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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: Gao, Wenlong | Zhi, Minqian | Ke, Yongsong | Wang, Xiaolong | Zhuo, Yun | Liu, Anping | Yang, Yi
Article Type: Research Article
Abstract: Structure learning is the core of graph model Bayesian Network learning, and the current mainstream single search algorithm has problems such as poor learning effect, fuzzy initial network, and easy falling into local optimum. In this paper, we propose a heuristic learning algorithm HC-PSO combining the HC (Hill Climbing) algorithm and PSO (Particle Swarm Optimization) algorithm, which firstly uses HC algorithm to search for locally optimal network structures, takes these networks as the initial networks, then introduces mutation operator and crossover operator, and uses PSO algorithm for global search. Meanwhile, we use the DE (Differential Evolution) strategy to select the …mutation operator and crossover operator. Finally, experiments are conducted in four different datasets to calculate BIC (Bayesian Information Criterion) and HD (Hamming Distance), and comparative analysis is made with other algorithms, the structure shows that the HC-PSO algorithm is superior in feasibility and accuracy. Show more
Keywords: Keywords. Bayesian network, structure learning, HC algorithm, PSO algorithm, DE algorithm
DOI: 10.3233/JIFS-236454
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4347-4359, 2024
Authors: Guo, Hairu | Wang, Jin’ge | Liu, Yongli | Zhang, Yudong
Article Type: Research Article
Abstract: The Aquila optimization (AO) algorithm has the drawbacks of local optimization and poor optimization accuracy when confronted with complex optimization problems. To remedy these drawbacks, this paper proposes an Enhanced aquila optimization (EAO) algorithm. To avoid elite individual from entering the local optima, the elite opposition-based learning strategy is added. To enhance the ability of balancing global exploration and local exploitation, a dynamic boundary strategy is introduced. To elevate the algorithm’s convergence rapidity and precision, an elite retention mechanism is introduced. The effectiveness of EAO is evaluated using CEC2005 benchmark functions and four benchmark images. The experimental results confirm EAO’s …viability and efficacy. The statistical results of Freidman test and the Wilcoxon rank sum test are confirmed EAO’s robustness. The proposed EAO algorithm outperforms previous algorithms and can useful for threshold optimization and pressure vessel design. Show more
Keywords: Aquila optimization algorithm, optimization function, kapur entropy, threshold optimization, pressure vessel design
DOI: 10.3233/JIFS-236804
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4361-4380, 2024
Authors: Nippatla, V. Ramanaiah | Mandava, Srihari
Article Type: Research Article
Abstract: The main contribution of this review work is to show how various control techniques are used to manage the speed of Permanent Magnet Synchronous Motor (PMSM). The PMSM’s are mostly used in electric vehicles, electric traction and high performance industrial drive applications. In this article conventional sensorless techniques are compared with machine learning techniques such as fuzzy logic, artificial neural network and neuro-fuzzy controllers to control the speed of PMSM drive based on vector control approach. The benefits of machine learning techniques used in sensorless PMSM drive are easy to design, less execution time and fast access speed control. The …various controlling techniques used in controller along with its complexity, advantages and drawbacks are discussed in this article. The above mentioned controlling techniques are implemented and simulated by using MATLAB R2019b/Simulink software based on sensorless Model Reference Adaptive System (MRAS) with the help of Field Oriented Control (FOC) strategy of PMSM drive. By comparing the all sensorless controlling techniques in simulation study, it is identified that the combination of neuro-fuzzy controller gives the best speed control performance than other controllers. Show more
Keywords: Field oriented control, fuzzy logic control, neuro-fuzzy control, PMSM drive, sensorless control
DOI: 10.3233/JIFS-222164
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4381-4395, 2024
Authors: Chen, Siting | You, Cuiling | Wu, Nan | Huang, Yan
Article Type: Research Article
Abstract: Cross-efficiency evaluation is an extension of data envelopment analysis (DEA), which can effectively distinguish between decision-making units (DMUs) through self- and peer-evaluation. The cross-efficiency of each DMU in a set of DMUs is measured in terms of intervals when the input–output data are represented by the number of intervals. Based on the interval cross-efficiency matrix, the interval entropy is defined in terms of the likelihood. Then, considering the influence of peer evaluation, the interval conditional cross-efficiency entropy is proposed and an aggregation model of the interval conditional cross-efficiency entropy is presented to create a ranking index for DMUs. Finally, a …simple example is provided to illustrate the effectiveness of the proposed method, which is applied to the evaluation of forest carbon sink efficiency in China. The results indicate that the final cross-efficiencies of all 30 provinces range from 0 to 0.6. Among these provinces, those with a relatively high efficiency include Guangdong, Guizhou, Hainan, Shandong, and Qinghai. Show more
Keywords: Data envelopment analysis, interval data, cross-efficiency, entropy, likelihood
DOI: 10.3233/JIFS-223071
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4397-4415, 2024
Authors: Huang, Zhengwei | Liu, Huayuan | Duan, Chen | Min, Jintao
Article Type: Research Article
Abstract: In the E-commerce environment, conversations between customers and businesses contain lots of useful information about customer sentiment. By mining that information, customer sentiment can be validly identified, which is helpful in accurately identifying customer needs and improving customer satisfaction. For conversational sentiment analysis, most existing approaches take contextual information into account. On this basis, we focus on the degree of association between utterances, which can more effectively capture overall and useful sentiment information in conversation. For this purpose, we propose a hybrid model to recognize customer sentiment in conversation. The model obtains utterance vectors with sentiment information through Sentiment Knowledge …Enhanced Pre-training (SKEP), then uses the bidirectional long short-term memory network (BiLSTM) to generate contextual semantic information, and further obtains customer sentiment information by applying the self-attention mechanism to focus on the degree of association between utterances. The experimental results on the JD Dialog dataset show that our model can more accurately recognize customer sentiment than other baseline models in customer service conversation. Show more
Keywords: Customer sentiment recognition, bidirectional long short-term memory network, self-attention mechanism, sentiment knowledge enhanced pre-training
DOI: 10.3233/JIFS-224183
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4417-4428, 2024
Authors: Ghiduk, Ahmed S. | Hashim, Marwa
Article Type: Research Article
Abstract: Mutation testing can evaluate the quality of the test inputs, generate test data, and simulate any test coverage criterion. Genetic algorithms and harmony search have been applied to reduce the cost of generating test inputs. Although hybridizing search algorithms enhances the efficiency of searching the solution domain, there is a shortage of applying the hybrid search techniques in mutation testing. This paper merges the genetic and harmony search algorithms to effectively generate test data to kill higher-order mutants. In addition, the performance of the proposed technique will be evaluated and compared with a stand-alone genetic algorithm and a stand-alone harmony …search algorithm through an empirical study using a set of benchmark programs. The experimental study shows that the proposed technique outperformed the compared algorithms, reaching a higher killing ratio, where the proposed approach kills 92.8% of higher-order mutants for all tested programs. In comparison, GA kills 88.7%, and HA kills 86.6%. Besides, the proposed algorithm overcame the compared algorithm in reaching a targeted killing ratio faster than the compared algorithms. HGA reduced the execution time for each program with a reduction ratio ranging from 58.9% to 89.8%. Show more
Keywords: Genetic algorithm, harmony search algorithm, higher-order mutation testing, test-data generation
DOI: 10.3233/JIFS-230226
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4429-4452, 2024
Authors: Liu, Xia | Chen, Benwei
Article Type: Research Article
Abstract: This paper defines an improved similarity degree based on inclusion degree as well as advanced information system based on interval coverage and credibility, and thus an attribute reduction framework embodying 4×2 = 8 reduct algorithms is systematically constructed for application and optimization in interval-valued decision systems. Firstly, a harmonic similarity degree is constructed by introducing interval inclusion degree and harmonic average mechanism, which has better semantic interpretation and robustness. Secondly, interval credibility degree and coverage degree are defined for information fusion, and they are combined to propose a δ -fusion condition entropy. The improved condition entropy achieves the information reinforcement and integrity …by dual quantization fusion of credibility and coverage, and it obtains measure development from granularity monotonicity to non-monotonicity. In addition, information and joint entropies are also constructed to obtain system equations. Furthermore, 8 reduct algorithms are designed by using attribute significance for heuristic searches. Finally, data experiments show that our five novel reduct algorithms are superior to the three contrast algorithms on classification performance, which also further verify the effectiveness of proposed similarity degree, information measures and attribute reductions. Show more
Keywords: Attribute reductions, interval-valued decision systems, information measurements, δ-fusion condition entropy, harmonic similarity degree, interval coverage degree and credibility degree
DOI: 10.3233/JIFS-231950
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4453-4466, 2024
Authors: Zhang, Xiang | Huang, Jianhua | Fang, Liting | Li, Qian
Article Type: Research Article
Abstract: Selecting suppliers for prefabricated components (PCs) involves a complex decision-making process, frequently relying on ambiguous information and subjective judgment. However, most existing methods use precise values to portray indicator information and overlook the uncertainty of weights and the subjective preferences of decision-makers (DMs). In order to address these limits, this paper proposes a novel approach to select suppliers of PCs. Initially, an evaluation index system for suppliers is established through literature analysis and a questionnaire survey. The system comprises six layers: product quality, price, service level, comprehensive ability, supply ability, and environmental sustainability. The group decision matrix is then constructed …using the set-valued statistical method and the prospect theory. The index weights are determined by a combination weighting method. Next, the cobweb model is introduced to analyze the disparity between the alternative and ideal solutions, describing their similarities in terms of area and shape. Lastly, cobweb similarity is employed instead of comprehensive distance, combined with the minimum sum of squares criterion, to improve the closeness algorithm and contrast the alternatives. The results demonstrate that this method facilitates a comprehensive evaluation of the benefits and drawbacks of various alternatives from diverse perspectives. Furthermore, it allows flexible adjustments based on the risk preferences of DMs, ensuring accurate and reliable decision results. Show more
Keywords: Select suppliers, risk preference, prospect theory, cobweb model, cobweb similarity
DOI: 10.3233/JIFS-232027
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4467-4479, 2024
Authors: Xu, Yi | Zhou, Meng
Article Type: Research Article
Abstract: As an important extension of classical rough sets, local rough set model can effectively process data with noise. How to effectively calculate three approximation regions, namely positive region, negative region and boundary region, is a crucial issue of local rough sets. Existing calculation methods for approximation regions are based on conditional probability, the time complexity is O (|X ||U ||C |). In order to improve the computational efficiency of three approximation regions of local rough sets, we propose a double-local conditional probability based fast calculation method. First, to improve the computational efficiency of equivalence class, we define the double-local equivalence …class. Second, based on the double-local equivalence class, we define the double-local conditional probability. Finally, given the probability thresholds and a local equivalence class, the monotonicity of double-local conditional probability is proved, on this basis, a double-local conditional probability based fast calculation method for approximation regions of local rough sets is proposed, and the time complexity is O (MAX (|X |2 |C |, |X ||X C ||C |)). Experimental results based on 9 datasets from UCI demonstrate the effectiveness of the proposed method. Show more
Keywords: Local rough sets, approximation regions, double-local equivalence class, double-local conditional probability
DOI: 10.3233/JIFS-232767
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4481-4493, 2024
Authors: Shi, Dingpu | Zhou, Jincheng | Wu, Feng | Wang, Dan | Yang, Duo | Pan, Qingna
Article Type: Research Article
Abstract: How to better grasp students’ learning preferences in the environment of rapid development of engineering and science and technology so as to guide them to high-quality learning is one of the important research topics in the field of educational technology research today. In order to achieve this goal, this paper utilizes the LDA (Latent Dirichlet Allocation) model for text mining of the survey results on the basis of a survey on students’ self-perception evaluation. The results show that the LDA model is capable of extracting terms from text, fuzzy identifying groups of students at different levels and presenting potential logical …relationships between the groups, and further analyzing the learning preferences of students at different levels for IT courses. Based on the student’s learning needs, this paper proposes recommendations for developing students’ learning effectiveness. The LDA method proposed in this paper is a feasible and effective method for assessing students’ learning dynamics as it generates cognitive content about students’ learning and allows for the timely discovery of students’ learning expectations and cutting-edge dynamics. Show more
Keywords: Latent Dirichlet Allocation model, educational data mining, self-perceptions, network modeling
DOI: 10.3233/JIFS-232971
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4495-4509, 2024
Authors: Wang, Hui | Liu, Ensheng | Wei, Hokai
Article Type: Research Article
Abstract: A machine for tunnel boring machine (TBM ) is recognized as productive equipment for tunnel construction. A dependable and precise tunnel boring machine’s performance (such as penetration rate (ROP )) prediction could reduce the cost and help choose the suitable construction method. Hence, this research develops new integrated artificial intelligence methods, i.e., biogeography-based multilayer perceptron neural network (BMLP ) and biogeography-based support vector regression (BSVR ), to forecast TBM PR . Using the biogeography-based optimization (BBO ) algorithm aims to improve the developed model’s performance by determining the optimized neuron number of hidden layers for MLP models and the …ideal values of the essential variables of SVR method. The results show that advanced methods can productively make a nonlinear relation among the ROP and its forecasters to obtain a satisfying forecast. Amongst the BMLP models with several hidden substrates, BM 5L with five hidden substrates could attain the total ranking score (TRS ) greatest rate, with root mean squared error (RMSE ) and coefficient of determination (R 2 ) equal to 0.017 and 0.9969. Simultaneously, the BSVR was the supreme model because of the fewer RMSE (0.00497 m /hr ) and a larger R 2 (0.999) compared with BMLP models. Overall, the acquired TRS s show that the BSVR outperforms the BMLP in terms of performance. As a consequence, the BSVR model may have been chosen as the suggested model if it had been able to accurately forecast the observed value even better than BM 5L . Show more
Keywords: Tunnel boring machine, penetration rate, biogeography-based multilayer perceptron neural network (BMLP), biogeography-based support vector regression (BSVR)
DOI: 10.3233/JIFS-232989
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4511-4528, 2024
Authors: Lin, Xiangyi | Luo, Hongyun | Lian, Yinghuan
Article Type: Research Article
Abstract: This research mainly evaluates the synergistic effect of “dual carbon” and high-quality economic development from four aspects: carbon reduction, pollution reduction, green expansion, and economic growth. Firstly, an indicator system of synergistic effect evaluation is constructed, and a FOPA-Cloud evaluation model is proposed based on the FOPA (Fuzzy Ordinal Priority Approach) and Cloud model. Based on the evaluation of experts’ language variables, it is calculated that a province’s “dual carbon” and high-quality economic development generally belong to a high-level synergistic effect. However, further improvement is still needed in reducing carbon, pollution reduction, and green expansion. The tedious work of pairwise …comparison can be overcome in the FOPA-Cloud model. Optimizing and solving to determine the weight of each indicator can not only determine the overall level but also analyze specific reasons, which can provide a basis for improving the synergistic effect of “dual carbon” and high-quality economic development. Show more
Keywords: Carbon peaking and carbon neutrality, high quality development, fuzzy ordinal priority approach, cloud model
DOI: 10.3233/JIFS-233119
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4529-4541, 2024
Authors: Yu, Ming | Liu, Jiali | Liu, Yi | Yan, Gang
Article Type: Research Article
Abstract: Most existing RGB-D salient object detection (SOD) methods extract features of both modalities in parallel or adopt depth features as supplementary information for unidirectional interaction from depth modality to RGB modality in the encoder stage. These methods ignore the influence of low-quality depth maps, and there is still room for improvement in effectively fusing RGB features and depth features. To address the above problems, this paper proposes a Feature Interaction Network (FINet), which performs bi-directional interaction through feature interaction module (FIM) in the encoder stage. The feature interaction module is divided into two parts: depth enhancement module (DEM) filters the …noise in the depth features through the attention mechanism; and cross enhancement module (CEM) effectively interacts RGB features and depth features. In addition, this paper proposes a two-stage cross-modal fusion strategy: high-level fusion adopts the semantic information of high level for coarse localization of salient regions, and low-level fusion makes full use of the detailed information of low level through boundary fusion, and then we progressively refine high-level and low-level cross-modal features to obtain the final saliency prediction map. Extensive experiments show that the proposed model achieves better performance than eight state-of-the-art models on five standard datasets. Show more
Keywords: RGB-D salient object detection, feature interaction, depth enhancement module, cross enhancement module, cross-modal fusion
DOI: 10.3233/JIFS-233225
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4543-4556, 2024
Authors: Jiang, Guangtian | Song, Anbin
Article Type: Research Article
Abstract: The dual probabilistic linguistic term sets (DPLTSs) are more effective than PLTSs in solving the problem of multi-attribute group decision-making (MAGDM). In this paper, an improved TOPSIS method is developed combining the TOPSIS method and projection measure of DPLTS to supplement the existing research. Firstly, considering the mathematical characteristics of DPLTS, this paper defines the concepts of the module, cosine function, and projection of DPLTS, and then proves the mathematical properties of the cosine function. Secondly, considering the uncertainty of decision-making problems, the weight-solving models are established respectively under the condition that the weight information is completely unknown and partially …known. Furthermore, a novel DPLPrj-TOPSIS approach is established based on the projection measure proposed. It involves integrating experts’ DPLTS evaluations, normalizing different DPLTSs, calculating alternatives’ relative closeness and score, etc. Secondly, the proposed method’s feasibility is demonstrated through a case study that entails selecting network promotion plans for food manufacturers. Finally, the proposed method’s effectiveness and validity are verified by comparing and analyzing it with the traditional TOPSIS method based on a distance measure and other existing decision methods. Show more
Keywords: Dual probabilistic linguistic term sets, multi-attribute group decision-making, technique for order preference by similarity to an ideal solution (TOPSIS), closeness degree
DOI: 10.3233/JIFS-233234
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4557-4572, 2024
Authors: Camgoz Akdag, Hatice | Menekse, Akin | Sahin, Fatih
Article Type: Research Article
Abstract: Cervical cancer is entirely preventable if diagnosed at an early stage; however, the current rate of cervical cancer screening participation is not very adequate, and early detection approaches are still open and demanding. Evaluating the risk levels of potential patients in a practical and economic way is crucial to direct risky candidates to screening and establishing potential treatments to conquer the disease. In this study, a machine learning-integrated fuzzy multi-criteria decision-making (MCDM) methodology is proposed to assess the cervical cancer risk levels of patients. In this context, based on behavioral criteria obtained from the publicly accessible cervical cancer behavior risk …data set from the UCI repository, the risk levels of patients are evaluated. The proposed methodology is established in three stages: In the first stage, using a machine learning technique, i.e., feature selection, the most effective criteria for predicting cervical cancer risk are selected. In the second stage, the criteria for importance through intercriteria correlation (CRITIC) method is used to assign objective importance levels to the criteria. In the third stage, the cervical cancer risk levels of candidate patients are prioritized using the technique for order preference by similarity to the ideal solution (TOPSIS) and, alternatively, the evaluation based on distance from the average solution (EDAS) techniques. The proposed methodology is developed in an interval-valued Pythagorean fuzzy atmosphere for quantifying the uncertainty in the nature of the problem. This study demonstrates that the feature selection algorithm can be efficiently utilized to determine the fundamental criteria of an MCDM problem and to aid in the early identification of cervical cancer. Show more
Keywords: Cervical cancer, machine learning, feature selection, pythagorean fuzzy, MCDM
DOI: 10.3233/JIFS-234647
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4573-4592, 2024
Authors: Shankari, R. | Leena Jasmine, J.S. | Mary Joans, S.
Article Type: Research Article
Abstract: Breast cancer poses a significant health risk for women, demanding early detection to mitigate its mortality impact. Leveraging the power of Deep Learning (DL) in medical imaging, this paper introduces a hybrid model that integrates YOLOv7 and Half UNet for feature extraction. YOLOv7 identifies and localizes potential cancerous regions, while Half UNet focuses on extracting pertinent features with its encoder-decoder structure. The fusion of these discriminative features, coupled with feature selection via Coati Optimization, ensures a comprehensive and optimized dataset. The selected features then feed into the CatBoost classification algorithm, refining parameters iteratively for precise predictions and minimizing the loss …function. Evaluation metrics, including precision, recall, specificity, and accuracy, demonstrate the model’s superior performance. Notably, the proposed model surpasses existing methods in early-stage breast cancer detection. Beyond numerical metrics, its significance lies in the potential to positively impact patient outcomes and increase survival rates. By amalgamating cutting-edge DL techniques, the model excels in identifying intricate patterns crucial for early cancer detection. The efficient fusion of YOLOv7 and Half UNet, coupled with feature optimization through Coati Optimization, sets this model apart. This research contributes to the evolving landscape of medical imaging and DL applications, emphasizing the potential for enhanced breast cancer diagnosis and improved patient prognoses. Show more
Keywords: Breast cancer prediction, YoloV7 model, HalfUNet feature extraction, feature Selection, cat Boost model, performance metrics
DOI: 10.3233/JIFS-235116
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4593-4607, 2024
Authors: Chen, Nongtian | Chen, Kai | Sun, Youchao
Article Type: Research Article
Abstract: The reliability level of general aviation fleet system directly affects the economic benefits and safe operation of general aviation fleet. In order to effectively evaluate the reliability level of general aviation fleet, using the entropy weight variable fuzzy recognition and 1D-CNN depth learning reliability evaluation method. Firstly, taking the Cessna 172 general aviation fleet as the research object, refers to the maintenance statistical analysis of general aviation fleet reliability data, and classifies the fleet reliability evaluation indexes according to the ATA100 chapter standard. Combined with index importance analysis and Delphi expert investigation, 14 key items are extracted as reliability evaluation …indexes of general aviation fleet. Secondly, using entropy weight method to obtain indexes weight objectively, and the evaluation level membership function is constructed based on variable fuzzy recognition method. Finally, a reliability evaluation model based on 1D-CNN deep learning method was established. Through training and testing the reliability data evaluation model of general aviation fleet, and comparing with the results of evaluation methods such as support vector machines. The results show that the recognition rate of the 1D-CNN deep learning method based on entropy weight variable fuzzy recognition can reach 91.95%, verifying the objective effectiveness of the evaluation method. Show more
Keywords: General aviation fleet, reliability evaluation, variable fuzzy recognition, 1D-CNN deep learning
DOI: 10.3233/JIFS-235280
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4609-4619, 2024
Authors: Ravindra Krishna Chandar, V. | Baskaran, P. | Mohanraj, G. | Karthikeyan, D.
Article Type: Research Article
Abstract: Unmanned robotics and autonomous systems (URAS) are integral components of contemporary Cyber-Physical Systems (CPS), allowing vast applications across many domains. However, due to uncertainties and ambiguous data in real-world environments, ensuring robust and efficient decision-making in URAS is difficult. By capturing and reasoning with linguistic data, fuzzy logic has emerged as a potent tool for addressing such uncertainties. Deep Iterative Fuzzy Pooling (DIFP) is a novel method proposed in this paper for improving decision-making in URAS within CPS. The DIFP integrates the capabilities of deep learning and fuzzy logic to effectively pool and aggregate information from multiple sources, thereby facilitating …more precise and trustworthy decision-making. This research presents the architecture and operational principles of DIFP and demonstrates its efficacy in various URAS scenarios through extensive simulations and experiments. The proposed method demonstrated a high-performance level, with an accuracy of 98.86%, precision of 95.30%, recall of 97.32%, F score of 96.26%, and a notably low false positive rate of 4.17%. The results show that DIFP substantially improves decision-making performance relative to conventional methods, making it a promising technique for enhancing the autonomy and dependability of URAS in CPS. Show more
Keywords: Unmanned robotics, autonomous systems, cyberphysical systems, decision-making, fuzzy logic, deep learning, iterative fuzzy pooling, information aggregation, uncertainty handling, reliability, autonomy
DOI: 10.3233/JIFS-235721
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4621-4639, 2024
Authors: Gao, Miaomiao
Article Type: Research Article
Abstract: To improve the effect of intelligent teaching in music classrooms, this paper combines the advanced music waveform iterative reconstruction algorithm to analyze the integration and reconstruction of the music curriculum. Aiming at the problem that the projection matrix occupies a large space and takes a long time to calculate in iterative reconstruction, a fast and real-time incremental method for generating a music wave matrix is proposed. The improved method avoids the judgment and comparison calculations performed by the incremental method when calculating the length and number of each voxel that the ray passes through. The research results show that the …music curriculum integration and reconstruction model based on the advanced music waveform iterative reconstruction algorithm can effectively improve the teaching effect of modern music classrooms. Show more
Keywords: Advanced iteration, reconstruction algorithm, music curriculum, integration, reconstruction
DOI: 10.3233/JIFS-236169
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4641-4655, 2024
Authors: Venkata Krishna, G.P.C. | Vivekananda Reddy, D.
Article Type: Research Article
Abstract: Ensuring data security in cloud computing is crucial due to the growing reliance on cloud-based services. Hybrid cryptography and image steganography have emerged as robust techniques to enhance data confidentiality in the cloud. In this research paper, we propose a novel algorithm, “Machine Learning-Enhanced Hybrid Cryptography and Image Steganography,” integrating these methods to provide comprehensive data protection. The algorithm employs key generation, encryption, steganography, cloud storage, data retrieval, and machine learning-based attack detection to defend against advanced cyber threats. Our experimentation demonstrates the algorithm’s effectiveness in detecting DoS attacks, data breaches, and data leakage attempts using SVM, Neural Network, Isolation …Forest, and Random Forest models. The proposed approach offers broad applicability, fortifying data security and fostering further advancements in cloud security research. Show more
Keywords: Data security, hybrid cryptography, security
DOI: 10.3233/JIFS-236229
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4657-4667, 2024
Authors: Xu, Chuannuo | Cheng, Xuezhen | Wang, Yi
Article Type: Research Article
Abstract: Rolling bearings are a key component of rotating machinery and their health directly affects the safe operation of mechanical equipment. Therefore, fault diagnose for rolling bearings is very important. The fault diagnosis process of rolling bearings includes three stages: signal decomposition, feature extraction, and pattern recognition. Variational mode decomposition (VMD) can suppress end effects, but improper parameter settings will cause information losses or excessive decomposition. In this work, an improved whale optimization algorithm (IWOA) is applied to parameter settings of VMD. Correspondingly, an IWOA-VMD signal decomposition method is proposed. The decomposed signal is combined with a Laplace score method and …classifier to remove the redundancy and noise in the feature set and obtain a low-dimensional sensitive feature subset. Then, aiming at the problem of the parameter settings of a least squares support vector machine (LSSVM) affecting the recognition performance and accuracy, a salp swarm algorithm (SSA) is used to globally optimize the penalty parameter and kernel width in the LSSVM to establish an SSA-LSSVM fault recognition model. This model is applied to the fault diagnosis of rolling bearings. In particular, rolling bearing fault samples at Case Western Reserve University are used to verify the method. The results indicate that the proposed method is effective and improves the speed and accuracy of fault diagnosis. Show more
Keywords: Least squares support vector machine, rolling fault, salp swarm algorithm, variational mode decomposition, whale algorithm
DOI: 10.3233/JIFS-236532
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4669-4680, 2024
Authors: Jothi, J. Sathiya | Chinnadurai, M.
Article Type: Research Article
Abstract: The most common type of disease that is normal among women is lung cancer. It is one of the main reasons among women, despite great efforts to prevent it through trackers. An automatic disease detection system helps doctors identify and provide accurate results, thus minimizing the mortality rate. Computer Aided Diagnosis (CAD) has minimal human intervention and produces more accurate results than humans. It will be a difficult and lengthy task that depends on the experience of the pathologists. Deep learning methods have been shown to give better results when correlated with ML and extract the best highlights from images. …The main objective of this article is to propose a deep learning technique in combination with a convolution neural network (CNN) with a chimpanzee optimization algorithm to diagnose lung cancer. Here, CNN is used for feature extraction and used for extracted feature detection. Experimental results show that the proposed system achieves 100% accuracy, 99% sensitivity, 99% recall, and 98% F1 score compared to other traditional models. As the system achieved correct results, it can help doctors easily investigate lung cancer. Show more
Keywords: Lung cancer, convolution neural network, computer aided diagnosis, chimp optimization
DOI: 10.3233/JIFS-237339
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4681-4696, 2024
Authors: Gnanasundari, P. | Sheela Sobana Rani, K.
Article Type: Research Article
Abstract: Wireless sensor networks (WSNs) are a new technology that helps with a variety of practical uses, involving healthcare and monitoring the environment. In recent years, security has been considered as important topic in WSN since it is vulnerable to several security threats. Recent works uses cryptographic techniques to ensure security in WSN. In existing works, the security methodologies require high resources but still assure low level security. To resolve this issue, this paper proposes a node validation method which is lightweight as well as assures high level security. The main idea behind this work is to integrate Blockchain technology with …WSN environment. We presented a novel Blockchain-assisted Node Validation (BlockNode) methodology for ensuring high level security. To maintain energy efficiency, the network is segregated into multiple clusters by Valid Cluster Formation (VCF) approach. In each cluster, optimum CH is selected by using type-II fuzzy algorithm. The VCF approach only allows the valid nodes which are authorized by Blockchain validation. Then, the data transmission is secured by Jacobian Curve Encryption (JCE) algorithm. For optimal route selection, Energy-aware Reinforcement Learning (ERL) algorithm is proposed. Overall, the proposed work high level security with minimum resource consumption. The experimental results obtained from NS-3.25 simulation tool confirms that the proposed work achieves better performance in security level, encryption & decryption time, delay, energy consumption, delivery ratio and throughput. Show more
Keywords: Node Validation, energy efficiency, cybersecurity, blockchain, WSN
DOI: 10.3233/JIFS-230020
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4697-4711, 2024
Authors: Chen, Zhe | Ye, Lin | Zhang, Hongli | Zhang, Yunting
Article Type: Research Article
Abstract: The purpose of the Chinese similar case matching task is to compare the similarity of two case texts with a given anchor text and find out which text is more similar to the anchor text. In the area of law, it plays an important role and has been of interest to many researchers. Previous approaches have compared legal texts only at the text semantic level, without incorporating article information of law. In addition, the position correlation of words in case texts is often important, but it has not been considered in previous approaches. This paper proposes a method which extracts …features from the semantic similarity level and from the level of related articles of law, respectively, to enable similarity comparisons of legal case texts. When similarity comparisons are made at the semantic similarity level, a novel capsule network method is proposed based on siamese structure that introduces the position correlation and the routing mechanism within the capsule network is improved so that deep text features between case pairs can be learned. When similarity comparisons are made at the level of related articles of law, related articles of law are selected and coded and interacted with the case text features to generate legal features. Experiment is conducted with a real-world legal text dataset, and the proposed model outperformed all baseline models, demonstrating effectiveness of the proposed model. Further, to confirm the generality of the improved capsule network proposed in the paper on long text datasets, this paper also carried out experiments on two long text datasets, demonstrating effectiveness of the improved capsule network proposed in the model. Show more
Keywords: Chinese similar case matching, integrating articles of law, capsule network, dynamic routing with position correlation
DOI: 10.3233/JIFS-232185
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4713-4731, 2024
Authors: Ramasamy, Karthikeyan | Sundaramurthy, Arivoli | Vaithiyalingam, Chitra
Article Type: Research Article
Abstract: The primary goal is to enhance the PSN by maintaining stable and consistent MGS operation and reestablishing stable operating conditions after generational interruptions. The artificial neural network is created using a bio-inspired optimization algorithm, such as particle swarm optimization, second generation particle swarm optimization, and new model particle swarm optimization., which directs the evolutionary learning process to determine the most optimal solution. For the best result, the ANN and bio-inspired algorithm (BIANN) are coupled. The suggested BIANN-based controller is made comprised of an internal current and an external power loop. The proper PI gain parameter is tuned using BIANN, allowing …the MGS to be stable. Three PSOs are used to investigate the suggested method, and the Matlab Simulink platform is used to create the fitness functions. The results are examined and contrasted. The new model’s particle swarm optimization provides MGS functioning and stability that is largely accurate and reliable. Show more
Keywords: Engineering optimization, Micro-grid, BIANN, stability assessment, mathematical model
DOI: 10.3233/JIFS-233112
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4733-4744, 2024
Authors: Chen, Minghao | Wang, Shuai | Zhang, Jiazhong
Article Type: Research Article
Abstract: The influence maximization problem is one of the hot research topics in the field of complex networks in recent years. The so-called influence maximization problem is how to select the seed set that propagates the largest amount of information on a given network. In practical applications, networks are often exposed to complicated environments, and both link-specific and node-specific attacks can have a significant impact on the network’s propagation performance. Several pilot studies have revealed the crux of the robust influence maximization problem, but the current work available is not comprehensive. On the one hand, existing studies only consider the case …that the network structure is stable or under link-specific attacks, and few researches have concentrated on the case when the network structure is under node-specific attacks. On the other hand, the current algorithm fails to combine the information of the search process well to solve the robust influence maximization problem. Aiming at these deficiencies, in this paper, a metric for evaluating the robust influence performance of seeds under node-specific attacks is developed. Guided by this, a genetic algorithm (GA) maintaining the principle of diversity concern (DC) to solve the Robust Influence Maximization (RIM) problem is designed, called DC-GA-RIM. DC-GA-RIM contains several problem-orientated operators and fully considers diverse information in the optimization process, which significantly improves the search ability of the algorithm. The effectiveness of DC-GA-RIM in solving the RIM problem is demonstrated on a variety of networks. The superiority of this algorithm over other approaches is shown. Show more
Keywords: Complex networks, influence maximization problem, robustness, optimization, genetic algorithm
DOI: 10.3233/JIFS-233222
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4745-4759, 2024
Authors: Wang, Xiaoli | Wang, Chongguo | Shi, Gang
Article Type: Research Article
Abstract: As a complex uncertain differential equation, how to solve the multi-dimensional uncertain differential equation is a complicated and difficult problem. This paper will be devoted to the α-path of some special multi-dimensional uncertain differential equations, namely, multi-factor uncertain differential equations, nested uncertain differential equations and multi-factor nested uncertain differential equations. The α-path method is used to study the numerical solution problems of the above three special multi-dimensional uncertain differential equations. At the same time, the inverse uncertainty distributions and expected values of these three special multi-dimensional uncertain differential equations are also obtained. At last, the numerical algorithm examples are given …to verify it. Show more
Keywords: Uncertainty theory, multi-dimensional uncertain differential equation, α-path, numerical algorithm
DOI: 10.3233/JIFS-234432
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4761-4776, 2024
Authors: Wu, Huimin
Article Type: Research Article
Abstract: Text summarization (TS) plays a crucial role in natural language processing (NLP) by automatically condensing and capturing key information from text documents. Its significance extends to diverse fields, including engineering, healthcare, and others, where it offers substantial time and resource savings. However, manual summarization is a laborious task, prompting the need for automated text summarization systems. In this paper, we propose a novel strategy for extractive summarization that leverages a generative adversarial network (GAN)-based method and Bidirectional Encoder Representations from Transformers (BERT) word embedding. BERT, a transformer-based architecture, processes sentence bidirectionally, considering both preceding and following words. This contextual understanding …empowers BERT to generate word representations that carry a deeper meaning and accurately reflect their usage within specific contexts. Our method adopts a generator and discriminator within the GAN framework. The generator assesses the likelihood of each sentence in the summary while the discriminator evaluates the generated summary. To extract meaningful features in parallel, we introduce three dilated convolution layers in the generator and discriminator. Dilated convolution allows for capturing a larger context and incorporating long-range dependencies. By introducing gaps between filter weights, dilated convolution expands the receptive field, enabling the model to consider a broader context of words. To encourage the generator to explore diverse sentence combinations that lead to high-quality summaries, we introduce various noises to each document within our proposed GAN. This approach allows the generator to learn from a range of sentence permutations and select the most suitable ones. We evaluate the performance of our proposed model using the CNN/Daily Mail dataset. The results, measured using the ROUGE metric, demonstrate the superiority of our approach compared to other tested methods. This confirms the effectiveness of our GAN-based strategy, which integrates dilated convolution layers, BERT word embedding, and a generator-discriminator framework in achieving enhanced extractive summarization performance. Show more
Keywords: Automated extractive summarization, generative adversarial network, bidirectional encoder representations from transformers, dilated convolution layer
DOI: 10.3233/JIFS-234709
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4777-4790, 2024
Authors: Adnan, R. Syed Aamir | Kumaravel, R.
Article Type: Research Article
Abstract: Weather Forecasting is very essential as it is helpful in saving lives and materials by predicting disasters such as cyclonic storms, tsunamis, extreme rainfall, etc. Within the defined range of rainfall rate approximation, this study investigates the application of Fuzzy Logic (FL) to forecast rainfall using the Interval Type –2 Fuzzy Inference System (IT2FIS). Environmental parameters which influence rainfall have been applied in this analysis and every implementation is carried out using MATLAB 9.13. The performance of IT2FIS model is compared with the actual data. Correlation coefficient (R 2 ) and Root Mean-Squared Error (RMSE) have been used to evaluate …the performance metrics of the proposed model. The results suggest that the IT2FIS model can capture the dynamic behavior of rainfall data and generate reasonable results, implying that it might be beneficial in long-term rainfall prediction. Show more
Keywords: Weather forecasting, fuzzy logic, interval type –2 Fuzzy Inference System, Environmental parameters
DOI: 10.3233/JIFS-235828
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4791-4802, 2024
Authors: Paul, Ann Rija | Grace Mary Kanaga, E.
Article Type: Research Article
Abstract: In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, …speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc. Show more
Keywords: Vehicle tracking, surveillance, collision detection, trajectory and angle of intersection, deep convolutional neural network
DOI: 10.3233/JIFS-235911
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4803-4816, 2024
Authors: Zhang, Kun | Zhou, Yu | Long, Haixia | Wu, Shulei | Wang, Chaoyang | Hong, Haizhuang | Fu, Xixi | Wang, Haifeng
Article Type: Research Article
Abstract: The complexity of marine information types, data diversity, data collection difficulties and other aspects makes the network security of marine information management more and more prominent, and has become a major issue affecting the stability of the country and society, so it is urgent to establish a marine information management network security system. Traditional network security technology adopts a passive approach and cannot actively detect viruses, trojans, and other hidden objects in the network. Antivirus software would only be used when attacked. If the risk of network attack is too great, the consequences would be unimaginable. This paper designed a …marine information management network security system based on artificial intelligence embedded technology, which improved the efficiency of marine information security management. This paper also applied the embedded technology of AI to the network security management, and proposed the k-means clustering algorithm (K-Means) of AI, which can greatly improve the network security. The experimental results in this paper showed that the intrusion detection rates of System 1 and System 2 were 56.3% and 78.3% respectively when the number of viruses was 50 at 30M, and 65.5% and 80.1% respectively when the number of viruses was 50 at 60M. It showed that the intrusion detection rate of System 2 was higher both at 30M and 60M. Show more
Keywords: Artificial intelligence, K-means clustering algorithm, marine information management, network security
DOI: 10.3233/JIFS-236018
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4817-4827, 2024
Authors: Haripriya, V. | Vishal Gupta, Mohan | Nadkarni, Nikita | Malik, Suraj | Yadav, Aditya | Joshi, Apoorva
Article Type: Research Article
Abstract: From online social networks to life-or-death security systems, multimedia files (photos, movies, and audio recordings) have grown common in today’s digital culture. Protecting people, businesses and infrastructure requires strict adherence to the encryption and decryption of multimedia data. We suggested an Ensemble Whale Optimized Recurrent Neural Network (EWO-RNN)used in this study to overcome these issues. With the help of this study, multimedia security will be evaluated in more accurate and comprehensive manner. Smarter decisions and proactive security measures may follow as a result of this. To increase the system quality and the overall performance, the collected data is pre-processed for …normalized data by using Min-Max Normalization. Pre-processed data is extracted by using Kernel Principle Component Analysis (K-PCA). The EWO-RNN evaluates the effectiveness and efficiency of an approach by analyzing the performance of Accuracy (97.85%), Precision (92.2%), F1-score (96.1%), Mean Square Error (MSE) (0.086), Root Mean Square (RMSE) (0.12%) and Sensitivity (95%). The Enhanced Radial Base Deep Learning Algorithm for Predicting Multimedia Security Issues proposes a solution with improved resilience, accuracy, generalization, and decision-making capabilities. In a dynamic and evolving digital environment, this makes the algorithm a viable tool for multimedia security assessments. Show more
Keywords: Multimedia, security issues, ensemble whale optimized recurrent neural network (EWO-RNN), min-max normalization, kernel principle component analysis (K-PCA)
DOI: 10.3233/JIFS-237041
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4829-4840, 2024
Authors: Jayachandran, A. | Ganesh, S.
Article Type: Research Article
Abstract: Microaneurysms, tiny, circular red dots that occur in retinal fundus images, are one of the earliest symptoms of diabetic retinopathy. Because microaneurysms are small and delicate, detecting them can be difficult. Their small size and cunning character make automatic detection of them difficult. In this study, a novel encoder-decoder network is proposed to segment the MAs automatically and accurately. The encoder part mainly consists of three parts: a low-level feature extraction module composed of a dense connectivity block (Dense Block), a High-resolution Block (HR Block), and an Atrous Spatial Pyramid Pooling (ASPP) module, of which the latter two modules are …used to extract high-level information. Therefore, the network is named a Multi-Level Features based Deep Convolutional Neural Network (MF-DCNN). The proposed decoder takes advantage of the multi-scale features from the encoder to predict MA regions. Compared with the existing methods on three datasets, it is proved that the proposed method is better than the current excellent methods in the segmentation results of the normal and abnormal fundus. In the case of fewer network parameters, MF-DCNN achieves better prediction performance on intersection over union (IoU), dice similarity coefficient (DSC), and other evaluation metrics. MF-DCNN is lightweight and able to use multi-scale features to predict MA regions. It can be used to automatically segment the MA and assist in computer-aided diagnosis. Show more
Keywords: Microaneurysm detection, fundus images, segmentation, features
DOI: 10.3233/JIFS-230154
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4841-4857, 2024
Authors: Long, Zuqiang | Luo, Zelong | Wang, Yunmeng
Article Type: Research Article
Abstract: The analytical structure plays an important role in system design and stability analysis of FLC. Structure analysis of traditional IT2 TS FLCs using Zadeh min operator and KM algorithm requires multiple IC dividing, which results in complex calculation and cumbersome parameter adjustment. This article proposes a new IT2 TS FLC by adopting product-type operator and NT algorithm. The proposed controller has such advantages: 1)use product-type operator to skip the partitioning in fuzzy inference process;2) use NT algorithm to avoid determining switching points and sorting rule consequents in type-reduction process. Then, the controller is proved to be universal approximator and sufficient …condition is deduced. Finally, we derive the analytical structure of the controller by substituting the parameters, and study the relationship between the uncertainty parameter θ and the analytical structure when the rule consequents are symmetric or asymmetric. Both the computational costs during operation and the computational workload for structural analysis can be reduced significantly by using the new FLC. Show more
Keywords: NT algorithm, IT2 TS FLC, analytical structure, universal approximation
DOI: 10.3233/JIFS-231866
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4859-4867, 2024
Authors: Yang, Hui | Wang, Yi-Na
Article Type: Research Article
Abstract: In this paper, we provide some new characterizations of L -convex systems. For this purpose, we first introduce the concept of partial hull operators and establish the categorical relationship between partial hull operators and convex systems. Then we abstract the relationship between a subset and its partially convex hull in convex system to a binary relation, called enclosed relation. Moreover, we prove that the enclosed relations are equivalent to convex systems. Subsequently, we generalize the concept of partial hull operators and enclosed relations to the fuzzy case, which will be called L -partial hull operators and L -enclosed relations respectively. …Finally we explore the categorical isomorphisms between them. Show more
Keywords: L-convex systems, partially L-convex hull operators, L-partial hull operators, L-enclosed relations
DOI: 10.3233/JIFS-232243
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4869-4879, 2024
Authors: dos Santos Lima, Matheus | Kich, Victor Augusto | Steinmetz, Raul | Tello Gamarra, Daniel Fernando
Article Type: Research Article
Abstract: The present study focuses on the implementation of Deep Reinforcement Learning (Deep-RL) techniques for a parallel manipulator robot, specifically the Delta Robot, within a simulated setting. We introduced a simulation framework designed to guide the Delta Robot’s end-effector to a designated spatial point accurately. Within this environment, the robotic agent undergoes a learning process grounded in trial and error. It garners positive rewards for successful predictions regarding the next action and faces negative repercussions for inaccuracies. Through this iterative learning mechanism, the robot refines its strategies, thereby establishing improved decision-making rules based on the ever-evolving environment states. Our investigation delved …into three distinct Deep-RL algorithms: the Deep Q-Network Algorithm (DQN), the Double Deep Q-Network (DDQN), and the Trust Region Policy Optimization Algorithm (TRPO). All three methodologies were adept at addressing the challenge presented, and a comprehensive discussion of the findings is encapsulated in the subsequent sections of the paper. Show more
Keywords: Deep reinforcement learning, parallel robots, delta robot
DOI: 10.3233/JIFS-232795
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4881-4894, 2024
Authors: Gao, Kaihan | Ju, Yiwei | Li, Shuai | Yang, Xuebing | Zhang, Wensheng | Li, Guoqing
Article Type: Research Article
Abstract: Recent advances in high-throughput electron microscopy (EM) have revolutionized the examination of microstructures by enabling fast EM image generation. However, accurately segmenting EM images remains challenging due to inherent characteristics, including low contrast and subtle grayscale variations. Moreover, as manually annotated EM images are limited, it is usually impractical to utilize deep learning techniques for EM image segmentation. To address these challenges, the pyramid multiscale channel attention network (PmcaNet) is specifically designed. PmcaNet employs a convolutional neural network-based architecture and a multiscale feature pyramid to effectively capture global context information, enhancing its ability to comprehend the intricate structures within EM …images. To enable the rapid extraction of channel-wise dependencies, a novel attention module is introduced to enhance the representation of intricate nonlinear features within the images. The performance of PmcaNet is evaluated on two general EM image segmentation datasets as well as a homemade dataset of superalloy materials, regarding pixel-wise accuracy and mean intersection over union (mIoU) as evaluation metrics. Extensive experiments demonstrate that PmcaNet outperforms other models on the ISBI 2012 dataset, achieving 87.85% pixel-wise accuracy and 73.11% mean intersection over union (mIoU), while also advancing results on the Kathuri and SEM-material datasets. Show more
Keywords: Electron microscopy, image segmentation, convolutional neural network, multiscale feature pyramid
DOI: 10.3233/JIFS-235138
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4895-4907, 2024
Authors: Shi, Liqing | Xiong, Taiping | Cui, Gengshen | Pan, Minghua | Zhu, Zhiguo | Cheng, Wei
Article Type: Research Article
Abstract: In order to accurately estimate the disparity of ill-posed regions, such as weak texture and occlusion regions, we propose DSPANet, a stereo matching network that incorporates a dual-stream pyramid module and a channel and spatial attention module. The dual-stream pyramid module captures numerous complementary features from different layers by utilizing multi-resolution inputs and feature extraction blocks. This approach enables the learning of local detailed features at various scales. These features at various scales are then combined to calculate the stereo matching cost. By incorporating channel and spatial attention module into the feature extraction process to obtain richer and more concise …contextual information, the matching cost can be constructed more accurately, which provides powerful conditions for subsequent cost aggregation. In the cost aggregation stage, we utilize the stacked hourglass module for both encoding and decoding. Additionally, we incorporate 3D global attention upsampling during the decoding stage, which enables high-level features to provide guidance information to low-level features in a simple way. We evaluate our proposed method on the Scene Flow dataset, as well as the KITTI2012 and KITTI2015 datasets. The experimental results demonstrate that our DSPANet achieves superior performance and effectively enhances the matching results in ill-posed regions. Our code has been implemented using PyTorch and will be released after paper publication at https://github.com/Shi-LiQing/DSPANet . Show more
Keywords: Stereo matching, dual-stream pyramid, attention mechanism, binocular vision
DOI: 10.3233/JIFS-235415
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4909-4922, 2024
Authors: Zhang, Baishun | Su, Xue
Article Type: Research Article
Abstract: In practical applications of machine learning, only part of data is labeled because the cost of assessing class label is relatively high. Measure of uncertainty is abbreviated as MU. This paper explores MU for partially labeled real-valued data via a discernibility relation. First, a decision information system with partially labeled real-valued data (p-RVDIS) is separated into two decision information systems: one is the decision information system with labeled real-valued data (l-RVDIS) and the other is the decision information system with unlabeled real-valued data (u-RVDIS). Then, based on a discernibility relation, dependence function, conditional information entropy and conditional information amount, four …degrees of importance on an attribute subset in a p-RVDIS are defined. They are calculated by taking the weighted sum of l-RVDIS and u-RVDIS based on the missing rate, which can be considered as four MUs for a p-RVDIS. Combining l-RVDIS and u-RVDIS provides a more accurate assessment of the importance and classification ability of attribute subsets in a p-RVDIS. This is precisely the novelty of this paper. Finally, experimental analysis on several datasets verify the effectiveness of these MUs. These findings will contribute to the comprehension of the essence of the uncertainty in a p-RVDIS. Show more
Keywords: Partially labeled real-valued data, p-RVDIS, Discernibility relation, Uncertainty, Measure
DOI: 10.3233/JIFS-236958
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4923-4940, 2024
Authors: Sun, Ruixia | Xiao, Ping | Tang, Wei | Chen, Long | Chen, Dandan
Article Type: Research Article
Abstract: In order to further enhance energy conservation and emission reduction, the header lifting structure of a harvester is studied. First, a double electric pushrod structure is used to replace the oil cylinder and air cylinder lifting structure of a traditional header to reduce fuel consumption and harmful gas emission. Furthermore, a mathematical model and a simulation model of the electric pushrod are established. To enhance the control effect of the header lifting structure, an improved version of the traditional gray wolf optimization (GWO) algorithm is designed. The nonlinear convergence factor, Kent chaotic mapping and convergence surrounding and spiral updating operations …are introduced to increase the convergence speed and optimization accuracy of this algorithm. The improved GWO (IGWO) algorithm is applied to optimize the proportional-integral-derivative (PID) controller of the double pushrod coordinated control system. Then, a new IGWO-PID control algorithm is also designed. The cross-coupling control strategy of header’s double pushrods is then studied. Results of the simulation and bench test show that the IGWO-PID control algorithm and the cross-coupling control strategy can effectively enhance controlling effect of the harvester header. The left and right pushrods can achieve good synchronous and coordinated movements. Show more
Keywords: Harvester header, electric pushrod, IGWO-PID control algorithm, cross-coupled collocated control
DOI: 10.3233/JIFS-231193
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4941-4953, 2024
Authors: Qin, Yong | Xu, Zeshui | Wang, Xinxin | Škare, Marinko
Article Type: Research Article
Abstract: Fuzzy decision-making is increasingly becoming a pivotal approach to solving complex and intricate issues in tourism and hospitality management. The primary objective of this study is to unveil the developmental status, key themes and research trends within fuzzy decision-making in tourism and hospitality management (FDMTH) using Bibliometrix, CorText Manager and VOSviewer tools. As such, we conduct a comprehensive bibliometric and content-wise analysis of selected 341 publications concerning FDMTH. For one thing, we use valuable bibliometric indicators to conduct a general feature peek and performance analysis of the audited corpus. The research findings reveal a sustained scholarly interest in FDMTH. As …a critical player, Pappas, Nikolaos leads the volume of publications. The Journal of Intelligent & Fuzzy Systems stands out as the preferred outlet for FDMTH research. For another, the contingency matrix and bump graph modules are employed to detect the knowledge flow and intellectual connections in FDMTH. The results of network mapping tentatively identify geographic and thematic biases in FDMTH. More importantly, bibliographic coupling analysis reveals four specific themes, namely multi-criteria decision-making and evaluation, factors identification, fuzzy programming and forecasting, and fuzzy intelligence. Our pioneer work will contribute to the present understanding of the complexity and interdisciplinarity of FDMTH. Show more
Keywords: Fuzzy decision-making, tourism and hospitality, CorText manager, intellectual connections, thematic bias
DOI: 10.3233/JIFS-236618
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4955-4980, 2024
Authors: Yang, Xiangfei | Zhang, Faping | Wei, Jianfeng
Article Type: Research Article
Abstract: To improve the accuracy of fault diagnosis for recoil systems under multiple operating conditions, a fuzzy RBF neural network (Radial Basis Function, RBF) fault diagnosis method based on knowledge and data fusion is proposed. A kinetic model for the recoil system is first established to describe the system’s behavior. Next, fuzzy RBF neural network is used to establish the relationship between abnormal operating parameters and fault causes, achieving a fault cause diagnosis accurately based on the integration of expert experience knowledge and system operation data. A study case demonstrate that the algorithm has strong knowledge and data fusion capabilities and …can effectively identify faults in recoil system. Show more
Keywords: Fuzzy method, RBF neural network, knowledge and data fusion, the recoil system, fault diagnosis
DOI: 10.3233/JIFS-230683
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4981-4994, 2024
Authors: Senthil Kumar, V. | Aruna, R. | Varalatchoumy, M. | Manikannan, P. | Santhana Krishnan, T. | Usha Rani, B. | Kumar, Ashok | Rajaram, A.
Article Type: Research Article
Abstract: As the world embraces the transition towards renewable energy, the optimization of solar power plants becomes paramount. In this research, we present a comprehensive framework that leverages advanced analytical methodologies to address critical operational challenges and elevate the efficiency of solar power generation. Our framework encompasses data preprocessing, time series analysis, anomaly detection, and equipment performance assessment, synergistically combining their strengths to offer a holistic solution. The heart of our proposed approach lies in the precision and efficacy of anomaly detection. We introduce two powerful techniques—LSTM Autoencoder and Isolation Forest—to identify anomalies and equipment underperformance. Through meticulous evaluation, we …showcase their comparative performance, revealing the nuanced strengths of each. Visualizations depict the model’s proficiency in pinpointing anomalies, with LSTM Autoencoder emerging as a standout performer, adept at capturing even subtle deviations from expected patterns. Our research extends beyond detection to equip stakeholders with real-time insights. The visualization of daily yield trends uncovers potential data anomalies, enabling timely intervention and rectification. Additionally, we address equipment failures by harnessing random forest modeling to establish a robust relationship between irradiance, temperature, and DC power. This approach provides a powerful tool for real-time condition monitoring and fault detection, enabling proactive maintenance and enhancing operational resilience. Show more
DOI: 10.3233/JIFS-235578
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4995-5011, 2024
Authors: Sivakumaran, V. | Sankar, K.
Article Type: Research Article
Abstract: Consider a simple graph G = 〈V , E 〉 with n vertices. Define a function f from vertex set of G to set of integers 1, 2, …, n subject to the condition that there exists an integer k > 0 such that the sum of adjacent labels of each vertex in G is equal to k . In this paper, we prove that the graph K n - ⌊ n 2 ⌋ e has DML for all n ≥ 1, decomposition of distance magic graph K 2n - {ne …} into n ( n - 1 ) 2 edge distinct copies of C 4 for all n ≥ 2 and DML of join of complete graphs gives unique mathematical model and it will be a useful model in big data analysis. Show more
Keywords: Distance magic labeling, magic constant, decomposition of distance magic graph, join of complete graphs, big data analysis
DOI: 10.3233/JIFS-224511
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5013-5020, 2024
Authors: Wang, Xiaoying | Chen, Xiaohai | Zhang, Zhongwen | He, Haisheng
Article Type: Research Article
Abstract: Intelligent Transportation Systems (ITS) have experienced significant growth over the past decade thanks to advances in control, communication, and information technology applied to vehicles, roads, and traffic control systems. Vehicle type classification plays a vital role in implementing ITS because of its ability to collect useful traffic information, enable future development of transport infrastructures, and increase human comfort. As a branch of machine learning, deep learning represents a frontier for artificial intelligence, which seeks to be closer to its primary goal. Deep learning is a powerful tool for classifying vehicle types because it can capture complex traffic data characteristics and …learn from large amounts of data. This means that it can be used to accurately classify traffic data and generate valuable insights that can be used to improve traffic management. Researchers have successfully adopted these algorithms as a solution to propose optimal vehicle-type classification strategies. This paper highlights the role of deep learning algorithms in solving the vehicle type classification problem, reviewing the state-of-the-art approaches in this field. Show more
Keywords: Transportation systems, ITS, vehicle type, deep learning, optimization
DOI: 10.3233/JIFS-233302
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5021-5032, 2024
Authors: Subbaian, Santhi | Balasubramanian, Anand | Marimuthu, Murugan | Chandrasekaran, Suresh | Muthusaravanan, Gokila
Article Type: Research Article
Abstract: Coconut farming is a significant agricultural activity in South India, but the coconut trees face challenges due to adverse weather conditions and environmental factors. These challenges include various leaf diseases and pest infestations. Identifying and locating these issues can be difficult because of the large foliage and shading provided by the coconut trees. Recent research has shown that Computer Vision algorithms are becoming increasingly important for solving problems related to object identification and detection. So, in this work, the YOLOv4 algorithm was employed to detect and pinpoint diseases and infections in coconut leaves from images. The YOLOv4 model incorporates advanced …features such as cross-stage partial connections, spatial pyramid pooling, contextual feature selection, and path-based aggregation. These features enhance the model’s ability to efficiently identify issues such as yellowing and drying of leaves, pest infections, and leaf flaccidity in coconut leaf images taken in various environmental conditions. Furthermore, the model’s predictive accuracy was enhanced through multi-scale feature detection, PANet feature learning, and adaptive bounding boxes. These improvements resulted in an impressive 88% F1-Score and an 85% Mean Average Precision. The model demonstrates its effectiveness and robustness even when dealing with medium-resolution images, offering improved accuracy and speed in disease and pest detection on coconut leaves. Show more
Keywords: Coconut leaf disease, YOLO v4, precision agriculture, pest, faster RCNN, YOLO-SPP
DOI: 10.3233/JIFS-233831
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5033-5045, 2024
Authors: Zhang, Mingming | Wu, Qingling
Article Type: Research Article
Abstract: High-performance concrete (HPC) is a specialized type of concrete designed to meet stringent performance and uniformity standards that are difficult to achieve with conventional materials and standard mixing, placing, and curing methods. The testing process to determine the mechanical properties of HPC specimens is complex and time-consuming, and making improvements can be difficult after the test result does not meet the required properties. Anticipating concrete characteristics is a pivotal facet in the realm of High-Performance Concrete (HPC) manufacturing. Machine learning (ML)-driven models emerge as a promising avenue to tackle this formidable task within this context. This research endeavors to employ …a synergy of ML hybrid and ensemble frameworks for the prognostication of the mechanical attributes within HPC, encompassing compressive strength (CS), slump (SL), and flexural strength (FS). The formulation of these hybrid and ensemble constructs was executed through the integration of Support Vector Regression (SVR) with three distinct meta-heuristic algorithms: Prairie Dog Optimization (PDO), Pelican Optimization Algorithm (POA), and Mountain Gazelle Optimizer (MGO). Some criteria evaluators were used in the training, validation, and testing phases to assess the robustness of the established models, and the best model was proposed for practical applications through comparative analysis of the results. As a result, the hybrid and ensemble models were the potential methods to predict concrete properties accurately and efficiently, thereby reducing the need for expensive and time-consuming testing procedures. In general, the ensemble model, i.e., SVPPM, had a more suitable performance with high values of R2 equal to 0.989 (MPa), 0.984 (mm), and 0.992 (MPa) and RMSE = 3.82 (MPa), 9.5 (mm), and 0.30 (MPa) for CS, SL, FS compared to other models, respectively. Show more
Keywords: High-Performance dynamic properties, support vector regression, prairie dog optimization, pelican optimization algorithm, mountain gazelle optimizer
DOI: 10.3233/JIFS-234125
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5047-5072, 2024
Authors: Wang, Haomiao | Li, Yibin | Jiang, Mingshun | Zhang, Faye
Article Type: Research Article
Abstract: Domain adaptation (DA) technology has the ability to solve fault diagnosis (FD) problems under variable operating conditions. However, DA technology faces two issues: (1) in general, vibration signals inevitably contain noise, which makes it difficult to extract discriminant features.(2) there are unknown fault types in target domain. These issues will lead to poor diagnostic performance. To solve above issues, a new cross-domain open-set transfer FD method called feature improvement adversarial network (FIAN) is proposed in this article. Specifically, to alleviate noise interference, a feature improvement module (FIM) is proposed and embedded into the backbone convolutional neural network to form new …feature extractor. FIM uses soft threshold function to enhance important information and suppresses redundant information. Furthermore,open-set DA by back-propagation (OSBP) is introduced into FIAN. OSBP can predict the probability that a target domain sample belongs to an unknown category, so that it can effectively identify unknown and known category samples. Experimental results demonstrated its effectiveness and superiority in two bearing datasets. Show more
Keywords: Fault diagnosis, rolling bearing, open-set domain adaptation, feature improvement module, adversarial network
DOI: 10.3233/JIFS-236593
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5073-5085, 2024
Authors: Chandana Mani, R.K. | Kamalakannan, J.
Article Type: Research Article
Abstract: Breast cancer (BC) is the most common cancer amongst women that threatens the health of women, initial diagnosis of BC becomes essential. Though there were several means to diagnose BC, the standard way is pathological analysis. Precise diagnosis of BC necessitates experienced histopathologists and needs more effort and time for completing this task. Recently, machine learning (ML) was successfully implemented in text classification, image recognition, and object recognition. With the emergence of computer aided diagnoses (CAD) technology, ML was effectively implemented for BC diagnosis. Histopathological image classification depends on deep learning (DL), particularly convolution neural network (CNN), which frequently needs …a large amount of labelled training models, whereas the labelled data was hard to obtain. This study develops an Aquila Optimizer(AO) with Hybrid ResNet-DenseNet Enabled Breast Cancer Classification on Histopathological Images (AOHRD-BC2HI). The proposed AOHRD-BC2HI technique inspects the histopathological images for the diagnosis of breast cancer. To accomplish this, the presented AOHRD-BC2HI technique uses hybridization of Resnet with Densenet (HRD) model for feature extraction. Moreover, the HRD method can be enforced for feature extracting procedure in which the DenseNet (feature value memory by concatenation) and ResNet (refinement of feature value by addition) were interpreted. For BC detection and classification, the DSAE model is utilized. The AO algorithm is exploited to improve the detection performance of DSAE model. The experimental validation of the presented AOHRD-BC2HI approach is tested using benchmark dataset and the results are investigated under distinct measures.Also the proposed model achieved the accuracy of 96%. The comparative result reports the improved performance of the presented AOHRD-BC2HI technique over other recent methods. Show more
Keywords: Breast cancer, histopathological images, aquila optimizer, computer aided diagnosis, deep learning
DOI: 10.3233/JIFS-236636
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5087-5102, 2024
Authors: Jyoti, | Singh, Jaspreeti | Gosain, Anjana
Article Type: Research Article
Abstract: Addressing missing values is a persistent challenge in the field of data mining. The presence of incomplete data can significantly compromise the overall data quality. Consequently, it is crucial to handle incomplete data efficiently. This paper presents a novel approach for imputing missing values that incorporates Kernelized Fuzzy C-Means (KFCM) clustering and proposes a method termed LIKFCM, which combines its benefits with Linear Interpolation (LI). The proposed LIKFCM’s performance is assessed through a comparison against nine state-of-the-art imputation techniques (mean, median, LI, EMI, KNNI, KMI, FKMI, LIFCM, and LIPFCM) across ten widely used real-world datasets from the UCI repository with …six combinations of missing ratios to assess the efficacy of the proposed imputation method. From the experimental results, it is evident that our proposed method outperforms the existing imputation methods with significant improvements in terms of RMSE & MAE for these datasets. Additionally, experiments examining the effect of missing values validate the robustness of the proposed approach by handling different missing ratios. The performance validation of the proposed approach against other state-of-the-art imputation methods has been conducted utilizing a Kendall’s W statistical test, involving a comparison of their mean ranks across different missing ratios. The outcomes indicate that LIKFCM has outperformed other imputation methods, attaining the highest rank in terms of different evaluation criteria. Show more
Keywords: Incomplete data, missing value imputation, fuzzy clustering, LI, LIKFCM
DOI: 10.3233/JIFS-236869
Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 5103-5123, 2024
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