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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: Weng, Peng | Xie, JingJing | Zou, Yang
Article Type: Research Article
Abstract: The estimation of compressive strength includes time-consuming, finance-wasting, and laboring approaches to undertaking High-performance concrete (HPC) production. On the other side, a vast volume of concrete consumption in industrial construction requires an optimal mix design with different percentages to reach the highest compressive strength. The present study considered two deep learning approaches to handle compressive strength prediction. The robustness of the deep model was put high through two novel optimization algorithms as a novelty in the research world that played their precise roles in charge of model structure optimization. Also, a dataset containing cement, silica fume, fly ash, the total …aggregate amount, the coarse aggregate amount, superplasticizer, water, curing time, and high-performance concrete compressive strength was used to develop models. The results indicate that the AMLP-I and GMLP-I models served the highest prediction accuracy. R2 and RMSE of AMLP-I stood at 0.9895 and 1.7341, respectively, which declared that the AMLP-I model could be presented as the robust model for estimating compressive strength. Generally, using optimization algorithms to boost the capabilities of prediction models by tuning the internal characteristics has increased the reliability of artificial intelligent approaches to substitute the more experimental practices. Show more
Keywords: HPC concrete, compressive strength, deep learning, arithmetic optimization algorithm, grasshopper optimization algorithm
DOI: 10.3233/JIFS-221714
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8711-8724, 2023
Authors: Nishanth, R. | Sulochana, C. Helen | Radhamani, A.S. | Ahilan, A.
Article Type: Research Article
Abstract: Approximate multipliers are a trending digital design that was specially developed for the implementation of low power and high-speed circuits. The main purpose of this design is to trade the necessity of accurate multipliers. In this work, a novel imprecise compressor was designed to develop the Hazy Multipliers for low-error resilient applications. These imprecise compressors are synthesized using a 40 nm CMOS technology. When compared with the previous approximate multiplier design the proposed Hazy Multipliers are can reduce the error up to 96%, 98.9%, 99.5% respectively than the existing methods. Finally, the proposed design is investigated on the image smoothening application …to show the performance metrics of Hazy Multipliers. Show more
Keywords: Approximate multipliers, accurate multipliers, imprecise compressors, image smoothening
DOI: 10.3233/JIFS-220418
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8725-8741, 2023
Authors: Zhang, Huiyuan | Wang, Hongjun | Cai, Qiang | Wei, Guiwu
Article Type: Research Article
Abstract: As an improved form of fuzzy sets (FSs), spherical fuzzy sets (SFSs) could provide decision makers (DMs) with more free space to express their preference information. In this article, we first develop some Hamacher power aggregation operators under SFSs by power operators and Hamacher operators, including spherical fuzzy Hamacher power average (SFHPA) operator, spherical fuzzy Hamacher power geometric (SFHPG) operator, spherical fuzzy Hamacher power weighted average (SFHPWA) operator, spherical fuzzy Hamacher power weighted geometric (SFHPWG) operator, spherical fuzzy Hamacher power ordered weighted average (SFHPOWA) operator, spherical fuzzy Hamacher power ordered weighted geometric (SFHPOWG) operator, spherical fuzzy Hamacher power hybrid average …(SFHPHA) operator and spherical fuzzy Hamacher power hybrid geometric (SFHPHG) operator. At the same time, some properties of the proposed operators are investigated, and the relationships between these operators and existing operators are discussed. Furthermore, a novel spherical fuzzy entropy measure is introduced to calculate unknown attribute weights. Then, some novel multiple attribute group decision making (MAGDM) methods are established by the proposed operators as well as entropy measure under SFSs. Lastly, the practicability of the presented methods is verified with a numerical case. Moreover, the robustness, availability and superiority for the developed methods are demonstrated via sensitivity analysis and further comparation with the existing methods. Show more
Keywords: Spherical fuzzy sets, Hamacher operators, power operators, spherical fuzzy Hamacher power aggregation operators, entropy measure, multiple attribute group decision making
DOI: 10.3233/JIFS-224468
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8743-8771, 2023
Authors: AlShammari, Naif Khalaf | Qazi, Emad Ul Haq | Gabr, Ahmed Maher | Alzamil, Ahmed A. | Alshammari, Ahmed S. | Albadran, Saleh Mohammad | Reddy, G. Thippa
Article Type: Research Article
Abstract: Technological development in biomedical procedures has given an upper understanding of the ease of evaluating and handling critical scenarios and diseases. A sustainable model design is required for the post-medical procedures to maintain the consistency of medical treatment. In this article, a telerobotic-based stroke rehabilitation optimization and recommendation technique cum framework is proposed and evaluated. Selecting optimal features for training deep neural networks can help in optimizing the training time and also improve the performance of the model. To achieve this, we have used Whale Optimization Algorithm (WOA) due to its higher convergence accuracy, better stability, stronger global search ability, …and faster convergence speed to streamline the dependency matrix of each attribute associated with post-stroke rehabilitation. Deep Neural Networking assures the selection of datasets from training and testing validation. The proposed framework is developed on providing decision support with a recommendation of activities and task flow, these recommendations are independent and have higher feasibility with the scenario of evaluation. The proposed model achieved a precision of 99.6%, recall of 99.5 %, F1-score of 99.7%, and accuracy of 99.9%, which outperform the other considered optimization algorithms such as antlion and gravitational search algorithms. The proposed technique has provided an efficient recommendation model compared to the trivial SVM-based models and techniques. Show more
Keywords: Exoskeleton robotic framework, whale-optimization algorithm, deep neural networks, stroke disease, Industry 4.0
DOI: 10.3233/JIFS-221295
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8773-8783, 2023
Authors: Hemanand, D. | Sridhar, P. | Priya, C. | Sathish Kumar, P.J.
Article Type: Research Article
Abstract: Wireless Sensor Networks are becoming increasingly popular in everyday life since they offer a variety of network structures for developing cutting-edge real-time applications. Wireless sensor devices have high energy consumption limitations, so it is necessary to handle excessive energy consumption by malicious nodes properly to improve network performance. Even though numerous studies have been conducted to increase the dependability of routing in WSNs, the existing routing strategies do not meet the required security constraints by using intelligent methods to protect the sensor nodes from malicious attack. To overcome this challenge a novel Trust Aware Clustering based Secure Routing Techniques (TAC-SRT) …has been proposed to minimize the overall energy consumption, improved security to nodes and to maximize the network lifetime. The proposed method is carried out in three phases. In the first phase, the cluster head is selected by using K mean clustering. In the second phase, the trust value of each node is evaluated by using Mamdani fuzzy inference rule. In the third phase, the Tversky similarity index is used to find the normal or malicious node and establishes the shortest route. The Fully Homomorphic Elliptic Curve Cryptography technique is then used to perform secure data transmission. The effectiveness of the proposed strategy is examined using several parameters, such as the lifetime of the network, data confidentiality, active nodes, and energy consumption. The proposed technique improves the network lifetime by 23.01%, 17.4%, and 13.2% better than MOSFA, SecDL, and CAR-MOSOA respectively. Finally, the proposed method demonstrated superior performance in terms of delay, throughput, encryption time, network lifetime, and packet delivery ratio compared with existing techniques. Show more
Keywords: Secure routing, fuzzy inference system, wireless sensor network, tversky similarity index, cluster head selection
DOI: 10.3233/JIFS-223197
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8785-8800, 2023
Article Type: Retraction
DOI: 10.3233/JIFS-219327
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 8801-8801, 2023
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