Journal of Intelligent & Fuzzy Systems - Volume 46, issue 3

Authors: Zhou, Yuzhong | Lin, Zhengping | Wu, Zhengrong | Zhang, Zifeng

Article Type: Research Article

Abstract: Due to the complexity of the calculation process of the existing methods, the efficiency of data fusion of the power grid model is low. In order to improve the knowledge fusion effect of power grid model, this paper studied the knowledge fusion method of power grid model based on Seq2seq half pointer and half label method. The Text Rank algorithm is used to calculate the weight of semantic nodes of each grid model, and combined with the topological potential method, the semantic information of the grid model is extracted according to the final weight value, and the Seq2Seq semi-pointer semi-label …model framework is constructed. The data of the scheduling automation system OMS and the production management system PMS are used as input. The extracted candidate mesh model semantics and the original mesh model semantics are encoded by Seq2Seq half-pointer half-label model. The semantic data of the power grid model is fused and sent to the Seq2Seq encoder. After the training is completed, the effective information is extracted from the power grid model through the Seq2Seq model to complete the knowledge fusion of the power grid model. Experimental results show that this method eliminates the redundant part of the basic attributes of each data source in the substation grid model after knowledge fusion, and the description of each basic attribute is more standardized, unified and perfect. Under different mesh model data dimensions, the support of the proposed method is all above 98%. The model trained by the proposed method tends to be stable after 120 iterations, and the precision, recall and F1 of the test set are 0.98, 0.93 and 0.91, respectively. At the same time, this method has high efficiency in the knowledge fusion processing of the power grid model, and its data processing speed is less than 160 s. The average integrity of the private data of the power grid model is 98.86%, indicating that the proposed method can better ensure the integrity of the data. Finally, compared with the application of other methods under different data amounts, the mean square error obtained by the proposed method is the smallest, indicating that the proposed method effectively improves the fusion accuracy. Show more

Keywords: Grid model, knowledge fusion method, half label method, LSTM neural network, Seq2seq half pointer, TPC TextRank algorithm

DOI: 10.3233/JIFS-236465

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6939-6950, 2024

Authors: Xiao, Yuteng | Liu, Zhaoyang | Yin, Hongsheng | Wang, Xingang | Zhang, Yudong

Article Type: Research Article

Abstract: Multivariate Time Series (MTS) forecasting has gained significant importance in diverse domains. Although Recurrent Neural Network (RNN)-based approaches have made notable advancements in MTS forecasting, they do not effectively tackle the challenges posed by noise and unordered data. Drawing inspiration from advancing the Transformer model, we introduce a transformer-based method called STFormer to address this predicament. The STFormer utilizes a two-stage Transformer to capture spatio-temporal relationships and tackle the issue of noise. Furthermore, the MTS incorporates adaptive spatio-temporal graph structures to tackle the issue of unordered data specifically. The Transformer incorporates graph embedding to combine spatial position information with long-term …temporal connections. Experimental results based on typical finance and environment datasets demonstrate that STFormer surpasses alternative baseline forecasting models and achieves state-of-the-art results for single-step horizon and multistep horizon forecasting. Show more

Keywords: Multivariate time series forecasting, Spatio-temporal structure, transformer, graph embedding, recurrent neural network

DOI: 10.3233/JIFS-237250

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6951-6967, 2024

Authors: Behera, Santi Kumari | Rao, Mannava Srinivasa | Amat, Rajat | Sethy, Prabira Kumar

Article Type: Research Article

Abstract: Mineral classification is a crucial task for geologists. Minerals are identified by their characteristics. In the field, geologists can identify minerals by examining lustre, color, streak, hardness, crystal habit, cleavage, fracture, and specific features. Geologists sometimes use a magnifying hand lens to identify minerals in the field. Surface color can assist in identifying minerals. However, it varies widely, even within a single mineral family. Some minerals predominantly show a single color. So, identifying minerals is possible considering surface color and texture. But, again, a limited database of minerals is available with large-scale images. So, the challenges arise to identify the …minerals using their images with limited images. With the advancement of machine learning, the deep learning approach with bi-layer feature fusion enhances the dimension of the feature vector with the possibility of high accuracy. Here, an experimental analysis is reported with three possibilities of bi-layer feature fusion of three CNN models like Alexnet, VGG16 & VGG19, and a framework is suggested. Alexnet delivers the highest performance with the bi-layer fusion of fc6 and fc7. The achieved accuracy is 84.23%, sensitivity 84.23%, specificity 97.37%, precision 84.7%, FPR 2.63%, F1 Score 84.17%, MCC 81.75%, and Kappa 53.59%. Show more

Keywords: Mineral identification, deep learning, bi-layer feature fusion, deep feature

DOI: 10.3233/JIFS-221987

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6969-6976, 2024

Authors: Danielraj, A. | Venugopal, P. | Padmapriya, N.

Article Type: Research Article

Abstract: Graph Neural Networks (GNNs) have gained popularity across various research fields in recent years. GNNs utilize graphs to construct an embedding that includes details about the nodes and edges in a graph’s neighborhood. In this work, a set of Region Adjacency Graphs (RAG) derives the attribute values from Static Signature (SS) images. These attribute values are used to label the nodes of the complete graph, which is formed by considering each signature as a node taken from the sample of signatures of a specific signer. The complete graph is trained by using GraphSAGE, an inductive representation learning method. This trained …model helps to determine any newly introduced node (static signature to be tested) as genuine or fake. Standard static signature datasets, notably GPDSsynthetic and MCYT-75 are used to test the prevailing model. Experimental results on genuine and counterfeit signature networks demonstrate that our computed model enables a high rate of accuracy (GPDSsynthetic 99.91% and MCYT-75 99.56%) and minimum range of loss (GPDSsynthetic 0.0061 and MCYT-75 0.0070) on node classification. Show more

Keywords: Signature verification, GNN, region adjacency graph, complete graph, GraphSAGE Node classifications

DOI: 10.3233/JIFS-231369

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6977-6994, 2024

Authors: Li, Jing | Hu, Yifan | Fan, Jiulun | Yu, Haiyan | Jia, Bin | Liu, Rui | Zhao, Feng

Article Type: Research Article

Abstract: The Fuzzy C-means (FCM) algorithm is one of the most widely used algorithms in unsupervised pattern recognition. As the intensity of observation noise increases, FCM tends to produce large center deviations and even overlap clustering problems. The relative entropy fuzzy C-means algorithm (REFCM) adds relative entropy as a regularization function to the fuzzy C-means algorithm, which has a good ability for noise detection and membership assignment to observed values. However, REFCM still tends to generate overlapping clusters as the size of the cluster increases and becomes imbalanced. Moreover, the convergence speed of this algorithm is slow. To solve this problem, …modified suppressed relative entropy fuzzy c-means clustering (MSREFCM) is proposed. Specifically, the MSREFCM algorithm improves the convergence speed of the algorithm while maintaining the accuracy and anti-noise capability of the REFCM algorithm by adding a suppression strategy based on the intra-class average distance measurement. In addition, to further improve the clustering performance of MSREFCM for multidimensional imbalanced data, the center overlapping problem and the center offset problem of elliptical data are solved by replacing the Euclidean distance in REFCM with the Mahalanobis distance. Experiments on several synthetic and UCI datasets indicate that MSREFCM can improve the convergence speed and classification performance of the REFCM for spherical and ellipsoidal datasets with imbalanced sizes. In particular, for the Statlog dataset, the running time of MSREFCM is nearly one second less than that of REFCM, and the accuracy of MSREFCM is 0.034 higher than that of REFCM. Show more

Keywords: Fuzzy c-means clustering, relative entropy fuzzy c-means clustering, modified suppressed relative entropy fuzzy c-means, Mahalanobis distance

DOI: 10.3233/JIFS-231515

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 6995-7019, 2024

Authors: Liu, Ziwei | Xu, Ziyu | Zheng, Xiyu | Zhao, Yongxing | Wang, Jinghua

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-232076

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7021-7034, 2024

Authors: Behera, Santi Kumari | Anitha, Komma | Amat, Rajat | Sethy, Prabira Kumar

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-233910

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7035-7045, 2024

Authors: Suganthi, J. Roselin | Rajeswari, K.

Article Type: Research Article

Abstract: Communication is an essential component of human nature. It connects humans, allowing them to learn, grow, col-laborate, and resolve conflicts. Several aspects of human society, relationships, and growth would be significantly hampered in the absence of efficient communication. Hand gesture recognition is a way to interact with technology that can be particularly useful for individuals with disabilities. This hand gesture recognition is mainly employed in sign language translation, healthcare, rehabilitation, prosthesis, and Human-Computer Interaction (HCI). The high degree of dexterity is a main challenge for prosthetic limbs. In order to meet this challenge, hand gesture recognition is employed for the …prosthetic limb, which can be used for rehabilitation. The objective of this article is to show the methodology for the recognition of hand gestures using Electromyography (EMG) signals. This article uses the pro-posed time domain feature extraction method called Absolute Fluctuation Analysis (AFA) along with the Root Mean Square (RMS) for the feature extraction method. Along with these feature extraction methods, repeated stratified K-fold cross validation is used for the validation of the classifiers such as the XGB classifier, the K-Nearest Neighbour (KNN) classifier, the Decision Tree classifier, the Random Forest classifier, and the SVM classifier, whose mean recognition accuracy is given by 93.26%, 87.42%, 85.26%, 92.23%, and 91.78%, respectively. The recognition accuracy of machine learning classifiers is being compared with state-of-the-art networks such as artificial neural net-works (ANN), long short-term memory (LSTM), bidirectional LSTM, gated recurrent units (GRU), and convolution-al neural networks (CNN), which provide recognition accuracy of 96.65%, 99.16%, 99.94%, and 99.99%, respectively. Show more

Keywords: Human computer interaction(HCI), Absolute fluctuation analysis, LSTM, GRU, CNN

DOI: 10.3233/JIFS-234196

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7047-7059, 2024

Authors: Lian, Jing | Chen, Shi | Li, Linhui | Sui, Duo | Ren, Weiwei

Article Type: Research Article

Abstract: Intelligent vehicles require accurate identification of traversable road areas and the ability to provide precise and real-time localization data in unstructured road environments. To address these issues, we propose a system for traversable map construction and robust localization in unstructured road environments based on a priori knowledge. The proposed method performs traversable area segmentation on the LiDAR point cloud and employs a submap strategy to jointly optimize multiple frames of data to obtain a reliable and accurate point cloud map of the traversable area, which is then rasterized and combined with the vehicle kinematic model for global path planning. Then, …it integrates priori map information and real-time sensor information to provide confidence and priori constraints to ensure the robustness of localization, and it fuses multi-sensor heterogeneous data to improve real-time localization. Experiments are conducted in a mining environment to evaluate the performance of the proposed method on an unstructured road. The experimental results demonstrate that the traversable map and localization results based on the proposed method can meet the requirements for autonomous vehicle driving on unstructured roads and provide reliable priori foundation and localization information for autonomous vehicle navigation. Show more

Keywords: Autonomous vehicles, traversability analysis, map construction, robust localization

DOI: 10.3233/JIFS-235063

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7061-7075, 2024

Authors: Srinivasa Rao Illapu, Sankara | Mula, Aswini | Malarowthu, Padmaja

Article Type: Research Article

Abstract: Wireless Body Area Network (WBAN) is an interconnection of tiny biosensors that are organized in/on several parts of the body. The developed WBAN is used to sense and transmit health-related data over the wireless medium. Energy efficiency is the primary challenges for increasing the life expectancy of the network. To address the issue of energy efficiency, one of the essential approaches i.e., the selection of an appropriate relay node is modelled as an optimization problem. In this paper, energy efficient routing optimization using Multiobjective-Energy Centric Honey Badger Optimization (M-ECHBA) is proposed to improve life expectancy. The proposed M-ECHBA is optimized …by using the energy, distance, delay and node degree. Moreover, the Time Division Multiple Access (TDMA) is used to perform the node scheduling at transmission. Therefore, the M-ECHBA method is used to discover the optimal routing path for enhancing energy efficiency while minimizing the transmission delay of WBAN. The performances of the M-ECHBA are analyzed using life expectancy, dead nodes, residual energy, delay, packets received by the Base Station (BS), Packet Loss Ratio (PLR) and routing overhead. The M-ECHBA is evaluated with some classical approaches namely SIMPLE, ATTEMPT and RE-ATTEMPT. Further, this M-ECHBA is compared with the existing routing approach Novel Energy Efficient hybrid Meta-heuristic Approach (NEEMA), hybrid Particle Swarm Optimization-Simulated Annealing (hPSO-SA), Energy Balanced Routing (EBR), Threshold-based Energy-Efficient Routing Protocol for physiological Critical Data Transmission (T-EERPDCT), Clustering and Cooperative Routing Protocol (CCRP), Intelligent-Routing Algorithm for WBANs namely I-RAW, distributed energy-efficient two-hop-based clustering and routing namely DECR and Modified Power Line System (M-POLC). The dead nodes of M-ECHBA for scenario 3 at 8000 rounds are 4 which is less when compared to the dead nodes of EBR. Show more

Keywords: Energy efficiency, life expectancy, multiobjective-energy centric honey badger optimization, time division multiple access, wireless body area network

DOI: 10.3233/JIFS-235387

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7077-7091, 2024

Authors: Qin, Zhi | Liu, Enyang | Zhang, Shibin | Chang, Yan | Yan, Lili

Article Type: Research Article

Abstract: Currently, word segmentation errors and polysemy problems are common in the field of Chinese relationship extraction. Although character-based model input can avoid word segmentation errors, in order to obtain the word information of a sentence, it is often necessary to introduce a dictionary or an external knowledge base to expand the word information, which requires a lot of manpower and time. In response to the above existing problems, this article uses characters as input, uses multiple embedding models to jointly form a character vector sequence, and obtains features containing character information through BiLSTM and attention layers; considering that convolutional neural …networks are good at extracting local features, obtain features containing word information through multi-kernel convolutional layers and multi-head self-attention layers, and finally use a gating mechanism to fuse the features. The model was tested on the public SanWen data set and our own cultural-travel data set, and obtained F1 values of 61.22% and 60.26% respectively. Experimental results show that our method can achieve better relationship extraction effects without using word segmentation tools and without building a dictionary or external knowledge base, and the effect is better than most commonly used models currently. Show more

Keywords: Chinese relation extraction, multiple embedded representations, muti-head self-attention, gating mechanism

DOI: 10.3233/JIFS-237391

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7093-7107, 2024

Authors: Sundar, R. | Choudhury, Ziaul Haque | Chiranjivi, M. | Parasa, Gayatri | Ravuri, Praseeda | Sivaram, M. | Subramanian, Balambigai | Muppavaram, Kireet | Lakshmi.Challa, Vijaya Madhavi

Article Type: Research Article

Abstract: Embracing Artificial Intelligence (AI) is becoming more common in a variety of areas, including healthcare, banking, and transportation, and it is based on substantial data analysis. However, utilizing data for AI raises a number of obstacles. This extensive article examines the challenges connected with using data for AI, including data quality, volume, privacy and security, bias and fairness, interpretability and ethical considerations, and the required technical knowledge. The investigation delves into each obstacle, providing insightful solutions for businesses and organizations to properly handle these complexities. Organizations may effectively harness AI’s capabilities to make educated decisions by understanding and proactively tackling …these difficulties, obtaining a competitive edge in the digital era. This review study, which provides a thorough examination of numerous solutions developed over the last decade to address data difficulties for AI, is expected to be a helpful resource for the scientific research community. It not only provides insights into current difficulties, but it also serves as a platform for creating novel ideas to alter our approaches to data strategies for AI. Show more

Keywords: Artificial intelligence, data quality, privacy, security, ethical consideration

DOI: 10.3233/JIFS-238830

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7109-7122, 2024

Authors: Xing, Haihua | Zhang, Min | Tong, Qixiang | Zeng, Xiya | Chen, Huannan

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-231883

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7123-7141, 2024

Authors: Ding, Ling | Xiong, Xiaobing | Bao, Zhenyu | Hu, Luokai | Chen, Yu | Li, Bijun | Cheng, Yong

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-234248

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7143-7153, 2024

Authors: Wei, Xiaohong

Article Type: Research Article

Abstract: Higher vocational mathematics education is advanced and related to real-time applications providing vast knowledge. Teaching and training peculiar mathematical problems improve their educational and career-focused performance. Therefore optimal performance assessment methods are required for reducing the lack of knowledge in mathematics learning. This article hence introduces an Articulated Performance Assessment Model (APAM) for consenting mathematics assessment. In this model, fuzzy optimization is used for consenting different factors such as understandability, problem-solving, and replication. The understandability is identified using similar problem progression by the students, whereas replication is the application of problem-solving skills for articulated mathematical models. From perspectives, problem-solving and …solution extraction is the theme that has to be met by the student. The assessments hence generate a perplexed outcome due to which the fuzzy optimization for high and low-level understandability is evaluated. The optimization recommends the change in varying steps in problem explanation and iterated replication for leveraging the students’ performance. This process swings between irrelevant and crisp inputs during fuzzification. In this process, the crisp inputs are the maximum replications produced by the students for better understanding. Therefore, the proposed model is evaluated using efficiency, maximum replication, fuzzification rate, and analytical time. Show more

Keywords: Fuzzy optimization, mathematics education, performance evaluation, student assessment

DOI: 10.3233/JIFS-235564

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7155-7171, 2024

Authors: Zhao, RongLe | Tang, Xiao

Article Type: Research Article

Abstract:  The theory of interval-valued formal contexts was originally derived from fuzzy formal contexts. While the fuzzy formal context can extract information from fuzzy formal contexts more precisely, it lacks theoretical analysis of formal contexts with interval-valued data types. This paper incorporates the three-way concept into interval-valued formal contexts, and partitions the interval value range of objects and attributes into three regions utilizing the notion of three-way decisions. On the basis of interval-valued information granules, the concepts of negative operators and interval-valued three-way concepts are proposed. They can conduct profounder knowledge discovery in interval-valued formal contexts, and a generation algorithm of …interval-valued three-way concepts is devised. Finally, the effectiveness of the algorithm is substantiated through experimentation Show more

Keywords: Three-way decision model, formal concept analysis, interval-valued, three-way concept, granular computing

DOI: 10.3233/JIFS-236146

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7173-7184, 2024

Authors: Feng, Rui | Weng, Lie’en

Article Type: Research Article

Abstract:  The text information processing technology of public health service is one of the hot research topics at present. To improve the defects of public health service texts, such as inaccurate word segmentation, spelling errors and professional vocabulary understanding, this study designed a character-level deep neural network model on the characteristics of public health service texts. In this model, the bidirectional short and short time memory and the attention pooling operation layer are introduced to make the model better classify the text according to the context. In addition, counter perturbation is introduced in this study to improve the robustness and generalization …ability of the model, thus improving its classification effect. The performance verification results show that the proposed model has better classification performance on the public health service text data set. The anti-disturbance samples generated by the model are all in the range of 0–0.2 when WMD deviation degree is measured, while most of the other methods are in the range of 0.4–0.6. The experimental object of this study is ultrasonic examination data. The experimental results show that the automatic analysis model of public health service text based on character level convolutional neural network constructed in this study has excellent accuracy and convergence speed, and has excellent performance in the classification of public health service text in different subject areas. Show more

Keywords: Public health service text, character level convolutional neural network, automatic analysis, counter sample, text classification

DOI: 10.3233/JIFS-236470

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7185-7197, 2024

Authors: Alrasheedi, Adel Fahad | Mishra, Arunodaya Raj | Pamucar, Dragan | Devi, Sarita | Cavallaro, Fausto

Article Type: Research Article

Abstract: In the theory of interval-valued intuitionistic fuzzy set (IVIFS), the rating/grade of an element is the subset of the closed interval [0, 1], therefore the IVIFS doctrine is more useful for the decision expert to present their judgments in terms of intervals rather than the crisp values. The present work develops an integrated decision-making methodology for evaluating sustainable wastewater treatment technologies within the context of IVIFS. The proposed decision-making framework is divided into three stages. First, some Yager weighted aggregation operators and their axioms are developed to combine the interval-valued intuitionistic fuzzy information. These operators can offer us a flexible …way to solve the realistic multi-criteria decision-making problems under IVIFS context. Second, an extension of Symmetry Point of Criterion model is introduced to determine the criteria weights under IVIFS environment. Third, an integrated alternative ranking order model accounting for two-step normalization (AROMAN) approach is proposed from IVIF information perspective. Next, the practicability and efficacy of the developed model is proven by implementing it on a case study of sustainable wastewater treatment technologies evaluation problem with multiple criteria and decision experts. Finally, comparative analysis is discussed to illustrate the consistency and robustness of the obtained outcomes. Show more

Keywords: Interval-valued intuitionistic fuzzy set, sustainability, waste water treatment, Yager aggregation operators, score function, AROMAN

DOI: 10.3233/JIFS-236697

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7199-7222, 2024

Authors: Shanthini, J. | Poovizhi, P. | Kavitha, M.S. | Karthik, S.

Article Type: Research Article

Abstract: PURPOSE: Increasing technological advancements in processing and storage have made it easier to handle formerly difficult jobs like disease diagnosis or semantic segmentation. Eye cancer is a rare but deadly disorder that, if misdiagnosed, can cause blindness or even death. It is essential to find eye cancer early in order to successfully treat it and enhance patient outcomes. The usage of DL methods for medical image analysis, particularly the identification of eye cancer, has fascinated increasing consideration in current era. The demand for efficient tool to detect the eye cancer emphasize the need for reliable detection systems. Examining how explainable …deep learning techniques, in which the model’s decision-making process can be understood and visualized, can increase confidence in and adoption of the deep learning-based approach for detecting eye cancer. Expert input is necessary to train machine learning algorithms properly. As it necessitates knowledge of ophthalmology, radiography, and pathology, this can be difficult for eye cancer identification. The main purpose of the study is to detect the eye cancer with at most accuracy with the utilization of Deep learning-based approach. METHODS: There are four steps involved to achieve the efficient detection system. They are pre-processing, segmentation, augmentation, feature extraction with classification. The Circle Hough Transform is applied to detect the edges in the image. The dataset size is increased by shifting, rotating and flipping augmentation techniques. Deep learning-based approach is suggested for the automatic detection of eye cancer. The two methods named 3XConPool and 10XCon5XPool were investigated using Python learning environment. The two techniques 3XConPool and 10XCon5XPool imply on the Sine Cosine Fitness Grey Wolf Optimization (SCFGWO) algorithm for the adjustment of the hyperparameters. The 3XConPool and 10XCon5XPool methods with SCFGWO are compared with each other and also with the other existing methods. RESULTS: As a comparison to the earlier techniques, the suggested configured Convolution Neural Network with SCFGWP exceeds with regard to high accuracy, recall and precision. The suggested 10XCon5XPool with SCFGWO obtains 98.01 as accuracy compared to other method 3XConPool which results 97.23% accuracy. CONCLUSION: The Proposed Method 1 and Proposed Method 2 is presented here, where Proposed Method 2 with 5 times convolution layer with pooling layer yields high accuracy compared to proposed method 1. The main contribution by the SCFGWO algorithm resulted in the achievement of accuracy. This study will open the door for further investigation and the creation of deep learning-based techniques with optimization for ophthalmic processing. Show more

Keywords: Eye cancer, deep learning model, Sine Cosine Fitness, Grey Wolf Optimizer, fully connected layer

DOI: 10.3233/JIFS-237083

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7223-7239, 2024

Authors: Selvin Prem Kumar, S. | Agees Kumar, C. | Venugopal, Anita | Sharma, Aditi

Article Type: Research Article

Abstract: The central nervous system can develop complex and deadly neoplastic growths called brain tumors. Despite being relatively uncommon in comparison to other cancers, brain tumors pose particular challenges because of their delicate anatomical placement and interactions with critical brain regions. The data are taken from TCIA (The Cancer Image Archive) and Kaggle Datasets. Images are first pre-processed using amplified median filter techniques. The pre-processed images are then segmented using the Grabcut method. Feature extraction is extracted using the Shape, ABCD rule, and GLCM are the features were retrieved. The MRI images are then classified into several classes using the Bi-directional …Encoder Representations from Transformers-Bidirectional Long Short Term Memory (BERT-Bi-LSTM) model. Kaggle and TICA datasets are used to simulate the proposed approach, and the results are evaluated in terms of F1-score, recall, precision and accuracy. The proposed model shows improved brain tumour identification and classification. To evaluate the expected technique’s efficacy, a thorough comparison of the current techniques with preceding methods is made. The trial results showed that an efficient hybrid bert model for brain tumor classification suggested strategy provided precision of 98.65%, F1-score of 98.25%, recall of 99.25%, and accuracy of 99.75%. Show more

Keywords: Brain tumor, BERT, Bi-LSTM, grabcut algorithm, classification, feature extraction

DOI: 10.3233/JIFS-237653

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7241-7258, 2024

Authors: Liu, Yang | Yi, Ran | Ma, Ding | Wang, Yongfu

Article Type: Research Article

Abstract: Due to the complexity of the maritime environment and the diversity of the volume and shape of monitored objects in the maritime, existing object detection algorithms based on Convolutional Neural Networks (CNN) are challenging to balance the requirements of high accuracy and high real-time simultaneously in the field of maritime object detection. In response to the characteristics of complex backgrounds, significant differences in object size between categories, and the characteristic of having a large number of small objects in maritime surveillance videos and images, the Maritime dataset with rich scenes and object categories was self-made, and the OS-YOLOv7 algorithm was …proposed based on the YOLOv7 algorithm. Firstly, a feature enhancement module named the TC-ELAN module based on the self-attention mechanism was designed, which enables the feature map used for detection to obtain enhanced semantic information fused from multiple scale features. Secondly, in order to enhance the attention to the area of dense small objects and further improve the positioning accuracy of occluded small objects, this study redesigned the SPPCSPC structure. Then, the network structure was improved to alleviate the problem of decreased object detection accuracy caused by the loss of semantic feature information. Finally, experimental results on self-made datasets and mainstream maritime object detection datasets show that OS-YOLOv7 has a better object detection effect compared to other state-of-the-art (SOTA) object detection algorithms at the cost of reasonable inference time and parameter quantity and can achieve good object detection accuracy on mainstream datasets with high real-time performance. Show more

Keywords: Object detection, real-time, maritime, object recognition, multi-scale

DOI: 10.3233/JIFS-237263

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7259-7271, 2024

Authors: Feng, Yue | Zhu, Yuanguo | He, Liu

Article Type: Research Article

Abstract: In recent years, there has been a great development in parameter estimation methods for uncertain differential equations (UDEs). However, the observations we can obtain in real life are limited, in which case the form of function in a UDE is unknown. When dealing with such UDEs, we may use observational data to make nonparametric estimates. There are many nonautonomous systems in real life, and nonautonomous UDEs can simulate some uncertain nonautonomous dynamical systems well. In this paper, a nonparametric estimation method based on the nonautonomous UDEs of the binary Legendre polynomial is proposed. Then, three numerical examples are given to …verify the reliability of nonparametric estimation. As an application, a real data example of global average monthly temperatures is used to illustrate the effectiveness of our method. Show more

Keywords: Uncertainty theory, uncertain differential equations, nonparametric estimation, global average monthly temperature

DOI: 10.3233/JIFS-235022

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7273-7281, 2024

Authors: Devulapalli, Praveen Kumar | Boppidi, Srikanth | Sake, Pothalaiah | Matta, Jagadeesh Chandra Prasad | Gopal, Dhanalakshmi | Maganti, Sushanth Babu

Article Type: Research Article

Abstract: High bit-error rates and high transmission rates are required for the Multimedia Wireless Sensor Networks (MWSN) to transmit high-quality pictures through smart devices. To fully utilize the advantages of the technology known as Multiple-Input Multiple-Output, MWSN heavily relies on cooperative communication. Large-scale wireless networks use multi-radio-multi-channel to improve performance by simultaneous broadcasts across symmetrical channels to reduce interference. Expanding cooperative communication in vast networks is subject to severe interference. And, as each node in the network is mobile, routing and transmission delay pose significant problems for cooperative multimedia wireless sensor networks. Mobility increases the MWSNs’ dynamic nature, which reflects in …the overhead control traffic. To address above issues, a Cluster-based Delay Aware Cooperative Relay Selection (CDACRS) was proposed by employing mobility and distance metrics and channel assignment (CA) using dynamic Global Table (GT). To minimize the end-to-end transmission latency without compromising aggregate throughput, our approach chooses a relay-node depending on the mobility and the maximum available channel capacity. Further, to improve the end-to-end energy consumption, Power Aware Transmission (PAT) protocol is developed by calculating maximum transmission power required to meet target bit error rate (BER). The proposed method’s performance is evaluated against the Cluster-based Cooperative Multi-Hop Optimal Relay Selection (CCORS); energy efficient and quality aware multi-hop cooperative image transmission; and Energy Aware Cooperative Image Transmission (EACIT) algorithms and observed that our approach improves the transmission delay by 37.5% (approx.) and end-to-end energy consumption by 48.8%. Show more

Keywords: Wireless multimedia sensor networks, Delay aware routing, Cooperative image transmission, Energy efficiency, Optimal relay selection

DOI: 10.3233/JIFS-234312

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7283-7293, 2024

Authors: Srivastava, Jyoti | Srivastava, Ashish Kumar | Muthu Kumar, B. | Anandaraj, S.P.

Article Type: Research Article

Abstract: Text summarizing (TS) takes key information from a source text and condenses it for the user while retaining the primary material. When it comes to text summaries, the most difficult problem is to provide broad topic coverage and diversity in a single summary. Overall, text summarization addresses the fundamental need to distill large volumes of information into more manageable and digestible forms, making it a crucial technology in the era of information abundance. It benefits individuals, businesses, researchers, and various other stakeholders by enhancing efficiency and comprehension in dealing with textual data. In this paper, proposed a novel Modified Generative …adversarial network (MGAN) for summarize the text. The proposed model involves three stages namely pre-processing, Extractive summarization, and summary generation. In the first Phase, the Text similarity dataset is pre-processed using Lowering Casing, Tokenization, Lemmatization, and, Stop Word Removal. In the second Phase, the Extractive summarization is done in three steps Generating similarity metrics, Sentence Ranking, and Sentence Extractive. In the third stage, a generative adversarial network (GAN) employs summary generation to jointly train the discriminative model D and the generative model G. To classify texts and annotate their syntax, Generative Model G employs a convolutional neural network called Bidirectional Gated Recursive Unit (CNN-BiGRU). The performance analysis of the proposed MGAN is calculated based on the parameters like accuracy, specificity, Recall, and Precision metrics. The proposed MGAN achieves an accuracy range of 99%. The result shows that the proposed MGAN improves the overall accuracy better than 9%, 6.5% and 5.4% is DRM, LSTM, and CNN respectively. Show more

Keywords: Text summarization, convolutional neural network, bidirectional gated recurrent unit

DOI: 10.3233/JIFS-236813

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7295-7306, 2024

Authors: Shu, Wenhao | Li, Shipeng | Qian, Wenbin

Article Type: Research Article

Abstract: In real-world scenarios, datasets generally exhibit containing mixed-type of attributes and imbalanced classes distribution, and the minority classes in the data are the primary research focus. Attribute reduction is a key step in the data preprocessing process, but traditional attribute reduction methods commonly overlook the significance of minority class samples, causing the critical information possessed in minority class samples to damage and decrease the performance of classification. In order to address this issue, we develop an attribute reduction algorithm based on a composite entropy-based uncertainty measure to handle imbalanced mixed-type data. To begin with, we design a novel oversampling method …based on the three-way decisions boundary region to synthesize the samples of minority class, for the boundary region to contain more high-quality samples. Then, we propose an attribute measure to select candidate attributes, which considers the boundary entropy, degree of dependency and weight of classes. On this basis, a composite entropy-based uncertainty measure guided attribute reduction algorithm is developed to select the attribute subset for the imbalanced mixed-type data. Experimental on UCI imbalanced datasets, as well as the results indicate that the developed attribute reduction algorithm is significantly outperforms compared to other attribute reduction algorithms, especially in total AUC, F1-Score and G-Mean. Show more

Keywords: imbalanced data, three-way decisions, neighborhood rough set, uncertainty measure, attribute reduction

DOI: 10.3233/JIFS-237211

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7307-7325, 2024

Authors: Li, Jingyan | Mo, Yuanbin | Hong, Lila | Gong, Rong

Article Type: Research Article

Abstract: Dynamic optimization problems exist widely in chemical industry, and its operational variables change with the evolution of both space and time. Therefore, dynamic optimization problems have important research significance and challenges. To solve this problem, a multi-strategy mayfly optimization algorithm (MMOA) combined with control variable parameterization method(CVP) is proposed in this paper. MMOA introduces three improvements on the basis of the original algorithm, namely, circle chaos crossover strategy, center wandering strategy and boundary correction strategy. The hybrid strategy can better balance the exploration and exploitation ability of the algorithm. Based on MATLAB simulation environment, MMOA was evaluated. The experimental results …show that MMOA has excellent performance in solving precision, convergence speed and stability for the benchmark function. For the six classical chemical dynamic optimization problems, MMOA obtained the performance indexes of 0.61071, 0.4776, 0.57486, 0.73768, 0.11861 and 0.13307, respectively. Compared with the data in the previous literature, MMOA can obtain more accurate control trajectory and better performance indicators. It provides an effective way to solve the dynamic optimization problem. Show more

Keywords: Chemical dynamic system, process control, dynamic optimization, mayfly optimization algorithm, control vector parameterization

DOI: 10.3233/JIFS-237786

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7327-7352, 2024

Authors: Zhang, Hong | Liu, Shaojie

Article Type: Research Article

Abstract: The amount of used new energy vehicle transactions is increasing quickly as the social economy matures, yet prices are typically low, making it increasingly difficult to select a fair trading system. Enhancing the score function is crucial in order to account for how different people’s attitudes affect the outcome of decisions and to choose an acceptable trading strategy that is applicable to other scenarios and has a favorable impact on transaction flow. The choice of a trading scheme for new energy-using vehicles is usually regarded as a multi-attribute decision problem. In this paper, the Intuitionistic Fuzzy Hybrid Averaging (IFHA) operator …integration operator with an improved score function is proposed based on the influence of herd mentality on decision-makers. In order to examine the correlation between the score function and the decision outcome using the Spearman rank correlation coefficient, an application to a real situation and some comparative analyses are provided. The outcomes demonstrate that the decision-making process for used car trading schemes can make use of the proposed improved score function. Show more

Keywords: Intuitionistic fuzzy set, multi-characteristic decision making, used new energy car, improved score function, spearman rank correlation coefficient

DOI: 10.3233/JIFS-231358

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7353-7365, 2024

Authors: Xiong, Yu | Cai, Ting | Zhong, Xin | Zhou, Song | Cai, Linqin

Article Type: Research Article

Abstract: Speech emotion recognition is of great significance in the industry such as social robots, health care, and intelligent education. Due to the obscurity of emotional expression in speech, most works on speech emotion recognition (SER) ignore the consistency of speech emotion recognition, leading to fuzzy expression and low accuracy in emotional recognition. In this paper, we propose a semantic aware speech emotion recognition model to alleviate this issue. Specifically, a speech feature extraction module based on CNN and Transformer is designed to extract local and global information from the speech. Moreover, a semantic embedding support module is proposed to use …text semantic information as auxiliary information to assist the model in extracting emotional features of speech, and can effectively overcome the problem of low recognition rate caused by emotional ambiguity. In addition, the model uses a key-value pair attention mechanism to fuse the features, which makes the fusion of speech and text features preferable. In experiments on two benchmark corpora IEMOCAP and EMO-DB, the recognition rates of 74.3% and 72.5% were obtained under respectively, which show that the proposed model can significantly improve the accuracy of emotion recognition. Show more

Keywords: Speech emotion recognition, obscure emotion, semantic awareness, deep learning, pattern recognition

DOI: 10.3233/JIFS-232280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7367-7377, 2024

Authors: Song, Kun | Yi, Huai’an | Song, Xinru | Shu, Aihua | Huang, Jiefeng

Article Type: Research Article

Abstract: The surface roughness of the workpiece is one of the important indicators to measure the quality of the workpiece. Vision-based detection methods are mainly based on human-designed image feature indicators for detection, while the self-extraction method of milling surface features based on deep learning has problems such as poor perception of details, and will be affected by surface rust. In order to solve these problems, this paper proposes a visual inspection method for surface roughness of milling rusted workpieces combined with local equilibrium histogram and CBB-yolo network. Experimental results show that local equilibrium histogram can enhance the milling texture and …improve the accuracy of model detection when different degrees of rust appear on the surface of the milled workpiece. The detection accuracy of the model can reach 97.9%, and the Map can reach 99.3. The inference speed can reach 29.04 frames per second. And the inspection of workpieces without rust, this method also has high detection accuracy, can provide automatic visual online measurement of milling surface roughness Theoretical basis. Show more

Keywords: Surface roughness detection, CBB-yolo, milling workpieces, local equilibrium histosquare

DOI: 10.3233/JIFS-233590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7379-7388, 2024

Authors: Zhou, Yancong | Ma, Yumei | Sun, Xiaochen | Peng, Aihuan | Zhang, Bo | Gu, Xiaoying | Wang, Yan | He, Xingxing | Guo, Zhen

Article Type: Research Article

Abstract: Kiwifruit has a high decay rate, in part because quality changes during storage cannot be easily monitored in real time. In order to better monitor the shelf life of kiwifruit and understand the quality changing process during storage, internal quality indexes such as hardness, respiratory intensity and TSS(Total Soluble Solid) were considered into the prediction models. The prediction models were constructed based on BPNN (Back Propagation Neural Network), Random Forest (RF) and XGBoost (eXtreme Gradient Boosting) respectively. And transfer learning algorithm was used to construct the quality prediction models with BPNN, RF, and XGBoost algorithms as the base learner. In …the experiments, sample data were augmented by adding Gaussian noise, which effectively prevented the model from over-fitting. The experimental results showed that the prediction accuracy of each index based on transfer learning was better than that of individual BPNN, RF and XGBoost. Moreover, the average prediction accuracy of the models based on transfer learning was 96.2%, and that of respiratory intensity was as high as 99.4%. Therefore transfer learning can be used to effectively analyze and predict changes of kiwifruit quality indexes during storage. Show more

Keywords: BPNN, RF, XGBoost, transfer learning, kiwifruit, quality prediction

DOI: 10.3233/JIFS-233718

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7389-7400, 2024

Authors: Yang, Ying | Liu, Shaoshuai | Wang, Xiaolong | Guo, Xiaopeng

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-236130

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7401-7412, 2024

Authors: Li, Shanglin | Xiao, Juan | Li, Yalan | Chen, Xuegang

Article Type: Research Article

Abstract: Favorite-longshot and reverse favorite-longshot biases have become widespread in various traditional sports betting markets in recent years. However, there is a limited number of investigations that have been conducted on the eSports betting market or the bettors that operate within it. In the present research, we have made efforts to re-examine the bias and market inefficiency in four typical eSport games: League of Legends , Counter-Strike: Global Offensive , Dota 2 , and King of Glory . Due to the natural characteristics of e-sports, we analyze the reasons for the market biases from 4 aspects: commission, region, match format, and …tournaments. We find that both the favorite-longshot and reverse favorite-longshot bias occur in eSports. Moreover, the distribution of these betting biases is completely different among different eSports game titles and tournaments. The results of the weighted linear regression model reveal that long match format is the important factor of long-short bias, while regional tournaments are the important factor of reverse long-short bias in League of Legends . Show more

Keywords: eSports, betting market, favorite-longshot, market bias, market inefficiency, statistical analysis

DOI: 10.3233/JIFS-232932

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7413-7426, 2024

Authors: Xing, Cheng | Wang, Jie-Sheng | Liu, Yu

Article Type: Research Article

Abstract: With the increasing complexity and difficulty of numerical optimization problems in the real world, many efficient meta-heuristic optimization methods have been proposed to solve these problems. An improved Fireworks Algorithm (FWA) with elitism-based selection and optimal particle guidance strategies (EO-FWA) was proposed to address the limitations of the traditional FWA in terms of optimization accuracy and convergence speed, which not only improves the efficiency of the searching agent but also accelerates its convergence speed. In addition, by adopting boundary-based mapping rules, EO-FWA eliminates the randomness of traditional modulo operation mapping rules, which improves its stability and reliability. Twelve benchmark functions …in CEC-BC-2022 are used to test the performance of EO-FWA, and the welded beam design problem is optimized at the end. The results show that EO-FWA exhibits stronger competitiveness than other algorithms in dealing with high-dimensional optimization problems and engineering optimization problem, and it can balance exploitation and exploration effectively so as to prevent the algorithm from falling into local optimal solutions. Show more

Keywords: Fireworks algorithm, elitist-based selection strategy, optimal particle guidance, optimization problem

DOI: 10.3233/JIFS-234536

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 3, pp. 7427-7446, 2024