Journal of Intelligent & Fuzzy Systems - Volume 45, issue 5

Authors: Liu, Pengyu

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-231529

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7507-7518, 2023

Authors: Zhang, Chengyutong | Tian, Jie

Article Type: Research Article

Abstract: With the deepening reform of the medical and health system, China’s community health services are also continuously improving. As the “gatekeeper” of community residents’ health, community medical and health services provide basic health protection for community residents. In the final analysis, community medical and health service is a kind of service. In today’s era where everyone pursues experience, improving service experience has become an important goal of modern health services. The community medical and health services evaluation is a multi-attribute group decision making (MAGDM) issue. The fuzzy number intuitionistic fuzzy sets (FNIFSs) are used as a tool for characterizing uncertain …information during the community medical and health services evaluation. In this paper, a novel MAGDM is built on given CoCoSo method under FNIFSs for community medical and health services evaluation. First of all, this paper extends the CoCoSo to FNIFSs environment to build the fuzzy number intuitionistic fuzzy CoCoSo (FNIF-CoCoSo) method. Secondly, a new MAGDM model for community medical and health services evaluation based on CoCoSo algorithm is built. Finally, the practical example for community medical and health services evaluation to show the practicability and some comparisons are supplied to prove the effectiveness of the decision algorithm. Show more

Keywords: Multi-attribute group decision making (MAGDM), FNIFSs, CoCoSo method, CRITIC method, community medical and health services evaluation

DOI: 10.3233/JIFS-231700

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7519-7531, 2023

Authors: Wang, Jun

Article Type: Research Article

Abstract: Beijing, Tianjin and Hebei are located in the Bohai Rim region of Northeast Asia, China. It is the region with the largest economic scale and strongest economic vitality in northern China. Due to historical development and administrative division, the economic strength of Beijing and Tianjin is strong, while the economic strength of Hebei Province is weak. The economic development of the Beijing-Tianjin-Hebei region is severely uneven. The “Beijing-Tianjin-Hebei Coordinated Development Strategy” is proposed and elevated to a national strategy in this context, aiming to explore the path of coordinated economic development in the Beijing-Tianjin-Hebei region, promote economic cooperation, balance economic …differences, and enhance the overall economic strength of the Beijing Tianjin Hebei region through national leadership. The economic collaborative development evaluation in the Beijing-Tianjin-Hebei region is a classical multiple attribute decision making (MADM) problems. Recently, the TODIM and Evaluation based on Distance from Average Solution (EDAS) method has been used to cope with MADM issues. The hesitant triangular fuzzy sets (HTFSs) are used as a tool for characterizing uncertain information during the economic collaborative development evaluation in the Beijing-Tianjin-Hebei region. In this paper, the hesitant triangular fuzzy TODIM-EDAS (HTF-TODIM-EDAS) method is built to solve the MADM under HTFSs. In the end, a numerical case study for economic collaborative development evaluation in the Beijing-Tianjin-Hebei region is given to validate the proposed method. The main contributions of this paper are summarized: (1) the HTF-TODIM-EDAS method is proposed under HTFSs. (2) The MADM method is designed based on the information entropy and HTF-TODIM-EDAS method under HTFSs. (3) A numerical case study for economic collaborative development evaluation in the Beijing-Tianjin-Hebei region is given to validate the proposed method. (4) A comparison between proposed method and existing methods is carried out to check its effectiveness. Show more

Keywords: Multiple attribute decision making (MADM), Hesitant triangular fuzzy sets (HTFSs), TODIM, EDAS, economic collaborative development evaluation

DOI: 10.3233/JIFS-232159

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7533-7545, 2023

Authors: Zhao, Lijuan | Du, Shuo

Article Type: Research Article

Abstract: In recent years, employers have continuously raised their requirements for college students, not only requiring a solid professional foundation, but also emphasizing personal professional literacy. As the first base for cultivating college students, major universities should not only guide them in their correct employment and entrepreneurship, but also help them find employment and entrepreneurship faster and better. However, in the context of the new era, universities still face some problems in the process of carrying out employment and entrepreneurship education, which hinder the progress of employment and entrepreneurship education. The probabilistic hesitant fuzzy sets (PHFSs), as an extension of hesitant …fuzzy sets (HFSs), can more effectively and accurately describe uncertain or inconsistent information during the quality evaluation of college student employment and entrepreneurship education. TODIM and TOPSIS methods are two commonly used multi-attribute decision-making (MADM) methods, each of which has its advantages and disadvantages. The quality evaluation of college student employment and entrepreneurship education is regarded as the defined multiple attribute group decision making (MAGDM). This paper proposes a novel method based on TODIM and TOPSIS to cope with multi-attribute group decision making (MAGDM) problems under PHFSs environment. After introducing the related theory of PHFSs and the traditional TODIM and TOPSIS methods, the novel method based on a combination of TODIM and TOPSIS methods is designed. And then, an illustrative example for quality evaluation of college student employment and entrepreneurship education proved the feasibility and validity of the proposed method. Finally, the result has been compared with some existing methods under the same example and the proposed method’s superiority has been proved. Show more

Keywords: Multi-attribute group decision making (MAGDM), probabilistic hesitant fuzzy sets (PHFSs), TODIM method, TOPSIS method, employment and entrepreneurship education

DOI: 10.3233/JIFS-233929

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7547-7562, 2023

Authors: Dinesh, E. | Sivakumar, M. | Rajalakshmi, R. | Sivakumar, P.

Article Type: Research Article

Abstract: Due to the COVID-19 virus, many educational institutions now encourage online learning. The National Program of Technology Enabled Learning (NPTEL) is a web portal that is used for e-learning applications. With this online course, students can access the lectures of all the respected experts from the best universities at any time and from any location. Due to privacy and security issues, many educational systems are hesitant to adopt the cloud. To avoid security issues, in this paper, a trust-based access control data hybrid cryptography model is proposed. The proposed system mainly focused on data confidentiality and the authentication process. For …data security, the hybrid Attribute-Based Encryption and Elliptical Curve Cryptography (ABE-ECC) algorithm is presented. Besides, for authentication, trust-based access control is introduced. The trust of the user is calculated using three parameters: the number of successful/failed interactions, the service satisfaction index, and the level of dishonesty. The performance of the proposed method is analyzed based on different metrics, namely throughput, latency, successful rate, service utilization, encryption time, decryption time, and retrieval time. Show more

Keywords: Trust, authentication, attribute-based encryption, elliptical curve cryptography, e-learning, education

DOI: 10.3233/JIFS-224287

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7563-7573, 2023

Authors: Xie, Wanli | Xu, Zhenguo | Liu, Caixia | Chen, Jianyue

Article Type: Research Article

Abstract: Grey system models have proven to be effective techniques in diverse fields and are crucial to global decision science. Amongst the various approaches of grey theory, the fractional-order grey model is fundamental and extends the cumulative generation method used in grey theory. Fractional-order cumulative generating operator offers numerous significant benefits, especially in educational funding that is often influenced by economic policies. However, their computational complexity complicates the generalization of fractional-order operators in real-world scenarios. In this paper, an enhanced fractional-order grey model is proposed based on a new fractional-order accumulated generating operator. The newly introduced model estimates parameters by utilizing …the method of least squares and determines the order of the model through the implementation of metaheuristic algorithms. Our results show that, after conducting both Monte Carlo simulations and practical case analyses, the newly proposed model outperforms both existing grey prediction models and machine learning models in small sample environments, thus demonstrating superior forecast accuracy. Moreover, our experiments reveal that the proposed model has a simpler structure than previously developed grey models and achieves greater prediction accuracy. Show more

Keywords: Grey system model, fractional-order accumulation, fractional-order derivative, educational fund

DOI: 10.3233/JIFS-230121

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7575-7586, 2023

Authors: Liu, Weifeng | Chang, Juan | He, Xia

Article Type: Research Article

Abstract: Bonferroni mean (BM) is an important aggregation operator in decision making. The desirable characteristic of the BM is that it can capture the interrelationship between the aggregation arguments or the individual attributes. The optimized weighted geometric Bonferroni mean (OWGBM) and the generalized optimized weighted geometric Bonferroni mean (GOWGBM) proposed by Jin et al in 2016 are the extensions of the BM. However, the OWGBM and the GOWGBM have neither the reducibility nor the boundedness, which will lead to the illogical and unreasonable aggregation results and might make the wrong decision. To overcome these existing drawbacks, based on the normalized weighted …Bonferroni mean (NWBM) and the GOWGBM, we propose the normalized weighted geometric Bonferroni mean (NWGBM) and the generalized normalized weighted geometric Bonferroni mean (GNWGBM), which can not only capture the interrelationship between the aggregation arguments, but also have the reducibility and the boundedness. Further, we extend the NWGBM and the GNWGBM to the intuitionistic fuzzy decision environment respectively, and develop the intuitionistic fuzzy normalized weighted geometric Bonferroni mean (IFNWGBM) and the generalized intuitionistic fuzzy normalized weighted geometric Bonferroni mean (GIFNWGBM). Subsequently, we prove some properties of these operators. Moreover, we present a new intuitionistic fuzzy decision method based on the IFNWGBM and the GIFNWGBM. Two application examples and comparisons with other existing methods are used to verify the validity of the proposed method. Show more

Keywords: Intuitionistic fuzzy number, Bonferroni mean, geometric Bonferroni mean, decision making

DOI: 10.3233/JIFS-231678

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7587-7601, 2023

Authors: Raman, Ramakrishnan | Barve, Amit | Meenakshi, R. | Jayaseelan, G.M. | Ganeshan, P. | Taqui, Syed Noeman | Almoallim, Hesham S. | Alharbi, Sulaiman Ali | Raghavan, S.S.

Article Type: Research Article

Abstract: Because of the two sequenced methods stated above, SG and AMP, are being used in different ways, present a deep learning methodology for taxonomic categorization of the metagenomic information which could be utilized for either. To place the suggested pipeline to a trial, 1000 16 S full-length genomes were used to generate either SG or AMP short-reads. Then, to map sequencing as matrices into such a number space, used a k-mer model. Our analysis of the existing approaches revealed several drawbacks, including limited ability to handle complex hierarchical representations of data and suboptimal feature extraction from grid-like structures. To overcome these …limitations, we introduce DBNs for feature learning and dimensionality reduction, and CNNs for efficient processing of grid-like metagenomic data. Finally, a training set for every taxon was obtained by training two distinct deep learning constructions, specifically deep belief network (DBN) and convolutional neural network (CNN). This examined the proposed methodology to determine the best factor that determines and compared findings to the classification abilities offered by the RDP classifier, a standard classifier for bacterium identification. These designs outperform using RDP classifiers at every taxonomic level. So, at the genetic level, for example, both CNN and DBN achieved 91.4% accuracy using AMP short-reads, but the RDP classifier achieved 83.9% with the same information. This paper, suggested a classification method for 16 S short-read sequences created on k-mer representations and a deep learning structure, that every taxon creates a classification method. The experimental findings validate the suggested pipelines as a realistic strategy for classifying bacterium samples; as a result, the technique might be included in the most commonly used tools for the metagenomic research. According to the outcomes, it could be utilized to effectively classify either SG or AMP information. Show more

Keywords: Deep neural network, RNA virus, metagenomic, convolutional neural network (CNN), taxonomic classification, Deep Belief Network (DBN), K-mer Representation

DOI: 10.3233/JIFS-231897

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7603-7618, 2023

Authors: Zhong, Yijie

Article Type: Research Article

Abstract: E-commerce is becoming a robust catalyst to enlarge the business actions and construct an active consumer based on emergence of a global economy. E-commerce is offering the opportunities for Small and Medium-sized Enterprises (SMEs) with limited resources to decrease the operating costs and improve the profitability by overcoming the operational problems. In addition, SMEs use e-commerce websitesas sales channels between the businesses, their competitor, and consumers. Between the success of e-commerce and manufacturing SMEs, however, the moderating influence of entrepreneurial competencies does not seem to be as significant. Hence, in this paper, Deep Convolutional Neural Network based onSales Prediction Model …(DCNN-SPM) has been suggested for analyzing SME enterprises’ e-commerce utilization and development. Consistent with the user decision-making requirements of online product sales, united with the impelling factors of online product sales in different SME industries and the benefits of Artificial Intelligence (AI), this study builds a sales prediction model appropriate for online products. Furthermore, it evaluates the model’s adaptability to different types of online products. Our model can automatically extract the useful features from raw log data and predict the sales utilizing those extracted features by DCNN. The experimental outcomes show that our suggested DCNN-SPM has achieved a high customer satisfaction ratio of 98.7% and a customer is buying behaviour analysis of 97.6%. Show more

Keywords: E-commerce utilization analysis, growth strategy for SMEs, artificial intelligence

DOI: 10.3233/JIFS-232406

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7619-7629, 2023

Authors: Chen, Jingfang

Article Type: Research Article

Abstract: Existing research on Chinese text classification primarily focuses on classifying data information at different granularities, such as character, word, sentence, and chapter. However, this approach often fails to capture the semantic information embedded in these different levels of granularity. To enhance the extraction of the text’s core content, this study proposes a text classification model that incorporates an attention mechanism to fuse multi-granularity information. The model begins by constructing embedding vectors for characters, words, and sentences. Character and word vectors are generated using the Word2Vec training model, allowing the data to be converted into these respective vectors. To capture contextual …semantic features, a bidirectional long and short-term memory network is employed for character and word vectors. Sentence vectors, on the other hand, are processed using the FastText model to extract the features they contain. To extract further important semantic information from the different feature vectors, they are fed into an attention mechanism layer. This layer enables the model to prioritize and emphasize the most significant information within the text. Experimental results demonstrate that the proposed model outperforms both single-granularity classification and combinations of two or more granularities. The model exhibits improved classification accuracy across three publicly available Chinese datasets. Show more

Keywords: Multi-granularity, information fusion, text classification, aattention mechanism

DOI: 10.3233/JIFS-233388

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7631-7645, 2023