Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Ramírez-Martínez, Angel | Chong-Quero, J. Enrique | Cervantes-Culebro, Héctor | Cruz-Villar, Carlos A.

Article Type: Research Article

Abstract: This paper presents a data-driven control approach for a five-bar robot with compliant joints. The robot consists of a parallel mechanism with compliant elements that introduce uncertainties in modeling and control. To address this fact, it is implemented a model-less data-driven controller based on a Feedforward Neural Network Module (FNNM) that identifies the inverse dynamics of the robot. The FNNM is incorporated into a coordination of Feedforward Control Method (CFCM) to achieve precise trajectory tracking. Experiments compare the compliant joints robot to a bearing-joint robot performing pick-and-place tasks from 0.15 to 3.15 Hz. Results show the compliant robot maintaining trajectory tracking …up to 1.25 Hz with a Root Mean Square Error (RMSE) of 9.02 mm. Show more

Keywords: Data-driven, five-bar robot, compliant joints, vision-based

DOI: 10.3233/JIFS-219364

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024

Authors: Karthikeyan, M. | Colak, Ilhami | Sagar Imambi, S. | Joselin Jeya Sheela, J. | Nair, Sruthi | Umarani, B. | Alagusabai, Andril | Suriyakrishnaan, K. | Rajaram, A.

Article Type: Research Article

Abstract: This research paper introduces a cutting-edge approach to electric demand forecasting by incorporating the Temporal Fusion Transformer (TFT). As the landscape of demand forecasting becomes increasingly intricate, precise predictions are vital for effective energy management. To tackle this challenge, we leverage the sequential and temporal patterns in an extensive electric demand dataset spanning from 2003 to 2014. Our proposed Temporal Fusion Transformer model combines attention mechanisms with the transformer architecture, enabling it to adeptly capture intricate temporal dependencies. Thorough data preprocessing, including temporal embedding and external features, enhances prediction accuracy. Through rigorous evaluation, the TFT model surpasses existing forecasting techniques, …showcasing its capacity for accurate, resilient, and adaptive predictions. This research contributes to the advancement of electric demand forecasting, harnessing the TFT’s capabilities to excel in capturing diverse temporal patterns. The findings hold the potential to enhance energy management and support decision-making in the energy sector, bridging the gap between innovation and practical utility. Show more

Keywords: Electric demand forecasting, temporal fusion transformer, energy management, time-series analysis, transformer architecture

DOI: 10.3233/JIFS-236036

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024

Authors: Arenas Muñiz, Andrés Antonio | Mújica-Vargas, Dante | Rendón Castro, Arturo | Luna-Álvarez, Antonio | Vela-Rincón, Virna V.

Article Type: Research Article

Abstract:  The selection of an appropriate trajectory for self-driving vehicles involves the analysis of several criteria that describe the generated trajectories. This problem evolves into an optimization problem when it is desired to increase or decrease the values for a specific criterion. The contribution of this thesis is to explore the use and optimization of another technique for decision-making, such as TOPSIS, with a sufficiently robust method that allows the inclusion of multiple parameters and their proper optimization, incorporating human experience. The proposed approach showed significantly higher safety and comfort performance, with about 20% better efficiency and 80% fewer safety violations …compared to other state-of-the-art methods, and in some cases outperforming in comfort by about 30.43%. Show more

Keywords: Decision-making, human experience, trajectory selection, self-driving

DOI: 10.3233/JIFS-219365

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Téllez-Velázquez, Arturo | Delice, Pierre A. | Salgado-Leyva, Rafael | Cruz-Barbosa, Raúl

Article Type: Research Article

Abstract: This paper performs an analysis comparing two evolutionary explainable fuzzy models that make inferences in a pipeline with a blood test data set for COVID-19 classification. Firstly, data is preprocessed by the following stages: cleaning, imputation and ranking feature selection. Later, we perform a comparative analysis between several clustering methods used in an Evolutionary Clustering-Structured Fuzzy Classifier (ECSFC) to solve this classification problem using the Differential Evolution (DE) algorithm. Complementarily, we find that the Fuzzy Decision Tree model produces similar performance when is tuned with the DE algorithm (EFDT). The obtained results show that, simpler models are easier to explain …qualitatively, i.e., increasing the number of clusters in ECSFC model or the maximum depth of the tree in EFDT model, does not necessarily help to obtain simplified and accurate models. In addition, although the EFDT model is by itself an intuitively explainable model, the ECSFC, with the help of the proposed Weighted Stacked Features Plot, generates more intuitive models that allow not only highlighting the features and the linguistic terms that defines a patient with COVID-19, but also allows users to visualize in a single graph and in specific colors the analyzed classes. Show more

Keywords: COVID-19, blood tests, fuzzy classifier, fuzzy decision tree, clustering, differential evolution

DOI: 10.3233/JIFS-219372

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Yao, Ziyang

Article Type: Research Article

Abstract: The traditional multi-task Takagi-Sugeno-Kang (TSK) fuzzy system modeling methods pay more attention to utilizing the inter-task correlation to learn the consequent parameters but ignore the importance of the antecedent parameters of the model. To this end, we propose a novel multi-task TSK fuzzy system modeling method based on multi-task fuzzy clustering. This method first proposes a novel multi-task fuzzy c-means clustering method that learns multiple specific clustering centers for each task and some common clustering centers for all tasks. Secondly, for the consequent parameters of the fuzzy system, the novel low-rank and row-sparse constraints are proposed to better implement multi-task …learning. The experimental results demonstrate that the proposed model shows better performance compared with other existing methods. Show more

Keywords: Multi-task fuzzy clustering, TSK fuzzy system, low-rank, row-sparsity, joint learning

DOI: 10.3233/JIFS-232312

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Gao, Lijun | Liu, Kai | Liu, Wenjun | Wu, Jiehong | Jin, Xiao

Article Type: Research Article

Abstract: As machine learning models become increasingly integrated into practical applications and are made accessible via public APIs, the risk of model extraction attacks has gained prominence. This study presents an innovative and efficient approach to model extraction attacks, aimed at reducing query costs and enhancing attack effectiveness. The method begins by leveraging a pre-trained model to identify high-confidence samples from unlabeled datasets. It then employs unsupervised contrastive learning to thoroughly dissect the structural nuances of these samples, constructing a dataset of high quality that precisely mirrors a variety of features. A mixed information confidence strategy is employed to refine the …query set, effectively probing the decision boundaries of the target model. By integrating consistency regularization and pseudo-labeling techniques, reliance on authentic labels is minimized, thus improving the feature extraction capabilities and predictive precision of the surrogate models. Evaluation on four major datasets reveals that the models crafted through this method bear a close functional resemblance to the original models, with a real-world API test success rate of 62.35%, which vouches for the method’s validity. Show more

Keywords: Model extraction, unsupervised learning, selection of strategies, active learning

DOI: 10.3233/JIFS-239504

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: López-Jasso, Edgar | Felipe-Riverón, Edgardo M. | Valdez-Rodríguez, José E.

Article Type: Research Article

Abstract:  This study underscores the crucial role of image preprocessing in enhancing the outcomes of multimodal image registration tasks using scale-invariant feature selection. The primary focus is on registering two types of retinal images, assessing a methodology’s performance on a set of retinal image pairs, including those with and without microaneurysms. Each pair comprises a color optical image and a gray-level fluorescein image, presenting distinct characteristics and captured under varying conditions. The SIFT methodology, encompassing five stages, with preprocessing as the initial and pivotal stage, is employed for image registration. Out of 35 test retina image pairs, 33 (94.28%) were successfully …registered, with the inability to extract features hindering automatic registration in the remaining pairs. Among the registered pairs, 42.42% were retinal images without microaneurysms, and 57.57% had microaneurysms. Instead of simultaneous registration of all channels, independent registration of preprocessed images in each channel proved more effective. The study concludes with an analysis of the fifth registration’s resulting image to detect abnormalities or pathologies, highlighting the challenges encountered in registering blue channel images due to high intrinsic noise. Show more

Keywords: Image SIFT registration, microaneurysms counting, retina image analysis, multimodal registration, image processing

DOI: 10.3233/JIFS-219374

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Wei, Xiao | Lin, Yidian

Article Type: Research Article

Abstract: Legal judgment prediction(LJP) has achieved remarkable results. However, existing methods still face problems such as difficulties in obtaining key feature words for charges, which impose limitations on the improvement of prediction results. To this end, we propose a legal judgment prediction model with legal feature Word subgraph Label-Embedding and Dual-knowledge Distillation(WLEDD). Compared with traditional methods, our method has two contributions: (1) To mitigate the impact of overly sparse tail class data and high similarity text representations, we capture the critical features related to the charges by fusing LDA and legal feature word subgraphs. Then we encode them as label information …to obtain highly distinguished representations of legal documents. (2) To solve the problem of high difficulty in some subtasks in LJP, we perform subtask-oriented compression of models to construct a student model with lower complexity and higher accuracy through dual knowledge distillation. Moreover, we exploit the logical association between the subtasks to constrain the labels of articles by charge prediction results. It greatly reduces the difficulty of article prediction. Experimental results on four datasets show that our approach significantly outperforms the baseline models. Compared with the state-of-art method, the F1 value of WLEDD for charge prediction has increased by an average of 2.57% . For article prediction, the F1 value has increased by an average of 1.09% . In addition, we demonstrate its effectiveness through ablation experiments and analytical experiments. Show more

Keywords: Legal judgment prediction, knowledge distillation, label embedding, legal text mining

DOI: 10.3233/JIFS-237323

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Yang, Hong | Wang, Lina

Article Type: Research Article

Abstract: The paper focuses on how to improve the prediction accuracy of time series and the interpretability of prediction results. First, a novel Prophet model based on Gaussian linear fuzzy approximate representation (GF-Prophet) is proposed for long-term prediction, which uniformly predicts the data with consistent trend characteristics. By taking Gaussian linear fuzzy information granules as inputs and outputs, GF-Prophet predicts with significantly smaller cumulative error. Second, noticing that trend extraction affects prediction accuracy seriously, a novel granulation modification algorithm is proposed to merge adjacent information granules that do not have significant differences. This is the first attempt to establish Prophet based …on fuzzy information granules to predict trend characteristics. Experiments on public datasets show that the introduction of Gaussian linear fuzzy information granules significantly improves prediction performance of traditional Prophet model. Compared with other classical models, GF-Prophet has not only higher prediction accuracy, but also better interpretability, which can clearly give the change information, fluctuation amplitude and duration of a certain trend in the future that investors actually pay attention to. Show more

Keywords: Fuzzy number, gaussian linear fuzzy information granule, the prophet model, long-term prediction

DOI: 10.3233/JIFS-230313

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Borse, Rushikesh | Das, Rochishnu | Dash, Devasish | Yadav, Akshay

Article Type: Research Article

Abstract: In the wake of the contemporary competitive business landscape, the retention of employees has become one of the most important yet difficult tasks for any corporate. Retaining top-performing employees not only improves organizational performance but also reduces recruitment costs. In this study, the authors investigate the major drivers leading to employee attrition and using machine learning algorithms implemented on a well proven and validated IBM HR data set. Although the data set tags the samples for a target variable (attrited and non-attrited), the work presented in this paper comes up with another labelling (1. likely to leave, 2. On the …verge of leaving, 3. will stay). The data set is evaluated over top 10 Machine learning algorithms and a competitive analysis is made between them based on various factors. The best model has shown a prediction accuracy of over 85% +. Managers are provided with insights and recommendations at the end that will help companies to proactively identify at-risk employees and implement effective retention strategies. Show more

Keywords: Employee attrition, machine learning, early detection of attrition, artificial neural network

DOI: 10.3233/JIFS-219410

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024

Authors: Senthamil Selvi, M. | Senthamizh Selvi, R. | Subbaiyan, Saranya | Murshitha Shajahan, M.S.

Article Type: Research Article

Abstract: Accurate prediction of grid loss in power distribution networks is pivotal for efficient energy management and pricing strategies. Traditional forecasting approaches often struggle to capture the complex temporal dynamics and external influences inherent in grid loss data. In response, this research presents a novel hybrid time-series deep learning model: Gated Recurrent Units with Temporal Convolutional Networks (GRU-TCN), designed to enhance grid loss prediction accuracy. The proposed model integrates the temporal sensitivity of GRU with the local context awareness of TCN, exploiting their complementary strengths. A learnable attention mechanism fuses the outputs of both architectures, enabling the model to discern significant …features for accurate prediction. The model is evaluated using well-established metrics across distinct temporal phases: training, testing, and future projection. Results showcase Resulting in encouraging Figures for mean absolute error, root mean squared error, and mean absolute percentage error, the model’s capacity to capture both long-term trends and transitory patterns. The GRU-TCN hybrid model represents a pioneering approach to power grid loss prediction, offering a flexible and precise tool for energy management. This research not only advances predictive accuracy but also lays the foundation for a smarter and more sustainable energy ecosystem, poised to transform the landscape of energy forecasting. Show more

Keywords: Accurate prediction, grid loss, power distribution networks

DOI: 10.3233/JIFS-235579

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Singh, Pardeep | Lamsal, Rabindra | Singh, Monika | Shishodia, Bhawna | Sitaula, Chiranjibi | Chand, Satish

Article Type: Research Article

Abstract:  Social media platforms play a crucial role in providing valuable information during crises, such as pandemics. The COVID-19 pandemic has created a global public health crisis, and vaccines are the key preventive measure for achieving herd immunity. However, some individuals use social media to oppose vaccines, undermining government efforts to eliminate the virus. This study introduces the “GeoCovaxTweets” dataset, consisting of 1.8 million geotagged tweets related to COVID-19 vaccines from January 2020 to November 2022, originating from 233 countries and territories. Each tweet includes state and country information, enabling researchers to analyze global spatial and temporal patterns. An extensive set …of analyses are performed on the dataset to identify prominent topic clusters and explore public opinions across different vaccines and vaccination contexts. The study outlines the dataset curation methodology and provides instructions for local reproduction. We anticipate that the dataset will be valuable for crisis computing researchers, facilitating the exploration of Twitter conversations surrounding COVID-19 vaccines and vaccination, including trends, opinion shifts, misinformation, and anti-vaccination campaigns. Show more

Keywords: COVID-19 discourse, COVID-19 pandemic, sentiment analysis, social media, topic clustering, twitter dataset

DOI: 10.3233/JIFS-219418

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Tripathi, Diwakar | Reddy, B. Ramachandra | Dwivedi, Shubhra | Shukla, Alok Kumar | Chandramohan, D. | Dewangan, Ram Kishan

Article Type: Research Article

Abstract: Nature-inspired algorithms as problem-solving methodologies are extremely effective in discovery of optimized solutions in multi-dimensional and multi-modal problems. Because of qualities like “self-optimization”, “flexibility” and etc., nature-inspired algorithms for problem solving are effectively optimal. Feature selection is an approach to find approximate optimal subset of the features which are more relevant towards the particular outcome. In this study, we focused on how feature selection may improve the credit scoring model’s performance for prediction. Nature-inspired algorithms are applied for feature selection to improve the predictive performance of the credit scoring model. Additionally, four benchmark credit scoring datasets collected from the UCI …repository are used to test feature selection by several Nature-inspired algorithms aggregated with “Random Forest (RF)”, “Logistic Regression (LR),” and “Multi-layer Perceptron (MLP)” for classification and results are compared in terms of classification accuracy and G-measures. Show more

Keywords: Nature-inspired algorithms, credit score, feature selection, classification

DOI: 10.3233/JIFS-219413

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: Faraz, Ansar Ali | Khan, Hina | Aslam, Muhammad | Albassam, Mohammed

Article Type: Research Article

Abstract: When data are hazy or uncertain, estimators given under classical statistics are ineffective. Given that it deals with uncertainty, neutrosophic statistics is the sole alternative. Due to the vast range of applications, extensive research has been done in this area. The objective of this study is to determine the most accurate predictions for the population mean with the least amount of mean square error. We have created neutrosophic ratio type estimators, when working with ambiguous, hazy, and neutrosophic-type data, the proposed estimation methods are very useful for computing results. These estimators produce findings that are not single-valued but rather have …an interval form, where our population parameter may lie more frequently. Since we have an estimated interval with the unknown population mean value given a minimal mean square error, it improves the estimators’ efficiency. Real life neutrosophic line losses data and simulation are both used to analyze the effectiveness of the proposed neutrosophic ratio-type estimators. Additionally, a comparison is made to show how helpful Neutrosophic ratio type estimator is in comparison to existing estimators. Show more

Keywords: Neutrosophic, conventional statistics, estimation, ratio estimators, mean square error

DOI: 10.3233/JIFS-240153

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Saravanan, Krithikha Sanju | Bhagavathiappan, Velammal

Article Type: Research Article

Abstract: The advancements in technology, particularly in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI) can be advantageous for the agricultural sector to enhance the yield. Establishing an agricultural ontology as part of the development would spur the expansion of cross-domain agriculture. Semantic and syntactic knowledge of the domain data is required for building such a domain-based ontology. To process the data from text documents, a standard technique with syntactic and semantic features are needed because the availability of pre-determined agricultural domain-based data is insufficient. In this research work, an Agricultural Ontologies Construction framework (AOC) is proposed for …creating the agricultural domain ontology from text documents using NLP techniques with Robustly Optimized BERT Approach (RoBERTa) model and Graph Convolutional Network (GCN). The anaphora present in the documents are resolved to produce precise ontology from the input data. In the proposed AOC work, the domain terms are extracted using the RoBERTa model with Regular Expressions (RE) and the relationships between the domain terms are retrieved by utilizing the GCN with RE. When compared to other current systems, the efficacy of the proposed AOC method achieves an exceptional result, with precision and recall of 99.6% and 99.1% respectively. Show more

Keywords: Anaphora resolution, term extraction, relationships identification, RoBERTa model, regular expressions, graph convolutional network, domain ontology

DOI: 10.3233/JIFS-237632

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-19, 2024

Authors: Immanuel, Rajeswari Rajesh | Sangeetha, S.K.B.

Article Type: Research Article

Abstract: Human emotions are the mind’s responses to external stimuli, and due to their dynamic and unpredictable nature, research in this field has become increasingly important. There is a growing trend in utilizing deep learning and machine learning techniques for emotion recognition through EEG (electroencephalogram) signals. This paper presents an investigation based on a real-time dataset that comprises 15 subjects, consisting of 7 males and 8 females. The EEG signals of these subjects were recorded during exposure to video stimuli. The collected real-time data underwent preprocessing, followed by the extraction of features using various methods tailored for this purpose. The study …includes an evaluation of model performance by comparing the accuracy and loss metrics between models applied to both raw and preprocessed data. The paper introduces the EEGEM (Electroencephalogram Ensemble Model), which represents an ensemble model combining LSTM (Long Short-Term Memory) and CNN (Convolutional Neural Network) to achieve the desired outcomes. The results demonstrate the effectiveness of the EEGEM model, achieving an impressive accuracy rate of 95.56%. This model has proven to surpass the performance of other established machine learning and deep learning techniques in the field of emotion recognition, making it a promising and superior tool for this application. Show more

Keywords: EEG signal, emotion, CNN, LSTM, ensemble learning, feature extraction

DOI: 10.3233/JIFS-237884

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Jansi Rani, J. | Manivannan, A.

Article Type: Research Article

Abstract: This paper focuses on solving the fully fuzzy transportation problem in which the parameters are triangular Type-2 fuzzy numbers due to the instinctive of human imprecision. To deal with uncertainty more precisely, a triangular Type-1 fuzzy transportation problem is reformed as a transportation problem with triangular Type-2 fuzzy parameters in this paper. In order to compare triangular Type-2 fuzzy numbers, a new ranking(ordering) technique is proposed by extending the Yager’s function. However, two efficient algorithmic approaches namely, triangular Type-2 fuzzy zero suffix method (TT2FZSM) and triangular Type-2 fuzzy zero average method (TT2FZAM) are proposed to generate the initial transportation cost …of the fully triangular Type-2 fuzzy transportation problem. Both TT2FZSM and TT2FZAM are converging towards an optimal solution. In addition to TT2FZSM and TT2FZAM, the modified distribution method is applied to ensure optimality. Subsequently, we carry out a comprehensive discussion of the obtained results to establish the validation of the proposed approach. Show more

Keywords: Transportation problem, triangular type-2 fuzzy number, ranking function, optimal solution

DOI: 10.3233/JIFS-237652

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Yan, Huiming | Yan, Zilin | Wang, Weiling | Liu, Shuyue

Article Type: Research Article

Abstract: In recent years, the burgeoning imperative of energy-efficient building management practices has surged dramatically, underscoring an urgent mandate for comprehensive studies that integrate cutting-edge optimization algorithms with precise heating load forecasting techniques. These studies are not merely endeavors; they represent concerted efforts to increase building energy efficiency and address mounting concerns regarding sustainability and resource utilization. In the intricate domain of heating, ventilation, and air conditioning (HVAC) systems, energy optimization challenges are being meticulously confronted through rigorous exploration and the application of innovative problem-solving methodologies. This pioneering study introduces groundbreaking methodologies by seamlessly integrating two state-of-the-art optimization algorithms— the Red …Fox Optimization and the Golden Eagle Optimizer— with the Decision Tree model. This fusion is aimed at enhancing the accuracy of heating load predictions and streamlining HVAC system optimization processes, marking a significant leap toward achieving heightened energy efficiency and operational efficacy in building management practices. The study emphasizes the significance of precise heating load prediction in advancing energy efficiency, realizing cost savings, and fostering environmental sustainability in building management. Furthermore, it delves into the multifaceted impact of various building features on heating load, encompassing variables such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. These insights furnish actionable intelligence for refined decision-making processes in both building design and operation. Based on the results, the DT single model experienced the weakest performance among the three models, with R 2  = 0.975 and RMSE  = 1.608. The model DTFO (DT + FOX) achieves an extraordinary R 2 value of 0.996 and RMSE value of 0.961 for heating load prediction, surpassing the performance benchmarks set by other models. This achievement holds considerable promise for aiding engineers in crafting energy-efficient buildings, particularly within the swiftly evolving landscape of smart home technologies. Show more

Keywords: Decision tree, heating load, red fox optimization, golden eagle optimizer

DOI: 10.3233/JIFS-240283

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Sriraam, Natarajan | Chinta, Babu | Suresh, Seshadhri | Sudharshan, Suresh

Article Type: Research Article

Abstract: Assessing fetal growth and development requires accurate identification of the fetal area contour and measurement of the Crown-Rump Length (CRL). In this paper, we presented a unique method for autonomously segmenting the fetal region in ultrasound images and calculating the CRL based on the U-Net architecture. Because of its capacity to capture both global and local information, the U-Net model is a popular choice for image segmentation tasks. Our method employs the U-Net model to extract the fetal region contour and measure the CRL, resulting in a dependable and efficient prenatal evaluation solution.

Keywords: Fetal, segmentation, U-Net, ultrasound image

DOI: 10.3233/JIFS-219403

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-7, 2024

Authors: Macias, Cesar | Soto, Miguel | Cardoso-Moreno, Marco A. | Calvo, Hiram

Article Type: Research Article

Abstract: Mental and cognitive well-being is of paramount significance for human beings. Consequently, the early detection of issues that may culminate in conditions such as depression holds great importance in averting adverse outcomes for individuals. Depression, a prevalent mental health disorder, can severely impact an individual’s quality of life. Timely identification and intervention are critical to prevent its progression. Our research delves into the application of Machine Learning (ML) and Deep Learning (DL) techniques to potentially facilitate the early recognition of depressive tendencies. By leveraging the cognitive triad theory, which encapsulates negative self-perception, a pessimistic outlook on the world, and a …bleak vision of the future, we aim to develop predictive models that can assist in identifying individuals at risk. In this regard, we selected The Cognitive Triad Dataset, which takes into account six different categories that encapsulate negative and positive postures about three different contexts: self context, future context and world context. Our proposal achieved great performance, by relying on a strict preprocessing analysis, which led to the models obtaining an accuracy value of 0.97 when classifying aspect contexts; 0.95 when classifying sentiment-aspects; and a value of 0.93 in accuracy was achieved under the aspect-sentiment paradigm. Our models outperformed those reported in the literature. Show more

Keywords: Cognitive triad inventory, depression detection, machine learning, deep learning, natural language processing

DOI: 10.3233/JIFS-219333

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Mundada, Shyamal | Jain, Pooja | Kumar, Nirmal

Article Type: Research Article

Abstract: Sustainable agriculture revolves around soil organic carbon (SOC), which is essential for numerous soil functions and ecological attributes. Farmers are interested in conserving and adding additional soil organic carbon to certain fields in order to improve soil health and productivity. The relationship between soil and environment that has been discovered and standardized throughout time has enhanced the progress of digital soil-mapping techniques; therefore, a variety of machine learning techniques are used to predict soil properties. Studies are thriving at how effectively each machine learning method maps and predicts SOC, especially at high spatial resolutions. To predict SOC of soil at …a 30 m resolution, four machine learning models—Random Forest, Support Vector Machine, Adaptive Boosting, and k-Nearest Neighbour were used. For model evaluation, two error metrics, namely R2 and RMSE have been used. The findings demonstrated that the calibration and validation sets’ descriptive statistics sufficiently resembled the entire set of data. The range of the calculated SOC content was 0.06 to 1.76 %. According to the findings of the study, Random Forest showed good results for both cases, i.e. evaluation using cross validation and without cross validation. Using cross validation, RF confirmed highest R2 as 0.5278 and lowest RMSE as 0.1683 for calibration dataset while without cross validation it showed R2 as 0.8612 and lowest RMSE as 0.0912 for calibration dataset. The generated soil maps will help farmers adopt precise knowledge for decisions that will increase farm productivity and provide food security through the sustainable use of nutrients and the agricultural environment. Show more

Keywords: Machine learning, remote sensing data, digital soil mapping, spatial predictions, precision farming

DOI: 10.3233/JIFS-240493

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Zheng, Danjing | Song, Xiaona | Song, Shuai | Peng, Zenglong

Article Type: Research Article

Abstract: This paper investigates an observer-based boundary controller design for interconnected nonlinear partial differential equation (PDE) systems. First, the Takagi–Sugeno (T–S) fuzzy model is adopted to accurately describe the target systems. Then, boundary measurements are employed to reduce the number of sensors. Next, considering the phenomenon of abnormal interference that may lead to measurement outliers and observer parameters’ uncertainties, an outlier-resistant non-fragile observer expressed by a saturation function is designed to guarantee the desired control objectives. Moreover, the boundary control approach is employed to trade-off the cost of system design and system performance. Furthermore, utilizing the membership function-dependent Lyapunov functions and …free-weight matrixes, sufficient conditions ensuring the closed-loop systems’ exponential stability are obtained while decreasing the conservativeness of the system stability analysis. Finally, the proposed method’s feasibility and effectiveness are validated by an example. Show more

Keywords: Boundary measurements, boundary control, interconnected nonlinear partial differential equation systems, membership function-dependent Lyapunov functions, outlier-resistant non-fragile observer

DOI: 10.3233/JIFS-238858

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Hayel, Rafa | El Hindi, Khalil | Hosny, Manar | Alharbi, Rawan

Article Type: Research Article

Abstract: Instance-Based Learning, such as the k Nearest Neighbor (kNN), offers a straightforward and effective solution for text classification. However, as a lazy learner, kNN’s performance heavily relies on the quality and quantity of training instances, often leading to time and space inefficiencies. This challenge has spurred the development of instance-reduction techniques aimed at retaining essential instances and discarding redundant ones. While such trimming optimizes computational demands, it might adversely affect classification accuracy. This study introduces the novel Selective Learning Vector Quantization (SLVQ) algorithm, specifically designed to enhance the performance of datasets reduced through such techniques. Unlike traditional LVQ algorithms that …employ random vector weights (codebook vectors), SLVQ utilizes instances selected by the reduction algorithm as the initial weight vectors. Importantly, as these instances often contain nominal values, SLVQ modifies the distances between these nominal values, rather than modifying the values themselves, aiming to improve their representation of the training set. This approach is crucial because nominal attributes are common in real-world datasets and require effective distance measures, such as the Value Difference Measure (VDM), to handle them properly. Therefore, SLVQ adjusts the VDM distances between nominal values, instead of altering the attribute values of the codebook vectors. Hence, the innovation of the SLVQ approach lies in its integration of instance reduction techniques for selecting initial codebook vectors and its effective handling of nominal attributes. Our experiments, conducted on 17 text classification datasets with four different instance reduction algorithms, confirm SLVQ’s effectiveness. It significantly enhances the kNN’s classification accuracy of reduced datasets. In our empirical study, the SLVQ method improved the performance of these datasets, achieving average classification accuracies of 82.55%, 84.07%, 78.54%, and 83.18%, compared to the average accuracies of 76.25%, 79.62%, 66.54%, and 78.19% achieved by non-fine-tuned datasets, respectively. Show more

Keywords: Machine learning, instance based learning, learning vector quantization, k-nearest neighbor, value difference metric (VDM)

DOI: 10.3233/JIFS-235290

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Reka, S | Karthik Sainadh Reddy, Dwarampudi | Dhiraj, Inti | Suriya Praba, T

Article Type: Research Article

Abstract: Polycystic Ovary Syndrome (PCOS) is a hormonal condition that typically affects female during the time of their reproduction. It is identified by the disruptions in hormonal balance, particularly an increase in levels of androgen (male hormone) in the female body. PCOS can lead to various symptoms and health complications including irregular menstrual cycles, ovarian cysts, fertility issues, insulin resistance, weight gain, acne, and excess hair growth. The real-world PCOS detection is a challenging task whilst PCOS specific cause is unknown and its symptoms are unclear. Thus, accurate and timely diagnosis of PCOS is crucial for effective management and prevention of …long-term complications. In such cases, Machine learning based PCOS prediction model support diagnostic process, address potential errors and time constraints. Machine learning algorithms can analyze large set of patient data, including medical history, hormonal profiles, and imaging results, to assist in the diagnosis of PCOS. In particular, the performance of data analysis chore and prediction model is improved by ensemble feature selection strategies. These methods concentrate on selecting a subset of pertinent features from a broader range of features. The unstable nature of the outcome of feature selection algorithm is a frequent issue in practical applications, when it is applied multiple times on similar dataset or with slight modifications in the data. Thus, evaluating the robustness of feature selection algorithm is most important. To address these issues and quantify the robustness, this study uses Jenson-Shannon divergence, an information theoretic approach with ensemble feature selection method to handle the various findings, such as complete ranking, half ranking and top-k lists (without ranking). Furthermore, this article proposes a hybrid machine learning classifier with SMOTE – SVM for the prompt detection of PCOS and the performance of the model is compared with a number of other individual classifiers including KNN (K-Nearest Neighbour), Support Vector Machine (SVM), AdaBoost, LR –Logistic Regression, NB –Nave Bayes, RF –Random Forest, Decision Tree. The proposed SWISS-AdaBoost classifier surpassed other models with 97.81% of accuracy and AUC of 99.08%. Show more

Keywords: Polycystic ovary syndrome (PCOS), Jenson-shannon divergence, SVM (Support Vector Machine), K-nearest neighbour, logistic regression, decision tree, naive bayes and AdaBoost

DOI: 10.3233/JIFS-219402

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Ezhilarasie, R. | MohanRaj, I. | Ramakrishnan, Thiruvikram Gopichettipalayam | Madhavan, Vyas | Narayan, Keshav | Umamakeswari, A.

Article Type: Research Article

Abstract: Internet of Things (IoT) devices are major stakeholders of contemporary network bandwidth. The proliferation of IoT devices and the demand for latency-free communication in time-critical applications has proven the drawback of cloud-based solutions. Edge computing is an paradigm that reduces the application’s response time by utilizing computation and storage proximate to each devices. Privacy in cloud computing is attained by system virtualization, containerization, among other evolved technologies. As privacy remains a primary concern, there is a need to test the feasibility of resource-constrained edge devices. Hence, this work aimed to examine the usability of such devices in edge computing by …benchmarking on different runtime environments. The results reveal that a standard mechanism was achieved for defining the criteria to identify the suitable edge devices for computation offloading, particularly for a set of smart traffic surveillance use cases. Further, an optimization algorithm was designed to generate an optimum schedule that decides the best device to execute a particular task from the set of suitable edge devices to enhance energy and execution time in a global view. Based on the feasibility study and optimal schedule, a makespan that is nearly 11 times better than local execution for the considered traffic surveillance workflow was achieved. Show more

Keywords: Container, docker, edge computing, IoT, LXC, offloading, single board computer

DOI: 10.3233/JIFS-219424

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Bukya, Hanumanthu | Bhukya, Raghuram | Harshavardhan, A.

Article Type: Research Article

Abstract: Fog computing has several undeniable benefits, such as enhancing near-real-time response, reducing transmission costs, and facilitating IoT analysis. This technology is poised to have a significant impact on businesses, organizations, and our daily lives. However, mobile user equipment struggles to handle the complex computing tasks associated with modern applications due to its limited processing power and battery life. Edge computing has emerged as a solution to this problem by relocating processing to nodes at the network’s periphery, which have more computational capacity. With the rapid evolution of wireless technologies and infrastructure, edge computing has become increasingly popular. Nevertheless, managing fog …computing resources remains challenging due to resource constraints, heterogeneity, and distant nodes. For delay-sensitive intelligent IoT applications within the fog computing architecture, cooperation and communication processing resources in 6 G and future networks are essential. This study proposes a joint computational and optimized resource allocation (JCORA) technique to accelerate the processing of data from intelligent IoT sensors in a cell association environment. The proposed technique utilizes an uplink and downlink power allocation factor and the shortest job first (SJF) task scheduling system to optimize user fairness and decrease data processing time. This is a complex assignment due to several non-convex limitations. The suggested JCORA-SJF model simultaneously optimizes time partitioning, computing task processing mode selection, and target sensing location selection to maximize the weighted total of task processing and communication performance. The simulation results demonstrate the effectiveness of the proposed JCORA-SJF algorithms, and the system’s scalability is also examined. Show more

Keywords: Fog computing, Internet of Things (IoT), resource allocation, edge computing networks, optimized resource allocation (JCORA), shortest job first (SJF)

DOI: 10.3233/JIFS-219421

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

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

Article Type: Research Article

Abstract:  Social media platforms play vital roles in disseminating information during crisis situations. Many rescue agencies, media outlets, and volunteers regularly monitor this data to identify and analyze disasters, ultimately mitigating life risks. However, effectively categorizing these messages based on information types is crucial for enhancing the situational awareness of emergency responders. This paper addresses the challenge of analyzing informal crisis-related social media texts by classifying disaster event tweets into 10 humanitarian categories associated with 19 major natural disaster events. We fine-tune seven state-of-the-art pre-trained transformer models and compare their performance with the recently introduced domain-specific models, i.e., CrisisTransformers. We empirically …found that CrisisTransformers outperform seven strong baseline transformer models in classifying disaster-specific tweets from the HumAID dataset, achieving a macro-averaged F1 score of 0.77. Our work contributes to the crisis computing field by improving the classification of disaster-related tweets and enhancing the capabilities of emergency responders and disaster management organizations. Show more

Keywords: Transformers, crisis computing, disaster classification, Twitter, disaster response

DOI: 10.3233/JIFS-219419

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Muppavarapu, Vamsee | Ramesh, Gowtham

Article Type: Research Article

Abstract: The W3C linked building data group is working on modeling the information for integrating building information with building life cycle data using Semantic Web technologies. The community has proposed a set of semantic models such as ifcOWL and Building Topology Ontology (BOT), to model various applications across Architecture, Engineering, Construction, and Operation (AECO) domain. On the other hand, the Semantic Web of Things (SWoT) group proposed standard semantic models such as M3-lite and BOSH ontologies for describing the sensor networks, observations, and sensor measurements. Both the aforementioned domains have their own siloed applications and with the evolution of the smart …home domain, there is a need to combine the knowledge of building information with the sensor knowledge to develop cross-domain applications. However, in order to develop such downstream applications leveraging advantages from both domains requires interoperable knowledge. This paper proposes an interoperable ontology, Building Topology Ontology for Smart Homes (BOTSH), with the aim of aligning the building domain with sensors domain semantic models. The BOTSH ontology facilitates capturing knowledge from both domains and helps in developing cross-domain applications. The potential of the proposed model was demonstrated using a real-life building model based on the competency questions framed by the domain experts. Show more

Keywords: Semantic web of things, building information models, building topology, sensors and observations, smart homes, knowledge graphs, semantic applications

DOI: 10.3233/JIFS-219425

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Pillai, Leena G. | Muhammad Noorul Mubarak, D. | Sherly, Elizabeth

Article Type: Research Article

Abstract: Speech production is a complex sequential process which involve the coordination of various articulatory features. Among them tongue being a highly versatile active articulator responsible for shaping airflow to produce targeted speech sounds that are intellectual, clear, and distinct. This paper presents a novel approach for predicting tongue and lip articulatory features involved in a given speech acoustics using a stacked Bidirectional Long Short-Term Memory (BiLSTM) architecture, combined with a one-dimensional Convolutional Neural Network (CNN) for post-processing with fixed weights initialization. The proposed network is trained with two datasets consisting of simultaneously recorded speech and Electromagnetic Articulography (EMA) datasets, each …introducing variations in terms of geographical origin, linguistic characteristics, phonetic diversity, and recording equipment. The performance of the model is assessed in Speaker Dependent (SD), Speaker Independent (SI), corpus dependent (CD) and cross corpus (CC) modes. Experimental results indicate that the proposed model with fixed weights approach outperformed the adaptive weights initialization with in relatively minimal number of training epochs. These findings contribute to the development of robust and efficient models for articulatory feature prediction, paving the way for advancements in speech production research and applications. Show more

Keywords: Acoustic-to-articulatory inversion, smoothing techniques, articulatory features, weight initialization, bidirectional long short-term memory

DOI: 10.3233/JIFS-219386

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Sheshadri, Shailashree K. | Gupta, Deepa

Article Type: Research Article

Abstract: Non-Autoregressive Machine Translation (NAT) represents a groundbreaking advancement in Machine Translation, enabling the simultaneous prediction of output tokens and significantly boosting translation speeds compared to traditional auto-regressive (AR) models. Recent NAT models have adeptly balanced translation quality and speed, surpassing their AR counterparts. The widely employed Knowledge Distillation (KD) technique in NAT involves generating training data from pre-trained AR models, enhancing NAT model performance. While KD has consistently proven its empirical effectiveness and substantial accuracy gains in NAT models, its potential within Indic languages has yet to be explored. This study pioneers the evaluation of NAT model performance for Indic …languages, focusing mainly on Kashmiri to English translation. Our exploration encompasses varying encoder and decoder layers and fine-tuning hyper-parameters, shedding light on the vital role KD plays in facilitating NAT models to capture variations in output data effectively. Our NAT models, enhanced with KD, exhibit sacreBLEU scores ranging from 16.20 to 22.20. The Insertion Transformer reaches a SacreBLEU of 22.93, approaching AR model performance. Show more

Keywords: Neural machine translation, auto-regressive translation, non-autoregressive translation, Levenshtein Transformer, insertion transformer, knowledge distillation

DOI: 10.3233/JIFS-219383

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Cruz, Elsy | Santos, Lourdes | Calvo, Hiram | Anzueto-Rios, Álvaro | Villuendas-Rey, Yenny

Article Type: Research Article

Abstract: In recent years, multiple studies have highlighted the growing correlation between breast density and the risk of developing breast cancer. In this research, the performance of two convolutional neural network architectures, VGG16 and VGG19, was evaluated for breast density classification across three distinct scenarios aimed to compare the masking effect on the models performance. These scenarios encompass both binary classification (fatty and dense) and multi-class classification based on the BI-RADS categorization, utilizing a subset of the ABC-Digital Mammography Dataset. In the first experiment, focusing on cases with no masses, VGG16 achieved an accuracy of 93.33% and 90.00% for two and …four-class classification. The second experiment, which involved cases with benign masses, yielded a remarkable accuracy of 95.83% and 93.33% with VGG16, respectively. In the third and last experiment, an accuracy of 88.00% was obtained using VGG16 for the two-class classification, while VGG19 delivered an accuracy of 93.33% for the four-class classification. These findings underscore the potential of deep learning models in enhancing breast density classification, with implications for breast cancer risk assessment and early detection. Show more

Keywords: Mammography, breast tissue density, convolutional neural networks

DOI: 10.3233/JIFS-219378

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024

Authors: Zheng, Z. | Gao, J.B. | Weng, Z.

Article Type: Research Article

Abstract: The body size parameter of cattle is an important index reflecting the growth and development and health condition of cattle. The traditional manual contact measurement is not only a large workload and difficult to measure, but also prone to problems such as affecting the normal life habits of cattle. In this paper, we address this problem by proposing a contactless body size measurement method for cattle based on machine vision. Firstly, the cattle is confined to a fixed space using a position-limiting device, and images of the body of the cattle are taken from three directions: top, left, and right, …using multiple cameras. Secondly, the image is segmented using a fuzzy clustering algorithm based on neighborhood adaptive local spatial information improvement, and the image is processed to extract the contour images of the top view and side view. The key points of body measurements were extracted using interval division and curvature calculation for the side view images, and the key point information was extracted using skeleton extraction and pruning for the top view images, which realized the measurements of body height(BH), rump height(RH), body slanting length(BSL), and abdominal circumference(AC) parameters of the cattle. The correlation between body size and weight data obtained by contactless methods was investigated and the modeled using one-factor linear regression, one-factor nonlinear regression, multivariate stepwise regression, RBF network fitting, BP neural network fitting, support vector machine, and particle swarm optimization-based support vector machine methods, respectively. Information on body size parameters was collected from 137 cattles, and the results showed that the maximum errors between the measured and actual values of BH, RH, BSL and AC were 5.0%, 4.4%, 3.6%, and 5.5%, respectively. Correlation of BH, RH, BSL and AC with weight obtained by non-contact methods was >  0.75. The BH parameter can be selected in the single-factor growth monitoring. The multi-body scale can reflect the growth status of cattle more comprehensively, in which RH, BSL and AC are important detection parameter; the multi-factor nonlinear model can reflect the growth characteristics of cattle more comprehensively. The contactless measurement method proposed in the paper can effectively improve the work efficiency and reduce the stress reaction of cattle, which is a long-term and effective monitoring method, and is of great significance in promoting accurate and welfare cattle rearing. Show more

Keywords: Image processing, body size measurement, fuzzy clustering, non-contact measurement, cattle weight estimation

DOI: 10.3233/JIFS-238016

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Vidhya, S.S. | Mathi, Senthilkumar | Anantha Narayanan, V. | Neelakanta Iyer, Ganesh

Article Type: Research Article

Abstract:  The Internet of Things lies in establishing low-power and lossy networks created by interconnecting many wireless devices with limited resources. Fascinatingly, an IPv6 routing protocol for low-power and lossy networks has become a common practice for these applications. Even though this protocol addresses the challenges of low-power networks, many issues concerning the quality of service and energy consumption are open to the research community. The protocol relies on a destination-oriented directed acyclic graph, and the root selection depends on some constraints and metrics associated with an objective function (OF). The conventional OFs select parents based on a single metric, such …as the expected transmission count or the number of nodes to travel. The current paper proposes an enhancement to the OF metric, aiming to decrease node energy and enhance the quality of service. This improvement is achieved by the factors, including the received signal strength indicator, node distance, power, link quality indicator, and expected transmission count, to select reliable communication links. The minimum power needed for reliable communication is predicted from the received signal strength indicator, node distance, receiver power, and link quality indicator using a nonlinear support vector machine. The OF value of the candidate node is computed from the power level and expected transmission count combined using the Takagi-Sugeno fuzzy model. The proposed OF is implemented in the Cooja simulator and compared against minimum rank with hysteresis OF and OF zero. A considerable improvement in the packet delivery ratio and a 37.5% reduction in energy consumption is obtained. Show more

Keywords: Classification, fuzzification, power prediction, received signal strength indicator, transmission power, link quality indicator, low power networks, TSK fuzzy model

DOI: 10.3233/JIFS-219420

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-11, 2024

Authors: Mathi, Senthilkumar | Ramalingam, Venkadeshan | Sree Keerthi, Angara Venkata | Abhirup, Kothamasu Ganga | Sreejith, K. | Dharuman, Lavanya

Article Type: Research Article

Abstract: Long-term evolution in wireless broadband communication aims to provide secure communication for users and a high data rate for a fourth-generation network. Even though the fourth-generation network provides security, some loopholes lead to several attacks on the fourth-generation network attacks. The denial-of-service attack occurs when the user communicates with a rogue base station, and the radio base station in fourth-generation long-term evolution networks ensures that the user is attached to the rogue node assigned network. The location leak attack occurs when the packets are sniffed to find any user’s location using its temporary mobile subscriber identity. Prevention of rogue base …station and location leak attacks helps the system achieve secure communication between the participating entities. Earlier works in long-term evolution mobility management do not address preventing attacks such as denial-of-service, rogue base stations and location leaks and suffer from computational costs while providing security features. Hence, the present paper addresses the vulnerability of these attacks. It also investigates how these attacks occur and exposes communication in the fourth-generation network. To mitigate these vulnerabilities, the paper proposes a novel authentication scheme. The proposed scheme is simulated using Network Simulator 3, and the security analysis of the proposed scheme is shown using AVISPA –a security tool. Numerical analysis demonstrates that the proposed scheme significantly reduces communication overhead and computational costs associated with the fourth-generation long-term evolution authentication mechanism. Show more

Keywords: Authentication, long-term evolution, denial-of-service, attack, location leak, confidentiality

DOI: 10.3233/JIFS-219406

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2024

Authors: Zheng, Lina | Wang, Yini | Wang, Sichun

Article Type: Research Article

Abstract: Due to the relatively high cost of labeling data, only a fraction of the available data is typically labeled in machine learning. Some existing research handled attribute selection for partially labeled data by using the importance of an attribute subset or uncertainty measure (UM). Nevertheless, it overlooked the missing rate of labels or the choice of the UM with optimal performance. This study uses discernibility relation and the missing rate of labels to UM for partially labeled data and applies it to attribute selection. To begin with, a decision information system for partially labeled data (pl-DIS) can be used to …induce two equivalent decision information systems (DISs): a DIS is constructed for labeled data (l-DIS), and separately, another DIS is constructed for unlabeled data (ul-DIS). Subsequently, a discernibility relation and the percentage of missing labels are introduced. Afterwards, four importance of attribute subset are identified by taking into account the discernibility relation and the missing rate of labels. The sum of their importance, which is determined by the label missing rates of two DISs, is calculated by weighting each of them and adding them together. These four importance may be seen as four UMs. In addition, numerical simulations and statistical analyses are carried out to showcase the effectiveness of four UMs. In the end, as its application for UM, the UM with optimal performance is used to attribute selection for partially labeled data and the corresponding algorithm is proposed. The experimental outcomes demonstrate the excellence of the proposed algorithm. Show more

Keywords: Partially labeled data, pl-DIS, uncertainty measure, attribute selection, the missing rate of labels, discernibility relation

DOI: 10.3233/JIFS-240581

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-18, 2024

Authors: Rao, Vishisht Srihari | Vinay, P. | Uma, D.

Article Type: Research Article

Abstract: A hazy image is characterized by atmospheric conditions that reduce the image’s clarity and contrast, thereby making it less visible. This degradation in image quality can hinder the performance of advanced computer vision tasks such as object detection and identifying open spaces which need to perform with high accuracy in important real world applications such as security surveillance and autonomous driving. In the recent past, the use of deep learning in image processing tasks have shown a remarkable improvement in performance, in particular, Convolutional Neural Networks (CNNs) perform superior to any other type of neural network in image related tasks. …In this paper, we propose the addition of Channel Attention and Pixel Attention layers to four state-of-the-art CNNs, namely, GMAN, U-Net, 123-CEDH and DMPHN, used for the task of image dehazing. We show that the addition of these layers yields a non-trivial improvement on the quality of the dehazed images which we show qualitatively with examples and quantitatively by obtaining PSNR and SSIM scores of 28.63 and 0.959 respectively. Through the experiments, we show that the addition of the mentioned attention layers to the GMAN architecture yields the best results. Show more

Keywords: Dehazing, deep neural network, convolutional neural network, attention

DOI: 10.3233/JIFS-219391

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Agrawalla, Bikash | Shukla, Alok Kumar | Tripathi, Diwakar | Singh, Koushlendra Kumar | Ramachandra Reddy, B.

Article Type: Research Article

Abstract: Software fault prediction, which aims to find and fix probable flaws before they appear in real-world settings, is an essential component of software quality assurance. This article provides a thorough analysis of the use of feature ranking algorithms for successful software failure prediction. In order to choose and prioritise the software metrics or qualities most important to fault prediction models, feature ranking approaches are essential. The proposed focus on applying an ensemble feature ranking algorithm to a specific software fault dataset, addressing the challenge posed by the dataset’s high dimensionality. In this extensive study, we examined the effectiveness of multiple …machine learning classifiers on six different software projects: jedit, ivy, prop, xerces, tomcat, and poi, utilising feature selection strategies. In order to evaluate classifier performance under two scenarios—one with the top 10 features and another with the top 15 features—our study sought to determine the most relevant features for each project. SVM consistently performed well across the six datasets, achieving noteworthy results like 98.74% accuracy on “jedit” (top 10 features) and 91.88% on “tomcat” (top 10 features). Random Forest achieving 89.20% accuracy on the top 15 features, on “ivy.” In contrast, NB repeatedly recording the lowest accuracy rates, such as 51.58% on “poi” and 50.45% on “xerces” (the top 15 features). These findings highlight SVM and RF as the top performers, whereas NB was consistently the least successful classifier. The findings suggest that the choice of feature ranking algorithm has a substantial impact on the fault prediction models’ predictive accuracy and effectiveness. When using various ranking systems, the research also analyses the trade-offs between computing complexity and forecast accuracy. Show more

Keywords: Software fault prediction, ensemble techniques, feature ranking, random forests, support vector machine

DOI: 10.3233/JIFS-219431

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Su, Xue | Chen, Lijun

Article Type: Research Article

Abstract: Incomplete real-valued data often misses some labels due to the high cost of labeling data. This paper investigates for partially labeled incomplete real-valued data and considers its application in semi-supervised attribute reduction. There are two decision information systems (DISs) in a partially labeled incomplete real-valued data DIS (p-IRVDIS): a labeled incomplete real-valued data DIS (l-IRVDIS) and a unlabeled incomplete real-valued data DIS (u-IRVDIS). The degree of importance on an attribute subset in a p-IRVDIS are defined using an indistinguishable relation and conditional information entropy. It is the weighted sum of l-IRVDIS and u-IRVDIS using the missing rate of label to …measure p-IRVDIS uncertainty. Based on the degree of importance, an adaptive semi-supervised attribute reduction algorithm in a p-IRVDIS is proposed. This algorithm can automatically adapt to various missing rates of label. The experimental results on 8 datasets reveal that the proposed algorithm performs statistically better than some state-of-the-art algorithms. Show more

Keywords: p-IRVDIS, the degree of importance, semi-supervised attribute reduction, indiscernibility relation, conditional information entropy

DOI: 10.3233/JIFS-239559

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2024

Authors: Tahir Kidwai, Umar | Akhtar, Nadeem | Nadeem, Mohammad | Alroobaea, Roobaea Salim

Article Type: Research Article

Abstract: In recent years, the surge in online content has necessitated the development of intelligent recommender systems capable of offering personalized suggestions to users. However, these systems often encapsulate users within a “filter bubble”, limiting their exposure to a narrow range of content. This study introduces a novel approach to address this issue by integrating a novel diversity module into a knowledge graph-based explainable recommender system. Utilizing the Movie Lens 1M dataset, this research pioneers in fostering a more nuanced and transparent user experience, thereby enhancing user trust and broadening the spectrum of recommendations. Looking ahead, we aim to further refine …this system by incorporating an explicit feedback loop and leveraging Natural Language Processing (NLP) techniques to provide users with insightful explanations of recommendations, including a comprehensive analysis of filter bubbles. This initiative marks a significant stride towards creating a more inclusive and informed recommendation landscape, promising users not only a wider array of content but also a deeper understanding of the recommendation mechanisms at play. Show more

Keywords: Recommender system, explainable recommendations, filter bubble, knowledge graph, diversity

DOI: 10.3233/JIFS-219416

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2024

Authors: Li, Xin | Hao, Miao | Ru, Changhai | Wang, Yong | Zhu, Junhui

Article Type: Research Article

Abstract: With the development of science and technology, people have higher and higher requirements for robots. The application of robots in industrial production is also increasing, and there are more applications in people’s lives. Therefore, robots must have a better ability to receive and process the external environment. Therefore, visual servo system appears. Pose estimation is a major problem in the current vision system. It has great application value in positioning and navigation, target tracking and recognition, virtual reality and motion estimation. Therefore, this paper put forward the research of robot arm pose estimation and control based on machine vision. This …paper first analyzed the technology of machine vision, and then carried out experiments. The accuracy and stability of the two methods for robot arm pose estimation were compared. The experimental results showed that when the noise of Kalman’s centralized data fusion method was 1 pixel, the maximum error of the X-axis angle was only 0.55, and the average error was 0.02. In Kalman’s distributed data fusion method, the average error of X-axis displacement was 0.06, and the maximum value was 17.66. In terms of accuracy, Kalman’s centralized data fusion method was better. In terms of stability, Kalman’s centralized data fusion method was also better. However, in general, these two methods had very good results, and could accurately control the position and posture of the manipulator. Show more

Keywords: Position and attitude estimation of manipulator, machine vision, kalman filter, world coordinate system

DOI: 10.3233/JIFS-237904

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Wang, Wei | Xu, Dehao | Lv, Jing | Rong, Jian | He, Donggang | Li, Shuangshuang

Article Type: Research Article

Abstract: The factors of water quality in the intensive marine stichopus japonicus aquaculture process are changing with seasons, so water temperature, salinity, pH value and nitrite were selected as auxiliary variables to measure the concentration of ammonia nitrogen. FCM (Fuzzy C-means) algorithm was adopted to classify them. Based on the EM (Expectation Maximization) algorithm, fuzzy sub-models of ammonia nitrogen concentration were constructed around each operating point, and finally the fuzzy sub-models were combined according to the posterior distribution of the characteristics of the sampling data. Based on the data collected at Xinyulong Marine Biological Seed Technology Co., Ltd, in Dalian China, …the ammonia nitrogen concentration prediction model was tested and verified. Show more

Keywords: Water quality, stichopus japonicus, expectation maximization, multi-model

DOI: 10.3233/JIFS-239032

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Shuangyuan, Li | Qichang, Li | Mengfan, Li | Yanchang, Lv

Article Type: Research Article

Abstract: With the development of information technology, the number and methods of cyber attacks continue to increase, making network security issues increasingly important. Intrusion detection has become a vital means of dealing with cyber threats. Current intrusion detection methods predominantly rely on machine learning. However, machine learning suffers from limitations in detection capability and the requirement for extensive feature engineering. Additionally, current intrusion detection datasets face the challenge of data imbalance. To address these challenges, this paper proposes a novel solution leveraging Generative Adversarial Networks (GANs) to balance the dataset and introduces an attention mechanism into the generator to efficiently extract …key feature information, the mechanism can effectively sort the key information of the data and quickly capture important features. Subsequently, a combination of 1D Convolutional Neural Networks (1DCNN) and Bidirectional Gated Recurrent Units (BiGRU) is employed to construct a classification model capable of extracting both spatial and temporal features. Furthermore, Particle Swarm Optimization (PSO) is utilized to optimize the input weights and hidden biases of the model, so as to further improve the accuracy and robustness of the model. Finally, the model is trained and implemented for network intrusion detection. To demonstrate the applicability of the model, experiments were conducted using the NSL-KDD dataset and the UNSW-NB15 dataset. The final results showed that the proposed model outperformed other models, achieving accuracies of 99.15% and 97.33% on the respective datasets. This indicates that the model improves the efficiency of network intrusion detection and better ensures the effectiveness of network security. Show more

Keywords: Intrusion detection, GAN, 1DCNN, BiGRU, PSO

DOI: 10.3233/JIFS-236285

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2024

Authors: Liu, Xia | Zhang, Xianyong | Chen, Jiaxin | Chen, Benwei

Article Type: Research Article

Abstract:  Attribute reduction is an important method in data analysis and machine learning, and it usually relies on algebraic and informational measures. However, few existing informational measures have considered the relative information of decision class cardinality, and the fusion application of algebraic and informational measures is also limited, especially in attribute reductions for interval-valued data. In interval-valued decision systems, this paper presents a coverage-credibility-based condition entropy and an improved rough decision entropy, further establishes corresponding attribute reduction algorithms for optimization and applicability. Firstly, the concepts of interval credibility, coverage and coverage-credibility are proposed, and thus, an improved condition entropy is defined …by virtue of the integrated coverage-credibility. Secondly, the fused rough decision entropy is constructed by the fusion of improved condition entropy and roughness degree. By introducing the coverage-credibility, the proposed uncertainty measurements enhance the relative information of decision classes. In addition, the nonmonotonicity of the improved condition entropy and rough decision entropy is validated by theoretical proofs and experimental counterexamples, with respect to attribute subsets and thresholds. Then, the two rough decision entropies drive monotonic and nonmonotonic attribute reductions, and the corresponding reduction algorithms are designed for heuristic searches. Finally, data experiments not only verify the effectiveness and improvements of the proposed uncertainty measurements, but also illustrate the reduction algorithms optimization through better classification accuracy than four comparative algorithms. Show more

Keywords: Rough sets, Attribute reduction, Interval-valued decision systems, Algebraic measures and informational measures, Coverage-credibility-based rough decision entropy

DOI: 10.3233/JIFS-239544

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Tian, Wen | Zhang, Yining | Fang, Qin | Liu, Weidong

Article Type: Research Article

Abstract: In order to solve the problem of imbalance between traffic demand and airspace capacity of high-altitude air route network, reduce unnecessary delay costs, and improve air route operation efficiency, the resource allocation problem of multi-objective air route network for CTOP program is studied. Taking the affected flights in the congested area of air routes as the research object, taking into account the constraints of actual flight operation, FCA time slot resource availability limit, FCA capacity limit, etc., aiming at minimizing the total delay time of each flight and maximizing the fairness of airlines, a multi-objective optimization model for air route …network resource allocation is established, and an improved NSGA-II algorithm is designed to solve the model. Based on the actual operation data of air routes in East China, the Pareto optimal solution set is obtained and compared with the traditional RBS algorithm, the average delay time is reduced by 5.49% and the average fair loss degree is reduced by 66.76%. The results show that the proposed multi-objective optimization model and the improved NSGA-II algorithm have better performance, which can take into account the fairness of each airline on the basis of reducing the total delay cost, realize the allocation of optimal flight trajectories and time slot resources, and provide a reference scheme for air traffic control resource scheduling. Show more

Keywords: Air traffic flow management, resource allocation, collaborative trajectory options program (CTOP), multi-objective optimization, genetic algorithm

DOI: 10.3233/JIFS-233588

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Gong, Zengtai | Zhang, Yuanyuan

Article Type: Research Article

Abstract: In this paper, we focus on generalized fuzzy complex numbers and propose a straightforward matrix method to solve the dual rectangular fuzzy complex matrix equations C · Z ˜ + L ˜ = R · Z ˜ + W ˜ , in which C and R are crisp complex matrices and Z ˜ , L ˜ and M ˜ …are fuzzy complex number matrices. The existing methods for solving fuzzy complex matrix equations involve separately calculating the extended solution and the corresponding parameters of the real and imaginary parts, whereby we obtain the algebraic solution of the equations. By means of the interval arithmetic and embedding approach, the n  × n dual rectangular fuzzy complex linear systems could be converted into 2n  × 2n fuzzy linear systems, which are also equivalent to the 4n  × 4n real linear systems. By directly solving the 4n  × 4n real linear systems, the algebraic solutions can be obtained. The general dual rectangular fuzzy complex matrix equations and dual rectangular fuzzy complex linear systems are investigated by the generalized inverses of matrices. Finally, some examples are given to illustrate the effectiveness of method. Show more

Keywords: Fuzzy number, fuzzy complex number, rectangular fuzzy complex number, dual rectangular fuzzy complex matrix equations

DOI: 10.3233/JIFS-239305

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-21, 2024

Authors: Aguilar-Canto, Fernando | Luján-García, Juan Eduardo | Espinosa-Juárez, Alberto | Calvo, Hiram

Article Type: Research Article

Abstract: Inferring phylogenetic trees in human populations is a challenging task that has traditionally relied on genetic, linguistic, and geographic data. In this study, we explore the application of Deep Learning and facial embeddings for phylogenetic tree inference based solely on facial features. We use pre-trained ConvNets as image encoders to extract facial embeddings and apply hierarchical clustering algorithms to construct phylogenetic trees. Our methodology differs from previous approaches in that it does not rely on preconstructed phylogenetic trees, allowing for an independent assessment of the potential of facial embeddings to capture relationships between populations. We have evaluated our method with …a dataset of 30 ethnic classes, obtained by web scraping and manual curation. Our results indicate that facial embeddings can capture phenotypic similarities between closely related populations; however, problems arise in cases of convergent evolution, leading to misclassifications of certain ethnic groups. We compare the performance of different models and algorithms, finding that using the model with ResNet50 backbone and the face recognition module yields the best overall results. Our results show the limitations of using only facial features to accurately infer a phylogenetic tree and highlight the need to integrate additional sources of information to improve the robustness of population classification. Show more

Keywords: Convolutional neural networks, deep learning, hierarchical clustering, phylogenetic tree

DOI: 10.3233/JIFS-219343

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-09, 2024

Authors: Li, Yuangang | Gao, Xinrui | Ni, Hongcheng | Song, Yingjie | Deng, Wu

Article Type: Research Article

Abstract: In this paper, an adaptive differential evolution algorithm with multi-strategy, namely ESADE is proposed to solve the premature convergence and high time complexity for complex optimization problem. In the ESADE, the population is divided into several sub-populations after the fitness value of each individual is sorted. Then different mutation strategies are proposed for different populations to balance the global exploration and local optimization. Next, a new self-adaptive strategy is designed adjust parameters to avoid falling into local optimum while the convergence accuracy has reached its maximum value. And a complex airport gate allocation multi-objective optimization model with the maximum flight …allocation rate, the maximum near gate allocation rate, and the maximum passenger rate at near gate is constructed, which is divided into several single-objective optimization model. Finally, the ESADE is applied solve airport gate allocation optimization model. The experiment results show that the proposed ESADE algorithm can effectively solve the complex airport gate allocation problem and achieve ideal airport gate allocation results by comparing with the current common heuristic optimization algorithms. Show more

Keywords: Differential evolution, multi-strategy, self-adaptive strategy, gate allocation, optimization

DOI: 10.3233/JIFS-238217

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Sowndeswari, S. | Kavitha, E. | Krishnamoorthy, Raja

Article Type: Research Article

Abstract: The development of tiny sensing nodes efficient for wireless communication in Wireless Sensor Networks (WSNs) can be attributed to the rapid advancements in processors and radio technology. Data transmission occurs through multi-hop routing in WSN, which relies on nodes’ cooperation. The collaboration between nodes has rendered these networks susceptible to various attacks. It is imperative to employ a security scheme to evaluate the dependability of nodes in distinctive malicious nodes from non-malicious nodes. In recent years, there has been a growing significance placed on security-based routing protocols with energy constraints as valuable mechanisms for enhancing the security and performance of …WSNs. A novel solution called the Deep Learning-based Hybrid Energy Efficient and Security System (DL-HE2S2) is introduced to address these challenges. The research workflow encompasses various essential stages, namely the deployment of nodes, the creation of clusters, the selection of cluster heads, the detection of malevolent nodes within each group, and the determination of optimal paths intra- and inter-clusters employing the routing algorithm for efficient packet transmission. The design of the algorithm is focused on achieving energy efficiency and enhancing network security while also taking into account various performance metrics, including a mean network lifetime of 187.244 hours, a throughput of 59.88 kilobits per second, an end-to-end latency of 11.939 milliseconds, a packet loss of 14.9%, a packet delivery ratio of 99.194%, network security at 92.026%, and energy usage of 19.424 J. This research examines the algorithm’s scalability and efficiency across various network sizes using a Network Simulator (NS-2). DL-HE2S2 offers valuable insights that can be applied to practical implementations in multiple applications. Show more

Keywords: Wireless sensor networks, energy efficiency, secured routing, cluster

DOI: 10.3233/JIFS-235322

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Xu, Liwen | Chen, Jiali

Article Type: Research Article

Abstract: Node classification in graph learning faces significant challenges due to imbalanced data, particularly for under-represented samples from minority classes. To address this issue, existing methods often rely on synthetic minority over-sampling techniques, introducing additional complexity during model training. In light of the challenges faced, we introduce GraphECC, an innovative approach that addresses numerical anomalies in large-scale datasets by supplanting the traditional CE loss function with an Enhanced Complementary Classifier (ECC) loss function’a novel modification to the CCE loss. This alteration ensures computational stability and mitigates potential numerical anomalies by incorporating a slight offset in the denominator during the computation of …the complementary probability distribution. In this paper, we present a novel training paradigm, the Enhanced Complementary Classifier (ECC), which offers “imbalance defense for free” without the need for extra procedures to improve node classification accuracy.The ECC approach optimizes model probabilities for the ground-truth class, akin to the cross-entropy method. Additionally, it effectively neutralizes probabilities associated with incorrect classes through a “guided” term, achieving a balanced trade-off between the two aspects. Experimental results demonstrate that our proposed method not only enhances model robustness but also surpasses the widely used cross-entropy training objective.Moreover, we demonstrate the versatility of our method by seamlessly integrating it with various well-known adversarial training techniques, resulting in significant gains in robustness. Notably, our approach represents a breakthrough, as it enhances model robustness without compromising performance, distinguishing it from previous attempts.The code for GraphECC can be accessed from the following link:https://github.com/12chen20/GraphECC . Show more

Keywords: Imbalanced node classification, trade-off optimization, enhanced complementary classifier (ECC), graph learning, minority classes

DOI: 10.3233/JIFS-239663

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Ali, Zeeshan | Yin, Shi | Yang, Miin-Shen

Article Type: Research Article

Abstract: In the context of fuzzy relations, symmetry refers to a property where the relationship between two elements remains the same regardless of the order in which they are considered. Natural language processing (NLP) in engineering documentation discusses the application of computational methods or techniques to robotically investigate, analyze, and produce natural language information for manufacturing contents. The NLP plays an essential role in dealing with large amounts of textual data normally recovered in engineering documents. In this paper, we expose the idea of a bipolar complex hesitant fuzzy (BCHF) set by combining the bipolar fuzzy set (BFS) and the complex …hesitant fuzzy set (CHFS). Further, we evaluate some algebraic and Schweizer-Sklar operational laws under the presence of BCHF numbers (BCHFNs). Additionally, using the above information as well as the idea of prioritized (PR) operators, we derive the idea of BCHF Schweizer-Sklar PR weighted averaging (BCHFSSPRWA) operator, BCHF Schweizer-Sklar PR ordered weighted averaging (BCHFSSPROWA) operator, BCHF Schweizer-Sklar PR weighted geometric (BCHFSSPRWG) operator, and BCHF Schweizer-Sklar PR ordered weighted geometric (BCHFSSPROWG) operator. Basic properties for the above operators are also discussed in detail, such as idempotency, monotonicity, and boundedness. Moreover, we evaluate the best way in which NLP can be applied to engineering documentations with the help of the proposed operators. Therefore, we illustrate the major technique of multi-attribute decision-making (MADM) problems based on these derived operators. Finally, we use some existing operators and try to compare their ranking results with our proposed ranking results to show the supremacy and validity of the investigated theory. Show more

Keywords: Fuzzy set (FS), hesitant FS, bipolar complex hesitant FS, Schweizer-Sklar prioritized aggregation operators, natural language processing, multi-attribute decision-making

DOI: 10.3233/JIFS-240116

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-27, 2024