Journal of Intelligent & Fuzzy Systems - Volume 45, issue 3

Authors: Muthulakshmi, S. | Chitra, R.

Article Type: Research Article

Abstract: Smart grid is proposed as a solution to the problems of production, distribution, monitoring, and control of the electricity in traditional power grids. Smart grid networks place IoT sensor nodes at various grid lines and collect large volume of data about power flow, usage etc. The collected data are analyzed for various applications like demand forecasting, fault diagnosis and fault prediction etc. The sensor nodes and the communication links can be compromised affecting the privacy of consumers. False data can be propagated with malicious intentions. This work proposes a secure and privacy preserving framework for smart grid IoT networks to …secure the data and decision at sensor nodes and communication links. The work proposes a novel Data and Decision rules Secure Efficient Smart Grid (DDSESG) framework integrating secure compressive sensing technique with blockchain and interplanetary file system (IPFS) for securing both data and decision. Through experimental analysis, the proposed solution is found to provide higher resiliency against data security attacks at comparative 12.4% lower computation cost, 15% lower communication cost, 19.9% lower storage cost. Forecasting on transformed data in proposed solution had only a marginal 1.08 % difference in accuracy compared to forecasting on original data. Show more

Keywords: Internet of things, blockchain, IPFS, smart grid, compressive sensing, transform coding

DOI: 10.3233/JIFS-231792

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3701-3714, 2023

Authors: Bhuvaneswary, N. | Deny, J. | Lakshmi, A.

Article Type: Research Article

Abstract: Universal Verification Methodology (UVM) caters to an essential role in verifying the different categories of circuits ranging from small-scale chips to complex system-on-chip architectures. Constrained random simulations are an indispensable part of UVM and are often used for design verification. However, the effort and time spent manually updating and analyzing the design input constraints result in high time complexity, which typically impacts the coverage goal and fault verification ratio. To overcome this problem, this paper proposes a novel hybrid optimized verification framework that combines Reinforcement Learning (RL) and Deep Neural Networks (DNN) for automatically optimizing the input constraints, accelerating faster …verification with a high coverage ratio. The proposed algorithm uses reinforcement learning to generate all possible vector sequences needed for testing the target devices and corresponding outputs of the target devices and potential design errors. Furthermore, the framework intends to use high-speed deep-feedforward neural networks to automate and optimize the constraints during runtime. The proposed framework was developed using Python interfaced with the TCL environment. Extensive experimentation was carried out using several circuits, including multi-core designs, and performance parameters such as coverage accuracy, speed, and computational complexity were calculated and analyzed. The experiment demonstrated the proposed framework remarkable results, showing its superior performance in faster coverage and fewer misclassification errors. Furthermore, the proposed framework is compared with existing verification frameworks and other classical learning models. Good results demonstrate that the proposed framework increases the 4.5x speed for verifying multi-core designs and the 99% accuracy of detection and coverage. Show more

Keywords: Universal verification methodology, reinforcement learning, deep feed forward neural network, multi-core designs

DOI: 10.3233/JIFS-232132

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3715-3728, 2023

Authors: Yuan, Ting | Qu, Huizhen | Pan, Dong

Article Type: Research Article

Abstract: The current article explores the affects of space-time discrete stochastic competitive neural networks. In line with a discrete-space and discrete-time constant variation formula, boundedness and stability are addressed to the space-time discrete stochastic competitive neural networks. Notably, the best convergence speed can be computed by a non-linear optimization problem. In the end, random periodic sequences with respect to time variable of the discrete-space and discrete-time stochastic competitive neural networks are discussed. The results indicate that spatial diffusion with non-negative density factors has no effect on the global mean square boundedness and stability and random periodicity of the network model. The …current article is precursory in consideration of space-time discrete competitive neural networks. Show more

Keywords: Competitive neural networks, space, random, periodicity, exponential difference

DOI: 10.3233/JIFS-230821

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3729-3748, 2023

Authors: Li, Zhaowen | Luo, Damei | Yu, Guangji

Article Type: Research Article

Abstract: Attribute reduction for incomplete data is a hot topic in rough set theory (RST). A fuzzy probabilistic information system (FPIS) combines of fuzzy relations that satisfy the probability distribution about objects, which can be regarded as an information system (IS) with fuzzy relations. This paper studies attribute reduction in an FPIS. Based on the available information of objects on an ISVIS, the probability distribution formula of objects is first defined. Then, an FPIS can be induced by an ISVIS. Next, attribute reduction in a FPIS is proposed similar to an IS. Moreover, information granulation and information entropy in an FPIS …is defined, and the corresponding algorithms are constructed. Finally, the effectiveness of the constructed algorithms is verified by k-means clustering, Friedman test and Nemenyi test. Show more

Keywords: Incomplete set-valued data, FPIS, attribute reduction, core, algorithm

DOI: 10.3233/JIFS-230865

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3749-3765, 2023

Authors: Turanoğlu Şirin, Betül

Article Type: Research Article

Abstract: The use of Unmanned Aerial Vehicle (UAV) platforms has been increasing day by day and it has become an important technology. In this study, how the engines should be selected in the design of a rotary wing UAV system is considered a multi-criteria decision-making (MCDM) problem. This MCDM problem has not yet been encountered in the literature. Therefore, a hybrid MCDM approach based on the fuzzy Best Worst Method (BWM) and Multi Attributive Ideal-Real Comparative Analysis (MAIRCA) is proposed to solve this problem. In the proposed approach, the decision makers determine 6 criteria (KV value, thrust, weight, efficiency, battery, electronic …speed controller (ESC)) and 6 different engine (A1 , A2 , A3 , A4 , A5 , A6 ) alternatives. The fuzzy BWM was used to calculate the weights of criteria, while the MAIRCA was used for the selection of alternatives. According to the results obtained, the three most effective criteria were thrust, KV value, and weight, respectively. The three best engine options were found as A3 , A1 , and A6 . Moreover, sensitivity analysis was performed to observe the change in the ranking of alternatives according to different weights of criteria. MABAC, MARCOS, and COPRAS methods were used to compare the alternative rankings found with the MAIRCA. Show more

Keywords: Multi criteria decision making, rotary wing unmanned aerial vehicle, selection of appropriate engine, fuzzy BWM, MAIRCA

DOI: 10.3233/JIFS-231143

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3767-3778, 2023

Authors: Xiang, Yan | Liu, Wei | Guo, Junjun | Zhang, Li

Article Type: Research Article

Abstract: Chinese medical named entity recognition (CMNER) aims to extract entities from Chinese unstructured medical texts. Existing character-based NER models do not comprehensively consider character’s characteristics from different perspectives, which limits their performance in applying to CMNER. In this paper, we propose a local and global character representation enhanced model for CMNER. For the input sentence, the model fuses the spacial and sequential character representation using autoencoder to get the local character representation; extracts the global character representation according to the corresponding domain words; integrates the local and global representation through gating mechanism to obtain the enhanced character representation, which has …better ability to perceive medical entities. Finally, the model sent the enhanced character representation to the Bi-LSTM and CRF layers for context encoding and tags decoding respectively. The experimental results demonstrate that our model achieves a significant improvement over the best baseline, increasing the F1 values by 1.04% and 0.62% on the IMCS21 and CMeEE datasets, respectively. In addition, we verify the effectiveness of each component of our model by ablation experiments. Show more

Keywords: Named entity recognition, Chinese characters, medical entity, local and global representation

DOI: 10.3233/JIFS-231554

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3779-3790, 2023

Authors: Geng, Xiaona | Geng, Xiaonan

Article Type: Research Article

Abstract: With the continuous deepening of higher education management reform, university leaders have realized that the merger of universities, annual expansion of enrollment, and expansion of educational scale have broadened the development space for universities. At the same time, many management problems have also emerged, and education management problems are particularly prominent, such as some decisions, plans, instructions, etc. of the school level education management department not being well implemented in various departments, and the channels for the school level education management department to understand the true situation of each department are not smooth. Therefore, deepening reform provides a good opportunity …for universities to strengthen management and streamline relationships. Teaching and scientific research must be upgraded, and the quality of teaching management must be improved. Establishing an education management quality evaluation system and emphasizing the quality of education management work are the key. The higher education management quality evaluation is affirmed as multi-criteria group decision-making (MCGDM). Interval-valued neutrosophic sets (IVNSs) have been widely used and researched in MCGDM. The interval-valued neutrosophic sets (IVNSs) could depict the uncertain information within the higher education management quality evaluation. The purpose of this article is to proposed a new improved grey relation analysis (GRA) method based on prospect theory (PT-GRA) to solve the MCGDM under IVNSs. At the end of this paper, an example for higher education management quality evaluation is illustrated through the built method and the comparison. Thus, the main contribution of this study is: (1) the PT-GRA method is used to deal with the MCGDM problems under IVNSs; (2) the weight information is obtained through entropy method; (3) an empirical example for higher education management quality evaluation has been given. (4) some comparative algorithms are given to show the rationality of PT-GRA method with IVNSs. Show more

Keywords: Multi-criteria group decision-making (MCGDM), interval-valued neutrosophic sets (IVNSs), grey relation analysis (GRA), prospect theory (PT), higher education management quality

DOI: 10.3233/JIFS-232146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3791-3805, 2023

Authors: Sun, Yanling | Liu, Xiaojing | Chen, Haoyue | Zhu, Li | Li, Yingji

Article Type: Research Article

Abstract: Brand authenticity perception is essential for territorial characteristic agricultural product e-commerce studies. From the complexity of consumer perception of brand authenticity, an e-commerce brand authenticity perception (EBAP) analysis model is proposed based on fuzzy cognitive map (FCM) and emotional analysis of online comments. Firstly, LDA model and snowNLP tools extract consumer perception attributes and their emotional inclination. After that, FCM and improved Bonferroni mean (BM) operator are used to accurately analyze the interrelationships between different attributes and comprehensively evaluate the brand authenticity of different enterprises under the same characteristic agricultural product. Finally, the model comparison experiment results show that the …model proposed can reflect the “attribute importance” and “emotional inclination” of the e-commerce brand authenticity perception of territorial characteristic agricultural products. Among them, “platform logistics” and “product benefits” are essential in promoting the authenticity of brand-consumer relationships. Meanwhile, “e-commerce aftersales service” is closely related to the positive evaluation of “platform logistics” and “product benefits.” This study expands the methodical approach to brand authenticity perception research; it provides a valuable reference for developing modern fine granularity management of e-commerce brand authenticity for characteristic agricultural products. Show more

Keywords: E-commerce brand authenticity, emotional analysis, fuzzy cognitive map

DOI: 10.3233/JIFS-230251

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3807-3822, 2023

Authors: Javaid, Sameena | Rizvi, Safdar

Article Type: Research Article

Abstract: Sign language recognition is a significant cross-modal way to fill the communication gap between deaf and hearing people. Automatic Sign Language Recognition (ASLR) translates sign language gestures into text and spoken words. Several researchers are focusing either on manual gestures or non-manual gestures separately; a rare focus is on concurrent recognition of manual and non-manual gestures. Facial expression and other body movements can improve the accuracy rate, as well as enhance signs’ exact meaning. The current paper proposes a Multimodal –Sign Language Recognition (MM-SLR) framework to recognize non-manual features based on facial expressions along with manual gestures in Spatio temporal …domain representing hand movements in ASLR. Our proposed architecture has three modules, first, a modified architecture of YOLOv5 is defined to extract faces and hands from videos as two Regions of Interest. Second, refined C3D architecture is used to extract features from the hand region and the face region, further, feature concatenation of both modalities is applied. Lastly, LSTM network is used to get spatial-temporal descriptors and attention-based sequential modules for gesture classification. To validate the proposed framework we used three publically available datasets RWTH-PHONIX-WEATHER-2014T, SILFA and PkSLMNM. Experimental results show that the above-mentioned MM-SLR framework outperformed on all datasets. Show more

Keywords: C3D, LSTM, manual gestures, non-manual gestures, sign language recognition, YOLOv5

DOI: 10.3233/JIFS-230560

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3823-3833, 2023

Authors: Shi, Qingguo | Hu, Yihuai | Yan, Guohua

Article Type: Research Article

Abstract: The failure mode and effect analysis (FMEA) is an effective tool to analyze risks and potential effects of complex systems, and it is one of the most widely used risk analysis methods for complex systems as there often exists various factors that could affect the operation of the complex systems. Conventional FMEA methods have been limited to using crisp values to represent the assessments, which has been criticized for having many deficiencies. Marine diesel fuel injection system is an important part of marine diesel engine, and its failure could directly affect the performance of the marine diesel engine and even …impact the safe operation of the ship. However, little attention has been paid to the FMEA of the marine diesel fuel injection system. To this end, this paper presents a novel FMEA method based on the best-worst method (BWM) and TOPSIS method with probabilistic linguistic term set (PLTS). Firstly, the PLTS is used to represent the uncertain and linguistic judgments of experts. Then, the BWM is extended with PLTS to determine the weights of different elements for FMEA, and the TOPSIS is extended with PLTS to assess and rank different failure modes. Finally, a case study on marine diesel fuel injection is presented, and the most critical failures are identified for improvement measures. The results show that the proposed method could help managers and engineerings identify the most important failure modes for marine diesel fuel injection system. Show more

Keywords: Failure mode and effect analysis, risk analysis, probabilistic linguistic term set, marine diesel fuel injection system

DOI: 10.3233/JIFS-230870

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3835-3854, 2023