Relevant bibliographies by topics / ANFS- adaptive neuro-fuzzy system

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'ANFS- adaptive neuro-fuzzy system'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "ANFS- adaptive neuro-fuzzy system"

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Senivarapu Ankit Reddy and Dr. Vustelamuri Padmavathi. "Integration of Neuro-Fuzzy Systems in Medical Diagnostics and Data Security - A Review." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 5 (2024): 196–200. http://dx.doi.org/10.32628/ijsrset24115113.

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Adaptive Neuro-Fuzzy Systems (ANFS) have become increasingly prevalent in a variety of fields due to their ability to process complex and uncertain data with high accuracy. This research article reviews three major contributions of ANFS: their application in deep neuro-fuzzy systems (DNFS) for healthcare and industrial systems, neuro-fuzzy logic controllers for paralysis estimation, and ANFIS-based solutions for secure cloud storage in medical IoT (MIoT). The findings emphasize the importance of ANFS in improving decision-making, diagnosis, and data security. This paper concludes with a discus
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Badvaji, Bhumika, Raunak Jangid, and Kapil Parikh. "PERFORMANCE ANALYSIS ON ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) BASED MPPT CONTROLLER FOR DC-DC CONVERTER FOR STANDALONE SOLAR ENERGY GENERATION SYSTEM." International Journal of Technical Research & Science 7, no. 06 (2022): 14–20. http://dx.doi.org/10.30780/ijtrs.v07.i06.003.

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This paper presents the development and performance analysis of Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller for a DC to DC converter. The proposed system consists of 2.0 kW PV array, DC to DC boost converter and load. The proposed algorithm has advantages of neural and fuzzy networks. To enhance of converter performance, Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller is used. In order to demonstrate the proposed ANFIS controller abilities to follow the reference voltage and current, its performance is simulated and compared with Artificial Intellige
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Vladov, Serhii, Maryna Bulakh, Victoria Vysotska, and Ruslan Yakovliev. "Onboard Neuro-Fuzzy Adaptive Helicopter Turboshaft Engine Automatic Control System." Energies 17, no. 16 (2024): 4195. http://dx.doi.org/10.3390/en17164195.

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A modified onboard neuro-fuzzy adaptive (NFA) helicopter turboshaft engine (HTE) automatic control system (ACS) is proposed, which is based on a circuit consisting of a research object, a regulator, an emulator, a compensator, and an observer unit. In this scheme, it is proposed to use the proposed AFNN six-layer hybrid neuro-fuzzy network (NFN) with Sugeno fuzzy inference and a Gaussian membership function for fuzzy variables, which makes it possible to reduce the HTE fuel consumption parameter transient process regulation time by 15.0 times compared with the use of a traditional system autom
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Sabet, Masumeh, Mehdi Naseri, and Hosein Sabet. "Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System." Annals of Warsaw University of Life Sciences - SGGW. Land Reclamation 42, no. 1 (2010): 159–67. http://dx.doi.org/10.2478/v10060-008-0074-6.

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Prediction of littoral drift with Adaptive Neuro-Fuzzy Inference System The amount of sand moving parallel to a coastline forms a prerequisite for many harbor design projects. Such information is currently obtained through various empirical formulae. Despite so many works in the past, an accurate and reliable estimation of the rate of sand drift has still remained a problem. It is a non-linear process and can be described by chaotic time-series. The current study addresses this issue through the use of Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS is about taking an initial fuzzy infere
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Barhoum, Tarek. "COMPARATIVE STUDY BETWEEN EXTENDED ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (EANFIS) AND CO-ACTIVE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (CANFIS) IN CONTROLLING FULL CAR ACTIVE SUSPENSION SYSTEM." INTERNATIONAL JOURNAL OF CURRENT RESEARCH 8, no. 0975-833X (2016): 36921–30. https://doi.org/10.5281/zenodo.15353284.

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This Paper presents two adaptive vehicle suspension control methods, which significantly improvethe performance of mechatronic suspension systems in full car model by absorbing shocks caused bybumpy roads and preventing vibrations from reaching the cockpit and providing stability andcoherence required. The first control approach is an extension to the Adaptive Neuro-Fuzzy InferenceSystem (ANFIS) called Extended adaptive Neuro fuzzy inference system (EANFIS). The secondcontrol approach is a special type of multi-inputs multi-outputs ANFIS model called Co-Activeadaptive Neuro fuzzy inference sys
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Alić, Senad, Safet Brdarević, Sabahudin Jašarević, and Mustafa Imamović. "INTRODUCTION OF EXSPERT SYSTEM ANFIS INTO MAINTENANCE SYSTEM OF PROCESS FANS." Mašinstvo 12, no. 2 (2015): 41–51. https://doi.org/10.62456/jmem.2015.02.041.

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<p style="text-align: justify;">Neuro-fuzzy systems represent a modern class of hybrid intelligent systems. They are described as artificial neural networks characterized by fuzzy parameters. The combination of two different concepts of artificial intelligence tries to take of individual advantages of fuzzy logic and artificial neural networks in hybrid systems of homogeneous structure. Such systems are increasingly being used for solving of everyday complex problems. The possibility to display fuzzy models in the form of neural network is commonly used in the p
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Jamali, A., H. Babaei, N. Nariman-Zadeh, SH Ashraf Talesh, and T. Mirzababaie Mostofi. "Multi-objective optimum design of ANFIS for modelling and prediction of deformation of thin plates subjected to hydrodynamic impact loading." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 234, no. 3 (2016): 368–78. http://dx.doi.org/10.1177/1464420716660332.

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Drop hammer impact experiments have been carried out to assess the dynamic plastic response of fully clamped circular and rectangular plates made of aluminum and steel subjected to hydrodynamic impact loading at various energy levels. Also, the effective parameters in forming process are proposed in non-dimensional forms for modeling and prediction of the central deflection of plates using adaptive neuro-fuzzy inference system in conjunction with genetic algorithm and singular value decomposition method. Genetic algorithm is used for optimal scheme of Gaussian membership function’s variables a
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Sangeetha, J., and P. Renuga. "Recurrent ANFIS-Coordinated Controller Design for Multimachine Power System with FACTS Devices." Journal of Circuits, Systems and Computers 26, no. 02 (2016): 1750034. http://dx.doi.org/10.1142/s0218126617500347.

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This paper proposes the design of auxiliary-coordinated controller for static VAR compensator (SVC) and thyristor-controlled series capacitor (TCSC) devices by adaptive fuzzy optimized technique for oscillation damping in multimachine power systems. The performance of the coordinated control of SVC and TCSC devices based on feedforward adaptive neuro fuzzy inference system (F-ANFIS) is compared with that of the adaptive neuro fuzzy inference system (ANFIS) structure based on recurrent adaptive neuro fuzzy inference system (R-ANFIS) network architecture. The objective of the coordinated control
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Blahová, Lenka, Ján Dvoran, and Jana Kmeťová. "Neuro-fuzzy control design of processes in chemical technologies." Archives of Control Sciences 22, no. 2 (2012): 233–50. http://dx.doi.org/10.2478/v10170-011-0022-2.

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Neuro-fuzzy control design of processes in chemical technologies The paper presents design of neuro-fuzzy control and its application in chemical technologies. Our approach to neuro-fuzzy control is a combination of the neural predictive controller and the neuro-fuzzy controller (Adaptive Network-based Fuzzy Inference System - ANFIS). These controllers work in parallel. The output of ANFIS adjusts the output of the neural predictive controller to enhance the control performance. Such design of an intelligent control system is applied to control of the continuous stirred tank reactor and labora
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Tahour, Ahmed, Hamza Abid, and Ghani Aissaoui. "Adaptive neuro-fuzzy controller of switched reluctance motor." Serbian Journal of Electrical Engineering 4, no. 1 (2007): 23–34. http://dx.doi.org/10.2298/sjee0701023t.

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This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy contro
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Dissertations / Theses on the topic "ANFS- adaptive neuro-fuzzy system"

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Guner, Evren. "Adaptive Neuro Fuzzy Inference System Applications In Chemical Processes." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1252246/index.pdf.

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Neuro-Fuzzy systems are the systems that neural networks (NN) are incorporated in fuzzy systems, which can use knowledge automatically by learning algorithms of NNs. They can be viewed as a mixture of local experts. Adaptive Neuro-Fuzzy inference system (ANFIS) is one of the examples of Neuro Fuzzy systems in which a fuzzy system is implemented in the framework of adaptive networks. ANFIS constructs an input-output mapping based both on human knowledge (in the form of fuzzy rules) and on generated input-output data pairs. Effective control for distillation systems, which are one of the importa
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Funsten, Brad Thomas Mr. "ECG Classification with an Adaptive Neuro-Fuzzy Inference System." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1380.

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Heart signals allow for a comprehensive analysis of the heart. Electrocardiography (ECG or EKG) uses electrodes to measure the electrical activity of the heart. Extracting ECG signals is a non-invasive process that opens the door to new possibilities for the application of advanced signal processing and data analysis techniques in the diagnosis of heart diseases. With the help of today’s large database of ECG signals, a computationally intelligent system can learn and take the place of a cardiologist. Detection of various abnormalities in the patient’s heart to identify various heart diseases
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Hamdan, Hazlina. "An exploration of the adaptive neuro-fuzzy inference system (ANFIS) in modelling survival." Thesis, University of Nottingham, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.594875.

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Medical prognosis is the prediction of the future course and outcome of a disease and an indication of the likelihood of recovery from that disease. Prognosis is important because it is used to guide the type and intensity of the medication administered to patients. Patients are usually concerned with how long they will survive after diagnosis. Survival analysis describes the analysis of data that corresponds to the time from when an individual enters a study until the occurrence of some particular event or end-point. It is concerned with the comparison of survival curves for different combina
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Khanfar, Ahmad A. "Forecasting failure of information technology projects using an adaptive neuro-fuzzy inference system." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2019. https://ro.ecu.edu.au/theses/2262.

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The role of information technology (IT) applications has become critical for organisations in various sectors such as education, health, finance, logistics, manufacturing and project management. IT applications provide many advantages at strategic, management and operational levels, and the investment in IT applications is therefore growing; however, the failure rate of IT projects is still high, despite the development of theories, methodologies and frameworks for IT project management in recent decades. The consequences of failure of an IT project can be devastating, and can threaten the exi
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Aslan, Muhittin. "Modeling The Water Quality Of Lake Eymir Using Artificial Neural Networks (ann) And Adaptive Neuro Fuzzy Inference System (anfis)." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12610211/index.pdf.

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Lakes present in arid regions of Central Anatolia need further attention with regard to water quality. In most cases, mathematical modeling is a helpful tool that might be used to predict the DO concentration of a lake. Deterministic models are frequently used to describe the system behavior. However most ecological systems are so complex and unstable. In case, the deterministic models have high chance of failure due to absence of priori information. For such cases black box models might be essential. In this study DO in Eymir Lake located in Ankara was modeled by using both Artificial Neural
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Kiani, Mavi Neda. "Forecasting project success in the construction industry using multi-criteria decision-making tools and adaptive neuro fuzzy inference system." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2024. https://ro.ecu.edu.au/theses/2791.

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The construction industry plays a significant role in the development of economies. This industry in Australia contributed around 20% Australian Trade and Investment Commission (2023) to its gross domestic product (GDP) of over US$1.80 trillion (approximately AUD 2.85 trillion) (OECD, 2023).The federal budget for 2022–23 allocates AUD 17.9 billion over a decade towards major infrastructure projects, encompassing substantial funding for nationwide road and rail projects. The overall investment in major public infrastructure is anticipated to surpass AU$218 billion from 2021 to 2025. Approximate
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Chotikorn, Nattapong. "Implementations of Fuzzy Adaptive Dynamic Programming Controls on DC to DC Converters." Thesis, University of North Texas, 2019. https://digital.library.unt.edu/ark:/67531/metadc1505139/.

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DC to DC converters stabilize the voltage obtained from voltage sources such as solar power system, wind energy sources, wave energy sources, rectified voltage from alternators, and so forth. Hence, the need for improving its control algorithm is inevitable. Many algorithms are applied to DC to DC converters. This thesis designs fuzzy adaptive dynamic programming (Fuzzy ADP) algorithm. Also, this thesis implements both adaptive dynamic programming (ADP) and Fuzzy ADP on DC to DC converters to observe the performance of the output voltage trajectories.
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Jain, Aakanksha. "Application of Artificial Intelligence Techniques in the Prediction of Industrial Outfall Discharges." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/39812.

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Artificial intelligence techniques have been widely used for prediction in various areas of sciences and engineering. In the thesis, applications of AI techniques are studied to predict the dilution of industrial outfall discharges. The discharge of industrial effluents from the outfall systems is broadly divided into two categories on the basis of density. The effluent with density higher than the water receiving will sink and called as negatively buoyant jet. The effluent with density lower than the receiving water will rise and called as positively buoyant jet. The effluent discharge in the
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Arsava, Kemal Sarp. "Modeling, Control and Monitoring of Smart Structures under High Impact Loads." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/105.

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In recent years, response analysis of complex structures under impact loads has attracted a great deal of attention. For example, a collision or an accident that produces impact loads that exceed the design load can cause severe damage on the structural components. Although the AASHTO specification is used for impact-resistant bridge design, it has many limitations. The AASHTO specification does not incorporate complex and uncertain factors. Thus, a well-designed structure that can survive a collision under specific conditions in one region may be severely damaged if it were impacted by a dif
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Spadotto, Marcelo Montepulciano [UNESP]. "Lógica ANFIS aplicada na estimação da rugosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/87176.

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Made available in DSpace on 2014-06-11T19:22:34Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-07-29Bitstream added on 2014-06-13T19:08:09Z : No. of bitstreams: 1 spadotto_mm_me_bauru.pdf: 1459647 bytes, checksum: c67d870286e648ad917f7e25b8b18d56 (MD5)<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>A necessidade de aplicação de novos equipamentos em ambientes cada vez mais agressivos demandou a busca por novos produtos capazes de suportar altas temperaturas, inertes às corroções químicas e com alta rigidez mecânica. O avanço tecnógico na produção de materia
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Books on the topic "ANFS- adaptive neuro-fuzzy system"

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Neelanarayanan, ed. Multi-step Prediction of Pathological Tremor With Adaptive Neuro Fuzzy Inference System (ANFIS). Association of Scientists, Developers and Faculties, 2014.

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Book chapters on the topic "ANFS- adaptive neuro-fuzzy system"

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Maurya, Akhilesh Kumar, and Devesh Kumar Patel. "Vehicle Classification Using Adaptive Neuro-Fuzzy Inference System (ANFIS)." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2220-0_11.

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Maurya, Akhilesh Kumar, and Devesh Kumar Patel. "Vehicle Classification Using Adaptive Neuro Fuzzy Inference System (ANFIS)." In Advances in Intelligent Systems and Computing. Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2220-0_54.

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Srivastava, Amit Kumar, Pooja, and Tanveer Jahan Siddiqui. "An adaptive neuro-fuzzy inference system (ANFIS) for sentiment analysis classification." In Intelligent Computing and Communication Techniques. CRC Press, 2025. https://doi.org/10.1201/9781003635680-54.

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Adedeji, P. A., S. O. Masebinu, S. A. Akinlabi, and N. Madushele. "Adaptive Neuro-fuzzy Inference System (ANFIS) Modelling in Energy System and Water Resources." In Optimization Using Evolutionary Algorithms and Metaheuristics. CRC Press, 2019. http://dx.doi.org/10.1201/9780429293030-7.

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Faycal, Djebbas, Zeddouri Aziez, and Belila Djilani. "Prediction of the Porosity Using Adaptive Neuro-Fuzzy Inference System (ANFIS) Technique." In Springer Series in Geomechanics and Geoengineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1964-2_87.

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Lounis, B., O. Raaf, L. Bouchemakh, and Y. Smara. "SeaWIFS Coastal Waters Mapping Using an Adaptive Neuro-fuzzy Inference System (ANFIS)." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4776-4_50.

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Zilouchian, Ali, David W. Howard, and Timothy Jordanides. "An Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to control of robotic manipulators." In Tasks and Methods in Applied Artificial Intelligence. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-64574-8_424.

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Hakim, Seyed Jamalaldin S., and H. Abdul Razak. "Damage Identification Using Experimental Modal Analysis and Adaptive Neuro-Fuzzy Interface System (ANFIS)." In Topics in Modal Analysis I, Volume 5. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2425-3_37.

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Nithish Kumar, K., V. C. Sai Santhosh, Aarya V. Kulkarni, and Ovee V. Kulkarni. "Adaptive Neuro-Fuzzy Inference System (ANFIS) for Enhanced 3D Brain Reconstruction from MRI Scans." In Proceedings of Ninth International Congress on Information and Communication Technology. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3305-7_27.

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Adeleke, Oluwatobi, Stephen A. Akinlabi, Paul A. Adedeji, and Tien-Chien Jen. "Energy Content Modelling for Municipal Solid Waste Using Adaptive Neuro-Fuzzy Inference System (ANFIS)." In Lecture Notes in Mechanical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5753-8_17.

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Conference papers on the topic "ANFS- adaptive neuro-fuzzy system"

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Vladov, Serhii, Victoria Vysotska, Valerii Sokurenko, Oleksandr Muzychuk, Vasyl Lytvyn, and Vitalii Danylyk. "Cyber-Physical System for Synthesizing Adaptive Fuzzy Controllers Based on Modified ANFIS Neuro-Fuzzy Network." In 2024 IEEE 19th International Conference on Computer Science and Information Technologies (CSIT). IEEE, 2024. https://doi.org/10.1109/csit65290.2024.10982649.

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S, Edy Victor Haryanto, Nita Sari Br Sembiring, Mikha Dayan Sinaga, and Noprita Elisabeth Sianturi. "Implementation of Adaptive Neuro-Fuzzy Inference System (ANFIS) Algorithm for Customer Credit Prediction." In 2024 6th International Conference on Cybernetics and Intelligent System (ICORIS). IEEE, 2024. https://doi.org/10.1109/icoris63540.2024.10903682.

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Riananda, Dimas Pristovani, Salsabila Ika Yuniza, Ryan Yudha Adhitya, et al. "System Protection Analysis on 1kW Solar Charge Hybrid Inverter Using Adaptive Neuro-Fuzzy Inference System (ANFIS)." In 2025 International Conference on Smart Computing, IoT and Machine Learning (SIML). IEEE, 2025. https://doi.org/10.1109/siml65326.2025.11080910.

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Amin, Ahmad Faishol, Ronny Cahyadi Utomo, and Khoirul Azis Rifa’i. "Comparing Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) for Auto-Cooling System in Generator Rotor Straightening." In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA). IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10747980.

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Faradilla, Arnes, and Taufik Djatna. "The Prevention Stroke for High-Risk Patients Using Prediction and Treatment Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)." In 14th International Seminar on Industrial Engineering and Management. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-qk2n23.

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Stroke is the second factor of mortality in the world. According to the World Health Organization (WHO), stroke is an acute brain dysfunction. The effects of stroke are disability and mortality. Therefore, this is a concern for world health. In early 2019, the Pandemic Covid-19 attacked the world and caused many mortalities. Especially, people who have complications with diseases such as heart attack, stroke, and asthma. The purpose of this research is to predict stroke diseases with input parameters (age, glucose level, heart rate, and BMI) and to test the accuracy of the system. Moreover, an
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Takács, Márta, József Kopják, and István Szúcs. "Relative SoC and Charging Session Duration Estimation for Electric Vehicle Charge Points Using Adaptive Neuro-Fuzzy Inference System (ANFIS)." In 2025 IEEE 19th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2025. https://doi.org/10.1109/saci66288.2025.11030184.

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Mehrabi, Mehdi, Mohsen Sharifpur, and Josua P. Meyer. "Adaptive Neuro-Fuzzy Modeling of the Thermal Conductivity of Alumina-Water Nanofluids." In ASME 2012 Third International Conference on Micro/Nanoscale Heat and Mass Transfer. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/mnhmt2012-75023.

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By using on Adaptive Neuro-Fuzzy Inference System (ANFIS) as well as experimental data, a model was established for the prediction of the thermal conductivity ratio of alumina (Al2O3)-water nanofluids. In the ANFIS the target parameter was the thermal conductivity ratio, and the nanoparticle volume concentration, temperature and Al2O3 nanoparticle size were considered as the input (design) parameters. In the development of the model, the empirical data was divided into train and test sections. The ANFIS network was instructed by eighty percent of the experimental data and the remaining data (t
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Choi, Gyu-Jong, Kyoung-Soo Lee, and Doo-Sung Ahn. "Visual servoing system based on ANFIS (adaptive neuro fuzzy inference system)." In Intelligent Systems and Advanced Manufacturing, edited by David P. Casasent and Ernest L. Hall. SPIE, 2001. http://dx.doi.org/10.1117/12.444185.

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Khalifa, Amine B., and Hichem Frigui. "MI-ANFIS: A multiple instance Adaptive Neuro-Fuzzy Inference System." In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7338077.

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Kalyani, K., and S. Kanagalakshmi. "Control of Trms using Adaptive Neuro Fuzzy Inference System (ANFIS)." In 2020 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2020. http://dx.doi.org/10.1109/icscan49426.2020.9262417.

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