Journal of Intelligent & Fuzzy Systems - Volume 44, issue 5

Authors: Arokiaraj, S. | Viswanathan, N.

Article Type: Research Article

Abstract: Human Activity Recognition (HAR) has reached its new dimension with the support of Internet of Things (IoT) and Artificial Intelligence (AI). To observe human activities, motion sensors like accelerometer or gyroscope can be integrated with microcontrollers to collect all the inputs and send to the cloud with the help of IoT transceivers. These inputs give the characteristics such as, angular velocity of movements, acceleration and apply them for an effective HAR. But reaching high recognition rate with less complicated computational overhead still represents a problem in the research. To solve this aforementioned issue, this work proposes a novel ensembling of …Capsule Networks (CN) and modified Gated Recurrent Units (MGRU) with Extreme Learning Machine (ELM) for an effective HAR classification based on data collected using IoT systems called Ensemble Capsule Gated (ECG)-Networks (NETS). The proposed system uses Capsule networks for spatio-feature extraction and modified (Gated Recurrent Unit) GRU for temporal feature extraction. The powerful feed forward training networks are then employed to train these features for human activity recognition. The proposed model is validated on real time IoT data and WISDM datasets. Experimental results demonstrates that proposed model achieves better results comparatively higher than existing (Deep Learning) DL models. Show more

Keywords: Artificial intelligence, capsule networks, human activity recognition, internet of things, gated recurrent units and spatio-feature extraction

DOI: 10.3233/JIFS-221551

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8219-8229, 2023

Authors: Srinivasa Rao, Illapu Sankara | Rajalakshmi, N.R.

Article Type: Research Article

Abstract: Since the IPv6 Wireless Personal Area Network (6LoWPAN) can be utilized for information dissemination, this network gains significant attention in recent years. Proxy mobile IPv6 (PMIPv6) is standard for mobility control based on network at entire IP wireless applications. But, group-based body area networks cannot respond effectively. A new improved group flexibility system decrease the number of control messages contain router requests as well as advertising messages when compared to the group-based PMIPv6 protocol, in order to minimize delay and signaling costs. The IEEE 802.15.4 standard for low-power personal area networks (6LoWPAN) complies through IPv6-compliant MAC and physical layers. If …the default parameters, excessive collisions, packet loss, and great latency occur arbitrarily in high traffic by default MAC parameters while using a great number of 6LoWPAN nodes. The implemented Whale optimization algorithm is based on artificial neural network optimization, genetic algorithm or particle swarm optimization to choose and authenticate MAC parameters. This manuscript proposes a novel intelligent method for choosing optimally configured MAC 6LoWPAN layer set parameters. Results of simulations based on the metrics such as Average delay time (ADT), Average signaling cost, Delivery ratio, Energy consumption, Latency, Network Life time (Nlt), Packet Overhead (PO), Packet loss. The performance of the proposed method provides 19.08%, 25.87%, 31.98%, 26.98%, 31.98%, 26.98% and 23.89% lower Latency, 12.67%, 25.98%, 31.98%, 26.98%, 27.98%, 31.97% and 27.85% lower Packet Overhead and 19.78%, 27.96%, 37.98%, 18.09%, 28.97%, 27.98% and 56.04% higher Delivery ratio compared with the existing methods such as 6LoWPAN-NUM-OHCA-FFA, 6LoWPAN-GTCCF-PSO, 6LoWPAN- DODAG-ACO, 6LoWPAN- MAC-GA-PSO, 6LoWPAN-NCG-DTC-NGIPSA and 6LoWPAN-TDMA-GTS-SHJA algorithms respectively. Show more

Keywords: Artificial neural networks, genetic algorithm, low power personal areas network (6LoWPAN), medium access control protocols (MAC Proxy mobile IPv6 (PMIPv6), particle swarm optimization and Whale optimization algorithm (WOA)

DOI: 10.3233/JIFS-222956

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8231-8255, 2023

Authors: Liu, Zhichao

Article Type: Research Article

Abstract: In the 75th session of the United Nations General Assembly, the Chinese government first proposed the goal of carbon neutrality and carbon peaking. Since then, China’s economy and society have undergone a comprehensive green and sustainable development upgrade and transformation. The development of green finance can provide financial support for achieving dual carbon goals and mitigate the impact of climate change. More importantly, it can contribute to the national economy’s and society’s sustainable development. We innovatively draw on the quality function deployment theory in marketing to logically formulate the research idea of this paper. On this basis, we also apply …the G1-entropy method from fuzzy mathematical theory for quantitative research. We innovatively address the actual national conditions in China and fully integrate green elements in constructing the index system from green finance and sustainability perspectives. Finally, we calculate index weights through G1-entropy quantification to assess the development quality of China’s green financial system and qualitatively propose countermeasures for the quality of China’s green financial development with respect to key index factors. Specifically, we sort out this paper in the following three aspects: (1) we innovatively combined the quality function deployment theory and built the quantitative analysis process architecture in this paper, which enhanced the readability of this paper (2) we realized the use of quantitative research for qualitative analysis and proposed the G1-entropy value method, which made up for the defects of the subjective and objective methods in the traditional assessment methods (3) we realized the organic combination of quantitative and qualitative analysis and proposed relevant countermeasure suggestions based on the quantitative index calculation results, which provided relevant countermeasure suggestions for promoting the sustainable and high-quality development of green finance in China. Our study will provide a set of perfect assessment methods for the quality improvement path and sustainable development strategy formulation after the construction of China’s future green financial system. It can also provide a reference assessment idea for the high-quality and sustainable development of China’s green finance, which will further help China’s economic transition to green and low-carbon and the achievement of the double carbon goal. Show more

Keywords: Economic quality assessment, dual carbon context, quality function deployment theory, G1-entropy value method, green financial system

DOI: 10.3233/JIFS-222935

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8257-8280, 2023

Authors: Wang, Shuai | Ning, Yufu | Huang, Hong | Chen, Xiumei

Article Type: Research Article

Abstract: Uncertain least squares estimation is one of the important methods to deal with imprecise data, which can fully consider the influence of given data on regression equation and minimize the absolute error. In fact, some scientific studies or observational data are often evaluated in terms of relative error, which to some extent allows the error of the forecasting value to vary with the size of the observed value. Based on the least squares estimation and the uncertainty theory, this paper proposed the uncertain relative error least squares estimation model of the linear regression. The uncertain relative error least squares estimation …minimizes the relative error, which can not only solve the fitting regression equation of the imprecise observation data, but also fully consider the variation of the error with the given data, so the regression equation is more reasonable and reliable. Two numerical examples verified the feasibility of the uncertain relative error least squares estimation, and compared it with the existing method. The data analysis shows that the uncertain relative error least squares estimation has a good fitting effect. Show more

Keywords: Relative error least squares estimation, relative error, least squares estimation, uncertainty theory

DOI: 10.3233/JIFS-222955

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8281-8290, 2023

Authors: Cui, Zhexin | Yue, Jiguang | Tao, Wei | Xia, Qian | Wu, Chenhao

Article Type: Research Article

Abstract: Collaboration is essential to improve the efficiency of product research and development (R&D), shorten the R&D cycle, and reduce the R&D costs in complex product lifecycle model management (CPLMM). However, disorganized processes and the unreliability of the result evaluation remain enormous challenges for efficient collaboration. This article proposes an active-passive collaboration mechanism to enable a regulated collaboration system, which can direct the self-organized collaboration of stakeholders. C-D-Petri Net is presented for the formal collaboration process modeling. The result evaluation in active-passive collaboration involves multi-source knowledge across disciplines and phases. To address the unreliable collaboration evaluation (Co-evaluation) caused by insufficient evaluation …knowledge and weak correlation between expertise and evaluation task, the collaborative fuzzy comprehensive evaluation (CFCE) model is established to support Co-evaluation actions, and its core improvement lies in the definition and introduction of collaboration volume. Finally, a simulated aircraft horizontal tail control system is regarded as an engineering application case to demonstrate and verify the effectiveness of the proposed method. Show more

Keywords: Active-passive collaboration, Knowledge-related fuzzy evaluation, C-D-Petri Net, Collaboration volume

DOI: 10.3233/JIFS-223978

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8291-8308, 2023

Authors: Li, Yufeng | Xu, Keyi | Ding, Yumei | Sun, Zhiwei | Ke, Ting

Article Type: Research Article

Abstract: Many traditional clustering algorithms are incapable of processing mixed-type datasets in parallel, limiting their applications in big data. In this paper, we propose a CF tree clustering algorithm based on MapReduce to handle mixed-type datasets. Mapper phase and reducer phase are the two primary phases of MR-CF. In the mapper phase, the original CF tree algorithm is modified to collect intermediate CF entries, and in the reducer phase, k -prototypes is extended to cluster CF entries. To avoid the high costs associated with I/O overheads and data serialization, MR-CF loads a dataset from HDFS only once. We first analyze the …time complexity, space complexity, and I/O complexity of MR-CF. We also compare it with sklearn BIRCH, Apache Mahout k -means, k -prototypes, and mrk-prototypes on several real-world datasets and synthetic datasets. Experiments on two mixed-type big datasets reveal that MR-CF reduces execution time by 45.4% and 61.3% when compared to k -prototypes, and it reduces execution time by 73.8% and 55.0% when compared to mrk-prototypes. Show more

Keywords: Clustering analysis, CF tree, mixed-type datasets, BIRCH, k-prototypes

DOI: 10.3233/JIFS-224234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8309-8320, 2023

Authors: Khan, Muhammad Zahir | Aslam, Muhammad | Albassam, Mohammed

Article Type: Research Article

Abstract: When the target value (T) is located in the midpoint of the specification interval (m). Traditional process capability indices (PCIs) are often employed for a process with a symmetric tolerance (T  = m). In case a process with asymmetric tolerance (T ≠m) traditional PCIs can be misleading. Process capability indices (PCIs) with asymmetric tolerance have been designed and successfully used in a crisp form in process capability analysis (PCA). These PCIs with asymmetric tolerance can benefit from the use of fuzzy set theory to deal with ambiguity and to add greater flexibility and sensitivity to mean variance, and target value (T), …and specification limits (SLs). In order to produce fuzzy SLs of PCIs with asymmetric tolerance fuzzy mean, fuzzy variance and the fuzzy target value have been used. Furthermore, these PCIs are graphically represented. It is concluded that the intermediate values of fuzzy SLs can be explored, which is not achievable with crisp SLs. Furthermore, it is recommended to utilize fuzzy SLs of PCIs with asymmetric tolerance to monitor goods that fall outside specification limits due to their flexibility and sensitivity in a fuzzy environment. The proposed FPCIs were illustrated with a real-life example using piston diameters that were produced in a factory. Show more

Keywords: Target value, fuzzy mean, fuzzy variance, asymmetric tolerance

DOI: 10.3233/JIFS-221993

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8321-8327, 2023

Authors: Jain, Archika | Sharma, Sandhya

Article Type: Research Article

Abstract: Hate speech on social media post is running now a days. Social media like YouTube, Twitter, and Facebook etc. are responsible for hated speech. Hated speech spreads through digital media, causing individuals to get confused and adopt prejudiced viewpoints. To limit the negative effects of disinformation on the digital platform, it is critical to detect it. Now a days, lots of digital platforms are available. Hate speech detection in dataset is very difficult. As a result, the Twitter dataset is of the size of 25296 is presented in this work. Many deep learning techniques are applied on Twitter dataset. The …Google Colab tool is used to scrape dataset material. Different deep learning approaches are utilized to boost the accuracy of the hated speech dataset. For training and validation accuracy and loss some models are used on Twitter dataset like Bi-directional Long Short Term Memory with Glove, Bi-LSTM, and Embedding from Language Model (Elmo) with deep learning, Convolutional Neural Network (CNN), Long Short Term Memory with Glove and LSTM. The performance of the proposed tweet dataset is evaluated using a variety of deep learning classifiers on text dataset. The planned deep learning techniques produced good results on tweet dataset. LSTM with Glove gave the highest accuracy 0.89 and minimum loss 0.19 on tweet dataset. So when compare our model on same dataset that was used earlier then we get highest accuracy and minimum loss. Show more

Keywords: Deep learning, classifiers, twitter dataset, LSTM, and accuracy

DOI: 10.3233/JIFS-222431

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8329-8341, 2023

Authors: Camgoz Akdag, Hatice | Menekse, Akin

Article Type: Research Article

Abstract: Breast cancer is the leading cause of cancer-related deaths, and choosing a suitable treatment plan for this disease has proved difficult for oncologists owing to the variety of criteria and alternatives that must be considered during the decision-making process. Since prospective treatment options influence patients’ health-related quality of life in a variety of ways, a methodology that can completely and objectively evaluate alternative treatments has become an essential issue. This paper proposes a novel multi-criteria decision-making (MCDM) methodology by integrating the CRiteria Importance Through Intercriteria Correlation (CRITIC) and the REGIME techniques and handles the problem of breast cancer treatment selection …problem. CRITIC enables the determination of objective criterion weights based on the decision matrix, while REGIME ranks the options without the need for lengthy computations or normalization procedures. The suggested methodology is demonstrated in a spherical fuzzy atmosphere, which allows decision experts to independently express their degrees of membership, non-membership, and hesitancy in a broad three-dimensional spherical space. In the numerical example provided, three oncologists evaluate four breast cancer treatment alternatives, namely, surgery, radiotherapy, chemotherapy, and hormone therapy, with respect to five criteria, which are disease or tumor type, stage of disease, patient type, side effects, and financial status of the patient. The tumor type is determined to be the most important assessment criterion, and surgery is selected as the best course of action. The stability and validity of the proposed methodology are verified through sensitivity and comparative studies. The discussions, limitations, and future research avenues are also given within the study. Show more

Keywords: CRITIC, REGIME, spherical fuzzy set, MCDM, breast cancer treatment selection

DOI: 10.3233/JIFS-222648

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8343-8356, 2023

Authors: Ma, Zhanyou | Gao, Yingnan | Li, Zhaokai | Li, Xia | Liu, Ziyuan

Article Type: Research Article

Abstract: The verification of reachability properties of fuzzy systems is usually based on the fuzzy Kripke structure or possibilistic Kripke structure. However, fuzzy Kripke structure or possibilistic Kripke structure is not enough to describe nondeterministic and concurrent fuzzy systems in real life. In this paper, firstly, we propose the generalized possibilistic decision process as the model of nondeterministic and concurrent fuzzy systems, and deduce the possibilities of sets of paths of the generalized possibilistic decision process relying on defining of schedulers. Then, we give fuzzy matrices calculation methods of the maximal possibilities and the minimal possibilities of eventual reachability, always reachability, …constrained reachability, repeated reachability and persistent reachability. Finally, we propose a model checking approach to convert the verification of safety property into the analysis of reachabilities. Show more

Keywords: Generalized possibilistic decision processes, scheduler, fuzzy matrices, reachability, safety property

DOI: 10.3233/JIFS-222803

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8357-8373, 2023