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

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