Journal of Intelligent & Fuzzy Systems - Volume 44, issue 2

Authors: Kang, Xinhui | Nagasawa, Shin’ya | Wu, Yixiang | Xiong, Xingfu

Article Type: Research Article

Abstract: Bamboo furniture is made of green and environmentally friendly bamboo, there is a unique hand temperature and weaving beauty in addition to bamboo texture and characteristics. In the past, making bamboo furniture relied on the traditional experience of craftsmen, which had less change in appearance and lack of communication with customers, and could not meet the fashion and aesthetic needs of modern people. Therefore, this paper connects deep convolution neural network (DCNN) and deep convolution generative adversarial network (DCGAN) to generate bamboo furniture design that meets customers’ emotional needs. First, based on collecting 17856 bamboo furniture in the market, DCNN …builds product image recognition models and enhances image recognition performance, thereby optimizing computational efficiency and obtaining high-quality output. The optimal recognition rate of emotional data set throughout the chair product is 98.7%, of which the modern chair has a recognition rate of 99.2%, and the recognition rate of fashion bamboo chairs is 98.2%. Second, DCGAN learns a good intermediate feature from a large quantity of non-marked images and automatically generates product styling that arouses the emotional resonance of customers. Finally, the fashion designers use this creative picture as the source of inspiration, cooperate with individual characteristics and trends of the times, then design green sustainable bamboo chairs. These design plans have increased the variety of product modalities, which greatly enhances customers’ emotional satisfaction and increases product sales. The collaborative design method proposed in this paper provides new ideas for generating the emotional design of bamboo furniture, which can also expand to other industrial product designs. Show more

Keywords: Emotional design, artificial intelligence, deep convolution generative adversarial networks, deep convolution neural network, bamboo furniture

DOI: 10.3233/JIFS-221754

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1977-1989, 2023

Authors: Huang, Xiaoqian | Hu, Yanrong | Liu, Hongjiu

Article Type: Research Article

Abstract: Most methods for evaluating a company’s financial performance currently focus on scoring, when there is a large amount of data, it is difficult to distinguish the company’s financial status. To cluster and predict the financial performance of companies, a hybrid model based on the fuzzy C-means clustering algorithm (FCM) and convolutional neural network (CNN) is proposed in this paper. Pearson correlation analysis was first performed on the indicators to ensure that they are not correlated with each other and to avoid indicator redundancy. The entropy method determined the weight of each index and ensured the high validity of the selected …indicators. Then, FCM clustering was carried out, and the performance of each company was clustered according to the indexes after data preprocessing with clustering labels. The processed data and labels were introduced into CNN to predict the level. The empirical study showed that the FCM-CNN model was superior to other machine learning models, which proved that this model has better clustering and forecasting ability, and could be applied to the prediction of corporate financial performance. Show more

Keywords: Fuzzy C-means clustering, convolutional neural network, performance clustering and prediction

DOI: 10.3233/JIFS-221995

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1991-2006, 2023

Authors: Shi, Zhihu

Article Type: Research Article

Abstract: In order to improve the accuracy of cloud manufacturing service recommendation results, improve recommendation efficiency and user satisfaction, a cloud manufacturing service recommendation model based on GA-ACO and carbon emission hierarchy is proposed. According to the concept of cloud manufacturing, a cloud manufacturing platform including resource layer, service layer, operation layer and application layer is constructed, and then a cloud manufacturing service quality perception model is established; genetic algorithm is used to realize cloud manufacturing service selection, and ACO algorithm is used to optimize cloud manufacturing service portfolio; According to the selection and combination results of the constructed cloud manufacturing …platform and cloud manufacturing service, taking the carbon emission field as an example, a hierarchical hierarchical model is constructed, and this model is used to further construct a cloud manufacturing service recommendation model from coarse to fine, from global to local; Identify user demand scenarios and implement cloud manufacturing service recommendations. The experimental results show that the recommendation results of the proposed method have high accuracy and efficiency, and can be recognized by most users. Show more

Keywords: GA-ACO, carbon emission hierarchy, service recommendation, quality perception model, cloud manufacturing platform

DOI: 10.3233/JIFS-222386

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2007-2017, 2023

Authors: Gao, Mengyuan | Ma, Shunagbao | Zhang, Yapeng | Xue, Yong

Article Type: Research Article

Abstract: Automatic identification picking robot is an important research content of agricultural modernization development. In order to overcome the difficulty of picking robots for accurate visual inspection and positioning of apples in a complex orchard, a detection method based on an instance segmentation model is proposed. To reduce the number of model parameters and improve the detection speed, the backbone feature extraction network is replaced from the Resnet101 network to the lightweight GhostNet network. Spatial Pyramid Pooling (SPP) module is used to increase the receptive field to enhance the semantics of the output network. Compared with Resnet101, the parameter quantity of …the model is reduced by 90.90%, the detection speed is increased from 5 frames/s to 10 frames/s, and the detection speed is increased by 100%. The detection result is that the accuracy rate is 91.67%, the recall rate is 97.82%, and the mAP value is 91.68%. To solve the repeated detection of fruits due to the movement of the camera, the Deepsort algorithms was used to solve the multi-tracking problems. Experiments show that the algorithm can effectively detect the edge position information and categories of apples in different scenes. It can be an automated apple-picking robot. The vision system provides strong technical support. Show more

Keywords: Instance segmentation, apple detection, GhostNet, Spatial Pyramid Pooling

DOI: 10.3233/JIFS-213072

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2019-2029, 2023

Authors: Vanam, Harika | JebersonRetna Raj, R | Janga, Vijaykumar

Article Type: Research Article

Abstract: Blogs, internet forums, social networks, and micro-blogging sites are some of the growing number of places where users can voice their opinions. Opinions on any given product, issue, service, or idea are contained in data, making them a valuable resource in their own right. Popular social networking services like Twitter, Facebook, and Google+ allows expressing views on a variety of topics, participating in discussions, or sending messages to a global user. Twitter sentiment analysis has received a lot of attention recently.Sentiment analysis is finding how a person feels about a topic from their written response about it and it can …be separated into positive and negative through its use. Doing so enables to classify the tweets made by a user in to appropriate classification category based on which some decisions can be made. The literature proposed approaches to develop the classifiers on the Twitter datasets. Operations, including tokenization, stop-word removal, and stemming will be performed. NLP converts the text to a machine-readable representation. Artificial Intelligence (AI) combines NLP data to evaluate if a situation is positive or negative. The document’s subjectivity can be identified using ML and NLP techniques to categorize them in to positive, neutral, or negative. Performing sentiment analysis in Twitter data can be tedious due to limited size, unstructured nature, misspellings, slang, and abbreviations. For this task, a Tweet Analyzing Model for Cluster Set Optimization with Unique Identifier Tagging (TAM-CSO-UIT) was built using prospects to determine positive or negative sentiment in tweets obtained from Twitter. This approach assigns a +ve/-ve value to each entry in the Tweet database based on probability assignment using n-gram model. To perform this effectively the tweet dataset is considered as a sliding window of length L. The proposed model accurately analyses and classifies the tweets. Show more

Keywords: Sentiment analysis, tweet analysis, tweet classification, unique identifier tagging

DOI: 10.3233/JIFS-220033

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2031-2039, 2023

Authors: Jiang, Yirong | Qiu, Jianwei | Meng, Fangxiu

Article Type: Research Article

Abstract: In this article, we explore the question of existence and finite time stability for fuzzy Hilfer-Katugampola fractional delay differential equations. By using the generalized Gronwall inequality and Schauder’s fixed point theorem, we establish existence of the solution, and the finite time stability for the presented problems. Finally, the effectiveness of the theoretical result is shown through verification and simulations for an example.

Keywords: Finite time stability, fuzzy Hilfer-Katugampola fractional differential equations, delay

DOI: 10.3233/JIFS-220588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2041-2050, 2023

Authors: Shanmugam, Gowri | Thanarajan, Tamilvizhi | Rajendran, Surendran | Murugaraj, Sadish Sendil

Article Type: Research Article

Abstract: Clustering plays a fundamental task in the process of data mining, which remains more demanding due to the ever-increasing dimension of accessible datasets. Big data is considered more populous as it has the ability to handle various sources and formats of data under numerous highly developed technologies. This paper devises a robust and effective optimization-based Internet of Things (IoT) routing technique, named Student Psychology Based Optimization (SPBO) -based routing for the big data clustering. When the routing phase is done, big data clustering is carried out using the Deep Fractional Calculus-Improved Invasive Weed Optimization fuzzy clustering (Deep FC-IIWO fuzzy clustering) …approach. Here, the Mapreduce framework is used to minimizing the over fitting issues during big data clustering. The process of feature selection is performed in the mapper phase in order to select the major features using Minkowski distance, whereas the clustering procedure is carried out in the reducer phase by Deep FC-IIWO fuzzy clustering, where the FC-IIWO technique is designed by the hybridization of Improved Invasive Weed Optimizer (IIWO) and Fractional Calculus (FC). The developed SPBO-based routing approach achieved effective performance in terms of energy, clustering accuracy, jaccard coefficient, rand coefficient, computational time and space complexity of 0.605 J, 0.935, 0.947, 0.954, 2100.6 s and 72KB respectively. Show more

Keywords: Internet of Things, routing, big data, big data clustering, student psychology based optimization

DOI: 10.3233/JIFS-221391

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2051-2063, 2023

Authors: Hou, Shuai | Yu, Junqi | Su, Yucong | Liu, Zongyi | Dai, Junwei

Article Type: Research Article

Abstract: An improved mayfly algorithm is proposed for the energy saving optimization of parallel chilled water pumps in central air conditioning system, with the minimum energy consumption of parallel pump units as the optimization objective and the speed ratio of each pump as the optimization variable for the solution. For the problem of uneven random initialization of mayflies, the variable definition method of Circle chaotic mapping is used to make the initial position of the population uniformly distributed in the solution space, and the mayfly fitness value and the optimal fitness value are incorporated into the calculation of the weight coefficient, …which better balances the global exploration and local exploitation of the algorithm. For the problem that the algorithm is easy to fall into the local optimum at the later stage, a multi-subpopulation cooperative strategy is proposed to improve the global search ability of the algorithm. Finally, the performance of the improved mayfly algorithm is tested with two parallel pumping system cases, and the stability and time complexity of the algorithm are verified. The experiments show that the algorithm can get a better operation strategy in solving the parallel water pump energy saving optimization problem, and can achieve energy saving effect of 0.72%  8.68% compared with other optimization algorithms, and the convergence speed and stability of the algorithm have been significantly improved, which can be better applied to practical needs. Show more

Keywords: Energy saving optimization, parallel water pump, improved mayfly algorithm, circle chaotic mapping, multi subpopulation cooperative strategy

DOI: 10.3233/JIFS-222783

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2065-2083, 2023

Authors: Zhang, Yun | Zhang, Yude | Yu, Shujuan | Wang, Xiumei | Zhao, Shengmei | Wang, Weigang | Liu, Yan | Ding, Keke

Article Type: Research Article

Abstract: The lack of training data in new domain is a typical problem for named entity recognition (NER). Currently, researchers have introduced “entity trigger” to improve the cost-effectiveness of the model. However, it still required the annotator to attach additional trigger label, which increases the workload of the annotator. Moreover, this trigger applies only to English text and lacks research into other languages. Based on this problem, we have proposed a more cost-effective trigger tagging method and matching network. The approach not only automatic tagging entity triggers based on the characteristics of Chinese text, but also adds mogrifier LSTM to the …matching network to reduce context-free representation of input tokens. Experiments on two public datasets show that our automatic trigger is effective. And it achieves better performances with automatic trigger than other state-of-the-art methods (The F1-scores increased by 1∼4). Show more

Keywords: Chinese NER, entity trigger, Mogrifier LSTM, TMN, m-TMN

DOI: 10.3233/JIFS-212824

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2085-2096, 2023

Authors: Liu, Jing | Tian, Shengwei | Yu, Long | Long, Jun | zhou, Tiejun | Wang, Bo

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-213501

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2097-2108, 2023