Journal of Intelligent & Fuzzy Systems - Volume 41, issue 1

Authors: Zhao, Yifan | Tian, Shuicheng

Article Type: Research Article

Abstract: Aiming at the problem of large error and long time of early warning response in the traditional system, this paper designs a hazard identification early warning system based on random forest algorithm in underground coal mine. By random classification decision forest created dangerous content in different areas of the downhole information input into the decision tree as a test sample, according to the result of the output of the leaf node determine the risk level of decision trees, and USES the high precision of decision forest classification ability the threat level assessment test sample, radically reducing hazards identification error. Then, …based on the evaluation results, combined with the threshold value of warning criteria to identify the gas exceeding limit area, and determine the fire source warning level, so as to realize the hazard source identification and warning. The simulation results show that the average hazard location identification error of the system is only 4.1%, and the warning response time can be controlled within 9 s. Show more

Keywords: Underground hazard sources, identify early warning, random forest algorithm, decision forest, risk assessment, alarm criteria threshold

DOI: 10.3233/JIFS-210105

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1193-1202, 2021

Authors: Kavitha, N | Ruba Soundar, K | Sathis Kumar, T

Article Type: Research Article

Abstract: In recent years, the Face recognition task has been an active research area in computer vision and biometrics. Many feature extraction and classification algorithms are proposed to perform face recognition. However, the former usually suffer from the wide variations in face images, while the latter usually discard the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the Key points Local Binary/Tetra Pattern (KP-LTrP) and Improved Hough Transform (IHT) with the Improved DragonFly Algorithm-Kernel Ensemble Learning Machine (IDFA-KELM) is proposed to address the face recognition …problem in unconstrained conditions. Initially, the face images are collected from the publicly available dataset. Then noises in the input image are removed by performing preprocessing using Adaptive Kuwahara filter (AKF). After preprocessing, the face from the preprocessed image is detected using the Tree-Structured Part Model (TSPM) structure. Then, features, such as KP-LTrP, and IHT are extracted from the detected face and the extracted feature is reduced using the Information gain based Kernel Principal Component Analysis (IG-KPCA) algorithm. Then, finally, these reduced features are inputted to IDFA-KELM for performing FR. The outcomes of the proposed method are examined and contrasted with the other existing techniques to confirm that the proposed IDFA-KELM detects human faces efficiently from the input images. Show more

Keywords: Face recognition, kernel ensemble learning machine, adaptive kuwahara filter, improved dragonfly algorithm

DOI: 10.3233/JIFS-210130

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1203-1216, 2021

Authors: Li, Lulu

Article Type: Research Article

Abstract: Set-valued data is a significant kind of data, such as data obtained from different search engines, market data, patients’ symptoms and behaviours. An information system (IS) based on incomplete set-valued data is called an incomplete set-valued information system (ISVIS), which generalized model of a single-valued incomplete information system. This paper gives feature selection for an ISVIS by means of uncertainty measurement. Firstly, the similarity degree between two information values on a given feature of an ISVIS is proposed. Then, the tolerance relation on the object set with respect to a given feature subset in an ISVIS is obtained. Next, λ …-reduction in an ISVIS is presented. What’s more, connections between the proposed feature selection and uncertainty measurement are exhibited. Lastly, feature selection algorithms based on λ -discernibility matrix, λ -information granulation, λ -information entropy and λ -significance in an ISVIS are provided. In order to better prove the practical significance of the provided algorithms, a numerical experiment is carried out, and experiment results show the number of features and average size of features by each feature selection algorithm. Show more

Keywords: Rough set theory, ISVIS, feature selection, similarity degree, λ-reduction, λ-discernibility matrix, λ-information granulation, λ-information entropy, λ-significance, algorithm

DOI: 10.3233/JIFS-210135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1217-1235, 2021

Authors: Wang, Lu

Article Type: Research Article

Abstract: With the prosperity of national economy and the development of highway construction, highway freight transportation plays an increasingly important role in the market economy. Due to its great flexible characteristic, highway freight transportation has been the main mode of transportation in China. On the macro level, there are many factors affecting the development of highway freight transportation especially under the background of the new era. Based on the historical data of the development of highway freight transportation, grey entropy analysis method is applied to analyze the significance of influencing factors for the development of highway freight transportation whose key indicator …is highway freight turnover. Then GM (1, N) model is established to predict the development trend of highway freight turnover and its significant influencing factors. Finally, main problems existing in highway freight transportation and development prospect were discussed and analyzed. The research results show that the three most significant factors affecting the development of road freight turnover in China are the total state revenue, GDP and average distance of highway freight. The established GM (1, N) model can conduct high precision prediction for the development of highway freight transportation. Opportunities and challenges of highway freight transportation industry coexist and its development prospect is promising. It is expected to provide beneficial references for the development strategy and decision-making of highway freight transportation in China. Show more

Keywords: Highway freight transportation, significance analysis, grey entropy analysis method, GM (1, N) prediction model

DOI: 10.3233/JIFS-210141

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1237-1246, 2021

Authors: Tao, Ning | Xiaodong, Duan | Lu, An | Tao, Gou

Article Type: Research Article

Abstract: A disruption management method based on cumulative prospect theory is proposed for the urgent with deteriorating effect arrival in flexible job shop scheduling problem (FJSP). First, the mathematical model of problem is established with minimizing the completion time of urgent order, minimizing the total process time of the system and minimizing the total cost as the target. Then, the cumulative prospect theory equation of the urgent arrival in job shop scheduling process is induced designed. Based on the selected model, an optimized multi-phase quantum particle swarm algorithm (MQPSO) is proposed for selecting processing route. Finally, using Solomon example simulation and …company Z riveting shop example as the study object, the performance of the proposed method is analyzed. It is compared with the current common rescheduling methods, and the results verify that the method proposed in this paper not only meets the goal of the optimized objects, but improves the practical requirements for the stability of production and processing system during urgent arrival. Lastly, the optimized multiphase quantum particle swarm algorithm is used to solve disruption management of urgent arrival problem. Through instance analysis and comparison, the effectiveness and efficiency of urgent arrival disruption management method with deteriorating effect are verified. Show more

Keywords: Flexible job-shop scheduling, deteriorating effect, emergency order insertion, disruption management, multi-phase quantum particle swarm optimization

DOI: 10.3233/JIFS-210166

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1247-1259, 2021

Authors: Bahrami, Vahid | Kalhor, Ahmad | Masouleh, Mehdi Tale

Article Type: Research Article

Abstract: This study intends to investigate the dynamic model estimation and the design of an adaptive neural network based controller for a passive planar robot, performing 2-DoF motion pattern which is in interaction with an actuated cable-driven robot. In fact, the main goal of applying this structure is to use a number of light cables to drive serial robot links and track the desired reference model by the robot’s end-effector. The under study system can be used as a rehabilitation setup which is helpful for those with arm disability. In this way, upon applying sliding mode error dynamics, it is necessary …to determine a vector that contains the matrices related to the robot dynamics. However, finding these matrices requires the use of computational approaches such as Newton-Euler or Lagrange. In addition, since the purpose of this paper is to express comprehensive methods, so with increasing the number of links and degrees of freedom of the robot, finding the dynamics of the robot becomes more difficult. Therefore, the Adaptive Neural Network (ANN) with specific inputs has been used for estimation unknown matrices of the system and the controller design has been performed based on it. So, the main idea in using an adaptive controller is the fact there is no pre-knowledge for the dynamic modeling of the system since the human arm could have different dynamic properties. Hence, the controller is formed by an ANN and robust term. In this way, the adaptation laws of the parameters are extracted by Lyapunov approach, and as a result, as aforementioned, the asymptotic stability of the whole of the system is guaranteed. Simulation results certify the efficiency of the proposed method. Finally, using the Roots Mean Square Error (RMSE) criteria, it has been revealed that, in the presence of bounded disturbance with different amplitude, adding the robust term to the controller leads to improve the tracking error about 34% and 62%, respectively. Show more

Keywords: Dynamic model estimation, adaptive neural network controller, lyapunov approach, passive planar robot, actuated cable-driven robot and rehabilitation setup

DOI: 10.3233/JIFS-210180

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1261-1280, 2021

Authors: Wenbo Huang, First A. | Changyuan Wang, Second B. | Hongbo Jia, Third C.

Article Type: Research Article

Abstract: Traditional intention inference methods rely solely on EEG, eye movement or tactile feedback, and the recognition rate is low. To improve the accuracy of a pilot’s intention recognition, a human-computer interaction intention inference method is proposed in this paper with the fusion of EEG, eye movement and tactile feedback. Firstly, EEG signals are collected near the frontal lobe of the human brain to extract features, which includes eight channels, i.e., AF7, F7, FT7, T7, AF8, F8, FT8, and T8. Secondly, the signal datas are preprocessed by baseline removal, normalization, and least-squares noise reduction. Thirdly, the support vector machine (SVM) is …applied to carry out multiple binary classifications of the eye movement direction. Finally, the 8-direction recognition of the eye movement direction is realized through data fusion. Experimental results have shown that the accuracy of classification with the proposed method can reach 75.77%, 76.7%, 83.38%, 83.64%, 60.49%,60.93%, 66.03% and 64.49%, respectively. Compared with traditional methods, the classification accuracy and the realization process of the proposed algorithm are higher and simpler. The feasibility and effectiveness of EEG signals are further verified to identify eye movement directions for intention recognition. Show more

Keywords: EM, EEG, tactile feedback, wireless sensor network, flying driving, brain electrical signals, data fusion

DOI: 10.3233/JIFS-210191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1281-1296, 2021

Authors: Narendiranath Babu, T. | Senthilnathan, N. | Pancholi, Shailesh | Nikhil Kumar, S.P. | Rama Prabha, D. | Mohammed, Noor | Wahab, Razia Sultana

Article Type: Research Article

Abstract: This study aims at developing a novel method for condition monitoring technique for detection and classification of developing faults and increase the working life of continuous variable transmission (CVT) using Daubechies Wavelet 06 (DB-06). The vibration data is collected for 4 different types of faults and healthy condition. Using a magnetic accelerometer and signal analyser, vibration data is collected from the system in the time-domain which is then used as input for a MATLAB code producing the plot of the frequency-domain signal. Maximum frequency is determined to diagnose the faults which are induced over three different belts. Collected data for …large scale automotive system (CVT) is used to train the network and then it is tested based on random data points. Faults were classified using ANN with a classification rate of 90.8 %. Show more

Keywords: Continuous variable transmission (CVT), Daubechies wavelet, fault diagnosis, fault classification, artificial neural network

DOI: 10.3233/JIFS-210199

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1297-1307, 2021

Authors: Guo, Wang | Liu, Xingmou | Ma, You | Zhang, Rongjie

Article Type: Research Article

Abstract: The correct identification of gene recombination cold/hot spots is of great significance for studying meiotic recombination and genetic evolution. However, most of the existing recombination spots recognition methods ignore the global sequence information hidden in the DNA sequence, resulting in their low recognition accuracy. A computational predictor called iRSpot-DCC was proposed in this paper to improve the accuracy of cold/hot spots identification. In this approach, we propose a feature extraction method based on dinucleotide correlation coefficients that focus more on extracting potential DNA global sequence information. Then, 234 representative features vectors are filtered by SVM weight calculation. Finally, a convolutional …neural network with better performance than SVM is selected as a classifier. The experimental results of 5-fold cross-validation test on two standard benchmark datasets showed that the prediction accuracy of our recognition method reached 95.11%, and the Mathew correlation coefficient (MCC) reaches 90.04%, outperforming most other methods. Therefore, iRspot-DCC is a high-precision cold/hot spots identification method for gene recombination, which effectively extracts potential global sequence information from DNA sequences. Show more

Keywords: Recombination spots, correlation coefficient, DNA property matrix, support vector machines, convolutional neural network

DOI: 10.3233/JIFS-210213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1309-1317, 2021

Authors: Shahzadi, Sundas | Rasool, Areen | Sarwar, Musavarah | Akram, Muhammad

Article Type: Research Article

Abstract: Bipolarity plays a key role in different domains such as technology, social networking and biological sciences for illustrating real-world phenomenon using bipolar fuzzy models. In this article, novel concepts of bipolar fuzzy competition hypergraphs are introduced and discuss the application of the proposed model. The main contribution is to illustrate different methods for the construction of bipolar fuzzy competition hypergraphs and their variants. Authors study various new concepts including bipolar fuzzy row hypergraphs, bipolar fuzzy column hypergraphs, bipolar fuzzy k -competition hypergraphs, bipolar fuzzy neighborhood hypergraphs and strong hyperedges. Besides, we develop some relations between bipolar fuzzy k …-competition hypergraphs and bipolar fuzzy neighborhood hypergraphs. Moreover, authors design an algorithm to compute the strength of competition among companies in business market. A comparative analysis of the proposed model is discuss with the existing models such bipolar fuzzy competition graphs and fuzzy competition hypergraphs. Show more

Keywords: Bipolar fuzzy competition hypergraphs, bipolar fuzzy k-competition hypergraphs, bipolar fuzzy neighborhood hypergraphs

DOI: 10.3233/JIFS-210216

Citation: Journal of Intelligent & Fuzzy Systems, vol. 41, no. 1, pp. 1319-1339, 2021