Journal of Intelligent & Fuzzy Systems - Volume 44, issue 5

Authors: Nath, Sudarshan | Das Gupta, Suparna | Saha, Soumyabrata

Article Type: Research Article

Abstract: Skin disease is currently considered to be one of the most common diseases in the globe. Most of the human population has experienced it at some point but not all skin illnesses are as severe as others. There are some diseases that are symptomless or show fewer symptoms. Skin cancer is a potentially fatal outcome of serious skin illnesses that might develop if they are not detected in time. Due to the fact that medical professionals aren’t always quick or reliable enough to make a proper diagnosis. There is a hefty price tag attached to employing sophisticated equipment. Therefore, we …propose a system capable of classifying skin diseases using deep learning approaches, such as CNN architecture and six preset models including MobileNet, VGG19, ResNet, EfficientNet, Inception, and DenseNet. Acne, blisters, cold sores, psoriasis, and vitiligo are some of the most often seen skin conditions, thus we scoured the web resources for relevant photographs of these conditions. We have applied data augmentation methods to extend the size of the dataset and include more image variations. In the validation dataset, we achieved an accuracy rate of approx 99 percent, while in the test dataset; we achieved an accuracy rate of approx 90 percent. Our proposed method would help to diagnose skin diseases in a faster and more cost-effective way. Show more

Keywords: Skin disease, deep learning, CNN, MobileNet, VGG19, ResNet, EfficientNet, Inception, DenseNet, Acne, blisters, cold sore, psoriasis, vitiligo

DOI: 10.3233/JIFS-222773

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7483-7499, 2023

Authors: Shen, Dong | Fang, Haoyu | Li, Qiang | Liu, Jiale | Guo, Sheng

Article Type: Research Article

Abstract: Visual Simultaneous Localization and Mapping (SLAM) is one of the key technologies for intelligent mobile robots. However, most of the existing SLAM algorithms have low localization accuracy in dynamic scenes. Therefore, a visual SLAM algorithm combining semantic segmentation and motion consistency detection is proposed. Firstly, the RGB images are segmented by SegNet network, the prior semantic information is established and the feature points of high-dynamic objects are removed; Secondly, motion consistency detection is carried out, the fundamental matrix is calculated by the improved Random Sample Consistency (RANSAC) algorithm, the abnormal feature points are output by the epipolar geometry method, and …the feature points of low-dynamic objects are eliminated by combining the prior semantic information. Thirdly, the static feature points are used for pose estimation. Finally, the proposed algorithm is tested on the TUM dataset, the algorithm in this paper reduces the average RMSE of ORB-SLAM2 by 93.99% in highly dynamic scenes, which show that the algorithm can effectively improve the localization accuracy of the visual SLAM system in dynamic scenes. Show more

Keywords: Simultaneous localization and mapping (SLAM), semantic segmentation, motion consistency detection, dynamic feature points

DOI: 10.3233/JIFS-222778

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7501-7512, 2023

Authors: Chen, Yong | Long, Feiyu | Kuang, Wei | Zhang, Tianbao

Article Type: Research Article

Abstract: Blast-induced ground vibration is highly possible to result in serious losses such as destroyed buildings. The crucial parameter of the mentioned vibration is peak particle velocity (PPV). Many equations have been developed to predict PPV, however, worse performance has been reported by multiple literatures. This paper developed a method for predicting PPV based on Mamdani Fuzzy Inference System. Firstly, Minimum Redundancy Maximum Relevance was employed to identify the blasting design parameters which significantly contribute to the PPV induced by blasting. Secondly, K-means method was applied to determine the value ranges of the selected parameters. The selected parameters and corresponding value …ranges were combined to input into Mamdani Fuzzy Inference System for obtaining predicted PPV. Totally, 280 samples were collected from a blasting site. 260 out of them were used to train the proposed method and 20 were assigned for test. The proposed method was tested in the comparison with empirical equation USBM, multiple linear regression analysis, pure Mamdani Fuzzy Inference System in terms of the difference between predicted PPV and measured PPV, coefficient of correlation, root-mean-square error, and mean absolute error. The results from that showed that the proposed method has the better performance in PPV prediction. Show more

Keywords: Blasting, peak particle velocity, parameter selection, k-means method, Mamdani Fuzzy Inference System

DOI: 10.3233/JIFS-223195

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7513-7522, 2023

Authors: Praveen, R. | Pabitha, P.

Article Type: Research Article

Abstract: The Internet of Medical Things (IoMT) is a network of medical devices, hardware infrastructure, and software that allows healthcare information technology to be communicated over the web. The IoMT sensors communicate medical data to server for the quick diagnosis. As, it handles private and confidential information of a user, security is the primary objective. The existing IoT authentication schemes either using two-factor(Username, password) or multi-factor (username, password, biometric) to authenticate a user. Typically the structural characteristics-based biometric trait like Face, Iris, Palm print or finger print is used as a additional factor. There are chances that these biometrics can be …fabricated. Thus, these structural biometrics based authentication schemes are fail to provide privacy, security, authenticity, and integrity. The biodynamic-based bioacoustics signals are gained attention in the era of human-computer interactions to authenticate a user as it is a unique feature to each user. So, we use a frequency domain based bio-acoustics as a biometric input. Thus, this work propose a Secure Lightweight Bioacoustics based User Authentication Scheme using fuzzy embedder for the Internet of Medical Things applications. Also, the IoT sensors tends to join and leave the network dynamically, the proposed scheme adopts chinese remainder technique for generate a group secret key to protect the network from the attacks of former sensor nodes. The proposed scheme’s security is validated using the formal verification tool AVISPA(Automated Validation of Internet Security Protocols and Applications). The system’s performance is measured by comparing the proposed scheme to existing systems in terms of security features, computation and communication costs. It demonstrates that the proposed system outperforms existing systems. Show more

Keywords: e-Healthcare, internet of medical things security, remote patient monitoring, user authentication, bioacoustics, fuzzy embedder

DOI: 10.3233/JIFS-223617

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7523-7542, 2023

Authors: Xu, Baohua | Chen, Jiayu | Li, Zhi | Yang, Tao

Article Type: Research Article

Abstract: In recent years, with the continuous occurrence of natural disasters, people have gradually realized the importance of improving emergency response capability, and the weight of time constraints for rational allocation of emergency materials has gradually increased. Therefore, a high-dimensional collaborative allocation method of disaster materials with time window constraints is studied. A high-dimensional collaborative distribution model of disaster materials with time window constraints is constructed by combining four dimensional decision-making indexes: maximizing the satisfaction of material demand, fairness of material distribution and minimizing the total cost of expected emergency response; Build SPEA2 + SDE hybrid algorithm, solve the model and output the …optimal solution set. The simulation results show that this method can have the ability of high-dimensional distribution of disaster materials, obtain the output of the optimal distribution scheme set of disaster materials, and the material satisfaction is more than 0.70. Under the condition of minimum distribution cost, the distribution of disaster materials can be completed. Show more

Keywords: Time window constraint, disaster materials, high dimensional collaborative allocation, multi-objective constraints, decision index

DOI: 10.3233/JIFS-224428

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7543-7552, 2023

Authors: Singh, Varsha | Agrawal, Prakhar | Tiwary, Uma Shanker

Article Type: Research Article

Abstract: Generating natural language description for visual content is a technique for describing the content available in the image(s). It requires knowledge of both the domains of computer vision and natural language processing. For this, various models with different approaches are suggested. One of them is encoder-decoder-based description generation. Existing papers used only objects for descriptions, but the relationship between them is equally essential, requiring context information. Which required techniques like Long Short-Term Memory (LSTM). This paper proposes an encoder-decoder-based methodology to generate human-like textual descriptions. Dense-LSTM is presented for better description as a decoder with a modified VGG19 encoder to …capture information to describe the scene. Standard datasets Flickr8K and Flickr30k are used for testing and training purposes. BLEU (Bilingual Evaluation Understudy) score is used to evaluate the generated text. For the proposed model, a GUI (Graphical User Interface) is developed, which produces the audio description of the output received and provides an interface for searching the related visual content and query-based search. Show more

Keywords: Convolutional neural network (CNN), dense-long short-term memory (Dense-LSTM), bilingual evaluation understudy score (BLEU), textual description generation

DOI: 10.3233/JIFS-222358

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7553-7565, 2023

Authors: Gao, Feng | Ahmadzade, Hamed | Gao, Rong | Zou, Zezhou

Article Type: Research Article

Abstract:  Gini coefficient is a device to characterize dispersion of uncertain variables. In order to measure variation of uncertain variables, the concept of Gini coefficient for uncertain variables is proposed. By invoking inverse uncertainty distribution, we obtain a formula for calculating Gini coefficient for uncertain variables. As an application of Gini coefficient, portfolio selection problems for uncertain returns are solved via mean-Gini models. For better understanding, several examples are provided.

Keywords: Uncertain variables, monte-carlo simulation, inverse uncertainty distribution, portfolio optimization, Gini coefficient

DOI: 10.3233/JIFS-222762

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7567-7575, 2023

Authors: Gao, Bin | Zhang, Naiwen

Article Type: Research Article

Abstract: For urban, the quantity and quality of talent is often an important measure of their development potential. Based on the theory of talent and environment comfort degree and the theory of urban comfort degree, this paper constructs the Evaluation Index System of urban talent development environment. This paper selects 7 coastal urban in Shandong province as the research objects, and uses entropy Weight-TOPSIS and cluster analysis to measure the talent development environment. The research results show that: (1) The talent development environment of seven coastal urban presents the phenomenon of “siphoning” and “distinctness” of talents. On the whole, Qingdao, Weifang, …and Yantai have certain advantages in the talent development environment. (2) Qingdao is the leading city, Weifang, Yantai, Weihai and Dongying are follow-up urban, and Binzhou and Rizhao are backward urban. (3) The environment of the eight first-level indicators forms a “magnetic field” for the development of talents. Only by fully releasing the “magnetic field effect” of the talent development environment can urban ensure that talents are “attracted, retained and used well". This paper puts forward some suggestions to optimize the talent de-velopment environment in coastal urban, which will help to stimulate the vitality and creativity of all kinds of talents in coastal urban. Show more

Keywords: Coastal urban, talent development environment, measurement, entropy weight-topsis method

DOI: 10.3233/JIFS-222889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7577-7587, 2023

Authors: Wei, Pingping | Zhang, Xin

Article Type: Research Article

Abstract: This paper proposes a robust autoencoder with Wasserstein distance metric to extract the linear separability features from the input data. To minimize the difference between the reconstructed feature space and the original feature space, using Wasserstein distance realizes a homeomorphic transformation of the original feature space, i.e., the so-called the reconstruction of feature space. The autoencoder is used for features extraction of linear separability in the reconstructed feature space. Experiment results on real datasets show that the proposed method reaches up 0.9777 and 0.7112 on the low-dimensional and high-dimensional datasets in extracted accuracies, respectively, and also outperforms competitors. Results also …confirm that compared with feature metric-based methods and deep network architectures-based method, the linear separabilities of those features extracted by distance metric-based methods win over them. More importantly, the linear separabilities of those features obtained by evaluating distance similarity of the data are better than those obtained by evaluating feature importance of data. We also demonstrate that the data distribution in the feature space reconstructed by a homeomorphic transformation can be closer to the original data distribution. Show more

Keywords: Autoencoder, distance measure, feature extraction, linear separability

DOI: 10.3233/JIFS-223017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7589-7598, 2023

Authors: Ahmed Seghir, Zianou | Djezzar, Meriem | Hemam, Mounir | Zeggari, Ahmed | Hachouf, Fella

Article Type: Research Article

Abstract: The application of 3D technology is rapidly expanding, and stereoscopic imagery is typically used to display 3D data. However, compression, transmission, and other necessary processes may reduce the quality of these images. Stereo image quality assessment (SIQA) has gained more attention to guarantee that customers have a positive watching experience. In order to provide the highest level of experience, it is necessary to develop a quality evaluation mechanism for stereoscopic content that is both dependable and precise. A full-reference method for SIQA is presented in this paper. Compared to previous measures, this method gives users more freedom to use distorted …pixel metrics and edge similarity. The binocular summation map is calculated by adding the left and right images for a stereo pair. Improved gradient similarity based distorted pixel measure (SGSDM) is used to calculate the quality of binocular summation. The scored 3D LIVE IQA database is used to evaluate the correlation of the proposed metric with the DMOS subjective score given by the database. The proposed method’s efficacy is demonstrated by experimental comparisons. Show more

Keywords: Gradient similarity, quality assessment, test image, distorted pixel measure, SIQA

DOI: 10.3233/JIFS-223375

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7599-7611, 2023

Authors: Saxena, Arti | Dubey, Y.M. | Kumar, Manish

Article Type: Research Article

Abstract: Models prediction is done for accurately anticipating metal removal rate (MRR), machine power (MP), and estimated tool life (ETL), which are vital in the industrial setup for better precision and higher speed. Cutting speed (CS) and feed rate (FR) were employed as controlling parameters for machining of P8 material on the SBCNC 60. By maintaining one of the two parameters constant at the mid-level, data from drilling experiments are sampled and examined. Application of ANOVA yields that the feed rate is 52.61 percent significant and the cutting speed is 46.49 percent significant for MRR, while cutting speed contributes 57.59 percent …and feed rate contributes 41.77 percent to the machine power, and the same cutting speed contributes 83 percent to ETL’s output. The analysis results that CS at 190 m/min and FR at 0.3 mm/rev are optimal combinations of input control parameters for all output of drilling operations. The development of prediction models is done by fuzzy and its comparison is carried out with classical regression method for the achievement of optimum MRR, MP and ETL. Numerical parameters for establishing the optimum model are calculated for MAPE, RMSE, MAD, and correlation coefficient between experimental values and the values obtained from regression, and fuzzy logic predictions. MAPE, RMSE, MAD, and correlation coefficient calculated 1.27%, 2.43, 1.89, and 0.99 for MRR,0.97%,0.10, 0.09 and 0.997 for MP and 5.12%,1.01,0.67 and 0.99 for ETL respectively. Hence, the proposed fuzzy logic rules effectively predict the MRR, MP, and ETL on P8 material with optimized performance. Show more

Keywords: ANOVA, Correlation coefficient (R), Estimated Tool Life, Fuzzy Logic, Mean Absolute difference (MAD), Mean Absolute Percentage Error (MAPE), Root mean square error (RMSE), Machine Power, Metal Removal Rate

DOI: 10.3233/JIFS-222768

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7613-7627, 2023

Authors: Venkatesh Kumar, M. | Lakshmi, C.

Article Type: Research Article

Abstract: Because significantly complex crypto procedures such as holomorphic encryption are robotically applied, despite the fact that consumer gadgets under our software circumstances are not, computational overhead is outrageously high. Simply hiding customers with the aid of nameless communications to act to protect the server and adversaries from linking suggestions made with the aid of the same customer makes the traditional method, which computes with the aid of any server based on the amount of provided services, impossible, and customers with charge features widely publicised with the aid of the server cause additional security concerns, impossible. To overcome the above existing …drawbacks, this research study presents a Privacy Preservation Data Collection and Access Control Using Entropy-Based Conic Curve. To safeguard the identity of clients and their requests, EBCC employs a unique group signature technic and an asymmetric cryptosystem. First, we ought to implement our EBCC method for data acquisition while maintaining privacy. Second, we consider looking at the properties of secure multiparty computation. EBCC employs lightweight techniques in encryption, aggregation, and decryption, resulting in little computation and communication overhead. Security research suggests that the EBCC is safe, can withstand collision attacks, and can conceal consumer distribution, which is required for fair balance checks in credit card payments. Finally, the results are analysed to illustrate the proposed method performance in addition to the more traditional ABC, AHRPA, ECC, and RSA methods. The proposed work should be implemented in JAVA. Show more

Keywords: Entropy-based conic curve, data mining, privacy-preserving, key generation, encryption, decryption

DOI: 10.3233/JIFS-223141

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7629-7642, 2023

Authors: Chen, Xue-gang | Sohn, Moo Young | Ma, De-xiang

Article Type: Research Article

Abstract: In real-life scenarios, both the vertex weight and edge weight in a network are hard to define exactly. We can incorporate the fuzziness into a network to handle this type of uncertain situation. Here, we use triangular fuzzy number to describe the vertex weight and edge weight of a fuzzy network G . In this paper, we consider weighted k -domination problem in fuzzy network. The weighted k -domination (WKD) problem is to find a k dominating set D which minimizes the cost f  (D ) : = ∑u ∈D w  (u ) + ∑v ∈V \D  min {∑u ∈S w … (uv ) |S  ⊆ N  (v ) ∩ D , |S | = k }. First, we put forward an integer linear programming model with a polynomial number of constrains for the WKD problem. If G is a cycle, we design a dynamic algorithm to determine its exact weighted 2-domination number. If G is a tree, we give a label algorithm to determine its exact weighted 2-domination number. Combining a primal-dual method and a greedy method, we put forward an approximation algorithm for general fuzzy network on the WKD problem. Finally, we describe an application of the WKD problem to police camp problem. Show more

Keywords: Fuzzy network, triangular fuzzy number, weighted k-domination, algorithm

DOI: 10.3233/JIFS-213120

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7643-7651, 2023

Authors: Punarselvam, E.

Article Type: Research Article

Abstract: Parkinson’s disease is neurological degenerative disorder cause by deficient dopamine production which in turn harms the motor functionality and speech. With latest IoT advancement in the health care era, we propose intelligent and smart Parkinson’s disease detection system based on voice signal analysis. Addition to PDs detection, we propose remote health monitoring feature that keep on monitoring and diagnosing PD person activity. To perform all tasks efficiently we divide our propose model in three phases: monitoring, diagnosing and analysis. During monitoring phase, PDs person voice signal is monitored and captured via IoT sensor enabled Smartphone device. This voices signal is …further processed for PD detection over MEC server during diagnosing phase. We use Tunable Q factor wavelet transform (TQWT) for extracting feature from voice sample, these extracted feature are reduced FRS methods. For feature reduction PCA and LDA are used. Theses processed feature are then applied to hybrid case-based reasoning neuro-fuzzy (ANFIS) classification system to detect Parkinson’s disease. On the detection of PDs abnormality, the proposed healthcare monitoring system immediately generates notification to the patient simultaneously send detection report to centralized healthcare cloud system. This PDs detection report is further analyzed and stored at cloud server during analysis phase where report is analyzed by professional health expert and send the appropriate treatment and medication to PD infected person or care taker. For experimentation and performance evaluation benchmark baseline UCI dataset of PDs are used. We analyzed our proposed hybrid ANFIS-CBR classifier with existing classifiers over the accuracy, sensitivity and specificity parameter. Based on the result analysis, it is observed that proposed hybrid classifier maximum accuracy, sensitivity, and specificity of 98.23%, 99.1%, and 95.3% in comparison to other classifier. Show more

Keywords: Parkinson’s Disease (PDs), Internet of things (IoT), Tunable Q-factor wavelet transform, Feature reduction and selection (FRS), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Forward feature selection (FFS), Backward feature selection (BFS), Adaptive Neuro-fuzzy interference System (ANFIS), Case-Based Reasoning (CBR), MEC (Mobile edge computing), Cloud computing

DOI: 10.3233/JIFS-220941

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7653-7668, 2023

Authors: Glukhikh, Igor | Chernysheva, Tatyana | Glukhikh, Dmitry

Article Type: Research Article

Abstract: The case-based reasoning method has a high potential for solving tasks of intelligence decision-support. To implement it, it is necessary to solve the problem of comparing situations and selecting the one that is most similar to the current situation in the knowledge base. The problem arises in the case of heterogeneous objects and situations with many different types of parameters and their possible uncertainty. In this paper, an approach based on machine (deep) learning is investigated for this task. It is proposed to carry out the process of selecting situations and solutions from the knowledge base in two stages: recognition …of the states of the elements of a complex object and the relationships between them, then the formation of a representation of the situation in the state space and its use for comparing situations using neural networks. An ensemble neural network model based on a multi-layer network is proposed. It successfully simulates the cognitive functions of a human (expert), correctly selects similar situations and ranks them according to the similarity parameter. Proposed neural network models provide the implementation of a hybrid-CBR approach for decision-making on complex objects. Show more

Keywords: Artificial intelligence, decision support systems, case-based reasoning, similarity assessment, neural network models, urban infrastructure

DOI: 10.3233/JIFS-221335

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7669-7682, 2023

Authors: Shi, Xiaolong | Kosari, Saeed | Rashmanlou, Hossein | Broumi, Said | Satham Hussain, S.

Article Type: Research Article

Abstract: The interval-valued quadripartitioned neutrosophic set is represented by the partition of the interval-valued neutrosophic set’s indeterminacy function into contradiction and ignorance parts. This article introduces the properties of interval quadripartitioned single valued neutrosophic graph. The properties like complementary, self-complementary, strong and complete interval-valued quadripartitioned neutrosophic graphs are investigated. The finest illustration of locating a climate conducive to apricot cultivation in Ladakh is provided by the notion that has been offered. The model gives us details on the location that should be chosen for apricot farming. Using the proposed concepts, we highlight potential applications of the usual apricot plant that thrives …in extremely cold climates and is appropriate for higher production. The adopted approach makes a superior fit to consider the problems in application viewpoint. Show more

Keywords: Interval quadripartitioned neutrosophic graph, properties on graphs, complement of interval quadripartitioned neutrosophic graph

DOI: 10.3233/JIFS-222572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7683-7697, 2023

Authors: Wang, Chu | Zhao, Xuefeng | Wang, Bin | Deng, Chao | Feng, Junlan

Article Type: Research Article

Abstract: Tabular data is a widely used data form in many fields such as product marketing. In some cases, the domain shift between source and target domain of tabular data may occur with the changing of collection conditions such as time. The extant methods on tabular data mainly consist of neural-network-based methods and tree-based methods. They both meet challenges induced by domain shift on tabular data. First, neural-network-based methods are lack of effective mechanism to extract the features of tabular data and the performance may not be higher than tree-based models. Second, tree-based methods are lack of effective feature representations to …model the associations between source domain and target domain. To improve the performance of tree-based methods for domain shift, a novel pseudo-label based domain adaptation method is proposed for the tree-based method called Xgboost. The proposed method consists of pseudo-label generation and selection strategies. The pseudo-label generation strategy can control the effects of pseudo-labels on Xgboost in a more flexible way by setting proper values of pseudo-labels. The pseudo-label selection strategy can select the pseudo-labels with high confidences under a consistency condition based on the outputs of Xgboost. The quality of pseudo-labels for the data in target domain is improved and so does the performance of Xgboost trained by the data in both source domain and target domain. In the experiment, several UCI datasets and 5G terminal datasets are used to show that the proposed methods can effectively improve the performance of Xgboost. Show more

Keywords: Domain adaptation, Pseudo-label, Tabular data, Xgboost

DOI: 10.3233/JIFS-223118

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7699-7708, 2023

Authors: Kang, Xinhui | Nagasawa, Shin’ya

Article Type: Research Article

Abstract: The automobile shows try to convey a clear product or service message to the audience in a short period of time. Therefore, the steps of materials, shape, display and other aspects need to be carefully designed to provide an important display platform for the business. However, most exhibitors depend on their subjective preferences to decide the size and planning of the booth, which fails to attract the attention of customers. In this paper, the evaluation grid method (EGM) and support vector regression (SVR) are combined to design the automobile booth, which provides an innovation process for booth planning and improves …the visual appeal of the booth. Firstly, the EGM is used to interview ten highly involved groups, thus obtaining the evaluation grid diagram of the connecting line among the upper emotional needs, the median design items, and the lower specific elements. Secondly, the importance ranking of upper emotional needs is determined by the grey relational analysis. Finally, the SVR is used to establish a mapping model between key emotional needs and lower design elements, thus obtaining the best combination of booth design features preferred by customers. The verification results show that the proposed method can significantly improve the emotional satisfaction of customers and provide clear trade exhibition guidance for exhibitors. Show more

Keywords: Evaluation grid method, support vector regression, grey relational analysis, automobile trade booth design

DOI: 10.3233/JIFS-223364

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7709-7722, 2023

Authors: Liu, Sijia | Guo, Zixue

Article Type: Research Article

Abstract: The digital economy based on the new generation of information technology has increasingly become an important driving force for economic development, and it is of great practical significance to study the evaluation of the development level of the digital economy. On the basis of summarizing the connotation of the digital economy, the evaluation index system of digital economy development level is firstly constructed from four dimensions of digital infrastructure, digital industry, digital application level and digital innovation ability. Secondly, the combination weighting method of CRITIC-entropy method is used to weight the indicators. Thirdly, the evaluation model of digital economy development …level based on grey correlation-VIKOR method is constructed, and the relevant data of 30 provinces in China in 2020 are taken as samples for empirical research. The results show that there is significant regional heterogeneity in the development level of digital economy in China. The development level of digital economy in eastern China is much higher than that in western China. The most important factor affecting the development level of China’s digital economy is the development of software industry. At the same time, digital innovation ability is also an important index to distinguish the development level of digital economy. Finally, corresponding policy suggestions are put forward in response to the problems in the development of China’s digital economy. Show more

Keywords: Digital economy, combination weighting method, improved VIKOR method, regional economy

DOI: 10.3233/JIFS-223567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7723-7738, 2023

Authors: Priyadharshini, P. | Pavalarajan, S.

Article Type: Research Article

Abstract: The Internet of Things (IoT) is a system of machines, computing devices, electronic equipment, and different sensors. It forms a network, where the transmission of device-related data can be accomplished. The devices in the IoT are connected to each other through wireless links and form ad-hoc networks. In IoT based applications, the lifetime of the communicating nodes is a greater concern. The network lifetime can be maximized by introducing energy efficient data transmission in the network. Therefore, a traffic and delay-aware energy-efficient routing (TADEER) protocol for IoT-based networks are proposed in this work. The proposed technique assigns delay for transmitting …data based on the criticality level of data and traffic rate at the forwarding nodes. Fixing delays for data transmission helps to avoid unnecessary transmissions. The route selection process is implemented using an optimization algorithm. A Fuzzy logic (FL) based biogeography-based optimization (BBO) algorithm is presented in this work. Thus, the number of data transmission and energy consumption can be reduced. The performance of the proposed method is evaluated by analyzing transmission delay, network lifetime, and energy consumption. By comparing the simulation results to the existing methods TEAR and ETASA, the simulation results are validated. Show more

Keywords: Internet of Things, smart home, energy management, demand response, energy consumption, wireless sensor network

DOI: 10.3233/JIFS-220399

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7739-7752, 2023

Authors: Yamuna, V. | Arun Prakash, K.

Article Type: Research Article

Abstract: The Fuzzy Incidence Coloring (FIC) of a graph is a mapping of its Fuzzy Incidence set to a color set in which adjacent Fuzzy Incidences (FIs) are colored with different colors. Using various sorts of fuzzy graph products, new graphs can be created from two existing graphs. In this paper, we determined the Fuzzy Incidence Coloring Number (FICN) of some cartesian product with two Fuzzy Incidence Paths (FIPs) ( P m ˜ × P n ˜ ) , two Fuzzy Incidence cycles ( C m ˜ …× C n ˜ ) , two Fuzzy Incidence complete graphs ( K m ˜ × K n ) ˜ , FIP and Fuzzy Incidence cycle ( P m ˜ × C n ) ˜ , FIP and Fuzzy Incidence complete graph ( P m ˜ × K n ) ˜ , Fuzzy Incidence cycle and Fuzzy Incidence complete graph ( C m ˜ × K n ) ˜ , respectively. Show more

Keywords: Fuzzy Incidence Path, Fuzzy Incidence cycle, Fuzzy Incidence complete graph, cartesian product

DOI: 10.3233/JIFS-221113

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7753-7774, 2023

Authors: Khan, Vakeel A. | Arshad, Mohammad | Alam, Masood

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-224035

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7775-7784, 2023

Authors: Kong, Yue | Sun, Huaijiang | Cui, Qiongjie | Pan, Jian | Li, Yanmeng

Article Type: Research Article

Abstract:  Human motion style transfer is a technique that aims to apply a desired style to neutral motions, which is an essential aspect of motion generation and retargeting. With the advancement of deep learning networks, significant progress has been made in this field. However, one of the main challenges is preserving the essential features of the original motions, such as velocities and trace, during the style transfer process. To overcome this challenge, we have proposed a novel method called Residual LSTM Generative Adversarial Networks (Res-LGAN) for motion style transfer. The Res-LGAN models consist of a transfer network and a refinement network, …which work together to generate smooth and natural stylized motions while preserving key features of the original motions. Additionally, we have introduced a reconstruction loss term to ensure the stylized motions closely retain the features of the original motions. Our experiments demonstrate that the proposed Res-LGAN model outperforms existing state-of-the-art models by generating high-quality stylized motions while preserving the original motion features. To the best of our knowledge, Res-LGAN is the leading method for preserving original content features during motion style transferring. Show more

Keywords: GANs, motion style transfer, motion feature preserved

DOI: 10.3233/JIFS-224175

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7785-7795, 2023

Authors: Rana, Shazia | Saeed, Muhammad | Qayyum, Madiha | Smarandache, Florentin

Article Type: Research Article

Abstract: This article is a preliminary draft for initiating and commencing a new pioneer dimension of expression. To deal with higher-dimensional data or information flowing in this modern era of information technology and artificial intelligence, some innovative super algebraic structures are essential to be formulated. In this paper, we have introduced such matrices that have multiple layers and clusters of layers to portray multi-dimensional data or massively dispersed information of the plithogenic universe made up of numerous subjects their attributes, and sub-attributes. For grasping that field of parallel information, events, and realities flowing from the micro to the macro level of …universes, we have constructed hypersoft and hyper-super-soft matrices in a Plithogenic Fuzzy environment. These Matrices classify the non-physical attributes by accumulating the physical subjects and further sort the physical subjects by accumulating their non-physical attributes. We presented them as Plithogenic Attributive Subjectively Whole Hyper-Super-Soft-Matrix (PASWHSS-Matrix) and Plithogenic Subjective Attributively Whole-Hyper-Super-Soft-Matrix (PSAWHSS-Matrix). Several types of views and level-layers of these matrices are described. In addition, some local aggregation operators for Plithogenic Fuzzy Hypersoft Set (PPFHS-Set) are developed. Finally, few applications of these matrices and operators are used as numerical examples of COVID-19 data structures. Show more

Keywords: Plithogenic, Hyper-Super-Soft-Matrix, matrix layers, parallel-universes, events, realities, aggregation operators, non-physical classifications, as COVID-19, data structures

DOI: 10.3233/JIFS-202792

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7797-7820, 2023

Authors: Senthil Mahesh, P.C. | Muthumanickam, K. | Vijayalakshmi, P.

Article Type: Research Article

Abstract: Recent technological advancements have enabled users to conduct more sophisticated business transactions via Wi-Fi enabled networks. Typically, a compromised access point (CAP) can handle all traffic between a user and an Internet server, thus becoming a serious security hazard. In addition, an attacker can easily control the entire network using the CAP remotely and compromise as many victims as possible to form a botnet. This paper presents a hybrid recommendation prediction model for forecasting CAP attacks based on network traffic in a private network. This model combines various prediction techniques likethe time-series model, the kNN model and cross association algorithm …for attack prediction. This hybrid blacklisting recommendation system effectively improves the prediction rate significantly as well as the robustness against poisoning attacks. Show more

Keywords: Access point, hybrid model, prediction model, recommendation system, security

DOI: 10.3233/JIFS-212979

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7821-7831, 2023

Authors: Mariyam, Farhana | Mehfuz, Shabana | Sadiq, Mohd.

Article Type: Research Article

Abstract: Goal oriented software requirements analysis method is used for the analysis of elicited functional goals (FGs) and non-functional goals (NFGs) of a system in which goals are decomposed and refined into sub-goals until requirements from the sub-goals are identified. Based on the critical analysis, we found that most of the attention of goal-oriented methods is on the crisp and fuzzy logic during the analysis of the software goals or requirements. In these methods’ prior information about the type of membership function is required; and the selection of membership function depends on the subjective justification. As a result, it lacks objectivity …and may affect the ranking values of the goals or requirements during the analysis. Therefore, this paper presents a rough attributed goal-oriented software requirements analysis (RAGOSRA) method in which rough preference matrix has been used to capture the opinions of different stakeholders. The results of the RAGOSRA method are compared by considering the following criteria, i.e., goal types, goal links, types of data used in the analysis, stakeholder perceptions and time complexity with some fuzzy based methods. Based on the time complexity analysis, it is found that RAGOSRA method requires only 24 operations for the selection of goals for the dataset having 3 NFGs and 4 FGs of an institute examination system. On the other hand, FAGOSRA method, fuzzy TOPSIS method, and fuzzy AHP method requires 36, 166, and 240 operations respectively. Show more

Keywords: Requirements engineering, goal-oriented requirements engineering, software requirements analysis, multi-criteria decision-making method, rough-set theory, institute examination system

DOI: 10.3233/JIFS-221300

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7833-7843, 2023

Authors: Akula, Naveen Kumar | S, Sharief Basha

Article Type: Research Article

Abstract: Group decision-making is a technique wherein professionals rank and select the most acceptable ones based on recognised criteria. The objective of the present study was to establish a strategy for solving issues with Laplacian energy and association coefficient measures of intuitionistic fuzzy graphs through group decision-making. Initially, making use of Laplacian energy, the load of each criterion is determined, and the entire criterion load vector is then computed by averaging the determined loads. The substitutes are then ranked using the association coefficient measure linked to every criterion. Finally, we used the proposed technique wherein professionals rank and select the most …acceptable based on recognised criteria with real-time application. Show more

Keywords: Association coefficient (AC), group decision making (GDM), correlation coefficient (CC), membership function (MF), non-membership function (NMF), Laplacian energy (LE), intuitionistic fuzzy graphs (IFGs), intuitionistic fuzzy preference relations (IFPRs), intuitionistic fuzzy laplacian matrix (IFLM)

DOI: 10.3233/JIFS-222510

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7845-7854, 2023

Authors: Shanthi, P. | Amutha, S. | Anbazhagan, N. | Bragatheeswara Prabu, S.

Article Type: Research Article

Abstract:  A graph G is an undirected finite connected graph. A function f  : V  (G ) → [0, 1] is called a fractional dominating function if, ∑u ∈N [v ] f  (u ) ≥1, for all v  ∈ V , where N  [v ] is the closed neighborhood of v . The weight of a fractional dominating function is w  (f ) = ∑v ∈V (G ) f  (v ). The fractional domination number γf  (G ) has the least weight of all the fractional dominating functions of G . In this paper, we analyze the effects on γf  (G ) of deleting a …vertex from G . Additionally, some bounds on γf  (G ) are discussed, and provide the exactness of some bounds. If we remove any leaves from any tree T , then the resulting graphs are , where |l | is the number of leaves. Some of the results are proved by the eccentricity value of a vertex e  (v ). Show more

Keywords: Domination number, edge domination number, fractional domination number, fractional edge domination number AMS Subject Classification: 05C72, 05C69.

DOI: 10.3233/JIFS-222999

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7855-7864, 2023

Authors: Gong, Zhiwei

Article Type: Research Article

Abstract: Entropy is an important tool to describe the degree of uncertainty of fuzzy sets. In this study, we first define a new entropy and distance measure in the linguistic q-Rung orthopair fuzzy (LIVqROF) environment, and verify its correctness and rationality. Secondly, in the LIVqROF environment, the new entropy formula is effectively applied to the multi-attribute decision making (MADM) with unknown attribute weights, which provides a new idea for solving the MADM problems. Finally, the feasibility and effectiveness of the proposed method are verified by a numerical example.

Keywords: Entropy measure, distance measure, linguistic interval-valued q-Rung orthopair fuzzy set, group decision making

DOI: 10.3233/JIFS-223729

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7865-7876, 2023

Authors: Sathiyavathi, V. | Thenmozhi, R. | Sai Ramesh, L. | Sabena, S. | Selvakumar, K.

Article Type: Research Article

Abstract: A Mobile Ad-hoc Network (MANET) is a self-constructed network consisting of spatially distributed nodes that cooperatively arrange themselves without any centralized manager or fixed based stations. In MANET, the nodes are deployed in a dynamic scenario, so, the nodes may fail as a result of energy depletion, hardware failure, communication link errors, and malicious attacks. Therefore, it is necessary to design energy-efficient fault-tolerant algorithms and protocols for MANETs. Since the development of Mobile Ad-hoc networks was originally motivated by military applications, such as battlefield surveillance and healthcare applications it is required to have a fast recovery mechanism to overcome the …fault condition. In this research work, a fault-tolerant Routing mechanism is designed and implemented to address the fault conditions such as node failure, link failure, and critical battery issues called Fault tolerant cluster based AODV with Error Reporting Routing (CAODVERR) protocol, and to improve the stability of the MANET. Also, an Error reporting mechanism has been incorporated with the Ad-hoc On-Demand Distance Vector (AODV) routing protocol. Show more

Keywords: MANET, fault-tolerant, clustering, routing, error reporting

DOI: 10.3233/JIFS-222718

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7877-7886, 2023

Authors: Awan, Tehreem | Khan, Khan Bahadar | Mannan, Abdul

Article Type: Research Article

Abstract: COVID-19 is an epidemic, causing an enormous death toll. The mutational changing of an RNA virus is causing diagnostic complexities. RT-PCR and Rapid Tests are used for the diagnosis, but unfortunately, these methods are ineffective in diagnosing all strains of COVID-19. There is an utmost need to develop a diagnostic procedure for timely identification. In the proposed work, we come up with a lightweight algorithm based on deep learning to develop a rapid detection system for COVID-19 with thorax chest x-ray (CXR) images. This research aims to develop a fine-tuned convolutional neural network (CNN) model using improved EfficientNetB5. Design is …based on compound scaling and trained on the best possible feature extraction algorithm. The low convergence rate of the proposed work can be easily deployed into limited computational resources. It will be helpful for the rapid triaging of victims. 2-fold cross-validation further improves the performance. The algorithm proposed is trained, validated, and testing is performed in the form of internal and external validation on a self-collected and compiled a real-time dataset of CXR. The training dataset is relatively extensive compared to the existing ones. The performance of the proposed technique is measured, validated, and compared with other state-of-the-art pre-trained models. The proposed methodology gives remarkable accuracy (99.5%) and recall (99.5%) for biclassification. The external validation using two different test dataset also give exceptional predictions. The visual depiction of predictions is represented by Grad-CAM maps, presenting the extracted features of the predicted results. Show more

Keywords: COVID-19, Chest X-rays, Deep learning, EfficientNets

DOI: 10.3233/JIFS-223704

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7887-7907, 2023

Authors: Suresh Kumar, M. | Sathish Kumar, G.A.

Article Type: Research Article

Abstract: This paper aims to formulate an enhanced ant colony optimization algorithm that helps find a suitable data route that improves energy efficiency and reduces the time consumption for the delivery of packets. Energy equations have been framed to analyze energy used at the transmitting, receiving, and relay nodes. The network is segmented into smaller virtual segments for easier analysis of the proposed algorithm. Each segment is assumed to have nodes with a cluster head. The sink gathers information from different cluster heads as it moves from one segment to another. The nodes not in close connection with the network used …the overhearing mechanism to share the information with the sink. The simulation has been done using Cisco Packet Tracer software. The proposed algorithm has been applied to different types of wireless networks to determine their efficiency. The wireless networks considered for this purpose are Bluetooth, Wi-Max, and Wi-Fi. Packet delivery ratio, end-to-end delay, collision, and lifetime are evaluated for the different types of wireless networks. The obtained results are analyzed, and graphs are plotted. Show more

Keywords: Ant colony optimization, energy efficient, mobile sink, wireless sensor networks, packet delivery ratio, WiFi, WiMax, energy

DOI: 10.3233/JIFS-221856

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7909-7917, 2023

Authors: Qi, Ju

Article Type: Research Article

Abstract: In the big data and “Internet+” era, the research related cybersecurity risk has attracted much attention. However, Premium pricing for cybersecurity insurance remains in its early days. In this paper, we established a premium pricing method for cybersecurity risks. Firstly, the losses during the cyber infection is modeled by an interacting Markov SIS (Susceptible-Infected-Susceptible) epidemic model. we also proposed a premium simulation method called the Gillespie algorithm, which can be used for simulation of a continuous-time stochastic process. At last, as an example, we calculated the premiums by using premium principles and simulation in a simple network respectively. The numerical …case studies demonstrate the premium pricing model performs well, and the premiums based on simulations are rather conservative, and recommended using in practice by comparing the results of premiums. Show more

Keywords: SIS epidemic model, cybersecurity insurance, premium pricing, renewal reward process, gillespie simulation algorithm

DOI: 10.3233/JIFS-222308

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7919-7933, 2023

Authors: Hu, Limei

Article Type: Research Article

Abstract: The traditional failure mode and effect analysis (FMEA), as an effective risk analysis technique, has several limitations in the uncertainty modeling and the weights determination of the risk indicators. This paper aims to propose a hybrid risk prioritization method simultaneously considering the characteristics of the reliability associated with the FMEA team members’ evaluation information and their psychological behavior to enhance the performance of the traditional FMEA model. The hybrid risk prioritization method is developed based on the generalized TODIM method and the weighted entropy measure with the linguistic Z-numbers (LZNs). First, the LZNs are adopted to depict the FMEA team …members’ cognition information and the reliability of these information. Second, a weighted entropy measure based on the fuzzy entropy and the LZNs is developed to obtain the risk indicators’ weights. Finally, the generalized TODIM method with the LZNs is constructed to obtain the risk priority orders of failure modes, which can effectively simulate the FMEA team members’ psychological character. The applicability and effectiveness of the proposed risk prioritization method is validated through an illustrative example of an integrated steel plant. The results of sensitivity analysis and comparative analysis indicate that the proposed hybrid risk prioritization method is effective and valid, and can get more accurate and practical risk ranking results to help enterprises formulate accurate risk prevention and control plans. Show more

Keywords: INDEX TERMS: Failure mode and effect analysis, risk prioritization, Linguistic Z-numbers, fuzzy entropy, the generalized TODIM method

DOI: 10.3233/JIFS-223132

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7935-7955, 2023

Authors: Parida, Subhashree | Acharya, Milu | Patnaik, Srikanta

Article Type: Research Article

Abstract: In the present era, the most delicate environmental issue is global warming, and because of this, countries across the globe are trying to manage the most hazardous emissions by making certain investments in projects to promote green industrial practices. The proposed study creates sustainable deteriorating inventory models in both crisp and fuzzy environments, with both cloudy and intuitionistic fuzzy considerations, where the demand is taken to be time-dependent. In the current study, the emission of CO2 from transportation is controlled by the optimum investments in green technology (GT). This work develops the previous research that has worked on a …sustainable inventory system with controllable greenhouse facilities through green investment. The present research includes an optimum GT investment in an inventory system with two warehouses to restrict carbon emissions due to the transportation of goods from owned to rented warehouses and then to customers. To have control over the total cost, this work considers two warehouses to manage the stock-out conditions and represents models with shortages for crisp, cloudy fuzzy (CF), and intuitionistic fuzzy (IF) environments. A multiple prepayment option for the purchasing cost involving an installment is provided to the retailers. In the present research, we develop non-linear crisp and fuzzy deteriorating inventory models and suggest an algorithm for the solution process. Model problems are illustrated through numerical examples and validated through sensitivity analysis. A comparative inquiry is conducted for the optimum results obtained in all three cases. Show more

Keywords: Carbon emissions, green technology, disposal cost, cloudy fuzzy, intuitionistic fuzzy, ranking method

DOI: 10.3233/JIFS-223385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7957-7976, 2023

Authors: Khan, Izaz Ullah | Shah, Jehanzeb Ali | Bilal, Muhammad | Faiza, | Khan, Muhammad Saqib | Shah, Sajid | Akgül, Ali

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-220781

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7977-7993, 2023

Authors: Sarfaraz, Amir Homayoun | Yazdi, Amir Karbassi | Hanne, Thomas | Hosseini, Raheleh Sadat

Article Type: Research Article

Abstract: Technology transfer plays an essential role in developing an organization’s capabilities to perform better in the market. Several protocols are defined for technology transfer. One of the main techniques in technology transfer is licensing, which significantly impacts profit and income. This study intends to develop a decision framework that integrates both a Fuzzy Inference System (FIS) and a two steps Fuzzy Quality Function Deployment (F-QFD) to assist an organization in selecting a licensor. To illustrate the decision framework’s performance, it has been implemented in an Iranian lubricant producer to select the best licensor among the 13 targeted companies. A complete …product portfolio, brand image enhancement, increasing the market share of the high-value products, and improving the technical knowledge of manufacturing products were identified as the most important expectations of the licensees. A sensitivity analysis for the recommended framework has been conducted. For doing so, 27 rules of the FIS were categorized into four group and then changed. The results are compared using the Pearson correlation coefficient. Inference rules detect unconventional changes, while logical changes are appropriately considered. Show more

Keywords: Technology transfer, licensing, fuzzy inference system, fuzzy quality function deployment, fuzzy QFD

DOI: 10.3233/JIFS-222232

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7995-8014, 2023

Authors: Monika, | Singh, Pardeep | Chand, Satish

Article Type: Research Article

Abstract:  Pedestrians are the most critical and vulnerable moving objects on roads and public areas. Learning pedestrian movement in these areas can be helpful for their safety. To improve pedestrian safety and enable driver assistance in autonomous driver assistance systems, recognition of the pedestrian direction of motion plays an important role. Pedestrian movement direction recognition in real world monitoring and ADAS systems are challenging due to the unavailability of large annotated data. Even if labeled data is available, partial occlusion, body pose, illumination and the untrimmed nature of videos poses another problem. In this paper, we propose a framework that considers …the origin and end point of the pedestrian trajectory named origin-end-point incremental clustering (OEIC). The proposed framework searches for strong spatial linkage by finding neighboring lines for every OE (origin-end) lines around the circular area of the end points. It adopts entropy and Q measure for parameter selection of radius and minimum lines for clustering. To obtain origin and end point coordinates, we perform pedestrian detection using the deep learning technique YOLOv5, followed by tracking the detected pedestrian across the frame using our proposed pedestrian tracking algorithm. We test our framework on the publicly available pedestrian movement direction recognition dataset and compare it with DBSCAN and Trajectory clustering model for its efficacy. The results show that the OEIC framework provides efficient clusters with optimal radius and minlines. Show more

Keywords: Unsupervised learning, line clustering method, origin-end point, pedestrian direction of motion, YOLOv5

DOI: 10.3233/JIFS-223283

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8015-8027, 2023

Authors: Zhang, Mingyue | Zhou, Zhiheng | Tao, Xiyuan | Zhang, Na | Deng, Ming

Article Type: Research Article

Abstract: The modern world contains a significant number of applications based on computer vision, in which human-computer interaction plays a crucial role, pose estimation of the hand is a crucial approach in the field of human-computer interaction. However, previous approaches suffer from the inability to accurately measure position in real-world scenes, difficulty in obtaining targets of different sizes, the structure of complex network, and the lack of applications. In recent years, deep learning techniques have produced state-of-the-art outcomes but there are still challenges that need to be overcome to fully exploit this technology. In this research, a fish skeleton CNN (FS-HandNet) …is proposed for hand posture estimation from a monocular RGB image. To obtain hand pose information, a fish skeleton network structure is used for the first time. Particularly, bidirectional pyramid structures (BiPS) can effectively reduce the loss of feature information during downsampling and can be used to extract features from targets of different sizes. It is more effective at solving problems of different sizes. Then a distribution-aware coordinate representation is employed to adjust the position information of the hand, and finally, a convex hull algorithm and hand pose information are applied to recognize multiple gestures. Extensive studies on three publicly available hand position benchmarks demonstrate that our method performs nearly as well as the state-of-the-art in hand pose estimation. Additionally, we have implemented hand pose estimation for the application of gesture recognition. Show more

Keywords: Hand pose estimation, FS-HandNet, distribution-aware coordinate representation, convex hull algorithm, the application of gesture recognition

DOI: 10.3233/JIFS-224271

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8029-8042, 2023

Authors: Guo, Xiaobin | Liu, Kun

Article Type: Research Article

Abstract: This paper discusses a new approximate solution of a class of fully fuzzy linear systems A ˜ x ˜ = b ˜ in which the coefficient matrix A ˜ is a positive fuzzy matrix. The original fuzzy linear systems is extended into simple crisp linear equation using the obtained approximate multiplication of positive fuzzy number and near zero fuzzy number. Two cases are analysed: (a) the unknown vector x ˜ is a near zero fuzzy vector with positive mean value; …(b) the unknown vector x ˜ is a near zero fuzzy vector with negative mean value. Two computing models are established and respective expression of the solution to fully fuzzy linear system are derived, and the sufficient condition for the existence of strong fuzzy solution are analyzed correspondingly. Some numerical examples are given to illustrated our proposed method. Show more

Keywords: Fuzzy numbers, matrix computation, fuzzy linear systems, fuzzy approximate solutions

DOI: 10.3233/JIFS-222421

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8043-8052, 2023

Authors: Ma, Hua | Tang, Yuqi | Zhang, Xuxiang | Zhu, Haibin | Huang, Peiji | Zhang, Hongyu

Article Type: Research Article

Abstract: An e-learning system should recommend learners appropriate learning resources according to their actual needs and cognitive status for improving their learning performance. To overcome the deficiencies of existing approaches (e.g., poor interpretability, limited efficiency and accuracy of recommendation), we propose a new recommendation approach to learning resources via knowledge graphs and learning style clustering. In this approach, the knowledge graphs of an online learning environment are constructed based on a generic ontology model, and the graph embedding algorithm and graph matching process are applied to optimize the efficiency of graph computation for identifying similar learning resources. By introducing learning style …theory, learners are clustered based on their learning styles. Based on the clustering results, the learners’ degrees of interest in similar learning resources are measured, and the recommendation results are obtained according to the degrees of interest. Finally, the experiments demonstrate that the proposed approach significantly enhances the computational efficiency and the quality of learning resource recommendation compared with the existing approaches in large-scale graph data scenarios. Show more

Keywords: Knowledge graphs, learning resource recommendation, learning style clustering, personalized learning

DOI: 10.3233/JIFS-222627

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8053-8069, 2023

Authors: Alnssyan, Badr | Hussein, Ekramy A. | Alizadeh, Morad | Afify, Ahmed Z. | Abdellatif, Ashraf D.

Article Type: Research Article

Abstract: We propose a new wider family called the weighted Lindley-G family. We derive some mathematical properties and special sub-models of the new family. We address the estimation of the model parameters by eight approaches of estimation. The estimation approaches are ranked and compared by using detailed simulations to develop a guideline for choosing the best approach for estimating the distribution parameters. The potentiality of the new family is illustrated via two applications to real-life data. It is shown that the proposed WLi-G family is more flexible as compared to some of the most cited families in the distribution theory literature …such as the exponentiated-G, beta-G, transmuted-G, and alpha-power-G families under the same baseline model. Show more

Keywords: Rényi entropy, exponential distribution, data analysis, Anderson–Darling estimation, maximum likelihood

DOI: 10.3233/JIFS-222758

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8071-8089, 2023

Authors: Duan, Chen | Liu, Yongli

Article Type: Research Article

Abstract: In fuzzy clustering algorithms, the possibilistic fuzzy clustering algorithm has been widely used in many fields. However, the traditional Euclidean distance cannot measure the similarity between samples well in high-dimensional data. Moreover, if there is an overlap between clusters or a strong correlation between features, clustering accuracy will be easily affected. To overcome the above problems, a collaborative possibilistic fuzzy clustering algorithm based on information bottleneck is proposed in this paper. This algorithm retains the advantages of the original algorithm, on the one hand, using mutual information loss as the similarity measure instead of Euclidean distance, which is conducive to …reducing subjective errors caused by arbitrary choices of similarity measures and improving the clustering accuracy; on the other hand, the collaborative idea is introduced into the possibilistic fuzzy clustering based on information bottleneck, which can form an accurate and complete representation of the data organization structure based on make full use of the correlation between different feature subsets for collaborative clustering. To examine the clustering performance of this algorithm, five algorithms were selected for comparison experiments on several datasets. Experimental results show that the proposed algorithm outperforms the comparison algorithms in terms of clustering accuracy and collaborative validity. Show more

Keywords: Possibilistic fuzzy clustering, collaborative clustering, information bottleneck, similarity measure

DOI: 10.3233/JIFS-223854

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8091-8102, 2023

Authors: Fu, Xingyu | Chen, Yingyue | Yan, Jingru | Chen, Yumin | Xu, Feng

Article Type: Research Article

Abstract:  The random forest is a combined classification method belonging to ensemble learning. The random forest is also an important machine learning algorithm. The random forest is universally applicable to most data sets. However, the random forest is difficult to deal with uncertain data, resulting in poor classification results. To overcome these shortcomings, a broad granular random forest algorithm is proposed by studying the theory of granular computing and the idea of breadth. First, we granulate the breadth of the relationship between the features of the data sets samples and then form a broad granular vector. In addition, the operation rules …of the granular vector are defined, and the granular decision tree model is proposed. Finally, the multiple granular decision tree voting method is adopted to obtain the result of the granular random forest. Some experiments are carried out on several UCI data sets, and the results show that the classification performance of the broad granular random forest algorithm is better than that of the traditional random forest algorithm. Show more

Keywords: Granular computing, Broad granular vector, Granular decision tree, Granular random forest, Classification

DOI: 10.3233/JIFS-223960

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8103-8117, 2023

Authors: Sun, Qiong | Jiang, Jingjing | Wang, Zhongsheng | Liao, Bin

Article Type: Research Article

Abstract: By using the interactive big data between enterprises and stakeholders in social media, this paper investigates the views of different stakeholders on the disclosure of enterprise digital transformation. In view of the social media platform brings together different stakeholders, this paper uses the organizational hypocrisy theory to explore the stakeholders’ Reflection on the hypocritical speech, decision-making and action strategies adopted in the disclosure of enterprise digital transformation. Through data mining and computer-aided emotion analysis, the posts of sina Weibo’s top 500 Chinese enterprises from December 31, 2020 to December 31, 2021 and the reactions of stakeholders are retrieved and analyzed. …It is found that stakeholders have different reactions to the hypocrisy strategies of enterprises. Although stakeholders pay more attention to information related to actions, and the disclosure of such actions will cause positive and negative reactions, the inconsistency of speech and decision-making will produce positive reactions and reduce negative impressions. Overall, research shows that the use of organizational hypocrisy strategies in social media can enable enterprises to manage the views and legitimacy of stakeholders. Show more

Keywords: Organizational hypocrisy, digital transformation, stakeholder, emotional analysis

DOI: 10.3233/JIFS-224092

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8119-8132, 2023

Authors: Geng, Xiaonan | Liu, Peng

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-224316

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8133-8145, 2023

Authors: Gong, Zengtai | Wang, Fangdi

Article Type: Research Article

Abstract: Complex fuzzy set, as an extension of classical fuzzy sets, could describe the fuzzy characters of things more detail and comprehensively and is very useful in dealing with vagueness and uncertainty of problems that include the periodic or recurring phenomena. Note that a complex fuzzy set is different from the fuzzy complex set introduced and discussed by many scholars, since the membership degree of a complex fuzzy set is a complex number with length less than or equal to 1 while a fuzzy complex set is a real number with membership degree less than or equal to 1, and the …universe is the complex plane. As the mathematical theoretical basis of fuzzy mathematics, fuzzy set and its mapping, corresponding fuzzy complex set and its mapping have been investigated in depth because they integrate and cross the methods and results of classical real analysis and complex analysis. However, there is no comprehensive investigation on complex fuzzy set and its corresponding mathematical theory, even include decomposition theorems, extension principles and the basic operations of the complex fuzzy set. As is well known, the cut set of fuzzy sets is the bridge between fuzzy sets and classical sets, which plays a significant role in fuzzy sets and fuzzy systems. In this paper, the concept of (r , θ)-cut sets of complex fuzzy sets is proposed and their properties are discussed. Meanwhile, the decomposition theorems and the extension principles of complex fuzzy set based on (r , θ)-cut sets are deduced and corresponding properties are investigated. All these conclusions not only deeply enrich the fundamental theory of complex fuzzy set, but also provide a powerful tool to investigate complex fuzzy set. Finally, an example application of signal detection demonstrates the utility of the (r , θ)-cut sets of complex fuzzy sets in practice. Show more

Keywords: Complex fuzzy set, (r, θ)-cut sets, decomposition theorems, extension principles

DOI: 10.3233/JIFS-221639

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8147-8162, 2023

Authors: Wang, Wenpu | Lin, Wei | Gao, Fengxiang | Chang, Shuli

Article Type: Research Article

Abstract: Business English teaching quality evaluation Business English is a new type of composite specialty, which is a discipline innovation made by China’s higher education to adapt to the new market demand and international standards since the reform and opening up. Over the past 20 years, it has cultivated a number of compound talents for the cause of China’s reform and opening up. However, the backwardness of business English theoretical research has greatly restricted the development of business English. At present, Business English has been officially approved as a new major for undergraduate enrollment by the Ministry of Education of the …People’s Republic of China. Its subject nature, specialty structure, training objectives, and specialty compound characteristics need to be qualitatively studied theoretically. The business English teaching quality evaluation is viewed as the multiple attribute decision making (MADM) issue. In this paper, we connect the geometric Heronian mean (GHM) operator and power geometric (PG) with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose the generalized 2-tuple linguistic neutrosophic power geometric HM (G2TLNPGHM) operator. Then, the G2TLNGHM operator is applied to deal with the MADM problems under 2TLNNs. Finally, an example for business English teaching quality evaluation is used to show the proposed methods. Some comparative analysis and parameter influence analysis are fully given. The results show that the built algorithms method is useful for business English teaching quality evaluation. Show more

Keywords: Multiple attribute decision making (MADM), 2-tuple linguistic neutrosophic numbers set (2TLNNSs), G2TLNPGHM operator, business English teaching quality

DOI: 10.3233/JIFS-223850

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8163-8175, 2023

Authors: Singh, Pardeep | Singh, Nitin Kumar | Monika, | Chand, Satish

Article Type: Research Article

Abstract: One major issue plaguing online social media is hate speech, a complex phenomenon whose identification and target categorization have been studied by the natural language processing community. In recent years, notable studies have been made towards hate speech detection using various mechanisms varying from traditional machine learning to complex deep neural network models. However, these studies mainly focus on high-resource English language. The multilingual societies such as the Indian subcontinent: English, Hindi and Hindi-English code-mixed languages are widespread and convenient for the users. The research works studying hate speech detection in these languages are still very limited. To fill this …gap, we propose an mBERT-GRU framework comprising of multilingual BERT embedding and bidirectional GRU layers to learn the cumulative features for hate speech detection and its target categorization. We evaluated our work on three datasets HASOC-2019, HS and HEOT to prove the competitive performance. Our results show that the proposed framework outperformed monolingual and state-of-the-art methods on English, Hindi and Hindi-English code-mixed datasets with Macro-F1 measure values of 0.87, 0.83 and 0.77, respectively. Show more

Keywords: Deep learning, GRU, hate speech detection, multilingual BERT, social media, text analysis

DOI: 10.3233/JIFS-222057

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8177-8192, 2023

Authors: Chughtai, Iqra Toheed | Naseer, Asma | Tamoor, Maria | Asif, Saara | Jabbar, Mamoona | Shahid, Rabia

Article Type: Research Article

Abstract: In the past few years, due to the increased usage of internet, smartphones, sensors and digital cameras, more than a million images are generated and uploaded daily on social media platforms. The massive generation of such multimedia contents has resulted in an exponential growth in the stored and shared data. Certain ever-growing image repositories, consisting of medical images, satellites images, surveillance footages, military reconnaissance, fingerprints and scientific data etc., has increased the motivation for developing robust and efficient search methods for image retrieval as per user requirements. Hence, it is need of the hour to search and retrieve relevant images …efficiently and with good accuracy. The current research focuses on Content-based Image Retrieval (CBIR) and explores well-known transfer learning-based classifiers such as VGG16, VGG19, EfficientNetB0, ResNet50 and their variants. These deep transfer leaners are trained on three benchmark image datasets i.e., CIFAR-10, CIFAR-100 and CINIC-10 containing 10, 100, and 10 classes respectively. In total 16 customized models are evaluated on these benchmark datasets and 96% accuracy is achieved for CIFAR-10 while 83% accuracy is achieved for CIFAR-100. Show more

Keywords: CBIR, transfer learning, CNN, VGG-16, VGG-19, ResNet-50, EfficientNet, deep learning

DOI: 10.3233/JIFS-223449

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8193-8218, 2023