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

Authors: Ghafour, Karzan Mahdi

Article Type: Research Article

Abstract: Demand forecasting is a fundamental element in industrial problems. Forecasts are crucial for accurately estimating intermittent demand to establish inventory measurements. The demand estimation by the Croston method gives less accurate values, which increases the standard deviation value. This increase indicates that the forecasted method is an inappropriate method of intermittent demand data because of the zero values. However, real data were adopted in an industrial sector for three years with constant lead-time. Furthermore, an integration of Bernoulli distribution and geometric distribution has been done to establish the new formulation, then extracting the mean equation and the standard deviation equation …of intermittent demand during lead-time. Relying on it, the optimal quantity of safety stock and reorder levels have been obtained. Furthermore, the proposed modified forecasting method was evaluated based on the criteria of CV and the results that obtained gives a less ratio dispersion of data thus accurate results. These procedures are very important to the industrial sectors in drawing future inventory policies. Show more

Keywords: Intermittent demand, forecasting, croston’s method, reorder level and safety stock

DOI: 10.3233/JIFS-211454

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3465-3475, 2022

Authors: Prajna, Yellamelli | Nath, Malaya Kumar

Article Type: Research Article

Abstract: Blood vessel segmentation is an essential element of automatic retinal disease screening systems. In particular, retinal blood vessel analysis from fundus image is vital in the identification and diagnosis of cardiovascular and ophthalmological diseases (Ex: Diabetic Retinopathy, Macular degeneration, Retinal Pigmentosa, Macular Edema, and various stages of Glaucoma, etc). Wherefore, the diagnosis of these diseases by automatic vessel segmentation has become essential, especially in disclosure of premature prognosis of vision condition. In general, blood vessel extraction is divided into vessel tracking and pixel classification. In vessel tracking a vasculature model is expanded from a seed point. In pixel classification, the …classifier classifies the pixels as either a vessel or background pixel, which is demonstrated in the proposed architecture. In this paper, deep learning based 19 layer U-Net architecture is proposed for the accurate and efficient segmentation of blood vessels. Prior to segmentation, a pre-processing block of AlexNet architecture is introduced for the classification of high-quality images from the experimented databases. This pre-classification stage helps in efficiently picking high-quality images determined by clarity, field definition, and sharpness. AlexNet classification is pivotal in enhancing the overall performance of the system by segmenting fine and tiny blood vessels. The proposed U-Net architecture has an encoder-decoder framework with 9 and 5 convolutional layers in each respectively. In order to boost the efficiency of the network as well as to reduce training and testing time, a proper choice of kernel dimension and number of filters are necessary. Our architecture was investigated on popular databases such as DRIVE, ARIA_d and MESSIDOR and various performance measures (accuracy, sensitivity, specificity, sensibility, Dice coefficient, and Jaccard coefficient) have been computed along with the Receiver Operating Characteristics. It is observed that the accuracy for DRIVE, ARIA_d and MESSIDOR are 90.60%, 87.60% and 83.42%, respectively. Area under curve in Receiver Operating Characteristics plot is found to be 98.54%, 93.28% and 88.18%, for DRIVE, ARIA_d and MESSIDOR databases, respectively. Results with the proposed architecture show remarkable improvement in the performance metrics for blood vessel segmentation. Show more

Keywords: Fundus image, U-Net, MSRI, accuracy, ROC curve

DOI: 10.3233/JIFS-211479

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3477-3489, 2022

Authors: Farooq, Muhammad | Qamar-uz-zaman, | Ijaz, Muhammad

Article Type: Research Article

Abstract: The Covid-19 infections outbreak is increasing day by day and the mortality rate is increasing exponentially both in underdeveloped and developed countries. It becomes inevitable for mathematicians to develop some models that could define the rate of infections and deaths in a population. Although there exist a lot of probability models but they fail to model different structures (non-monotonic) of the hazard rate functions and also do not provide an adequate fit to lifetime data. In this paper, a new probability model (FEW) is suggested which is designed to evaluate the death rates in a Population. Various statistical properties of …FEW have been screened out in addition to the parameter estimation by using the maximum likelihood method (MLE). Furthermore, to delineate the significance of the parameters, a simulation study is conducted. Using death data from Pakistan due to Covid-19 outbreak, the proposed model applications is studied and compared to that of other existing probability models such as Ex-W, W, Ex, AIFW, and GAPW. The results show that the proposed model FEW provides a much better fit while modeling these data sets rather than Ex-W, W, Ex, AIFW, and GAPW. Show more

Keywords: FEW, MLE, statistical properties, applications, and simulation

DOI: 10.3233/JIFS-211519

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3491-3499, 2022

Authors: Tripathy, Santosh Kumar | Sudhamsh, Repala | Srivastava, Subodh | Srivastava, Rajeev

Article Type: Research Article

Abstract: Crowd panic detection (CPD) is crucial to control crowd disasters. The recent CPD approaches fail to address crowd shape change due to perspective distortion in the frame and across the frames. To this end, we are motivated to design a simple but most effective model known as multiscale spatial-temporal atrous-net and principal component analysis (PCA) guided one-class support vector machine (OC-SVM), i.e., MuST-POS for the CPD. The proposed model utilizes two multiscale atrous-net to extract multiscale spatial and multiscale temporal features to model crowd scenes. Then we adopted PCA to reduce the dimension of the extracted multiscale features and fed …them into an OC-SVM for modeling normal crowd scenes. The outliers of the OC-SVM are treated as crowd panic behavior. Three publicly available datasets: the UMN, the MED, and the Pets-2009, are used to show the effectiveness of the proposed MuST-POS. The MuST-POS achieves the detection accuracy of 99.40%, 97.61%, and 98.37% on the UMN, the MED, and the Pets-2009 datasets, respectively, and performs better to recent state-of-the-art approaches. Show more

Keywords: Atrous (Dilated)-CNN, multiscale spatial-temporal features, dimension reduction, PCA, OC-SVM

DOI: 10.3233/JIFS-211556

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3501-3516, 2022

Authors: Khan, Izaz Ullah | Aftab, Muhammad

Article Type: Research Article

Abstract: This research is about the development of a dynamic programming model for solving fuzzy linear programming problems. Initially, fuzzy dynamic linear programming model FDLP is developed. This research revises the established dynamic programming model for solving linear programming problems in a crisp environment. The mentioned approach is upgraded to address the problem in an uncertain environment. Dynamic programming model can either be passing forward or backward. In the proposed approach backward dynamic programming approach is adopted to address the problem. It is then followed by implementing the proposed method on the education system of Pakistan. The education system of Pakistan …comprises of the Primary, Middle, Secondary, and Tertiary education stages. The problem is to maximize the efficiency of the education system while achieving the targets with minimum usage of the constrained resources. Likewise the model tries to maximize the enrollment in the Primary, Middle, Secondary and Tertiary educational categories, subject to the total available resources in a fuzzy uncertain environment. The solution proposes that the enrollment can be increased by an amount 9997130, by increasing the enrollment in the Middle and Tertiary educational categories. Thus the proposed method contributes to increase the objective function value by 30%. Moreover, the proposed solutions violate none of the constraints. In other words, the problem of resources allocation in education system is efficiently managed to increase efficiency while remaining in the available constrained resources. The motivation behind using the dynamic programming methodology is that it always possesses a numerical solution, unlike the other approaches having no solution at certain times. The proposed fuzzy model takes into account uncertainty in the linear programming modeling process and is more robust, flexible and practicable. Show more

Keywords: Fuzzy Linear Programming (FLP), fuzzy mathematical programming, dynamic programming, education, fuzzy sets and fuzzy modeling, resource allocation

DOI: 10.3233/JIFS-211577

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3517-3535, 2022

Authors: Sun, Quan | Yu, Xianghai | Li, Hongsheng | Peng, Fei | Sun, Guodong

Article Type: Research Article

Abstract: With the rapid development of new energy vehicles, the reliability and safety of Brushless DC motor drive system, the core component of new energy vehicles, has been widely concerned. The traditional open circuit fault detection method of power electronic converters have the problem of poor feature extraction ability because of inadequate signal processing means, which lead to low recognition accuracy. Therefore, a fault recognition method based on continuous wavelet transform and convolutional neural network (CWT-CNN) is proposed. It can not only adaptively extract features, but also avoid the complexity and uncertainty of artificial feature extraction. The three-phase current signal is …converted into time-frequency spectrum by continuous wavelet transform as the input data of AlexNet. At the same time, the changes of time domain and frequency domain under different fault modes are analyzed. Finally, the softmax classifier with Adam optimizer is used to classify the fault features extracted by CNN to realize the state recognition of different fault modes of power electronic converter. The experimental results show that the CWT-CNN model achieves satisfactory fault detection accuracy under different working conditions and different fault modes. The effectiveness and superiority of the proposed method are verified by comparing with other networks. Show more

Keywords: Continuous wavelet transform, convolution neural network, fault detection, power electronic converter

DOI: 10.3233/JIFS-211632

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3537-3549, 2022

Authors: Jin, LeSheng | Mesiar, Radko | Yager, Ronald | Kaya, Sema Kayapinar

Article Type: Research Article

Abstract: The recently proposed basic uncertain information can directly present numerical uncertainties for given real values, but it cannot handle given interval values which themselves also have uncertainties. Against this background, this work proposes the concept of interval basic uncertain information which serves as a generalization of basic uncertain information and involves two types of uncertainties. We analyze some basic operations, weighted arithmetic mean and preference transformation for interval basic uncertain information. The Rule-based decisions and the comprehensive certainty of interval basic uncertain information are also discussed. An illustrative example of multi-source multi-criteria evaluation under interval basic uncertain information environment is …presented. Show more

Keywords: Aggregation operators, basic uncertain information, decision making, interval basic uncertain information, preference aggregation

DOI: 10.3233/JIFS-211635

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3551-3558, 2022

Authors: Li, Feng

Article Type: Research Article

Abstract: Many experts and scholars focus on the Maclaurin symmetric mean (MSM) equation, which can reflect the interrelationship among the multi-input arguments. It has been generalized to different fuzzy environments and put into use in various actual decision problems. The fuzzy data intuitionistic fuzzy numbers (FNIFNs) could well depict the uncertainties and fuzziness during the security evaluation of Wireless Sensor Network (WSN). And the WSN security evaluation is frequently viewed as the multiple attribute decision-making (MADM) issue. In this paper, we expand the generalized Maclaurin symmetric mean (GMSM) equation with FNIFNs to propose the fuzzy number intuitionistic fuzzy generalized MSM (FNIFGMSM) …equation and fuzzy number intuitionistic fuzzy weighted generalized MSM (FNIFWGMSM) equation in this study. A few MADM tools are developed with FNIFWGMSM equation. Finally, taking WSN security evaluation as an example, this paper illustrates effectiveness of the depicted approach. Moreover, by comparing and analyzing the existing methods, the effectiveness and superiority of the FNIFWGMSM method are further certified. Show more

Keywords: Multiple attribute decision making (MADM), fuzzy number intuitionistic fuzzy sets (FNIFSs), fuzzy number intuitionistic fuzzy GMSM (FNIFGMSM) equation, fuzzy number intuitionistic fuzzy weighted GMSM (FNIFWGMSM) equation, Wireless Sensor Network (WSN)

DOI: 10.3233/JIFS-211731

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3559-3573, 2022

Authors: Varatharaj, Nagaraj | Ramalingam, Sumithira Thulasimani

Article Type: Research Article

Abstract: Most revolutionary applications extending far beyond smartphones and high configured mobile device use to the future generation wireless networks’ are high potential capabilities in recent days. One of the advanced wireless networks and mobile technology is 5G, where it provides high speed, better reliability, and amended capacity. 5 G offers complete coverage, which is accommodates any IoT device, connectivity, and intelligent edge algorithms. So that 5 G has a high demand in a wide range of commercial applications. Ambrosus is a commercial company that integrates block-chain security, IoT network, and supply chain management for medical and food enterprises. This paper proposed a …novel framework that integrates 5 G technology, Machine Learning (ML) algorithms, and block-chain security. The main idea of this work is to incorporate the 5 G technology into Machine learning architectures for the Ambrosus application. 5 G technology provides continuous connection among the network user/nodes, where choosing the right user, base station, and the controller is obtained by using for ML architecture. The proposed framework comprises 5 G technology incorporate, a novel network orchestration, Radio Access Network, and a centralized distributor, and a radio unit layer. The radio unit layer is used for integrating all the components of the framework. The ML algorithm is evaluated the dynamic condition of the base station, like as IoT nodes, Ambrosus users, channels, and the route to enhance the efficiency of the communication. The performance of the proposed framework is evaluated in terms of prediction by simulating the model in MATLAB software. From the performance comparison, it is noticed that the proposed unified architecture obtained 98.6% of accuracy which is higher than the accuracy of the existing decision tree algorithm 97.1%. Show more

Keywords: 5 G Technology, internet of things, block-chain security, wireless communication, machine learning models

DOI: 10.3233/JIFS-211745

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3575-3590, 2022

Authors: Garg, Harish | Alodhaibi, Sultan S. | Khalifa, Hamiden Abd El-Wahed

Article Type: Research Article

Abstract: Rough set theory, introduced by Pawlak in 1981, is one of the important theories to express the vagueness not by means of membership but employing a boundary region of a set, i.e., an object is approximately determined based on some knowledge. In our real-life, there exists several parameters which impact simultaneously on each other and hence dealing with such different parameters and their conflictness create a multi-objective nonlinear programming problem (MONLPP). The objective of the paper is to deal with a MONLPP with rough parameters in the constraint set. The considered MONLPP with rough parameters are converted into the two-single …objective problems namely, lower and upper approximate problems by using the weighted averaging and the ɛ - constraints methods and hence discussed their efficient solutions. The Karush-Kuhn-Tucker’s optimality conditions are applied to solve these two lower and upper approximate problems. In addition, the rough weights and the rough parameter ɛ are determined by the lower and upper the approximations corresponding each efficient solution. Finally, two numerical examples are considered to demonstrate the stated approach and discuss their advantages over the existing ones. Show more

Keywords: Multiobjective nonlinear programming, rough set, lower approximation programming problem, upper approximation programming problem, weighting method, ɛ- constraints method, parametric analysis

DOI: 10.3233/JIFS-211747

Citation: Journal of Intelligent & Fuzzy Systems, vol. 42, no. 4, pp. 3591-3604, 2022