Journal of Intelligent & Fuzzy Systems - Volume 45, issue 3

Authors: Kaladevi, P. | Punitha, V.V. | Muthusankar, D. | Praveen, R.

Article Type: Research Article

Abstract: Early detection and classification of breast cancer can be facilitated to initiate the most effective treatment. As the second leading cause of death among women, early breast cancer screening is essential for reducing mortality rates. In this context, Convolutional neural networks (CNNs) are the ideal candidate for increasing the rate of identification and classification of tumours with efficiency, particularly in medical imaging. This research proposes a hybridised CNN with the Orca Predation Optimization Algorithm (OPOA) as a novel classification model for the effective detection of abnormalities in breast cancer diagnosis. Specifically, the OPOA technique is used to determine the optimal …hyperparameter values for the hybrid CNN architecture being deployed. As the pretrained CNN model, the suggested model utilizeds a ResNet50 residual network. It merged OPOA with the ResNet50 residual network to construct the OPOA-ResNet-50 Architecture. The experimental validation of the proposed OPOA-ResNet-50 model utilising the datasets of curated breast imaging subset of DDSM (CBIS-DDSM) shown improved classification accuracy of 99.04%, specificity of 98.56%, and sensitivity of 97.78% in comparison to the baseline techniques. The results also revealed that the proposed under mammographic image analysis society (MIAS) OPOA-ResNet-50 model demonstrated superior classification accuracy of 98.64%, specificity of 98.79%, and sensitivity of 98.82% compared to the benchmarked methods. The adopted OPOA algorithm is determined to achieve more optimal hyperparameter values for the ResNet50 architecture than the comparative algorithms Improved Marine Predator Optimization Algorithm (IMPOA), Whale Optimization Algorithm (WOA), Harris hawk’s optimization (HHO), and gravitational search algorithm (GSA). Show more

Keywords: Deep Learning Architecture, ResNet-50 model, Convolutional neural networks (CNNs), Hyperparameters Optimization, Orca Predation Optimization Algorithm (OPOA)

DOI: 10.3233/JIFS-231176

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3855-3873, 2023

Authors: Mao, Bingbo | Feng, Tao | Su, Hang | Ma, Xicheng

Article Type: Research Article

Abstract: With the continuous extension and deepening of college education reform, the research on the future employment of college students and the evaluation of employment quality has become a major focus topic. The traditional evaluation system for the employment quality of college graduates is relatively outdated and unitary, lacking a vision of the future development status of college graduates, as well as an effective understanding and mastery of the overall feedback and evaluation of the entire employment market for college graduates. Moreover, most colleges and universities mainly focus on the level of competence that college graduates should achieve five years after …graduation from college in terms of talent cultivation goals, The lack of specific evaluation work for long-term employment tracking of graduates has resulted in universities being unable to grasp and understand the degree of fit and matching between the comprehensive abilities of university graduates and the future employment market, and thus unable to provide effective feedback and summary of talent cultivation and innovation strategies. Therefore, it is imperative to comprehensively innovate the employment quality evaluation system and methods for college graduates. The employment quality evaluation of college graduates is a classical multiple attribute group decision making (MAGDM) problems. Recently, the TODIM and VIKOR method has been used to cope with MAGDM issues. The probabilistic linguistic term sets (PLTSs) are used as a tool for characterizing uncertain information during the employment quality evaluation of college graduates. In this manuscript, the probabilistic linguistic TODIM-VIKOR (PL-TODIM-VIKOR) method is built to solve the MAGDM under PLTSs. In the end, a numerical case study for employment quality evaluation of college graduates is given to validate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), probabilistic linguistic term sets (PLTSs), information entropy, TODIM, VIKOR, employment quality evaluation

DOI: 10.3233/JIFS-231388

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3875-3886, 2023

Authors: Jaikumar, R.V. | Raman, Sundareswaran | Pal, Madhumangal

Article Type: Research Article

Abstract: The picture fuzzy set (PFS) is a more frequent platform for describing the degree of positive, neutral, and negative membership functions that generalizes the concept of fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs). Neutrality is a crucial component of PFS, and the score function plays a crucial role in ranking the alternatives in decision-making situations. In the decision-making process, some researchers concentrate on the various aggregation operators’ development while ignoring the development of score functions. This factor causes several errors in the existing score function. If there are two separate picture fuzzy numbers (PFNs), there should be two different …scores or accuracy values. Some researchers failed to rank the alternatives when the score and accuracy values for various PFNs were equal. To overcome the shortcomings, we proposed the perfect score function in this paper for ranking PFNs and introduced strong and weak PFSs. The shortcoming of the existing score function in PFNs has been highlighted in this paper. Furthermore, the decision-making approach has been depicted based on the proposed score function, and real-world applications have been shown by ranking the COVID-19 affected regions to demonstrate its efficacy. Show more

Keywords: Decision-making problem, perfect score function, strong perfect score, strong PFS, weak perfect score, weak PFS

DOI: 10.3233/JIFS-223234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3887-3900, 2023

Authors: Amshi, Ahmad Hauwa | Prasad, Rajesh | Sharma, Birendra Kumar

Article Type: Research Article

Abstract: Throughout history, cholera has posed a public health risk, impacting vulnerable populations living in areas with contaminated water and poor sanitation. Many studies have found a high correlation between the occurrence of cholera and environmental issues such as geographical location and climate change. Developing a cholera forecasting model might be possible if a relationship exists between the cholera epidemic and meteorological elements. Given the auto-regressive character of cholera as well as its seasonal patterns, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was utilized for time-series study from 2017 to 2022 cholera datasets obtained from the NCDC. Cholera incidence correlates positively to humidity, precipitation, …minimum temperature, and maximum temperature with r  = 0.1045, r  = 0.0175, r  = 0.0666, and r  = 0.0182 respectively. Improving a SARIMA model, autoregressive integrated moving average (ARIMA), and Long short-term memory (LSTM) with the k-means clustering and discrete wavelet transform (DWT) for feature selection, the improved model is known as MODIFIED SARIMA Outperforms the LSTM, ARIMA, and SARIMA and also outperformed both the modified LSTM and ARIMA with an RSS = 0.502 and an accuracy = 97%. Show more

Keywords: Cholera forecasting, SARIMA, K-means clustering, discrete wavelet transform

DOI: 10.3233/JIFS-223901

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3901-3913, 2023

Authors: Zhu, Shuaiwei | Fan, Xiaobin | Qi, Gengxin | Wang, Pan | Chen, Xinbo

Article Type: Research Article

Abstract: Aiming at the problem that the current ABS control algorithm cann’t make full use of the ground braking force to complete the braking when the complex road surface is in emergency braking, the ABS sliding mode variable structure control method based on road surface identification is proposed. Combined with the in-wheel motor of in-wheel motor electric vehicle, a coordinated control method of motor hydraulic composite is designed. Based on the fuzzy logic control method, the road adhesion coefficient is estimated to realize the identification of typical roads and dynamically obtain the optimal slip rate of different roads. The ABS sliding …mode variable structure controller is designed with the optimal slip ratio and the actual slip ratio as input, and the saturation function is used to replace the sign function in the traditional sliding mode variable structure control to weaken the ’ chattering ’ phenomenon in the sliding mode variable structure control, and then the ABS controller is designed. Taking the experimental prototype vehicle driven by four-wheel hub motor as the research object, an eight-degree-of-freedom dynamic simulation model of the whole vehicle is established. Compared with the traditional PID controller, the braking time is shortened by 0.2 s and the braking distance is shortened by 2.3 m, which shows the feasibility of the designed controller. Through the simulation braking experiment of the docking road, the adaptability and real-time performance of the ABS sliding mode controller are verified, and the importance of the road adhesion coefficient identification to the ABS controller is verified. Show more

Keywords: Vehicle engineering, vehicle anti-lock braking system, road identification system, sliding mode control, slip rate

DOI: 10.3233/JIFS-220989

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3915-3928, 2023

Authors: Zhang, Zhuo | Zhang, Ning | Sun, Jing-he | Wang, Jian-ling

Article Type: Research Article

Abstract: Green supplier management (GSM) gained significant importance in addressing environmental concerns, promoting resource efficiency, and enhancing eco-efficiency within the green supply chain system. This study presents a systematic review to provide insights into the current research status and prospects in GSM literature. Results indicate that the research about GSM is gaining consistently growing attention over the past decades. However, there exists a regional imbalance in academic research, with a substantial portion of the authors originating from developing countries in China and India. The topics of green supplier selection and evaluation have received considerable attention in academia. In addition, the multi-attribute …decision-making methods, such as TOPSIS, VIKOR, and AHP, and some mathematical modeling approaches have played a crucial role in the methodology employed for GSM research. As a fundamental algorithm in the artificial intelligence area, fuzzy sets theory has also been extensively employed in supplier selection and evaluation studies, whereas other big data analysis approaches have received little attention. Considering the inherent risks and uncertainties in the business strategy environment and developing more big data and artificial intelligence techniques represent promising avenues for future research in the field. Show more

Keywords: Green supplier management, bibliometric, literature review, green supplier selection and evaluation

DOI: 10.3233/JIFS-222019

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3929-3949, 2023

Authors: Zhao, Jin | Wang, Zhaohan | Jianjun, Zhang

Article Type: Research Article

Abstract: In the Big-data Era, the construction of precise personalized learning evaluation system forms an important part of analyzing learners’ learning behavior and predicting precise personalized learning performance. The CIPP evaluation model is introduced into the precise personalized learning evaluation, and 3 first-level indicators, 9 second-level indicators and 25 third-level indicators are designed to evaluate the learning process in terms of pre-class preview, in-class teaching and after-class consolidation. And then through the application of questionnaire survey, AHP method and fuzzy comprehensive evaluation method, the indicators are condensed and weighted, and the corresponding fuzzy comprehensive judgment matrix is figured out. Finally, a …learning evaluation system for the whole process of precise personalized learning is constructed. An empirical study based on the learning behavior data of a certain number of online learners is carried out to test the value and feasibility of this learning evaluation system. Show more

Keywords: CIPP evaluation model, learning evaluation, precise personalized learning, analytic hierarchy process, fuzzy comprehensive evaluation method

DOI: 10.3233/JIFS-230004

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3951-3963, 2023

Authors: Li, Guo | Geng, Xiuli | Yuan, Yong

Article Type: Research Article

Abstract: Under the COVID-19 pandemic, sports event is facing enormous challenges. Logistics and security are affected seriously. The ability of service suppliers to deal with uncertainty is critical. Considering complex uncertainty, evaluating the service suppliers of sports events is reasonable. This study proposes a new framework for selecting sports suppliers, which combines a hesitant fuzzy set (HFS) and Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) method. MARCOS is based on determining the reference values of alternatives about the ideal and is a comprehensively rational and reasonable application methodology. HFS has the advantage of expressing fuzzy and hesitant …evaluation information, which is seldom used in the MARCOS framework. A case study of a sports supplier selection for the 2022 China National Youth U Series Floorball Championship is given to demonstrate the practicability of the proposed approach. Finally, a comprehensive sensitivity analysis is performed to verify the proposed methodology’s stability and effectiveness. Show more

Keywords: Sports suppliers selection, COVID-19, hesitant fuzzy set, MARCOS, multi-criteria decision-making

DOI: 10.3233/JIFS-230601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3965-3984, 2023

Authors: Barokab, Omar M. | Khan, Asghar | Khan, Sher Afzal | Jun, Young Bae | Rushdi, Ali Muhammad Ali

Article Type: Research Article

Abstract: In comparison to intuitionistic fuzzy set (IFS) and Pythagorean fuzzy set (PFS), the Fermatean Fuzzy Set (FFS) is more efficacious in dealing ambiguous and imprecise data when making decisions. In this paper, we propose unique operations on Fermatean fuzzy information based on prioritized attributes, as well as Einstein’s operations based on adjusting the priority of characteristics in the Fermatean fuzzy environment. We use Einstein’s operations with prioritized attributes to propose new operations on Fermatean fuzzy numbers (FFNs), and then introduce basic aspects of these operations. Motivated by Einstein operations on FFNs, we develop Fermatean fuzzy Einstein prioritized arithmetic and geometric …aggregation operators (AOs). In the first place, the concepts of a Fermatean fuzzy Einstein prioritized average (FFEPA), Fermatean fuzzy Einstein prioritized weighted average (FFEPWA), and Fermatean fuzzy Einstein prioritized ordered weighted average (FFEPOWA)-operators are introduced. Then, Fermatean fuzzy Einstein prioritized geometric (FFEPG) operator, Fermatean fuzzy Einstein prioritized weighted geometric (FFEPWG) operator, Fermatean fuzzy Einstein prioritized ordered weighted geometric (FFEPOWG) operator, and Fermatean fuzzy Einstein hybrid geometric (FFEHG) operator are given. We also go through some of the key characteristics of these operators. Moreover, using these operators, we establish algorithm for addressing a multiple attribute decision-making issue using Fermatean fuzzy data and attribute prioritizing. The case of university faculty selection is taken as a scenario to analyze and demonstrate the applicability of our suggested model. In addition, a comparison of the proposed and current operators is conducted, and the impact of attribute priority on the ranking order of alternatives is explored. Show more

Keywords: MADM, FFE prioritized average (FFEPA) operator, FFE prioritized weighted average (FFEPWA) operator, FFE prioritized ordered weighted average (FFEPOWA) operator, FFE prioritized geometric (FFEPG) operator, FFE prioritized weighted geometric (FFEPWG) operator, FFE prioritized ordered weighted geometric (FFEPOWG) operator

DOI: 10.3233/JIFS-230681

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3985-4008, 2023

Authors: He, Keke | Tang, Haojun | Gou, Fangfang | Wu, Jia

Article Type: Research Article

Abstract: Artificial intelligence image processing has been of interest to research investigators in tumor identification and determination. Magnetic resonance imaging for clinical detection is the technique of choice for identifying tumors because of its advantages such as accurate localization with tomography in any orientation. Nevertheless, owing to the complexity of the images and the heterogeneity of the tumors, existing methodologies have insufficient field of view and require expensive computations to capture semantic information in the view, rendering them lacking in universality of application. Consequently, this thesis developed a medical image segmentation algorithm based on global field of view attention network (GVANet). …It focuses on replacing the original convolution with a transformer structure and views in a larger field-of-view domain to build a global view at each layer, which captures the refined pixel information and category information in the region of interest with fewer parameters so as to address the defective tumor edge segmentation problem. The dissertation exploits the pixel-level information of the input image, the category information of the tumor region and the normal tissue region to segment the MRI image and assign weights to the pixel representatives. This medical image recognition algorithm enables to undertake the ambiguous tumor edge segmentation task with low computational complexity and to maximize the segmentation accuracy and model property. Nearly four thousand MRI images from the Monash University Research Center for Artificial Intelligence were applied for the experiments. The outcome indicates that the approach obtains outstanding classification capability on the data set. Both the mask (IoU) and DSC quality were improved by 7.6% and 6.3% over the strong baseline. Show more

Keywords: Tumor recognition, image analysis, atention, companion diagnostics, global view

DOI: 10.3233/JIFS-231053

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4009-4021, 2023