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

Authors: Xu, Meiling | Fu, Yongqiang | Tian, Boping

Article Type: Research Article

Abstract: The fraud problem has drastically increased with the rapid growth of online lending. Since loan applications, approvals and disbursements are operated online, deceptive borrowers are prone to conceal or falsify information to maliciously obtain loans, while lenders have difficulty in identifying fraud without direct contacts and lack binding force on customers’ loan performance, which results in the frequent occurrence of fraud events. Therefore, it is significant for financial institutions to apply valuable data and competitive technologies for fraud detection to reduce financial losses from loan scams. This paper combines the advantages of statistical methods and ensemble learning algorithms to design …the grouped trees and weighted ensemble algorithm (GTWE), and establishes fraud prediction models for online loans based on mobile application usage behaviors(App behaviors) by logistic regression, extreme gradient boosting (XGBoost), long short-term memory (LSTM) and the GTWE algorithm, respectively. The experimental results show that the fraud prediction model based on the GTWE algorithm achieves outstanding classification effect and stability with satisfactory interpretability. Meanwhile, the fraud probability of customers detected by the fraud prediction model is as high as 84.19%, which indicates that App behaviors have a considerable impact on predicting fraud in online loan application. Show more

Keywords: Fraud prediction, mobile application usage behaviors, extreme gradient boosting, long short-term memory, grouped trees, weighted ensemble algorithm

DOI: 10.3233/JIFS-222405

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7181-7194, 2023

Authors: Zhang, Ruifan | Wang, Hao | Yang, Gongping

Article Type: Research Article

Abstract: Embedding similarity-based methods obtained good results in unsupervised anomaly detection (AD). This kind of method usually used feature vectors from a model pre-trained by ImageNet to calculate scores by measuring the similarity between test samples and normal samples. Ultimately, anomalous regions are localized based on the scores obtained. However, this strategy may lead to a lack of sufficient adaptability of the extracted features to the detection of anomalous patterns for industrial anomaly detection tasks. To alleviate this problem, we design a novel anomaly detection framework, MFFA, which includes a pseudo sample generation (PSG) block, a local-global feature fusion perception (LGFFP) …block and an anomaly map compensation (AMC) block. The PSG block can make the pre-trained model more suitable for real-world anomaly detection tasks by combining the CutPaste augmentation. The LGFFP block aggregates shallow and deep features on different patches and inputs them to CaiT (Class-attention in image Transformers) to guide self-attention, effectively interacting local and global information between different patches, and the AMC block can compensate each other for the two anomaly maps generated by the nearest neighbor search and multivariate Gaussian fitting, improving the accuracy of anomaly detection and localization. In experiments, MVTec AD dataset is used to verify the generalization ability of the proposed method in various real-world applications. It achieves over 99.1% AUROCs in detection and 98.4% AUROCs in localization, respectively. Show more

Keywords: Anomaly detection, pseudo sample, feature fusion, transformer, anomaly map compensation

DOI: 10.3233/JIFS-222595

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7195-7210, 2023

Authors: Jain, Manish | Kumar, Sanjay | Park, Choonkil

Article Type: Research Article

Abstract: The question of relaxing the compatible hypothesis of the pair of mappings in fixed point theory has always been remained an open problem. We address such an open problem raised by Choudhury et al. [4 ] and also explicitly settles the issue of monotone and continuity hypotheses of the involved mappings in coupled coincidence point results. Moreover, we state a gap in an example given in [3 ] and repair it. Application to the dynamic programming problem shows the usability of present work. Finally, we also propose an open problem for further investigation.

Keywords: GV-fuzzy metric space,φ-contractions, Hadɘić type t-norm, mixed monotone property, coupled coincidence point 2010 Mathematics Subject Classification. 47H10, 54H25.

DOI: 10.3233/JIFS-222637

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7211-7223, 2023

Authors: Angel, A. Sheeba | Jayaparvathy, R.

Article Type: Research Article

Abstract: Despite the numerous risks that high-rise buildings face, fire accidents happen most frequently. Studying fire accidents in high-rise buildings is crucial because they can result in harm to people’s health, fatalities, property damage, and pollution. The number of accidental fires in buildings is very large since it is difficult to isolate a single cause and all processes and control measures are not appropriately implemented. This paper proposes a fuzzy-bow tie approach to evaluating the risk of fire accidents by taking into account the various fire sources and effects. The fourteen-floor high-rise residential building is used as a case study for …the proposed fuzzy bow tie approach. The fuzzy fault tree approach estimates that there is a 0.0968% risk of a fire accident occurring in that high-rise building, with a possibility for 9 out of 100 accidental fires annually. The fuzzy event tree model predicts that loss of life and loss of property are the most likely consequences of an accidental fire. Accordingly, mitigation strategies can be developed by building officials and fire safety practitioners. Show more

Keywords: Risk assessment, fire alarm and control system, safety, fault tree, sensitivity analysis

DOI: 10.3233/JIFS-223307

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7225-7242, 2023

Authors: Li, Runya | Pang, Ling

Article Type: Research Article

Abstract: Remote sensing image technology is of great significance for dynamic management and monitoring of ground buildings. In order to improve the data fusion ability of remote sensing image of ground buildings, a data fusion method of remote sensing image of ground buildings based on multi-level fuzzy evaluation is proposed. This method constructs a remote sensing image acquisition model of ground buildings, and uses image enhancement methods to realize the gray information analysis and image enhancement of the remote sensing image rate of ground buildings. Finally, combining the remote sensing image data fusion method and the fuzzy region reconstruction method, it …reconstructs the pixels of the dynamically changed ground buildings. The simulation results show that the remote sensing image data fusion accuracy of ground buildings is good, and the remote sensing feature extraction accuracy of ground buildings is high. The dynamic real-time monitoring of remote sensing image of ground buildings is realized. Show more

Keywords: Multistage fuzzy evaluation, remote sensing image, data fusion, enhancement, building image

DOI: 10.3233/JIFS-223434

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7243-7255, 2023

Authors: Prabhu, Akshatha | Shobha Rani, N. | Basavaraju, H.T.

Article Type: Research Article

Abstract: One of the most essential factors in classifying and qualitatively evaluating mangoes for various industrial uses is weight. To meet grading requirements during industrial processing, this paper presents an orientation-independent weight estimation method for the mango cultivar “Alphonso.” In this study, size and geometry are considered as key variables in estimating weight. Based on the visual fruit geometry, generalized hand-crafted local and global features, and conventional features are calculated and subjected to the proposed feature selection methodology for optimal feature identification. The optimal features are employed in regression analysis to estimate the predicted weight. Four regression models –MLR, Linear SVR, …RBF SVR, and polynomial SVR—are used during the experimental trials. A self-collected mango database with two orientations per sample is obtained using a CCD camera. Three different weight estimation techniques are used in the analysis concerning orientation 1, orientation 2, and combining both orientations. The SVR RBF kernel yields a higher correlation between predicted and actual weights, and experiments demonstrate that orientation 1 is symmetric to orientation 2. By exhibiting a correlation coefficient of R2  = 0.99 with SVR-RBF for weight estimation using both orientations as well as individual orientations, it is observed that the correlation between predicted and estimated weights is nearly identical Show more

Keywords: Mass estimation, computer vision, mango processing, Alphonso mangoes, automated weight estimation

DOI: 10.3233/JIFS-223510

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7257-7275, 2023

Authors: Sahoo, Amit Kumar | Mishra, Sudhansu Kumar | Acharya, Deep Shekhar | Sahu, Sitanshu Sekhar | Paul, Sanchita | Gupta, Vikash Kumar

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-222238

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7277-7289, 2023

Authors: Cui, Wanqiu | Wang, Dawei | Feng, Wengang

Article Type: Research Article

Abstract: Image semantic learning techniques are crucial for image understanding and classification. In social networks, image data is widely disseminated thanks to convenient acquisition and intuitive expression characteristics. However, due to the freedom of users to publish information, the image has apparent context dependence and semantic fuzziness, which brings difficulties to image representation learning. Fortunately, social attributes such as hashtags carry rich semantic relations, which can be conducive to understanding the meaning of images. Therefore, this paper proposes a new method named Social Heterogeneous Graph Networks (SHGN) for image semantic learning in social networks. First, a heterogeneous graph is built to …expand image semantic relations by social attributes. Then the consistent semantic space is reconstructed through cross-media feature alignment. Finally, an image semantic extended learning network is designed to capture and integrate the social semantics and visual feature, which obtains a rich semantic representation of images from a social context. The experiments demonstrate that SHGN can achieve efficient image representation, and favorably against many baseline algorithms. Show more

Keywords: Social networks image, representation learning, heterogeneous graph, social semantic aggregation

DOI: 10.3233/JIFS-222981

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7291-7304, 2023

Authors: Manikandan, N.K. | Kavitha, M.

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-213422

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7305-7318, 2023

Authors: Ge, Liang | Jia, Yixuan | Li, Qinhong | Ye, Xiaofeng

Article Type: Research Article

Abstract:  Traffic speed prediction is a crucial task of the intelligent traffic system. However, due to the highly nonlinear temporal patterns and non-static spatial dependence of traffic data, timely and accurate traffic forecasting remains a challenge. The existing methods usually use a static adjacency matrix to model spatial dependence while ignoring the spatial dynamic characteristics of the road network.Meanwhile, the dynamic influence of different time steps on the prediction target is ignored. Thus, we propose a dynamic multi-graph convolution recurrent neural network (DMGCRNN), which models the dynamic correlations of road networks over time based on various information of road network. Dynamic …correlation is an essential factor for accurate traffic prediction, because it reflects the change of the traffic conditions in real-time. In this model, we design a dynamic graph construction method, which utilizes the local temporal and spatial characteristics of each road segment to construct dynamic graphs. Then, a dynamic multi-graph convolution fusion module is proposed, which considers the dynamic characteristics of spatial correlations and global information to model the dynamic trend of spatial dependence. Moreover, by combing the global context information, temporal attention is provided to capture the dynamic temporal dependence among different time steps. The experimental results from two real-world traffic datasets demonstrate that our method outperforms the state-of-the-art baselines. Show more

Keywords: Traffic speed prediction, dynamic graph construction, Spatio-temporal dependence

DOI: 10.3233/JIFS-222857

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7319-7332, 2023

Authors: Simin, Wang | Lulu, Qin | Chunmiao, Ma | Weiguo, Wu

Article Type: Research Article

Abstract: With the rapid development of cloud computing, there are more and more large-scale data centers, which makes the energy management of data centers more complex. In order to achieve better energy-saving effect, it is necessary to solve the problems of concurrent management and interdependence of IT, refrigeration, storage, and network equipment. Reinforcement learning learns by interacting with the environment, which is a good way to realize the independent management of the data center. In this paper, a overall energy consumption method for data center based on deep reinforcement learning is proposed to achieve collaborative energy saving of data center task …scheduling and refrigeration equipment. A new multi-agent architecture is proposed to separate the training process from the execution process, simplify the interaction process during system operation and improve the operation effect. In the deep learning stage, a hybrid deep Q network algorithm is proposed to optimize the joint action value function of the data center and obtain the optimal strategy. Experiments show that compared with other reinforcement learning methods, our method can not only reduce the energy consumption of the data center, but also reduce the frequency of hot spots. Show more

Keywords: Energy consumption, data center, job scheduling, cooling system, deep reinforcement learning, multi-agent system

DOI: 10.3233/JIFS-223769

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7333-7349, 2023

Authors: Hu, Wujin | Shao, Yi | Liu, Yefei

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-224539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7351-7365, 2023

Authors: Zhang, Yu | Liu, Fan | Hu, Yupeng | Li, Xiaoli | Dong, Xiangjun | Cheng, Zhiyong

Article Type: Research Article

Abstract: Cross-domain recommendation aims to alleviate the target domain’s data sparsity problem by leveraging source domain knowledge. Existing GCN-based approaches perform graph convolution operations in each domain separately. However, the direct effect of item feature and topological structure information in the source domain are neglected for user preference modeling in the target domain. In this paper, we propose a novel Dual Attentive Graph Convolutional Network for Cross-Domain Recommendation (DAG4CDR). Specifically, we integrate the source and target domain’s interaction data to construct a unified user-item bipartite graph and then perform GCN propagation on the graph to learn user and item embeddings. Over …the unified graph, the interaction data from both domains can be leveraged to learn user and item embeddings via information propagation. In the embedding aggregation phase, the messages passed from different items of two domains to users are weighted by a designed dual attention mechanism, which considers the contributions of different items from both node- and domain-level. We conducted extensive experiments to validate the effectiveness of our method on several publicly available datasets, and the results demonstrate the superiority of our model on preference modeling for both common and non-common users. Show more

Keywords: Cross-domain recommendation, graph convolutional network, attention mechanism

DOI: 10.3233/JIFS-222411

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7367-7378, 2023

Authors: Yousif, Majeed A. | Hamasalh, Faraidun K.

Article Type: Research Article

Abstract: In this paper, a novel numerical scheme is developed using a new construct by non-polynomial spline for solving the time fractional Generalize Fisher equation. The proposed models represent bacteria, epidemics, Brownian motion, kinetics of chemicals and fuzzy systems. The basic concept of the new approach is constructing a non-polynomial spline with different non-polynomial trigonometric and exponential functions to solve fractional differential equations. The investigated method is demonstrated theoretically to be unconditionally stable. Furthermore, the truncation error is analyzed to determine the or-der of convergence of the proposed technique. The presented method was tested in some examples and compared graphically with …analytical solutions for showing the applicability and effectiveness of the developed numerical scheme. In addition, the present method is compared by norm error with the cubic B-spline method to validate the efficiency and accuracy of the presented algorithm. The outcome of the study reveals that the developed construct is suitable and reliable for solving nonlinear fractional differential equations. Show more

Keywords: Non-polynomial spline, generalize fisher equation, truncation error, stability analysis

DOI: 10.3233/JIFS-222445

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7379-7389, 2023

Authors: Tuo, Meimei | Yang, Wenzhong

Article Type: Research Article

Abstract: In today’s big data era, there are a large number of unstructured information resources on the web. Natural language processing researchers have been working hard to figure out how to extract useful information from them. Entity Relation Extraction is a crucial step in Information Extraction and provides technical support for Knowledge Graphs, Intelligent Q&A systems and Intelligent Retrieval. In this paper, we present a comprehensive history of entity relation extraction and introduce the relation extraction methods based on Machine Mearning, the relation extraction methods based on Deep Learning and the relation extraction methods for open domains. Then we summarize the …characteristics and representative results of each type of method and introduce the common datasets and evaluation systems for entity relation extraction. Finally, we summarize current entity relation extraction methods and look forward to future technologies. Show more

Keywords: Information extraction, relation extraction, natural language processing, machine learning, deep learning

DOI: 10.3233/JIFS-223915

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7391-7405, 2023

Authors: Sun, Quan | Yang, Lichen | Li, Hongsheng | Sun, Guodong

Article Type: Research Article

Abstract: Aluminum electrolytic capacitor (AEC) is one of the most pivotal components that affect the reliability of power electronic systems. The electrolyte evaporation and dielectric degradation are the two main reasons for the parametric degradation of AEC. Remaining useful life (RUL) prediction for AEC is beneficial for obtaining the health state in advance and making reasonable maintenance strategies before the system suffers shutdown malfunction, which can increase the reliability and safety. In this paper, a hybrid machine learning (ML) model with GRU and PSO-SVR is proposed to realize the RUL prediction of AEC. The GRU is used for the recursive multi-step …prediction of AEC to model the times series of AEC, SVR optimized by PSO for hyper-parameters is applied for error compensation caused by recursive GRU. Finally, the proposed model is validated by two kinds of data sets with accelerated degradation experiments. Compared with the other methods, the results show that the proposed scheme can obtain greater prediction performance index of RUL under different prediction time points, which can support the technology of health management for power electronic system. Show more

Keywords: Aluminum electrolytic capacitor, remaining useful life, machine learning, error compensation

DOI: 10.3233/JIFS-220866

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7407-7417, 2023

Authors: Dhumras, Himanshu | Bajaj, Rakesh Kumar

Article Type: Research Article

Abstract: Systematic assessment of insufficiencies and inexactness in the information along with parametrization of multi-sub attributes is one of the substantial features in the field of decision-making. In the present communication, a new way of defining Picture Fuzzy Hypersoft Set (PFHSS) has been presented which contains an additional capacity of accommodating the components of neutral membership (abstain) and refusal compared to Intuionistic Fuzzy Hypersoft Set (IFHSS). The main objective of the present study is to establish the novelty of PFHSS with some of the basic operations and introduce various important aggregation operators. Some of the important properties and operational laws related …to the introduced picture fuzzy hypersoft weighted average/ordered weighted average operator (PFHSWA/PFHSOWA) and weighted geometric/ordered weighted geometric operator (PFHSWG/PFHSOWG) have been proved in detail. On the basis of these aggregation operators and obtained results, a new algorithm for solving a decision-making problem, involving the multi-sub attributes and their parametrization in the shade of abstain and refusal feature, has been proposed. A numerical example of the selection process of employees for a company has been solved in order to suitably ensure and validate the implementation of the proposed methodology. Some of the advantageous features of the proposed notions and algorithm have been listed along with the comparative analysis in contrast with the existing literature. Finally, the efficacy of the proposed notion and methodology has been duly concluded with the scope for future work. Show more

Keywords: Picture fuzzy set, soft set, hypersoft set, aggregation operators, decision-making

DOI: 10.3233/JIFS-222437

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7419-7447, 2023

Authors: Shamia, D. | Balasamy, K. | Suganyadevi, S.

Article Type: Research Article

Abstract: Security, secrecy, and authenticity problems have arisen as a result of the widespread sharing of medical images in social media. Copyright protection for online photo sharing is becoming a must. In this research, a cutting-edge method for embedding encrypted watermarks into medical images is proposed. The proposed method makes use of fuzzy-based ROI selection and wavelet-transformation to accomplish this. In the first step of the process, a fuzzy search is performed on the original picture to locate relevant places using the center region of interest (RoI) and the radial line along the final intensity. The suggested method takes a digital …picture and divides it into 4×4 non-overlapping blocks, with the intent of selecting low information chunks for embedding in order to maximize invisibility. By changing the coefficients, a single watermark bit may be inserted into both the left and right singular SVD matrices. The absence of false positives means the suggested technique can successfully integrate a large amount of data. Watermarks are encrypted using a pseudorandom key before being embedded. Discrete wavelet transform saliency map, block mean method, and cosine functions are used to construct an adaptively-generated pseudo-random key from the cover picture. Images uploaded to social media platforms must have a high degree of invisibility and durability. These watermarking features, however, come with a price. The optimal scaling factor is used to strike a balance between the two in the proposed system. Furthermore, the suggested scheme’s higher performance is confirmed by comparison with the latest state-of-the-art systems. Show more

Keywords: Watermarking, key component, wavelet transform, Fuzzy ROI, encryption

DOI: 10.3233/JIFS-222618

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7449-7457, 2023

Authors: Guo, Xiaobin | Chen, Ying | Zhuo, Quanxiu

Article Type: Research Article

Abstract:  In paper the generalized real eigenvalue and fuzzy eigenvector of a crisp real symmetric matrix with respect to another real symmetric matrix is studied. The original generalized fuzzy eigen problem is extended into a crisp generalized eigen problem of a real symmetric matrix with high orders using the arithmetic operation of LR fuzzy matrix and vector. Two cases are analysed: (a) the unknown eigenvalue λ is a non negative real number; (b) the unknown eigenvalue λ is a negative real number. Two computing models are established and an algorithm for finding the generalized fuzzy eigenvector of a real symmetric matrix …is derived. Moreover, a sufficient condition for the existence of a strong generalized fuzzy eigenvector is given. Some numerical examples are shown to illustrated our proposed method. Show more

Keywords: Fuzzy numbers, fuzzy eigenvectors, matrix computation, fuzzy linear systems

DOI: 10.3233/JIFS-222641

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7459-7467, 2023

Authors: Li, Xiaoning | Yu, Qiancheng | Yang, Yufan | Tang, Chen | Wang, Jinyun

Article Type: Research Article

Abstract: This paper proposes an evolutionary ensemble model based on a Genetic Algorithm (GAEEM) to predict the transmission trend of infectious diseases based on ensemble again and prediction again. The model utilizes the strong global optimization capability of GA for tuning the ensemble structure. Compared with the traditional ensemble learning model, GAEEM has three main advantages: 1) It is set to address the problems of information leakage in the traditional Stacking strategy and overfitting in the Blending strategy. 2) It uses a GA to optimize the combination of base learners and determine the sub. 3) The feature dimension of the data …used in this layer is extended based on the optimal base learner combination prediction information data, which can reduce the risk of underfitting and increase prediction accuracy. The experimental results show that the R2 performance of the model in the six cities data set is higher than all the comparison models by 0.18 on average. The MAE and MSE are lower than 42.98 and 42,689.72 on average. The fitting performance is more stable in each data set and shows good generalization, which can predict the epidemic spread trend of each city more accurately. Show more

Keywords: Evolutionary ensemble, genetic algorithm, ensemble strategy, epidemics transmission prediction

DOI: 10.3233/JIFS-222683

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7469-7481, 2023

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

Authors: Arokiaraj, S. | Viswanathan, N.

Article Type: Research Article

Abstract: Human Activity Recognition (HAR) has reached its new dimension with the support of Internet of Things (IoT) and Artificial Intelligence (AI). To observe human activities, motion sensors like accelerometer or gyroscope can be integrated with microcontrollers to collect all the inputs and send to the cloud with the help of IoT transceivers. These inputs give the characteristics such as, angular velocity of movements, acceleration and apply them for an effective HAR. But reaching high recognition rate with less complicated computational overhead still represents a problem in the research. To solve this aforementioned issue, this work proposes a novel ensembling of …Capsule Networks (CN) and modified Gated Recurrent Units (MGRU) with Extreme Learning Machine (ELM) for an effective HAR classification based on data collected using IoT systems called Ensemble Capsule Gated (ECG)-Networks (NETS). The proposed system uses Capsule networks for spatio-feature extraction and modified (Gated Recurrent Unit) GRU for temporal feature extraction. The powerful feed forward training networks are then employed to train these features for human activity recognition. The proposed model is validated on real time IoT data and WISDM datasets. Experimental results demonstrates that proposed model achieves better results comparatively higher than existing (Deep Learning) DL models. Show more

Keywords: Artificial intelligence, capsule networks, human activity recognition, internet of things, gated recurrent units and spatio-feature extraction

DOI: 10.3233/JIFS-221551

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8219-8229, 2023

Authors: Srinivasa Rao, Illapu Sankara | Rajalakshmi, N.R.

Article Type: Research Article

Abstract: Since the IPv6 Wireless Personal Area Network (6LoWPAN) can be utilized for information dissemination, this network gains significant attention in recent years. Proxy mobile IPv6 (PMIPv6) is standard for mobility control based on network at entire IP wireless applications. But, group-based body area networks cannot respond effectively. A new improved group flexibility system decrease the number of control messages contain router requests as well as advertising messages when compared to the group-based PMIPv6 protocol, in order to minimize delay and signaling costs. The IEEE 802.15.4 standard for low-power personal area networks (6LoWPAN) complies through IPv6-compliant MAC and physical layers. If …the default parameters, excessive collisions, packet loss, and great latency occur arbitrarily in high traffic by default MAC parameters while using a great number of 6LoWPAN nodes. The implemented Whale optimization algorithm is based on artificial neural network optimization, genetic algorithm or particle swarm optimization to choose and authenticate MAC parameters. This manuscript proposes a novel intelligent method for choosing optimally configured MAC 6LoWPAN layer set parameters. Results of simulations based on the metrics such as Average delay time (ADT), Average signaling cost, Delivery ratio, Energy consumption, Latency, Network Life time (Nlt), Packet Overhead (PO), Packet loss. The performance of the proposed method provides 19.08%, 25.87%, 31.98%, 26.98%, 31.98%, 26.98% and 23.89% lower Latency, 12.67%, 25.98%, 31.98%, 26.98%, 27.98%, 31.97% and 27.85% lower Packet Overhead and 19.78%, 27.96%, 37.98%, 18.09%, 28.97%, 27.98% and 56.04% higher Delivery ratio compared with the existing methods such as 6LoWPAN-NUM-OHCA-FFA, 6LoWPAN-GTCCF-PSO, 6LoWPAN- DODAG-ACO, 6LoWPAN- MAC-GA-PSO, 6LoWPAN-NCG-DTC-NGIPSA and 6LoWPAN-TDMA-GTS-SHJA algorithms respectively. Show more

Keywords: Artificial neural networks, genetic algorithm, low power personal areas network (6LoWPAN), medium access control protocols (MAC Proxy mobile IPv6 (PMIPv6), particle swarm optimization and Whale optimization algorithm (WOA)

DOI: 10.3233/JIFS-222956

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8231-8255, 2023

Authors: Liu, Zhichao

Article Type: Research Article

Abstract: In the 75th session of the United Nations General Assembly, the Chinese government first proposed the goal of carbon neutrality and carbon peaking. Since then, China’s economy and society have undergone a comprehensive green and sustainable development upgrade and transformation. The development of green finance can provide financial support for achieving dual carbon goals and mitigate the impact of climate change. More importantly, it can contribute to the national economy’s and society’s sustainable development. We innovatively draw on the quality function deployment theory in marketing to logically formulate the research idea of this paper. On this basis, we also apply …the G1-entropy method from fuzzy mathematical theory for quantitative research. We innovatively address the actual national conditions in China and fully integrate green elements in constructing the index system from green finance and sustainability perspectives. Finally, we calculate index weights through G1-entropy quantification to assess the development quality of China’s green financial system and qualitatively propose countermeasures for the quality of China’s green financial development with respect to key index factors. Specifically, we sort out this paper in the following three aspects: (1) we innovatively combined the quality function deployment theory and built the quantitative analysis process architecture in this paper, which enhanced the readability of this paper (2) we realized the use of quantitative research for qualitative analysis and proposed the G1-entropy value method, which made up for the defects of the subjective and objective methods in the traditional assessment methods (3) we realized the organic combination of quantitative and qualitative analysis and proposed relevant countermeasure suggestions based on the quantitative index calculation results, which provided relevant countermeasure suggestions for promoting the sustainable and high-quality development of green finance in China. Our study will provide a set of perfect assessment methods for the quality improvement path and sustainable development strategy formulation after the construction of China’s future green financial system. It can also provide a reference assessment idea for the high-quality and sustainable development of China’s green finance, which will further help China’s economic transition to green and low-carbon and the achievement of the double carbon goal. Show more

Keywords: Economic quality assessment, dual carbon context, quality function deployment theory, G1-entropy value method, green financial system

DOI: 10.3233/JIFS-222935

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8257-8280, 2023

Authors: Wang, Shuai | Ning, Yufu | Huang, Hong | Chen, Xiumei

Article Type: Research Article

Abstract: Uncertain least squares estimation is one of the important methods to deal with imprecise data, which can fully consider the influence of given data on regression equation and minimize the absolute error. In fact, some scientific studies or observational data are often evaluated in terms of relative error, which to some extent allows the error of the forecasting value to vary with the size of the observed value. Based on the least squares estimation and the uncertainty theory, this paper proposed the uncertain relative error least squares estimation model of the linear regression. The uncertain relative error least squares estimation …minimizes the relative error, which can not only solve the fitting regression equation of the imprecise observation data, but also fully consider the variation of the error with the given data, so the regression equation is more reasonable and reliable. Two numerical examples verified the feasibility of the uncertain relative error least squares estimation, and compared it with the existing method. The data analysis shows that the uncertain relative error least squares estimation has a good fitting effect. Show more

Keywords: Relative error least squares estimation, relative error, least squares estimation, uncertainty theory

DOI: 10.3233/JIFS-222955

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8281-8290, 2023

Authors: Cui, Zhexin | Yue, Jiguang | Tao, Wei | Xia, Qian | Wu, Chenhao

Article Type: Research Article

Abstract: Collaboration is essential to improve the efficiency of product research and development (R&D), shorten the R&D cycle, and reduce the R&D costs in complex product lifecycle model management (CPLMM). However, disorganized processes and the unreliability of the result evaluation remain enormous challenges for efficient collaboration. This article proposes an active-passive collaboration mechanism to enable a regulated collaboration system, which can direct the self-organized collaboration of stakeholders. C-D-Petri Net is presented for the formal collaboration process modeling. The result evaluation in active-passive collaboration involves multi-source knowledge across disciplines and phases. To address the unreliable collaboration evaluation (Co-evaluation) caused by insufficient evaluation …knowledge and weak correlation between expertise and evaluation task, the collaborative fuzzy comprehensive evaluation (CFCE) model is established to support Co-evaluation actions, and its core improvement lies in the definition and introduction of collaboration volume. Finally, a simulated aircraft horizontal tail control system is regarded as an engineering application case to demonstrate and verify the effectiveness of the proposed method. Show more

Keywords: Active-passive collaboration, Knowledge-related fuzzy evaluation, C-D-Petri Net, Collaboration volume

DOI: 10.3233/JIFS-223978

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8291-8308, 2023

Authors: Li, Yufeng | Xu, Keyi | Ding, Yumei | Sun, Zhiwei | Ke, Ting

Article Type: Research Article

Abstract: Many traditional clustering algorithms are incapable of processing mixed-type datasets in parallel, limiting their applications in big data. In this paper, we propose a CF tree clustering algorithm based on MapReduce to handle mixed-type datasets. Mapper phase and reducer phase are the two primary phases of MR-CF. In the mapper phase, the original CF tree algorithm is modified to collect intermediate CF entries, and in the reducer phase, k -prototypes is extended to cluster CF entries. To avoid the high costs associated with I/O overheads and data serialization, MR-CF loads a dataset from HDFS only once. We first analyze the …time complexity, space complexity, and I/O complexity of MR-CF. We also compare it with sklearn BIRCH, Apache Mahout k -means, k -prototypes, and mrk-prototypes on several real-world datasets and synthetic datasets. Experiments on two mixed-type big datasets reveal that MR-CF reduces execution time by 45.4% and 61.3% when compared to k -prototypes, and it reduces execution time by 73.8% and 55.0% when compared to mrk-prototypes. Show more

Keywords: Clustering analysis, CF tree, mixed-type datasets, BIRCH, k-prototypes

DOI: 10.3233/JIFS-224234

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8309-8320, 2023

Authors: Khan, Muhammad Zahir | Aslam, Muhammad | Albassam, Mohammed

Article Type: Research Article

Abstract: When the target value (T) is located in the midpoint of the specification interval (m). Traditional process capability indices (PCIs) are often employed for a process with a symmetric tolerance (T  = m). In case a process with asymmetric tolerance (T ≠m) traditional PCIs can be misleading. Process capability indices (PCIs) with asymmetric tolerance have been designed and successfully used in a crisp form in process capability analysis (PCA). These PCIs with asymmetric tolerance can benefit from the use of fuzzy set theory to deal with ambiguity and to add greater flexibility and sensitivity to mean variance, and target value (T), …and specification limits (SLs). In order to produce fuzzy SLs of PCIs with asymmetric tolerance fuzzy mean, fuzzy variance and the fuzzy target value have been used. Furthermore, these PCIs are graphically represented. It is concluded that the intermediate values of fuzzy SLs can be explored, which is not achievable with crisp SLs. Furthermore, it is recommended to utilize fuzzy SLs of PCIs with asymmetric tolerance to monitor goods that fall outside specification limits due to their flexibility and sensitivity in a fuzzy environment. The proposed FPCIs were illustrated with a real-life example using piston diameters that were produced in a factory. Show more

Keywords: Target value, fuzzy mean, fuzzy variance, asymmetric tolerance

DOI: 10.3233/JIFS-221993

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8321-8327, 2023

Authors: Jain, Archika | Sharma, Sandhya

Article Type: Research Article

Abstract: Hate speech on social media post is running now a days. Social media like YouTube, Twitter, and Facebook etc. are responsible for hated speech. Hated speech spreads through digital media, causing individuals to get confused and adopt prejudiced viewpoints. To limit the negative effects of disinformation on the digital platform, it is critical to detect it. Now a days, lots of digital platforms are available. Hate speech detection in dataset is very difficult. As a result, the Twitter dataset is of the size of 25296 is presented in this work. Many deep learning techniques are applied on Twitter dataset. The …Google Colab tool is used to scrape dataset material. Different deep learning approaches are utilized to boost the accuracy of the hated speech dataset. For training and validation accuracy and loss some models are used on Twitter dataset like Bi-directional Long Short Term Memory with Glove, Bi-LSTM, and Embedding from Language Model (Elmo) with deep learning, Convolutional Neural Network (CNN), Long Short Term Memory with Glove and LSTM. The performance of the proposed tweet dataset is evaluated using a variety of deep learning classifiers on text dataset. The planned deep learning techniques produced good results on tweet dataset. LSTM with Glove gave the highest accuracy 0.89 and minimum loss 0.19 on tweet dataset. So when compare our model on same dataset that was used earlier then we get highest accuracy and minimum loss. Show more

Keywords: Deep learning, classifiers, twitter dataset, LSTM, and accuracy

DOI: 10.3233/JIFS-222431

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8329-8341, 2023

Authors: Camgoz Akdag, Hatice | Menekse, Akin

Article Type: Research Article

Abstract: Breast cancer is the leading cause of cancer-related deaths, and choosing a suitable treatment plan for this disease has proved difficult for oncologists owing to the variety of criteria and alternatives that must be considered during the decision-making process. Since prospective treatment options influence patients’ health-related quality of life in a variety of ways, a methodology that can completely and objectively evaluate alternative treatments has become an essential issue. This paper proposes a novel multi-criteria decision-making (MCDM) methodology by integrating the CRiteria Importance Through Intercriteria Correlation (CRITIC) and the REGIME techniques and handles the problem of breast cancer treatment selection …problem. CRITIC enables the determination of objective criterion weights based on the decision matrix, while REGIME ranks the options without the need for lengthy computations or normalization procedures. The suggested methodology is demonstrated in a spherical fuzzy atmosphere, which allows decision experts to independently express their degrees of membership, non-membership, and hesitancy in a broad three-dimensional spherical space. In the numerical example provided, three oncologists evaluate four breast cancer treatment alternatives, namely, surgery, radiotherapy, chemotherapy, and hormone therapy, with respect to five criteria, which are disease or tumor type, stage of disease, patient type, side effects, and financial status of the patient. The tumor type is determined to be the most important assessment criterion, and surgery is selected as the best course of action. The stability and validity of the proposed methodology are verified through sensitivity and comparative studies. The discussions, limitations, and future research avenues are also given within the study. Show more

Keywords: CRITIC, REGIME, spherical fuzzy set, MCDM, breast cancer treatment selection

DOI: 10.3233/JIFS-222648

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8343-8356, 2023

Authors: Ma, Zhanyou | Gao, Yingnan | Li, Zhaokai | Li, Xia | Liu, Ziyuan

Article Type: Research Article

Abstract: The verification of reachability properties of fuzzy systems is usually based on the fuzzy Kripke structure or possibilistic Kripke structure. However, fuzzy Kripke structure or possibilistic Kripke structure is not enough to describe nondeterministic and concurrent fuzzy systems in real life. In this paper, firstly, we propose the generalized possibilistic decision process as the model of nondeterministic and concurrent fuzzy systems, and deduce the possibilities of sets of paths of the generalized possibilistic decision process relying on defining of schedulers. Then, we give fuzzy matrices calculation methods of the maximal possibilities and the minimal possibilities of eventual reachability, always reachability, …constrained reachability, repeated reachability and persistent reachability. Finally, we propose a model checking approach to convert the verification of safety property into the analysis of reachabilities. Show more

Keywords: Generalized possibilistic decision processes, scheduler, fuzzy matrices, reachability, safety property

DOI: 10.3233/JIFS-222803

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8357-8373, 2023

Authors: Wu, Yisheng | Jin, Xin | Huang, Haiping

Article Type: Research Article

Abstract: This paper focuses on the task of Point-of-interest (POI) recommendation whose goal is to generate a list of POIs for a target user based on his or her history check-in records. Different from the traditional recommendation tasks (e.g., movie recommendation), there are many factors, like temporal factor and geographical factor, which make a great influence on user preference. Though existing POI recommendation methods tend to model the user preference from temporal factor, geographical factor or social factor, they fail to model these factors into a jointly model, leading to learn the suboptimal user preference. To tackle this issue, we propose …a Muti-channel Graph Attention Network (MGAN) for POI recommendation which learns the user preference from multiple aspects in a unify model. Specifically, MGAN first constructs several graphs with corresponding contextual features to capture the user preference from temporal, geographical, semantic and social aspects. Then MGAN leverages the graph attention networks to learn the representations of POIs from these graphs. Finally, MGAN estimates the user preference from the history check-in records and other similar users via the learned POI representations. We conduct extensive experiments on real-world datasets. And the results indicate that our proposed MGAN outperforms mainstream POI recommendation methods. Show more

Keywords: Point-of-interest, recommendation, graph attention network, temporal, geographical

DOI: 10.3233/JIFS-222952

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8375-8385, 2023

Authors: Deng, Xue | Geng, Fengting | Fang, Wen | Huang, Cuirong | Liang, Yong

Article Type: Research Article

Abstract: By considering the stock market’s fuzzy uncertainty and investors’ psychological factors, this paper studies the portfolio performance evaluation problems with different risk attitudes (optimistic, pessimistic, and neutral) by the Data Envelopment Analysis (DEA) approach. In this work, the return rates of assets are characterized as trapezoidal fuzzy numbers, whose membership functions with risk attitude parameters are described by exponential expression. Firstly, these characteristics with risk attitude are strictly derived including the possibilistic mean, variance, semi-variance, and semi-absolute deviation based on possibility theory. Secondly, three portfolio models (mean-variance, mean-semi-variance, and mean-semi-absolute-deviation) with different risk attitudes are proposed. Thirdly, we prove the …real frontiers determined by our models are concave functions through mathematical theoretical derivation. In addition, two novel indicators are defined by difference and ratio formulas to characterize the correlation between DEA efficiency and portfolio efficiency. Finally, numerical examples are given to verify the feasibility and effectiveness of our model. No matter what risk attitude an investor holds, the DEA can generate approximate real frontiers. Correlation analysis indicates that our proposed approach outperforms in evaluating portfolios with risk attitudes. At the same time, our model is an improvement of Tsaur’s work (2013) which did not study the different risk measures, and an extension of Chen et al.’s work (2018) which only considered risk-neutral attitude. Show more

Keywords: Portfolio performance evaluation, risk attitude, data envelopment analysis (DEA), possibility theory, real frontier

DOI: 10.3233/JIFS-223543

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8387-8411, 2023

Authors: Lin, Sihong | Zhang, Kunbin | Guan, Dun | He, Linjie | Chen, Yumin

Article Type: Research Article

Abstract: Intrusion detection systems have become one of the important tools for network security due to the frequent attacks brought about by the explosive growth of network traffic. Autoencoder is an unsupervised learning model with a neural network structure. It has a powerful feature learning capability and is effective in intrusion detection. However, its network construction suffers from overfitting and gradient disappearance problems. Traditional granular computing methods have advantages in solving such problems, but the process is relatively complex, the granularity dimension is high, and the computational cost is large, which is not suitable for application in intrusion detection systems. To …address these problems, we propose a novel autoencoder: Granular AutoEncoders (GAE). The granulation reference set is constructed by random sampling. The granulation of training samples is based on single-feature similarity in a reference set to form granules. The granulation of multiple features results in granular vectors. Some operations of granules are defined. Furthermore, we propose some granular measures, including granular norms and granular loss functions. The GAE is further applied to the field of intrusion detection by designing an anomaly detection algorithm based on the GAE. The algorithm determines whether the network flows are anomalous by comparing the difference between an input granular vector and its output granular vector that is reconstructed by the GAE. Finally, some experiments are conducted using an intrusion detection dataset, comparing multiple metrics in terms of precision, recall, and F1-Score. The experimental results validate the correctness and effectiveness of the intrusion detection method based on GAE. And contrast experiments show that the proposed method has stronger ability for detecting anomalies than the correlation algorithms. Show more

Keywords: Granular computing, intrusion detection, autoencoder, deep Learning, anomaly detection

DOI: 10.3233/JIFS-223649

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8413-8424, 2023

Authors: Shanmukha, M.C. | Lee, Sokjoon | Usha, A. | Shilpa, K.C. | Azeem, Muhammad

Article Type: Research Article

Abstract: Topological indices and coindices are numerical invariants that relate to quantitative structure property/activity connections. The purpose of topological indices and coindices were introduced to draw the data related to chemical graphs with respect to adjacent & non adjacent pairs of vertex degrees respectively. These indices equip the researchers with a lot of information related to the properties and structure of the chemical compound. In this article, CoM-polynomials for molecular graph of linear and multiple Anthracene are computed from which eleven degree based topological coindices are derived.

Keywords: CoM-polynomial, topological coindex, anthracene

DOI: 10.3233/JIFS-223947

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8425-8436, 2023

Authors: Devi Sri Nandhini, M. | Pradeep, Gurunathan

Article Type: Research Article

Abstract: Sentiment analysis is the contextual analysis of words to retrieve the social opinion of a brand which aids the business firms/institutions to know the impact of their products/services. It is habitual that users may express different opinions regarding various aspects of the same entity. Therefore, there is a strong demand to extract all the opinion targets may those be explicitly mentioned aspects or implicit aspects which are not directly specified in the reviews. In this context, comparatively less amount of work has been carried out concerning implicit aspect detection. The proposed work has been dedicated solely to extracting the implicit …aspects using a dynamic approach based on the type of sentence containing the clues for implicit aspect. A novel aspect pointer compendium (APC) has been developed that catalyzes the task of finding implicit aspects to the maximum extent possible. The APC incorporates the usage of different types of clues such as synonym clues, context clues, phrase clues, and partially implicit aspects that aid in the detection of hidden aspects. Based on this idea, the proposed work classifies the implicit aspect sentences into six types and proceeds with the task in an efficient manner. To strengthen the task of implicit aspect detection, the proposed work utilizes a hybrid technique encompassing APC, domain-specific adjective-noun collocation list (DSANCL), and the explicit aspect-opinion word pairs extracted from the reviews. The experimentation and results reveal that the proposed hybrid approach shows a good improvement in terms of the efficacy of extracting the implicit aspects as compared to the existing baseline models. Show more

Keywords: Implicit aspect detection, aspect pointer compendium, partially implicit aspects

DOI: 10.3233/JIFS-222927

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8437-8450, 2023

Authors: Yang, Yali | Zhang, Tianwei

Article Type: Research Article

Abstract: This paper firstly establishes the discrete-time lattice networks for nonlocal stochastic competitive neural networks with reaction diffusions and fuzzy logic by employing a mix techniques of finite difference to space variables and Mittag-Leffler time Euler difference to time variable. The proposed networks consider both the effects of spatial diffusion and fuzzy logic, whereas most of the existing literatures focus only on discrete-time networks without spatial diffusion. Firstly, the existence of a unique ω-anti-periodic in distribution to the networks is addressed by employing Banach contractive mapping principle and the theory of stochastic calculus. Secondly, global exponential convergence in mean-square sense to …the networks is discussed on the basis of constant variation formulas for sequences. Finally, an illustrative example is used to show the feasible of the works in the current paper with the help of MATLAB Toolbox. The work in this paper is pioneering in this regard and it has created a certain research foundations for future studies in this area. Show more

Keywords: Lattice, reaction diffusion, stochastic, finite difference method

DOI: 10.3233/JIFS-223495

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8451-8470, 2023

Authors: Comakli Sokmen, Özlem | yılmaz, Mustafa

Article Type: Research Article

Abstract: The hierarchical Chinese postman problem (HCPP) aims to find the shortest tour or tours by passing through the arcs classified according to precedence relationship. HCPP, which has a wide application area in real-life problems such as shovel snow and routing patrol vehicles where precedence relations are important, belongs to the NP-hard problem class. In real-life problems, travel time between the two locations in city traffic varies due to reasons such as traffic jam, weather conditions, etc. Therefore, travel times are uncertain. In this study, HCPP was handled with the chance-constrained stochastic programming approach, and a new type of problem, the …hierarchical Chinese postman problem with stochastic travel times, was introduced. Due to the NP-hard nature of the problem, the developed mathematical model with stochastic parameter values cannot find proper solutions in large-size problems within the appropriate time interval. Therefore, two new solution approaches, a heuristic method based on the Greedy Search algorithm and a meta-heuristic method based on ant colony optimization were proposed in this study. These new algorithms were tested on modified benchmark instances and randomly generated problem instances with 817 edges. The performance of algorithms was compared in terms of solution quality and computational time. Show more

Keywords: Optimization, arc routing problems, chance-constrained stochastic programming, new efficient algorithm, hierarchical Chinese postman problem

DOI: 10.3233/JIFS-222097

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8471-8492, 2023

Authors: Zhong, Cheng | Wang, Jie-Sheng | Liu, Yu

Article Type: Research Article

Abstract: The rolling bearing fault diagnosis is affected by industrial environmental noise and other factors, leading to the existence of some redundant components after signal decomposition. At the same time, the existence of the modal aliasing phenomenon in empirical mode decomposition (EMD) and the relevant improved algorithms also leads to the existence of many invalid features in the components. These phenomena have great influence on the bearing fault diagnosis. So a rolling bearing bidirectional-long short term memory (Bi-LSTM) fault diagnosis method was proposed based on segmented interception auto regressive (SIAR) spectrum analysis and information fusion. The ensemble empirical mode decomposition (EEMD), …the complementary ensemble empirical mode decomposition (CEEMD) and the robust EMD (REMD) algorithms decompose the rolling bearing fault signals, and AR spectrum analysis is performed on the obtained components respectively. By comparing the AR spectra of the components corresponding to different fault locations, the effective AR spectral values are intercepted as the eigenvalues of the data, and finally all the eigenvalues are fused to achieve the purpose of screening effective features more efficiently so as to reduce the impact of feature redundancy caused by mode aliasing on neural network training. Then the Bi-LSTM neural network was used as a rolling bearing fault diagnosis classifier, and the simulation experiments were conducted based on the rolling bearing fault signal data from Case Western Reserve University to verify the effectiveness of the proposed feature extraction and fault diagnosis method. Show more

Keywords: Rolling bearing, fault diagnosis, AR spectrum analysis, information fusion, empirical mode decomposition, LSTM

DOI: 10.3233/JIFS-222476

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8493-8519, 2023

Authors: Rajesh, Thota Radha | Rajendran, Surendran | Alharbi, Meshal

Article Type: Research Article

Abstract: Multi-agent reinforcement learning (MARL) is a generally researched approach for decentralized controlling in difficult large-scale autonomous methods. Typical features create RL system as an appropriate candidate to develop powerful solutions in variation of healthcare fields, whereas analyzing decision or treatment systems can be commonly considered by a prolonged and sequential process. This study develops a new Penguin Search Optimization Algorithm with Multi-agent Reinforcement Learning for Disease Prediction and Recommendation (PSOAMRL-DPR) model. This research aimed to use a unique PSOAMRL-DPR algorithm to forecast diseases based on data collected from networks and the cloud by a mobile agent. The major intention of …the proposed PSOAMRL-DPR algorithm is to identify the presence of disease and recommend treatment to the patient. The model manages the agent container with different mobile agents and fetched data from dissimilar locations of the network as well as cloud. For disease detection and prediction, the PSOAMRL-DPR technique exploits deep Q-network (DQN) technique. In order to tune the hyperparameters related to the DQN technique, the PSOA technique is used. The experimental result analysis of the PSOAMRL-DPR technique is validated on heart disease dataset. The simulation values demonstrate that the PSOAMRL-DPR technique outperforms the other existing methods. Show more

Keywords: Multi-agent reinforcement learning, penguin search optimization, deep Q-learning, disease prediction, treatment recommendation

DOI: 10.3233/JIFS-223933

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8521-8533, 2023

Authors: Wang, Jing | Cai, Qiang | Wang, Hongjun | Wei, Guiwu | Liao, Ningna

Article Type: Research Article

Abstract: Green supply chain management attaches great importance to the coordinated development of social economy and ecological environment, and requires enterprises to consider environmental protection factors in product design, packaging, procurement, production, sales, logistics, waste and recycling. Suppliers are the “source” of the entire supply chain, and the choice of green suppliers is the basis of green supply chain management, and their quality will directly affect the environmental performance of enterprises. The green supplier selection is a classical multiple attribute group decision making (MAGDM) problems. Interval-valued intuitionistic fuzzy sets (IVIFSs) are the extension of intuitionistic fuzzy sets (IFSs), and are utilized …to depict the complex and changeable circumstance. To better adapt to complex environment, the purpose of this paper is to construct a new method to solve the MAGDM problems for green supplier selection. Taking the fuzzy and uncertain character of the IVIFSs and the psychological preference into consideration, the original MABAC method based on the cumulative prospect theory (CPT) is extended into IVIFSs (IVIF-CPT-MABAC) method for MAGDM issues. Meanwhile, the method to evaluate the attribute weighting vector is calculated by CRITIC method. Finally, a numerical example for green supplier selection has been given and some comparisons is used to illustrate advantages of IVIF-CPT-MABAC method and some comparison analysis and sensitivity analysis are applied to prove this new method’s effectiveness and stability. Show more

Keywords: Multiple attribute group decision making (MAGDM), interval-valued intuitionistic fuzzy sets (IVIFSs), MABAC, cumulative prospect theory (CPT), CRITIC, green supplier selection

DOI: 10.3233/JIFS-224206

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8535-8560, 2023

Authors: Gokul Pran, S. | Raja, Sivakami

Article Type: Research Article

Abstract: Network flaws are used by hackers to get access to private systems and data. This data and system access may be extremely destructive with losses. Therefore, this network intrusions detection is utmost significance. While investigating every feature set in the network, deep learning-based algorithms require certain inputs. That’s why, an Adaptive Artificial Neural Network Optimized with Oppositional Crow Search Algorithm is proposed for network intrusions detection (IDS-AANN-OCSA). The proposed method includes several phases, including feature selection, preprocessing, data acquisition, and classification. Here, the datas are gathered via CICIDS 2017 dataset. The datas are fed to pre-processing. During pre-processing, redundancy eradication …and missing value replacement is carried out with the help of random forest along Local least squares for removing uncertainties. The pre-processed datas are fed to feature selection to select better features. The feature selection is accomplished under hybrid genetic algorithm together with particle swarm optimization technique (GPSO). The selected features are fed to adaptive artificial neural network (AANN) for categorization which categorizes the data as BENIGN, DOS Hulk, PortScan, DDoS, DoS Golden Eye. Finally, the hyper parameter of adaptive artificial neural network is tuned with Oppositional Crow Search Algorithm (OCSA) helps to gain better classification of network intrusions. The proposed approach is activated in Python, and its efficiency is evaluated with certain performance metrics, like accuracy, recall, specificity, precision, F score, sensitivity. The performance of proposed approach achieves better accuracy 99.75%, 97.85%, 95.13%, 98.79, better sensitivity 96.34%, 91.23%, 89.12%, 87.25%, compared with existing methods, like One-Dimensional Convolutional Neural Network Based Deep Learning for Network Intrusion Detection (IDS-CNN-GPSO), An innovative network intrusion detection scheme (IDS-CNN-LSTM) and Application of deep learning to real-time Web intrusion detection (IDS-CNN-ML-AIDS) methods respectively. Show more

Keywords: Adaptive artificial neural network, feature selection, genetic particle swarm optimization, intrusion detection systems

DOI: 10.3233/JIFS-222120

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8561-8571, 2023

Authors: Liu, Jing | Zhang, ErZi | Ma, Chao | Yager, Ronald R. | Senapati, Tapan | Yatsalo, Boris | Jin, LeSheng

Article Type: Research Article

Abstract: In many multi criteria group decision making problems, the individual evaluation values offered by experts are with uncertainties. Therefore, when assigning weights to those experts using preferences induced weights allocation, we can have two types of bi-polar preferences. The first one is the optimism-pessimism preference over evaluation values; the second one is the uncertainty aversion preference over the attached numerical certainty/uncertainty degrees. When performing preferences induced weights allocation, the certainty/uncertainty degrees will affect the optimism-pessimism preference induced weights allocation because the magnitudes of those evaluation values might not be the exact ones. Moreover, the importance of those experts in multi …criteria group decision making can also have influence over the two types of preference induced weights allocation processes, and the importance can also be with uncertainties and can be expressed using basic uncertain information. Therefore, to handle this situation with multiple inducing variables and uncertainties, we simultaneously consider the influence of the uncertainties attached to evaluation values and the influence of uncertain importance of experts, and thus we at the same time adopt the method of confidence threshold and the method of uncertain importance level function to propose some synthesized method to adjust the induced weights allocation processes. We also propose a complete multi criteria group decision making problems to show the feasibility and reasonability of the proposed decision model for the complex situation where both evaluation values and expert importance are expressed by basic uncertain information. Show more

Keywords: Aggregation operators, basic uncertain information, induced ordered weighted averaging operators, information fusion, multi criteria group decision making, uncertain decision making

DOI: 10.3233/JIFS-222590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8573-8583, 2023

Authors: Wang, Chong | Yang, Gongping | Huang, Yuwen | Liu, Yikun | Zhang, Yan

Article Type: Research Article

Abstract: Fruit detection is essential for harvesting robot platforms. However, complicated environmental attributes such as illumination variation and occlusion have made fruit detection a challenging task. In this study, a Transformer-based mask region-based convolution neural network (R-CNN) model for tomato detection and segmentation is proposed to address these difficulties. Swin Transformer is used as the backbone network for better feature extraction. Multi-scale training techniques are shown to yield significant performance gains. Apart from accurately detecting and segmenting tomatoes, the method effectively identifies tomato cultivars (normal-size and cherry tomatoes) and tomato maturity stages (fully-ripened, half-ripened, and green). Compared with existing work, the …method has the best detection and segmentation performance for these tomatoes, with mean average precision (mAP) results of 89.4% and 89.2%, respectively. Show more

Keywords: Mask R-CNN, Swin Transformer, tomato detection, instance segmentation

DOI: 10.3233/JIFS-222954

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8585-8595, 2023

Authors: Amanulla Khan, M. | Sithi Shameem Fathima, S.M.H.

Article Type: Research Article

Abstract: Gait recognition is the process of recognizing a person based on their walking style. Each person’s walking gait is distinctive and cannot be imitated by others. However, the walking motion of a person will be changed based on their behaviour but their walking pattern doesn’t change. In this paper, a novel Clustering based Faster RCNN has been proposed to identify the single, double and multi-gait. The gait images from the publicly available dataset are pre-processed using Multi scale Retinex (MSR) to reduce the noise artifacts. The Faster RCNN is used for extracting the relevant features from the gait images via …the two modules namely CNN and RPN. The CNN layers extract the most relevant features as feature maps and RPN is used for creating the bounding boxes for the extracted features. Fuzzy K-means clustering is used to group the features based on their labels, and it specifies the features acquired using CNN and RPN as input. Finally, the Fast RCNN is employed for classifying the gait images into suspicious and non-suspicious walking pattern. The proposed Clustering based Faster RCNN net achieves the high accuracy rate of 98.74% and 99.19% for suspicious and non-suspicious walking pattern respectively. The proposed Clustering based Faster RCNN model was compared with other traditional models like CNN, U-net, Fab net and Fast R-CNN. The proposed Clustering based Faster RCNN model improves the overall accuracy of 8.86% 33.77% 3.12% and 5.48% better than mmGait, LSTM Net, STDNN and RNN respectively. Show more

Keywords: Gait recognition, deep learning, faster R-CNN, fuzzy K-means clustering, multi scale Retinex

DOI: 10.3233/JIFS-224114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8597-8606, 2023

Authors: Chen, Kejia | Wu, Qianqian | Yan, Minru | Li, Xuannan

Article Type: Research Article

Abstract: The purpose of this paper is to explore how port enterprises can scientifically select a better logistic service provider (LSP) to achieve a high efficiency. An empirical study is conducted to verify the effectiveness of the combination weighting-grey synthetic decision-making method by helping the LSP selection of a port enterprise in China. Data are collected from questionnaires administered to port logistics’ industry professionals. The method is proposed, which associates the analysis network process method with the entropy method to determine the combined weights of the evaluation indexes. The improved centre-point triangular whitenization weight function is introduced to cluster the alternative …port LSPs and judge the corresponding grey classes. Subsequently, the synthetic weighted decision-making vectors are used to determine the grey synthetic decision-making coefficient vectors. The grey synthetic clustering decision-making coefficients are calculated to establish a synthetic decision-making rank of the alternative plans. The combined method can help the port enterprises realize the selection of better LSPs in a scientific manner. Show more

Keywords: Combination weighting, grey synthetic decision-making, logistics service, provider selection

DOI: 10.3233/JIFS-222156

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8607-8626, 2023

Authors: Tao, Zhifu | Wang, Xinyu | Zhu, Benji | Wu, Peng

Article Type: Research Article

Abstract: The aim of this paper is to introduce a combination of Basic Uncertain Information (BUI) and a Bag Based Technique for Order Preference by Similarity to Ideal Solution (BBTOPSIS), which is further applied to multi-attribute decision making (MADM) with BUI. To realize the decision process, a novel comparison law is developed to derive the superiority, inferiority and noninferiority multi-attribute canonical fuzzy bags. Mathematical properties of the developed comparison law is discussed. Besides, to extend traditional TOPSIS method in BUI, a novel distance measure between BUI is also introduced, which is composed by distance between transformed intervals and similarity between BUI. …Superiority of the developed distance measure is illustrated. Finally, a decision algorithm is presented to solve MADM with BUI by using the developed BBTOPSIS under BUI. A numerical example on location of medical warehouse is presented to illustrate the feasibility and validity of the developed decision method. Show more

Keywords: Basic uncertain information, BBTOPSIS, multi-attribute decision making, medical warehouse, location

DOI: 10.3233/JIFS-223835

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8627-8636, 2023

Authors: Liu, Chang

Article Type: Research Article

Abstract: The “3 + 2” segmented training between higher vocational colleges and applied undergraduate courses has opened up the rising channel of vocational education from junior college level to undergraduate level, and promoted the organic connection between higher vocational colleges and Universities of Applied Sciences. It is one of the important ways to establish a modern vocational education system. Exploring the monitoring mechanism of talent training quality is an important measure to ensure the achievement of the segmented training goal, and it is a necessary condition to successfully train high-quality skilled applied talents. The talent training quality evaluation of segmented education is viewed …as multiple attribute decision-making (MADM) issue. In this paper, an extended probabilistic simplified neutrosophic number GRA (PSNN-GRA) method is established for talent training quality evaluation of segmented education. The PSNN-GRA method integrated with CRITIC method in probabilistic simplified neutrosophic sets (PSNSs) circumstance is applied to rank the optional alternatives and a numerical example for talent training quality evaluation of segmented education is used to proof the newly proposed method’s practicability along with the comparison with other methods. The results display that the approach is uncomplicated, valid and simple to compute. Show more

Keywords: Multiple attributes decision making (MADM), probabilistic simplified neutrosophic sets (PSNSs), GRA method, talent training quality evaluation

DOI: 10.3233/JIFS-224494

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8637-8647, 2023

Authors: Fang, Jian | Lin, Xiaomei | Liu, Weida | An, Yi | Sun, Haoran

Article Type: Research Article

Abstract: The purpose of facial expression recognition is to capture facial expression features from static pictures or videos and to provide the most intuitive information about human emotion changes for artificial intelligence devices to use effectively for human-computer interaction. Among the factors, the excessive loss of locally valid information and the irreversible degradation trend of the information at different expression semantic scales with increasing network depth are the main challenges faced currently. To address such problems, an enhanced pyramidal network model combining with triple attention mechanisms is designed in this paper. Firstly, three attention mechanism modules, i.e. CBAM, SK, and SE, …are embedded into the backbone network model in stages, and the key features are sensed by using spatial or channel information mining, which effectively reduces the effective information loss caused by the network depth. Then, the pyramid network is used as an extension of the backbone network to obtain the semantic information of expression features across scales. The recognition accuracy reaches 96.25% and 73.61% in the CK+ and Fer2013 expression change datasets, respectively. Furthermore, by comparing with other current advanced methods, it is shown that the proposed network architecture combining with the triple attention mechanism and multi-scale cross-information fusion can simultaneously maintain and improve the information mining ability and recognition accuracy of the facial expression recognition model. Show more

Keywords: Facial expression recognition, attention mechanism, Resnet-50, pyramid network

DOI: 10.3233/JIFS-222252

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8649-8661, 2023

Authors: Zuo, Haichun

Article Type: Research Article

Abstract: The rapid growth of cloud services for hosting applications in the scientific, commercial, web, and social networks has led to enormous growth in the number of large-scale data centers. By shifting the costs of data center maintenance, hardware, and software from customers to service providers using a pay-as-you-go policy, service providers and customers are benefited. On the other hand, the massive growth of data centers has been accompanied by challenges that have limited the boundaries of this technology. Thus, researchers in this field tend to focus on eliminating these limitations. Since virtualization is at the core of cloud computing, allocating …Virtual Machines (VMs) to physical hosts in the Infrastructure as a Service layer (IaaS) is one of the most significant challenges. Nonetheless, the VM allocation problem is a combinatorial optimization problem that is known to be NP-Hard. In this paper, we presented a comprehensive analysis of virtual machine placement problem and outlined different approaches to solving it. This paper aims to provide insight into the challenges and issues for recent virtual machine placement strategies. The current study aims to comprehensively classify the physical resource allocation for VMs by overviewing available trends. Show more

Keywords: Cloud computing, virtual machine allocation, virtualization, resource utilization, review

DOI: 10.3233/JIFS-222896

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8663-8696, 2023

Authors: Navaneethan, M. | Janakiraman, S.

Article Type: Research Article

Abstract: E-commerce, often known as electronic commerce, is the purchasing and selling of goods over the internet using electronic devices to share data. Banks and other financial institutions are frequently added as third-party platforms to traditional e-commerce platforms. As a result, it raises issues with integrity and cyber security. We suggest a deep learning-based strategy called the Hybrid Interactive Autodidactic School-Based Teaching-Learning Optimization (HIASTLO) algorithm to address these issues. The IoT-based e-commerce blockchain is used to extract and reject the various cyberattacks in the network, and deep learning is utilised to improve the weight and bias of the neural networks. We …used a variety of performance indicators, including accuracy, precision, and recall, to identify cyberattacks. We also evaluated how well our work performed when compared to previous BSIoTNET, BCFC, DRNN, DNN-KNN, MOO-FS, LRNN, and HDLM efforts. Furthermore, MudraChain and NormaChain are used to examine the transaction time of our suggested task. The results show that our suggested work performs better than any other methods and offers highly secure internet services. Show more

Keywords: E-commerce, blockchain, deep learning, cyber attacks, IoT

DOI: 10.3233/JIFS-220743

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8697-8709, 2023