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

Authors: Yang, Liu | Li, Ao

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Abstract: This article has been retracted.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-220600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1823-1834, 2023

Authors: Xu, Lan | Yang, Long

Article Type: Research Article

Abstract: The lack of a scientific and complete service quality evaluation system for Medical Caring and Nursing Combined Institutions for the Aged is a critical factor that makes it difficult to improve the quality of their services. Based on the SERVQUAL model, the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged is constructed from tangibles, security, reliability, responsiveness, and empathy. Considering the ambiguity, randomness, grey characteristics, and the interaction between indicators in the service evaluation process of Medical Caring and Nursing Combined Institutions for the Aged, the interval Mahalanobis-Taguchi system (MTS) is introduced into …the grey cloud clustering model, and a service quality evaluation model of the interval MTS— grey cloud clustering is proposed. The Medical Caring and Nursing Combined Institutions for the Aged in four typical cities of Jiangsu Province are taken as examples in this study. Feasibility of the proposed method is verified, and targeted measures are thus proposed to provide stronger support and reference for improving the service quality of these institutions. Show more

Keywords: Service quality, medical caring and nursing combined institutions for the aged, interval Mahalanobis-Taguchi system, grey cloud clustering

DOI: 10.3233/JIFS-221358

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1835-1846, 2023

Authors: Samimi, Navid | Nejatian, Samad | Parvin, Hamid | Bagherifard, Karamollah | Rezaei, Vahideh

Article Type: Research Article

Abstract: Existing fuzzy clustering ensemble approaches do not consider dependability. This causes those methods to be fragile in dealing with unsuitable basic partitions. While many ensemble clustering approaches are recently introduced for improvement of the quality of the partitioning, but lack of a median partition based consensus function that considers more participate reliable clusters, remains unsolved problem. Dealing with the mentioned problem, an innovative weighting fuzzy cluster ensemble framework is proposed according to cluster dependability approximation. For combining the fuzzy clusters, a fuzzy co-association matrix is extracted in a weighted manner out of initial fuzzy clusters according to their dependabilities. The …suggested objective function is a constrained nonlinear objective function and we solve it by sparse sequential quadratic programming (SSQP). Experimentations indicate our method can outperform modern clustering ensemble approaches. Show more

Keywords: Fuzzy cluster ensemble, cluster dependability, consensus function, base clustering, sequential quadratic programming

DOI: 10.3233/JIFS-201950

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1847-1863, 2023

Authors: Xia, Fangfang

Article Type: Research Article

Abstract: For thousands of years, the Chinese people have accumulated and inherited profound cultural traditions. The uniqueness of this cultural tradition lies in its amazing creative wisdom and power. “The ideological and political education of the integration of Chinese regional culture into international students refers to the educative influence of excellent regional culture that can run through the entire international education management system, curriculum system and extracurricular practice system to achieve “all-round, full-process, full-staff” Education goals. The sustainable education value evaluation based on the integration of regional culture into international students’ ideological education is a classical multiple-attribute decision-making (MADM) issue. In …this paper, we extend the geometric Heronian mean (GHM) operator to fuzzy number intuitionistic fuzzy numbers (FNIFNs) to propose the fuzzy number intuitionistic fuzzy GHM (FNIFGHM) operator. Then, the multiple-attribute decision-making (MADM) methods are built on FNIFGHM operator. Finally, a numerical example for sustainable education value evaluation based on the integration of regional culture into international students’ ideological education and some comparative analysis are used to prove the built methods’ credibility and reliability. Show more

Keywords: Multiple-attribute decision-making (MADM), fuzzy number intuitionistic fuzzy numbers (FNIFNs), fuzzy number intuitionistic fuzzy GHM (FNIFGHM) operator, sustainable education value evaluation

DOI: 10.3233/JIFS-222651

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1865-1880, 2023

Authors: Nakshathram, Sajithra | Duraisamy, Ramyachitra

Article Type: Research Article

Abstract: Protein Remote Homology and fold Recognition (PRHR) is the most crucial task to predict the protein patterns. To achieve this task, Sequence-Order Frequency Matrix-Sampling and Deep learning with Smith-Waterman (SOFM-SDSW) were designed using large-scale Protein Sequences (PSs), which take more time to determine the high-dimensional attributes. Also, it was ineffective since the SW was only applied for local alignment, which cannot find the most matches between the PSs. Hence, in this manuscript, a rapid semi-global alignment algorithm called SOFM-SD-GlobalSW (SOFM-SDGSW) is proposed that facilitates the affine-gap scoring and uses sequence similarity to align the PSs. The major aim of this …paper is to enhance the alignment of SW algorithm in both locally and globally for PRHR. In this algorithm, the Maximal Exact Matches (MEMs) are initially obtained by the bit-level parallelism rather than to align the individual characters. After that, a subgroup of MEMs is obtained to determine the global Alignment Score (AS) using the new adaptive programming scheme. Also, the SW local alignment scheme is used to determine the local AS. Then, both local and global ASs are combined to produce a final AS. Further, this resultant AS is considered to train the Support Vector Machine (SVM) classifier to recognize the PRH and folds. Finally, the test results reveal the SOFM-SDGSW algorithm on SCOP 1.53, SCOP 1.67 and Superfamily databases attains an ROC of 0.97, 0.941 and 0.938, respectively, as well as, an ROC50 of 0.819, 0.846 and 0.86, respectively compared to the conventional PRHR algorithms. Show more

Keywords: PRHR, SOFM-SMSW, DCNN, local and global alignment, adaptive programming, maximal exact match, affine-gap scoring, SVM

DOI: 10.3233/JIFS-213522

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1881-1891, 2023

Authors: Zhang, Nian | Zhou, Yifan | Zhou, Qin | Wei, Guiwu

Article Type: Research Article

Abstract: In this paper, an integrated decision-making methodology is proposed to solve the subjectivity and fuzziness in the selection of cold chain logistics service providers (LSPs). Firstly, the social network analysis (SNA) method is applied to select the evaluation criteria of cold chain LSPs, which is based on the systematic literature analysis. Then, a novel combination weighting method that combines the advantages of entropy weight (EW) method and improved analytic hierarchy process (AHP) is constructed to calculate the weight of criteria. Further, the fuzzy comprehensive evaluation (FCE) method is utilized to generate a ranking order of providers and recommend the optimal …provider. Finally, the illustrative example and comparison analysis are provided to prove the validity and feasibility of the approach. In addition, a sensitivity analysis is presented to discuss the stability of the proposed method. In conclusion, this paper innovatively constructs an index system of cold chain LSPs evaluation and selection, and the process of evaluation and selection is also objective. Show more

Keywords: Cold chain logistics service provider, social network analysis, combination weighting method, fuzzy comprehensive evaluation

DOI: 10.3233/JIFS-220780

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1893-1905, 2023

Authors: Kodavali, Lakshminarayana | Kuppuswamy, Sathiyamurthy

Article Type: Research Article

Abstract: Ethereum is one of the popular Blockchain platform. The key component in the Ethereum Blockchain is the smart contract. Smart contracts (SC) are like normal computer programs which are written mostly in solidity high-level object-oriented programming language. Smart contracts allow completing transactions directly between two parties in the network without any middle man or mediator. Modification of the smart contracts are not possible once deployed into the Blockchain. Thus smart contract has to be vulnerable free before deploying into the Blockchain. In this paper, Bayesian Network Model was designed and constructed based on Bayesian learning concept to detect smart contract …security vulnerabilities which are Reentrancy, Tx.origin and DOS. The results showed that the proposed BNMC (Bayesian Network Model Construction) design is able to detect the severity of each vulnerability and also suggest the reasons for the vulnerability. The accuracy of the proposed BNMC results are improved (accuracy 8% increased for both Reentracy and Tx.origin, 6% increased for DOS), compared with traditional method LSTM. This proposed BNMS design and implementation is the first attempt to detect smart contract vulnerabilities using Bayesian Networks. Show more

Keywords: Blockchain, smart contracts, vulnerabilities, Ethereum, Bayesian network, expert knowledge

DOI: 10.3233/JIFS-221898

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1907-1920, 2023

Authors: Guo, Yingchun | Wang, Dan | Yan, Gang | Zhu, Ye

Article Type: Research Article

Abstract: With the increasing variety of display devices, image retargeting has become an indispensable technology for adjusting the aspect ratio of images to adapt to different display terminals. Since the retargeting operation would cause geometric distortion and content loss of the image, the image retargeting quality assessment (IRQA) is necessary to guide the retargeting algorithm’s optimization, selection, and design. Our paper mainly works for systematically reviewing the state-of-the-art technologies in IRQA. And then, this paper further discusses image registration algorithms for matching the original image and the retargeted image. Next, we investigate the feature measurement methods for image retargeting quality evaluation. …To facilitate the quantitative assessment of the IRQA methods, this paper gives a list of publicly open datasets and the performance of the mainstream methods. Finally, some promising research directions towards IRQA are pointed out. From this survey, engineers from the industry may find skills to improve their image retargeting systems, and researchers from academia may find ideas to conduct some innovative work. Show more

Keywords: Registration algorithm, image retargeting, quality assessment

DOI: 10.3233/JIFS-220456

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1921-1942, 2023

Authors: Chen, Junfeng | Zheng, Kaijun | Li, Qingwu | Ayush, Altangerel

Article Type: Research Article

Abstract: The traveling thief problem (TTP) is a typical combinatorial optimization problem that integrates the computational complexity of the traveling salesman problem (TSP) and the knapsack problem (KP). The interdependent and mutually restrictive relationship between these two sub-problems brings new challenges to the heuristic optimization algorithm for solving the TTP problem. This paper first analyzes the performance of three sub-component combined iterative algorithms: Memetic Algorithm with the Two-stage Local Search (MATLS), S5, and CS2SA algorithms, which all employ the Chained Lin-ighan (CLK) algorithm to generate the circumnavigation path. To investigate the influence of different traveling routes on the performance of TTP …solving algorithms, we propose a combinatorial iterative TTP solving algorithm based on the Ant Colony Optimization (ACO) and MAX-MIN Ant System (MMAS). Finally, the experimental investigations suggest that the traveling route generation method dramatically impacts the performance of TTP solving algorithms. The sub-component combined iterative algorithms based on the MMAS algorithm to generate the circumnavigation path has the best practical effect. Show more

Keywords: Traveling thief problem, traveling salesman problem, knapsack problem, ant colony optimization, MAX-MIN ant system

DOI: 10.3233/JIFS-221032

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1943-1956, 2023

Authors: Zhou, Yuqian | Wang, Dong | Li, Qing

Article Type: Research Article

Abstract: Motivated by Hema Freshs new-retail case, we noticed that an effective recommender system is a common way to attract the consumers’ purchasing behaviors and thus enlarge the profit of platform as well as retailers. With the aim of increasing the benefits of all parties in the platform, this paper focusing on not only increasing the effectiveness of the recommender platform but also the evaluation system of measuring the interests of consumer, retailers and platform. In this paper, the interests of the third-party platform are added into the evaluation system, the profit of the third-party platform as an evaluation index is …taken and a 0–1 integer programming model is established which sets the profit of the platform as the objective function. The result of the proposed model and algorithm indicate that: (1) The relevance of products has a significant impact on platform recommendation when the consumers are selecting products. When the correlations of the products are high, the algorithms of selecting the products will have a lower capacity of 1% compared with the algorithm without products correlations. (2) The evaluation of the target products from the target consumers is quite different from the heterogeneity assumptions. When the consumer presentation is taken into consideration, it is hard to evaluate the consumer presence because of the strictly requirement of data for the platform recommendation system. (3) The proposed two-stage solution for the platform recommendation system is optimized in time and space complexity. Total optimization of the proposed method is 30% higher than the greedy algorithms. The two stages are combined together to obtain the approximate solution, and finally provide a reasonable and feasible recommendation for the third-party platform. Show more

Keywords: Third-party platform, advertising recommendation, two-stage model, integer programming algorithm

DOI: 10.3233/JIFS-221236

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1957-1975, 2023

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

Authors: Chandnani, Neeraj | Verma, Kirti

Article Type: Research Article

Abstract: Smart gadgets have created a buzz in the market today; you will find everything smart today. Like a smartwatch, smart band, smart led, smart heater, etc., and transmitting data securely between all these devices is necessary as an outcome; IoT devices developed defenseless to numerous devices. Faith replicas were predictable, significant simultaneous approaches to defend a large communication system in contrast to evil virtual outbreaks. In this research paper, the various Type-II fuzzy logic models are evaluated, which provides enhanced data security for IoT devices. Also, compression is applied between all data encryption techniques based on the parameters like Reproduction …time (circles), Program series (m), Quantity of device nodes, Number of spiteful nodes, and Total interval. Show more

Keywords: Type-II fuzzy logic, internet of things, encryption

DOI: 10.3233/JIFS-220570

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2109-2116, 2023

Authors: Chen, Deguang | Zhou, Jie

Article Type: Research Article

Abstract: MobileBert is a generic lightweight model suffering from a large network depth and parameter cardinality. Therefore, this paper proposes a secondary lightweight model entitled LightMobileBert, which retains the bottom 12 Transformers structure of the pre-trained MobileBert and utilizes the tensor decomposition technique to process the model to skip pre-training and further reduce the parameters. At the same time, the joint loss function is constructed based on the improved Supervised Contrastive Learning loss function and the Cross-Entropy loss function to improve performance and stability. Finally, the LMBert_Adam optimizer, an improved Bert_Adam optimizer, is used to optimize the model. The experimental results …demonstrate that LightMobileBert has a comparatively higher performance than MobileBert and other popular models while requiring 57% fewer network parameters than MobileBert, confirming that LightMobileBert retains a higher performance while being lightweight. Show more

Keywords: Natural language processing, lightweight model, tensor decomposition, supervised contrastive learning

DOI: 10.3233/JIFS-221985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2117-2129, 2023

Authors: Jayachandran, Shana | Dumala, Anveshini

Article Type: Research Article

Abstract: The Corona virus pandemic has affected the normal course of life. People all over the world take the social media to express their opinions and general emotions regarding this phenomenon. In a relatively short period of time, tweets about the new Corona virus increased by an amount never before seen on the social networking site Twitter. In this research work, Sentiment Analysis of Social Media Data to Identify the Feelings of Indians during Corona Pandemic under National Lockdown using recurrent neural network is proposed. The proposed method is analyzed using four steps: that is Data collection, data preparation, Building sentiment …analysis model and Visualization of the results. For Data collection, the twitter dataset are collected from social networking platform twitter by application programming interface. For Data preparation, the input data set are pre-processed for removing URL links, removing unnecessary spaces, removing punctuations and numbers. After data cleaning or preprocessing entire particular characters and non-US characters from Standard Code for Information Interchange, apart from hash tag, are extracted as refined tweet text. In addition, entire behaviors less than three alphabets are not assumed at analysis of tweets, lastly, tokenization and derivation was carried out by Porter Stemmer to perform opinion mining. To authenticate the method, categorized the tweets linked to COVID-19 national lockdown. For categorization, recurrent neural method is used. RNN classify the sentiment classification as positive, negative and neutral sentiment scores. The efficiency of the proposed RNN based Sentimental analysis classification of COVID-19 is assessed various performances by evaluation metrics, like sensitivity, precision, recall, f-measure, specificity and accuracy. The proposed method attains 24.51%, 25.35%, 31.45% and 24.53% high accuracy, 43.51%, 52.35%, 21.45% and 28.53% high sensitivity than the existing methods. Show more

Keywords: COVID 19, sentiment analysis, data analytics, lockdown, classification, recurrent neural network

DOI: 10.3233/JIFS-221883

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2131-2146, 2023

Authors: Liu, Zhongpu | Liu, Jianjuan

Article Type: Research Article

Abstract: For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slow optimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and “reward or punishment” update mechanism is used to updates the global pheromone concentration directionally, …which increases the pheromone concentration of excellent path nodes and the heuristic effect; Finally, a prediction-backward mechanism to deal with the “deadlock” problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved; the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved. Show more

Keywords: Mobile robot, Path planning, Path prediction model, Ant colony optimization algorithm, Reward and punishment update

DOI: 10.3233/JIFS-222211

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2147-2162, 2023

Authors: Guan, Xuechong

Article Type: Research Article

Abstract: Soft separation axioms and their properties are popular topic in the research of soft topological spaces. Two types of separation axioms T i -I and T i -II (i  = 0, 1, ⋯ , 4) which take single point soft sets and soft points as separated objects have been given in [18 ] and [30 ] respectively. In this paper we show that a soft T 0 -II(T 1 -II, T 2 -II, and T 4 -II respectively) space is a soft T 0 -I(T 1 -I, T 2 -I, and T 4 -I respectively) space, if the initial universe …set X and the parameter set E are sets of two elements. Some examples are given to explain that a soft T i -I may not to be a soft T i -II space (i  = 0, 1, ⋯ , 4). Show more

Keywords: Soft set, soft topological space, single point soft set, soft point, separation axiom

DOI: 10.3233/JIFS-212432

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2163-2171, 2023

Authors: Marimuthu, Poorani | Vaidehi, V.

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-220539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2173-2190, 2023

Authors: Han, Chao-Qun | Zhang, Xiao-Hong | Ma, Hong-Wei | Hu, Zhi-Hua

Article Type: Research Article

Abstract: Since the tax of carbon emission is popular and consumers are exhibiting low-carbon preference, a manufacturer may invest to adopt carbon emission reduction (CER) technologies to produce green products. In face of high cost of CER investment and random yield in low carbon production processes for the manufacturer, this paper explores the incentive role of the contracts of revenue-sharing (RS) and cost-sharing with subsidy (CSS) offered by a retailer in a low-carbon supply chain. Theoretical analysis and numerical experiments show that both RS and CSS can promote the manufacturer’s Carbon Emission Reduction (CER) efforts and improve the efficiency of the …supply chain, and RS boosts these more than CSS. RS and CSS can also decrease firms’ profit losses due to yield uncertainty, and RS also decreases firms’ profit losses more than CSS. Moreover, to motivate manufacturer’s CER efforts, the government should levy the highest-possible carbon tax under RS, the medium-level carbon tax under CSS, and the lowest-possible carbon tax for the decentralized case, and levy the same carbon tax on the centralized case with that under RS. Show more

Keywords: Yield uncertainty, retailer-driven incentive, carbon emission reduction, carbon tax, revenue-sharing, cost-sharing with su

DOI: 10.3233/JIFS-220354

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2191-2206, 2023

Authors: Jin, Feifei | Jiang, Hao | Pei, Lidan

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-220566

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2207-2216, 2023

Authors: Altinsoy, Ufuk | Aktepe, Adnan | Ersoz, Suleyman

Article Type: Research Article

Abstract: In today’s understanding, the universities are considered as service providers besides their institutional functions. Because the universities shape the future of the country via the services they provide, it is a necessity that their service quality must be assessed by using scientific analyses, and their service quality must be improved based on such scientific findings. The Generation Z, whose members are currently receiving university education carries unique features that distinguish them from the previous generations. When this fact is considered, it is understood that the constant research and monitoring of the learning environment of the Generation Z is important. In …this study, as a result of a detailed literature search, a scale consisting of 7 dimensions and 36 indicators was developed in order to measure the higher education service quality of the Z generation. The validity and reliability tests of this scale are completed via the convergent and divergent validity analyses, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). Because the answers provided to the surveys reflect the personal evaluation of the participants, the Fuzzy Logic is employed, and the study is conducted by using the fuzzy modelling and fuzzy ranking. As a result of this study, the General Satisfaction Index is created, and improving recommendations are carried out based on the scores. Show more

Keywords: Service quality, fuzzy logic, artificial intelligence, higher education, generation-z

DOI: 10.3233/JIFS-220985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2217-2230, 2023

Authors: Han, Yongguang | Yan, Rong | Gou, Chang

Article Type: Research Article

Abstract: Today’s higher vocational colleges have already put innovation and entrepreneurship education at the top of vocational education, and integrated it into the entire education and teaching work, in order to continuously improve the innovation and entrepreneurship ability of students in higher vocational colleges and improve their job competition. strength, and improve the quality of education in higher vocational colleges. The quality evaluation of innovation and entrepreneurship education in vocational colleges is a classical multiple attribute decision making (MADM) problems. In this paper, we introduced some calculating laws on interval-valued intuitionistic fuzzy sets (IVIFSs), Hamacher sum and Hamacher product and further …propose the induced interval-valued intuitionistic fuzzy Hamacher power ordered weighted geometric (I-IVIFHPOWG) operator. Meanwhile, we also study some ideal properties of built operator. Then, we apply the I-IVIFHPOWG operator to deal with the MADM problems under IVIFSs. Finally, an example for quality evaluation of innovation and entrepreneurship education in vocational colleges is used to test this new approach. Show more

Keywords: Multiple attribute decision making (MADM), interval-valued intuitionistic fuzzy sets (IVIFSs), IOWG operator, I-IVIFHPOWG operator, innovation and entrepreneurship education

DOI: 10.3233/JIFS-221701

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2231-2249, 2023

Authors: Fathy, E. | Ammar, E.

Article Type: Research Article

Abstract: In this research, we use the harmonic mean technique to present an interactive strategy for addressing neutrosophic multi-level multi-objective linear programming (NMMLP) problems. The coefficients of the objective functions of level decision makers and constraints are represented by neutrosophic numbers. By using the interval programming technique, the NMMLP problem is transformed into two crisp MMLP problems, one of these problems is an MMLP problem with all of its coefficients being upper approximations of neutrosophic numbers, while the other is an MMLP problem with all of its coefficients being lower approximations of neutrosophic numbers. The harmonic mean method is then used …to combine the many objectives of each crisp problem into a single objective. Then, a preferred solution for NMMLP problems is obtained by solving the single-objective linear programming problem. An application of our research problem is how to determine the optimality the cost of multi-objective transportation problem with neutrosophic environment. To demonstrate the proposed strategies, numerical examples are solved. Show more

Keywords: Neutrosophic number, multi-level linear programming, multi-objective programming, harmonic mean technique, transportation problem

DOI: 10.3233/JIFS-211374

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2251-2267, 2023

Authors: Yang, Ruicheng | Wang, Pucong | Qi, Ji

Article Type: Research Article

Abstract: Categorical Boost (CatBoost) is a new approach in credit rating. In the process of classification and prediction using CatBoost, parameter tuning and feature selection are two crucial parts, which affect the classification accuracy of CatBoost significantly. This paper proposes a novel SSA-CatBoost model, which mixes Sparrow Search Algorithm (SSA) and CatBoost to improve classification and prediction accuracy for credit rating. In terms of parameter tuning, the SSA-CatBoost optimization obtains the most optimal parameters by iterating and updating the sparrow’s position, and utilize the optimal parameter to improve the accuracy of classification and prediction. In terms of feature selection, a novel …wrapping method called Recursive Feature Elimination algorithm is adopted to reduce the adverse impact of noise data on the results, and further improves calculation efficiency. To evaluate the performance of the proposed SSA-CatBoost model, P2P lending datasets are employed to assess the prediction results, then the interpretable Shap package is used to explain the reason why the proposed model considers a sample as good or bad. Consequently, the experimental results show that the SSA-CatBoost model has an ideal accuracy in classification and prediction for credit rating by comparing the SSA-CatBoost model with the CatBoost model and other well-known machine learning models. Show more

Keywords: CatBoost, sparrow search algorithm, parameter tuning, feature selection, credit rating

DOI: 10.3233/JIFS-221652

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2269-2284, 2023

Authors: Sophia, Sundar Singh Sheeba Jeya | Diwakaran, S.

Article Type: Research Article

Abstract: Glaucoma is an irreversible blindness that affects the people over the age of 40 years. Many approaches are proposed to detect glaucoma in image by dealing with its complex data. Redundancy is the major problem in medical image which could lead to increased false positive and false negative rates. This paper proposed a three-structure CNN optimized with Hybrid optimization approach for glaucoma detection and severity differentiation. The CNN structure is designed with three sub-groups to do attention prediction, segmentation and classification. The mathematical equation for Loss function is derived for the CNN structure with three hyper-parameters which is optimized with …Hybrid approach. Hybrid optimization approach consist of Muddy Electric fish Optimization and Grass hopper optimization algorithm for exploration and exploitation processes. The proposed method is designed in a Matlab and validated with LAG and Rim-One database. The proposed method achieved accuracy greater than 95% and other metrics like F2 and AUC has reached 98%. Show more

Keywords: Hybrid optimization, Glaucoma detection, image processing, convolutional neural network

DOI: 10.3233/JIFS-221262

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2285-2303, 2023

Authors: Tatar, Veysel | Yazicioglu, Osman | Ayvaz, Berk

Article Type: Research Article

Abstract: Work-related musculoskeletal disorders (WMSDs) are the most common occupational health problems in agriculture workers due to repetitive and excessive force movement activities involved in their job processes. The Fine-Kinney method has been commonly used as a quantitative evaluation method in risk assessment studies. Classically, the risk value via Fine–Kinney is calculated by the mathematical multiplication irrespective of the degree of importance of each risk parameter (probability (P), exposure (E), and consequence (C)). Hence, a novel risk management model was proposed based on integrating Fine-Kinney and spherical fuzzy AHP-TOPSIS. First, each risk parameter is weighted using the spherical fuzzy AHP (SF-AHP). …Second, the spherical fuzzy TOPSIS (SF-TOPSIS) method is used for hazard ranking. The proposed model is applied to evaluate risks in tea harvesting workers for work-related musculoskeletal disorders. Subsequently, a sensitivity analysis is carried out to test the proposed model. Finally, we compare the proposed model’s applicability and effectiveness with the spherical fuzzy COmbinative Distance-based ASsessment (SF-CODAS) method based on Fine-Kinney. The ranking similarity between the proposed Fine-Kinney-based SF-TOPSIS and SF-CODAS methods is checked by applying Spearman’s rank correlation coefficient, in which 92% of rankings are matched. Show more

Keywords: Risk assessment, Fine–Kinney method, Spherical fuzzy sets, Work-related musculoskeletal disorders (WMSDs), AHP-TOPSIS

DOI: 10.3233/JIFS-222652

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2305-2323, 2023

Authors: He, Mingjun | Che, Jinxing | Jiang, Zheyong | Zhao, Weihua | Wan, Bingrong

Article Type: Research Article

Abstract: Understanding and forecasting air quality index (AQI) plays a vital role in guiding the reduction of air pollution and helping social sustainable development. By combining fuzzy logic with decomposition techniques, ANFIS has become an important means to analyze the data resources, uncertainty and fuzziness. However, few studies have paid attention to the noise of decomposed subseries. Therefore, this paper presents a novel decomposition-denoising ANFIS model named SSADD-DE-ANFIS (Singular Spectrum Analysis Decomposition and Denoising-Differential Evolution-Adaptive Neuro-Fuzzy Inference System). This method uses twice SSA to decompose and denoise the AQI series, respectively, then fed the subseries obtained after the decomposition and denoising …into the constructed ANFIS for training and predicting, and the parameters of ANFIS are optimized using DE. To investigate the prediction performance of the proposed model, twelve models are included in the comparisons. The experimental results of four seasons show that: the RMSE of the proposed SSADD-DE-ANFIS model is 1.400628, 0.63844, 0.901987 and 0.634114, respectively, which is 19.38%, 21.27%, 20.43%, 21.27% and 87.36%, 88.12%, 88.97%, 88.71% lower than that of the single SSA decomposition and SSA denoising. Diebold-Mariano test is performed on all the prediction results, and the test results show that the proposed model has the best prediction performance. Show more

Keywords: Air quality index forecasting, decomposition-denoising, Adaptive Neuro-Fuzzy Inference System, singular spectrum analysis, differential evolution algorithm

DOI: 10.3233/JIFS-222920

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2325-2349, 2023

Authors: Nizarudeen, Shanu | Shunmugavel, Ganesh R.

Article Type: Research Article

Abstract: Intracerebral haemorrhage (ICH) is defined as bleeding occurs in the brain and causes vascular abnormality, tumor, venous Infarction, therapeutic anticoagulation, trauma property, and cerebral aneurysm. It is a dangerous disease and increases high mortality rate within the age of 15 to 24. It may be cured by finding what type of ICH is affected in the brain within short period with more accuracy. The previous method did not provide adequate accuracy and increase the computational time. Therefore, in this manuscript Detection and Categorization of Acute Intracranial Hemorrhage (ICH) subtypes using a Multi-Layer DenseNet-ResNet Architecture with Improved Random Forest Classifier (IRF) …is proposed to detect the subtypes of ICH with high accuracy, less computational time with maximal speed. Here, the brain CT images are collected from Physionet repository publicly dataset. Then the images are pre-processed to eliminate the noises. After that, the image features are extracted by using multi layer Densely Connected Convolutional Network (DenseNet) combined with Residual Network (ResNet) architecture with multiple Convolutional layers. The sub types of ICH (Epidural Hemorrhage (EDH), Subarachnoid Hemorrhage (SAH), Intracerebral Hemorrhage (ICH), Subdural Hemorrhage (SDH), Intraventricular Hemorrhage (IVH), normal is classified by using Improved Random Forest (IRF) Classifier with high accuracy. The simulation is activated in MATLAB platform. The proposed Multilayer-DenseNet-ResNet-IRF approach attains higher accuracy 23.44%, 31.93%, 42.83%, 41.9% compared with existing approaches, like Detection with classification of intracranial haemorrhage on CT images utilizing new deep-learning algorithm (ICH-DC-CNN), Detection with classification of intracranial haemorrhage on CT images utilizing new deep-learning algorithm (ICH-DC-CNN-ResNet-50), Shallow 3D CNN for detecting acute brain hemorrhage from medical imaging sensors (ICH-DC-S-3D-CNN), Convolutional neural network: a review of models, methods and applications to object detection (ICH-DC-CNN-AlexNet) respectively. Show more

Keywords: Acute Intracranial Hemorrhage (ICH), Computerized Tomography (CT), Residual Network (ResNet), Densely Connected Convolutional Networks (DenseNet), Extreme Gradient Boosting (XGBoost) Classifier

DOI: 10.3233/JIFS-221177

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2351-2366, 2023

Authors: Ma, Zhipeng | Guo, Hongyue | Wang, Lidong

Article Type: Research Article

Abstract: Forecasting trend and variation ranges for time series has been challenging but crucial in real-world modeling. This study designs a hybrid time series forecasting (FIGDS) model based on granular computing and dynamic selection strategy. Firstly, with the guidance of the principle of justifiable granularity, a collection of interval-based information granules is formed to characterize variation ranges for time series on a specific time domain. After that, the original time series is transformed into granular time series, contributing to dealing with time series at a higher level of abstraction. Secondly, the L 1 trend filtering method is applied to extract …trend series and residual series. Furthermore, this study develops hybrid predictors of the trend series and residual series for forecasting the variation range of time series. The ARIMA model is utilized in the forecasting task of the residual series. The dynamic selection strategy is employed to identify the ideal forecasting models from the pre-trained multiple predictor system for forecasting the test pattern of the trend series. Eventually, the empirical experiments are carried out on ten time series datasets with a detailed comparison for validating the effectiveness and practicability of the established hybrid time series forecasting method. Show more

Keywords: Granular computing, information granule, time series forecasting, dynamic selection, L1 trend filtering

DOI: 10.3233/JIFS-222746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2367-2379, 2023

Authors: Riali, Ishak | Fareh, Messaouda | Ibnaissa, Mohamed Chakib | Bellil, Mounir

Article Type: Research Article

Abstract: Medical decisions, especially when diagnosing Hepatitis C, are challenging to make as they often have to be based on uncertain and fuzzy information. In most cases, that puts doctors in complex yet uncertain decision-making situations. Therefore, it would be more suitable for doctors to use a semantically intelligent system that mimics the doctor’s thinking and enables fast Hepatitis C diagnosis. Fuzzy ontologies have been used to remedy the shortcomings of classical ontologies by using fuzzy logic, which allows dealing with fuzzy knowledge in ontologies. Moreover, Fuzzy Bayesian networks are well-known and widely used to represent and analyze uncertain medical data. …This paper presents a system that combines fuzzy ontologies and Bayesian networks to diagnose Hepatitis C. The system uses a fuzzy ontology to represent sequences of uncertain and fuzzy data about patients and some features relevant to Hepatitis C diagnosis, enabling more reusable and interpretable datasets. In addition, we propose a novel semantic diagnosis process based on a fuzzy Bayesian network as an inference engine. We conducted an experimental study on 615 real cases to validate the proposed system. The experimentation allowed us to compare the results of existing machine learning algorithms for the Hepatitis C diagnosis with the results of our proposed system. Our solution shows promising results and proves effective for fast medical assistance. Show more

Keywords: Fuzzy ontology, medical diagnosis, semantic representation, fuzzy Bayesian networks, uncertainty, reasoning

DOI: 10.3233/JIFS-213563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2381-2395, 2023

Authors: Jindaluang, Wattana

Article Type: Research Article

Abstract: A machine learning method is now considered capable of accurately segmenting images. However, one significant disadvantage of this strategy is that it requires a lengthy training phase and an extensive training dataset. This article uses an image segmentation by histogram thresholding approach that does not require training to overcome this difficulty. This article proposes straightforward and time-optimal algorithms, which are guaranteed by mathematical proofs. Furthermore, we experiment with the proposed algorithms using 100 images from a standard database. The results show that, while their performances are not significantly different, the two proposed methods are roughly 10 and 20 times faster …than the most simple and optimal method, Brute Force. They also show that the proposed algorithms can deal with bimodal images and images with various shapes of the image histogram. Because our proposed algorithms are the most efficient and effective. As a result, they can be used for real-time segmentations and as a pre-processing approach for multiple object segmentation. Show more

Keywords: Image segmentation, histogram thresholding, dynamic programming, optimization problem, time-optimal algorithm

DOI: 10.3233/JIFS-222259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2397-2411, 2023

Authors: Zhang, Luyang | Wang, Huaibin | Wang, Haitao

Article Type: Research Article

Abstract: Unconstrained video face recognition is an extension of face recognition technology, and it is an indispensable part of intelligent security and criminal investigation systems. However, general face recognition technology cannot be directly applied to unconstrained video face recognition, because the video contains fewer frontal face image frames and a single image contains less face feature information. To address the above problems, this work proposes a Feature Map Aggregation Network (FMAN) to achieve unconstrained video face recognition by aggregating multiple face image frames. Specifically, an image group is used as the input of the feature extraction network to replace a single …image to obtain a multi-channel feature map group. Then a quality perception module is proposed to obtain quality scores for feature maps and adaptively aggregate image features from image groups at the feature map level. Finally, extensive experiments are conducted on the challenging face recognition benchmarks YTF, IJB-A and COX to evaluate the proposed method, showing a significant increase in accuracy compared to the state-of-the-art. Show more

Keywords: Video face recognition, Aggregation, Deep convolutional neural network, Feature map

DOI: 10.3233/JIFS-212382

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2413-2425, 2023

Authors: Yao, Linjie | Zhang, Guidong | Sheng, Yuhong

Article Type: Research Article

Abstract: Multi-dimensional uncertain differential equations (MUDEs) are often used to describe complex systems that vary with time. In this paper, the generalized moment estimation method is employed to estimate the MUDEs’ unknown parameters. A method to optimize parameters with multiple estimation results is proposed. The hypothesis test and α-path are proposed to verify the feasibility of the parameter results. Several examples of parameter estimation for MUDEs are given, as well as two numerical examples to verify the feasibility of the method.

Keywords: Uncertainty theory, multi-dimensional uncertain differential equation, generalized moment estimation, parameter estimation

DOI: 10.3233/JIFS-213503

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2427-2439, 2023

Authors: Sathish, S. | Kavitha, K. | Poongodi, J.

Article Type: Research Article

Abstract: The industrial world including the merits of Internet of Things (IoT) paradigm has wide opened the evolution of new digital technology to facilitate promising and revolutionizing dimensions in diversified industrial application. However, handling the deployment challenges of security awareness, energy consumption, resource optimization, service assurance and real-time big data analytics in Industrial IoT Networks is a herculean task. In this paper, Dantzig Wolfe Decomposition Algorithm-based Service Assurance and Parallel Optimization Algorithm (DWDA-SAPOA) is proposed for guaranteeing QoS in energy efficient Software-Defined Industrial IoT Networks. This DWDA-SAPOA is proposed for achieving minimized energy consumption on par with the competitive network routing …algorithms which fails in satisfying the strict requirements of heterogeneous Quality of Service (QoS) during the process of optimizing resources under industrial communications. It is proposed as a service assurance and centralized route optimization strategy using the programmability and flexibility characteristics facilitating by the significant Software Defined Networking (SDN) paradigm which is implemented over a multi-layer programmable industrial architecture. It supports bandwidth-sensitive service and ultra-reliable low-latency communication type of heterogeneous flows that represents a routing optimization problem which could be potentially modelled as a multi-constrained shortest path problem. It further adopts Dantzig Wolfe Decomposition Algorithm (DWDA) to handle the complexity of NP-hard involved in solving the multi-constrained shortest path problems. The simulation experiments of the proposed DWDA-SAPOA prove its predominance in minimizing energy consumption by 24.28%, flow violation by 19.21%, packet loss by 21.28%, and end-to-end delay by 29.82%, and bandwidth utilization by up to 26.22% on par with the benchmarked QoS provisioning and energy-aware routing problem. Show more

Keywords: Software defined networking, Dantzig Wolfe Decomposition algorithm, industrial internet of things networks, multi-constrained shortest path problem, centralized route optimization

DOI: 10.3233/JIFS-221776

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2441-2454, 2023

Authors: Akalya devi, C. | Karthika Renuka, D. | Pooventhiran, G. | Harish, D. | Yadav, Shweta | Thirunarayan, Krishnaprasad

Article Type: Research Article

Abstract: Emotional AI is the next era of AI to play a major role in various fields such as entertainment, health care, self-paced online education, etc., considering clues from multiple sources. In this work, we propose a multimodal emotion recognition system extracting information from speech, motion capture, and text data. The main aim of this research is to improve the unimodal architectures to outperform the state-of-the-arts and combine them together to build a robust multi-modal fusion architecture. We developed 1D and 2D CNN-LSTM time-distributed models for speech, a hybrid CNN-LSTM model for motion capture data, and a BERT-based model for text …data to achieve state-of-the-art results, and attempted both concatenation-based decision-level fusion and Deep CCA-based feature-level fusion schemes. The proposed speech and mocap models achieve emotion recognition accuracies of 65.08% and 67.51%, respectively, and the BERT-based text model achieves an accuracy of 72.60%. The decision-level fusion approach significantly improves the accuracy of detecting emotions on the IEMOCAP and MELD datasets. This approach achieves 80.20% accuracy on IEMOCAP which is 8.61% higher than the state-of-the-art methods, and 63.52% and 61.65% in 5-class and 7-class classification on the MELD dataset which are higher than the state-of-the-arts. Show more

Keywords: Emotion recognition, time-distributed models, CNN-LSTM, BERT, DCCA

DOI: 10.3233/JIFS-220280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2455-2470, 2023

Authors: Han, Meng | Li, Ang | Gao, Zhihui | Mu, Dongliang | Liu, Shujuan

Article Type: Research Article

Abstract: In reality, the data generated in many fields are often imbalanced, such as fraud detection, network intrusion detection and disease diagnosis. The class with fewer instances in the data is called the minority class, and the minority class in some applications contains the significant information. So far, many classification methods and strategies for binary imbalanced data have been proposed, but there are still many problems and challenges in multi-class imbalanced data that need to be solved urgently. The classification methods for multi-class imbalanced data are analyzed and summarized in terms of data preprocessing methods and algorithm-level classification methods, and the …performance of the algorithms using the same dataset is compared separately. In the data preprocessing methods, the methods of oversampling, under-sampling, hybrid sampling and feature selection are mainly introduced. Algorithm-level classification methods are comprehensively introduced in four aspects: ensemble learning, neural network, support vector machine and multi-class decomposition technique. At the same time, all data preprocessing methods and algorithm-level classification methods are analyzed in detail in terms of the techniques used, comparison algorithms, pros and cons, respectively. Moreover, the evaluation metrics commonly used for multi-class imbalanced data classification methods are described comprehensively. Finally, the future directions of multi-class imbalanced data classification are given. Show more

Keywords: Classification, multi-class imbalance data, data preprocessing method, algorithm-level classification method

DOI: 10.3233/JIFS-221902

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2471-2501, 2023

Authors: Xiao, Yanjun | Zhao, Churui | Qi, Hao | Liu, Weiling | Meng, Zhaozong | Peng, Kai

Article Type: Research Article

Abstract: In the control system of a lithium battery rolling mill, the correction system was crucial. This was because the correction system had a significant impact on the performance of the lithium battery rolling mill, including high precision and efficient rolling quality. However, the non-linearity of the correction system and the uncertainty of the correction system made it a challenging problem to achieve a high precision correction control. The contribution and innovation of this paper was a genetic fuzzy PID control strategy based on Kalman filter, which was proposed and applied to the control of lithium battery rolling mill correction technology. …In order to achieve intelligent control of a high-precision electrode rolling mill correction system, an algorithm fusion control scheme was proposed. Firstly, a novel and detailed correction system model was presented. Next, the initial PID parameters of the correction were optimized by means of a genetic algorithm so that the PID parameters could be adapted to the correction control process and then optimized again by adding an extended Kalman filter. Finally, the lithium battery rolling mill correction control system was validated, tested and commissioned in the field. The results showed that the designed algorithm could meet the working requirements of the lithium battery rolling mill and that it improved the accuracy of the correction system. In the actual lithium battery rolling mill production process, the algorithm was compared with a conventional PID. Compared with the common single algorithm, the fusion algorithm proposed in this paper was a complete set of high precision correction control system algorithm to solve the high precision problem faced by the correction system in the actual lithium battery rolling mill correction system. Show more

Keywords: Pole piece rolling mill, deviation correction system, fuzzy PID, genetic algorithm, algorithm fusion, extended kalman filter

DOI: 10.3233/JIFS-221028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2503-2523, 2023

Authors: Vo, Tham

Article Type: Research Article

Abstract: The wind power is considered as a potential renewable energy resource which requires less management cost and effort than the others like as tidal, geothermal, etc. However, the natural randomization and volatility aspects of wind in different regions have brought several challenges for efficiently as well as reliably operating the wind-based power supply grid. Thus, it is necessary to have centralized monitoring centers for managing as well as optimizing the performance of wind power farms. Among different management task, wind speed prediction is considered as an important task which directly support for further wind-based power supply resource planning/optimization, hence towards …power shortage risk and operating cost reductions. Normally, considering as traditional time-series based prediction problem, most of previous deep learning-based models have demonstrated significant improvement in accuracy performance of wind speed prediction problem. However, most of recurrent neural network (RNN) as well as sequential auto-encoding (AE) based architectures still suffered several limitations related to the capability of sufficient preserving the spatiotemporal and long-range time dependent information of complex time-series based wind datasets. Moreover, previous RNN-based wind speed predictive models also perform poor prediction results within high-complex/noised time-series based wind speed datasets. Thus, in order to overcome these limitations, in this paper we proposed a novel integrated convolutional neural network (CNN)-based spatiotemporal randomization mechanism with transformer-based architecture for wind speed prediction problem, called as: RTrans-WP. Within our RTrans-WP model, we integrated the deep neural encoding component with a randomized CNN learning mechanism to softy align temporal feature within the long-range time-dependent learning context. The utilization of randomized CNN component at the data encoding part also enables to reduce noises and time-series based observation uncertainties which are occurred during the data representation learning and wind speed prediction-driven fine-tuning processes. Show more

Keywords: Wind speed prediction, deep learning, transformer, randomization, nomenclatures

DOI: 10.3233/JIFS-222446

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2525-2541, 2023

Authors: Yu, Wenmei | Xia, Lina | Cao, Qiang

Article Type: Research Article

Abstract: With the development of big data, Internet finance, the digital economy is developing rapidly and has become an important force to drive the continuous transformation of the global economy and society. China has put forward plans for the development of digital economy from 2021 to 2025, requiring the number of core industries of digital economy to reach 10% of GDP by 2025, while continuously improving China’s digital economy to achieve high-quality development of China’s digital economy. Aiming at China’s digital economy, we use the adaptive lasso method and select feature variables based on quantitative and qualitative perspectives, so as to …predict the development trend of China’s digital economy from 2021 to 2025 based on the TDGM (1, 1, r) grey model optimized by the particle swarm algorithm. Meanwhile, we have added the comparative analyses with TDGM(1,1), Grey Verhulst, GM(1,1) models and evaluate the prediction results both Ex-ante and Ex-post, demonstrating the feasibility of the proposed model and the accuracy. Finally, we find that the future of China’s digital economy will meet the planned objectives in terms of quantity and quality, but the trend of digital economy development in quantity is faster, thanks to the development of digital technology application industry. Show more

Keywords: Digital economy development, adaptive lasso grey model, TDGM(1, 1, r) model, quantity and quality

DOI: 10.3233/JIFS-222520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2543-2560, 2023

Authors: Muthumanickam, Arunkumar | Balasubramanian, Gomathy | Chakrapani, Venkatesh

Article Type: Research Article

Abstract: The field of self-driving cars is one that is rapidly growing in popularity. The goal of autonomous vehicles has always been to avoid accidents. It has long been argued that human errors while driving are the primary cause of traffic accidents, and autonomous cars have the potential to remove this. An intelligent transportation system based on the Internet of Things (IoT) is required at some point for the vehicle to make an instant choice to evade accidents, regardless of the competence of a decent driver Mishaps on the road and in the weather are those that occur due to unfavourable …weather circumstances such as fog, gusts, snow, rain, slick pavement, sleet, etc. There are many factors that might cause a vehicle to lose control, including speed, weight, momentum, poor fleet maintenance. It has the potential to lessen the number of collisions caused by poor weather and deteriorating road circumstances. An IoT-based intelligent accident escaping system for poor weather and traffic circumstances is presented here. A variety of sensors are used to check the health of the vehicle. Data from sensors is processed by a microcontroller and displayed on the dashboard of a car after it has been received. The proposed model combines both an IoT system that monitors weather and road conditions and an intelligent system based on deep learning that learns the adverse variables that impact an accident in order to anticipate and prescribe a harmless speed to the driver. The experimental results show that the proposed deep learning technique achieved 94% of accuracy, where the existing LeNet model achieved 80% of accuracy for the prediction process. The proposed ResNet is more effective than LeNet, because identity mapping is used to solve the vanishing gradient problems. Show more

Keywords: Accidents-free driving, autonomous vehicles, deep learning, fleet management, internet of things, microcontroller, sensors

DOI: 10.3233/JIFS-222719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2561-2576, 2023

Authors: Little Flower, X. | Poonguzhali, S.

Article Type: Research Article

Abstract: For real-time applications, the performance in classifying the movements should be as high as possible, and the computational complexity should be low. This paper focuses on the classification of five upper arm movements which can be provided as a control for human-machine interface (HMI) based applications. The conventional machine learning algorithms are used for classification with both time and frequency domain features, and k-nearest neighbor (KNN) outplay others. To further improve the classification accuracy, pretrained CNN architectures are employed which leads to computational complexity and memory requirements. To overcome this, the deep convolutional neural network (CNN) model is introduced with …three convolutional layers. To further improve the performance which is the key idea behind real-time applications, a hybrid CNN-KNN model is proposed. Even though the performance is high, the computation costs of the hybrid method are more. Minimum redundancy maximum relevance (mRMR), a feature selection method makes an effort to reduce feature dimensions. As a result, better performance is achieved by our proposed method CNN-KNN with mRMR which reduces computational complexity and memory requirement with a mean prediction accuracy of about 99.05±0.25% with 100 features. Show more

Keywords: Empirical mode decomposition, minimum redundancy maximum relevance, spectrogram representation, k-nearest neighbor, deep learning

DOI: 10.3233/JIFS-220811

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2577-2591, 2023

Authors: Annapandi, P. | Ramya, R. | Kotaiah, N.C. | Rajesh, P. | Subramanian, Arun

Article Type: Research Article

Abstract: This manuscript proposes an efficient hybrid strategy to obtain the optimal solution of operational cost reduction, size reduction of hybrid renewable energy sources and optimal power flow control for off-grid system. Here, off-grid is incorporated with photovoltaic array, wind turbine, Diesel generator, and battery energy storage system. The hybrid method is joint execution of Giza Pyramids Construction (GPC) and Billiards-inspired optimization algorithm (BOA) hence it is named GPC-BOA technique. The major purpose of proposed method is minimizing the operational cost as well as size of hybrid renewable energy sources and improves the power flow of system. In this energy management …system of off-grid provides cost reduction which includes the generation, replacement, operating and maintenance, cost of fuel consumption, cost of exchanged power with grid, and the penalty for emissions. Here, the GPC method is employed for forecasting the load requirement of system. The BOA technique optimizes the off-grid system through the deliberation of forecasted load requirement. At last, the proposed approach is performed on MATLAB platform and the performance is assessed using existing techniques. Show more

Keywords: Energy management system, cost, power flow, photovoltaic array, wind turbine, Diesel generator, battery energy storage system

DOI: 10.3233/JIFS-221176

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2593-2614, 2023