Journal of Intelligent & Fuzzy Systems - Volume 45, issue 1

Authors: Guan, Hao | Ejaz, Farukh | ur Rehman, Atiq | Hussain, Muhammad | Kosari, Saeed

Article Type: Research Article

Abstract:  In this paper, we have defined some fuzzy topological invariants for particular types of uniform fuzzy graph. Some particular useful types of uniform fuzzy graphs are Uniform Edge Fuzzy Graph, Uniform Vertex Fuzzy Graph, Uniform Vertex-Edge Fuzzy Graph and Totally Uniform Fuzzy Graph. For each particular type we have defined different kinds of degrees in a graph in accordance with the unique nature of it. In the end, we have applied all our output results to a cellular neural fuzzy graph as an example, to verify the predicting ability of topological invariants. The aim of this paper is to define …more significant fuzzy topological invariants in fuzzy graphs. Our ideas will help to create a link between fuzzy graph theory and simple (crisp) graph theory. Show more

Keywords: Uniform Edge Fuzzy Graphs, Fuzzy Topological Invariants, Fuzzy degrees

DOI: 10.3233/JIFS-223402

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1653-1662, 2023

Authors: Bao, Qingfeng | Zhang, Sen | Guo, Jin | Ding, Dawei | Zhang, Zhenquan

Article Type: Research Article

Abstract:  In order to improve the optimal setting temperature problem to achieve the global optimum of product performance, costs and benefits. In this article, a hierarchical structure optimal setting approach of production indexes for the rolling heating furnace temperature field (RHFTF) is proposed. It is composed of three layers with different functions to obtain the temperature control setting model of the RHFTF. In the first layer, the bi-feature Gaussian mixture model clustering (BFGMMC) algorithm of loading plan is proposed to optimize the setting of a limited number of slabs. In the second layer, the type-2 fuzzy rule interpolation (T2FRI) setting method …is developed to obtain the optimal setting curve. Meanwhile, an improved KH (Kóczy-Hirota) α-cut distance (IKHCD) algorithm is proposed to get the miss information between any two adjacent interpolation points. In the third layer, knowledge feedforward compensation of rule matrices (KFCRM) algorithm is presented to improve the anti-interference ability of the setting model. The results of the study can demonstrate that the proposed method improves the accuracy of the model and optimizes the control strategy. Furthermore, the experimental results show that the proposed method meets the process technical requirements. Show more

Keywords: Hierarchical structure, bi-feature Gaussian mixture model clustering (BFGMMC), type-2 fuzzy rules interpolation (T2FRI), improved KH (Kóczy-Hirota) α-cut distance (IKHCD), knowledge feedforward compensation of rule matrices(KFCRM)

DOI: 10.3233/JIFS-223441

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1663-1681, 2023

Authors: Vasavi, J. | Abirami, M.S.

Article Type: Research Article

Abstract: Latent Lip groove application is been a notable topic in forensic applications like crime and other investigations. The detection of lip movement is been a challenging task since it is a smaller integral part of the human face. The conventional models operate on the available public or private dataset but it is constrained to the large population and unconstrained environment. The study aims at developing a deep learning model in a multimodal system using the deep U-Net Convolutional Neural Network architecture. It also aims at improving biometric authentication through a deep pattern recognition that involves the feature extraction of grooves …present in the human lips. An examination of grooves present in the input lip image is conducted by the present system to check the authenticity of the person entering the cyber-physical systems. The lip images are collected from the public security cameras via high-definition cameras in crowded areas that help the proposed method in forensic investigation and further, it considers various unconstrained scenarios to improve the efficacy of the system. The study involves initially pre-processing of lip image, and feature extraction of lip grooves to improve the efficacy of the lip trait. The simulation is conducted on the MATLAB tool to examine the efficacy of the model against various existing methods. Further, the study does not take into account the datasets available on the websites and lip images are only collected from a large set population in a real-time environment. The results of the simulation show that the proposed method achieves a higher degree of accuracy in extracting the grooves from the input lip images. Show more

Keywords: Biometric authentication, lip pattern, U-Net, grooves, multimodal

DOI: 10.3233/JIFS-223488

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1683-1693, 2023

Authors: Gao, Yu | Zhang, Qinghua | Zhao, Fan | Gao, Man

Article Type: Research Article

Abstract:  Fuzzy sets provide an effective method for dealing with uncertain and imprecise problems. For data of intermediate fuzzy distribution, membership degrees of objects whose attribute values are larger or smaller than the normal value would be the same and carried out the same decision. However, objects with different values mean that the information they contain is different for the decision-making problem. The decision process of calculating membership degrees in fuzzy set will lose the information of data itself. Therefore, bilateral fuzzy sets and their three-way decisions are proposed. First, the deviation degree is proposed in order to distinguish these objects. …Compared with the membership degree, the deviation degree extends the mapping range from [0, 1] to [- 1, 1]. For six typical membership functions, their corresponding deviation functions are discussed and deduced. Second, the concept of bilateral fuzzy sets is proposed and the corresponding operation rules are analyzed and proved. Then, three-way decisions and approximations based on bilateral fuzzy sets are constructed. Next, for the optimization of threshold, principle of least cost is extended to the three-way decisions model based on bilateral fuzzy sets, and theoretical derivation is carried out. Finally, based on probability statistics, the principle based on confidence interval is proposed, which provides a new perspective for threshold calculation. Show more

Keywords: Fuzzy sets, three-way decisions, confidence interval, Bilateral fuzzy sets

DOI: 10.3233/JIFS-230638

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1695-1715, 2023

Authors: Komala, C.R. | Velmurugan, V. | Maheswari, K. | Deena, S. | Kavitha, M. | Rajaram, A.

Article Type: Research Article

Abstract: Internet of Things (IoT) technologies increasingly integrate unmanned aerial vehicles (UAVs). IoT devices that are becoming more networked produce massive data. The process and memory of this enormous volume of data at local nodes, particularly when utilizing artificial intelligence (AI) algorithms to collect and utilize useful information, have been declared vital issues. In this paper, we introduce UAV computing to solve greater energy consumption, delay difficulties using task offload and clustered approaches, and make cloud computing operations accessible to IoT devices. First, we present a clustering technique to group IoT devices for data transmission. After that, we apply the Q-learning …approach to accomplish task offloading and allocate the difficult tasks to UAVs that are not yet fully loaded. The sensor readings from the CHs are then collected using UAV path planning. Furthermore, We use a convolutional neural network (CNN) to achieve UAV route planning. In terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets, the effectiveness of the current study is finally compared with the existing techniques using UAVs. The results showed that the suggested strategy outperformed the current approaches in terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets. Additionally, the proposed technique consumed less energy due to CNN-based route planning and dynamic positioning, which reduced UAV transmits power. Overall, the study concluded that the suggested approach is effective for improving energy-efficient and responsive data transmission in crises. Show more

Keywords: UAV computing, Internet of Things, clustering, energy reduction, task offloading, and UAV path planning

DOI: 10.3233/JIFS-231242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1717-1730, 2023

Authors: Jayapriya, P. | Umamaheswari, K. | Kavitha, A. | Ahilan, A.

Article Type: Research Article

Abstract: In recent years, finger vein recognition has gained a lot of attention and been considered as a possible biometric feature. Various feature selection techniques were investigated for intrinsic finger vein recognition on single feature extraction, but their computational cost remains undesirable. However, the retrieved features from the finger vein pattern are massive and include a lot of redundancy. By using fusion methods on feature extraction approaches involving weighted averages, the error rate is minimized to produce an ideal weight. In this research, a novel combinational model of intelligent water droplets is proposed along with hybrid PCA LDA feature extraction for …improved finger vein pattern recognition. Initially, finger vein images are pre-processed to remove noise and improve image quality. For feature extraction, Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) are employed to identify the most relevant characteristics. The PCA and LDA algorithms combine features to accomplish feature fusion. A global best selection method using intelligent water drops (GBS-IWD) is employed to find the ideal characteristics for vein recognition. The K Nearest Neighbour Classifier was used to recognize finger veins based on the selected optimum features. Based on empirical data, the proposed method decreases the equal error rate by 0.13% in comparison to existing CNN, 3DFM, and JAFVNet techniques. The overall accuracy of the proposed GBSPSO-KNN is 3.89% and 0.85% better than FFF and GWO, whereas, the proposed GBSIWD-KNN is 4.37% and 1.35% better than FFF and GWO respectively. Show more

Keywords: Principle component analysis, finger vein recognition, linear discriminant analysis, k-nearest neighbor, intelligent water drops

DOI: 10.3233/JIFS-222717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1731-1742, 2023

Authors: Jiang, Feng | Lin, Chunhua | Chen, Jing | Wu, Chutian

Article Type: Research Article

Abstract: New energy integration is thought to be one of the most potential solutions to support the power system with a sustainable energy infrastructure. However, new energy is an uncertain power generation resource, and the electricity generated by it has the characteristics of randomness, intermittency and reverse peak regulation. Its large-scale integration into the power grid makes the operation and reliability scheduling of the power system more challenging. It was important to build a wireless sensing and monitoring network to monitor the power and change trend of the new energy field (station) in real time. The energy consumption of wireless sensing …monitoring network is an important factor to improve the reliability of new energy scheduling. Based on the energy consumption of the wireless sensing monitoring network built by the new energy scheduling, the compression sensing technology was integrated and the network routing protocol (I-LEACH protocol) was optimized. The sampling data was transmitted by the cluster head node at the compression rate of 0.6, the improved OMP (Orthogonal Matching Pursuit) algorithm was reconstructed to achieve reliable data transmission, and the network energy consumption was further reduced. Compared with the I-LEACH routing protocol network, the experiments show that the network residual energy of the proposed method increased by 22% and the life cycle increased by about 30%. This method is helpful to improve the reliability of new energy power dispatching system and it can provide reference for realizing the reliability scheduling of new energy power system. Show more

Keywords: I-LEACH, cluster head node, OMP

DOI: 10.3233/JIFS-222980

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1743-1756, 2023

Authors: Yue, Guanli | Deng, Ansheng | Qu, Yanpeng | Cui, Hui | Liu, Jiahui

Article Type: Research Article

Abstract: Ensemble clustering helps achieve fast clustering under abundant computing resources by constructing multiple base clusterings. Compared with the standard single clustering algorithm, ensemble clustering integrates the advantages of multiple clustering algorithms and has stronger robustness and applicability. Nevertheless, most ensemble clustering algorithms treat each base clustering result equally and ignore the difference of clusters. If a cluster in a base clustering is reliable/unreliable, it should play a critical/uncritical role in the ensemble process. Fuzzy-rough sets offer a high degree of flexibility in enabling the vagueness and imprecision present in real-valued data. In this paper, a novel fuzzy-rough induced spectral ensemble …approach is proposed to improve the performance of clustering. Specifically, the significance of clusters is differentiated, and the unacceptable degree and reliability of clusters formed in base clustering are induced based on fuzzy-rough lower approximation. Based on defined cluster reliability, a new co-association matrix is generated to enhance the effect of diverse base clusterings. Finally, a novel consensus spectral function is defined by the constructed adjacency matrix, which can lead to significantly better results. Experimental results confirm that the proposed approach works effectively and outperforms many state-of-the-art ensemble clustering algorithms and base clustering, which illustrates the superiority of the novel algorithm. Show more

Keywords: Rough set, fuzzy-rough set, ensemble clustering, cluster reliability, spectral clustering

DOI: 10.3233/JIFS-223897

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1757-1774, 2023

Authors: Santhadevi, D. | Janet, B.

Article Type: Research Article

Abstract: Many Internet of Things (IoT) devices are susceptible to cyber-attacks. Attackers can exploit these flaws using the internet and remote access. An efficient Intelligent threat detection framework is proposed for IoT networks. This paper considers four key layout ideas while building a deep learning-based intelligent threat detection system at the edge of the IoT. Based on these concepts, the Hybrid Stacked Deep Learning (HSDL) model is presented. Raw IoT traffic data is pre-processed with spark. Deep Vectorized Convolution Neural Network (VCNN) and Stacked Long Short Term Memory Network build the classification model (SLSTM). VCNN is used for extracting meaningful features …of network traffic data, and SLSTM is used for classification and prevents the DL model from overfitting. Three benchmark datasets (NBaIoT-balanced, UNSW-NB15 & UNSW_BOT_IoT- imbalanced) are used to test the proposed hybrid technique. The results are compared with state-of-the-art models. Show more

Keywords: Hybrid stacked deep learning, stacked LSTM, Vectorized Convolutional Neural Network, IoT-network security, edge computing

DOI: 10.3233/JIFS-223246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1775-1790, 2023

Authors: Li, Huan

Article Type: Research Article

Abstract: The difficulties in determining the compressive strength of concrete are inherited due to the various nonlinearities rooted in the mix designs. These difficulties raise dramatically considering the modern mix designs of high-performance concrete. Presents study tries to define a simple approach to link the input ingredients of concrete with the resulted compressive with a high accuracy rate and overcome the existing nonlinearity. For this purpose, the radial base function is defined to carry out the modeling process. The optimal results were obtained by determining the optimal structure of radial base function neural networks. This task was handled well with two …precise optimization algorithms, namely Henry’s gas solubility algorithm and particle swarm optimization algorithm. The results defined both models’ best performance earned in the training section. Considering the root mean square error values, the best value stood at 2.5629 for the radial base neural network optimized by Henry’s gas solubility algorithm, whereas the same value for the the radial base neural network optimized by particle swarm optimization was 2.6583 although both hybrid models provided acceptable output results, the radial base neural network optimized by Henry’s gas solubility algorithm showed higher accuracy in predicting high performance concrete compressive strength. Show more

Keywords: High-performance concrete, Henry’s gas solubility algorithm, particle swarm optimization algorithm, radial base function neural network

DOI: 10.3233/JIFS-221342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1791-1803, 2023