Journal of Intelligent & Fuzzy Systems - Volume 42, issue 4

Authors: Zhu, Xiaowei | Han, Yu | Li, Shichong | Wang, Xinyin

Article Type: Research Article

Abstract: With the rapid growth of social network users, the social network has accumulated massive social network topics. However, due to the randomness of content, it becomes sparse and noisy, accompanied by many daily chats and meaningless topics, which brings challenges to bursty topics discovery. To deal with these problems, this paper proposes the spatial-temporal topic model with sparse prior and recurrent neural networks (RNN) prior for bursty topic discovering (ST-SRTM). The semantic relationship of words is learned through RNN to alleviate the sparsity. The spatial-temporal areas information is introduced to focus on bursty topics for further weakening the semantic sparsity …of social network context. Besides, we introduced the “Spike and Slab” prior to decouple the sparseness and smoothness. Simultaneously, we realized the automatic discovery of social network bursts by introducing the burstiness of words as the prior and binary switching variables. We constructed multiple sets of comparative experiments to verify the performance of ST-SRTM by leveraging different evaluation indicators on real Sina Weibo data sets. The experimental results confirm the superiority of our ST-SRTM. Show more

Keywords: Social network, bursty topic, topic model, RNN, sparse prior

DOI: 10.3233/JIFS-212135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3909-3922, 2022

Authors: Rashwan, Rashwan A. | Hammad, Hasanen A. | Nafea, A.

Article Type: Research Article

Abstract: In this manuscript, the concept of a cyclic tripled type fuzzy cone contraction mapping in the setting of fuzzy cone metric spaces is introduced. Also, some theoretical results concerned with tripled fixed points are given without a mixed monotone property in the mentioned space. Moreover, under this concept, some strong tripled fixed point results are obtained. Ultimately, to support the theoretical results non-trivial examples are listed and the existence of a unique solution to a system of integral equations is presented as an application.

Keywords: Strong tripled fixed point, fuzzy cone metric space, contraction condition, integral equation, cyclic tripled type fuzzy cone contraction mapping

DOI: 10.3233/JIFS-212188

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3923-3943, 2022

Authors: Wang, Yini | Wang, Sichun

Article Type: Research Article

Abstract: Fuzzy relation is one of the main research contents of fuzzy set theory. This paper obtains some results on fuzzy relations by studying relationships between fuzzy relations and their uncertainty measurement. The concepts of equality, dependence, partial dependence and independence between fuzzy relations are first introduced. Then, uncertainty measurement for a fuzzy relation is investigated by using dependence between fuzzy relations. Moreover, the basic properties of uncertainty measurement are obtained. Next, effectiveness analysis is carried out. Finally, an application of the proposed measures in attribute reduction for heterogeneous data is given. These results will be helpful for understanding the essence …of a fuzzy relation. Show more

Keywords: Fuzzy relation, dependence, uncertainty, measurement, attribute reduction, heterogeneous data

DOI: 10.3233/JIFS-212215

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3945-3961, 2022

Authors: Su, Nan | Lin, Zhishuo | You, Wenlong | Zheng, Nan | Ma, Kun

Article Type: Research Article

Abstract: Management of garbage classification is a general term for a series of activities to sort, store and transport garbage into public resources according to certain regulations or standards. Current garbage classification systems have several drawbacks, such as inability to identify multiple garbage categories, and high dependence on the surrounding environment. To address these issues, this paper has proposed the Real Time Multi-Modal Garbage classification System (abbreviated as RMGCS). It consists of two sub systems: an indoor garbage classification applet (abbreviated as IGCA) and an outdoor garbage classification system (abbreviated as OGCS). IGCA provides users with three methods of garbage classification, …and OGCS provides users with outdoor real-time multi-target garbage classification and can dynamically update the recognition model. RMGCS achieves real-time, accurate, and multimodal classification. Finally, the experiments with RMGCS show that our approaches are effective and efficient. Show more

Keywords: Garbage classification, multi-modality, picture recognition, real-time video recognition

DOI: 10.3233/JIFS-212225

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3963-3973, 2022

Authors: Li, Dong | Gong, Lanlan | Liu, Shulin | Sun, Xin | Gu, Ming | Qian, Kun

Article Type: Research Article

Abstract: The traditional batch learning classification methods need to obtain all kinds of data once before training. This makes them unable to recognize the data from the unseen types and cannot continuously enhance their classification ability through learning the testing data in the testing process, because they lack continual learning ability. Inspired by the continual learning mechanism of the biological immune system (BIS), this paper proposed a continual learning classification method with single-label memory cells (S-CLCM). The type of testing data is identified by memory cells, and the data type from unseen types is determined by an affinity threshold. New memory …cells are cultivated continuously by learning the testing data to enhance the classification ability of S-CLCM gradually. Every memory cell has the same size and a unique type. It becomes a standard batch learning classification method or a standard clustering method under certain conditions. Take the experiments on twenty benchmark datasets to estimate its classification performance and possible superiority. Results show S-CLCM has good performance when it becomes a standard batch learning classification method, and S-CLCM is superior to the other classical classification algorithms when the data from unseen types or new labeled data appear during the testing process. It can improve the classification accuracy by up to 33%, and by at least 14%. Show more

Keywords: Classification, continual learning, biological immune system, machine learning, artificial immune algorithm

DOI: 10.3233/JIFS-212226

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3975-3991, 2022

Authors: Yang, Jie | Luo, Tian | Zeng, Lijuan | Jin, Xin

Article Type: Research Article

Abstract: Neighborhood rough sets (NRS) are the extended model of the classical rough sets. The NRS describe the target concept by upper and lower neighborhood approximation boundaries. However, the method of approximately describing the uncertain target concept with existed neighborhood information granules is not given. To solve this problem, the cost-sensitive approximation model of the NRS is proposed in this paper, and its related properties are analyzed. To obtain the optimal approximation granular layer, the cost-sensitive progressive mechanism is proposed by considering user requirements. The case study shows that the reasonable granular layer and its approximation can be obtained under certain …constraints, which is suitable for cost-sensitive application scenarios. The experimental results show that the advantage of the proposed approximation model, moreover, the decision cost of the NRS approximation model will monotonically decrease with granularity being finer. Show more

Keywords: Neighborhood rough sets, approximation model, cost-sensitive, Granular layer selection

DOI: 10.3233/JIFS-212234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3993-4003, 2022

Authors: Yu, Zhiqiang | Huang, Yuxin | Guo, Junjun

Article Type: Research Article

Abstract: It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions. Thai-Lao is a typical low-resource language pair of tiny parallel corpus, leading to suboptimal NMT performance on it. However, Thai and Lao have considerable similarities in linguistic morphology and have bilingual lexicon which is relatively easy to obtain. To use this feature, we first build a bilingual similarity lexicon composed of pairs of similar words. Then we propose a novel NMT architecture to leverage the similarity between Thai and Lao. Specifically, besides the prevailing sentence encoder, we introduce an extra similarity lexicon encoder …into the conventional encoder-decoder architecture, by which the semantic information carried by the similarity lexicon can be represented. We further provide a simple mechanism in the decoder to balance the information representations delivered from the input sentence and the similarity lexicon. Our approach can fully exploit linguistic similarity carried by the similarity lexicon to improve translation quality. Experimental results demonstrate that our approach achieves significant improvements over the state-of-the-art Transformer baseline system and previous similar works. Show more

Keywords: Neural machine translation, Thai-Lao, linguistic similarity, structure improving, lexicon

DOI: 10.3233/JIFS-212236

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4005-4014, 2022

Authors: Binh, Nguyen Thanh | Hien, Nguyen Mong | Tin, Dang Thanh

Article Type: Research Article

Abstract: The central retinal artery and its branches supply blood to the inner retina. Vascular manifestations in the retina indirectly reflect the vascular changes and damage in organs such as the heart, kidneys, and brain because of the similar vascular structure of these organs. The diabetic retinopathy and risk of stroke are caused by increased venular caliber. The degrees of these diseases depend on the changes of arterioles and venules. The ratio between the calibers of arterioles and venules (AVR) is various. AVR is considered as the useful diagnostic indicator of different associated health problems. However, the task is not easy …because of the lack of information of the features being used to classify the retinal vessels as arterioles and venules. This paper proposed a method to classify the retinal vessels into the arterioles and venules based on improving U-Net architecture and graph cuts. The accuracy of the proposed method is about 97.6%. The results of the proposed method are better than the other methods in RITE dataset and AVRDB dataset. Show more

Keywords: Arterioles, venules, U-Net architecture, graph cuts, retinal blood vessels

DOI: 10.3233/JIFS-212259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4015-4026, 2022

Authors: Zhong, Xianyou | Xia, Tianyi | Zhao, Yankun | Zhao, Xiao

Article Type: Research Article

Abstract: The weak fault characteristics of rolling bearings are difficult to identify due to strong background noise. To address this issue, a bearing fault detection scheme combining swarm decomposition (SWD) and frequency-weighted energy operator (FWEO) is presented. First, SWD is applied to decompose the bearing fault signal into single mode components. Then, a new evaluation index termed LEP is constructed by combining the advantages of envelope entropy, Pearson correlation coefficient and L-kurtosis, and it is utilized to choose the sensitive component containing the richest bearing fault characteristics. Finally, FWEO is employed for extracting the bearing fault features from the sensitive component. …Simulation and experimental analyses indicate that the LEP index has better performance than the L-kurtosis index in determining the sensitive component. The method has the effect of suppressing noise and enhancing impulse characteristics, which is superior to the SWD-based envelope demodulation method. Show more

Keywords: Swarm decomposition, frequency-weighted energy operator, fault diagnosis, rolling bearing

DOI: 10.3233/JIFS-212305

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4027-4039, 2022

Authors: Guo, Wenbin | Zhang, Juan

Article Type: Research Article

Abstract: This article propose s a network that is mainly used to deal with a single image polluted by raindrops in rainy weather to get a clean image without raindrops. In the existing solutions, most of the methods rely on paired images, that is, the rain image and the real image without rain in the same scene. However, in many cases, the paired images are difficult to obtain, which makes it impossible to apply the raindrop removal network in many scenarios. Therefore this article proposes a semi-supervised rain-removing network apply to unpaired images. The model contains two parts: a supervised network …and an unsupervised network. After the model is trained, the unsupervised network does not require paired images and it can get a clean image without raindrops. In particular, our network can perform training on paired and unpaired samples. The experimental results show that the best results are achieved not only on the supervised rain-removing network, but also on the unsupervised rain-removing network. Show more

Keywords: Rain removal, raindrop detection, semi-supervised learning, image restoration, shared weight

DOI: 10.3233/JIFS-212342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4041-4049, 2022

Authors: Kalimuthu, Raj Kumar | Thomas, Brindha

Article Type: Research Article

Abstract: In today’s world, cloud computing plays a significant role in the development of an effective computing paradigm that adds more benefits to the modern Internet of Things (IoT) frameworks. However, cloud resources are considered to be dynamic and the demands necessitated for resource allocation for a certain task are different. These diverse factors may cause load and power imbalance which also affect the resource utilization and task scheduling in the cloud-based IoT environment. Recently, a bio-inspired algorithm can work effectually to solve task scheduling problems in the cloud-based IoT system. Therefore, this work focuses on efficient task scheduling and resource …allocation through a novel Hybrid Bio-Inspired algorithm with the hybridized of Improvised Particle Swarm Optimization and Ant Colony Optimization. The vital objective of hybridizing these two approaches is to determine the nearest multiple sources to attain discrete and continuous solutions. Here, the task has been allocated to the virtual machine through a particle swarm and continuous resource management can be carried out by an ant colony. The performance of the proposed approach has been evaluated using the CloudSim simulator. The simulation results manifest that the proposed Hybridized algorithm efficiently scheduling the task in the cloud-based IoT environment with a lesser average response time of 2.18 sec and average waiting time of 3.6 sec as compared with existing state-of-the-art algorithms. Show more

Keywords: Metaheuristic algorithm, Internet of Things, cloud computing, resource optimization, scheduling algorithms

DOI: 10.3233/JIFS-212370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4051-4063, 2022

Authors: Huang, Xiaoqing | Wang, Zhilong | Liu, Shihao

Article Type: Research Article

Abstract: In order to solve the problem of health evaluation of CNC machine tools, an evaluation method based on grey clustering analysis and fuzzy comprehensive evaluation was proposed. The health status grade of in-service CNC machine tools was divided, and the performance indicator system of CNC machine tools was constructed. On the above basis, the relative importance of each performance and its indicators were combined, and grey clustering analysis and fuzzy comprehensive evaluation was utilized to evaluate the health status of in-service CNC machine tools to determine their health grade. The proposed health status evaluation method was applied to evaluate the …health level of an in-service gantry CNC machine that can be used for the machining propellers, and the results shown that the health status of the whole gantry CNC machine tool is healthy. The proposed evaluation method provides useful references for further in-depth research on the health status analysis and optimization of CNC machine tools. Show more

Keywords: CNC machine tools, grey clustering, fuzzy comprehensive evaluation, health evaluation, green performance

DOI: 10.3233/JIFS-212406

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4065-4082, 2022

Authors: Javid, Irfan | Zager Alsaedi, Ahmed Khalaf | Ghazali, Rozaida | Mohmad Hassim, Yana Mazwin | Zulqarnain, Muhammad

Article Type: Research Article

Abstract: In previous studies, various machine-driven decision support systems based on recurrent neural networks (RNN) were ordinarily projected for the detection of cardiovascular disease. However, the majority of these approaches are restricted to feature preprocessing. In this paper, we concentrate on both, including, feature refinement and the removal of the predictive model’s problems, e.g., underfitting and overfitting. By evading overfitting and underfitting, the model will demonstrate good enactment on equally the training and testing datasets. Overfitting the training data is often triggered by inadequate network configuration and inappropriate features. We advocate using Chi2 statistical model to remove irrelevant features when …searching for the best-configured gated recurrent unit (GRU) using an exhaustive search strategy. The suggested hybrid technique, called Chi2 GRU, is tested against traditional ANN and GRU models, as well as different progressive machine learning models and antecedently revealed strategies for cardiopathy prediction. The prediction accuracy of proposed model is 92.17%. In contrast to formerly stated approaches, the obtained outcomes are promising. The study’s results indicate that medical practitioner will use the proposed diagnostic method to reliably predict heart disease. Show more

Keywords: Gated recurrent unit, heart disease, overfitting, underfitting, feature selection

DOI: 10.3233/JIFS-212438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4083-4094, 2022

Authors: Ulusu, Uğur | Gülle, Esra

Article Type: Research Article

Abstract: The main purpose of this paper is introduced the concept of deferred Cesàro mean in the Wijsman sense for double sequences of sets and then presented the concepts of strongly deferred Cesàro summability and deferred statistical convergence in the Wijsman sense for double sequences of sets. Also, investigate the relationships between these concepts and then to prove some theorems associated with the concepts of deferred statistical convergence in the Wijsman sense for double sequences of sets is purposed.

Keywords: Deferred Cesàro summability, deferred statistical convergence, double sequences of sets, convergence in the Wijsman sense

DOI: 10.3233/JIFS-212486

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4095-4103, 2022

Authors: Zhang, Qinghui | Wu, Meng | Lv, Pengtao | Zhang, Mengya | Yang, Hongwei

Article Type: Research Article

Abstract: In the medical field, Named Entity Recognition (NER) plays a crucial role in the process of information extraction through electronic medical records and medical texts. To address the problems of long distance entity, entity confusion, and difficulty in boundary division in the Chinese electronic medical record NER task, we propose a Chinese electronic medical record NER method based on the multi-head attention mechanism and character-word fusion. This method uses a new character-word joint feature representation based on the pre-training model BERT and self-constructed domain dictionary, which can accurately divide the entity boundary and solve the impact of unregistered words. Subsequently, …on the basis of the BiLSTM-CRF model, a multi-head attention mechanism is introduced to learn the dependency relationship between remote entities and entity information in different semantic spaces, which effectively improves the performance of the model. Experiments show that our models have better performance and achieves significant improvement compared to baselines. The specific performance is that the F1 value on the Chinese electronic medical record data set reaches 95.22%, which is 2.67%higher than the F1 value of the baseline model. Show more

Keywords: Chinese electronic medical records, name entity recognition, character-word information fusion, multi-head attention

DOI: 10.3233/JIFS-212495

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4105-4116, 2022

Authors: Albert, Johny Renoald | Sharma, Aditi | Rajani, B. | Mishra, Ashish | Saxena, Ankur | Nandagopal, C. | Mewada, Shivlal

Article Type: Research Article

Abstract: A new Symmetric Solar Fed Inverter (SSFI) proposed with a reduced number of components compared to the classical, modified, conventional type of Multilevel Inverter (MLI). The objective of this architecture is to design fifteen-level SSFI, this circuit uses a single switch with minimizing harmonics, and Modulation Index (MI) values. Power Quality (PQ) is developed by using the optimization algorithms like as Particle Swarm Optimization (PSO), Genetic algorithm (GA), Modified Firefly Algorithm (MFA). It’s determined to generate the gating pulse and finding optimum firing angle values calculate as per the input of MPP intelligent controller schemes. The proposed circuit is solar …fed inverter used for optimization techniques governed by switching controller approach delivers a major task. The comparison is made for different optimization algorithm has significantly reduced the harmonic content by varying the modulation index and switching angle values. SSFI generates low distortion output uses through without any additional filter component through utilizing MATLAB Simulink software (2020a). The SSFI circuit assist Xilinx Spartan 3-AN Filed Program Gate Array (FPGA) tuned by optimization techniques are presented for the effectiveness of the proposed model. Show more

Keywords: Symmetric solar fed inverter, particle swarm optimization, genetic algorithm, modified firefly algorithm, power quality

DOI: 10.3233/JIFS-212559

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4117-4133, 2022

Authors: Do Xuan, Cho | Duong, Duc

Article Type: Research Article

Abstract: Nowadays, early detecting and warning Advanced Persistent Threat (APT) attacks is a major challenge for intrusion monitoring and prevention systems. Current studies and proposals for APT attack detection often focus on combining machine-learning techniques and APT malware behavior analysis techniques based on network traffic. To improve the efficiency of APT attack detection, this paper proposes a new approach based on a combination of deep learning networks and ATTENTION networks. The proposed process for APT attack detection in this study is as follows: Firstly, all data of network traffic is pre-processed, and analyzed by the CNN-LSTM deep learning network, which is …a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Then, instead of being used directly for classification, this data is analyzed and evaluated by the ATTENTION network. Finally, the output data of the ATTENTION network is classified to identify APT attacks. The optimization proposal for detecting APT attacks in this study is a novel proposal. It hasn’t been proposed and applied by any research. Some scenarios for comparing and evaluating the method proposed in this study with other approaches (implemented in section 4.4) show the superior effectiveness of our proposed approach. The results prove that the proposed method not only has scientific significance but also has practical significance because the model combining deep learning with ATTENTION network has helped improve the efficiency of analyzing and detecting APT malware based on network traffic. Show more

Keywords: APT, APT attack detection, Network traffic, Abnormal behavior, Deep Learning, attention

DOI: 10.3233/JIFS-212570

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4135-4151, 2022

Authors: Adline Priya, G. | Sundar, C. | Pavalarajan, S.

Article Type: Research Article

Abstract: The adoption of a new transmission line is extremely complex because of its socio-economic problems such as environmental clearances. Thus, there is a prominence of better utility over available transmission infrastructure. The Flexible Alternating Current Transmission System (FACTS) devices can offer transmission capability enhancement, power compensation, and stability as well as voltage improvement. However, the FACTS devices have a higher penetration impact of wind generation for the dynamic stability of power networks. In this work, an efficient Intellectual Control system has been proposed to stabilize the FACTS devices placement. The Squirrel Search Optimization is adapted with an intellectual control system …to enhance the steady-state voltage stability of FACTS devices. The proposed system has been evaluated with the assist of IEEE 14 and 26 standard bus systems to handle the multi-objective functions like cost, reduction in power loss, reducing risks, and maximizing user’s benefit. These multi-objective functions facilitate to attain the optimal placement and load flows at various sites. The simulation can be carried out with MATLAB/SIMULINK environment and the results manifest that the proposed system outperforms well when compared with existing approaches. Show more

Keywords: FACTS devices, squirrel search optimization, voltage stability, multi-objective, optimal load flow

DOI: 10.3233/JIFS-212573

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4153-4171, 2022

Authors: Sen, Rikta | Goswami, Saptarsi | Mandal, Ashis Kumar | Chakraborty, Basabi

Article Type: Research Article

Abstract: Jeffries-Matusita (JM) distance, a transformation of the Bhattacharyya distance, is a widely used measure of the spectral separability distance between the two class density functions and is generally used as a class separability measure. It can be considered to have good potential to be used for evaluation of the effectiveness of a feature in discriminating two classes. The capability of JM distance as a ranking based feature selection technique for binary classification problems has been verified in some research works as well as in our earlier work. It was found by our simulation experiments with benchmark data sets that JM …distance works equally well compared to other popular feature ranking methods based on mutual information, information gain or Relief. Extension of JM distance measure for feature ranking in multiclass problems has also been reported in the literature. But all of them are basically rank based approaches which deliver the ranking of the features and do not automatically produce the final optimal feature subset. In this work, a novel heuristic approach for finding out the optimum feature subset from JM distance based ranked feature lists for multiclass problems have been developed without explicitly using any specific search technique. The proposed approach integrates the extension of JM measure for multiclass problems and the selection of the final optimal feature subset in a unified process. The performance of the proposed algorithm has been evaluated by simulation experiments with benchmark data sets in comparison with two other previously developed multiclass JM distance measures (weighted average JM distance and another multiclass extension equivalent to Bhattacharyya bound) and some other popular filter based feature ranking algorithms. It is found that the proposed algorithm performs better in terms of classification accuracy, F-measure, AUC with a reduced set of features and computational cost. Show more

Keywords: Feature selection, JM distance multiclass extension, feature ranking and subset selection

DOI: 10.3233/JIFS-202796

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4173-4190, 2022

Authors: Karimi, Saeed | Mirzamohammadi, Saeed | Pishvaee, MirSaman

Article Type: Research Article

Abstract: As a major concern of chief managers in each organization, project portfolio selection has a special place in their responsibilities. To assist managers in making decisions, applicable optimization models play an essential role in such processes. In this regard, this paper provides a stochastic optimization model for a project portfolio selection problem under different scenarios. Providing the novelty in the model along with making it closer to reality, the interdependency between revenue and cost of projects is considered. Due to the inherent uncertainty of parameters, the revenue and cost of each project, as well as contributed capital, follow triangular fuzzy …parameters. Contrary to the previous model, the appreciation of assets is considered in the proposed model as the other novelty of the proposed model. To tackle the uncertainty of parameters, a robust possibilistic approach is used, which has been first-ever devised in such problems. Being both optimistic and pessimistic approaches available for decision-makers, a new measure is introduced to make the model inclusive. Moreover, by considering the confidence level as both parameter and decision variables, the robust possibilistic programming approach is adopted to solve the proposed model. Using the new proposed measure, the optimal average value of robust model are obtained under different confidence level. Finally, solving the optimization model, the results are provided by implementing the realization for uncertain parameters, and regarding the obtained results, discussions are made to provide some insights to the managers. Show more

Keywords: Project portfolio selection, project interdependencies, possibilistic robustness, fuzzy uncertainty

DOI: 10.3233/JIFS-210144

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4191-4204, 2022

Authors: Sun, Kexin | Xin, Yuelan | Ma, Yide | Lou, Meng | Qi, Yunliang | Zhu, Jie

Article Type: Research Article

Abstract: U-Net is a commonly used deep learning model for mammogram segmentation. Despite outstanding overall performance in segmenting, U-Net still faces from two aspects of challenges: (1) the skip-connections in U-Net have limitations, which may not be able to effectively extract multi-scale features for breast masses with diverse shapes and sizes. (2) U-Net only merges low-level spatial information and high-level semantic information through concatenating, which neglects interdependencies between channels. To address these two problems, we propose the U-shape adaptive scale network (ASU-Net), which contains two modules: adaptive scale module (ASM) and feature refinement module (FRM). In each level of skip-connections, ASM …is used to adaptively adjust the receptive fields according to the different scales of the mass, which makes the network adaptively capture multi-scale features. Besides, FRM is employed to allows the decoder to capture channel-wise dependencies, which make the network can selectively emphasize the feature representation of useful channels. Two commonly used mammogram databases including the DDSM-BCRP database and the INbreast database are used to evaluate the segmentation performance of ASU-Net. Finally, ASU-Net obtains the Dice Index (DI) of 91.41% and 93.55% in the DDSM-BCRP database and the INbreast database, respectively. Show more

Keywords: Mammograms, mass segmentation, convolutional neural network, adaptive scale module, feature refinement module

DOI: 10.3233/JIFS-210393

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4205-4220, 2022

Authors: Liu, Chaojie | Lu, Jie | Fu, Wenjing | Zhou, Zhuoyi

Article Type: Research Article

Abstract: How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier …Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model. Show more

Keywords: Housing price, big data, MGWR, GTWR, BLAAP, batch evaluation model

DOI: 10.3233/JIFS-210917

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4221-4240, 2022

Authors: Xiang, Chen | Xing, Wang | Hubiao, Zhang | Yuheng, Xu | You, Chen | Xiaotian, Wu

Article Type: Research Article

Abstract: Threat evaluation (TE) is essential in battlefield situation awareness and military decision-making. The current processing methods for uncertain information are not effective enough for their excessive subjectivity and difficulty to obtain detailed information about enemy weapons. In order to optimize TE on uncertain information, an approach based on interval Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and the interval SD-G1 (SD standard deviation) method is proposed in this article. By interval SD-G1 method, interval number comprehensive weights can be calculated by combining subjective and objective weights. Specifically, the subjective weight is calculated by interval G1 method, …which is an extension of G1 method into interval numbers. And the objective weight is calculated by interval SD method, which is an extension of SD method with the mean and SD of the interval array defined in this paper. Sample evaluation results show that with the interval SD-G1 method, weights of target threat attributes can be better calculated, and the approach combining interval TOPSIS and interval SD-G1 can lead to more reasonable results. Additionally, the mean and SD of interval arrays can provide a reference for other fields such as interval analysis and decision-making. Show more

Keywords: Interval number, multiple attribute decision making (MADM), interval TOPSIS, comprehensive weight, threat evaluation

DOI: 10.3233/JIFS-210945

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4241-4257, 2022

Authors: Zhou, Weibin | Chen, Tao | Huang, Huafang | Sheng, Chang | Wang, Yangfeng | Wang, Yang | Zhang, Daqiang

Article Type: Research Article

Abstract: Iris segmentation is one of the most important steps in iris recognition. The current iris segmentation network is based on convolutional neural network (CNN). Among these methods, there are still problems with the segmentation networks such as high complexity, insufficient accuracy, etc. To solve these problems, an improved low complexity DenseUnet is proposed to this paper based on U-net for acquiring a high-accuracy iris segmentation network. In this network, the improvements are as follows: (1) Design a dense block module that contains five convolutional layers and all convolutions are dilated convolutions aimed at enhancing feature extraction; (2) Except for the …last convolutional layer, all convolutional layers output feature maps are set to the number 64, and this operation is to reduce the amounts of parameters without affecting the segmentation accuracy; (3) The solution proposed to this paper has low complexity and provides the possibility for the deployment of portable mobile devices. DenseUnet is used on the dataset of IITD, CASIA V4.0 and UBIRIS V2.0 during the experimental stage. The results of the experiments have shown that the iris segmentation network proposed in this paper has a better performance than existing algorithms. Show more

Keywords: Iris segmentation, iris recognition, CNN, U-net, low complexity

DOI: 10.3233/JIFS-211396

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4259-4275, 2022

Authors: Sworna Kokila, M.L. | Gomathi, V.

Article Type: Research Article

Abstract: Automatic Person Re-identification by video surveillance is commonly used in different applications. Perhaps the human uniqueness criteria for tracking the presence of the same person across multiple camera views and a person’s growth identification is extremely challenging. To solve the above problem, we propose an efficient Auto Track Regression System (ATRF) based on a deep learning technique that uses an eminent representation strategy along with recognition. In this work, the Auto Wiley Detective (AWD) approach is proposed for the representation of features that can collect valuable information by monitoring individuals. After obtaining important information on the characteristics, it is possible …to define the personal growth identity of the generation. The OPVC (Original Pick Virtual Classifier) is used for accurate classification of the queried person from a dense area by utilizing features of a person’s growth identity extracted from feature extraction by the Auto Wiley Detection Method. The proposed Originated Pick Virtual Classifier (OPVC) uses Platt scaling (originated pick) on probit regression (virtual) to train the featured data set for accurate person re-identification, which is boosted by the Karush–Kuhn–Tucker (KKT) conditions to reduce false re-identification. Since the gallery information is trained using the Backpropagation method and smoothened analysis through approximated output, the Auto Wiley Detection Method proficiently detects the required information automatically. This also helps to detect the person query image from the database, which contains a vast collection of video images based on the similarity features identified in the query image and the detailed features extracted from the query image. The classification is completed automatically, and then the Person Re-Identification from the databases is performed accurately and efficiently. Henceforth, the proposed work effectively extracts reliable height and age estimates with improved flexibility and individual re-identifying capabilities. Show more

Keywords: Auto track regression framework, auto wiley detection, originated pick virtual classifier

DOI: 10.3233/JIFS-201977

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4277-4294, 2022

Article Type: Retraction

DOI: 10.3233/JIFS-219267

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4295-4295, 2022

Article Type: Retraction

DOI: 10.3233/JIFS-219268

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 4297-4297, 2022