Journal of Intelligent & Fuzzy Systems - Volume 42, issue 3

Authors: Zhang, Yu | Xiao, Qunli | Deng, Xinyang | Jiang, Wen

Article Type: Research Article

Abstract: The ship target recognition (STR) is greatly related to the battlefield situation awareness, which has recently gained prominence in the military domains. With the diversification and complexity of military missions, ship targets are mostly performed in the form of formations. Therefore, using the formation information to improve the accuracy of the ship target type recognition is worth studying. To effectively identify ship target type, we in this paper jointly consider the ship dynamic, formation, and feature information to propose a STR method based on Bayesian inference and evidence theory. Specifically, we first calculate the ship position distance matrix and the …directional distance matrix with the Dynamic Time Warping (DTW) and the difference-vector algorithm taken into account. Then, we use the two distance matrices to obtain the ship formation information at different distance thresholds by the hierarchical clustering method, based on which we can infer the ship type. Thirdly, formation information and other attribute information are as nodes of the Bayesian Network (BN) to infer the ship type. Afterward, we can convert the recognition results at different thresholds into body of evidences (BOEs) as multiple information sources. Finally, we fuse the BOEs to get the final recognition. The proposed method is verified in simulation battle scenario in this paper. The simulation results demonstrate that the proposed method achieves performance superiority as compared with other ship recognition methods in terms of recognition accuracy. Show more

Keywords: Ship target recognition, multi-source information, formation information, Bayesian inference, evidence theory

DOI: 10.3233/JIFS-211638

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2331-2346, 2022

Authors: Jin, Zhen-Yu | Yan, Cong-Hua

Article Type: Research Article

Abstract: In this paper, a notion of fuzzifying bornological linear spaces is introduced and the necessary and sufficient condition for fuzzifying bornologies to be compatible with linear structure is discussed. The characterizations of convergence and separation in fuzzifying bornological linear spaces are showed. In particular, some examples with respect to linear fuzzifying bornologies induced by probabilistic normed spaces and fuzzifying topological linear spaces are also provided.

Keywords: Fuzzifying bornological linear spaces, fuzzifying bornological convergence, separation, Product linear fuzzifying bornologies, quotient linear fuzzifying bornologies

DOI: 10.3233/JIFS-211644

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2347-2358, 2022

Authors: Chen, Zhixiong | Tian, Shengwei | Yu, Long | Zhang, Liqiang | Zhang, Xinyu

Article Type: Research Article

Abstract: In recent years, the research on object detection has been intensified. A large number of object detection results are applied to our daily life, which greatly facilitates our work and life. In this paper, we propose a more effective object detection neural network model ENHANCE_YOLOV4. We studied the effects of several attention mechanisms on YOLOV4, and finally concluded that spatial attention mechanism had the best effect on YOLOV4. Therefore, based on previous studies, this paper introduces Dilated Convolution and one-by-one convolution into the spatial attention mechanism to expand the receptive field and combine channel information. Compared with CBAM and BAM, …which are composed of spatial attention and channel attention, this improved spatial attention module reduces model parameters and improves detection capabilities. We built a new network model by embedding improved spatial attention module in the appropriate place in YOLOV4. And this paper proves that the detection accuracy of this network structure on the VOC data set is increased by 0.8%, and the detection accuracy on the coco data set is increased by 7%when the calculation performance is increased a little. Show more

Keywords: DCNN, object detection, spatial attention, dilated convolution, COCO

DOI: 10.3233/JIFS-211648

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2359-2368, 2022

Authors: Wu, Guoqiang | Li, Qingping

Article Type: Research Article

Abstract: Population structure changes interact with economic development, moderate population and reasonable population structure are important guarantees for sustainable social and economic development. The research ignores the specific impact of the change of population age structure on economic growth, and proposes and establishes a population economic function model based on data mining algorithm. Based on the changes of population structure in Liaoning Province in the past 20 years, Grey correlation analysis method is selected. The analysis shows that there is a close relationship between population structure and economic growth. Based on this research, the econometric method is used to construct a …multiple linear regression model to further analyze the specific impact of population structure changes on economic growth. The analysis results show that the total population of urban areas, the total number of employed people in the primary industry, the number of middle school students per 10,000 people, and the total number of employed people in the tertiary industry are the four most significant demographic indicators for the per capita GDP of the study area. There is a significant positive correlation between the total number of employed people in the tertiary industry and per capita GDP and there is a significant negative correlation between the total number of employed people in the primary industry and the number of middle school students per capita and per capita GDP. The impact of other indicators on per capita GDP is not significant. According to the conclusion, countermeasures and suggestions to ease population structure change and promote the coordinated development of population and economy in the study area are put forward. Show more

Keywords: Grey correlation analysis method, data mining algorithm, population economic function, model

DOI: 10.3233/JIFS-211663

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2369-2382, 2022

Authors: Zhou, Daxin | Qian, Yurong | Ma, Yuanyuan | Fan, Yingying | Yang, Jianeng | Tan, Fuxiang

Article Type: Research Article

Abstract: Low-illumination image restoration has been widely used in many fields. Aiming at the problem of low resolution and noise amplification in low light environment, this paper applies style transfer of CycleGAN(Cycle-Consistent Generative Adversarial Networks) to low illumination image enhancement. In the design network structure, different convolution kernels are used to extract the features from three paths, and the deep residual shrinkage network is designed to suppress the noise after convolution. The color deviation of the image can be resolved by the identity loss of CycleGAN. In the discriminator, different convolution kernels are used to extract image features from two paths. …Compared with the training and testing results of Deep-Retinex network, GLAD network, KinD and other network methods on LOL-dataset and Brightening dataset, CycleGAN based on multi-scale depth residuals contraction proposed in this experiment on LOL-dataset results image quality evaluation indicators PSNR = 24.62, NIQE = 4.9856, SSIM = 0.8628, PSNR = 27.85, NIQE = 4.7652, SSIM = 0.8753. From the visual effect and objective index, it is proved that CycleGAN based on multi-scale depth residual shrinkage has excellent performance in low illumination enhancement, detail recovery and denoising. Show more

Keywords: Style migration, cycle-consistent generative adversarial networks, depth residual shrinkage, image enhancement

DOI: 10.3233/JIFS-211664

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2383-2395, 2022

Authors: Ramalingam, Anita | Navaneethakrishnan, Subalalitha Chinnaudayar

Article Type: Research Article

Abstract: Thirukkural, a Tamil classic literature, which was written in 300 BCE is a didactic literature. Though Thirukkural comprises 1330 couplets which are organized into three sections and 133 chapters, in order to retrieve meaningful Thirukkural for a given query in search systems, a better organization of the Thirukkural is needed. This paper lays such a foundation by classifying the Thirukkural into ten new categories called superclasses that is helpful for building a better Information Retrieval (IR) system. The classifier is trained using Multinomial Naïve Bayes algorithm. Each superclass is further classified into two subcategories based on the didactic information. The …proposed classification framework is evaluated using precision, recall and F-score metrics and achieved an overall F-score of 82.33% and a comparison analysis has been done with the Support Vector Machine, Logistic Regression and Random Forest algorithms. An IR system is built on top of the proposed system and the performance comparison has been done with the Google search and a locally built keyword search. The proposed classification framework has achieved a mean average precision score of 89%, whereas the Google search and keyword search have yielded 59% and 68% respectively. Show more

Keywords: Natural language processing, text classification, information retrieval, multinomial naive bayes classifier, the Thirukkural , morphological analysis

DOI: 10.3233/JIFS-211667

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2397-2408, 2022

Authors: Wei, Pengfei | Zeng, Bi | Liao, Wenxiong

Article Type: Research Article

Abstract: Intent detection and slot filling are recognized as two very important tasks in a spoken language understanding (SLU) system. In order to model these two tasks at the same time, many joint models based on deep neural networks have been proposed recently and archived excellent results. In addition, graph neural network has made good achievements in the field of vision. Therefore, we combine these two advantages and propose a new joint model with a wheel-graph attention network (Wheel-GAT), which is able to model interrelated connections directly for single intent detection and slot filling. To construct a graph structure for utterances, …we create intent nodes, slot nodes, and directed edges. Intent nodes can provide utterance-level semantic information for slot filling, while slot nodes can also provide local keyword information for intent detection. The two tasks promote each other and carry out end-to-end training at the same time. Experiments show that our proposed approach is superior to multiple baselines on ATIS and SNIPS datasets. Besides, we also demonstrate that using bi-directional encoder representation from transformer (BERT) model further boosts the performance of the SLU task. Show more

Keywords: Spoken language understanding, graph neural network, attention mechanism, joint learning

DOI: 10.3233/JIFS-211674

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2409-2420, 2022

Authors: Hou, Long | Yu, Long | Tian, Shengwei | Zhang, Yanhan

Article Type: Research Article

Abstract: Underwater image enhancement has always been a hot spot in underwater vision research. However, due to complicated underwater environment, a lot of problems such as the color distortion and low brightness of underwater raw images are very likely to occur. In response to the above situation, we proposed a generative adversarial network that integrated multiple attention to enhance underwater images. In the generator, we introduced multi-layer dense connections and CSAM modules, of which the former could capture more detailed features and make use of previous features, while the latter could improve the utilization of the feature map. Meanwhile, we improved …the enhancement effect of the generated image by combining VGG19 content loss function and SmoothL1 loss function. Finally, we verified the effectiveness of the proposed model through qualitative and quantitative experiments, and compared the results with the performance of several latest models. The results show that the methods proposed in this paper are superior to the existing methods. Show more

Keywords: Deep learning, attentional mechanism, underwater image, image enhancement.

DOI: 10.3233/JIFS-211680

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2421-2433, 2022

Authors: Wei, Jinpeng | Qu, Shaojian | Jiang, Shan | Feng, Can | Xu, Yuting | Zhao, Xiaohui

Article Type: Research Article

Abstract: Individual opinion is one of the vital factors influencing the consensus in group decision-making, and is often uncertain. The previous studies mostly used probability distribution, interval distribution or uncertainty distribution function to describe the uncertainty of individual opinions. However, this requires an accurate understanding of the individual opinions distribution, which is often difficult to satisfy in real life. In order to overcome this shortcoming, this paper uses a robust optimization method to construct three uncertain sets to better characterize the uncertainty of individual initial opinions. In addition, we used three different aggregation operators to obtain collective opinions instead of using …fixed values. Furthermore, we applied the numerical simulations on flood disaster assessment in south China so as to evaluate the robustness of the solutions obtained by the robust consensus models that we proposed. The results showed that the proposed models are more robust than the previous models. Finally, the sensitivity analysis of uncertain parameters was discussed and compared, and the characteristics of the proposed models were revealed. Show more

Keywords: Group decision making, aggregation operator, consensus models, uncertainty set, robust optimization

DOI: 10.3233/JIFS-211704

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2435-2449, 2022

Authors: Yang, Gang | Li, Tianbin | Ma, Chunchi | Meng, Lubo | Zhang, Hang | Ma, Junjie

Article Type: Research Article

Abstract: Accurate prediction of surrounding rock grades holds great significance to tunnel construction. This paper proposed an intelligent classification method for surrounding rock based on one-dimensional convolutional neural networks (1D CNNs). Six indicators collected in some tunnel construction sites are considered, and the degree of linear correlation between these indicators has been analyzed. The improved one-hot encoding method is put forward for transforming these non-image indicators into one-dimensional structural data and avoiding the sampling error in the indicators of surrounding rock collected in the field. We found that the 1D CNNs model based on the improved one-hot encoding method can best …extract the features of surrounding rock classification indicators (in terms of both accuracy and efficiency). We applied the well-trained classification model of tunnel surrounding rock to a series of expressway tunnels in China, and the results show that our model could accurately predict the surrounding rock grade and has great application value in the construction of tunnel engineering. It provides a new research idea for the prediction of surrounding rock grades in tunnel engineering. Show more

Keywords: Tunnel engineering, surrounding rock classification, index-based classification, one-dimensional convolutional neural network, non-image data

DOI: 10.3233/JIFS-211718

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2451-2469, 2022

Authors: Liu, Mingzhou | Xu, Xin | Hu, Jing | Jiang, Qiannan

Article Type: Research Article

Abstract: Road detection algorithms with high robustness as well as timeliness are the basis for developing intelligent assisted driving systems. To improve the robustness as well as the timeliness of unstructured road detection, a new algorithm is proposed in this paper. First, for the first frame in the video, the homography matrix H is estimated based on the improved random sample consensus (RANSAC) algorithm for different regions in the image, and the features of H are automatically extracted using convolutional neural network (CNN), which in turn enables road detection. Secondly, in order to improve the rate of subsequent similar frame detection, …the color as well as texture features of the road are extracted from the detection results of the first frame, and the corresponding Gaussian mixture models (GMMs) are constructed based on Orchard-Bouman, and then the Gibbs energy function is used to achieve road detection in subsequent frames. Finally, the above algorithm is verified in a real unstructured road scene, and the experimental results show that the algorithm is 98.4% accurate and can process 58 frames per second with 1024×960 pixels. Show more

Keywords: Unstructured road detection, Gibbs energy function, CNN

DOI: 10.3233/JIFS-211733

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2471-2489, 2022

Authors: Connie, Tee | Tan, Yee Fan | Goh, Michael Kah Ong | Hon, Hock Woon | Kadim, Zulaikha | Wong, Li Pei

Article Type: Research Article

Abstract: In the recent years, Artificial Intelligence (AI) has been widely deployed in the healthcare industry. The new AI technology enables efficient and personalized healthcare systems for the public. In this paper, transfer learning with pre-trained VGGFace model is applied to identify sick symptoms based on the facial features of a person. As the deep learning model’s operation is unknown for making a decision, this paper investigates the use of Explainable AI (XAI) techniques for soliciting explanations for the predictions made by the model. Various XAI techniques including Integrated Gradient, Explainable region-based AI (XRAI) and Local Interpretable Model-Agnostic Explanations (LIME) are …studied. XAI is crucial to increase the model’s transparency and reliability for practical deployment. Experimental results demonstrate that the attribution method can give proper explanations for the decisions made by highlighting important attributes in the images. The facial features that account for positive and negative classes predictions are highlighted appropriately for effective visualization. XAI can help to increase accountability and trustworthiness of the healthcare system as it provides insights for understanding how a conclusion is derived from the AI model. Show more

Keywords: Explainable AI, health prediction, transfer learning, deep learning

DOI: 10.3233/JIFS-211737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2491-2503, 2022

Authors: Rajagopal, Sureshkumar | Umapathy, Prabha

Article Type: Research Article

Abstract: As the move towards Grid Integrated-Photovoltaic (GI-PV) system is proposed to improve the power quality development. A novel Adaptive Neuro-Fuzzy Inference System (ANFIS) based on improved Moth Flame Optimization (MFO) algorithm is described for grid integrated approach. The solar integration of Maximum Power Point (MPP) fed into modified Switched Boost Inverter (SBI) is presented, this GI-PV connected circuit has become prominent research in a recent scenario for energy demand. Proposed MFOA-ANFIS controller has generated the duty cycle pulses to each converter circuit. The benefit of grid-tied SBI is direct control outer-loop employed to obtain MFO-ANFIS techniques. To maintain a constant …voltage DC-link is employed for inner-loop, this presence of constant DC-power to grid loads with support of MFO-ANFIS assists Proportional Integral Differential (PID) method. The results acquired by the simulation expressed that the proposed controller is addressed to maintain active and reactive power exchange, regulate DC bus-link voltages, grid voltage, and grid current. The effectiveness of the practical implication research is achieved by the output as represented as minimum grid harmonics, load current, and compensator current as verified in MATLAB/Simulink platform. Show more

Keywords: Grid Integrated-Photovoltaic, Maximum Power Point, Adaptive Neuro-Fuzzy Inference System, Moth Flame Optimization Algorithm

DOI: 10.3233/JIFS-211748

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2505-2519, 2022

Authors: Muhiuddin, G. | John, J. Catherine Grace | Elavarasan, B. | Jun, Y.B. | Porselvi, K.

Article Type: Research Article

Abstract: The concept of a hybrid structure in X -semimodules, where X is a semiring, is introduced in this paper. The notions of hybrid subsemimodule and hybrid right (resp., left) ideals are defined and discussed in semirings. We investigate the representations of hybrid subsemimodules and hybrid ideals using hybrid products. We also get some interesting results on t -pure hybrid ideals in X . Furthermore, we show how hybrid products and hybrid intersections are linked. Finally, the characterization theorem is proved in terms of hybrid …structures for fully idempotent semirings. Show more

Keywords: Hybrid semirings, hybrid X-semimodules, fully idempotent hybrid ideals, t-pure hybrid ideals, weakly regular

DOI: 10.3233/JIFS-211751

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2521-2531, 2022

Authors: Li, Wenyi | Zhang, Cuixia | Liu, Conghu | Liu, Xiao

Article Type: Research Article

Abstract: In order to improve the quality of remanufacturing assembly with uncertainty for the sustainability of remanufacturing industry, an error propagation model of the remanufacturing assembly process and its optimal control method are established. First, the state space model of error propagation is established by taking the work-in-process parameter errors of each process as the initial state of the procedure and the parameters of remanufactured parts and operation quantities as the input. Then, the quality control issue of remanufacturing assembly is transformed into a convex quadratic programming with constraints based on this model. Finally, the proposed method is used to control …the remanufactured-crankshaft assembly quality. The experimental results show that the axial-clearance consistency and the crankshaft torque are improved, and the one-time assembly success rate of a remanufactured crankshaft is increased from 96.97%to 99.24%. This study provides a theoretical model and method support for the quality control of remanufacturing assembly and has a practical effect on improving the quality of remanufactured products. Show more

Keywords: Remanufacturing, assembly, error propagation, optimal control, state space, convex quadratic programming

DOI: 10.3233/JIFS-211791

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2533-2547, 2022

Authors: Jha, Sunil Kumar | Marina, Ninoslav | Wang, Jinwei | Ahmad, Zulfiqar

Article Type: Research Article

Abstract: Machine learning approaches have a valuable contribution in improving competency in automated decision systems. Several machine learning approaches have been developed in the past studies in individual disease diagnosis prediction. The present study aims to develop a hybrid machine learning approach for diagnosis predictions of multiple diseases based on the combination of efficient feature generation, selection, and classification methods. Specifically, the combination of latent semantic analysis, ranker search, and fuzzy-rough-k-nearest neighbor has been proposed and validated in the diagnosis prediction of the primary tumor, post-operative, breast cancer, lymphography, audiology, fertility, immunotherapy, and COVID-19, etc. The performance of the proposed approach …is compared with single and other hybrid machine learning approaches in terms of accuracy, analysis time, precision, recall, F-measure, the area under ROC, and the Kappa coefficient. The proposed hybrid approach performs better than single and other hybrid approaches in the diagnosis prediction of each of the selected diseases. Precisely, the suggested approach achieved the maximum recognition accuracy of 99.12%of the primary tumor, 96.45%of breast cancer Wisconsin, 94.44%of cryotherapy, 93.81%of audiology, and significant improvement in the classification accuracy and other evaluation metrics in the recognition of the rest of the selected diseases. Besides, it handles the missing values in the dataset effectively. Show more

Keywords: Hybrid machine learning, fuzzy nearest neighbor, disease diagnosis prediction, feature generation and selection

DOI: 10.3233/JIFS-211820

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2549-2563, 2022

Authors: Yao, Jianrong | Wang, Zhongyi | Wang, Lu | Zhang, Zhebin | Jiang, Hui | Yan, Surong

Article Type: Research Article

Abstract: With the in-depth application of artificial intelligence technology in the financial field, credit scoring models constructed by machine learning algorithms have become mainstream. However, the high-dimensional and complex attribute features of the borrower pose challenges to the predictive competence of the model. This paper proposes a hybrid model with a novel feature selection method and an enhanced voting method for credit scoring. First, a novel feature selection combined method based on a genetic algorithm (FSCM-GA) is proposed, in which different classifiers are used to select features in combination with a genetic algorithm and combine them to generate an optimal …feature subset. Furthermore, an enhanced voting method (EVM) is proposed to integrate classifiers, with the aim of improving the classification results in which the prediction probability values are close to the threshold. Finally, the predictive competence of the proposed model was validated on three public datasets and five evaluation metrics (accuracy, AUC, F-score, Log loss and Brier score). The comparative experiment and significance test results confirmed the good performance and robustness of the proposed model. Show more

Keywords: Credit scoring, hybrid model, feature selection, machine learning, ensemble learning

DOI: 10.3233/JIFS-211828

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2565-2579, 2022

Authors: Li, Lin | Yu, Xiaolei | Liu, Zhenlu | Zhao, Zhimin | Wu, Chao | Zhang, Ke | Zhou, Shanhao

Article Type: Research Article

Abstract: As a non-contact automatic identification technology, Radio Frequency Identification (RFID) is of great significance to improve the simultaneous identification of multi-target. This paper designs a more efficient and accurate multi-tag reading performance measurement system based on the fusion of YOLOv3 and Elman neural network. In the machine vision subsystem, multi-tag images are collected by dual CCD and detected by neural network algorithm. The reading distance of 3D distributed multi-tag is measured by laser ranging to evaluate the reading performance of RFID system. Firstly, the multi-tag are detected by YOLOv3, which realizes the measurement of 3D coordinates, improves the prediction accuracy, …enhances the recognition ability of small targets, and improves the accuracy of 3D coordinate detection. Secondly, the relationship between the 3D coordinates and the corresponding reading distance of RFID multi-tag are modelled by Elman recurrent neural network. Finally, the reading performance of RFID multi-tag is optimized. Compared with the state-of-the-arts, the multi-tag detection rate of YOLOv3 is 17.4% higher and the time is 3.27 times higher than that of the previous template matching algorithm. In terms of reading performance, the MAPE of Elman neural network is 1.46 %, which is at least 21.43 % higher than other methods. In running time, Elman only needs 1.69s, which is at least 28.40% higher than others. Thus, the system not only improves the accuracy, but also improves the speed, which provides a new insight for the measurement and optimization of RFID performance. Show more

Keywords: RFID, neural network, Elman, YOLOv3, performance optimization

DOI: 10.3233/JIFS-211838

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2581-2594, 2022

Authors: Wu, Jian | Jin, Yuting | Zhou, Mi | Cao, Mingshuo | Liu, Yujia

Article Type: Research Article

Abstract: Sustainable supplier selection (SSS) plays an increasingly critical role in the stability and development of the organization with increasing environmental awareness. This article proposes a linguistic multiple attribute group decision-making method to select the appropriate sustainable supplier by combing Decision Making and Trial Evaluation Laboratory(DEMATEL) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). To do that, a distribution linguistic based DEMATEL technique is developed to deal with the complexity in criteria of SSS. To eliminate the inconsistency among multiple decision-makers providing the preference information of evaluation criteria, a minimum adjusting cost feedback mechanism is utilized to reach group consensus. Therefore, the …proposed weights obtaining method can not only deal with the subjectivity of evaluation criterion but also satisfy group decision-makers with different profits and backgrounds. Then, based on the evaluation matrices of supplier performance, it calculates the ranking of alternative suppliers by the VIKOR method. Hence, it can deal with the ambiguity of decision makers’ evaluation and provide the best solution for decision-makers, as a consequence, it makes the final evaluation result more feasible and operable. Finally, the effectiveness and efficiency of this method are verified based on the actual situation of ABC Company. This study proposed a linguistic multiple attribute group decision-making method to select the appropriate sustainable supplier by combing Decision Making and Trial Evaluation Laboratory(DEMATEL) and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). What’s more, the proposed method considered the group consensus reaching processes. Show more

Keywords: Sustainable supplier selection, , Group decision making, DEMATEL, VIKOR, Consensus

DOI: 10.3233/JIFS-211929

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2595-2613, 2022

Authors: Guo, Dayong | Hu, Qing

Article Type: Research Article

Abstract: Aiming at the problems of low precision, slow data transmission speed and long response time of silk quality and temperature control in tobacco intelligent production line, a multi-index testing system is designed. According to the characteristics of PROFIBUS fieldbus technology, combined with PROFIBUS transmission technology, a factory level information network is formed with PROFIBUS-DP as the exchange mode. Based on the PROFIBUS technology, the dual redundancy structure of control ring network and management information ring network is adopted, and the whole network architecture is constructed by logic layering. From the point of view of building enterprise MES system, it locates …real-time production monitoring, production task receiving and production line related data collection, integrates equipment control layer, centralized monitoring layer and production management layer, and designs system function structure. The functional structure of the system, and the establishment of a number of data tables, to achieve a tobacco intelligent production line silk quality detection system design. Experimental results show that this method can effectively speed up the data transmission speed and shorten the system response time. Show more

Keywords: PROFIBUS fieldbus technology, tobacco intelligent production line, silk quality, multi-index integrated test system

DOI: 10.3233/JIFS-211936

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2615-2627, 2022

Authors: Zhou, Yinfeng | Li, Jinjin | Wang, Hongkun | Sun, Wen

Article Type: Research Article

Abstract: In knowledge space theory (KST), knowledge structure is an effective feature to evaluate individuals’ knowledge and guide future learning. How to construct knowledge structures is one of the key research problems in KST. At present, the knowledge structure has been generalized to the polytomous knowledge structure. This article mainly focuses on the special polytomous knowledge structures delineated by skills, which are called fuzzy knowledge structures. We consider how to construct fuzzy knowledge structures based on the relationship between items and skills, and how to find the learning paths for specific knowledge domains. First, we construct knowledge structures in four models, …which are the conjunctive model of skill maps, the disjunctive and conjunctive models of fuzzy skill maps, and the competency model of fuzzy skill multimaps. Second, we assess individuals’ skills and find the learning paths for the specific knowledge domains in the first three models. Finding the learning paths for a specific knowledge domain can guide learning and improve the learning efficiency of individuals. Finally, we analyze some data sets to show that the algorithms proposed are effective and applicable. These works can be applied to adaptive learning systems, which bring great convenience for assessing individuals’ knowledge and guiding future learning. Show more

Keywords: Fuzzy knowledge structure, learning path, disjunctive model, conjunctive model, competency model

DOI: 10.3233/JIFS-212018

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2629-2645, 2022

Authors: Prakash, R | Ayyar, K

Article Type: Research Article

Abstract: This paper presents an Enhanced Whale Optimization Algorithm (EWO) approach for tuning to perfection of Fractional Order Proportional Integral and integral order Controller (FOPI λ ) is used to sensorless speed control of permanent magnet Brushless DC (PMBLDC) motor under the operating dynamic condition such as (i) speed change by set speed command signal (ii) varying load conditions, (iii) integrated conditions and (iv) controller parameters uncertainty. On the other hand, it deals with a reduced THD (Total Harmonic Distortion) under dynamic operating conditions to improve the power quality for the above control system. Here present are three optimization techniques, namely …(i) Enhanced Whale Optimization (EWO), (ii) Invasive Weed Optimization (IWO), and (iii) Social Spider Optimization (SSO) for fine-tuning of the FOPI λ controller parameters with reduction of THD. The proposed optimization algorithm optimized FOPI λ controller are compared under various BLDC motor operating conditions. Based on the results of MATLAB/Simulink models, the proposed algorithms are evaluated. Here, both the simulation and the results of the experiments are validated for the proposed controller technique. It demonstrates that the effectiveness of the proposed controllers is completely validated by comparing the three intelligent optimization techniques mentioned above. The EWO optimized FOPI λ controller for speed control of sensorless PMBLDC motor clearly outperforms the other two intelligent controllers by minimizing the time domain parameters, THD, performance Indices error, convergence time, control efforts, cost function, mean and standard deviation. Show more

Keywords: BLDC motor drives, fractional order PID controller (FOPIλ), whale optimization algorithm, sensorless speed control techniques

DOI: 10.3233/JIFS-212167

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2647-2666, 2022

Authors: Murugesan, Malathi | Kaliannan, Kalaiselvi | Balraj, Shankarlal | Singaram, Kokila | Kaliannan, Thenmalar | Albert, Johny Renoald

Article Type: Research Article

Abstract: Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage. The primary goal of this effort is to properly identify lung cancer, which is critical in preserving a person’s life. Lung cancer has been a source of concern for people all around the world for decades. Several researchers presented numerous issues and solutions for various stages of a computer-aided system for diagnosing lung cancer in its early stages, as well as information about lung cancer. Computer vision is one of the field of artificial intelligence this is a better way to detect and prevent the …lung cancer. This study focuses on the stages involved in detecting lung tumor regions, namely pre-processing, segmentation, and classification models. An adaptive median filter is used in pre-processing to identify the noise. The work’s originality seeks to create a simple yet effective model for the rapid identification and U-net architecture based segmentation of lung nodules. This approach focuses on the identification and segmentation of lung cancer by detecting picture normalcy and abnormalities. Show more

Keywords: Lung cancer, pre-processing, support vector machine, deep learning, U-Net, classification accuracy

DOI: 10.3233/JIFS-212189

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2667-2679, 2022

Authors: Angappamudaliar Palanisamy, Senthil Kumar | Selvaraj, Dinesh | Ramasamy, SivaBalaKrishnan

Article Type: Research Article

Abstract: In the field of mobile robot decision making and control, path planning is an essential element as it defines the performance of the design. It is one of the hot topics in artificial intelligence and researchers pay more attention to develop an efficient model. The key requirements that must be considered while designing a navigational system for mobile robots are origin point, obstacles, destination point, path planning, and realistic decision mechanism. However, conventional systems have limitations as slow response, long planning, large turns, and unsafe factors. Aiming at the problems, this research work presents a hybrid optimized path planning model …for a mobile robot. Improved particle swarm optimization and Modified Whale optimization models are incorporated as a hybrid multi-objective approach to obtain the shortest, smoothest, and safest path for a mobile robot. Experimental results demonstrate that the proposed hybrid optimization model is suitable for mobile robot navigation for dynamic environments by obtaining a shorter, smoother, and safer path than existing algorithms. Show more

Keywords: Path planning, path optimization, hybrid optimization, mobile robot, improved particle swarm optimization

DOI: 10.3233/JIFS-211801

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2681-2693, 2022

Authors: Iqbal, M. Mohamed | Latha, K.

Article Type: Research Article

Abstract: Link prediction plays a predominant role in complex network analysis. It indicates to determine the probability of the presence of future links that depends on available information. The existing standard classical similarity indices-based link prediction models considered the neighbour nodes have a similar effect towards link probability. Nevertheless, the common neighbor nodes residing in different communities may vary in real-world networks. In this paper, a novel community information-based link prediction model has been proposed in which every neighboring node’s community information (community centrality) has been considered to predict the link between the given node pair. In the proposed model, the …given social network graph can be divided into different communities and community centrality is calculated for every derived community based on degree, closeness, and betweenness basic graph centrality measures. Afterward, the new community centrality-based similarity indices have been introduced to compute the community centralities which are applied to nine existing basic similarity indices. The empirical analysis on 13 real-world social networks datasets manifests that the proposed model yields better prediction accuracy of 97% rather than existing models. Moreover, the proposed model is parallelized efficiently to work on large complex networks using Spark GraphX Big Data-based parallel Graph processing technique and it attains a lesser execution time of 250 seconds. Show more

Keywords: Link prediction, social network, performance evaluation, prediction accuracy, parallel louvain algorithm

DOI: 10.3233/JIFS-211821

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2695-2711, 2022

Authors: Pang, Jing | Yao, Bingxue | Li, Lingqiang

Article Type: Research Article

Abstract: In this paper, we point out that Lin’s general neighborhood systems-based rough set model is an extension of Qian’s optimistic rough set model, and thus called optimistic general neighborhood systmes-based rough set model. Then we present a new rough set model based on general neighborhood systems, and prove that it is an extension of Qian’s pessimistic rough set model. Later, we study the basic properties of the proposed pessimistic rough sets, and define the serial, reflexive, symmetric, transitive and Euclidean conditions for general neighborhood systems, and explore the further properties of related rough sets. Furthermore, we apply the pessimistic general …neighborhood systems-based rough set model in the research of incomplete information system, and build a three-way decision model based on it. A simple practical example to show the effectiveness of our model is also presented. Show more

Keywords: Rough set, neighborhood system, pessimistic rough approximation operator, three-way decision

DOI: 10.3233/JIFS-211851

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2713-2725, 2022

Authors: Luo, Wentao | Feng, Pingfa | Zhang, Jianfu | Yu, Dingwen | Wu, Zhijun

Article Type: Research Article

Abstract: As the service life of the assembly equipment are short, the tightening data it produces are very limited. Therefore, data-driven assembly quality diagnosis is still a challenge task in industries. Transfer learning can be used to address small data problems. However, transfer learning has strict requirements on the training dataset, which is hard to satisfy. To solve the above problem, an Improved Deep Convolution Generative Adversarial Transfer Learning Model (IDCGAN-TM) is proposed, which integrates three modules: The generative learning module automatically produces source datasets based on small target datasets by using the improved generative-adversarial theory. The feature learning module improves …the feature extraction ability by building a lightweight deep learning model (DL). The transfer learning module consists of a pre-trained DL and a one fully connected layer to better perform the intelligent quality diagnosis on the training small sample data. A parallel computing method is adopted to obtain produced source data efficiently. Real assembly quality diagnosis cases are designed and discussed to validate the advance of the proposed model. In addition, the comparison experiments are designed to show that the proposed approach holds the better transfer diagnosis performance compared with the existing three state-of-art approaches. Show more

Keywords: Transfer learning, generative adversarial learning, small sample learning, quality diagnosis

DOI: 10.3233/JIFS-211860

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2727-2741, 2022

Authors: Sambath Kumar, K. | Rajendran, A.

Article Type: Research Article

Abstract: Manual segmentation of brain tumor is not only a tedious task that may bring human mistakes. An automatic segmentation gives results faster, and it extends the survival rate with an earlier treatment plan. So, an automatic brain tumor segmentation model, modified inception module based U-Net (IMU-Net) proposed. It takes Magnetic resonance (MR) images from the BRATS 2017 training dataset with four modalities (FLAIR, T1, T1ce, and T2). The concatenation of two series 3×3 kernels, one 5×5, and one 1×1 convolution kernels are utilized to extract the whole tumor (WT), core tumor (CT), and enhance tumor (ET). The modified inception module …(IM) collects all the relevant features and provides better segmentation results. The proposed deep learning model contains 40 convolution layers and utilizes intensity normalization and data augmentation operation for further improvement. It achieved the mean dice similarity coefficient (DSC) of 0.90, 0.77, 0.74, and the mean Intersection over Union (IOU) of 0.79, 0.70, 0.70 for WT, CT, and ET during the evaluation. Show more

Keywords: Brain tumor, automatic segmentation, deep neural network, inception, convolution

DOI: 10.3233/JIFS-211879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2743-2754, 2022

Authors: Liu, Wei | Wang, Yuhong

Article Type: Research Article

Abstract: In view of the present situation that most aggregation methods of fuzzy preference information are extended or mixed by classical aggregation operators, which leads to the aggregation accuracy is not high. The purpose of this paper is to develop a novel method for spatial aggregation of fuzzy preference information. Thus we map the fuzzy preference information to a set of three-dimensional coordinate and construct the spatial aggregation model based on Steiner-Weber point. Then, the plant growth simulation algorithm (PGSA) algorithm is used to find the spatial aggregation point. According to the comparison and analysis of the numerical example, the aggregation …matrix established by our method is closer to the group preference matrices. Therefore, the optimal aggregation point obtained by using the optimal aggregation method based on spatial Steiner-Weber point can best represent the comprehensive opinion of the decision makers. Show more

Keywords: Fuzzy preference information, Steiner-Weber point, spatial aggregation model, aggregation operator, PGSA

DOI: 10.3233/JIFS-211913

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2755-2773, 2022

Authors: Honghong, Zhang | Xusheng, Gan | Ying, Liu | Yarong, Wu | Jingjuan, Sun | Liang, Tong | Feng, Yang

Article Type: Research Article

Abstract: To provide real-time safety assessment for low-altitude unmanned aerial vehicle (UAV) air traffic management, and to ensure the UAVs safe operation in low-altitude airspace, a risk assessment framework is proposed. It considers the accidents probability and the accidents hazards. Firstly, accidents probability model based on the System Theoretic Process Analysis-Bayesian Network (STPA-BN) algorithm is built. Potential system hazards are effectively identified and analyzed through the STPA process. The accidents cause identified based on the STPA process is taken as the root node. The relevant failure probability table is given respectively. It constitutes the BN used to analyze the system accidents …probability. This method uses a combination of qualitative and quantitative methods to calculate the accidents probability. Then, based on the UAV fall model, considering the uncertainty of the UAV operation process, the UAV fall point distribution is determined based on the Monte-Carlo method, and the impact area of the fall is calculated. Thus the system risk value is obtained. Finally, through case analysis, the validity and rationality of the proposed risk assessment framework are verified. Show more

Keywords: Unmanned aerial vehicle system, air traffic management, risk assessment

DOI: 10.3233/JIFS-211927

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2775-2792, 2022

Authors: Jeyaprakash, P. | Agees Kumar, C. | Ravi, A.

Article Type: Research Article

Abstract: Electricity is the most critical facility for humans. All traditional energy supplies are rapidly depleting. As a result, the energy resources are moved from traditional to non-conventional. In this research, mixture of two energy tools, namely wind and solar energy are used. Using a Hybrid Energy Storage System (HESS), continuous power can be provided. Electricity can be produced at a cost that is affordable. The integration of solar and wind in a hybrid system cause an increase in the system’s stability, which is the key benefit of this research. The system’s power transmission efficiency and reliability can be greatly enhanced …by integrating these two intermittent sources. When one of the energy source is unavailable or inadequate to meet load demands, the other energy source will supply the power. The major contribution in this research is that, the proposed bidirectional single-inductor multiple-port (BSIMP) converter significantly lowers the component count, smaller circuit size and lower cost, allowing HESS to be integrated into DC microgrid. Minimum number of components are used for the same number of ESs in HESS in the proposed BSIMP converter. The hybridization of battery and supercapacitor (SC) for storage purpose is more cost effective, as compared to the battery energy storage system, thus improving the battery stress and hence used for large scale grid energy storage. SC’s are accepted as backup and found very useful in delivering high power, not possible with batteries. The use of SC in addition to batteries can be one solution for achieving the low life cycle economy. The Single Objective Adaptive Firefly Algorithm (SOAFA) is introduced for optimising the Proportional-Integral (PI) controller parameters. The system cost is reduced by about 32%, with the constraints on wind turbine swept area, PV area, total battery and SC capacity with the proposed optimisation algorithm. Show more

Keywords: Bidirectional single-inductor multiple-port (BSIMP) converter, Single Objective Adaptive firefly Algorithm (SOAFA), PI controller, Hybrid Energy Storage System (HESS)

DOI: 10.3233/JIFS-212262

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2793-2808, 2022

Authors: Sha, Gang | Wu, Junsheng | Yu, Bin

Article Type: Research Article

Abstract: Purpose: at present, more and more deep learning algorithms are used to detect and segment lesions from spinal CT (Computed Tomography) images. But these algorithms usually require computers with high performance and occupy large resources, so they are not suitable for the clinical embedded and mobile devices, which only have limited computational resources and also expect a relative good performance in detecting and segmenting lesions. Methods: in this paper, we present a model based on Yolov3-tiny to detect three spinal fracture lesions, cfracture (cervical fracture), tfracture (thoracic fracture), and lfracture (lumbar fracture) with a small size model. We …construct this novel model by replacing the traditional convolutional layers in YoloV3-tiny with fire modules from SqueezeNet, so as to reduce the parameters and model size, meanwhile get accurate lesions detection. Then we remove the batch normalization layers in the fire modules after the comparative experiments, though the overall performance of fire module without batch normalization layers is slightly improved, we can reduce computation complexity and low occupations of computer resources for fast lesions detection. Results: the experiments show that the shrank model only has a size of 13 MB (almost a third of Yolov3-tiny), while the mAP (mean Average Precsion) is 91.3%, and IOU (intersection over union) is 90.7. The detection time is 0.015 second per CT image, and BFLOP/s (Billion Floating Point Operations per Second) value is less than Yolov3-tiny. Conclusion: the model we presented can be deployed in clinical embedded and mobile devices, meanwhile has a relative accurate and rapid real-time lesions detection. Show more

Keywords: Deep learning, Yolov3-tiny, shrank model, fire module, detection and location

DOI: 10.3233/JIFS-212255

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 3, pp. 2809-2828, 2022