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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: Pushpa, B.R. | Shobha Rani, N.
Article Type: Research Article
Abstract: Low resolution mobile photographed images pose a complex set of research challenges as compared to non-mobile captured images, which really is a significant issue these days. For non-mobile captured and high-resolution photos, current plant recognition systems are the best solution providers. This study proposes the identification and extraction of leaf regions from complex backgrounds to meet the automatic recognition needs of a variety of mobile phone users. Additionally multiple factors complicate the leaf region extraction from complex backgrounds such as varying background patterns, clutters, varying leaf shape/size and varying illumination due to volatile weather conditions. In this paper, a simple …and efficient method for leaf extraction from complex background of mobile photographed low resolution images is proposed based on color channel thresholding and morphological operations. A self-built database of 5000 mobile photographed images in realistic environments is adapted for experimentations. Experiments were conducted on various resolution categories, and it was discovered that the proposed model has an average dice similarity measure of 99.5 percent for successful extraction of the leaf region in 13MP mobile photographed images. Furthermore, our comparative investigation reveals that the suggested model outperforms both traditional and state-of-the-art techniques. Show more
Keywords: Leaf extraction, color thresholding, morphological operations, realistic backgrounds, mobile camera images, gradient image analysis
DOI: 10.3233/JIFS-212451
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 773-789, 2022
Authors: Sreejith, S. | Subramanian, R. | Karthik, S.
Article Type: Research Article
Abstract: Ischemic stroke is a universal ailment that endangers the life of patients and makes them bedridden until death. Over a decade, doctors and radiologists have been dissecting patient status straightforwardly from the printouts of the slice images delivered by different diagnostic imaging modalities. Computed Tomography (CT) is a frequently used imaging strategy for therapeutic analysis and neuroanatomical investigations. The main objective of the paper is to develop a simple technique with less architectural complication and power consumption. The proposed work is to section the ischemic stroke lesion more efficiently from multi-succession CT images using patching the asymmetric region. The Hough …transform segment and extracts the features from the asymmetric region of the CT image and finally, the random forest is implemented to classify the unusual tissues from the CT image dependent on their pathological properties. RF classifier has been trained for different parts of the cerebrum for fragmenting the stroke lesion. The acquired outcomes produce better segmentation accuracy when compared with different strategies. The overall efficiency of the proposed method determines the Ischemic stroke with an accuracy of 95% with an RF classifier. Hence this method can be used in the segmentation process of stroke lesions. Show more
Keywords: Segmentation of ischemic stroke lesion, preprocessing, patching asymmetric region, Hough line symmetry axis, Random forest classifier
DOI: 10.3233/JIFS-212457
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 791-800, 2022
Authors: Senthil Vadivu, M. | Kavithaa, G.
Article Type: Research Article
Abstract: Fetal Electrocardiogram (ECG) signal extraction from non-invasive abdominal ECG signal is one of the important clinical practices followed to observe the fetal health state. Information about heart growth and health conditions of a fetus can be observed from fetal ECG signals. However, acquiring fetal ECG from abdominal ECG signals is still considered as a challenging task in biomedical analysis. This is mainly due to corrupted high amplitude maternal ECG signals, low signal to noise ratio of fetal ECG signal, difficulties in reduction of QRS (Q wave, R wave, S wave) complexities, fetal ECG signal superimposed characteristics, other motion, and electromyography …artifacts. To reduce these conventional challenges, in fetal ECG analysis of a novel Conditional Generative adversarial network (CGAN) is introduced in this research work to extract the fetal ECG signal. The proposed classification model was classified efficiently in fetal ECG signals from non-invasive abdominal ECG signals. The experimental analysis demonstrates that the proposed network model provides better results in terms of sensitivity, specificity, and accuracy compared to the conventional fetal ECG extraction models like singular value decomposition, periodic component analysis, and Adaptive neuro-fuzzy inference system. Show more
Keywords: Fetal ECG, generative adversarial networks (GAN), classification
DOI: 10.3233/JIFS-212465
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 801-811, 2022
Authors: Lin, Jiang | Jianjun, Zhu | Nanehkaran, Y.A.
Article Type: Research Article
Abstract: The problem of bilateral matching of teams and scientific and technical talents is studied in new R&D institutions with different forms of uncertain assessment information. A decision method is proposed based on a combination of grey correlation and cloud model. The method firstly applies interval grey numbers to characterize uncertain assessment score information and cloud models to characterize uncertain linguistic assessment information; secondly, the two different pieces of information are converted into grey correlation coefficients by applying grey correlation analysis methods to the assessment values, so as to solve indicator weights, and assemble assessment data based on indicator weights and …cloud models; finally, the bilateral matching model is constructed and the matching results are solved based on the cloud model data features and the dual objectives of maximum satisfaction and minimum uncertainty. The case analysis and method comparison show that the method is feasible and effective. Show more
Keywords: New R&D institutions, scientific and technical talents, evaluation, grey correlation, cloud model, bilateral matching
DOI: 10.3233/JIFS-212467
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 813-840, 2022
Authors: Muhiuddin, G. | Talebi, A. A. | Sadati, S. H. | Rashmanlou, Hossein
Article Type: Research Article
Abstract: The cubic set, introduced as a combination of a fuzzy set and an interval-valued fuzzy set, provided researchers with more flexibility than the previous two sets in dealing with complex and uncertain problems. Fuzzy graphs, based on this type of set, are among the emerging fuzzy graphs that have a great potential to model the surrounding phenomena. Consistent with the special role that cubic graphs play in decision-making and selecting superior options, dominating these graphs is of great importance and value. In this paper, we introduce the domination of the cubic graphs in terms of strong edges and examine their …properties. In addition, we examine domination in terms of independent sets and since many of the phenomena surrounding us are hybrid, we also discuss the domination concept on its fuzzy operations. Finally, we present an application of this graph on the subject of domination. Show more
Keywords: Cubic graph, dominating set, independent cubic set, cubic operations
DOI: 10.3233/JIFS-212534
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 841-857, 2022
Authors: Wei, Mingrun | Wang, Hongjuan | Cheng, Ru | Yu, Yue
Article Type: Research Article
Abstract: Clear images are generally desirable in high-level computer vision algorithms which are mostly deployed outdoors. However, affected by the changeable weather in the real world, images are inevitably contaminated by rain streaks. Deep convolutional neural networks (CNNs) have shown significant potential in rain streaks removal. The performance of most existing CNN-based deraining methods is often enhanced by stacking vanilla convolutional layers and some other methods use dilated convolution which can only model local pixel relations to provide the necessary but limited receptive field. Therefore, long-range contextual information is rarely considered for this specific task, thus, deraining a single image remains …challenging problem. To address the above problem, an effective residual deep attention network (RDANet) for single image rain removal is proposed. Specifically, we design a strong basic unit that contains dilated convolution, spatial and channel attention module (SCAM) simultaneously. As contextual information is very important for rain removal, the proposed basic unit can capture global long-distance dependencies among pixels in feature maps and model feature relations across channels. Compared with a single dilated convolution, the spatial and channel attention enhance the feature expression ability of the network. Moreover, some previous works have proven that the no-rain information in a rain image will be missing during deraining. To enrich the detailed information in the clean images, we present a residual feature processing group (RFPG) that contains several source skip connections to inject rainy shallow source information into each basic unit. In summary, our model can effectively handle complicated long rain streaks in spatial and the outputs of the network can retain most of the details of the original rain images. Experiments demonstrate the superiority of our RDANet over state-of-the-art methods in terms of both quantitative metrics and visual quality on both synthetic and real rainy images. Show more
Keywords: Single image deraining, convolutional neural network, spatial and channel attention, source skip connection
DOI: 10.3233/JIFS-212571
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 859-875, 2022
Authors: Zhang, Feng | Luo, Xiaoying | Li, Fengling | Li, Yun | Li, Yanbin | Zhang, Pengyu
Article Type: Research Article
Abstract: Although smart grids are characterized by self-healing, economy, high efficiency, and security, many hidden dangers exist in the development of smart grids due to a gradually expanding power grid and the continuous access of new energy to the power grid. Therefore, the development of smart grids, especially their reliability, security, and vulnerability, warrants further investigation. In this study, the vulnerability of smart grids is identified, and the vulnerability elements of smart grids are selected. Based on relevant theories, such as credibility and the combination of the credibility-based moment-generating function and the distortion function, a calculation model and framework of the …vulnerability index of a smart grid are constructed. An empirical analysis is also conducted. This study provides a scientific basis for analyzing the vulnerability of smart grids and suggesting reasonable preventive measures and auxiliary decision-making information for relevant planning, design, and operation personnel, which contributes to the sustainable and healthy development of smart grids. Show more
Keywords: Smart grids, vulnerability index, moment-generating function, distortion function
DOI: 10.3233/JIFS-212575
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 877-888, 2022
Authors: Li, Jie | Song, Li | Cao, Lianglin
Article Type: Research Article
Abstract: In this paper, to reduce the redundant attractions and incorrect directions of firefly algorithm (FA), a distance-guided selection approach (DSFA) is proposed, which consists of a distance-guided mechanism and selection strategy. Where the designed distance-guided mechanism reduces the attractions and plays as a classifier for global search and local search, the suggested selection strategy can avoid local search falling into traps, thereby increasing the probability of correct direction. With the good cooperation of these two approaches, DSFA obtains a good balance of exploration and exploitation. To confirm the performance of the proposed algorithm, excessive experiments are conducted on CEC2013 benchmark …functions, large-scale optimization problems CEC2008, and software defect prediction (SDP). In the comparison with the 5 advanced FA variants, DSFA provides the optimal solutions to most CEC2013 problems. Besides, when facing the problems of class imbalance and the dimensional explosion of datasets, DSFA greatly improves the performance of machine learning classifiers employed by SDP. It can be concluded that DSFA is an effective method for global continuous optimization problems. Show more
Keywords: Firefly algorithm, distance guided mechanism, selection strategy, global continuous optimization, software defect prediction
DOI: 10.3233/JIFS-212587
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 889-906, 2022
Authors: Zeeshan, Muhammad | Khan, Madad | Iqbal, Sohail
Article Type: Research Article
Abstract: In this paper, we introduce the notion of amplitude interval-valued complex Pythagorean fuzzy sets (AIVCPFSs). The motivation for this extension is the utility of interval-valued complex fuzzy sets in membership and non-membership degree which can express the two dimensional ambiguous information as well as the interaction among any set of parameters when they are in the form of interval-valued. The principle of AIVCPFS is a mixture of the two separated theories such as interval-valued complex fuzzy set and complex Pythagorean fuzzy set which covers the truth grade (TG) and falsity grade (FG) in the form of the complex number whose …real part is the sub-interval of the unit interval. We discuss some set-theoretic operations and laws of the AIVCPFSs. We study some particular examples and basic results of these operations and laws. We use AIVCPFSs in signals and systems because its behavior is similar to a Fourier transform in certain cases. Moreover, we develop a new algorithm using AIVCPFSs for applications in signals and systems by which we identify a reference signal out of the large number of signals detected by a digital receiver. We use the inverse discrete Fourier transform for the membership and non-membership functions of AIVCPFSs for incoming signals and a reference signal. Thus a method for measuring the resembling values of two signals is provided by which we can identify the reference signal. Show more
Keywords: Amplitude interval-valued complex Pythagorean fuzzy set, Complex fuzzy set, Fuzzy set, Inverse discrete Fourier transform
DOI: 10.3233/JIFS-212615
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 907-925, 2022
Authors: Kanika, | Singla, Jimmy
Article Type: Research Article
Abstract: Since the introduction of online payment systems, people have started doing online transactions which has also led to the rise of fraudulent transactions causing loss of money to the users and created distrust in the usage of online payment systems. Hence, fraud detection systems are the need of the hour. Among the transactions occurring on daily basis, frauds are less in number as compared to the genuine transactions, so class imbalance naturally exists in fraud detection systems. In this research work, a novel framework for online transaction fraud detection system based on Deep Neural Network (DNN) has been proposed by …utilizing algorithm-level method capable to detect frauds from imbalanced data and to maintain the overall performance of the model as well. The proposed system optimizes the decision threshold by utilizing the validation data efficiently for deciding whether an incoming transaction is a Fraud or not. For demonstration of the efficiency of our proposed system, three class imbalanced publicly available datasets have been used. Proposed system has shown better performance than data-level method. The results produced by the proposed fraud detection system have also been compared with existing machine learning techniques-based fraud detection systems. The experimental results show that the deep learning-based fraud detection system is more efficient for detecting frauds from imbalanced datasets having large number of input features as compared to the machine learning models. Show more
Keywords: Deep learning, machine learning, fraud detection, imbalanced data, thresholding
DOI: 10.3233/JIFS-212616
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 927-937, 2022
Authors: Gupta, Bhavna | Kaur, Harmeet | Bedi, Punam
Article Type: Research Article
Abstract: A robust collaborative system of active products (a product is called active when its ownership does not get transferred from provider to requestor at the time of its usage) should have an in-built mechanism which can make entities (service provider(s) and requestor(s)) to decide with whom to collaborate. In the absence of such a mechanism, the system is bound to have high job failure rate, resulting in wastage of resources. This paper proposes a Trust based Multi-Agent Framework (TbMAF) for collaborative systems of active products which enable only trustworthy entities to collaborate, safeguarding both users’ sensitive applications and providers’ resources. …The trustworthiness of service provider(s) and requestor(s) is computed using Fuzzy Inference System (FIS) and Radial Basis Function Neural Network (RBFNN) methodologies, respectively. A prototype based on the proposed system has been tested using real time data of a collaborative system namely, EGEE (Enabling Grids for E-science). This paper finds evidence that the job failure rate is lower when collaborations take place only between trustworthy entities. Further, the proposed framework is found to be robust against malicious entities and can capture the evolving behavior of entities as well. Show more
Keywords: Trust, reputation, recommendation, active product, collaborative system
DOI: 10.3233/JIFS-212691
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 939-956, 2022
Authors: Shan, Chuanhui | Chen, Xiumei
Article Type: Research Article
Abstract: Because of the advantages of deep learning and information fusion technology, it has drawn much attention for researchers to combine them to achieve target recognition, positioning, and tracking. However, when the existing neural network process multichannel images (e.g., color images), multiple channels as a whole input into neural networks, which makes it hard for networks to fully learn information in R, G, and B channels of images. Therefore, it is not conducive to the final learning effect of the networks. To solve the problem, using different combinations of R, G, and B channels of color images for feature-level fusion, this …paper proposes three fusion types as “R/G/B”, “R+G/G+B/B+R”, and “R+G+B/R+G+B/R+G+B” multichannel concat-fusional convolutional neural networks. Experimental results show that multichannel concat-fusional convolutional neural networks with fusional types of “R+G/G+B/B+R” and “R+G+B/R+G+B/R+G+B” achieve better performance than the corresponding non-fusional convolutional neural networks on different datasets. It shows that networks with fusion types of “R+G/G+B/B+R” and “R+G+B/R+G+B/R+G+B” can learn more fully information of R, G, and B channels of color images and improve the learning performance of networks. Show more
Keywords: Information fusion technology, “R+G/G+B/B+R” fusional type, “R+G+B/R+G+B/R+G+B” fusional type, multichannel concat-fusional convolutional neural network, convolutional neural network
DOI: 10.3233/JIFS-212718
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 957-969, 2022
Authors: Poornima, R. | Elangovan, Mohanraj | Nagarajan, G.
Article Type: Research Article
Abstract: The evolving new and modern technologies raise the risks in the network which will be affected by several attacks and thus give rise to developing efficient network attack detection and classification methods. Here in this article for predicting and classifying the network attacks, the LSTM neural network with XGBoost is suggested in which the NSL-KDD dataset was utilized to train the LSTM in the study. In the beginning, the unnecessary data and the noisy data will be eliminated using the dataset and the feature subset with the most compelling features will be selected using the feature selection. By utilizing the …essential data, the proposed system will be trained and the training parameter values will be modified for maximizing the functionality of the proposed system. Then, the result of the proposed system will be evaluated with some of the existing machine learning and deep learning algorithms such as SVM, LR, RF, DNN, and CNN with the performance metrics like Accuracy, F1 score, Recall, and Precision. It was found that the proposed model outperforms better than the other algorithms as this model is trained with the most important features and due to this, the training time and overfitting of the learning model was reduced thereby increasing the model effectiveness Show more
Keywords: Deep learning, feature selection, LSTM, network attack, XGBoost
DOI: 10.3233/JIFS-212731
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 971-984, 2022
Authors: Yadav, Yadavendra | Chand, Satish | Sahoo, Ramesh Ch. | Sahoo, Biswa Mohan | Kumar, Somesh
Article Type: Research Article
Abstract: Machine learning and deep learning methods have become exponentially more accurate. These methods are now as precise as experts of respective fields, so it is being used in almost all areas of life. Nowadays, people have more faith in machines than men, so, in this vein, deep learning models with the concept of transfer learning of CNN are used to detect and classify diabetic retinopathy and its different stages. The backbone of various CNN-based models such as InceptionResNetV2, InceptionV3, Xception, MobileNetV2, VGG19, and DenceNet201 are used to classify this vision loss disease. In these base models, transfer learning has been …applied by adding some layers like batch normalization, dropout, and dense layers to make the model more effective and accurate for the given problem. The training of the resulting models has been done for the Kaggle retinopathy 2019 dataset with about 3662 fundus fluorescein angiography colored images. Performance of all six trained models have been measured on the test dataset in terms of precision, recall, F1 score, macro average, weighted average, confusion matrix, and accuracy. A confusion matrix is based on maximum class probability prediction that is the incapability of the confusion matrix. The ROC-AUC of different classes and the models are analyzed. ROC-AUC is based on the actual probability of different categories. The results obtained from this study show that InceptionResNetV2 is proven the best model for diabetic retinopathy detection and classification, among other models considered here. It can work accurately in case of less training data. Thus, this model may detect and classify diabetic retinopathy automatically and accurately at an early stage. So it would be beneficial for humans to reduce the effects of diabetes. As a result of this, the impact of diabetes on vision loss can be minimized, and that would be a blessing in the medical field. Show more
Keywords: Diabetic retinopathy, nonproliferative, proliferative, maculopathy, transfer learning
DOI: 10.3233/JIFS-212771
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 985-999, 2022
Authors: Wong, Shi-Ting | Too, Chian-Wen | Yap, Wun-She | Khor, Kok-Chin
Article Type: Research Article
Abstract: With technological advancement, visual search has become an effective tool for searching important information by providing images. We propose a practical medical equipment recognition that can be used in visual search through deep transfer learning. We evaluated three deep learning models, i.e., VGG-16, ResNet-50, and Inception-v3, to recognise ten different classes of medical equipment. A data set consisting of 2,666 images had been collected and augmented to measure the models’ effectiveness. The models pre-trained with the ImageNet data set were transferred to the final models, and the last layers were replaced and trained with the collected data set. A grid …search method was then used to find the best combination of hyperparameters, such as optimiser, batch size, epoch number, dropout rate, and learning rate. We tested the models using photos captured using smartphones. The results showed that Inception-v3 outperformed the other two models with the highest accuracy of 0.9454. This is the first study that uses deep transfer learning for recognising medical equipment to our best knowledge. Such recognition technology can potentially be implemented in visual search for helping consumers to check the validity of medical equipment. Show more
Keywords: Medical equipment, object recognition, deep transfer learning
DOI: 10.3233/JIFS-212786
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1001-1010, 2022
Authors: Wei, Xiaolong | Huang, Xianglin | Yang, LiFang | Cao, Gang | Tao, Zhulin | Wang, Bing | An, Jing
Article Type: Research Article
Abstract: Structural models based on Attention can not only record the relationships between features’ position, but also can measure the importance of different features based on their weights. By establishing dynamically weighted parameters for choosing relevant and irrelevant features, the key information can be strengthened, and the irrelevant information can be weakened. Therefore, the efficiency of Deep Learning algorithms can be significantly elevated and improved. Although Transformer have been performed very well in many fields including Reinforcement Learning (RL). We tried to integrate Transformers into RL, however there are some challenge in this task. Especially, MARL (known as Multi-Agent Reinforcement Learning), …which can be recognized as a set of independent agents trying to adapt and learn through their way to reach the goal. In order to emphasize the relationship between each MDP decision in a certain time period, we applied the hierarchical coding method and validated the effectiveness of this method. This paper proposed a Hierarchical Transformer MADDPG based on recurrent neural network(RNN) which we call it Hierarchical RNNs-Based Transformers MADDPG(HRTMADDPG). It consists of a lower level encoder based on RNNs that encodes multiple step sizes in each time sequence, and it also consists of an upper sequence level encoder based on Transformer for learning the correlations between multiple sequences. Then we can capture the causal relationship between sub-time sequences and make HRTMADDPG more efficient. Show more
Keywords: MADDPG, Attention, RNN
DOI: 10.3233/JIFS-212795
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1011-1022, 2022
Authors: Elayaraja, P. | Kumarganesh, S. | Martin Sagayam, K. | Dang, Hien | Pomplun, Marc
Article Type: Research Article
Abstract: Cervical cancer can be cured if it is initially screened and giving timely treatment to the patients. This paper proposes an optimization technique for exposing and segmenting the cancer portion in cervical images using transform and windowing technique. The image processing steps are preprocessing, transformation, feature extraction, feature optimization, classification, and segmentation involved in the proposed work. Initially, Gabor transform is enforced on the cervical test image to modify the pixels associated with the spatial domain into multi-resolution domain. Subsequently, the parameters of the multi-level features are extracted from the Gabor transformed cervical image. Then, the extracted features are optimized …using the Genetic Algorithm (GA), and the optimistic prominent part is classified by the Convolutional Neural Networks (CNN). Finally, the Finite Segmentation Algorithm (FSA) is used to detect and segment the cancer region in cervical images. The proposed GA based CNN classification method describes the effectual detection and classification of cervical cancer by the parameters such as sensitivity, specificity and accuracy. The experimental results are shown 99.37% of average sensitivity, 98.9% of average specificity and 99.21% of average accuracy, 97.8% of PPV, 91.8% of NPV, 96.8% of FPR and 90.4% of FNR. Show more
Keywords: Cervical cancer, Gabor, features, optimization, ANFIS, classification, Artificial Neural Network
DOI: 10.3233/JIFS-212871
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1023-1033, 2022
Authors: Bhatia, Tanveen Kaur | Kumar, Amit | Sharma, M.K. | Appadoo, S.S.
Article Type: Research Article
Abstract: To the best of author’s knowledge, only one approach is proposed in the literature to solve fuzzy linear fractional minimal cost flow problems (minimal cost flow problems in which each known arc cost is represented either by a triangular fuzzy number or a trapezoidal fuzzy number). In this paper, the mathematical incorrect assumptions, considered in the existing approach to solve fuzzy linear fractional minimal cost flow problems, are pointed out. Also, by generalizing an existing approach for solving fuzzy linear fractional programming problems, an approach (named as Mehar approach) is proposed to solve fuzzy linear fractional minimal cost flow problems. …Furthermore, two numerical examples are solved to illustrate the proposed Mehar approach. Show more
Keywords: Linear fractional minimal cost flow problem, triangular fuzzy numbers, trapezoidal fuzzy numbers, lexicographic approach
DOI: 10.3233/JIFS-212909
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1035-1051, 2022
Authors: Wang, Xiaohan | He, Zengyu | Wang, Pei | Zha, Xinmeng | Gong, Zimin
Article Type: Research Article
Abstract: Due to the limitation of positioning devices, there is a certain error between GPS positioning data and the real location on the map, and the positioning data needs to be processed to have better usability. For example, accurate location is needed for traffic flow control, automatic driving navigation, logistics tracking, etc. There are few studies specifically for circular road sections. In addition, many existing map matching methods based on Hidden Markov model (HMM) also have the problem that GPS points are easily to be matched to tangent or non-adjacent road sections at circular road sections. Therefore, the contextual voting map …matching method for circular road sections (STDV-matching) is proposed. The method proposes multiple subsequent point direction analysis methods based on STD-matching to determine entry into the circular section, and adds candidate section frequency voting analysis to reduce matching errors. The effectiveness of the proposed method is verified at the circular section by comparing it with three existing HMM methods through experiments using two real map and trajectory datasets. Show more
Keywords: Map matching, circular road, GPS, voting
DOI: 10.3233/JIFS-213054
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1053-1063, 2022
Authors: Lin, Yang | Ling, Yiqun | Yang, Zhe | Wang, Chunli | Li, Chuankun
Article Type: Research Article
Abstract: In the modern industrial process, a complete production process is achieved by requiring a variety of equipment to cooperate with each other. The abnormality in any equipment will have a large or small impact on process safety or product quality, resulting in increased risk. In recent years, many data-driven early-warning methods have been developed in academia. However, most of the methods need to be implemented on the support of normal and fault data. In order to overcome the problem, this paper establishes a new early-warning model based on negative selection algorithm (NSA) for centrifugal compressor unit without fault data. Firstly, …a nearest neighbor fixed boundary negative selection algorithm (NFB-NSA) is proposed by optimizing detector generation mechanism and matching rules for test samples. Secondly, the performance of NFB-NSA is tested by Iris dataset. The experimental results among NFB-NSA, V-detector, and other anomaly detection methods for Iris dataset shows that NFB-NSA can achieve the highest detection accuracy and the lowest false alarm rate in most cases. Finally, the early-warning of centrifugal compressor unit under normal samples is carried on by NFB-NSA in this paper. Validated by field data, NFB-NSA is demonstrated to be of excellent accuracy and robustness by results of experiments. Moreover, the influence of size of training sample on performance of NFB-NSA is obtained. Show more
Keywords: Early-warning, Centrifugal compressor unit, Fault data, NFB-NSA
DOI: 10.3233/JIFS-213075
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1065-1075, 2022
Authors: Yang, Shaojun | Huang, Xinyi
Article Type: Research Article
Abstract: The numbers of Helly, Carathéodory and Radon are a central theme in classic convex spaces and each of them is invariant under isomorphism. The theory of M -fuzzifying convex spaces is an active research field since it is presented. This paper focus on convex invariants of M -fuzzifying convex spaces. The degrees of Helly independent, Carathéodory independent and Radon independent of non-empty set are defined in the framework of M -fuzzifying convex spaces. By those definitions, we introduce the Helly number, Carathéodory number and Radon number of M -fuzzifying convex spaces. Finally, we inspect M -fuzzifying topology and M -fuzzifying …JHC which are characterized by convex invariants of M -fuzzifying convex spaces. Show more
Keywords: Helly number, Carathéodory number, Radon number
DOI: 10.3233/JIFS-213081
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1077-1090, 2022
Authors: Thangamuthu, Logeswaran | Albert, Johny Renoald | Chinnanan, Kalaivanan | Gnanavel, Banu
Article Type: Research Article
Abstract: To minimize real-time errors in a Photovoltaic (PV) system performances must be forecasted through precise simulation design before continuing into a practical application. However, due to the scarcity of data in datasheets and the inherent transcendental connections are between PV current and PV voltage, to determining the Single Diode Model (SDM) parameters becomes a more challenging problems. This paper offers a simulated study of a SDM and Double Diode Model (DDM) solar PV system under various irradiation represents, and the performance was developed by incorporating an optimization-based Maximum Power Point (MPP) tracking techniques. According to the present simulation presented in …this article, a mathematical model for a SDM/DDM as well as optimization methodologies has been estimated MATLAB platform. The present MPP circuit model designed and compared with BAT optimization algorithms. The nonlinear relationship between Voltage (V) - Current (I) and Voltage (V) –Power (W) acknowledged as characteristic curves for different temperature (∘c) and irradiance (W/m2 ) values are verified in numerical simulation results. MPP tracking power and efficiency are examined for maximum power (Pmax ) to test the optimization based system. The simulation results show that the BAT optimization model was achieved the highest tracking efficiency better than other heuristic algorithms. Show more
Keywords: Photovoltaic, maximum power point, BAT Optimization, firefly algorithm, solar irradiations
DOI: 10.3233/JIFS-213241
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1091-1102, 2022
Authors: Ali, Zeeshan | Mahmood, Tahir | Panityakul, Thammarat
Article Type: Research Article
Abstract: Bonferroni means (BM) operator is the extended form of the arithmetic mean operator, used for simplifying non-dominant and non-feasible problems diagnosed in genuine life scenarios. A lot of aggregation operators are the specific parts of the BM operators under the consideration of different values of parameters which are the main parts of the BM operators. In the presence of the BM operator and a very well-known conception in the scenario of fuzzy set, called complex Pythagorean fuzzy (CPF) setting, the objective of this scenario is to diagnose the CPF power BM (CPFPBM) operator and utilize their beneficial results with important …properties. Moreover, a multi-attribute decision-making (MADM) technique is evaluated in the presence of invented operators for CPF settings. In the last of this study, we diagnosed the superiority and efficiency of the invented works with the help of sensitive analysis and graphical illustrations to enhance the gap of the research works. Show more
Keywords: Complex pythagorean fuzzy sets, power Bonferroni mean operators, decision-making methods
DOI: 10.3233/JIFS-212546
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1103-1121, 2022
Authors: Liu, Nana | Wang, Chang
Article Type: Research Article
Abstract: In this paper, we show that the inappropriateness of Example 3.10 and the error of Theorem 4.7 in [M.Balamurugan, G.Balasubramanian, C.Ragavan. Intuitionistic Fuzzy Soft Ideals in BCK/BCI-algebras. Materials today: proceedings 16(2019)496-503.], then give a more perfect example and a counterexample respectively.
Keywords: Intuitionistic fuzzy soft BCK/BCI-algebra, intuitionistic fuzzy soft ideal, intuitionistic fuzzy soft p-ideal
DOI: 10.3233/JIFS-212589
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1123-1127, 2022
Authors: Ahsanullah, T. M. G.
Article Type: Research Article
Abstract: Starting with an approximation space as the underlying structure, we look at the rough uniformity of a topological rough group. Next, taking L as a complete residuated lattice, we consider L -subgroup and normal L -subgroup of a group to create the L -fuzzy upper rough subgroup, and the L -fuzzy lower rough subgroup within the framework of the L -fuzzy approximation spaces. Here we particularly focus on a category of L -fuzzy upper rough subgroups, and a special kind of category of L -closure groups that arises naturally. We introduce the notion of the L -fuzzy approximation group, …and study some of its properties including the usual function space structure for the L -fuzzy approximation spaces. Furthermore, using the notion of an L -fuzzy upper approximation operator, we investigate some categorical connection between the L -fuzzy approximation groups, and the L -closure groups. In a similar fashion, using an L -fuzzy lower approximation operator, we investigate the categorical connection between the L -fuzzy approximation groups, and the L -interior groups. Show more
Keywords: Approximation space, rough group, topological rough group, rough uniformity, L-fuzzy approximation space, L-fuzzy upper rough subgroup, L-fuzzy lower rough subgroup, L-fuzzy approximation group, L-closure group, L-interior group, category theory
DOI: 10.3233/JIFS-212634
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1129-1139, 2022
Authors: Feng, Xiangqian | Shi, Hui | Wei, Cuiping
Article Type: Research Article
Abstract: As a core resource of the company, employees play a major role to implement green management related behaviors in enterprises. Management department is also working hard to improve the ability of employees to perform these green behaviors for the company’s sustainable development capabilities. This study is the first effort that evaluation of effect factors of employee green behavior ability (EGBA) by intuitionistic fuzzy number-best worst method (IFN-BWM). To reach the study objective, a total of four criteria and twenty-seven sub-criteria for evaluation of EGBA are collected from the existing literatures. Subsequently, the PFN-VIKOR methodology (Pythagorean Fuzzy Number-Visekriterijumska Optimizacija I Kom-promisno …Resenje) is proposed to rank EGBA levels. The results of this study show that employee self-efficacy and employee initiative in learning relevant green knowledge are important factors to enhance EGBA. Moreover, findings confirm that extended fuzzy semantic values and novel algorithm can accurately measure the decision makers’ mind and improve the accuracy of evaluation. This study also provides a framework for managers to evaluate their employee’ green behavior ability. Show more
Keywords: Green behavior, Best-worst method, VIKOR, sustainability development
DOI: 10.3233/JIFS-212660
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1141-1162, 2022
Authors: Palanisamy, Rajarathinam | Govindaraj, Vijayakumar | Siddhan, Saravanan | Albert, Johny Renoald
Article Type: Research Article
Abstract: A super-lift mechanism has made tremendous progress in DC/DC conversion technology. In comparison to the asymmetrical form of MLI, the novel Asymmetric Multilevel Inverter (AMLI) technology proposes a minimized number of components. The Fuzzy-PI (Proportional integral) and Modified Genetic Algorithm (MGA) utilizes to minimize the harmonic content considerably using a variety of modulation index and firing angle values in open-loop and closed-loop control. This architecture for designing single-phase 7-level AMLI with an intelligent algorithm proposed for Renewable Energy (RE) applications. This circuit uses a single MOSFET switch with less switching stress and a single DC source. The effectiveness of the …proposed MGA optimization eliminates the lower-order harmonics. MGA and Fuzzy-PI based Distributed Power Flow Intelligent Control (DPFIC) algorithms are applied with multilevel structures while maintaining the fundamental frequency for both MATLAB platform and hardware implementation. During this analysis, the losses is also find to investigate the influence of modulation index and output power factor on inverter efficiency. Simulations and experimental findings confirm the proposed inverter capacity to create high-quality multilayer output voltage. However, the proposed closed loop simulation circuit gives 0.47% minimum THD level, and 10.4% in experimental results. Show more
Keywords: Asymmetric multilevel inverter, modified genetic algorithm, proportional integral, fuzzy logical control, distributed power flow intelligent control
DOI: 10.3233/JIFS-212668
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1163-1176, 2022
Authors: Akmal, Muhammad | Zubair, Syed | Jochumsen, Mads | Zia ur rehman, Muhammad | Nlandu Kamavuako, Ernest | Irfan Abid, Muhammad | Niazi, Imran Khan
Article Type: Research Article
Abstract: To design a prosthetic hand which can classify movements based on the electromyography (EMG) signals, complete and good quality signals are essential. However, due to different reasons such as disconnection of electrodes or muscles fatigue the recorded EMG data can be incomplete, which degrades the classification of test movements. In this paper, we first acquire multiday intramuscular EMG (iEMG) signals (which are invasive) with higher Signal-to-Noise Ratio (SNR) compared to surface EMG (sEMG) signals; followed by application of matrix (non-negative matrix factorization – NMF) and tensor factorization methods (Canonical Polyadic Decomposition (CPD), Tucker decomposition (TD) & Canonical Polyadic-Weighted Optimization (CP-WOPT)) …for recovering structured missing data i.e., chunks of missing samples in channels. Furthermore, we tested the scalability of NMF, CPD, TD and CP-WOPT by employing them on the large multiday (seven days) iEMG data where the size of missing data is increased from day 1 to day 7, and for each day a fixed percentage of missing data is introduced from 10% to worst case of 50%. Results show that CP-WOPT outperformed NMF, CPD and TD to recover large percentage of missing data in terms of Relative Mean Error (RME) even when 7 days of data is considered. CP-WOPT showed robustness even for the worse case even when 50% iEMG data is removed from day 1 to day 7 where it’s RME degraded slightly from 0.08 to 0.1. Show more
Keywords: Multiday intramuscular EMG, missing data, tensor factorization
DOI: 10.3233/JIFS-212715
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1177-1187, 2022
Authors: Guo, Qing | Xin, Xiao Long
Article Type: Research Article
Abstract: Pseudo EQ-algebras were introduced by X.L. Xin as possible algebraic semantics for fuzzy type theory. The main goal of this paper is to investigate pseudo EQ-algebras with internal states. To begin with, we introduce the notion of pseudo EQ-algebras with internal state(simplify, ISPEQ-algebra) and study its properties. Moreover, we discuss the relation between ISPEQ-algebras and states on pseudo EQ-algebras. In the following, we study state filters(simplify S-fliters) and state prefilters(simplify S-prefliters) of ISPEQ-algebras. We construct ISPEQ-algebras from S-fliters and derive congruence relation θ of ISPEQ-algebras. In the end, we give the generating formula of prefilters on residuated pseudo EQ-algebras …and based on that we get the generating formula of S-prefilters on residuated pseudo EQ-algebras. Show more
Keywords: Pseudo EQ-algebra, state operator, S-fliter, S-prefliter, generating formula
DOI: 10.3233/JIFS-212723
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1189-1202, 2022
Authors: Yu, Xiaobing | Liu, Zhenjie
Article Type: Research Article
Abstract: Grey Wolf Optimizer (GWO) is competitive to other population-based algorithms. However, considering that the conventional GWO has inadequate global search capacity, a GWO variant based on multiple strategies, i.e., adaptive Evolutionary Population Dynamics (EPD) strategy, differential perturbation strategy, and greedy selection strategy, named as ADGGWO, is proposed in this paper. Firstly, the adaptive EPD strategy is adopted to enhance the search capacity by updating the position of the worst wolves according to the best ones. Secondly, the exploration capacity is extended by the use of differential perturbation strategy. Thirdly, the greedy selection improves the exploitation capacity, contributing to the balance …between exploration and exploitation capacity. ADGGWO has been examined on a suite from CEC2014 and compared with the traditional GWO as well as its three latest variants. The significance of the results is evaluated by two non-parametric tests, Friedman test and Wilcoxon test. Furthermore, constrained portfolio optimization is applied in this paper to investigate the performance of ADGGWO on real-world problems. The experimental results demonstrate that the proposed algorithm, which integrates multiple strategies, outperforms the traditional GWO and other GWO variants in terms of both accuracy and convergence. It can be proved that ADGGWO is not only effective for function optimization but also for practical problems. Show more
Keywords: Multiple strategies, GWO, constrained portfolio optimization
DOI: 10.3233/JIFS-212729
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1203-1227, 2022
Authors: Pan, Hongguang | Zhang, Huipeng | Lei, Xinyu | Xin, Fangfang | Wang, Zheng
Article Type: Research Article
Abstract: Object detection is a very important part of computer vision, and the most common method of object detection is the Faster region convolutional neural network (RCNN), which uses CNN to extract image features. However, the parameters to be learned in CNN are enormous and may affecting the efficiency. In this paper, hybrid dilated Faster RCNN (HDF-RCNN) is proposed to solve this problem, and the main contributions are: 1) HDF-RCNN is built through replacing the VGG16 in Faster RCNN with HDC (hybrid dilated CNN) to achieve a fast and accurate object detection algorithm, and the LeakyReLU activation function is used to …increase the ability of mapping input information; 2) the portability of HDC, namely, the possibility of embedding the HDC into object detection network with independent feature extraction part is verified. The Microsoft COCO data set is used to verify the performance of HDF-RCNN, and the experiments show that, compared with the traditional Faster RCNN, the testing accuracy of HDF-RCNN is averagely improved by 7.11%, the training loss and training time are averagely reduced by 40.06% and 34.29%, respectively. Therefore, the HDF-RCNN can significantly improve the efficiency of object detection and the HDC can be used as an independent feature extraction network to adapt to many different frameworks. Show more
Keywords: object detection, hybrid dilated convolution, faster RCNN
DOI: 10.3233/JIFS-212740
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1229-1239, 2022
Authors: Haque, Md. Rakibul | Mishu, Sadia Zaman | Palash Uddin, Md. | Al Mamun, Md.
Article Type: Research Article
Abstract: Hyperspectral Image (HSI) is usually composed of hundreds of capturing wavelength bands, which not only increase the size of the HSI rapidly but also impose various obstacles in classifying the objects accurately. Moreover, the traditional machine learning schemes utilize only the spectral features for HSI classification, which, therefore, neglect the spatial features that have a significant impact on the classification improvement. To address the aforementioned issues, in this paper, we propose to employ the principal component analysis (PCA), the baseline feature extraction method, and a thoughtfully designed stacked autoencoder, a deep learning-based feature extraction approach, for reducing the high dimensionality …of the HSI and then propose a novel lightweight 3D-2D convolutional neural network (CNN) framework to concurrently exploit both spatial and spectral features from the dimensionality-reduced HSI for classification. In particular, PCA and stacked autoencoder are applied to reduce the high dimensionality of the original HSI and then the proposed 3D-2D CNN provides a combination of 3D and 2D convolution operations to extract the subtle spatial and spectral features for efficient classification. We well-adjust the proposed 3D-2D CNN architecture, and perform extensive experiments on three benchmark HSI datasets and compare our approach with the state-of-the-art classical and deep learning methods. Experimental results illustrate that we have achieved an overall accuracy of 99.73%, 99.90%, and 99.32% on Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively, which outperform the classical machine learning and independent 2D and 3D CNN-based state-of-the-art methods. Show more
Keywords: Feature extraction, principal component analysis, deep learning, stacked autoencoder, classification, convolutional neural network
DOI: 10.3233/JIFS-212829
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1241-1258, 2022
Authors: Jose, Merin | Mathew, Sunil C.
Article Type: Research Article
Abstract: In this paper, the authors introduce catalyzed LM -G-filter spaces, a special case of weakly inspired LM -G-filter spaces and identify certain properties of these spaces. It is proved that C LM -G , the category of catalyzed LM -G-filter spaces, is isomorphic to I LM -G , the category of inspired LM -G-filter spaces. Moreover, the categorical connection between WI LM -G , the category of weakly inspired LM -G-filter spaces, and C LM -G is investigated through interior and exterior catalyzation of weakly inspired LM -G-filter spaces. It is proved that C LM -G is an …isomorphism-closed, bireflective and bicoreflective full subcategory of WI LM -G and LM -G . Show more
Keywords: Category, functor, reflective subcategory, coreflective subcategory
DOI: 10.3233/JIFS-212923
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1259-1269, 2022
Authors: Yan, Zhiwen | Chen, Ying | Wang, Xianqing | Zhu, Jia | Li, Jianbo
Article Type: Research Article
Abstract: The detection of molars in this paper is mainly for children around seven years old. The first molars of children in this age group have just erupted. We primarily check whether the teeth need to pit and fissure sealing to protect the teeth from caries. Our dataset comes from dental photos taken by mobile phones. We use these images to train the deep learning model and use the trained deep learning model to detect whether the teeth are healthy. However, this task has enormous challenges. The main difficulties are as follows: first, the teeth are closely arranged, and the individuals …are relatively small, so the detection was a bit tricky. Second, the camera’s shooting angles varied greatly, which might cause uneven image quality. Third, the image dataset is relatively small, which might result in the inability to obtain important features when training classification. By analyzing the dataset, we divided the task into two steps to build the model. First, object detection is used to detect the position of the first molar. Second, we classify the tested teeth into three categories. In response to the above problems, both of the two parts of the model are improved. An attention mechanism and a bounding boxes screening mechanism are added to the object detection part. For the classification part, we propose the MCGan model to extend the dataset. The dataset came from children’s dental images collected in a stomatological hospital, and professional dentists annotate the dental images. For molar identification, the accuracy of our model is 98.5%, and the accuracy of tooth classification is 85.6%. Show more
Keywords: Pit and fissure sealant, object detection, classification, GAN
DOI: 10.3233/JIFS-212994
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1271-1283, 2022
Authors: Luo, Hongyun | Lin, Xiangyi | Niu, Yi
Article Type: Research Article
Abstract: This research explores indicators and methods for an enterprise to measure and evaluate user satisfaction with enterprise social media for knowledge management. This paper presents qualitative indicators, including three service levels of enterprise social media for knowledge management (KM) from a techno-social perspective. This research puts forward a synthetic evaluation model mixed with linguistic variables, consistent fuzzy preference relations (CFPR) and cloud model for measuring and evaluating user satisfaction. The synthetic evaluation model can transform linguistic variables into quantitative data to obtain user satisfaction levels and determine the distance between the expected satisfaction level and actual performance. This research can …help an enterprise to improve the service ability of its social media to meet users’ requirements for knowledge management. Show more
Keywords: User satisfaction, enterprise social media, knowledge management, linguistic variables, consistent fuzzy preference relations, cloud model
DOI: 10.3233/JIFS-213026
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1285-1297, 2022
Authors: Li, Yan | Guo, Junjun | Yu, Zhengtao | Gao, Shengxiang
Article Type: Research Article
Abstract: Semantic alignment is a key component in Cross-Language Text Matching (CLTM) to facilitate matching (e.g., query-document matching) between two languages. The current solutions for semantic alignment mainly perform word-level translation directly, without considering the contextual information for the whole query and documents. To this end, we propose a Dual-Level Collaborative Rough-to-Fine Filter Alignment Network (DLCCFA) to achieve better cross-language semantic alignment and document matching. DLCCFA is devised with both a coarse-grained filter in word-level and a fine-grained filter in sentence-level. Concretely, for the query in word-level, we firstly extract top-k translation candidates for each token in the query …through a probabilistic bilingual lexicon. Then, a Translation Probability Attention (TPA) mechanism is proposed to obtain coarse-grained word alignment, which generates the corresponding query auxiliary sentence. Afterwards, we further propose a Bilingual Cross Attention and utilize Self-Attention to achieve fine-grained sentence-level filtering, resulting in the cross-language representation of the query. The idea is that each token in the query works as an anchor to filter the semantic noise in the query auxiliary sentence and accurately align semantics of different languages. Extensive experiments on four real-world datasets of six languages demostrate that our method can outperform the mainstream alternatives of CLTM. Show more
Keywords: Cross-language text matching, Alignment, Probabilistic bilingual lexicon, Translation probability attention, Bilingual cross attention
DOI: 10.3233/JIFS-213070
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1299-1314, 2022
Authors: Lu, Yu | Song, Jingjing | Wang, Pingxin | Xu, Taihua
Article Type: Research Article
Abstract: In the era of big data for exploring attribute reduction/rough set-based feature selection related problems, to design efficient strategies for deriving reducts and then reduce the dimensions of data, two fundamental perspectives of Granular Computing may be taken into account: breaking up the whole into pieces and gathering parts into a whole. From this point of view, a novel strategy named label-specific guidance is introduced into the process of searching reduct. Given a formal description of attribute reduction, by considering the corresponding constraint, we divide it into several label-specific based constraints. Consequently, a sequence of these label-specific based constraints can …be obtained, it follows that the reduct related to the previous label-specific based constraint may have guidance on the computation of that related to the subsequent label-specific based constraint. The thinking of this label-specific guidance runs through the whole process of searching reduct until the reduct over the whole universe is derived. Compared with five state-of-the-art algorithms over 20 data sets, the experimental results demonstrate that our proposed acceleration strategy can not only significantly accelerate the process of searching reduct but also offer justifiable performance in the task of classification. This study suggests a new trend concerning the problem of quickly deriving reduct. Show more
Keywords: Accelerator, attribute reduction, label-specific guidance, rough set
DOI: 10.3233/JIFS-213112
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1315-1329, 2022
Authors: Saravanan, C. | Anbalagan, P.
Article Type: Research Article
Abstract: Congestion not only affects the power flow, but also leads certain issues, like market power, market inefficiency and security. When the transmission line exceeds their limits congestion is occurred (voltage, thermal, stability). Congestion management is a technique that helps to deal the issue corresponding to congestion. Many methods have been developed to manage congestion, and also several countries execute various strategies for the smooth functioning of their network. In this manuscript, the rescheduling of congestion management in a deregulated environment using DA-MRFO is proposed. The proposed hybrid technique is the combined execution of both the dragonfly algorithm (DA) and manta …ray foraging optimization (MRFO). Dragonfly algorithm is enhanced using Manta ray Foraging optimization (MRFO), hence it is named DA-MRFO technique. The proposed method is used to alleviate transmission grid congestion on group-based electricity market via reprogramming active power of generators and also to reprogram the generator power. Congestion is the major Independent System Operator (ISO) concern on deregulated electricity market that is traditionally controlled by reprogramming generator output power. However, the effects of changes in the generator output power on the overloaded line flow are not identical. All the generators do not represent a desirable approach for congestion management. Here, a generator sensitivity factor is adapted for supporting the optimal generator selection in a congestion management (CM). In a congestion relief process, it is provided at the lowest possible cost. The reduction of power flow with collection of congested lines is probable through coordinated response of reactive energy dispatch as wind farms. The proposed approach is executed in modified IEEE 30 bus system and IEEE 57 bus system, then the efficiency is compared with the various existing optimization approaches. Show more
Keywords: Congestion management, rescheduling, deregulated environment, dragonfly, manta ray foraging optimization
DOI: 10.3233/JIFS-213138
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1331-1345, 2022
Authors: Iqbal, Saeed | Qureshi, Adnan N.
Article Type: Research Article
Abstract: Breast cancer diagnosis utilizes histopathological images to get best results as per standards. For detailed diagnosis of breast cancer, microscopic analysis is necessary. During analysis, pathologists examine breast cancer tissues under different magnification levels and it takes a long time, can be hampered by human interpretation and requires expertise of different magnifications. A single patient usually requires dozens of such images during examination. Since, labelling the data is a computationally expensive task, it is assumed that the images for all patients have the same label in conventional image-based classification and is not usually tested practically. In this study, we are …intending to investigate the significance of machine learning techniques in computer aided diagnostic systems based on analysis of histopathological breast cancer images. Publicly available BreakHis data set containing around 8,000 histopathological images of breast tumours is used for conducting experiments. The recently proposed non-parametric approach is proven to show interesting results when compared in detail with machine learning approaches. Our proposed model ’Deep-Hist’ is magnification independent and achieves > 92.46% accuracy with Stochastic Gradient Descent (SGD) which is better than the pretrained models for image classification. Hence, our approach can be used in processing data for use in research and clinical environments to provide second opinions very close to the experts’ intuition. Show more
Keywords: Breast cancer, deep learning, convolutional neural network, batch normalisation, feature selection, classification, histopathology
DOI: 10.3233/JIFS-213158
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1347-1364, 2022
Authors: Cuong, Nguyen Ha Huy | Trinh, Trung Hai | Meesad, Phayung | Nguyen, Thanh Thuy
Article Type: Research Article
Abstract: A computational method for detecting pineapple ripening could lead to increased agricultural productivity. It is possible to predict fruit maturity before harvesting to increase agricultural productivity. A ripe fruit’s quality, its standard content of physical and chemical properties will increase the value of a good when traded outside the market. This paper studies and improves the Tiny YOLO-v4 model for identifying the pineapple ripening period. Researchers studied pineapples in a pineapple garden in Vietnam’s central region. They wanted to determine when pineapples were ripe. The API and the website are based on the YOLO innovation model. Apps and website APIs …will be available for mobile devices so that people can monitor fruits. Technology transfer and academic research are combined in this study. We prepared the pineapple data set by using 5,000,000 pineapples harvested from the pineapple farm at different stages of growth. To make the measurements, we improved the YOLO-v4 algorithm. This results in a more accurate training model and reduced train-ing time. A 98.26% recognition accuracy is quite impressive. Research takes place at large-scale plantations, so the models are created from the data collected at the plantations and are used as labels; training takes a long time for the tiniest details about pineapples, and finding pineapple-growing regions takes a long time. The deep learning classifier was able to process pineapple plantation photos by using the camera on the mobile phone. Show more
Keywords: Deep learning, computer vision, deep convolutional networks, YOLO, pineapples, segmentation, classifier, loss function
DOI: 10.3233/JIFS-213251
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1365-1381, 2022
Authors: Wu, Yan | Wang, Ling-ying | Fang, Yiling
Article Type: Research Article
Abstract: Smart city refers to the use of various information technologies to integrate urban systems and services so as to improve the efficiency of resource utilisation and improve the quality of life for citizens. For many activities related to smart cities, such as the selection of pilot cities, a large number of experts from different functionalities or departments are usually invited to make evaluations of multiple attributes. The wide-spanning nature of smart cities needs cross-functional integration of various types of expertise. Therefore, it is necessary to develop a multi-criteria large group decision-making model to gather expert opinions from a wide range …of sources to solve these problems. To do this, we first use the simple and fast algorithm for K-medoids clustering to classify experts into different subgroups and thereby reduce the complexity of the decision-making problem. Subgroup leaders will be selected at the same time to represent subgroups in subsequent decision-making processes. We then use the DEMATEL method to determine the weights of attributes. Next, to ensure that the decision outcome is supported by the majority of experts, a consensus-reaching process is proposed to reduce discrepancies in opinions. An illustrative example is adopted which involves the selection of pilot cities in Sichuan Province in order to verity the applicability of the model. Comparative analyses will be provided to verify the advantages of the proposed model. The results show that our model can effectively address evaluation problems associated with smart city activities involving a large group of experts. Show more
Keywords: Smart cities, multi-criteria large group decision making, subgroup leader, consensus
DOI: 10.3233/JIFS-213267
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1383-1398, 2022
Authors: Cai, Yi | Guo, Jinlu | Tang, Zhenpeng
Article Type: Research Article
Abstract: The regularly issued low frequency data, such as the change of fund position (weekly), and Producer Price Index (monthly), can affect the subsequent trend of stock returns. However, the forecasting effect of low frequency data on high frequency has not been discussed amply. This paper proposes a new mixed frequency neural network that helps to fill this research gap. The original time series is decomposed into several components through ensemble empirical mode decomposition, then the frequency alignment method is applied to integrate the high frequency component with low frequency variable as inputs, and the CNN-BiLSTM-Attention network completes the remaining forecasting …work. The empirical results show that compared with other benchmark models, the proposed procedures perform better when predicting the high frequency components and obtain a smaller statistical error in the final ensemble results. The proposed model has great potential for the forecasting of reverse mixed time series. Show more
Keywords: Stock returns, Ensemble empirical mode decomposition, Deep learning, Attention mechanism, Mixed frequency prediction
DOI: 10.3233/JIFS-213276
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1399-1415, 2022
Authors: Ngoc, Vo Truong Nhu | Viet, Do Hoang | Tuan, Tran Manh | Hai, Pham Van | Thang, Nguyen Phu | Tuyen, Do Ngoc | Son, Le Hoang
Article Type: Research Article
Abstract: Periapical Inflammation (PI) is one of the most popular diseases in adults due to complication of endodontitis or dental trauma with corresponding consequences to quality-of-life like tiredness and signs of infection. Specifically, patients having severe PI are often tiredness and high fever accompanied by signs of infection such as dry lips, dirty tongue, lymph node reaction in the area under the jaw. In X-Ray images, PI could be recognized by vague boundaries with signs of periapical ligament extensions. It is necessary to design a computerized diagnosis system based on the Deep Learning models for supporting clinicians in diagnosis of PI …from X-Ray images. In this paper, we propose a new medical system called VNU for diagnosis of PI from X-Rays images. The VNU system uses Deep Learning to classify whether X-Ray images being PI or not. The Residual Neural Network (ResNet) with 34 layers is utilized with proper data augmentation and learning algorithms. The system is designed based on 7-layer enterprise architecture (User, Business, Application, Data, Technology, Infrastructure, and Security). It is used by both the clinicians and IT operators. The system has been validated on real data from Hanoi Medical University, Vietnam consisting of 900 images with PI and 500 normal images. Two scenarios of validation namely hyperparameter selection and performance comparison with other CNN-based Deep Learning models have been performed. It has been found from the experiments that the proposed system has better performance than the others in terms of sensitivity and specificity with the corresponding values of 96.70% and 93.87%. The system is deployed on the web interface that offers flexibility for clinicians in diagnosis and training. Show more
Keywords: Apical lesions, periapical radiograph, ResNet, deep learning, VNU system
DOI: 10.3233/JIFS-213299
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1417-1427, 2022
Authors: Khan, Vakeel A. | Ali Khan, Izhar | Esi, Ayhan | Alam, Masood
Article Type: Research Article
Abstract: The main purpose of this paper is to introduce invariant convergence in intuitionistic fuzzy normed space. Following which we present some characteristics of this notion with respect to intuitionistic fuzzy norm. We also define strongly invariant convergence, ideal invariant convergence and invariant ideal convergence in intuitionistic fuzzy normed space. After that, we establish the relationship between these notions with respect to intuitionistic fuzzy norm. Lastly, we define ideal invariant Cauchy and invariant ideal Cauchy criteria for sequences in intuitionistic fuzzy normed space and relate it to their convergence notion.
Keywords: Intuitionistic fuzzy normed space, Ideal convergent, Invariant mean
DOI: 10.3233/JIFS-213327
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1429-1438, 2022
Authors: Radhika, K. | Arun Prakash, K.
Article Type: Research Article
Abstract: Multi-objective optimization is an emerging field concerning optimization problems associated with more than one objective function, each of them has to be optimized simultaneously. Multi-objective optimization is widely used in logistics and supply chains to reduce the cost and time involved in transportation. With the increase in Global Supply Chains, many organizations are facing the challenges of delivering products to their customers at a fast pace, low cost, and high reliability. There are numerous factors that may affect the goal of an organization to optimize the cost, time, and effort during the transportation of their products to the end customers. …For instance, in the existing transportation problems, the type of vehicles used for the movement of the products is not focused. Transportation of the goods is considered to utilize any type of vehicle irrespective of the nature of the goods. However, in real-life scenarios, there are certain constraints in the vehicle used to transport the finished goods or raw materials from a source to a destination. Vehicles such as tanker trucks, top open trucks, closed trucks, etc. need to be booked based on the nature of goods to be transported. Also, the cost and time of transportation are uncertain in nature. In this paper, we formulate the Multi-Objective Solid Transportation Problem (MOSTP) by considering the above issue. The uncertain parameters of the problem are considered as Pentagonal Intuitionistic Fuzzy Numbers (PIFN). Magnitude method is used for defuzzification. An algorithm to find the solution of formulated Intuitionistic Fuzzy Multi-Objective Solid Transportation problem (IFMOSTP) is provided. The proposed model is illustrated by a numerical example which is solved with the help of LINGO software. Show more
Keywords: Intuitionistic fuzzy sets, intuitionistic fuzzy numbers, magnitude ranking, multi-objective solid transportation problems, fuzzy programming
DOI: 10.3233/JIFS-213517
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1439-1452, 2022
Authors: Ramachandran, Lakshmanan | Mohan, Veerasamy
Article Type: Research Article
Abstract: Image segmentation is an essential part of almost any image processing methodology and it play a critical role in protecting the region of interest on any substrate image before its actual analysis is prescribed. In fact, the accuracy of any processing done by image segmentation will largely depends on the efficiency of the segmentation algorithm employed. A typical segmentation method employing a important features of Canny–GLCM (Gray Level Co-occurrence Matrix) incorporated with a simple Artificial Neural Network (ANN) model is proposed in this research work for segmentation of shrimp variability. Performance metrics related to accuracy have been compared with benchmark …of this method, and the sensitivity of efficiency level has been described. The segmentation in the proposed research work is targeted towards Penaeus Monodon (PM), and Litopenaeus Vannamei (LV) diversities for main threats detection of White Spot Syndrome (WSS). The proposed model has better performance metrics, such as (94.67%), sensitivity (94.79%), specificity (94.51%) and positive predictive (94.79%) while compared to other existing methods. Show more
Keywords: Image segmentation, white spot syndrome, gray level cooccurence matrix, neural network, detection accuracy
DOI: 10.3233/JIFS-220172
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1453-1466, 2022
Authors: Jin, Xin
Article Type: Research Article
Abstract: With the development of the Internet and mobile networks, social networks have gradually become an essential tool and widespread application. Therefore, the research on short text semantic modelling of social networks has attracted widespread attention. However, modelling short texts encounter the semantics sparsity and multiple meanings of a word in social networks. To solve the above problems, we propose a user-based topic model with topical word embeddings semantic modelling method, namely SM-UTM. Firstly, we construct the user topic model to aggregate short text. Secondly, we build word pair in the user topic model to alleviate semantics sparsity in social networks. …In addition, we introduce the time information of social networks into the topic model to jointly constrain the generation process of topics, to improve the quality of semantic representation of social network short texts. Finally, we use the topic word embedding learning based on deep learning to train and optimize the word vector according to the learning results of the user topic model, to alleviate the problem of polysemy in social networks. We build multiple groups of quantitative and qualitative experiments based on the crawled real Sina Weibo data. The experimental results show that our SM-UTM is significantly better than the comparison method in the evaluation indicators of topic consistency, purity and entropy. Show more
Keywords: Topic model, topical word embeddings, social network, semantic modelling, semantics sparsity
DOI: 10.3233/JIFS-212614
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1467-1480, 2022
Authors: Mariappan, Gengaraj | Lakshmanan, Kalaivani
Article Type: Research Article
Abstract: In this manuscript, a hybrid technique is proposed for Torque Ripple (TR) minimization of Switched Reluctance Motor (SRM). The proposed technique is the consolidation of Wingsuit flying search (WFS) optimization and Gradient Boosting Decision Tree (GBDT) algorithm, hence it is known as WFS-GBDT technique. The control mechanisms consists of fractional order proportional integral derivative (FOPID) speed controller on external loop as well as current controller on internal loop with controlling turn activate and deactivate angles for SRM. The complexity of acquiring the ideal evaluation of proportional, integral and derivative gains for speed and current controller including turn activate and deactivate …angles are deemed as a multi-objective optimization issue. Here, the WFS optimize the gain parameters of external speed loop along internal current loop with commutation angles for turn activate and deactivate switches. The WFS optimization processing is used to productive machine learning dataset under the types of SRM parameter. By using the satisfied dataset, the GBDT is predicted and mandatory forecasting is implemented in the entire machine operating stage. The optimized gain parameters based, the fractional order proportional integral derivative controller is tuned exactly. The proposed WFS-GBDT control technique lessens the torque ripple and quick settling time with this proper control, because of its systematic random search capabilities, thereby enhancing the dynamic execution of SRM drive. Finally, the proposed technique is activated in MATLAB/Simulink site, its performance is analyzed with existing techniques, like Base, ALO and WFS. The best, worst, mean, standard deviation for ISEspeed using proposed technique attains 230.5364, 231.5934, 230.952 and 0.05314. The best, worst, mean and standard deviation for torque ripple using proposed technique attains 0.4571, 0.6548, 0.585 and 0.472. The best, worst, mean, standard deviation for ISEcurrent using proposed technique attains 3.1257, 3.9754, 3.5783 and 0.0472. Show more
Keywords: Switched reluctance motor, torque ripple minimization, speed, current, gain parameters and dynamic efficiency
DOI: 10.3233/JIFS-212519
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1481-1504, 2022
Authors: Khalil, Alamgir | Arshad, Kainat | Ijaz, Muhammad | Khan, Sajjad Ahmad | Manzoor, Sadaf | El-Morshedy, Mahmoud
Article Type: Research Article
Abstract: Statistical lifetime distributions play a very important role in modeling data sets in various fields. Extending the existing distributions is of great interest in statistical research. The modification of the distributions provides more flexible model as compared to existing one. In this article, we propose a new probability model using Quadratic Rank Transmutation Map technique, named as Transmuted Lomax Exponential Distribution (TLED). The new distribution can model data sets with increasing, decreasing and bathtub shape hazard rates. Various statistical properties of the proposed distribution such as moments, order statistics, quantile function, mean residual life function and characteristic function are derived. …Further, the parameter estimates are obtained through Maximum Likelihood method along with asymptotic confidence intervals. The utility of the new model is evaluated by analyzing two real data sets. In order to access the performance of the new model, several goodness of fit measures is used. The results indicate that the new model best fits the data as compared to the other extensions of the Lomax distribution. Show more
Keywords: Lomax distributions, maximum likelihood, transmuted, estimation, hazard function
DOI: 10.3233/JIFS-212544
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1505-1518, 2022
Authors: Mehmood, Arif | Al Ghour, Samer | Afzal, Farkhanda | Nordo, Giorgio | Saleem, Najma
Article Type: Research Article
Abstract: This paper concern the study of the notions of some new definitions and results. Three new definitions are given namely, neutrosophic soft quad semi-open, neutrosophic soft quad pre-open and neutrosophic soft quad *b open sets in neutrosophic soft quad-topological spaces. Among these one of the interesting neutrosophic soft quad generalized open set known as neutrosophic soft quad pre-open set is chosen then on the basis of this definition some fundamentals are generated. These are including, neutrosophic soft quad interior, neutrosophic soft quad boundary, neutrosophic soft quad exterior and neutrosophic soft quad closer. In continuation, attention has been focused …on neutrosophic soft separation axioms which are defined in terms of neutrosophic soft p-open sets with respect to soft points then on the basis of definitions and results given, neutrosophic soft separation axioms are discussed mostly in terms of neutrosophic soft closer of the sets. In addition, some more results are addressed in neutrosophic soft quad-opological spaces with respect to soft points. Stress has been given on the neutrosophic soft quad topological property. Lastly, results on product of neutrosophic soft quad topological spaces, Bolzano-Weierstrass Property, compactness, countably compactness and sequentially compactness are addressed in terms of neutrosophic soft p-open sets in neutrosophic soft quad topological spaces. Show more
Keywords: Neutrosophic soft set, neutrosophic soft points, neutrosophic soft quad-topological space, neutrosophic soft p-open set, set and neutrosophic softp-separation axioms
DOI: 10.3233/JIFS-212547
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1519-1540, 2022
Authors: Yang, Eunsuk
Article Type: Research Article
Abstract: This paper deals with semilinear extensions of implicational tonoid and partial Galois logics. To this end, first the class of implicational tonoid prelinear logics is defined and it is verified that these logics are semilinear in an algebraic context, namely an implicational tonoid logic is semilinear if it is complete over linearly ordered matrices. Next, a relational semantics is introduced for finitary implicational tonoid prelinear logics and it is proved that these logics are complete on the semantics. Thirdly the term “semilinear” is generalized to a notion to be applied in a set-theoretic context and it is verified …that finitary implicational tonoid prelinear logics are semilinear in this context. Finally some extensions satisfying abstract Galois, dual Galois properties are introduced together with similar relational semantics for them and it is shown that these logics are semilinear in both contexts. Show more
Keywords: Implicational tonoid logic, weakly implicative logic, tonoid, Routley–Meyer–style semantics, semilinear logic
DOI: 10.3233/JIFS-212549
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1541-1552, 2022
Authors: Uz, Mehmet E.
Article Type: Research Article
Abstract: Two 10-storey benchmark buildings exposed to different earthquakes are considered in the study in order to analyse the performance and capability of the design of the Tuned Mass Damper (TMD) with the optimal properties. Two optimisation algorithms, i.e. the Modified Genetic Algorithm (MGA) and the Grey Wolves Optimization (GWO) method, are used in the investigation. Firstly, the effectiveness of MGA and GWO, under optimally designed TMD system is verified by comparing the results with the ones obtained by other methods. In a second part, the optimum design of TMD system is determined by including mass of TMD as a design …variable so as to assess the feasibility of MGA and GWO. The MGA and GWO methods hold the better responses based on the reduction in the displacement, drift and acceleration of all stories subjected to different seismic excitations. The smaller properties of the TMD are attained using the methods of MGA and GWO as compared to the ones obtained by the Den Hartog and Warburton methods based on the objective function. Therefore, the MGA and GWO approaches lead to more practical and efficient solutions, which allows us to design economically the TMD systems rather than that of the other methods based on the reduction of structural responses. The results show that the efficiency of the parameters and modifications can be enhanced by selecting the proper access in the regulation output with requirements to be diminished. Show more
Keywords: Seismic response, TMD, optimization, genetic algorithm, grey wolfs optimization
DOI: 10.3233/JIFS-212553
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1553-1567, 2022
Authors: Saxena, Arti | Dubey, Y.M. | Kumar, Manish
Article Type: Research Article
Abstract: On the everlasting demand for better accuracy, high speed, and the inevitable approach for the high-quality surface finish as the basic requirements in the process industry, there felt the requirement to develop models which are reliable for predicting surface roughness (SR) as it is having a crucial role in the process industries. In this paper, SBCNC-60 of HMT make used to study the purpose of machining, while cutting speed (CS), feed rate (FR), and the depth of cut (DoC) were considered as parameters for machining of P8 material. Turning experiments data is studied by keeping two parameters constant at the …mid-level out of three parameters. An artificial intelligence technique named fuzzy was engaged in working out for surface roughness and material removal rate (MRR) to design the models of reliable nature for the predictions. The accurate prediction performance of the fuzzy logic model was then better analyzed by calculating MAPE, RMSE, MAD, and correlation coefficient between experimental values and fuzzy logic predictions. MAPE, RMSE, MAD, and correlation coefficient calculated 2.66%, 8.20, 6.44, and 0.98 for MRR and 4.19%,1.16, 0.86 and 0.90 for SR, respectively. Hence, the proposed fuzzy logic rules efficiently predict the SR and MRR on P8 material with higher accuracy and computational cost. Show more
Keywords: Correlation coefficient (R), Fuzzy Logic (FL), Mean Absolute difference (MAD), Mean Absolute Percentage Error (MAPE), MRR, Root mean square error (RMSE), Surface roughness (SR)
DOI: 10.3233/JIFS-212566
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1569-1582, 2022
Authors: Muthunagai, S.U. | Anitha, R.
Article Type: Research Article
Abstract: As a result of the advancements in Industry 4.0, the amount of data collected within industries are continuously expanding to achieve an innovative environment within the industry by maximizing asset usage. Meanwhile, the redundancy rate is increasing in cloud storage, which has an impact on data storage and analysis. To lower the rate of redundancy, the proposed system comprises a Time series-based deduplication technique. In the Time series-based deduplication technique, the Adaptive Multi-Pattern Boyer Moore Horspool (AM-BMH) algorithm, and Merkle tree were used to produce time-series data. Another significant challenge is that the geographically distributed cloud system has encountered that …the data placement methodology with high-priced transportation costs for data transmission. To overcome this issue, an optimal data placement strategy using Modified Distribution is proposed. Thus the proposed Time Series-based Deduplication and Optimal Data Placement Strategy (TDOPS) is found to be effective when compared with the existing system. The various parameters like space reduction, efficient retrieval, data transportation costs, and data transmission time are taken into the account in the cloud environment for an evaluation. The proposed scheme saves 98 percent of storage space, 55 percent computation overhead, and improves 60% of cloud storage efficacy. Show more
Keywords: Data placement, deduplication, modified distribution, Merkle tree, time series analysis
DOI: 10.3233/JIFS-212568
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1583-1597, 2022
Authors: Lin, Rongde | Li, Jinjin | Chen, Dongxiao | Chen, Yingsheng | Huang, Jianxin
Article Type: Research Article
Abstract: Attribute reduction is an important issue in data mining, machine learning and other applications of big data processing. Covering-based rough set and intuitionistic fuzzy (IF) set models are both the effective theoretical tools of uncertainty or imprecise computation, and thus IF covering rough set model has been acknowledged as a positive approach to attribute reduction. Based on IF covering rough set model, this study explores a kind of parameterized IF observational consistency in IF multi-covering decision system, and proposes an attribute reduction method. This article firstly defines the concepts of regular IF β -covering, parameterized IF observational sets on the …regular IF β -covering approximation space. Secondly, the parameterized IF observational consistency is defined to be the principal of attribute reduction in the IF multi-covering decision system, and the related IF discernibility matrix is developed to provide a way of attribute reduction. For multi-observational consistency corresponding to an observational parameters set, an unified multi-observational discernibility matrix is constructed, which avoids the disadvantage of needing to construct multiple corresponding discernibility matrices separately. Furthermore, an attribute reduction algorithm based on iterative dissolving of unified multi-observational discernibility matrix is proposed, and the experiment to demonstrate effectiveness of algorithm is presented. Experiments with UCI datasets shows that, the proposed method is a good way for improving both the rates of attribute-reduced and the classification accuracy of reduced datasets. Show more
Keywords: Attribute reduction, intuitionistic fuzzy covering-based rough set, parameterized observational consistency, intuitionistic fuzzy discernibility matrix
DOI: 10.3233/JIFS-212585
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1599-1619, 2022
Authors: Khatatneh, Khalaf | Filist, Sergey | Al-Kasasbeh, Riad Taha | Aikeyeva, Altyn Amanzholovna | Namazov, Manafaddin | Shatalova, Olga | Shaqadan, Ashraf | Miroshnikov, Andrey
Article Type: Research Article
Abstract: Modern medical risk classification systems focus on traditional risk factors and modeling methods. The available modeling tools do not allow reliable prediction of the of disease severity. In this study we develop prediction model of recurrent myocardial infarction in the rehabilitation period using several health variables generated in virtual flows. Hybrid decision modules with health data flows were used to build prognostic model for the prediction of disease. The vector of input information features consists of two subvectors: the first reflects real flows, the second reflects virtual flows. Complex interrelations among input data are modelled using Neural Network structure. …The model classification quality of the intellectual cardiovascular catastrophe prediction system was tested on a sample composed of 230 patients who had acute myocardial infarction. For prediction, three categories of risk factors were identified: traditional factors, factors associated with stressful overloads, and risk factors derived from bio-impedance studies. During the rehabilitation period, the level of molecular products of lipid peroxidation and the antioxidant potential of blood serum were also studied. Experimental studies of various modifications of the proposed classifier model were conducted, consisting of sequential disconnection from the aggregator of solutions of “weak” classifiers at various hierarchical levels. The mathematical model show predictions accuracy of correct prognosis for the risk of myocardial infarction exceeding 0.86. Prediction quality indicators are higher than the known ASCORE cardiovascular catastrophe prediction system, on average, by 14%. Show more
Keywords: Hybrid decision module, latent variable, GMDH model, neural network, aggregators of fuzzy decision rules, recurrent myocardial infarction
DOI: 10.3233/JIFS-212617
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1621-1632, 2022
Authors: Tran, Van Quan | Nguyen, Linh Quy
Article Type: Research Article
Abstract: Taking advantage of dredged sediments as lightweight materials is a useful solution to protect the environment and save natural materials in the field of construction. In which unconfined compression strength is an important criterion to determine the application in the construction project. It is difficult to find the optimal mixing ratio based on design standards or construction conditions because the unconfined compression strength (UCS) is affected by the mixing ratio of the materials and additives. In this study, the Machine Learning (ML) models consisting of Extreme Gradient Boosting (XGB) model and Linear regression models are investigated to design components for …reinforced lightweight soil based on the influence of unconfined compression strength of the test sample which is water content, cement content, air foam content, waste fishing net. To evaluate the effectiveness of the proposed ML models, several evaluation criteria including Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and coefficient of determination (R2 ) are proposed. The results show that the predictions of the XGB model have high accuracy with R2 = 0.9695, RMSE = 5.5849 kPa and MAE = 4.1583 kPa for the testing dataset. Sensitivity analysis evaluates the influence of input variables on UCS and the interaction between input variables to help design RLS components optimally. Show more
Keywords: Unconfined compressive strength, reinforced lightweight soil, extreme gradient boosting, machine learning
DOI: 10.3233/JIFS-212621
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1633-1650, 2022
Authors: Vivek Joe Bharath, Amaladoss | Thirumarimurugan, Marimuthu
Article Type: Research Article
Abstract: Early detection and diagnosis of faulty events in industrial processes can represent economic, social and environmental profits. When the process has a great quantity of sensors or actuators, the Fault Detection and Isolation (FDI) task is very difficult. Advanced statistical based FDI methods are extensively used for fault detection and isolation purposes. In this work, three multivariate statistical techniques such as neural network based Principal Component Analysis (PCA), neural network-based Fischer Discriminant Analysis (FDA) and Correspondence Analysis (CA) was applied to the multivariate data extracted from laboratory scale shell and tube heat exchanger. Performance metric such as detection delay, estimated …time of occurrence of fault, misclassification rate was computed for those three techniques for the detection and isolation of biases in sensors and actuators. Correspondence Analysis was proven to perform better when compared to PCA and FDA. CA was observed to perform FDI with minimal detection delay (which is less than or equal to 7 seconds) and lower misclassification rate (which is less than or equal to 6%) in case of both sensor & actuator faults. PCA and FDA showed significant detection delay and missed alarm rate for single and multiple fault identification. Show more
Keywords: Orthogonal projection, χ2 distance, neural network, misclassification rate, detection delay
DOI: 10.3233/JIFS-212631
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1651-1668, 2022
Authors: Ravindran, Vijay | Vennila, C.
Article Type: Research Article
Abstract: Internet of Things (IoT) proposed a new digital computing paradigm enabling interaction between devices and machines. It deliberately creates connectivity between the internet, electronics, and other forms of hardware. A novel modern cluster supervisor-based cluster head selection algorithm (MCSBCH) is proposed for the Wireless Sensor Network (WSN). The proposed cluster supervisor mechanism is responsible for controlling and monitoring the network effectively. In this approach, the cluster supervisor is the heart of the network, and the whole mechanism work under its supervision. The Cluster supervisor (CS) monitors the node’s energy level and allocates CH (cluster head) node. Each node’s energy level …is considered for electing the CH. Obviously when the cluster head energy level gets drained, then it allocates the next higher energy node as cluster head. The assigned CH is the next node with the highest energy level known as the backup node. This cluster supervisor (CS) is supported by the cluster head (CH) and other backup nodes in the network. The proposed MCSBCH is boosted with an enhanced clustering routing protocol. An experimental result is based on the aspect of a lifetime, energy consumption, and throughput, to test the proposed mechanism performance. Show more
Keywords: WSN, IoT, energy consumption, cluster communication and management
DOI: 10.3233/JIFS-212632
Citation: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 1, pp. 1669-1679, 2022
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