Journal of Intelligent & Fuzzy Systems - Volume 44, issue 6

Authors: Zhang, Dongping | Lan, Hao | Ma, Zhennan | Yang, Zhixiong | Wu, Xin | Huang, Xiaoling

Article Type: Research Article

Abstract: The key to solving traffic congestion is the accurate traffic speed forecasting. However, this is difficult owing to the intricate spatial-temporal correlation of traffic networks. Most existing studies either ignore the correlations among distant sensors, or ignore the time-varying spatial features, resulting in the inability to extract accurate and reliable spatial-temporal features. To overcome these shortcomings, this study proposes a new deep learning framework named spatial-temporal gated graph convolutional network for long-term traffic speed forecasting. Firstly, a new spatial graph generation method is proposed, which uses the adjacency matrix to generate a global spatial graph with more comprehensive spatial features. …Then, a new spatial-temporal gated recurrent unit is proposed to extract the comprehensive spatial-temporal features from traffic data by embedding a new graph convolution operation into gated recurrent unit. Finally, a new self-attention block is proposed to extract global features from the traffic data. The evaluation on two real-world traffic speed datasets demonstrates the proposed model can accurately forecast the long-term traffic speed, and outperforms the baseline models in most evaluation metrics. Show more

Keywords: Traffic speed forecasting, graph convolution operation, gated recurrent unit, self-attention block

DOI: 10.3233/JIFS-224285

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10437-10450, 2023

Authors: Han, Nana | Qiao, Junsheng

Article Type: Research Article

Abstract: Lately, Jiang and Hu (H.B. Jiang, B.Q. Hu, On ( O , G ) -fuzzy rough sets based on overlap and grouping functions over complete lattices, Int. J. Approx. Reason. 144 (2022) 18-50.) put forward ( O , G ) -fuzzy rough sets via overlap and grouping functions over complete lattices. Meanwhile, they showed the characterizations of O -upper and G -lower L -fuzzy rough approximation operators in ( O , G ) -fuzzy rough set …model based on some of specific L -fuzzy relations and studied the topological properties of the proposed model. Nevertheless, we discover that the partial results given by Jiang and Hu could be further optimized. So, as a replenish of the above article, in this paper, based on G -lower L -fuzzy rough approximation operator in ( O , G ) -fuzzy rough set model, we further explore several new conclusions on the relationship between G -lower L -fuzzy rough approximation operator and different L -fuzzy relations. In particular, the equivalent descriptions of relationship between G -lower L -fuzzy rough approximation operator and O -transitive ( O -Euclidean) L -fuzzy relations are investigated, which are not involved in above literature and can make the theoretical results of this newly fuzzy rough set model more perfect. Show more

Keywords: (𝔒, 𝔊)-fuzzy rough set, 𝔏-fuzzy relation, overlap function, grouping function, complete lattice

DOI: 10.3233/JIFS-224286

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10451-10457, 2023

Authors: Liu, Lin | Yang, Lijun

Article Type: Research Article

Abstract: The level of education in colleges is career and development-focused compared to that from high schools. Quality education relies on the teachers’ qualifications, knowledge, and experience over the years. However, the demand for technical and knowledge-based education is increasing with the world’s demands. Therefore, assessing the knowledge of teaching professionals to meet external demand becomes mandatory. This article introduces an Acceded Data Evaluation Method (ADEM) using Fuzzy Logic (FL) for teaching quality assessment. The proposed method inputs the teachers’ skills and students’ productivity for evaluation. The teachers’ knowledge and updated skills through training and self-learning are the key features for …evaluating the independents’ performance. The impact of the above features on the student qualifying ratio and understandability (through examination) are analyzed periodically. Depending on the qualifications and performance, the teachers’ knowledge update is recommended with the new training programs. In this evaluation process, fuzzy logic is implied for balancing and identifying the maximum validation criteria that satisfy the quality requirements. The recommendations using partial and fulfilled quality constraints are identified using the logical truth over the varying assessments. The proposed method is analyzed using the metrics evaluation rate, quality detection, recommendations, evaluation time, and data balancing. Show more

Keywords: Data balancing, decision recommendations, fuzzy logic, teaching quality

DOI: 10.3233/JIFS-224290

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10459-10475, 2023

Authors: Al-Andoli, Mohammed Nasser | Tan, Shing Chiang | Sim, Kok Swee | Goh, Pey Yun | Lim, Chee Peng

Article Type: Research Article

Abstract: Malicious software, or malware, has posed serious and evolving security threats to Internet users. Many anti-malware software packages and tools have been developed to protect legitimate users from these threats. However, legacy anti-malware methods are confronted with millions of potential malicious programs. To combat these threats, intelligent anti-malware systems utilizing machine learning (ML) models are useful. However, most ML models have limitations in performance since the training depth is usually limited. The emergence of Deep Learning (DL) models allow more training possibilities and improvement in performance. DL models often use gradient descent optimization, i.e., the Back-Propagation (BP) algorithm; therefore, their …training and optimization procedures suffer from local sub-optimal solutions. In addition, DL-based malware detection methods often entail single classifiers. Ensemble learning overcomes the shortcomings of individual techniques by consolidating their strengths to improve the performance. In this paper, we propose an ensemble DL classifier stacked with the Fuzzy ARTMAP (FAM) model for malware detection. The stacked ensemble method uses several heterogeneous deep neural networks as the base learners. During the training and optimization process, these base learners adopt a hybrid BP and Particle Swarm Optimization algorithm to combine both local and global optimization capabilities for identifying optimal features and improving the classification performance. FAM is selected as a meta-learner to effectively train and combine the outputs of the base learners and achieve robust and accurate classification. A series of empirical studies with different benchmark data sets is conducted. The results ascertain that the proposed ensemble method is effective and efficient, outperforming many other compared methods. Show more

Keywords: Ensemble learning, fuzzy ARTMAP, deep learning, malware detection, particle swarm optimization, backpropagation algorithm

DOI: 10.3233/JIFS-230009

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10477-10493, 2023

Authors: Rose, Biji | Aruna Devi, B.

Article Type: Research Article

Abstract: From the signal received on a particular frequency band, spectrum sensing (SS) is used in cognitive radio (CR) to assess whether the primary user (PU) is using the spectrum and, consequently, whether the secondary user (SU) can utilize the spectrum. The main issue with SS is determining the presence of the primary signal in a low signal-to-noise ratio (SNR). Compared to conventional technologies, machine learning techniques are more effective and accurate at identifying the qualities of input data. This paper proposes a machine learning (ML) based SS model for CR with effective feature extraction and reduction techniques. The proposed work …comprises five phases: noise removal, wavelet transform, feature extraction, dimensionality reduction, and classification. Firstly, noise filtering is done on the received signal to remove the noise present in the input signal using the filters such as moving median filter (MMF), Gaussian filter (GF), and Gabor filter (GBF). After that, the filtered signal is transformed into a wavelet domain using Discrete Wavelet Transform (DWT) algorithm. Then the statistical features such as average absolute value, wavelet energy, variance, standard deviation, and peak value features are extracted from the DWT. Next, the dimensionality reduction (DR) is performed using Linear Discriminant Analysis (LDA). Finally, the classification is performed using the ensemble ML classifiers such as Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbour (KNN), which classify whether the PU signal is active or not. Simulations are carried out to analyze the efficiency of the presented models for SS. The results proved that SVM obtains the best performance for SS with higher accuracy and lower SNR. Show more

Keywords: Cognitive radio, spectrum sensing, discrete wavelet transform, machine learning, signal-to-noise ratio

DOI: 10.3233/JIFS-230438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10495-10509, 2023

Authors: Li, Huiru | Hu, Yanrong | Liu, Hongjiu

Article Type: Research Article

Abstract: Stock price volatility is influenced by many factors, including unstructured data that is not easy to quantify, such as investor sentiment. Therefore, given the difficulty of quantifying investor sentiment and the complexity of stock price, the paper proposes a novel LASSO-ATT-LSTM intelligent stock price prediction system based on multi-source data. Firstly, establish a sentiment dictionary in the financial field, conduct sentiment analysis on news information and comments according to the dictionary, calculate sentiment scores, and then obtain daily investor sentiment. Secondly, the LASSO (Least absolute shrinkage and selection operator) is used to reduce the dimension of basic trading indicators, valuation …indicators, and technical indicators. The processed indicators and investor sentiment are used as the input of the prediction model. Finally, the LSTM (Long short-term memory) model that introduces the attention mechanism is used for intelligent prediction. The results show that the prediction of the proposed model is close to the real stock price, MAPE, RMSE, MAE and R2 are 0.0118, 0.0685, 0.0515 and 0.8460, respectively. Compared with the existing models, LASSO-ATT-LSTM has higher accuracy and is an effective method for stock price prediction. Show more

Keywords: Stock price forecast, sentiment analysis, LSTM, attention, multi-source data

DOI: 10.3233/JIFS-221919

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10511-10521, 2023

Authors: Yanhu, Han | Huimin, Xin

Article Type: Research Article

Abstract: The location and capacity of precast concrete component factories (PC component factories) are not only the key factors for manufacturers to gain competitive advantage, but also the important factors affecting the operational efficiency of the prefabricated construction supply chain. This paper takes the capacitated location problem of PC component factories as the research object. Drawing on the model of traditional capacitated plant location problem, the model of capacitated location problem of PC component factories is constructed by setting the optional production scale by stages. According to the characteristics of this model, the optimal strategy of location is determined by using …the Tabu search algorithm. Taking the location problem of PC component factory in the Beijing-Tianjin-Hebei region as the object, the calculation example is designed, in which the influence of the distance parameters on the results of location problem is analyzed. The results can make the configuration of regional PC component factories more reasonable and balanced. Show more

Keywords: Prefabricated construction, location, PC component factories, capacity limitation

DOI: 10.3233/JIFS-222923

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10523-10535, 2023

Authors: Chiranjeevi, Phaneendra | Rajaram, A.

Article Type: Research Article

Abstract: Recommender systems based on sentiment analysis become challenging due to the presence of enormous data available over the internet. With the lack of proper data cleaning and analysis methods, existing machine learning (ML) techniques fail to generate accurate recommendations. To overcome this issue, this paper proposes a Light Deep Learning (LightDL)-based recommender system that uses Twitter-based reviews. First, the data is collected from Twitter and cleaned by subsequent data cleaning processes. Then, this pre-processed data is fed into the LightDL model, which learns the important features like hashtags, unigrams, multigrams, etc. from each piece of data. Here, we have learned …about four groups of features, including semantic features, syntactic features, symbolic features, and tweet-based features. Finally, the data is classified into positive, negative, and neutral categories according to the learned features. On the basis of classified sentiment, the review is generated to the users. Finally, the model is evaluated in terms of accuracy, precision, recall, f-measure, and error rate through extensive experiments in Matlab. The proposed LightDL model outperforms in all performance measures; specifically, it achieves 95% accuracy for the Twitter dataset. Show more

Keywords: Lightweight Dl, sentiment analysis, recommender system, twitter data

DOI: 10.3233/JIFS-223871

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10537-10550, 2023

Authors: Weng, Zhi | He, Dongchang | Zheng, Yan | Zheng, Zhiqiang | Zhang, Yong | Gong, Caili

Article Type: Research Article

Abstract: As the basis of intelligent breeding management and animal husbandry insurance, the identification of individual cattle is important in animal husbandry management. Given the difficulty of data acquisition caused by the non-rigid and lacking cooperation of cattle, this study proposes a method for cattle face image acquisition and processing that can efficiently adapt to the harsh environment of cattle barns. When processing the non-rigid cow face, the method of approximating the cow face to a rigid body is used to establish the cow face image data set., and the cattle face image data set is established. The Three Dimensional(3D) reconstruction …method of cattle face uses a 3D image reconstruction method based on multiple perspectives. First, the scale-invariant feature transform algorithm is used to extract the image feature points. The fast library for approximate nearest neighbors algorithm is used to match feature points. The matching results are selected via random sampling consensus. Second, the structure of the motion method is used for the sparse reconstruction of point clouds, and the dense point cloud is then generated using the three-dimensional multi-view stereo vision algorithm. Finally, the Poisson surface reconstruction method is used for surface reconstruction. The results indicate that this method can effectively realize the three-dimensional reconstruction of cattle faces; the reconstructed images have obvious color, clear texture, and complete shape features. Show more

Keywords: 3D Reconstruction, approximate rigidity, multi-perspective, surface reconstruction

DOI: 10.3233/JIFS-224260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10551-10563, 2023

Authors: Lin, Tiantai | Yang, Bin

Article Type: Research Article

Abstract: In social life, conflict situations occur frequently all the time. To analyse a conflict situation, not only the intrinsic reason of the conflict but also the resolution of the conflict should be given. In this paper, we propose a combine conflict analysis model under q -rung fuzzy orthopair information system that contain conflict resolution, which is called discern function-based three-way group conflict analysis. Firstly, we propose three novel form conflict distances which are induced by discern functions, and examine their properties, then the comprehensive conflict distances are given based on the normality and symmetry they share. Thus, the conflict analysis …and resolution method in our model can be directly gained based on these novel form conflict distances. Secondly, from the view of group decision, the comprehensive q -rung fuzzy loss function is attained by aggregating a group of q -rung fuzzy loss functions through the q -rung orthopair fuzzy weighted averaging operator in the procedure of conflict resolution. Finally, we employ an example of the governance of a local government to demonstrate the process of finding an optimal feasible strategy in our model. Show more

Keywords: Conflict analysis, resolution of conflict analysis, q-rung orthopair fuzzy set, three-way decisions

DOI: 10.3233/JIFS-224589

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 10565-10580, 2023