Journal of Intelligent & Fuzzy Systems - Volume 45, issue 4

Authors: Chen, Liuxin | Wang, Yutai | Liu, Jinyuan

Article Type: Research Article

Abstract: In the emergency decision-making process, decision-makers usually cannot give rational evaluations, and existing decision-making methods do not adequately consider the risk attitude of decision-makers either. To solve these problems, a combined method based on the prospect theory and the multi-attributive ideal-real comparative analysis (MAIRCA) method is put forward in the picture fuzzy environment. Firstly, the optimal aggregation (OA) model is proposed to obtain the ideal evaluations with the least disagreement among decision-makers. Regarding the evaluations as reference systems, the OA-based prospect theory is put forward, which could calculate the prospect matrix more reasonably. Secondly, considering the prospect matrix and alternative …preference, the improved MAIRCA method is proposed, which overcomes the defects of theory and has the better ranking ability. Then, the OA-based prospect theory-MAIRCA method is further put forward to effectively complete the decision-making process with risk attitudes. Finally, an illustrative example of earthquake emergency assessment and a series of comparative experiments are presented. The analyses of results show that the proposed method has great guiding significance in the field of emergency decision-making management. Show more

Keywords: Picture fuzzy set, optimal aggregation model, prospect theory, MAIRCA method

DOI: 10.3233/JIFS-223279

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5493-5507, 2023

Authors: Gautam, Devendra | Dixit, Anurag | Banda, Latha | 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. 45, no. 4, pp. 5509-5522, 2023

Authors: Qian, Zhuoyi | Guo, Peng | Wang, Yifan | Xiao, Fangcheng

Article Type: Research Article

Abstract: Self-driving cars are expected to replace human drivers shortly, bringing significant benefits to society. However, they have faced opposition from various organizations that argue it is challenging to respond to instances involving unavoidable personal injury. In situations involving deadly collisions, self-driving cars must make decisions that balance life and death. This paper investigates the ethical and moral decision-making challenges for self-driving cars from an algorithmic perspective. To address this issue, we introduce the accident-prioritized replay mechanism to the Deep Q-Networks (DQN) algorithm based on early humanities research. The mechanism quantifies a reward function that takes priority into account. RGB (red, …green, blue) images obtained by the camera installed in front of the self-driving cars are fed into the Xception network for training. To evaluate our approach, we compare it to the conventional DQN algorithm. The simulation results indicate that the Rawlsian DQN algorithm has superior stability and interpretability in decision-making. Furthermore, the majority of respondents to our survey accept the final decision made by our algorithm. Our experiment demonstrates that it is possible to incorporate ethical considerations into self-driving car decision-making, providing a solution for rational decision-making in emergency and dilemma circumstances. Show more

Keywords: Rawlsian maximin principle, carla simulator, depth-wise separable convolution, deep Q-network, ethical decision-making

DOI: 10.3233/JIFS-224553

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5523-5540, 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. 45, no. 4, pp. 5541-5550, 2023

Authors: Jeyabalan, Saranya Devi | Yesudhas, Nancy Jane | Sathyanarayanan, Jayashree | Harichandran, Khanna Nehemiah

Article Type: Research Article

Abstract: Coronavirus disease 2019 (Covid-19) is a contagious pandemic illness characterized by severe acute respiratory syndrome. The daily rise of Covid-19 instances and fatalities has resulted in worldwide lockdowns, quarantines and social distancing. Researchers have been working incredibly to develop precisely focused strategies to warfare the Covid-19 pandemic. This study aims to develop a cyclical learning rate optimized stacked generalization computational models (CLR-SGCM) for predicting Covid-19 pandemic outbreaks. Stacked generalization framework performs hierarchical two-phase prediction. In the first phase, deep learning models namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and statistical model Auto Regressive Integrated Moving Average (ARIMA) …are used as sub models to create pooled datasets (PDS). Cyclical learning rate (CLR) optimizer is used to enhance learning rate of ensemble deep learning models namely LSTM and GRU. In the second phase, meta learner is trained on dataset PDS using four different regression algorithms such as linear regression, polynomial regression, lasso regression and ridge regression to perform the final predictions. Time series data from India, Brazil, and the United States were utilized to forecast the Covid-19 pandemic outbreak. According to experimental finding, the presented stacking ensemble model outpaces the individual learners in terms of accuracy and error rate. Show more

Keywords: Covid-19, forecasting, time series prediction, stacked generalization, CLR optimization, deep learning models

DOI: 10.3233/JIFS-231229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5551-5566, 2023

Authors: Ge, Hongping | Liu, Huaying | Luo, Yun

Article Type: Research Article

Abstract: Aiming at the troubles of difficult extraction of fault features and low fault recognition rate in rotating equipment fault detection approach, a new technique for intelligent diagnosis based on modified hierarchical diversity entropy (MHDE) and extension theory (ET) is proposed in the thesis. Firstly, MHDE employs to comprehensively describe the fault information of the given signals. Secondly, the MHDE feature sets are regarded as the characteristic parameters of the extension matter element model, and the matter element model in various states is established. Finally, the testing datasets are fed into the matter element model for each operating conditions, and the …correlation function is used to compute the comprehensive correlation between the testing datasets and the various conditions of the rotating machinery, so as to realize the qualitative and quantitative identification of the testing datasets. The reliability and superiority of the proposed new approach is validated by real experimental datasets on various rotating machinery types. The analysis results show that the proposed novel technology can effectively excavate the feature information and accurately identify various fault conditions of rotating machinery. In addition, compared with other combined model technology in the paper, the proposed intelligent fault diagnosis technology has better classification performance. Show more

Keywords: Rotating machinery, modified hierarchical diversity entropy, extension theory, correlation function, fault diagnosis technology

DOI: 10.3233/JIFS-231363

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5567-5586, 2023

Authors: Peng, Peng | Ni, Zhiwei | Zhu, Xuhui | Chen, Qian

Article Type: Research Article

Abstract: A framework for spatial crowdsourcing task allocation based on centralized differential privacy is proposed for addressing the problem of worker’s location privacy leakage. Firstly, by combining two stages of differential privacy noise addition and clustering matching, a spatial crowdsourcing worker dataset with high differential privacy protection can be obtained; Secondly, the dynamic problem of spatial crowdsourcing task allocation is transformed into a static combinatorial optimization problem by dividing the spatiotemporal units and the “delay matching” strategy; Finally, the improved discrete glowworm swarm optimization algorithm is used to calculate the results of spatial crowdsourcing task allocation. It has been demonstrated that, …compared to the direct differential privacy noise-adding assignment method and the discrete glowworm swarm optimization assignment method, the proposed method achieves better task assignment results, with the total travel distance reduced by 12.42% and 3.56%, respectively, and the task assignment success rate increased by 11.75% and 3.34%, respectively. Show more

Keywords: Differential privacy, k-means clustering, space crowdsourcing, task allocation, the glowworm swarm optimization algorithm

DOI: 10.3233/JIFS-230734

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5587-5600, 2023

Authors: Zhu, Xuemin | Liu, Sheng | Zhu, Xuelin | You, Xiaoming

Article Type: Research Article

Abstract: An enhancing sparrow optimization algorithm with hybrid multi-strategy (EGLTA-SSA) is proposed, to improve the defects of the sparrow search algorithm (SSA), which is easy to fall into local optimum. Firstly, the elite backward learning strategy is introduced to initialize the sparrow population, to generate high-quality initial solutions. Secondly, the leader position is updated by fusing multi-strategy mechanisms. On one hand, the high distributivity of arithmetic optimization algorithm operators are used to deflate the target position, and enhance the ability of SSA to jump out of the local optimum. On the other hand, the leader position is perturbed by adopting the …golden levy flight method and the t-distribution perturbation strategy to improve the shortcoming of SSA in the late iteration when the population diversity decreases. Further, a probability factor is added for random selection to achieve more effective communication among leaders. Finally, to verify the effectiveness of EGLTA-SSA, CEC2005 and CEC2019 functions are tested and compared with state-of-the-art algorithms, and the experimental results show that EGLTA-SSA has a better performance in terms of convergence rate and stability. EGLTA-SSA is also successfully applied to three practical engineering problems, and the results demonstrate the superior performance of EGLTA-SSA in solving project optimization problems. Show more

Keywords: Sparrow optimization algorithm (SSA), arithmetic optimization algorithm, golden levy flight distribution, t-distribution perturbation, engineering design problems

DOI: 10.3233/JIFS-231114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5601-5632, 2023

Authors: Bai, Xiaojun | Pan, Zhaofeng | Meng, Gong | Wang, Shenhang | Fu, Yanfang

Article Type: Research Article

Abstract: Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, …and the monitoring index with the lowest correlation is selected as the fault feature; secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method. Show more

Keywords: Hard disk drives, failure prediction, association analysis, long-short term memory, SSA

DOI: 10.3233/JIFS-231268

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5633-5645, 2023

Authors: Suo, Jiafeng | Han, Dongchen | Zhao, Hui

Article Type: Research Article

Abstract: In the entity extraction task, there are some complex extraction problems, such as nested entity, entity boundary recognition, context ambiguity, and multi-instance entity recognition. Entity nesting is an important challenge in relational extraction. The main reason of entity nesting problem is that the boundary information between entities is not clear. In order to solve the entity nesting problem at the fragment level, while preserving the relationship between fragments with the same characteristics and improving efficiency, we proposed a brand new fragment annotation method. On the basis of traditional fragment annotation method, combined with pointer annotation method, we designed an annotation …method of "ergodic enumeration + group mapping". On the basis of this method, an entity extraction model is designed: Span-Extraction Based Entity Extraction Model (LMA). Our model underwent a series of validations in the English data sets New York Times(NYT) and WEBNLG, showing significant improvements over the baseline model F1. It can effectively alleviate the above problems. Show more

Keywords: Entity extraction, relational extraction, nested entity, context ambiguity

DOI: 10.3233/JIFS-231766

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5647-5657, 2023