%, respectively. Moreover, tool wear values for HSS drills decreased by 28.51%, uncoated drills by 34.7%, and coated drills by 33.71%, based on the relative error approach. MRR values for HSS drills increased by 20.51 %, uncoated drills by 23.08%, and coated drills by 23.5%, respectively. For PCA (principal component analysis), feed (47%), and for GRA (gray relation analysis), feed (40.1%) will be the significant parameter followed by speed, and both methods have identified the same experimental run values for optimization of cutting parameters. The theoretical values were predicted using machine learning methods, which utilized the Python language using the Google Colab and then validated with experimental values. The predicted values obtained by the decision tree are close to the measured values as compared to support vector regression and K-nearest neighbor based on relative error. The estimated values obtained by the ANN (artificial neural networks) approach, using Easy NN plus software, match well with the actual values, with a slight deviation. Show more

Keywords: Monel K500, principal component analysis, grey relation analysis with S/N ratio, machine learning methods, ANN

DOI: 10.3233/JIFS-212087

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5605-5625, 2022

Authors: Pan, Lujia | Kalander, Marcus | Wang, Pinghui

Article Type: Research Article

Abstract: Classification algorithms are widely applied to predict failures and detect anomalies in various application areas. It is common to assume that the data and labels are correct when training, but this is challenging to guarantee in the real world. If there are erroneous labels in the training data, a model can easily overfit to these, resulting in poor performance. How to handle label noise has been previously researched, however, few works focus on label noise in anomaly detection. In this work, we propose LDAAD, a novel algorithm framework for label de-noising for anomaly detection that combines unsupervised learning and semi-supervised …learning methods. Specifically, we apply anomaly detection to partition the training data into low-risk and high-risk sets. We subsequently build upon ideas from cross-validation and train multiple classification models on segments of the low-risk data. The models are used both to relabel the samples in the high-risk set and to filter the low-risk samples. Finally, we merge the two sets to obtain a final sample set with more confident labels. We evaluate LDAAD on multiple real-world datasets and show that LDAAD achieves robust results that outperform the benchmark methods. Specifically, LDAAD achieves a 5% accuracy improvement over the second-best method for symmetric noise while having a minimal detrimental impact when no label noise is present. Show more

Keywords: Label noise, anomaly detection, ensemble learning, semi-supervised learning

DOI: 10.3233/JIFS-212096

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5627-5637, 2022

Authors: Borza, Mojtaba | Rambely, Azmin Sham

Article Type: Research Article

Abstract: In the multi-objective programming problem (MOPP), finding an efficient solution is challenging and partially encompasses some difficulties in practice. This paper presents an approach to address the multi-objective linear fractional programing problem with fuzzy coefficients (FMOLFPP). In the method, at first, the concept of α  - cuts is used to change the fuzzy numbers into intervals. Therefore, the fuzzy problem is further changed into an interval-valued linear fractional programming problem (IVLFPP). Afterward, this problem is transformed into a linear programming problem (LPP) using a parametric approach and the weighted sum method. It is proven that the solution resulted from the …LPP is at least a weakly ɛ  - efficient solution. Two examples are given to illustrate the method. Show more

Keywords: Efficient solution, fuzzy numbers, fuzzy programming, interval arithmetic, weighted sum approach

DOI: 10.3233/JIFS-212105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5639-5652, 2022

Authors: Zhao, Hang | Chu, Jianjie | Mo, Rong | Chen, Chen | Ding, Ning

Article Type: Research Article

Abstract: At present, high-speed trains have become popular modern transportation. As a significant part of the high-speed train riding activity, the stowing and unloading luggage task has its characteristics. To comprehensively and reasonably evaluate passenger comfort of the stowing and unloading luggage task in high-speed trains. In this paper, passenger behavior characteristics are firstly analyzed by the author, the theoretical architecture of passenger comfort evaluation is constructed with the perspective of product aesthetics and ergonomics, and then the process of the passenger comfort evaluation is put forward. Secondly, a combination of Rough Number (RN) and Decision Making Trial and Evaluation Laboratory …(DEMATEL) (i.e. R-DEMATEL) is utilized to solve the centrality degree of comfort influencing factors and determine comfort evaluation indexes. Furthermore, the passenger comfort evaluation model with Fuzzy Neural Network (FNN) is constructed and trained. After that, the sample data of the evaluation are collected through the simulated experiment of the stowing and unloading luggage task, and they are trained with FNN comparing to Back Propagation Neural Network (BPNN). Eventually, the result of examples testing is verified that the effectiveness of the proposed method. Show more

Keywords: Comfort evaluation, stowing and unloading luggage, Rough-DEMATEL (R-DEMATEL), FNN, high-speed trains

DOI: 10.3233/JIFS-212109

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5653-5665, 2022

Authors: Senthilkumar, D. | George Washington, D. | Reshmy, A.K. | Noornisha, M.

Article Type: Research Article

Abstract: Predicting the quality of water is a very important issue in an ecosystem and it can be used to control the increase of water contamination. Also, water quality prediction is a prominent complex non-linear multi-target learning problem and extracting a relevant subset of features from a large number of features with multiple targets is a challenging task. Existing water quality prediction model not focused on multi-target learning process simultaneously and not identifying the non-linear relationship between the features and target variables. Therefore, this study proposes a multi-task learning method dealing with multi-target regression using non-linear machine learning technique. Finally, experiments …are conducted to build a prediction model based on the proposed methods to evaluate accuracy on water quality dataset. The experimental results indicate that our method increases the overall accuracy of the experimental dataset compared with the existing methods with the reduced number of significant features. Show more

Keywords: Water quality prediction, multi-target, non-linear, MARS, CART

DOI: 10.3233/JIFS-212117

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 6, pp. 5667-5679, 2022