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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: Mishra, Rajiv Kumar | Yadav, Rajesh Kumar | Nath, Prem
Article Type: Research Article
Abstract: The massive amounts of data produced and gathered by smart devices through the internet support a wide range of applications, considerably improving our daily lives. Data sharing among smart devices must be safeguarded due to the sensitivity of the data involved in the transmission. The Internet of Things (IoT) environment must be protected from unauthorised access due to a variety of variables, including its attractiveness to cybercriminals, previous successful cyber-attacks, and consumers’ perceptions of security and reliability. Blockchain technology appears to be one promising technology that appears to address these security challenges extremely effectively. However, given the volume and rate …at which smart devices generate data, Blockchain appears to be inefficient for storing it. The pace of data collection in the IoT context and the speed of transaction confirmation in the Blockchain network are the two key elements behind this. We connect the Blockchain and the Inter-Planetary File System (IPFS) in this study to permit data recording on a distributed storage and a mechanism to restrict access to recorded data to authorised organisations only. Over the Blockchain network, the access policy definition for safe data sharing and cryptographic hash content is stored. The real IoT-generated data, on the other hand, is collected via a distributed storage network, which improves availability and security. The proposed scheme’s analysis and performance evaluation show that it is secure and feasible. Furthermore, simulations are undertaken to assess the operating costs of smart contracts and to test the efficacy and viability of the suggested architecture. Show more
Keywords: IoT, secure data sharing, unauthorized access, blockchain, IPFS, etc.
DOI: 10.3233/JIFS-232483
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10951-10966, 2023
Authors: Rubia J, Jency | Lincy R, Babitha
Article Type: Research Article
Abstract: Deep learning strategies have been achieved over the historical decades to resolve many computer vision applications. Recently, these deep learning algorithms have been extensively used as a tool in classification problems. Generally, the deep learning algorithms trained with gradient-based optimizers, which has some downsides such as the slow speed of convergence and stuck in local minima. As a solution, the planned work using meta-heuristic based Grey Wolf and Whales optimization algorithms for the automatic plant disease detection model. The planned work has explored the application of automatic plant disease identification through the leaf images with the help of the image …processing approach. The planned research has evaluated the deep learning algorithm with Grey Wolf and Whales optimization techniques using the three types of datasets, such as Plant Village, New Plant Disease, and Rice Leaf Disease databases. The simulation consequences illustrate that the computational efficiency of the Grey Wolf and Whales based automatic disease identification process is boosted when coupled with the deep learning method. Show more
Keywords: Plant disease detection, deep learning, meta-heuristic optimizers, Grey Wolf optimization algorithms, Whales optimization algorithms
DOI: 10.3233/JIFS-213423
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10967-10983, 2023
Authors: Wang, Ming
Article Type: Research Article
Abstract: Cloud platforms rapidly develop and supply many cloud-based services with various functions but varying Quality of Service (QoS) attributes. In the context of cloud computing, a major concern is how to incorporate disparate cloud services to maximize QoS value, which is underpinned by meeting the local QoS requirements of users. To deal with multiple QoS parameters in service composition, we propose a new mechanism using the Capuchin Search Algorithm (CapSA) that provides solutions to satisfy connectivity constraints. The CapSA algorithm mimics the dynamic behavior of capuchin monkeys in the forest to solve global and local optimization problems. The CapSA was …selected for its simplicity, reduced computational complexity, and exploration/exploitation balance. The approach takes the form of an optimization problem to minimize energy consumption and total cost. Compared with other approaches, the proposed algorithm improves the efficiency of cloud service composition by convergent rapidly and obtaining better compositions. Show more
Keywords: Cloud computing, service composition, QoS, capuchin search
DOI: 10.3233/JIFS-224523
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10985-10996, 2023
Authors: Ren, Zenggen | Guo, Fu | Hu, Mingcai | Qu, Qingxing | Li, Fengxiang
Article Type: Research Article
Abstract: Generating kansei profiles for products represent fundamental aspects of kansei engineering (KE). Conventionally, the semantic differential (SD) method has been extensively employed to construct product kansei profiles, aiming to delve into consumers’ perceptions of products. However, this approach is associated with significant time consumption and inefficiency. In light of this, we introduce an innovative kansei evaluation approach that incorporates consumers’ kansei preferences, thereby enhancing the efficiency of the evaluation process. This approach comprises three integral modules: Firstly, the generation of product kansei profiles and the construction of a kansei database for decision alternatives are achieved through the analysis of online …reviews. Subsequently, the kansei data is adjusted based on consumers’ kansei preferences. Finally, the rank correlation analysis (RCA) is conducted to establish the prioritization of decision alternatives. Notably, this method facilitates the ranking of products in accordance with consumers’ kansei preferences, thereby assisting consumers in navigating through an array of functionally similar products to identify their preferred choices. A comprehensive case study illustrates the implementation procedure and validates the practicality of our proposed method. Show more
Keywords: Kansei evaluation, kansei preferences, kansei profiles, online reviews, rank correlation analysis
DOI: 10.3233/JIFS-230654
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10997-11012, 2023
Authors: Shafi, Muhammad Ammar | Rusiman, Mohd Saifullah | Jacob, Kavikumar | Musa, Aisya Natasya
Article Type: Research Article
Abstract: With relevant computational software, fuzzy prediction, a new intelligent modelling technique, is utilised to resolve unclear phenomena in various disciplines. Excellent software risk prediction is essential for effective prediction, such as risk management, case planning, and control. We provide an intelligent modelling strategy for software risk prediction in this research. We are applying a support vector machine model and two phases of hybrid fuzzy linear regression clustering (SVM). This method may produce the most accurate risk predictions for various continuous data. The best model with even less error value, acceptable interpretability, and imprecise uncertainty inputs is a fuzzy linear regression …with symmetric parameter clustering with a support vector machine (FLRWSPCSVM), a new intelligent modelling technique. The model’s predictive accuracy is demonstrably higher than other prediction models, according to validation utilising simulation data and four software packages such as SPSS, MATLAB and Weka Explorer. Show more
Keywords: Intelligent modelling, fuzzy hybrid, Prediction data, computation software, statistical error
DOI: 10.3233/JIFS-231814
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11013-11019, 2023
Authors: Sun, Peixi | Wang, Yixuan | Song, Jaehoon
Article Type: Research Article
Abstract: A brand is an enterprise’s market image and huge intangible assets. A brand is an enterprise’s market image and huge intangible assets, and it is also a comprehensive embodiment of an enterprise’s core competitiveness. Therefore, continuous improvement of brand competitiveness undoubtedly has far-reaching significance for manufacturing enterprises. Using the brand competitiveness evaluation index system and selected evaluation methods of manufacturing enterprises constructed in this article, the brand competitiveness evaluation index system and selected evaluation methods can not only study the overall brand competitiveness of the participating enterprises, but also understand the advantages and disadvantages of the brand competitiveness of the …participating enterprises from different perspectives, To help and guide manufacturing enterprises to strengthen brand building in a targeted manner and continuously improve the brand competitiveness of manufacturing enterprises. The brand competitiveness evaluation of manufacturing enterprises is a classical MAGDM problems. Recently, the TODIM and VIKOR method has been used to cope with MAGDM issues. The interval neutrosophic sets (INSs) are used as a tool for characterizing uncertain information during the brand competitiveness evaluation of manufacturing enterprises. In this manuscript, the interval neutrosophic number TODIM-VIKOR (INN-TODIM-VIKOR) method is built to solve the MAGDM under INSs. In the end, a numerical case study for brand competitiveness evaluation of manufacturing enterprises is given to validate the proposed method. Show more
Keywords: Multiple attribute group decision making (MAGDM), Interval neutrosophic sets (INSs), TODIM, VIKOR, Brand competitiveness evaluation
DOI: 10.3233/JIFS-232001
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11021-11034, 2023
Authors: Kalaiarasan, D. | Ahilan, A. | Ramalingam, S.
Article Type: Research Article
Abstract: Image security plays a vital role in communication networks. Despite advancements in encryption, securing protective image data remains a computationally challenging problem, requiring the use of a secure environment to secure data transmitted from the device through the network. Encryption is essential for safeguarding essential data against unauthorized access or malfunction, especially for images. The contribution of the proposed model is to provide an analytical hybrid Harris Hawk Optimization (HHO) with a chaotic map approach which can be used to improve the overall performance of standard encryption techniques in image-based encrypted communication. Previously, the algorithm computes numerous chaotic Logistic map …and an encrypted images, where the pending plain image calculates the session key for the map’s input parameters. Afterwards, encrypted images are presented as hawks and made to work well with HHO. The optimized ciphered image is expressed as a fitness function using correlation coefficients related to nearby pixels. When compared to existing algorithms, the proposed NPCR (99%), UACI (33%), and entropy (7.97) demonstrate superior performance. The proposed hybrid approach outperforms traditional algorithms in terms of security while preserving image quality. Show more
Keywords: Harris Hawk Optimization, image security, multimedia, chaotic map, cryptography
DOI: 10.3233/JIFS-213337
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11035-11057, 2023
Authors: Zhang, Shiguang | Guo, Di | Zhou, Ting
Article Type: Research Article
Abstract: Extreme learning machine (ELM) has received increasingly more attention because of its high efficiency and ease of implementation. However, the existing ELM algorithms generally suffer from the drawbacks of noise sensitivity and poor robustness. Therefore, we combine the advantages of twin hyperplanes with the fast speed of ELM, and then introduce the characteristics of heteroscedastic Gaussian noise. In this paper, a new regressor is proposed, which is called twin extreme learning machine based on heteroskedastic Gaussian noise (TELM-HGN). In addition, the augmented Lagrange multiplier method is introduced to optimize and solve the presented model. Finally, a significant number of experiments …were conducted on different data-sets including real wind-speed data, Boston housing price dataset and stock dataset. Experimental results show that the proposed algorithms not only inherits most of the merits of the original ELM, but also has more stable and reliable generalization performance and more accurate prediction results. These applications demonstrate the correctness and effectiveness of the proposed model. Show more
Keywords: Extreme learning machine, heteroscedastic Gaussian noise, least squares support vector regression, twin hyperplanes, wind-speed forecasting
DOI: 10.3233/JIFS-232121
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11059-11073, 2023
Authors: Vidyabharathi, D. | Sivanesh, S. | Theetchenya, S. | Vidhya, G.
Article Type: Research Article
Abstract: Detecting of cracks and damages, especially in multi storied buildings is a crucial aspect of infrastructure and building maintenance, as it ensures safety and reliability. An enhanced framework for the crack detection is proposed to identify the fine cracks which are present at greater heights and not captured to the human vision from the ground. The cracks are identified and classified by the deep convolutional neural network model. The Oriented Non-Maximal Suppression module reduces the false positives to improve the classification accuracy and reliability. The proposed method O-CNN(CNN with ONMS)can be used in real-world for the infrastructure inspection and potential …applications in civil engineering construction. The ability to input different types of data, including images and videos, makes the proposed system user-friendly and easy to use. Furthermore the system reduces the risk of human error and prevents the huge damages caused to the building. Also, it prevents the major loss which may be caused to the lives. Overall, the proposed system contributes to the field of deep learning and computer vision by providing an effective and better solution for crack detection in real-world scenarios. Show more
Keywords: Deep learning, convolutional neural networks, oriented non-maximal suppression, O-CNN
DOI: 10.3233/JIFS-232793
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11075-11091, 2023
Authors: Alphy, Anna | Rajamohamed, | Velusamy, Jayaraj | Vidhya, K. | Ravi, G. | Rajasekaran, Arun Sekar
Article Type: Research Article
Abstract: Age-Related Macular Degeneration is a progressive, irreversible eye condition that causes vision loss and impairs quality of life. The lost potential of the optic nerve cannot be regained, but a patient with Age-Related Macular Degeneration must have early diagnosis and treatment in order to prevent visual loss. The diagnosis of Age-Related Macular Degeneration is based on visual field loss tests, a patient’s medical history, intraocular pressure, and a physical fundus evaluation. Age-Related Macular Degeneration must be diagnosed early in order to avoid irreparable structural damage and vision loss. The objective of the proposed study is to develop a new optimization-driven …strategy-based recurrent neural network using the Internet of Things for the identification of age-related macular degeneration. The Recurrent Neural Network (RNN) classifier is trained using the Particle Swarm Optimization (PSO) technique included into the RNN-IoMT. Initially, the input picture is sent through pre-processing in order to remove noise and artefacts. The generated preprocessed picture is simultaneously sent to optical disc detection and blood vessel detection. In addition, picture level characteristics are extracted from the image that has been preprocessed. Finally, the image-level, optic disc-level, and blood vessel-level features are retrieved and compiled into a feature vector. The acquired feature vector is fed into the RNN classifier, with the suggested PSO used to train the RNN for Age-Related Macular Degeneration detection via the Internet of Medical Things. The suggested PSO+RNN exhibits better performance with enhanced precision of 97.194%, sensitivity of 97.184%, and specificity of 97.2044%, respectively. Show more
Keywords: Wearables, internet of things, teleophthalmology, deep learning, fundus images
DOI: 10.3233/JIFS-233044
Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11093-11105, 2023
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