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

Authors: Liu, Ting

Article Type: Research Article

Abstract: In this paper, a new timing synchronization algorithm for the main synchronous signal (PSS) is proposed for the risk identification of cross-border e-commerce in the Internet of things, aiming at the problems of poor performance of anti frequency bias and high computational complexity of the improved PSS timing synchronization algorithm. Based on the piecewise correlation algorithm, the normalized frequency deviation of PSS sequence is preset. The segmented ones are pre stored at the terminal by using the conjugate symmetry of PSS sequence. The fast correlation of each segment correlation window is realized by combining convolution and overlapping reservation block method. …Then, the threshold judgment is made after the time delay accumulation of the correlation values of the segments is made, so as to complete the joint detection of timing synchronization and coarse frequency deviation. The simulation results show that the algorithm can improve the performance of the system anti frequency offset effectively, reduce the complexity of the calculation and show that the timing synchronization conditions of the Internet can be satisfied. At the same time, under the background of the current development of cross-border logistics, this paper reviews the current research status of Transnational E-commerce logistics and Transnational E-commerce logistics risk. By comparing the advantages and disadvantages of various risk assessment methods, neural network and genetic algorithm are selected as the basic risk assessment methods in this paper. Based on the improved PSS timing synchronization algorithm and the Internet of things, the risk indicators of e-commerce logistics transnational will be selected from five risk dimensions: platform risk, customs clearance risk, organizational risk, process risk and environmental risk. Through the comprehensive literature and expert’s opinion, the logistics risk assessment index system of cross-border e-commerce is established. Show more

Keywords: PSS timing synchronization algorithm, internet of things, cross border e-commerce, risk identification

DOI: 10.3233/JIFS-221194

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1325-1340, 2023

Authors: Gao, Pengcheng | Chen, Mingxian | Zhou, Yu | Zhou, Ligang

Article Type: Research Article

Abstract: In order to estimate the deficiency of a city in its ability to prevent and control risks, as well as to evaluate the corresponding measures, this paper focuses on multi-attribute decision making based on LINMAP method and Manhattan distance at linguistic q-rung orthopair fuzzy. Manhattan distance is a new product that combines clustering distance with linguistic q-rung orthopair fuzzy to be able to use the data more effectively for measurement. LINMAP method is a decision making method based on ideal points, which can solve the weights as well as provide ideal solutions by linear programming model. The combination of the …two can create a new decision-making method, which can effectively evaluate the decision scheme of social public facilities according to the actual needs of decision-makers. The new method has the following advantages: (1) the conditions of linguistic fuzzy numbers can be applied more comprehensively, making the decision more realistic and effective; (2) the Manhattan distance is more in line with the human way of thinking and closer to life; (3) after comparative study, the results produced by this method have certain reliability. Show more

Keywords: Multi-attribute decision making, linguistic q-rung orthopair fuzzy, LINMAP method, Manhattan distance

DOI: 10.3233/JIFS-221750

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1341-1355, 2023

Authors: Padmanaban, K. | Shunmugalatha, A.

Article Type: Research Article

Abstract: A novel metaheuristic algorithm has been presented based on the physical significance of palm tree leaves and petioles, which can themselves water and fertilize with their unique architecture. Palm tree leaves collect almost all the raindrops that fall on the tree, which drags the nutrient-rich dropping of crawlers and birds that inhabit it and funnel them back to the palm tree’s roots. The proposed Palm Tree Optimization (PTO) algorithm is based on two main stages of rainwater before it reaches the trunk. Stage one is that the rainwater drops search for petioles in the local search space of a particular …leaf, and stage two involves that the rainwater drops after reaching the petioles search for trunk to funnel back to the root along with nutrients. The performance of PTO in searching for global optima is tested on 33 Standard Benchmark Functions (SBF), 29 constrained optimization problems from IEEE-CEC2017 and real-world optimization problems from IEEE-CEC2011 competition especially for testing the evolutionary algorithms. Mathematical benchmark functions are classified into six groups as unimodal, multimodal, plate & valley-shaped, steep ridges, hybrid functions and composition functions which are used to check the exploration and exploitation capabilities of the algorithm. The experimental results prove the effectiveness of the proposed algorithm with better search ability over different classes of benchmark functions and real-world applications. Show more

Keywords: PTO-palm tree optimization, exploration, exploitation, petioles, crankshaft

DOI: 10.3233/JIFS-222413

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1357-1385, 2023

Authors: Wang, Wei | Zhang, Weidong | Zhang, Zhe

Article Type: Research Article

Abstract: The complexity of the cohesive soil structure necessitates settlement modeling beneath shallow foundations. The goal of this research is to use recently discovered machine learning techniques called the hybridized radial basis function neural network (RBFNN ) with sine cosine algorithm (SCA ) and firefly algorithm (FFA ) to detect settlement (S m ) of shallow foundations. The purpose of using optimization methods was to find the optimal value for the primary attributes of the model under investigation. With R 2 values of at least 0.9422 for the learning series and 0.9271 for the assessment series, both the produced …SCA  - RBFNN and FFA  - RBFNN correctly replicated the S m , which indicates a considerable degree of efficacy and even a reasonable match between reported and modeled S m . In comparison to FFA  - RBFNN and ANFIS  - PSO , the SCA  - RBFNN is believed to be the more correct method, with the values of R 2 , RMSE and MAE was 0.9422, 7.2255 mm and 5.1257 mm, which is superior than ANFIS  - PSO and FFA  - RBFNN . The SCA  - RBFNN could surpass FFA one by 25% for the learning component and 14.2% for the test data, according to the values of PI index. Ultimately, it is apparent that the RBFNN combined with SCA could score higher than the FFA and even the ANFIS  - PSO , which is the proposed system in the S m forecasting model, after assessing the reliability and considering the assumptions. Show more

Keywords: Shallow foundation settlement, prediction, RBF neural network, sine cosine algorithm

DOI: 10.3233/JIFS-223907

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1387-1396, 2023

Authors: Keerthika, V. | Muhiuddin, G. | Jun, Y. B. | Elavarasan, B.

Article Type: Research Article

Abstract: Fuzzy sets, soft sets, and their generalisations have always been important tools for mathematicians and researchers working with uncertainty. Jun proposed a hybrid structure that combined the concepts of a fuzzy set and a soft set. It should be noted that hybrid structures are a combination of soft set and fuzzy set speculation. Our aim is to explore the concept of hybrid ordered ideals and hybrid interior ideals in ordered semirings, as well as look at some of their related properties, which is one of the important aspects of this paper. In order to investigate the structure theory of hybrid …ideals in ordered semirings, we define hybrid composition and hybrid addition. We also establish and characterise the regularity of ordered semirings in terms of hybrid structures. Show more

Keywords: Semiring, ideals, hybrid structure, hybrid interior ideals, ordered semirings

DOI: 10.3233/JIFS-224060

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1397-1408, 2023

Authors: Singh, Nitin Kumar | Singh, Pardeep | Das, Prativa | Chand, Satish

Article Type: Research Article

Abstract: Social media platforms allow people across the globe to share their thoughts and opinions and conveniently communicate with each other. Apart from various advantages of social media, it is also misused by a set of users for hate-mongering with toxic and offensive comments. The majority of the earlier proposed toxicity detection methods are primarily focused on the English language, but there is a lack of research on low-resource languages and multilingual text data. We propose an XRBi-GAC framework comprising XLM-RoBERTa, Bi-GRU with self-attention and capsule networks for multilingual toxic text detection. A loss function is also presented, which fuses the …binary cross-entropy loss and focal loss to address the class imbalance problem. We evaluated the proposed framework on two datasets, namely, the Jigsaw Multilingual Toxic Comment dataset and HASOC 2019 dataset and achieved F1-score of 0.865 and 0.829, respectively. The results of the experiments show that the proposed framework has outperformed the state-of-the-art multilingual models XLM-RoBERTa and mBERT on both datasets, which shows the versatility and robustness of the proposed XRBi-GAC framework. Show more

Keywords: Toxicity, multilingual text, XLM-RoBERTa, Bi-GRU, self-attention, capsule network

DOI: 10.3233/JIFS-224536

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1409-1421, 2023

Authors: Xu, Siyu | Qin, Keyun | Pan, Xiaodong | Fu, Chao

Article Type: Research Article

Abstract:  Both fuzzy set and rough set are important mathematical tools to describe incomplete and uncertain information, and they are highly complementary to each other. What is more, most fuzzy rough sets are obtained by combining Zadeh fuzzy sets and Pawlak rough sets. There are few reports about the combination of axiomatic fuzzy sets and Pawlak rough sets. For this reason, we propose the axiomatic fuzzy rough sets (namely rough set model with respect to the axiomatic fuzzy set) establishing on fuzzy membership space. In this paper, we first present a similarity description method based on vague partitions. Then the concept …of similarity operator is proposed to describe uncertainty in the fuzzy approximation space. Finally, some characterizations concerning upper and lower approximation operators are shown, including basic properties. Furthermore, we give a algorithm to verify the effectiveness and efficiency of the model. Show more

Keywords: Rough sets, axiomatic fuzzy rough sets, residuated lattices, fuzzy relations, approximation operators

DOI: 10.3233/JIFS-223643

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1423-1436, 2023

Authors: Sharma, Rahul | Singh, Amar

Article Type: Research Article

Abstract: In the recent decade, plant disease classification using convolution neural networks has proven to be superior because of its ability to extract key features. Obtaining the optimum feature subset with the necessary discriminant information is challenging. The main objective of this paper is to design an efficient hybrid plant disease feature selection approach and validate it on standard image datasets. The raw input image features were transformed into 8192 learned features by employing the VGG16. To reduce the training time and enhance classification accuracy, the dimensionality reduction technique Principal Component Analysis (PCA) is integrated with the big bang-big crunch (BBBC) …optimization algorithm. The PCA-BBBC feature selection method reduces computing time by eliminating unnecessary and redundant features. The proposed approach was evaluated on plant diseases and benchmarked image datasets. Experimental results reveal that the Artificial Neural Network (ANN) classifier integrated with the VGG16-PCA-BBBC approach enhanced the performance of the classifier. The proposed approach outperformed the VGG16-PCA-ANN method and other popular image classification techniques. For the rice disease dataset, the proposed hybrid approach reduced the VGG16 extracted 8192 deep features to 200 relevant principal components. The recommended reduced features were used for training ANN. The test dataset was classified by ANN with an accuracy of 99.12%. Experimental results demonstrate that the proposed approach improved the performance of the classifier and accurately labeled image and plant diseases datasets aiding farmers to adopt remedial measures. Show more

Keywords: BBBC, dimensionality reduction, feature selection, PCA, plant disease detection

DOI: 10.3233/JIFS-222517

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1437-1451, 2023

Authors: Vinodha, D. | Mary Anita, E.A.

Article Type: Research Article

Abstract: Industrial revolutions and demand of novel applications drive the development of sensors which offer continuous monitoring of remote hostile areas by collecting accurate measurement of physical phenomena. Data aggregation is considered as one of the significant energy-saving mechanism of resource constraint Wireless Sensor Networks (WSNs) which reduces bandwidth consumption by eliminating redundant data. Novel applications demand WSN to provide information about the monitoring region in multiple aspects in large scale. To meet this requirement, different kinds of sensors of different parameters are deployed in the same region which in turn demands the aggregator node to integrate diverse data in a …smooth and secure manner. Novelty in applications also requires Base station (BS) to apply multiple statistical functions. Hence, we propose to develop a novel secure cost-efficient data aggregation scheme based on asymmetric privacy homomorphism to aggregate data of multiple parameters and facilitate the BS to compute multiple functions in one round of data collection by providing elaborated view of monitoring region. To meet the claim of large scale WSN which requires dynamic change in size, vector-based data collection method is adopted in our proposed scheme. The security aspect is strengthened by allowing BS to verify the authenticity of source node and validity of data received. The performance of the system is analyzed in terms of computation and communication overhead using the mathematical model and simulation results. Show more

Keywords: Wireless sensor networks, secured data aggregation, privacy homomorphism

DOI: 10.3233/JIFS-223511

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1453-1472, 2023

Authors: Tian, Chang | Liu, Yanjung | Li, Meng | Fen, Chaofan

Article Type: Research Article

Abstract:  The key step in the intelligence of tongue diagnosis is the segmentation of the tongue image, and the accuracy of the segmented edges has a significant impact on the subsequent medical judgment. Deep learning can predict the class of pixel points to achieve pixel-level segmentation of images, so it can be used to handle tongue segmentation tasks. However, different models have different segmentation effects, and they did not learn the connection between space and channels, resulting in inaccurate tongue segmentation. This paper first discussed the choice of model and loss function and then compared the results of different options to …find the better model. Associating the red feature of the tongue is very conducive to segmentation as a feature, this paper tested many methods to try to get the color features of the original image to be paid attention to. Finally, this paper proposed an improved Encoder-Decoder network model to solve the problem based on the results. Start with Resnet as the backbone network, then introduce the U-Net model, and then we fused the attention layer, obtained from the source image through convolution and CBAM attention mechanism, and the feature layer obtained from the last upsampling in U-Net. Experimental results show that: The new, improved algorithm results are 2-3 percentage points higher than the popular algorithm, making it more suitable for tongue segmentation tasks. Show more

Keywords: Deep convolutional neural network, attention mechanism, tongue image, image segmentation

DOI: 10.3233/JIFS-221411

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1473-1480, 2023