Journal of Intelligent & Fuzzy Systems - Volume 44, issue 6

Authors: Geo Jenefer, G. | Deepa, A.J.

Article Type: Research Article

Abstract: Globally, diabetes directly causes 1.5 million fatalities each year. It is necessary to predict such diseases at an earlier stage and cure them. Since modern healthcare data comprises huge amounts of information, it is tough to process such data in conventional databases. Previously, various machine learning (ML) algorithms were used to predict diabetics, and their performance was evaluated. But still, those existing algorithms result in poor accuracy and performance.This work proposes a FOCB (Firefly Optimization-based CatBoost) classifier for predicting diabetes. The PIMA Indian diabetic dataset has been taken as the input dataset. The proposed FOCB algorithm has been compared with …various machine learning algorithms. From the results, we can see that the FOCB classifier gives the best accuracy of 96% with improved performance. The proposed system has been compared with other FO-based machine learning algorithms like NB, KNN, RF, AB, GB, XGB, CNN, DBN, and CB, and it has been proven that CB based on FO produces better accuracy with less hamming loss. Show more

Keywords: CatBoost(CB), feature scaling, machine learning

DOI: 10.3233/JIFS-223105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9943-9954, 2023

Authors: Dhiyanesh, B. | Rameshkumar, M. | Karthick, K. | Radha, R.

Article Type: Research Article

Abstract: Healthcare data is the most sensitive information for processing through machine learning and cloud computing in the various healthcare organizations. Electronic Health Record (EHR) manipulation are now on the rise, and we need to focus on using the data generated by the healthcare applications. Many sensitive data are associated with various health care domains, particularly neurology and cardiology. Previous approaches, such as manual data records, had significant disadvantages, and hence disease prediction based on the above records was found ineffective resulting with improper diagnosis on the patients. These data records require special attention, and current frameworks focused on these areas …must implement sophisticated technologies to predict specific patterns. To address the above concerns, the proposed work incorporates the integration of Neuro Fuzzy Logistic Regression (NFLR) machine learning algorithm and cloud computing storage management to solve these problems. The usage of cloud storage reduces data duplication while handling the storage of EHRs where the proposed ML algorithm accurately predict the disease. In the proposed research, the features are extracted using a specific algorithm –Self-organizing Clustering (SOC) which forms a clustered data with highest weight. To select the maximum number of features, and to predict the disease risk factors, the S2 NO algorithm and NFLR algorithms are used in this work. Further, the database storage estimation with fuzzy rules, logistic analysis, and other benefits such as experimental learning of different ML tools, data privacy constraints related to healthcare are considered in this paper. Show more

Keywords: Neuro-Fuzzy Logistic Regression (NFLR), Social Spider Neural Optimization (S2NO), Self-organizing Clustering (SOC), Electronic Health Record (EHR), Healthcare Medical database

DOI: 10.3233/JIFS-223280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9955-9964, 2023

Authors: Zhang, Yunqi | Sheng, Yuhong

Article Type: Research Article

Abstract:  Risk measurement and insurance pricing have always been issues of concern in actuarial science. Under the framework of uncertainty theory, this paper puts forward a new premium principle: uncertain standard deviation premium principle, proposes some of its properties about risk and compares the premiums of different risks. Based on the utility function of risk aversion, the additional premium coefficient is derived and two specific numerical examples are used to calculate the maximum premium. Furthermore, the unknown parameters of the policy with deductible are estimated by uncertain moment estimation and uncertain maximum likelihood estimation.

Keywords: Uncertainty theory, standard deviation premium principle, additional premium coefficient, utility function, parameter estimation

DOI: 10.3233/JIFS-223297

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9965-9975, 2023

Authors: Yu, Yan | Qiu, Dong | Yan, Ruiteng

Article Type: Research Article

Abstract: To mine more semantic information between words, it is important to utilize the different semantic correlations between words. Focusing on the different degrees of modifying relations between words, this article provides a quantum-like text representation based on syntax tree for fuzzy semantic analysis. Firstly, a quantum-like text representation based on density matrix of individual words is generalized to represent the relationship of modification between words. Secondly, a fuzzy semantic membership function is constructed to discuss the different degrees of modifying relationships between words based on syntax tree. Thirdly, the tensor dot product is defined as the sentence semantic similarity by …combining the operation rules of the tensor to effectively exploit the semantic information of all elements in the quantum-like sentence representation. Finally, extensive experiments on STS’12, STS’14, STS’15, STS’16 and SICK show that the provided model outperforms the baselines, especially for the data set containing multiple long-sentence pairs, which confirms there are fuzzy semantic associations between words. Show more

Keywords: Quantum-like text representation, fuzzy semantic analysis, fuzzy semantic membership function, neural networks, syntax tree

DOI: 10.3233/JIFS-223499

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9977-9991, 2023

Authors: Dai, Songsong | Zheng, Jianwei

Article Type: Research Article

Abstract: In a recent work (Wang et al. 2020), a partial order ⪯, a join operation ⊔ and a meet operation ⊓ of probabilistic linguistic term sets (PLTSs) were introduced and it was proved that L 1  ⊓ L 2  ⪯ L 1  ⪯ L 1  ⊔ L 2 and L 1  ⊓ L 2  ⪯ L 2  ⪯ L 1  ⊔ L 2 . In this paper, we demonstrate that its join and meet operations are not satisfy the above requirement. To satisfy this requirement, we modify its join and meet operations. Moreover, we define a negation operation of PLTSs based on the partial order ⪯. The …combinations of the proposed negation, the modified join and meet operations yield a bounded, distributive lattice over PLTSs. Meanwhile, we also define a new join operation and a new meet operation which, together with the negation operation, yield a bounded De Morgan over PLTSs. Show more

Keywords: Probabilistic linguistic term sets, operations, orders, lattices

DOI: 10.3233/JIFS-223747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9993-10003, 2023

Authors: Fu, Chao | Qin, Keyun | Yang, Lei | Hu, Qian

Article Type: Research Article

Abstract: Covering rough sets have been successfully applied to decision analysis because of the strong representing capability for uncertain information. As a research hotspot in decision analysis, hesitant fuzzy multi-attribute decision-making (HFMADM) has received increasing attention. However, the existing covering rough sets cannot handle hesitant fuzzy information, which limits its application. To tackle this problem, we set forth hesitant fuzzy β-covering rough set models and discuss their application to HFMADM. Specifically, we first construct four types of hesitant fuzzy β-covering ( T , I ) rough set models via hesitant fuzzy logic operators and hesitant fuzzy …β-neighborhoods, which can handle hesitant fuzzy information without requiring any prior knowledge other than the data sets. Then, some intriguing properties of these models and their relationships are also discussed. In addition, we design a new method to deal with HFMADM problems by combining the merits of the proposed models and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. In this method, we not only consider the risk preferences of decision-makers, but also present a new hesitant fuzzy similarity measure expressed by hesitant fuzzy elements to measure the degree of closeness between two alternatives. Finally, an enterprise project investment problem is applied to illustrate the feasibility of our proposed method. Meanwhile, the stability and effectiveness of our proposed method are also verified by sensitivity and comparative analyses. Show more

Keywords: Hesitant fuzzy sets, covering rough sets, hesitant fuzzy logic operators, hesitant fuzzy β-covering, multi-attribute decision-making

DOI: 10.3233/JIFS-223842

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10005-10025, 2023

Authors: Wali, Aamir | Ahmad, Muzammil | Naseer, Asma | Tamoor, Maria | Gilani, S.A.M.

Article Type: Research Article

Abstract: Deep networks require a considerable amount of training data otherwise these networks generalize poorly. Data Augmentation techniques help the network generalize better by providing more variety in the training data. Standard data augmentation techniques such as flipping, and scaling, produce new data that is a modified version of the original data. Generative Adversarial networks (GANs) have been designed to generate new data that can be exploited. In this paper, we propose a new GAN model, named StynMedGAN for synthetically generating medical images to improve the performance of classification models.  StynMedGAN builds upon the state-of-the-art styleGANv2 that has produced remarkable results …generating all kinds of natural images. We introduce a regularization term that is a normalized loss factor in the existing discriminator loss of styleGANv2. It is used to force the generator to produce normalized images and penalize it if it fails. Medical imaging modalities, such as X-Rays, CT-Scans, and MRIs are different in nature, we show that the proposed GAN extends the capacity of styleGANv2 to handle medical images in a better way. This new GAN model (StynMedGAN) is applied to three types of medical imaging: X-Rays, CT scans, and MRI to produce more data for the classification tasks. To validate the effectiveness of the proposed model for the classification, 3 classifiers (CNN, DenseNet121, and VGG-16) are used. Results show that the classifiers trained with StynMedGAN-augmented data outperform other methods that only used the original data. The proposed model achieved 100%, 99.6%, and 100% for chest X-Ray, Chest CT-Scans, and Brain MRI respectively. The results are promising and favor a potentially important resource that can be used by practitioners and radiologists to diagnose different diseases. Show more

Keywords: GANs, deep learning, synthetic data, data augmentation, CNN, styleGANv2, brain tumor, MRI, CT-scan, CXRs

DOI: 10.3233/JIFS-223996

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10027-10044, 2023

Authors: Zhu, Sheng | Tan, Min Keng | Lim, Kit Guan | Chin, Renee Ka Yin | Chua, Bih Lii | Teo, Kenneth Tze Kin

Article Type: Research Article

Abstract: Misfire fault is a common engine failure which is caused by incomplete combustion in the engine cylinders. Conventionally, the misfire fault is diagnosed manually by mechanics, but the diagnosis process is time-consuming. Therefore, this study aims to explore the feasibility of using Subtractive Clustering based Adaptive Neuro-Fuzzy Inference System (SC-ANFIS) algorithm to assist in diagnosing misfire faults. The Subtractive Clustering (SC) approach initializes the parameters of Adaptive Neuro-Fuzzy Inference System (ANFIS), whereas Back Propagation (BP) and Least Square Estimation (LSE) approaches are implemented to optimize the ANFIS parameters. The proposed algorithm will pre-diagnose the cause of misfire faults based on …the engine exhaust gas. In this work, exhaust gases for different causes of misfire faults are collected from Volkswagen 1.8TSI 4-cylinder petrol engine. These collected data are used to train the proposed algorithm. The performances of the proposed algorithm are compared to two commonly used algorithms, namely Fuzzy C-Mean Clustering based ANFIS (FCM-ANFIS) and BP algorithms. The simulation results show the proposed algorithm has improved 2.4% to 5.5% averagely in terms of accuracy, efficiency and stability. Show more

Keywords: Engine misfire, fault diagnosis, SC-ANFIS, FCM-ANFIS

DOI: 10.3233/JIFS-224059

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10045-10066, 2023

Authors: Wu, Meiqin | Song, Jiawen | Fan, Jianping

Article Type: Research Article

Abstract: As COVID-19 swept through, production in various industries was affected. Epidemic control leads to logistical disruptions from time to time and suppliers have to make production and shipping decisions after analyzing the customer’s situation. Therefore, the majority of manufacturers need to establish effective methods for the selection of distribution customers. The method presented in this paper can classify customers into three regions and rank their status to help suppliers effectively make decisions. The three-way decision (3WD) is a well-known fast sorting method in multi-attribute decision-making (MADM). In this paper, we proposed the 3WD model based on Indifference Threshold based Attribute …Ratio Analysis (ITARA), ELimination Et Choix Traduisant la REalite III (ELLECTRE III) in the spherical fuzzy environment. Then, we used the SF-ITARA-ELECTRE III-3WD method to select the suitable customers for dispensing. In addition, comparison with the conventional SF-PROMETHEE-3WD, SF-EVAMIX-3WD, SF-TOPSIS-3WD and SF-VIKOR-3WD are created to verify the effectiveness of the proposed method. An effective risk-averse solution to the MADM problem for spherical fuzzy environment is provided. Show more

Keywords: 3WD, ITARA, ELECTRE III, spherical fuzzy number (SFN), customers selection

DOI: 10.3233/JIFS-224062

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10067-10084, 2023

Authors: Liu, Xinyu | Liu, Lu | Jiang, Tianhua

Article Type: Research Article

Abstract: Energy-aware scheduling has been viewed as a feasible way to reduce energy consumption during the production process. Recently, energy-aware job shop scheduling problems (EAJSPs) have received wide attention in the manufacturing area. However, the majority of previous literature about EAJSPs supposed that all jobs are fabricated in the in-house workshop, while the outsourcing of jobs to some available subcontractors is neglected. To get close to practical production, the outsourcing and scheduling are simultaneously determined in an energy-aware job shop problem with outsourcing option (EAJSP-OO). To formulate the considered problem, a novel mathematical model is constructed to minimize the sum of …completion time cost, outsourcing cost and energy consumption cost. Considering the strong complexity, a self-learning interior search algorithm (SLISA) is developed based on reinforcement learning. In the SLISA, a new Q-learning algorithm is embedded to dynamically select search strategies to prevent blind search in the iteration process. Extensive experiments are carried out to evaluate the performance of the proposed algorithm. Simulation results indicate that the SLISA is superior to the compared existing algorithms in more than 50% of the instances of the considered EAFJSP-OO problem. Show more

Keywords: Job shop, outsourcing option, energy-aware scheduling, interior search algorithm

DOI: 10.3233/JIFS-224624

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10085-10100, 2023