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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: Rajkumar, K. | Dhanakoti, V.
Article Type: Research Article
Abstract: Storage consumption is increasing significantly these days, with consumers trying to find an effective approach to safe storage space. In these situations, a deduplication in cloud storage services is a significant way to reduce bandwidth and service space by omitting unnecessary information and keeping only a single copy of the information. This raises computational, privacy and storage issues when large numbers of handlers outsource the similar data to cloud service storage. To overcome these problems, an effective Fuzzy-Dedup framework is designed in this research by integrating four steps namely is introduced, which breaks down the data into fixed size chunks …and is immediately fingerprinted by a hashing algorithm for ensuring data authentication and then indexing is done with the help of traditional b-tree indexing, similarity function is calculated to compute the similarity value in the documents. After calculating the similar values, the fuzzy interference system is designed by formulating appropriate rules for the decision-making process that determines duplicate and non-duplicate files by obtaining an effective de-duplication ratio over existing methods. After detecting duplicate files, the inline based deduplication policy checks that the new data is ready to send for storage against existing data and does not store any redundant data it discovers. The proposed model is implemented in MATLAB software is carried out several performance metrics and these parameter attained better performance such as, deduplication ratio of 1.2, memory utilization of 12500 bytes in inline and 9550 bytes in offline, throughput of 32500 Mb/s in inline and 25500 Mb/s in offline and processing time of 0.4494 s in inline and 0.1139 s in offline. Thus when compared to previous methods, such as Two Thresholds Two Divisors deduplication (TTTD) approach proposed design shows high range of performance. Show more
Keywords: De-duplication, Fuzzy-Dedup, cosine similarity, chunking, fingerprinting, indexing, fuzzy interference system, cloud storage, inline, encryption, decryption
DOI: 10.3233/JIFS-210511
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2819-2832, 2022
Authors: Yuan, Zhizhu | Hou, Lijuan | Gao, Zihuan | Wu, Meiqin | Fan, Jianping
Article Type: Research Article
Abstract: Single-valued neutrosophic sets can efficiently depict a great deal of imprecise, uncertain and discordant information. Hamy mean operator can consider the interrelationships among multiple integrated arguments and Schweizer-Sklar operations express great flexibility in the process of information aggregation. To give full consideration to these advantages, we merge the Hamy mean operator with the Schweizer-Sklar operations in single-valued neutrosophic environment, proposing a single-valued neutrosophic Schweizer-Sklar Hamy mean operator and a single-valued neutrosophic Schweizer-Sklar weighted Hamy mean operator. Besides, we illustrate some specific cases and attributes of the two operators. Moreover, based on the entropy weight method and the single-valued neutrosophic Schweizer-Sklar …weighted Hamy mean operator, this paper presents a single-valued neutrosophic Schweizer-Sklar entropic weighted Hamy mean method to tackle multi-attribute decision making problems. At last, the method and other three existing methods are applied to solve a practical multi-attribute decision making problem, which validates the credibility and validity of the single-valued neutrosophic Schweizer-Sklar entropic weighted Hamy mean method by comparing the differences among them. Show more
Keywords: Single-valued neutrosophic sets, Schweizer-Sklar operations, Hamy mean operator, the entropy weight method, multi-attribute decision making
DOI: 10.3233/JIFS-212818
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2833-2851, 2022
Authors: Ramachandraarjunan, Senthilkumar | Perumalsamy, Venkatakrishnan | Narayanan, Balaji
Article Type: Research Article
Abstract: Monitoring indoor air quality stays needed for human health because people use more than 95% of air in their indoor rooms. An Intelligent Internal Air Quality Monitoring (IIAQM) system built on the Internet of Things (IoT) devices has been developed and tested in Quantanics Techserv Private Limited, Tamilnadu, India. To monitor the levels of CO2 , PM2.5 (Particle Matters 2.5), and moisture measurement, the IIAQM model has been used to monitor the present level of air quality. The gateway collects IIAQM sensor data in a few seconds and transfers data to cloud server. Approved users can incorporate the cloud …systems through mobile applications or web servers. Installation of sensor networks, instrument transformers, and IoT-powered microcontrollers will provide air quality monitoring for buildings. The proposed window controller configuration is designed with the help of a Recurrent Neural Network (RNN) to predict the air quality level in advance. If the air quality level is above the normal level, the window controller automatically will open with the help of sensor activity control system. After the AQI (Air Quality Index) becomes normal, hence the window controller is closed automatically. The air quality index, CO2, and humidity data are visualized on the Grafana dashboard. Show more
Keywords: Internet of things, machine learning, recurrent neural networks humidity sensor, intelligent internal air quality monitoring system
DOI: 10.3233/JIFS-212955
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2853-2868, 2022
Authors: Zhang, Shuai | Chen, Qian | Zeng, Wenhua | Guo, Shanshan | Xu, Jiyuan
Article Type: Research Article
Abstract: The coronavirus disease 2019 pandemic has significantly impacted the world. The sudden decline in electricity load demand caused by strict social distancing restrictions has made it difficult for traditional models to forecast the load demand during the pandemic. Therefore, in this study, a novel transfer deep learning model with reinforcement-learning-based hyperparameter optimization is proposed for short-term load forecasting during the pandemic. First, a knowledge base containing mobility data is constructed, which can reflect the changes in visitor volume in different regions and buildings based on mobile services. Therefore, the sudden decline in load can be analyzed according to the socioeconomic …behavior changes during the pandemic. Furthermore, a new transfer deep learning model is proposed to address the problem of limited mobility data associated with the pandemic. Moreover, reinforcement learning is employed to optimize the hyperparameters of the proposed model automatically, which avoids the manual adjustment of the hyperparameters, thereby maximizing the forecasting accuracy. To enhance the hyperparameter optimization efficiency of the reinforcement-learning agents, a new advance forecasting method is proposed to forecast the state-action values of the state space that have not been traversed. The experimental results on 12 real-world datasets covering different countries and cities demonstrate that the proposed model achieves high forecasting accuracy during the coronavirus disease 2019 pandemic. Show more
Keywords: COVID-19, deep learning, load forecasting, reinforcement learning, transfer learning
DOI: 10.3233/JIFS-213103
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2869-2882, 2022
Authors: Mohammed Hashim, B.A. | Amutha, R.
Article Type: Research Article
Abstract: Human Activity Recognition (HAR) is the most popular research area in the pervasive computing field in recent years. Sensor data plays a vital role in identifying several human actions. Convolutional Neural Networks (CNNs) have now become the most recent technique in the computer vision phenomenon, but still, it is premature to use CNN for sensor data, particularly in ubiquitous and wearable computing.Deep CNN requires huge dataset and models which increases the computational complexity. Transfer learning that uses the pre trained CNNwith fine tuning is the better alternative to reduce the training cost.In this paper, we have proposed the idea of …transforming the raw accelerometer and gyroscope sensor data to the visual domain by using our novel activity image creation method (NAICM). Pre-trained CNN (AlexNet) has been used on the converted image domain information. The proposed method is evaluated on several online available human activity recognition dataset. The results show that the proposed novel activity image creation method (NAICM) has successfully created the activity images with a classification accuracy of 98.36% using pre trained CNN. Show more
Keywords: Human activity recognition, CNN, pervasive computing, NAICM, transfer learning
DOI: 10.3233/JIFS-213174
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2883-2890, 2022
Authors: Vijaya Karthik, S.V. | Arputha Vijaya Selvi, J.
Article Type: Research Article
Abstract: Information Centric Network (ICN) is a newer technology in handling web content distribution that has recently emerged in order to tackle the risk of data security. For handling content distribution, ICN provides data security via a name-based approach. Named Data Networking (NDN) is an ongoing ICN realisation that was incorporated recently. Named Data Networking (NDN) has recently grown in popularity and significance as a new internet design that solves certain limitations in traditional internet communications. NDN is perfectly adapted for the Internet of Things (IoT), which is today dominated by huge, and emerging applications. In this work, we propose an …IoT enabled hybrid cluster-based routing protocol with mitigation of content poisoning attack for information-centric Wireless Sensor Network (WSN)-NDN. In this method, hybrid K-medoids clustering is used with African Buffalo Optimization Algorithm (ABOA), which is to find an optimal shortest path between the cluster heads, and light weight encryption. It is developed by using Hyperelliptic Curve Cryptography (HCC) to mitigate content poisoning. Our proposed system has effective data security as it has encrypted data in the cluster head. The smart health care monitoring system has been used for our proposed method. The proposed method has been subjected to extensive analysis by comparing with other existing methods that should improve performance justified in terms of several metrics by introducing the malicious nodes (10%, 20%, and 30%). Show more
Keywords: Named data network, clustering algorithm, content poisoning, african buffalo optimization, hyperelliptic curve cryptography
DOI: 10.3233/JIFS-212674
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2891-2905, 2022
Authors: Abin, Deepa | Thepade, Sudeep D.
Article Type: Research Article
Abstract: In today’s digital times, the quality of video frames is ubiquitous, and the presence of shadows is undesirable in computer vision applications. Shadow suppression is of paramount significance in crucial application areas, especially in outdoor scene environments. The objects present in the environment occlude the light. Most of the work in literature focuses on single shadow regions in a frame or an image. Different methods are proposed in the literature. This challenging area of shadows suppression is addressed with the proposed method, as a novel amalgamation, with Adaptive Gamma Weighted Correction and modified Exemplar based inpainting method. The paper discusses …different single shadow scenarios and multiple distributed shadow regions. Across four datasets, and objective evaluation using three performance metrics, the obtained average Entropy of 7.032, ‘Blind Reference Image Spatial Quality Evaluator (BRISQUE)’ of 26.2031, and ‘No-Reference Image Quality Evaluator (NIQE)’ of 3.699 have demonstrated considerable results. Show more
Keywords: Shadow suppression, AGWCD, exemplar inpainting, outdoor scene, color space
DOI: 10.3233/JIFS-212823
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2907-2919, 2022
Authors: Li, Fu | Su, PeiYu | Qin, Feng
Article Type: Research Article
Abstract: In this paper, the ternary soft set is discussed based on the soft triadic (complete) formal context. The ternary soft set is a generalization of Molodsov’s soft set, which can characterize the objects universe more clearly by the attributes set, and different from type-2 soft set. The definitions of the ternary soft set and ternary formal context are given and illustrated with some examples. A mindmap is used to show the idea of ternary soft set visually. The usage of bijective soft set enables fast decision making process by our work. We demonstrate the idea with a flowchart and a …case study. Meanwhile, the soft operations among the ternary soft sets are defined and their properties are studied. Show more
Keywords: Soft set, soft (formal) context, ternary soft set, bijective soft set
DOI: 10.3233/JIFS-213155
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2921-2931, 2022
Authors: Yang, Mengyin | Chen, Junfen | Wang, Wenjie | He, Qiang
Article Type: Research Article
Abstract: Deep unsupervised learning extracts meaningful features from unlabeled images and simultaneously serves downstream tasks in computer vision. The basic process of deep clustering methods can include features learning and clustering assignment. To enhance the discriminative ability of the features and further improve the clustering performances, a new deep clustering method namely ACMEC (asymmetric convolutional denoising autoencoder with manifold spatial embedding clustering) is proposed. In this method, an asymmetric convolution denoising autoencoder is employed to extract visual features from images, and a manifold learning algorithm is used to obtain more distinctive features, followed by a Gaussian Mixture Model (GMM) is for …clustering learning. The stability of feature space is guaranteed using separately training mechanism. In addition, reconstruction from noisy images enhances the robustness of feature networks. Experimental results on nine benchmark datasets demonstrate that the proposed ACMEC method can provide the better performances such as 0.979 clustering accuracy on the MNIST dataset and 0.668 on the fashion-MNIST dataset. ACMEC is a comparable competitor to the N2D (not too deep clustering) algorithm that is with 0.979 and 0.672 clustering accuracies respectively. Moreover, it is 16.1% higher than DEC algorithm on the fashion-MNIST dataset. Show more
Keywords: Clustering analysis, feature learning, asymmetric convolutional denoising autoencoder, manifold embedding, Gaussian mixture models (GMM)
DOI: 10.3233/JIFS-213468
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2933-2944, 2022
Authors: Luo, Wenguan | Yu, Xiaobing
Article Type: Research Article
Abstract: Cuckoo search algorithm (CS) is an excellent nature-inspired algorithm that has been widely introduced to solve complex, multi-dimensional global optimization problems. However, the traditional CS algorithm has a low convergence speed and a poor balance between exploration and exploitation. In other words, the single search strategy of CS may make it easier to trap into local optimum and end in premature convergence. In this paper, we proposed a new variant of CS called Novel Enhanced CS Algorithm (NECSA) to overcome these drawbacks mentioned above inspired by the cuckoos’ behaviors in nature and other excellent search strategies employed in intelligent optimization …algorithms. NECSA introduces several enhancement strategies, namely self-evaluation operation and modified greedy selection operation, to improve the searchability of the original CS algorithm. The former is proposed to enhance the exploration ability and ensure population diversity, and the latter is employed to enhance the exploitation ability and increase search efficiency. Besides, we introduced adaptive control parameter settings based on the fitness and iteration number to increase the convergence speed and the accuracy of the search process. The experimental results and analysis on the CEC2014 test have demonstrated the reliable performance of NECSA in comparison with the other five CS algorithm variants. Show more
Keywords: Cuckoo search, self-evaluation mechanism, greedy selection
DOI: 10.3233/JIFS-220179
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 3, pp. 2945-2962, 2022
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