Journal of Intelligent & Fuzzy Systems - Volume 43, issue 6

Authors: Bochkarev, Vladimir V. | Maslennikova, Yulia S. | Shevlyakova, Anna V.

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Abstract: This article has been retracted.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-212179

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6965-6977, 2022

Authors: Rico-Preciado, Erick | Laureano, Mayte H. | Calvo, Hiram

Article Type: Research Article

Abstract: Learning relationships between nodes in a directed graph is a task that has been widely studied and it has been applied to a large number of topics and research areas. We establish a definition of particular kind of relationship, called analogy in a directed multigraph. An analogy can be defined for a certain pair of concepts, and the paths connecting them are called explanation of this analogy. We experiment with a structure built from real oneiric stories obtained from psychoanalytic descriptions (e.g. mother is represented as a bull; book represents power). Analogies found by the analysts are automatically identified by …means of linguistically motivated patterns. Analogies have degrees of similarity based on the words used to describe them: represents, is a, is like a, can be a, refers to, etc. Once they are identified and graded, they are represented in the multidigraph, allowing us to provide different hypotheses in how these analogies can be explained. In order to enrich the concept graph, we added information from ConceptNet and WordNet. In addition, we propose a learning method for association rules that, given the degree of the analogy and a starting concept, allow reaching a destination concept. For example, starting from “dream”, we obtain the path <dream, psychic, neurosis, symptom>, being "dream is a symptom" a description previously given by a psychoanalyst, that was not included when training the algorithm. We evaluated 100 analogies on 171 concepts with 8,034 properties using Leave One Out cross validation, and found that the correct analogy was found within the all the possible paths for 94% of the analogies, restricted to 85% if only the top 20% possible paths are considered. This implies that, by using our method, it is possible to learn analogies between two concepts by reconstructing paths of different lengths based on local decisions considering concept, property and degree of analogy. Show more

Keywords: Directed graphs, analogy, concept representation, explainable artificial intelligence, psychoanalysis

DOI: 10.3233/JIFS-211895

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6979-6994, 2022

Authors: Balouchzahi, Fazlourrahman | Shashirekha, Hosahalli Lakshmaiah | Sidorov, Grigori | Gelbukh, Alexander

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-212872

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6995-7005, 2022

Authors: Zhang, Ming | Du, Qian | Yang, Jianxun | Liu, Song

Article Type: Research Article

Abstract: The Pile movement is one of the most crucial matters in designing piles and foundations that need to be estimated for any project failure. Over the variables used in forecasting Pile Settlement, many methods have been introduced to appraise it. However, existing a wide range of theoretical strategies to investigate the pile subsidence, the soil-pile interactions are still ambiguous for academic researchers. Most studies have tried to work out the subsidence rate in piles after loading passing time by artificial intelligence methods. Generally, the Artificial Neural Network (ANN) has drawn attention to show the actual views of pile settlement over …the loading phase vertically. This research aims to present the Hybrid Radial Basis Function neural network integrated with the Novel Arithmetic Optimization Algorithm and Biogeography-Based Optimization to calculate the optimal number of neurons embedded in hidden layers. The transportation network of Klang Valley, Mass Rapid Transit in Kuala Lumpur, Malaysia, was chosen to analyze the piles’ settlement and earth features using HRBF-AOA and HRBF-BBO scenarios. Over the prediction process, the R-values of HRBF-AOA and HRBF-BBO were obtained at 0.9825 and 0.9724, respectively. The MAE also shows a similar trend as 0.2837 and 0.323, respectively. Show more

Keywords: Pile in rock, settlement, prediction, radial basis function, biogeography-based optimization, arithmetic optimization algorithm, r-value correlation

DOI: 10.3233/JIFS-221021

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7009-7022, 2022

Authors: Dharaniya, R. | Indumathi, J. | Uma, G.V.

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-212271

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7023-7039, 2022

Authors: Li, Yueen | Feng, Qi | Huang, Tao | Wang, Shennan | Cong, Weifeng | Knighton, Edwin

Article Type: Research Article

Abstract: The Artificial Neural Networks (ANN) are more widely used in the New Product Development (NPD) process in recent years. The product data generation process is a prerequisite for the application of the ANN algorithm. In the development of new products, the Kansei Engineering (KE) method is an effective emotion-based data generation method. The Semantic Difference (SD) method is usually used to obtain data to apply to design idea generation. Facing the data demand of product creativity, it is important to establish the relationship between consumer perception and product expression. Numerical relationships are not linear and several methods are required for …solving these problems. The method of the Back Propagation (BP) neural network is simple and effective to be used in this case. This paper proposes an innovative data modeling method using digital coding and KE. This model explores a rational design method of perceptual intention and builds an intelligent model. Compared with traditional method, the modified model can quickly and accurately reflect the users’ perceptual needs, make the design more scientific, improve the design efficiency, and reduce design costs. This method is used in the design of electric welding machines, and this process can effectively provide technical support for NPD process in small and medium-sized enterprises. Show more

Keywords: New product development, KE, semantic difference, ANN, BP

DOI: 10.3233/JIFS-212441

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7041-7055, 2022

Authors: Chen, Shuang | Ren, Tao | Qv, Ying | Shi, Yang

Article Type: Research Article

Abstract: Dealing with the explosive growth of web sources on the Internet requires the use of efficient systems. Automatic text summarization is capable of addressing this issue. Recent years have seen remarkable success in the use of graph theory on text extractive summarization. However, the understanding of why and how they perform so well is still not clear. In this paper, we intend to seek a better understanding of graph models, which can benefit from graph extractive summarization. Additionally, analysis has been performed qualitatively with the graph models in the design of recent graph extractive summarization. Based on the knowledge acquired …from the survey, our work could provide more clues for future research on extractive summarization. Show more

Keywords: Text summarization, extractive summarization, graph theory, extraction scheme, sentiment analysis

DOI: 10.3233/JIFS-220433

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7057-7065, 2022

Authors: Elanangai, V. | Vasanth, K.

Article Type: Research Article

Abstract: In today’s world, Steel plates play essential materials for various industries like the national defense industry, chemical industry, automobile industry, machinery manufacturing, etc. However, some defects may occur in a few plates during the manufacture of stainless-steel plates which directly impact the quality of the stainless-steel plate. If the faulted plate detection can be done manually, then it leads to errors and a time-consuming process. Hence, a computerized automated system is necessary to detect the abnormalities. In this paper, a novel Adaptive Faster Region Convolutional Neural Networks (AFRCNN) scheme has been proposed for automatic fault detection of stainless-steel plates. The …proposed AFRCNN scheme comprises three phases: identification, detection, and recognition. Primarily, the damaged plates are identified using Region Proposal Network and Fully Convolutional Neural Network functioning as a combined process under AFRCNN. In the next phase, the number corresponding to the particular plate is recognized through the standard Automated Plate Number Recognition approach with the support of the character recognition technique. The simulation results manifest that the proposed AFRCNN scheme obtains a superior classification accuracy of 99.36%, specificity of 99.24%, and F1-score of 98.18% as compared with the existing state-of-the-art schemes. Show more

Keywords: Fault detection, stainless steel plates, convolutional neural network, classification, region proposal network

DOI: 10.3233/JIFS-213031

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7067-7079, 2022

Authors: Alqhtani, Samar M.

Article Type: Research Article

Abstract: Disasters occur due to naturally stirring events like earthquake, floods, tsunamis, storms hurricanes, wildfire, and other geologic measures. Social media fake image posting influence is increasing day by day regarding the natural disasters. A natural disaster can result in the death or destruction of property, as well as economic damage, the severity of which is determined by the resilience of the affected population and the infrastructure available. Many researchers applied different machine learning approaches to detect and classification of natural disaster types, but these algorithms fail to identify fake labelling occurs on disaster events images. Furthermore, when many natural disaster …events occur at a time then these systems couldn’t handle the classification process and fake labelling of images. Therefore, to tackle this problem I have proposed a FLIDND-MCN: Fake Label Image Detection of Natural Disaster types with Multi Model Convolutional Neural Network for multi-phormic natural disastrous events. The main purpose of this model is to provide accurate information regarding the multi-phormic natural disastrous events for emergency response decision making for a particular disaster. The proposed approach consists of multi models’ convolutional neural network (MMCNN) architecture. The dataset used for this purpose is publicly available and consists of 4,428 images of different natural disaster events. The evaluation of proposed model is measured in the terms of different statistical values such as sensitivity, specificity, accuracy, precision, and f1-score. The proposed model shows the accuracy value of 0.93 percent for fake label disastrous images detection which is higher as compared to the already proposed state-of-the-art models. Show more

Keywords: Convolutional neural network, fake labeling, natural disaster, image classification

DOI: 10.3233/JIFS-213308

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7081-7095, 2022

Authors: Kavitha, P. | Latha, L. | Palaniswamy, Thangam

Article Type: Research Article

Abstract: Big Data is a popular research area where a vast amount of data is created, replicated, and consumed by society. The quality of the data used directly influences big data knowledge discovery. The existence of noise is the most prevalent problem influencing data quality. The following techniques were developed to reduce noise in data with a distributed setting: Homogenous Ensemble for Big Data (HME-BD) and Heterogeneous Ensemble for Big Data (HTE-BD). In this article, the performance of HTE-BD is improved further by developing Enhanced HTE-BD (EHTE-BD), which combines Logistic Regression based Support Vector Machine (LR-SVM) in conjunction with RF, LR, …and KNN to reduce noisy data. Furthermore, the Multi-Objective Evolutionary Fuzzy Method for Subgroup Discovery throughout Big Data (MEFASD-BD) was used to resolve the multi-objective optimization challenge, and the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) was utilized to handle the rising dimensionality issue through subgroup discovery. To address the NSGA-II’s slow convergence rate, an Improved Multi-Objective Meta-Heuristic Fuzzy approach for discovering subgroups in big data is described, that contains a meta-heuristic method for subgroup discovery known as the Multi-Objective Differential Search Algorithm (MODSA). It selects the most relevant subgroups from vast amounts of data, reducing the data’s dimensionality. The Fuzzy Deep Neural Network (FDNN) classifier assesses the main subgroups. By removing noisy data and selecting the most relevant subgroups, the performance of FDNN in classifying vast amounts of data is improved. Show more

Keywords: Big data analysis, logistic regression-based support vector machine, multi-objective differential search algorithm, fuzzy deep neural network, random forest, high dimensionality problem, subgroup discovery, slow convergence rate

DOI: 10.3233/JIFS-220171

Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 7097-7113, 2022