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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: Rajalakshmi, R. | Sivakumar, P. | Prathiba, T. | Chatrapathy, K.
Article Type: Research Article
Abstract: In healthcare (HC), Internet of Things (IoT) integrated cloud computing provides various features and real-time applications. However, owing to the nature of IoT architecture, their types, various modes of communication and the density of data transformed in the network, security is currently a critical issue in the IoT healthcare (IoT-HC) field. This paper proposes a deep learning (DL) model, namely Adaptive Swish-based Deep Multi-Layer Perceptron (ASDMLP) that identifies the intrusions or attacks in the IoT healthcare (IoT-HC) platform. The proposed model starts by clustering the patients’ sensor devices in the network using the Probability-based Fuzzy C-Means (PFCM) model. After clustering …the devices, the cluster heads (CHs) among the cluster members are selected based on the energy, distance and degree of the sensor devices for aggregating the data sensed by the medical sensor devices. The base station (BS) sends the patient’s data collected by the CHs to the cloud server (CS). At the cloud end, the proposed model implements an IDS by applying training of the DL model in publicly available databases. The DL approach first performs preprocessing of the data and then selects optimal features from the dataset using the Opposition and Greedy Levy mutation-based Coyotes Optimization Algorithm (OGCOA). The ASDMLP trains these optimal features for the detection of HC data intrusions. The outcomes confirm that the proposed approach works well on real-time IoT datasets for intrusion detection (ID) without compromising the energy consumption (EC) and lifespan of the network. Show more
Keywords: Smart healthcare, Internet of Things (IoT), intrusion detection system, deep learning, healthcare security
DOI: 10.3233/JIFS-223166
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2753-2768, 2023
Authors: Lovelyn Rose, S. | Ravitha Rajalakshmi, N. | Sabari Nathan, M. | Suraj Subramanian, A. | Harishkumar, R.
Article Type: Research Article
Abstract: Recently computer vision and NLP based techniques have been employed for document layout analysis where different types of elements in the document and their relative position are identified. This process is trickier as there are blocks which are structurally similar but semantically different such as title, text etc. This works attempts to use region-based CNN architecture (F-RCNN) for determining five different sections in the scientific articles. To improve the performance of detection algorithm, reading order is used as an additional feature and this model is known as MF-RCNN. First, an algorithm is formulated to find the reading order in documents …which adopts Manhattan-layout using a color-coding scheme. Secondly, this information is fused with the input image without changing its shape. Experimental results show that MF-RCNN which uses the reading order performs better when compared with F-RCNN when tested on Publaynet dataset. Show more
Keywords: FRCNN, reading order, XY tree, multiple channels, manhattan layout
DOI: 10.3233/JIFS-220705
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2769-2778, 2023
Authors: Xu, Juan | Ma, Zhen Ming | Xu, Zeshui
Article Type: Research Article
Abstract: Heronian mean (HM) operators, which can capture the interrelationship between input arguments with the same importance, have been a hot research topic as a useful aggregation technique. In this paper, we propose the generalized normalized cross weighted HM operators on the unit interval which can not only capture the interrelationships between input arguments but also aggregate them with different weights, some desirable properties are derived. Then, generalized cross weighted HM operators are extended to real number set and applied to binary classification. We list the detailed steps of binary classification with the developed aggregation operators, and give a comparison of …the proposed method with the existing ones using the Iris dataset with 5-fold cross-validation (5-f cv), the accuracy of the proposed method for the training sets and the testing sets are both 100%. Show more
Keywords: Generalized cross weighted HM operator, cross weight vector, binary classification
DOI: 10.3233/JIFS-221152
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2779-2789, 2023
Authors: Osman, Mawia | Xia, Yonghui
Article Type: Research Article
Abstract: This paper proposes a method for solving fuzzy linear and nonlinear partial q -differential equations by the fuzzy q -differential transform. Further, we implemented the fuzzy fractional q -differential transform for solving some types of fuzzy fractional q -differential equations . The technique investigated is based on gH -differentiability, fuzzy q-derivative, and fuzzy q-fractional derivative. Various concrete problems have been tested by implementing the new method, and the results show great performance. The results also reveal that the method is a very effective and quite accurate mathematical tool for solving fuzzy fractional and integer q -differential equations. Finally, we …have provided some examples illustrating our method. Show more
Keywords: Fuzzy numbers, fuzzy-valued functions, fuzzy q-derivative; fuzzy q-fractional derivative, gH-differentiability, fuzzy q-differential transform method
DOI: 10.3233/JIFS-222567
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2791-2846, 2023
Authors: Wang, Biao | Wei, Hongquan | Li, Ran | Liu, Shuxin | Wang, Kai
Article Type: Research Article
Abstract: Spotting rumors from social media and intervening early has always been a daunting challenge. In recent years, Deep neural networks have begun to discover rumors by exploring the way of rumor propagation. The existing static graph models either only focus on the spatial structure information of rumor propagation or on time series propagation information but do not effectively combine them. This paper proposes the Static Spatiotemporal Model (SSM), which first extracts the textual semantic information and constructs undirected and directed propagation trees. Then obtains spatial structure information of rumor propagation through Graph Convolutional Network and extracts time series propagation information …through the Recurrent Neural Network. The extracted spatiotemporal information is enhanced using different source node information hopping. Finally, SSM uses a weighted connection ensemble to rumor classification. Experimentally validated on datasets such as Weibo and Twitter, the results show that the proposed method outperforms several state-of-the-art static graph models. To better apply SSM in early detection and characterize early concepts, this paper presents a new data collection index for early detection, which can detect events that spread faster and have more significant influence in a targeted manner. The experimental results on the new indicators further verify the superiority of SSM as it can extract sufficient information in early detection or events with fewer participants. Show more
Keywords: Rumor detection, deep learning, SSM, spatiotemporal information, early detection, data collection index
DOI: 10.3233/JIFS-220417
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2847-2862, 2023
Authors: Ramkumar, N. | Sadasivam, G. Sudha | Renuka, D. Karthika
Article Type: Research Article
Abstract: Multimodal analysis focuses on the internal and external manifestations of cancer cells to provide physicians, oncologists and surgeons with timely information on personalized diagnosis and treatment for patients. Decision fusion in multimodal analysis reduces manual intervention, and improves classification accuracy facilitating doctors to make quick decisions. Genetic characteristics extracted on biopsies do not, however, provide details on adjacent cells. Images can only provide external observable details of cancer cells. While mammograms can detect breast cancer, region wise details can be obtained from ultrasound images. Hence, different types of imaging techniques are used. Features are extracted using the SelectKbest method in …the Wisconsin Breast Cancer, Clinical and gene expression datasets. The features are extracted using Gray Level Co-occurrence Matrix from Histology, Mammogram and Sonogram images. For image datasets, the Convolution Neural Network (CNN) is used as a classifier. The combined features from clinical, gene expression and image datasets are used to train an Integrated Stacking Classifier. The integrated multimodal system’s effectiveness is shown by experimental findings. Show more
Keywords: Convolution neural networks, multimodal analysis, gray level co-occurrence matrix, histopathological, mammogram, sonogram and integrated stacking classifier
DOI: 10.3233/JIFS-220633
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2863-2880, 2023
Authors: Xie, Ying | Zhu, Yuan | Lu, Zhenjie
Article Type: Research Article
Abstract: In view of the large-scale and high-dimensional problems of industrial data and fault-tracing problems, a fault detection and diagnosis method based on multi-block probabilistic kernel partial least squares (MBPKPLS) is proposed. First, the process variables are divided into several blocks in a decentralized manner to address the large-scale and high-dimensional problems. The probabilistic characteristics and relationship between the corresponding process variables and the quality variables of each block are analyzed using latent variables, and the PKPLS model of each block is established separately. Second, the MBPKPLS model is applied to process monitoring, statistics of each block are established in a …high-dimensional space, and the monitoring indicators in each block are used to detect faults. Third, based on fault detection, the multi-block concept is further used to locate the cause of fault, thereby solving the problem of fault tracing. Finally, a numerical example and the penicillin fermentation process (PFP) are used to test the effectiveness of the MBPKPLS method. The results demonstrate that the proposed method is suitable for processing large-scale, high-dimensional data with strong nonlinear characteristics, and the MBPKPLS process monitoring method is effective for improving the performance of fault detection and diagnosis. Show more
Keywords: Large-scale industrial process, multi-block probabilistic kernel partial least squares, fault detection, fault diagnosis
DOI: 10.3233/JIFS-220605
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2881-2894, 2023
Authors: Ma, Yizhe | Yu, Long | Lin, Fangjian | Tian, Shengwei
Article Type: Research Article
Abstract: In increasingly complex scenes, multi-scale information fusion becomes more and more critical for semantic image segmentation. Various methods are proposed to model multi-scale information, such as local to global, but this is not enough for the scene changes more and more, and the image resolution becomes larger and larger. Cross-Scale Sampling Transformer is proposed in this paper. We first propose that each scale feature is sparsely sampled at one time, and all other features are fused, which is different from all previous methods. Specifically, the Channel Information Augmentation module is first proposed to enhance query feature features, highlight part of …the response to sampling points and enhance image features. Next, the Multi-Scale Feature Enhancement module performs a one-time fusion of full-scale features, and each feature can obtain information about other scale features. In addition, the Cross-Scale Fusion module is used for cross-scale fusion of query feature and full-scale feature. Finally, the above three modules constitute our Cross-Scale Sampling Transformer(CSSFormer). We evaluate our CSSFormer on four challenging semantic segmentation benchmarks, including PASCAL Context, ADE20K, COCO-Stuff 10K, and Cityscapes, achieving 59.95%, 55.48%, 50.92%, and 84.72% mIoU, respectively, outperform the state-of-the-art. Show more
Keywords: Multi-scale fusion, Segmentation, Transformer
DOI: 10.3233/JIFS-220976
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2895-2907, 2023
Article Type: Research Article
Abstract: In this paper, a class of Clifford-valued neutral fuzzy neural-type networks with proportional delay and D operator and whose self feedback coefficients are also Clifford numbers are considered. By using the Banach fixed point theorem and some differential inequality techniques, we directly study the existence and global asymptotic stability of pseudo almost periodic solutions by not decomposing the considered Clifford-valued systems into real-valued systems. Finally, two examples are given to illustrate our main results. Our results of this paper are new.
Keywords: Clifford-valued neural network, fuzzy neural network, proportional delay, D operator, pseudo almost periodic solution, global asymptotic stability.
DOI: 10.3233/JIFS-221017
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2909-2925, 2023
Authors: Deepa, S. | Sridhar, K.P. | Baskar, S. | Mythili, K.B. | Reethika, A. | Hariharan, P.R.
Article Type: Research Article
Abstract: A smart healthcare network can use sensors and the Internet of Things (IoT) to enhance patient care while decreasing healthcare expenditures. It has become more difficult for healthcare providers to keep track and analyze the massive amounts of data it generates. Health care data created by IoT devices and e-health systems must be handled more efficiently. A wide range of healthcare industries can benefit from machine learning (ML) algorithms in the digital world. However, each of these algorithms has to be taught to anticipate or solve a certain problem. IoT-enabled healthcare data and health monitoring-based machine learning algorithms (IoT-HDHM-MLA ) …have been proposed to solve the difficulties faced by healthcare providers. Sensors and IoT devices are vital for monitoring an individual’s health. The proposed IoT-HDHM-MLA aims to deliver healthcare services via remote monitoring with experts and machine learning algorithms. In this system, patients are monitored in real-time for various key characteristics using a collection of small wireless wearable nodes. The health care business benefits from systematic data collection and efficient data mining. Thus, the experimental findings demonstrate that IoT-HDHM-MLA enhances efficiency in patient health surveillance. Show more
Keywords: Health monitoring, machine learning algorithms, IoT, smart healthcare
DOI: 10.3233/JIFS-221274
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2927-2941, 2023
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