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Abstract: This article has been retracted.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

Authors: Yadav, Vishakha | Ganesh, P. | Thippeswamy, G.

Article Type: Research Article

Abstract: The determination and categorization of red blood cells (RBCs) from microscopic pictures is a critical step in the diagnosis of sickle cell disease (SCD). Traditionally, such procedures are performed manually by pathologists using a light microscope. Furthermore, manual visual evaluation is a time-consuming operation that relies on subjective judgment, resulting in variations in RBC recognition and counts. Mature If there is a blood problem, RBCs suffer morphological alterations. There are both automated and manual systems available on the market for counting the number of RBCs. Manual counting entails collecting blood cells with a Hemocytometer. The traditional procedure of exposing the …smear below a microscope and physically measuring the cells yields inaccurate findings, putting clinical laboratory staff under stress. Automatic counters are incapable of detecting aberrant cell. The computer-aided method will assist in achieving accurate outcomes in minimum time. In this study presents an image processing method for separating red blood cells from several other blood products. Its goal is to analyze and interpret blood smear images to aid in the categorizing of red blood cells across 11 categories. The WBCs are extracted from the image using the K-Medoids technique, that is resistant to exterior disturbance. Granulometric assessment has been used to distinguish between red and WBCs. Feature extraction is used to obtain important features that aid in categorization. The categorization outcomes aid in a rapid diagnosis of disorders such as Normochromic, Iron Deficiency, Hypochromic, Sickle Cell, and Megaloblastic. Show more

Keywords: Red blood cells (RBCs), determination, categorization, computer-aided framework, diagnosing disorder, Sickle cell

DOI: 10.3233/JIFS-234129

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7647-7659, 2023

Authors: Liu, Ning | Zhao, Jianhua

Article Type: Research Article

Abstract: With the explosive increase of information, recommendation system is applied in a variety of areas. However, the performance of recommendation system is limited due to issues such as data sparsity, cold starts and poor semantic understanding. In order to make full use of external information to assist recommendation, deeply mine the semantic information of review text and further improve the performance of recommendation system, a deep recommendation system based on knowledge graph and review text (Drs-kgrt) is proposed in this paper. In Drs-kgrt, knowledge graph, review text and the social records between users are used as auxiliary information to improve …recommendation performance. Firstly, the review text is divided into user review text and item review text. BERT (Bidirectional Encoder Representation from Transformers) is used to accurately understand semantic information in user review text and the social records between users. The trust relationship between users and user preferences are fully mined to form user feature vectors. Secondly, BERT and knowledge graph entity recognition link technology are combined to extract item attribute feature entities and their associated entities. The fine-grained features of the items are analyzed to form item feature vectors. Thirdly, based on the scoring matrix, latent vectors of users and items are obtained by matrix decomposition. The deep features of users and items are generated based on user feature vectors, item feature vectors, latent vectors of users and items, the deep recommendation system is established to predict user scores for items. Finally, experiments are conducted on the Douban dataset and Amazon Movie Review dataset, the results show that the proposed algorithm can achieve better performance compared with other benchmark recommendation algorithms. Show more

Keywords: Knowledge graph, personalized recommendation, user review, item review, social relationships

DOI: 10.3233/JIFS-230584

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7661-7673, 2023

Authors: Aramuthakannan, S. | Ramya Devi, M. | Lokesh, S. | Kumar, R.

Article Type: Research Article

Abstract: The increased usage of the internet and social networks generates a large volume of information. Exploring through the large collection is time-consuming and hard to find the required one, so there is a serious need for a recommendation system. Based on this context several movie recommendation (MR) systems have been recently established. In addition, they have poor data analytics capability and cannot handle changing user preferences. As a result, there are many movies listed on the recommendation page, which provides for a poor user experience is the major issue. Therefore, in this work, a novel Taymon Optimized Deep Learning network …(TODL net) for recommending top best movies based on their past choices, behaviour and movie contents. The deep neural network is a combination of Dilated CNN with Bi-directional LSTM. The DiCNN-BiLSTM model eliminates the functionality pooling operations and uses a dilated convolution layer to address the issue of information loss. The DiCNN is employed to learn the movie contents by mining user behavioral pattern attributes. The BiLSTM is applied to recommend the best movies on basis of the extracted features of the movie rating sequences of users in other social mediums. Moreover, for providing better results the DiCNN-BiLSTM is optimized with Taymon optimization algorithm to recommend best movies for the users. The proposed TODL net obtains the overall accuracy of 97.24% for best movies recommendation by using TMDB and MovieLens datasets. Show more

Keywords: Movie recommender system, deep learning, user experience, taymon, accuracy, movie rating

DOI: 10.3233/JIFS-231041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7675-7690, 2023

Authors: Niu, Lili

Article Type: Research Article

Abstract: As a convenient learning tool in the We Media era, mobile apps have been paid more and more attention by college students because of their accompanying timeliness and practicality. With the increasing number of English learning apps, many such apps provide college students with new ways to obtain learning resources and diversified learning modes. The related research in the field of mobile-assisted language learning at home and abroad has developed over nearly 20 years, basically following the route from theory to application in practice, but there have been few process studies on learners’ individual language skill learning behaviors based on …mobile platform data. In this study, the time series clustering method was adopted, and the learning behavior of college students in an English vocabulary learning app in China was selected for data mining. Firstly, taking the “single-day memorization amount” as the measurement index, the memorization records of college students in the whole use cycle were extracted and processed into trajectory data, and the KmL algorithm was used to cluster the trajectory of the memorization amount in the time series. According to the intra-class average trajectory, the characteristics of learning behavior changes among the different college students are summarized, and two learning modes are depicted. Secondly, through the experimental analysis, it was found that adopting the English learning model three weeks before an exam can effectively stimulate college students and improve their willingness to learn and continue using the app. Show more

Keywords: Time series clustering, English app, data mining, learning mode

DOI: 10.3233/JIFS-231476

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7691-7700, 2023

Authors: Mahaboob Basha, S.K. | Kalaiselvan, S.A.

Article Type: Research Article

Abstract: Quality of Experience (QoE) is a critical aspect of multimedia applications, which directly impacts user satisfaction and adoption. QoE predictions are used to optimize various parameters such as video quality, bitrate, and network bandwidth to enhance the user experience. However, accurate QoE prediction is a challenging task, as it involves various factors such as network conditions, video content, and user preferences. Therefore, there is a need for enhancing QoE predictions with advanced techniques to improve user satisfaction and adoption. This paper proposes incorporating more complex neural network architectures and using more diverse datasets to improve the accuracy and generalization of …Quality of Experience (QoE) predictions. The paper suggests experimenting with more advanced architectures such as convolutional neural networks and recurrent neural networks, which have been shown to be effective in various applications. Additionally, the paper highlights the limitation of using a single dataset and proposes using more diverse datasets that capture different types of video content and network conditions. Enhancing QoE predictions with complex neural networks and diverse datasets include improved accuracy, better generalization, more sophisticated models, enhanced user satisfaction and increased adoption. These enhancements are expected to lead to more accurate and reliable QoE predictions, which are crucial for improving user experience in multimedia applications. Show more

Keywords: Quality of Experience (QoE), Neural networks, multimedia applications

DOI: 10.3233/JIFS-233777

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7701-7711, 2023

Authors: Bera, Sanchari | Muhiuddin, Ghulam | Pal, Madhumangal

Article Type: Research Article

Abstract: Graph theory plays a crucial role in the era of computer science, medical science and information technology. The fundamental motivation behind this paper is to present some availability ideas in the m polar interval-valued fuzzy graph (m -PIVFG), which are utilized to portray the interval of the uncertainty of items. What’s more, the m -PIVFG graphs are utilized to portray the underlying connection between ideas in which the vertices and edges are of multi-poles and in the form of interval values to feature the uncertainty conditions. The dominating set involves a basic situation in graph analysis. This paper essentially …adds to expanding the idea of double domination in the fuzzy graph to the m -PIVFG and getting the related extended ideas of m -PIVFG. In the interim, the ways to get the particular double dominating sets are introduced. At long last, a numeral model on ambulance service on some villages information in India is introduced to clarify the necessity of double domination in m -PIVFG in the particular application. Show more

Keywords: m-PIVFG, double domination in m-PIVFG, acurate dominating set on m-PIVFG, accurate double dominating set on m-PIVFG, facility location problem

DOI: 10.3233/JIFS-223054

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7713-7726, 2023

Authors: Al-shami, Tareq M. | Hosny, Rodyna A. | Mhemdi, Abdelwaheb | Abu-Gdairi, Radwan | Saleh, Salem

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-230436

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7727-7738, 2023

Authors: Dang, Trong Hop | Do, Viet Duc | Mai, Dinh Sinh | Ngo, Long Thanh | Trinh, Le Hung

Article Type: Research Article

Abstract:  In image processing, segmentation is a fundamental problem but an important step for advanced image processing problems. When dealing with hyperspectral image data, the task becomes much more challenging due to the large number of features (dimension), higher nonlinearity, and greater capacity of the data. This paper proposes a solution of features reduction collaborative fuzzy c-means clustering (FR-CFCM) for hyperspectral remote sensing image analysis using random projection. The dimensional reduction technique is based on the Johnson Lindenstrauss lemma algorithm, preserving the relative distance between data samples. This can make clustering easier without affecting the clustering results. Moreover, by reducing dimensionality …and sharing information among sub-data in collaborative clustering, it is possible to improve the performance and accuracy of hyperspectral remote sensing image analysis results. The experiments conducted on two hyperspectral image data sets with five validity indexes show that the proposed methods perform better compared with the other methods. Show more

Keywords: hyperspectral image, fuzzy clustering, collaborative clustering, feature reduction

DOI: 10.3233/JIFS-230511

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7739-7752, 2023

Authors: Lv, Qian

Article Type: Research Article

Abstract: English teaching at college levels is more sophisticated and advanced compared to high schools and professionals. The teaching must have high-quality meetings, real-world interactions, and professional applications. Therefore teaching quality evaluation periodically is performed internally and externally through skill validation and joint training. This article introduces a Regressive Fuzzy Evaluation Model (RFEM) for analyzing the quality of college classroom English teaching quality. This evaluation model operates over the teaching quality metrics such as performance, student understandability, and application. The understandability and English application to the real world is modeled by referring to the performance as the regressive factor. The regressive …factor is analyzed for two fuzzification outputs: high and low, by analyzing the individual factors over cumulative teaching grades. The regression for low fuzzy outputs is analyzed using mean understandability and application score from the previous assessment instance. This is required for training the fuzzification from the mean score rather than the low level. Therefore the quality improvements from the lagging features are addressed by providing a new teaching method. Further fuzzy regression is initiated from the mean to the high level reducing the computation time and errors. Show more

Keywords: English teaching, fuzzy logic, quality evaluation, regressive analysis

DOI: 10.3233/JIFS-231321

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7753-7767, 2023

Authors: Ju, Hongmei | Yi, Huan

Article Type: Research Article

Abstract: The classification problem is a key area of research in machine learning. The Least Squares Support Vector Machine (LSSVM) is an important classifier that is commonly used to solve classification problems. Its widespread use stems from its replacement of the inequality constraint in the Support Vector Machine (SVM) with the equality constraint, which transforms the convex quadratic programming (QP) problem of SVM into the solution of linear equations. However, when dealing with multi-class classification problems, LSSVM faces the challenges of lack of sparsity and sample noises, which can negatively impact its performance. Based on the modeling characteristics and data distribution …of the multi-class LSSVM model, this paper proposes two improvements and establishes an improved fuzzy sparse multi-class least squares support vector machine (IF-S-M-LSSVM). The first improvement adopts a non-iterative sparse algorithm, which can delete training sample points to different degrees by adjusting the sparse ratio. The second improvement addresses the impact of sample noise on determining the optimal hyperplane by adding a fuzzy membership degree based on sample density. The advantages of the new model, in terms of training speed and classification accuracy, are verified through UCI machine learning standard data set experiments. Finally, the statistical significance of the IF-S-M-LSSVM model is tested using the Friedman and Bonferroni-Dunn tests. Show more

Keywords: Least squares support vector machine, multi-class classification problem, fuzzy membership, sparse

DOI: 10.3233/JIFS-231738

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7769-7783, 2023

Authors: Liao, Yi | Gui, Zhen

Article Type: Research Article

Abstract: In this paper, a sparse feature extraction method is presented based on sparse decomposition and multiple musical instrument component dictionaries to address the challenges of existing methods in component-recognition and analysis of mixed musical instrument music data. These methods, which are often dependent on data labels, and rely primarily on frequency domain or physical features, can be improved significantly using this technique. Through the in-depth analysis of the sparse coefficient vectors, this method is capable of generating independent sparse music features that are highly interpretable and have been shown to intuitively express the composition of musical instruments, and capture the …variations of emotion in the music. Consequently, this approach has great potential for application in the field of mixed musical instrument composition analysis and other time-varying signal analysis. Show more

Keywords: Feature extraction, sparse decomposition, sparse feature, hybrid instrument recognition, music time domain analysis

DOI: 10.3233/JIFS-231290

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7785-7796, 2023

Authors: Wu, Jing | Shi, Yuxin | Sheng, Yuhong

Article Type: Research Article

Abstract: Uncertain time series analysis is a method of predicting future values by analyzing imprecise observations. In this paper, the least absolute deviation (LAD) method is applied to solve for the unknown parameters of the uncertain max-autoregressive (UMAR) model. The predicted value and confidence interval of the future data are calculated using the fitted UMAR model. Moreover, the relative change rate of parameter is proposed to test the robustness of different estimation methods. Then, two comparative analyses demonstrate the LAD estimation can handle outliers better than the least squares (LS) estimation and the necessity of introducing the UMAR model. Finally, a …numerical example displays the LAD estimation in detail to verify the effectiveness of the method. The LAD estimation is also applied to a collection of actual data with cereal yield. Show more

Keywords: Uncertain time series, uncertain max-autoregressive model, least absolute deviation estimation, relative change rate

DOI: 10.3233/JIFS-232789

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7797-7809, 2023

Authors: Sangeetha, K. | Shanthini, J. | Karthik, S.

Article Type: Research Article

Abstract: Wireless sensor networks consist of a large number of randomly distributed nodes in a given area. WSN nodes are battery-powered, so they lose all their energy after a certain period and this energy constraint affects the network lifetime. This study aims to maximize network lifetime while minimizing overall energy use. In this study, a novel Energy Efficient Cluster based Adaptive Routing (ECAR) approach has been proposed for large-scale WSNs. Initially, the Genetic Bee Colony algorithm (GBCA) is introduced, which provides an effective way for selecting cluster heads based on node degrees, node centralities, distances to neighbors, and residual energy. Consequently, …the Quantum Inspired African Vulture Optimization algorithm (QIAVO) is utilized to find a routing path between the source and the destination over the cluster heads. To optimize the network performance, QIAVO considers multiple objectives, including residual energy, distance, and node degree. The proposed method is evaluated based on average packet delivery ratios, energy consumption, and average end-to-end delays. According to simulation results, the proposed protocol successfully balances the energy consumption of all sensor nodes and increases network lifespan. Show more

Keywords: Clustering, wireless sensor network, routing, energy efficiency, ECAR

DOI: 10.3233/JIFS-233445

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7811-7825, 2023

Authors: Rajarajan, S. | Kavitha, M.G.

Article Type: Research Article

Abstract: Technology development brought numerous lifestyle changes. People move around with smart gadgets and devices in the home, work environment, and familiar places. The Internet acts as a backbone for all applications and connecting multiple devices to set up a smart environment is technically termed as IoT (Internet of Things). The feature merits of IoT are explored in numerous fields from simple psychical data measurement to complex trajectory data measurement. Where the place is inaccessible to humans, IoT devices are used to analyze the region. Though IoT provides numerous benefits, due to its size and energy limitations, it faces security and …privacy issues. Intrusions in IoT networks have become common due to these limitations and various intrusion detection methods are introduced in the past decade. Existing learning-based methods lag in performance while detecting multiple attacks. Conventional detection models could not be able to detect the intrusion type in detail. The diverse IoT network data has several types of high dimensional features which could not be effectively processed by the conventional methods while detecting intrusions. Recently improvements in learning strategies proved the performance of deep learning models in intrusion detection systems. However, detecting multiple attacks using a single deep learning model is quite complex. Thus, in this research a multi deep learning model is presented to detect multiple attacks. The initial intrusion features are extracted through the AlexNet, and then essential features are selected through bidirectional LSTM. Finally, the selected features are classified using the decision tree C5.0 algorithm to attain better detection accuracy. Proposed model experimentations include benchmark NSL-KDD dataset to verify performances and compared the results with existing IDSs based on DeepNet, Multi-CNN, Auto Encoder, Gaussian mixture, Generative adversarial Network, and Convolutional Neural Network models. The proposed model attained maximum detection accuracy of 98.8% over conventional methods. Overall, an average of 15% improved detection performance is attained by the proposed model in detecting several types of intrusions in the IoT network. Show more

Keywords: Internet of Things (IoT), Intrusion detection system (IDS), deep learning, AlexNet, Bidirectional Long short-term memory (BiLSTM)

DOI: 10.3233/JIFS-233575

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7827-7840, 2023

Authors: Hou, Jundan | Liu, Qian | Dong, Qi

Article Type: Research Article

Abstract: In recent years, with the rapid growth of the public’s demand for cultural connotation and cultural taste of tourism products, promoting the rapid development of the integration of cultural tourism, the development of cultural tourism boom has been set off nationwide. Cultural tourism resources are the premise and foundation of cultural tourism development. With the rise of cultural tourism fever, the collation and excavation of the cultural connotation and cultural value of various types of cultural tourism resources around the world has entered a more in-depth stage, which undoubtedly promotes the industrial transformation and utilization of resources, but in terms …of the evaluation of the value of resources, there are more qualitative evaluations and few quantitative evaluations, which is largely due to the current academic classification of cultural tourism resources is not uniform, so that the evaluation of resources This is largely due to the difficulty of establishing the index system in the current academic community. The comprehensive value evaluation of cultural tourism resources is looked as the multiple attribute decision making (MADM) issue. In this paper, we extended the dua Hamy mean (DHM) operator and power avergae (PA) operator to 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the 2-tuple linguistic neutrosophic power DHM (2TLNPDHM) operator. Finally, a decision example for comprehensive value evaluation of cultural tourism resources is employed to show the 2TLNPDHM operator. Show more

Keywords: Multiple attribute decision making (MADM), 2TLNSs, 2TLNPDHM, cultural tourism resources, comprehensive value evaluation

DOI: 10.3233/JIFS-224492

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7841-7858, 2023

Authors: Ji, YingZhou | Niuo, Qiang

Article Type: Research Article

Abstract: High-performance concrete performs better than normal concrete because of using additional components than usual concrete components. Several artificially based analytics were used to evaluate the compressive strength (CS) of high-performance concrete (HPC) made with fly ash and blast furnace slag. In the present research, the Aquila optimizer (AO) was used to find the best values for the determinants of the adaptive neuro-fuzzy inference system (ANFIS), and radial basis function neural network (RBFNN) that may be changed to enhance performance. The suggested approaches were established using 1030 tests, eight inputs (a primary component of mix designs, admixtures, aggregates, and curing age), …and the CS as the forecasting objective. The results of the outperformed model were then contrasted with those found in the existing scientific literature. Calculation results point to the potential benefit of combining AO-RBFNN and AO-ANFIS study. The AO-ANFIS demonstrated significantly higher R 2 (Train: 0.9862, Test: 0.9922) and lower error metrics (such as: RMSE at 2.1434 (train) and 1.2763 (Test)) when compared to the AO-RBFNN and previously published articles. In summation, the proposed method for determining the CS of HPC supplemented with blast furnace slag and fly ash may be established using the suggested AO-ANFIS analysis. Show more

Keywords: High-performance concrete, estimation; artificial intelligence, ANFIS, optimization algorithm

DOI: 10.3233/JIFS-230374

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7859-7873, 2023

Authors: Wang, Shu | Wei, Nan | Zhu, Jie | Xu, Qinzheng

Article Type: Research Article

Abstract: Various fluid mechanics software, due to inherent factors such as algorithms and boundary conditions, cannot quickly simulate 3D flow fields in batches, and the calculation of each model still takes a lot of time.In order to realize the rapid prediction of the three-dimensional flow field around the airfoil, this paper uses a new SDF geometric expression to describe the shape of the airfoil, and combines the prediction accuracy of the velocity and pressure channels, and proposes a two-stage Unet3d convolution prediction model based on the SDF expression, which greatly improves the prediction accuracy of the pressure channel.In addition, the introduced …two-stage convolutional network is optimized by combining lightweight network and attention mechanism. On the premise of ensuring the accuracy of the network, it can effectively reduce the parameters of the network model and improve the operating efficiency of the network. The two-stage method was tested on the Naca0012 and RAE2822 three-dimensional datasets, and the average accuracy rates were 95.44% and 98.22% respectively, which were 2 to 3 percentage points higher than the original method. Show more

Keywords: deep learning, 3D flow field prediction, lightweight network, two-stage convolution, attention mechanism

DOI: 10.3233/JIFS-230692

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7875-7892, 2023

Authors: Shen, Xiajiong | Yang, Huijing | Hu, Xiaojie | Qi, Guilin | Shen, Yatian

Article Type: Research Article

Abstract: Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of a specified aspect in a sentence. Graph neural networks (GNN) based on dependency trees have been shown to be effective for ABSA by explicitly modeling the connection between aspect and opinion terms and exploiting local semantic and syntactic information in the sentence. However, most previous works have overlooked the use of global dependency information. In this paper, we propose a novel Graph Convolutional Network (GCN) with an Interactive Memory Fusion (IMF) mechanism (IMF-GCN) that incorporates both global and local structural information for aspect-based sentiment classification. The IMF mechanism efficiently …fuses global and local structural dependency information by assigning different weights to global and local dependency modules. Syntactic constraints are also imposed to prevent the graph convolution propagation unrelated to the target words, further improving the model’s performance. The evaluation metrics used in the paper are accuracy and macro-average F1 scores, and the proposed approach achieves optimal results on three datasets with F1 scores of 79.60%, 82.19%, and 77.75%, which outperform the baseline model. Show more

Keywords: Aspect-based sentiment analysis, GNN, dependency tree, GCN, interactive memory fusion

DOI: 10.3233/JIFS-230703

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7893-7903, 2023

Authors: Zhang, Ping | Lv, Wangyong | Zhang, Ce | Song, Jiacheng

Article Type: Research Article

Abstract: Probabilistic interval-valued intuitionistic hesitant fuzzy sets (PIVIHFSs) can well describe the evaluation information of decision-makers (DMs) in multi-attribute decision-making (MADM) problems. However, PIVIHFSs only depict the situation where both membership and non-membership information occur with equal probability while ignoring the situations of non-equal possibility due to DMs’ subjective preferences. In this paper, we develop dual probabilistic interval-valued intuitionistic hesitant fuzzy sets (DPIVIHFSs) concept based on the truncated normal distribution. The DPIVIHFSs overcome the shortcomings of PIVIHFSs and are more interpretable. Then, the operations and ranking method of DPIVIHFSs are introduced. Furthermore, we study MADM methods in dual probabilistic interval-valued intuitionistic …hesitant fuzzy environments by aggregation operators (AOs). We propose a series of AOs including the DPIVIHF heronian mean (DPIVIHFHM) operator and the DPIVIHF weighted heronian mean (DPIVIHFWHM) operator. The basic properties of the presented are discussed and proved. Finally, a novel method for solving the MADM problem is proposed based on the DPIVIHFWHM operator and a numerical example of express company selection strategy is used to illustrate the effectiveness of the method. The proposed method in this article can capture more fuzzy and uncertain information when solving MADM problems and have a wider application range. Show more

Keywords: Multi-attribute decision-making, DPIVIHFS, truncated normal distribution, DPIVIHFWHM, express company selection strategy

DOI: 10.3233/JIFS-231146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7905-7920, 2023

Authors: Sun, Peixi | Cui, Tong | Qi, Shixin

Article Type: Research Article

Abstract: Corporate culture is the sum of corporate values, systems, and behavioral norms formed in the long-term survival and development of an enterprise. It is the long-term accumulation of consensus among all employees in the enterprise. In the context of today’s global economic integration trend, the role of corporate culture construction in promoting enterprise development, improving business performance, and enhancing internal cohesion and external competitiveness is becoming increasingly significant. How to strengthen the construction of corporate culture and establish excellent corporate culture is increasingly receiving widespread attention from the academic and business communities. The comprehensive evaluation of corporate cultural competitiveness is …regarded as multi-attribute decision-making (MADM). The 2TLNSs are employed as a useful tool for characterizing uncertain information during the comprehensive evaluation of corporate cultural competitiveness. In this paper, the dual Hamy mean (DHM) and the power average (PA) are connected with 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the 2-tuple linguistic neutrosophic numbers power weighted DHM (2TLNPWDHM) operator. Then, use the 2TLNPWDHM operator to handle MADM with 2TLNS. Finally, taking the comprehensive evaluation of corporate cultural competitiveness as an example, the proposed method is explained. The main contributions of this study are summarized: the establishment of the 2TLNPWDHM operator; (2) The 2TLNPWDHM operator was developed to handle MADM with 2TLNS; (3) Through the empirical application of the comprehensive evaluation of corporate cultural competitiveness, the proposed method is validated; (4) Some comparative studies have shown the rationality of the 2TLNPWDHM operator. Show more

Keywords: Multi-attribute decision making (MADM), neutrosophic numbers, 2-tuple linguistic neutrosophic sets (2TLNSs), 2TLNPWDHM operator, corporate cultural competitiveness

DOI: 10.3233/JIFS-232024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7921-7937, 2023

Authors: Sasirekha, N. | Poonguzhali, I. | Shekhar, Himanshu | Vimalnath, S.

Article Type: Research Article

Abstract: The image of liver which is the area of interest in this work is obtained from abdominal CT scan. It also contains details of other abdominal organs such as pancreas, spleen, stomach, gall bladder, intestine etc. Since all these organs are of soft tissues, the pixel intensity values differ marginally in the CT scan output and the organs overlap each other at their boundaries. Hence it is very difficult to trace out the exact contour of liver and liver tumor. The overlapping and obscure boundaries are to be avoided for proper diagnosis. Image segmentation process helps to meet this requirement. …The normal perception of the CT image can be improved by suitable segmentation techniques. This will help the physician to extract more information from the image and give an accurate diagnosis and better treatment. The projected images are processed using the Partial Differential Technique (PDT) to isolate the liver from the other organs. The Level Set Methodology (LSM) is then used to separate the cancerous tissue from the healthy tissue around it. The classification of stages may be done with the assistance of an Enhanced Convolutional Classifier. The classification of LSM is evaluated by producing many metrics of accuracy, sensitivity, and specificity using an Improved Convolutional classifier. Compared to the two current algorithms, the proposed technique has a sensitivity and specificity of 96% and 93%, respectively, with 95% confidence intervals of [0.7513 1.0000] and [0.7126 1.0000] for sensitivity, and specificity respectively. Show more

Keywords: Liver cancer, improved convolutional classifier, level set methodology, partial differential technique, accuracy

DOI: 10.3233/JIFS-232218

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7939-7955, 2023

Authors: Megala, A. | Veeramani, C.

Article Type: Research Article

Abstract: Researchers in science and engineering face various obstacles due to a lack of specific and full data. Many different approaches have been devised to deal with these restrictive requirements, but two notable schools of thought are the fuzzy set (FS) theory and the rough set (RS) theory, both of which have spawned many extensions and hybridizations. Although RS theory originated from an indiscernibility relation (also known as an equivalence relation in mathematics), emphasis rapidly shifted to similarity or coverings (and their fuzzy analogues). Many other hybrid schemes were suggested with this goal in mind. The gap between those concepts shrank …because to this thorough analysis. Fuzzy set theory is a legitimate way to convey the ambiguity of assessment data, yet it is still inadequate for dealing with certain intricate problems in the actual world. In reality, decision makers will undoubtedly provide different kinds of ambiguous and nuanced assessments. Atanassov’s intuitionistic fuzzy set theory broadened the application of fuzzy set theory by imbuing it with an element of uncertainty. Sometimes in real life, you have to deal with a neutral element on top of the indeterminate one. Picture fuzzy sets were developed specifically for this purpose. Membership roles may be positive, neutral, or negative/refusal. In contrast, hesitant fuzzy sets and its hybrid models are useful when decision makers are on the fence about which option to choose. As a binary relation on a set, a graph is symmetric. It is a staple in mathematical modelling and is used in almost every scientific and technological discipline. Graph theory has been essential in the mathematical modelling and subsequent resolution of several real-world situations. Information about connections between things is often best represented using graph theory, which uses vertices to stand in for the items and edges for the relationships between them. The suggested dynamic algorithm is better to the static approach in dealing with the multidimensional dynamic changes of the hybrid incomplete decision system, according to a series of experiments carried out on nine UCI datasets. Show more

Keywords: Intuitionistic fuzzy set theory, graph theory, rough set theory, varying object sets and values

DOI: 10.3233/JIFS-232314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7957-7974, 2023

Authors: Huang, Zhen | Gao, Feng | Li, Xuesong | Jiang, Min

Article Type: Research Article

Abstract: The static risk assessment method has difficulty tracking variations of the risk level, which is not conducive to the dynamic control of construction. Tunnel collapse during the construction of mountain tunnels has a dynamic evolution law and contains great risk of harm, and the corresponding dynamic risk assessment is extremely important. This study proposes a static and dynamic fuzzy uncertainty assessment method for the collapse risk of mountain tunnels. First, 150 tunnel collapse accidents were investigated and analysed, and the static and dynamic risk assessment index system of mountain tunnel construction collapse was established. Second, the DEMATEL method is processed …by applying fuzzy logic, the subjective weight of each index is calculated, and the interaction between the indexes is analysed. Finally, the traditional VIKOR method is improved upon, and the weight of each assessment index is coupled and analysed. A static and dynamic uncertainty assessment model of the construction collapse risk of multiple construction sections is constructed. This method has been successfully applied to the risk assessment of tunnel collapse, and the assessment results are consistent with the actual construction situation. This study provides a new method for the static and dynamic assessment of mountain tunnel collapse risk. Show more

Keywords: Mountain tunnel, collapse, risk assessment, VIKOR method, DEMATEL method, Uncertainty analysis

DOI: 10.3233/JIFS-233149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7975-7999, 2023

Authors: Narayanan, M. Badri | Ramesh, Arun Kumar | Gayathri, K.S. | Shahina, A.

Article Type: Research Article

Abstract: Fake news production, accessibility, and consumption have all increased with the rise of internet-connected gadgets and social media platforms. A good fake news detection system is essential because the news readers receive can affect their opinions. Several works on fake news detection have been done using machine learning and deep learning approaches. Recently, the deep learning approach has been preferred over machine learning because of its ability to comprehend the intricacies of textual data. The introduction of transformer architecture changed the NLP paradigm and distinguished itself from recurrent models by enabling the processing of sentences as a whole rather than …word by word. The attention mechanisms introduced in Transformers allowed them to understand the relationship between far-apart tokens in a sentence. Numerous deep learning works on fake news detection have been published by focusing on different features to determine the authenticity of a news source. We performed an extensive analysis of the comprehensive NELA-GT 2020 dataset, which revealed that the title and content of a news source contain discernible information critical for determining its integrity. To this objective, we introduce ‘FakeNews Transformer’ — a specialized Transformer-based architecture that considers the news story’s title and content to assess its veracity. Our proposed work achieved an accuracy of 74.0% on a subset of the NELA-GT 2020 dataset. To our knowledge, FakeNews Transformer is the first published work that considers both title and content for evaluating a news article; thus, we compare the performance of our work against two BERT and two LSTM models working independently on title and content. Our work outperformed the BERT and LSTM models working independently on title by 7.6% and 9.6% , while performing better than the BERT and LSTM models working independently on content by 8.9% and 10.5% , respectively. Show more

Keywords: Fake news detection, FakeNews transformer, transformer encoder, NELA-GT 2020

DOI: 10.3233/JIFS-223980

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8001-8013, 2023

Authors: Zhenlin, Wei | Chuantao, Wang | Xuexin, Yang | Wei, Zhao

Article Type: Research Article

Abstract: The purpose of sentiment classification is to accomplish automatic judssssgment of the sentiment tendency of text. In the sentiment classification task of online reviews, traditional models focus on the optimization of algorithm performance, but ignore the imbalanced distribution of the number of sentiment classifications of online reviews, which causes serious degradation in the classification performance of the model in practical applications. The experiment was divided into two stages in the overall context. The first stage trains SimBERT using online review data so that SimBERT can fully learn the semantic features of online reviews. The second stage uses the trained SimBERT …model to generate fake minority samples and mix them with the original samples to obtain a distributed balanced dataset. Then the mixed data set is input into the deep learning model to complete the sentiment classification task. Experimental results show that this method has excellent classification performance in the sentiment classification task of hotel online reviews compared with traditional deep learning models and models based on other imbalanced processing methods. Show more

Keywords: Sentiment classification, imbalance classification, deep learning, BERT, SimBERT

DOI: 10.3233/JIFS-230278

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8015-8025, 2023

Authors: Xia, Yan | Yu, Shun | Jiang, Liu | Wang, Liming | Lv, Haihua | Shen, Qingze

Article Type: Research Article

Abstract: Power system load forecasting is a method that uses historical load data to predict electricity load data for a future time period. Aiming at the problems of general prediction accuracy and slow prediction speed in using typical machine learning methods, an improved fuzzy support vector regression machine method is proposed for power load forecasting. In this method, the boundary vector extraction technique is employed in the design of the membership function for fuzzy support vectors to differentiate the importance of different samples in the regression process. This method utilizes a membership function based on boundary vectors to assign differential weights …to different sample points that used to differentiate the importance of different types of samples in the regression analysis process in order to improve the accuracy of electricity load prediction. The key parameters of the fuzzy support vector regression model are optimized, further enhancing the precision of the forecasting results. Simulation experiments are conducted using real power load data sets, and the experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and speed in predicting power load data compared to other prediction models. This method can be widely applied in real power production and scheduling processes. Show more

Keywords: Machine learning, fuzzy support vector regressive machine, power load prediction, membership function, boundary vector

DOI: 10.3233/JIFS-230589

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8027-8048, 2023

Authors: Zhang, Ruihua | Han, Meng | He, Feifei | Meng, Fanxing | Li, Chunpeng

Article Type: Research Article

Abstract: In recent years, there has been an increasing demand for high utility sequential pattern (HUSP) mining. Different from high utility itemset mining, the “combinatorial explosion” problem of sequence data makes it more challenging. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of HUSP from a novel perspective. Firstly, from the perspective of serial and parallel, the data structure used by the mining methods are illustrated and the pros and cons of the algorithms are summarized. In order to protect data privacy, many HUSP hiding algorithms have been proposed, which are classified into array-based, …chain-based and matrix-based algorithms according to the key technologies. The hidden strategies and evaluation metrics adopted by the algorithms are summarized. Next, a taxonomy of the most common and the state-of-the-art approaches for incremental mining algorithms is presented, including tree-based and projection-based. In order to deal with the latest sequence in the data stream, the existing algorithms often use the window model to update dynamically, and the algorithms are divided into methods based on sliding windows and landmark windows for analysis. Afterwards, a summary of derived high utility sequential pattern is presented. Finally, aiming at the deficiencies of the existing HUSP research, the next work that the author plans to do is given. Show more

Keywords: Survey, high utility sequential patterns, incremental data, data streams, hidden patterns

DOI: 10.3233/JIFS-232107

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8049-8077, 2023

Authors: Wu, Xiaopeng

Article Type: Research Article

Abstract: In wireless-sensing networks (WSNs), the energy economy has lately emerged as the main problem. Since sensor networks run on batteries, they eventually run out of power. To increase the packet transmission ratio for sensing devices, it becomes more difficult to enhance data loss in an energy-efficient manner. In WSNs, the mobile drain causes high network energy usage and data delay. This paper suggests an Improved Ant Colony Clustering-Based Data Transmission Algorithm (EACODT) that first develops the network nodes’ energy density function before allocating sensing nodes with higher residual energy as cluster leaders using the energy density function. The EACODT is …thoroughly modeled for different WSN situations with variable numbers of sensing nodes and CHs, and the findings are contrasted with some recently developed meta-heuristic algorithms. As a consequence, it is discovered that EACODT gets 34% of energy usage, 98.8% of network lifespan, 95% of packet delivery ratio, 854 kbps of transmission, and a 98% convergence rate. Show more

Keywords: Wireless sensor networks, optimization, energy efficiency, packet delivery, data transmission

DOI: 10.3233/JIFS-232295

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8079-8089, 2023

Authors: Zhao, Xiao-Rui | Wang, Jie-Sheng | Bao, Yin-Yin | Hou, Jia-Ning | Ma, Xin-Ru | Li, Yi-Xuan

Article Type: Research Article

Abstract: Wild Horse Optimizer (WHO) is a population-based metaheuristic algorithm inspired by animal behavior, which mainly imitates the decent behavior, grazing behavior, mating behavior and leadership dominance behavior of wild horses in nature to find the optimal. The initialization of the population by imitating the behavior of wild horses is prone to uneven distribution of population positions, and its position updating method is prone to local optimal problems while improving the efficiency of the search. In order to enhance the population diversity and to break out of the local optimum, an adaptive weighted wild horse optimizer based on backward learning and …small-hole imaging strategy is proposed. The backward learning strategy is used to enhance the population diversity and improve the uneven distribution of individuals; The adaptive weight and small-hole imaging strategy are added to the local search strategy to improve the global search ability and jump out of the local optimum. To verify the effectiveness of the proposed algorithm, simulation experiments were conducted by using 23 benchmark test functions to test the search ability and Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Rat Swarm Optimizer (RSO) and Multi-Verse Optimizer (MVO) algorithms are compared in terms of their search performance, and finally four real engineering design problems are solved. The simulation results indicate that the proposed FHPWHO has excellent merit-seeking capability. Show more

Keywords: Wild horse optimizer, inverse learning, adaptive weights, small-hole imaging strategy, function optimization, engineering optimization

DOI: 10.3233/JIFS-232342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8091-8117, 2023

Authors: Cao, Jiangzhong | Liao, Siyi

Article Type: Research Article

Abstract: 3D shape recognition is a critical research topic in the field of computer vision, attracting substantial attention. Existing approaches mainly focus on extracting distinctive 3D shape features; however, they often neglect the model’s robustness and lack refinement in deep features. To address these limitations, we propose the point-view fusion attention network that aims to extract a concise, informative, and robust 3D shape descriptor. Initially, our approach combines multi-view features with point cloud features to obtain accurate and distinguishable fusion features. To effectively handle these fusion features, we design a dual-attention convolutional network which consists of a channel attention module and …a spatial attention module. This dual-attention mechanism greatly enhances the generalization ability and robustness of 3D recognition models. Notably, we introduce a strip-pooling layer in the channel attention module to refine the features, resulting in improved fusion features that are more compact. Finally, a classification process is performed on the refined features to assign appropriate 3D shape labels. Our extensive experiments on the ModelNet10 and ModelNet40 datasets for 3D shape recognition and retrieval demonstrate the remarkable accuracy and robustness of the proposed method. Show more

Keywords: 3D Shape recognition, multimodal feature fusion, feature refinement, attention mechanism

DOI: 10.3233/JIFS-232800

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8119-8133, 2023

Authors: Huang, Hangxing | Ma, Lindong

Article Type: Research Article

Abstract: In late 2019, coronavirus disease (COVID-19) began to spread globally and is highly contagious. Due to its exceptionally rapid spread and high mortality rate, it is not yet possible to be eradicated. In order to halt the spread of COVID-19, there is a pressing need for effective screening of infected patients and immediate medical intervention. The absence of rapid and accurate methods to identify infected patients has led to a need for a model for early diagnosis of patients with and suspected of having COVID-19 to reduce the probability of missed diagnosis and misdiagnosis. Modern automatic image recognition techniques are …an important diagnostic method for COVID-19. The aim of this thesis is to propose a novel deep learning technique for the automatic diagnosis and recognition of coronavirus disease (COVID-19) on X-ray images using a transfer learning approach. A new dataset containing COVID-19 information was created by merging two publicly available datasets. This dataset includes 912 COVID-19 images, 4273 pneumonia images, and 1583 normal chest X-ray images. We used this dataset to train and test the deep learning algorithm. With this new dataset, two pre-trained models (Xception and ResNetRS50) were trained and validated using transfer learning techniques. 3-class images were identified (Pneumonia vs. COVID-19 vs. Normal), and the two models generated validation accuracies of 90% and 97.21%, respectively, in the experiments. This demonstrates that our proposed algorithm can be well applied in diagnosing patients with lung diseases. In this study, we found the ResNetRS50 model to be superior. Show more

Keywords: ResNetRS50, deep learning, X-ray images, transfer learning, COVID-19

DOI: 10.3233/JIFS-232866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8135-8144, 2023

Authors: Yao, Jingkun | Guo, Beibei | Pang, Zheng

Article Type: Research Article

Abstract: In order to improve the coordinated control effect of hierarchical power balance of new energy microgrid, this paper applies fuzzy control method to this system, and proposes a hierarchical control strategy based on event-triggered communication. Each DG is regarded as a proxy, and the continuous actual value of output is replaced by the state prediction value. Moreover, two different event trigger condition functions for frequency and voltage are designed based on Lyapunov method respectively. At the same time, each DG only communicates with its neighbor DG aperiodic at the event trigger time, and finally all DG are restored to the …reference value provided by the virtual leader. Finally, this paper constructs a coordinated fuzzy control simulation system for hierarchical power balance of new energy microgrid. Combined with the simulation results, the method proposed in this paper is feasible. Show more

Keywords: New energy, microgrid, hierarchical power, balance, fuzzy control

DOI: 10.3233/JIFS-232963

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8145-8158, 2023

Authors: Natarajan, Kirthika | Chelliah, Jeyalakshmi | Mariyarose, Jemin Vijayaselvan | Andi, Senthilkumar | Venkatachalam, Bharathi | Alagarsamy, Manjunathan

Article Type: Research Article

Abstract: This is contrary for Voice impaired people since their speech is tough for others to recognize even by their parents and teachers. Provided if their parents are illiterate. So our TTS system can be used for converting their written text to speech for their illiterate parents and friends around them. Though many methods have been adopted for the concatenation of the basic sound units, the HMM-based approach in modeling the sound is utilized by many researchers in many languages. In this paper, we have tried to implement, text to speech systems of synthesis for a Tamil text uses a phonemic …concatenation approach in MATLAB. Instead of utilizing Tamil letters as it is, due to its difficulty in production, Tamil text is transliterated into English then it is converted into intelligible speech. The performance of the output is verified for various examples by changing its parameters, in which the quality of the sound is comparable to that of English text. So the proposed system is utilized for all languages other than Tamil also if it is properly transliterated for limited vocabulary. Show more

Keywords: Phoneme, text normalization, voice impaired, subharmonic ratio, pitch, transliteration

DOI: 10.3233/JIFS-231680

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8159-8169, 2023

Authors: Syed Anwar Hussainy, F. | Thillaigovindan, Senthil Kumar | Sabhanayagam, T.

Article Type: Research Article

Abstract: The present growth in Internet of Medical Things (IoMT) and Artificial Intelligence (AI) paved a way for advanced healthcare systems from conventional methods. The integration of AI and IoMT provides varied chances in medical domain. With that concern, the proposed model derives a novel model for Heart Disease Prediction (HDP), incorporates IoMT and AI. The proposed model comprises of different phases of functions, as, data collection, data preparation, feature optimization and selection, classification. IoMT devices include medical or wearable sensors are used for continuous collection of medical statistics while machine learning model process the data for disease prediction. Here, a …new feature selection model called Enhanced Binary Particle Swarm Optimization (EBPSO) for reducing joint feature selection problems. With the extracted features, classification is performed with Cascaded Long Short Term Memory (CLSTM) model for attaining better accuracy of medical data classification. During evaluation, the proposed HDP model achieved the maximal accuracy in disease prediction. Hence, the model can be effectively used for diagnosing heart disease in Smart Healthcare Models. Show more

Keywords: Internet of medical things, Artificial Intelligence, Enhanced Binary Particle Swarm Optimization, machine learning, Heart Disease

DOI: 10.3233/JIFS-232517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8171-8180, 2023

Authors: Wang, Lu

Article Type: Research Article

Abstract: In recent years, due to the further development of the market economy, the internal competition in the large-cargo transportation industry has become increasingly fierce, and the profit space has been greatly compressed. Therefore, large-cargo logistics enterprises are paying more and more attention to the research of highway transportation route plan. The highway transportation scheme selection is looked as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic numbers (TFNN) grey relational analysis (TFNN-GRA) method is established based on the classical grey relational analysis (GRA) and triangular fuzzy neutrosophic sets (TFNSs) with completely unknown weight information. In order to …obtain the weight values, the information Entropy is established to obtain the weight values based on the score and accuracy functions under TFNSs. Then, combining the traditional fuzzy GRA model with TFNSs information, the TFNN-GRA method is set up and the computing steps for MADM are established. Finally, a numerical example for highway transportation scheme selection was established and some comparisons are established to study the advantages of TFNN-GRA. The main contributions of this paper are established as follows: (1) the information Entropy is established to obtain the weight values based on the score and accuracy functions under TFNSs; (2) the TFNN-GRA method is established with completely unknown weight information. (2) the TFNN-GRA method is established and the computing steps for MADM are established. (3) Finally, a numerical example for highway transportation scheme selection was established and some comparisons is employed to study advantages of TFNN-GRA method. Show more

Keywords: Multiple attribute decision making (MAGDM) problems, triangular fuzzy neutrosophic sets (TFNSs), GRA method; highway transportation scheme selection

DOI: 10.3233/JIFS-233620

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8181-8195, 2023

Authors: Dawlet, Omirzhan | Bao, Yan-Ling

Article Type: Research Article

Abstract:  As dual hesitant fuzzy sets can express the uncertainty of data efficiently, the aggregation of dual hesitant fuzzy information plays an important role in both theory and application. However, some existing dual hesitant fuzzy aggregation operators are not rigorous enough actually. In this note, we show that some theorems in an earlier paper by Ju et al. [1 ] (Journal of Intelligent & Fuzzy Systems 27 (2014) 2481–2495) are not correct, i.e., the dual hesitant fuzzy Hamacher weighted averaging operator (DHFHWA) and some other aggregation operators proposed by Ju et al. don’t satisfy idempotency and boundedness. Therefore, the purpose of …this paper is to make researchers aware of that some aggregation operators in literature [1 ] are flawed and limited for many applications. Show more

Keywords: Dual hesitant fuzzy set, Aggregation operator, Idempotency

DOI: 10.3233/JIFS-230764

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8197-8201, 2023

Authors: Chandra Murty, Patnala S.R. | Anuradha, Chinta | Appala Naidu, P. | Balaswamy, C. | Nagalingam, Rajeswaran | Jagatheesaperumal, Senthil Kumar | Ponnusamy, Muruganantham

Article Type: Research Article

Abstract: This study quantifies individual stress levels through real-time analysis of wearable sensor data. An embedded setup utilizes artificial neural networks to analyze R-R intervals and Heart Rate Variability (HRV). Emotion recognition of happiness, sadness, surprise, fear, and anger is explored using seven normalized HRV features. Statistical analysis and classification with a neural network model are performed on approximately 20,700 segments,with participants within the age ranged from 23 to 40, mixed gender, and normal health status, along with other pertinent demographics included. Findings show stress observation’s potential for mental well-being and early detection of stress-related disorders. Three classification algorithms (LVQ, BPN, …CART) are evaluated, comparing ECG signal correlation features with traditional ones. BPN achieves the highest emotional recognition accuracy, surpassing LVQ by 5.9% – 8.5% and CART by 2% – 6.5%. Maximum accuracy is 82.35% for LVQ and 97.77% for BPN, but does not exceed 95%. Using only 72 feature sets yields the highest accuracy, surpassing S1 by 17.9% – 20.5% and combined S1/S2 by 11% – 12.7%. ECG signal correlation features outperform traditional features, potentially increasing emotion recognition accuracy by 25%. This study contributes to stress quantification and emotion recognition, promoting mental well-being and early stress disorder detection. The proposed embedded setup and analysis framework offer real-time monitoring and assessment of stress levels, enhancing health and wellness. Show more

Keywords: Psychological behavior, stress monitoring, artificial neural networks, wearable embedded sensors, heart rate variability, ECG

DOI: 10.3233/JIFS-233791

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8203-8216, 2023

Authors: Dutta, Kusumika Krori | Manohar, Premila | Indira, K.

Article Type: Research Article

Abstract: Although epilepsy is one of the most prevalent and ancient neurological disorder, but, still difficult to identify the specific type of seizure, due to artefacts, noise, and other disturbances, because of acquisition of Scalp EEG. It necessitating the use of skilled medical professionals as incorrect diagnosis lead to wrong Anti Seizure Drug (ASDs) and face it’s side effects. On the other hand machine learning plays a crucial role in seizure detection by analyzing and identifying patterns in brain activity data that are indicative of seizures. It can be used to develop predictive models that can detect the onset of seizures …in real-time, allowing for early intervention and improved patient outcomes. Most of the research work focuses on seizure detection using various machine learning techniques pre-processed by different mathematical models. But, very less attention is paid towards seizure type detection. In this study, multiple Machine and Deep Learning algorithms were used in conjunction with time-domain and frequency-domain pre-processing to classify epileptic seizures into multiple types. The ictal period of various seizure types were extracted from Temple University Hospital EEG (TUHEEG) and the pre-processed data was tried out with multiple classifiers, including support vector classifiers (SVC), K- Nearest Neighbor (KNN), and Long short term memory (LSTM), among others. By using SVM, KNN, and LSTM, multiclass classification of seven types of epileptic seizures with 19 channels were considered for each EEG data and a 75–25 train–test ratio was accomplished with 90.41%, 94.46%, and 86.2% accuracy respectively. Epileptic seizure’s ictal phase EEG signals are categorized using a variety of machine learning(ML) and deep learning(DL) methods after being pre-processed using time domain and frequency domain approaches. The KNN yields the best results of all. Show more

Keywords: Seizure classification, TUHEEG, ABSZ, CPSZ, FNSZ, GNSZ, SPSZ, TNSZ, TCSZ, SVM, KNN, LSTM, EEG

DOI: 10.3233/JIFS-224570

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8217-8226, 2023

Authors: Mahalingam, Priyadarshini | Kalpana, D. | Thyagarajan, T.

Article Type: Research Article

Abstract: This paper disseminates an extra dimension of substantial analysis demonstrating the trade-offs between the performance of Parametric (P) and Non-Parametric (NP) classification algorithms when applied to classify faults occurring in pneumatic actuators. Owing to the criticality of the actuator failures, classifying faults accurately may lead to robust fault tolerant models. In most cases, when applying machine learning, the choice of existing classifier algorithms for an application is random. This work, addresses the issue and quantitatively supports the selection of appropriate algorithm for non-parametric datasets. For the case study, popular parametric classification algorithms namely: Naïve Bayes (NB), Logistic Regression (LR), Linear …Discriminant Analysis (LDA), Perceptron (PER) and non-parametric algorithms namely: Multi-Layer Perceptron (MLP), k Nearest Neighbor (kNN), Support Vector Machine (SVM), Decision Tree (DT) and Random Forest (RF) are implemented over a non-parametric, imbalanced synthetic dataset of a benchmark actuator process. Upon using parametric classifiers, severe adultery in results is witnessed which misleads the interpretation towards the accuracy of the model. Experimentally, about 20% improvement in accuracy is obtained on using non-parametric classifiers over the parametric ones. The robustness of the models is evaluated by inducing label noise varying between 5% to 20%. Triptych analysis is applied to discuss the interpretability of each machine learning model. The trade-offs in choice and performance of algorithms and the evaluating metrics for each estimator are analyzed both quantitatively and qualitatively. For a more cogent reasoning through validation, the results obtained for the synthetic dataset are compared against the industrial dataset of the pneumatic actuator of the sugar refinery, Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS). The efficiency of non-parametric classifiers for the pneumatic actuator dataset is well proved. Show more

Keywords: Parametric classifiers, non-parametric classifiers, trade-offs, pneumatic actuator, DAMADICS, accuracy, interpretability

DOI: 10.3233/JIFS-231026

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8227-8247, 2023

Authors: Yan, Zhenggang

Article Type: Research Article

Abstract: With the continuous deepening of the construction of urban-rural economic integration in China, rural construction activities supported by rural revitalization strategies have changed the development thinking of rural economy. While implementing the goal of rural ecological economy, optimizing the rural living environment has become one of the important contents of rural revitalization, including the planning and design of rural landscapes. Rural landscape planning and design need to comprehensively consider the adaptability of landscape and rural ecological environment, emphasize the impact of rural spatial structure differences on landscape planning and design, and achieve scientific and humanized landscape planning and design, thereby …creating a more warm, natural, and comfortable rural living space. The quality evaluation of tourism rural landscape planning and design is a multiple attribute group decision making (MAGDM) problems. Recently, the TODIM (an acronym in Portuguese of interactive and multicriteria decision making) and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method has been inaugurated to cope with MAGDM issues. The 2-tuple linguistic neutrosophic sets (2TLNSs) are inaugurated as a effective tool for characterizing uncertain information during the quality evaluation of tourism rural landscape planning and design. In this paper, the 2-tuple linguistic neutrosophic TODIM-VIKOR (2TLN-TODIM-VIKOR) method is inaugurated to solve the MAGDM under 2TLNSs. In the end, a numerical case study for quality evaluation of tourism rural landscape planning and design is inaugurated to confirm the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2-tuple linguistic neutrosophic sets (2TLNSs), TODIM, VIKOR, tourism rural landscape planning and design

DOI: 10.3233/JIFS-231400

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8249-8261, 2023