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

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