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

Authors: Crnković, Dean | Švob, Andrea

Article Type: Research Article

Abstract: Tolerance graphs were introduced in 1982 by M. C. Golumbic and C. L. Monma as a generalization of interval graphs. In this paper, we introduce tolerance fuzzy graphs as a generalization of tolerance graphs, and apply them to a modeling of a transmission of airborne diseases.

Keywords: tolerance graph, fuzzy graph, random graph, airborne disease

DOI: 10.3233/JIFS-231606

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4023-4029, 2023

Authors: Bipin Nair, B.J. | Shobha Rani, N. | Khan, Mustaqeem

Article Type: Research Article

Abstract: The method for document image classification presented in this paper mainly focuses on six different Malayalam palm leaf manuscripts categories. The proposed approach consists of three phases: dataset analysis, building a bag of words repository followed by recognition and classification using a voting approach. The palm leaf manuscripts are initially subject to pre-processing and subjective analysis techniques to create a bag of words repository during the dataset analysis phase. Next, the textual components from the manuscripts are extracted for recognition using Tesseract 4 OCR with default and self-adapted training sets and a deep-learning algorithm. The Bag of Words approach is …used in the third phase to categorize the palm leaf manuscripts based on textual components recognized by OCR using a voting process. Experimental analysis was done to analyze the proposed approach with and without the voting techniques, varying the size of the Bag of Words with default/self-adapted training datasets using Tesseract OCR and a deep learning model. Experimental analysis proves that the proposed approach works equally well with/ without voting with a bag of words technique using Tesseract OCR. It is noticed that, for document classification, an overall accuracy of 83% without voting and 84.5% with voting is achieved with an F-score of 0.90 in both cases using Teserract OCR. Overall, the proposed approach proves to be high generalizable based on trial wise experiments with Bag of Words, offering a reliable way for classifying deteriorated Malayalam handwritten palm manuscripts. Show more

Keywords: Document image classification, palm leaf manuscripts, handwritten document analysis, Tesseract OCR, deep learning, ancient document images

DOI: 10.3233/JIFS-223713

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4031-4049, 2023

Authors: An, Qing | Gao, Cuifen | Deng, Qian

Article Type: Research Article

Abstract: Due to the corrosion and aging caused by the special oceanic environment, the characteristic of coastal photovoltaic (PV) system significantly drift after years of operation. In this study, the maximum power point tracking (MPPT) problem for coastal PV system is addressed and a novel MPPT methodology based on deep neural network (DNN) integrated with the corrosion evaluation index (CE-index) and dynamic training-sample (DTS) mechanism is developed. To be specific, the detailed effect of corrosion and aging for the PV modules installed in coastal areas is comprehensively analysed, and a composite indicator for evaluating the PV parameter drift, namely CE-index, is …proposed. Then, a novel DNN-based offline MPPT methodology for the large-scale coastal PV system is developed, in which the DTS mechanism is also introduced for overcoming the effect caused by PV module corrosion and aging phenomenon. Finally, the optimal length of DTS for different degrees of CE-index is comprehensively verified by case studies. Experimental result shows that the developed DNN-based MPPT methodology can accurately forecast the maximum power point (MPP) voltage for large-scale coastal PV-system with robust performance, and cooperation of the developed DTS-mechanism and CE-index corrosion evaluation strategy can also effectively overcome the disturbance caused by the harsh oceanic environment. Show more

Keywords: Coastal PV system, PV module corrosion, corrosion evluation, maximum power point tracking, deep neural network

DOI: 10.3233/JIFS-223428

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4051-4070, 2023

Authors: Van Pham, Hai | Thuy, Linh Hoang Thi | Hung, Nguyen Chan | Dich, Nguyen Quang | Ngoc, Son Luong | Moore, Philip

Article Type: Research Article

Abstract: Pedagogic systems are gaining traction in the provision of training, learning, and continuing professional development (often required to maintain professional qualifications). An essential element in pedagogic systems is the matching of teachers (mentors) and students (mentees). In this paper we present an intelligent context-aware learning system based on profile criteria developed using big data analytic solutions. The proposed system is designed to provide systematic support for mentors based on student profiles. The goal of the proposed system is to match the mentor profiles with the type of pedagogic system, the student profile, the student requirements, and the student’s goals and …expectations. The proposed system is predicated on the use of fuzzy logic definitions with a maximal length matching algorithm using expert knowledge. The proposed system implements a mentor (teacher) and mentee (student) matching algorithm based on their profile criteria. The proposed system has been successfully tested by matching mentor and mentee profiles and preferences. Experimental results show that the proposed system can access multi-factorial mentor and mentee profiles, effectively match suitable mentors (teachers) with appropriate mentees (students), and meet the mentee expectations. Show more

Keywords: Mentor, Mentee, Mentoring, context awareness, profile matching, intelligent pedagogic systems

DOI: 10.3233/JIFS-223820

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4071-4087, 2023

Authors: Zhao, Jin | Shi, Liying

Article Type: Research Article

Abstract: This paper uses two optimizers (Improved Gray Wolf Optimizer (I_GWO) and Dragonfly Optimization Algorithm (DA)) for the sensitivity and robustness of artificial intelligence (AI) techniques, namely radial basis functions (RBFs). The purpose is to evaluate and analyze the predictive strength of high-performance concrete (HPC). 170 samples were collected for this purpose. This includes eight input parameters, cement, silica fume, fly ash, water, coarse aggregate, total aggregate, high water reducing agent, concrete age, and one output parameter, the compressive strength, to produce Increase learning and validation data sets. The proposed AI model was validated against several standard criteria: coefficient of determination …(R2), root mean square error (RMSE), scatter index (SI), RMSE-observations standard deviation ratio (RSR), and coefficient of persistence (CP), n10_index. Many runs were performed to analyze the sensitivity and robustness of the model. The results show that I_GWO using RBF performs better than DA. Furthermore, sensitivity analysis indicated that cement content and HPC test age are the most essential and sensitive factors for predicting the compressive strength of HPC, according to the evaluations performed on the models, it was seen that the IGWO_RBF model provided better results compared to other models and can be introduced as the practical model for the prediction of HPC’s CS. In conclusion, this study can help to select appropriate AI models and suitable input parameters to accurately and quickly estimate the compressive strength of HPC. Show more

Keywords: High-performance concrete, compressive strength, improved Grey Wolf optimizer, Dragonfly optimization algorithm, radial basis function

DOI: 10.3233/JIFS-224382

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4089-4103, 2023

Authors: Arukonda, Srinivas | Cheruku, Ramalingaswamy

Article Type: Research Article

Abstract: Disease diagnosis is very important in the medical field. It is essential to diagnose chronic diseases such as diabetes, heart disease, cancer, and kidney diseases in the early stage. In recent times, ensembled-based approaches giving effective predictive performance than individual classifiers and gained attention in assisting doctors with early diagnosis. But one of the challenges in these approaches is dealing with class-imbalanced data and improper configuration of ensemble classifiers with optimized parameters. In this paper, a novel 3-level stacking approach with ADASYN oversampling technique with PSO Optimized SVM meta-model (Stacked-ADASYN-PSO) is proposed. Our proposed Stacked-ADASYN-PSO model uses base models such …as Logistic regression(LR), K-Nearest neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Multi-Layer Perceptron (MLP) in layer-0. In layer-1 three meta classifiers namely LR, KNN, and Bagging DT are used. In layer-2 PSO optimized SVM used as the final meta-model to combine the previous layer predictions. To evaluate the robustness of the proposed model It is tested on five benchmark disease datasets from the UCI machine learning repository. These results are compared with state-of-the-art ensemble models and non-ensemble models. Results demonstrated that the proposed model performance is superior in terms of AUC, accuracy, specificity, and precision. We have performed statistical analysis using paired T -tests with a 95% confidence level and our proposed stacking model is significantly differs when compared to base classifiers. Show more

Keywords: Disease diagnosis, particle swarm optimization, oversampling, stacking, class imbalance, ensemble

DOI: 10.3233/JIFS-232268

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4105-4123, 2023

Authors: Chen, Jilan

Article Type: Research Article

Abstract: The vast usage of concrete made it the second most used material after water. This volume of concrete consumes an enormous number of natural sources and chronically enhances environmental pollution by CO2 emission. Cementitious supplementary materials such as fly ash and micro silica help decrease the usage of cohesive materials in the concrete and improve concrete’s properties, specifically compressive strength. In addition, due to being the by-product materials of other industries, applying these materials contribute to the decline of environmental pollution. On the other hand, fly ash and micro silica decrease the ratio of water to cement and increase the …compressive strength (CS) of concrete. High-Performance Concrete (HPC) is one of the types of concrete used in dams, bridges, etc. In order to achieve the compressive strength of HPC, it is necessary to conduct laboratory tests, which are not economical in terms of time and cost. For this reason, in the present study, the prediction of the CS of the mentioned concrete can be done based on soft-based and artificial intelligence. Furthermore, various mixed designs of HPC, such as fly ash and silica fume coupled with different percentages of plasticizers, are considered the base dataset for developing the prediction models. Neural network-based model hybridized with antlion optimization algorithm and biography-based optimization algorithm developed for compressive strength estimation. The result showed that the AMLP-I model with R2 and RMSE values of 0.9879 and 1.9003 accurately predicted compressive strength and can be referred to as the most qualitative prediction model compared to the BMLP model. Show more

Keywords: Compressive strength, high-performance concrete, antlion optimization, biography-based optimization, artificial neural network

DOI: 10.3233/JIFS-221544

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4125-4138, 2023

Authors: Huo, Xiaoyan

Article Type: Research Article

Abstract: Automated visual inspection on PCB boards is a critical process in electronic industries. Misalignment component detection is one of the challenging tasks in the PCB inspection process. Defects during the production process might include missing and misaligned components as well as poor solder connections. Inspection of PCB is therefore required to create practically defect-free products. There are various methods have been developed to perform this task in literature. The significance of this research is to propose an efficient with low-cost system is still require in small scale manufacturing to perform the misalignment or missing component detection on PCB boards. However, …an efficient, low-cost system is still required in small-scale manufacturing to perform the misalignment or missing component detection on PCB boards. In this study, a real-time visual inspection system is developed for misalignment component detection. The proposed system consists of hardware and software frameworks. The hardware framework involves the setup of devices and modules. The software framework is composed of pre-processing and post-processing. In pre-processing, image enhancement is applied to remove noises from captured images and You Only Look Once (YOLO) object detector for components detection. Subsequently, the detected components are compared to the corresponding defined pattern using a template-matching algorithm. As experimental shown, the proposed system satisfies the requirement of missing component detection on PCB boards. Show more

Keywords: Surface defect detection, visual inspection, PCB, YOLO, fuzzy logic

DOI: 10.3233/JIFS-223773

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4139-4145, 2023

Authors: Yadav, Shilpi | Patel, Raj K. | Singh, Vijay P.

Article Type: Research Article

Abstract: The study introduces a novel approach to classify faulty bearings using a combination of the Teager-Kaiser Energy Operator (TKEO) and Artificial Intelligence. The TKEO signal is used for statistical feature extraction to distinguish between healthy and abnormal bearings and two datasets were used to evaluate the proposed method. Total 11 statistical features were extracted from the raw and processed signals using the TKEO operator. The obtained feature set was used as input for various machine learning algorithms, and their performance was compared. Additionally, statistical features were calculated using the Hilbert Transform and compared to the proposed method. The study found …that when the TKEO features were used as input for the classifier in the acoustic signal, the CART model achieved the highest accuracy of 99.62% compared to the raw and Hilbert transform signal features. In the case of vibration signals, the TKEO signal feature outperformed the raw signal feature with 100% accuracy for all artificial intelligence models. The proposed methodology revealed that using TKEO signal features as input significantly enhanced the classification accuracy. Show more

Keywords: Statistical feature, hilbert transform, TKEO, artificial intelligence, CART

DOI: 10.3233/JIFS-224221

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4147-4164, 2023

Authors: Zhu, Yinghui | Jiang, Yuzhen

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-230517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4165-4177, 2023

Authors: Zheng, Yue | Xing, Cheng | Wang, Jie-Sheng | Song, Hao-Ming | Bao, Yin-Yin | Zhang, Xing-Yue

Article Type: Research Article

Abstract: The reptile search algorithm (RSA) is a dynamic and effective meta-heuristic algorithm inspired by the behavior of crocodiles in nature and the way of hunting prey. Unlike other crawler search algorithms, it uses four novel mechanisms to update the location of the solutions, such as walking at high or on the belly, and hunting in a coordinated or cooperative manner. In this algorithm, the total number of iterations is divided into four intervals, and different position-updating strategies are used to make the algorithm easily fall into the local optimum. Therefore, an improved reptile search algorithm based on a mathematical optimization …accelerator (MOA) and elementary functions is proposed to improve its search efficiency and make it not easily fall into local optimum. MOA was used to realize the switching of RSA’s four searching modes by introducing random perturbations of six elementary functions (sine function, cosine function, tangent function, arccosine function, hyperbolic secant function and hyperbolic cosecant function), four mechanisms are distinguished by random number instead of the original RSA algorithm’s inherent four mechanisms by iteration number, which increases the randomness of the algorithm and avoids falling into local optimum. The random perturbations generated by elementary functions are added to the variation trend of parameter MOA to improve the optimization accuracy of the algorithm. To verify the effectiveness of the proposed algorithm, 30 benchmark functions in CEC2017 were used for carrying out simulation experiments, and the optimization performance was compared with BAT, PSO, ChOA, MRA and SSA. Finally, two practical engineering design problems are optimized. Simulation results show that the proposed sechRSA has strong global optimization ability. Show more

Keywords: Reptile search algorithm, mathematically optimized accelerator, elementary function, function optimization, engineering optimization

DOI: 10.3233/JIFS-223210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4179-4208, 2023

Authors: Cai, Huiwang | Luan, Ji | Zhou, Changlin | Zhang, Ji | Ma, Lu

Article Type: Research Article

Abstract: High-performance concrete (HPC) is one of the most important elements in constructing bridges, skyscrapers, and dams. This concrete additive plays a very important role in performance and response to inflow loads such as earthquakes and dead loads. Fly ash (Fa) and Micro-silica (Ms) are additives added to concrete by cement to reduce water to cement. Increase the ratio and increase the hardening of the cement. This will improve the compressive strength (Cs) of the concrete. Modeling is required for this type of structure. The radial basis function (RBF) is one of the models that can produce better and more rational …results. This model combines two optimizers, the Sine Cosine Algorithm (SCA) and the Artificial hummingbird algorithm (AHA), in the framework of RBF-SCA and RBF-AHA, which are considered to be new and effective initiatives in the field of algorithms. The lowest amount of error parameters contains: (RMSE = 2.58), (NMSE = 6.59), and (U95 = 7.16) for RBF-AHA in the train section and the test section (MBE = – 0.1929). The (Tstate = 0.285) in the train section of the RBF-SCA has the lowest compared to another section. RBF-AHA has the highest R2 value of 97.15% in the training area. Both hybrid models can have the desired error and the correct percentage based on the given output. However, the RBF-AHA model may look more powerful in this modeling. Show more

Keywords: High-performance concrete, compressive strength, radial basis function, artificial hummingbird algorithm, sine cosine algorithm

DOI: 10.3233/JIFS-224343

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4209-4221, 2023

Authors: Wu, Cuiling | Duan, Xiaodong | Ning, Tao

Article Type: Research Article

Abstract: Machine vision-based semi-automatic sorting in parcel sorting relies on specific sensors to read form information and synchronize it to the control system to complete a sort. The cost of traditional Faster RCNN parameter calculation is high, and the requirements for hardware equipment are high. In order to reduce the consumption of hardware resources and improve efficiency, we redesigned the traditional Faster RCNN to reduce the hardware cost requirements. The number of categories in package data sets varies greatly, and category imbalance is also one of the problems. To solve the express parcel category imbalance problem, an adaptive Mosaic method is …proposed to improve the recognition accuracy of fine-grained similar parcels. To be deployed on edge devices with limited computational resources, a new lightweight network, Reparameterization Large Depthwise conv Normalization-based Attention (ReLDWNAM), is proposed. The experimental results show that compared with MobileNetV2, the number of parameters is reduced by 3.07M, and the computing resources are reduced by more than twice, 10 times faster time for feature extraction network, and more than double the overall detection speed of Faster RCNN with little difference in accuracy. Show more

Keywords: Parcel detection, form recognition, Mosaic method, faster RCNN

DOI: 10.3233/JIFS-230255

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4223-4238, 2023

Authors: Zhou, Shaoling | Tan, Xiaoman | Wang, Xiaosheng

Article Type: Research Article

Abstract: Uncertain differential equations are widely used in the fields of finance, chemistry, and so forth. In this paper, the problem of parameter estimation in uncertain differential equations is discussed. The trapezoidal scheme is derived to approximate the uncertain differential equations, then a difference scheme named the composite Heun scheme is proposed to obtain the difference equations of uncertain differential equations. The method of moments based on the composite Heun scheme is given to estimate the parameters in uncertain differential equations. Several examples are used to illustrate the viability of the composite Heun scheme.

Keywords: Composite Heun scheme, uncertain differential equation, method of moments, parameter estimation

DOI: 10.3233/JIFS-230288

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4239-4248, 2023

Authors: Yang, Wenguang | Ren, Baitong | Xu, Bingbing | Pang, Xiaona | Liu, Ruitian

Article Type: Research Article

Abstract: In this study, a novel approach based on the reduction of the attribution and the rank preservation is analyzed, which intends to solve the issue of multi-attribute decision making (MADM) with the hesitant fuzzy information. Firstly, several new concepts are shown to simplify the representation of hesitant fuzzy information, such as single point fuzzification estimated value, and single point fuzzification weighted Euclidean distance. Secondly, a new improved HF-TOPSIS method based on the overall situation and these new concepts are put forward, in which the positive and negative ideal solutions are fixed to calculate the complex hesitant fuzzy decision process. The …proposed method in this paper achieves the purpose of compression of the complex hesitant fuzzy information, and the calculation is relatively simple and easy to operate. Finally, two examples are presented to test and verify the credibility and effectiveness of the TOPSIS-Based rank preservation approach, which can achieve the consistency of results before and after evaluation, as well as ensuring rank preservation, while other HF-TOPSIS methods may cause rank reversal problems. Show more

Keywords: Rank preservation, TOPSIS, MADM, hesitant fuzzy set, single point fuzzification

DOI: 10.3233/JIFS-230713

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4249-4260, 2023

Authors: Sugumaran, V.R. | Rajaram, A.

Article Type: Research Article

Abstract: This paper focuses on achieving high-level security in Mobile Adhoc Networks (MANET) by incorporating Blockchain technology-based Intrusion Detection systems (IDS). The existing works on MANET security focus on either security prevention or detection. Thus, the security level attained by the prior works is unable to cope with the increasing attacks. To resolve this main issue, this research paper introduces Lightweight Blockchain assisted Intrusion Detection System (LB-IDS) which jointly prevents and detects the attacks held on mobile networks. Initially, the network nodes are authenticated by a lightweight Blockchain-based Multi-Factor Authentication (LBMFA) scheme. This procedure prevents the malicious nodes entry to the …network. Then, data packets are transmitted through the optimal route which is selected by Multi-Objective Strawberry Optimization (MOSO) algorithm. The collected data packets are fed into IDS which classifies the data into normal and malicious packets. For IDS, we proposed Deep Q-Learning (DQL) algorithm which takes actions by learning the environment. As the mitigation step, the Blockchain is updated with the trust value according to the data packet classification. For such continuous monitoring, K-Mode Clustering (KMC) algorithm is proposed. On the whole, the proposed work improves the network security in MANET through Prevention, Detection, and Mitigation. The results of the presented work attains better security level, packet delivery ratio (PDR), energy efficiency, delay, and detection accuracy. Show more

Keywords: Blockchain, Mobile Adhoc Network (MANET), Deep Q-Learning (DQL), energy efficient, security

DOI: 10.3233/JIFS-231340

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4261-4276, 2023

Authors: Liu, Anlei | Ma, Xun | Jia, Xuchao | Liu, Kai | Ji, Ming | Feng, Jian | Wang, Junlong

Article Type: Research Article

Abstract: In order to ensure the efficiency of power user’s requirements processing, an automatic classification method for demand test of power users based on parallel naive Bayesian algorithm is proposed. Polynomial naive Bayes is selected to build Hadoop cluster, and the feature words of power user’s requirements are selected through chi square test. The weight of each feature item is calculated by word frequency-inverse text frequency index method, and the weight sum of each category is calculated. The weight sum is input into naive Bayes algorithm to output the text classification results of power user’s requirements. At the same time, The …naive Bayes classification algorithm is parallelized and encapsulated to reduce the cost of data movement and exchange in the classification process, and improve the operation efficiency of demand text classification of power user. The experimental results show that this method can accurately extract the feature words of power user’s requirements, effectively realize the automatic classification of power user’s requirements text, and have a more accurate classification effect. The average fitness value of the proposed method tends to be stable after more than 20 training times, and the number of network convergence steps is 7. When the ratio of energy function is about 0.4 and 0.6, the average IU value is the highest. When the required number of texts ranges from 500 to 1500, the delay time of text classification is 0.02 s, and the peak signal-to-noise ratio is more than 33, among which the highest peak signal-to-noise ratio is 42.52, and the normalization coefficient is 1. Show more

Keywords: MapReduce, Naive Bayes, power user’s requirements, automatic text classification, parallel processing

DOI: 10.3233/JIFS-224170

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4277-4289, 2023

Authors: Cui, Zheng | Li, Xiaoqi | Guo, Jie | Lu, Yunhang

Article Type: Research Article

Abstract: Basketball has always been a relatively hot sport. However, the level of basketball in China does not maintain the synchronous development trend with competitive sports, which can be seen from the achievements of various international competitions. Many basketball players have retired due to sports injuries. How to avoid and delay the occurrence of injuries to the maximum extent, and make the best competitive state to get the longest time is an urgent problem to be solved in the current basketball training and competition process. Therefore, how to reduce sports damage in basketball sports has become a crucial problem. The …artificial neural network algorithm is widely used in complex system hardware fault detection, medical diagnosis, medical image processing and other complex task, to classify and forecast, and achieved good results. But in the use of the sports injury risk prevention is very limited, in sports injury risk early warning research, predecessors to sports injury factors made a lot of research and the qualitative model was established, but no quantitative evaluation research, and artificial neural network algorithm has good performance in complex system classification and prediction, so the artificial neural network algorithm is applied to sports injury risk early warning study is a very meaningful work, can carry on the accurate to the athlete sports injury risk assessment. Using RBF neural network to achieve dimensional reduction preprocessing of high-dimensional data not only has sufficient theoretical basis, but also it is more superior. Based on the optimization study of RBF neural network algorithm, we study the data-based feature selection RBF neural network, and apply it in the high-dimensional multi-objective optimization decision space and pare to quality and disadvantages prediction. Through the evaluation of the test sample, the early warning model achieves ideal results, so it is feasible to apply to the sports injury risk warning. Show more

Keywords: Keywords. Basketball, RBF neural network algorithm, sports injury early warning, athletes

DOI: 10.3233/JIFS-224601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4291-4300, 2023

Authors: Li, Hao | Niu, Haisha | Zhang, Yong | Yu, Zhengxian

Article Type: Research Article

Abstract: Traditional mechanical models and sensors face challenges in obtaining the dynamometer diagram of the sucker rod pump system (SRPS) due to difficulties in model solving, high application costs, and maintenance difficulties. Since the electric motor powers the SRPS, its power output is highly correlated with the working state of the entire device. Therefore, a hy-brid method based on electric motor power and SPRS mechanical parameter prediction is proposed to predict the dyna-mometer diagram. First, a long short-term memory neural network (LSTM) is used to establish the LSTM-L model for predicting the dynamometer load based on electric motor power. Then, a …mathematical and physical calculation model (FLM-D) of the dynamometer diagram displacement at the hanging point is constructed by combining the four-bar linkage structure of the sucker rod pump. Finally, the experimental production data of oil wells are collected through an edge computing device to verify the prediction performance of the LSTM-L&FLM-D hybrid model. Experimental results show that the proposed LSTM-L&FLM-D model has a high fitting degree of 99.3%, which is more robust than other models considered in this study, and exhibits better generalization ability. Show more

Keywords: Long-short term memory neural network, dynamometer diagram, indirect measurement, edge computing

DOI: 10.3233/JIFS-230253

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4301-4313, 2023

Authors: Guo, Fu-Jun | Sun, Wei-Zhong | Wang, Jie-Sheng | Zhang, Min | Hou, Jia-Ning | Song, Hao-Ming | Wang, Yu-Cai

Article Type: Research Article

Abstract: Dealing with classification problems requires the crucial step of feature selection (FS), which helps to reduce data dimensions and shorten classification time. Feature selection and support vector machines (SVM) classification method for banknote dirtiness recognition based on marine predator algorithm (MPA) with mathematical functions was proposed. The mathematical functions were mainly used to improve the optimizatio of MPA for feature parameter selection, and the loss function and kernel function parameters of the SVM are optimized by slime mold optimization algorithm (SMA) and marine predator algorithm. According to the experimental results, the accuracy of identifying dirtiness on the entire surface of …the banknote reaches 89.07%. At the same time, according to the image pattern distribution of the banknoteS, the white area image in the middle left of the collected banknote is selected by the same method to select the feature parameters and identify the dirtiness of the banknoteS. The accuracy of dirtiness recognition in the middle left white area reached 86.67%, this shows that the white area in the middle left can basically completely replace the entire banknote. To confirm the effectiveness of the feature selection method, the proposed optimization method has been compared with four other swarm intelligent optimization algorithms to verify its performance. The experiment results indicate that the enhanced strategy is successful in improving the performance of MPA. Moreover, the robustness analysis proves its effectiveness. Show more

Keywords: Banknote dirtiness, marine predator algorithm, feature selection, mathematical function, support vector machine

DOI: 10.3233/JIFS-230459

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4315-4336, 2023

Authors: Ye, Qiang | Zhang, Juwei | Chen, Quankun

Article Type: Research Article

Abstract: Different number of broken wires produce different grooves on the surface of steel wire rope. Based on the local structural features of these grooves, a new broken wire identification method is proposed. By comparing the processing effects of various image enhancement methods, a processing method called adaptive histogram equalization is selected to process the broken wire image. Aiming at a large amount of useless information in structural features extracted by HOG algorithm, a encoder-decoder neural network is designed to reduce the dimension of features. In addition, to effectively avoid information loss caused by the output layer of the BP neural …network, a joint algorithm of the BP neural network and the support vector machine is proposed. The experimental results show that using image enhancement technology to process broken wire images can effectively improve the recognition rate of broken wires; The structural features extracted by HOG algorithm are more beneficial to the quantitative recognition of broken wires than the texture features extracted by LBP operator; Compared with various dimensionality reduction methods, neural network can retain more effective information; The joint algorithm can improve the recognition rate of broken wire by at least 0.25% on the basis of BP neural network. Show more

Keywords: Steel wire rope, neural network, HOG, support vector machine

DOI: 10.3233/JIFS-231259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4337-4347, 2023

Authors: Dingjun, He | Liang, Xu | Hui, Yang

Article Type: Research Article

Abstract: Box girders are commonly utilized in bridge engineering because of their economical and visually appealing form. Due to recent advancements in the design sector. However, safety in the economy is the fundamental demand of the current generation, therefore, it is vital to pick an optimal design. Prestrained concrete is used for large-span bridges. Standard heuristic optimization is frequently used to do structural optimization because of how complex structural concerns remain. However, traditional heuristic optimization still takes a significant amount of time. Particle Swarm trained Hierarchically Stepped Adversarial Networks (PS-HSAN) are presented as an alternative approach to speeding up the optimization …of complex problems, and their use reduces the cost of computation for optimization. To find the best design for “a three-span continuous box-girder pedestrian bridge, this research” will apply both classical heuristic optimization and PS-HSAN. This will include analyzing and assessing a variety of crime types and sample sizes. Particle swarm optimization is shown to be as effective as conventional heuristic optimization but with significant time savings. Therefore, using a PS-HSAN in structural design challenges provides an original method for handling certain structural difficulties that need a great degree of computing power while simplifying the solution of other problems. Show more

Keywords: Box girder bridge, Particle Swarm trained hierarchically stepped adversarial networks (PS-HSAN), Particle swarm optimization, structural optimization, the optimal solution

DOI: 10.3233/JIFS-231309

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4349-4360, 2023

Authors: Xia, Zhile | Mou, Jinping

Article Type: Research Article

Abstract: In this paper, the containment control problem of second-order nonlinear heterogeneous multi-agent system is studied. In order to deal with complex uncertainties such as unknown parts, uncertainties, and input constraints in the system, we designed a distributed fuzzy adaptive controller. The interval type-II (IT2) fuzzy set is adopted to deal with the uncertainty of membership functions. We construct a matrix equality and a matrix inequality to deal with the asymmetric Laplace matrix. The controller designed is simple and the designed controller only uses the information of itself and its neighbors. Therefore, it is very easy to be compensated in practice. …Finally, a simulation example is introduced to verify the effectiveness of the proposed methods. Show more

Keywords: Containment problem, fractional-order systems, heterogeneous multi-agent systems, distributed type-II fuzzy adaptive controller

DOI: 10.3233/JIFS-231350

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4361-4370, 2023

Authors: Pushpa, M. | Sornamageswari, M.

Article Type: Research Article

Abstract: The requisite of detecting Autism in the initial stage proposed dataset is exceptionally high in the recent era since it affects children with severe impacts on social and communication developments by damaging the neural system in a broader range. Thus, it is highly essential to identify this Autism in the primary stage. So many methods are employed in autism detection but fail to produce accurate results. Therefore, the present study uses the data mining technique in the process of autism detection, which provides multiple beneficial impacts with high accuracy as it identifies the essential genes and gene sequences in a …gene expression microarray dataset. For optimally selecting the genes, the Artificial Bee Colony (ABC) Algorithm is utilized in this study. In contrast, the feature selection process is carried out by five different algorithms: tabu search, correlation, information gain ratio, simulated annealing, and chi-square. The proposed work utilizes a hybrid Extreme Learning Machine (ELM) algorithm based Adaptive Neuro-Fuzzy Inference System (ANFIS) in the classification process, significantly assisting in attaining high-accuracy results. The entire work is validated through Java. The obtained outcomes have specified that the introduced approach provides efficient results with an optimal precision value of 89%, an accuracy of 93%, and a recall value of 87%. Show more

Keywords: Autism, data mining, gene expression, gene selection, hybrid classifier

DOI: 10.3233/JIFS-231608

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4371-4382, 2023

Authors: Prasad, Padavala Sai | Nair, Prabha Shreeraj | Patil, Anagha | Patil, Nilesh Madhukar | Chaturvedi, Abhay | Taqui, Syed Noeman | Almoallim, Hesham S. | Alharbi, Sulaiman Ali | Raghavan, S.S.

Article Type: Research Article

Abstract: For many, Covid-19 is a short-term, mildly debilitating disease. But some people are still struggling with monthly symptoms with persistent inflammation, chronic pain and shortness of breath. The situation of “long-term cowardice” has become so debilitating that it is now common for some to say that they are tired even if they walk a short distance. So far, the focus has been on saving lives from the plague. But now there are growing concerns about people facing the long-term consequences of the COVID epidemic. The fundamental question, with the uncertainty of whether those with chronic goiter, or all those affected, …will fully recover is raised. In this paper a smart monitoring model was proposed to keep monitoring the COVID patient’s health conditions. The smart method keep on watching the different changes reflected in the body conditions and ensure the changes in the database. In case any emergency is raised, then these smart monitoring tools inform the information to the doctors. This can very much helpful for the patients to communicate with the doctors. Show more

Keywords: Health care, inflammation, chronic pain, long-term consequences, COVID epidemic

DOI: 10.3233/JIFS-231899

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4383-4393, 2023

Authors: Dou, Weiwei

Article Type: Research Article

Abstract: The so-called “college English” teaching quality evaluation is to provide a basic, comprehensive, and realistic evaluation of the relevant aspects and management of teaching implementation on the basis of following the general laws of higher education; It is a comprehensive inspection of “College English” teaching and an important means of quality monitoring and policy adjustment for “College English”. As mentioned earlier, teaching evaluation is a comprehensive evaluation of teaching. Therefore, our evaluation of the quality of university public education is actually an examination of our specific measures in evaluating teaching, teaching methods and methods, teaching literature, and other aspects. The …college public English teaching quality evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy sets (TPFSs) and investigate the MADM problems under TPFSs. Based on the traditional dual generalized weighted Bonferroni mean (DGWBM) operator and dual generalized weighted geometric Bonferroni mean (DGWGBM) operator, some triangular Pythagorean fuzzy operators are proposed: triangular Pythagorean fuzzy DGWBM (TPFDGWBM) operator and triangular Pythagorean fuzzy DGWGBM (TPFDGWGBM) operator. Accordingly, we have took advantage of these operators to develop some approaches to work out the triangular Pythagorean fuzzy MADM. Ultimately, a practical example for college public English teaching quality evaluation is took advantage of to validate the developed approach, and an influence analysis of the parameter on the final results is been presented to attest its availability and validity. Show more

Keywords: Multiple attribute decision making (MADM), Triangular Pythagorean fuzzy set, Dual generalized weighted Bonferroni mean (DGWBM) operator, Dual generalized weighted geometric Bonferroni mean (DGWGBM) operator, English teaching quality evaluation

DOI: 10.3233/JIFS-232581

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4395-4414, 2023

Authors: Shi, Huanyu | Li, Ning | Liu, Yinuo

Article Type: Research Article

Abstract: In the wake of the wide promotion of 5G network, the era of super-high-speed networks and the Internet of Everything is approaching. Combining digital technologies led by 5G with landscape architecture has become an important way for the sustainable development of garden ecology. In order to achieve refined management of gardens and improve the accuracy and consistency of garden environmental data monitoring, this study constructs a new IoT sensor multi data fusion algorithm model. Considering the high redundancy and large error data collected by multiple sensors, this paper proposes a multi data fusion algorithm based on adaptive trust estimation and …improved D-S evidence theory. The experimental data demonstrates that matched with IGA-BP, algorithm in this paper obtained the largest fitness value and the fastest convergence speed in three sensor application scenarios with different numbers of nodes. The lowest values were obtained in terms of unit energy consumption and network latency indicators. In the monitoring experiment for environmental data of landscape architecture, the algorithm obtained lower relative error and mean square error than IGA-BP in four environmental parameters of temperature, humidity, light intensity and carbon dioxide concentration. Therefore, the algorithm is effective in real-time monitoring of landscape garden environmental data, and can provide decision-making data for garden management as a reference. Show more

Keywords: 5G, sensor, multi-data fusion algorithm, internet of things, landscape architecture

DOI: 10.3233/JIFS-223961

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4415-4425, 2023

Authors: Kumar, Amit | Dhiman, Pooja

Article Type: Research Article

Abstract: The reliability of an industrial system plays a significant role in new technological era where everyone is concerned about the performance of associated industry. With the frequent demands of the customers, the job of the production industries becomes more tedious to produce the required products at a high rate. To fulfill the customer’s demand, the initial focus of the industries is to work well without any interruption/failure in the entire production process. In this paper, Reliability, Availability, MTTF, MTTR, MTBF, ENOF is proposed for an industrial system namely “Injection moulding machine”. For this, the membership function for right triangular generalized …fuzzy numbers (RTrGFN) is proposed with the certain level of confidence. The real data of the Injection moulding machine is taken to validate the proposed methodology. The input data is extracted from the records/maintenance sheets of several years and found uncertainty due to the loss of any information or human mistakes. The parameters of the system like failure rate and repair time is retrieved. Based on data, AND-OR combination for the system is constructed. The lambda-tau methodology along with RTrGFN and its corresponding arithmetic operations is used for the performance analysis of the considered “Injection moulding machine”. For better understanding the results are discussed with the aid of graphs. Also after seeing the result one can conclude that this methodology is one of the best methodology for the performance analysis of a conventional system. Also authors have added the highlights and future scope of the research work at the end of the manuscript. Show more

Keywords: Reliability indicators, right triangular generalized fuzzy number, alpha cuts, injection moulding machine

DOI: 10.3233/JIFS-224022

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4427-4445, 2023

Authors: Karunanidhi, Bavithra | Ramasamy, Latha | Sathiasamuel, Charles Raja | Manivannan Sudha, Vasanth

Article Type: Research Article

Abstract: Among the list of reliability issues in Photovoltaic (PV) systems, partial shading is one of the crucial issues that affect the row current creating a wide range of current differences between rows these results in reduced output power and panel life span by creating hotspots. It also creates difficulty in tracking the power, because of multiple hotspot peaks obtainable in PV and IV (Current-Voltage) curves. Physical relocation of panels during shade occurrence is not an encouraging solution because of rooftop solar and domestic PV systems, where the area for PV installation is a ceiling. The optimization-based controller is retrofitted for …the electrical relocation of panels. It is developed based on the Cuckoo Search Algorithm (CSA), which aims to reduce the row current difference with a minimum reposition of panels as constraints. For the 9*9 PV arrangement, the row current ranges from 3.747 A to 8.424 A. It is reduced and almost made zero. Hence, the Fill factor raises from 38.073 to 51.707%. The power output is enhanced by about 20%. To prove the algorithm’s novelty a shading case for 4*3 asymmetric array arrangement is also considered for simulation studies. The proposed system proves to be economically beneficent for PV users. The performance of CSA is compared with PSO, Skyscraper, and SuDoKu. An economic analysis is carried out that adds the PV efficiency value to the proposed CSA algorithm. The real-time experimental validation holds good for 3*3 solar array agreement with theoretically simulated results. Show more

Keywords: Optimization-based shade dispersion controller, Cuckoo Search Algorithm, power output enhancement, fill factor, mismatch losses

DOI: 10.3233/JIFS-224137

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4447-4468, 2023

Authors: Huang, Ke | Zhang, Limin

Article Type: Research Article

Abstract: In the construction process, wearing a safety helmet is an important guarantee for personnel safety. However, manual detection is time-consuming, labor-intensive, and unable to provide real-time monitoring. To address this issue, a helmet-wearing detection algorithm has been proposed based on YOLOv5s. The algorithm uses the YOLOv5s network and introduces the CoordAtt coordinate attention mechanism module into its backbone to consider global information and improve the network’s ability to detect small targets. To improve feature fusion, the residual block in the backbone network has been replaced by a Res2NetBlock structure. The experimental results show that compared to the original YOLOv5 algorithm, …the accuracy and speed of the self-made helmet data set have improved by 2.3 percentage points and 18 FPS, respectively. Compared to the YOLOv3 algorithm, accuracy and speed have improved by 13.8 percentage points and 95 FPS, respectively, resulting in a more accurate, lightweight, efficient, and real-time helmet-wearing detection. Show more

Keywords: Helmet wearing detection, YOLOv5s, CoordAtt, Res2NetBlock

DOI: 10.3233/JIFS-230666

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4469-4482, 2023

Authors: Yu, Junqi | Su, Yucong | Feng, Chunyong | Cheng, Renyin | Hou, Shuai

Article Type: Research Article

Abstract: Global path planning is one of the key technologies for airport energy station inspection robots to achieve autonomous navigation. Due to the complexity of airport energy station buildings with numerous mechanical and electrical equipment and narrow areas, planning an optimal global path remains a challenge. This paper aimed to study global path planning for airport energy station inspection robots using an improved version of the Grey Wolf Optimizer (IGWO) algorithm. Firstly, the initialization process of the Grey Wolf Optimizer algorithm selects several grey wolf individuals closer to the optimal solution as the initial population through the lens imaging reverse learning …strategy. The algorithm introduces nonlinear convergence factors in the control parameters, and adds an adaptive adjustment strategy and an elite individual reselection strategy to the location update to improve the search capability and to avoid falling into local optima. Benchmark function and global path planning simulation experiments were carried out in MATLAB to test the proposed algorithm’s effectiveness. The results showed that compared to other swarm intelligent optimization algorithms, the proposed algorithm outperforms them in terms of higher convergence speed and optimization accuracy. Friedman’s test ranked this algorithm first overall. The algorithm outperforms others in terms of average path length, standard deviation of path length, and running time. Show more

Keywords: Airport energy station, inspection robot, global path planning, improved grey wolf optimizer

DOI: 10.3233/JIFS-230894

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4483-4500, 2023

Authors: Priyadharshini, S. | Mahapatra, Ansuman

Article Type: Research Article

Abstract: With the advances in video technology, the advent of spherical video (360° video) recorded using an omnidirectional camera offers a limitless field-of-view (FoV) to the viewers. However, they suffer from the fear of missing out (FOMO) because they can only see a particular FoV at a time. Reviewing a long recorded surveillance video i.e., 24 hours a day is a time-consuming process due to temporal and spatial redundancy. A solution to this problem is to compactly represent the video synopsis by shifting the objects along the time domain. Using a multi-camera setup for surveillance creates blind spots. This problem is …solved by using a spherical camera. Therefore, in this paper, we focus on creating and visualizing the video synopsis recorded by the spherical camera. The optimization algorithm plays a key role in condensing the recorded video. Hence, a novel spherical video synopsis optimization framework has been introduced to generate compact videos that eliminate FOMO. The synopsis is generated by shifting objects on the temporal axis and displays them simultaneously by optimizing multiple constraints. It minimizes activity loss, virtual collisions, temporal inconsistencies, and synopsis video length by preserving interactions between objects. The proposed multiobjective optimization includes a new constraint to restrict the number of objects displayed per frame due to the limitation of the human visual system. Direction-based visualization methods have been proposed to improve the viewer’s experience without FOMO. Comparative performance of the proposed framework using the latest metaheuristic optimization algorithms with existing video synopsis optimization algorithms is performed. It is found that chronological disorder ratio and overall virtual collision are minimized effectively through the recent metaheuristics optimization algorithms compared to the related works on video synopsis. Show more

Keywords: Display constraint, object-based video synopsis, optimization, panoramic surveillance video, spherical video synopsis

DOI: 10.3233/JIFS-232227

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4501-4516, 2023

Authors: Xuan, Cho Do | Nguyen, Hoa Dinh

Article Type: Research Article

Abstract: Advanced persistent threat (APT) attacking campaigns have been a common method for cyber-attackers to attack and exploit end-user computers (workstations) in recent years. In this study, to enhance the effectiveness of the APT malware detection, a combination of deep graph networks and contrastive learning is proposed. The idea is that several deep graph networks such as Graph Convolution Networks (GCN), Graph Isomorphism Networks (GIN), are combined with some popular contrastive learning models like N-pair Loss, Contrastive Loss, and Triplet Loss, in order to optimize the process of APT malware detection and classification in endpoint workstations. The proposed approach consists of …three main phases as follows. First, the behaviors of APT malware are collected and represented as graphs. Second, GIN and GCN networks are used to extract feature vectors from the graphs of APT malware. Finally, different contrastive learning models, i.e. N-pair Loss, Contrastive Loss, and Triplet Loss are applied to determine which feature vectors belong to APT malware, and which ones belong to normal files. This combination of deep graph networks and contrastive learning algorithm is a novel approach, that not only enhances the ability to accurately detect APT malware but also reduces false alarms for normal behaviors. The experimental results demonstrate that the proposed model, whose effectiveness ranges from 88% to 94% across all performance metrics, is not only scientifically effective but also practically significant. Additionally, the results show that the combination of GIN and N-pair Loss performs better than other combined models. This provides a base malware detection system with flexible parameter selection and mathematical model choices for optimal real-world applications. Show more

Keywords: APT malware detection, end-point workstations, event ID, deep graph networks, contrastive learning

DOI: 10.3233/JIFS-231548

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4517-4533, 2023

Authors: Zhao, Hu | Hu, Xia | Chen, Gui-Xiu

Article Type: Research Article

Abstract:  In order to give a characterization of the product of (L , M )-fuzzy convex structures, the notion of convex (L , M )-fuzzy hull operators is presented, it is proved that the category of (L , M )-fuzzy convex structures and the category of convex (L , M )-fuzzy hull operators are isomorphic. In particular, the lattices structure of convex (L , M )-fuzzy hull operators and a new characterization of the product of (L , M )-fuzzy convex structures are given.

Keywords: (L, M)-fuzzy convex structures, (L, M)-fuzzy weak hull operators, Sayed’s (L, M)-fuzzy hull operators, convex (L, M)-fuzzy hull operators, product

DOI: 10.3233/JIFS-231909

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4535-4545, 2023

Authors: Wang, Lu

Article Type: Research Article

Abstract: After entering the 21st century, China’s national economy has shown a rapid growth momentum, the comprehensive transportation system has been continuously improved, the road traffic infrastructure has made remarkable achievements, and the modern logistics industry has also risen rapidly and grown rapidly, which has greatly changed the market demand for road transport hubs. The road transport hub is the main node of the road transport network, the hub of passenger and freight distribution of road transport, and the organizational center for the interconnection of road transport and other transport modes and the development of comprehensive transport. Highway transportation hub is …an important part of highway transportation infrastructure and plays an important role in highway transportation. The planning scheme evaluation of highway transportation hub is a multi-attribute decision making (MADM). This paper intends to propose a MADM methodology based on cross-entropy (CE) method under interval-valued intuitionistic fuzzy sets (IVIFSs) for planning scheme evaluation of highway transportation hub. First of all, this paper extends the cross entropy method under the IVIFSs to propose the interval-valued intuitionistic fuzzy number CE(IVIFN-CE) method, it enlarges the application range of the CE method. Secondly, a new MADM model for planning scheme evaluation of highway transportation hub based on IVIFN-CE algorithm is proposed. Show more

Keywords: Multi-attribute decision making (MADM), interval-valued intuitionistic fuzzy sets (IVIFSs), CRITIC method cross-entropy (CE), planning scheme evaluation

DOI: 10.3233/JIFS-232668

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4547-4558, 2023

Authors: Tahir, Zaigham | Khan, Hina | Alamri, Faten S. | Aslam, Muhammad

Article Type: Research Article

Abstract: The current work is one step in filling a large void in the research left by the advent of neutrosophic Statistics (NS), a philosophized variant of classical statistics (CS). The philosophy of NS deals with techniques for investigating data that is ambiguous, hazy, or uncertain. The traditional techniques of estimation utilizing auxiliary information work under specific determinate data, which in the case of neutrosophic data may lead to mistakes (over/ under-estimation). This study presents a generalized neutrosophic ratio-type exponential estimator (NRTEE) for estimating location parameters and achieving the lowest mean square error (MSE) possible for interval neutrosophic data (IND). The …offered NRTEE helps to deal with the uncertainty and ambiguity of data. Unlike typical estimators, its findings are not single-valued but rather in interval form, which reduces the possibility of over-or under-estimation caused by single crisp outcomes and also increases the likelihood of the parameter dwelling in the interval. It improves the efficiency of the estimator since we have an estimated interval that contains the unknown value of the population mean with a minimal MSE. The suggested NRTEE’s efficiency is further addressed by utilizing real-life IND of temperature and simulations. A comparison is also performed to establish the superiority of the proposed estimator over the traditional estimators. The limits are calculated and discussed in cases when our suggested estimator is always efficient. The suggested estimator is the most efficient of all estimators and outperformed all others on both IND and classical data. Show more

Keywords: Neutrosophic statistics, classical statistics, estimation, ratio estimators, bias, mean square error

DOI: 10.3233/JIFS-223539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4559-4583, 2023

Authors: Qian, Yurong | Shao, Jinxin | Zhang, Zhe | Leng, Hongyong | Ma, Mengnan | Li, Zichen

Article Type: Research Article

Abstract: In traditional user portrait construction methods, static word vectors can extract only shallow semantic representations, which cannot manage word polysemy. Moreover, the common clustering algorithm K-means has the problems of initial K values and unstable initial centroid selection. A Bert-CK model based on Bert and CK-means+ is proposed. First, Bert is used to extract semantic and syntactic text features at various levels, and word vectors and sentence vectors are obtained according to the context. Then, the CK-means+ algorithm is improved based on canopy and mean calculation. Next, the K value and initial centroid are determined. The sentence vectors are input …to CK-means+ to obtain user classification and topic features. Finally, semantic features and topic features are fused and classified. CK-means+ is evaluated on the Sogou user portrait dataset. The experimental results verify that Bert-CK is better than the baseline model. Show more

Keywords: User profile, bert, canopy, K-means, text classification

DOI: 10.3233/JIFS-224531

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4585-4597, 2023

Authors: Kumari, Rani | Ramachandran, Prakash

Article Type: Research Article

Abstract: The deformation of speech caused by glottic vocal tract is an early bio marker for Parkinson’s disease. A novel idea of Line Spectral Frequency trajectory spectrum image representation of the speech signals of the subjects in Deep Convolution Neural Network is proposed for Parkinson’s disease classification in which the convolution layer automatically learn the features from the input images and no separate feature calculation stage in required. The human vocal tract that produces a short phonetics is assumed as an all-pole Infinite impulse response system and the Line spectral frequency trajectory spectrum images represents the poles of the system and …reflects the voice defects due to Parkinson’s disease. It is shown that the proposed method outperforms the existing state of the art work for two different utterance tasks one for sustained phonation and another for natural running speech dataset. It is demonstrated that the Deep Convolution Neural Network results in a training accuracy of 92.5% for sustained phonation dataset and training accuracy of 99.18% for King’s college running speech dataset. The validation accuracies for both the datasets are 100%. The proposed work is much better than another recent benchmark work in which Mel Frequency Cepstral Coefficient parameters are used in machine learning for Parkinson’s disease detection in running speech. The high performance of the proposed method for King’s college running speech dataset which is collected through mobile device voice recordings, gains attention. Rigorous performance analysis is performed for running speech dataset by using separate isolated test set for repeated 50 trials and the performance metrics are F1 score of 99.37%, sensitivity of 100%, precision of 98.75% and specificity of 99.27%. Show more

Keywords: Deep convolution neural network, line spectral frequency, Parkinson’s disease, running speech, sustained phonation

DOI: 10.3233/JIFS-230183

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4599-4615, 2023

Authors: Muhsen, Yousif Raad | Husin, Nor Azura | Zolkepli, Maslina Binti | Manshor, Noridayu

Article Type: Research Article

Abstract:  The Fuzzy-Weighted Zero-Inconsistency (FWZIC) and Fuzzy-Decision-by-Opinion-Score-Method (FDOSM) are considered the recent advance methods. FDOSM generates a ranking for possible alternatives, while FWZIC produces a weight for criterion. Keeping up with the stream of academic publications on the FDOSM and FWZIC methods is complicated. This study aims to provide a comprehensive review of the literature on the latest advanced methods of MCDM in order to reorganize the findings of the previous literature and provide decisive evidence for ongoing research and future studies. Based on previous literature, the current study used the Prisma method to collect data from multiple databases such as …IEEE Xplore®, ScienceDirect, and Web of Science. There were 45 papers discovered relevant to this subject; however, only 23 studies were relevant for the FDOSM & FWZIC study. The results included theoretical and practical implications. Theoretically, additions of new aggregation operators or usage of new fuzzy sets in the FDOSM & FWZIC model to solve the uncertainty problem are the key obstacles. Practically, agriculture and architectural fields are considered to be a hotspot of research. Finally, a number of potential points for future research to develop methods with high certainty and low ambiguity are presented. Show more

Keywords: Multi-criteria decision-making, fuzzy set, FWZIC, FDOSM

DOI: 10.3233/JIFS-230803

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4617-4638, 2023

Authors: Hsu, Pi-Shan | Huang, Chien-Chung | Sung, Wei-Ying | Tsai, Han-Ying | Wu, Zih-Xin | Lin, Ting-Yu | Lin, Kuo-Ping | Liu, Gia-Shie

Article Type: Research Article

Abstract: This study attempts to develop the adaptive neuro-fuzzy inference system (ANFIS) with biogeography-based optimization (BBO) (ANFIS-BBO) for a case study of the actual number of COVID-19 vaccinations in a medical center, considering the variables of the date and time of vaccination, the brand of vaccine, and the number of open appointments on the government network platform in Taiwan. The COVID-19 has brought about a great burden on the health and economy of the world since the end of 2019. Many scholars have proposed a prediction model for the number of confirmed cases and deaths. However, there is still a lack …of research in the prediction model for mass vaccination. In this study, ANFIS-BBO is developed to predict the number of COVID-19 vaccination, and three other forecasting models, support vector machines (SVM), least-square support vector machines (LSSVM) and general regression neural network (GRNN) are employed for forecasting the same data sets. Empirical results show that the ANFIS-BBO with trapezoidal membership function model can achieve better performance than other methods and provide robust predictions for the actual number of COVID-19 mass vaccination. Show more

Keywords: COVID-19, mass vaccination, adaptive neuro-fuzzy inference system, biogeography-based optimization, prediction

DOI: 10.3233/JIFS-231165

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4639-4650, 2023

Authors: Sasirekha, N. | Karuppaiah, Jayakumar | Shekhar, Himanshu | Naga Saranya, N.

Article Type: Research Article

Abstract: Cancer is a devastating disease that has far-reaching effects on our culture and economy, in addition to the human lives it takes. Regarding budgetary responsibility, investing just in cancer treatment is not an option. Early diagnosis is a crucial part of the remedy that sometimes gets overlooked. Malignancy is often diagnosed and evaluated using Histopathology Images (HI), which are widely accepted as the gold standard in the field. Yet, even for experienced pathologists, analysing such images is challenging, which raises concerns of inter- and intra-observer variability. The analysis also requires a substantial investment of time and energy. One way that …such an examination may be sped up is by making use of computer-assisted diagnostics devices. The purpose of this research is to create a comprehensive cancer detection system using images of breast and prostate histopathology stained with haematoxylin and eosin (H&E). Proposed here is work on improving colour normalisation methods, constructing an integrated model for nuclei segmentation and multiple objects overlap resolution, introducing and evaluating multi-level features for extracting relevant histopathological image and interpretable information, and developing classification algorithms for tasks such as cancer diagnosis, tumor identification, and tumor class labelling. Mini-Batch Stochastic Gradient Descent and Convolutional Neural Network which obtained statistical kappa value for breast cancer histopathology images shows a high degree of consistency in the classification task, with a range of 0.610.80 for benign and low grades and a range of 0.811.0 for medium and high rates. The Support Vector Machine (SVM), on the other hand, shows an almost perfect degree of consistency (0.811.0) across the several breast cancer picture classifications (benign, low, medium, and high). Show more

Keywords: Breast cancer, Mini-Batch Stochastic Gradient Descent and Convolutional Neural Network, computer-assisted diagnostic systems, histopathology images

DOI: 10.3233/JIFS-231480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4651-4667, 2023

Authors: Sahayaraj, L. Remegius Praveen | Muthurajkumar, S.

Article Type: Research Article

Abstract: Preserving the integrity of log data and using the same for forensic analysis is one of the prime concerns of cloud-oriented applications. Since log data collates sensitive information, providing confidentiality and privacy is of at most importance. For data auditors, maintaining the integrity of the log data is a prime concern. Existing models focus on providing models and frameworks that relies on any third-party entity or the cloud service provider (CSP) to handle the logs, which lacks in securing the integrity due to the presence of the external entities. Sole dependence on CSP is a major flaw together with a …drawback, since the CSP itself is prone to data theft alliance. In this paper, we instantiate a mechanism which maintains the integrity of the log without compromising the performance efficiency of the system. The influence of machine learning classification techniques is leveraged in order to efficiently classify the log data before it is processed. Progressively the log data integrity is maintained through the proposed Propagated Chain of Log Blocks (PCLB), the Hybrid Vector Committed BST (HVCBST) and lightweight Multikey Hybrid Storage (MKHS) structures. The results of the implemented systems have proven to be efficient and tamper proof compared to the existing systems and can be easily rendered in any private or public cloud deployments. Show more

Keywords: Data integrity, cloud, security, log, block chain, encryption, decryption

DOI: 10.3233/JIFS-224585

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4669-4687, 2023

Authors: Hou, Jia-Ning | Zhang, Min | Wang, Jie-Sheng | Wang, Yu-Cai | Song, Hao-Ming

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-230081

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4689-4714, 2023

Authors: Zhang, Yiwen | Zhang, Li | Dong, Yunchun | Chu, Jun | Wang, Xing | Ying, Zuobin

Article Type: Research Article

Abstract: Traditional collaborative filtering algorithms use user history rating information to predict movie ratings Other information, such as plot and director, which could provide potential connections are not fully mined. To address this issue, a collaborative filtering recommendation algorithm named a movie recommendation method based on knowledge graph and time series is proposed, in which the knowledge graph and time series features are effectively integrated. Firstly, the knowledge graph gains a deep relationship between users and movies. Secondly, the time series could extract user features and then calculates user similarity. Finally, collaborative filtering of ratings can calculate the user similarity and …predicts ratings more precisely by utilizing the first two phases’ outcomes. The experiment results show that the A Movie Recommendation Method Fusing Knowledge Graph and Time Series can reduce the MAE and RMSE of user-based collaborative filtering and Item-based collaborative filtering by 0.06,0.1 and 0.07,0.09 respectively, and also enhance the interpretability of the model. Show more

Keywords: Knowledge graph, rating prediction, collaborative filtering

DOI: 10.3233/JIFS-230795

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4715-4724, 2023

Authors: Zhang, Fang | Wang, Hongjuan | Wang, Lukun | Wang, Yue

Article Type: Research Article

Abstract: Human body pose transfer is to transform the character image from the source image pose to the target pose. In recent years, the research has achieved great success in transforming the human body pose from the source image to the target image, but it is still insufficient in the detailed texture of the generated image. To solve the above problems, a new two-stage TPIT network model is proposed to process the detailed texture of the pose-generated image. The first stage is the source image self-learning module, which extracts the source image features by learning the source image itself and further …improves the appearance details of pose-generated image. The other stage is to change the pose of the figure gradually from the source image pose to the target pose. Then, by learning the feature correlation between source and target images through cross-modal attention, texture transmission between images is promoted to generate finer-grained details of the generated image. A large number of experiments show that the model has superior performance on the Market-1501 and DeepFashion datasets, especially in the quantitative and qualitative evaluation of Market-1501, which is superior to other advanced methods. Show more

Keywords: Posture transfer, self-attention mechanism, dual-tasking mechanism, character image generation, generating adversarial networks

DOI: 10.3233/JIFS-231289

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4725-4735, 2023

Authors: Ponniah, Krishna Kumar | Retnaswamy, Bharathi

Article Type: Research Article

Abstract: The internet of things (IoT) has significantly influenced day-to-day life in large industrial systems. The Internet of Things (IoT) offers a platform for information systems to integrate effectively with network servers. In contrast, cyber threats are becoming critical, especially for IoT servers. A strong strategy must be in place to protect the network system from multiple attacks. In order to detect malicious behaviors that deteriorate network performance, an intrusion detection system (IDS) is crucial. An IDS use a detection method to monitor network activity to alert IoT users regularly. This paper proposes a novel IDS for IoT using log-sigmoid kernel …principal component analysis (LSK-PCA) and activation updated deep feed-forward neural network (AU-DFFNN) based dimensionality reduction (DR) and classification technique. Initially, the input data is taken from the NSLKDD dataset and undergoes pre-processing. Afterwards, attribute extraction is carried out, followed by Fisher’s Yates Adapted Golden Eagle Optimizer (FY-GEO) based feature selection. Then, DR of the feature selected data is done using the LSK-PCA model. Finally, the reduced dataset is given as an input to the classifier for classifying the data as attacked and normal data. As a final point, experimental analysis is performed using performance metrics like precision (PR), recall (RC), f-score (FS), accuracy (AC), false alarm rate (FAR) and computational time (CT). The results proved that the proposed work detects intrusion effectively compared to state-of-art techniques. Show more

Keywords: Intrusion Detection System (IDS), Internet of Things (IoT), Golden Eagle Optimizer (GEO), Feed Forward Neural Network (FFNN), Attribute extraction, Dimensionality reduction, Principal Component Analysis (PCA)

DOI: 10.3233/JIFS-223437

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4737-4751, 2023

Authors: Ashok Kumar, M. | Saravanan, K.

Article Type: Research Article

Abstract: In multicasting packets of data from a node will be sent to a group of receiver nodes at the same time. Multicasting lowers transmission costs. Energy conservation is critical to a sensor network’s long-term viability. Sensor networks have limited and non-replenishable energy supplies, maximizing network lifetime is crucial in sensor nodes. As a result, clustering has become one of the popular methods for extending the lifetime of an entire system by integrating information at the cluster head. Cluster head (CH) selection is the important serving node in each cluster in the Wireless sensor networks (WSN). This paper introduces a High …Power Node (HPN) multicasting approach which embeds a cluster of sink node data in packet headers to allow receiver for utilizing a approach for transferring multicast packet data via the shortest paths. The proposed Energy efficient multicasting cluster based routing (EEMCR) protocol utilized high power nodes, which shall play a critical role in minimal energy usage. The implementation findings demonstrate that, when compared with the previous methodologies, the suggested algorithm has enhanced in terms of packet delivery ratio (PDR), End to end delivery rate, efficiency and achieves low energy consumption. The proposed EEMCR obtain 95% efficiency. The results are then compared to other existing algorithms to determine the superiority of the proposed methodology. Show more

Keywords: Routing, wireless sensor networks, multicasting, cluster head selection, clustering

DOI: 10.3233/JIFS-223536

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4753-4766, 2023

Authors: Nathezhtha, T. | Vaidehi, V. | Sangeetha, D.

Article Type: Research Article

Abstract: In recent days, malicious users try to captivate the consumers using their fraudulent marketing URL post in social networking sites. Such malicious URL posted by fake users in Social Networking Services (SNS) is hard to identify. Therefore, there occurs a need to detect such fraudulent URLs in SNS. In order to detect such URLS, this paper proposes a SNS Fraudulent Detection (SFD) scheme. The proposed SFD scheme includes a Deterministic Finite Automata Tokenization (DFA-T) and Web Crawler (WC) based Neuro Fuzzy System (WC-NFS). DFA-T extracts the URL features and calculates a Penalty Score (PS) based on the malicious words in …the extracted URL. The DFA extracted URL features with PS are fed into WC-NFS. Subsequently, the WC fetches the numeric WC-Index (WCI) value from the URLs which are added to the WC-NFS. The existing URL data set is used to identify the malicious web links and suitable machine learning techniques are used to identify the malicious URLs. From the experimental results, it is found that the proposed SFD provides 92.6 % accuracy in classifying the benign from malicious URLs when compared with the existing methods. Show more

Keywords: Consumer electronics, fraudulent, web crawler, social networking service, malicious users

DOI: 10.3233/JIFS-223569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4767-4775, 2023

Authors: Bai, Zhiqiang | Yang, Zhiyong | Jiang, Yusheng | Gao, Hongji | Sun, Zhengyang | Sun, Wei

Article Type: Research Article

Abstract: The earth pressure balance (EPB) shield tunneling efficiency is greatly affected by the choice of soil transport mode. In this study, the influence of two soil transport modes, such as the continuous belt conveyor and rail train, on the efficiency of shield excavation was analyzed using the Markov chain model. A method was proposed to define the ideal and non-ideal excavation states and quantitatively evaluate the excavation efficiency of the two soil transportation modes of the EPB shield. Based on this model framework, a profitable Markov chain model was established to predict the expected profits of the two soil transportation …modes. The Beijing Metro New Airport Line first-phase project was used as a case study to verify the model established. The results show that under the same conditions, the continuous belt conveyor soil transport mode can have a higher excavation efficiency and expected profit. This advantage gradually increases over time. Show more

Keywords: Markov chain, soil transport, excavation efficiency, expect profit, shield construction

DOI: 10.3233/JIFS-223833

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4777-4790, 2023

Authors: Jegajothi, B. | Kathir, I. | Shukla, Neeraj Kumar | Prakash, R.B.R.

Article Type: Research Article

Abstract: Because of environmental issues and energy crises, significant attention has been received in the domain of renewable and clean energy systems. Solar energy is the most effective source of renewable energy technologies. Recently, photovoltaic (PV) system have become common in grid-linked applications and plays a vital part in power production. MPPT algorithms enable PV systems to capture the maximum available power from the solar panels, regardless of variations in solar irradiance, temperature, and other environmental factors. By continuously tracking the MPP, MPPT techniques ensure that the PV system operates at its highest efficiency, resulting in increased energy harvesting and improved …overall performance. Meanwhile, the frequent modifications in irradiance and temperature pose a major challenging issue which can be resolved by the use of artificial intelligence MPPT methodologies like artificial neural networks (ANN), fuzzy logic (FL), and metaheuristics systems. In this aspect, this work presents a new quasi-oppositional artificial algae optimization (QOAAO) with an adaptive neuro-fuzzy inference system (ANFIS) technique, named QOAAO-ANFIS for maximum efficiency MPPT technique for minimizing the present ripple and power oscillations over the MPP. The presented QOAAO-ANFIS model mainly depends upon the integration of the ANFIS and QOHOA techniques. In addition, the presented QOAAO-ANFIS model involves optimal MF selection of the ANFIS model to estimate the irradiation level and compute PV voltage equivalent to maximal power point. The QOAAO model can be utilized for enhancing the optimization process of membership function variables under varying conditions and awareness of global optima. The simulation result analysis of the QOAAO-ANFIS model takes place in terms of different evaluation measures. Extensive comparative results reported the better performance of the QOAAO-ANFIS model with maximum tracking efficiency of 99.89% and a minimum convergence time of 13.51 ms. Show more

Keywords: Membership function, photovoltaic systems, maximum power point tracking, artificial intelligence, fuzzy logic controller

DOI: 10.3233/JIFS-223889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4791-4805, 2023

Authors: Zheng, Zhangqi | Liu, Yongshan | Zhang, Bing | Ren, Jiadong | Zong, Yongsheng | Wang, Qian | Yang, Xiaolei | Liu, Qian

Article Type: Research Article

Abstract: A software defect is a common cyberspace security problem, leading to information theft, system crashes, and other network hazards. Software security is a fundamental challenge for cyberspace security defense. However, when researching software defects, the defective code in the software is small compared with the overall code, leading to data imbalance problems in predicting software vulnerabilities. This study proposes a heterogeneous integration algorithm based on imbalance rate threshold drift for the data imbalance problem and for predicting software defects. First, the Decision Tree-based integration algorithm was designed following sample perturbation. Moreover, the Support Vector Machine (SVM)-based integration algorithm was designed …based on attribute perturbation. Following the heterogeneous integration algorithm, the primary classifier was trained by sample diversity and model structure diversity. Second, we combined the integration algorithms of two base classifiers to form a heterogeneous integration model. The imbalance rate was designed to achieve threshold transfer and obtain software defect prediction results. Finally, the NASA-MDP and Juliet datasets were used to verify the heterogeneous integration algorithm’s validity, correctness, and generalization based on the Decision Tree and SVM. Show more

Keywords: Software defect, imbalance rate, heterogeneous, integration, threshold shift

DOI: 10.3233/JIFS-224457

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4807-4824, 2023

Authors: Jin, Xiu | Li, He | Hou, Yuting

Article Type: Research Article

Abstract: Emerging markets, such as the Chinese financial market, are occasionally subject to extreme risk events that result in investor losses during the investment process. To address the challenge of investment selection amidst market fluctuations, considering the fuzzy uncertainty and tail risk compensation based on the asymmetric perspective, we propose to use the lower VaR ratio and the upper VaR ratio as investment objectives to construct a multi-period credibilistic portfolio selection model. The study reveals that the cumulative returns and terminal wealth of the constructed model surpassed those of the benchmark models, delivering greater social and economic welfare to investors. During …extreme events, investors could promptly adjust their portfolio structure to achieve higher investment returns. Investors who prefer the lower VaR ratio tend to make conservative investment decisions and allocate a higher proportion to defensive assets, such as bonds and risk-free assets. Conversely, investors who favor the upper VaR ratio are inclined to adopt aggressive investment strategies and allocate a larger proportion to high-risk stocks. The findings demonstrate that the proposed model offers differentiated investment decisions, and the research conclusions serve as valuable references for investors engaged in multi-period asset allocation and risk management. Show more

Keywords: Lower VaR ratio, upper VaR ratio, multi-period portfolio selection, generalized fuzzy numbers, credibility measure

DOI: 10.3233/JIFS-224517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4825-4845, 2023

Authors: Onat Bulak, Fatma | Bozkurt, Hacer

Article Type: Research Article

Abstract: In this study, we define soft quasilinear functionals on soft normed quasilinear spaces and we examine some of its qualities. By using the soft quasilinear operator defined in [6 ] we specify and prove some theorem related to the continuity and boundedness of soft quasilinear operators and functionals. Furthermore, we give some examples in favor of the soft quasilinear functionals.

Keywords: Soft set, soft quasilinear space, soft normed quasilinear space, soft quasilinear operator, soft quasilinear functional

DOI: 10.3233/JIFS-230035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4847-4856, 2023

Authors: Zhang, Hengshan | Wang, Yun | Chen, Tianhua

Article Type: Research Article

Abstract: Methods on the basis of the consensus reaching process are prevalent in Group Decision Making (GDM), which typically forces some evaluators to revise initial opinions in order to reach group consensus without being able to precisely reflect original viewpoints. Furthermore, in case the correct opinion is embedded in the hand of the minority, existing methods may not reach the correct consensus. With the aim to tackle these observations, a novel approach of the Positive and Negative Prediction Selection Rate (PNPSR) is proposed on the basis of the Pythagorean Fuzzy Preference Relation (PFPR) which enables to present individuals’ opinions in a …pairwise manner using the linguistic preference relation. The PFPR expressed opinions then serve as input for the computation of the proposed PNPSR, the minimum of which is subsequently selected as the correct one. Finally, the full ranking of the alternatives can be calculated through the proposed iterative algorithm. In the process, the evaluators’ original opinions are not required to modify, and the correct result can be achieved when the minority evaluators provide the correct opinions. Experimental results demonstrate the efficacy of the proposed approach in comparison with two state-of-the-art methods. Show more

Keywords: Group decision making, Pythagorean fuzzy preference relation, positive and negative prediction selection rate, consensus measure, consensus reaching process

DOI: 10.3233/JIFS-230395

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4857-4870, 2023

Authors: Hu, Hongqiang | Zhai, Ce | Chu, Yunxia | Feng, Jiu | Shi, Jianfeng | Liu, Xuning | Zhang, Genshan

Article Type: Research Article

Abstract: The prediction of coal and gas outburst is very necessary for the prevention of gas disaster, so an outburst prediction model coupled with feature extraction and feature weighting using optimized classifier is proposed. First, Pearson correlation coefficient(PCC) and symmetric uncertainty(SU) are employed to measure the effective information in outburst sample data. Second, Kernel principal component analysis(KPCA) and linear discriminant analysis(LDA) methods are used to extract the exiting discriminate information, and the extracted linear and nonlinear feature information can effectively reflect significant information of outburst influencing factors. Third, the combination of gradient boost decision tree(GBDT) and grey relation analysis(GRA) is used …to weight and fuse the extracted linear and nonlinear feature components, then form a new feature set as important discriminant information. Forth, the weighted and fused features of the coal and gas outburst influencing factors are used as the input of support vector machine(SVM) classifier with optimized parameters, it can classify outburst states, and the achieved classification accuracy can obtain 95%. Finally, the proposed model and the existing outburst classification models in literatures are used to predict outburst, then the experiment results verify the effectiveness of the proposed model and conclude that the performance of the proposed predication model are significant than present outburst prediction models. Show more

Keywords: Coal and gas outburst, KPCA, LDA, GBDT, GRA, SVM

DOI: 10.3233/JIFS-222979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4871-4884, 2023

Authors: Liu, Weiling | Xu, Jinliang | Ren, Guoqing | Xiao, Yanjun

Article Type: Research Article

Abstract: Due to the dynamic nature of work conditions in the manufacturing plant, it is difficult to obtain accurate information on process processing time and energy consumption, affecting the implementation of scheduling solutions. The fuzzy flexible job shop scheduling problem with uncertain production parameters has not yet been well studied. In this paper, a scheduling optimization model with the objectives of maximum completion time, production cost and delivery satisfaction loss is developed using fuzzy triangular numbers to characterize the time parameters, and an improved quantum particle swarm algorithm is proposed to solve it. The innovations of this paper lie in designing …a neighborhood search strategy based on machine code variation for deep search; using cross-maintaining the diversity of elite individuals, and combining it with a simulated annealing strategy for local search. Based on giving full play to the global search capability of the quantum particle swarm algorithm, the comprehensive search capability of the algorithm is enhanced by improving the average optimal position of particles. In addition, a gray target decision model is introduced to make the optimal decision on the scheduling scheme by comprehensively considering the fuzzy production cost. Finally, simulation experiments are conducted for test and engineering cases and compared with various advanced algorithms. The experimental results show that the proposed algorithm significantly outperforms the compared ones regarding convergence speed and precision in optimal-searching. The method provides a more reliable solution to the problem and has some application value. Show more

Keywords: Fuzzy flexible job shop scheduling, PSO, QPSO, simulated annealing, local search

DOI: 10.3233/JIFS-231640

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4885-4905, 2023

Authors: Yang, Juan

Article Type: Research Article

Abstract: In order to improve the accuracy of English online course teaching effect evaluation results, a paper proposed an English online course teaching effect evaluation method based on ResNet algorithm. The effect of College English online teaching was evaluated from five aspects: pre-class preparation, teaching content, basic skills, ability training, and teaching methods. Each evaluation item was set with seven levels of scoring standards. An evaluation model of the classroom teaching effect was constructed based on convolutional neural network according to the internal relationship between facial expression recognition and classroom teaching effect evaluation. The problem of network depth deepening affecting the …accuracy of evaluation in convolutional neural network models was innovatively solved by utilizing the ResNet algorithm. The evaluation of the effectiveness of English online course teaching was achieved. The experimental results showed that this method could effectively improve the effect of English online course teaching evaluation and improve the teaching quality of English online courses. Show more

Keywords: ResNet algorithm, English online teaching, teaching evaluation, face recognition, convolutional neural network

DOI: 10.3233/JIFS-230048

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4907-4916, 2023

Authors: Xu, Wan | Zhang, Yuhao | Yu, Leitao | Zhang, Tingting | Cheng, Zhao

Article Type: Research Article

Abstract: In order to solve the problem that the traditional DWA algorithm cannot have both safety and speed because of the fixed parameters in the complex environment with many obstacles, a parameter adaptive DWA algorithm (PA-DWA) is proposed to improve the robot running speed on the premise of ensuring safety. Firstly, the velocity sampling space is optimized by the current pose of the mobile robot, and a criterion of environment complexity is proposed. Secondly, a parameter-adaptive method is presented to optimize the trajectory evaluation function. When the environment complexity is greater than a certain threshold, the minimum distance between the mobile …robot and the obstacle is taken as the input, and the weight of the velocity parameter is adjusted according to the real-time obstacle information dynamically. The current velocity of the mobile robot is used as input to dynamically adjust the weight of the direction angle parameter. In the Matlab simulation, the total time consumption of PA-DWA is reduced by 47.08% in the static obstacle environment and 39.09% in the dynamic obstacle environment. In Gazebo physical simulation experiment, the total time of PA-DWA was reduced by 26.63% in the case of dynamic obstacles. The experimental results show that PA-DWA can significantly reduce the total time of the robot under the premise of ensuring safety. Show more

Keywords: Speed sampling space, parameter adaptation, DWA, local path planning

DOI: 10.3233/JIFS-221837

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4917-4933, 2023

Authors: Huang, Haojian | Liu, Zhe | Han, Xue | Yang, Xiangli | Liu, Lusi

Article Type: Research Article

Abstract: Dempster-Shafer theory (DST) has attracted widespread attention in many domains owing to its powerful advantages in managing uncertain and imprecise information. Nevertheless, counterintuitive results may be generated once Dempster’s rule faces highly conflicting pieces of evidence. In order to handle this flaw, a new belief logarithmic similarity measure ( BLSM ) based on DST is proposed in this paper. Moreover, we further present an enhanced belief logarithmic similarity measure ( EBLSM ) to consider the internal discrepancy of subsets. In parallel, we prove that EBLSM satisfies several desirable properties, …like bounded, symmetry and non-degeneracy. Finally, a new multi-source data fusion method based on EBLSM is well devised. Through its best performance in two application cases, specifically those pertaining to fault diagnosis and target recognition respectively, the rationality and effectiveness of the proposed method is sufficiently displayed. Show more

Keywords: Dempster-Shafer theory, basic belief assignment, logarithmic similarity measure, belief entropy, data fusion

DOI: 10.3233/JIFS-230207

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4935-4947, 2023

Authors: Park, Choonkil | Rehman, Noor | Ali, Abbas | Alahmadi, Reham A. | Khalaf, Mohammed M. | Hila, Kostaq

Article Type: Research Article

Abstract: In clasical logic, it is possible to combine the uniary negation operator ¬ with any other binary operator in order to generate the other binary operators. In this paper, we introduce the concept of (N ∗ , O , N , G )-implication derived from non associative structures, overlap function O , grouping function G and two different fuzzy negations N ∗ and N are used for the generalization of the implication p  → q  ≡ ¬ [p  ∧ ¬ (¬ p  ∨ q )] . We show that (N ∗ , O , N , G )-implication are fuzzy implication without any restricted …conditions. Further, we also study that some properties of (N ∗ , O , N , G )-implication that are necessary for the development of this paper. The key contribution of this paper is to introduced the concept of circledcircG ,N -compositions on (N ∗ , O , N , G )-implications. If ( N 1 ∗ , O ( 1 ) , N 1 , G ( 1 ) ) - or ( N 2 ∗ , O ( 2 ) , N 2 , G ( 2 ) ) -implications constructed from the tuples ( N 1 ∗ , O ( 1 ) , N 1 , G ( 1 ) ) or ( N 2 ∗ , O ( 2 ) , N 2 , G ( 2 ) ) satisfy a certain property P , we now investigate whether circledcircG ,N -composition of ( N 1 ∗ , O ( 1 ) , N 1 , G ( 1 ) ) - and ( N 2 ∗ , O ( 2 ) , N 2 , G ( 2 ) ) -implications satisfies the same property or not. If not, then we attempt to characterise those implications ( N 1 ∗ , O ( 1 ) , N 1 , G ( 1 ) ) -, ( N 2 ∗ , O ( 2 ) , N 2 , G ( 2 ) ) -implications satisfying the property P such that circledcircG ,N -composition of ( M 1 ∗ , O ( 1 ) , M 1 , G ( 1 ) ) - and ( M 2 ∗ , O ( 2 ) , M 2 , G ( 2 ) ) -implications also satisfies the same property. Further, we introduced sup-circledcircO -composition of (N ∗ , O , N , G )-implications constructed from tuples (N ∗ , O , N , G ) . Subsequently, we show that under which condition sup-circledcircO -composition of (N ∗ , O , N , G )-implications are fuzzy implication. We also study the intersections between families of fuzzy implications, including R O -implications (residual implication), (G , N )-implications, QL -implications, D -implications and (N ∗ , O , N , G )-implications. Show more

Keywords: Overlape function, grouping function, fuzzy implication, fuzzy negation

DOI: 10.3233/JIFS-222878

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4949-4977, 2023

Authors: Wang, Jing

Article Type: Research Article

Abstract: The traditional text-image confrontation model utilizes a convolutional network in the discriminator to extract image features, yet this fails to involve the spatial relationship between underlying objects, resulting in a poor-quality generated image. To remedy this, a capsule network is proposed to improve the model. The convolutional network in the discriminator is replaced with a capsule network, thereby improving the robustness of the images. Through experiments on the Oxford-102 and CUB datasets, it has been found that the new model can effectively improve the quality of generated text-image. The FID value of the generated flower image decreased by 14.49%, and …the FID value of the generated bird image decreased by 9.64%. Additionally, the Inception Score of images generated on the Oxford-102 and CUB datasets increased by 22.60% and 26.28%, respectively, indicating that the improved model generated richer and more meaningful image features. Show more

Keywords: Generating images, capsule network, generation adversarial network, convolutional network, robustness

DOI: 10.3233/JIFS-223741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4979-4989, 2023

Authors: Li, Yuqi

Article Type: Research Article

Abstract: The method based on entropy was used for the Bayesian optimization. Based on compelling information theory, Entropy Search (ES) and Predictive Entropy Search (PES) maximized information about the unknown function when the loss function reaches the maximum value. However, both methods were plagued by complicated calculations for estimating entropy. The most important motivation of this article is to improve and modularize the entropy search itself, making this method more flexible and effective for model adaptation. After the initial optimization and pruning module processing, a reasonable initial configuration for the complex model was successfully established, further reducing the space required for …secondary optimization hyper-parameter search. The advantage of this method is that, on the one hand, the basic method of Bayesian optimization is used to get the best result of the iteration, while ensuring that the algorithm has theoretical boundedness. On the other hand, through the maximum entropy, the information features of the original space and data set are retained as much as possible to reduce the loss of information due to the initialization process, so as to improve the precision of the secondary optimization of the model. Further, a new algorithm framework is proposed, integrating MES and Sequential Model-Based Optimization (SMBO). With MES as the final module of the whole optimization process, a more accurate and reasonable algorithmic model was built, which lays a solid mathematical basis for the final empirical analysis. Show more

Keywords: Bayesian optimization, SMBO, hyperparameter optimization, entropy search

DOI: 10.3233/JIFS-230470

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 4991-5006, 2023

Authors: Chen, Chuen-Jyh | Huang, Chieh-Ni | Yang, Shih-Ming

Article Type: Research Article

Abstract: Weather forecasts are essential to aviation safety. Unreliable forecasts not only cause problems to pilots and air traffic controllers, but also lead to aviation accidents and incidents. To enhance the forecast accuracy, an integrated model comprising a convolutional neural network (CNN) and long short-term memory (LSTM) network is developed to achieve improved weather visibility forecasting. In this model, the CNN acts as the precursor of the LSTM network and classifies weather images to increase the visibility forecasting accuracy achieved with the LSTM network. For a dataset with 1500 weather images, the training, validation, and testing accuracy achieved with the integrated …model is 100.00%, 97.33%, and 97.67%, respectively. On a numerical dataset of 10 weather features over 10 years, the RMSE and MAPE of an LSTM forecast can be reduced by multiple linear regression from RMSE 12.02 to 11.91 and 44.46% to 39.02%, respectively, and further by the Pearson’s correlation coefficients to 10.12 and 36.77%, respectively. By using CNN result as precursor to LSTM, the visibility forecast by integrating both can decrease the RMSE and MAPE to 2.68 and 13.41%, respectively. The integration by deep learning is shown an effective, accurate aviation weather forecast. Show more

Keywords: Aviation weather, convolutional neural network, long short-term memory network, weather forecasting

DOI: 10.3233/JIFS-230483

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5007-5020, 2023

Authors: Wang, Yuxian | Li, Zhaowen | Zhang, Jie | Yu, Guangji

Article Type: Research Article

Abstract: Gene selection is an important research topic in data mining. A gene decision space means a real-valued decision information system (RVDIS) where objects, conditional attributes and information values are cells, genes and gene expression values, respectively. This paper explores gene selection in a gene decision space based on information entropy and considers its application for gene expression data classification. In the first place, the distance between two cells in a given decision subspace is constructed. In the next place, the binary relations induced by this decision subspace are defined. After that, some information entropy for a gene decision space are …investigated. Lastly, several gene selection algorithms in a gene decision space are presented by using the presented information entropy. The presented algorithms are applied to gene expression data classifications. Multiple publicly available gene expression datasets are employed to evaluate the gene selection performances of the proposed algorithms, while two commonly-used classifiers, KNN and CART, are utilized to obtain 10 fold cross validation accuracy of classification (ACC ). The classification results demonstrated that the proposed algorithms can lower significantly the number genes selected, achieve the higher ACC , and outperform the other competing methods, such as raw data, Fisher, tSNE, PCA, FMIFRFS and DNEAR, with respect to gene number and ACC . Show more

Keywords: Gene expression data, Gene decision space, Gene selection, Uncertainty measurement

DOI: 10.3233/JIFS-231569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5021-5044, 2023

Authors: Zhang, Yihao | Chen, Ruizhen | Hu, Jiahao | Zhang, Guangjian | Zhu, Junlin | Liao, Weiwen

Article Type: Research Article

Abstract: The key to sequential recommendation modeling is to capture dynamic users’ interests. Existing sequential recommendation methods (e.g., self-attention mechanism) have achieved extraordinary success in modeling users’ interests. However, these models ignore that users have different levels of preferences for different aspects of items, failing to capture users’ most concerning aspects. In addition, they are highly dependent on the quality of training data, which may lead to overfitting of the model when the training data is insufficient. To address the above issues, we propose a novel sequence-aware model (Multi-Aspect Features of Items for Time-Ordered Sequential Recommendation, MFITSRec), which combines the features …of items with user behavior sequences to learn more complex item-item and item-attribute relationships. Moreover, the model uses a self-attention network based on an absolute time relationship, which can better represent the changes in users’ interests and capture users’ preferences for particular aspects of items. Extensive experiments on five datasets demonstrate that our model outperforms various baseline models. In particular, the model’s prediction accuracy has been significantly improved on sparse datasets. Show more

Keywords: Sequential recommendation, multi-aspect preferences of users, data sparsity, absolute time relationship

DOI: 10.3233/JIFS-230274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5045-5061, 2023

Authors: Wen, Haolan | Chen, Yu | Wang, Weizhong | Ding, Ling

Article Type: Research Article

Abstract: Sustainable food consumption and production (SFCP) has become increasingly significant for creating new value, reducing costs, and reducing greenhouse gas emissions. However, there are some challenges and barriers to implementing SFCP in practice. Moreover, current methods for prioritizing barriers to SFCP seldom consider the behavioral preference of experts and interactions among factors, especially with q-Rung orthopair fuzzy set (q-ROFS)-based information. Thus, this study aims to construct a hybrid q-ROFS-based framework for ranking these barriers. First, the q-ROFS is introduced to express the experts’ uncertain information. Then, the q-ROF- CRITIC (CRiteria importance through intercriteria correlation) method is utilized to determine criteria …weights considering the interrelations among barriers. Next, the q-ROF generalized TODIM method is built to rank the barriers to SFCP by considering the impact of experts’ behavioral preferences. Finally, a numerical case of barriers analysis for SFCP is organized to display the application procedures of the constructed ranking method. The result indicates that the top-priority set is education and culture (a 4 ), with the most significant overall dominance value (0.839). Further, a comparison exploration is given to demonstrate the preponderances of the present barriers ranking method. The outcomes demonstrate that the proposed ranking method can provide a synthetic and reliable framework to handle the prioritizing issue for the barriers to SFCP within a complex and uncertain context. Show more

Keywords: Sustainable food consumption and production, q-Rung orthopair fuzzy set, generalized TODIM method, CRITIC approach

DOI: 10.3233/JIFS-230526

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5063-5074, 2023

Authors: IssanRaj, R. | Visalakshi, S.

Article Type: Research Article

Abstract: Triple Diode Solar Cell Module (TDSCM) circuit with nine parameters for various environmental circumstances represents the behavior and practical performance of solar cell.The precise extraction of photovoltaic (PV) module parameters is essential for optimising the energy conversion efficiency of PV systems. Usually the equations describing solar panels are implicit in nature, and parameter extraction has been very complicated. The solar cell is mathematically modelled with nonlinear I-V (Current – Voltage) characteristics behavior, and it cannot be directly determined from the PV’s datasheet due to the lack of data offered by the PV manufacturers. On the basis of the technical datasheet …of the photovoltaic module (PV), only four equations can be obtained in single diode, double diode, and triple diode parameters. To be implemented with fifth equation, many researchers have been done with multiple approximations and it becomes with low accuracy, complexity of computation, convergence problem. To resolve these issues, a new multi-objective optimization (GA) genetic algorithm method is prescribed to frame the fifth equation using the Boole rules implemented with the curved area concept. The proposed Boole’s rule based model offers superior non-linearity performance and high precision modelling, and the error shows a significant reduction when compared to the single and double diode approaches used in the existing approach. The effectiveness of the proposed I-V curve characteristics efficiency was improved by the implementation of the proposed Boole’s rule with RMSE error 0.000034. Show more

Keywords: Photovoltaic cell model, solar cell modelling, multi objective genetic algorithm, triple diode model, boole’s rule

DOI: 10.3233/JIFS-230663

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5075-5092, 2023

Authors: Zhu, Cunxin | Huang, Xuhong | Chen, Yanyi | Tang, Shengping | Zhao, Nan | Xiao, Weihao

Article Type: Research Article

Abstract: Chinese couplet is one of the important forms of expression in Chinese and even world literature, with its own unique charm and beauty. In order to meet the needs of users who only need one image to obtain corresponding couplets, realize the function of computer automatically writing couplets with images, and improve the literary expression ability of couplets to images, this paper proposes an image based intelligent generative model of couplets. The model first outputs corresponding descriptions based on image extraction features, and then extracts keywords through an improved hybrid algorithm according to the descriptions. Then, based on the keywords, …the Chinese GPT-2 model automatically expands the first line of a couplet, and finally generates the second line of a couplet from the first line of a couplet through the encoding and decoding framework. Through experiments, it has been shown that the generated couplets of the model meet the requirements for image description, and the effectiveness of the model has been confirmed by manual evaluation results. Show more

Keywords: Chinese couplet, image description, keyword extraction, Encoding and decoding framework

DOI: 10.3233/JIFS-231155

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5093-5105, 2023

Authors: Segura Dorado, Jhon | Anacona Mopan, Yesid Ediver | Solis Pino, Andrés Felipe | Paz Orozco, Helmer

Article Type: Research Article

Abstract: Colombia exhibits a considerable incidence rate of natural disasters because of its location within the intertropical zone, which exposes it to various meteorological and geological phenomena, including the Nevado del Huila volcano. The identification of suitable areas for the installation of temporary shelters is critical for managing these disasters. However, the task of identifying such locations is a complex problem that involves multiple criteria. This study uses a fuzzy systems approach to identify suitable sites for establishing temporary shelters in the Paez municipality during natural disasters, considering the essential criteria determined by experts through pairwise comparisons. The study results indicate …that responsiveness is the most significant criterion, followed by area profile. Using these criteria, it identified a specific locality in the Paez municipality as suitable for establishing temporary shelters during natural disasters caused by volcanic phenomena. The findings were compared with those obtained from existing scientific literature and validated by experts in natural disasters. The methodological process described in this study provides a valuable tool for public entities to make informed decisions concerning natural disasters in indigenous territories caused by volcanic phenomena. Show more

Keywords: Location temporary shelters, multiple criteria decisions making, analytic network process

DOI: 10.3233/JIFS-231453

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5107-5121, 2023

Authors: Sakthi, K. | Nirmal Kumar, P.

Article Type: Research Article

Abstract: Rapid technological advances and network progress has occurred in recent decades, as has the global growth of services via the Internet. Consequently, piracy has become more prevalent, and many modern systems have been infiltrated, making it vital to build information security tools to identify new threats. An intrusion detection system (IDS) is a critical information security technology that detects network fluctuations with the help of machine learning (ML) and deep learning (DL) approaches. However, conventional techniques could be more effective in dealing with advanced attacks. So, this paper proposes an efficient DL approach for network intrusion detection (NID) using an …optimal weight-based deep neural network (OWDNN). The network traffic data was initially collected from three openly available datasets: NSL-KDD, CSE-CIC-IDS2018 and UNSW-NB15. Then preprocessing was carried out on the collected data based on missing values imputation, one-hot encoding, and normalization. After that, the data under-sampling process is performed using the butterfly-optimized k-means clustering (BOKMC) algorithm to balance the unbalanced dataset. The relevant features from the balanced dataset are selected using inception version 3 with multi-head attention (IV3MHA) mechanism to reduce the computation burden of the classifier. After that, the dimensionality of the selected feature is reduced based on principal component analysis (PCA). Finally, the classification is done using OWDNN, which classifies the network traffic as normal and anomalous. Experiments on NSL-KDD, CSE-CIC-IDS2018 and UNSW-NB15 datasets show that the OWDNN performs better than the other ID methods. Show more

Keywords: Intrusion detection system, deep learning, dimensionality reduction, butterfly optimization, k-means clustering, inception v3, multi head attention, deep neural network

DOI: 10.3233/JIFS-231758

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5123-5140, 2023

Authors: Wajahat, Ahsan | He, Jingsha | Zhu, Nafei | Mahmood, Tariq | Nazir, Ahsan | Pathan, Muhammad Salman | Qureshi, Sirajuddin | Ullah, Faheem

Article Type: Research Article

Abstract: Positive developments in smartphone usage have led to an increase in malicious attacks, particularly targeting Android mobile devices. Android has been a primary target for malware exploiting security vulnerabilities due to the presence of critical applications, such as banking applications. Several machine learning-based models for mobile malware detection have been developed recently, but significant research is needed to achieve optimal efficiency and performance. The proliferation of Android devices and the increasing threat of mobile malware have made it imperative to develop effective methods for detecting malicious apps. This study proposes a robust hybrid deep learning-based approach for detecting and predicting …Android malware that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). It also presents a creative machine learning-based strategy for dealing with unbalanced datasets, which can mislead the training algorithm during classification. The proposed strategy helps to improve method performance and mitigate over- and under-fitting concerns. The proposed model effectively detects Android malware. It extracts both temporal and spatial features from the dataset. A well-known Drebin dataset was used to train and evaluate the efficacy of all creative frameworks regarding the accuracy, sensitivity, MAE, RMSE, and AUC. The empirical finding proclaims the projected hybrid ConvLSTM model achieved remarkable performance with an accuracy of 0.99, a sensitivity of 0.99, and an AUC of 0.99. The proposed model outperforms standard machine learning-based algorithms in detecting malicious apps and provides a promising framework for real-time Android malware detection. Show more

Keywords: Android malware detection, deep learning, CNN, LSTM, Drebin dataset

DOI: 10.3233/JIFS-231969

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5141-5157, 2023

Authors: Zhou, Qiaozhen | Wang, Fang | Zhao, Xuanyu | Hu, Kai | Zhang, Yujian | Shan, Xin | Lin, Xin | Zhang, Yupeng | Shan, Ke | Zhang, Kailiang

Article Type: Research Article

Abstract: Resistive random access memory (RRAM) has lots of advantages that make it a promising candidate for ultra-high-density memory applications and neuromorphic computing. However, challenges such as high forming voltage, low endurance, and poor uniformity have hampered the development and application of RRAM. To improve the uniformity of the resistive memory, this paper systematically investigates the HfOx -based RRAM by embedding nanoparticles. In this paper, the HfOx -Based RRAM with and without tungsten nanoparticles (W NPs) is fabricated by magnetron sputtering, UV lithography, and stripping. Comparing the various resistive switching behaviors of the two devices, it can be observed that the …W NPs device exhibits lower switching voltage (including a 69.87% reduction in Vforming and a reduction in Vset /Vreset from 1.4 V/-1.36 to 0.7 V/-1.0 V), more stable cycling endurance (>105 cycles), and higher uniformity. A potential switching mechanism is considered based on the XPS analysis and the research on the fitting of HRS and LRS: Embedding W NPs can improve the device performance by inducing and controlling the conductive filaments (CFs) size and paths. This thesis has implications for the performance enhancement and development of resistive memory. Show more

Keywords: Resistive random access memory (RRAM), HfOx, embedding W nanoparticles, uniformity, conduction mechanism

DOI: 10.3233/JIFS-232028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5159-5167, 2023

Authors: Li, Zhenjiang | Zhang, Qianxue

Article Type: Research Article

Abstract: The writer identification task infers the writer by analyzing the texture, structure, and other representative features of the handwriting. Inspired by the attention mechanism, an end-to-end writer identification model is proposed in this paper, which combines both global features and local features. The Vision Transformer is used as the backbone network, and the Convolutional block attention module (CBAM) is introduced to enhance the ability of global feature awareness of the model. The proposed method is evaluated on two public data sets, IAM and CVL respectively. In the task of word-level writer identification, the accuracy rates in two data sets were …90.1% and 92.3% respectively. In the task of page-level writer identification, the accuracy rates were 98.6% and 99.5%, as a state-of-the-art performance. Show more

Keywords: Biometrics, writer identification, computer vision, neural network, vision transformer

DOI: 10.3233/JIFS-232134

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5169-5179, 2023

Authors: Wang, Chia-Hung | Cai, Jiongbiao | Ye, Qing | Suo, Yifan | Lin, Shengming | Yuan, Jinchen

Article Type: Research Article

Abstract: In recent years, it has been shown that deep learning methods have excellent performance in establishing spatio-temporal correlations for traffic speed prediction. However, due to the complexity of deep learning models, most of them use only short-term historical data in the time dimension, which limits their effectiveness in handling long-term information. We propose a new model, the Multi-feature Two-stage Attention Convolution Network (MTA-CN), to address this issue. The MTA-CN intercepts longer single-feature historical data, converts them into shorter multi-feature data with multiple time period features, and uses the most recent past point as the main feature. Furthermore, two-stage attention mechanisms …are introduced to capture the importance of different time period features and time steps, and a Temporal Graph Convolutional Network (T-GCN) is used instead of traditional recurrent neural networks. Experimental results on both the Los Angeles Expressway (Los-loop) and Shen-zhen Luohu District Taxi (Sz-taxi) datasets demonstrate that the proposed model outperforms several baseline models in terms of prediction accuracy. Show more

Keywords: Traffic speed prediction, attentional mechanisms, temporal dependence, spatial dependence, graph convolutional network

DOI: 10.3233/JIFS-231133

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5181-5196, 2023

Article Type: Retraction

DOI: 10.3233/JIFS-219329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 5197-5197, 2023