Journal of Intelligent & Fuzzy Systems - Volume 45, issue 6

Authors: Xia, Wenxin | Che, Jinxing

Article Type: Research Article

Abstract: Wind energy needs to be used efficiently, which depends heavily on the accuracy and reliability of wind speed forecasting. However, the volatility and nonlinearity of wind speed make this difficult. In volatility and nonlinearity reduction, we sequentially apply complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) to secondarily decompose the wind speed data. This framework, however, requires effectively modeling multiple uncertainty components. Eliminating this limitation, we integrate crow search algorithm (CSA) with deep belief network (DBN) to generate a unified optimal deep learning system, which not only eliminates the influence of multiple uncertainties, but …also only adopts DBN as a predictor to realize parsimonious ensemble. Two experiments demonstrate the superiority of this system. Show more

Keywords: Parsimonious ensemble, secondary decomposition, optimal deep learning, crow search algorithm

DOI: 10.3233/JIFS-233782

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10799-10822, 2023

Authors: Kumari, Ritika | Singh, Jaspreeti | Gosain, Anjana

Article Type: Research Article

Abstract: Class imbalance problem (CIP) exists when the class distribution is not uniform. Many real-world scenarios face CIP which attracted the researcher’s attention to this problem. Training machine learning (ML) models with class imbalanced datasets is a challenging problem. Ensemble methods in ML involve training multiple classifiers, combining or averaging their predictions to come to a final prediction. Specifically designed ensemble-based methods can overcome the difficulty faced by traditional classifiers and can handle the CIP. The performance of 19 ensemble methods for 44 unbalanced datasets is assessed in this paper in order to observe the effects of the class imbalance ratio …(CIR). For performance evaluation, we divide these datasets into three categories, i.e., Slightly Imbalance (SI), Moderately Imbalance (MI) and Highly Imbalance (HI) based on CIR. With the proposed perspective, we observe that different ensemble methods perform well in different categories suggesting that the percentage of minority or majority class could be a criterion for the selection of ensemble methods for class imbalance datasets. Moreover, visual representations and different non-parametric statistical tests are also used to have more reliable results. Show more

Keywords: Ensemble methods, boosting, bagging, hybrid approaches, classification

DOI: 10.3233/JIFS-223333

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10823-10834, 2023

Authors: Koshti, Dipali | Gupta, Ashutosh | Kalla, Mukesh

Article Type: Research Article

Abstract: Visual question Answering (VQA) is a computer vision task that requires a system to infer an answer to a text-based question about an image. Prior approaches did not take into account an image’s positional information or the questions’ grammatical and semantic relationships during image and question processing. Featurization, which leads to the false answering of the question. Hence to overcome this issue CNN –Graph based LSTM with optimized BP Featurization technique is introduced for feature extraction of image as well as question. The position of the subjects in the image has been determined using CNN with a dropout layer and …the optimized momentum backpropagation during the extraction of image features without losing any image data. Then, using a graph-based LSTM with loopy backpropagation, the questions’ syntactic and semantic dependencies are retrieved. However, due to their lack of external knowledge about the input image, the existing approaches are unable to respond to common sense knowledge-based questions (open domain). As a result, the proposed Spatial GCNN knowledge retrieval with PDB Model and Spatial Graph Convolutional Neural Network, which recovers external data from Wikidata, have been used to address the open domain problems. Then the Probabilistic Discriminative Bayesian model, based Attention mechanism predicts the answer by referring to all concepts in question. Thus, the proposed method answers the open domain question with high accuracy of 88.30%. Show more

Keywords: Visual Question Answering, graph-based LSTM, SVO triples sentence, Discriminative Bayesian model, dynamic memory network

DOI: 10.3233/JIFS-230198

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10835-10852, 2023

Authors: My, Bui T.T. | Ta, Bao Q.

Article Type: Research Article

Abstract: Credit scoring is a typical example of imbalanced classification, which poses a challenge to conventional machine learning algorithms and statistical classifiers when attempting to accurately predict outcomes for defaulting customers. In this paper, we propose a credit scoring classifier called Decision Tree Ensemble model (DTE). This model effectively addresses the challenge of imbalanced data and identifies significant features that influence the likelihood of credit status. An experiment demonstrates that DTE exhibits superior performance metrics in comparison to well-known based-tree ensemble classifiers such as Bagging, Random Forest, and AdaBoost, particularly when integrated with resampling techniques for handling imbalanced data.

Keywords: Classifiers, credit scoring, decision tree, ensemble classifiers, imbalanced data

DOI: 10.3233/JIFS-230825

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10853-10864, 2023

Authors: Catherine Grace John, J. | Deepika, M. | Elavarasan, B.

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-232591

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10865-10872, 2023

Authors: Thumilvannan, S. | Balamanigandan, R.

Article Type: Research Article

Abstract: The survival of patients’ deaths owing to Heart Disease (HD) could be improved with the assistance of an enhanced approach for predicting the risk of diabetes and HD. Nevertheless, such schemes are developed rarely. Thus, this paper proposes a new Power Lognormal Distribution-Semi-Supervised Learning-centric Restricted Boltzmann Machine (PLD-SSL-RBM) diabetes and HD risk level prediction model for IoT data. The missing data are removed by partial Derivation of the Hamilton-Cluster Centered-K-means Clustering (DH-CC-KC) to efficiently train the classifier and then, the data are aggregated. Next, to reduce the dataset size, the features are reduced with Shell Sort-Principal Component Analysis (SS-PCA). Then, …the fuzzy rule-based decisions are created with the T -test-centric Uniform Distribution-Elephant Herd Optimization Algorithm (T -test-UDEHOA) Correlated Features (CF) to classify the risk levels accurately. Lastly, the risk levels of HD and diabetes are predicted; in addition, by employing the Elliptic Curve Cryptography (ECC)7encryption technique, the data is securely stored on the medical database. The proposed risk prediction model’s performance is analyzed on the Framingham dataset. As per the experimental outcomes, when analogized to the prevailing methodologies, the proposed technique attained a higher accuracy of 99.55%. Show more

Keywords: Internet of Things (IoT), heart disease and diabetes risk, Restricted Boltzmann Machine (RBM), correlated features, Elephant Herd Optimization Algorithm (EHOA), Correlated Feature (CF)

DOI: 10.3233/JIFS-232851

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10873-10886, 2023

Authors: Thakur, Divya | Lalwani, Praveen

Article Type: Research Article

Abstract: The use of mobile devices has contributed greatly to the expansion of Human Activity Recognition (HAR) studies in recent years. Researchers find it attractive because of its versatility, low cost, compact size, ease of usage, and wide range of possible applications. Conventional, biological, and control-based systems are just some of the methods that have been created for humanoid robot movement in recent years. This article specifically targeted improvement in the proposed method, which is different from previous papers. This is being done with the use of the publicly available Human Activity Gait (HAG) data set, which documents a wide range …of different types of activities. IMU sensors were used to collect this data set. Several experiments were conducted using different machine-learning strategies, each with its own set of hyper-parameters, to determine how best to utilize these data. In our proposed model Cuckoo Search Optimization is being used for optimum feature selection. On this data set, we have tested a number of machine learning models, including LR, KNN, DT, and proposed CSOEM (Cuckoo Search-Based Optimized Ensemble Model). The simulation suggests that the proposed model CSOEM achieves an impressive accuracy of 98%. This CSOEM is built by combining the feature selection strategy of Cuckoo Search Optimizations with the ensembling of the LR, KNN, and DT. Show more

Keywords: Bipedal robot locomotion, CSO: cuckoo search optimization, HAG: human activity gait, HAR: human activity recognition

DOI: 10.3233/JIFS-232986

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10887-10900, 2023

Authors: Lian, Jing | Chen, Shi | Pi, Jiahao | Li, Linhui | Li, Qingfeng

Article Type: Research Article

Abstract: Localization through intricate traffic scenes poses challenges due to their dynamic, light-variable, and low-textured nature. Existing visual Simultaneous Localization and Mapping (SLAM) methods, which are based on static and texture-rich assumptions, struggle with drift and tracking failures in such complex environments. To address this, we propose a visual SLAM algorithm based on semantic information and geometric consistency in order to solve the above issues and further realize autonomous driving applications in road environments. In dynamic traffic scenes, we employ an object detection network to identify moving objects and further classify them based on geometric consistency as dynamic objects or potential …dynamic objects. This method permits us to preserve more reliable static feature points. In low-texture environments, we propose a method that employs key object categories and geometric parameters of static scene objects for object matching between consecutive frames, effectively resolving the problem of tracking failure in such scenarios. We conducted experiments on the KITTI and ApolloScape datasets for autonomous driving and compared them to current representative algorithms. The results indicate that in the dynamic environment of the KITTI dataset, our algorithm improves the compared metrics by an average of 29.68%. In the static environment of the KITTI dataset, our algorithm’s performance is comparable to that of the other compared algorithms. In the complex traffic scenario R11R003 from the ApolloScape dataset, our algorithm improves the compared metrics by an average of 25.27%. These results establish the algorithm’s exceptional localization accuracy in dynamic environments and its robust localization capabilities in environments with low texture. It provides development and support for the implementation of autonomous driving technology applications. Show more

Keywords: Autonomous vehicles, SLAM, traffic environments, object detection

DOI: 10.3233/JIFS-233068

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10901-10919, 2023

Authors: Liu, Zhichao | Wang, Yachao | Ma, Zhiyuan | Cao, Mengnan | Liu, Mingda | Yang, Xiaochu

Article Type: Research Article

Abstract: Real-time monitoring of electricity usage details through load monitoring techniques is a crucial aspect of smart power grid management and monitoring, allowing for the acquisition of information on the electricity usage of individual appliances for power users. Accurate detection of electricity load is essential for refined load management and monitoring of power supply quality, facilitating the improvement of power management at the user side and enhancing power operation efficiency. Non-intrusive load monitoring (NILM) techniques require only the analysis of total load data to achieve load monitoring of electricity usage details, and offer advantages such as low cost, easy implementation, high …reliability, and user acceptance. However, with the increasing number of distributed new load devices on the user side and the diversification of device development, simple load recognition algorithms are insufficient to meet the identification needs of multiple devices and achieve high recognition accuracy. To address this issue, a non-intrusive load recognition (NILR) model that combines an adaptive particle swarm optimization algorithm (PSO) and convolutional neural network (CNN) has been proposed. In this model, pixelated images of different electrical V-I trajectories are used as inputs for the CNN, and the optimal network layer and convolutional kernel size are determined by the adaptive PSO optimization algorithm during the CNN training process. The proposed model has been validated on the public dataset PLAID, and experimental results demonstrate that it has achieved a overall recognition accuracy of 97.26% and F-1 score of 96.92%, significantly better than other comparison models. The proposed model effectively reduces the confusion between various devices, exhibiting good recognition and generalization capabilities. Show more

Keywords: Smart grid, non-intrusive load recognition, DL, Convolutional Neural Network, adaptive Particle Swarm Optimization

DOI: 10.3233/JIFS-233813

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10921-10935, 2023

Authors: Afzali, Parvaneh | Rezapour, Abdoreza | Rezaee Jordehi, Ahmad

Article Type: Research Article

Abstract: Handwriting is an individual trait that serves as evidence to authenticate a particular writer. Identifying the writer of a handwritten text has shown encouraging results in examining historical and forensic documents. In this paper, we propose a novel offline writer identification system based on the challenging analysis of small amount of data to extract distinct patterns. In our deep network, the feature extraction process relies on a specially designed dual-path architecture, and the resulting embeddings are concatenated to produce the final learned features. To deal with a variety of uncertainties such as high intra-class variations and noises, we leverage the …fuzzy logic in the design of a custom Convolutional Neural Network (CNN) with a type-2 fuzzy activation function for the first path. Additionally, the second path utilizes the transfer learning-based CNN to enhance the discriminability of the learned features. Our method allows for text-independent writer identification, eliminating the need for identical handwriting samples to train and test the model. Considering that various factors can influence the handwriting style, a dataset containing right-to-left handwriting samples is assembled. The proposed method is evaluated on our developed dataset and four widely-known public datasets, namely KHATT, CVL, Firemaker, and IAM. High accuracy values are achieved, with results of 99.85%, 99.83%, 99.79%, 99.64%, and 98.17% for each dataset, respectively. One noteworthy aspect of this study is that the evaluation results on diverse datasets demonstrate the applicability of the proposed model to various languages. Moreover, the model performs effectively in real-world scenarios with limited handwritten data. Show more

Keywords: Writer identification, convolutional neural networks, Type-2 fuzzy logic, deep feature concatenation

DOI: 10.3233/JIFS-231889

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10937-10949, 2023

Authors: Mishra, Rajiv Kumar | Yadav, Rajesh Kumar | Nath, Prem

Article Type: Research Article

Abstract: The massive amounts of data produced and gathered by smart devices through the internet support a wide range of applications, considerably improving our daily lives. Data sharing among smart devices must be safeguarded due to the sensitivity of the data involved in the transmission. The Internet of Things (IoT) environment must be protected from unauthorised access due to a variety of variables, including its attractiveness to cybercriminals, previous successful cyber-attacks, and consumers’ perceptions of security and reliability. Blockchain technology appears to be one promising technology that appears to address these security challenges extremely effectively. However, given the volume and rate …at which smart devices generate data, Blockchain appears to be inefficient for storing it. The pace of data collection in the IoT context and the speed of transaction confirmation in the Blockchain network are the two key elements behind this. We connect the Blockchain and the Inter-Planetary File System (IPFS) in this study to permit data recording on a distributed storage and a mechanism to restrict access to recorded data to authorised organisations only. Over the Blockchain network, the access policy definition for safe data sharing and cryptographic hash content is stored. The real IoT-generated data, on the other hand, is collected via a distributed storage network, which improves availability and security. The proposed scheme’s analysis and performance evaluation show that it is secure and feasible. Furthermore, simulations are undertaken to assess the operating costs of smart contracts and to test the efficacy and viability of the suggested architecture. Show more

Keywords: IoT, secure data sharing, unauthorized access, blockchain, IPFS, etc.

DOI: 10.3233/JIFS-232483

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10951-10966, 2023

Authors: Rubia J, Jency | Lincy R, Babitha

Article Type: Research Article

Abstract: Deep learning strategies have been achieved over the historical decades to resolve many computer vision applications. Recently, these deep learning algorithms have been extensively used as a tool in classification problems. Generally, the deep learning algorithms trained with gradient-based optimizers, which has some downsides such as the slow speed of convergence and stuck in local minima. As a solution, the planned work using meta-heuristic based Grey Wolf and Whales optimization algorithms for the automatic plant disease detection model. The planned work has explored the application of automatic plant disease identification through the leaf images with the help of the image …processing approach. The planned research has evaluated the deep learning algorithm with Grey Wolf and Whales optimization techniques using the three types of datasets, such as Plant Village, New Plant Disease, and Rice Leaf Disease databases. The simulation consequences illustrate that the computational efficiency of the Grey Wolf and Whales based automatic disease identification process is boosted when coupled with the deep learning method. Show more

Keywords: Plant disease detection, deep learning, meta-heuristic optimizers, Grey Wolf optimization algorithms, Whales optimization algorithms

DOI: 10.3233/JIFS-213423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10967-10983, 2023

Authors: Wang, Ming

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-224523

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10985-10996, 2023

Authors: Ren, Zenggen | Guo, Fu | Hu, Mingcai | Qu, Qingxing | Li, Fengxiang

Article Type: Research Article

Abstract: Generating kansei profiles for products represent fundamental aspects of kansei engineering (KE). Conventionally, the semantic differential (SD) method has been extensively employed to construct product kansei profiles, aiming to delve into consumers’ perceptions of products. However, this approach is associated with significant time consumption and inefficiency. In light of this, we introduce an innovative kansei evaluation approach that incorporates consumers’ kansei preferences, thereby enhancing the efficiency of the evaluation process. This approach comprises three integral modules: Firstly, the generation of product kansei profiles and the construction of a kansei database for decision alternatives are achieved through the analysis of online …reviews. Subsequently, the kansei data is adjusted based on consumers’ kansei preferences. Finally, the rank correlation analysis (RCA) is conducted to establish the prioritization of decision alternatives. Notably, this method facilitates the ranking of products in accordance with consumers’ kansei preferences, thereby assisting consumers in navigating through an array of functionally similar products to identify their preferred choices. A comprehensive case study illustrates the implementation procedure and validates the practicality of our proposed method. Show more

Keywords: Kansei evaluation, kansei preferences, kansei profiles, online reviews, rank correlation analysis

DOI: 10.3233/JIFS-230654

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10997-11012, 2023

Authors: Shafi, Muhammad Ammar | Rusiman, Mohd Saifullah | Jacob, Kavikumar | Musa, Aisya Natasya

Article Type: Research Article

Abstract: With relevant computational software, fuzzy prediction, a new intelligent modelling technique, is utilised to resolve unclear phenomena in various disciplines. Excellent software risk prediction is essential for effective prediction, such as risk management, case planning, and control. We provide an intelligent modelling strategy for software risk prediction in this research. We are applying a support vector machine model and two phases of hybrid fuzzy linear regression clustering (SVM). This method may produce the most accurate risk predictions for various continuous data. The best model with even less error value, acceptable interpretability, and imprecise uncertainty inputs is a fuzzy linear regression …with symmetric parameter clustering with a support vector machine (FLRWSPCSVM), a new intelligent modelling technique. The model’s predictive accuracy is demonstrably higher than other prediction models, according to validation utilising simulation data and four software packages such as SPSS, MATLAB and Weka Explorer. Show more

Keywords: Intelligent modelling, fuzzy hybrid, Prediction data, computation software, statistical error

DOI: 10.3233/JIFS-231814

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11013-11019, 2023

Authors: Sun, Peixi | Wang, Yixuan | Song, Jaehoon

Article Type: Research Article

Abstract: A brand is an enterprise’s market image and huge intangible assets. A brand is an enterprise’s market image and huge intangible assets, and it is also a comprehensive embodiment of an enterprise’s core competitiveness. Therefore, continuous improvement of brand competitiveness undoubtedly has far-reaching significance for manufacturing enterprises. Using the brand competitiveness evaluation index system and selected evaluation methods of manufacturing enterprises constructed in this article, the brand competitiveness evaluation index system and selected evaluation methods can not only study the overall brand competitiveness of the participating enterprises, but also understand the advantages and disadvantages of the brand competitiveness of the …participating enterprises from different perspectives, To help and guide manufacturing enterprises to strengthen brand building in a targeted manner and continuously improve the brand competitiveness of manufacturing enterprises. The brand competitiveness evaluation of manufacturing enterprises is a classical MAGDM problems. Recently, the TODIM and VIKOR method has been used to cope with MAGDM issues. The interval neutrosophic sets (INSs) are used as a tool for characterizing uncertain information during the brand competitiveness evaluation of manufacturing enterprises. In this manuscript, the interval neutrosophic number TODIM-VIKOR (INN-TODIM-VIKOR) method is built to solve the MAGDM under INSs. In the end, a numerical case study for brand competitiveness evaluation of manufacturing enterprises is given to validate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), Interval neutrosophic sets (INSs), TODIM, VIKOR, Brand competitiveness evaluation

DOI: 10.3233/JIFS-232001

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11021-11034, 2023

Authors: Kalaiarasan, D. | Ahilan, A. | Ramalingam, S.

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-213337

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11035-11057, 2023

Authors: Zhang, Shiguang | Guo, Di | Zhou, Ting

Article Type: Research Article

Abstract: Extreme learning machine (ELM) has received increasingly more attention because of its high efficiency and ease of implementation. However, the existing ELM algorithms generally suffer from the drawbacks of noise sensitivity and poor robustness. Therefore, we combine the advantages of twin hyperplanes with the fast speed of ELM, and then introduce the characteristics of heteroscedastic Gaussian noise. In this paper, a new regressor is proposed, which is called twin extreme learning machine based on heteroskedastic Gaussian noise (TELM-HGN). In addition, the augmented Lagrange multiplier method is introduced to optimize and solve the presented model. Finally, a significant number of experiments …were conducted on different data-sets including real wind-speed data, Boston housing price dataset and stock dataset. Experimental results show that the proposed algorithms not only inherits most of the merits of the original ELM, but also has more stable and reliable generalization performance and more accurate prediction results. These applications demonstrate the correctness and effectiveness of the proposed model. Show more

Keywords: Extreme learning machine, heteroscedastic Gaussian noise, least squares support vector regression, twin hyperplanes, wind-speed forecasting

DOI: 10.3233/JIFS-232121

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11059-11073, 2023

Authors: Vidyabharathi, D. | Sivanesh, S. | Theetchenya, S. | Vidhya, G.

Article Type: Research Article

Abstract: Detecting of cracks and damages, especially in multi storied buildings is a crucial aspect of infrastructure and building maintenance, as it ensures safety and reliability. An enhanced framework for the crack detection is proposed to identify the fine cracks which are present at greater heights and not captured to the human vision from the ground. The cracks are identified and classified by the deep convolutional neural network model. The Oriented Non-Maximal Suppression module reduces the false positives to improve the classification accuracy and reliability. The proposed method O-CNN(CNN with ONMS)can be used in real-world for the infrastructure inspection and potential …applications in civil engineering construction. The ability to input different types of data, including images and videos, makes the proposed system user-friendly and easy to use. Furthermore the system reduces the risk of human error and prevents the huge damages caused to the building. Also, it prevents the major loss which may be caused to the lives. Overall, the proposed system contributes to the field of deep learning and computer vision by providing an effective and better solution for crack detection in real-world scenarios. Show more

Keywords: Deep learning, convolutional neural networks, oriented non-maximal suppression, O-CNN

DOI: 10.3233/JIFS-232793

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11075-11091, 2023

Authors: Alphy, Anna | Rajamohamed, | Velusamy, Jayaraj | Vidhya, K. | Ravi, G. | Rajasekaran, Arun Sekar

Article Type: Research Article

Abstract: Age-Related Macular Degeneration is a progressive, irreversible eye condition that causes vision loss and impairs quality of life. The lost potential of the optic nerve cannot be regained, but a patient with Age-Related Macular Degeneration must have early diagnosis and treatment in order to prevent visual loss. The diagnosis of Age-Related Macular Degeneration is based on visual field loss tests, a patient’s medical history, intraocular pressure, and a physical fundus evaluation. Age-Related Macular Degeneration must be diagnosed early in order to avoid irreparable structural damage and vision loss. The objective of the proposed study is to develop a new optimization-driven …strategy-based recurrent neural network using the Internet of Things for the identification of age-related macular degeneration. The Recurrent Neural Network (RNN) classifier is trained using the Particle Swarm Optimization (PSO) technique included into the RNN-IoMT. Initially, the input picture is sent through pre-processing in order to remove noise and artefacts. The generated preprocessed picture is simultaneously sent to optical disc detection and blood vessel detection. In addition, picture level characteristics are extracted from the image that has been preprocessed. Finally, the image-level, optic disc-level, and blood vessel-level features are retrieved and compiled into a feature vector. The acquired feature vector is fed into the RNN classifier, with the suggested PSO used to train the RNN for Age-Related Macular Degeneration detection via the Internet of Medical Things. The suggested PSO+RNN exhibits better performance with enhanced precision of 97.194%, sensitivity of 97.184%, and specificity of 97.2044%, respectively. Show more

Keywords: Wearables, internet of things, teleophthalmology, deep learning, fundus images

DOI: 10.3233/JIFS-233044

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11093-11105, 2023

Authors: Nguyen, Vy Duong Kim | Do, Phuc

Article Type: Research Article

Abstract: People will increasingly get expedited and diverse means of accessing news as societies progress. Furthermore, there is a noticeable increase in the prevalence of incorrect and misleading information. Our research is motivated by the significant concerns regarding the detrimental impacts of disinformation on the general public, political stability, and trust in the media. The scarcity of Vietnamese-language datasets can be attributed to the predominant focus of false news detection studies on datasets only in English. Detection investigations of fake news have predominantly relied on supervised machine learning algorithms, which possess notable limitations when confronted with unclassified news articles that are …either authentic or untrue. The utilization of Knowledge Graphs (KG) and Graph Convolutional Networks (GCN) holds promise in addressing the constraints of supervised machine learning algorithms. To address these problems, we propose an approach that integrates KG)into the procedure for detecting fake news. We utilize the Vietnamese Fake News Detection dataset (VFND-vietnamese-fake-news), comprising authentic and deceptive news articles from reputable Vietnamese newspapers such as vnexpress, tuoitre, and have been collected from 2018 to 2023. News articles are only labeled as real or fake after experiencing independent verification. The Glove embedding (Global Vectors for Word Representation) is employed to establish a knowledge network for the given dataset. This knowledge graph’s construction is accomplished using the Word Mover’s Distance (WMD) algorithm in conjunction with the K-nearest neighbor approach; GCN approach and the input KG train models to discern between real and fake news. With labeling half of the input dataset, the experimental findings indicate a notable level of accuracy, reaching up to 85%. Our research holds significant importance in identifying fake news, particularly within the context of the Vietnamese language. Show more

Keywords: Fake news detection, graph convolutional network, semi-supervised, K-nearest neighbor, word mover’s distance

DOI: 10.3233/JIFS-233260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11107-11119, 2023

Authors: Bo, Lina

Article Type: Research Article

Abstract: In college education, English is a required course for every college student, and undergraduate colleges have certain requirements for college English proficiency. At the same time, English is directly related to its learning, so improving the quality of college English teaching (CET) is very important. Teaching quality is a key indicator for measuring the effectiveness of English teaching. Learning quality evaluation is a very complex process that involves many factors, such as evaluation indicators, evaluation methods, etc. Therefore, establishing an objective and scientific English quality evaluation system is a challenging issue. The CET quality evaluation is a MAGDM. Then, the …TODIM and VIKOR was used to set up MAGDM. The interval-valued intuitionistic fuzzy sets (IVIFSs) are employed as a tool for depicting uncertain information during the CET quality evaluation. In this study, the entropy and score values are employed to establish the objective weights. Then, an integrated interval-valued intuitionistic fuzzy TODIM-VIKOR (IVIF-TODIM-VIKOR) is developed to cope with the MAGDM problem. An illustrative example for CET quality evaluation and some comparative analysis are developed to demonstrate the validity and reliability of IVIF-TODIM-VIKOR method. Show more

Keywords: Multiple attribute group decision making (MAGDM), 2 interval-valued intuitionistic fuzzy sets (IVIFSs), TODIM, VIKOR, CET quality evaluation

DOI: 10.3233/JIFS-234149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11121-11133, 2023

Authors: Sreelatha, Tammineni | Maheswari, M. | Ravi, G. | Manikanda Devarajan, N. | Arun, M.

Article Type: Research Article

Abstract: Data compression is the ancestor of image compression, which uses fewer bits to represent the same picture. It is categorised as lossy or lossless depending on the quality required. In a lossless compression situation, no information is lost during the decompression process. Data loss is possible with the lossy technique since it is not reversible. In an effort to boost compression efficiency while maintaining a high xiv reconstruction quality of picture, near lossless approaches have evolved. The medical pictures consist of a large number of items, each of which may be described in detail and utilised for a variety of …purposes. The clinically relevant item in 2D medical pictures is referred to as the Region of Interest (ROI), whereas in 3D images, it is referred to as the Volume of Interest (VOI). Saving energy is crucial since it is one of the most limited resources in these networks. However, DTN has an additional difficulty since communication between nodes is maintained so long as they are in physical proximity to one another. However, because to the nodes’ mobility, this may not be long enough to provide the necessary multimedia data transmission. Wireless networks are susceptible to security assaults, and traditional security solutions are computationally demanding, making them unsuitable for networks that constantly need to recharge their batteries. All of these are reasons for tackling the problems of multimedia data processing and transmission via wireless networks in this dissertation. With this in mind, it has been attempted to investigate low-overhead and safe multimedia data compression as a solution to the issue that energy-constrained nodes in these networks limit complex multimedia processing while keeping at least basic security features. LZW-OMCA compression using the Octagonal Multimedia Compression Algorithm is part of the suggested method. The purpose of this is to improve the compression ratio. The proposed approach uses a little bit of crypt to compress data, which makes the data unreadable to anybody except the intended receiver, hence providing network security. The previous proposed works analysed the performance of several compression algorithms applied to multimedia material. Performance assessment utilising MSE, SSIM, and other metrics are used to show the pros and cons of each segment. Show more

Keywords: Octagonal multimedia compression algorithm, data compression, LZW-OMCA compression, MSE, SSIM

DOI: 10.3233/JIFS-234314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11135-11147, 2023

Authors: Shi, Jianzhong

Article Type: Research Article

Abstract: Compared with type-2 fuzzy sets, the secondary membership degree of interval type-3 fuzzy sets is an interval rather than crisp value, which makes interval type-3 fuzzy sets can obtain more degree of freedoms. This article studies an interval type-3 fuzzy PID controller based on interval type-3 fuzz sets. The framework of interval type-3 fuzzy PID controller is identical with type-2 fuzzy PID controller, but it contains more adjustment controller parameters and its type reduction procedure is more complex. In this paper, type reduction of interval type-3 fuzzy sets is derived from general type-2 fuzzy sets represented by α-plane and a …direct NT type reduction algorithm is applied. The control effects of interval type-3 fuzzy PID controller are firstly tested by 2 nonlinear plants, the simulation results show that interval type-3 fuzzy PID controller has better control performance indexes than PID controller, type-1 fuzzy PID controller, interval type-2 fuzzy PID controller and general type-2 fuzzy PID controller. Furthermore, the interval type-3 fuzzy PID controller will be applied in rated voltage control of solid oxide fuel cells (SOFC) power plant. The output voltage control of SOFC is quite challenging because of the strong nonlinearity, limited fuel flow, and rapid variation of the load disturbance. The simulation results demonstrate the advantages and robustness of proposed interval type-3 fuzzy PID controller. Show more

Keywords: Type-2 fuzzy sets, interval type-3 fuzzy sets, interval type-3 fuzzy control system, type reduction, solid oxide fuel cells

DOI: 10.3233/JIFS-231460

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11149-11162, 2023

Authors: Chandra, Harshit | Bajpai, Shrish | Alam, Monauwer | Chandel, Vishal Singh | Pandey, Amit Kumar | Pandey, Digvijay

Article Type: Research Article

Abstract: Hyperspectral (HS) images contain rich spatial and spectral information. Due to its large size, it is difficult to store, process, analyze, or transmit the critical information contained in it. The compression of hyperspectral images is inevitable. Many transform based Hyper Spectral Image Compression Algorithms (HSICAs) have been proposed in the past that work for both lossy and lossless compression processes. The transform based HSICA uses linked lists or dedicated markers or array structure to keep track of significant and insignificant sets or coefficients of a transformed HS image. However, these algorithms either suffered from low coding efficiency, high memory requirements, …or high coding complexity. This work proposes a transform based HSICA using a curvelet transform to improve the directional elements and the ability to represent edges and other singularities along curves. The proposed HSICA aims to provide superior quality compressed HS images by representing HS images at different scales and directions and to achieve a high compression ratio. Experimental results show that the proposed algorithm has a low coding memory requirement with a 2% to 5% increase in coding gain compared to the other state of art compression algorithms. Show more

Keywords: Complexity theory, curvelet transform, hyperspectral image, hyperspectral image compression, transform coding

DOI: 10.3233/JIFS-231684

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11163-11187, 2023

Authors: Yang, Lei | Gao, Ge

Article Type: Research Article

Abstract: As China’s economic and social development enters a new stage, the role of innovation and entrepreneurship is becoming increasingly prominent, and its importance is also being emphasized. As the main force for future employment and national economic construction, college students naturally become the new force for innovation and entrepreneurship. Therefore, it is imperative for universities to carry out in-depth innovation and entrepreneurship education (IEE) for college students. Then, with the continuous development of social development needs and the professional growth needs of college students, the “innovation and entrepreneurship” education for college students should also be adjusted in a timely manner …in terms of educational concepts, models, and methods. The IEE environment evaluation in universities under the background of “ Double Innovation” is looked as multiple attribute decision-making (MADM). In this paper, the information entropy model is employed to calculate the objective weight of the evaluation attribute. Then, interval-valued intuitionistic fuzzy Combined Compromise Solution (IVIF-CoCoSo) is built based on the Hamming distance and Euclid distance to cope with MADM under interval-valued intuitionistic fuzzy sets (IVIFSs). The new MADM method is proposed for IEE environment evaluation in universities under the background of “ Double Innovation”. Finally, the IVIF-CoCoSo approach is compared with existing methods to verify the effectiveness of IVIF-CoCoSo algorithm. The main contributions of this constructed paper are: (1) the IVIF-CoCoSo method is built based on the Hamming distance and Euclid distance. (2) the information entropy model is employed to calculate the objective weight of the evaluation attribute. (3) The new MADM method is proposed for IEE environment evaluation in universities under the background of “ Double Innovation” based on IVIF-CoCoSo. (4) The IVIF-CoCoSo model is compared with existing methods to verify the effectiveness of the IVIF-CoCoSo algorithm. Show more

Keywords: Multi-attribute decision making (MADM), interval-valued intuitionistic fuzzy sets (IVIFSs), IVIF-CoCoSo method, information entropy method; IEE environment

DOI: 10.3233/JIFS-232151

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11189-11201, 2023

Authors: Abiyev, Rahib H. | Aliev, Rafik | Kaynak, Okyay

Article Type: Research Article

Abstract: In this paper, a novel Z-number based Fuzzy Neural Network (Z-FNN) based on the integration of Z-valued fuzzy logic and neural networks is proposed. Z-valued fuzzy rule base is presented and its inference process is described using interpolative approximate reasoning. Accordingly, the structure of the Z-FNN is proposed using a distance measure and interpolative approximate reasoning scheme. Based on presented architecture the learning algorithm of Z-FNN is designed. The updating of the unknown parameters of the network is carried out using Genetic Algorithms (GA). The proposed Z-FNN system is utilized for dynamic plant identification. The effectiveness of Z-FNN has been …tested by comparing its performance with the performances of other fuzzy systems available in the literature. The proposed approach has been proven to be a suitable alternative for the identification of nonlinear systems characterized by uncertain and imprecise information. Show more

Keywords: Fuzzy neural networks, Z-number, fuzzy rule, learning

DOI: 10.3233/JIFS-232741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11203-11216, 2023

Authors: Ashwin Shenoy, M. | Thillaiarasu, N.

Article Type: Research Article

Abstract: Automated identification of human activities remains a complex endeavor, particularly in unique settings like temple environments. This study focuses on employing machine learning and deep learning techniques to analyze human activities for intelligent temple surveillance. However, due to the scarcity of standardized datasets tailored for temple surveillance, there is a need for specialized data. In response, this research introduces a pioneering dataset featuring Eight distinct classes of human activities, predominantly centered on hand gestures and body postures. To identify the most effective solution for Human Activity Recognition (HAR), a comprehensive ablation study is conducted, involving a variety of conventional machine …learning and deep learning models. By integrating YOLOv4’s robust object detection capabilities with ConvLSTM’s ability to model both spatial and temporal dependencies in spatio-temporal data, the approach becomes capable of recognizing and understanding human activities in sequences of images or video frames. Notably, the proposed YOLOv4-ConvLSTM approach emerges as the optimal choice, showcasing a remarkable accuracy of 93.68%. This outcome underscores the suitability of the outlined methodology for diverse HAR applications in temple environments. Show more

Keywords: Dataset, machine learning, deep learning, YOLOv4, ConvLSTM, Human Activity Recognition

DOI: 10.3233/JIFS-233919

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11217-11232, 2023

Authors: Zhou, Jiwen

Article Type: Research Article

Abstract: With the proposal of the “Double First Class” construction concept in Chinese universities, cultivating high-quality talents has become the main direction of university education. At present, local universities in China are actively adapting to the changes of the times, taking the construction of the Double First Class as an opportunity, continuously adjusting and deepening the reform of the double innovation and innovation talent cultivation mode, further improving the curriculum system, increasing investment in professional practical teaching, stimulating students’ enthusiasm for innovation and entrepreneurship (IAE), and focusing on improving students’ comprehensive abilities, achieving significant results in talent cultivation. The “Entrepreneurship and …Entrepreneurship” career ability evaluation of local college students could be considered as multiple attribute decision-making (MADM). Recently, the Combined Compromise Solution (CoCoSo) method and information entropy method was employed to deal with MADM. The triangular fuzzy neutrosophic sets (TFNSs) are employed as a better tool for expressing uncertain information during the “Entrepreneurship and Entrepreneurship” career ability evaluation of local college students. In this paper, the triangular fuzzy neutrosophic number CoCoSo (TFNN-CoCoSo) based on the Hamming distance and Euclid distance is constructed to cope with the MADM under TFNSs. The information entropy method is employed to compute the weight values based on the Hamming distance and Euclid distance under TFNSs. Finally, a numerical example of “Entrepreneurship and Entrepreneurship” career ability evaluation of local college students is constructed and some decision comparisons are constructed to verify the TFNN-CoCoSo method. Show more

Keywords: Multiple attribute decision making (MADM), TFNSs, CoCoSo model, information entropy, career education quality evaluation

DOI: 10.3233/JIFS-234138

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11233-11246, 2023

Authors: Tan, Ruipu | Chen, Chong | Zhang, Wende | Yang, Lehua | Ma, Hangfei

Article Type: Research Article

Abstract: With the rising incidences of emergencies, it is both challenging and meaningful to study how to make decisions quickly and take appropriate measures to control the spread and evolution of the situation. However, most current emergency decision-making focuses on mathematical model construction, whereas fuzzy decision-making is biased towards subjective assumptions, which are both insufficient for practicability. We studied the intelligent acquisition of single-valued neutrosophic numbers based on emotional tendency analysis and applied them to emergency decision-making. First, Python programming technology was used to crawl, preprocess, and statistically analyse the network comment data of emergencies, and a quantised single-value neutrosophic number …was obtained. Second, the attribute values, represented as neutrosophic numbers, were uniformly converted into cloud droplets, and the weight of the attribute values was objectively determined according to the digital feature entropy in the cloud droplets. Subsequently, a case-based reasoning approach was used to calculate the combined weighted similarity between the alternatives and ideal solution (target case) to obtain a ranking of the alternatives and historical cases in the case base that best matches the target case. Finally, a typhoon disaster assessment was considered as an example to verify the feasibility and effectiveness of the proposed method, and the advantages of the proposed method were emphasised through multi-aspect and multi-angle comparative analyses. The relevant research can be used for public opinion monitoring during emergencies and emergency handling. Show more

Keywords: Emergency decision-making, intelligent acquisition of single-valued neutrosophic numbers, emotional tendency analysis, cloud model, case-based reasoning (CBR)

DOI: 10.3233/JIFS-231039

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11247-11268, 2023

Authors: Deng, Ming | Zhou, Zhiheng | Liu, Guoqi | Zeng, Delu | Zhang, Mingyue

Article Type: Research Article

Abstract: Some active contour models proposed based on intensity inhomogeneity are sensitive to initialization and cannot achieve ideal segmentation results for real images. An adaptive active contour model based on local bias field estimation and saliency is proposed in this paper. First of all, this model proposes an adaptive multi-local search algorithm, which avoids the initialization sensitivity by adaptively setting of the initial contour; Secondly, the local bias field is estimated by fusing the saliency map and fuzzy c-means clustering; Finally, the new bias field and the corrected energy fitting constant are used to define the new energy functional. The desired …target object is obtained by minimizing the energy functional. The experimental results show that the segmentation accuracy of the model proposed in this paper is higher than that of the models participating in the comparison. The proposed model can not only avoid the interference of initialization and redundant information, but also segment images with intensity inhomogeneity effectively. Show more

Keywords: Active contour model, intensity inhomogeneity, bias field, saliency map

DOI: 10.3233/JIFS-231741

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11269-11283, 2023

Authors: Shahi, Samira | Navidi, Hamidreza

Article Type: Research Article

Abstract: This paper proposes an efficient interval solidarity value that operates well for interval cooperative games. In addition to the axioms of symmetry, efficiency, and additivity, this value also satisfies two new axioms, namely, interval-egalitarian A-null player and interval differential marginality. The interval-egalitarian A-null player axiom equally divides the result of the difference between the grand coalition value and the sum of the solidarity value of players in the degenerate interval game among A-null players. The interval differential marginality axiom is an interval version of the Casajus differential marginality axiom. This property states that the difference in the interval solidarity value …of two players is determined by the difference between their average marginal contributions in the degenerate interval game. Eventually, the efficiency results and applicability of the proposed approach are compared with those of the other methods. Show more

Keywords: Interval cooperative games, solidarity value, efficiency, uncertainty

DOI: 10.3233/JIFS-223736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11285-11293, 2023

Authors: Mostafa, Ayman Mohamed | Rushdy, Ehab | Medhat, Reham | Hanafy, Asmaa

Article Type: Research Article

Abstract: Cloud computing is a cost-effective way for organizations to access and use IT resources. However, it also exposes data to security threats. Authentication and authorization are crucial components of access control that prevent unauthorized access to cloud services. Organizations are turning to identity management solutions to help IT administrators face and mitigate security concerns. Identity management (IDM) has been recognized as a more robust solution for validating and maintaining digital identities. Identity management (IDM) is a key security mechanism for cloud computing that helps to ensure that only authorized users have access to data and resources. Traditional IDM solutions are …centralized and rely on a single authority to manage user identities, which makes them vulnerable to attack. However, existing identity management solutions need to be more secure and trustworthy. Blockchain technology can create a more secure and trustworthy cloud transaction environment. Purpose: This paper investigates the security and trustworthiness of existing identity management solutions in cloud computing. Comparative results: We compared 14 traditional IDM schemes in cloud systems to explore contributions and limitations. This paper also compared 17 centralized, decentralized, and federated IDM models to explain their functions, roles, performance, contribution, primary metrics, and target attacks. About 17 IDM models have also been compared to explore their efficiency, overhead consumption, effectiveness to malicious users, trustworthiness, throughput, and privacy. Major conclusions: Blockchain technology has the potential to make cloud transactions more secure and reliable. It featured strong authentication and authorization mechanisms based on smart contracts on the Ethereum platform. As a result, it is still regarded as a reliable and immutable solution for protecting data sharing between entities in peer-to-peer networks. However, there is still a large gap between the theoretical method and its practical application. This paper also helps other scholars in the field discover issues and solutions and make suggestions for future research. Show more

Keywords: Cloud computing, identity management, blockchain, security-as-a-service, single-sign-on model

DOI: 10.3233/JIFS-231911

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11295-11317, 2023

Authors: Yu, Xulong | Yu, Qiancheng | Zhang, Yue | Wang, Aoqiang | Wang, Jinyun

Article Type: Research Article

Abstract: Traditional methods for detecting surface defects on ceramic tiles result in misdetection and missed detection, which makes it difficult to guarantee product stability and consistency within the same batch. Therefore, this article proposes an improved YOLOv5 algorithm for detecting surface defects on ceramic tiles. Firstly, the Res2Net module is combined with self-attention to fully utilize local and global information and improve the feature extraction effect of defects. Secondly, the GS-BiFPN neck network is designed to enhance the fusion capability of shallow detail and deep semantic information and alleviate ambiguity and redundancy on the feature map. Then, a lightweight attention module …is introduced to improve the detection capability of difficult-to-recognize defects and anti-background interference. Finally, the SIoU loss function improves the model’s convergence speed and accuracy. Experimental results demonstrate that the improved algorithm’s mean average precision (mAP) reaches 73.3%, 6.3% higher than the baseline model. Even when compared with YOLOv7-tiny, the mAP of the improved algorithm has increased by 8.7%. Additionally, the detection speed of the model can reach 92 frames per second, which can meet the requirements of ceramic tile surface defect detection in industrial scenarios. Show more

Keywords: Defect detection, YOLO, Attention mechanism, multi-scale feature fusion, SIoU

DOI: 10.3233/JIFS-231991

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11319-11331, 2023

Authors: Ji, Bin | Zhou, Chuhao | Chen, Ze | Zheng, Shuai

Article Type: Research Article

Abstract: The use of similarity measures in interval-valued neutrosophic sets (IVNSs) theory is essential for comparing and assessing the degree of IVNSs difference. However, existing similarity measures for IVNSs suffer from several issues such as lacking precise axiomatic definitions, counterintuitive results, division by zero errors, inability to distinguish between positive and negative differences, and failure to satisfy the ranking definition. To address these limitations, we propose a novel multi-parameter similarity measure for IVNSs based on the tangent function. We demonstrate that our measure satisfies the axiomatic definition and apply it to medical diagnosis, achieving accurate diagnostic results. Additionally, we consider the …interactions between symptoms, adjust the proposed similarity measure using Choquet integrals, and provide analytical comparisons to demonstrate the advantages of our improved similarity measure, highlighting its stability and high confidence in the field of medical diagnosis.This study contributes to the advancement of similarity measures in IVNSs theory and provides valuable insights for the field of medical diagnosis. Show more

Keywords: Interval-valued neutrosophic sets, similarity measures, medical diagnosis, tangent function, choquet integrals

DOI: 10.3233/JIFS-232444

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11333-11351, 2023

Authors: Lv, Zhaoming

Article Type: Research Article

Abstract: Metaheuristics are widely used in science and industry because it as a high-level heuristic technique can provide robust or advanced solutions compared to classical search algorithms. Flow Regime Algorithm is a novel physics-based optimization approach recently proposed, and it is one of the candidate algorithms for solving complex optimization problems because of its few parameter configurations, simple coding, and good performance. However, the population that initialized randomly may have poor diversity issues, resulting in insufficient global search, and premature convergence to local optimum. To solve this problem, in this paper, a novel enhanced Flow Regime Algorithm based on opposition learning …scheme is proposed. The proposed algorithm introduces the opposition-based learning strategy into the generation of some populations to enhance the global search performance while maintaining a fast convergence rate. In order to verify the performance of the proposed algorithm, 23 benchmark numerical optimization functions were studied experimentally in detail and compared with six well-known algorithms. Experimental results show that the proposed algorithm outperforms all other metaheuristic algorithms in all unimodal functions with higher accuracy, and can obtain competitive results on more multimodal cases. A statistical comparison shows that the proposed algorithm has superiority. Finally, that the proposed algorithm can achieve higher quality alignment compared to most other metaheuristic-based systems and OAEI ontology alignment systems. Show more

Keywords: Meta-heuristic algorithms, flow regime algorithm, opposition-based learning, benchmark functions

DOI: 10.3233/JIFS-233329

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11353-11368, 2023

Authors: Saraswathi, C. | Pushpa, B.

Article Type: Research Article

Abstract: Alopecia Areata (AA) is one of the most widespread diseases, which is generally classified and diagnosed by the Computer Aided Diagnosis (CAD) models. Though it improves AA diagnosis, it has limited interoperability and needs skilled radiologists in medical image interpretation. This problem can be solved by developing Deep Learning (DL) models with CAD for accurately diagnosing AA patients. Many studies engaged only in specific DL models such as Convolutional Neural Network (CNN) in medical imaging, which provides different independent results and many parameters, which limits their generalizability for different datasets. To combat this limitation, this work proposes an Ensemble Pre-Learned …DL and an Optimized Long Short-Term Memory (EPL-OLSTM) model for AA classification. Initially, many healthy and AA scalp hair images are separately fed to the pre-learned CNN structures, i.e. AlexNet, ResNet, and InceptionNet to extract the deep features. Then, these features are passed to the OLSTM, in which the Battle Royale Optimization (BRO) algorithm is applied to optimize the LSTM’s hyperparameters. Moreover, the output of the LSTM is classified by the fuzzy-softmax into the associated AA classes, including mild, moderate, and severe. Thus, this model can increase the accuracy of differentiating between healthy and multiple AA scalp hair classes. Finally, an extensive experiment using the Figaro1k (for healthy scalp hair images) and DermNet (for different AA scalp hair images) datasets demonstrates that the EPL-OLSTM achieves 93.1% accuracy compared to the state-of-the-art DL models. Show more

Keywords: Alopecia areata, computer-aided diagnosis, deep learning, pre-learned CNN, LSTM, battle royale optimizer, fuzzy-softmax

DOI: 10.3233/JIFS-232172

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11369-11380, 2023

Authors: Yu, Shanshan | Wang, Yajun

Article Type: Research Article

Abstract: The street design and landscape in China include cultural elements representing the Heritage and history of this generation. Such designs are planned, fabricated, and implemented based on previous elements and novel findings from the past. The novel findings are identified using sophisticated technologies like the Internet of Things (IoT). Therefore, this article introduces a Cultural Design Planning Method (CDPM) for Street Landscape (SL) in maintaining the renowned Heritage of Chinese roads. The proposed method relies on IoT-based data and cultural elements from the previous design and its impact on society. In this case, the impact is computed using attraction and …cultural progression from the tourists and location. The cultural element’s connectivity and resemblance to the current location display the cultural progression. Such progression and impacts are recurrently validated using deep learning; the learning process identifies the elements and their associated impact on society. The previous and current street designs are augmented in the learning process to leverage placement and street design precision. The landscapes are periodically validated based on the current trends and associations. Show more

Keywords: Chinese cultural elements, deep learning, IoT, street landscape

DOI: 10.3233/JIFS-232292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11381-11395, 2023

Authors: Karthikeyan, P. | Brindha, K.

Article Type: Research Article

Abstract: Decentralised fog computing can provide real-time interaction, minimize latency, heterogeneity, and provide networking services between edge devices and cloud data centers. One of the biggest challenges in the fog layer network is finding a trustworthy fog node. Trust management encompasses the process of being trustworthy and the act of assessing the reliability of other nodes. It is essential to carry out a comprehensive review using a systematic approach in this field to advance our understanding, address emerging challenges, and foster secure and efficient trust management practices. This research paper considers a comprehensive analysis of high-quality fog computing trust management literature …from 2018 to 2022. A variety of distinct approaches have been chosen by fog computing-based trust management and these techniques are classified into three categories: algorithms, challenges, and limitations. Further, it reviews the various trust attacks in fog environments, details the solutions proposed in the current literature, and concludes with a discussion of the open challenges and potential future research directions in fog computing. Show more

Keywords: Trust management, fog computing, cloud computing, edge devices, security

DOI: 10.3233/JIFS-232892

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11397-11423, 2023

Authors: Ma, Junpeng | Liu, Feiyan | Xiao, Chenggang | Wang, Kairan | Liu, Zirui

Article Type: Research Article

Abstract: The wake effect of wind farm can reduce the incoming wind speed at the wind turbine located in the downstream direction, resulting in the decrease of global output. WRF model adopts a three-layer two-way nested grid division scheme to simulate the upper atmospheric circulation, obtain wind speed, wind direction and other data that can truly reproduce the fluid characteristics of the regional wind farm group. The boundary conditions and solution conditions of CFD model are set, and the computational fluid dynamics model of the region is obtained. WRF is coupled with CFD, and Fitch wake model is introduced into it. …By introducing the drag coefficient of wind turbine into the calculation of wind speed and turbulent kinetic energy in CFD-WRF coupling model, the wind field characteristics and wake effect of wind farm are simulated online. Monte Carlo sampling method is used to obtain random wind resource data in CFD-WRF coupling model, and then the sampled data is used to calculate the group output of wind farms, and evaluate the impact of wake effect on wind farm treatment. The experimental results show that this method can effectively analyze the characteristic data of regional wind field, and the calculation time of RANS method is about 3 s. Due to the wake effect, the overall output and efficiency of wind field will be significantly reduced. Show more

Keywords: CFD-WRF coupling, wind resource map, wind farm group, wake effect evaluation, wind speed and direction data, fitch wake model

DOI: 10.3233/JIFS-233273

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11425-11437, 2023

Authors: Pi, Feng | Tian, Shengwei | Pei, Xinjun | Chen, Peng | Wang, Xin | Wang, Xiaowei

Article Type: Research Article

Abstract: With the development of the Internet of Things (IoT), mobile devices are playing an increasingly important role in our daily lives. There are various malware threats present in these mobile devices, which can steal users’ personal information. Some malware exploits Inter-Component Communication (ICC) to execute malicious activities for unauthorized data access and system control, enabling communication between different components within an app and between different apps. In this paper, we propose an Adaptive Transformer-based malware framework (named AdaTrans) that combines sensitive Application Programming Interface (API)- and ICC-related features. The framework first extracts sensitive function call subgraphs (SFCS) to reflect the …caller-callee relationships, and then utilizes ICC interactions to reveal hidden communication patterns in malicious activities. Moreover, we propose a novel adaptive Transformer model to detect malicious behaviors. We evaluate our framework on real-world datasets and demonstrate that AdaTrans consistently outperforms other existing state-of-the-art systems. Show more

Keywords: Internet of things, ICC, Malware detection, transformer

DOI: 10.3233/JIFS-233556

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11439-11452, 2023

Authors: Lyu, Yucheng | Mo, Yuanbin | Yue, Songqing | Hong, Lila

Article Type: Research Article

Abstract: Optimization problems in the field of industrial engineering usually involve massive amounts of information and complex scheduling process with the characteristics of high-dimension and non-convexity, which bring many challenges to finding an optimal solution. We proposed an improved beetle swarm optimization (IBSO) algorithm demonstrating the potential to solve different problems of path planning in static environment with good performance. Firstly, the algorithm is an upgrade of the original beetle antennae search (BAS) algorithm and the search strategy is improved by replacing a single beetle by multiple beetles. Secondly, the global search ability gets enhanced, and the diversity of optimization is …improved through introducing nonlinear sinusoidal disturbance with Levy flight mechanism in beetles’ position. Finally, the search performance of beetle swarm is improved by simulating the characteristics of employment bees to search for a better solution near the honey source field in the Artificial Bee Colony (ABC) algorithm. Our experiment results show that IBSO algorithm can achieve higher search efficiency and wider search ranges through well balancing the advantages of local search and fast optimization of the BAS algorithm with the global search of the improved mechanism. The IBSO algorithm has shown the potential to provide a new solution for several optimization problems in path planning in static environment. Show more

Keywords: Path planning, beetle antennae search algorithm, Levy flight, artificial bee colony algorithm

DOI: 10.3233/JIFS-224163

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11453-11479, 2023

Authors: Zhang, Lei | Gu, Yue | Xia, Pengfei | Wei, Chuyuan | Yang, Chengwei

Article Type: Research Article

Abstract: Knowledge graphs are knowledge bases that represent entities and relations in the objective world through graph structure, and they have a boosting effect on many artificial intelligence tasks. To facilitate the development of downstream artificial intelligence tasks, knowledge graph embedding (KGE) is proposed. It aims to express semantic information for each entity and relation in the knowledge graph within a low-dimensional space. However, when it comes to semantic hierarchy, multiple relation patterns and multi-fold relational structures in knowledge graphs, most of the existing models tend to focus on only one or two aspects, often neglecting the importance of considering all …three simultaneously. Therefore, we propose a new knowledge graph embedding model, Hierarchical relation and Entity Rotation-based Multi-Feature knowledge graph Embedding (HERotMFE). Concerning hierarchical relation rotation and entity rotation, it can represent semantic hierarchy, multiple relation patterns and multi-fold relations simultaneously. Self-attention mechanism is used to learn the weights of the two-part rotation to further enhance the model’s performance. According to the findings of the experiments, HERotMFE outperforms existing models on most metrics and achieves state-of-the-art results. Show more

Keywords: Knowledge graph embedding, multi-feature, multi-fold relation, hierarchical relation, self-attention mechanism

DOI: 10.3233/JIFS-231774

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11481-11493, 2023

Authors: Zhou, Xueling | Sun, Lei | Wei, Cuiping

Article Type: Research Article

Abstract: With the advancement of technology and growing social demand, large scale group decision making has gained significant importance in the field of decision making. Clustering analysis plays a crucial role in enhancing the efficiency of large scale group decision making processes. Linguistic evaluation is more in line with people’s cognitive and expressive habits. The hesitant fuzzy linguistic term set (HFLTS) offers more flexibility in expressing evaluation information. This paper is dedicated to designing a fuzzy C -means clustering model that is specifically applicable for the hesitant fuzzy linguistic preference relation (HFLPR). The objective function of the model is built based …on the dissimilarity between HFLPRs and the initial cluster centers to obtain the fuzzy membership matrix and cluster centers. Since initializing cluster centers is a crucial step to produce a reasonable cluster result, three methods are proposed for generating initial centers for HFLPRs. The first and second methods are improvements over existing approaches that dealt with the clustering problem with numerical values. The third approach considers both the preference relation of preferring an alternative and the distribution of the actual preference relations. Based on this, a fuzzy C -means clustering algorithm with HFLPR is designed to obtain the cluster centers and membership matrix for there types of initializing clustering centers. Finally, based on the quality and speed of clustering, a numerical example and comparative analyses illustrate that the proposed clustering algorithm is efficient and effective. Show more

Keywords: Large scale group decision making, Fuzzy C-means clustering, Hesitant fuzzy linguistic term set, Probabilistic linguistic term set

DOI: 10.3233/JIFS-224098

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11495-11514, 2023

Authors: Muthulakshmi, V. | Hemapriya, N.

Article Type: Research Article

Abstract: The advent of deep learning techniques has ignited interest in medical image processing. The proposed work in this paper suggests one of the edge technologies in deep learning, which is recommended, based on a Radiomics feature extraction model for the effective detection of Kaposi sarcoma, a vascular skin lesion expression that indicates the most prevalent cancer in AIDS patients. This work investigates the role and impact of medical image fusion on deep feature learning based on ensemble learning in the medical domain. The model is crafted wherein the pre-built ResNet50 (Residual network) and Visual Geometry Group (VGG16) are fine-tuned and …an ensemble learning approach is applied. The pre-defined CNN was incrementally regulated to determine the appropriate standards for classification efficiency improvements. Our findings show that layer-by-layer fine-tuning can improve the performance of middle and deep layers. This work would serve the purpose of masking and classification of skin lesion images, primarily sarcoma using an ensemble approach. Our proposed assisted framework could be deployed in assisting radiologists by classifying Kaposi sarcoma as well as other related skin lesion diseases, based on the positive classification findings. Show more

Keywords: Kaposi sarcoma, vascular skin lesions, ensemble learning, ResNet50, VGG16, radiomics

DOI: 10.3233/JIFS-230426

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11515-11534, 2023

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

Article Type: Research Article

Abstract: Uncertainty exists in numerous evaluation and decision making problems and therefore it also provides space for the subjective preferences of decision makers to affect the aggregation and evaluation results. Recently, relative basic uncertain information is proposed to further generalize basic uncertain information, but currently there is no research on how to apply this type of uncertainty in both theory and practices. There is also a paucity of decision methodology about how to build systematic preference involved decision model considering this new type of uncertainty. The relative basic uncertain information can serve as a general frame to enable the possibility for …simultaneously handling heterogeneous uncertain information including interval information, basic uncertain information, and relative basic uncertain information. Different types of bipolar subjective preferences commonly should be taken into consideration in practical decision making. With the individual heterogeneous uncertain information and the involved two types of subjective preferences, namely bipolar preferences for uncertainties and bipolar optimism-pessimism preferences, the evaluation and decision making become more complex. This work proposes a systematic intersubjective decision model which can effectively and reasonably deal with the decision scenario with such complex uncertainty, in which Yager preference induced weights allocation is applied. Some novel preference conversion and transformation functions, specified techniques, and the related decision making procedures and sub-modules are proposed and analyzed. An application is also presented to showthe practicality of the proposed decision models and related conversion and transformation functions. Show more

Keywords: Basic uncertain information, decision making, information fusion, relative basic uncertain information, uncertain decision making, Yager induced weights allocation

DOI: 10.3233/JIFS-231395

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11535-11547, 2023

Authors: Gong, Kaixin | Ma, Weimin | Ren, Zitong | Wang, Jia

Article Type: Research Article

Abstract: Large-scale group decision-making (LSGDM) issues are increasingly prevalent in modern society across various domains. The preference information has emerged as a widely adopted approach to tackle LSGDM problems. However, a significant challenge lies in facilitating consensus among decision-makers (DMs) with diverse backgrounds while considering their hesitation and psychological behavior. Consequently, there is a pressing need to establish a novel model that enables DMs to evaluate alternatives with heterogeneous preference relations (HPRs). To this end, this research presents a new consensus-building method to address LSGDM problems with HPRs. First, a novel approach for solving collective priority weight is introduced based on …cosine similarity and prospect theory. In particular, a new cosine similarity measure is defined for HPRs. Subsequently, a consensus index is provided to gauge the consensus level among DMs by considering their psychological behavior and risk attitudes. Further, a consensus-reaching model is developed to address LSGDM with HPRs. Finally, an instance of supplier selection is presented to demonstrate the practicality and efficacy of the proposed method. Show more

Keywords: Large-scale group decision-making, prospect theory, heterogeneous preference relations, consensus reaching, risk attitudes

DOI: 10.3233/JIFS-231456

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11549-11566, 2023

Authors: Huang, Feidan | Deng, Zexi | Cao, Fasheng

Article Type: Research Article

Abstract: Concepts of generalized commutative intuitionistic fuzzy finite state machines and generalized switching intuitionistic fuzzy finite state machines are proposed in this paper, and properties of these two kinds of intuitionistic fuzzy finite state machines are investigated. By using the conditions associated with generalized commutativity, a kind of congruences of intuitionistic fuzzy finite state machines is defined. Homomorphic properties of generalized commutative intuitionistic fuzzy finite state machine are also discussed. Moreover, products of generalized commutative intuitionistic fuzzy finite state machines and products of generalized switching intuitionistic fuzzy finite state machines are studied, and some related properties are proved.

Keywords: Intuitionistic fuzzy finite state machine, generalized commutativity, congruence, homomorphism, product

DOI: 10.3233/JIFS-231549

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11567-11583, 2023

Authors: Wang, Liming | Liu, Yingming | Pang, Xinfu | Wang, Qimin | Wang, Xiaodong

Article Type: Research Article

Abstract: A low-carbon economic scheduling method based on a Q-learning-based multiobjective memetic algorithm (Q-MOMA) is proposed to improve the economy of cogeneration system scheduling and reduce carbon emission. First, the model incorporates a carbon capture device, a heat storage device, and a demand response mechanism to enhance the system’s flexibility and wind power consumption. In addition, the Q-MOMA algorithm combines global and local search and uses a Q-learning algorithm to dynamically adjust the crossover and mutation probabilities to improve the algorithm’s searchability. Finally, the fuzzy membership function method is used to make a multiobjective decision, which balances the economy and low …carbon of the system, and a compromise scheduling scheme is given. The effectiveness of the proposed model and solution method is verified through the simulation calculation of the improved system and compared with the simulation results of various optimization algorithms. The simulation results show that the proposed model can improve the wind power consumption space and the system’s economy and reduce carbon emissions. The Q-MOMA algorithm has a relatively better optimization ability in the low-carbon economic scheduling of the cogeneration system. Show more

Keywords: Bi-objective optimization, carbon capture, demand response, memetic algorithm, Q-learning

DOI: 10.3233/JIFS-231824

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11585-11600, 2023

Authors: Wenjun, Zhou | Jianmin, Ma

Article Type: Research Article

Abstract: In the speaker verification task based on Gaussian Mixture Model-Universal Background Model (GMM-UBM), by constructing the UBM as a tree structure, the kernel Gaussians suitable for different speakers can be quickly selected, which speeds up the modeling of speaker acoustic space by GMM. The tree-based kernel selection algorithm (TBKS) introduces a beam-width, which increases the candidate range of kernels and improves the kernel selection accuracy. In this paper, we improve the TBKS algorithm by introducing a recall rate to adjust the number of nodes recalled in each layer of the tree structure. This adjustment refines the quantity and resolution of …Gaussian distributions in various subspaces within the acoustic space, compensating for the loss caused by discarding some significant Gaussians erroneously. Speaker verification experiments are carried out based on the Aishell2 dataset. The results reveal that the modified TBKS algorithm reduces EER by 7.5% relatively and increses computational reduction factor to 42.93, enhancing both recognition accuracy and speed. In addition, the test speech is spliced into different lengths and common environmental noise is added to verify the universality of the improved algorithm. Show more

Keywords: Speaker verification, fast scoring, gaussian mixture model, tree-based kernel selection

DOI: 10.3233/JIFS-232304

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11601-11611, 2023

Authors: Sermakani, A.M. | Paulraj, D.

Article Type: Research Article

Abstract: The contemporary development of cloud is a next generation federated cloud technology envisioned by virtualization to enable cost-efficient usage of computing resources. The resources are intended on scalability as data grows enormously with on demand services. Federated cloud is an efficient networked computing environment that can adopt infrastructure which aims for virtual unlimited pool during on demand services. The challenging task for federated cloud includes managing workloads of individual cloud, progressing virtual machine volumes, cost utilization, fair load distribution. In order to addresses these challenges, this approach uses “Optimized Bit Matrix based Node Acquisition for Federated cloud (BMNF)”. The framework …process two different approaches: managing bit matrix and fulfilling load distribution in federated cloud based on cost aware workloads. The formation of bit matrix designed by each member in cloud services that validates load availability status. Load distribution factor concentrates on fair allocation with cost aware policy at individual level. BMNF policy segregates the request among various clouds by analyzing bits patterns. In addition to load distribution using bit matrix, it also focuses on improving cost utilization and targets with better quality of load distribution. The proposed working model is highly efficient with computation and communication overhead for federated cloud. Show more

Keywords: Cloud computing, load distribution, virtualization, federated cloud, virtual machine allocation, quality of service

DOI: 10.3233/JIFS-232897

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11613-11627, 2023

Authors: Minh, K.D. | Nguyen, X.H. | Nguyen, V.P.

Article Type: Research Article

Abstract: With the rapid expansion of artificial intelligence (AI) and machine learning, the evaluation of AI cloud platforms has become a critical research topic. Given the availability of many platforms, selecting the best AI cloud services that can satisfy the requirements and budget of an organization is crucial. Several solutions, each with its advantages and disadvantages, are available. In this study, a combinative-distance-based assessment approach was proposed in probabilistic linguistic hesitant fuzzy sets (PLHFSs) to accommodate the multiple characteristics of group decision-making. The original data were normalized using a standardized process that integrated numerous methodologies. Furthermore, under PLHFSs, the statistical variance …approach was used to generate the weighted objective of the vector of assessment criteria. Finally, an AI cloud platform evaluation and comparison analysis case study was used to validate the feasibility of this method. Show more

Keywords: Combinative-distance-based assessment (CODAS) method, probabilistic linguistic hesitant fuzzy sets (PLHFSs), AI cloud platform evaluation, multiple attribute decision-making (MADM), fuzzy environments

DOI: 10.3233/JIFS-232546

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11629-11646, 2023

Authors: Wang, Xin-Fan | Zhang, Li-Na | Zhou, Huan | Wang, Xue-Bin

Article Type: Research Article

Abstract: The intuitionistic uncertain linguistic information aggregation problems considering different priority levels of criteria are investigated. Firstly, we extended the prioritized averaging (PA) operator to intuitionistic uncertain linguistic environment, defined two new prioritized aggregation operators called the intuitionistic uncertain linguistic prioritized weighted average (IULPWA) operator and the intuitionistic uncertain linguistic prioritized weighted geometric (IULPWG) operator, and established various desirable properties of the proposed operators. Secondly, we developed a multi-criteria decision making (MCDM) approach based on the IULPWA operator (or the IULPWG operator) to deal with the MCDM problems in which the criterion values take the form of intuitionistic uncertain linguistic numbers …(IULNs) and the criteria are in different priority levels. Finally, an example is given to illustrate the feasibility and effectiveness of the proposed method, and a comparison analysis is conducted to make clear the differences among the IULPWA operator, the IULPWG operator, the intuitionistic uncertain linguistic number weighted averaging (IULNWA) operator and the intuitionistic uncertain linguistic weighted geometric average (IULWGA) operator. Show more

Keywords: Multi-criteria decision making (MCDM), intuitionistic uncertain linguistic number (IULN), intuitionistic uncertain linguistic prioritized weighted average (IULPWA) operator, intuitionistic uncertain linguistic prioritized weighted geometric (IULPWG) operator

DOI: 10.3233/JIFS-223829

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11647-11661, 2023

Authors: Hajmirfattahtabrizi, Mahboobehalsadat | Feylizadeh, Mohammad Reza | Song, Huaming

Article Type: Research Article

Abstract: In the past two years, 2020-2022, the developing construction industry has been a huge issue according to the negative effect of Covid-19 with the increasing pandemic situation in cities and areas. In Covid-19 pandemic situation, the cement manufacturing industry has been crucial and needed more scrutiny. As cement is the second significant component after water in concrete and construction industry. Meanwhile, locating a cement plant in a special area of the city is challenging and affecting more by local communities and other involved environmental factors. The location selection decisions need to grow by environmental, economic, technical and social attributes. This …study aims to present the site suitability decisions through a case study of locating a new manufacturing plant for cement production in Tehran surrounding, Iran. In this process, some required technical and tactical criteria are deserved for evaluating and suitability of the plant through decision-makers for cement manufacturing. All the feasible industrial alternative locations were evaluated under various criteria and regarding the Covid-19 pandemic’s negative impact to identify the most appropriate location for the cement industry. The authors proposed two Multi-Criteria Decision Attributes (MCDA) methods of MacBeth and COmplex PRoportional ASsessment (COPRAS) to evaluate and select the most suitable location for site suitability of the cement plant in this problem. Though the MacBeth method does not need to calculate weights of the Geographical Information System (GIS) criteria, the COPRAS method determined and used BWM (Best-Worst Method) as the weighing method. In sum, the comparison of the two methods was obtained according to the given results and ranks of volunteer cement suppliers for site suitability of the cement plant. Show more

Keywords: Cement plant, site suitability, GIS, BWM, COPRAS, entropy, MacBeth

DOI: 10.3233/JIFS-224534

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11663-11678, 2023

Authors: Prabakaran, G. | Jayanthi, K.

Article Type: Research Article

Abstract: Coronavirus 2019 (COVID-19) is a severe disease in respiratory syndrome. Early identification and efficient treatment of COVID-19 are not presented which provides ineffective treatment. This research develops an efficient system for early detection and segmentation of COVID-19 severity with the consideration of CT images. To overcome the abovementioned drawbacks, we develop the optimized Mask R-CNN method to train and test the dataset to classify and segment the COVID-19 disease. The proposed technique contains three phases which are, pre-processing, segmentation, and severity analysis. Initially, the patient’s CT images are collected from a different clinic. Then, the noise present in the images …is detached with a Gaussian filter. Then, the pre-processed images are given to the optimized mask region-based convolution neural network (OMRCNN) classifier to detect, classify and segment the image. After segmentation, the severity of the disease is examined. To enhance the performance of the mask RCNN classifier, the parameter is efficiently chosen by using the adaptive red deer algorithm. In the adaptive red deer algorithm, the levy flight is utilized to enhance the updating process. The performance of the proposed technique is analyzed based on various metrics. Show more

Keywords: COVID-19 segmentation, detection, recurrent neural network, gaussian filter adaptive red deer algorithm, and severity analysis

DOI: 10.3233/JIFS-230312

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11679-11693, 2023

Authors: Gulistan, Muhammad | Pedrycz, Witold | Yaqoob, Naveed

Article Type: Research Article

Abstract: We explore switching techniques between q-fractional fuzzy sets (qFr sets) and various other classes of fuzzy sets to establish connections and provide a comprehensive framework. In particular, we examine the relationships between qFr sets and interval-valued fuzzy sets (IVFS), type 2 fuzzy sets(T2FS), intuitionistic fuzzy sets(IFS), Pythagorean fuzzy sets(PFS), q-rung orthopair fuzzy sets (q-ROFS), and linear diophantine fuzzy sets(LDFS). By examining these interconnections, we aim to understand better qFr sets and their applications in a wide range of fuzzy systems. It is possible to convert qFr sets into other fuzzy set models using the derived switching techniques, facilitating the utilization …of existing methods and algorithms. The versatility of qFr sets, combined with the bridging techniques presented, holds promise for addressing complex problems in decision-making, pattern recognition, and other applications where uncertainty and imprecision play significant roles. Through case studies and practical applications, we illustrate the effectiveness and usefulness of the proposed switching techniques, showcasing their potential impact on real-world scenarios. Show more

Keywords: q-fractional fuzzy sets, fuzzy set, interval-valued fuzzy sets, type 2 fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets, q-rung orthopair fuzzy sets, linear diophantine fuzzy sets, switching techniques, uncertainty, imprecision

DOI: 10.3233/JIFS-233563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11695-11706, 2023

Authors: Ponniah, Krishna Kumar | Retnaswamy, Bharathi

Article Type: Research Article

Abstract: The Internet of Things (IoT) integrated Cloud (IoT-Cloud) has gotten much attention in the past decade. This technology’s rapid growth makes it even more critical. As a result, it has become critical to protect data from attackers to maintain its integrity, confidentiality, protection, privacy, and the procedures required to handle it. Existing methods for detecting network anomalies are typically based on traditional machine learning (ML) models such as linear regression (LR), support vector machine (SVM), and so on. Although these methods can produce some outstanding results, they have low accuracy and rely heavily on manual traffic feature design, which has …become obsolete in the age of big data. To overcome such drawbacks in intrusion detection (ID), this paper proposes a new deep learning (DL) model namely Morlet Wavelet Kernel Function included Long Short-Term Memory (MWKF-LSTM), to recognize the intrusions in the IoT-Cloud environment. Initially, to maintain a user’s privacy in the network, the SHA-512 hashing mechanism incorporated a blockchain authentication (SHABA) model is developed that checks the authenticity of every device/user in the network for data uploading in the cloud. After successful authentication, the data is transmitted to the cloud through various gateways. Then the intrusion detection system (IDS) using MWKF-LSTM is implemented to identify the type of intrusions present in the received IoT data. The MWKF-LSTM classifier comes up with the Differential Evaluation based Dragonfly Algorithm (DEDFA) optimal feature selection (FS) model for increasing the performance of the classification. After ID, the non-attacked data is encrypted and stored in the cloud securely utilizing Enhanced Elliptical Curve Cryptography (E2 CC) mechanism. Finally, in the data retrieval phase, the user’s authentication is again checked to ensure user privacy and prevent the encrypted data in the cloud from intruders. Simulations and statistical analysis are performed, and the outcomes prove the superior performance of the presented approach over existing models. Show more

Keywords: Internet of Things (IoT), deep learning, cloud computing, data security, IoT authentication, intrusion detection system, Elliptical Curve Cryptography

DOI: 10.3233/JIFS-221873

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11707-11724, 2023

Authors: Nunsanga, Morrel V.L. | Pakray, Partha | Devi, Toijam Sonalika | Singh, L. Lolit Kr

Article Type: Research Article

Abstract: The process of associating words with their relevant parts of speech is known as part-of-speech (POS) tagging. It takes a substantial amount of well-organized data or corpora and significant target language research to obtain good performance for a tagger. Mizo is a language that needs more research attention in computational linguistics due to its under-resourced nature. The limited availability of corpora and relevant literature adds complexity to the task of assigning POS labels to Mizo text. This paper explores two methods to potentially improve the Hidden Markov Model (HMM)-based POS tagger for the Mizo language. The proposed taggers are compared …with the baseline HMM tagger and the N-gram taggers on the designed Mizo corpus, which consists of 72,077 manually tagged tokens. The experimental results proved that the two proposed taggers enhanced the HMM-based Mizo POS tagger, achieving 81.52% and 84.29% accuracy, respectively. Moreover, a comprehensive analysis of the performance of the suggested hybrid tagger was conducted, yielding a weighted average precision, recall, and F1-score of 83.09%, 77.88%, and 79.64% respectively. Show more

Keywords: Hybrid POS tagger, rule-based POS tagger, N-gram tagger, Mizo POS tagger, Hidden Markov Model

DOI: 10.3233/JIFS-224220

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11725-11736, 2023

Authors: Srihari, Pasala | Harikiran, Jonnadula | Sai Chandana, B. | Surendra Reddy, Vinta

Article Type: Research Article

Abstract: Recognizing human activity is the process of using sensors and algorithms to identify and classify human actions based on the data collected. Human activity recognition in visible images can be challenging due to several factors of the lighting conditions can affect the quality of images and, consequently, the accuracy of activity recognition. Low lighting, for example, can make it difficult to distinguish between different activities. Thermal cameras have been utilized in earlier investigations to identify this issue. To solve this issue, we propose a novel deep learning (DL) technique for predicting and classifying human actions. In this paper, initially, to …remove the noise from the given input thermal images using the mean filter method and then normalize the images using with min-max normalization method. After that, utilizing Deep Recurrent Convolutional Neural Network (DRCNN) technique to segment the human from thermal images and then retrieve the features from the segmented image So, here we choose a fully connected layer of DRCNN as the segmentation layer is utilized for segmentation, and then the multi-scale convolutional neural network layer of DRCNN is used to extract the features from segmented images to detect human actions. To recognize human actions in thermal pictures, the DenseNet-169 approach is utilized. Finally, the CapsNet technique is used to classify the human action types with Elephant Herding Optimization (EHO) algorithm for better classification. In this experiment, we select two thermal datasets the LTIR dataset and IITR-IAR dataset for good performance with accuracy, precision, recall, and f1-score parameters. The proposed approach outperforms “state-of-the-art” methods for action detection on thermal images and categorizes the items. Show more

Keywords: Human action recognition, deep recurrent convolutional neural network, thermal images, classification, CapsNet, feature extraction, DenseNet-169

DOI: 10.3233/JIFS-230505

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11737-11755, 2023

Authors: Xiao, Jian | Meng, Linglong | Wu, Kaiyin

Article Type: Research Article

Abstract: A supplier portrait generation method based on Big data analysis and deep learning was proposed to help users make reasonable decisions in core links such as procurement and contract signing. This method establishes a label element analysis model for each level in the vertical label system of power supply enterprises, and divides it into target layer, standard layer, and solution layer based on the logic and attributes of the elements, and establishes a hierarchical structure. Compare the index labels of each level with the labels of the upper and lower levels by considering the logical relationship and correlation between each …level. Utilize deep learning algorithms to sort hierarchically, and use a multidimensional structural model to represent and fuse portrait labels of power supply enterprises. Based on the imaging results of supplier vertical rating, combined with objective factors such as material production cycle, supply cycle, market supply and demand, price fluctuations, etc., it helps power enterprises effectively predict the supplier’s performance ability. The simulation results show that the reliability of the power supply enterprise portrait generated by this method is high, and the credibility of the portrait identification system for all levels of power supply enterprises is high. This supplier portrait method can effectively improve the supplier management capabilities of power enterprises. Show more

Keywords: Deep learning BCCM, multi-aspect, electricity supplier, portrait generation, information management

DOI: 10.3233/JIFS-230722

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11757-11767, 2023

Authors: Zhu, Yiping | Huang, Jiajia | Zhu, Yi | Guo, Yang

Article Type: Research Article

Abstract: Online teaching platforms have developed into mainstream knowledge learning and exchange platform. The research on the quality evaluation of online teaching platforms and the construction of an applicable and scientific evaluation index system model can help explore the key factors affecting the quality of online teaching platforms and provide some references for evaluating online teaching platforms and improving online teaching quality. This study combines the rough set theory (RS) with the BP (Back Propagation) neural networks to build an RS-BP neural network model to evaluate the quality of online teaching platforms. Firstly, an initial online teaching platform quality evaluation index …system is constructed based on knowledge transfer theory from four aspects: course content, knowledge transmitter, knowledge receiver and teaching platform. Then, 12 core evaluation indicators were generated by attribute reduction using rough set theory, and the weights of each core indication were determined. The normalized data input was then trained, validated, and tested to generate a rough set neural network quality evaluation model for online teaching platforms. After that, three representative online education platforms of content, interaction and compatibility are selected for empirical research. The accuracy of the model is first tested by the error between the simulated and output values, after which the core metric scores and the overall scores are calculated for the three types of platforms. The empirical results demonstrate that the model has certain advantages in terms of index simplification and adaptive training when evaluating online teaching platforms, as well as strong operability and practicality. The evaluation results show that the content online teaching platform has the highest comprehensive score, followed by the compatible and interactive online teaching platforms. According to the index scores, the quality of the course content, stage assessments, and contact between professors and students were identified as major elements influencing the quality of the online teaching platform. Finally, suggestions for optimization for each of the three types of online teaching platforms were made based on the core indicators and their weights, as well as the scores and characteristics of the three types of online teaching platforms, with the goal of improving the quality of online teaching platforms. Show more

Keywords: Knowledge transfer, online teaching platform, rough set theory, BP neural network

DOI: 10.3233/JIFS-231381

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11769-11789, 2023

Authors: Wen, Shuting | Wen, Fangcheng

Article Type: Research Article

Abstract: Culture and tourism development through public services rely on accumulated big data and overall country/ province development. Accumulated data relies on various cultures, people, places, etc. attributes for which a heterogeneous and multi-faced analysis is required. This article introduces a Development-focused Data Handling Process (D-DHP) for providing insights through culture and tourism accumulated information. The proposed process relies on heterogeneous data attributes for identifying economic and society-based development stagnancies. The data analysis is performed for identifying missing sequences and invariable information that shows development stagnancies. The stagnancy rates between successive quarters (per annum) are accounted for identifying development drops. If …such drops are identified, the accumulated data outputs are analyzed through classification learning. In this classification, the development and drop-associated data are split for an independent analysis. This analysis helps to replace the mode of development focusing on tourism or culture or both based on dependability. The classification process is updated based on the replaced information for further improvements across various accumulated data inputs. Therefore, the proposed process is viable in identifying development-focused information from the accumulated data. Show more

Keywords: Big data, classification learning, culture and tourism, public services

DOI: 10.3233/JIFS-232318

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11791-11806, 2023

Authors: Chen, Xin | Wang, Yan | Li, Fuzhen

Article Type: Research Article

Abstract: A singular system, assumed to possess both regularity and freedom from impulses, is categorized as a causal system. Noncausal systems (NSs) are a class of singular systems anticipated to exhibit regularity. This study focuses on investigating zero-sum games (ZSGs) in the context of NSs. We introduce recurrence equations grounded in Bellman’s optimality principle. The saddle-point solution for multistage two-player ZSGs can be obtained by solving these recurrence equations. This methodology has demonstrated its effectiveness in addressing two-player ZSGs involving NSs. Analytical expressions that characterize saddle-point solutions for two types of two-player ZSGs featuring NSs, encompassing both linear and quadratic control …scenarios, are derived in this paper. To enhance clarity, we provide an illustrative example that effectively highlights the utility of our results. Finally, we apply our methodology to analyze a ZSG in the realm of environmental management, showcasing the versatility of our findings. Show more

Keywords: Zero-sum game, noncausal system, saddle-point solution, recurrence equations

DOI: 10.3233/JIFS-232401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11807-11833, 2023

Authors: Sun, Xiujing

Article Type: Research Article

Abstract: With the rapid development and application of internet technology, cross-border e-commerce (CBEC) has begun to popularize globally and play an important role in China’s foreign trade. The Chinese government has successively introduced multiple policies and regulations to strongly support its rapid development. Compared to the booming trend of CBEC, the development of its supply chain is slightly lacking in momentum, which has formed a certain obstacle to the overall development of CBEC. The supply chain is the foundation of successful CBEC transactions, and the foundation of the supply chain is logistics. The primary task to improve the backwardness of supply …chain development is to solve logistics problems. Therefore, while enjoying the dividends brought by the rapid development of CBEC, international logistics enterprises should continuously improve their logistics service capabilities, effectively evaluate their service quality, and then identify problems based on the evaluation results, analyze and improve them. The service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain is a classical multiple attribute group decision making (MAGDM). The Spherical fuzzy sets (SFSs) provide more free space for DMs to portray uncertain information during the service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain. Therefore, this paper expands the partitioned Maclaurin symmetric mean (PPMSM) operator and IOWA operator to SFSs based on the power average (PA) technique and construct induced spherical fuzzy weighted power partitioned MSM (I-SFWPPMSM) technique. Subsequently, a novel MAGDM method is constructed based on I-SFWPPMSM technique and SFNWG technique under SFSs. Finally, a numerical example for service quality evaluation of international logistics enterprises from the perspective of CBEC supply chain is employed to verify the constructed method, and comparative analysis with some existing techniques to testy the validity and superiority of the I-SFWPPMSM technique. Show more

Keywords: MAGDM, Spherical fuzzy sets (SFSs), I-SFWPPMSM operator, Service quality evaluation

DOI: 10.3233/JIFS-233384

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11835-11851, 2023

Authors: Liu, Wenxiu | Xu, Lijun | Zhou, Yijia | Yu, Bo

Article Type: Research Article

Abstract: In this paper, we propose two novel Alternating Direction Method of Multipliers (ADMM) algorithms for the sparse portfolio problem via sorted ℓ1 -norm penalization (SLOPE). The first algorithm (FADMM) is presented by adding a prediction-correction step to the classic ADMM framework. Since the problem is not strongly convex, the second fast ADMM (FADMMR) is proposed by utilizing both prediction-correction step and restarting rules. Numerical experiments show that the FADMMR algorithm converges faster than the FADMM algorithm and ADMM algorithm when tuning parameters are relatively small. On the other hand, when tuning parameters are relative large, the FADMM algorithm performs better …than the FADMMR algorithm and ADMM algorithm. The FADMM algorithm and FADMMR algorithm converge faster than the ADMM algorithm in terms of convergence time for different sizes of tuning parameters. For large-scale portfolio problem, the proposed algorithms have highly performance as well. Finally, empirical analysis on five datasets of stocks index show that the proposed algorithms are efficient and superior for solving sparse portfolio problems via SLOPE. Show more

Keywords: Fast ADMM, fast ADMM with restart, sparse portfolio, sort ℓ1-norm penalty

DOI: 10.3233/JIFS-234381

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11853-11872, 2023

Authors: Jin, Huilong | Du, Ruiyan | Wen, Tian | Zhao, Jia | Shi, Lei | Zhang, Shuang

Article Type: Research Article

Abstract: Compared with other facial expression recognition, classroom facial expression recognition should pay more attention to the feature extraction of a specific region to reflect the attention of students. However, most features are extracted with complete facial images by deep neural networks. In this paper, we proposed a new expression recognition based on attention mechanism, where more attention would be paid in the channel information which have much relationship with the expression classification instead of depending on all channel information. A new classroom expression classification has also been concluded with considering the concentration. Moreover, activation function is modified to reduce the …number of parameters and computations, at the same time, dropout regularization is added after the pool layer to prevent overfitting of the model. The experiments show that the accuracy of our method named Ixception has an maximize improvement of 5.25% than other algorithms. It can well meet the requirements of the analysis of classroom concentration. Show more

Keywords: Deep learning, classroom facial expression recognition, attention mechanism, activation function, dropout regularization

DOI: 10.3233/JIFS-235541

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11873-11882, 2023

Authors: Luo, Dang | Ambreen, Muffarah | Latif, Assad | Wang, Xiaolei | Samreen, Mubbarra | Muhammad, Aown

Article Type: Research Article

Abstract: Almost all cities of Pakistan are economically affected by the electricity shortage due to the continuously increasing demand for electricity. To correctly forecast the seasonal fluctuations of the electricity consumption of Lahore city in Pakistan, we proposed the SDGPM(1,1,N) model, which is a seasonal discrete grey polynomial model combined with seasonal adjustment. We conducted an empirical analysis using the proposed model based on the seasonal electricity consumption data of Lahore city in Pakistan from 2014 to 2021. The findings from the SDGPM (1,1,N) model are compared with those collected through the original grey model DGPM(1,1,N) and other eight models. The …comparison’s findings demonstrated that the SDGPM(1,1,N) model is indeed capable of correctly identifying seasonal fluctuations of electricity consumption in Lahore city and its prediction accuracy is significantly higher than the original DGPM(1,1,N) model and the other seven models. The SDGPM(1,1,N) model’s forecast findings for Lahore from 2022 to 2025 indicate that the city’s energy consumption is expected to rise marginally, although there will still be significant seasonal fluctuations. It is predicted that the annual electricity consumption from 2022 to 2025 will be 26249, 26749, 27928, and 28136 with an annual growth rate of 7.18%. This forecast can provide policymakers ahead start in planning to ensure that supply and demand are balanced. Show more

Keywords: Seasonal factor, Lahore electricity forecasting, seasonal discrete grey polynomial model, seasonal DGPM(1, 1,N)

DOI: 10.3233/JIFS-231106

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11883-11894, 2023

Authors: Sangeetha, J. | Priyanka, M. | Jayakumar, C.

Article Type: Research Article

Abstract: Audio Event Detection (AED) and classification of acoustic events has become a notable task for machines to interpret the auditory information around us. Nevertheless, it has been a difficult and cumbersome task to extract the most basic characteristics of acoustic events that encapsulate the fundamental elements of the audio events. Previous works on audio event classification utilized supervised pre-training as well as meta-learning approaches that happened to depend on labeled data therefore facing instability. Deep Learning is progressing in an increasingly mature direction, and the application of deep learning methods to detect acoustic event has become more and more sought …after. The proposed hybrid method called Greedy Regression-based Convolutional Neural Network and Differential Convex Bidirectional Gated Recurrent Unit (GRCNN-DCBGRU) is introduced to learn a vector representation of an audio sequence for Audio Event Classification (AEC). Differential Convex Bidirectional Gated Recurrent Unit is analogous to long short-term memory and involves time-cyclic long-term dependencies with a lesser processing complexity. The model first extracts acoustic features from the sound event dataset through a Differential Convex Bidirectional Gated Recurrent Unit employing Gabor Filter bank features and then extracts the local static acoustic features through the Greedy Regression-based Convolutional Neural Network by utilizing Mel Frequency Cepstral Coefficients (MFCC). Finally, the Differential Convex Meta-Learning classifier is used for the final acoustic event classification. Extensive evaluation on large-size publicly available acoustic event database like Findsounds2016 will be performed in Python programming language to demonstrate the efficiency of the proposed method for the AEC task. To demonstrate the visualizations of individual modules and their influence on overall representation learning for AEC tasks, several parameters like audio detection time, audio detection accuracy, precision, and recall are measured. Show more

Keywords: Audio event detection, audio event classification, deep learning, greedy regression, convolutional neural network, differential convex, bidirectional gated recurrent unit

DOI: 10.3233/JIFS-232561

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11895-11908, 2023

Authors: Tan, Mindong | Qu, Liangdong

Article Type: Research Article

Abstract: Oral English teaching quality evaluation is a complex nonlinear relationship, which is affected by many factors and has low accuracy. Aiming at the problem, a teaching quality evaluation method based on a BP neural network optimized by the improved crow search algorithm (ICSA) is proposed. First, ICSA is put forward and five algorithms are used to compare with the proposed algorithm on 10 benchmarks functions. The results show that ICSA outperforms the other five algorithms on 10 functions. Second, a feature selection method based on the improved binary crow search algorithm (BICSA) is used to select teaching quality evaluation indexes, …and 10 standard datasets from the UCI repository are used for testing experiments. Finally, an oral English teaching evaluation model based on BP neural network is designed, in which BICSA is used for feature selection and ICSA is used to optimize the initial weights of the BP neural network. In the experiment, we designed 5 first-grade indexes and 15 second-grade indexes, and then we collects 23 groups of oral English teaching quality data. BICSA selected 10 features from a set of 15 features. Experimental results show that this method can effectively evaluate the quality of oral English teaching with high accuracy and real-time performance. Show more

Keywords: BP neural network, crow search algorithm, feature selection, oral English teaching, quality evaluation

DOI: 10.3233/JIFS-222455

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11909-11924, 2023

Authors: Srivatsun, G. | Thivaharan, S.

Article Type: Research Article

Abstract: Writing is a crucial component of the language requirement and is an effective method for correctly reflecting language proficiency. Manually evaluating Tamil language exams becomes time-consuming and costly for standardized language administrators as they grow in popularity. Numerous studies on computerized English assessment systems have been conducted in recent years. Due to Tamil text’s complicated grammatical structures, less research has been done on computerized evaluation methods. In this research, we present a Tamil review comment analysis system using a novel multivariate naïve Bayes classifier (mv  - NB ) where the comments are acquired from an online social network and performed training …using the database for further analysis. Experiments show that the graded Kappa of 0.4239, error rate of 2.55 and precision of 85% was achieved on the online dataset by our contents grading system, which is superior in grading compared to the other widely used machine learning algorithms training on big datasets. Our findings are promising. Additionally, our contents analysis may provide beneficial criticism on Tamil writing on YouTube posts including comments, spelling errors and morphological issues that help to analyze thelanguage correlation. Show more

Keywords: Writing, Tamil content, grading system, reviews, morphological issues

DOI: 10.3233/JIFS-222504

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11925-11936, 2023

Authors: Cheng, Shumin | Zhou, Yan | Bao, Yanling

Article Type: Research Article

Abstract: With the increasing diversification and complexity of information, it is vital to mine effective knowledge from information systems. In order to extract information rapidly, we investigate attribute reduction within the framework of dynamic incomplete decision systems. Firstly, we introduce positive knowledge granularity concept which is a novel measurement on information granularity in information systems, and further give the calculation method of core attributes based on positive knowledge granularity. Then, two incremental attribute reduction algorithms are presented for incomplete decision systems with multiple objects added and deleted on the basis of positive knowledge granularity. Furthermore, we adopt some numerical examples to …illustrate the effectiveness and rationality of the proposed algorithms. In addition, time complexity of the two algorithms are conducted to demonstrate their advantages. Finally, we extract five datasets from UCI database and successfully run the algorithms to obtain corresponding reduction results. Show more

Keywords: Incomplete decision system, positive knowledge granularity, incremental attribute reduction

DOI: 10.3233/JIFS-230349

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11937-11947, 2023

Authors: Wei, Qiuyue | Yang, Dong | Zhang, Mingjie

Article Type: Research Article

Abstract: Aspect-based sentiment analysis is a fine-grained task in the field of sentiment analysis. Various GCN approaches have recently emerged to work on this, but many approaches ignored the critical role of aspectual word information and the effect of noise. In view of this situation, we propose an aspect-based word embedding graph convolutional network (AWEGCN) model. In order to make good use of the aspect information and distinguish the contextual information that is more important for a particular aspect, the aspect information is embedded in the output of the hidden layer. To reduce the noise effect when multiple aspect words appear …in a sentence, after going through the bidirectional graph convolutional network, the aspect information is embedded. A specific contextual representation is computed through an attention mechanism, which is used as the final classification feature. Experiments show that our model achieves impressive performance on five public datasets, and we also apply BERT and XLNet pre-trained models to this task and obtain advanced results that validate the effectiveness of our model. Show more

Keywords: Aspect-level sentiment classification, aspect word embeddings, graph convolutional networks, attention mechanisms

DOI: 10.3233/JIFS-230537

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11949-11962, 2023

Authors: Cheng, Tao | Cheng, Hua | Fang, Yiquan | Liu, Yufei | Gao, Caiting

Article Type: Research Article

Abstract: As prototype-based Few-Shot Learning methods, Prototypical Network generates prototypes for each class in a low-resource state and classify by a metric module. Therefore, the quality of prototypes matters but they are inaccurate from the few support instances, and the domain-specific information of training data are harmful to the generalizability of prototypes. We propose a C onceptual P rototype (CP), which contains both rich instance and concept features. The numerous query data can inspire the few support instances. An interactive network is designed to leverage the interrelation between support set and query-detached set to acquire a rich Instance Prototype which is …typical on the whole data. Besides, class labels are introduced to prototype by prompt engineering, which makes it more conceptual. The label-only concept makes prototype immune to domain-specific information in training phase to improve its generalizability. Based on CP, C onceptual P rototypical C ontrastive L earning (CPCL) is proposed where PCL brings instances closer to its corresponding prototype and pushes away from other prototypes. “2-way 5-shot” experiments show that CPCL achieves 92.41% accuracy on ARSC dataset, 2.30% higher than other prototype-based models. Meanwhile, the 0-shot performance of CPCL is comparable to Induction Network in the 5-shot way, indicating that our model is adequate for 0-shot tasks. Show more

Keywords: Prototypical network, text classification, Few-Shot learning, prompt learning

DOI: 10.3233/JIFS-231570

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11963-11975, 2023

Authors: AlAlaween, Wafa’ H. | AlAlawin, Abdallah H. | AbuHamour, Saif O. | Gharaibeh, Belal M.Y. | Mahfouf, Mahdi | Alsoussi, Ahmad | AbuKaraky, Ashraf E.

Article Type: Research Article

Abstract: Right-first-time production enables manufacturing companies to be profitable as well as competitive. Ascertaining such a concept is not as straightforward as it may seem in many industries, including 3D printing. Therefore, in this research paper, a right-first-time framework based on the integration of fuzzy logic and multi-objective swarm optimization is proposed to reverse-engineer the radial based integrated network. Such a framework was elicited to represent the fused deposition modelling (FDM) process. Such a framework aims to identify the optimal FDM parameters that should be used to produce a 3D printed specimen with the desired mechanical characteristics right from the first …time. The proposed right-first-time framework can determine the optimal set of the FDM parameters that should be used to 3D print parts with the required characteristics. It has been proven that the right-first-time model developed in this paper has the ability to identify the optimal set of parameters successfully with an average error percentage of 4.7%. Such a framework is validated in a real medical case by producing three different medical implants with the desired mechanical characteristics for a 21-year-old patient. Show more

Keywords: Fuzzy logic, particle swarm optimization, radial based integrated network, right-first-time production

DOI: 10.3233/JIFS-232135

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11977-11991, 2023

Authors: Uganya, G. | Bommi, R.M. | Muthu Krishnammal, P. | Vijayaraj, N.

Article Type: Research Article

Abstract: Internet of things (IoT) is a recent developing technology in the field of smart healthcare. But it is difficult to transfer the patient’s health record as a centralized network. So, “blockchain technology” has excellent consideration due to its unique qualities such as decentralized network, openness, irreversible data, and cryptography functions. Blockchain technology depends on cryptography hash techniques for safe transmission. For increased security, it transforms the variable size inputs into a constant length hash result. Current cryptographic hash algorithms with digital signatures are only able to access keys up to a size of 256 bytes and have concerns with single …node accessibility. It just uses the bits that serve as the key to access the data. This paper proposes the “Revised Elliptic Curve Cryptography Multi-Signature Scheme” (RECC-MSS) for multinode availability to find the nearest path for secure communications with the medical image as keys. Here, the input image key can be converted into an array of data that can be extended up to 512 bytes of size. The performance of the proposed algorithm is analyzed with other cryptography hash functions like Secure Hashing Algorithms (SHAs) such as “SHA224”, “SHA256”, “SHA384”, “SHA512”, “SHA3-224”, “SHA3-256”, “SHA3-384”, “SHA3-512”, and “Message Digest5” (MD5) by “One-way ANOVA” test in terms of “accuracy”, “throughput” and “time complexity”. The proposed scheme with ECC achieved the throughput of 17.07 kilobytes per 200 nano seconds, 93.25% of accuracy, 1.5 nanoseconds latency of signature generation, 1.48 nanoseconds latency of signature verification, 1.5 nanoseconds of time complexity with 128 bytes of hash signature. The RECC-MSS achieved the significance of 0.001 for accuracy and 0.002 for time complexity which are less than 0.05. From the statistical analysis, the proposed algorithm has significantly high accuracy, high throughput and less time complexity than other cryptography hash algorithms. Show more

Keywords: Internet of Things, blockchain technology, multi-signature, Secure Hash Algorithm, Revised Elliptic Curve Cryptography, medical image

DOI: 10.3233/JIFS-232802

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11993-12012, 2023

Authors: Zhang, Guowei | Tang, Yutong | Tang, Hulin | Li, Wuzhi | Wang, Li

Article Type: Research Article

Abstract: Unmanned sorting technology can significantly improve the transportation efficiency of the logistics industry, and package detection technology is an important component of unmanned sorting. This paper proposes a lightweight deep learning network called EPYOLO, in which a lightweight self-attention feature extraction backbone network named EPnet is also designed. It also reduces the Floating-Point Operations (FLOPs) and parameter count during the feature extraction process through an improved Contextual Transformer-slim (CoTs) self-attention module and GSNConv module. To balance network performance and obtain semantic information for express packages of different sizes and shapes, a multi-scale pyramid structure is adopted using the Feature Pyramid …Network (FPN) and the Path Aggregation Network (PAN). Finally, comparative experiments were conducted with the state-of-the-art (SOTA) model by using a self-built dataset of express packages by using a self-built dataset of express packages, results demonstrate that the mean Average Precision (mAP) of the EPYOLO network reaches 98.8%, with parameter quantity only 11.63% of YOLOv8 s and FLOPs only 9.16% of YOLOv8 s. Moreover, compared to the YOLOv8 s network, the EPYOLO network shows superior detection performance for small targets and overlapping express packages. Show more

Keywords: Object detection, express package detection, lightweight, deep learning

DOI: 10.3233/JIFS-232874

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12013-12025, 2023

Authors: Li, Yue | Mao, Liang

Article Type: Research Article

Abstract: Automatic detection of defects in mature litchi plays a vital role in the classification of fruit grades. The existing method mainly relies on manual, it is difficult to meet the needs of different varieties of litchi various types of commodity packaging, and there are problems such as low efficiency, high cost and poor quality of goods. To address the above problems, this paper proposes an improved You Only Look Once(YOLO)v7 algorithm for the automatic detection of post-harvest mature litchi epidermal defects. First, a dataset of litchi defects (black spot, fall off, crack) was constructed, in which the train and test …sets had 4133 and 516; Next, A Simple Parameter-Free Attention(SimAM) mechanism is introduced into the original YOLOv7 backbone network, while GSconv is used in the neck instead of convolution, and the shallow network is used instead of the deep network for lateral linking, finally, the Mish function is used as the activation function. Experimental results show the precious and mAP of the original YOLOv7 are 87.66% and 88.98%, and those of the improved YOLOv7 are 91.56% and 93.42%, improvements of 3.9% and 4.44%. A good foundation is laid for the automated classification of ripe litchi after harvesting. Show more

Keywords: YOLOv7, litchi epidermal defects, SimAM, GSconv, shallow networks

DOI: 10.3233/JIFS-233440

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12027-12036, 2023

Authors: He, Ping | Chen, Jingfang

Article Type: Research Article

Abstract: In this paper, a question answering method is proposed for educational knowledge bases (KBQA) using a question-aware graph convolutional network (GCN). KBQA provides instant tutoring for learners, improving their learning interest and efficiency. However, most open domain KBQA methods model question sentences and candidate answer entities independently, limiting their effectiveness. The proposed method extracts description information and query entity sets for a specific question, processes them with Transformer and pre-trained embeddings of the knowledge base, and extracts a subgraph of candidate answer sets from the knowledge base. The node information is updated by GCN with two attention mechanisms expressed by …the question description and query entity set, respectively. The query description information, query entity set, and candidate entity representation are fused to calculate the score and predict the answer. Experiments on MOOC Q&A dataset show that the proposed method outperforms benchmark models. Show more

Keywords: Educational knowledge base, data-driven intelligent education, question answering method, Graph convolutional network (GCN), prediction accuracy

DOI: 10.3233/JIFS-233915

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12037-12048, 2023

Authors: Wang, Yashao

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-234605

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12049-12063, 2023

Authors: Zhao, Jie | Wang, Shuo | Wu, Haotian

Article Type: Research Article

Abstract: To effectively enhance the safety, stability, and economic operation capability of DC microgrids, an optimized control strategy for DC microgrid hybrid energy storage system (HESS)(The abbreviation table is shown in Table 2 ) based on model predictive control theory is proposed. Based on the characteristics of supercapacitors and batteries, system safety requirements, and various constraints, a predictive model for a hybrid energy storage DC microgrid is established. By defining its optimization indicators, designing an energy optimization management strategy, and transforming it into a quadratic programming problem for solution, the reasonable scheduling of power in the DC microgrid has been achieved. In …addition, a power control method was proposed for the system without constraints. The simulation experiment results show that at the initial sampling time, the system operates normally, and the MPC algorithm allocates two types of energy storage devices to discharge to meet the net load demand, without absorbing electricity from the external network. At the 30th sampling point, the net load increases, and the MPC controller obtains the optimal solution of the control problem based on the known net load prediction data at the previous sampling time. It outputs the operating reference values of each output unit at the next time. Starting from the 100th to 199th sampling points, SOC UC falls below the lower limit of the safety interval, and the system enters situation 4 mode. The external network output assists the battery in working. At the 131st sampling point, the net load decreases, the system enters Situation 3 mode, and the battery operates independently. Until the 179th point, SOC B was also below the lower limit of its safety interval, and the system entered situation 5 mode, completely maintaining system power balance by external network power. Starting from point 201, the net load becomes negative, and the system charges the HESS according to instructions and stops the external power grid energy transmission. Conclusion: The feasibility and effectiveness of the proposed optimization management strategy have been verified. Show more

Keywords: DC microgrid, model predictive control, mixed energy storage, objective function, secondary planning

DOI: 10.3233/JIFS-234849

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12065-12077, 2023

Authors: Maguluri, Lakshmana Phaneendra | Vinya, Viyyapu Lokeshwari | Goutham, V. | Uma Maheswari, B. | Kumar, Boddepalli Kiran | Musthafa, Syed | Manikandan, S. | Srivastava, Suraj | Munjal, Neha

Article Type: Research Article

Abstract: Depression is a prevalent mental health disorder that affects people of all ages and origins; therefore, early detection is essential for timely intervention and support. This investigation proposes a novel method for detecting melancholy in young, healthy individuals by analysing their gait and balance patterns. In order to accomplish this, a comprehensive system is designed that incorporates cutting-edge technologies such as a Barometric Pressure Sensor, Beck Depression Inventory (BDI), and t-Distributed Stochastic Neighbour Embedding (t-SNE) algorithm. The system intends to capitalize on the subtle motor and physiological changes associated with melancholy, which may manifest in a person’s gait and balance. …The Barometric Pressure Sensor is used to estimate variations in altitude and vertical velocity, thereby adding context to the evaluation. The mood states of participants are evaluated using the BDI, a well-established psychological assessment instrument that provides insight into their emotional health. Integrated and pre-processed data from the Barometric Pressure Sensor, BDI responses, and gait and balance measurements. The t-SNE algorithm is then used to map the high-dimensional data into a lower-dimensional space while maintaining the local structure and identifying underlying patterns within the dataset. The t-SNE algorithm improves visualization and pattern recognition by reducing the dimensionality of the data, allowing for a more nuanced analysis of depression-related markers. As the proposed system combines objective physiological measurements with subjective psychological assessments, it has the potential to advance the early detection and prediction of depression in young, healthy individuals. The results of this exploratory study have implications for the development of non-intrusive and easily accessible instruments that can assist healthcare professionals in identifying individuals at risk and implementing targeted interventions. Show more

Keywords: Depression, barometric pressure sensor, beck depression inventory, t-SNE, mental health

DOI: 10.3233/JIFS-235058

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12079-12093, 2023

Authors: Vidhya, R. | Banavath, Dhanalaxmi | Kayalvili, S. | Naidu, Swarna Mahesh | Charles Prabu, V. | Sugumar, D. | Hemalatha, R. | Vimal, S. | Vidhya, R.G.

Article Type: Research Article

Abstract: Early Alzheimer’s disease detection is essential for facilitating prompt intervention and enhancing the quality of care provided to patients. This research presents a novel strategy for the diagnosis of Alzheimer’s disease that makes use of sophisticated sampling methods in conjunction with a hybrid model of deep learning. We use stratified sampling, ADASYN (Adaptive Synthetic Sampling), and Cluster- Centroids approaches to ensure a balanced representation of Alzheimer’s and non-Alzheimer’s cases during model training in order to meet the issues posed by imbalanced data distributions in clinical datasets. This allows us to solve the challenges posed by imbalanced data distributions in clinical …datasets. A strong hybrid architecture is constructed by combining a Residual Neural Network (ResNet) with Residual Neural Network (ResNet) units. This architecture makes the most of both the feature extraction capabilities of ResNet and the capacity of LSTM to capture temporal dependencies. The findings demonstrate that the model is superior to traditional approaches to machine learning and single-model architectures in terms of accuracy, sensitivity, and specificity. The hybrid deep learning model demonstrates exceptional capabilities in identifying early indicators of Alzheimer’s disease with a high degree of accuracy, which paves the way for early diagnosis and treatment. In addition, an interpretability study is carried out in order to provide light on the decision-making process underlying the model. This helps to contribute to a better understanding of the characteristics and biomarkers that play a role in the identification of Alzheimer’s disease. In general, the strategy that was provided provides a promising foundation for accurate and reliable Alzheimer’s disease identification. It does this by harnessing the capabilities of hybrid deep learning models and sophisticated sampling approaches to improve clinical decision support and, as a result, eventually improve patient outcomes. Show more

Keywords: Alzheimer’s disease, Residual Neural Network (ResNet), Residual Neural Network (ResNet), Cluster Centroids, stratified sampling, ADASYN (Adaptive Synthetic Sampling)

DOI: 10.3233/JIFS-235059

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12095-12109, 2023

Authors: Nandhini, Ramesh Sneka | Lakshmanan, Ramanathan

Article Type: Research Article

Abstract: Cyber-physical systems (CPS) play a pivotal role in various critical applications, ranging from industrial automation to healthcare monitoring. Ensuring the reliability and accuracy of sensor data within these systems is of paramount importance. This research paper presents a novel approach for enhancing fault detection in sensor data within a cyber-physical system through the integration of machine learning algorithms. Specifically, a hybrid ensemble methodology is proposed, combining the strengths of AdaBoost and Random Forest with Rocchio’s algorithm, to achieve robust and accurate fault detection. The proposed approach operates in two phases. In the first phase, AdaBoost and Random Forest classifiers are …trained on a diverse dataset containing normal and faulty sensor data to develop individual base models. AdaBoost emphasizes misclassified instances, while Random Forest focuses on capturing complex interactions within the data. In the second phase, the outputs of these base models are fused using Rocchio’s algorithm, which exploits the similarities between faulty instances to improve fault detection accuracy. Comparative analyses are conducted against individual classifiers and other ensemble methods to validate the effectiveness of the hybrid approach. The results demonstrate that the proposed approach achieves superior fault detection rates. Additionally, the integration of Rocchio’s algorithm significantly contributes to the refinement of the fault detection process, effectively leveraging the strengths of AdaBoost and Random Forest. In conclusion, this research offers a comprehensive solution to enhance fault detection capabilities in cyber-physical systems by introducing a novel ensemble framework. By synergistically combining AdaBoost, Random Forest, and Rocchio’s algorithm, the proposed methodology provides a robust mechanism for accurately identifying sensor data anomalies, thus bolstering the reliability and performance of cyber-physical systems across a multitude of critical applications. Show more

Keywords: Cyber-physical systems, fault detection, sensor data, ensemble learning, random forest

DOI: 10.3233/JIFS-235809

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12111-12122, 2023

Authors: Ng, Grace Yee Lin | Ang, Kim Loon | Tan, Shing Chiang | Ong, Chia Sui | Ngeow, Yun Fong

Article Type: Research Article

Abstract: Multilocus variable number tandem repeat analysis (MLVA) utilizes short DNA repeat polymorphism in genomes, which is termed variable number tandem repeat (VNTR), to differentiate closely related organisms. One research challenge is to find an optimal set of VNTR to distinguish different members accurately. An intuitive method is to use an exhaustive search method. However, this method is not an efficient way to find optimal solutions from a dataset comprising many attributes (loci) due to the curse of dimensionality. In this study, metaheuristic methods are proposed to find an optimal set of loci combination. Basic genetic algorithm (BGA) and modified genetic …algorithm (MGA) were proposed in our previous work for this purpose. However, they require prior knowledge from an experienced user to specify the minimum number of loci for achieving good results. To impose no such expertise requirement for parameter setting, a GA with Duplicates (GAD), which allows the inclusion of duplicated loci in a chromosome (potential solution) during the search process, is developed. The study also investigates the search performance of a hybrid metaheuristic method, namely quantum-inspired differential evolution (QDE). Hunter-Gaston Discriminatory Index (HGDI) is used to indicate the discriminatory power of a loci combination. Two Mycobacterium tuberculosis MLVA datasets obtained from a public portal and a local laboratory respectively, are used. The results obtained by using exhaustive search and metaheuristic methods are first compared, followed by a performance comparison among BGA, MGA, GAD, and QDE by a statistical approach. The best-performing GA method (i.e., GAD) and QDE are selected for a performance comparison with several recent metaheuristic methods using both MLVA datasets by a statistical approach. The statistical results show that both GAD and QDE could achieve higher HGDI than the recent methods using a small but informative set of loci combination. Show more

Keywords: Variable number tandem repeat (VNTR), multiple locus VNTR analysis (MLVA), genotyping, metaheuristic algorithms, genetic algorithm

DOI: 10.3233/JIFS-231367

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12123-12142, 2023

Authors: Yaqoot, Iqra | Riaz, Muhammad | Al-Quran, Ashraf | Tehreem,

Article Type: Research Article

Abstract: This research work proposes a novel approach for multi stage decision analysis (MSDA) using innovative concepts of cubic intuitionistic fuzzy set (CIFS) theory. The paper introduces CIF-technique for order preference by similarity to ideal solution (TOPSIS) as a robust method for MSDA problems, particularly for the diagnosis of epilepsy disorders. To achieve this goal, new similarity measures (SMs) are developed for CIFS, including the Cosine angle between two vectors, a new distance measure, and the Cosine function, presented as three different types of Cosine similarity measures. The proposed CIF-TOPSIS approach is found to be suitable for precise value performance ratings …and is expected to be a viable approach for case studies in the diagnosis of epilepsy disorders. The efficiency and reliability of the proposed MSDA methods is efficiently carried through numerical examples and comparative analysis. Show more

Keywords: CIF information, CIF-TOPSIS, similarity, measures, epilepsy disorders, multi stage decision analysis

DOI: 10.3233/JIFS-232085

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12143-12166, 2023

Authors: Zhou, Zilong | Yu, Yue | Song, Chaoyang | Liu, Zhen | Shi, Manman | Zhang, Jingxiang

Article Type: Research Article

Abstract: Reducing noise in CT images and extracting key features are crucial for improving the accuracy of medical diagnoses, but it remains a challenging problem due to the complex characteristics of CT images and the limitations of existing methods. It is worth noting that multiple views can provide a richer representation of information compared to a single view, and the unique advantages of the wavelet transform in feature analysis. In this study, a novel Multi-View Weighted Feature Fusion algorithm called MVWF is proposed to address the challenge of enhancing CT image recognition utilizing wavelet transform and convolutional neural networks. In the …proposed approach, the wavelet transform is employed to extract both detailed and primary features of CT images from two views, including high frequency and low frequency. To mitigate information loss, the source domain is also considered as a view within the multi-view structure. Furthermore, AlexNet is deployed to extract deeper features from the multi-view structure. Additionally, the MVWF algorithm introduces a balance factor to account for both specific information and global information in CT images. To accentuate significant multi-view features and reduce feature dimensionality, random forest is used to assess feature importance followed by weighted fusion. Finally, CT image recognition is accomplished using the SVM classifier. The performance of the MVWF algorithm has been compared with classical multi-view algorithms and common single-view methods on COVID-CT and SARS-COV-2 datasets. The experimental results indicate that an average improvement of 6.8% in CT image recognition accuracy can be achieved by utilizing the proposed algorithm. Particularly, the MVF algorithm and MVWF algorithm have attained AUC values of 0.9972 and 0.9982, respectively, under the SARS-COV-2 dataset, demonstrating outstanding recognition performance. The proposed algorithms can capture more robust and comprehensive high-quality feature representation by considering feature correlations across views and feature importance based on Multi-view. Show more

Keywords: Multi-view, CT image recognition, feature fusion, wavelet transform, random forest

DOI: 10.3233/JIFS-233373

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12167-12183, 2023

Authors: Xu, Le | Wang, Jinghua | Kuang, Ciwei | Xu, Yong

Article Type: Research Article

Abstract: The 0-1 grid method is commonly used to divide a fire building into fully passable and fully impassable areas. Firefighters are only able to perform rescue tasks in the fully passable areas. However, in an actual building fire environment, there are three types of areas: fully impassable areas (areas blocked by obstacles or with heavy smoke and fire), fully passable areas, and partially passable areas (areas without obstacles or fire, but with some smoke risk). Due to the urgency of rescue, firefighters can consider conducting rescue tasks in both fully passable and partially passable areas to save valuable rescue time. …To address this issue, we propose a three-value grid method, which classifies the fire environment into fully impassable areas, fully passable areas, and partially passable areas, represented by 1, 0, and 0.5, respectively. Considering that the ACO algorithm is prone to local optimum, we propose an enhanced ant colony algorithm (EACO) to solve the fire rescue path planning problem. The EACO introduces an adaptive heuristic function, a new pheromone increment strategy, and a pheromone segmentation rule to predict the shortest rescue path in the fire environment. Moreover, the EACO takes into account both the path length and the risk to balance rescue effectiveness and safety. Experiments show that the EACO obtains the shortest rescue path, which demonstrates its strong path planning capability. The three-value grid method and the path planning algorithm take reasonable application requirements into account. Show more

Keywords: Fire rescue, path planning, 0-1 grid method, three-value grid method, EACO

DOI: 10.3233/JIFS-233862

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12185-12200, 2023

Authors: Deng, Qiao

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-234396

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12201-12212, 2023

Authors: Xing, Yu-Xuan | Wang, Jie-Sheng | Zhang, Shi-Hui | Bao, Yin-Yin | Zheng, Yue | Zhang, Yun-Hao

Article Type: Research Article

Abstract: The p-Hub allocation problem is a classic problem in location assignment, which aims to optimize the network by placing Hub devices and allocating each demand node to the corresponding Hub. A mutation Transit search (TS) algorithm with the introduction of the black hole swallowing strategy was proposed to solve the p-Hub allocation problem. Firstly, the mathematical model for the p-Hub allocation problem is established. Six mutation operators specifically designed for p-Hub allocation problem are introduced to enhance the algorithm’s ability to escape local optima. Additionally, the black hole swallowing strategy was incorporated into TS algorithm so as to accelerate its …convergence rate while ensuring sufficient search in the solution space. The improved TS algorithm was applied to optimize three p-Hub location allocation problems, and the simulation results are compared with those of the basic TS algorithm. Furthermore, the improved TS algorithm is compared with the Honey Badger Algorithm (HBA), Sparrow Search Algorithm (SSA), Harmony Search Algorithm (HS), and Particle Swarm Optimization (PSO) to solve three of p-Hub allocation problems. Finally, the impact of the number of Hubs on the cost of three models was studied, and the simulation results validate the effectiveness of the improved TS algorithm. Show more

Keywords: p-Hub allocation problem, transit search algorithm, black hole strategy, mutation operator

DOI: 10.3233/JIFS-234695

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12213-12232, 2023

Authors: Migdadi, Hatim Solayman | Al-Olaimat, Nesreen M.

Article Type: Research Article

Abstract: In this paper, a new extension of the standard Rayleigh distribution called the Power Rayleigh distribution (PRD) is investigated for the accelerated life test (ALT) using the geometric process (GP) under Type-I censored data. Point estimates of the formulated model parameters are obtained via the likelihood estimation approach. In addition, interval estimates are obtained based on the asymptotic normality of the derived estimators. To evaluate the performance of the obtained estimates, a simulation study of 4, 5 and 6 levels of stress is conducted for ALT in different combinations of sample sizes and censored times. Simulation results indicated that point …estimates are very close to their initial true values, have small relative errors, are robust and are efficient for estimating the model parameters. Similarly, the interval estimates have small lengths and their coverage probabilities are almost converging to their 95% nominated significance level. The estimation procedure is also improved by the approach of finding optimum values of the acceleration factor to have optimum values for the reliability function at the specified design stress level. This work confirms that PRD has the superiority to model the lifetimes in ALT using GP under any censoring scheme and can be effectively used in reliability and survival analysis. Show more

Keywords: Accelerated life test, geometric process, power ryleigh distribution, maximum likelihood estimation, optimum test plan

DOI: 10.3233/JIFS-232084

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12233-12242, 2023

Authors: Jiang, Shaojie | Wu, Jiang

Article Type: Research Article

Abstract: Point-of-Interest (POI) recommendation is one of the most important tasks in the field of social network analysis. Many efforts have been proposed to enhance the model performance for the POI recommendation task in recent years. Existing studies have revealed that the temporal factor and geographical factor are two crucial contextual factors which influence user decisions. However, they only learn representations of POIs and users from the single contextual factor and fuse the learned representations in the final stage, which ignores the interactions of different contextual factors, leading to learning suboptimal representations of POIs and users. To overcome this gap, we …propose a novel Temporal-Geographical Attention-based Transformer (TGAT) for the POI recommendation task. Specifically, TGAT develops a hybrid sequence sampling strategy that samples the sequence of POIs from the different contextual factor POI graphs generated by the users’ check-in records. In this way, the interactions of different contextual factors can be care-fully pre-served. Then TGAT conducts a Transformer-based neural network backbone to learn representations of POIs from the sampling sequences. In addition, a weighted aggregation strategy is proposed to fuse the representations learned from different context factors. The extensive experimental results on real-world datasets have demonstrated the effectiveness of TGAT. Show more

Keywords: Point-of-interest, social network, contextual factor, hybrid sequence sampling, transformer

DOI: 10.3233/JIFS-234824

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12243-12253, 2023

Authors: Feng, Dongmei | Kang, Yifan

Article Type: Research Article

Abstract: With the continuous development of China’s economic system, the development of the construction industry is becoming more and more rapid, and the number and scale of construction projects are increasing. Due to the characteristics of large projects and long cycles, there are a large number of construction parties involved in construction projects. The increase in the number of participating partners makes it difficult for their projects to be integrated and managed by management departments such as owners, let alone for various parties to collaborate in the construction of projects. In order to effectively solve this problem, the engineering procurement construction …(EPC) general contracting model has emerged. The risk assessment of EPC project is classical multiple attributes group decision making (MAGDM). The probabilistic hesitancy fuzzy sets (PHFSs) are used as a tool for characterizing uncertain information during the risk assessment of EPC project. In this paper, the classical grey relational analysis (GRA) method is extended to PHFSs. Firstly, the basic concept, comparative formula and Hamming distance of PHFSs are introduced. Then, the definition of the score values is employed to obtain the attribute weights based on the information entropy. Then, probabilistic hesitancy fuzzy GRA (PHF-GRA) method is built for MAGDM under PHFSs. Finally, a practical case study for risk assessment of EPC project is designed to validate the proposed method and some comparative studies are also designed to verify the applicability. Show more

Keywords: Multiple attributes group decision making (MAGDM), probabilistic hesitant fuzzy sets, grey relational analysis method (GRA), information entropy, risk assessment of EPC project

DOI: 10.3233/JIFS-231726

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12255-12266, 2023

Authors: Jiang, Wenchao | Yang, Xiaolei | Zang, Yuqi | Yuan, Xumei | Liu, Rui

Article Type: Research Article

Abstract: In view of the technical defects of the existing grey relational projection method, a new grey compromise relational bidirectional projection method is proposed. By incorporating the information expression advantage of picture hesitant fuzzy number, the distance formula of picture hesitant fuzzy statistics is constructed based on the centralized trend measurement and discrete trend measurement in descriptive statistics. On this basis, a multi-attribute recommendation method of picture hesitant fuzzy grey compromise relational bidirectional projection is proposed by combining compromise idea and bidirectional projection technology. The validity and advantage of this method are verified by numerical analysis, which also suggested the rationality …of the picture hesitant fuzzy statistical distance and the grey compromise relational bidirectional projection method. Show more

Keywords: Picture hesitant fuzzy number, grey compromise relational, bidirectional projection, multi-attribute recommendation

DOI: 10.3233/JIFS-233016

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12267-12278, 2023

Authors: Wang, Jia-Li | Jiang, Wen-Qi | Tao, Xi-Wen | Yang, Shan-Shan

Article Type: Research Article

Abstract: The processing method of fuzzy information is a critical element in multi-criteria group decision-making (MCGDM). The hesitant Pythagorean fuzzy set (HPFS) has a higher capacity in express the uncertainty of human inherent preference. A composite weighted mathematical programming model with prospect theory and best-worst method (BWM) is proposed to solve the uncertainty of criterion weight acquisition and decision-makers (DMs) psychological behavior under the HPF environment. The decision-making process is as follows: Firstly, a novel spatial distance measurement method is designed which considers the extension space of HPFSs space by five parameters under the HPF environment. Secondly, the optimal criteria weights …model minimizes the total distance between the alternatives and the HPF positive ideal solution (HPFPIS), as well as minimizes the consistency ratio of BWM. Thirdly, we propose the prospect decision matrix by the prospect theory and optimal weights, then use the ordered weighted average operator under the normal distribution to calculate the weight of DMs and rank the decision alternatives. Finally, an example is illustrated here, sensitivity and reliability, and comparative analysis are conducted to verify the effectiveness of the proposed method. Show more

Keywords: Multiple-criteria group decision-making, BWM, prospect theory, mathematical programming model, combination weights, spatial distance measure

DOI: 10.3233/JIFS-233339

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12279-12299, 2023

Authors: Wang, Lei

Article Type: Research Article

Abstract: The core of logistics is scheduling and monitoring. After the modern interprise logistics development concept change, the development prospect of enterprise logistics is more optimistic. Major enterprises have begun to use intelligent logistics scheduling platforms. In order to solve the problem that heterogeneous information fusion is complex in the temporal heterogeneous graphs, this paper proposes to dynamically store and update node representation through an augmented memory matrix in a memory network. At the same time, the model also designs a novel read-write module for the memory matrix, which can effectively capture the timing information in the long interaction sequence and …has high flexibility. The model has significantly improved in tasks such as node classification, timing recommendation and visualization. This paper studies the logistics supply chain of modern enterprises and establishes the mathematical model of vehicle scheduling. This paper takes the non-full load scheduling model as the critical research object. Based on the research of logistics supply chain, the vehicle scheduling model is established. The intelligent heuristic algorithm is applied to solve it, and the effective vehicle distribution scheme and driving route are formed. The simulation results show that the approximate Pareto optimal solution obtained by our designed model and algorithm has good robustness. NSGAIIROELSDR can get a better solution in small-scale scheduling. However, in large-scale numerical experiments, the final solution obtained by MOEA/DROELSDR is obviously better than that of NSGAIIROELSDR, and the running time of MOEA/DROELSDR is also shorter. Therefore, we conclude that MOEA/DROELSDR is more suitable for large-scale scheduling, and NSGAIIROELSDR is more suitable for more minor scheduling. Show more

Keywords: Logistics scheduling, heterogeneous graph neural network, edge feature coding, memory network

DOI: 10.3233/JIFS-234562

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12301-12312, 2023

Authors: Mannanuddin, Khaja | Vimal, V.R. | Srinivas, Angalkuditi | Uma Mageswari, S.D. | Mahendran, G. | Ramya, J. | Kumar, Ashok | Das, Pranjal | Vidhya, R.G.

Article Type: Research Article

Abstract: Diseases of the retina continue to be a leading cause of blindness and visual impairment around the world. In the field of medical image analysis, specifically retinal disease identification, deep learning techniques, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have showed remarkable potential. In this paper, we present a unique method for detecting retinal diseases by combining the advantages of the Inception-V3, ResNet-50, and Vision Transformer architectures into a single model called a Cascade CNN-ViT. The suggested Cascade CNN-ViT model extracts local features from retinal pictures by leveraging the spatial hierarchy learning capabilities of Inception-V3 and ResNet-50. …The Vision Transformer takes these regional characteristics and uses self-attention mechanisms to pick up global context information and long-range interdependence. The model successfully combines fine-grained local information with semantically significant global contextual cues by merging the output representations from the CNNs and Vision Transformer. undertaking comprehensive experiments on a large and varied dataset of multimodal retinal pictures to evaluate the performance of the proposed technique. Cascade CNN-ViT model outperforms standalone CNNs and Vision Transformers, as shown by the experimental findings. The model is also resilient across all classes of retinal diseases and is able to successfully deal with the complications introduced by using multiple picture types. Overall, the power of cascading Inception-V3, ResNet-50, and Vision Transformer topologies for improved retinal illness diagnosis has been demonstrated. Potentially improving the management of retinal illnesses and preserving visual health, the proposed approach could have important consequences for early detection and timely intervention. Show more

Keywords: Multimodal retinal images, deep learning, Inception-V3, vision transformer, cascade CNN-ViT

DOI: 10.3233/JIFS-235055

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12313-12328, 2023

Authors: Liu, Qingyang | Yahyapour, Ramin

Article Type: Research Article

Abstract: The considerable fluctuation of the stock market caused by COVID-19 tends to have a tremendous and long-lasting adverse impact on the economy. In this work, we propose a novel methodology to investigate this impact on the Chinese medical stock market. We examine changes in the stock network structure using the Triangulated Maximally Filtered Graph (TMFG ), which is computationally faster and more adaptable to enormous datasets. Additionally, we develop the LoGo model, which combines a local-global approach in its construction, to predict the stock prices of the Chinese medical stock market. In addition to traditional predictors, we incorporate …daily new infected numbers as an additional predictor to reflect the impact of COVID-19 . We select data from the 2019-2020 period and divide it into two datasets: one for the period during COVID-19 and another for the period before COVID-19 . Firstly, we compute the grey correlation coefficients between stocks instead of standard correlation coefficients. We use these coefficients to build the TMFG , enabling us to identify which stocks played the leading roles. Subsequently, we choose six stocks to build the price prediction models. Compared with the LSTM and SVR models, the LoGo models demonstrates higher accuracy, achieving an average accuracy of 71.67 percent. Furthermore, the execution time of the Logo models is 200 times faster than that of the SVR models and 50 times faster than that of the LSTM models. Show more

Keywords: Grey relation analysis (GRA), LoGo, TMFG, Information filtering networks, Stock price

DOI: 10.3233/JIFS-232479

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12329-12339, 2023

Authors: Bisht, Garima | Pal, A.K.

Article Type: Research Article

Abstract: In today’s complex decision-making environment, accounting for attribute interdependencies and expert relationships is crucial. Traditional models often assume attribute independence and overlook the significant impact of expert relationships on decision outcomes. Also, amidst the dynamic and ever-changing decision-making landscape, the effect of news and real-time updates on alternative rankings is significant. In complex decision-making environments, information is constantly evolving, and staying up-to-date with the latest developments is paramount. To overcome these limitations, this study aims to develop a novel model that effectively captures attribute dependencies and incorporates the influence of social media on alternative ordering. To establish the model, the …Decision-making trial and evaluation laboratory (DEMATEL) method and regression analysis are integrated to capture attribute dependencies. Furthermore, social network analysis (SNA) is employed to develop a trust propagation model for determining experts’ weights. Additionally, we present a two-stage multi-skilled and high potential multi-criteria decision-making (MCDM) framework, where the base-criterion method (BCM) is adopted to evaluate attribute weights and the well-known traditional Vlekriterijumsko KOmpromisno Rangiranje (VIKOR) method is redefined using Heronian mean (HM) operator to capture the relationships between arguments. Despite uncertainties, the proposed fuzzy-BCM-VIKOR-Heronian (F-BCM-VIKOR-H) approach enhances flexibility by addressing inconsistent data in complex decision-making problems. Similarly, certain news or future updates about any alternative or attribute can significantly affect the ranking. Acknowledging the significance of timely information, the proposed approach actively considers the effect of such news through the formation of an updated matrix. By factoring in the latest developments, we ensure that the proposed decision-making model remains relevant and adaptable, capturing the most current insights into alternative performance. To demonstrate the model’s effectiveness, we apply the proposed approach to a numerical illustration in the electronics industry, specifically for ranking cars. Sensitivity analysis evaluates the model’s stability, and comparing the results with existing approaches showcases its advantage and superiority. Show more

Keywords: Group decision making, VIKOR, SNA, attribute dependencies, news influence

DOI: 10.3233/JIFS-232608

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12341-12363, 2023

Authors: Khan, Majid | Batool, Syeda Iram | Munir, Noor | Alshammari, Fahad Sameer

Article Type: Research Article

Abstract: The design and development of secure nonlinear cryptographic Boolean function plays an unavoidable measure for modern information confidentiality schemes. This ensure the importance and applicability of nonlinear cryptographic Boolean functions. The current communication is about to suggest an innovative and energy efficient lightweight nonlinear multivalued cryptographic Boolean function of modern block ciphers. The proposed nonlinear confusion element is used in image encryption of secret images and information hiding techniques. We have suggested a robust LSB steganography structure for the secret hiding in the cover image. The suggested approach provides an effective and efficient storage security mechanism for digital image protection. …The technique is evaluated against various cryptographic analyses which authenticated our proposed mechanism. Show more

Keywords: Nonlinear multivalued cryptographic Boolean function, lightweight, encryption, information hiding

DOI: 10.3233/JIFS-233823

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12365-12379, 2023

Authors: Guo, Hongyue | Deng, Qiqi | Jia, Wenjuan | Wang, Lidong | Sui, Cong

Article Type: Research Article

Abstract: The implied volatility plays a pivotal role in the options market, and a collection of implied volatilities across strike and maturity is known as the implied volatility surface (IVS). To capture the dynamics of IVS, this study examines the latent states of IVS and their relationship based on the regime-switching framework of the hidden Markov model (HMM). The cross-sectional models are first built for daily implied volatilities, and the obtained regression factors are regarded as the proxies of the IVS. Then, having these latent factors, the HMM is employed to model the dynamics of IVS. Take the advantages of HMM, …the hidden state for each daily data is identified to achieve the corresponding time distribution, the characteristics, and the transition between the hidden states. The empirical study is conducted on the Shanghai 50ETF options, and the analysis results indicate that the HMM can capture the latent factors of IVS. The achieved states reflect different financial characteristics, and some of their typical features and transfer are associated with certain events. In addition, the HMM exploited to predict the regression factors of the cross-sectional models enables the further forecasting of implied volatilities. The autoregressive integrated moving average model, the vector auto-regression model, and the support vector regression model are regarded as benchmarks for comparison. The results show that the HMM performs better in the implied volatility prediction compared with other models. Show more

Keywords: Hidden Markov model, regime-switching frameworks, implied volatility surface, prediction

DOI: 10.3233/JIFS-232139

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 12381-12394, 2023