Journal of Intelligent & Fuzzy Systems - Volume 44, issue 6

Authors: Senthilkumar, V.M. | Thenmozhi, S. | Kumudavalli, M.V. | Yedukondalu, U.

Article Type: Research Article

Abstract: The Severe Acute Respiratory Syndrome (SARS) are caused by the strain of the corona virus causes cold and influenza. In recent years, the covid pandemic spread throughout the world killing millions of people. The fatality rate has increased and it also leads to pneumonia for breathing problems. Several methods like wavelet filter banks, time series methods, Neural networks was developed for the diagnosis of severe acute respiratory syndrome coronavirus, still the accuracy can be improved. Less works is carried out for hardware implementation for syndrome detectors. This proposed work represents the FPGA (Field Programmable Gate Array) implementation of the hybrid …method using Convolutional Recurrent neural network and Independent Components Analysis (ICA). The architecture extracts the ccomplex features from ECG (Electrocardiogram) samples. The hybrid Statistical and Recurrent Neural Network (RNN) Architecture implementation in a real time hardware detects the Severe Acute Respiratory Syndrome presented. The proposed method can be implemented in MATLAB, Embedded and DSP (Digital Signal Processor). But, the FPGAs consume less power computationally efficient. Since, ICA is an efficient method due to its blind source separation property accumulate the extraction of features accurate described. The mathematical model for the analysis of ECG signal using RNN is analyzed and based on that the proposed model is selected. On investigation the hybrid method using the statistical and neural network model is efficient in the analysis of biomedical signal especially ECG. The proposed ICA based RNN model is mathematically evaluated and tested with real time data. For implementation, Quartus software is used for effectiveness of the proposed model. Show more

Keywords: Field programmable gate array, recurrent neural network, independent component analysis, electrocardiogram, severe acute respiratory syndrome

DOI: 10.3233/JIFS-224289

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8803-8816, 2023

Authors: Wang, Hua | Wang, Zhi-Ming | Cui, Xiu-Tao | Li, Long

Article Type: Research Article

Abstract: Considering the heterogeneity, diffusive shape, and complex background of tumors, automatic segmentation of hepatic lesions in computed tomography (CT) images has been considered a challenging task. The performance of existing methods remains subject to segmentation uncertainties, especially in tumor boundary regions. The pixel information in these regions will be affected by both sides, thereby exposing the segmentation results to missing marks. To this end, a new network architecture named Two Direction Segmentation U-Net (TDS-U-Net) is hereby designed based on the classic Attention U-Net to tackle this problem. As the most important blocks of the Attention U-Net network, attention gates (AGs) …focus on the target structures of different shapes and sizes. In the last layer of TDS-U-Net, two dichotomous convolutional networks are applied to obtain the segmentation maps of the liver and the tumor respectively. Superimposing two segmented maps to obtain the final image addresses the above problems. The entire structure has been verified on two widely accepted public CT datasets, LiTS17 and KiTS19. Compared with the state of the art, this method exhibits superior performance and excellent shape extractions with high detection sensitivity, perfectly demonstrating its effectiveness in medical image segmentation. Show more

Keywords: Attention gates, CT, deep learning, liver tumor segmentation, kidney tumor segmentation, U-Net

DOI: 10.3233/JIFS-221111

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8817-8825, 2023

Authors: Pavithra, R. | Ramachandran, Prakash

Article Type: Research Article

Abstract: A spectrum-image based representation of machine vibration signals with deep convolution neural network is proposed for machine fault classification in which the convolution layer is used for automatic feature extraction as an alternate to the conventional feature-based methods. Two different forms of spectrum representations are proposed, one based on the short time Fourier transform of the original signals and the other based on the short time Fourier transform of the intrinsic mode functions acquired by empirical mode decomposition. Empirical mode decomposition has its own merits in discriminating non stationary signals and the novelty of the work is to use the …short time Fourier transform of intrinsic mode functions with deep convolution neural network model. The classification and validation accuracy of the model are investigated with respect to epochs. It is demonstrated that both spectrum-based techniques perform good with 100% model accuracies in a numerical experiment of binary classification on a bearing dataset that comprises of normal and faulty signals. In another experiment using milling data set, short time Fourier transform of intrinsic mode functions representation performs better with 100% training accuracy, F1 score of 0.8933 which is better than that of using short time Fourier transform of raw signals whose training accuracy is 64% and F1 score of 0.7486. The numerical study shows that the empirical mode decomposition based spectrum representation delivers the highest accuracy in the learning model obviating the necessity for independent feature extraction, feature selection, and dimension reduction. The numerical experiment is extended using empirical mode decomposition based spectrums for multiple class classification problems in bearing dataset. The confusion matrix obtained for 10 classes, shows that validation accuracy is 100% for all classes. The performance comparison throws light on the merits of empirical mode decomposition spectrum method over other state of the art methods. Show more

Keywords: Convolutional neural network (CNN), empirical mode decomposition (EMD), intrinsic mode function (IMF), short-time Fourier transform (STFT)

DOI: 10.3233/JIFS-223012

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8827-8840, 2023

Authors: Qiu, Guangying | Tao, Dan | Su, Housheng

Article Type: Research Article

Abstract: The fault diagnosis of vessel power equipment is established by the manual work with low efficiency. The knowledge graph(KG) usually is applied to extract the experience and operation logic of controllers into knowledge, which can enrich the means of fault judgment and recovery decision. As an important part of KG building, the performance of named entity recognition (NER) is critical to the following tasks. Due to the challenges of information insufficiency and polysemous words in the entities of vessel power equipment fault, this study adopts the fusion model of Bidirectional Encoder Representations from Transformers (BERT), revised Convolutional neural network (CNN), …bidirectional long short-term memory (BiLSTM), and conditional random field (CRF). Firstly, the adjusted BERT and revised CNN are respectively adopted to acquire the multiple embeddings including semantic information and contextual glyph features. Secondly, the local context features are effectively extracted by adopting the channel-wised fusion structures. Finally, BiLSTM and CRF are respectively adopted to obtain the semantic information of the long sequences and the prediction sequence labels. The experimental results show that the performance of NER by the proposed model outperforms other mainstream models. Furthermore, this work provides the foundation of the tasks of intelligent diagnosis and NER in other fields. Show more

Keywords: Vessel, power equipment, named entity recognition, BERT

DOI: 10.3233/JIFS-223200

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8841-8850, 2023

Authors: Sharma, Surya Prakash | Singh, Laxman | Tiwari, Rajdev

Article Type: Research Article

Abstract: In the current market scenario, online customer reviews had a significant impact on boosting the sale of online products. Recently, there has been exponential growth in e-commerce industry owning to the online customer reviews. Over the years, researchers has observed the importance of online consumer reviews for purchasing online products. Hence, in this study, authors made an attempt to develop an efficient convolutional neural network (CNN) based classification model that aims to predict the usefulness of product reviews with higher accuracy on two different types of data sets (i.e., search product and experienced product). In our proposed study, to determine …the usefulness of a review in terms of structural, linguistic, sentimental, lexical, and voting feature sets, we build a deep learning model to predict the review helpfulness as a binary classification problem. The performance of the proposed method is evaluated in terms of accuracy, precision, F1 score etc. and had been compared against the various leading machine learning (ML) state of art models viz., K-nearest neighbor (KNN), Linear regression (LR), Gaussian Naive Bays (GNB), Linear Discriminant Analysis (LDA) etc. The results demonstrate that CNN achieved better classification performance in comparison to other state of art models, with highest accuracy of 99.26% and 98.97%, precision of 99% and 99.01%, F1 score of 99% and 99.89%, AUC of 0.9999 and 0.9998, Average Precision (AP) of 0.9999 and 0.9997 and recall of 100% and 100% for two different amazon product datasets. Show more

Keywords: CNN, reviews helpfulness, online reviews, machine learning, binary classification, reviews feature set

DOI: 10.3233/JIFS-223546

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8851-8868, 2023

Authors: Selvarajan, L. | Venkataramanan, K. | Rajavel, R. | Senthilkumar, T.S.

Article Type: Research Article

Abstract: Electro discharge machining (EDM) is a cycle for molding tough materials and framing profound contour formed openings by warm disintegration in all sort of electrically conductive materials. The goal of the venture to be concentrating because of working parameters of EDM for machining of silicon nitride-titanium nitride in the machining qualities with copper electrode, for example input Spark on time (Son ), current (Ip ), Spark off time (Soff ), spark gap and dielectric pressure on the metal removal rate (MRR) and Electrode Wear Rate (EWR) were analyzed. Subsequently, using Taguchi analysis of various plots like Mean effect plots, Interaction …plots, and contour plots, performance characteristics are looked at in relation to multiple process factors. Fuzzy logic and Regression analysis is utilized to combine various reactions into a solitary trademark record known as the Multi Response Performance Index (MRPI).The trial and anticipated qualities were in a decent programming instrument for discovering the MRPI esteem. For numerous performance aspects, such as material removal rate, electrode wear rate and so on, the optimal process parameter combination was established using fuzzy logic analysis. The key process factors, which included spark off time and current, were found using an ANOVA based on a fuzzy algorithm. Topography on machined surface and cross-sectional view of conductive Si3 N4 -TiN composite and surface characteristics of machined electrode is examined by SEM analysis and identified the best hole surface and worst hole surface. Sensitivity analysis is being utilized to determine how much the input values, such as Ip, Son and Soff , will need to alter in order to get the desired, optimal result. In the complexity analysis, each constraint of the machine, composite and process is addressed. Future researches might look into various electrodes to assess geometrical tolerances including angularity, parallelism, total run out, flatness, straightness, concentricity, and line profile employing other optimization methodologies to achieve the best outcome. The findings of the confirmatory experiment have been established, indicating that it may be feasible to successfully strengthen the spark eroding technique. Show more

Keywords: EDM, Si3N4–TiN, fuzzy logic optimization, MRPI, surface texture

DOI: 10.3233/JIFS-223650

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8869-8888, 2023

Authors: Rajeswary, C. | Thirumaran, M.

Article Type: Research Article

Abstract: Phishing is a major problem on darknets. Phishing is the practice of attacking an unaware person by pretending to be someone else to steal their digital data. In anonymous platforms such as the dark web or deep web of Tor, detecting the attacker or phishing attacks is a much more complicated practice. Generic phishing attacks can be easy to spot. Today’s challenge is detecting the various attacks in the anonymous network is very hard. The intelligent factor of attacks can bypass traditional detection solutions. To solve the problem of complications in the Tor Network, this work focuses on the development …of automated detection of vulnerable attacks in phishing-based Tor hidden services. The proposed model initially divides the attack parameters into three categories into Class A, Class B, and Class C based on technical perspectives and some defined threshold values. Next, the class A attacks (i.e. top level domain and protocol similarity) attacks are detected by a random forest (RF) classifier. Then, the class B attacks can be identified by the convolutional neural network (CNN). Finally, the LSTM model is applied for the accurate classification of multiple attacks in the Tor network. The experimental validation of the proposed model is tested using the CIRCL and AIL datasets. The experimental values highlighted the promising performance of the proposed model over other methods with a maximum overall detection accuracy of 95.60% and 95.77% on CIRCL and AIL datasets respectively. Therefore, the proposed model effectively detects multiple attacks in the Tor network under dynamic and real-time environments. Show more

Keywords: Phishing detection, attacks, tor network, random forest, CNN, LSTM

DOI: 10.3233/JIFS-224142

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8889-8903, 2023

Authors: Pei, Mengjiao | Liu, Shuli | Wen, Haolan | Wang, Weizhong

Article Type: Research Article

Abstract: Failure mode and effect analysis (FMEA) is one of the most effective means for potential systematic risk assessment in a real work environment. Nevertheless, the traditional FMEA approach has been extensively criticized for many deficiencies in coping with risk evaluation and prioritization problems under inter-uncertain environments. To overcome the limitations, in this paper, a synthesized risk priority calculation framework is proposed for FMEA by combining the gained and lost dominance score (GLDS) method, the combination ordered weighted averaging (C-OWA) operator, and Fermatean fuzzy set (FFS). Firstly, we use FFS to express the experts’ uncertain risk evaluation information which can depict …the fuzziness and ambiguity of the information. Secondly, the C-OWA operator combined with FFS is introduced to build the group risk matrix which can provide a more reasonable risk analysis result. Then, the developed GLDS method with FFS is presented to calculate the risk priority of each failure mode which takes both individual and group risk attitudes into consideration. Finally, a medical device risk analysis case is introduced to demonstrate the proposed FMEA framework. We also perform comparison analyses to confirm the effectiveness and rationality of the hybrid risk prioritization framework for FMEA under a complex and uncertain situation. Show more

Keywords: Failure mode and effect analysis, GLDS, Fermatean fuzzy set, C-OWA operator

DOI: 10.3233/JIFS-222692

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8905-8923, 2023

Authors: Wang, W.-C. | Yeh, Y.-W. | Chen, R.

Article Type: Research Article

Abstract: In the cooperative multi-agent pathfinding and motion planning, given a unique start position and a unique goal position for each agent, all agents are able to pursue their own goals without colliding with each other. To aim at realizing the collision-free motion of the agents within the tractable time, this work proposes a polynomial-time solver, called the HBD-AOI, hybridizing centralized and decentralized schemes. Firstly, an algorithm of centralized pathfinding is utilized to plan the optimal paths of all agents. Afterwards, each of the agents updates the local motion pattern to tracks its own planned waypoints with the obstacle avoidance in …a decentralized manner. Furthermore, to resolve unavoidable egoistic conflicts occurring in the decentralized scheme, a centralized intervener with the route replanning is invoked to coach the involved agents to abort the existing deadlocks. Bounded by an amount of time, the performances of the proposed and benchmarked algorithms are simulated on the same instance, from the evaluated testbeds that consists of various maps and scenarios. In the simulations, it is proved that this work outperforms other benchmarked algorithms for all presented instances in the term of the success rate. The experimental results are also demonstrated to verify the feasibility of the proposed methodology. Show more

Keywords: Multi-agent, pathfinding, motion navigation, hybrid approach, deadlock

DOI: 10.3233/JIFS-223157

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8925-8941, 2023

Authors: He, Xiaoxu

Article Type: Research Article

Abstract: Complex time series appear in numerous applications and are related to some essential physiological and natural systems. Their comparison faces big challenges: 1) with different complexity; 2) with significant phase shift in one series or shift∖on the time axis. Existing methods work well for periodic time-series data, but fail to produce satisfactory results in complex time-series. In this paper, we introduce a novel distance function based on the evolution rule for complex time series comparison. Here, the evolution rule, as the innate generative mechanism of time series, is creatively used to characterize complicated dynamics from complex time series. The comparison …includes different level comparisons: the coarse level is to compare the difference in complexity, and the fine level is to compare the difference in actual evolution behavior. The proposed method is inspired by the observation that similar sequences come from the same source, e.g. a person’s heart, in the case of ECG, thus two similar series will have the same innate generative mechanism. The performance has been verified by the conducting experiments, and the experiment results show that the proposed method is superior to the previously existing methods in clustering and classification on some real data sets. Show more

Keywords: Complex time series, evolution rule, complex system, data mining, non-parametric

DOI: 10.3233/JIFS-223338

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 8943-8955, 2023