Journal of Intelligent & Fuzzy Systems - Volume 45, issue 5

Authors: Sasirekha, N. | Poonguzhali, I. | Shekhar, Himanshu | Vimalnath, S.

Article Type: Research Article

Abstract: The image of liver which is the area of interest in this work is obtained from abdominal CT scan. It also contains details of other abdominal organs such as pancreas, spleen, stomach, gall bladder, intestine etc. Since all these organs are of soft tissues, the pixel intensity values differ marginally in the CT scan output and the organs overlap each other at their boundaries. Hence it is very difficult to trace out the exact contour of liver and liver tumor. The overlapping and obscure boundaries are to be avoided for proper diagnosis. Image segmentation process helps to meet this requirement. …The normal perception of the CT image can be improved by suitable segmentation techniques. This will help the physician to extract more information from the image and give an accurate diagnosis and better treatment. The projected images are processed using the Partial Differential Technique (PDT) to isolate the liver from the other organs. The Level Set Methodology (LSM) is then used to separate the cancerous tissue from the healthy tissue around it. The classification of stages may be done with the assistance of an Enhanced Convolutional Classifier. The classification of LSM is evaluated by producing many metrics of accuracy, sensitivity, and specificity using an Improved Convolutional classifier. Compared to the two current algorithms, the proposed technique has a sensitivity and specificity of 96% and 93%, respectively, with 95% confidence intervals of [0.7513 1.0000] and [0.7126 1.0000] for sensitivity, and specificity respectively. Show more

Keywords: Liver cancer, improved convolutional classifier, level set methodology, partial differential technique, accuracy

DOI: 10.3233/JIFS-232218

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7939-7955, 2023

Authors: Megala, A. | Veeramani, C.

Article Type: Research Article

Abstract: Researchers in science and engineering face various obstacles due to a lack of specific and full data. Many different approaches have been devised to deal with these restrictive requirements, but two notable schools of thought are the fuzzy set (FS) theory and the rough set (RS) theory, both of which have spawned many extensions and hybridizations. Although RS theory originated from an indiscernibility relation (also known as an equivalence relation in mathematics), emphasis rapidly shifted to similarity or coverings (and their fuzzy analogues). Many other hybrid schemes were suggested with this goal in mind. The gap between those concepts shrank …because to this thorough analysis. Fuzzy set theory is a legitimate way to convey the ambiguity of assessment data, yet it is still inadequate for dealing with certain intricate problems in the actual world. In reality, decision makers will undoubtedly provide different kinds of ambiguous and nuanced assessments. Atanassov’s intuitionistic fuzzy set theory broadened the application of fuzzy set theory by imbuing it with an element of uncertainty. Sometimes in real life, you have to deal with a neutral element on top of the indeterminate one. Picture fuzzy sets were developed specifically for this purpose. Membership roles may be positive, neutral, or negative/refusal. In contrast, hesitant fuzzy sets and its hybrid models are useful when decision makers are on the fence about which option to choose. As a binary relation on a set, a graph is symmetric. It is a staple in mathematical modelling and is used in almost every scientific and technological discipline. Graph theory has been essential in the mathematical modelling and subsequent resolution of several real-world situations. Information about connections between things is often best represented using graph theory, which uses vertices to stand in for the items and edges for the relationships between them. The suggested dynamic algorithm is better to the static approach in dealing with the multidimensional dynamic changes of the hybrid incomplete decision system, according to a series of experiments carried out on nine UCI datasets. Show more

Keywords: Intuitionistic fuzzy set theory, graph theory, rough set theory, varying object sets and values

DOI: 10.3233/JIFS-232314

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7957-7974, 2023

Authors: Huang, Zhen | Gao, Feng | Li, Xuesong | Jiang, Min

Article Type: Research Article

Abstract: The static risk assessment method has difficulty tracking variations of the risk level, which is not conducive to the dynamic control of construction. Tunnel collapse during the construction of mountain tunnels has a dynamic evolution law and contains great risk of harm, and the corresponding dynamic risk assessment is extremely important. This study proposes a static and dynamic fuzzy uncertainty assessment method for the collapse risk of mountain tunnels. First, 150 tunnel collapse accidents were investigated and analysed, and the static and dynamic risk assessment index system of mountain tunnel construction collapse was established. Second, the DEMATEL method is processed …by applying fuzzy logic, the subjective weight of each index is calculated, and the interaction between the indexes is analysed. Finally, the traditional VIKOR method is improved upon, and the weight of each assessment index is coupled and analysed. A static and dynamic uncertainty assessment model of the construction collapse risk of multiple construction sections is constructed. This method has been successfully applied to the risk assessment of tunnel collapse, and the assessment results are consistent with the actual construction situation. This study provides a new method for the static and dynamic assessment of mountain tunnel collapse risk. Show more

Keywords: Mountain tunnel, collapse, risk assessment, VIKOR method, DEMATEL method, Uncertainty analysis

DOI: 10.3233/JIFS-233149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 7975-7999, 2023

Authors: Narayanan, M. Badri | Ramesh, Arun Kumar | Gayathri, K.S. | Shahina, A.

Article Type: Research Article

Abstract: Fake news production, accessibility, and consumption have all increased with the rise of internet-connected gadgets and social media platforms. A good fake news detection system is essential because the news readers receive can affect their opinions. Several works on fake news detection have been done using machine learning and deep learning approaches. Recently, the deep learning approach has been preferred over machine learning because of its ability to comprehend the intricacies of textual data. The introduction of transformer architecture changed the NLP paradigm and distinguished itself from recurrent models by enabling the processing of sentences as a whole rather than …word by word. The attention mechanisms introduced in Transformers allowed them to understand the relationship between far-apart tokens in a sentence. Numerous deep learning works on fake news detection have been published by focusing on different features to determine the authenticity of a news source. We performed an extensive analysis of the comprehensive NELA-GT 2020 dataset, which revealed that the title and content of a news source contain discernible information critical for determining its integrity. To this objective, we introduce ‘FakeNews Transformer’ — a specialized Transformer-based architecture that considers the news story’s title and content to assess its veracity. Our proposed work achieved an accuracy of 74.0% on a subset of the NELA-GT 2020 dataset. To our knowledge, FakeNews Transformer is the first published work that considers both title and content for evaluating a news article; thus, we compare the performance of our work against two BERT and two LSTM models working independently on title and content. Our work outperformed the BERT and LSTM models working independently on title by 7.6% and 9.6% , while performing better than the BERT and LSTM models working independently on content by 8.9% and 10.5% , respectively. Show more

Keywords: Fake news detection, FakeNews transformer, transformer encoder, NELA-GT 2020

DOI: 10.3233/JIFS-223980

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8001-8013, 2023

Authors: Zhenlin, Wei | Chuantao, Wang | Xuexin, Yang | Wei, Zhao

Article Type: Research Article

Abstract: The purpose of sentiment classification is to accomplish automatic judssssgment of the sentiment tendency of text. In the sentiment classification task of online reviews, traditional models focus on the optimization of algorithm performance, but ignore the imbalanced distribution of the number of sentiment classifications of online reviews, which causes serious degradation in the classification performance of the model in practical applications. The experiment was divided into two stages in the overall context. The first stage trains SimBERT using online review data so that SimBERT can fully learn the semantic features of online reviews. The second stage uses the trained SimBERT …model to generate fake minority samples and mix them with the original samples to obtain a distributed balanced dataset. Then the mixed data set is input into the deep learning model to complete the sentiment classification task. Experimental results show that this method has excellent classification performance in the sentiment classification task of hotel online reviews compared with traditional deep learning models and models based on other imbalanced processing methods. Show more

Keywords: Sentiment classification, imbalance classification, deep learning, BERT, SimBERT

DOI: 10.3233/JIFS-230278

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8015-8025, 2023

Authors: Xia, Yan | Yu, Shun | Jiang, Liu | Wang, Liming | Lv, Haihua | Shen, Qingze

Article Type: Research Article

Abstract: Power system load forecasting is a method that uses historical load data to predict electricity load data for a future time period. Aiming at the problems of general prediction accuracy and slow prediction speed in using typical machine learning methods, an improved fuzzy support vector regression machine method is proposed for power load forecasting. In this method, the boundary vector extraction technique is employed in the design of the membership function for fuzzy support vectors to differentiate the importance of different samples in the regression process. This method utilizes a membership function based on boundary vectors to assign differential weights …to different sample points that used to differentiate the importance of different types of samples in the regression analysis process in order to improve the accuracy of electricity load prediction. The key parameters of the fuzzy support vector regression model are optimized, further enhancing the precision of the forecasting results. Simulation experiments are conducted using real power load data sets, and the experimental results demonstrate the effectiveness of the proposed method in terms of accuracy and speed in predicting power load data compared to other prediction models. This method can be widely applied in real power production and scheduling processes. Show more

Keywords: Machine learning, fuzzy support vector regressive machine, power load prediction, membership function, boundary vector

DOI: 10.3233/JIFS-230589

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8027-8048, 2023

Authors: Zhang, Ruihua | Han, Meng | He, Feifei | Meng, Fanxing | Li, Chunpeng

Article Type: Research Article

Abstract: In recent years, there has been an increasing demand for high utility sequential pattern (HUSP) mining. Different from high utility itemset mining, the “combinatorial explosion” problem of sequence data makes it more challenging. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods of HUSP from a novel perspective. Firstly, from the perspective of serial and parallel, the data structure used by the mining methods are illustrated and the pros and cons of the algorithms are summarized. In order to protect data privacy, many HUSP hiding algorithms have been proposed, which are classified into array-based, …chain-based and matrix-based algorithms according to the key technologies. The hidden strategies and evaluation metrics adopted by the algorithms are summarized. Next, a taxonomy of the most common and the state-of-the-art approaches for incremental mining algorithms is presented, including tree-based and projection-based. In order to deal with the latest sequence in the data stream, the existing algorithms often use the window model to update dynamically, and the algorithms are divided into methods based on sliding windows and landmark windows for analysis. Afterwards, a summary of derived high utility sequential pattern is presented. Finally, aiming at the deficiencies of the existing HUSP research, the next work that the author plans to do is given. Show more

Keywords: Survey, high utility sequential patterns, incremental data, data streams, hidden patterns

DOI: 10.3233/JIFS-232107

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8049-8077, 2023

Authors: Wu, Xiaopeng

Article Type: Research Article

Abstract: In wireless-sensing networks (WSNs), the energy economy has lately emerged as the main problem. Since sensor networks run on batteries, they eventually run out of power. To increase the packet transmission ratio for sensing devices, it becomes more difficult to enhance data loss in an energy-efficient manner. In WSNs, the mobile drain causes high network energy usage and data delay. This paper suggests an Improved Ant Colony Clustering-Based Data Transmission Algorithm (EACODT) that first develops the network nodes’ energy density function before allocating sensing nodes with higher residual energy as cluster leaders using the energy density function. The EACODT is …thoroughly modeled for different WSN situations with variable numbers of sensing nodes and CHs, and the findings are contrasted with some recently developed meta-heuristic algorithms. As a consequence, it is discovered that EACODT gets 34% of energy usage, 98.8% of network lifespan, 95% of packet delivery ratio, 854 kbps of transmission, and a 98% convergence rate. Show more

Keywords: Wireless sensor networks, optimization, energy efficiency, packet delivery, data transmission

DOI: 10.3233/JIFS-232295

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8079-8089, 2023

Authors: Zhao, Xiao-Rui | Wang, Jie-Sheng | Bao, Yin-Yin | Hou, Jia-Ning | Ma, Xin-Ru | Li, Yi-Xuan

Article Type: Research Article

Abstract: Wild Horse Optimizer (WHO) is a population-based metaheuristic algorithm inspired by animal behavior, which mainly imitates the decent behavior, grazing behavior, mating behavior and leadership dominance behavior of wild horses in nature to find the optimal. The initialization of the population by imitating the behavior of wild horses is prone to uneven distribution of population positions, and its position updating method is prone to local optimal problems while improving the efficiency of the search. In order to enhance the population diversity and to break out of the local optimum, an adaptive weighted wild horse optimizer based on backward learning and …small-hole imaging strategy is proposed. The backward learning strategy is used to enhance the population diversity and improve the uneven distribution of individuals; The adaptive weight and small-hole imaging strategy are added to the local search strategy to improve the global search ability and jump out of the local optimum. To verify the effectiveness of the proposed algorithm, simulation experiments were conducted by using 23 benchmark test functions to test the search ability and Whale Optimization Algorithm (WOA), Moth-Flame Optimization (MFO), Rat Swarm Optimizer (RSO) and Multi-Verse Optimizer (MVO) algorithms are compared in terms of their search performance, and finally four real engineering design problems are solved. The simulation results indicate that the proposed FHPWHO has excellent merit-seeking capability. Show more

Keywords: Wild horse optimizer, inverse learning, adaptive weights, small-hole imaging strategy, function optimization, engineering optimization

DOI: 10.3233/JIFS-232342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8091-8117, 2023

Authors: Cao, Jiangzhong | Liao, Siyi

Article Type: Research Article

Abstract: 3D shape recognition is a critical research topic in the field of computer vision, attracting substantial attention. Existing approaches mainly focus on extracting distinctive 3D shape features; however, they often neglect the model’s robustness and lack refinement in deep features. To address these limitations, we propose the point-view fusion attention network that aims to extract a concise, informative, and robust 3D shape descriptor. Initially, our approach combines multi-view features with point cloud features to obtain accurate and distinguishable fusion features. To effectively handle these fusion features, we design a dual-attention convolutional network which consists of a channel attention module and …a spatial attention module. This dual-attention mechanism greatly enhances the generalization ability and robustness of 3D recognition models. Notably, we introduce a strip-pooling layer in the channel attention module to refine the features, resulting in improved fusion features that are more compact. Finally, a classification process is performed on the refined features to assign appropriate 3D shape labels. Our extensive experiments on the ModelNet10 and ModelNet40 datasets for 3D shape recognition and retrieval demonstrate the remarkable accuracy and robustness of the proposed method. Show more

Keywords: 3D Shape recognition, multimodal feature fusion, feature refinement, attention mechanism

DOI: 10.3233/JIFS-232800

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 5, pp. 8119-8133, 2023