Journal of Intelligent & Fuzzy Systems - Volume 45, issue 6

Authors: Zhu, Yiping | Huang, Jiajia | Zhu, Yi | Guo, Yang

Article Type: Research Article

Abstract: Online teaching platforms have developed into mainstream knowledge learning and exchange platform. The research on the quality evaluation of online teaching platforms and the construction of an applicable and scientific evaluation index system model can help explore the key factors affecting the quality of online teaching platforms and provide some references for evaluating online teaching platforms and improving online teaching quality. This study combines the rough set theory (RS) with the BP (Back Propagation) neural networks to build an RS-BP neural network model to evaluate the quality of online teaching platforms. Firstly, an initial online teaching platform quality evaluation index …system is constructed based on knowledge transfer theory from four aspects: course content, knowledge transmitter, knowledge receiver and teaching platform. Then, 12 core evaluation indicators were generated by attribute reduction using rough set theory, and the weights of each core indication were determined. The normalized data input was then trained, validated, and tested to generate a rough set neural network quality evaluation model for online teaching platforms. After that, three representative online education platforms of content, interaction and compatibility are selected for empirical research. The accuracy of the model is first tested by the error between the simulated and output values, after which the core metric scores and the overall scores are calculated for the three types of platforms. The empirical results demonstrate that the model has certain advantages in terms of index simplification and adaptive training when evaluating online teaching platforms, as well as strong operability and practicality. The evaluation results show that the content online teaching platform has the highest comprehensive score, followed by the compatible and interactive online teaching platforms. According to the index scores, the quality of the course content, stage assessments, and contact between professors and students were identified as major elements influencing the quality of the online teaching platform. Finally, suggestions for optimization for each of the three types of online teaching platforms were made based on the core indicators and their weights, as well as the scores and characteristics of the three types of online teaching platforms, with the goal of improving the quality of online teaching platforms. Show more

Keywords: Knowledge transfer, online teaching platform, rough set theory, BP neural network

DOI: 10.3233/JIFS-231381

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11769-11789, 2023

Authors: Wen, Shuting | Wen, Fangcheng

Article Type: Research Article

Abstract: Culture and tourism development through public services rely on accumulated big data and overall country/ province development. Accumulated data relies on various cultures, people, places, etc. attributes for which a heterogeneous and multi-faced analysis is required. This article introduces a Development-focused Data Handling Process (D-DHP) for providing insights through culture and tourism accumulated information. The proposed process relies on heterogeneous data attributes for identifying economic and society-based development stagnancies. The data analysis is performed for identifying missing sequences and invariable information that shows development stagnancies. The stagnancy rates between successive quarters (per annum) are accounted for identifying development drops. If …such drops are identified, the accumulated data outputs are analyzed through classification learning. In this classification, the development and drop-associated data are split for an independent analysis. This analysis helps to replace the mode of development focusing on tourism or culture or both based on dependability. The classification process is updated based on the replaced information for further improvements across various accumulated data inputs. Therefore, the proposed process is viable in identifying development-focused information from the accumulated data. Show more

Keywords: Big data, classification learning, culture and tourism, public services

DOI: 10.3233/JIFS-232318

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11791-11806, 2023

Authors: Chen, Xin | Wang, Yan | Li, Fuzhen

Article Type: Research Article

Abstract: A singular system, assumed to possess both regularity and freedom from impulses, is categorized as a causal system. Noncausal systems (NSs) are a class of singular systems anticipated to exhibit regularity. This study focuses on investigating zero-sum games (ZSGs) in the context of NSs. We introduce recurrence equations grounded in Bellman’s optimality principle. The saddle-point solution for multistage two-player ZSGs can be obtained by solving these recurrence equations. This methodology has demonstrated its effectiveness in addressing two-player ZSGs involving NSs. Analytical expressions that characterize saddle-point solutions for two types of two-player ZSGs featuring NSs, encompassing both linear and quadratic control …scenarios, are derived in this paper. To enhance clarity, we provide an illustrative example that effectively highlights the utility of our results. Finally, we apply our methodology to analyze a ZSG in the realm of environmental management, showcasing the versatility of our findings. Show more

Keywords: Zero-sum game, noncausal system, saddle-point solution, recurrence equations

DOI: 10.3233/JIFS-232401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11807-11833, 2023

Authors: Sun, Xiujing

Article Type: Research Article

Abstract: With the rapid development and application of internet technology, cross-border e-commerce (CBEC) has begun to popularize globally and play an important role in China’s foreign trade. The Chinese government has successively introduced multiple policies and regulations to strongly support its rapid development. Compared to the booming trend of CBEC, the development of its supply chain is slightly lacking in momentum, which has formed a certain obstacle to the overall development of CBEC. The supply chain is the foundation of successful CBEC transactions, and the foundation of the supply chain is logistics. The primary task to improve the backwardness of supply …chain development is to solve logistics problems. Therefore, while enjoying the dividends brought by the rapid development of CBEC, international logistics enterprises should continuously improve their logistics service capabilities, effectively evaluate their service quality, and then identify problems based on the evaluation results, analyze and improve them. The service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain is a classical multiple attribute group decision making (MAGDM). The Spherical fuzzy sets (SFSs) provide more free space for DMs to portray uncertain information during the service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain. Therefore, this paper expands the partitioned Maclaurin symmetric mean (PPMSM) operator and IOWA operator to SFSs based on the power average (PA) technique and construct induced spherical fuzzy weighted power partitioned MSM (I-SFWPPMSM) technique. Subsequently, a novel MAGDM method is constructed based on I-SFWPPMSM technique and SFNWG technique under SFSs. Finally, a numerical example for service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain is employed to verify the constructed method, and comparative analysis with some existing techniques to testy the validity and superiority of the I-SFWPPMSM technique. Show more

Keywords: MAGDM, Spherical fuzzy sets (SFSs), I-SFWPPMSM operator, Service quality evaluation

DOI: 10.3233/JIFS-233384

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11835-11851, 2023

Authors: Liu, Wenxiu | Xu, Lijun | Zhou, Yijia | Yu, Bo

Article Type: Research Article

Abstract: In this paper, we propose two novel Alternating Direction Method of Multipliers (ADMM) algorithms for the sparse portfolio problem via sorted ℓ1 -norm penalization (SLOPE). The first algorithm (FADMM) is presented by adding a prediction-correction step to the classic ADMM framework. Since the problem is not strongly convex, the second fast ADMM (FADMMR) is proposed by utilizing both prediction-correction step and restarting rules. Numerical experiments show that the FADMMR algorithm converges faster than the FADMM algorithm and ADMM algorithm when tuning parameters are relatively small. On the other hand, when tuning parameters are relative large, the FADMM algorithm performs better …than the FADMMR algorithm and ADMM algorithm. The FADMM algorithm and FADMMR algorithm converge faster than the ADMM algorithm in terms of convergence time for different sizes of tuning parameters. For large-scale portfolio problem, the proposed algorithms have highly performance as well. Finally, empirical analysis on five datasets of stocks index show that the proposed algorithms are efficient and superior for solving sparse portfolio problems via SLOPE. Show more

Keywords: Fast ADMM, fast ADMM with restart, sparse portfolio, sort ℓ1-norm penalty

DOI: 10.3233/JIFS-234381

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11853-11872, 2023

Authors: Jin, Huilong | Du, Ruiyan | Wen, Tian | Zhao, Jia | Shi, Lei | Zhang, Shuang

Article Type: Research Article

Abstract: Compared with other facial expression recognition, classroom facial expression recognition should pay more attention to the feature extraction of a specific region to reflect the attention of students. However, most features are extracted with complete facial images by deep neural networks. In this paper, we proposed a new expression recognition based on attention mechanism, where more attention would be paid in the channel information which have much relationship with the expression classification instead of depending on all channel information. A new classroom expression classification has also been concluded with considering the concentration. Moreover, activation function is modified to reduce the …number of parameters and computations, at the same time, dropout regularization is added after the pool layer to prevent overfitting of the model. The experiments show that the accuracy of our method named Ixception has an maximize improvement of 5.25% than other algorithms. It can well meet the requirements of the analysis of classroom concentration. Show more

Keywords: Deep learning, classroom facial expression recognition, attention mechanism, activation function, dropout regularization

DOI: 10.3233/JIFS-235541

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11873-11882, 2023

Authors: Luo, Dang | Ambreen, Muffarah | Latif, Assad | Wang, Xiaolei | Samreen, Mubbarra | Muhammad, Aown

Article Type: Research Article

Abstract: Almost all cities of Pakistan are economically affected by the electricity shortage due to the continuously increasing demand for electricity. To correctly forecast the seasonal fluctuations of the electricity consumption of Lahore city in Pakistan, we proposed the SDGPM(1,1,N) model, which is a seasonal discrete grey polynomial model combined with seasonal adjustment. We conducted an empirical analysis using the proposed model based on the seasonal electricity consumption data of Lahore city in Pakistan from 2014 to 2021. The findings from the SDGPM (1,1,N) model are compared with those collected through the original grey model DGPM(1,1,N) and other eight models. The …comparison’s findings demonstrated that the SDGPM(1,1,N) model is indeed capable of correctly identifying seasonal fluctuations of electricity consumption in Lahore city and its prediction accuracy is significantly higher than the original DGPM(1,1,N) model and the other seven models. The SDGPM(1,1,N) model’s forecast findings for Lahore from 2022 to 2025 indicate that the city’s energy consumption is expected to rise marginally, although there will still be significant seasonal fluctuations. It is predicted that the annual electricity consumption from 2022 to 2025 will be 26249, 26749, 27928, and 28136 with an annual growth rate of 7.18%. This forecast can provide policymakers ahead start in planning to ensure that supply and demand are balanced. Show more

Keywords: Seasonal factor, Lahore electricity forecasting, seasonal discrete grey polynomial model, seasonal DGPM(1, 1,N)

DOI: 10.3233/JIFS-231106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11883-11894, 2023

Authors: Sangeetha, J. | Priyanka, M. | Jayakumar, C.

Article Type: Research Article

Abstract: Audio Event Detection (AED) and classification of acoustic events has become a notable task for machines to interpret the auditory information around us. Nevertheless, it has been a difficult and cumbersome task to extract the most basic characteristics of acoustic events that encapsulate the fundamental elements of the audio events. Previous works on audio event classification utilized supervised pre-training as well as meta-learning approaches that happened to depend on labeled data therefore facing instability. Deep Learning is progressing in an increasingly mature direction, and the application of deep learning methods to detect acoustic event has become more and more sought …after. The proposed hybrid method called Greedy Regression-based Convolutional Neural Network and Differential Convex Bidirectional Gated Recurrent Unit (GRCNN-DCBGRU) is introduced to learn a vector representation of an audio sequence for Audio Event Classification (AEC). Differential Convex Bidirectional Gated Recurrent Unit is analogous to long short-term memory and involves time-cyclic long-term dependencies with a lesser processing complexity. The model first extracts acoustic features from the sound event dataset through a Differential Convex Bidirectional Gated Recurrent Unit employing Gabor Filter bank features and then extracts the local static acoustic features through the Greedy Regression-based Convolutional Neural Network by utilizing Mel Frequency Cepstral Coefficients (MFCC). Finally, the Differential Convex Meta-Learning classifier is used for the final acoustic event classification. Extensive evaluation on large-size publicly available acoustic event database like Findsounds2016 will be performed in Python programming language to demonstrate the efficiency of the proposed method for the AEC task. To demonstrate the visualizations of individual modules and their influence on overall representation learning for AEC tasks, several parameters like audio detection time, audio detection accuracy, precision, and recall are measured. Show more

Keywords: Audio event detection, audio event classification, deep learning, greedy regression, convolutional neural network, differential convex, bidirectional gated recurrent unit

DOI: 10.3233/JIFS-232561

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11895-11908, 2023

Authors: Tan, Mindong | Qu, Liangdong

Article Type: Research Article

Abstract: Oral English teaching quality evaluation is a complex nonlinear relationship, which is affected by many factors and has low accuracy. Aiming at the problem, a teaching quality evaluation method based on a BP neural network optimized by the improved crow search algorithm (ICSA) is proposed. First, ICSA is put forward and five algorithms are used to compare with the proposed algorithm on 10 benchmarks functions. The results show that ICSA outperforms the other five algorithms on 10 functions. Second, a feature selection method based on the improved binary crow search algorithm (BICSA) is used to select teaching quality evaluation indexes, …and 10 standard datasets from the UCI repository are used for testing experiments. Finally, an oral English teaching evaluation model based on BP neural network is designed, in which BICSA is used for feature selection and ICSA is used to optimize the initial weights of the BP neural network. In the experiment, we designed 5 first-grade indexes and 15 second-grade indexes, and then we collects 23 groups of oral English teaching quality data. BICSA selected 10 features from a set of 15 features. Experimental results show that this method can effectively evaluate the quality of oral English teaching with high accuracy and real-time performance. Show more

Keywords: BP neural network, crow search algorithm, feature selection, oral English teaching, quality evaluation

DOI: 10.3233/JIFS-222455

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11909-11924, 2023

Authors: Srivatsun, G. | Thivaharan, S.

Article Type: Research Article

Abstract: Writing is a crucial component of the language requirement and is an effective method for correctly reflecting language proficiency. Manually evaluating Tamil language exams becomes time-consuming and costly for standardized language administrators as they grow in popularity. Numerous studies on computerized English assessment systems have been conducted in recent years. Due to Tamil text’s complicated grammatical structures, less research has been done on computerized evaluation methods. In this research, we present a Tamil review comment analysis system using a novel multivariate naïve Bayes classifier (mv  - NB ) where the comments are acquired from an online social network and performed training …using the database for further analysis. Experiments show that the graded Kappa of 0.4239, error rate of 2.55 and precision of 85% was achieved on the online dataset by our contents grading system, which is superior in grading compared to the other widely used machine learning algorithms training on big datasets. Our findings are promising. Additionally, our contents analysis may provide beneficial criticism on Tamil writing on YouTube posts including comments, spelling errors and morphological issues that help to analyze thelanguage correlation. Show more

Keywords: Writing, Tamil content, grading system, reviews, morphological issues

DOI: 10.3233/JIFS-222504

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11925-11936, 2023