Journal of Intelligent & Fuzzy Systems - Volume 45, issue 6

Authors: Umamageswari, A. | Deepa, S. | Bhagyalakshmi, A. | Sangari, A. | Raja, K.

Article Type: Research Article

Abstract: To assess non-verbal reactions to commodities, services, or products, sentiment analysis is the technique of identifying exhibited human emotions utilizing artificial intelligence-based technology. The facial muscles flex and contract differently in response to each facial expression that a person makes, which facilitates the deep learning AI algorithms’ ability to identify an emotion. Facial emotion analysis has numerous applications across various industries and domains, leveraging the understanding of human emotions conveyed through facial expressions, so it is very much required in healthcare, security and survelliance, Forensics, Autism and cultural studies etc,.. In this study, facially expressed sentiments in real-time photographs as …well as in an existing dataset are classified using object detection techniques based on deep learning. Fast Region-based Convolution Neural Network (R-CNN) is an object detection system that uses suggested areas to categorize facial expressions of emotion in real-time. Using a high-quality video collection made up of 24 actors who were photographed facially expressing eight distinct emotions (Happy, Sad, Disgust, Anger, Surprise, Fear, Contempt and Neutral). The Fast R-CNN and Mouth region-based feature extraction and Maximally Stable Extremal Regions (MSER) method used for classification and feature extraction respectively. In order to assess the deep network’s performance, the proposed work builds a confusion matrix. The network generalizes to new images rather well, as seen by the average recognition rate of 97.6% for eight emotions. The suggested deep network approach may deliver superior recognition performance when compared to CNN and SVM methods, and it can be applied to a variety of applications including online classrooms, video game testing, healthcare sectors, and automated industry. Show more

Keywords: Deep learning, R-CNN, emotion recognition, facial expression, computer vision

DOI: 10.3233/JIFS-233842

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10141-10155, 2023

Authors: Liu, Jun

Article Type: Research Article

Abstract: Composite cylindrical shells play a crucial role in aerospace and marine structures. This study investigates the optimal structure for cylindrical multilayer composite shells under the effect of axial pressure using the finite element method and NSGA-II genetic algorithm to determine the maximum buckling load capacity. The critical buckling load of multilayer composite shells depends on various parameters, such as fiber angle, the number of layers, the material of the layers, and their thickness. The objective functions are used to increase the structure load capacity and reduce its weight. ABAQUS software was used to perform finite element analysis on the composite …cylindrical shell for determining the buckling load. Using the response surface model, the relationship between variables and objective functions has been determined. Results of the proposed response surface model for the training stages are evaluated using various statistical indices and the root mean square error for buckling load and shell weight variables is 0.065 and 0.140, respectively. In the next step, the NSGA-II genetic optimization algorithm was used to modify the layout and thickness of the composite layers to optimize the buckling strength and weight of the structure. A genetic algorithm based on NSGA-II was used to optimize the geometric characteristics. Show more

Keywords: Multi-objective optimization, NSGA-II, buckling load, genetic algorithm, cylindrical shell

DOI: 10.3233/JIFS-230826

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10157-10165, 2023

Authors: Sun, Tiantian

Article Type: Research Article

Abstract: A scientific financial transfer payment system is an important guarantee for promoting the modernization of the national governance system and governance capacity. The new development concept puts forward new requirements for fiscal governance at a new historical stage. The reform and improvement of the special transfer payment system match the responsibility and responsibility of fiscal governance, and better serve the national strategic pattern of rural revitalization and common prosperity. The performance evaluation of financial special poverty alleviation (SPA) development funds is conducive to improving the efficiency of fund utilization, achieving the radiation effect and multiple effect of financial SPA development …funds, improving the ability of financial transfer payments, and enhancing the modernization of national financial governance capabilities. The performance evaluation of financial SPA development funds under the background of rural revitalization is a multiple attribute group decision making (MAGDM). Based on the existing MABAC model, the MABAC model is extended to 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs). Firstly, the definitions of 2TLPFSs, 2TLPFWA operator and 2TLPFWG operator is introduced. Then, the existing MABAC method is also introduced. The 2-tuple linguistic Pythagorean fuzzy number MABAC method (2TLPFN-MABAC) is constructed to cope with the MAGDM under 2TLPFSs. Finally, a case study for performance evaluation of financial SPA development funds under the background of rural revitalization is constructed and some comparative analyses is employed to verify the 2TLPFN-MABAC method. Show more

Keywords: Multiple attribute group decision making (MAGDM), MABAC method, 2-tuple linguistic Pythagorean fuzzy numbers (2TLPFNs), Financial SPA development funds

DOI: 10.3233/JIFS-232168

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10167-10181, 2023

Authors: Sun, Yin-Kun | Hua, Jun | Li, Yan-Na | Chen, Guang-Wei

Article Type: Research Article

Abstract: With the rapid development of science and technology, automatic control systems have been applied in more and more fields. At the same time, the requirements for the stability, accuracy, response speed, and self-regulation ability of the system are also increasing. In the wood processing industry, the heating system of factories is an important link to ensure normal production. Therefore, in order to further improve the production efficiency of the wood processing industry and enhance the stability and controllability of the heating system in wood processing production, this article takes into account the delay and coupling effects in the rapid heating …process, and combines fuzzy control with temperature control to study and establish a woodworking thermal mechanical coupling model based on fuzzy control algorithm. The results show that compared with traditional PID control, fuzzy control has advantages such as short response time, small overshoot, high steady-state accuracy, fast steady-state recovery, and good dynamic and static performance. Under the condition of rapid heating, the existence of delay effect weakens the effect of Thermal shock, while the coupling effect not only affects the propagation of thermoelastic waves in the elastic body, but also weakens the weakening effect of delay effect on Thermal shock to a certain extent. Show more

Keywords: Woodworking, fuzzy control, thermal-mechanical coupling, thermodynamic coupling, delay effect

DOI: 10.3233/JIFS-232242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10183-10192, 2023

Authors: Sritha, P. | Valarmathi, R.S. | Poongodi, C.

Article Type: Research Article

Abstract: One of the best methods for assessing a baby’s health is foetal electrocardiography (FECG). Previously restricted to more widespread global disorders such as common ischemia, it is new way to investigating foetal heart rate irregularities. Current prenatal monitoring practices ignore critical FECG waveform elements that are the foundation of both pediatric, adult cardiac assessment, and instead of focusing solely on the foetal heart rate. In this paper we proposed Double Multiply-and-Accumulate (MAC) approach used for package operators into a single DSP block of commercial FPGAs, theoretically doubling the calculation speed for FECG monitoring. For a variety of technical reasons, they …were using the Space-Time Block Code (STBC) monitoring mode of operation. To strengthen the security of FECG monitoring, the Advanced Encryption Standard (AES) method may be used with the double MAC operators using STBC based FECG monitoring that has been developed. The solution was then assessed using state-of-the-art the Space-Time Block Code (STBC) based FECG techniques, and its validity was confirmed using Verilog simulation and FPGA synthesis. The calculation throughput of an STBC-based FECG monitoring system was found to be doubled using the Double MAC approach. Our implementation result demonstrates that keys are necessary for 128-bit AES encoding and decoding operations via VHDL-coded transformations. It is now more vital than ever to do a feasibility analysis of any hardware design due to the increase in the number of ways presented for minimizing noise. The efficiency increased (92%), and the delay was decreased to 19.35 ns by employing this double MAC architecture. The simulation results demonstrate that transformations for coding on an FPGA are implemented using the Xilinx VIVADO tool. Show more

Keywords: FECG monitoring, Double MAC, STBC based FECG, Verilog, AES, Xilinx

DOI: 10.3233/JIFS-234164

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10193-10211, 2023

Authors: Wang, Hui

Article Type: Research Article

Abstract: In Visual Communication Design (VCD), noise data is easy to appear, which reduces image quality and affects the effect of VCD. The non local mean image denoising algorithm is a good filtering denoising algorithm, but there are still issues of information interference and missing. To improve the performance of noise recognition and image denoising technology, this study proposes a non local mean image denoising algorithm based on machine learning technology. The whale optimization algorithm, as a machine learning technique, has good performance in seeking optimal solutions. Therefore, it is applied to optimize the filtering parameters of non local mean image …denoising algorithms to improve the perforGAmance of non local mean image denoising algorithms. To address the shortcomings of the whale optimization algorithm, BP neural network is introduced for optimization. Finally, the experiment uses the improved particle swarm optimization algorithm to optimize the BPNN and applies it to the recognition and classification of noise data. Combining the above contents, the IBINLM image denoising algorithm is constructed experimentally. It is verified that the IPSO-BPNN model’s loss value is 0.12; The recognition accuracy of the model for noise pixels is 98.64%; F1 value reaches 96.32%; The fitting degree reaches 0.983. The PSNR of IBINLM algorithm is 35.86 dB; MSE is 0.29; AUC value reaches 0.903. The results show that the IPSO-BPNN model and IBINLM image denoising algorithm have better performance compared to other models, which can improve the quality of visual communication works, playing an essential role in image transmission and storage in visual communication design. Show more

Keywords: Machine learning, non-local mean, image denoising, whale optimization algorithm, visual communication design

DOI: 10.3233/JIFS-234632

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10213-10225, 2023

Authors: Guo, Ying | Peng, Jinzhu | Ding, Shuai | Liang, Jing | Wang, Yaonan

Article Type: Research Article

Abstract: In this paper, a variable impedance control method is proposed for uncertain robotic systems based on a nonlinear force contact-based flexible environmental model. First, a nonlinear force contact model between the rigid manipulator and flexible environment is applied to the compliant control of the manipulator, which can avoid excessive force overshoot that usually exists in the traditional spring-damping environmental model. Then, to achieve better force/position tracking performances, a fuzzy-based adaptive variable impedance controller is designed based on the force contact-based flexible environmental model, where the impedance parameters are adjusted online through the force and position feedback of the robotic system, …and the fuzzy logic system is used to compensate the uncertainties. Moreover, the stability of the adaptive variable impedance control scheme is proved by the Routh stability criterion, and the boundness of all the signals in the closed-loop control system is guaranteed by the Lyapunov stability theorem. Finally, the effectiveness of the proposed method is verified by the simulation of a two-link manipulator, and the results demonstrate that the performances of position tracking are improved, while the force overshoot and oscillation time are reduced. Show more

Keywords: Variable impedance control, flexible environment, fuzzy logic system, force contact model, robotic system

DOI: 10.3233/JIFS-224250

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10227-10241, 2023

Authors: Kayyidavazhiyil, Abhilash

Article Type: Research Article

Abstract: Prediction of malicious attacks and monitoring of network behaviour is significant for providing security and mitigating the loss of credential information. In order to monitor network traffic and identify different types of attacks in the network, numerous existing algorithms have been provided for classifying unauthorized access from the authorized access. However, the traditional techniques have faced complications in satisfying the accuracy while making predictions of malicious activities. Detection accuracy have been addressed as a drawback which hinders in making appropriate identification of threats. In order to overcome such challenges, the proposed work is designed with effective IDS mechanism for detecting …and classifying the attacks taken from the UNSW-NB15 and NSL-KDD dataset. IDS (Intrusion Detection System) implementation is accomplished with three stages such as pre-processing is the initial phase in which scaling re-sizing of all images to similar width and height. Process of checking missing values reduces the computational complexities and enhances accuracy. Second stage is the novel feature-selection process accomplished by E-GSS (Enhanced Genetic Sine Swarm Intelligence) for selecting significant and optimal features. Finally, classification is the final phase in which intrusion is classified using novel DMH-ANN (Deep Meta-Heuristics Artificial Neural Network) which is internally being compared to three classifiers such as RF (Random Forest), NB (Naïve Bayes) and XG-Boost (Extreme Gradient). Experimental evaluation is carried out with the performance metrics such as accuracy, precision and recall and compared with existing algorithms for exhibiting the effectiveness of the proposed model. The research outcome reveals its efficiency in detecting and classifying attacks with greater accuracy. Show more

Keywords: Intrusion detection, UNSW-NB15 dataset, NSL-KDD, Genetic Sine Swarm, Metaheuristic ANN, Naïve Bayes, XG-Boost, random forest

DOI: 10.3233/JIFS-224283

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10243-10265, 2023

Authors: Vaishnavi, D. | Balaji, G.N.

Article Type: Research Article

Abstract: Due to the drastic increase in the generation of high-quality fake images in social networking, it is essential to design effective recognition approaches. Image/video manipulation defines any set of actions which can be carried out on digital content by the use of software editing approaches or artificial intelligence. A major kind of image and video editing comprises replicating the regions of the image, named as copy-move technique. Conventional image processing methods physically search for the pattern relevant to the replicated contents, restricting the utilization in massive classification of data. Contrastingly, the recently developed deep learning (DL) models have exhibited promising …performance over the traditional models. In this aspect, this paper presents a novel intelligent deep learning based copy move image forgery detection (IDL-CMIFD) technique. The proposed IDL-CMIFD technique intends to design a DL model to classify the candidate images into two classes: original and forged/tampered and then localized the copy moved regions. In addition, the proposed IDL-CMIFD technique involves the Adam optimizer with Efficient Net based feature extractor to derive a useful set of feature vectors. Moreover, chaotic monarch butterfly optimization (CMBO) with deep wavelet neural network (DWNN) model is applied for classification purposes. The CMBO algorithm is utilized to optimally tune the parameters involved in the DWNN model in such a way that the classification performance gets improved. The performance validation of the proposed model takes place on benchmark MICC-F220, MICC-F2000, MICC-F600 datasets. A wide range of comparative analyses is performed and the results ensured the better performance of the IDL-CMIFD technique in terms of different evaluation parameters. Show more

Keywords: Copy Move technique, image forgery, deep learning, hyperparameter tuning, metaheuristics

DOI: 10.3233/JIFS-230291

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10267-10280, 2023

Authors: Sri Geetha, M. | Grace Selvarani, A.

Article Type: Research Article

Abstract: Breast cancer is responsible for the deaths of hundreds of women every year. The manual identification of breast cancer has more difficulties, and have the possibility of error. Many imaging approaches are being researched for their potential to identify breast cancer (BC). Incorrect identification might sometimes result in unneeded therapy and diagnosis. Because of this, accurate identification of breast cancer may save a great number of patients from needing unneeded surgery and biopsies. Deep learning’s (DL) performance in the processing of medical images has substantially increased as a result of recent breakthroughs in the sector. Because of their improved capacity …to anticipate outcomes, deep learning algorithms are able to reliably detect BC from ultrasound pictures. Transfer learning is a kind of machine learning that reuses knowledge representations from public models that were built with the use of large-scale datasets. Transfer learning has been shown to often result in overfitting. The primary purpose of this research is to develop and provide suggestions for a deep learning model that is effective and reliable in the detection and classification of breast cancer. A tissue biopsy is obtained from the suspicious region in order to ascertain the nature of a breast tumor and whether or not it is cancerous. Tumors may take any of these forms. When the images have been reconstructed with the help of a variational autoencoder (VAE) and a denoising variational autoencoder (DVAE), a convolutional neural network (CNN) model is used. This will be the case because it opens up a new area of the field to be investigated. The histological subtypes of breast cancer are used in conjunction with the degree of differentiation to execute the task of breast cancer categorization. Show more

Keywords: Medical image classification, disease detection, deep learning, breast cancer, convolutional neural network (CNN), variationalautoencoder, histopathology image

DOI: 10.3233/JIFS-231345

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10281-10294, 2023