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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: Li, Tao | Liu, C. | Qu, Xingle | Guo, Linjia | Fang, Jiangping
Article Type: Research Article
Abstract: The conventional evaluation methods for the state of agricultural environmental geological system mainly use the support vector regression (SVR) model to process the evaluation samples, which is vulnerable to the influence of the sensitive loss function, resulting in the high difference of the evaluation entropy. Therefore, a new evaluation method for the state of agricultural environmental geological system needs to be designed based on the optimized particle swarm optimization algorithm. That is to say, combining with the evolution process of regional agricultural environmental geology, the accurate state evaluation target is selected, the state evaluation system of agricultural environmental geology system …is constructed, and the state evaluation model of agricultural environmental geology system is designed combined with the optimized particle swarm optimization algorithm, so as to complete the state evaluation of geological system. The results demonstrated the suggested methodology assesses the state of an agricultural environmental geological system. Key factors included soil texture (0.254), soil nutrient (0.118), and soil pH (0.256). It showed that the designed evaluation method of agricultural environmental geological system state based on optimized particle swarm optimization algorithm has good evaluation effect, reliability and certain application value, and has made certain contributions to the formulation of reasonable agricultural ecological protection scheme. Show more
Keywords: Optimized particle swarm optimization, agriculture, environmental science, geology, system, status, evaluation method
DOI: 10.3233/JIFS-236184
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3569-3576, 2024
Authors: Rajalakshmi, K. | Priyan, S. Vishnu | Inbakumar, J. Parivendhan | Kumar, C.
Article Type: Research Article
Abstract: The distribution system plays a pivotal role in connecting power generation sources to vital facilities like nuclear reactors. In this intricate network, losses occur while supplying electricity, demanding a reduction for enhanced performance. The quality of power reaching the nuclear plant is imperative due to the susceptibility of sensitive equipment to poor power conditions. This study presents a reconfiguration strategy to bolster dependability and curtail power losses in distribution networks. Leveraging the Modified Genetic Optimization Algorithm (MGOA), the reconfiguration conundrum is tactfully addressed to determine optimal switch operation schemes. The MGOA-based reconfiguration not only minimizes energy wastage but also refines …voltage profiles, elevating operational efficiency. The effectiveness of this approach is substantiated through its successful application to radial distribution systems comprising 33, 69, and 136 buses. Embracing diverse scenarios encompassing normal and abnormal operating states, as well as varying loads, the method’s robustness is showcased. The validity of the proposed methodology is reinforced by comprehensive simulation results, underscoring its reliability and potential for real-world implementation. Show more
Keywords: Distribution network reconfiguration, genetic algorithm firework algorithm, runner-root model, fuzzy shuffled frog-leaping algorithm, grey wolf optimizer and PSO method
DOI: 10.3233/JIFS-233917
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3577-3591, 2024
Authors: He, Jianshe | Chen, Zhong
Article Type: Research Article
Abstract: Dynamical systems that exhibit a high degree of sensitivity to the parameters of their initial states are referred to as chaotic. Natural selection and the process of evolution are the models that inspire a group of optimization algorithms collectively referred to as evolutionary algorithms (EA). EA is quite beneficial when handling difficult optimization difficulties, especially in situations where traditional procedures are either not practical or insufficient. The resolution of goal conflicts is accomplished through multi-objective optimization (MOO). The study proposed using chaotic systems and evolutionary algorithms to address the issue of multi-objective optimization.An initially chaotic time series of wind speed …predictions was gathered from three locations in Penglai, China. The preprocessing of these data was carried out using Z-score normalization. We suggested using multi-objective particle swarm optimization (MOPSO) to gather information. Before the suggested design can be applied to the MOPSO of the chaotic system itself, it is required to evaluate the architecture of the proposed that will be utilized, the functioning of the chaotic systems, and the problems in the design of the system. Studies using currently available methods demonstrate that the proposed method outperforms all parameter measurements in terms of 15bits of throughput, active power loss 6.4812 MVA, 0.6495 voltages, 6.8% of RMSE, 0.8% of MAPE, and 0.1 sec of time. The finding of combining evolutionary algorithms with chaotic systems yields a powerful and effective framework for addressing multi-objective optimization problems, which bodes well for practical implementations in fields like building design, economics, and time management. Show more
Keywords: Multi-objective optimization (MOO), problem-solving, Z-score normalization, particle swarm optimization (PSO), chaotic systems
DOI: 10.3233/JIFS-236000
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3593-3603, 2024
Authors: Zhang, Zhao Zhao | Pan, Hao Ran | Zhu, Ying Qin
Article Type: Research Article
Abstract: Modular neural networks (MNNs) have garnered substantial attention in the field of nonlinear system modeling. However, even though MNNs require fewer hyperparameters due to their hierarchical structure compared to traditional NNs, determining the optimal module arrangement remains challenging. To address these issues, a novel approach named fuzzy modular neural networks (FMNN) is introduced. This method employs conditional fuzzy clustering and incremental radial basis function (RBF) neural networks to automatically construct sub-modules within the MNN framework. The resultant sub-modules are chosen utilizing a distance-based fuzzy integrative strategy, effectively diminishing the necessity for manual intervention. To showcase the superiority of the FMNN …approach, a series of experiments are carried out employing three benchmark examples. These experiments encompass a comparison of modeling accuracy against other extensively employed neural network models. The experimental findings illustrate that FMNN surpasses alternative neural network models in terms of model precision. Show more
Keywords: Automated modeling, fuzzy clustering, modular neural network, radial basis function network
DOI: 10.3233/JIFS-232396
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3605-3621, 2024
Authors: Zhang, Jiarui | Ling, Bingo Wing-Kuen
Article Type: Research Article
Abstract: The patients with the nasopharyngeal cancer are required to breath through their mouth after performing the surgery. Hence, it is required to perform the breathing site classification and employs the classification results to indicate whether the patients breath correctly or not. Nevertheless, there is currently no such a medical aided tool in the market. To address this issue, this paper extracts both the mel frequency cepstral coefficients (MFCCs) based features and the gammatone frequency cepstral coefficients (GFCCs) based features as well as employs the random forest as the classifier for performing the breathing site classification. The data lasted for a …few minutes acquired from 10 volunteers are employed to demonstrate the effectiveness of our proposed method. The computer numerical simulation results show that the average accuracy, the average specificity and the average sensitivity yielded by our proposed method are 95.30±2.00%, 93.27±3.87% and 97.15±1.87%, respectively. Although this paper proposes a method based on the fusion of two types of the acoustic features for classifying different breathing sites, the computer numerical simulation results show that our proposed method outperforms the common respiration or speech processing based methods. Besides, our proposed method is also compared to a series of relevant methods. It is found that our proposed method achieves the highest classification results at the majority signal to noise ratios among the state of the arts methods. Show more
Keywords: Nasopharyngeal cancer, mel frequency cepstral coefficients, gammatone frequency cepstral coefficients, random forest, breathing site classification
DOI: 10.3233/JIFS-235446
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3623-3634, 2024
Authors: Mukiri, RajaKumari | Burra, Vijaya Babu
Article Type: Research Article
Abstract: The convergence of healthcare and deep learning has engendered transformative solutions for myriad medical challenges. Amid the COVID-19 pandemic, innovative strategies are imperative to mitigate the propagation of misinformation and myths, which can exacerbate the crisis. This study embarks on a pioneering research quest, harnessing advanced deep learning methodologies, including the novel Vision Transformer (ViT) model and state-of-the-art (SOTA) models, to predict and quell the dissemination of rumors within the COVID-19 milieu. By synergizing the capabilities of Vision Transformers (ViTs) with cutting-edge SOTA models, the proposed approach strives to elevate the precision of information disseminated through traditional and digital media …platforms, thereby cultivating informed decision-making and public awareness. Central to this inquiry is the development of a bespoke vision transformer architecture, adeptly tailored to scrutinize CT images associated with COVID-19 cases. This model adeptly captures intricate patterns, anomalies, and features within the images, facilitating precise virus detection. Extending beyond conventional methodologies, the model adroitly harnesses the scalability and hierarchical learning intrinsic to deep learning frameworks. It delves into spatial relationships and finer intricacies within CT scans. An extensive dataset of COVID-19-related CT images, encompassing diverse instances, stages, and severities, is meticulously curated to fully exploit the innovative potential of the vision transformer model. Thorough training, validation, and testing refine the model’s predictive prowess. Techniques like data augmentation and transfer learning bolster generalization and adaptability for real-world scenarios. The efficacy of this research is gauged through comprehensive assessments, encompassing sensitivity, specificity, and prediction accuracy. Comparative analyses against existing methods underscore the superior performance of the novel model, highlighting its transformative influence on predicting and mitigating rumor propagation during the COVID-19 pandemic. Enhanced interpretability sheds light on the decision-making process, augmenting the model’s utility within real-world decision support systems. By harnessing the transformative capabilities of vision transformers and synergizing them with advanced SOTA models, this study offers a robust solution to counter the dissemination of misinformation during the pandemic. The model’s proficiency in discerning intricate patterns in COVID-19-related CT scans signifies a pivotal leap toward combating the infodemic. This endeavor culminates in more precise public health communication and judicious decision-making, ushering in a new era of leveraging cutting-edge deep learning for societal well-being amidst the challenges posed by the COVID-19 era. Show more
Keywords: Healthcare, deep learning, COVID-19, vision transformer, rumor prediction, misinformation
DOI: 10.3233/JIFS-236842
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3635-3648, 2024
Authors: Yamuna, K.S. | Thirunavukkarasu, S. | Manjunatha, B. | Karthikeyan, B.
Article Type: Research Article
Abstract: Lung sound (LS) signals are a vital source of information for the identification of pulmonary disorders. Heart sound (HS) is the most common contaminant of lung sounds during auscultation from the chest walls. This directly affects the efficiency of lung sound processing in diagnosing lung diseases. In this work, Adaptive Variational Mode Decomposition (AVMD) technique is proposed to remove heart sound contaminants from lung sounds. The proposed AVMD method initially breakdown the noisy lung sound signal into a collective of bandlimited modes called variational mode functions (VMF). Then, based on the frequency spectrum, the HS is filtered out from the …LS. The real time lung sound data is collected from 95 participants and the performance of VMD technique is evaluated using the statistical metrics measures. Thus, the proposed topology exhibits Higher SNR (29.6587dB, lowest Root Mean Square (RMSE) of 0.0102, lowest normalized Mean Absolute Error (nMAE) of 0.0336, and highest percentage in correlation coefficient Factor (CCF) of 99.79% respectively. These experimental results are found to be superior and outperform all other recently proposed techniques. Show more
Keywords: Variational mode decomposition (VMD), adaptive VMD, lung sound signals, heart sound signals
DOI: 10.3233/JIFS-231127
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3649-3657, 2024
Authors: Elamrani Abou Elassad, Dauha | Elamrani Abou Elassad, Zouhair | Ed-dahbi, Abdel Majid | El Meslouhi, Othmane | Kardouchi, Mustapha | Akhloufi, Moulay
Article Type: Research Article
Abstract: The concept of endorsing AI in embedded systems is growing in all sectors including the development of Accident Avoidance Systems. Although real-time road crash prediction is vital for enhancing road user safety, there has been limited focus on the analysis of real-time crash events within ensemble and deep learning fused systems. The main aim of this paper is to design an advanced Accident Avoidance System established on a deep learning and ensemble fusion strategy in order to acquire more performant crash predictions. As such, four highly optimized models for crash prediction have been designed based on the popular ensemble techniques: …CatBoost, AdaBoost and Bagging and the deep learning CNN. Additionally, four categories of features, including driver inputs, vehicle kinematics, driver states and weather conditions, were measured during the execution of various driving tasks performed on a driving simulator. Moreover, given the infrequent nature of crash events, an imbalance-control procedure was adopted using the SMOTE and ADASYN techniques. The highest performances results have been acquired using CatBoost along with ADASYN on almost all the adopted metrics during the different weather conditions, and more than 50% of all crashes have occurred in rainy weather conditions, whereas 31% have been exhibited in fog patterns. The sensitivity analysis results indicate that the fusing all the acquired features has the highest impact on the prediction performance. To our knowledge, there has been a limited interest, if not at all, at adopting a fused ensemble deep learning system examining the real-time impact of the adopted features’ combinations on the prediction of road crashes while taking into account class imbalance. The findings provide new insights into crash prediction and emphasize the relevance of the explanatory features which can be endorsed in designing efficient Accident Avoidance Systems. Show more
Keywords: Accident avoidance system, machine learning, class-imbalance, ensemble learning, deep learning, sensitivity analysis
DOI: 10.3233/JIFS-232446
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3659-3676, 2024
Authors: Wang, Jing | Yu, Liying | Rong, Yuan
Article Type: Research Article
Abstract: Quality function deployment (QFD) is a customer-driven product development technique that converts customer requirements (CRs) into design attributes (DAs) of a product and service. Nevertheless, in real situations, the traditional QFD method has been found that possesses some deficiencies, such as the accuracy assessment of relationships between CRs and DAs, and the inter-relationships among DAs. To fill in the above gaps, this study develops a new QFD approach by a CoCoSo-based ranking method under Pythagorean fuzzy environment. To begin with, an extended Pythagorean fuzzy decision-making trial and evaluation laboratory (DEMATEL) method is proposed to identify the relationships within DAs. Second, …the aggregation method of the weighted average method and objective penalty function are propounded to construct the programming models for calculating the importance of DAs under Pythagorean fuzzy setting. Third, a new CoCoSo-based ranking method for Pythagorean triangular fuzzy numbers (PTrFNs) is proposed to obtain the ranking of DAs. Lastly, a case regarding “Ping An Health” mobile medical App is carried out to verify the effectiveness and superiority of the proposed QFD approach. The results show that the top DA is perceptibility. Therefore, perceptibility should be focus on firstly in the “Ping An Health” App design, such as system fluency, interface comfort and network stability. Additionally, the results show that the new QFD can express experts’ hesitant assessment information, deal with the interrelations among DAs, and yield more precise rankings of DAs in QFD. Show more
Keywords: Quality function deployment, DEMATEL method, CoCoSo-based ranking method, mobile medical App
DOI: 10.3233/JIFS-233229
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3677-3700, 2024
Authors: Wang, Cong | Teng, Yue | Zhang, Tianhang
Article Type: Research Article
Abstract: Establishing a closed-loop system that could facilitate the reusing, renovation, and recycling of the various garbage products generated by this business could prove significant value to the particular business chains involved. A system of shipping that is mindful of the surroundings and takes accountability regarding all the relevant money, sustainable, and societal concerns. The sustainability Closed-Loop Supply-Chain Networks (CLSCN) architecture and the marketplace are brought together in the present article, which serves as the study’s primary part in the body of knowledge. As a result, an optimization with multiple objectives paradigm has been offered to arrive at their choices regarding …position, allocations, and stock in relation to the challenge under consideration. The goals of the optimized model, derived from the triple bottom line strategy, are aimed at lowering overall expenditure and emissions of CO2 as much as possible while increasing the number of employment possibilities. In this study, we have proposed Hybrid electromagnetism with a genetic algorithm (HEGA) and compare our proposal with the existing methods. The obtained results show that the proposed model integrated with HEGA gives significant improvements with significant outcomes in terms of sensitivity (97%), specificity (95%), transportation cost (30%), and computational time (5.3s). This knowledge serves as a driving force behind the development of CLSN in the sector to establish a viable and affordable approach. Show more
Keywords: Supply chain (SC), Closed-Loop Supply Chain Network (CLSCN), industries, fuzzy logic, Hybrid electromagnetism (HE)
DOI: 10.3233/JIFS-236612
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3701-3712, 2024
Authors: Lei, Fan | Cai, Qiang | Wang, Hongjun | Wei, Guiwu | Mo, Zhiwen
Article Type: Research Article
Abstract: Urban fire accident is a common dangerous accident in urban sudden accidents, which threatens the safety of people’s lives and property. For this reason, in recent years, all cities have incorporated the prevention and emergency management of urban fire accidents into their urban development planning, and actively improved their fire accident emergency management capabilities. However, how to evaluate the urban fire accident emergency management capacity of each city to ensure that people’s lives and property are protected to the greatest extent is an urgent problem to be considered and solved. Therefore, this paper defines a class of probabilistic double hierarchy …linguistic Heronian mean (PDHLHM) operator, probabilistic double hierarchy linguistic Power Heronian mean (PDHLPHM) operators, and their dual operators that can reflect the relationship between two attributes during aggregation. Taking urban fire accident risk monitoring and early warning capability, fire infrastructure and communication system, fire-fighting and rescue capability, recovery and reconstruction capability as evaluation attributes, the probabilistic double hierarchy linguistic weight Power Heronian mean (PDHLWPHM) operator model and the probabilistic double hierarchy linguistic weight Power geometric Heronian mean (PDHLWPGHM) operator model are constructed for group decision-making. In addition, the idempotence, boundedness and monotonicity of these operators are studied, and the sensitivity of the parameters involved in the operator model is analyzed. Finally, the new model proposed in this paper is compared with the existing model to verify its scientificity. Show more
Keywords: Group decision-making, probabilistic double hierarchy linguistic term set (PDHLTS), PDHLWPHM operator and PDHLWPGHM operator, urban fire emergency management capability
DOI: 10.3233/JIFS-230485
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3713-3760, 2024
Authors: Zhao, Chen | Sun, Lijun | Li, Gang | Tang, Yiming
Article Type: Research Article
Abstract: Relevancy transformation operators (RET operators) have been widely used in fuzzy systems modelling and the construction of weighted aggregation functions. Several construction methods of RET operators based on different aggregation functions such as t-norm, t-conorm and copula, have been proposed. In this paper, the attention is paid to the expression of RET operators, which is an important feature from an application the point of view. Polynomial RET operators are introduced as those RET operators in the form of polynomial functions of two variables. A complete characterisation of polynomial RET operators of degree less than 4 are presented.
Keywords: Relevancy transformation operators, Polynomial functions, Fuzzy systems, Monotonicity
DOI: 10.3233/JIFS-231017
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3761-3771, 2024
Authors: Wang, Jiaguo | Li, Wenheng | Lei, Chao | Yang, Meng | Pei, Yang
Article Type: Research Article
Abstract: Recently, actor-critic architectures such as deep deterministic policy gradient (DDPG) are able to understand higher-level concepts for searching rich reward, and generate complex actions in continuous action space, and widely used in practical applications. However, when action space is limited and has dynamic hard margins, training DDPG can be problematic and inefficiency. Since real-world actuators always have margins and interferences, after initialization, the actor network is likely to be stuck at a local optimal point on action space margin: actor gradient orients to the outside of action space but actuators stop at the margin. If the hard margins are complex, …dynamic and unknown to the DDPG agent, it is unable to use penalty functions to recover from local optimum. If we enlarge the random process for local exploration, the training could be in potential risk of failure. Therefore, simply relying on gradient of critic network to train the actor network is not a robust method in real environment. To solve this problem, in this paper we modify DDPG to deep comparative policy (DCP). Rather than leveraging critic-to-actor gradient, the core training process of DCP is regulated by a T-fold compare among random proposed adjacent actions. The performance of DDPG, DCP and related algorithms are tested and compared in two experiments. Our results show that, DCP is effective, efficient and qualified to perform all tasks that DDPG can perform. More importantly, DCP is less likely to be influenced by the action space margins, DCP can provide more safety in avoiding training failure and local optimum, and gain more robustness in applications with dynamic hard margins in the action space. Another advantage is that, complex penalty for margin touching detection is not required, the reward function can always be brief and short. Show more
Keywords: Actor-critic, deep reinforcement learning, intelligent agent, iterative learning
DOI: 10.3233/JIFS-233747
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3773-3788, 2024
Authors: Mukesh Krishnan, M. | Thanga Ramya, S. | Ramar, K.
Article Type: Research Article
Abstract: Unusual crowd activity detection is a challenging problem in surveillance video applications because feature extraction is difficult process in crowded scenes. The main objective of this research work is to detect unusual crowd activities and to detect unusual splits of moving objects. Various methods have been employed to address these challenges. However, there is still a lack of appropriate handling of this problem due to frames having occlusion, noise, and congestion. This paper proposes a novel clustering approach to detect unusual crowd activities. The proposed method consists of five phases including foreground extraction, foreground enhancement, foreground estimation, clustering crowds, and …the Unusual Crowd Activities (UCA) model. The UCA model can find unusual crowd activities and unusual splits of moving objects using the Laplacian Matrix formulation. Two public datasets viz. PETS 2009 and UMN dataset are used for evaluating the proposed methodology. To estimate the effectiveness of the proposed work, several unusual event detection methods are compared with the proposed work results. The experimental results revealed that the proposed method gives better results than the existing methods. Show more
Keywords: Unusual event detection, crowd detection, crowd clustering, and unusual crowd activities model
DOI: 10.3233/JIFS-233833
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3789-3798, 2024
Authors: Li, Tao | Wang, Xiaolong | Li, Xinkun | Jia, Xinyu | Wu, Lijie | Yang, Weihong
Article Type: Research Article
Abstract: Tunnel stability is mainly concerned with the object of symmetric tunnels, shallow buried unsymmetric(SBU) tunnels should also be emphasized as the focus of the computational analysis of tunnel engineering. It is especially important to solve the expressions of ultimate support force and damage surface function for SBU tunnels. In this paper, considering the effect of unsymmetrical action, based on the Hoke-Brown(H-B) damage criterion, the optimal upper bound(UB) solution expression is derived by using the limit analysis method. The expression can be used to express the support force and collapse pattern of a SBU rectangular tunnel. The results show that q …1 and q 2 decrease with the increase of parameters A and σ c , and increase with the increase of parameters B , γ , and h . q 1 increases with the increase of α , and vice versa for q 2 . The range of damage surface decreases with increasing parameter A , σ c and increases with increasing parameter B , γ , d , h . After the feasibility study and results analysis, it is concluded that the results obtained in this study are consistent with common engineering knowledge. The training results using Feedforward neural network verify the feasibility of the method for SBU tunnels and can be generalized for shallow buried(SB) symmetrical tunnels. The proposed method can provide a theoretical basis for the support design of SBU tunnels. Show more
Keywords: Shallow buried unsymmetrical tunnels, Hoke-Brown criterion, collapsed rock mass, limit analysis method
DOI: 10.3233/JIFS-234766
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3799-3809, 2024
Authors: Arulkumar, V. | Sandana Karuppan, A. | Alex, Sini Anna | Lathamanju, R.
Article Type: Research Article
Abstract: In an era marked by the widespread adoption of cloud services, individuals and businesses face the daunting task of navigating a complex landscape to make informed choices. The inherent opacity of the cloud service environment underscores the need for methods that can effectively handle imprecise information. This research presents a novel and superior approach to aid customers in selecting the most suitable cloud services. Our work introduces a distinctive fuzzy decision-making paradigm, surpassing current methodologies. We leverage an innovative analytic hierarchy process technique to quantify the semantic similarity between concepts and employ a fuzzy ontology to elucidate the uncertain relationships …among database items, facilitating precise service matching. Furthermore, we present a multi-faceted evaluation framework for ranking cloud services. To substantiate the efficacy of our similarity matching based on the fuzzy ontology, we conduct comprehensive testing. The results of our experiments provide compelling evidence of the viability and effectiveness of the proposed method. This research offers a valuable contribution to the challenging realm of cloud service selection, empowering individuals and organizations to make well-informed decisions amidst the cloud service abundance. Show more
Keywords: Semantic information retrieval, fuzzy ontology, ontology, the Semantic Web
DOI: 10.3233/JIFS-235130
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3811-3826, 2024
Authors: Cheng, Long | Wang, Lei | Cai, Jingcao
Article Type: Research Article
Abstract: For solving the distributed assembly flow shop scheduling problem with fuzzy processing time (FDAPFSP), a regional biogeography-based optimization algorithm (RBBO) is proposed to minimize the maximum fuzzy completion time. The mathematical model is provided. In RBBO, all habitats are divided into regions based on the habitat suitability index, and the habitats of each region are subject to cross-regional migration and replacement procedures. A critical factory optimization strategy is developed to enhance local search capability. Taguchi method is used to determine the parameters of RBBO. In ten FDAPFSP instances, comparative testing of RBBO algorithm with various heuristic and swarm intelligence algorithms …are conducted. The computation results show that in ten FDAPFSP cases, the proposed RBBO outperforms other algorithms in nine out of ten FDAPFSP cases. Show more
Keywords: Fuzzy scheduling, distributed scheduling, permutation flow-shop, regional biogeography-based optimization algorithm
DOI: 10.3233/JIFS-235854
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3827-3841, 2024
Authors: Vidhya, K. | Krishnamoorthi, K.
Article Type: Research Article
Abstract: In this manuscript, a hybrid approach is proposed for multi-functional grid connected photovoltaic (PV) interleaved inverter using power quality(PQ) enhancement. The proposed method is the integration of Spherical Evolution Search Algorithm (LSE) and Wild Horse Optimizer (WHO), thus it is called LSE-WHO method. The key objective of the proposed method lessens the DC voltage fluctuation and enhances the PQ. At the grid side, the interleaved inverter is used and it consists of 4 legs and every leg has a power electronic switch and a diode. Because of the structure of interleaved inverter, the shoot-through effect overcomes. The system performance is …improved by the utilization of interleaved inverter. The operation of proposed method is divided into 2 parts, like harmonics reduction and power harvesting. The LSE method is used to improve the maximal power of photovoltaic and the WHO method is used to lessen the harmonics distortion and eliminated the DC-link voltage fluctuation by double band hysteresis current controller (DBHCC). The switching losses are low because the DBHCC gives lesser switching frequency. Then, the LSE-WHO method is done in MATLAB, and its performance is compared to the existing methods. From the simulation, it conclude that the LSE-WHO method provides the THD as 2.12% and improves the PQ. Show more
Keywords: Power quality, power harvesting, grid connected PV, interleaved inverter, DC voltage fluctuation, switching losses, harmonic
DOI: 10.3233/JIFS-221561
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3843-3865, 2024
Authors: Al-Essa, Laila A. | Khan, Zahid | Alduais, Fuad S.
Article Type: Research Article
Abstract: The logistic distribution is frequently encountered to model engineering, industrial, healthcare and other wide range of scientific data. This work introduces a flexible neutrosophic logistic distribution (LDN ) constructed using the neutrosophic framework. The LDN is considered to be ideal for evaluating and quantifying the uncertainties included in processing data. The suggested distribution offers greater flexibility and superior fit to numerous commonly used metrics for assessing survival, such as the hazard function, reliability function, and survival function. The mode, skewness, kurtosis, hazard function, and moments of the new distribution are established to determine its properties. The theoretical findings are …experimentally proven by numerical studies on simulated data. It is observed that the suggested distribution provides a better fit than the conventional model for data involving imprecise, vague, and fuzzy information. The maximum likelihood technique is explored to estimate the parameters and evaluate the performance of the method for finite sample sizes under the neutrosophic context. Finally, a real dataset on childhood mortality rates is considered to demonstrate the implementation methodology of the proposed model. Show more
Keywords: Uncertain data, neutrosophic probability, neutrosophic distribution, uncertain estimators, Monte Carlo simulation
DOI: 10.3233/JIFS-233357
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3867-3880, 2024
Authors: Liu, Yicheng | Hu, Zewei | Nie, Haiwen
Article Type: Research Article
Abstract: With the rapid economic development and high concentration of urban population, people’s income level and quality of life continue to improve, resulting in more and more crowded scenes caused by people going out. Especially in urban commercial centers, transportation hubs, sports venues during important events, tourist attractions, etc., crowd gatherings occur frequently. However, accidents involving crowd gatherings in public places occur frequently, causing heavy casualties and property losses. Therefore, for crowd recognition, this paper proposes a new method to accurately estimate the number of dense crowds. In this method, a density map with accurate pedestrian locations is first generated using …the focal inverse distance transform and used as ground truth labels for network training. Then, a multi-scale feature fusion algorithm based on residual network is designed, combining spatial and channel attention mechanisms to improve the accuracy and stability of crowd density estimation. In dense crowds, the phenomenon of overlapping and occlusion of people is very common and serious, making it difficult for existing pedestrian detection methods to distinguish each individual and accurately count the flow of people. To solve this problem, this paper proposes a density map-based method that uses a local maximum detection strategy and a K-nearest neighbor algorithm to convert the density map into the corresponding dense head bounding box. This method can effectively reduce the impact of occlusion and improve the accuracy of people counting. In order to further improve the estimation accuracy, a pattern recognition density peak clustering algorithm is introduced to study the clustered crowds. By treating the head bounding box as an element point, the distance between each element point is calculated, and the density of each point is calculated. Then perform clustering to find the cluster center with the highest density in each class. Finally, by comparing the density of each cluster center with the corresponding density threshold and adopting the corresponding decision-making method, the accuracy of people counting is further improved. Show more
Keywords: Deep learning, residual networks, public places, crowd recognition, clustering
DOI: 10.3233/JIFS-236811
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3881-3893, 2024
Authors: Feng, Kan | Yang, Ke | Shi, Haopeng | Jia, Najuan | Zhang, Pingjuan
Article Type: Research Article
Abstract: Overload service in the communication network of smart substation will cause congestion, resulting in low overload service throughput, high congestion rate and long congestion control time in the average smart substation. A congestion control method for overloaded services in smart substations with high concurrent users is proposed. According to the characteristics of overload service request of smart substation, the mathematical model of the algorithm is defined by describing the overload service request of smart substation on the basis of network topology model. Combined with the wavelength rotation strategy, the congestion rate of overloaded services in smart substations is reduced, and …the throughput rate of overloaded services in smart substations is improved. Considering the factors of high concurrent users, by judging and feeding back the congestion of the overloaded services of smart substations, the congestion control of overloaded services of smart substations under high concurrent users is realized. The experimental results show that the proposed method has better effect and scalability in the congestion control of the overloaded service of the smart substation, and can effectively shorten the congestion control time of the overloaded service of the smart substation. Show more
Keywords: High concurrent users, smart substation, wavelength rotation strategy, overload service, congestion control, D_WA algorithm, overload service congestion model, congestion degree value, interest packet forwarding rate
DOI: 10.3233/JIFS-224276
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3895-3906, 2024
Authors: Wu, Zhongyi | Liu, Weidong | Zheng, Weijie
Article Type: Research Article
Abstract: This research presents a novel model for optimizing process information in manufacturing steps through the utilization of Process Constituent Elements (PCE), with the aim of enhancing the effectiveness of product process information design. To achieve this objective, a systematic analysis is conducted on six dimensions: input, output, resources, value-adding activities, environment, and process control and inspection content. In addition, specific attributes of PCE are investigated, and an improved FP-growth algorithm is employed to extract the optimized structural expressions of typical PCE, thus determining specific expression requirements. The PCE and their attribute relationships are organized into modular mapping rules, resulting in …an optimized representation structure based on a polychromatic set approach. The effectiveness of this approach is quantitatively assessed by developing a comprehensive quality indicator evaluation system for process information and using a fuzzy comprehensive evaluation model for analysis. Show more
Keywords: Process constituent elements, process design, optimization, data mining, process quality, fuzzy evaluation
DOI: 10.3233/JIFS-231198
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3907-3932, 2024
Authors: Siva Senthil, D. | Sivarani, T.S.
Article Type: Research Article
Abstract: Detecting abnormal events in surveillance involves identifying unexpected behavior through video analysis. This involves recognizing patterns or deviations from normal behavior and taking actions to mitigate potential risks. However, the distribution of data can change over time, leading to concept drift, which can make it challenging to accurately detect abnormal events. To address this issue, a new approach using a global density network (GDN) has been proposed. The GDN allows for more efficient identification of object distributions in surveillance videos, leading to improved accuracy in abnormal event detection. The proposed method combines features extracted by a backbone network with a …global density joined network (GDJN), which refines density features using dilated convolutional networks. A multistage long short-term memory (LSTM) network is then used to classify abnormal events. The experimental results are conducted on two datasets, UMN and UCSD Ped2. The achieved F1 scores were 93.42 and 94.46 respectively, with corresponding AUC values of 93.5 and 94.8. Show more
Keywords: Keywords:Video analysis, abnormal event detection, GDN, GDJN, LSTM, deep learning
DOI: 10.3233/JIFS-232177
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3933-3944, 2024
Authors: Li, Weidong | Fan, Jinsheng | Li, Zhenying | Wang, Chisheng | Zhang, Xuehai | Duan, Jinlong
Article Type: Research Article
Abstract: The accuracy of predicting river-suspended sediment concentration (SSC) is crucial for evaluating the functional lifespan of reservoirs, analyzing river geomorphological evolution, and assessing riverbed stability. In this study, we aim to develop new models for SSC prediction at two hydrological stations near Puerto Rico, USA, by integrating the bacterial foraging optimization algorithm and adaptive neural fuzzy inference network (ANFIS). The models comprise ANFIS with grid partition (ANFIS-GP), ANFIS with subtractive clustering (ANFIS-SC), and ANFIS with fuzzy c-means clustering (ANFIS-FCM). Additionally, we employ an artificial neural network (ANN) and the sediment rating curve (SRC) for predicting daily series data of flow …discharge-suspended sediment concentration (SSC). Different scenarios are considered based on varying input and output variables, leading to predictions for four distinct scenarios. At the Rio Valenciano Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-GP, ANFIS-SC, ANN, and SRC are 2.2172, 2.5389, 2.6627, 2.7549, 2.7994, and 3.7882, respectively. For the Quebrada Blanca Station, the MRSE values for ANFIS-BFO, ANFIS-FCM, ANFIS-SC, ANFIS-GP, ANN, and SRC are 0.8295, 0.8664, 0.8964, 0.9110, 0.9684, and 1.6742, respectively. It can be inferred that ANFIS-BFO exhibits superior prediction results compared to all other models. Furthermore, ANFIS-SC and ANFIS-FCM demonstrate slightly better prediction performance than ANFIS-GP. In comparison to ANN, ANFIS-GP, ANFIS-SC, and ANFIS-FCM exhibit slightly superior prediction performance. Show more
Keywords: ANFIS, ANN, bacterial foraging optimization algorithm, modeling, suspended sediment
DOI: 10.3233/JIFS-232277
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3945-3961, 2024
Authors: Sahapudeen, Farjana Farvin | Krishna Mohan, S.
Article Type: Research Article
Abstract: Patients with lung cancer can only be diagnosed and treated surgically. Early detection of lung cancer through medical imaging could save numerous lives. Adding advanced techniques to conventional tests that offer high accuracy in diagnosing lung cancer is essential. U-Net has excelled in diversified tasks involving the segmentation of medical image datasets. A significant challenge remains in determining the ideal combination of hyper parameters for designing an optimized U-Net for detailed image segmentation. In our work, we suggested a technique for automatically generating evolutionary U-Nets to detect and segregate lung cancer anomalies. We used three distinct datasets, namely the LIDC-IRDC …Dataset, Luna 16 Dataset, and Kaggle Dataset, for training the proposed work on lung images. Our results, examined with six distinct evaluation criteria used for medical image segmentation, consistently demonstrated the highest performance. More specifically, the GA-UNet outperforms conventional approaches in terms of an impressive accuracy rate of 97.5% and a Dice similarity coefficient (DSC) of 92.3%. Show more
Keywords: Genetic programming, deep learning, attention blocks, residual network, UNets, optimized U-Net
DOI: 10.3233/JIFS-233006
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3963-3974, 2024
Authors: Demirtaş, Naime | Dalkılıç, Orhan | Riaz, Muhammad | Al-Quran, Ashraf
Article Type: Research Article
Abstract: Introduction: The soft set theory has drawn the attention of many researchers, particularly for dealing with uncertainty in decision-making problems. Despite its remarkable advantages, the soft set theory has only been used to tackle decision-making problems that aim to choose the best option. However, there exist different forms of decision-making problems that involve different forms of uncertainty. Methods: In this study, we present various algorithms based on the soft set theory in order to handle the cases where one has different uncertainty forms in decision-making problems. Some new concepts such as object code, personal object code, parameter significance …weight and new distance measures have been introduced to the literature for the construction of these algorithms. Furthermore, we show the application results of those algorithms and provide several examples. Results and Conclusions: As a result, a comparison among the application results of the algorithms implies that the best objects might not always yield the most efficient outcomes. Show more
Keywords: Soft set, D-metric space, parametric distance, algorithm, decision making
DOI: 10.3233/JIFS-234481
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3975-3985, 2024
Authors: Li, Zhixin | Liu, Hao | Huan, Zhan | Liang, Jiuzhen
Article Type: Research Article
Abstract: Human activity recognition (HAR) plays a crucial role in remotely monitoring the health of the elderly. Human annotation is time-consuming and expensive, especially for abstract sensor data. Contrastive learning can extract robust features from weakly annotated data to promote the development of sensor-based HAR. However, current research mainly focuses on the exploration of data augmentation methods and pre-trained models, disregarding the impact of data quality on label effort for fine-tuning. This paper proposes a novel active contrastive coding model that focuses on using an active query strategy to evenly select small, high-quality samples in downstream tasks to complete the update …of the pre-trained model. The proposed uncertainty-based balanced query strategy mines the most indistinguishable hard samples according to the data posterior probability in the unlabeled sample pool, and imposes class balance constraints to ensure equilibrium in the labeled sample pool. Extensive experiments have shown that the proposed method consistently outperforms several state-of-the-art baselines on four mainstream HAR benchmark datasets (UCI, WISDM, MotionSense, and USCHAD). With approximately only 10% labeled samples, our method achieves impressive F1-scores of 98.54%, 99.34%, 98.46%, and 87.74%, respectively. Show more
Keywords: Contrastive learning, active learning, human activity recognition, hard sample mining, mobile medical system
DOI: 10.3233/JIFS-234804
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3987-3999, 2024
Authors: Zeng, Zengpei
Article Type: Research Article
Abstract: Visual communication design, as a type of artistic and three-dimensional design behavior, helps to spread visual behavior by designing it. The rapid development of new media technology has provided rich channels and vast space for visual communication design, and the elements and modes of visual communication design are constantly being updated, better promoting the development of visual communication technology. The Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities based on the cultivation of innovative and creative abilities is a multiple-attribute decision-making (MADM). In this paper, some calculating laws on IVIFSs, Hamacher sum, …Hamacher product are introduced, and the induced interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted averaging (I-IVIFHIHWA) operator is proposed based on the interval-valued intuitionistic fuzzy Hamacher interactive hybrid weighted averaging (IVIFHIHWA) operator and induced ordered weighted averaging (I-OWA) operator. Meanwhile, some ideal properties of I-IVIFHIHWA operator are studied. Then, the I-IVIFHIHWA operator is employed to cope with the MADM under IVIFSs. Finally, an example for Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities is employed to test the I-IVIFHIHWA operator. Thus, the main research aim of this paper is concluded as follows: [1 ] the I-IVIFHIHWA operator is constructed based on classical IOWA operator; [2 ] the I-IVIFHIHWA operator is put forward to cope with the MADM under IVIFSs; [3 ] an empirical example for Teaching quality evaluation of visual communication design based on the cultivation of innovative and creative abilities has been put forward to show the I-IVIFHIHWA operator. Show more
Keywords: Multiple-attribute decision-making (madm), interval-valued intuitionistic fuzzy sets (ivifss), i-ivifhihwa operator, quality evaluation
DOI: 10.3233/JIFS-235960
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4001-4013, 2024
Authors: Li, Fang | Li, Pengfei
Article Type: Research Article
Abstract: Currently, the digital economy continues to deepen its development, and it has become a consensus among all sectors as the direction of global future development. Digital finance, as a fleet in the wave of digital economy, is rapidly heading towards the sunny shore of benefiting the public and serving entities driven by the digital technology engine. Xinwang Bank is a fast boat in the digital finance fleet, always adhering to the principle of technology, building an open platform, and actively promoting the construction of an open, shared, and secure digital credit ecosystem from three levels: institutional, industry, and ecological, to …assist in the development of the digital economy. The digital commercial bank security evaluation is a classical multiple attribute group decision making (MAGDM) problems. Recently, the Evaluation based on Distance from Average Solution (EDAS) method has been employed to manage MAGDM issues. The intuitionistic fuzzy sets (IFSs) are used as a tool for portraying uncertain information during the digital commercial bank security evaluation. In this paper, the intuitionistic fuzzy nunmber EDAS (IFN-EDAS) method is cultivated to manage the MAGDM based on Hamming distance and Euclidean distance under IFSs. In the end, a numerical case study for digital commercial bank security evaluation is supplied to validate the proposed method. The main contributions of this paper are outlined: (1) the EDAS method has been extended to IFSs based on Hamming distance and Euclidean distance; (2) the CRITIC method is used to derive weight based on Hamming distance and Euclidean distance under IFSs. (3) the IFN-EDAS method based on Hamming distance and Euclidean distance is founded to manage the MAGDM based on the Hamming distance and Euclidean distance under IFSs; (4) a numerical case study for digital commercial bank security evaluation and some comparative analysis is supplied to validate the proposed method. Show more
Keywords: Multiple attribute group decision making (MAGDM), intuitionistic fuzzy sets (IFSs), EDAS method, CRITIC method, digital commercial bank security evaluation
DOI: 10.3233/JIFS-236058
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4015-4027, 2024
Authors: Zhang, Bin
Article Type: Research Article
Abstract: In recent years, e-commerce live streaming and short video marketing supported by big data and artificial intelligence technology have flourished, adding new sales models for e-commerce products to mass consumption, promoting the multimodal development of the e-commerce industry, giving new impetus and connotation to economic and social development, and being an effective means to achieve high-quality development in the new era. The effectiveness evaluation of short video marketing strategies is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Exponential TODIM technique and Combined Compromise Solution (CoCoSo) technique has been employed to cope with MAGDM issues. The interval-valued Pythagorean fuzzy sets …(IVPFSs) are employed as a tool for characterizing uncertain information during the effectiveness evaluation of short video marketing strategies. In this paper, the interval-valued Pythagorean fuzzy Exponential TODIM (ExpTODIM) (IVPF-ExpTODIM-CoCoSo) technique is constructed to solve the MAGDM under IVPFSs. In the end, a numerical case study for effectiveness evaluation of short video marketing strategies is given to validate the proposed technique. The main contributions of this paper are outlined: (1) the Exp-TODIM and CoCoSo technique has been extended to IVPFSs; (2) Information Entropy is employed to manage the weight values under IVPFSs. (3) the IVPF-ExpTODIM-CoCoSo technique is founded to implement the MAGDM under IVPFSs; (4) a numerical case study for effectiveness evaluation of short video marketing strategies and some comparative analysis is supplied to verify the IVPF-ExpTODIM-CoCoSo technique. Show more
Keywords: Multiple attribute group decision making (MAGDM), interval-valued Pythagorean fuzzy sets (IVPFSs), Exponential TODIM (ExpTODIM) technique, CoCoSo technique, effectiveness evaluation
DOI: 10.3233/JIFS-236767
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4029-4042, 2024
Authors: Ma, Junwen | Bi, Wenhao | Mao, Zeming | Zhang, An | Tang, Changhong
Article Type: Research Article
Abstract: The weaponized unmanned aerial vehicle (UAV) swarms have posed a significant threat to maritime civilian and military installations. For effective defense deployment, threat assessment has become a critical part of maritime defense decision-making. However, due to the uncertainty of threat information and the ignorance of decision-makers’ psychological behaviors, there are great challenges in obtaining a reliable and accurate threat assessment result to assist in maritime defense decision-making. To this end, this paper proposes an integrated threat assessment method for maritime defense against UAV swarms based on improved interval type-2 fuzzy best-worst method (IT2FBWM), prospect theory and VIKOR (VlseKriterijumska Optimizacija I …Kompromisno Resenje, in Serbian). Firstly, the improved IT2FBWM is designed by introducing interval type-2 fuzzy set (IT2FS) and entropy-based information to obtain attribute weights with high reliability. Then, the hybrid fuzzy scheme covering IT2FS and interval number is constructed to express the uncertainty of different types of threat information. Next, VIKOR is extended to hybrid fuzzy environment and combined with prospect theory to consider the influence of psychological behaviors of decision-makers. Finally, the improved IT2FBWM and extended VIKOR are integrated to determine the threat ranking of targets and the priority defense targets. A case study of maritime threat assessment is provided to illustrate the performance of the proposed method. Moreover, sensitivity and comparative experiments were conducted, and the results indicate that the proposed method not only obtain the reliable threat assessment result but also outperforms the other methods in terms of attribute weight determination, decision preference consideration and decision mechanism. Show more
Keywords: Threat assessment, interval type-2 fuzzy, best-worst method, prospect theory, multi-attribute decision-making
DOI: 10.3233/JIFS-231675
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4043-4061, 2024
Authors: Cai, Buqing | Tian, Shengwei | Yu, Long | Long, Jun | Zhou, Tiejun | Wang, Bo
Article Type: Research Article
Abstract: With the rapid growth of Internet penetration, identifying emergency information from network news has become increasingly significant for emergency monitoring and early warning. Although deep learning models have been commonly used in Chinese Named Entity Recognition (NER), they require a significant amount of well-labeled training data, which is difficult to obtain for emergencies. In this paper, we propose an NER model that combines bidirectional encoder representations from Transformers (BERT), bidirectional long-short-term memory (BILSTM), and conditional random field (CRF) based on adversarial training (ATBBC) to address this issue. Firstly, we constructed an emergency dataset (ED) based on the classification and coding …specifications of the national emergency platform system. Secondly, we utilized the BERT pre-training model with adversarial training to extract text features. Finally, BILSTM and CRF were used to predict the probability distribution of entity labels and decode the probability distribution into corresponding entity labels.Experiments on the ED show that our model achieves an F1-score of 85.39% on the test dataset, which proves the effectiveness of our model. Show more
Keywords: Named Entity Recognition, BERT, BILSTM, CRF, Adversarial Training
DOI: 10.3233/JIFS-232385
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4063-4076, 2024
Authors: Wang, Chuantao | Wang, Xiumin | Zhai, Jiliang | Shao, Shuo
Article Type: Research Article
Abstract: In recent years, UNet and its derivative networks have gained widespread recognition as major methods of medical image segmentation. However, networks like UNet often struggle with Point-of-Care (POC) healthcare applications due to their high number of parameters and computational complexity. To tackle these challenges, this paper introduces an efficient network designed for medical image segmentation called MCU-Net, which leverages ConvNeXt to enhance UNet. 1) Based on ConvNeXt, MCU-Net proposes the MCU Block, which employs techniques such as large kernel convolution, depth-wise separable convolution, and an inverted bottleneck design. To ensure stable segmentation performance, it also integrates global response normalization (GRN) …layers and Gaussian Error Linear Unit (GELU) activation functions. 2) Additionally, MCU-Net introduces an enhanced Multi-Scale Convolution Attention (MSCA) module after the original UNet’s skip connections, emphasizing medical image features and capturing semantic insights across multiple scales. 3)The downsampling process replaces pooling layers with convolutions, and both upsampling and downsampling stages incorporate batch normalization (BN) layers to enhance model stability during training. The experimental results demonstrate that MCU-Net, with a parameter count of 2.19 million and computational complexity of 19.73 FLOPs, outperforms other segmentation models. The overall performance of MCU-Net in medical image segmentation surpasses that of other models, achieving a Dice score of 91.8% and mIoU of 84.7% on the GlaS dataset. When compared to UNet on the BUSI dataset, MCU-Net shows an improvement of 2% in Dice and 2.9% in mIoU. Show more
Keywords: Convolution neural network, deep learning, medical image processing, semantic segmentation
DOI: 10.3233/JIFS-233232
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4077-4092, 2024
Authors: Ragul Vignesh, M. | Srihari, K. | Karthik, S.
Article Type: Research Article
Abstract: The rapid development of Internet of Things (IoT) technology has enabled the emergence of the Internet of Medical Things (IoMT), especially in body area network applications. To protect sensitive medical data, it is essential to ensure privacy preservation and detect intrusions in this context. This study proposes a novel intrusion detection system that protects the privacy of IoMT networks, specifically in the context of body area networks. For feature extraction, the system employs a recurrent U-Net autoencoder algorithm, which effectively captures temporal dependencies in IoMT data. In addition, privacy is protected through the combination of data anonymization techniques and data …classification using Principal Component Analysis (PCA). Combining the recurrent U-Net autoencoder algorithm, privacy preservation mechanisms, and PCA-based data classification, the proposed system architecture comprises the U-Net autoencoder algorithm. The proposed method is superior to existing approaches in terms of accuracy, precision, recall, F-measure, and classification loss, as demonstrated by experimental evaluations. This research contributes to the field of privacy protection and intrusion detection in IoMT, specifically in body area network applications. Show more
Keywords: Biomedical, Internet of Medical Things, intrusion detection, privacy preservation, recurrent neural networks, U-Net
DOI: 10.3233/JIFS-234441
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4093-4104, 2024
Authors: Liu, Yongfei
Article Type: Research Article
Abstract: The improved Sparse Signal Reconstruction (SR) algorithm for Trusted Artificial Intelligence (AI) and Distributed Compressed Sensing (DCS) technology was thoroughly investigated. The study verified its effectiveness and advantages in trusted AI and DCS systems, which have significant implications for enhancing the credibility, security, and performance of signal processing and AI algorithms. The reconstruction performance was evaluated using Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), and Least Absolute Shrinkage and Selection Operator (LASSO). The analysis primarily focused on runtime, refactoring errors, and the number of successful reconstruction attempts. When K = 4, K = 6, K = 8, and K = 10, OMP outperformed BP and LASSO in terms …of successful reconstructions, demonstrating better performance and higher reconstruction precision. Show more
Keywords: Trusted artificial intelligence, distributed compressed sensing technology, sparse signal reconstruction algorithm, orthogonal matching pursuit, basis pursuit, least absolute shrinkage and selection operator
DOI: 10.3233/JIFS-234771
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4105-4118, 2024
Authors: Luo, Minxia | Gu, Xiaojing | Li, Wenling
Article Type: Research Article
Abstract: As the theory of picture fuzzy sets has been developed, more information in life can be expressed in mathematical terms. Similarity measure is a special tool for quantifying the similarity between two sets, so studying similarity measure on picture fuzzy sets has become a trending topic. This new research direction has drawn a great deal of attention from experts and has led to a number of important results which have led to significant results in a number of practical applications. By examining these new findings, we discovered that there are many studies on similarity measure of picture fuzzy sets, some …of them are deficient in solving certain problems, and such similarity measures can lead to the calculation of unreasonable data in practical applications, affecting the final results. Secondly, there is still room for research similarity measures on exponential functions. Considering these two aspects, we propose two new similarity measures based on exponential function, which not only satisfy the axiomatic definition of similarity measures, but also show reasonable computational results in practical applications. Show more
Keywords: Picture fuzzy set, similarity measure, pattern recognition, degree of confidence
DOI: 10.3233/JIFS-235571
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4119-4126, 2024
Authors: Mao, Cui
Article Type: Research Article
Abstract: With the acceleration of economic globalization, enterprises are facing fierce competition and huge challenges, requiring deep financial management transformation. In this context, the integration of industry and finance has gradually demonstrated extremely important value. The integration of industry and finance can not only effectively improve the efficiency of financial management, prevent business risks, and improve operational efficiency, but also enhance the comprehensive ability of enterprise financial management, providing a more flexible, transparent, and efficient financial management system for enterprises. The operational quality evaluation of industry-finance integration enterprises under lean management accounting is a multiple-attribute decision-making (MADM). In this paper, some …calculating laws on IVIFSs, Hamacher sum, Hamacher product are introduced, and the interval-valued intuitionistic fuzzy Hamacher interactive power averaging (IVIFHIPA) technique is proposed based on the interval-valued intuitionistic fuzzy (IVIF) Hamacher interactive weighted averaging (IVIFHIWA) technique and power average (PA) technique. Meanwhile, some ideal properties of IVIFHIPA technique are studied. Then, the IVIFHIPA technique is employed to cope with the MADM under IVIFSs. Finally, an example for operational quality evaluation of industry-finance integration enterprises under lean management accounting is employed to test the IVIFHIPA technique. Thus, the main research aim of this paper is concluded as follows: (1) the IVIFHIPA technique is constructed based on IVIFHIWA technique and classical power average (PA) technique; (2) the IVIFHIPA technique is put forward to cope with the MADM under IVIFSs; (3) an empirical example for operational quality evaluation of industry-finance integration enterprises under lean management accounting has been put forward to show the IVIFHIPA technique. Show more
Keywords: Multi-attribute decision making (MADM), Interval-valued intuitionistic fuzzy sets (IVIFSs), IVIFHIPA technique, operational quality evaluation
DOI: 10.3233/JIFS-235820
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4127-4146, 2024
Authors: Banitalebi, S. | Ahn, S.S. | Borzooei, R.A.
Article Type: Research Article
Abstract: Recently, the neutrosophic graph has been introduced as an extension of fuzzy graphs and intuitionistic fuzzy graphs, which offers more compatibility and flexibility than these two types in modeling and structuring many actual issues. In this article, using neutrosophic highly strong arc, the new notions of (totally) special irregular, highly special irregular, strongly special irregular, neighborly special irregular and special arc-irregular of neutrosophic graphs are stated. Finally, one of their utilizations relevant to offering a fixed optimization model in decision making in diverse conditions is presented. In fact,we present a decision-making problem in real-world applied example which discusses the factors …influencing a companys efficiency. The presented model is, in fact, a factor-based model wherein the impact score of each factor is divided into two types of direct and indirect influences, in which the concept of neutrosophic special dominating set plays a significant role. Show more
Keywords: Neutrosophic graph, special irregular neurosophic graph, special homomorphism, special isomorphism
DOI: 10.3233/JIFS-221785
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4147-4157, 2024
Authors: Dong, Yumin | Che, Xuanxuan | Fu, Yanying | Liu, Hengrui | Sun, Lina
Article Type: Research Article
Abstract: Previously, single classification models were mainly studied to classify human protein cell images, i.e., to identify a certain protein based on a set of different cells. However, a classifier can identify only one protein, in fact, a single cell usually consists of multiple proteins, and the proteins are not completely independent of each other. In this paper, we build a human protein cell classification model by multi-label learning. The logical relationship and distribution characteristics among the labels are analyzed to determine the different proteins contained in a set of different cells (i.e., containing multiple elements in the output space). In …this paper, using human protein image data, we conducted comparison experiments on pre-trained Xception and InceptionResnet V2 to optimize the two models in terms of data augmentation, channel settings, and model structure. The results show that the Optimized InceptionResnet V2 model achieves high performance in the classification task. The final accuracy of the Optimized InceptionResnet V2 model we obtained reached 96.1%, which is a 2.82% improvement relative to that before the optimized model. Show more
Keywords: Human protein atlas images data set, multi-label learning, deep convolutional neural network
DOI: 10.3233/JIFS-223464
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4159-4172, 2024
Authors: Kamber, Eren | Baskak, Murat
Article Type: Research Article
Abstract: In this study, it is aimed to integrate CODAS method with circular intuitionistic fuzzy sets as a new solution method for MCDM problems. Containing a radius notation with degrees of central membership and non-membership degrees is the main advantage of circular intuitionistic fuzzy in decision making. On the other side, Combinative Distance-based Assessment (CODAS) method contains many advantages such as basing on two types of distance calculations (Euclidean and Taxicab distances) comparing with other MCDM methods. When the advantages of circular intuitionistic fuzzy sets and CODAS method are considered, proposed circular intuitionistic fuzzy CODAS method (CIFS-CODAS) presents many superiorities compared …to other MCDM techniques. By this way, an application for green logistics park location selection will be handled by using CIFS-CODAS to show the validity of the methodology. After, a comparative analysis with intuitionistic fuzzy CODAS (IFS-CODAS), intuitionistic fuzzy TOPSIS (IFS-TOPSIS) and intuitionistic fuzzy EDAS (IFS-EDAS) methods will be performed for green logistics park location selection problem to confirm the robustness of presented method. Green logistics and Green Deal are also emphasized considering environmental factors as a scope of the article. Finally, the results will be evaluated in the context of the logistics sector and green logistics. Show more
Keywords: Green logistics, circular intuitionistic fuzzy sets, fuzzy, CODAS method, location selection
DOI: 10.3233/JIFS-231843
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4173-4189, 2024
Authors: Tamizharasi, A. | Ezhumalai, P.
Article Type: Research Article
Abstract: A novel approach to enhance software testing through intelligent test case selection is proposed in this work. The proposed method combines feature extraction, clustering, and a hybrid optimization algorithm to improve testing effectiveness while reducing resource overhead. It employs a context encoder to extract relevant features from software code, enhancing the accuracy of subsequent testing. Through the use of Fuzzy C-means (FCM) clustering, the test cases are classified into groups, streamlining the testing process by identifying similar cases. To optimize feature selection, a Hybrid Whale Optimized Crow Search Algorithm (HWOCSA), which intelligently combines the strengths of both Whale Optimization Algorithm …(WOA) and Crow Search Algorithm (CSA) is introduced. This hybrid approach mitigates limitations while maximizing the selection of pertinent features for testing. The ultimate contribution of this work lies in the proposal of a multi-SVM classifier, which refines the test case selection process. Each classifier learns specific problem domains, generating predictions that guide the selection of test cases with unprecedented precision. Experimental results demonstrate that the proposed method achieves remarkable improvements in testing outcomes, including enhanced performance metrics, reduced computation time, and minimized training data requirements. By significantly streamlining the testing process and accurately selecting relevant test cases, this work paves the way for higher quality software updates at a reduced cost. Show more
Keywords: Context encoder, pre-processing, FCM, WOA, HWOCSA
DOI: 10.3233/JIFS-232700
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4191-4207, 2024
Authors: Dong, Yue-Fang | Fu, Wei-wei | Zhou, Zhe | Shi, Guo-Hua
Article Type: Research Article
Abstract: Relative pupillary afferent disorder (RAPD) plays a crucial role in diagnosing optic nerve dysfunction. This paper introduces an innovative equipment design with a high-speed pupil detection algorithm and a binocular independent stimulation optical path. The proposed algorithm utilizes the grayscale characteristics of the pupil region to achieve rapid and accurate pupil detection and tracking. Initially, a pupil threshold is estimated using eigenvalues, enabling the calculation of the pupil centroid. Subsequently, leveraging the unique characteristics of the pupil region, a dynamic tracking algorithm, a second-order partial derivative threshold algorithm, and a pupil diameter extraction algorithm are employed to precisely locate the …centroid. By incorporating a binocular independent stimulus light path design, the algorithm overcomes limitations associated with the current measurement equipment. The experimental results demonstrate the algorithm’s high robustness and fast detection speed, meeting the tracking speed requirement of 1250 frames per second for a single eye. These advancements have the potential to significantly enhance the diagnosis and assessment of optic nerve dysfunction. Show more
Keywords: RAPD, pupil detection, gray level features, dynamic tracking
DOI: 10.3233/JIFS-232752
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4209-4218, 2024
Authors: Ma, Xiaoqin | Liu, Jianming | Wang, Pei | Yu, Wenchang | Hu, Huanhuan
Article Type: Research Article
Abstract: Feature selection can remove data noise and redundancy and reduce computational complexity, which is vital for machine learning. Because the difference between nominal attribute values is difficult to measure, feature selection for hybrid information systems faces challenges. In addition, many existing feature selection methods are susceptible to noise, such as Fisher, LASSO, random forest, mutual information, rough-set-based methods, etc. This paper proposes some techniques that consider the above problems from the perspective of fuzzy evidence theory. Firstly, a new distance incorporating decision attributes is defined, and then a relation between fuzzy evidence theory and fuzzy β covering with an anti-noise …mechanism is established. Based on fuzzy belief and fuzzy plausibility, two robust feature selection algorithms for hybrid data are proposed in this framework. Experiments on 10 datasets of various types have shown that the proposed algorithms achieved the highest classification accuracy 11 times out of 20 experiments, significantly surpassing the performance of the other 6 state-of-the-art algorithms, achieved dimension reduction of 84.13% on seven UCI datasets and 99.90% on three large-scale gene datasets, and have a noise tolerance that is at least 6% higher than the other 6 state-of-the-art algorithms. Therefore, it can be concluded that the proposed algorithms have excellent anti-noise ability while maintaining good feature selection ability. Show more
Keywords: Feature selection, fuzzy β covering, fuzzy belief, fuzzy plausibility, hybrid information systems
DOI: 10.3233/JIFS-233070
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4219-4242, 2024
Authors: Lu, Tianjun | Zhong, Xian | Zhong, Luo
Article Type: Research Article
Abstract: Convolutional neural networks (CNNs) have received significant attention for change detection (CD) on multimodal remote sensing images, but they struggle to capture global cues due to the locality of convolution operations. In contrast, the transformer can learn global semantic information by dividing the input image into patches, adding position encodings, and utilizing the self-attention mechanism. Motivated by this, we propose mSwinUNet, a novel end-to-end multi-modal model with swin-transformer-based and U-shaped siamese network architectures for supervised CD using Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 Multispectral Imager (MSI) data. mSwinUNet contains multi-modal encoder with difference module, bottleneck, and fused decoder, and …all of them are based on swin transformer. Firstly, tokenized multi-modal bitemporal image patches are fed into multiple Siamese encoder branches to extract multi-level multi-modal difference feature maps in parallel. Subsequently, the last level multi-modal difference maps are fused to generate the smallest scale change map in the bottleneck. Then, the hierarchical decoder incorporates patch expansion and fusion operations to fuse multi-scale difference and change maps, effectively recuperating the details of the change information. Finally, the last patch expansion and a linear projection are applied to output the final change map, which preserves the identical spatial resolution as the input image. Extensive experiments have shown that mSwinUNet outperforms several the state-of-the-art multi-modal CD methods on OSCD dataset and the corresponding Sentinel-1 SAR data. Show more
Keywords: Change detection (CD), multi-modal siamese network, swin transformer, remote sensing image
DOI: 10.3233/JIFS-233868
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4243-4252, 2024
Authors: Chen, Wenda | Shi, Cao
Article Type: Research Article
Abstract: Accurate segmentation of knee cartilage in MR images is crucial for early diagnosis and treatment of knee conditions. Manual segmentation is time-consuming, leading researchers to explore automatic deep learning methods. However, the choice between 2D and 3D networks for organ segmentation remains debated. In this paper, we propose a hybrid 2D and 3D deep neural network approach, named UVNet, which combines the strengths of both techniques to enhance segmentation performance. Within this network structure, the 3D segmentation network serves as the backbone for feature extraction, while the 2D segmentation network functions as an information supplement network. Local and global MIP …images are generated by employing various maximum intensity projection modes of knee MRI volumes as input for the information supplement network. By constructing a local and global MIP feature fusion module, the supplementary information obtained from the 2D segmentation network is fully integrated into the backbone network. We assess the quality of the proposed method using the Osteoarthritis Initiative (OAI) dataset and the 2010 Grand Challenge Knee Image Segmentation (SKI-10) dataset, comparing it to the Baseline Network and other advanced 2D and 3D segmentation methods. The experiments demonstrate that UVNet achieves competitive performance in the aforementioned two cartilage segmentation tasks. Show more
Keywords: Convolutional neural network, maximum intensity projection, segmentation of knee cartilage
DOI: 10.3233/JIFS-234050
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4253-4264, 2024
Authors: Wu, Rong | Yu, Long | Tian, Shengwei | Long, Jun | Zhou, Tiejun | Wang, Bo
Article Type: Research Article
Abstract: Event Detection (ED) has long struggled with the ambiguous definition of event categories, making it challenging to accurately classify events. Previous endeavors aimed to tackle this problem by constructing prototypes for specific event categories. However, they overlooked potential correlations among instances of distinct event categories, resulting in trigger misclassifications across event types. In this research, we introduce KEPA-CRF to train enhanced event prototypes and address the issue of limited samples in few-shot event detection. By integrating external knowledge from the Glove knowledge base into the model training process, we augment synonymous examples, mitigating the problem of insufficient samples in few-shot …event detection. Additionally, through prototype adversarial training, we contrast prototypes of different event categories to augment the representational capabilities of prototype vectors. Experimental results showcase that our approach attains superior performance on the benchmark dataset FewEvent, surpassing comparative models with reduced training time. Show more
Keywords: Few-shot event detection, PA-CRF, Contrast Learning, Glove
DOI: 10.3233/JIFS-234368
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4265-4275, 2024
Authors: Sikkandar, Mohamed Yacin | Sabarunisha Begum, S. | Algamdi, Musaed Saadullah | Alanazi, Ahmed Bakhit | Alotaibi, Mashhor Shlwan N. | Alenazi, Nadr Saleh F. | AlMutairy, Habib Fallaj | Almutairi, Abdulaziz Fallaj | Almutairi, Mohammed Sulaiman
Article Type: Research Article
Abstract: Alzheimer’s disease (AD) is the predominant aetiology of dementia among the elderly population, accounting for about 60–70% of all instances of cognitive decline. Diffusion tensor imaging (DTI) is a contemporary methodology that enables the cartography of alterations in the microstructure of white matter (WM) in neurological diseases. Nevertheless, the effort of analysing substantial amounts of medical pictures poses significant challenges, prompting researchers to shift their focus towards machine learning. This approach encompasses a collection of computer algorithms that possess the ability to autonomously adjust their output to align with the desired goal. This work proposed the use of a combined …approach using Hidden Markov Model (HMM) and MR-DTI, where Diffusion Tensor Imaging (DTI) is employed as a magnetic resonance imaging technique. The purpose of this method is to forecast the occurrence of AD. Furthermore, the statistical analysis demonstrated a significant correlation between microstructural WM changes with both output in the patient groups and cognitive functioning. This finding suggests that these abnormalities in WM might potentially serve as a biomarker for AD. The proposed method is named as Graphcut Hidden MorkovModel (Graph_HMM) is evaluated on ADNI database with statistical analysis and found that it achieves 99.8% of accuracy, 96.4% of sensitivity, 97.4% of specificity and 12.3% of MSE. Show more
Keywords: Hidden Morkov Model, Alzhemier disease, prediction, segmentation, diffusion tensor imaging (DTI), statistical analysis
DOI: 10.3233/JIFS-234613
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4277-4289, 2024
Authors: Chinnamuniyandi, Maharajan | Chandran, Sowmiya | Xu, Changjin
Article Type: Research Article
Abstract: This research investigates the presence of unique solutions and quasi-uniform stability for a class of fractional-order uncertain BAM neural networks utilizing the Banach fixed point concept, the contraction mapping principle, and analysis techniques. In order to guarantee the equilibrium point of fractional-order BAM neural networks with undetermined parameters, some new adequate criteria are devised, and both time delays result in quasi-uniform stability. The acquired results, which are simple to verify in practice, enhance and extend several earlier research works in some ways. Finally, two illustrative examples are provided to show the value of the suggested outcomes.
Keywords: BAM neural networks, quasi-uniform stability, caputo fractional-order differential equation, uncertain parameters, linear matrix inequality
DOI: 10.3233/JIFS-234744
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4291-4313, 2024
Authors: Saranya, K. | Paulraj, M. | Hema, C.R. | Nithya, S.
Article Type: Research Article
Abstract: Exploring and finding Significant features for colour visualization tasks using the EEG signals is crucial in developing a robust Brain-machine Interface (BMI). The visually evoked potential carries multiple pieces of information, and finding its best feature is a tedious task. The main objective of this research is to concentrate on various linear and non-linear features which classifies the visually evoked potential when visualizing various colours for a certain period with reduced computational time and with higher accuracy. The feature extraction techniques utilized for extracting the features of EEG signals while visualizing various colours are Power Spectral Intensity (PSI), Spectral Entropy …(SE), Detrended Fluctuation analysis (DFA), Higuchi Fractal Dimension (HFD), Petrossian Fractal Dimension (PFD), Multifractal Detrended Fluctuation Analysis (MFDFA). The extracted features were classified using the Multiclass classifier using one vs rest technique Support Vector Machine algorithm. The result shows that the MFDFA method with multiclass classifier combination has achieved 97.4 percent of classification accuracy when compared with other features. Show more
Keywords: Electroencephalogram (EEG), Brain Machine Interface (BMI), Detrended Fluctuation analysis (DFA), Higuchi Fractal Dimension (HFD), Petrossian Fractal Dimension (PFD), Multifractal Detrended Fluctuation Analysis (MFDFA)
DOI: 10.3233/JIFS-235469
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4315-4324, 2024
Authors: Ma, Xiuqin | Sun, Huanling | Qin, Hongwu | Wang, Yibo | Zheng, Yan
Article Type: Research Article
Abstract: When handling complex uncertainty information for multi-attribute decision-making (MADM) problems, interval-valued Fermatean fuzzy sets (IVFFSs) are a novel and powerful tool with a wide range of prospective applications. However, existing MADM methods based on IVFFS ignore associations between attributes and are vulnerable to extreme values. Thus, this research proposes a novel MADM method based on IVFFSs. First, taking into consideration attribute relationships, we propose interval-valued Fermatean fuzzy Bonferroni mean (IVFFBM) operators and interval-valued Fermatean fuzzy weighted Bonferroni mean (IVFFWBM) operators based on IVFFSs. Further, interval-valued Fermatean fuzzy power Bonferroni mean (IVFFPBM) operator and interval-valued Fermatean fuzzy weighted power Bonferroni mean …(IVFFWPBM) operator are suggested considering the impact of extreme values. Secondly, Attribute weights are a key component of MADM. A novel method for determining attribute weights based on fuzzy entropy is developed. Finally, a novel MADM approach is proposed based on the proposed operator and weight determination method. Experimental results on one real-life case demonstrate the superiority and effectiveness of our method. Show more
Keywords: Interval-valued fermatean fuzzy set, bonferroni mean operator, multi-attribute decision making
DOI: 10.3233/JIFS-235495
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4325-4345, 2024
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