Journal of Intelligent & Fuzzy Systems - Volume 45, issue 5

Authors: Subhashini, R. | Hemalakshmi, G.R. | Rajalakshmi, R. | Chen, Chuang

Article Type: Research Article

Abstract: The quality of sleep plays a crucial role in physical well-being, and individuals are becoming increasingly concerned about sleep quality and its associated health issues. Although various sleep monitoring devices exist, there remains a need for a highly accurate sleep state identification algorithm. To address this, we present a paper that utilizes machine learning techniques to identify human sleep states based on electroencephalogram (EEG) signals collected by an EEG instrument. We propose a model that incorporates two nonlinear characteristic parameters, MSE and PSE, extracted from artificially designed EEG signals as input. Additionally, we employ a Support Vector Machine (SVM) classifier …algorithm to accurately identify sleep states, eliminating uncertainties associated with manually designed feature parameters. Experimental results demonstrate the superior accuracy of our proposed model for sleep state analysis, offering valuable insights for improving sleep quality and addressing related health concerns. Show more

Keywords: Sleeping quality, health, electroencephalograph, support vector machine, machine learning

DOI: 10.3233/JIFS-230765

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8703-8716, 2023

Authors: Zhao, Aiwu | Du, Chuantao | Guan, Hongjun

Article Type: Research Article

Abstract: Based on the double hierarchy linguistic term sets (DHLTS), a novel forecasting model is proposed considering both the internal fluctuation rules and the external correlation of different time series. The innovative aspects of this model consist of: (i) It can expresses more internal fluctuation and external correlation information, providing guarantees for improving the predictive performance of the model. (ii) The equivalent transformation function of DHLTS reduces the fuzzy granularity and improves the prediction accuracy. (iii) The application of similarity measures can extract the closest rules from historical states based on the distance operators of DHLTS. In addition, experiments on TAIEX …considering the impact of the U.S. stock market and other data show that the model has good predictive performance. Show more

Keywords: Fuzzy time series, double hierarchy linguistic term set, forecast, Co-movements of stock markets

DOI: 10.3233/JIFS-230810

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8717-8733, 2023

Authors: Gao, Gengjun | Wang, Yiwen

Article Type: Research Article

Abstract: The rationality of profit distribution affects the stability of the multimodal transport alliance. In the multimodal transport alliance, the participation rate of the carrier and the communication structure of the alliance are important influencing factors of profit distribution results. To get a reasonable profit distribution scheme, this paper constructs a profit distribution model considering the characteristics of multimodal transport, called the Choquet Cloud Gravity Center AT model. Firstly, considering the communication structure of the alliance, the Cloud Gravity Center Average Tree method is used as the base model for profit distribution. Secondly, considering the multimodal transport alliance is a fuzzy …coalition, the profit for each alliance subset in the base model is calculated by the Choquet integral. Then, the profit distribution model considering participation rate and communication structure is obtained. Finally, a numerical example is given to illustrate the applicability of the model, and comparative analysis is conducted to verify the rationality of the model. This study provides a suitable profit-sharing model for multimodal transport alliances, which is conducive to the stable and efficient operation of alliances. Show more

Keywords: Multimodal transport, profit distribution, fuzzy coalition, communication structure, Choquet integral

DOI: 10.3233/JIFS-231370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8735-8746, 2023

Authors: Cano-Izquierdo, Jose-Manuel | Ibarrola, Julio | Almonacid, Miguel

Article Type: Research Article

Abstract:  Deep-learning (DL) is a new paradigm in the artificial intelligence field associated with learning structures able to connect directly numeric data with high-level patterns or categories. DL seems to be a suitable technique to deal with computationally challenging Brain Computer Interface (BCI) problems. Following DL strategy, a new modular and self-organized architecture to solve BCI problems is proposed. A pattern recognition system to translate the measured signals in order to establish categories representing thoughts, without previous pre-processing, is developed. To achieve an easy interpretability of the system internal functioning, a neuro-fuzzy module and a learning methodology are carried out. The …whole learning process is based on machine learning. The architecture and the learning method are tested on a representative BCI application to detect and classify motor imagery thoughts. Data is gathered with a low-cost device. Results prove the efficiency and adaptability of the proposed DL architecture where the used classification module (S-dFasArt) exhibits a better behaviour compared with the usual classifiers. Additionally, it employs neuro-fuzzy modules which allow to offer results in a rules format. This improves the interpretability with respect to the black-box description. A DL architecture, going from the raw data to the labels, is proposed. The proposed architecture, based on Adaptive Resonance Theory (ART) and Fuzzy ART modules, performs data processing in a self-organized way. It follows the DL paradigm, but at the same time, it allows an interpretation of the operation stages. Therefore this approach could be called Transparent Deep Learning. Show more

Keywords: Transparent deep learning, brain computer interface, neuro-fuzzy modular architecture, s-dFasArt, motor imagery

DOI: 10.3233/JIFS-231387

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8747-8760, 2023

Authors: Buche, Arti | Chandak, M.B.

Article Type: Research Article

Abstract: In the field of finance, deep learning techniques have been extensively researched for predicting stock prices. In this research, we propose a novel approach for predicting stock price movements using a combination of reviews and historical price data for SBI and HDFC stocks. As market volatility is influenced by numerous factors, it is crucial to consider it while predicting stock prices. To capture the interactions between the price and text data effectively, we create a fusion mix and utilize a hybrid information mixing module, designed using BERT and BiLSTM, to extract the multimodal interactions between the time series and semantic …features. The proposed model, the hybrid information mixing module, is based on a multilayer perceptron and achieves high accuracy in predicting price fluctuations in highly volatile stock markets. Future research can extend this approach to include additional data sources and explore other deep learning techniques for better performance. Show more

Keywords: Natural language processing, deep learning, multilayer perceptron, BiLSTM, BERT, Indian stock market

DOI: 10.3233/JIFS-231472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8761-8773, 2023

Authors: Adaikkan, Kalaivani | Thenmozhi, Durairaj

Article Type: Research Article

Abstract: Social media has become one of the most popular medium of communication and the post may be predominantly unstructured, informal, and frequently misspelled. It has become increasingly common for users to use abusive language in their comments. Detecting offensive language on social media platforms and the presence of such language on the Internet has become a major challenge for modern society. To overcome this challenge, Offensive Language Classification based on the Chaotic Antlion optimization algorithm has been proposed. Initially, the dataset is pre-processed using NLP languages for removing irrelevant data. Consequently, statistical, synthetic, and lexicon features are extracted using various …feature extraction techniques. A Chaotic Antlion Optimization Algorithm is used to select the most relevant features during the feature selection phase. After selecting the features, a Ghost network classifies the input data into four classes namely offensive, non-offensive, swear, and offensive but not offensive. The proposed method was evaluated based on a number of variables, including precision, accuracy, specificity, recall, and F-measure. The best classification accuracy is achieved by the suggested method, which is 99.27% for the SOLID dataset and 98.99% for the OLID dataset. The suggested method outperforms the DCNN, Simple Logistics, and CNN methods in terms of overall accuracy by 4.99%, 8.72%, and 10.4%, respectively. Show more

Keywords: Chaotic Antlion optimization algorithm, detecting offensive language, SOLID dataset, Ghost network, DCNN

DOI: 10.3233/JIFS-232217

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8775-8788, 2023

Authors: Zhang, Xu | Lv, Mingrui | Yuan, Xumei

Article Type: Research Article

Abstract: In order to solve the problems of insufficient uncertainty information measure, inaccuracy of weight calculation and incommensurability of indices in hybrid multi-criteria decision making, this paper introduces the Cloud-CRITIC weight calculation method and Cloud-CRITIC-PDR method, which combine cloud model, CRITIC method and Probabilistic Dominance Relation (PDR). In these two methods, the cloud model is used to characterize uncertainty, the Comprehensive information of CRITIC method has been modified in order to adapt to uncertain situation, the PDR method is used to compare schemes. A case study concerning supplier evaluation is given to demonstrate the merits of the cloud-CRITIC and cloud-CRITIC-PDR. The …effectiveness and superiority of the developed methods are further illustrated through method comparison and sensitivity analysis. These combined methods can be used for dealing with decision-making problems with complex index types and strong data uncertainty, such as supplier evaluation and risk assessment. There are few papers about combining the cloud model, CRITIC method, and PDR method under hybrid indices decision-making situation at present, so this paper can provide a new perspective on hybrid MCDM. Show more

Keywords: Hybrid multi-criteria decision making, cloud model, probabilistic dominance relationship, CRITIC, Gaussian criterion

DOI: 10.3233/JIFS-232605

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8789-8803, 2023

Authors: Guo, Xiao | Feng, Qinrong | Zhao, Lin

Article Type: Research Article

Abstract:  Fuzzy soft set as a tool to deal with uncertainty can effectively handle decision making problems. However, there are many redundant parameters in the decision making process. In order to remove redundant parameters to improve the efficiency of decision making, different parameter reduction algorithms for fuzzy soft sets based on different decision criteria have been proposed. This paper focuses on the problem of parameter reduction of fuzzy soft sets based on choice value criteria. The restrictions of the strict conditions about parameter reduction lead to a very low applicability of some previous algorithms based on choice value criteria. To address …this limitation, we introduce a flexible definition of parameter reduction for fuzzy soft sets. Further a difference-based parameter reduction algorithm for fuzzy soft sets is proposed. Compared with some previous algorithms based on choice value criteria, the proposed algorithm not only has wider applicability, but also can reduce more redundant parameters making the found parameter reduction with a lower cardinality, and it is easier to find the parameter reduction of fuzzy soft sets. Show more

Keywords: Soft set, fuzzy soft set, parameter reduction, difference

DOI: 10.3233/JIFS-232657

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8805-8821, 2023

Authors: Muthazhagan, B. | Ravi, T. | Rajinigirinath, D.

Article Type: Research Article

Abstract: Lung cancer is the prevalent malignancy afflicting both men and women, mostly affects the chain smokers. The lung CT images are examined to identifying the abnormalities, but diagnosing lung cancer with CT images is time-consuming and difficult task. In this work, a novel Sooty-LuCaNet has been proposed in which the best features are selected using sooty tern optimization to reduces computational complexity of neural network. Initially, the denoised CT images are segmented using Grabcut technique to separate the lung nodules by eliminating the background distortions. The deep learning based Shufflenet is used to extract the structural features from the segmented …nodule and the textural features from the enhanced images. Afterwards, the sooty tern optimization (STO) algorithm is applied to select the most relevant features from the extracted features from the ShuffleNet. Finally, the classification process is carried out to differentiate the normal, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) from the CT images. The experimental findings show the robustness of the proposed Sooty-LuCaNet based on the specific metrics namely sensitivity, accuracy, specificity, recall, precision and F1 score. An average classification accuracy of 99.16% is achieved for detection and classification of lung cancer. Show more

Keywords: Lung cancer, computed tomography, deep learning, Shufflenet, sooty tern optimization algorithm

DOI: 10.3233/JIFS-232875

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8823-8836, 2023

Authors: Ren, Rongrong | Wang, Hailong | Meng, Xinyu | Zhao, Meng

Article Type: Research Article

Abstract: Many businesses and organizations consider group decision making (GDM) to be an important decision-making strategy for dealing with complex decision-making difficulties. Although it is acknowledged that the difference in decision makers’ assessment scales has a significant impact on decision results, how to eliminate the difference in decision makers’ evaluation scales in the decision-making process has not been investigated further. In this research, the non-consensus of MAGDM is studied considering the difference of expert evaluation scale, and an improved two-stage multi-attribute group decision making method (MAGDM) is proposed. The example and comparative analysis of annual bonus allocation in engineering businesses validate …the effectiveness and operability of this system. Simultaneously, the approach is improved to handle the MAGDM problem of tiny samples, and the method’s problem of inadequate information is illustrated by numerical examples. The research presented in this work gives a practicable approach and idea for investigating the eradication of decision-maker evaluation scale disparities in MAGDM, and it demonstrates the importance of decision-maker evaluation scale differences in theoretical research and practical management. Show more

Keywords: Multi-attribute group decision making (MAGDM), expert evaluation scale, relative entropy, massive alternatives, normal distribution

DOI: 10.3233/JIFS-233618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8837-8858, 2023