Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Gautam, Devendra | Dixit, Anurag | Lathabanda, | Goyal, S.B. | Verma, Chaman | Kumar, Manoj

Article Type: Research Article

Abstract: In recent generations of the digital world medical data in Recommender Systems. Health Care Recommender System (HCRS) analyses the medical data and then predicts the user’s or patient’s illness. Nowadays, healthcare data is used by various users or patients in recommendation systems which are useful for everyone. Analysing and predicting medical data provides awareness to users and these data predictions may be enriched using various techniques of RS. Machine learning techniques are used to make sure that health data is reliable and of high quality. In every RS the issues are targeted such as scalability, sparsity and cold start problems. …In many social networking applications, these issues are resolved using ML algorithms. However, there is a significant gap between IT systems and medical diagnosis. The fuzzy genetic method is used in HCRS in order to bridge the gap between IT and healthcare applications. Through the use of the mutation and crossover operators, a real-value genetic method is used in this to compute similarity. With the user’s extra personalized information, fuzzy rules are later generated for the database. The Hybrid fuzzy-genetic method, also known as this situation, combines both techniques to improve recommendation quality. Utilizing this method will improve the quality of the recommendation process by discovering the most precise similarity measures among different users. Six factors are subjected to fuzzification, including age, gender, employment, height, weight, and region. Genre-interesting measure weights are then used, including Very Light, Light, Average, Heavy, and Very Heavy. Finally, the evaluation metrics used MAE and RMSE to evaluate the recommendation accuracy which showed the best results in comparison with baseline approaches such as Convolutional Neural Networks and Restricted Boltzman Machine. Show more

Keywords: Recommender system, confidentiality, deep learning, convolutional neural networks, fuzzy logics

DOI: 10.3233/JIFS-224257

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Li, Eryang | Feng, Xiangqian | Wei, Cuiping

Article Type: Research Article

Abstract: Internet of Things (IoT) technology now has a new purpose and relevance as a result of the digitalization wave. In this setting, businesses start to plan how they will use IoT technology. But some critical factors can prevent the successful deployment of IoT, and businesses must get beyond these critical factors if they want to do so. The literature review, system literature review, and Delphi technique are used to identify 15 critical factors. These critical factors are then divided into four categories: organization, technology, process, and environment. The PFN-weighted power harmonic operator is proposed with the aim of more effectively …obtaining assessment data from experts and lessening the inaccuracy of outcomes caused by information loss. The best and worst method (BWM) is used to determine the ideal weight of critical factors. Results indicate that the primary critical factors to the effective adoption of the Internet of Things are talent, resource limitations, integration complexity, technical operations, equipment power consumption, technical dependability, and data governance. This research will benefit corporate managers in recognizing the significance of the effective deployment of the Internet of Things, identifying major critical factors to this achievement, and making decisions to remove these factors. Thus, an organization may support the effective adoption of the animal Internet of Things. Show more

Keywords: Internet of things, critical factors, PFN, weighted power harmonic operator, BWM

DOI: 10.3233/JIFS-231023

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2023

Authors: Kai, Wei

Article Type: Research Article

Abstract: In this study, we focus on the analysis of factors influencing the siting decision of coal emergency reserve centers. Specifically, we first draw on the quality function deployment theory in marketing to logically integrate the ideas of this study. On this basis, we adopted an interdisciplinary fuzzy decision-making method, namely the G1-entropy method, to quantitatively evaluate the research of this paper. Thereafter, we constructed a three-level index system based on the characteristics of the coal emergency reserve site selection, and used the G1-entropy value method to calculate the weights of the indicators in the coal emergency reserve center siting decision …index system and obtain the results. Our research findings have found that the three key indicators of coal conventional reserve, emergency coal transportation methods, and emergency response time play a crucial role in the decision-making of coal emergency reserve center location. Therefore, we propose specific countermeasures and suggestions for these three key indicators. Our study can provide support for the government to better select the location of emergency coal reserves, better improve the national energy layout, and provide support for relevant decision makers on how to better reserve coal. The location of the emergency coal reserve center can better play the role of strategic reserve to stabilize the market function, effectively respond to the impact of various events on the energy market, and can make corresponding suggestions to the construction of the national energy security reserve system. Show more

Keywords: Emergency reserve center, site selection decision, quality function deployment theory, G1 method, entropy value method

DOI: 10.3233/JIFS-232299

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2023

Authors: Lv, Bailin | Wang, Sijia | Xia, Kaijian | Jiang, Yizhang

Article Type: Research Article

Abstract: Machine learning methods have become an effective strategy commonly used in quantitative hedge funds, which can maximize profits and reduce investment risks to a certain extent. Traditional stock forecasting systems are usually based on a single attribute of stock data. There are two main challenges in this process: 1) Use suitable processing methods to deal with highly nonlinear time series data such as stocks. 2) Using a single class of stock data for training does not capture the correlation between other related data and the training data. Based on RBF neural network, this research introduces view weighting and collaborative learning …mechanism, and proposes a MV-RBF model. It mainly includes the following contributions: 1) By comparing the experimental results of MV-RBF model with the single-view model, its effectiveness and feasibility are verified. 2) The MV-RBF model was compared with other commonly used classification models to analyze its advantages and disadvantages. 3) Study the relevant parameters, stability and other indicators of MV-RBF model. The experimental results show that compared with the single view model and most common classification models, MV-RBF has certain improvement in the prediction accuracy. Show more

Keywords: Multi-view learning, stock price prediction, collaborative learning, view weighting mechanism, RBF neural network

DOI: 10.3233/JIFS-223202

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2023

Authors: Jia, Lijuan | Hou, Fang

Article Type: Research Article

Abstract: The evaluation of physical education teaching effectiveness is an important component of physical education teaching, and plays a multifaceted role in the process of physical education teaching. The information provided by it can control and regulate the progress of physical education teaching activities as a whole, ensuring that physical education teaching activities develop towards predetermined goals. With the development of the popularization of physical education, people’s requirements for the quality of physical education continue to improve, and the role and position of evaluation in teaching has become increasingly evident. Evaluation of physical education teaching effectiveness has become an indispensable process …in teaching activities. The college physical education teaching effect evaluation can be regarded as a multiple attribute decision making (MADM). Thus, this paper collected information in probabilistic hesitant fuzzy sets (PHFSs) and using CRITIC method to obtain the unknown weight among attributes. Further, a novel probabilistic hesitant fuzzy QUALIFLEX (PHF-QUALIFLEX) method was constructed for MADM. Finally, a numerical case for college physical education teaching effect evaluation was illustrated with this proposed model and other methods were utilized to compare with PHF-QUALIFLEX method to verify the feasibility and applicability. Show more

Keywords: Multiple attributes decision making (MADM), probabilistic hesitant fuzzy sets (PHFSs), QUALIFLEX method, CRITIC method, teaching effect evaluation

DOI: 10.3233/JIFS-231769

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Chang, Yung-Chia | Chang, Kuei-Hu | Chen, Wei-Ting

Article Type: Research Article

Abstract: In vehicle leasing industry which presents a great business opportunity, information completed by applicants was assessed and judged by leasing associates manually in most cases; therefore, assessment results would be affected by their personal experience of leasing associates and decisions would be further affected accordingly. There are few researches on applicant credit risk assessment due to not easy to obtain of vehicle leasing data. Further, the difficulty in vehicle leasing risk assessment is increased due to class imbalance problems in vehicle leasing data. In order to address such issue, a research on credit risk assessment in vehicle leasing industry was …conducted in this study. The great disparity in the ratio of high risk and low risk data was addressed by applying synthetic minority over-sampling technique (SMOTE). Then, classification effect of risk assessment model was improved by applying logistic regression in a two-phase manner. In the section of empirical analysis, the feasibility and effectiveness of the approach proposed in this study was validated by using data of actual vehicle leasing application cases provided by a financial institution in Taiwan. It is found that the proposed approach provided a simple yet effective way to build a credit risk assessment model for companies that provide vehicle leasing. Show more

Keywords: Credit risk assessment model, logistic regression, synthetic minority over-sampling technique, category asymmetry

DOI: 10.3233/JIFS-231344

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023

Authors: Shan, Yuxiang | Lu, Hailiang | Lou, Weidong

Article Type: Research Article

Abstract: Exploiting dynamic spatial and temporal features of location information for robot modeling is of great importance in many real applications. It has gained increasing attention in the era of the Internet of Things (IoT). However, successful modeling and accurate localization for robot in indoor environment is still a challenge, where the environment factors are complex and unpredictable, such as signal noise, obstacles and spare fingerprints. Existing studies usually employ data driven and learning based models to capture spatial and temporal features for robot location estimation, modeling dynamics of robot and make robot decision. However, the modeling and localization performance is …not satisfied. In this paper, to address above challenges, a novel deep learning framework called multi-faceted deep learning based dynamics modeling and robot localization learning (DMLoc) method is proposed. Specifically, a localization attention module is designed to capture the features from original fingerprints and optimized fingerprints information. Then, a multi-faceted localization module is proposed, which integrates extraction model and optimized model with long short-term memory (LSTM) and gate recurrent unit (GRU). Moreover, a multi-feature fusion layer is designed to fuse the extracted features and generate localization results. Extensive simulation results show the efficiency of the proposed DMLoc. Show more

Keywords: Robot localization, dynamics modeling, learning-based robot decision

DOI: 10.3233/JIFS-230895

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2023

Authors: Sreekrishna, M. | Jacob, T.P.

Article Type: Research Article

Abstract: Unstructured pathology report plays a major role in definitive cancer diagnosis. Accessing or searching unstructured textual information from the clinical pathology reports is one of the major concerns in cancer healthcare sector to provide precise medicine, analysis of cancer outcomes, providing cancer care services, accurate measurement for future prediction, treatment history, and comparative future research work. An efficient methodology has to be introduced for to extract quantitative information from the unstructured cancer data. Integrating computational intelligence in Robotic Process Automation can be done to process this data and automate repetitive activities for evaluating patients clinical pathology report. RPA-based NLP BERT …system is designed and evaluated to automatically extract information on these variables for the patients from pathology report. In order to detect tumour and outcomes from documented pathology reports, a supervised machine learning keyword based extraction algorithm was developed in which the pathology data are examined to extract keywords from 2087 reports with 1579 of data reports being processed for the development phase and 508 of data being used for evaluation. The precision recall and accuracy are calculated for organ specimens for cancer test as (0.984, 0.982, 0.9839), test methodology(0.986, 0.981,0.9956) and pathological result(0.986, 0.9938, 0.9795) were achieved. The feasibility of autonomously extracting pre-defined data from clinical narratives for cancer research were established in this work. The outcomes showed that our methodology was suitable for actual use in obtaining essential information from pathology reports. Show more

Keywords: Unstructured data, intelligent automation, bots, feature analysis, prediction, supervised learning, NLP, pathology, information extraction, diagnosis

DOI: 10.3233/JIFS-231625

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2023

Authors: Xinyi, Zhang

Article Type: Research Article

Abstract: Visual art was originally measured by viewing and appreciating graphic works, and there was no previous research into ways to improve the quality of visual art. With the rapid development of visual arts and technology, the question of how to improve quality has become an urgent one. As the most cutting-edge and hottest concept in the international arena today, the development and application of metaverse technology has widely drawn the close attention of various industries, including management, economy, education, and art. However, there is no in-depth and clear research on the concept of metaverse in the field of art, especially …in the field of visual art. We believe that the creation of visual art in the context of metaverse will be an important direction for art development in the future, and can also greatly contribute to the improvement of the quality of metaverse visual art presentation. Therefore, we focus on the issue of visual art quality assessment in our research, and propose a theory and method of metaverse-oriented future visual art quality assessment. The method focuses on the G1-entropy value method to calculate the weights in visual arts, combines qualitative research with quantitative research, and proposes the improvement path and countermeasures for visual arts. In summary, our research aims to address the theoretical approaches to the design of the metaverse field architecture and the assessment of art quality for the future introduction of the metaverse. The main contributions of our research are focused on the following three aspects: 1. The construction of the visual art field architecture draws on the functional requirements analysis method of system science simulation, considering that the entire visual art metaverse field architecture is constructed at three levels: the bottom data support layer, the middle technical support layer and the upper technical application layer. 2. The G1-entropy combination weighting method is used to derive the importance ranking of visual art quality indicators and identify key factors, and to derive suggestions for quality improvement based on the key indicator factors. More importantly, we also build a field architecture for future-oriented visual arts in this study, which bridges the gap in the structural design of visual arts after the introduction of the future concept. Our present study makes a great contribution to the application of visual art quality enhancement, focusing on the analysis of new concepts and the improvement of old methods, building a new scene of organic combination of new technologies and traditional visual art, with practical research theoretical support for the promotion and progress of the disciplinary field. Show more

Keywords: Visual art, metaverse, field structure, G1 entropy method, index weights

DOI: 10.3233/JIFS-224571

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2023

Authors: Luo, Jiangnan | Cai, Jinyu | Li, Jianping | Gao, Jiuhua | Zhou, Feng | Chen, Kailang | Liu, Lei | Hao, Mengda

Article Type: Research Article

Abstract: During the process of gas hole drilling, automatic loading and unloading drilling rod by robotic arm ensures the safety of personnel and drilling efficiency. Accurate recognition of drilling rod target is a prerequisite for precise positioning. However, the presence of dark and dust underground coal mines presents the great challenge in detecting and recognizing drilling rods during the automatic drill loading and uploading process. To solve this problem, We have designed a drilling rod target detection and segmentation technology based on generating adversarial network(GAN). Furthermore, we carried out experiments to compare the recognition performance of drilling rods of different colors, …including black, blue, and yellow, in the dark and dusty environment. The results indicate that the drilling rod recognition method proposed in this paper demonstrates high accuracy and robustness even in dark and dusty environment, better than other commonly used segmentation networks. Notably, the recognition accuracy of yellow drilling rods surpasses that of blue and black drilling rods. Show more

Keywords: Dark and dusty environment, drilling rod recognition segmentation, deep learning, GAN, spatial attention mechanism

DOI: 10.3233/JIFS-232162

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023