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

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