Journal of Intelligent & Fuzzy Systems - Volume 45, issue 1

Authors: Wajid, Mohd Anas | Zafar, Aasim | Terashima-Marín, Hugo | Wajid, Mohammad Saif

Article Type: Research Article

Abstract: Recent advances in technology and devices have caused a data explosion on the Internet and on our home PCs. This data is predominantly obtained in various modalities (text, image, video, etc.) and is essential for e-commerce websites. The products on these websites have both images and descriptions in text form, making them multimodal in nature. Earlier categorization and information retrieval methods focused mostly on a single modality. This study employs multimodal data for classification using neutrosophic fuzzy sets for uncertainty management for information retrieval tasks. This effort utilizes image and text data and, inspired by past techniques of embedding text …over an image, attempts to classify the images using neutrosophic classification algorithms. For classification tasks, Neutrosophic Convolutional Neural Networks (NCNNs) are used to learn feature representations of the produced images. We demonstrate how a pipeline based on NCNN can be utilized to learn representations of the innovative fusion method. Traditional convolutional neural networks are vulnerable to unknown noisy conditions in the test phase, and as a result, their performance for the classification of noisy data declines. Comparing our method against individual sources on two large-scale multi-modal categorization datasets yielded good results. In addition, we have compared our method to two well-known multi-modal fusion methodologies, namely early fusion and late fusion. Show more

Keywords: Multimodal data, early & late fusion, fuzzy logic, neutrosophic logic, convolutional neutral network

DOI: 10.3233/JIFS-223752

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1039-1055, 2023

Authors: Thao, Le Quang | Linh, Le Khanh | Thien, Nguyen Duy | Cuong, Duong Duc | Bach, Ngo Chi | Dang, Nguyen Ha Thai | Hieu, Nguyen Ha Minh | Minh, Nguyen Trieu Hoang | Diep, Nguyen Thi Bich

Article Type: Research Article

Abstract: The detection and prediction of cleaning conditions in school restrooms are crucial for reducing health risks and improving service quality. Traditional methods like manual hygienic inspection, fixed cleaning schedules, and automatic flushing devices have required large investments of money and effort from cleaning businesses to maintain cleanliness in school restrooms. To address this issue, we propose a prediction model based on Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) architecture. The model uses a dataset obtained from real-time conditions of the toilet via a wireless sensor network, enabling more efficient scheduling of toilet cleaning tasks. By predicting patterns of …Ammoniac (NH3) concentrations and Relative Humidity (RH) levels over time, our LSTM model is superior to the RNN model in performance, significantly reducing deviations in the NH3 and RH values with RMSE values of 3.32 and 2.85 , respectively. Furthermore, the model’s flexibility allows a variety of inputs to evaluate the need for cleaning at specific times, achieving maximum efficiency without requiring excessive neurons. Show more

Keywords: Wireless sensor network, manage clean restroom, LSTM, prediction

DOI: 10.3233/JIFS-230056

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1057-1065, 2023

Authors: Thao, Nguyen Xuan | Duong, Truong Thi Thuy

Article Type: Research Article

Abstract: Online reviews play a vital role in providing multidimensional information for tourists. It also has an effect on the ranking and overall score of hotels. As a powerful tool, the Fermatean fuzzy set efficiently models dealing with uncertain information. Considering that there is no study using the correlation coefficient in Fermatean fuzzy context to assess the effect of online reviews on ratings and overall score of hotels. Therefore, a correlation coefficient measure is put forward to determine the relationship between two Fermaten fuzzy numbers and then they are utilized to assess the impact of online reviews on ranking and overall …rating of hotels. The paper first introduces the TOPSIS–based ranking model using a new distance under Fermatean set. Then, we construct a new correlation coefficient between two Fermatean fuzzy numbers to measure the effect of online reviews with ranking, overall score and score of hotels under given criteria. A case study on TripAdvisor.com is performed to illustrate the proposed operator and model. Show more

Keywords: Hotels, decision making, picture fuzzy set, intuitionistic fuzzy set, Fermatean fuzzy set, correlation coefficient

DOI: 10.3233/JIFS-230667

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1067-1087, 2023

Authors: Babiyola, A. | Aruna, S. | Sumithra, S. | Buvaneswari, B.

Article Type: Research Article

Abstract: The need for a monitoring system has grown as a result of rising crime and anomalous activity. To avoid unusual incidents, the common man initiated video surveillance of important areas, which was then passed on to the government. In typical surveillance operations, surveillance devices create a vast volume of data that must be manually analysed. Manually handling huge data sets in real time results in information loss. To prevent abnormal incidents, the actions in sensitive areas can be properly monitored, evaluated, and alerted to the appropriate authorities. Previous deep learning-based activity identification methods have appeared, but the findings are inaccurate, …and the proposed Hybrid Machine Learning Algorithms (HMLA) incorporate two detection methods for surveillance videos like as Transfer Learning (TL) and Continual Learning (CL). As a result, the suspicious activity in the video may be missed. Consequently, numerous image processing and computer vision technologies were used in activity detection to decrease human effort and mistakes in surveillance operations. Activities in sensitive areas can be properly monitored and evaluated to avoid unusual incidents, and the appropriate authorities may be alerted. Hence, in order to decrease human error and effort in surveillance operations, activity recognition embraced a variety of image processing and computer vision technologies. In this present work, the capacity has constraints that impact recognition accuracy. Consequently, this research paper presents a HMLA based technique that uses feature extraction using multilayer (Long Short Term Memory) LSTM, Convolutional Neural Networks (CNN), and Temporal feature extraction using multilayer LSTM to improve identification accuracy by 96% while requiring minimal execution time. To show the superior performance of the proposed hybrid machine learning technique, a standard UCF crime dataset was utilised for experimental analysis and compared to existing deep learning algorithms. Show more

Keywords: Hybrid machine learning algorithms, surveillance videos, transfer learning, continual learning, recognition abnormal events

DOI: 10.3233/JIFS-231187

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1089-1102, 2023

Authors: Deng, Wentao | Ma, Guoqing

Article Type: Research Article

Abstract: The quality evaluation of Chinese universities ideological and political (IAP) education has gone through the stages of defining tasks, proposing standards and exploring and carrying out, and has completed the stage tasks and accumulated practical experience. To construct the quality evaluation system of IAP education of Chinese universities in the new era, it is necessary to find the quality positioning in the fundamental task of establishing moral education and pay attention to the synergy between the internal and external parts of the quality of IAP education of Chinese universities. The IAP education quality evaluation of Chinese universities are the multiple-attribute …decision-making (MADM) issue. In this paper, we extend the geometric Heronian mean (GHM) operator to fuzzy number intuitionistic fuzzy numbers (FNIFNs) to propose the fuzzy number intuitionistic fuzzy weighted geometric HM (FNIFWGHM) operator. Then, the MADM method are built on FNIFWGHM operator. Finally, a numerical example for IAP education quality evaluation of Chinese universities and some comparative studies are used to prove the built methods’ credibility and reliability. Show more

Keywords: Multiple-attribute decision-making (MADM), Fuzzy number intuitionistic fuzzy numbers (FNIFNs), FNIFWHM operator, education quality evaluation

DOI: 10.3233/JIFS-224145

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1103-1118, 2023

Authors: Subha, K.J. | Rajavel, R. | Paulchamy, B.

Article Type: Research Article

Abstract: The Retinal image analysis has received significant attention from researchers due to the compelling need of early detection systems that aid in the screening and treatment of diseases. Several automated retinal disease detection studies are carried out as part of retinal image processing. Heren an Improved Ensemble Deep Learning (IEDL) model has been proposed to detect the various retinal diseases with a higher rate of accuracy, having multiclass classification on various stages of deep learning algorithms. This model incorporates deep learning algorithms which automatically extract the properties from training data, that lacks in traditional machine learning approaches. Here, Retinal Fundus …Multi-Disease Image Dataset (RFMiD) is considered for evaluation. First, image augmentation is performed for manipulating the existing images followed by upsampling and normalization. The proposed IEDL model then process the normalized images which is computationally intensive with several ensemble learning strategies like heterogeneous deep learning models, bagging through 5-fold cross-validation which consists of four deep learning models like ResNet, Bagging, DenseNet, EfficientNet and a stacked logistic regression for predicting purpose. The accuracy rate achieved by this method is 97.78%, with a specificity rate of 97.23%, sensitivity of 96.45%, precision of 96.45%, and recall of 94.23%. The model is capable of achieving a greater accuracy rate of 1.7% than the traditional machine learning methods. Show more

Keywords: Improved Ensemble Deep learning (IEDL), bagging through 5-fold cross-validation, Retinal Fundus Multi-Disease Image Dataset (RFMiD), Stacked logistic regression

DOI: 10.3233/JIFS-230912

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1119-1130, 2023

Authors: Yang, Xu

Article Type: Research Article

Abstract: The Petri net structure of workflow is used to model, and the moment generating function is used to analyze the time performance of workflow, and the complexity of analysis is given. It provides basic theory and basis for analysis and verification. The calculation of time complexity is given for sequence, concurrency, cycle, conflict (selection) and mutual exclusion. The performance analysis method based on moment generating function can be used to analyze the performance of arbitrarily distributed bounded or unbounded random Petri nets. Establish a broad-random Petri net model that conforms to the concept of workflow. Then, based on statistical analysis …and experience estimation of relevant data in the actual system, analyze the time nature of the on-demand service based on the analysis method based on behavioral expression, and obtain some valuable performance and index information. A necessary and sufficient condition for maintaining reliability of a workflow network model is given; A polynomial decomposition algorithm for P-invariants is proposed; Combining the moment function, a performance analysis method for workflow systems is established. An example is given to verify the effectiveness of the algorithm. Show more

Keywords: Performance analysis, workflow net, concurrent selection structure, read arcs, loop structure

DOI: 10.3233/JIFS-231137

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1131-1139, 2023

Authors: Al Ghour, Samer

Article Type: Research Article

Abstract: We use soft ω s -open sets to define soft ω s -irresoluteness, soft ω s -openness, and soft pre-ω s -openness as three new classes of soft mappings. We give several characterizations for each of them, specially via soft ω s -closure and soft ω s -interior soft operators. With the help of examples, we study several relationships regarding these three notions and their related known notions. In particular, we show that soft ω s -irresoluteness is strictly weaker than soft ω s -continuity, soft ω s -openness lies strictly …between soft openness and soft semi-openness, pre-ω s -openness is strictly weaker than ω s -openness, soft ω s -irresoluteness is independent of each of soft continuity and soft irresoluteness, soft pre-ω s -openness is independent of each of soft openness and soft pre-semi-openness, soft ω s -irresoluteness and soft continuity (resp. soft irresoluteness) are equivalent for soft mappings between soft locally countable (resp. soft anti-locally countable) soft topological spaces, and soft pre-ω s -openness and soft pre-semi-continuity are equivalent for soft mappings between soft locally countable soft topological spaces. Moreover, we study the relationship between our new concepts in soft topological spaces and their topological analog. Show more

Keywords: Soft ωs-open sets, soft ωs-continuous function, soft irresolute soft mapping, soft semi-open soft mapping, soft pre-semi-open soft mapping

DOI: 10.3233/JIFS-223332

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1141-1154, 2023

Authors: Tan, Guimei | Yu, Xichang

Article Type: Research Article

Abstract: As a powerful tool to model some unsharp concepts in real life, uncertain sets have been studied by more and more scholars. In order to characterize the degree of difficulty of uncertain sets, the hyperbolic entropy of an uncertain set and the hyperbolic relative entropy of uncertain sets are introduced in this paper. After that, this paper derived a key formula to calculate the hyperbolic entropy of an uncertain set via membership function, and some mathematical properties of hyperbolic entropy are also investigated in this paper. Finally, the hyperbolic entropy is applied in some research fields such as uncertain learning …curve, clustering of rare books and portfolio selection of collecting rare books. Show more

Keywords: Uncertainty theory, uncertain set, hyperbolic entropy, uncertain learning curve

DOI: 10.3233/JIFS-223626

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1155-1168, 2023

Authors: Xie, Wenxuan | Wu, Jiali | Sheng, Yuhong

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-223641

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1169-1178, 2023

Authors: Bhuvanya, R. | Kavitha, M.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-223754

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1179-1193, 2023

Authors: Jannu, Chaitanya | Vanambathina, Sunny Dayal

Article Type: Research Article

Abstract: Over the past ten years, deep learning has enabled significant advancements in the improvement of noisy speech. Due to the short time stability of speech signal, previous speech enhancement (SE) methods concentrated only on magnitude estimation, and these methods added a phase of the mixture in reconstructing the speech. The performance is limited in these approaches since the phase will also carry some of the speech information. Some of the speech enhancement approaches were developed later to jointly estimate both magnitudes as well as phases. Recently, complex-valued models, like deep complex convolution recurrent network (DCCRN), are proposed, but the computation …of the model is very huge. In this work, we propose a Discrete Cosine Transform-based Densely Connected Convolutional Gated Recurrent Unit (DCTDCCGRU) model using dilated dense block and stacked GRU. The dense connectivity strengthens the gradient propagation by concatenating features from previous layers at the input. The advantage of the dense block is that at various resolutions, the dilated convolutions aid with context aggregation, and the dense connectivity provides a feature map with more precise target information by passing through multiple layers. To represent the correlation between neighboring noisy speech frames, a two Layer GRU is added in the bottleneck of U-Net. The experimental findings demonstrate that the proposed model outperformed the other existing models in terms of STOI (short-time objective intelligibility), PESQ (perceptual evaluation of the speech quality), and output SNR (signal-to-noise ratio). Show more

Keywords: SE-Speech enhancement, DTC-Discrete cosine transform, SNR-Signal to noise ratio, dense block

DOI: 10.3233/JIFS-223951

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1195-1208, 2023

Authors: Bektur, Gulcin

Article Type: Research Article

Abstract: In this study, an energy-efficient distributed flow shop scheduling (DFSS) problem with total tardiness minimisation and machine-sequence dependent setup times is addressed. A mixed integer linear programming (MILP) model is proposed for the problem. A variant of the NSGA II algorithm is suggested for the solution of large scale problems. The proposed algorithm is compared with the state-of-the-art NSGA II, SPEA II, and multiobjective iterated local search algorithm. The computational results show that the proposed algorithm is efficient and effective for the problem. This is the first study to propose a heuristic algorithm for the distributed flow shop scheduling problem …with total tardiness minimisation, speed scaling and setups. Show more

Keywords: Energy efficient scheduling, distributed flow shop scheduling, multiobjective optimisation, heuristic algorithms, minimisation of total tardiness, speed scaling mechanism

DOI: 10.3233/JIFS-224199

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1209-1222, 2023

Authors: Xu, Qian

Article Type: Research Article

Abstract: As the problem of sub-health continues to expand among urban residents, forestry tourism has been further developed, and forest wellness travel for the purpose of recuperation has gradually become the focus of transformation and upgrading of the current big health industry. In order to refine the evaluation of the development potential of regional forest health tourism and achieve further promotion of regional forest health tourism, the study first established the construction principles of the evaluation system, combined with expert consultation and theoretical analysis methods to select evaluation indicators, and used analytic hierarchy process to obtain the weight of each indicator. …An adaptive variational genetic algorithm was then proposed to improve the BP neural network to form the AGA-BP model, which was finally applied to the assessment of the progression potentiality of forest wellness travel. The outcomes demonstrate that among the assessment indicators of forest wellness travel progression potentiality, the environmental quality has the largest weight of 0.4598; the convergence and precision of the AGA-BP model proposed by the research have been upgraded by 80% and 50% respectively, with a faster global search speed; in the assessment of the regional forest wellness travel progression potentiality, the method is highly consistent with the actual assessment outcomes, with an average precision rate of 98% indicating that it can accurately and effectively conduct potentiality assessment, providing a methodological reference for the sustainable progression of forest wellness travel. Show more

Keywords: Forest recreation, tourism, progression potentiality, BP neural network

DOI: 10.3233/JIFS-230582

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1223-1234, 2023

Authors: Zou, Wang | Zhang, Wubo | Tian, Zhuofeng | Wu, Wenhuan

Article Type: Research Article

Abstract: In the field of text classification, current research ignores the role of part-of-speech features, and the multi-channel model that can learn richer text information compared to a single model. Moreover, the method based on neural network models to achieve final classification, using fully connected layer and Softmax layer can be further improved and optimized. This paper proposes a hybrid model for text classification using part-of-speech features, namely PAGNN-Stacking1 . In the text representation stage of the model, introducing part-of-speech features facilitates a more accurate representation of text information. In the feature extraction stage of the model, using the multi-channel attention …gated neural network model can fully learn the text information. In the text final classification stage of the model, this paper innovatively adopts Stacking algorithm to improve the fully connected layer and Softmax layer, which fuses five machine learning algorithms as base classifier and uses fully connected layer Softmax layer as meta classifier. The experiments on the IMDB, SST-2, and AG_News datasets show that the accuracy of the PAGNN-Stacking model is significantly improved compared to the benchmark models. Show more

Keywords: Text classification, part-of-speech features, multi-channel, stacking algorithm

DOI: 10.3233/JIFS-231699

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1235-1249, 2023

Authors: Chen, Huakun | Jia, Qianlei | Huang, Wei | Shi, Jingping | Safwat, Ehab

Article Type: Research Article

Abstract: There is a growing body of literature that recognises the importance of Z-numbers proposed by Prof Zadeh. However, due to the complicated structure and short presentation time, many unknowns about Z-numbers still exist. To fill these gaps, this study aims to make use of rectangular coordinate system to express linguistic Z-numbers. Simultaneously, this study sets out to design a score function to quantify the information contained in different Z-numbers. Subsequently, distance measure and similarity measure are also presented from the perspective of coordinate system. Besides, linguistic discrete Z-numbers and belief rule base (BRB) model are combined to construct a novel …reasoning model on the basis of implication operators. To verify the validity of the proposed method, three representative examples of epidemic level assessment, multicriteria group decision-making (MCGDM), and network security assessment are employed. The comparison with other widely used methods are performed to further demonstrate the superiority of the proposed method. Show more

Keywords: Linguistic Z-numbers, score function, rectangular coordinate system, distance measure, similarity measure

DOI: 10.3233/JIFS-223025

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1251-1268, 2023

Authors: Zhu, Zhihui | Zhu, Meifang

Article Type: Research Article

Abstract: In recent years, chronic diseases, an aging population, and high healthcare costs have become global concerns. The Internet of Things (IoT) is transforming society by enabling physical objects to sense and collect data about their surroundings. It has evolved to encompass a wide range of sensing strategies, and it continues to improve in terms of sophistication and cost reduction. IoT can play an important role in enhancing human health through remote healthcare. The application of advanced IoT technology in healthcare is still a significant challenge due to a number of issues, such as the shortage of cost-effective and accurate smart …medical sensors, the absence of standardized IoT architectures, the heterogeneity of connected wearable devices, the multidimensionality of data generated, and the need for interoperability. In order to provide insight into the advance of IoT technologies in healthcare, this paper presents a comprehensive discussion on IoT device capabilities, focusing on the hardware and software systems, as well as the processing abilities, operating systems, and built-in tools. Show more

Keywords: Healthcare, internet of things, medical device, wireless sensor networks, data management, literature review

DOI: 10.3233/JIFS-224166

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1269-1288, 2023

Authors: Yuan, Pei

Article Type: Research Article

Abstract: With the globalization of the world’s economy, culture, science and technology, and the increasing frequency of international cooperation and exchanges, English will play an increasingly important role. For non-English majors in Chinese colleges and universities, college English is a public compulsory basic course, which plays a very important role in expanding students’ knowledge, improving foreign language cultural literacy and comprehensive language use ability. An important part of college English classroom teaching is teaching evaluation, which not only helps teachers obtain teaching feedback information, improve teaching management, and ensure teaching quality, but also effectively helps students adjust learning strategies, improve learning …methods, and improve learning efficiency. The English classroom teaching quality evaluation could be deemed as a classic multiple attribute group decision making (MAGDM) problem. In this paper, as a useful outranking approach, the extended QUALIFLEX method is utilized to address some MAGDM issues by using picture 2-tuple linguistic sets (P2TLSs). In addition, integrating the QUALIFLEX method with P2TLSs, the extended QUALIFLEX method with P2TLNs is constructed and all calculating procedures are simply depicted. Eventually, an empirical application of English classroom teaching quality evaluation has been offered to demonstrate this novel method. Show more

Keywords: Multiple attribute group decision making (MAGDM), picture fuzzy sets (PFSs), picture 2-tuple linguistic sets (P2TLSs), the extended QUALIFLEX method, English classroom teaching quality evaluation

DOI: 10.3233/JIFS-230969

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1289-1302, 2023

Authors: Jin, Xiaofang

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-231191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1303-1312, 2023

Authors: Li, Yan

Article Type: Research Article

Abstract: With the development of socialist market economy, the exhibition industry has emerged as the tertiary industry matures in a globalized economic environment. As a new economic form, the exhibition economy presents new opportunities for economic development. The research on the exhibition industry at home and abroad has been relatively mature, and there has been a scientific analysis of the industrial linkage effect of the exhibition industry. The strong industrial linkage effect has made the exhibition industry occupy a very important position in the economic development of cities. However, in the development of China’s urban exhibition industry today, it is no …longer a simple question of developing and enhancing the strategic position of the exhibition industry in economic development, but rather a question of how to enhance the competitiveness of China’s urban exhibition industry. Only when the level of competitiveness is improved can the economic and social benefits brought by the exhibition industry be brought into full play. The fuzzy comprehensive competitiveness evaluation of urban exhibition industry is a classical multiple attribute decision making (MADM) problems. Recently, the TODIM and VIKOR method has been used to cope with MAGDM issues. The hesitant fuzzy sets (HFSs) are used as a tool for characterizing uncertain information during the fuzzy comprehensive competitiveness evaluation of urban exhibition industry. In this manuscript, the hesitant fuzzy TODIM-VIKOR (HF-TODIM-VIKOR) method is built to solve the MADM under HFSs. In the end, a numerical case study for fuzzy comprehensive competitiveness evaluation of urban exhibition industry is given to validate the proposed method. Show more

Keywords: Multiple attribute decision making(MAGDM), Hesitant fuzzy sets (HFSs), TODIM, VIKOR, urban exhibition industry

DOI: 10.3233/JIFS-231672

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1313-1323, 2023

Authors: Liu, Ting

Article Type: Research Article

Abstract: In this paper, a new timing synchronization algorithm for the main synchronous signal (PSS) is proposed for the risk identification of cross-border e-commerce in the Internet of things, aiming at the problems of poor performance of anti frequency bias and high computational complexity of the improved PSS timing synchronization algorithm. Based on the piecewise correlation algorithm, the normalized frequency deviation of PSS sequence is preset. The segmented ones are pre stored at the terminal by using the conjugate symmetry of PSS sequence. The fast correlation of each segment correlation window is realized by combining convolution and overlapping reservation block method. …Then, the threshold judgment is made after the time delay accumulation of the correlation values of the segments is made, so as to complete the joint detection of timing synchronization and coarse frequency deviation. The simulation results show that the algorithm can improve the performance of the system anti frequency offset effectively, reduce the complexity of the calculation and show that the timing synchronization conditions of the Internet can be satisfied. At the same time, under the background of the current development of cross-border logistics, this paper reviews the current research status of Transnational E-commerce logistics and Transnational E-commerce logistics risk. By comparing the advantages and disadvantages of various risk assessment methods, neural network and genetic algorithm are selected as the basic risk assessment methods in this paper. Based on the improved PSS timing synchronization algorithm and the Internet of things, the risk indicators of e-commerce logistics transnational will be selected from five risk dimensions: platform risk, customs clearance risk, organizational risk, process risk and environmental risk. Through the comprehensive literature and expert’s opinion, the logistics risk assessment index system of cross-border e-commerce is established. Show more

Keywords: PSS timing synchronization algorithm, internet of things, cross border e-commerce, risk identification

DOI: 10.3233/JIFS-221194

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1325-1340, 2023

Authors: Gao, Pengcheng | Chen, Mingxian | Zhou, Yu | Zhou, Ligang

Article Type: Research Article

Abstract: In order to estimate the deficiency of a city in its ability to prevent and control risks, as well as to evaluate the corresponding measures, this paper focuses on multi-attribute decision making based on LINMAP method and Manhattan distance at linguistic q-rung orthopair fuzzy. Manhattan distance is a new product that combines clustering distance with linguistic q-rung orthopair fuzzy to be able to use the data more effectively for measurement. LINMAP method is a decision making method based on ideal points, which can solve the weights as well as provide ideal solutions by linear programming model. The combination of the …two can create a new decision-making method, which can effectively evaluate the decision scheme of social public facilities according to the actual needs of decision-makers. The new method has the following advantages: (1) the conditions of linguistic fuzzy numbers can be applied more comprehensively, making the decision more realistic and effective; (2) the Manhattan distance is more in line with the human way of thinking and closer to life; (3) after comparative study, the results produced by this method have certain reliability. Show more

Keywords: Multi-attribute decision making, linguistic q-rung orthopair fuzzy, LINMAP method, Manhattan distance

DOI: 10.3233/JIFS-221750

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1341-1355, 2023

Authors: Padmanaban, K. | Shunmugalatha, A.

Article Type: Research Article

Abstract: A novel metaheuristic algorithm has been presented based on the physical significance of palm tree leaves and petioles, which can themselves water and fertilize with their unique architecture. Palm tree leaves collect almost all the raindrops that fall on the tree, which drags the nutrient-rich dropping of crawlers and birds that inhabit it and funnel them back to the palm tree’s roots. The proposed Palm Tree Optimization (PTO) algorithm is based on two main stages of rainwater before it reaches the trunk. Stage one is that the rainwater drops search for petioles in the local search space of a particular …leaf, and stage two involves that the rainwater drops after reaching the petioles search for trunk to funnel back to the root along with nutrients. The performance of PTO in searching for global optima is tested on 33 Standard Benchmark Functions (SBF), 29 constrained optimization problems from IEEE-CEC2017 and real-world optimization problems from IEEE-CEC2011 competition especially for testing the evolutionary algorithms. Mathematical benchmark functions are classified into six groups as unimodal, multimodal, plate & valley-shaped, steep ridges, hybrid functions and composition functions which are used to check the exploration and exploitation capabilities of the algorithm. The experimental results prove the effectiveness of the proposed algorithm with better search ability over different classes of benchmark functions and real-world applications. Show more

Keywords: PTO-palm tree optimization, exploration, exploitation, petioles, crankshaft

DOI: 10.3233/JIFS-222413

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1357-1385, 2023

Authors: Wang, Wei | Zhang, Weidong | Zhang, Zhe

Article Type: Research Article

Abstract: The complexity of the cohesive soil structure necessitates settlement modeling beneath shallow foundations. The goal of this research is to use recently discovered machine learning techniques called the hybridized radial basis function neural network (RBFNN ) with sine cosine algorithm (SCA ) and firefly algorithm (FFA ) to detect settlement (S m ) of shallow foundations. The purpose of using optimization methods was to find the optimal value for the primary attributes of the model under investigation. With R 2 values of at least 0.9422 for the learning series and 0.9271 for the assessment series, both the produced …SCA  - RBFNN and FFA  - RBFNN correctly replicated the S m , which indicates a considerable degree of efficacy and even a reasonable match between reported and modeled S m . In comparison to FFA  - RBFNN and ANFIS  - PSO , the SCA  - RBFNN is believed to be the more correct method, with the values of R 2 , RMSE and MAE was 0.9422, 7.2255 mm and 5.1257 mm, which is superior than ANFIS  - PSO and FFA  - RBFNN . The SCA  - RBFNN could surpass FFA one by 25% for the learning component and 14.2% for the test data, according to the values of PI index. Ultimately, it is apparent that the RBFNN combined with SCA could score higher than the FFA and even the ANFIS  - PSO , which is the proposed system in the S m forecasting model, after assessing the reliability and considering the assumptions. Show more

Keywords: Shallow foundation settlement, prediction, RBF neural network, sine cosine algorithm

DOI: 10.3233/JIFS-223907

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1387-1396, 2023

Authors: Keerthika, V. | Muhiuddin, G. | Jun, Y. B. | Elavarasan, B.

Article Type: Research Article

Abstract: Fuzzy sets, soft sets, and their generalisations have always been important tools for mathematicians and researchers working with uncertainty. Jun proposed a hybrid structure that combined the concepts of a fuzzy set and a soft set. It should be noted that hybrid structures are a combination of soft set and fuzzy set speculation. Our aim is to explore the concept of hybrid ordered ideals and hybrid interior ideals in ordered semirings, as well as look at some of their related properties, which is one of the important aspects of this paper. In order to investigate the structure theory of hybrid …ideals in ordered semirings, we define hybrid composition and hybrid addition. We also establish and characterise the regularity of ordered semirings in terms of hybrid structures. Show more

Keywords: Semiring, ideals, hybrid structure, hybrid interior ideals, ordered semirings

DOI: 10.3233/JIFS-224060

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1397-1408, 2023

Authors: Singh, Nitin Kumar | Singh, Pardeep | Das, Prativa | Chand, Satish

Article Type: Research Article

Abstract: Social media platforms allow people across the globe to share their thoughts and opinions and conveniently communicate with each other. Apart from various advantages of social media, it is also misused by a set of users for hate-mongering with toxic and offensive comments. The majority of the earlier proposed toxicity detection methods are primarily focused on the English language, but there is a lack of research on low-resource languages and multilingual text data. We propose an XRBi-GAC framework comprising XLM-RoBERTa, Bi-GRU with self-attention and capsule networks for multilingual toxic text detection. A loss function is also presented, which fuses the …binary cross-entropy loss and focal loss to address the class imbalance problem. We evaluated the proposed framework on two datasets, namely, the Jigsaw Multilingual Toxic Comment dataset and HASOC 2019 dataset and achieved F1-score of 0.865 and 0.829, respectively. The results of the experiments show that the proposed framework has outperformed the state-of-the-art multilingual models XLM-RoBERTa and mBERT on both datasets, which shows the versatility and robustness of the proposed XRBi-GAC framework. Show more

Keywords: Toxicity, multilingual text, XLM-RoBERTa, Bi-GRU, self-attention, capsule network

DOI: 10.3233/JIFS-224536

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1409-1421, 2023

Authors: Xu, Siyu | Qin, Keyun | Pan, Xiaodong | Fu, Chao

Article Type: Research Article

Abstract:  Both fuzzy set and rough set are important mathematical tools to describe incomplete and uncertain information, and they are highly complementary to each other. What is more, most fuzzy rough sets are obtained by combining Zadeh fuzzy sets and Pawlak rough sets. There are few reports about the combination of axiomatic fuzzy sets and Pawlak rough sets. For this reason, we propose the axiomatic fuzzy rough sets (namely rough set model with respect to the axiomatic fuzzy set) establishing on fuzzy membership space. In this paper, we first present a similarity description method based on vague partitions. Then the concept …of similarity operator is proposed to describe uncertainty in the fuzzy approximation space. Finally, some characterizations concerning upper and lower approximation operators are shown, including basic properties. Furthermore, we give a algorithm to verify the effectiveness and efficiency of the model. Show more

Keywords: Rough sets, axiomatic fuzzy rough sets, residuated lattices, fuzzy relations, approximation operators

DOI: 10.3233/JIFS-223643

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1423-1436, 2023

Authors: Sharma, Rahul | Singh, Amar

Article Type: Research Article

Abstract: In the recent decade, plant disease classification using convolution neural networks has proven to be superior because of its ability to extract key features. Obtaining the optimum feature subset with the necessary discriminant information is challenging. The main objective of this paper is to design an efficient hybrid plant disease feature selection approach and validate it on standard image datasets. The raw input image features were transformed into 8192 learned features by employing the VGG16. To reduce the training time and enhance classification accuracy, the dimensionality reduction technique Principal Component Analysis (PCA) is integrated with the big bang-big crunch (BBBC) …optimization algorithm. The PCA-BBBC feature selection method reduces computing time by eliminating unnecessary and redundant features. The proposed approach was evaluated on plant diseases and benchmarked image datasets. Experimental results reveal that the Artificial Neural Network (ANN) classifier integrated with the VGG16-PCA-BBBC approach enhanced the performance of the classifier. The proposed approach outperformed the VGG16-PCA-ANN method and other popular image classification techniques. For the rice disease dataset, the proposed hybrid approach reduced the VGG16 extracted 8192 deep features to 200 relevant principal components. The recommended reduced features were used for training ANN. The test dataset was classified by ANN with an accuracy of 99.12%. Experimental results demonstrate that the proposed approach improved the performance of the classifier and accurately labeled image and plant diseases datasets aiding farmers to adopt remedial measures. Show more

Keywords: BBBC, dimensionality reduction, feature selection, PCA, plant disease detection

DOI: 10.3233/JIFS-222517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1437-1451, 2023

Authors: Vinodha, D. | Mary Anita, E.A.

Article Type: Research Article

Abstract: Industrial revolutions and demand of novel applications drive the development of sensors which offer continuous monitoring of remote hostile areas by collecting accurate measurement of physical phenomena. Data aggregation is considered as one of the significant energy-saving mechanism of resource constraint Wireless Sensor Networks (WSNs) which reduces bandwidth consumption by eliminating redundant data. Novel applications demand WSN to provide information about the monitoring region in multiple aspects in large scale. To meet this requirement, different kinds of sensors of different parameters are deployed in the same region which in turn demands the aggregator node to integrate diverse data in a …smooth and secure manner. Novelty in applications also requires Base station (BS) to apply multiple statistical functions. Hence, we propose to develop a novel secure cost-efficient data aggregation scheme based on asymmetric privacy homomorphism to aggregate data of multiple parameters and facilitate the BS to compute multiple functions in one round of data collection by providing elaborated view of monitoring region. To meet the claim of large scale WSN which requires dynamic change in size, vector-based data collection method is adopted in our proposed scheme. The security aspect is strengthened by allowing BS to verify the authenticity of source node and validity of data received. The performance of the system is analyzed in terms of computation and communication overhead using the mathematical model and simulation results. Show more

Keywords: Wireless sensor networks, secured data aggregation, privacy homomorphism

DOI: 10.3233/JIFS-223511

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1453-1472, 2023

Authors: Tian, Chang | Liu, Yanjung | Li, Meng | Fen, Chaofan

Article Type: Research Article

Abstract:  The key step in the intelligence of tongue diagnosis is the segmentation of the tongue image, and the accuracy of the segmented edges has a significant impact on the subsequent medical judgment. Deep learning can predict the class of pixel points to achieve pixel-level segmentation of images, so it can be used to handle tongue segmentation tasks. However, different models have different segmentation effects, and they did not learn the connection between space and channels, resulting in inaccurate tongue segmentation. This paper first discussed the choice of model and loss function and then compared the results of different options to …find the better model. Associating the red feature of the tongue is very conducive to segmentation as a feature, this paper tested many methods to try to get the color features of the original image to be paid attention to. Finally, this paper proposed an improved Encoder-Decoder network model to solve the problem based on the results. Start with Resnet as the backbone network, then introduce the U-Net model, and then we fused the attention layer, obtained from the source image through convolution and CBAM attention mechanism, and the feature layer obtained from the last upsampling in U-Net. Experimental results show that: The new, improved algorithm results are 2-3 percentage points higher than the popular algorithm, making it more suitable for tongue segmentation tasks. Show more

Keywords: Deep convolutional neural network, attention mechanism, tongue image, image segmentation

DOI: 10.3233/JIFS-221411

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1473-1480, 2023

Authors: Liang, Baohua | Lu, Zhengyu

Article Type: Research Article

Abstract: Attribute reduction is a widely used technique in data preprocessing, aiming to remove redundant and irrelevant attributes. However, most attribute reduction models only consider the importance of attributes as an important basis for reduction, without considering the relationship between attributes and the impact on classification results. In order to overcome this shortcoming, this article firstly defines the distance between samples based on the number of combinations formed by comparing the samples in the same sub-division. Secondly, from the point of view of clustering, according to the principle that the distance between each point in the cluster should be as small …as possible, and the sample distance between different clusters should be as large as possible, the combined distance is used to define the importance of attributes. Finally, according to the importance of attributes, a new attribute reduction mechanism is proposed. Furthermore, plenty of experiments are done to verify the performance of the proposed reduction algorithm. The results show that the data sets reduced by our algorithm has a prominent advantage in classification accuracy, which can effectively reduce the dimensionality of high-dimensional data, and at the same time provide new methods for the study of attribute reduction models. Show more

Keywords: Rough sets, attribute reduction, clustering, combined distance

DOI: 10.3233/JIFS-222666

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1481-1496, 2023

Authors: Sakthivel, S. | Vinotha, N.

Article Type: Research Article

Abstract: Concerns of security as well as privacy are the chief obstacles which have prevented the public cloud’s extensive adoption in Intel IT as well as across the industry. Generally, IT organizations are quite reluctant to store sensitive as well as valuable data in infrastructures which are out of their control. The technique of anonymization is employed by enterprises to raise the security of the public cloud’s data whilst facilitating the data’s analysis as well as application. The procedure of data anonymization will modify how the data is either employed or published in such a way that it will prevent the …key information’s identification. The privacy issues are addressed using k-anonymity. However, the issue of selecting the variables for anonymization and suppression of variables without the loss of knowledge is an optimization problem. To address the selection of variables for anonymization and suppression, metaheuristic algorithms are used. Diverse research groups have successfully utilized the River Formation Dynamics (RFD) metaheuristic to handle numerous problems of discrete combinatorial optimization. Even so, this metaheuristic has never been adapted for use in domains of continuous optimization. To mitigate the local minima problem, hybridization of the algorithms is proposed. In this work, a modified K-Anonymity technique’s proposal has been given by using the Modified Hill Climbing (MHC) optimization, the RFD-MHC optimization, the RFD-PSO optimization, the RFD-MHC suppression as well as the RFD-PSO suppression. Furthermore, proposal for a suppression technique has also been given in this work. Experiments demonstrated that the RFD-PSO optimization has higher classification accuracy in the range of 6.73% to 8.55% when compared to manual K-anonymization. The work has also given better trade off for security analysis and data utility effectiveness. Show more

Keywords: Privacy preservation, security, K-anonymity model, river formation dynamics (RFD) and particle swarm optimization (PSO) algorithm, modified hill climbing (MHC)

DOI: 10.3233/JIFS-223509

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1497-1512, 2023

Authors: Wang, Changjing | Jiang, Huiwen | Wang, Yuxin | Huang, Qing | Zuo, Zhengkang

Article Type: Research Article

Abstract: The smart contract, a self-executing program on the blockchain, is key to programmable finance. However, the rise of smart contract use has also led to an increase in vulnerabilities that attract illegal activity from hackers. Traditional manual approaches for vulnerability detection, relying on domain experts, have limitations such as low automation and weak generalization. In this paper, we propose a deep learning approach that leverages domain-specific features and an attention mechanism to accurately detect vulnerabilities in smart contracts. Our approach reduces the reliance on manual input and enhances generalization by continuously learning code patterns of vulnerabilities, specifically detecting various types …of vulnerabilities such as reentrancy, integer overflow, forced Ether injection, unchecked return value, denial of service, access control, short address attack, tx.origin, call stack overflow, timestamp dependency, random number dependency, and transaction order dependency vulnerabilities. In order to extract semantic information, we present a semantic distillation approach for detecting smart contract vulnerabilities. This approach involves using a syntax parser, Slither, to segment the code into smaller slices and word embedding to create a matrix for model training and prediction. Our experiments indicate that the BILSTM model is the best deep learning model for smart contract vulnerability detection task. We looked at how domain features and self-attentiveness mechanisms affected the ability to identify 12 different kinds of smart contract vulnerabilities. Our results show that by including domain features, we significantly increased the F1 values for 8 different types of vulnerabilities, with improvements ranging from 7.35% to 48.58%. The methods suggested in this study demonstrate a significant improvement in F1 scores ranging from 4.18% to 38.70% when compared to conventional detection tools like Oyente, Mythril, Osiris, Slither, Smartcheck, and Securify. This study provides developers with a more effective method of detecting smart contract vulnerabilities, assisting in the prevention of potential financial losses. This research provides developers with a more effective means of detecting smart contract vulnerabilities, thereby helping to prevent potential financial losses. Show more

Keywords: Smart contract, vulnerability detection, attention mechanism, domain features, recurrent neural network 2010 MSC: 00-01, 99-00

DOI: 10.3233/JIFS-224489

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1513-1525, 2023

Authors: Xu, Haiyan | Zhang, Hao | Zhu, Anfeng | Xu, Gang

Article Type: Research Article

Abstract: In order to improve the accuracy and security of encrypted holographic 3D geographic information data acquisition and improve the actual resolution of geographic information files, a blind watermarking algorithm for encrypted holographic 3D geographic information data based on mapping mechanism is proposed. According to the characteristics of the mapping mechanism, a mapping mechanism structure diagram is constructed; Under the mapping mechanism technology, blind watermark data is preprocessed. Then, a watermark embedding operation is performed to obtain the watermark information image, and then a blind watermark that encrypts the holographic three-dimensional geographic information data is extracted. Finally, using the blind watermark …signal as input, the blind watermark information is obtained by using the watermark strength, and the holographic 3D geographic data information is segmented and encrypted to complete blind watermark detection. The blind watermark algorithm for encrypting the holographic 3D geographic information data is studied. The results show that the maximum difference between the correlation coefficient of the algorithm in this paper and the correlation coefficient of the unaffected algorithm is only 0.04, which has better anti attack performance, high security, good terrain information collection ability, high data accuracy, and can achieve curvature repair of information data. Show more

Keywords: Mapping mechanism, encrypted holography, 3D geographic information data, blind watermarking algorithm

DOI: 10.3233/JIFS-230064

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1527-1537, 2023

Authors: Smitha, E.S. | Sendhilkumar, S. | Mahalakshmi, G.S.

Article Type: Research Article

Abstract: Multi-modal information outbreak is consistently increasing in social media. Classification of tweet sentiments using various information modalities will help the recommender systems to achieve success in digital marketing. Moreover, aspect-level sentiment analysis categorizes a target’s sentiment polarity in a specific environment. Using topic modelling in aspect-level sentiment analysis enables the identification of more accurate aspect-based tweet sentiments. The existing sentiment classification techniques used for the development of recommendation systems do not focus on the aspect-based approach modelled using deep learning classifier with temporal analysis on the social media data. Hence, this paper proposes an efficient sentiment classification model that highlights …the impact of topic modelling-based word feature embedding for improvising the classification of Twitter sentiments and product reviews based on temporal reasoning and analysis for performing predictive analysis. For tweets context analysis, Latent Dirichlet Allocation based topic modelling is used in this work which generates the topics. For each topic, the sentiment is calculated separately and the topic guided feature expansion is done using Senti-wordnet. Moreover, an extended deep learning classification algorithm called Long Short-Term Memory (LSTM) with word embedding and temporal reasoning(LSTMWTR) is proposed in this paper for improving the classification accuracy. Finally, the labelled data are classified using the existing machine learning algorithms namely Naïve Bayes, Support Vector Machines and also using the deep learning models such as Convolution Neural Network(CNN),LSTM, Recurrent Neural Networks (RNN) and the transformer model namelyBi-directional Encoder Representation from Transformers (BERT),Convolution Bi-directional Recurrent Neural Network (CBRNN) and the proposed deep learning algorithm namelyLSTMWTR. These sentiment classification algorithms have been evaluated with word embedding for tweet sentiment classification and product review classification. The results obtained from this work show that the proposed LSTMWTR algorithm emerges as the highly accurate model for tweet sentiment and product review classification. Show more

Keywords: Sentiment, classification, word embedding, temporal reasoning, NB, multinomial NB, SVM, LSTM, LSTMWTR, BERT, CNN, RNN, and CBRNN

DOI: 10.3233/JIFS-230246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1539-1565, 2023

Authors: Ali, Imran | Li, Yongming | Pedrycz, Witold

Article Type: Research Article

Abstract:  In literature granular computing and formal concept analysis algorithm use only single-value attributes to knowledge discovery for the data of spatio-temporal aspects. However, most of the datasets like forest fires and tornado storms involve multiscale values for attributes. The limitation of single-value attributes of the existing approaches indicates only the data related to event occurrence which may be missing the elicitation of important knowledge related to severity of event occurrence. Motivated by these limitations, this research article proposes a novel and generalized method which uses ordinal semantic weighted multiscale values for attributes in formal concept analysis with granular computing measures …especially when spatio-temporal attributes are not given. The originality of proposed methodology is using ordinal semantic weighted multiscale values for attributes that give complete information of event occurrences. Moreover, the use of ordinal semantic weighted multiscale values improves the results of granular computing measures. The significance of proposed approach is well explained by experimental evaluation performed on publicly available datasets on storm occurring in different States of America. Show more

Keywords: Formal concept analysis, granular computing, granulation measures, ordinal semantic weighted multiscales

DOI: 10.3233/JIFS-223764

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1567-1586, 2023

Authors: Hati, Santu

Article Type: Research Article

Abstract: In present world the major cause of global warming and climate change are emission of carbon and greenhouse gas. Governments and policymakers around the world want to put their best efforts to control the pollution and climate change to save our environment. To reduce greenhouse gas emissions Government and policymakers takes carbon tax policy on carbon emission. Also in real world uncertainty is a pervasive phenomenon. Humans have a significant ability to make logical decisions based on uncertain information. For this purpose, we are developing a pollution control fuzzy production inventory model with imperfect and break-ability items under preservation technology …investment and carbon tax policy. In this model, the break-ability rate is dependent on inventory level as the break-ability rate of breakable items depends on the collected stress of inventory stock level. Here the unit production cost is dependent on raw material cost, wear-tear cost and development cost. Carbon emission is controlled by investing in carbon reduction technology and a fraction of product items are imperfect. In this study demand of the product depends on selling price and inventory stock level of product. Finally, this optimal control problem solved by using Pontryagin Maximum principle and the optimal results are illustrated graphically and numerically using MATLAB software. Subsequently, some sensitivity analysis is investigated as the impact of parameters on total profit. Show more

Keywords: Break-ability, deteriorating items, preservation technology, environment pollution control, fuzzy granular differentiability, fuzzy optimal control production inventory

DOI: 10.3233/JIFS-224019

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1587-1601, 2023

Authors: Murugesan, Malathi | Jeyali Laseetha, T.S. | Sundaram, Senthilkumar | Kandasamy, Hariprasath

Article Type: Research Article

Abstract: Glaucoma is a condition of the eye that is caused by an increase in the eye’s intraocular pressure that, when it reaches its advanced stage, causes the patient to lose all of their vision. Thus, glaucoma screening-based treatment administered in a timely manner has the potential to prevent the patient from losing all of their vision. However, because glaucoma screening is a complicated process and there is a shortage of human resources, we frequently experience delays, which can lead to an increase in the proportion of people who have lost their eyesight worldwide. In order to overcome the limitations of …current manual approaches, there is a critical need to create a reliable automated framework for early detection of Optic Disc (OD) and Optic Cup (OC) lesions. In addition, the classification process is made more difficult by the high degree of overlap between the lesion and eye colour. In this paper, we proposed an automatic detection of Glaucoma disease. In this proposed model is consisting of two major stages. First approach is segmentation and other method is classification. The initial phase uses a Stacked Attention based U-Net architecture to identify the optic disc in a retinal fundus image and then extract it. MobileNet-V2 is used for classification of and glaucoma and non-glaucoma images. Experiment results show that the proposed method outperforms other methods with an accuracy, sensitivity and specificity of 98.9%, 95.2% and 97.5% respectively. Show more

Keywords: Medical image segmentation, classification, convolutional neural network, U-Net, MobileNet-V2

DOI: 10.3233/JIFS-230659

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1603-1616, 2023

Authors: Jebril, Akram H. | Rashid, Rozeha A.

Article Type: Research Article

Abstract: Low power wide area networks (LPWANs) are made to survive conditions of extensive installation. Technological innovations, including Global Network Operator, Long Range Wide Area Network (LoRaWAN), Narrowband Internet of Things (NB-IoT), Weightless, Sigfox, etc., have adopted LPWANs. LoRaWAN is currently regarded to be one of the most cutting-edge and intriguing technology for the widespread implementation of the IoT. Although LoRaWAN offers the best features that make it fit with Internet - of - things specifications, there are still certain technical issues to overcome, such as link coordination, resource allocation and reliable transmission. In LoRaWAN, End-devices transmit randomized uplink frames to …the gateways using un-slotted random-access protocol. This randomness with the restrictions placed on the gateways is a reason that leads to a considerable decline in network performance, in particular downlink frames. In this paper, we propose a new approach to increase Acknowledgement (ACK) messages throughput. The suggested method takes advantage of both class A and class B features to enhance and assist LoRaWAN’s reliability by ensuring that an ACK message is sent for every confirmed uplink while retaining the minimum energy level that is utilized by nodes. Show more

Keywords: Internet of Things, LoRaWAN, Downlink Frame, Differential Evolution optimization, Collision, Acknowledgement Message

DOI: 10.3233/JIFS-230730

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1617-1631, 2023

Authors: Prusty, Sashikanta | Das, Priti | Dash, Sujit Kumar | Patnaik, Srikanta | Prusty, Sushree Gayatri Priyadarsini

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-223265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1633-1652, 2023

Authors: Guan, Hao | Ejaz, Farukh | ur Rehman, Atiq | Hussain, Muhammad | Kosari, Saeed

Article Type: Research Article

Abstract:  In this paper, we have defined some fuzzy topological invariants for particular types of uniform fuzzy graph. Some particular useful types of uniform fuzzy graphs are Uniform Edge Fuzzy Graph, Uniform Vertex Fuzzy Graph, Uniform Vertex-Edge Fuzzy Graph and Totally Uniform Fuzzy Graph. For each particular type we have defined different kinds of degrees in a graph in accordance with the unique nature of it. In the end, we have applied all our output results to a cellular neural fuzzy graph as an example, to verify the predicting ability of topological invariants. The aim of this paper is to define …more significant fuzzy topological invariants in fuzzy graphs. Our ideas will help to create a link between fuzzy graph theory and simple (crisp) graph theory. Show more

Keywords: Uniform Edge Fuzzy Graphs, Fuzzy Topological Invariants, Fuzzy degrees

DOI: 10.3233/JIFS-223402

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1653-1662, 2023

Authors: Bao, Qingfeng | Zhang, Sen | Guo, Jin | Ding, Dawei | Zhang, Zhenquan

Article Type: Research Article

Abstract:  In order to improve the optimal setting temperature problem to achieve the global optimum of product performance, costs and benefits. In this article, a hierarchical structure optimal setting approach of production indexes for the rolling heating furnace temperature field (RHFTF) is proposed. It is composed of three layers with different functions to obtain the temperature control setting model of the RHFTF. In the first layer, the bi-feature Gaussian mixture model clustering (BFGMMC) algorithm of loading plan is proposed to optimize the setting of a limited number of slabs. In the second layer, the type-2 fuzzy rule interpolation (T2FRI) setting method …is developed to obtain the optimal setting curve. Meanwhile, an improved KH (Kóczy-Hirota) α-cut distance (IKHCD) algorithm is proposed to get the miss information between any two adjacent interpolation points. In the third layer, knowledge feedforward compensation of rule matrices (KFCRM) algorithm is presented to improve the anti-interference ability of the setting model. The results of the study can demonstrate that the proposed method improves the accuracy of the model and optimizes the control strategy. Furthermore, the experimental results show that the proposed method meets the process technical requirements. Show more

Keywords: Hierarchical structure, bi-feature Gaussian mixture model clustering (BFGMMC), type-2 fuzzy rules interpolation (T2FRI), improved KH (Kóczy-Hirota) α-cut distance (IKHCD), knowledge feedforward compensation of rule matrices(KFCRM)

DOI: 10.3233/JIFS-223441

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1663-1681, 2023

Authors: Vasavi, J. | Abirami, M.S.

Article Type: Research Article

Abstract: Latent Lip groove application is been a notable topic in forensic applications like crime and other investigations. The detection of lip movement is been a challenging task since it is a smaller integral part of the human face. The conventional models operate on the available public or private dataset but it is constrained to the large population and unconstrained environment. The study aims at developing a deep learning model in a multimodal system using the deep U-Net Convolutional Neural Network architecture. It also aims at improving biometric authentication through a deep pattern recognition that involves the feature extraction of grooves …present in the human lips. An examination of grooves present in the input lip image is conducted by the present system to check the authenticity of the person entering the cyber-physical systems. The lip images are collected from the public security cameras via high-definition cameras in crowded areas that help the proposed method in forensic investigation and further, it considers various unconstrained scenarios to improve the efficacy of the system. The study involves initially pre-processing of lip image, and feature extraction of lip grooves to improve the efficacy of the lip trait. The simulation is conducted on the MATLAB tool to examine the efficacy of the model against various existing methods. Further, the study does not take into account the datasets available on the websites and lip images are only collected from a large set population in a real-time environment. The results of the simulation show that the proposed method achieves a higher degree of accuracy in extracting the grooves from the input lip images. Show more

Keywords: Biometric authentication, lip pattern, U-Net, grooves, multimodal

DOI: 10.3233/JIFS-223488

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1683-1693, 2023

Authors: Gao, Yu | Zhang, Qinghua | Zhao, Fan | Gao, Man

Article Type: Research Article

Abstract:  Fuzzy sets provide an effective method for dealing with uncertain and imprecise problems. For data of intermediate fuzzy distribution, membership degrees of objects whose attribute values are larger or smaller than the normal value would be the same and carried out the same decision. However, objects with different values mean that the information they contain is different for the decision-making problem. The decision process of calculating membership degrees in fuzzy set will lose the information of data itself. Therefore, bilateral fuzzy sets and their three-way decisions are proposed. First, the deviation degree is proposed in order to distinguish these objects. …Compared with the membership degree, the deviation degree extends the mapping range from [0, 1] to [- 1, 1]. For six typical membership functions, their corresponding deviation functions are discussed and deduced. Second, the concept of bilateral fuzzy sets is proposed and the corresponding operation rules are analyzed and proved. Then, three-way decisions and approximations based on bilateral fuzzy sets are constructed. Next, for the optimization of threshold, principle of least cost is extended to the three-way decisions model based on bilateral fuzzy sets, and theoretical derivation is carried out. Finally, based on probability statistics, the principle based on confidence interval is proposed, which provides a new perspective for threshold calculation. Show more

Keywords: Fuzzy sets, three-way decisions, confidence interval, Bilateral fuzzy sets

DOI: 10.3233/JIFS-230638

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1695-1715, 2023

Authors: Komala, C.R. | Velmurugan, V. | Maheswari, K. | Deena, S. | Kavitha, M. | Rajaram, A.

Article Type: Research Article

Abstract: Internet of Things (IoT) technologies increasingly integrate unmanned aerial vehicles (UAVs). IoT devices that are becoming more networked produce massive data. The process and memory of this enormous volume of data at local nodes, particularly when utilizing artificial intelligence (AI) algorithms to collect and utilize useful information, have been declared vital issues. In this paper, we introduce UAV computing to solve greater energy consumption, delay difficulties using task offload and clustered approaches, and make cloud computing operations accessible to IoT devices. First, we present a clustering technique to group IoT devices for data transmission. After that, we apply the Q-learning …approach to accomplish task offloading and allocate the difficult tasks to UAVs that are not yet fully loaded. The sensor readings from the CHs are then collected using UAV path planning. Furthermore, We use a convolutional neural network (CNN) to achieve UAV route planning. In terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets, the effectiveness of the current study is finally compared with the existing techniques using UAVs. The results showed that the suggested strategy outperformed the current approaches in terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets. Additionally, the proposed technique consumed less energy due to CNN-based route planning and dynamic positioning, which reduced UAV transmits power. Overall, the study concluded that the suggested approach is effective for improving energy-efficient and responsive data transmission in crises. Show more

Keywords: UAV computing, Internet of Things, clustering, energy reduction, task offloading, and UAV path planning

DOI: 10.3233/JIFS-231242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1717-1730, 2023

Authors: Jayapriya, P. | Umamaheswari, K. | Kavitha, A. | Ahilan, A.

Article Type: Research Article

Abstract: In recent years, finger vein recognition has gained a lot of attention and been considered as a possible biometric feature. Various feature selection techniques were investigated for intrinsic finger vein recognition on single feature extraction, but their computational cost remains undesirable. However, the retrieved features from the finger vein pattern are massive and include a lot of redundancy. By using fusion methods on feature extraction approaches involving weighted averages, the error rate is minimized to produce an ideal weight. In this research, a novel combinational model of intelligent water droplets is proposed along with hybrid PCA LDA feature extraction for …improved finger vein pattern recognition. Initially, finger vein images are pre-processed to remove noise and improve image quality. For feature extraction, Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) are employed to identify the most relevant characteristics. The PCA and LDA algorithms combine features to accomplish feature fusion. A global best selection method using intelligent water drops (GBS-IWD) is employed to find the ideal characteristics for vein recognition. The K Nearest Neighbour Classifier was used to recognize finger veins based on the selected optimum features. Based on empirical data, the proposed method decreases the equal error rate by 0.13% in comparison to existing CNN, 3DFM, and JAFVNet techniques. The overall accuracy of the proposed GBSPSO-KNN is 3.89% and 0.85% better than FFF and GWO, whereas, the proposed GBSIWD-KNN is 4.37% and 1.35% better than FFF and GWO respectively. Show more

Keywords: Principle component analysis, finger vein recognition, linear discriminant analysis, k-nearest neighbor, intelligent water drops

DOI: 10.3233/JIFS-222717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1731-1742, 2023

Authors: Jiang, Feng | Lin, Chunhua | Chen, Jing | Wu, Chutian

Article Type: Research Article

Abstract: New energy integration is thought to be one of the most potential solutions to support the power system with a sustainable energy infrastructure. However, new energy is an uncertain power generation resource, and the electricity generated by it has the characteristics of randomness, intermittency and reverse peak regulation. Its large-scale integration into the power grid makes the operation and reliability scheduling of the power system more challenging. It was important to build a wireless sensing and monitoring network to monitor the power and change trend of the new energy field (station) in real time. The energy consumption of wireless sensing …monitoring network is an important factor to improve the reliability of new energy scheduling. Based on the energy consumption of the wireless sensing monitoring network built by the new energy scheduling, the compression sensing technology was integrated and the network routing protocol (I-LEACH protocol) was optimized. The sampling data was transmitted by the cluster head node at the compression rate of 0.6, the improved OMP (Orthogonal Matching Pursuit) algorithm was reconstructed to achieve reliable data transmission, and the network energy consumption was further reduced. Compared with the I-LEACH routing protocol network, the experiments show that the network residual energy of the proposed method increased by 22% and the life cycle increased by about 30%. This method is helpful to improve the reliability of new energy power dispatching system and it can provide reference for realizing the reliability scheduling of new energy power system. Show more

Keywords: I-LEACH, cluster head node, OMP

DOI: 10.3233/JIFS-222980

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1743-1756, 2023

Authors: Yue, Guanli | Deng, Ansheng | Qu, Yanpeng | Cui, Hui | Liu, Jiahui

Article Type: Research Article

Abstract: Ensemble clustering helps achieve fast clustering under abundant computing resources by constructing multiple base clusterings. Compared with the standard single clustering algorithm, ensemble clustering integrates the advantages of multiple clustering algorithms and has stronger robustness and applicability. Nevertheless, most ensemble clustering algorithms treat each base clustering result equally and ignore the difference of clusters. If a cluster in a base clustering is reliable/unreliable, it should play a critical/uncritical role in the ensemble process. Fuzzy-rough sets offer a high degree of flexibility in enabling the vagueness and imprecision present in real-valued data. In this paper, a novel fuzzy-rough induced spectral ensemble …approach is proposed to improve the performance of clustering. Specifically, the significance of clusters is differentiated, and the unacceptable degree and reliability of clusters formed in base clustering are induced based on fuzzy-rough lower approximation. Based on defined cluster reliability, a new co-association matrix is generated to enhance the effect of diverse base clusterings. Finally, a novel consensus spectral function is defined by the constructed adjacency matrix, which can lead to significantly better results. Experimental results confirm that the proposed approach works effectively and outperforms many state-of-the-art ensemble clustering algorithms and base clustering, which illustrates the superiority of the novel algorithm. Show more

Keywords: Rough set, fuzzy-rough set, ensemble clustering, cluster reliability, spectral clustering

DOI: 10.3233/JIFS-223897

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1757-1774, 2023

Authors: Santhadevi, D. | Janet, B.

Article Type: Research Article

Abstract: Many Internet of Things (IoT) devices are susceptible to cyber-attacks. Attackers can exploit these flaws using the internet and remote access. An efficient Intelligent threat detection framework is proposed for IoT networks. This paper considers four key layout ideas while building a deep learning-based intelligent threat detection system at the edge of the IoT. Based on these concepts, the Hybrid Stacked Deep Learning (HSDL) model is presented. Raw IoT traffic data is pre-processed with spark. Deep Vectorized Convolution Neural Network (VCNN) and Stacked Long Short Term Memory Network build the classification model (SLSTM). VCNN is used for extracting meaningful features …of network traffic data, and SLSTM is used for classification and prevents the DL model from overfitting. Three benchmark datasets (NBaIoT-balanced, UNSW-NB15 & UNSW_BOT_IoT- imbalanced) are used to test the proposed hybrid technique. The results are compared with state-of-the-art models. Show more

Keywords: Hybrid stacked deep learning, stacked LSTM, Vectorized Convolutional Neural Network, IoT-network security, edge computing

DOI: 10.3233/JIFS-223246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1775-1790, 2023

Authors: Li, Huan

Article Type: Research Article

Abstract: The difficulties in determining the compressive strength of concrete are inherited due to the various nonlinearities rooted in the mix designs. These difficulties raise dramatically considering the modern mix designs of high-performance concrete. Presents study tries to define a simple approach to link the input ingredients of concrete with the resulted compressive with a high accuracy rate and overcome the existing nonlinearity. For this purpose, the radial base function is defined to carry out the modeling process. The optimal results were obtained by determining the optimal structure of radial base function neural networks. This task was handled well with two …precise optimization algorithms, namely Henry’s gas solubility algorithm and particle swarm optimization algorithm. The results defined both models’ best performance earned in the training section. Considering the root mean square error values, the best value stood at 2.5629 for the radial base neural network optimized by Henry’s gas solubility algorithm, whereas the same value for the the radial base neural network optimized by particle swarm optimization was 2.6583 although both hybrid models provided acceptable output results, the radial base neural network optimized by Henry’s gas solubility algorithm showed higher accuracy in predicting high performance concrete compressive strength. Show more

Keywords: High-performance concrete, Henry’s gas solubility algorithm, particle swarm optimization algorithm, radial base function neural network

DOI: 10.3233/JIFS-221342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1791-1803, 2023

Authors: Caroline Misbha, J. | Ajith Bosco Raj, T. | Jiji, G.

Article Type: Research Article

Abstract: The research aims to provide network security so that it can be protected from several attacks, especially DoS (Denial-of-Service) or DDoS (Distributed Denial-of-Service) attacks that could at some point render the server inoperable. Security is one of the main obstacles. There are a lot of network risks and attacks available today. One of the most common and disruptive attacks is a DDoS attack. In this study, upgraded deep learning Elephant Herd Optimization with random forest classifier is employed for early DDos attack detection. The DDoS dataset’s number of characteristics is decreased by the proposed IDN-EHO method for classifying data learning …that works with a lot of data. In the feature extraction stage, deep neural networks (DNN) approach is used, and the classified data packages are compared to return the DDoS attack traffic characteristics with a significant percentage. In the classification stage, the proposed deep learning Elephant Herd Optimization with random forest classifier used to classify the data learning which deal with a huge amount of data and minimise the number of features of the DDoS dataset. During the detection step, when the extracted features are used as input features, the attack detection model is trained using the improved deep learning Elephant Herd Optimization. The proposed framework has the potential to be a promising method for identifying unidentified DDoS attacks, according to experiments. 99% recall, precision, and accuracy can be attained using the suggested strategy, according on the findings of the experiments. Show more

Keywords: Effective fuzzy, elephant herd optimization, DDoS attack, hybrid deep learning method

DOI: 10.3233/JIFS-224149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1805-1816, 2023

Authors: Xue, Junxiao | Kong, Xiangyan | Wang, Gang | Dong, Bowei | Guan, Haiyang | Shi, Lei

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-211999

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1817-1831, 2023

Authors: Li, Hui

Article Type: Research Article

Abstract: The scientific research work of colleges and universities has attracted more and more social attention because of its large number of multidisciplinary scientific and technological talents, hardware facilities and good scientific research environment, and the quality of scientific and technological management work of colleges and universities directly affects the level of scientific and technological work of colleges and universities. Starting from the common problems of scientific research management in colleges and universities, this paper explores the ideas and methods to further promote scientific research work by improving the quality of scientific research management. The quality evaluation of scientific research management …in application-oriented universities is classical multiple attribute group decision making (MAGDM). Based on this, we extend the traditional CODAS method to the Pythagorean 2-tuple linguistic sets (P2TLSs) and propose the Pythagorean 2-tuple linguistic CODAS (P2TL-CODAS) method for quality evaluation of scientific research management in application-oriented universities. The P2TL-CODAS method is established and all computing steps are simply presented. Furthermore, we apply the P2TL-CODAS method to evaluate the quality evaluation of scientific research management in application-oriented universities. Show more

Keywords: Multiple attribute group decision making (MAGDM), Pythagorean 2-tuple linguistic sets (P2TLSs), CODAS method, P2TL-CODAS model, quality evaluation of scientific research management

DOI: 10.3233/JIFS-230629

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1833-1845, 2023

Authors: Agitha, T. | Sivarani, T.S.

Article Type: Research Article

Abstract: This research work focus on level control in quadruple tank systems based on proposed Deep Neural Fuzzy based Fractional Order Proportional Integral Derivative (DN-FFOPID) controller system. This is used for controlling the liquid level in these non- linear cylindrical systems. These model helps in identifying the dynamics of the tank system which gives the control signal feed forwarded from the reference liquid level. But, it fails to minimize the error and the system is also subjected to external disturbances. Hence, to minimize this drawback a novel controller must be introduced in it. The proposed Deep Neural model is a six …layered network which are optimized with the back-propagation algorithm. It effectively trains the system thus reducing the steady state error, offset model errors and unmeasured disturbances. This neural intelligent system maintains the liquid level which fulfils the required design criteria like time constant, no overshoot, less rise time and less settling time, which can be applied to various fields. MATLAB/simulink at FOMCON toolbox is used to perform the simulation. Real time liquid control experimental results and simulation results are demonstrated which proves the effectiveness and feasibility of the proposed methods for the quadruple tank system which finds applications in effluent treatment, petrochemical, pharmaceutical and aerospace fields. Show more

Keywords: Proposed deep neural fuzzy based fractional order proportional integral derivative controller, non- linear quadruple tank systems, back propagation, MATLAB/simulink –FOMCON toolbox

DOI: 10.3233/JIFS-221674

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1847-1861, 2023

Authors: Liu, Pingqing | Wang, Hongjun | Ning, Baoquan | Wei, Guiwu

Article Type: Research Article

Abstract: The recruitment of university researchers can be considered a multi-attribute group decision-making (MAGDM) problem. MAGDM is a familiar issue with uncertainty and fuzziness in the decision-making field. Generalized hesitation fuzzy numbers (GHFNs) as a new expanded form of hesitation fuzzy numbers (HFNs) can better express the uncertain information in MAGDM. The TODIM is a very classical and widely used method to deal with the MAGDM issue. In this paper, we integrate cumulative prospect theory (CPT) into TODIM to consider not only decision makers’ subjective risk preferences but also their confidence level to obtain more reasonable choices under risk conditions. Therefore, …we propose the GHF CPT-TODIM approach to tackle the MAGDM issue. Meanwhile, in the GHF environment, it is proposed to use the volatility of attribute information (entropy weighting method) to obtain the importance of attributes, obtain the unknown attribute weight, and enhance the rationality of weight information. Finally, the validity and usefulness of the technique are verified by applying the GHF CPT-TODIM technique to the recruitment of university researchers and comparing it with the existing GHF MAGDM method, which offers a new way to solve the MAGDM problem with GHFNs. Show more

Keywords: Multi-attribute group decision-making (MAGDM), generalized hesitant fuzzy numbers (GHFNs), TODIM, cumulative prospect theory (CPT), recruitment of university researchers

DOI: 10.3233/JIFS-224437

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1863-1880, 2023

Article Type: Retraction

DOI: 10.3233/JIFS-219328

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1881-1882, 2023