Journal of Intelligent & Fuzzy Systems - Volume 45, issue 1

Authors: Sun, Hanjie

Article Type: Research Article

Abstract: With the development of information technology, online learning has become an important way of teaching in colleges and universities. The importance of online learning is particularly prominent, especially during the COVID-19 pandemic. How to improve online learning quality is a common problem faced by educators. Online learning quality is closely related to information presentation form, so it is necessary to study the influence of information presentation form on online learning. Based on the dynamics theory of visual perception form and its operating principle, this study compares the differences in post-test scores, cognitive load and satisfaction between the information dynamics presentation …form and the traditional information presentation form through a two-factor random experiment. The data analysis shows that information presentation form plays a significant role in improving students’ academic performance and reducing cognitive load. To a certain extent, there search proves the effectiveness of the information presentation form based on dynamics theory of visual perception form in promoting online learning. Relevant improvement suggestions are proposed to provide a reference and basis for the in-depth development of online learning and the improvement of online learning quality. Show more

Keywords: Dynamics theory of visual perception form, information presentation form, online learning, associative cues, CLC Number: G434 Document Code: A

DOI: 10.3233/JIFS-230083

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 463-475, 2023

Authors: Deva, K. | Mohanaselvi, S.

Article Type: Research Article

Abstract: Picture fuzzy aggregation operators are the standard mathematical tools for the combination of several inputs with respect to attributes into one unique output. The Choquet integral operator has been proven more ideal than traditional aggregation operators in the modelling of interaction phenomena among the attributes in decision-making problems. Firstly, we propose the Choquet integral picture fuzzy Einstein geometric aggregation operator and Choquet integral picture fuzzy Einstein ordered geometric aggregation operator with certain properties of these operators being established. We validate the functioning of the operators with illustrative examples. The proposed operators clearly capture the comprehensive correlative relationships of attributes in …a simpler manner. Furthermore, the algorithm for a multi attribute decision-making problem based on proposed operators is given. The application of the proposed operators was explored to deal with the selection of the best mobile apps for online education. Finally, comparisons are conducted to illustrate the discussion and advantages of the proposed operators. Show more

Keywords: Multi attribute decision-making, picture fuzzy set, choquet integral, aggregation opertaors

DOI: 10.3233/JIFS-230472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 477-490, 2023

Authors: Savitha, S. | Rajiv Kannan, A.

Article Type: Research Article

Abstract: Chronic Kidney Disease (CKD) is a crucial life-threatening condition due to impaired kidney functionality and renal disease. In recent studies, Kidney disorder is considered one of the essential and deadliest issues that threaten patients’ survival with the lack of earlier prediction and classification. The earlier prediction process and the proper diagnosis help delay or stop the chronic disease progression into its final stage, where renal transplantation or dialysis is a known way of saving the patient’s life. Global studies reveal that nearly 10% of the population is affected by Chronic Kidney Disease (CKD), and millions die because of non-affordable treatment. …Early detection of CKD from the biological parameters would save people from this crisis. Machine Learning algorithms are playing a predominant role in disease diagnosis and prognosis. This work generates compound features from CKD indicators by two novel algorithms: Correlation-based Weighted Compound Feature (CWCF) and Feature Significance based Weighted Compound Feature (FSWCF). Any learning algorithm is as good as its features. Hence, the features generated by these algorithms are validated on different machine learning algorithms as a test for generality. The simulation is done in MATLAB 2020a environment where various metrics like prediction accuracy gives superior results compared to multiple other approaches. The accuracy of CWCF over different methods like LR is 97.23%, Gaussian NB is 99%, SVM is 99.18%, and RF is 99.89%, which is substantially higher than the approaches without proper methods feature analysis. The results suggest that generated compound features improve the predictive power of the algorithms. Show more

Keywords: Feature selection, correlation, feature significance, chronic kidney disease, feature projection, mutual information

DOI: 10.3233/JIFS-222401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 491-504, 2023

Authors: Karuppuchamy, V. | Palanivelrajan, S.

Article Type: Research Article

Abstract: Chronic diseases like diabetes, Heart Failure (HF), malignancy, and severe respiratory sickness are the leading cause of mortality around the globe. Dissimilar indications or traits are extremely difficult to identify in HF patients. IoT solutions are becoming increasingly commonplace as smart wearable gadgets become more popular. Sudden heart attacks have a short life expectancy, which is terrible. As a result, a patient monitoring of heart patients based on IoT-centered Machine Learning (ML) is presented to help with HF prediction, and treatment is administered as necessary. Verification, Encryption, and Categorization are the three phases that make up this developed model. Initially, …the datasets from the IoT sensor gadget are gathered by authenticating with a specific hospital through encryption. The patient’s integrated IoT sensor module then transfers sensing information to the cloud. The Improved Blowfish Encryption (IBE) approach is used to protect the sensor data transfer to the cloud. Then the encrypted data is decrypted, and the classification is performed using the Adaptive Fuzzy-Based Long Short-Term Memory with Recurrent Neural Network (AF-LSTM-RNN) algorithm. The results are classed as malignant or benign. It assesses the patient’s cardiac state and sends an alert text to the doctor for treatment. The AF-LSTM-RNN-based HF prediction outperforms the existing techniques. Accuracy, sensitivity, specificity, precision, F-measure and Matthews Correlation Coefficient (MCC) are compared to existing procedures to ensure the planned research is genuine. Using the Origin tool, these metrics are shown as research findings. Show more

Keywords: Heart failure (HF), IoT, machine learning, improved blowfish encryption (IBE), adaptive fuzzy-based long short-term memory with recurrent neural network (AF-LSTM-RNN), origin tool

DOI: 10.3233/JIFS-224298

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 505-520, 2023

Authors: Dhivya, S. | Mohanavalli, S. | Kavitha, S.

Article Type: Research Article

Abstract: Breast cancer can be successfully treated if diagnosed at its earliest, though it is considered as a fatal disease among women. The histopathology slide turned images are the gold standard for tumor diagnosis. However, the manual diagnosis is still tedious due to its structural complexity. With the advent of computer-aided diagnosis, time and computation intensive manual procedure can be managed with the development of an automated classification system. The feature extraction and classification are quite challenging as these images involve complex structures and overlapping nuclei. A novel nuclei-based patch extraction method is proposed for the extraction of non-overlapping nuclei patches …obtained from the breast tumor dataset. An ensemble of pre-trained models is used to extract the discriminating features from the identified and augmented non-overlapping nuclei patches. The discriminative features are further fused using p-norm pooling technique and are classified using a LightGBM classifier with 10-fold cross-validation. The obtained results showed an increase in the overall performance in terms of accuracy, sensitivity, specificity, and precision. The proposed framework yielded an accuracy of 98.3% for binary class classification and 95.1% for multi-class classification on ICIAR 2018 dataset. Show more

Keywords: Breast cancer, histopathology, nuclei-based patches, nuclei feature fusion, LightGBM

DOI: 10.3233/JIFS-222136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 521-535, 2023

Authors: Yang, Biqin | Deng, Yu

Article Type: Research Article

Abstract: Due to the increasingly strengthened role of finance in modern economic development, theoretical research on regional financial competitiveness in the study of regional economic competitiveness becomes very important. For China at this stage, finance is in a period of rapid development, and its role has penetrated into all aspects of social and economic life. Especially after China’s entry into the WTO, the pace of opening up the financial market has been further accelerated, and comprehensive evaluation and analysis of financial competitiveness is of great significance for comprehensively understanding and accurately grasping China’s national conditions, national strength, and international competitiveness, promoting …the long-term growth of China’s financial competitiveness, and the sustainable development of the financial industry. The competitiveness evaluation of regional financial centers is looked as the multiple attribute decision-making (MADM) problem. This paper intends to propose a MADM methodology based on CoCoSo (Combined Compromise Solution) method under interval-valued intuitionistic fuzzy sets (IVIFSs) for sustainable competitiveness evaluation of regional financial centers. At the end of this study, we noticed to a comparison between the proposed IVIF-CoCoSo approach with other existing methods to verify the effectiveness of the algorithm. Show more

Keywords: Multi-attribute decision making (MADM), interval-valued intuitionistic fuzzy sets (IVIFSs), IVIF-CoCoSo method, CRITIC method, competitiveness evaluation

DOI: 10.3233/JIFS-222607

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 537-547, 2023

Authors: Kadeeja Mole, K.P. | Sameena, Kalathodi

Article Type: Research Article

Abstract: In this work, several operations on fuzzy graphs are introduced: u -product, strong edge product, and k th power. The relationship between the fuzzy chromatic number of resultant fuzzy graphs of operations union, join, and newly developed operations and the fuzzy chromatic number of associated fuzzy graphs is also investigated using fuzzy colouring techniques. The number of captures in a chess puzzle move is calculated using the fuzzy colouring approach.

Keywords: Fuzzy graph, fuzzy chromatic number, operations of fuzzy graphs, strong edge, fuzzy colouring

DOI: 10.3233/JIFS-223263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 549-561, 2023

Authors: Sun, Ke | Zhao, Xiaojie | Huang, He | Yan, Yunyang | Zhang, Haofeng

Article Type: Research Article

Abstract: Zero-Shot Learning (ZSL) has made significant progress driven by deep learning and is being promoted further with the advent of generative models. Despite the success of these methods, the type and number of unseen categories are nailed in the generative models, which makes it challenging to recognize unseen categories in an incremental manner, and the profits of some superior performance algorithms largely arise from their advanced capability of feature extraction, such as Transformers. This paper rigidly follows the assumptions introduced in conventional ZSL and proposes a visual feature filtering method based on a semantic mapping model, namely, filtering visual features …through class-specific filters to effectively remove class-agnostic information. Extensive experiments are conducted on four benchmark datasets and have achieved very competitive performance. Show more

Keywords: Generalized zero-shot learning, class-specific filter, matching score calculation

DOI: 10.3233/JIFS-224297

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 563-576, 2023

Authors: Li, Wenqiao | Wang, Ruijie | Ai, Qisheng | Liu, Qian | Lu, Shu Xian

Article Type: Research Article

Abstract: The compressive strength and slump of concrete have highly nonlinear functions relative to given components. The importance of predicting these properties for researchers is greatly diagnosed in developing constructional technologies. Such capacities should be progressed to decrease the cost of expensive experiments and enhance the measurements’ accuracy. This study aims to develop a Radial Basis Function Neural Network (RBFNN) to model the hardness features of High-Performance Concrete (HPC) mixtures. In this function, optimizing the predicting process via RBFNN will be aimed to be accurate, as the aim of this research, conducted with metaheuristic approaches of Henry gas solubility optimization (HGSO) …and Multiverse Optimizer (MVO). The training phase of models RBHG and RBMV was performed by the dataset of 181 HPC mixtures having fly ash and superplasticizer. Regarding the results of hybrid models, the MVO had more correlation between the predicted and observed compressive strength and slump values than HGSO in the R2 index. The RMSE of RBMV (3.7 mm) was obtained 43.2 percent lower than that of RBHG (5.3 mm) in the appraising slump of HPC samples, while, for compressive strength, RMSE was 3.66 MPa and 5 MPa for RBMV and RBHG respectively. Moreover, to appraise slump flow rates, the R2 correlation rate for RBHG was computed at 96.86 % while 98.25 % for RBMV in the training phase, with a 33.30% difference. Generally, both hybrid models prospered in doing assigned tasks of modeling the hardness properties of HPC samples. Show more

Keywords: Compressive strength, slump flow, multiverse optimization algorithm, concrete hardness, neural network

DOI: 10.3233/JIFS-230005

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 577-591, 2023

Authors: Liu, Lin

Article Type: Research Article

Abstract: With the rapid development of the construction industry, people’s requirements for the construction quality continue to improve, and the supervision and management of the construction project quality has been paid more and more attention. The perfect quality supervision and management system is not only an important guarantee for the whole construction project implementation process, but also provides support for the smooth implementation of the construction project. With the increasing number of high-rise buildings in cities and the increasing difficulty of construction, it has posed great challenges to the construction industry, which also means that the quality supervision and management of …construction projects are facing new challenges. Therefore, the project quality supervision and management department should review the situation, optimize the quality supervision and management work according to the current situation and needs of the construction project development, effectively improve the system guarantee and content optimization, maximize the role of quality supervision and management, and provide assistance for the high-quality and sustainable development of the construction industry. The quality evaluation of construction project is a classical multiple attribute group decision making (MAGDM). In this paper, we extended multi-attributive border approximation area comparison (MABAC) method for MAGDM with Pythagorean 2-tuple linguistic sets (P2TLSs). Firstly, a brief review of the definition of P2TLSs is given. Next, two aggregation operators of P2TLSs are used to fuse overall evaluation information. Moreover, combining traditional MABAC model with P2TLSs, Pythagorean 2-tuple linguistic number MABAC (P2TLN-MABAC) is built with all computing steps depicted in detail. Furthermore, a numerical example related to quality evaluation of construction project is conducted to demonstrate the effectiveness of the proposed method. Finally, some comparisons with P2TLWA and P2TLWG operators are also carried out. Show more

Keywords: Multiple attribute group decision making (MAGDM), Pythagorean 2-tuple linguistic sets (P2TLSs), MABAC method, quality evaluation, construction project

DOI: 10.3233/JIFS-230963

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 593-602, 2023