Journal of Intelligent & Fuzzy Systems - Volume 45, issue 2

Authors: Shu, Wenhao | Chen, Ting | Qian, Wenbin | Yan, Zhenchao

Article Type: Research Article

Abstract: Feature selection focuses on selecting important features that can improve the accuracy and simplification of the learning model. Nevertheless, for the ordered data in many real-world applications, most of the existing feature selection algorithms take the single-measure into consideration when selecting candidate features, which may affect the classification performance. Based on the insights obtained, a multi-measure feature selection algorithm is developed for ordered data, which not only considers the certain information by the dominance-based dependence, but also uses the discern information provided by the dominance-based information granularity. Extensive experiments are performed to evaluate the performance of the proposed algorithm on …UCI data sets in terms of the number of selected feature subset and classification accuracy. The experimental results demonstrate that the proposed algorithm not only can find the relevant feature subset but also the classification performance is better than, or comparably well to other feature selection algorithms. Show more

Keywords: Ordered decision system, dominance-based rough set, multi-measure, feature selection

DOI: 10.3233/JIFS-224474

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3379-3392, 2023

Authors: Kalawi, Dana | Cakar, Tarık | Gurul, Binnur

Article Type: Research Article

Abstract: This study aims to investigate the sustainable campus criteria, the variations made or require to be done to become an ecologically sustainable campus. In this context, the major goal of the research is assessing the sustainable campus design principles and indicators, setting the targets and deciding the precedencies with the Fuzzy Multi-Criteria Decision-Making methods (MCDM) for the sustainable campus design at Istanbul Gelisim University. In this study, model-based methods have been used to evaluate the sustainable campus performance of universities. In this respect, the study differs from other studies in the literature. Another difference of this study is that three …different Fuzzy Multi-Criteria Decision-Making methods has been used, these methods are Fuzzy-AHP, Fuzzy-TOPSIS and Fuzzy-ELECTRE. All three have different inference mechanisms. A common solution has been obtained by using the results of these three different Fuzzy-MCDM methods as hybrid dominance and superiority criteria. Here, the Copeland method, which takes the superiority criterion as a reference, has been used in the options where we could not provide the dominance criterion. At the end of this study, a recommendation report has been prepared according to these results. Show more

Keywords: Sustainable campus, fuzzy multicriteria decision making, fuzzy AHP, fuzzy TOPSIS, fuzzy ELECTRE

DOI: 10.3233/JIFS-223778

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3393-3415, 2023

Authors: Sherubha, P. | Jubair Ahmed, L. | Kannan, K.S. | Sasirekha, S.P.

Article Type: Research Article

Abstract: The aggressive form of cancer commonly in breast cells is breast cancer. The highly aggressive form of cancer is frequently created in breast cells. The need for the predictive model to accurately measure the prognosis prediction of breast cancer in the earlier stage is highly recommended. This development of methods for protecting people from fatal diseases by the researchers from the different disciplines who are all working altogether. An accurate breast cancer prognosis prediction is made by using a good predictive model to assist Medical Internet of Things (mIoT). Various advantages such as cancer detection in an earlier stage, medical …expenses related to treatment, and having unwanted treatment gives the accurate prediction attains spare patients. Existing models lie on the uni-modal data such as chosen gene expression to predict the model’s design. Few learning-based predictive models are used in the proposed method to improve breast cancer prognosis prediction from the current data sets. Most of the peculiar benefits of the suggested method rely on the model’s architecture. Here, a novel adaptive boosting model (a-BM) is used to measure the loss function of every individual and intends to reduce the error rate. Various performances metrics are used to evaluate the predictive performance, which provides the model gives a good outcome rather than the previous techniques. Show more

Keywords: Machine learning, breast cancer, prediction rate, loss function, error rate

DOI: 10.3233/JIFS-230086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3417-3431, 2023

Authors: Kaspar, A. John | Christy, D.K. Sheena | Gloria, D.K. Shirley

Article Type: Research Article

Abstract: A two dimensional language is a collection of two dimensional words, which are rectangular array of symbols made up of finite alphabets. Fuzzy Petri nets are the generalization of classical Petri nets designed to deal with imprecise and ambiguous data which usually occurs in knowledge based systems, image processing, etc. They have been widely used to represent fuzzy production rules and fuzzy rule-based reasoning. In this paper, a new model called array token fuzzy Petri net to generate two dimensional fuzzy regular languages has been introduced. Array token fuzzy Petri nets are used to deal with impreciseness and uncertainties occurring …in two dimensional regular languages. Furthermore, proved that for every two dimensional fuzzy regular grammar there exists an array token fuzzy Petri net that generates the same two dimensional fuzzy regular language and also establish some closure properties of the languages generated by array token fuzzy Petri net. Show more

Keywords: Array grammars, array token petri nets, fuzzy petri nets, fuzzy languages, picture languages

DOI: 10.3233/JIFS-222833

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3433-3443, 2023

Authors: Pandithurai, O. | Urmela, S. | Murugesan, S. | Bharathiraja, N.

Article Type: Research Article

Abstract: The Wireless IoT sensor network (IWSN) has significant potential in industrial settings, but to fully realize its benefits, a robust and scalable computer system is required to handle the continuous influx of data from various applications. In this research study, we propose an IoT sensor-cloud architecture that integrates WSN with cloud technology, providing a unique data analytics framework for highly secure analysis of sensor data. The proposed architecture emphasizes effective interoperability mechanisms in the cloud, and provides an IPv6 extensible enterprise WSN design and simulation technique. To demonstrate the effectiveness of our proposed architecture, we track the pH, resistivity, and …dissolved oxygen levels of industrial effluents that are discharged into water sources. We use AT instructions in conjunction with the HTTP GET technique to gather and upload detector data to the ThingSpeak cloud through a GPRS internet connection, enabling real-time online monitoring and control using IoT functionality. The proposed architecture uses a distributed approach to handle high volumes of incoming data from the IoT sensors, storing the data in a scalable and accessible way for analysis. Real-time analysis is performed using a combination of batch and stream processing frameworks and machine learning algorithms, and the results are visualized using a web-based dashboard that provides real-time updates on key metrics and allows users to explore the data in different ways. Security is a top priority in our proposed architecture, and we use encryption technologies such as SSL/TLS and access control mechanisms such as OAuth2 to ensure the secure transmission and storage of sensitive industrial IoT data. The architecture is designed to be scalable and adaptable to handle a wide range of IoT use cases in industrial settings. The proposed IoT sensor-cloud architecture provides a robust and scalable solution for the collection, analysis, and exchange of significant amounts of IoT sensor information, enabling real-time monitoring and control of critical environmental parameters in industrial settings. Show more

Keywords: WSN, cloud computing, IoT, IoT sensor, industrial case study

DOI: 10.3233/JIFS-224174

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3445-3460, 2023

Authors: Zhu, Xiaorong

Article Type: Research Article

Abstract: The quality management of higher vocational education has become an important part of talent training in colleges and universities. With the increasing demand for social talents, the adaptability of traditional teaching management concepts has gradually emerged. In this case, the innovation and practice of education quality management will become the key research content of higher vocational colleges in the new era in combination with the actual situation of higher vocational colleges and from the perspective of the overall development of talents in vocational colleges. The higher vocational education management quality evaluation is viewed as the multi-attribute decision-making (MADM). In this …paper, the cross-entropy method under The fuzzy number intuitionistic fuzzy sets (FNIFSs) is built based on the traditional cross-entropy method. Firstly, the FNIFSs is introduced. Then, combine the traditional fuzzy cross-entropy method with FNIFSs information, the cross-entropy method is established for MADM under FNIFSs. Finally, a numerical example for higher vocational education management quality evaluation has been given and some comparisons is used to illustrate advantages of cross-entropy method with FNIFSs. Show more

Keywords: Multiple attribute decision making (MADM) problems, fuzzy number intuitionistic fuzzy sets (FNIFSs), cross-entropy method; higher vocational education, management quality evaluation

DOI: 10.3233/JIFS-230094

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3461-3471, 2023

Authors: he, Jia-long | zhang, Xiao-Lin | wang, Yong-Ping | zhang, Huan-Xiang | gao, Lu | xu, En-Hui

Article Type: Research Article

Abstract:  In recent years, contrastive learning has been very successful in unsupervised tasks of representation learning and has received a lot of attention in supervised tasks. In supervised tasks, the discrete nature of natural language makes the construction of sample pairs difficult and the models are poorly robust to adversarial samples, so it remains a challenge to make contrastive learning effective for text classification tasks and to guarantee the robustness of the models. This paper presents a contrastive adversarial learning framework built using data augmentation with labeled insertion data. Specifically,By adding perturbation to the word-embedding matrix, adversarial samples are generated as …positive examples of contrastive learning, and external semantic information is introduced to construct negative examples. Contrastive learning is used to improve the sensitivity and generalization ability of the model, and adversarial training is used to improve robustness, thereby improving the classification accuracy. In addition, the momentum contrast from unsupervised tasks is also introduced into the text classification task to increase the number of sample pairs. Experimental results on several datasets show that the proposed approach outperforms the baseline comparison approach, and in addition some experiments are conducted to verify the effectiveness of the proposed framework under low-resource conditions. Show more

Keywords: Contrastive learning, adversarial training, text classification

DOI: 10.3233/JIFS-230787

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3473-3484, 2023

Authors: Rana, Anurag | Vaidya, Pankaj | Kautish, Sandeep | Kumar, Manoj | Khaitan, Supriya

Article Type: Research Article

Abstract: Parameters related to earthquake origins can be broken down into two broad classes: source location and source dimension. Scientists use distance curves versus average slowness to approximate the epicentre of an earthquake. The shape of curves is the complex function to the epicentral distance, the geological structures of Earth, and the path taken by seismic waves. Brune’s model for source is fitted to the measured seismic wave’s displacement spectrum in order to estimate the source’s size by optimising spectral parameters. The use of ANFIS to determine earthquake magnitude has the potential to significantly alter the playing field. ANFIS can learn …like a person using only the data that has already been collected, which improves predictions without requiring elaborate infrastructure. For this investigation’s FIS development, we used a machine with Python 3x running on a core i5 from the 11th generation and an NVIDIA GEFORCE RTX 3050ti GPU processor. Moreover, the research demonstrates that presuming a large number of inputs to the membership function is not necessarily the best option. The quality of inferences generated from data might vary greatly depending on how that data is organised. Subtractive clustering, which does not necessitate any type of normalisation, can be used for prediction of earthquakes magnitude with a high degree of accuracy. This study has the potential to improve our ability to foresee quakes larger than magnitude 5. A solution is not promised to the practitioner, but the research is expected to lead in the right direction. Using Brune’s source model and high cut-off frequency factor, this article suggests using machine learning techniques and a Brune Based Application (BBA) in Python. Application accept input in the Sesame American Standard Code for Information Interchange Format (SAF). An application calculates the spectral level of low frequency displacement (Ω0 ), the corner frequency at which spectrum decays with a rate of 2(fc ), the cut-off frequency at which spectrum again decays (fmax ), and the rate of decay above fmax on its own (N ). Seismic moment, stress drop, source dimension, etc. have all been estimated using spectral characteristics, and scaling laws. As with the maximum frequency, fmax, its origin can be determined through careful experimentation and study. At some sites, the moment magnitude was 4.7 0.09, and the seismic moment was in the order of (107 0.19) 1023. (dyne.cm). The stress reduction is 76.3 11.5 (bars) and the source-radius is (850.0 38.0) (m). The ANFIS method predicted pretty accurately as the residuals were distributed uniformly near to the centrelines. The ANFIS approach made fairly accurate predictions, as evidenced by the fact that the residuals were distributed consistently close to the centerlines. The R2, RMSE, and MAE indices demonstrate that the ANFIS accuracy level is superior to that of the ANN. Show more

Keywords: Artificial neural networks, brune based application, adaptive neuro fuzzy inference system, source dimension, earthquake occurrence, prediction

DOI: 10.3233/JIFS-224423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3485-3500, 2023

Authors: Tu, Feng Miao | Wei, Ming Hui | Liu, Jun | Liao, Lu Lu

Article Type: Research Article

Abstract: In steel surface inspection, an accurate steel surface defect identification method is needed to evaluate the impact of defects on structural performance and system maintenance. Traditionally, the recognition accuracy of methods based on handcrafted features is limited, but the system performance can be improved by feature fusion extracted by different methods. Therefore, this research uses the pre-trained convolutional neural network (CNN) combined with transfer learning to extract effective abstract features, and carries out adaptive weighting multimodal fusion of three the abstract features and handcrafted feature sets at the decision-making level, that is, proposes an adaptive weighting multimodal fusion classification system. …The system uses handcrafted features as a supplement to abstract features, and accurately classifies steel surface defects in completely different feature representation spaces. Based on the NEU steel plate surface defect benchmark database, the classification results of feature sets before and after fusion are compared and analyzed. The experimental results show that the classification accuracy of the fusion system is improved by at least 3.4% compared with that before fusion, and the final accuracy rate is 99.0%, which proves the effectiveness of the proposed system. Show more

Keywords: CNN-based features, feature extraction, steel plate surface defect, decision-level fusion

DOI: 10.3233/JIFS-230170

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3501-3512, 2023

Authors: Ye, Kangrui | Jiang, Huiqin | Sadati, Seyed Hossein | Talebi, Ali Asghar

Article Type: Research Article

Abstract: A cubic fuzzy graph is a fuzzy graph that simultaneously supports fuzzy membership and interval-valued fuzzy membership. This simultaneity leads to a better flexibility in modeling problems regarding uncertain variables. The cubic fuzzy graph structure, as a combination of cubic fuzzy graphs and graph structures, shows better capabilities in solving complex problems, especially where there are multiple relationships. Since many problems are a combination of different relationships, as well, applying some operations on them creates new problems; therefore, in this article, some of the most important product operations on cubic fuzzy graph structure have been investigated and some of their …properties have been described. Studies have shown that the product of two strong cubic fuzzy graph structures is not always strong and sometimes special conditions are needed to be met. By calculating the vertex degree in each of the products, a clear image of the comparison between the vertex degrees in the products has been obtained. Also, the relationships between the products have been examined and the investigations have shown that the combination of some product operations with each other leads to other products. At the end, the cubic fuzzy graph structure application in the diagnosis of brain lesions is presented. Show more

Keywords: Cubic fuzzy graph structure, lexicographic max-product, residue product, tensor product

DOI: 10.3233/JIFS-222984

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3513-3538, 2023

Authors: Liu, Wuniu | Li, Zhihui | Li, Yongming

Article Type: Research Article

Abstract:  Multi-objective optimization can be used to address possible conflicting relationships between multiple objectives. However, some objectives have a fuzzy temporal relationship between them, making it difficult to give a common method to portray the fuzzy temporal relationship. To fill this gap, we propose the concept of complex objectives, which can be described by fuzzy temporal logic that includes both temporal and logical operators. Furthermore, we investigated the optimal control problems of complex objectives and developed a fuzzy system called possibilistic decision systems (PDSs) to establish a framework for optimal control. In PDSs, states of fuzzy systems are determined by a …family of variables, and transitions induced by actions between fuzzy states of systems are also fuzzy uncertain and determined by a possibility degree. Importantly, we proved that memoryless strategies are sufficient for optimal control of complex objectives. Finally, the theory presented in this paper is illustrated by a mobile robot simulation. Show more

Keywords: Multi-objective optimization, complex objectives, fuzzy temporal logic, decision systems, possibility theory

DOI: 10.3233/JIFS-221966

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 2, pp. 3539-3553, 2023