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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
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