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

Authors: Sun, Hanjie

Article Type: Research Article

Abstract: With the development of information technology, online learning has become an important way of teaching in colleges and universities. The importance of online learning is particularly prominent, especially during the COVID-19 pandemic. How to improve online learning quality is a common problem faced by educators. Online learning quality is closely related to information presentation form, so it is necessary to study the influence of information presentation form on online learning. Based on the dynamics theory of visual perception form and its operating principle, this study compares the differences in post-test scores, cognitive load and satisfaction between the information dynamics presentation …form and the traditional information presentation form through a two-factor random experiment. The data analysis shows that information presentation form plays a significant role in improving students’ academic performance and reducing cognitive load. To a certain extent, there search proves the effectiveness of the information presentation form based on dynamics theory of visual perception form in promoting online learning. Relevant improvement suggestions are proposed to provide a reference and basis for the in-depth development of online learning and the improvement of online learning quality. Show more

Keywords: Dynamics theory of visual perception form, information presentation form, online learning, associative cues, CLC Number: G434 Document Code: A

DOI: 10.3233/JIFS-230083

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 463-475, 2023

Authors: Deva, K. | Mohanaselvi, S.

Article Type: Research Article

Abstract: Picture fuzzy aggregation operators are the standard mathematical tools for the combination of several inputs with respect to attributes into one unique output. The Choquet integral operator has been proven more ideal than traditional aggregation operators in the modelling of interaction phenomena among the attributes in decision-making problems. Firstly, we propose the Choquet integral picture fuzzy Einstein geometric aggregation operator and Choquet integral picture fuzzy Einstein ordered geometric aggregation operator with certain properties of these operators being established. We validate the functioning of the operators with illustrative examples. The proposed operators clearly capture the comprehensive correlative relationships of attributes in …a simpler manner. Furthermore, the algorithm for a multi attribute decision-making problem based on proposed operators is given. The application of the proposed operators was explored to deal with the selection of the best mobile apps for online education. Finally, comparisons are conducted to illustrate the discussion and advantages of the proposed operators. Show more

Keywords: Multi attribute decision-making, picture fuzzy set, choquet integral, aggregation opertaors

DOI: 10.3233/JIFS-230472

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 477-490, 2023

Authors: Savitha, S. | Rajiv Kannan, A.

Article Type: Research Article

Abstract: Chronic Kidney Disease (CKD) is a crucial life-threatening condition due to impaired kidney functionality and renal disease. In recent studies, Kidney disorder is considered one of the essential and deadliest issues that threaten patients’ survival with the lack of earlier prediction and classification. The earlier prediction process and the proper diagnosis help delay or stop the chronic disease progression into its final stage, where renal transplantation or dialysis is a known way of saving the patient’s life. Global studies reveal that nearly 10% of the population is affected by Chronic Kidney Disease (CKD), and millions die because of non-affordable treatment. …Early detection of CKD from the biological parameters would save people from this crisis. Machine Learning algorithms are playing a predominant role in disease diagnosis and prognosis. This work generates compound features from CKD indicators by two novel algorithms: Correlation-based Weighted Compound Feature (CWCF) and Feature Significance based Weighted Compound Feature (FSWCF). Any learning algorithm is as good as its features. Hence, the features generated by these algorithms are validated on different machine learning algorithms as a test for generality. The simulation is done in MATLAB 2020a environment where various metrics like prediction accuracy gives superior results compared to multiple other approaches. The accuracy of CWCF over different methods like LR is 97.23%, Gaussian NB is 99%, SVM is 99.18%, and RF is 99.89%, which is substantially higher than the approaches without proper methods feature analysis. The results suggest that generated compound features improve the predictive power of the algorithms. Show more

Keywords: Feature selection, correlation, feature significance, chronic kidney disease, feature projection, mutual information

DOI: 10.3233/JIFS-222401

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 491-504, 2023

Authors: Karuppuchamy, V. | Palanivelrajan, S.

Article Type: Research Article

Abstract: Chronic diseases like diabetes, Heart Failure (HF), malignancy, and severe respiratory sickness are the leading cause of mortality around the globe. Dissimilar indications or traits are extremely difficult to identify in HF patients. IoT solutions are becoming increasingly commonplace as smart wearable gadgets become more popular. Sudden heart attacks have a short life expectancy, which is terrible. As a result, a patient monitoring of heart patients based on IoT-centered Machine Learning (ML) is presented to help with HF prediction, and treatment is administered as necessary. Verification, Encryption, and Categorization are the three phases that make up this developed model. Initially, …the datasets from the IoT sensor gadget are gathered by authenticating with a specific hospital through encryption. The patient’s integrated IoT sensor module then transfers sensing information to the cloud. The Improved Blowfish Encryption (IBE) approach is used to protect the sensor data transfer to the cloud. Then the encrypted data is decrypted, and the classification is performed using the Adaptive Fuzzy-Based Long Short-Term Memory with Recurrent Neural Network (AF-LSTM-RNN) algorithm. The results are classed as malignant or benign. It assesses the patient’s cardiac state and sends an alert text to the doctor for treatment. The AF-LSTM-RNN-based HF prediction outperforms the existing techniques. Accuracy, sensitivity, specificity, precision, F-measure and Matthews Correlation Coefficient (MCC) are compared to existing procedures to ensure the planned research is genuine. Using the Origin tool, these metrics are shown as research findings. Show more

Keywords: Heart failure (HF), IoT, machine learning, improved blowfish encryption (IBE), adaptive fuzzy-based long short-term memory with recurrent neural network (AF-LSTM-RNN), origin tool

DOI: 10.3233/JIFS-224298

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 505-520, 2023

Authors: Dhivya, S. | Mohanavalli, S. | Kavitha, S.

Article Type: Research Article

Abstract: Breast cancer can be successfully treated if diagnosed at its earliest, though it is considered as a fatal disease among women. The histopathology slide turned images are the gold standard for tumor diagnosis. However, the manual diagnosis is still tedious due to its structural complexity. With the advent of computer-aided diagnosis, time and computation intensive manual procedure can be managed with the development of an automated classification system. The feature extraction and classification are quite challenging as these images involve complex structures and overlapping nuclei. A novel nuclei-based patch extraction method is proposed for the extraction of non-overlapping nuclei patches …obtained from the breast tumor dataset. An ensemble of pre-trained models is used to extract the discriminating features from the identified and augmented non-overlapping nuclei patches. The discriminative features are further fused using p-norm pooling technique and are classified using a LightGBM classifier with 10-fold cross-validation. The obtained results showed an increase in the overall performance in terms of accuracy, sensitivity, specificity, and precision. The proposed framework yielded an accuracy of 98.3% for binary class classification and 95.1% for multi-class classification on ICIAR 2018 dataset. Show more

Keywords: Breast cancer, histopathology, nuclei-based patches, nuclei feature fusion, LightGBM

DOI: 10.3233/JIFS-222136

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 521-535, 2023

Authors: Yang, Biqin | Deng, Yu

Article Type: Research Article

Abstract: Due to the increasingly strengthened role of finance in modern economic development, theoretical research on regional financial competitiveness in the study of regional economic competitiveness becomes very important. For China at this stage, finance is in a period of rapid development, and its role has penetrated into all aspects of social and economic life. Especially after China’s entry into the WTO, the pace of opening up the financial market has been further accelerated, and comprehensive evaluation and analysis of financial competitiveness is of great significance for comprehensively understanding and accurately grasping China’s national conditions, national strength, and international competitiveness, promoting …the long-term growth of China’s financial competitiveness, and the sustainable development of the financial industry. The competitiveness evaluation of regional financial centers is looked as the multiple attribute decision-making (MADM) problem. This paper intends to propose a MADM methodology based on CoCoSo (Combined Compromise Solution) method under interval-valued intuitionistic fuzzy sets (IVIFSs) for sustainable competitiveness evaluation of regional financial centers. At the end of this study, we noticed to a comparison between the proposed IVIF-CoCoSo approach with other existing methods to verify the effectiveness of the algorithm. Show more

Keywords: Multi-attribute decision making (MADM), interval-valued intuitionistic fuzzy sets (IVIFSs), IVIF-CoCoSo method, CRITIC method, competitiveness evaluation

DOI: 10.3233/JIFS-222607

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 537-547, 2023

Authors: Kadeeja Mole, K.P. | Sameena, Kalathodi

Article Type: Research Article

Abstract: In this work, several operations on fuzzy graphs are introduced: u -product, strong edge product, and k th power. The relationship between the fuzzy chromatic number of resultant fuzzy graphs of operations union, join, and newly developed operations and the fuzzy chromatic number of associated fuzzy graphs is also investigated using fuzzy colouring techniques. The number of captures in a chess puzzle move is calculated using the fuzzy colouring approach.

Keywords: Fuzzy graph, fuzzy chromatic number, operations of fuzzy graphs, strong edge, fuzzy colouring

DOI: 10.3233/JIFS-223263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 549-561, 2023

Authors: Sun, Ke | Zhao, Xiaojie | Huang, He | Yan, Yunyang | Zhang, Haofeng

Article Type: Research Article

Abstract: Zero-Shot Learning (ZSL) has made significant progress driven by deep learning and is being promoted further with the advent of generative models. Despite the success of these methods, the type and number of unseen categories are nailed in the generative models, which makes it challenging to recognize unseen categories in an incremental manner, and the profits of some superior performance algorithms largely arise from their advanced capability of feature extraction, such as Transformers. This paper rigidly follows the assumptions introduced in conventional ZSL and proposes a visual feature filtering method based on a semantic mapping model, namely, filtering visual features …through class-specific filters to effectively remove class-agnostic information. Extensive experiments are conducted on four benchmark datasets and have achieved very competitive performance. Show more

Keywords: Generalized zero-shot learning, class-specific filter, matching score calculation

DOI: 10.3233/JIFS-224297

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 563-576, 2023

Authors: Li, Wenqiao | Wang, Ruijie | Ai, Qisheng | Liu, Qian | Lu, Shu Xian

Article Type: Research Article

Abstract: The compressive strength and slump of concrete have highly nonlinear functions relative to given components. The importance of predicting these properties for researchers is greatly diagnosed in developing constructional technologies. Such capacities should be progressed to decrease the cost of expensive experiments and enhance the measurements’ accuracy. This study aims to develop a Radial Basis Function Neural Network (RBFNN) to model the hardness features of High-Performance Concrete (HPC) mixtures. In this function, optimizing the predicting process via RBFNN will be aimed to be accurate, as the aim of this research, conducted with metaheuristic approaches of Henry gas solubility optimization (HGSO) …and Multiverse Optimizer (MVO). The training phase of models RBHG and RBMV was performed by the dataset of 181 HPC mixtures having fly ash and superplasticizer. Regarding the results of hybrid models, the MVO had more correlation between the predicted and observed compressive strength and slump values than HGSO in the R2 index. The RMSE of RBMV (3.7 mm) was obtained 43.2 percent lower than that of RBHG (5.3 mm) in the appraising slump of HPC samples, while, for compressive strength, RMSE was 3.66 MPa and 5 MPa for RBMV and RBHG respectively. Moreover, to appraise slump flow rates, the R2 correlation rate for RBHG was computed at 96.86 % while 98.25 % for RBMV in the training phase, with a 33.30% difference. Generally, both hybrid models prospered in doing assigned tasks of modeling the hardness properties of HPC samples. Show more

Keywords: Compressive strength, slump flow, multiverse optimization algorithm, concrete hardness, neural network

DOI: 10.3233/JIFS-230005

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 577-591, 2023

Authors: Liu, Lin

Article Type: Research Article

Abstract: With the rapid development of the construction industry, people’s requirements for the construction quality continue to improve, and the supervision and management of the construction project quality has been paid more and more attention. The perfect quality supervision and management system is not only an important guarantee for the whole construction project implementation process, but also provides support for the smooth implementation of the construction project. With the increasing number of high-rise buildings in cities and the increasing difficulty of construction, it has posed great challenges to the construction industry, which also means that the quality supervision and management of …construction projects are facing new challenges. Therefore, the project quality supervision and management department should review the situation, optimize the quality supervision and management work according to the current situation and needs of the construction project development, effectively improve the system guarantee and content optimization, maximize the role of quality supervision and management, and provide assistance for the high-quality and sustainable development of the construction industry. The quality evaluation of construction project is a classical multiple attribute group decision making (MAGDM). In this paper, we extended multi-attributive border approximation area comparison (MABAC) method for MAGDM with Pythagorean 2-tuple linguistic sets (P2TLSs). Firstly, a brief review of the definition of P2TLSs is given. Next, two aggregation operators of P2TLSs are used to fuse overall evaluation information. Moreover, combining traditional MABAC model with P2TLSs, Pythagorean 2-tuple linguistic number MABAC (P2TLN-MABAC) is built with all computing steps depicted in detail. Furthermore, a numerical example related to quality evaluation of construction project is conducted to demonstrate the effectiveness of the proposed method. Finally, some comparisons with P2TLWA and P2TLWG operators are also carried out. Show more

Keywords: Multiple attribute group decision making (MAGDM), Pythagorean 2-tuple linguistic sets (P2TLSs), MABAC method, quality evaluation, construction project

DOI: 10.3233/JIFS-230963

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 593-602, 2023

Authors: Wang, Xiaomin | Zhang, Xueyuan | Zhou, Rui

Article Type: Research Article

Abstract: In this paper, we introduce a new hybrid model called probabilistic hesitant N-soft sets by a suitable combination of probability with hesitant N-soft sets, a model that extends hesitant N-soft sets. Our novel concept extends the ability of hesitant N-soft set by considering the occurrence probability of hesitant grades, which could effectively avoid the loss of decision-making information. Moreover, we investigate some basic properties of probabilistic hesitant N-soft sets and construct fundamental operations on them. Then we describe group decision-making methods including TOPSIS, VIKOR, choice value and weighted choice value based on probabilistic hesitant N-soft sets. The corresponding algorithms are …put forward and their validity is proved by examples. Show more

Keywords: N-soft set, hesitant N-soft set, probabilistic hesitant N-soft set, probabilistic hesitant fuzzy set, decision-making

DOI: 10.3233/JIFS-222563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 603-617, 2023

Authors: Haj Seyed Javadi, Mohammadreza | Haj Seyyed Javadi, Hamid | Rahmani, Parisa

Article Type: Research Article

Abstract: The Internet of Things (IoT) is a future-generation networking environment in which distributed smart objects can communicate directly and create a connection between different types of heterogeneous networks. Knowing the accurate localization of IoT-based devices is one of the most challenging issues in expanding the IoT network performance. This paper was done to propose a new fuzzy type2-based scheme to enhance the position accurateness of sensors deployed in the Internet of Things environments. Our proposed scheme is based on the weighted centralized localization strategy, in which the location of unknown nodes calculates using the fuzzy type-2 system. The flow measurement …via the wireless channel to calculate the separation distance between the sensor/anchor nodes is employed as the fuzzy system input. Also, the fuzzy membership functions to better adaptivity of our scheme with lossy IoT environments via learning automata algorithm are tuned. Then, in the proposed method, the fuzzy type-2 calculations are restricted by comparing the received signal strength with a predefined threshold value to extend the network lifetime. The effectiveness of the proposed scheme has been proven through extensive simulation. Based on the simulation results, our scheme, on average, reduced the localization error by 35.9% and 9.5% decreased the energy consumption by 13% and 7.2%, and reduced the convergence rate by 33.1% and 12.37 % compared to the HSPPSO and IMRL methods, respectively. Show more

Keywords: IoT, location, learning automata, fuzzy logic, signal strength

DOI: 10.3233/JIFS-223103

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 619-635, 2023

Authors: Zhao, Xue | Li, Qiaoyan | Xing, Zhiwei | Dai, Xuezhen

Article Type: Research Article

Abstract: Selecting appropriate features can better describe the characteristics and structure of data, which play an important role in further improving models and algorithms whether for supervised or unsupervised learning. In this paper, a new unsupervised feature selection regression model with nonnegative sparse constraints (URNS) is proposed. The algorithm combines nonnegative orthogonal constraint, L 2,1 -norm minimum optimization and spectral clustering. Firstly, the linear regression model between the features and the pseudo labels is given, and the indicator matrix, which describes feature weight, is subject to nonnegative and orthogonal constraints to select better features. Secondly, in order to reduce redundant and …even noisy features, L 2,1 -norm for indicator matrix is added to the regression model for exploring the correlation between pseudo labels and features by the row sparsity property of L 2,1 -norm. Finally, pseudo labels of all samples are established by spectral clustering. In order to solve the regression model efficiently and simply, the method of nonnegative matrix decomposition is used and the complexity of the given algorithm is analysed. Moreover, a large number of experiments and analyses have been carried out on several public datasets to verify the superiority of the given model. Show more

Keywords: Non-negative matrix factorization, L2,1-norm, feature selection, spectral clustering, unsupervised

DOI: 10.3233/JIFS-224132

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 637-648, 2023

Authors: Duman, Ekrem

Article Type: Research Article

Abstract: The main function of the internal control department of a bank is to inspect the banking operations to see if they are performed in accordance with the regulations and bank policies. To accomplish this, they pick up a number of operations that are selected randomly or by some rule and, inspect those operations according to some predetermined check lists. If they find any discrepancies where the number of such discrepancies are in the magnitude of several hundreds, they inform the corresponding department (usually bank branches) and ask them for a correction (if it can be done) or an explanation. In …this study, we take up a real-life project carried out under our supervisory where the aim was to develop a set of predictive models that would highlight which operations of the credit department are more likely to bear some problems. This multi-classification problem was very challenging since the number of classes were enormous and some class values were observed only a few times. After providing a detailed description of the problem we attacked, we describe the detailed discussions which in the end made us to develop six different models. For the modeling, we used the logistic regression algorithm as it was preferred by our partner bank. We show that these models have Gini values of 51 per cent on the average which is quite satisfactory as compared to sector practices. We also show that the average lift of the models is 3.32 if the inspectors were to inspect as many credits as the number of actual problematic credits. Show more

Keywords: Predictive modeling, multi-classification, banking, internal control, data mining

DOI: 10.3233/JIFS-223679

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 649-658, 2023

Authors: Liu, Xuwang | Liu, Yanyang | Qi, Wei | Luo, Xinggang

Article Type: Research Article

Abstract: With the rapid development of O2O, offline experience and online purchase have become a method of purchase for more and more customers. Through offline experience, consumers can feel the quality of products directly. Such channel switching behavior of consumers will produce a “showroom” effect and affect the return rate of online channels. This study adopts the multinomial logit model to maximize profits by considering the difference in quality between online and offline products, quality defects, and offline service. Then, a pricing decision model is developed to analyze the influence of returning goods due to quality problems on the retailers’ optimal …pricing and profit. The result shows that retailers can obtain the optimal profit when the offline service is maintained at a certain level. As the return rate increases, the optimal pricing increases, but the maximum profit decreases. The optimal pricing decreases with the increase in online product quality, but the maximum profit increases accordingly. In the omni-channel environment, customers can freely switch between channels according to utility and preference when purchasing products. Based on customer returns, retailers can dynamically adjust their service, control product quality, and set optimal product pricing, thus achieving maximum profits. This study can provide a theoretical basis and decision support for omni-channel retailers in platform operation and revenue management. Show more

Keywords: Channel switching behavior, return behavior, omni-channel marketing, multinomial logit model, product pricing

DOI: 10.3233/JIFS-230078

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 659-673, 2023

Authors: Lin, Haofeng | Ullah, Inam | Abbas, Syed Zaheer | Shakeel, Muhammad | Ali, Asad

Article Type: Research Article

Abstract: To deal with the ambiguity in real-world problems, researchers strive to obtain extensions to classical set theory. They introduced ideas like fuzzy set theory, spherical, intuitionistic, and Pythagorean fuzzy sets. In comparison to fuzzy sets, spherical fuzzy sets are more realistic at handling uncertainty. Fundamentals are classified in Spherical Fuzzy Set according to an attribute, and each feature has a variety of criteria. In this study, we have created a new extended algebraic structure called Confidence Spherical Fuzzy Aggregation Operators by applying the idea of Confidence Levels to the already-existing Spherical Fuzzy Aggregation Operators. We have created a Confidence Spherical …Fuzzy Aggregation Operators-based end-product. We demonstrated various intriguing characteristics of Confidence Spherical Fuzzy Aggregation Operators, including operational laws. The study is validated by addressing the decision-making processes. Show more

Keywords: Spherical fuzzy numbers, confidence level, operational laws, aggregation operators, decision-making

DOI: 10.3233/JIFS-220102

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 675-686, 2023

Authors: Richard, Amala S. | Jose Parvin Praveena, N. | Rajkumar, A.

Article Type: Research Article

Abstract: This research paper elucidates the significant role of Replacement problem in reliability optimization problems. Ambiguity and indeterminacy act as a plight in scheduling maintenance problems. When there is a need for replacement the devices of components work under the circumstances of the problem and the sustentation characteristics to reinstitute or restore the decrepit components of the systems. There is a vagueness associated with the elements performing intervals, erroneous, following assessment period create a new task in adjudicating optimal constituents’ distribution where it assessing future task effectively. In this paper, the group replacement model is solved using a special single valued …octagonal Neutrosophic number. The formula for the De-Neutrosophication of the Octagonal Neutrosophic number is deduced by using the area removal method. MATLAB code is used in De-Neutrosophication and also delineating this effective work. The MATLAB program is being used in the replacement problem to find the optimal year of replacement. A numerical illustration is used for validating the replacement model to determine its persuasiveness. This replacement problem using MATLAB has not been initiated by any researchers. Analytically, the time consumption for this method is less and very effective when compared with other methods. A comparative analysis has also been conducted using SVNN. Show more

Keywords: Neutrosophic number, replacement problem, Matlab, area removal method

DOI: 10.3233/JIFS-221567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 687-698, 2023

Authors: Bi, Shunjie | Wu, Zhiyong | Gao, Peng | Ding, Hangqi

Article Type: Research Article

Abstract: Evolutionary multitasking algorithms (EMT) study how to solve multiple optimization tasks simultaneously by evolutionary computation, and investigate how knowledge sharing can accelerate the convergence of individual tasks, meaning that useful knowledge gained in solving one task can be used to solve other tasks. However, as the evolutionary search continues, the learnability among tasks may decrease, leading to a decrease in the efficiency of knowledge transfer and affecting the population evolution. To solve this problem, a new multifactorial evolutionary algorithm (MFEA-VOM) is proposed in this paper, which applies to three strategies, namely, implicit conversion strategy, opposition matrix strategy, and regulatory gene …fusion strategy. The implicit conversion strategy is applied to minimize the threat of negative knowledge migration and reduce the impact caused by negative knowledge migration. The proposed opposition matrix strategy explores more unknown areas of the population and improves the exploration ability of the population by further exploring and utilizing the unified search space, transforming the parent individuals into an appropriate task through mapping relationships, and reducing the gap between tasks. The proposed regulatory gene fusion strategy is applied to the reproduction of individuals to produce better individuals applicable to the task, submitting the efficiency of knowledge transfer. Through a comprehensive experimental analysis of the EMT optimization problem, the experimental results demonstrate the better performance of MFEA-VOM compared to other EMT algorithms. Show more

Keywords: Evolutionary multitasking, knowledge transfer, opposition matrix, implicit conversion, regulatory gene fusion

DOI: 10.3233/JIFS-222267

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 699-718, 2023

Authors: Gu, Ming | Li, Dong | Gong, Lanlan | Liu, Jia | Liu, Shulin

Article Type: Research Article

Abstract: The traditional negative selection algorithm with a randomly generated hypersphere detector is unable to satisfy the development needs of continuous learning due to the inherent defects of the detector. This paper proposes a novel negative selection algorithm for hyper-rectangle detectors that overcomes the shortcomings of randomly generated hyper-sphere detectors and lays the foundation for a negative selection algorithm with continuous learning capability. It uses self-sample clusters of equal-sized hypercubes instead of self-samples for training. The hyper-rectangle detectors are generated by cutting the nonself-space along the boundary of the self-sample clusters. The state space is covered without overlapping each other by …self-sample clusters and detectors. The anomaly detection performance of the proposed method was demonstrated using Iris data, vowel recognition data (Vowel), and Wisconsin Breast Cancer (BCW) data. The experimental results show that the proposed method outperforms other artificial immune algorithms and clustering algorithms under the same parameter conditions. Show more

Keywords: Artificial immune algorithm, negative selection algorithm, anomaly detection, hyper-rectangle detectors, artificial intelligence

DOI: 10.3233/JIFS-222994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 719-730, 2023

Authors: Jain, Vipin | Kashyap, Kanchan Lata

Article Type: Research Article

Abstract: COVID-19 epidemic is one of the worst disaster which affected people worldwide. It has impacted whole civilization physically, monetarily, and also emotionally. Sentiment analysis is an important step to handle pandemic effectively. In this work, systematic literature review of sentiment analysis of Indian population towards COVID-19 and its vaccination is presented. Recent exiting works are considered from four primary databases including ACM, Web of Science, IEEE Explore, and Scopus. Total 40 publications from January 2020 to August 2022 are selected for systematic review after applying inclusion and exclusion algorithm. Existing works are analyzed in terms of various challenges encountered by …the existing authors with collected datasets. It is analyzed that mainly three techniques namely lexical, machine and deep learning are used by various authors for sentiment analysis. Performance of various applied techniques are comparative analyzed. Direction of future research works with recommendations are highlighted. Show more

Keywords: Sentiment analysis, COVID-19, opinion mining, neural networks, text classification

DOI: 10.3233/JIFS-224086

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 731-742, 2023

Authors: Dey, Aniruddha | Ghosh, Manas | Chowdhury, Shiladitya | Kahali, Sayan

Article Type: Research Article

Abstract: This paper presents a novel decision-making method for face recognition where the features were extracted from the original image fused with its corresponding true and partial diagonal images. To extract features, we adopted the generalized two-dimensional FLD (G2DFLD) feature extraction technique. The feature vectors from a test image are given as input to neural network-based classifier. It is trained with the feature vectors of original image and diagonally fused images and thereby the merit weights with respect to different classes were generated. To address the factors that affect the face recognition accuracy and uncertainty related to raw biometric data, a …fuzzy score for each of the classes is generated by treating a type-2 fuzzy set. This type-2 fuzzy set is formed by the feature vectors of both the diagonally fused training samples and the test image of the respective classes. A concluding score for each of the classes under consideration is computed by fusing complemented merit weight with the complemented fuzzy score. These class-wise concluding scores are considered in the face recognition process. In this study, the well-known face databases (AT&T, UMIST and CMU-PIE) are used to evaluate the performance of the proposed method. The experimental results illustrate the fact that the proposed method has exhibited superior classification precision as compared with other state-of-art methods. Our T2FMFImg F method achieves highest face recognition accuracies of 99.41%, 98.36% and 89.80% in case of AT&T, UMIST and CMU-PIE (with expression), respectively while for CMU-PIE (with Light) the highest recognition accuracy is 97.957%. In addition to it, the presented method is quite successful in fusing and classifying textural information from the original and partial diagonal images by integrating them with type-2 fuzzy set-based treatment. Show more

Keywords: Image-level fusion, confidence factor, face recognition, fuzzy type-2

DOI: 10.3233/JIFS-224288

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 743-761, 2023

Authors: Badshah, Noor | Arif, Muhammad | Khan, Tufail Ahmad | Ullah, Asmat | Rabbani, Hena | Atta, Hadia | Begum, Nasra

Article Type: Research Article

Abstract: Segmenting outdoor images in the presence of haze, fog or smog (which fades the colors and diminishes the contrast of the observed objects) has been a challenging task in image processing with several important applications. In this paper, we propose a new fractional-order variational model that will be able to de-haze and segment a given image simultaneously. The proposed method incorporates the atmospheric veil estimation based on the dark channel prior (DCP). This transmission map can reduce significantly the edge artifacts and enhance estimation precision in the resulting image. The transmission map is then changed over to the high-quality depth …map, with which the new fractional-order variational model can be framed to look for the haze free segmenting image for both grey and color outdoor images. An explicit gradient descent scheme is employed to find efficiently the minimizer of the proposed energy functional. Experimental tests on real world scenes show that the proposed method can jointly de-haze and segment hazy or foggy images effectively and efficiently. Show more

Keywords: Foggy or hazy images, fractional-order total variation, image de-hazing, image segmentation, inhomogeneous intensity, object detection

DOI: 10.3233/JIFS-230385

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 763-781, 2023

Authors: Li, Dongping | Shen, Shikai | Yang, Yingchun | He, Jun | Shen, Haoru

Article Type: Research Article

Abstract: In order to solve the problems of inaccurate trajectory time prediction and poor privacy protection of dataset publishing mechanism, the study adds deep learning models into the trajectory time prediction model and designs the SLDeep model. Its performance is compared with LRD, STTM and DeepTTE models for experiments, and the results show that the SLDeep model. The lowest mean absolute error value was 116.357, indicating that it outperformed the other models. The study designed the Travelet publishing mechanism by incorporating differential privacy methods into the publishing mechanism, and compared it with Li’s and Hua’s publishing mechanisms for experiments. The results …show that the mutual information index value of Travelet publishing mechanism is 0.06, which is better than Li’s and Hua’s publishing mechanisms. The experimental results show that the performance of the trajectory time prediction model incorporating deep learning and the dataset publishing mechanism incorporating differential privacy methods has been greatly improved, which can provide new ideas to obtain a more accurate and all-round trajectory big data management system. Show more

Keywords: Deep learning, differential privacy, trajectory time prediction, release mechanism

DOI: 10.3233/JIFS-231210

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 783-795, 2023

Authors: Chola Raja, K. | Kannimuthu, S.

Article Type: Research Article

Abstract: Autism Spectrum Disorder (ASD) is a complicated neurodevelopment disorder that is becoming more common day by day around the world. The literature that uses machine learning (ML) and deep learning (DL) approaches gained interest due to their ability to increase the accuracy of diagnosing disorders and reduce the physician’s workload. These artificial intelligence-based applications can learn and detect patterns automatically through the collection of data. ML approaches are used in various applications where the traditional algorithms have failed to obtain better results. The major advantage of the ML algorithm is its ability to produce consistent and better performance predictions with …the help of non-linear and complex relationships among the features. In this paper, deep learning with a meta-heuristic (MH) approach is proposed to perform the feature extraction and feature selection processes. The proposed feature selection phase has two sub-phases, such as DL-based feature extraction and MH-based feature selection. The effective convolutional neural network (CNN) model is implemented to extract the core features that will learn the relevant data representation in a lower-dimensional space. The hybrid meta-heuristic algorithm called Seagull-Elephant Herding Optimization Algorithm (SEHOA) is used to select the most relevant and important features from the CNN extracted features. Autism disorder patients are identified using long-term short-term memory as a classifier. This will detect the ASD using the fMRI image dataset ABIDE (Autism Brain Imaging Data Exchange) and obtain promising results. There are five evaluation metrics such as accuracy, precision, recall, f1-score, and area under the curve (AUC) used. The validated results show that the proposed model performed better, with an accuracy of 98.6%. Show more

Keywords: Autism spectrum disorder, Meta-Heuristic, Deep learning, Convolution neural network, seagull and elephant herding optimization, LSTM, fMRI.

DOI: 10.3233/JIFS-223694

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 797-807, 2023

Authors: Wu, Chong | Mao, Zengli | Zhan, Baoqiang | Wu, Yahui

Article Type: Research Article

Abstract: The ocean plays a crucial role in human society’s survival and development. While China’s marine economy has grown rapidly in recent years, it has also led to serious problems inhibiting ecosystem sustainability. This paper proposes high-quality development of the marine economy and combines the improved entropy value method, fuzzy hierarchical analysis method (FAHP), and data envelopment analysis (DEA) method to establish a quadratic relative evaluation model. A two-layer comprehensive index framework with 19 indicators is built to measure various aspects of the marine economy, including innovation, coordination, green, openness, and sharing. Empirical analysis conducted on 11 coastal provinces in China …using data mainly collected from the Chinese Statistical Yearbook reveals significant spatial patchiness in the high-quality development level of the marine economy. This discrepancy is largely due to differences in geographical locations, resources, and government policies. The study analyzes four benchmark provinces of high-quality development and summarizes their experiences. The paper concludes by providing suggestions and implications to support government decision-making. Show more

Keywords: Marine economy, high-quality, DEA, quadratic relative evaluation

DOI: 10.3233/JIFS-224173

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 809-830, 2023

Authors: Sathish Kumar, P.J. | Ponnusamy, Muruganantham | Radhika, R. | Dhurgadevi, M.

Article Type: Research Article

Abstract: Underwater wireless sensor networks (UWSNs) are designed to perform cooperative monitoring and data collection tasks by combining several elements, such as automobiles and sensors located in a particular acoustic area. Several studies have been carried out to improve energy efficiency and routing reliability. However, UWSN faces several challenges, such as high ocean interference and noise, long transmission delays, limited bandwidth, and low sensor node battery energy. In this work, a novel underwater clustering-based hybrid routing protocol (UC-HRP) has been proposed to address these issues. The overall process is carried out in three phases. In the first phase, the fuzzy-ELM approach …is used to initialize the cluster based on parameters such as Doppler spread, path loss, noise, and multipath. In the second phase, the cluster head is selected using Cluster Centre Cluster Head Selection (C3HS) based on Link quality, distance, node degree, and residual energy. In the third phase, Hybrid Artificial Bee Colony (HABC) algorithm is used for selecting an optimal route based on the parameters such as reliability, bandwidth effectiveness, average path loss, and average transmission latency. The performance of the proposed UC-HRP method is evaluated using a variety of parameters, including the network lifetime, packet delivery ratio, alive nodes, and energy consumption. The proposed technique improves the network lifetime by 14.03%, 16.25%, and 18.34% better than ACUN, ANC-UWSNS, and MERP respectively. Show more

Keywords: Underwater wireless sensor networks, fuzzy extreme learning machine, cluster centre cluster head selection, hybrid artificial bee colony algorithm

DOI: 10.3233/JIFS-230172

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 831-843, 2023

Authors: Chen, Guomin | Jin, Yingwei | Cheng, Shili | Jiao, Huihua

Article Type: Research Article

Abstract: Fuel Cells are novel devices that have been proposed as new power generation systems. The advantages of solid oxide fuel cells are higher efficiency, higher stability, fuel flexibility, lower emissions, and generally lower cost. In the present study, the fuzzy model is employed to build the model of the solid oxide fuel cell considering various sputtering power, thickness of electrolyte, and temperatures of cell. The maximum iterations for the adaptive neuro-fuzzy inference model was considered 50 iterations. About 3500 samples were applied for the training process, and almost 900 samples were utilized for the testing. After modeling process, the genetic …algorithm, particle swarm, simulated annealing, and hybrid firefly-particle swarm optimizers are applied to achieve the optimum value of current and power densities. The results showed that proposed fuzzy model could approximate the model the system with a good agreement with experimental data. Additionally, the obtained data confirm the accuracy, high convergence speed, and robustness of the proposed hybrid optimizer compared to three efficient optimization algorithms. Accordingly, the correlation factor for the proposed fuzzy model for the training and testing dataset was obtained to be 0.9298 and 0.9289, correspondingly. Show more

Keywords: Performance improvement of SOFC, adaptive neuro-fuzzy inference model, various optimization algorithms, experimental dataset accuracy, comparative study

DOI: 10.3233/JIFS-221125

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 845-862, 2023

Authors: Li, Lin

Article Type: Research Article

Abstract: In recent years, the use of Gas Turbines (GTs) to generate electricity has grown exponentially. Therefore, for the optimal performance of gas power plants, a lot of research has been done on modeling different parts of GTs, estimating model parameters, and controlling them. But most of the available methods are not accurate enough, like most linear methods, or are model-based, which require an accurate model of the system (like most nonlinear methods), or there is a constant need to adjust the controller parameters. To address these shortcomings, this study uses a new hybrid method including the brain emotional learning-based intelligent …controller, the nonlinear multivariate method in the form of feedback linearization, and an adaptive control method of mode predictive reference model used to quickly control the GT. The Rowen model is used to simulate the nonlinear model of the GT. Owing to the influence of exhaust temperature on the speed of GT and the multivariate system model, nonlinear multivariate controller design is considered. First, the adaptive control method of the state-predictive reference model for a multi-output multi-input system, in general, is presented, and then, the proposed method for a GT with real dynamic values is implemented. The simulation results show the ability of the proposed controller to control the GT. In order to prove the efficiency of the proposed method, the obtained results are compared with the PID industrial controller method and the classical reference model method. Show more

Keywords: GTs, speed control, brain emotional learning based intelligent controller, feedback linearization, dynamic simulation

DOI: 10.3233/JIFS-221408

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 863-876, 2023

Authors: Ammasaikutti, Pradeep | Palanisamy, Kannan

Article Type: Research Article

Abstract: A single phase Soft Switching-Solid State Transformer (SS-SST) design is proposed with H-bridge topology as an alternative solution to fulfil the demand of low (or) medium grid power applications. A medium/low frequency transformers fed with H-bridge circuit are incorporate without DC-voltage link, and it’s provided sinusoidal output voltage into the grid. An optimization of Cuckoo Search Firefly (CSF) algorithm was proposed in this research to find optimum switching angle and duty cycle in bridge circuit unit. At present optimum grid power is achieved a maximum efficiency of medium/low power frequency with the help of proposed SS-SST (MS4T) model. For proposed …design is used to electric aircraft, ship power systems, battery energy storage systems (BESS) and fast charging electric vehicles (EV). Which are appealing the networks of medium-voltage DC (MVDC). Proposed MS4T design is based on soft-switching transformer with low conduction loss, low EMI and high efficiency via H-bridge converter circuit. The capacitor voltage balancing control between cascade module and design of the component including a medium level voltage frequency transformer that is implement a 1 kV to 0.25 kV MS4T described. Therefore, the efficacy of the present investigations are established with MATLAB platform. The medium voltage Micro Grid (MG) output is estimated under different operation load conditions. A simulation result of the grid power is measured minimum harmonics level by using optimum switching angle, switching frequency and duty cycle arrangements. Show more

Keywords: Soft switching-solid state transformer, cuckoo search firefly algorithm, H-bridge circuit, medium level voltage, grid

DOI: 10.3233/JIFS-224393

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 877-890, 2023

Authors: Yang, Wendong | Wang, Jingyi | Yang, Sibo | Zhang, Kai

Article Type: Research Article

Abstract: Short-term load prediction has always played an increasingly important part in power system administration, load dispatch, and energy transfer scheduling. However, how to build a novel model to improve the accuracy of load forecasts is not only an extremely challenging problem but also a concerning problem for the power market. Specifically, the individual model pays no attention to the significance of data selection, data preprocessing, and model optimization. So these models cannot always satisfy the time series forecasting’s requirements. With these above-mentioned ignored factors considered, to enhance prediction accuracy and reduce computation complexity, in this study, a novel and robust …method were proposed for multi-step forecasting, which combines the power of data selection, data preprocessing, artificial neural network, rolling mechanism, and artificial intelligence optimization algorithm. Case studies of electricity power data from New South Wales, Australia, are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed model has significantly increased the accuracy of load prediction in all quarters. As a result, the proposed method not only is simple, but also capable of achieving significant improvement as compared with the other forecasting models, and can be an effective tool for power load forecasting. Show more

Keywords: Short-term load prediction, data selection, data preprocessing, optimization, forecasting

DOI: 10.3233/JIFS-224567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 891-909, 2023

Authors: Sun, Shuwan | Bian, Weixin | Xie, Dong | Xu, Deqin | Huang, Yi

Article Type: Research Article

Abstract: With the development of wireless communication technology and the rapid increase of user data, multi-server key agreement authentication scheme has been widely used. In order to protect users’ privacy and legitimate rights, a two-factor multi-server authentication scheme based on device PUF and users’ biometrics is proposed. The users’ biometrics are combined with the physical characteristics of the Physically Unclonable Functions (PUF ) as authentication factors, which not only ensures the security of the scheme, but it also is user-friendly without a password. The proposed scheme can be applied to telemedicine, smart home, Internet of Vehicles and other fields to …achieve mutual authentication and key agreement between users and servers. In order to prove the security of the proposed scheme, the widely accepted ROR model and BAN logic are used for formal security analysis. The scheme can effectively resist various security attacks, and the comparison with existing schemes shows that it has better performance in terms of communication cost and computational complexity. Show more

Keywords: Multi-server, physical unclonable function, password-free, mutual authentication, biometric security and privacy

DOI: 10.3233/JIFS-221354

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 911-928, 2023

Authors: Nishy Reshmi, S. | Shreelekshmi, R.

Article Type: Research Article

Abstract: In this paper, we propose a method exploiting syntactic structure, semantic relations and word embeddings for recognizing textual entailment. The sentence pairs are analyzed using their syntactic structure and categorization of sentences in active voice, sentences in passive voice and sentences holding copular relations. The main syntactic relations such as subject, verb and object are extracted and lemmatized using a lemmatization algorithm based on parts-of-speech. The subject-to-subject, verb-to-verb and object-to-object similarity is identified using enhanced Wordnet semantic relations. Further similarity is analyzed using modifier relation, number relation, nominal modifier relation, compound relation, conjunction relation and negative relation. The experimental evaluation …of the method on Stanford Natural Language Inference dataset shows that the accuracy of the method is 1.4% more when compared to the state-of-the-art zero shot domain adaptation methods. Show more

Keywords: GloVe, natural language processing, textual entailment, Wordnet

DOI: 10.3233/JIFS-223275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 929-939, 2023

Authors: Liu, Boting | Guan, Weili | Yang, Changjin | Fang, Zhijie

Article Type: Research Article

Abstract: Word vector is an important tool for natural language processing (NLP) tasks such as text classification. However, existing static language models such as Word2vec cannot solve the polysemy problem, leading to a decline in text classification performance. To solve this problem, this paper proposes a method for making Chinese word vector dynamic (MCWVD). The part of speech (POS) is used to solve the ambiguity problem caused by different POS. The POS structure graph is constructed and the syntactic structure information of POS features is extracted by GCN (Graph Convolutional Network). POS vector and word vector are concatenated into PW (POS-Word) …vector. Parametric matrix is added to improve the fusion effect of POS and word features. Multilayer attention is used to distinguish the importance of different features and further update the vector expression of word vectors about the current context. Experiments on Chinese datasets THUCNews and SogouNews show that MCWVD effectively improves the accuracy of text classification and achieves better performance than CoVe (Context Vectors) and ELMo (Embeddings from Language Models). MCWVD also achieves similar performance to BERT and GPT-1 (Generative Pre-Training), but with a much lower computational cost and only 4% of BERT parameters. Show more

Keywords: Word vector, Word2vec, part of speech, Graph Convolutional Network, multi-layer attention

DOI: 10.3233/JIFS-224052

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 941-952, 2023

Authors: Tong, Shekun | Peng, Jie

Article Type: Research Article

Abstract: In this work, with the aim of separating the genuine and forgery samples of the signature, we developed a new dual-path architecture using deep neural network and a traditional descriptor for feature extraction toward an automatic offline signature recognition. The proposed approach is an extended version of VGG-16, which is enhanced using our two paths architecture. In the first path, we explore features using a deep convolutional neural network, and in the second path, we discover global features using a traditional heuristic approach. For classical feature extraction, an innovative idea is presented, in which the descriptor is stable for some …common changes, such as magnification and epoch, in the signature samples. Our traditional approach extracts global features that are stable with rotation and scaling. The proposed method was analyzed and compared with three well-known databases of CEDAR, UTsig, and GPDS signature images. A dual-patched model architecture is significantly more accurate than the basic model when compared to the basic model. In agreement with the proposed method, the best signature recognition accuracy on the CEDAR database is in the range of 98.04-99.96%, while the best recognition accuracy on the GPDS and UTsig databases is 98.04% and 99.56%, respectively. Furthermore, this technique has been compared with four popular methods such as VGG-S, VGG-M, VGG-16, and LS2Net. The presented approach achieved a recognition rate of 99.96% using a diverse signature database. Experimental results demonstrate that the proposed VGG-16 based signature recognition system is superior over texture-based and deep-learning methods and also outperforms the existing state-of-the-art results in this regard. It is expected that the proposed system will provide fresh acumen to the researchers in developing offline signature verification and recognition systems in other scripts. Show more

Keywords: Signature recognition, offline, deep learning, VGG 16-layer neural network, feature extraction

DOI: 10.3233/JIFS-224326

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 953-964, 2023

Authors: Devika, M. | Shaby, Maflin

Article Type: Research Article

Abstract: One of the major challenge in Wireless Sensor Networks (WSN’s) deployment is efficient energy consumption. Critical distance, proper routing algorithm and error control coding techniques can be used for energy optimization. As WSN contains a large number of power constrained sensors, the sensed data from the environment should be transmitted in a cooperative way to the base station (BS). The pattern of the clustering structure can extend the sensor network life time, reduce the total consumed energy and regulate the data transmission. Clustering concept combines group of sensors which are located in the same communication range. Some of the routing …protocol like, SEED, LEACH, SEP, Z-SEP etc., suffers from idle listening problem, which cannot cope with an environment with sensor nodes. It leads to energy wastage across the network. To manage energy efficiency and traffic heterogeneity issues, a new routing protocol called enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA) is proposed. Here, one sensor in each group will be in active mode whereas other sensors entered in sleep mode. Based on the nodes energy, sleep and awake node pairs will be altered. In the proposed method, one slot is allotted for group of pairs. The proposed approach is evaluated and compared against LEACH, SEP and Z-SEP protocols. Simulation results show that EEESAA protocol performs better than LEACH, SEP, Z-SEP in terms of cluster head selection, throughput, number of alive & dead nodes and network lifetime. Show more

Keywords: Wireless sensor network, enhanced energy efficient sleep awake aware intelligent sensor network (EEESAA), low-energy adaptive clustering hierarchy (LEACH), stable election protocol, zonal stable election protocol

DOI: 10.3233/JIFS-224380

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 965-973, 2023

Authors: Yao, Zhuangkai | Zeng, Bi | Hu, Huiting | Wei, Pengfei

Article Type: Research Article

Abstract: In recent mathematical reasoning tasks, self-attention has achieved better results in public datasets. However, self-attention performs poorly on more complex mathematical problems due to the lack of capacity to capture local features and the ill-conditioned training after deepening the number of layers. To tackle the problem and enhance its ability of extracting local features while learning the global contexts, we propose an implicit mathematical reasoning model that improves Transformer by combining self-attention and convolution to achieve joint modeling of global and local context. Also, by introducing Reweight connection and adversarial loss function, we prevent the model gradient from disappearing or …exploding in a deep neural network while ensuring the convergence speed and avoiding overfitting. Experimental results show that the proposed model improves the accuracy by 4.47% on average for complex mathematical problems compared to the best existing results. In addition, we verify the validity of our model using ablation analysis and further demonstrate the interpretability of the model by attention mapping and task role analysis. Show more

Keywords: Implicit mathematical reasoning, self-attention, depth separable convolution, causal language model, adversarial loss

DOI: 10.3233/JIFS-224598

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 975-988, 2023

Authors: Wang, Wei | Zhang, Ning | Peng, Weishi | Liu, Zhengqi

Article Type: Research Article

Abstract: Intonation evaluation is an important precondition that offers guidance to music practices. This paper present a new intonation quality evaluation method based on self-supervised learning to solve the fuzzy evaluation problem at the critical intonations. Firstly, the effective features of audios are automatically extracted by a self-supervised learning-based deep neural network. Secondly, the intonation evaluation of the single tones and pitch intervals are carried out by combining with the key local features of the audios. Finally, the intonation evaluation method characterized by physical calculations, which simulates and enhances the manual assessment. Experimental results show that the proposed method achieved the …accuracy of 93.38% which is the average value of multiple experimental results obtained by randomly assigning audio data, which is much higher than that of the frequency-based intonation evaluation method(37.5%). In addition, this method has been applied in music teaching for the first time and delivers visual evaluation results. Show more

Keywords: Music practice, intonation evaluation, self-supervised learning, deep neural network, audio feature extraction

DOI: 10.3233/JIFS-230165

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 989-1000, 2023

Authors: Lin, Fucai | Wu, Tingyi | Cao, Xiyan | Li, Jinjin

Article Type: Research Article

Abstract: The theory of knowledge spaces (KST) which is regarded as a mathematical framework for the assessment of knowledge and advices for further learning. Now the theory of knowledge spaces has many applications in education. From the topological point of view, we discuss the language of the theory of knowledge spaces by the axioms of separation and the accumulation points of pre-topology respectively, which establishes some relations between topological spaces and knowledge spaces; in particular, we show that the language of the regularity of pre-topology in knowledge spaces and give a characterization for knowledge spaces by inner fringe of knowledge states. …Moreover, we study the relations of Alexandroff spaces and quasi ordinal spaces; then we give an application of the density of pre-topological spaces in primary items for knowledge spaces, which shows that one person in order to master an item, she or he must master some necessary items. In particular, we give a characterization of a skill multimap such that the delineated knowledge structure is a knowledge space, which gives an answer to a problem in [14 ] or [18 ] whenever each item with finitely many competencies; further, we give an algorithm to find the set of atom primary items for any finite knowledge space. Show more

Keywords: Knowledge space, knowledge structure, learning space, pre-topological space, skill multimap, quasi ordinal space, Alexandroff space, separation of axiom, primary item, Primary 54A05, secondary 54A25, 54B05, 54B10, 54D05, 54D70

DOI: 10.3233/JIFS-230498

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1001-1013, 2023

Authors: Wang, Encheng | Liu, Xiufeng | Wan, Jiyin

Article Type: Research Article

Abstract: Received Signal Strength Indication (RSSI) fluctuates with the change of indoor noise, resulting in a large positioning error of the trained Back Propagation Neural Network (BPNN). An adaptive indoor positioning model based on Cauchy particle swarm optimization (Cauchy-PSO) BPNN is proposed to solve the problem. In the off-line training phase, the signal with less noise intensity acquired in a good environment is selected as the original training set in the localization phase. The variance of the received set of signals is used as a measure of the noise intensity of the current environment. In the localization phase, the variance of …each set of signals received is calculated at equal intervals. If the variance of adjacent intervals differs significantly, the system adjusts the original training set data according to the current noise intensity and re-trains the BP model online. Meanwhile, the particle swarm optimization algorithm using Cauchy variance to optimize the BP network tends to fall into the disadvantage of local optimum. Considering that the collected fingerprint database may generate “high-dimensional disasters”, Principal Component Analysis (PCA) is used to select and downscale the features of the wireless Access Point (AP). The proposed adaptive localization model can be trained online. The improved Cauchy-PSO algorithm and data dimensionality reduction can further improve the localization accuracy and training speed of the BP model. The experimental results show that the adaptive indoor localization model has strong adaptive capability in a noise-varying environment. Show more

Keywords: RSSI, adaptive BP model (AI-BP), BPNN, PCA, Cauchy-PSO

DOI: 10.3233/JIFS-231082

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1015-1027, 2023

Authors: Qiao, Wenbao

Article Type: Research Article

Abstract: Computer network security evaluation is a basic work to determine the security performance of the network system and implement the network security management. It involves organizational management, network technology, personnel psychology, social environment and other factors. In recent years, with the rapid development of information technology in China, the problem of computer network security has become increasingly prominent. Although domestic and foreign scholars have sought effective methods of network security evaluation from different aspects and using different methods, many factors involved in network security are difficult to quantify, so far, there is no relatively mature quantitative evaluation method of network …security. The computer network security evaluation is classical multiple attribute decision making (MADM) problems. In this article, based on projection measure, we shall introduce the projection models with q-rung orthopair fuzzy information. First of all, the definition of q-rung orthopair fuzzy sets (q-ROFSs) is introduced. In addition, to fuse overall q-rung orthopair fuzzy evaluation information, two aggregation operators including q-ROFWA and q-ROFWG operators is introduced. Furthermore, combine projection with q-ROFSs, we develop the projection models with q-rung orthopair fuzzy information. Based on developed weighted projection models, the multiple attribute decision making model is established and all computing steps are simply depicted. Finally, a numerical example for computer network security evaluation is given to illustrate this new model and some comparisons between the new proposed models and q-ROFWA and q-ROFWG operators are also conducted to illustrate advantages of the new built method. Show more

Keywords: Multiple attribute decision making (MADM) problems, q-rung orthopair fuzzy sets (q-ROFSs), q-rung orthopair fuzzy projection model, computer network security evaluation

DOI: 10.3233/JIFS-231351

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1029-1038, 2023

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