Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Qian, Jin | Han, Xing | Yu, Ying | Liu, Caihui

Article Type: Research Article

Abstract: Fuzzy rough sets and multi-granularity rough sets are essential extensions of Pawlak rough sets, which have become artificial intelligence research hotspots. Previous studies of the rough sets based on the fuzzy T-equivalence relation did not take the multi-granularity into account. The multi-granularity data is typically the multi-view cognition obtained by different granularity of the data, and its distinctive feature is that the data can be presented in different granularity spaces. In this paper, we integrate the idea of multi-granularity and propose four new models of “optimistic,” “pessimistic,” “optimistic-pessimistic,” and “pessimistic-optimistic” decision-theoretic rough sets based on the fuzzy T-equivalence relation for …the first time, followed by a preliminary analysis of the intrinsic relations and properties of these new decision-theoretic rough set models by a concrete example. At last, we use experiments to show the effectiveness of suggested models, proving that they are both rational and practical. Show more

Keywords: Three-way decision, fuzzy similarity relationship, multi-granularity, decision-theoretic rough set, rough set

DOI: 10.3233/IFS-222910

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2022

Authors: Li, Jingyi | Chao, Shiwei

Article Type: Research Article

Abstract: Most existing classifiers are better at identifying majority classes instead of ignoring minority classes, which leads to classifier degradation. Therefore, it is a challenge for binary classification to imbalanced data, to address this, this paper proposes a novel twin-support vector machine method. The thought is that majority classes and minority classes are found by two support vector machines, respectively. The new kernel is derived to promote the learning ability of the two support vector machines. Results show that the proposed method wins over competing methods in classification performance and the ability to find minority classes. Those classifiers based-twin architectures have …more advantages than those classifiers based-single architecture in classification ability. We demonstrate that the complexity of imbalanced data distribution has negative effects on classification results, whereas, the advanced classification results and the desired boundaries can be gained by optimizing the kernel. Show more

Keywords: Binary classification, imbalanced data, support vector machine

DOI: 10.3233/JIFS-222501

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-10, 2022

Authors: Sahaya Elsi, S. | Michael Raj, F. | Prince Mary, S.

Article Type: Research Article

Abstract: Grey wolf-optimized artificial neural networks used in DC–AC hybrid distribution networks, to regulate the energy consumption, is presented in this study. Energy management system that takes into consideration, the distributed generation, load demand, and battery state of charge are being considered. The artificial neural network have been trained, utilising the profile data, based on the energy storage system’s charging and discharging characteristics, under various distribution network power conditions. Moreover, the error rate was kept, well under 10%. The suggested energy management system, that employs an artificial neural network, has been trained to function in the optimal mode, utilising grey wolf …optimization for each grid-connected power converter. Small-scale hybrid DC/AC microgrids have been developed and tested, in order to simulate and verify the proposed energy management system. The grey wolf optimized neural network energy management system has been proven to provide 99.48 % efficiency, which is superior when compared to other methods existing in the literatures. Show more

DOI: 10.3233/JIFS-222112

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2022

Authors: Lekkoksung, Somsak | Iampan, Aiyared | Julatha, Pongpun | Lekkoksung, Nareupanat

Article Type: Research Article

Abstract: It is known that any ordered semigroup embeds into the structure consisting of the set of all fuzzy sets together with an associative binary operation and a partial order with compatibility. In this study, we provide two classes of ordered semigroups in which any model in these classes is a representation of any ordered semigroup. Moreover, we give an interconnection of a class we constructed.

Keywords: ordered semigroup, fuzzy ordered semigroup, representation

DOI: 10.3233/JIFS-223356

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2022

Authors: Wang, Yaqin | Xu, Jing | Luo, Chen

Article Type: Research Article

Abstract: The mechanical properties of the ultra-great workability concrete (UGWC ) are deeply related to the weights of components, curing period and condition, and occasionally property of admixtures. This study aimed to appraise the usefulness of the adaptive neuro-fuzzy inference system (ANFIS) technique for forecasting the compressive strength of UGWC and enhancing the accuracy of the literature. To outline the forecasting process, two improved ANFIS were suggested, in which determinative variables of them were determined by metaheuristic algorithms named imperialist competitive algorithm (ICA) and multi-verse optimizer (MVO) algorithms. For this purpose, 170 data samples were collected from published literature separated …accidentally for the train and test phase. The calculated performance criteria for proposed ANFIS models demonstrate that both ICA-ANFIS and MVO-ANFIS models can result in justifiable workability for f c of the UGWC prediction procedure. The MVO-ANFIS model could outperform ICA-ANFIS regarding all criteria. For instance, the value of R 2 and VAF for the ICA-ANFIS model are roughly smaller than the MVO-ANFIS model, at 0.9012 and 90% in the training dataset and 0.8973 and 89% in the testing stage, respectively. While the best values of criteria have belonged to the MVO-ANFIS model, with R 2 at 0.937 and 0.944 for the train and test phases, respectively. Overall, the hybrid MVO-ANFIS model can obtain higher workability than ICA-ANFIS and literature (R2 at 0.801), where causes are recognized as the proposed model. Show more

Keywords: Terms— Ultra great workability concrete, compressive strength prediction, adaptive neuro-fuzzy inference system, Hybrid ANFIS

DOI: 10.3233/JIFS-221409

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-15, 2022

Authors: Shao, Sijie | Li, Zhiyong

Article Type: Research Article

Abstract: The new power system information network has the security problem of computer virus attack, and the study of its transmission mechanism is helpful to discover the law and influence of virus transmission. In this paper, the research method of epidemic theory is introduced, and a new Susceptible-Exposed-Infectious-Recovered-Susceptible(SEIR-S) virus model is proposed. The immune time-delay parameter is introduced to simulate the evolution and mutation of the virus so that nodes immune to the virus can still be re-infected after a certain time interval. At the same time, the immune time of different nodes is different, and the distributed immune time delay …is used to enhance the authenticity of the simulated virus transmission; and considering the influence of the scale-free characteristics of the information network, this paper establishes a continuous Markov chain based on time. The transmission process of the virus, and then deduce the theoretical analysis results of the virus infection rate threshold. Based on theoretical analysis, the propagation process of the SEIR-S virus model with distributed immune time delay was simulated by using the Monte Carlo method, and the accuracy of the threshold formula of virus infection rate was verified. The influence rule of the hysteresis parameter, that is, increasing the average immune time of nodes to viruses can reduce the infection density of the network in a steady, and at the same time, making the immune time of network nodes obey a normal distribution can effectively reduce the oscillation effect of viruses on the network. Show more

Keywords: New power system, information network, computer virus, SEIR-S model, distributed immune time-delay

DOI: 10.3233/JIFS-220575

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2022

Authors: Jun-Fang, Song | Yan, Chen

Article Type: Research Article

Abstract: In order to alleviate the increasingly serious traffic congestion problem in China, realize intelligent traffic control, and provide accurate and real-time traffic flow prediction data for traffic flow guidance and traffic travel, this paper designs a GPS-based vehicle trajectory fusion optimization deep model BN-LSTM-CNN which makes full use of the temporal and spatial correlation characteristics of dynamic traffic flow to improve the accuracy of short-term traffic flow prediction. The parameters of the historical GPS dynamic trajectory of the traffic network link are converted into a two-dimensional matrix image of time and space relationship. First, the spatial features are input to …the CNN network, and the spatial dependence relationship between the links is mined, then the traffic flow time series modeling is performed with a four-layer ConvLSTM network, and the BN normalization layer is added to normalize the activation value of the previous layer on each batch, so that the model can obtain higher training accuracy and quickly complete the prediction of the traffic flow state in a certain period of time in the future. The experimental results show that the prediction model is fast to optimize, the prediction error is the smallest compared with other methods, and it can meet the real-time requirements of urban traffic control. Show more

Keywords: Traffic flow state prediction, vehicle trajectory, GPS, matrix image, CNN, ConvLSTM

DOI: 10.3233/JIFS-212998

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2022

Authors: Amutha, S.

Article Type: Research Article

Abstract: White blood cell (WBC) leukemia is caused by an excess of leukocytes in the bone marrow, and image-based identification of malignant WBCs is important for its detection. This research describes a new hybrid technique for accurate classification of WBC leukemia. To increase the image quality, the preprocessing is done using Contrast Limited Adaptive Histogram Equalization (CLAHE). The images are then segmented using Hidden Markov Random Fields (HMRF). To extract features from WBC images, Visual Geometry Group Network (VGGNet), a powerful Convolutional Neural Network (CNN) architecture, is used After that, an Efficient Salp Swarm Algorithm (ESSA) is used to optimize the …extracted features. The proposed method is tested on two Acute Lymphoblastic Leukemia Image Databases, yielding good accuracy of 98.1% for dataset 1 and 98.8% for dataset 2. While enhancing accuracy, the ESSA optimization picked just 1K out of 25K features retrieved with VGGNet. The combination of CNN feature extraction with ESSA feature optimization could be effective for a variety of additional image classification tasks. Show more

Keywords: WBC leukemia, VGGNet-CNN, ALLIDB, efficient scalp swarm algorithm

DOI: 10.3233/JIFS-221302

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-17, 2022

Authors: Aswini, Arockia | Sivarani, T.S.

Article Type: Research Article

Abstract: Diabetic retinopathy becomes an increasingly popular cause of vision loss in diabetic patients. Deep learning has recently received attention as one of the most popular methods for boosting performance in a range of sectors, including medical image analysis and classification. The proposed system comprises three steps; they are image preprocessing, image segmentation, and classification. In preprocessing, the image will be resized, denoising the image and enhancing the contrast of the image which is used for further processing. The lesion region of diabetic retinopathy fundus image is segmented by using Feature Fusion-based U-Net architecture. A blood vessel of a retinal image …is extracted by using the spatial fuzzy c means clustering (SFCM) algorithm. Finally, the diabetic retinopathy images are classified using a modified capsule network. The convolution and primary capsule layers collect features from fundus images, while the class capsule and softmax layers decide whether the image belongs to a certain class. Using the Messidor dataset, the proposed system’s network efficiency is evaluated in terms of four performance indicators. The modified contrast limited adaptive histogram equalization technique enhanced the Peak Signal to Noise Ratio (PSNR), mean square error, and Structural Similarity Index Measure (SSIM) have average values of 36.18, 6.15, and 0.95, respectively. After enhancing the image, segmentation is performed to segment the vessel and lesion region. The segmentation accuracy is measured for the proposed segmentation algorithm by using two metrics namely intersection over union (IoU) and Dice similarity coefficient. Then modified capsule network is constructed for classifying the stages of diabetic retinopathy. The experimental result shows that the proposed modified capsule network got 98.57% of classification accuracy. Show more

Keywords: Diabetic retinopathy, Messidor dataset, Image preprocessing, segmentation, classification

DOI: 10.3233/JIFS-221112

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2022

Authors: Suresh, K.S. | Ravichandran, K.S. | Venugopal, S.

Article Type: Research Article

Abstract: Due to the problem’s high level of complexity, the optimization strategies used for the mobile robot path planning problem are quite expensive. The Mobile Robot Path Search based on a Multi-objective Genetic Algorithm (MRPS-MOGA) is suggested as a solution to the complexity. The MRPS-MOGA resolves path planning issues while taking into account a number of different factors, including safety, distance, smoothness, trip duration, and a collision-free path. In order to find the best approach, the suggested MRPS-MOGA takes into account five main objectives. The MOGA is used to pick the best path from a variety of viable options. Paths produced …at random are used to initialise the population with viable paths. By using objective functions for various objectives, the fitness value is assessed for the quantity of potential candidate paths. In order to achieve diversity in the population, another GA operator mutation is carried out at random on the sequence. Once more, the individual fitness criterion is supported in order to derive the best path from the population. With various situations, an experimental research of the suggested MRPS-MOGA is conducted. The outcome shows that the suggested MRPS-MOGA performs better when choosing the best path with the least amount of time complexity. MRPS-MOGA is more effective than the currently used approaches, according to the experimental analysis. Show more

Keywords: Mobile robot path planning, Multiple objectives, meta-heuristic search, Fitness, tournament selection, ring crossover, adaptive bit string mutation

DOI: 10.3233/JIFS-220886

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-14, 2022