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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Ren, Yonghui | Shi, Yan | Li, Chenglin | Jin, Yanxu
Article Type: Research Article
Abstract: Robots can help people complete repetitive and high-risk tasks, such as industrial production, medical care, environmental monitoring, etc. The control system of robots is the key to their ability to complete tasks, and studying robot control systems is of great significance. This article used Convolutional Neural Network (CNN) and Robotic Process Automation (RPA) technologies to optimize and train the robot control system and constructed a robot control system. This article conducts perception and decision-making experiments and execution experiments in the experimental section. According to the experimental results, it can be concluded that the average image recognition accuracy of the robot …control system in perception and decision-making experiments was 94.62% . The average decision accuracy was 87.5%, and the average time efficiency was 176 seconds. During the execution of the experiment, the deviation of the motion trajectory shall not exceed 5 cm, and the oscillation amplitude shall not exceed 6°; the distance from the obstacle shall not exceed 20 cm, and the movement speed shall not exceed 0.6 m/s; the operating time shall not exceed 25 hours, and the number of faults shall not exceed 0.2 times per hour, all within the normal range. The robot control system based on Deep Learning (DL) and RPA has broad application prospects and research value, which would bring new opportunities and challenges to the development and application of robot technology. Show more
Keywords: Robot control system, Robotic Process Automation (RPA), Convolutional Neural Network (CNN), Deep Learning (DL)
DOI: 10.3233/JIFS-233056
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024
Authors: Fan, Lin | Wang, Wenli
Article Type: Research Article
Abstract: The ability, interest, and prior accomplishments of students with varying proficiency levels all impact how they learn English. Exact validation is essential for facilitating efficient evaluation and training models. The research’s innovative significance resides in incorporating personal attributes, progressive appraisal, and Fuzzy Logic-based appraisal in English language learning. The PA2M model, which addresses the shortcomings of existing models, offers a thorough and accurate assessment, enabling personalized recommendations and enhanced teaching tactics for students with varied skill levels. This research proposes the Fuzzy Logic System (FLS)-based Persistent Appraisal Assessment Model (PA2M). Based on the students’ evolving performance and accumulated data, this …model evaluates the students’ English learning capabilities. The model assesses the student’s ability using fuzzification approaches to reduce variations in appraisal verification by linking personal attributes with performance. Mamdani FIS offers a clear and thorough evaluation of student’s English learning capacity within the framework of the appraisal methodology. The inputs are updated utilizing performance and accumulated ability data to improve validation consistently and reduce converge errors. During the fuzzification process, pre-convergence from unavailable appraisal sequences is eliminated. The PA2M approach determines precise improvements and evaluations depending on student ability by merging prior and current data. Several appraisal validations and verifications result in clear fresh suggestions. According to experimental data, the suggested model enhances 9.79% of recommendation rates, 8.79% of appraisal verification, 8.25% of convergence factor, 12.56% error ratio, and verification time with 8.77% over a range of inputs. The PA2M model provides a fresh and useful way to evaluate English learning potential, filling in some gaps in the body of knowledge and practice. Show more
Keywords: Big data, English learning, fuzzy logic system, student ability
DOI: 10.3233/JIFS-232619
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Zou, Yu | Fu, Deyu | Mo, Honghuai | Chen, Henglong | Wang, Deyin
Article Type: Research Article
Abstract: Foreign objects identification in the distribution network is an important link in the security of electric power, and is of great significance to the normal transportation of electric power. At present, a lot of equipment in the distribution network is in the open air environment, facing a large number of foreign interference. These foreign objects not only bring potential safety hazards to the distribution network, but also easily lead to short circuit, causing power supply difficulties within the region. Therefore, the research first constructs an optimized triplet feature learning model. On this basis, the HOG-SVM depth feature recognition model is …proposed. In HOG-SVM, AM is introduced to improve recognition accuracy. In addition, the research enhances the night vision ability of the model by standardizing the features in the image region block. The results show that the AP of the model is stable at more than 90.54%, the average FPR is 2.21%, and the average FNR is 3.17% . The performance of HOG-SVM is significantly better than that of traditional SVM. It verifies the contribution of this research in the field of foreign object recognition and application value in ensuring the security of distribution network. Show more
Keywords: Distribution network, foreign objects, depth characteristics, attention
DOI: 10.3233/JIFS-237868
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Yi, Lingzhi | Peng, Xinlong | Fan, Chaodong | Wang, Yahui | Li, Yunfan | Liu, Jiangyong
Article Type: Research Article
Abstract: Reliable and accurate short-term forecasting of residential load plays an important role in DSM. However, the high uncertainty inherent in single-user loads makes them difficult to forecast accurately. Various traditional methods have been used to address the problem of residential load forecasting. A single load forecast model in the traditional method does not allow for comprehensive learning of data characteristics for residential loads, and utilizing RNNs faces the problem of long-term memory with vanishing or exploding gradients in backpropagation. Therefore, a gated GRU combined model based on multi-objective optimization is proposed to improve the short-term residential load forecasting accuracy in …this paper. In order to demonstrate the effectiveness, GRUCC-MOP is first experimentally tested with the unimproved model to verify the model performance and forecasting effectiveness. Secondly the method is evaluated experimentally with other excellent forecasting methods: models such as DBN, LSTM, GRU, EMD-DBN and EMD-MODBN. By comparing simulation experiments, the proposed GRU combined model can get better results in terms of MAPE on January, April, July, and November load data, so this proposed method has better performance than other research methods in short-term residential load forecasting. Show more
Keywords: Short-term residential load forecasting, gate recurrent unit, multi-objective optimization algorithm, deep learning
DOI: 10.3233/JIFS-237189
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Zhang, Jianhua | Liu, Chan | Geng, Na | Zhang, Yixuan | Yang, Liqiang
Article Type: Research Article
Abstract: An improved Ant Colony Optimization (ACO) algorithm, named IACO, is proposed to address the inherent limitation of slow convergence, susceptibility to local optima and excessive number of inflection in traditional ACO when solving path planning problems. To this end, firstly, the search direction number is expanded from 4 or 8 into 32; Secondly, the distance heuristic information is replaced by an area heuristic function, which deviated from the traditional approach that only considers pheromone information between two points; Then, the influence of path angle and number of turns is taken into account in the local pheromone update. Additionally, a reward …and punishment mechanism is employed in the global pheromone update to adjust the pheromone concentrations of different paths; Furthermore, an adaptive update strategy for pheromone volatility factor adaptive is proposed to expand the search range of the algorithm. Finally, simulation experiments are conducted under various scenarios to verify the superiority and effectiveness of the proposed algorithm. Show more
Keywords: Ant colony optimization, mobile robot, path planning, search direction, area-inspired, grid map
DOI: 10.3233/JIFS-238095
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Sangeetha, M. | Nimala, K.
Article Type: Research Article
Abstract: NLP, or natural language processing, is a subfield of AI that aims to equip computers with the ability to understand and analyze human language. Sentiment analysis is a widely used application of NLP, particularly for examining attitudes expressed in online conversations. Nevertheless, many social media comments are written in languages that are not native to the authors, making sentiment analysis more difficult, especially for languages with limited resources, such as Tamil. To tackle this issue, a code-mixed and sentiment-annotated corpus in Tamil and English was created. This article will explain how the corpus was established, including the process of data …collection and the assignment of polarities. The article will also explore the agreement between annotators and the results of sentiment analysis performed on the corpus. This work signifies various performance metrics such as precision, recall, support, and F1-score for the transformer-based model such as BERT, RoBerta, and XLM-RoBerta. Among the various models, XLM-Robert shows slightly significant positive results on the code-mixed corpus when compared to the state of art models. Show more
Keywords: Sentiment analysis, Tamil-English Code-mix, natural language processing, corpus, grammar rule
DOI: 10.3233/JIFS-236971
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Yang, Jingling | Chen, Liren | Chen, Huayou | Liu, Jinpei | Han, Bing
Article Type: Research Article
Abstract: The conventional approaches to constructing Prediction Intervals (PIs) always follow the principle of ‘high coverage and narrow width’. However, the deviation information has been largely neglected, making the PIs unsatisfactory. For high-risk forecasting tasks, the cost of forecast failure may be prohibitive. To address this, this work introduces a multi-objective loss function that includes Prediction Interval Accumulation Deviation (PIAD) within the Lower Upper Bound Estimation (LUBE) framework. The proposed model can achieve the goal of ‘high coverage, narrow width, and small bias’ in PIs, thus minimizing costs even in cases of prediction failure. A salient feature of the LUBE framework …is its ability to discern uncertainty without explicit uncertainty labels, where the data uncertainty and model uncertainty are learned by Deep Neural Networks (DNN) and a model ensemble, respectively. The validity of the proposed method is demonstrated through its application to the prediction of carbon prices in China. Compared with conventional uncertainty quantification methods, the improved interval optimization method can achieve narrower PI widths. Show more
Keywords: Prediction interval, uncertainty prediction, deep neural networks, carbon price
DOI: 10.3233/JIFS-237524
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Shakkeera, L. | Dhiyanesh, B. | Asha, A. | Kiruthiga, G.
Article Type: Research Article
Abstract: To address this storage issue, we propose a Content-Aware Deduplication Clustering Analysis for Cloud Storage Optimization (CADC-FPRLE) based on a file partitioning running length encoder. At first, preprocessing was done by indexing, counting terms, cleansing, and tokenizing. Further multi-objective clustering points are analysed based on the bisecting divisible partition block, which divides a set of documents. The count terms are filtered from the divisible blocks and make up the count terms content block. Using Content-Aware Multi-Hash Ensemble Clustering (CAMH-EC) to group the similar blocks into clusters. This creates a high-dimensional Euclidean interval to create the number of clusters, and points …are performed randomly to set the initial collection. Then, the Magnitude Vector Space Rate (MVSR) estimates the similarity distance between the groups to select the highest scatter value content for indexing. Finally, the Running Block Parity Encoder (RBPE) generates similarity parity in order to reduce the content to a redundant, singularized file in order to optimise storage. This implementation proves a higher level of storage optimization compared to the previous system than other methods. Show more
Keywords: Data deduplication, semantic analysis, cloud storage, magnitude vector space, cluster analysis, running length encoder
DOI: 10.3233/JIFS-231223
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Saichand, N. Venkata | Naik, S. Gopiya
Article Type: Research Article
Abstract: Epilepsy is considered a most general neurological disorder related to brain activity disruption. In epileptic seizures detection and classification, EEG (Electroencephalogram) measurements that record the brain’s electrical activities are used frequently. Generally, physicians investigate the abnormalities in the brain. However, this technique is time-consuming, faced complexity in seizure detection, and poor consistency because of data imbalance. To overcome these difficulties, Improved Empirical Mode Decomposition for feature extraction and Improved Weight Updated KNN (K-Nearest Neighbor) algorithm for classification are proposed. In the case of pre-processing, a rule-based filter, namely a wiener scalar filter with integer wavelet transform is used for multiple …channels conversion and further signal to noise ratio is increased. Further in feature extraction, better features are extracted using an improved empirical mode decomposition-based bandpass filter. By using the Improved Weight updated KNN, feature extracted samples are classified incorrect manner, avoiding data imbalance issues. Feature vectors’ effective classification is performed attains higher computational speed and sensitivity. The EEG input signal of the proposed study utilizing the BONN dataset and different performance metrics such as accuracy, sensitivity, specificity, recall, f-score, and error values were performed and compared with various existing studies. From the results, it is clear that the proposed method provides effective detection for seizure and non-seizure patients compared with existing studies. Show more
Keywords: Seizure detection, bandpass filter, rule-based filter, improved empirical mode decomposition, improved weight updated KNN
DOI: 10.3233/JIFS-222960
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Guo, Sheng | Tan, Mian | Cai, Shan | Zhang, Zaijun | Liang, Yihui | Feng, Hongxi | Zou, Xue | Wang, Lin
Article Type: Research Article
Abstract: Although facial expression recognition (FER) has a wide range of applications, it may be difficult to achieve under local occlusion conditions which may result in the loss of valuable expression features. This issue has motivated the present study, as a part of which an effective multi-feature cross-attention network (MFCA-Net) is proposed. The MFCA-Net consists of a two-branch network comprising a multi-feature convolution module and a local cross-attention module. Thus, it enables decomposition of facial features into multiple sub-features by the multi-feature convolution module to reduce the impact of local occlusion on facial expression feature extraction. In the next step, the …local cross-attention module distinguishes between occluded and unoccluded sub-features and focuses on the latter to facilitate FER. When the MFCA-Net performance is evaluated by applying it to three public large-scale datasets (RAF-DB, FERPlus, and AffectNet), the experimental results confirm its good robustness. Further validation is performed on a real FER dataset with local occlusion of the face. Show more
Keywords: Facial expression recognition, deep convolution, multi-feature convolution module, local cross-attention module
DOI: 10.3233/JIFS-233748
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Wang, Lai-Wang | Hung, Chen-Chih
Article Type: Research Article
Abstract: In response to the low efficiency and poor quality of current seed optimization algorithms for multi-threshold image segmentation, this paper proposes the utilization of the normal distribution in the cluster distribution mathematical model, the Levy flight mechanism, and the differential evolution algorithm to address the deficiencies of the seed optimization algorithm. The main innovation lies in applying the BBO algorithm to image multi threshold segmentation, providing a new perspective and method for image segmentation tasks. The second significant progress is the combination of Levy flight dynamics and differential evolution algorithm (DEA) to improve the BBO algorithm, thereby enhancing its performance …and image segmentation quality. Therefore, a multi-threshold image segmentation model based on the optimized seed optimization algorithm is developed. The experimental results showed that on the function f1, the iteration of the improved seed optimization algorithm was 53, the Generational Distance value was 0.0020, the Inverted Generational Distance value was 0.098, and the Spacing value was 0.051. Compared with the other two algorithms, the improved seed optimization algorithm has better image segmentation performance and clearer image segmentation details. In summary, compared with existing multi-threshold image segmentation methods, the proposed multi-threshold image segmentation model based on the improved seed optimization algorithm has a better image segmentation effect and higher efficiency, can significantly improve the quality of image segmentation, has positive significance for the development of image processing technology, and also provides references for the improvement and application of optimization algorithms. Show more
Keywords: Seed optimization algorithm, differential evolution algorithm, image segmentation, levy flight mechanism
DOI: 10.3233/JIFS-237994
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Duan, Wenbiao | Yang, Mingjin | Sun, Weiliang | Xia, Mingmin | Zhu, Hui | Gu, Chijiang | Zhang, Haiqiang
Article Type: Research Article
Abstract: OBJECTIVE: A comprehensive evaluation of studies using DNA microarray datasets for screening and identifying key genes in gastric cancer is the goal of this systematic review and meta-analysis. To better understand the molecular environment associated with stomach cancer, this study aims to provide a quantitative synthesis of findings. PURPOSE: Using DNA microarray databases in a systematic manner, this study aims to analyze gastric cancer (GC) screening and gene identification efforts. Through a literature review spanning 2002–2022, this research aims to identify key genes associated with GC and develop strategies for screening and prognosis based on these …findings. METHODS: The following databases were searched extensively: Science Direct, NCKI, Web of Science, Springer, and PubMed. Fifteen studies met the inclusion and exclusion criteria; 10,134 tissues served as controls and 11,724 as GCs. The levels of critical genes, including COL1A1, COL1A2, THBS2, SPP1, SPARC, COL6A3, and COL3A1, were compared in normal and GC tissues. Rev Man 5.3 was used to do the meta-analysis. While applying models with fixed or random effects, 95% confidence intervals and weighted mean differences were computed. RESULTS According to the meta-analysis, GC tissues exhibited substantially elevated levels of important genes when contrasted with the control group. In particular, there were statistically significant increases in COL1A1 (MD = 2.43, 95% CI: 1.84–3.02), COL1A2 (MD = 2.75, 95% CI: 1.09–4.41), THBS2 (MD = 2.54, 95% CI: 1.66–3.41), SPP1 (MD = 3.64, 95% CI: 3.40–3.88), SPARC (MD = 1.57, 95% CI: 0.37–2.77), COL6A3 (MD = 2.31, 95% CI: 2.02–2.60), and COL3A1 (MD = 2.21, 95% CI: 1.59–2.82). CONCLUSIONS: The COL1A1, THBS2, SPP1, COL6A3, and COL3A1 genes were shown to have potential use in germ cell cancer screening and prognosis, according to this research. Clinical assessment and prognosis of heart failure patients may be theoretically supported by the results of this study. Show more
Keywords: DNA microarray database, gastric cancer, key genes, meta-analysis
DOI: 10.3233/JIFS-236416
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Li, Tao | Zhang, Zhongyu | Tao, Zhigang | Jia, Xinyu | Wang, Xiaolong | Wang, Jian
Article Type: Research Article
Abstract: Rock crack is one of the main factors responsible for rock failure. Uniaxial compression creep tests are performed using acoustic emission techniques, a high-sensitivity, non-radiative, non-destructive testing method to understand the influence of crack number on the precursor characteristics of short-term creep damage in the fractured rock mass. Based on the Grassberger-Procaccia (G-P) algorithm, the calculation step size for the correlation dimension value (D 2 ) of the acoustic emission ringing count rate is consistent with that for the acoustic emission b -value. The influence of the number of pre-cracks on the Acoustic emission precursor characteristics of red sandstone …creep is analyzed. The results show that near the destabilization of the specimen, the Acoustic emission accumulative ringing count surges in a stepwise manner, the Acoustic emission b -value decreases, the D 2 -value increases, the Acoustic emission amplitude shows high intensity and high frequency, and the ringing count increases sharply, all with the characteristics of failure precursors. During the accelerated creep stage of the specimens, with the increase of pre-cracks number, the precursory time points of acoustic emission b -value and D 2 -value advance, and their acoustic emission ringing counts increase sharply. Show more
Keywords: Acoustic emission, b-value, correlation dimension value (D2), precursor information, pre-cracks
DOI: 10.3233/JIFS-238964
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Hou, Xiaoyu | Luo, Chao | Gao, Baozhong
Article Type: Research Article
Abstract: Candlesticks are widely used as an effective technical analysis tool in financial markets. Traditionally, different combinations of candlesticks have formed specific bullish/bearish patterns providing investors with increased opportunities for profitable trades. However, most patterns derived from subjective expertise without quantitative analysis. In this article, combining bullish/bearish patterns with ensemble learning, we present an intelligent system for making stock trading decisions. The Ensemble Classifier through Multimodal Perturbation (ECMP) is designed to generate a diverse set of precise base classifiers to further determine the candlestick patterns. It achieves this by: first, introducing perturbations to the sample space through bootstrap sampling; second, employing …an attribute reduction algorithm based on neighborhood rough set theory to select relevant features; third, perturbing the feature space through random subspace selection. Ultimately, the trading decisions are guided by the classification outcomes of this procedure. To evaluate the proposed model, we apply it to empirical investigations within the context of the Chinese stock market. The results obtained from our experiments clearly demonstrate the effectiveness of the approach. Show more
Keywords: Trading system, ensemble learning, multimodal perturbation method, neighborhood rough set theory
DOI: 10.3233/JIFS-237087
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Zhao, Bin | Cao, Wei | Zhang, Jiqun | Gao, Yilong | Li, Bin | Chen, Fengmei
Article Type: Research Article
Abstract: Aiming at the issue that the current click-through rate prediction methods ignore the varying impacts of different input features on prediction accuracy and exhibit low accuracy when dealing with large-scale data, a click-through rate prediction method (GBIFM) which combines Gradient Boosting Decision Tree (GBDT) and Input-aware Factorization Machine (IFM) is proposed in this paper. The proposed GBIFM method employs GBDT for data processing, which can flexibly handle various types of data without the need for one-hot encoding of discrete features. An Input-aware strategy is introduced to refine the weight vector and embedding vector of each feature for different instances, adaptively …learning the impact of each input vector on feature representation. Furthermore, a fully connected network is incorporated to capture high-order features in a non-linear manner, enhancing the method’s ability to express and generalize complex structured data. A comprehensive experiment is conducted on the Criteo and Avazu datasets, the results show that compared to typical methods such as DeepFM, AFM, and IFM, the proposed method GBIFM can increase the AUC value by 10% –12% and decrease the Logloss value by 6% –20%, effectively improving the accuracy of click-through rate prediction. Show more
Keywords: Click-through rate estimation, GBIFM, GBDT, IFM
DOI: 10.3233/JIFS-234713
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Wang, Shuo | Yang, Jing | Yang, Yue
Article Type: Research Article
Abstract: Personalized recommendation systems fundamentally assess user preferences as a reflection of their emotional responses to items. Traditional recommendation algorithms, focusing primarily on numerical processing, often overlook emotional factors, leading to reduced accuracy and limited application scenarios. This paper introduces a collaborative filtering recommendation method that integrates features of facial information, derived from emotions extracted from such data. Upon user authorization for camera usage, the system captures facial information features. Owing to the diversity in facial information, deep learning methods classify these features, employing the classification results as emotional labels. This approach calculates the similarity between emotional and item labels, reducing …the ambiguity inherent in facial information features. The fusion process of facial information takes into account the user’s emotional state prior to item interaction, which might influence the emotions generated during the interaction. Variance is utilized to measure emotional fluctuations, thereby circumventing misjudgments caused by sustained non-interactive emotions. In selecting the nearest neighboring users, the method considers not only the similarity in user ratings but also in their emotional responses. Tests conducted using the Movielens dataset reveal that the proposed method, modeling facial features, more effectively aligns recommendations with user preferences and significantly enhances the algorithm’s performance. Show more
Keywords: Collaborative filtering algorithm, facial information features, emotional factors, non-interactive emotion
DOI: 10.3233/JIFS-232718
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Zhai, Shanshan | Fan, Jianping | Liu, Lin
Article Type: Research Article
Abstract: Neutrosophic cubic set (NCS) can process complex information by choosing both interval value and single value membership and indeterminacy and falsehood components. The aggregation operators based on Aczel-Alsina t-norm and t-corm are quite effective for evaluating the interrelationship among attributes. The purpose of this paper is to diagnose the interrelationship among attributes with neutrosophic cubic information, and propose a multi-attribute decision-making(MADM) method for supplier selection problem with unknown weight under neutrosophic cubic environment. We defined neutrosophic cubic Aczel-Alsina (NC-AA) operator and neutrosophic cubic Aczel–Alsina weighted arithmetic average (NCAAWAA) operator, then we discussed various important results and some properties of the …proposed operators. Additionally, we proposed a MADM method under the presence of the NC-AAWAA operator. When the weights of attributes are unknown, we use the MEREC method to determine the weights. Later, the NC-AAWAA operator and MEREC method are applied to address the supplier selection problem. Finally, a sensitivity analysis and a comparative analysis are conducted to illustrate the stability and superiority of the proposed method. The results show the NC-AAWAA operator can handle the interrelationship among complex information more effectively, and MEREC method can weight the attributes based on the removal effect of a neutrosophic cubic attribute. Show more
Keywords: Multi-attribute decision-making (MADM), neutrosophic cubic set (NCS), Aczel-Alsina aggregation operators, MEREC method
DOI: 10.3233/JIFS-235274
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024
Authors: Hong, Yuntao
Article Type: Research Article
Abstract: Obsessive-compulsive disorder (OCD) is a chronic disease and psychosocial disorder that significantly reduces the quality of life of patients and affects their personal and social relationships. Therefore, early diagnosis of this disorder is of particular importance and has attracted the attention of researchers. In this research, new statistical differential features are used, which are suitable for EEG signals and have little computational load. Hilbert-Huang transform was applied to EEGs recorded from 26 OCD patients and 30 healthy subjects to extract instant amplitude and phase. Then, modified mean, variance, median, kurtosis and skewness were calculated from amplitude and phase data. Next, …the difference of these statistical features between various pairs of EEG channels was calculated. Finally, different scenarios of feature classification were examined using the sparse nonnegative least squares classifier. The results showed that the modified mean feature calculated from the amplitude and phase of the interhemispheric channel pairs produces a high accuracy of 95.37%. The frontal lobe of the brain also created the most distinction between the two groups among other brain lobes by producing 90.52% accuracy. In addition, the features extracted from the frontal-parietal network produced the best classification accuracy (93.42%) compared to the other brain networks examined. The method proposed in this paper dramatically improves the accuracy of EEG classification of OCD patients from healthy individuals and produces much better results compared to previous machine learning techniques. Show more
Keywords: Obsessive-compulsive disorder (OCD), Electroencephalogram (EEG), Hilbert-Huang transform, statistical features, classification
DOI: 10.3233/JIFS-237946
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Zhao, Xixi | Gu, Liang | Duan, Xiaorong | Wang, Liguo | Li, Zhenxi
Article Type: Research Article
Abstract: Cloud storage attribute libraries usually store a large amount of sensitive data such as personal information and trade secrets. Attackers adopt diverse and complex attack methods to target the cloud storage attribute database, which makes the defense work more challenging. In order to realize the secure storage of information, an attribute based cloud storage anti-attack algorithm based on dynamic authorization access is proposed. According to the characteristic variables of the sample, the data correlation matrix is calculated, and the principal component analysis method is adopted to reduce the dimension of the data, build the anti-attack code model, simulate the dynamic …authorization access rights, and calculate the packet loss rate according to the anti-attack flow. Design the initialization stage, cluster stage and cluster center update stage to realize the attack prevention of cloud storage attribute database. The experimental results show that the proposed algorithm can accurately classify the anti-attack code, has good packet processing ability, relatively short page request time, and anti-attack success rate is higher than 90%, which can effectively ensure the stability of the algorithm. Show more
Keywords: Dynamic authorization access, cloud storage attributes, basic anti-attack algorithm, anti-attack code model, access permissions
DOI: 10.3233/JIFS-237409
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Mahendran, S. | Gomathy, B.
Article Type: Research Article
Abstract: This study addresses the escalating energy demands faced by global industries, exerting pressure on power grids to maintain equilibrium between supply and demand. Smart grids play a pivotal role in achieving this balance by facilitating bidirectional energy flow between end users and utilities. Unlike traditional grids, smart grids incorporate advanced sensors and controls to mitigate peak loads and balance overall energy consumption. The proposed work introduces an innovative deep learning strategy utilizing bi-directional Long Short Term Memory (b-LSTM) and advanced decomposition algorithms for processing and analyzing smart grid sensor data. The application of b-LSTM and higher-order decomposition in the analysis …of time-series data results in a reduction of Mean Absolute Percentage Error (MAPE) and Minimal Root Mean Square (RMSE). Experimental outcomes, compared with current methodologies, demonstrate the model’s superior performance, particularly evident in a case study focusing on hourly PV cell energy patterns. The findings underscore the efficacy of the proposed model in providing more accurate predictions, thereby contributing to enhanced management of power grid challenges. Show more
Keywords: Smart grids, deep learning, PV cells, error rate and absolute error, prediction
DOI: 10.3233/JIFS-238850
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Ning, Yi | Liu, Meiyu | Guo, Xifeng | Liu, Zhiyong | Wang, Xinlu
Article Type: Research Article
Abstract: Accurate load forecasting is an important issue for safe and economic operation of power system. However, load data often has strong non-stationarity, nonlinearity and randomness, which increases the difficulty of load forecasting. To improve the prediction accuracy, a hybrid short-term load forecasting method using load feature extraction based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and refined composite multi-scale entropy (RCMSE) and improved bidirectional long short time memory (BiLSTM) error correction is proposed. Firstly, CEEMDAN is used to separate the detailed information and trend information of the original load series, RCMSE is used to reconstruct the feature …information, and Spearman is used to screen the features. Secondly, an improved butterfly optimization algorithm (IBOA) is proposed to optimize BiLSTM, and the reconstructed components are predicted respectively. Finally, an error correction model is constructed to mine the hidden information contained in error sequence. The experimental results show that the MAE, MAPE and RMSE of the proposed method are 645 kW, 0.96% and 827.3 kW respectively, and MAPE is improved by about 10% compared with other hybrid models. Therefore, the proposed method can overcome the problem of inaccurate prediction caused by data and inherent defects of models and improve the prediction accuracy. Show more
Keywords: Short-term load forecasting, complete ensemble empirical mode decomposition with adaptivenoise, refined composite multi-scale entropy, improved butterfly optimization algorithm, bidirectional long short time memory neural network
DOI: 10.3233/JIFS-237993
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Arulmurugan, A. | Jose Moses, G. | Gandhi, Ongole | Sheshikala, M. | Arthie, A.
Article Type: Research Article
Abstract: In the current scenario, feature selection (FS) remains one of the very important functions in machine learning. Decreasing the feature set (FSt) assists in enhancing the classifier’s accuracy. Because of the existence of a huge quantity of data within the dataset (DS), it remains a colossal procedure for choosing the requisite features out of the DS. Hence, for resolving this issue, a new Chaos Quasi-Oppositional-based Flamingo Search Algorithm with Simulated Annealing Algorithm (CQOFSASAA) has been proffered for FS and for choosing the optimum FSt out of the DSs, and, hence, this lessens the DS’ dimension. The FSA technique can be …employed for selecting the optimal feature subset out of the DS. Generalized Ring Crossover has been as well embraced for selecting the very pertinent features out of the DS. Lastly, the Kernel Extreme Learning Machine (KELM) classifier authenticates the chosen features. This proffered paradigm’s execution has been tested by standard DSs and the results have been correlated with the rest of the paradigms. From the experimental results, it has been confirmed that this proffered CQOFSASAA attains 93.74% of accuracy, 92% of sensitivity, and 92.1% of specificity. Show more
Keywords: Quasi-oppositional, feature selection, Flamingo Search Algorithm, Simulated Annealing, convergence rate
DOI: 10.3233/JIFS-233557
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2023
Authors: Harikumar, Yedhu | Muthumeenakshi, M.
Article Type: Research Article
Abstract: The Indian stock market is a dynamic, complicated system that is impacted by many different variables, making it difficult to anticipate its future. The utilization of deep learning and optimization techniques to forecast stock market movements has gained popularity in recent years. To foresee the Indian stock market, an innovative approach is presented in this study that combines the Grey Wolf Optimization algorithm with a hybrid Convolutional Neural Network (CNN) and Bi-Directional Long-Short Term Memory (Bi-LSTM) framework. The stock market information is first pre-processed utilizing a CNN to extract pertinent features. The Bi-LSTM system, that is intended to capture the …long-term dependencies and temporal correlations of the stock market statistics, is then fed the CNN’s outcome. The model parameters are then optimized utilizing the Grey Wolf Optimization (GWO) technique, which also increases forecasting accuracy. The findings demonstrate that, in terms of forecasting accuracy, the suggested method outperforms a number of contemporary methods, including conventional time series models, neural networks, and evolutionary algorithms. Thus, the suggested methodology provides an effective way to forecast the Indian stock market by combining deep learning and optimization approaches. Show more
Keywords: Indian stock market, grey wolf optimization, deep learning approach, bi-directional long-short term memory, convolutional neural network
DOI: 10.3233/JIFS-233716
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2023
Authors: Li, Xiaoli | Du, Linhui | Yu, Xiaowei | Wang, Kang | Hu, Yongkang
Article Type: Research Article
Abstract: During the operation of HVAC (Heating, Ventilation, and Air-Conditioning) systems, precise energy consumption prediction plays an important role in achieving energy savings and optimizing system performance. However, the HVAC system is a complex and dynamic system characterized by a large number of variables that exhibit significant changes over time. Therefore, it is inadequate to rely on a fixed offline model to adapt to the dynamic changes in the system that consume tremendous computation time. To solve this problem, a deep neural network (DNN) model based on Just-in-Time learning with hyperparameter R (RJITL) is proposed in this paper to predict …HVAC energy consumption. Firstly, relevant samples are selected using Euclidean distance weighted by Spearman coefficients. Subsequently, local models are constructed using deep neural networks supplemented with optimization techniques to enable real-time rolling energy consumption prediction. Then, the ensemble JITL model mitigates the influence of local features, and improves prediction accuracy. Finally, the local models can be adaptively updated to reduce the training time of the overall model by defining the update rule (hyperparameter R ) for the JITL model. Experimental results on energy consumption prediction for the HVAC system show that the proposed DNN-RJITL method achieves an average improvement of 5.17% in accuracy and 41.72% in speed compared to traditional methods. Show more
Keywords: HVAC, energy consumption, weighted similarity measure, deep neural network, Just-in-Time learning
DOI: 10.3233/JIFS-233544
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Maleki, Monavareh | Ebrahimi, Mohamad | Davvaz, B.
Article Type: Research Article
Abstract: The concept of entropy and information gain of BE-algebras in scientific disciplines such as information theory, data science, supply chain and machine learning assists us to calculate the uncertanity of the scientific processes of phenomena. In this respect the notion of filter entropy for a transitive BE-algebra is introduced and its properties are investigated. The notion of a dynamical system on a transitive BE-algebra is introduced. The concept of the entropy for a transitive BE-algebra dynamical system is developed and, its characteristics are considered. The notion of equivalent transitive BE-algebra dynamical systems is defined, and it is proved the fact …that two equivalent BE-algebra dynamical systems have the same entropy. Theorems to help calculate the entropy are given. Specifically, a new version of Kolmogorov– Sinai Theorem has been proved. The study introduces the concept of information gain of a transitive BE-algebra with respect to its filters and investigates its properties. This study proposes the use of filter entropy to approximate the level of risk introduced by a BE-algebra dynamical system. This aim is reached by defining the information gain with respect to the filters of a BE-algebra. This methodology is well developed for use in engineering, especially in industrial networks. This paper proposes a novel approach to assess the quantity of uncertainty, and the impact of information gain of a BE-algebra dynamical system. Show more
Keywords: Generator, transitive BE-algebra, dynamical system, entropy, information gain
DOI: 10.3233/JIFS-232363
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024
Authors: Yang, Jiyun | Gui, Can
Article Type: Research Article
Abstract: Malware attack is a growing problem on the Android mobile platform due to its popularity and openness. Although numerous malware detection approaches have been proposed, it still remains challenging for malware detection due to a large amount of constantly mutating apps. The opcode, as the most fundamental part of Android app, possesses good resistance against obfuscation and Android version updates. Due to the large number of opcodes, most opcode-based methods employ statistical-based feature selection, which disrupts the correlation and semantic information among opcodes. In this paper, we propose an Android malware detection framework based on sensitive opcodes and deep reinforcement …learning. Firstly, we extract sensitive opcode fragments based on sensitive elements and then encode the features using n -gram. Next, we use deep reinforcement learning to select the optimal subset of features. During the process of handling opcodes, we focus on preserving semantic information and the correlation among opcodes. Finally, our experimental results show an accuracy of 0.9670 by using the 25 opcode features we obtained. Show more
Keywords: Android malware, deep reinforcement learning, feature selection, machine learning
DOI: 10.3233/JIFS-235767
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024
Authors: Badshah, Noor | Begum, Nasra | Rada, Lavdie | Ashfaq, Muniba | Atta, Hadia
Article Type: Research Article
Abstract: Joint segmentation and registration of images is a focused area of research nowadays. Jointly segmenting and registering noisy images and images having weak boundaries/intensity inhomogeneity is a challenging task. In medical image processing, joint segmentation and registration are essential methods that aid in distinguishing structures and aligning images for precise diagnosis and therapy. However, these methods encounter challenges, such as computational complexity and sensitivity to variations in image quality, which may reduce their effectiveness in real-world applications. Another major issue is still attaining effective joint segmentation and registration in the presence of artifacts or anatomical deformations. In this paper, a …new nonparametric joint model is proposed for the segmentation and registration of multi-modality images having weak boundaries/noise. For segmentation purposes, the model will be utilizing local binary fitting data term and for registration, it is utilizing conditional mutual information. For regularization of the model, we are using linear curvature. The new proposed model is more efficient to segmenting and registering multi-modality images having intensity inhomogeneity, noise and/or weak boundaries. The proposed model is also tested on the images obtained from the freely available CHOAS dataset and compare the results of the proposed model with the other existing models using statistical measures such as the Jaccard similarity index, relative reduction, Dice similarity coefficient and Hausdorff distance. It can be seen that the proposed model outperforms the other existing models in terms of quantitatively and qualitatively. Show more
Keywords: Image segmentation, image registration, linear curvature (LC), conditional mutual information (CMI), Jaccard similarity index (JSI)
DOI: 10.3233/JIFS-233306
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Wang, Tianxiong | Xu, Mengmeng | Yang, Liu | Zhou, Meiyu | Sun, Xin
Article Type: Research Article
Abstract: Kansei Engineering (KE) is a product design method that aims to develop products to meet users’ emotional preferences. However, traditional KE faces the problem that the acquisition of Kansei factors does not represent the real consumers demands based on manual and reports, and using tradition methods to calculate relationship between Kansei factors and specific design elements, which can lead to the omission of key information. To address these problems, this study adopts text mining and backward propagation neural networks (BPNN) to propose a product form design method from a multi-objective optimization perspective. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) and WordNet …are used to extract key user Kansei requirements from online review texts to obtain more accurate Kansei knowledge. Secondly, the BPNN is used to establish the non-linear relationship between product Kansei factors and specific design elements, and a preference mapping prediction model is constructed. Finally, BPNN is transformed into an iterative prediction value of non-dominated sorting genetic algorithm-II (NSGA-II), and the model is solved through multi-objective evolutionary algorithm (MOEA) to obtain the Pareto optimal solution set that satisfies the user’s multiple emotional needs, and the fuzzy Delphi method is used to obtain the best product form design scheme that meets the user’s multiple emotional images. Using the example of electric bicycle form design show that this proposed method can effectively complete multi-objective product solutions innovation design. Show more
Keywords: Text mining, Back propagation neural network (BPNN), Multi-objective evolutionary algorithm (MOEA), Non-dominated sorting genetic algorithm-II (NSGA-II), Kansei engineering
DOI: 10.3233/JIFS-230668
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Jianping, Liu | Yingfei, Wang | Jian, Wang | Meng, Wang | Xintao, Chu
Article Type: Research Article
Abstract: To better understand users’ behavior patterns in web search, numerous click models are proposed to extract the implicit interaction feedback. Most existing click models are heavily based on the implicit information to model user behaviors, ignoring the impact of explicit information between queries and documents in search sessions. In this paper, we fully consider the topic relevance between queries and documents in search sessions and propose a novel topic relevance-aware click model (TRA-CM) for web search. TRA-CM consists of a relevance estimator and an examination predictor. The relevance estimator consists of a topic relevance predictor and a click context encoder. …In the topic relevance predictor, we utilize the pre-trained BERT model to model the content information of queries and documents in search sessions. Meanwhile, we use transformer to encode users’ click behaviors in the click context encoder. We further apply a two-stage fusion strategy to obtain the final relevance scores. The examination predictor estimates the examination probability of each document. We further utilize learnable filters to attenuate log noise and obtain purer input features in both relevance estimator and examination predictor, and investigate different combination functions to integrate relevance scores and examination probabilities into click prediction. Extensive experiment results on two real-world session datasets prove that TRA-CM outperforms existing click models in both click prediction and relevance estimation tasks. Show more
Keywords: BERT, click model, click prediction, deep learning, web search
DOI: 10.3233/JIFS-236894
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Lizhu, Yue | Qian, Wang
Article Type: Research Article
Abstract: With the rapid development of big data and continuous optimization of online shopping platforms, personalized recommendation has become a standard feature of recommendation methods. In order to effectively provide personalized recommendations to customers, improve recommendation accuracy, and customer satisfaction, it is necessary to consider customers’ preferences for multiple product attributes when making product recommendations. However, existing recommendation methods require precise calculation of product attribute weights, which is computationally expensive, complex, and often results in unstable weight values. This paper proposes a multi-attribute recommendation method based on consumer decision preference information that overcomes the need for weights and reflects personalized customer …preferences. Based on the acquisition of customer product attribute preference sequences, a partial order relation for recommended products is constructed using partial order set theory. Finally, the recommended products are determined through the partial order Hasse diagram, where the top layer elements of the Hasse diagram represent the recommended product set. This method addresses challenges that traditional content-based recommendations cannot overcome. The experiment in this paper uses a dataset of 30,000 records from Beeradvocate beer reviews. The experimental results show that, compared to traditional multi-attribute recommendation methods, this method only requires decision-maker preference information to complete product recommendations, requiring less information and having lower computational costs, resulting in more robust results. Show more
Keywords: Multi-attribute recommendation, partial order set, decision preference, hasse diagram, personalization
DOI: 10.3233/JIFS-231724
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Huang, Mengtao | Wang, Jiaxuan
Article Type: Research Article
Abstract: Pedestrian trajectory prediction plays a crucial role in autonomous driving, as its accuracy directly affects the autonomous driving system’s comprehension of the environment and subsequent decision-making processes. Current trajectory prediction methods tend to oversimplify pedestrians to mere point coordinates, utilizing positional information to infer interactions among individuals while overlooking the temporal correlations between them, thereby excessively simplifying pedestrian characteristics. To address the aforementioned issues, this paper proposes a trajectory prediction model for autonomous driving applications, that takes into account both pedestrian motion characteristics and scene interaction. The model optimizes the LSTM unit structure twice, serving to learn correlations among long …trajectories of pedestrians and to integrate multiple forms of information into the neighborhood interaction module. Furthermore, our model introduces dual attention mechanisms for individuals and scenes, focusing on the key motion points of individual pedestrians and their interactive behavior with others in busy scenarios. The efficacy of the model was validated on the MOT16 pedestrian dataset and the Daimler pedestrian path prediction dataset, outperforming mainstream methods with 8% and 10% reductions in Average Displacement Error and Final Displacement Error respectively. Show more
Keywords: Trajectory prediction, automated driving, CNN-LSTM, deep learning
DOI: 10.3233/JIFS-236271
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Yu, Xingping | Yang, Yang
Article Type: Research Article
Abstract: The rapid advancement of communication and information technology has led to the expansion and blossoming of digital music. Recently, music feature extraction and classification have emerged as a research hotspot due to the difficulty of quickly and accurately retrieving the music that consumers are looking for from a large volume of music repositories. Traditional approaches to music classification rely heavily on a wide variety of synthetically produced aural features. In this research, we propose a novel approach to selecting the musical genre from user playlists by using a classification and feature selection machine learning model. To filter, normalise, and eliminate …missing variables, we collect information on the playlist’s music genre and user history. The characteristics of this data are then selected using a convolutional belief transfer Gaussian model (CBTG) and a fuzzy recurrent adversarial encoder neural network (FRAENN). The experimental examination of a number of music genre selection datasets includes measures of training accuracy, mean average precision, F-1 score, root mean squared error (RMSE), and area under the curve (AUC). Results show that this model can both create a respectable classification result and extract valuable feature representation of songs using a wide variety of criteria. Show more
Keywords: Music genre selection, user playlists, machine learning, classification, feature selection
DOI: 10.3233/JIFS-235478
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Gul, Rimsha | Bashir, Maryam
Article Type: Research Article
Abstract: As the volume of data continues to grow, the significance of text classification is on the rise. This vast amount of data majorly exists in the form of texts. Effective data preparation is essential to extract sentiment data from this vast amount of text, as irrelevant and redundant information can impede valuable insights. Feature selection is an important step in the data preparation phase as it eliminates irrelevant and insignificant features from the huge features set. There exist a large body of work related to feature selection for image processing but limited research is done for text data. While some …studies recognize the significance of feature selection in text classification, but there is still need for more efficient sentiment analysis models that optimize feature selection and reduce computational. This manuscript aims to bridge these gaps by introducing a hybrid multi-objective evolutionary algorithm as a feature selection mechanism, combining the power of multiple objectives and evolutionary processes. The approach combines two feature selection techniques within a binary classification model: a filter method, Information Gain (IG), and an evolutionary wrapper method, Binary Multi-Objective Grey Wolf Optimizer (BMOGWO). Experimental evaluations are conducted across six diverse datasets. It achieves a reduction of over 90 percent in feature size while improving accuracy by nearly nine percent. These results showcase the model’s efficiency in terms of computational time and its efficacy in terms of higher classification accuracy which improves sentiment analysis performance. This improvement can be beneficial for various applications, including recommendation systems, reviews analysis, and public opinion observation. However, it’s crucial to acknowledge certain limitations of this study. These encompass the need for broader classifier evaluation, and scalability considerations with larger datasets. These identified limitations serve as directions for future research and the enhancement of the proposed approach. Show more
Keywords: Feature selection, sentiment analysis, multi-objective optimization, evolutionary algorithms
DOI: 10.3233/JIFS-234615
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Zhao, Xiaoqing | Xu, Miaomiao | Li, Yanbing | Huang, Hao | Silamu, Wushour
Article Type: Research Article
Abstract: This research focuses on Scene Text Recognition (STR), a crucial component in various applications of artificial intelligence such as image retrieval, office automation, and intelligent traffic systems. Recent studies have shown that semantic-aware approaches significantly improve the performance of STR tasks, with context-aware STR methods becoming mainstream. Among these, the fusion of visual and language models has shown remarkable effectiveness. We propose a novel method (PABINet) that incorporates three key components: a Visual-Language Decoder, a Language Model, and a Fusion Model. First, during training, the Visual-Language Decoder masks the original labels in the Transformer decoder using permutation masks, with each …mask being unique. This enhances word memorization and learning through contextual semantic information, resulting in robust semantic knowledge. During the inference stage, the Visual-Language Decoder employs autonomous Autoregressive model (AR) inference to generate results. Subsequently, the Language Model scrutinizes and corrects the output of the Visual-Language Encoder using a cloze mask approach, achieving context-aware, autonomous, bidirectional inference. Finally, the Fusion Model concatenates and refines the outputs of both models through iterative layers.Experimental results demonstrate that our PABINet performs exceptionally well when handling various quality images. When trained with synthetic data, PABINet achieves a new STR benchmark (average accuracy of 92.41%), and when trained with real data, it establishes new state-of-the-art results (average accuracy of 96.28%). Show more
Keywords: Scene text recognition, language model, visual-language decoder
DOI: 10.3233/JIFS-237135
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Chen, Hongan | Zhang, Zongfu | Luo, Qingjia | Chen, Rongbin | Zhao, Yang
Article Type: Research Article
Abstract: Existing methods for recognizing partial discharge patterns in power cables do not utilize fuzzy clustering of the discharge signals, resulting in poor quality and low recall and precision of the pattern recognition. To address this, we propose a new approach for partial discharge pattern recognition in cables using Gustafson-Kessel(GK) Fuzzy Clustering. The method involves acquiring signals from a power cable partial discharge monitoring system and then processing the signals with GK fuzzy clustering. The clustered discharge signals are filtered with wavelet packet transforms before input into an improved adaptive resonance theory(ART) neural network for final pattern recognition. Experiments demonstrate the …new technique achieves up to 98.7% recall and 85.6% precision for discharge pattern recognition, with discharge signal Signal Noise Ratio(SNR) between 55 dB and 62 dB and maximum recognition accuracy reaching 98%. The proposed fuzzy clustering-based pattern recognition approach significantly enhances partial discharge diagnostics for power cable monitoring. Show more
Keywords: Gustafson-Kessel(GK) fuzzy clustering, power cable, partial discharge, pattern recognition, wavelet packet transform
DOI: 10.3233/JIFS-235945
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Sharma, Itika | Gupta, Sachin Kumar
Article Type: Research Article
Abstract: UAVs or Drones can be used to support wireless communication by acting as flying or mobile Base Stations for the accumulation of the different types of data to train the models. However, in traditional or DL-based UAVs, the raw data is sent from the devices to the centralized server, which causes problems in terms of the privacy of the devices and the UAVs’ communication resources or limited processing. Therefore, the issue with DL-based UAVs is that sending the original data to the centralized body raises questions about security and privacy. The transmission of distributed, unprocessed data from the drones to …the cloud, including interactive media information data types, requires a significant amount of network bandwidth and more energy, which has an enormous effect on several trade-offs, including communication rates and computation latencies. Data packet loss caused by asynchronous transmission, which doesn’t prevent peer-to-peer communication, is a concern with AFL-based UAVs. Therefore, in order to address the aforementioned issues, we have introduced SFL-based UAVs that focus on creating algorithms in which the models simultaneously update the server as they wait for all of the chosen devices to communicate. The proposed framework enables a variety of devices, including mobile and UAV devices, to train or learn their algorithms for machine learning before updating the models and parameters simultaneously to servers or manned aerial data centers for model buildup without transferring their original private information. This decreases packet loss and privacy threats while also enhancing round effectiveness as well as model accuracy. The comparative analysis of AFL and SFL techniques in terms of accuracy, global rounds, and communication rounds are offered. Simulation findings suggest that the proposed methodology improves in terms of global rounds and accuracy. Show more
Keywords: UAV, training, raw data, FL, AFL, SFL etc
DOI: 10.3233/JIFS-235275
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Guo, Hong | Yang, Jin | Yang, Jun
Article Type: Research Article
Abstract: This paper proposes a method of using machine learning and an evolutionary algorithm to solve the flexible job shop problem (FJSP). Specifically, a back propagation (BP) neural network is used as the machine learning method, the most widely used genetic algorithm (GA) is employed as the optimized object to address the machine-selection sub-problem of the FJSP, and particle swarm optimization (PSO) is utilized to solve the operation-order sub-problem of the FJSP. At present, evolutionary algorithms such as the GA, PSO, ant colony algorithm, simulated annealing algorithm, and their optimization algorithms are widely used to solve the FJSP; however, none of …them optimizes the initial solutions. Because each of these algorithms only focuses on solving a single FJSP, they can only use randomly generated initial solutions and cannot determine whether the initial solutions are good or bad. Based on these standard evolutionary algorithms and their optimized versions, the JSON object was introduced in this study to cluster and reconstruct FJSPs such that the machine learning strategies can be used to optimize the initial solutions. Specifically, the BP neural networks are trained so that the generalization of BP neural networks can be used to judge whether the initial solutions of the FJSPs are good or bad. This approach enables the bad solutions to be filtered out and the good solutions to be maintained as the initial solutions. Extensive experiments were performed to test the proposed algorithm. They demonstrated that it was feasible and effective. The contribution of this approach consists of reconstructing the mathematical model of the FJSP so that machine learning strategies can be introduced to optimize the algorithms for the FJSP. This approach seems to be a new direction for introducing more interesting machine learning methodologies to solve the FJSP. Show more
Keywords: Flexible job shop scheduling problem, mechanical engineering, evolutionary algorithms, machine learning
DOI: 10.3233/JIFS-224021
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024
Authors: Tomy, Navin | Johnson, T.P.
Article Type: Research Article
Abstract: This paper deals with lattice isomorphic L -topological spaces. We are concerned with a question: Under what conditions will a lattice isomorphic L -topological spaces be L -homeomorphic. We give contributions to this question in three different ways.
Keywords: L-homeomorphism, quasi L-homeomorphism, lattice isomorphism, pL-homeomorphism
DOI: 10.3233/JIFS-234375
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Chen, Junzhuo | Lu, Zonghan | Kang, Shitong
Article Type: Research Article
Abstract: In the wake of the global spread of monkeypox, accurate disease recognition has become crucial. This study introduces an improved SE-InceptionV3 model, embedding the SENet module and incorporating L2 regularization into the InceptionV3 framework to enhance monkeypox disease detection. Utilizing the Kaggle monkeypox dataset, which includes images of monkeypox and similar skin conditions, our model demonstrates a noteworthy accuracy of 96.71% on the test set, outperforming conventional methods and deep learning models. The SENet module’s channel attention mechanism significantly elevates feature representation, while L2 regularization ensures robust generalization. Extensive experiments validate the model’s superiority in precision, recall, and F1 score, …highlighting its effectiveness in differentiating monkeypox lesions in diverse and complex cases. The study not only provides insights into the application of advanced CNN architectures in medical diagnostics but also opens avenues for further research in model optimization and hyperparameter tuning for enhanced disease recognition. Show more
Keywords: CNN, InceptionV3, SENet, L2 regularization, monkeypox disease, deep learning
DOI: 10.3233/JIFS-237232
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024
Authors: Pandey, Vibha | Choubey, Siddhartha | Patra, Jyotiprakash | Mall, Shachi | Choubey, Abha
Article Type: Research Article
Abstract: Automated reading of license plate and its detection is a crucial component of the competent transportation system. Toll payment and parking management e-payment systems may benefit from this software’s features. License plate detection and identification algorithms abound, and each has its own set of strengths and weaknesses. Computer vision has advanced rapidly in terms of new breakthroughs and techniques thanks to the emergence and proliferation of deep learning principles across several branches of AI. The practice of automating the monitoring process in traffic management, parking management, and police surveillance has become much more effective thanks to the development of Automatic …License Plate Recognition (ALPR). Even though license plate recognition (LPR) is a technology that is extensively utilized and has been developed, there is still a significant amount of work to be done before it can achieve its full potential. In the last several years, there have been substantial advancements in both the scientific community’s methodology and its level of efficiency. In this era of deep learning, there have been numerous developments and techniques established for LPR, and the purpose of this research is to review and examine those developments and approaches. In light of this, the authors of this study suggest a four-stage technique to automated license plate detection and identification (ALPDR), which includes, image pre-processing, license plate extraction, character segmentation, and character recognition. And the first three phases are known as “extraction,” “pre-processing,” and “segmentation,” and each of these processes has been shown to benefit from its own unique technique. In light of the fact that character recognition is an essential component of license plate identification and detection, the Convolution Neural Network (CNN), MobileNet, Inception V3, and ResNet 50 have all been put through their paces in this regard. Show more
Keywords: Data security, secure image analysis, automatic license plate recognition, segmentation, image classification, convolution neural network, character recognition
DOI: 10.3233/JIFS-235400
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-23, 2024
Authors: Wu, Chengding | Xu, Zhaoping | Liu, Liang | Yang, Tao
Article Type: Research Article
Abstract: There are limitations of personalization in Advanced Driver Assistance Systems (ADAS) that have a serious impact on driver acceptance and satisfaction. This study investigates driving style recognition method to achieve personalization of longitudinal driving behavior. Currently, driving style recognition algorithms for Personalized Adaptive Cruise Control (PACC) rely on integrated recognition. However, disturbances in the driving cycle may lead to changes in a driver’s integrated driving style. Therefore, the integrated driving style cannot accurately and comprehensively reflect the driver’s driving style. To solve this problem, a new driving style recognition method for PACC is proposed, which considers integrated driving style and …driving cycle. Firstly, the method calculates the constructed feature parameters of driving cycle and style, and then reduces the dimensionality of the feature parameter matrix by principal component analysis (PCA). Secondly, a two-stage clustering algorithm with self-organizing mapping networks and K-means clustering (SOM-K-means) is used to obtain the type labels. Then, a transient recognition model based on random forest (RF) is established and the hyperparameters of this model are optimized by sparrow search algorithm (SSA). Based on this, a comprehensive driving style recognition model is established using analytic hierarchy process (AHP). Finally, the validity of the proposed method is verified by a natural dataset. The method incorporates the driving cycle into driving style recognition and provides guidance for improving the personalization of adaptive cruise control system. Show more
Keywords: Personalized adaptive cruise control, SOM-K-means two-stage clustering, random forest (RF), sparrow search algorithm (SSA), driving style recognition
DOI: 10.3233/JIFS-235045
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024
Authors: Atef, Shimaa | El-Seidy, Essam | Reda, Naglaa M.
Article Type: Research Article
Abstract: Decisions in many dilemmas are based on a combination of factors, including as incentive, punishment, reputation, and memory. The impact of memory information on cooperative evolution in multi-round games is a decision-making process in group evolution. The iterated prisoner’s dilemma is an excellent model for the development of cooperation amongst the payoff-maximizing individuals. Since tit-for-tat proved successful in Axelrod’s repeated prisoner’s dilemma tournaments, there has been a great deal of interest in creating new strategies. Every iterative prisoner’s dilemma method bases its decision-making on a specific duration of past contacts with the opponent, which is referred to as the memory’s …size. This study examines the impact of strategy memory size on the evolutionary stability of n-person iterated prisoner’s dilemma strategies. In this paper, we address the role that memory plays in decision-making. We interested in the model of the Iterated Prisoner’s Dilemma game for three players with memory two, and we will look at strategies with similar behavior, such as Tit-For-Tat (TFT) strategies as well as Win Stay-Lose Shift (WSLS) strategies. As a result of this paper, we have shown that the effect of memory length is almost non-existent in the competitions of strategies that we studied. Show more
Keywords: Memory-Two, Tit-For-Tat strategies (TFT), three-players iterated prisoner’s dilemma game (3P-IPD), transition matrix, Win Stay-Lose Shift strategies (WSLS)
DOI: 10.3233/JIFS-233690
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024
Authors: Chen, Zhipeng | Liu, Xiao | Qin, Jianhua
Article Type: Research Article
Abstract: To solve the problem that the walking jitter of quadruped robots leads to the degradation of clarity of visual imaging, a quadruped robot visual imaging jitter compensation algorithm based on the theory of walking jitter is proposed. The D-H coordinate transformation method is used to establish the coordinate system of each joint of the leg. The kinetic equations of the leg are derived from the relationship between the rotational velocity and the moment of the leg joint, and the kinetic equilibrium equations of the quadruped robot body are established based on the spatial moment equilibrium theorem; the spring-mass model of …the leg of the quadruped robot is used to construct the kinetic equations of the leg jittering, and the kinetic equations of the body jittering are derived using the moment equilibrium condition of the body center of gravity position and under the effect of the leg and body jitter to obtain the visual imaging device jitter quantity; finally, the tremor quantity is combined with the jitter quantity and rotation matrix to derive the walking jitter mathematical model of the quadruped robot visual imager, and the jitter compensation algorithm of quadruped robot visual imager is verified. The experimental results show that compared with the traditional Wiener filter algorithm for jitter compensation and the BP neural network jitter compensation algorithm, this algorithm improves the visual imaging by 10.8% and 3.3% in the two evaluation indexes of peak signal-to-noise ratio and structural similarity, respectively, and the de-jittering effect is better. Show more
Keywords: Quadruped robot, visual imaging, walking jitter, compensation algorithm
DOI: 10.3233/JIFS-235345
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Liu, Gan | Qi, Guirong | Wan, Sanyu
Article Type: Research Article
Abstract: Imbalanced data is a serious binary classification difficulty in forecasting the well-being of the elderly. This paper improves the Smote algorithm from the algorithm and sample dimensions to tackle the issue of imbalanced distribution of questionnaire data. The k-means Smote is combined with RBFNN as K-RBFNN Smote in the algorithm dimension and add FCM link to resample the minority set in the sample dimension as FCM K-RBFNN Smote. In order to improve the generalization of models, the RUS module is added to the algorithm. Experiments are carried out on four improved Smote technologies and two existing Smote technologies combined with …XGBoost, which is superior than the other five conventional classification models. The experimental results indicate that the performance order is RUS FCM K-RBFNN Smote > K-RBFNN Smote > FCM K-RBFNN Smote > RUS K-RBFNN Smote > K-Means Smote > FCM Smote. The RUS FCM K-RBFNN method has been identified as the optimal approach for enhancing performance, resulting in a 98.58% accuracy rate. In conclusion, Smote algorithm undergoes the implementation of K-RBFNN shows greater performance and the enhancement of FCM and RUS relies on the structure of sampling. Show more
Keywords: RUS FCM K-RBFNN Smote, XGBoost, imbalanced data, elderly well-being classification
DOI: 10.3233/JIFS-235213
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Allouche, Moez | Dahech, Karim | Gaubert, Jean-Paul
Article Type: Research Article
Abstract: This paper proposes a multi-objective H2 /H ∞ maximum power tracking control of a variable speed wind turbine to minimize the H2 tracking error and ensure the H ∞ model reference-tracking performance, simultaneously. The optimal condition is obtained via a boost converter use, which adapts the load impedance to the wind turbine generator. Thus, based on the fuzzy T-S model, a multi-objective Maximum Power Point Tracking (MPPT) controller is developed, ensuring maximum power transfer, despite wind speed variation and system uncertainty. To specify the optimal trajectory to follow, a TS reference model is proposed taking as input the optimal …rectified DC current. The conditions of stability and stabilization are expressed in terms of linear matrix inequality (LMI) for uncertain and disturbed T-S models leading to determining the controller gains. Finally, an example of MPP tracking applied to a Wind Energy Conversion System (WECS) illustrates the effectiveness of the proposed fuzzy control law. Show more
Keywords: Multi-objective fuzzy tracking control, maximum power point tracking (MPPT), linear matrix inequalities (LMIs), robust control, T-S fuzzy model
DOI: 10.3233/JIFS-222887
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024
Authors: Xiong, Haoyu | Yang, Leixin | Fang, Gang | Li, Junwei | Xiang, Yu | Zhang, Yaping
Article Type: Research Article
Abstract: Test-time augmentation (TTA) has become a widely adopted technique in the computer vision field, which can improve the prediction performance of models by aggregating the predictions of multiple augmented test samples without additional training or hyperparameter tuning. While previous research has demonstrated the effectiveness of TTA in visual tasks, its application in natural language processing (NLP) tasks remains challenging due to complexities such as varying text lengths, discretization of word elements, and missing word elements. These unfavorable factors make it difficult to preserve the label invariance of the standard TTA method for augmented text samples. Therefore, this paper proposes a …novel TTA technique called Defy, which combines nearest-neighbor anomaly detection algorithm and an adaptive weighting network architecture with a bidirectional KL divergence entropy regularization term between the original sample and the aggregated sample, to encourage the model to make more consistent and reliable predictions for various augmented samples. Additionally, by comparing with Defy, the paper further explores the problem that common TTA methods may impair the semantic meaning of the text during augmentation, leading to a shift in the model’s prediction results from correct to corrupt. Extensive experimental results demonstrate that Defy consistently outperforms existing TTA methods in various text classification tasks and brings consistent improvements across different mainstream models. Show more
Keywords: Test-time augmentation, test-time robustification, text classification, language model, anomaly detection
DOI: 10.3233/JIFS-236010
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024
Authors: Shengbin, Liang | Haoran, Sun | Fuqi, Sun | Hongjian, Wu | Wencai, Du
Article Type: Research Article
Abstract: Mild cognitive impairment (MCI) is a syndrome that occurs in the preclinical stage of Alzheimer’s disease (AD) and is also an early signal of the onset of AD. Early detection and accurate differentiation between MCI and AD populations, and providing them with effective intervention and treatment, are of great significance for preventing or delaying the onset of AD. In this paper, we propose a deep learning model, SE-DenseNet, that combines channel attention and dense connectivity networks and apply it to the field of magnetic resonance imaging (MRI) data recognition for the diagnosis of AD and MCI. First, to extract MRI …features with high quality, a slicing algorithm based on two-dimensional image information entropy is proposed to obtain AD brain lesion features with stronger representation ability. Second, in terms of model structure, SENet is introduced as a channel attention module and redistribute the weight of image features in the channel dimension; use DenseNet as the main architecture to maximize information flow, and each layer is directly interconnected with subsequent layers. It enables the network to learn and extract relevant features from the input data and improve the classification ability of the network. Finally, our proposed model is validated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, the results have shown that the accuracy for the four classification tasks of AD-NC, AD-MCI, NC-MCI, and AD-NC-MCI can reach 98.12%, 97.42%, 97.42%, and 95.24%, respectively. At the same time, the sensitivity and specificity have also achieved satisfactory results, exhibited a high performance in comparison with the classic machine learning algorithm and several existing state-of-the-art deep learning methods, demonstrating the proposed method is a powerful tool for the early diagnosis and detection of AD. Show more
Keywords: Alzheimer’s disease classification, computer aided diagnosis, medical image processing, megnetic resonance imaging, deep learning
DOI: 10.3233/JIFS-236542
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024
Authors: Karthika, K. | Rangasamy, Devi Priya
Article Type: Research Article
Abstract: In today’s digital era, the security of sensitive data such as Aadhaar data is of utmost importance. To ensure the privacy and integrity of this data, a conceptual framework is proposed that employs the Diffie-Hellman key exchange protocol and Hash-based Message Authentication Code (HMAC) to enhance the security. The proposed system begins with the preprocessing phase, which includes removing noise, standardizing formats and validating the integrity of the data. Next, the data is segmented into appropriate sections to enable efficient storage and retrieval in the cloud. Each segment is further processed to extract meaningful features, ensuring that the relevant information …is preserved while reducing the risk of unauthorized access. For safeguarding the stored Aadhaar data, the system employs the Diffie-Hellman key exchange protocol which allows the data owner and the cloud service provider to establish a shared secret key without exposing it to potential attackers. Additionally, HMAC is implemented to verify the identity of users during the login process. HMAC enhances security by leveraging cryptographic hash functions and a shared secret key to produce a distinct code for each login attempt. This mechanism effectively protects the confidentiality and integrity of stored data. The combination of Diffie-Hellman key exchange and HMAC authentication provides a robust security framework for Aadhaar data. It ensures that the data remains encrypted and inaccessible without the secret key, while also verifying the identity of users during the login process. This comprehensive approach helps preventing unauthorized access thereby protecting against potential attacks, instilling trust and confidence in the security of Aadhaar data stored in the cloud. Results of the article depict that the proposed scheme achieve 0.19 s of encryption time and 0.05 s of decryption time. Show more
Keywords: Hash based message authentication code (HMAC), cryptographic hash functions, Diffie Hellman, communications
DOI: 10.3233/JIFS-234641
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-20, 2024
Authors: Vijaya Lakshmi, A. | Vaitheki, K. | Suresh Joseph, K.
Article Type: Research Article
Abstract: Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a novel meta-heuristic approach employing the slender Loris optimization algorithm (SLOA), miming slender Loris behavior. The behavior includes foraging, hunting, migration and communication with each other. The ultimate goal of the devised algorithm is to replicate the food-foraging behaviour of Slender Loris (SL) and the quick movement of SL when threatened (i.e.) their escape from predators and also mathematically modelled the special communication techniques of SL using their urine scent smell. SLOA modelled …SL’s slow food foraging behaviour as the exploitation phase, and moving between the tree and escaping from a predator is modelled as the exploration phase. The Eyesight of slender Loris plays a vital role in food foraging during nighttime in dim light. The operator’s Eyesight is modelled based on the angle of inclination of SL. The urine scent intensity is used here to be instrumental in preventing already exploited territory activities, which improves algorithm performance. The suggested algorithm is assessed and tested against nineteen benchmark test operations and evaluated for effectiveness with standard widely recognized meta-heuristics algorithms. The result shows SLOA performing better and achieving near-optimal solutions and dominance in exploration–exploitation balance in most cases than the existing state-of-the-art algorithms. Show more
Keywords: Slender loris optimization algorithm, exploitation and exploration, optimization problems, swarm intelligence algorithm, metaheuristic
DOI: 10.3233/JIFS-236737
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024
Authors: Dai, Songsong
Article Type: Research Article
Abstract: The well-known iterative boolean-like law a →(a → b ) = a → b can be generalized to the functional equation I (x , I (x , y )) = I (x , y ), where I is a fuzzy implication. In this paper, we discuss an approximation of the equation, I (x , I (x , y )) ≈ I (x , y ), i.e., the law is approximately valid. Furthermore, we study the property of approximation preserving with respect to compositions of fuzzy implications. Finally, we give a necessary condition and a sufficient condition for the approximate equation of (S , N )-implications.
Keywords: Functional equation, iterative boolean-like law, fuzzy implication, (S, N)-implication
DOI: 10.3233/JIFS-233435
Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024
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