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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: He, Xiaoxu
Article Type: Research Article
Abstract: In clinical practice, segmenting polyps from colonoscopy images plays an important role in the diagnosis and treatment of colorectal cancer since it provides valuable information. However, accurate polyp segmentation is full of changes due to the following reasons: (1) the small training datasets with a limited number of samples and the lack of data variability; (2) the same type of polyps with a variation in texture, size, and color; (3) the weak boundary between a polyp and its surrounding mucosa. To address these challenges, we propose a novel robust deep neural network based on data augmentation, called Robust Multi-center Multi-resolution …Unet (RMMSUNet), for the polyp segmentation task. Data augmentation and Multi-center training are both utilized to increase the amount and diversity of training dataset. The new multi-resolution blocks make up for the lack of fine-grained information in U-Net, and ensures the generation of more accurate pixel-level segmentation prediction graphs. Region-based refinement is added as the post-processing for the network output, to correct some wrongly predicted pixels and further refine the segmentation results. Quantitative and qualitative evaluations on the challenging polyp dataset show that our RMMSUNet improves the segmentation accuracy significantly, when comparing to other SOTA algorithms. Show more
Keywords: Image segmentation, colon cancer, U-Net, polyp segmentation, data augmentation
DOI: 10.3233/JIFS-223340
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4593-4604, 2023
Authors: Ramamurthy, Priyadarshini | Nandagopal, Malarvizhi
Article Type: Research Article
Abstract: Fog computing enables the data analysis done nearer to the place of data generated, which makes a very short response time. Trust is essential for the effective performance of the fog nodes to overcome uncertainty, vulnerability, and risk. To enhance the trusted connection in fog computing, blockchain technology is adopted as a solution. Inclusion of blockchain in fog environment ensures consistency and security among the fog nodes. Each fog node mines every transaction and stores them as block creating a chain of blocks. In this proposed work, the adaption of blockchain technology is designed as a suitable solution for establishing …trusted security between fog nodes and for which a qualitative assessment is done. Show more
Keywords: Fog computing, fog nodes, blockchain, trust, data security, ethereum blockchain, smart contract
DOI: 10.3233/JIFS-222014
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4605-4612, 2023
Authors: Eti, Serkan | Dinçer, Hasan | Yüksel, Serhat | Gökalp, Yaşar
Article Type: Research Article
Abstract: In this study, a new fuzzy decision-making model is created to evaluate whether the solar panels are efficient to minimize energy costs of the hospitals. The weights of the criteria are calculated by considering T-Spherical fuzzy decision-making trial and evaluation laboratory (DEMATEL) method. Moreover, for the purpose of measuring the coherency of the findings, analysis results are also calculated for different t values. Additionally, by making improvements to some criticisms to the classical DEMATEL method, a new technique is created by the name of TOP-DEMATEL while integrating some steps of technique for order preference by similarity to ideal solution (TOPSIS) …to the DEMATEL technique. The main novelty of this study is that it is analyzed whether the solar panels are effective in reducing the costs of hospitals with an original decision-making model. It is concluded that generating own energy in the long run is the most crucial item according to both T-Spherical fuzzy DEMATEL and TOP-DEMATEL methods. The analysis results are quite similar for different t values. This situation gives information about the coherency and reliability of the findings. This situation gives information that the solar panels should be taken into consideration for the hospitals because they will minimize energy dependency of the hospitals. On the other side, the results of T-Spherical fuzzy TOP-DEMATEL indicate that the high initial investment cost is the second most critical factor in this respect. This finding is quite different by comparing with the results of T-Spherical fuzzy TOP-DEMATEL. Hence, it is seen that cost effectiveness should also be taken into consideration for the decision of generating the solar panels in the hospitals. Show more
Keywords: T-Spherical fuzzy sets, TOP-DEMATEL, solar energy, health industry
DOI: 10.3233/JIFS-222968
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4613-4625, 2023
Authors: Mathina Kani, Mohamed Ali Jinna | Parvathy, Meenakshi Sundaram | Maajitha Banu, Samsammal | Abdul Kareem, Mohamed Saleem
Article Type: Research Article
Abstract: In this article, a methodological approach to classifying malignant melanoma in dermoscopy images is presented. Early treatment of skin cancer increases the patient’s survival rate. The classification of melanoma skin cancer in the early stages is decided by dermatologists to treat the patient appropriately. Dermatologists need more time to diagnose affected skin lesions due to high resemblance between melanoma and benign. In this paper, a deep learning based Computer-Aided Diagnosis (CAD) system is developed to accurately classify skin lesions with a high classification rate. A new architecture has been framed to classify the skin lesion diseases using the Inception v3 …model as a baseline architecture. The extracted features from the Inception Net are then flattened and are given to the DenseNet block to extracts more fine grained features of the lesion disease. The International Skin Imaging Collaboration (ISIC) archive datasets contains 3307 dermoscopy images which includes both benign and malignant skin images. The dataset images are trained using the proposed architecture with the learning rate of 0.0001, batch size 64 using various optimizer. The performance of the proposed model has also been evaluated using confusion matrix and ROC-AUC curves. The experimental results show that the proposed model attains a highest accuracy rate of 91.29 % compared to other state-of-the-art methods like ResNet, VGG-16, DenseNet, MobileNet. A confusion matrix and ROC curve are used to evaluate the performance analysis of skin images. The classification accuracy, sensitivity, specificity, testing accuracy, and AUC values were obtained at 90.33%, 82.87%, 91.29%, 87.12%, and 87.40%. Show more
Keywords: Image processing, deep learning, feature extraction, image classification, Inception v3 model, computer aided diagnosis
DOI: 10.3233/JIFS-221386
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4627-4641, 2023
Authors: Liang, Shaohui | Wei, Botao
Article Type: Research Article
Abstract: Teaching-learning-based optimization algorithm (TLBO) is a swarm intelligence optimization algorithm that simulates classroom teaching phenomenon. In order to solve the problem that TLBO algorithm is easy to fall into local optimum and has poor stability, an improved teaching-learning-based optimization algorithm based on fusion difference mutation (IDMTLBO) is proposed. Firstly, adaptive teaching factors are introduced. Secondly, in the teaching stage, each student studies according to the gap between himself and the teacher, which improves the convergence speed and convergence accuracy of the algorithm. Finally, in the learning stage, students are divided into two levels according to their learning level, and two …students are randomly selected to improve the iterative equation in the learning stage with the difference mutation strategy, It improves the disadvantage that the algorithm is easy to fall into local optimum. Numerical experiments show that the convergence speed and convergence accuracy of the algorithm are obviously better than TLBO algorithm, DMTLBO algorithm, DSTLBO algorithm. Show more
Keywords: Teaching-learning-based optimization, adaptive teaching factors, the improved teaching stage, learning stages, differential mutation
DOI: 10.3233/JIFS-221019
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4643-4651, 2023
Authors: Fathima Perveen, P. A. | John, Sunil Jacob | Kamacı, Hüseyin | Baiju, T.
Article Type: Research Article
Abstract: Picture fuzzy sets are a direct extension of fuzzy sets and intuitionistic fuzzy sets, recently developed as a mathematical tool for solving uncertainty-related problems. In this paper, a novel similarity measure and corresponding weighted similarity measure between two picture fuzzy sets are proposed after indicating some disadvantages of the current similarity measures of picture fuzzy sets through some exemplary numerical examples. Also, some of their basic properties are discussed. Further, a picture fuzzy decision making algorithm based on the similarity aggregation method is constructed and then applied to the decision making problem. It is also used to deal with a …medical diagnosis problem to detect which disease a patient may be suffering from. Finally, the effectiveness of the proposed similarity measure is demonstrated by making comparison with the present picture fuzzy similarity measures. Show more
Keywords: Fuzzy sets, picture fuzzy sets, similarity measure, medical diagnosis, decision making
DOI: 10.3233/JIFS-222334
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4653-4665, 2023
Authors: Wang, Chaofeng
Article Type: Research Article
Abstract: For the spread COVID-19 between countries through air transportation, the influencing factors are analyzed, a system dynamics model which passes the validity test is established. Then, three key indicators, Basic Reproduction Rate, Initial Infection Individuals and Transfer Rate, are selected as independent variables to analyze the system changes through computer simulation. Based on the analysis, the infectivity of COVID-19 and the number of people transferred by air transportation are important factors affecting the import of epidemics for non-epidemic countries, and the sooner or later control of infectious diseases by epidemic countries has little effect on non-epidemic countries.
Keywords: COVID-19, air transportation, spread virus, international spread, computer simulation
DOI: 10.3233/JIFS-212862
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4667-4679, 2023
Authors: Elavarasan, B. | Muhiuddin, G. | Porselvi, K. | Jun, Y. B.
Article Type: Research Article
Abstract: Many uncertainties arise in real-world problems, making them impossible to solve using conventional approaches. Researchers all over the world have developed new mathematical theories like fuzzy set theory and rough set theory to better understand the uncertainties that occur in various fields. Soft set theory, which was recently introduced, offers a novel approach to real-world problem solving by removing the need to set the membership function. This is helpful in resolving a variety of issues, and much progress is being made these days. Recently, Jun introduced the concept of a hybrid structure, which blends the concepts of a fuzzy set …as well as a soft set. In this paper, we define the hybrid k -sum and hybrid k -product of k -ideals of semiring and investigate their properties. We illustrate with an example that the hybrid sum and hybrid product of two k-ideals are not always hybrid ideals. We also describe semiring regularity constraints in terms of hybrid k -ideal structures. Show more
Keywords: Semiring, hybrid structure, ideal, hybrid product, k-ideal, hybrid k-product, hybrid ideals, hybrid k-ideals
DOI: 10.3233/JIFS-222335
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4681-4691, 2023
Authors: Fu, Xue | Zhu, Liangkuan | Wu, Bowen | Wang, Jingyu | Zhao, Xiaohan | Ryspayev, Arystan
Article Type: Research Article
Abstract: To improve the traditional image segmentation, an efficient multilevel thresholding segmentation method based on improved Chimp Optimization Algorithm (IChOA) is developed in this paper. Kapur entropy is utilized as the objective function. The best threshold values for RGB images’ three channels are found using IChOA. Meanwhile, several strategies are introduced including population initialization strategy combining with Gaussian chaos and opposition-based learning, the position update mechanism of particle swarm algorithm (PSO), the Gaussian-Cauchy mutation and the adaptive nonlinear strategy. These methods enable the IChOA to raise the diversity of the population and enhance both the exploration and exploitation. Additionally, the search …ability, accuracy and stability of IChOA have been significantly enhanced. To prove the superiority of the IChOA based multilevel thresholding segmentation method, a comparison experiment is conducted between IChOA and 5 six meta-heuristic algorithms using 12 test functions, which fully demonstrate that IChOA can obtain high-quality solutions and almost does not suffer from premature convergence. Furthermore, by using 10 standard test images the IChOA-based multilevel thresholding image segmentation method is compared with other peers and evaluated the segmentation results using 5 evaluation indicators with the average fitness value, PSNR, SSIM, FSIM and computational time. The experimental results reveal that the presented IChOA-based multilevel thresholding image segmentation method has tremendous potential to be utilized as an image segmentation method for color images because it can be an effective swarm intelligence optimization method that can maintain a delicate balance during the segmentation process of color images. Show more
Keywords: Multi-threshold color image segmentation, chimp optimization algorithm, particle swarm algorithm, self-adaptive strategy, Kapur’s entropy
DOI: 10.3233/JIFS-223224
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4693-4715, 2023
Authors: Perumal, T. Sudarson Rama | Jegatheesan, A. | Jayachandran, A.
Article Type: Research Article
Abstract: Brain tumor is one of the deadliest cancerous diseases and their severity has turned them into the leading cause of cancer-related mortality. Automatic detection and classification of severity-level for a brain tumor using MRI is a complex process in multilevel classification and needs an improved learning method without computational complexity. In this research article, we propose an innovative Multi-Dimensional Cascades Neural Network work (MDCNet) that takes full advantage of two networks with different dimensions, which can balance the complete semantic information and high-resolution detail information of a large-volume MRI image. In stage 1, a shallow-layer-enhanced 3D location net obtains the …location and rough segmentation of brain lesions. In stage 2, a high-resolution attention map is used to obtain the 2D high-resolution image slice sets from the original image and the output of stage 1. The high-resolution images pick up the lost detailed information, refining the boundaries further. Moreover, a multi-view 2.5D net composed of three 2D refinement sub-networks is applied to deeply explore the morphological characteristics of all brain lesions from different perspectives, which compensates for the mistakes and missing spatial information of a single view, increasing the stability of the whole algorithm. The robustness of the proposed model is analyzed using several performance metrics of three different data sets. Through the prominent performance, the proposed model can outperform other existing models attaining an average accuracy of 99.13%. Here, the individual accuracy for Dataset 1, Dataset 2, and Dataset 3 is 99.67%, 98.16%, and 99.76% respectively. Show more
Keywords: Keywords Brain tumor, classification, convolutional neural network, two stage ensemble, magnetic resonance imaging
DOI: 10.3233/JIFS-220308
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 3, pp. 4717-4732, 2023
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