Journal of Intelligent & Fuzzy Systems - Volume 45, issue 4

Authors: Chen, Haoying

Article Type: Research Article

Abstract: Big data is changing our lives and the way we understand the world, as well as the operational patterns of business and social organizations. Fully understanding the value of data and knowing how to use big data to provide a basis for business decision-making has gradually become the most basic thinking that business organizations should possess in the era of big data. Under the thinking mode of data-driven decision-making, many information science researchers have discussed the model, architecture, operation mechanism and other aspects of big data competitive intelligence system. At the same time, more and more enterprises, such as IBM, …Amazon, Google, Microsoft, Wal Mart, etc., have begun to attach importance to the development and construction of big data competitive intelligence software systems, and have achieved certain results. The enterprise competitive intelligence system evaluation in the context of big data is regarded as multi-attribute decision-making (MADM). In this paper, the Hamy mean (HM) and the power average (PA) are connected with 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the 2-tuple linguistic neutrosophic numbers power HM (2TLNPHM) operator. Then, use the 2TLNPHM operator to handle MADM with 2TLNS. Finally, taking the enterprise competitive intelligence system evaluation in the context of big data as an example, the proposed method is explained. The main contributions of this study are summarized: the establishment of the 2TLNPHM operator; (2) The 2TLNPHM operator was developed to handle MADM with 2TLNS; (3) Through the empirical application of the enterprise competitive intelligence system evaluation, the proposed method is validated; (4) Some comparative studies have shown the rationality of the 2TLNPHM operator. Show more

Keywords: Multi-attribute decision making (MADM), neutrosophic numbers, 2-tuple linguistic neutrosophic sets (2TLNSs), 2TLNPHM operator, enterprise competitive intelligence system evaluation

DOI: 10.3233/JIFS-231768

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5955-5970, 2023

Authors: Wu, Huiyong | Yang, Tongtong | Wu, Harris | Li, Hongkun | Zhou, Ziwei

Article Type: Research Article

Abstract: Good air quality is one of the prerequisites for stable urban economic growth and sustainable development. Air quality is influenced by a range of environmental elements. In this study, seven common air pollutants and six kinds of meteorological data in a major city in China are studied. In this urban setting, the air quality index will be estimated based on a Long Short-term Memory (LSTM)model. To improve prediction accuracy, the Random Forest (RF) method is adopted to choose important features and pass them to the LSTM model as input, an improved sparrow search algorithm (ISSA) is used to optimize the …hyperparameters of the LSTM model. According to the experimental findings, the RF-ISSA-LSTM model demonstrates superior accuracy compared to both the basic LSTM model and the ISSA-LSTM fusion model. Show more

Keywords: Sustainable development, long short-term memory, sparrow search algorithm, random forest, air quality index

DOI: 10.3233/JIFS-232308

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5971-5985, 2023

Authors: Prabakaran, S. | Mary Praveena, S.

Article Type: Research Article

Abstract: Osteosarcomas are a type of bone tumour that can develop anywhere in the bone but most typically do so around the metaphyseal growth plates at the ends of long bones. Death rates can be lowered by early detection. Manual osteosarcoma identification can be difficult and requires specialised knowledge. With the aid of contemporary technology, medical photographs may now be automatically analysed and categorised, enabling quicker and more effective data processing. This paper proposes a novel hyperparameter-tuned deep learning (DL) approach for predicting osteosarcoma on histology images with effective feature selection mechanism which aims to improve the prediction accuracy of the …classification system for bone tumor detection. The proposed system mainly consists of ‘6’ phases: data collection, preprocessing, segmentation, feature extraction, feature selection, and classification. Firstly, the dataset of histology images is gathered from openly available sources. Then Median Filtering (MEF) is utilized as the preprocessing step that enhances the quality of the input images for accurate prediction by eliminating unwanted information from them. Afterwards, the pre-processed image was segmented using Harmonic Mean-based Otsu Thresholding (HMOTH) approach to obtain the tumor-affected regions from the pre-processed data. Then the features from the segmented tumor portions are extracted using the Self-Attention Mechanism-based MobileNet (SAMMNet) model. A Van der Corput sequence and Adaptive Inertia Weight included Reptile Search Optimization Algorithm (VARSOA) is used to select the more relevant features from the extracted features. Finally, a Hyperparameter-Tuned Deep Elman Neural Network (HTDENN) is utilized to diagnose and classify osteosarcoma, in which the hyperparameters of the neural network are obtained optimally using the VARSOA. The proposed HTDENN attains the higher accuracy of 0.9531 for the maximum of 200 epochs, whereas the existing DENN, MLP, RF, and SVM attains the accuracies of 0.9492, 0.9427, 0.9413, and 0.9387. Likewise, the proposed model attains the better results for precision (0.9511), f-measure (0.9423), sensitivity (0.9345) and specificity (0.9711) than the existing approaches for the maximum of 200 epochs. Simulation outcomes proved that the proposed model outperforms existing research frameworks for osteosarcoma prediction and classification. Show more

Keywords: Deep Elman Neural Network, osteosarcoma diagnosis, histology images, median filter, convolutional neural network

DOI: 10.3233/JIFS-233484

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5987-6003, 2023

Authors: Ullah, Sami | Kashif, Muhammad | Aslam, Muhammad | Haider, Gulfam | AlAita, Abdulrahman | Saleem, Muhammad

Article Type: Research Article

Abstract: The application of classical statistical methods is not feasible given the presence of imprecise, fuzzy, uncertain, or undetermined observations in the underlying dataset. This is due to the existence of uncertainties pervading every aspect of real-life situations, which cannot always be accurately addressed by classical statistical approaches. In order to tackle this problem, a new methodology known as neutrosophic analysis of variance (NANOVA) has been developed as an extension of classical approaches to analyze datasets with uncertainty. The proposed approach can be applied regardless of the number of factors and replications. Moreover, NANOVA introduces a novel matrix-based approach to derive …the F_N-test in an uncertain environment. To assess the effectiveness of NANOVA, various real datasets have been employed, and research findings on single- and two-factor NANOVAs with measures of indeterminacy have been presented. According to our comparisons, NANOVA provides a more informative, efficient, flexible, and reliable approach to deal with uncertainties than classical statistical methods. Therefore, there is a need to go beyond conventional statistical techniques and adopt advanced methodologies that can effectively handle uncertainties. Show more

Keywords: Imprecise data, classical statistics, interval statistics, analysis of variance, F-test

DOI: 10.3233/JIFS-223636

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6005-6017, 2023

Authors: Patidar, Ritu | Patel, Sachin

Article Type: Research Article

Abstract: Many people have been severely affected by the COVID-19 outbreak, which has left them anxious, terrified, and other difficult feelings. Since the introduction of coronavirus vaccinations, people’s emotional spectrum has broadened and become more sophisticated.We want to observe and interpret their sentiments using deep learning techniques in this work. The most efficient way to convey one’s thoughts and feelings right now is via social media, and using Twitter may help one better understand what is popular and what is going through other people’s minds. Analyzing and visualization of data play a vital role in Data Science; as customers over e-commerce …increase, feedback/reviews shared by them increase significantly, and decisions by a new customer to buy a product or not rely on these reviews; reviews might falsely be displayed which may be involving in controlling if any products demand and supply so, reviews analyzing and visualizationto understand they are genuinely playing an important role over e-commerce nowadays. Our primary objective in conducting this study was to understand better the various perspectives individuals held on the vaccination process and reviews of products purchased online. As shown by the presented study, analysis and visualization approaches may be used to facilitate rapid and easy comprehension of e-commerce data, despite its high dimensionality.All correlation and non-correlation factors were mapped and examined, providing a comprehensive picture of the proposed data and its connection to other parameters.The proposed work provides an overview of sentiment observations across arguments and the relationships between parameters; it opens the door for modeling to extract some decision-making insights from the data, which can be used to improve the efficiency of application areas like product quality and customer satisfaction. Show more

Keywords: E-commerceproduct, COVID-19 vaccines, NLTK, CNN model, XLnet model, TextBlob

DOI: 10.3233/JIFS-230662

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6019-6034, 2023

Authors: Fan, Jianping | Tian, Ge | Wu, Meiqin

Article Type: Research Article

Abstract: Cross-efficiency in data envelopment analysis is widely used in production as an evaluation method that includes input and output indicators and allows for self-evaluation and mutual evaluation of decision making units (DMUs). However, as the application scenarios continue to expand, the traditional methods gradually fail to meet the needs. Many researchers have proposed improved methods and made great progress in weight determination, but the existing studies still have shortcomings in considering the psychological behavior of decision makers (DMs) and there is still relatively little research on cross-efficiency in fuzzy environments. In this paper, we proposed a method to apply CRITIC …to determine weights and introduce both prospect theory and regret theory into the evaluation method of cross-efficiency to obtain the prospect cross-efficiency matrix and regret cross-efficiency matrix respectively, and then applied the Pythagorean hesitant fuzzy operator to aggregate them to achieve the ranking of DMUs through the fraction function. This largely takes into account the subjective preference and regret avoidance psychology of DMs. The applicability of this paper’s method is also verified through an example of shopping for a new energy vehicle. Finally, the effectiveness of this paper’s method is verified by comparing three traditional methods with this paper’s method, which provides an effective method for considering risk preferences in the decision-making process. Show more

Keywords: Data envelopment analysis, cross-efficiency, CRITIC, prospect theory, regret theory, Pythagorean hesitant fuzzy set

DOI: 10.3233/JIFS-231371

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6035-6045, 2023

Authors: Ismail, Isaudin | Abd Mutalip, Fatin Noor Najihah | Jacob, Kavikumar

Article Type: Research Article

Abstract: The Copula concept has long been used in many applications, especially in the financial field. This concept was first used in 1959 by Sklar in his mathematical work and greatly assisted in the applications of financial and insurance areas. The copula functions have been widely used in dependence modeling. In this study, we look at how the copula began to develop from a basic form to a more advanced form through studies that previous researchers have made. Throughout this study, we find various types of the copula, and each exhibits its own characteristics lying under two main families, Elliptical and …Archimedean copulas. Our findings suggest that copula is vital in solving problems in statistical dependence measures and joint marginal distribution functions. This comprehensive study served as a review paper on the development of copulas from their initial existence to their latest evolution. Show more

Keywords: Copula, financial field, decision-making, insurance, marginal distribution

DOI: 10.3233/JIFS-223481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6047-6062, 2023

Authors: Yu, Zhongliang

Article Type: Research Article

Abstract: The aerospace target tracking is difficult to achieve due to the dataset is intrinsically rare and expensive, and the complex space background, and the large changes of the target in the size. Meta-learning can better train a model when the data sample is insufficient, and tackle the conventional challenges of deep learning, including the data and the fundamental issue of generalization. Meta-learning can quickly generalize a tracker for new task via a few adapt. In order to solve the strenuous problem of object tracking in aerospace, we proposed an aerospace dataset and an information fusion based meta-learning tacker, and named …as IF-Mtracker. Our method mainly focuses on reducing conflicts between tasks and save more task information for a better meta learning initial tracker. Our method was a plug-and-play algorithms, which can employ to other optimization based meta-learning algorithm. We verify IF-Mtracker on the OTB and UAV dataset, which obtain state of the art accuracy than some classical tracking method. Finally, we test our proposed method on the Aerospace tracking dataset, the experiment result is also better than some classical tracking method. Show more

Keywords: Aerospace tracking dataset, meta learning, information fusion, aerospace tracking dataset

DOI: 10.3233/JIFS-230265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6063-6075, 2023

Authors: Ramaswamy, Srividhya Lakshmi | Chinnappan, Jayakumar

Article Type: Research Article

Abstract: The deep learning revolution in the current decade has transformed the artificial intelligence industry. Eventually, deep learning techniques have become essential for many computational modeling tasks. Nevertheless, deep neural models provide a high degree of automation for natural language processing (NLP) applications. Deep neural models are extensively used to decode public reviews subjective to specific products, services, and other social activities. Further, to improve sentiment classification accuracy, several neural architectures have been developed. Convolutional neural networks (CNN) and Long-short term memory (LSTM) are the popular deep models employed in ensemble architectures for sentiment classification tasks. This review article extensively compares …the competence of CNN and LSTM-based ensemble models to improve the sentiment accuracy for online review datasets. Further, this article also provides an empirical study on various ensemble models concerning the position of LSTM and CNN for efficient sentiment classification. This empirical study provides deep learning researchers with insights into building effective multilayer LSTM and CNN models for many sentiment analysis tasks. Show more

Keywords: Sentiment analysis, convolutional neural network, long-short term memory, multilayer ensemble architectures, review dataset

DOI: 10.3233/JIFS-230917

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6077-6105, 2023

Authors: Jhansi Rani, Challapalli | Devarakonda, Nagaraju

Article Type: Research Article

Abstract: The study addresses the challenges of human action recognition and analysis in computer vision, with a focus on classifying Indian dance forms. The complexity of these dance styles, including variations in body postures and hand gestures, makes classification difficult. Deep learning models require large datasets for good performance, so standard data augmentation techniques are used to increase model generalizability. The study proposes the Indian Classical Dance Generative Adversarial Network (ICD-GAN) for augmentation and the quantum-based Convolutional Neural Network (QCNN) for classification. The research consists of three phases: traditional augmentation, GAN-based augmentation, and a combination of both. The proposed QCNN is …introduced to reduce computational time. Different GAN variants DC-GAN, CGAN, MFCGAN are employed for augmentation, while transfer learning-based CNN models VGG-16, VGG-19, MobileNet-v2, ResNet-50, and new QCNN are implemented for classification. The study demonstrates that GAN-based augmentation outperforms traditional methods, and QCNN reduces computational complexity while improving prediction accuracy. The proposed method achieves a precision rate of 98.7% as validated through qualitative and quantitative analysis. It provides a more effective and efficient approach compared to existing methods for Indian dance form classification. Show more

Keywords: Quantum convolution neural network, data augmentation, generative adversarial network, Indian classical dance, transfer learning

DOI: 10.3233/JIFS-231183

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6107-6125, 2023

Authors: Zhang, Chaoqin | Li, Ting | Yin, Yifeng | Ma, Jiangtao | Gan, Yong | Zhang, Yanhua | Qiao, Yaqiong

Article Type: Research Article

Abstract: With the continuous development of knowledge graph completion (KGC) technology, the problem of few-shot knowledge graph completion (FKGC) is becoming increasingly prominent. Traditional methods for KGC are not effective in addressing this problem due to the lack of sufficient data samples. Therefore, completing the task of knowledge graph with few-shot data has become an urgent issue that needs to be addressed and solved. This paper first presents a concise introduction to FKGC, which covers relevant definitions and highlights the advantages of FKGC techniques. We then categorize FKGC methods into meta-learning-based, metric-based, and graph neural network-based methods, and analyze the unique …characteristics of each model. We also introduced the research on FKGC in a specific domain - Temporal Knowledge Graph Completion (TKGC). Subsequently, we summarized the commonly used datasets and evaluation metrics in existing methods and evaluated the completion performance of different models in TKGC. Finally, we presented the challenges faced by FKGC and provided directions for future research. Show more

Keywords: Knowledge graph, few-shot learning, knowledge graph completion, temporal knowledge graph completion

DOI: 10.3233/JIFS-232260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6127-6143, 2023

Authors: Marimuthu, M. | Mohanraj, G. | Karthikeyan, D. | Vidyabharathi, D.

Article Type: Research Article

Abstract: Web browsers have become an integral part of our daily lives, granting us access to vast information and services. However, this convenience significantly risks personal information and data security. One common source of this risk is browser extensions, which users often employ to add new features to their browsers. Unfortunately, these extensions can also pose a security threat, as malicious ones may access and steal sensitive information such as passwords, credit card details, and personal data. The vulnerability of web browsers to malicious extensions is a significant challenge that effectively tackles through robust defence mechanisms. To address this concern, Secure …Vault – API is proposed and designed to safeguard confidential web page content from malicious extensions. The Web Crypto API provides cryptographic functions that protect data during transmission and storage. The Secure Vault encompasses a Sentinel extension responsible for parsing the web page’s Document Object Model (DOM) content and querying for all “vault” elements. The extension then verifies that the DOM content has not been tampered with by any malicious extension by calculating the SHA512 hash value of the concatenated vault elements as a string, with no whitespace between them. With its encryption, hashing, and isolation techniques, the Secure Vault effectively protects confidential web page content from malicious extensions. It provides a secure environment for storing and processing sensitive data, reducing the risk of data breaches caused by malicious extensions. The proposed approach offers significant advantages over existing strategies in terms of protecting confidential web page content from malicious extensions. This not only improves the efficiency and effectiveness of the browser extensions but also ensures compatibility, interoperability and performance across different web browsers with respect to the load time of HTML elements. Users can browse the web and carry out sensitive transactions with peace of mind, knowing their data is safeguarded against theft or manipulation by malicious extensions. Show more

Keywords: Browser security, chrome extensions, secure browsing, Web Crypto API, malicious extension

DOI: 10.3233/JIFS-233122

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6145-6160, 2023

Authors: Sundarakumar, M.R. | Sharma, Ravi | Fathima, S.K. | Gokul Rajan, V. | Dhayanithi, J. | Marimuthu, M. | Mohanraj, G. | Sharma, Aditi | Johny Renoald, A.

Article Type: Research Article

Abstract: For large data, data mining methods were used on a Hadoop-based distributed infrastructure, using map reduction paradigm approaches for rapid data processing. Though data mining approaches are established methodologies, the Apriori algorithm provides a specific strategy for increasing data processing performance in big data analytics by applying map reduction. Apriori property is used to increase the efficiency of level-wise creation of frequent itemsets by minimizing the search area. A frequent itemset’s subsets must also be frequent (Apriori property). If an itemset is rarely, then all of its supersets are infrequent as well. We refined the apriori approach by varying the …degree of order in locating frequent item sets in large clusters using map reduction programming. Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC) is a classical algorithm which are used for data processing from the huge datasets but their accuracy is not up to the mark. In this article, updated Apriori algorithms such as multiplied-fixed-pass combined counting (MFPC) and average time-based dynamic combined counting (ATDFC) are used to successfully achieve data processing speed. The proposed approaches are based on traditional Apriori core notions in data mining and will be used in the map-reduce multi-pass phase by ignoring pruning in some passes. The optimized-MFPC and optimized-ATDFC map-reduce framework model algorithms were also presented. The results of the experiments reveal that MFPC and ATDFC are more efficient in terms of execution time than previously outmoded approaches such as Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC). In a Hadoop multi-node cluster, this paradigm accelerates data processing on big data sets. Previous techniques were stated in terms of reducing execution time by 60–80% through the use of several passes. Because of the omitted trimming operation in data pre-processing, our proposed new approaches will save up to 84–90% of that time. Show more

Keywords: Algorithms, pruning, data mining, hadoop cluster, map reduce

DOI: 10.3233/JIFS-232048

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6161-6177, 2023

Authors: Zhang, Hang | Liu, Yongli | Chao, Hao

Article Type: Research Article

Abstract: The density peak clustering algorithm (DPC) quickly divides each cluster based on high-density peak points and shows better clustering performance. In order to address the issue that the local density is constrained by the preset cut-off distance in DPC and the Euclidean distance cannot capture the possible correlation between different features, a DPC algorithm based on improved dung beetle optimization (IDBO) and Mahalanobis metric is proposed, called IDBO-MDDPC. The IDBO algorithm enhances the ball dung beetle individual by incorporating nonlinear dynamic factors to increase the search and development capabilities of the algorithm and by incorporating an adaptive cosine wave inertial …weight strategy to more precisely determine the optimal position of the thief dung beetle in order to improve the convergence speed and accuracy of the algorithm. The IDBO algorithm is simulated on eight benchmark functions, and the results demonstrate that it is superior to other comparison algorithms in terms of convergence speed and accuracy. In the DPC algorithm, the Mahalanobis metric is used to capture the correlation between features to improve clustering performance. The IDBO algorithm is integrated with the DPC algorithm, and the F-Measure evaluation index is used to design the objective function so that the optimal value of the cut-off distance can be automatically selected. In order to evaluate the efficiency of the algorithm, three sets of artificially synthesized datasets and five sets of UCI standard datasets were chosen for studies. Experimental results show that the IDBO-MDDPC algorithm can automatically determine a better cut-off distance value and ensure higher clustering accuracy. Show more

Keywords: Density peak clustering, nonlinear dynamic factor, adaptive cosine wave inertia weight, mahalanobis metric

DOI: 10.3233/JIFS-232334

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6179-6191, 2023

Authors: Cheng, Chen | Li, Bixin | Chen, Dong

Article Type: Research Article

Abstract: Intelligent Traffic Management System (ITMS) is a complex and intelligent cyber-physical system (CPS) with multi-subsystem interaction, which plays a significant role in traffic safety. However, the quality evaluation requirements of ITMS, particularly its running quality, cannot be satisfied by the current quality evaluation metrics. Moreover, the present ITMS evaluation techniques are arbitrary. The effectiveness of road traffic is impacted because ITMS quality cannot be adequately assured. To fill this gap, this paper proposes a quality evaluation (QE) methodology based on the ITMS business data flow. First, the ITMS QE dimension extraction process was introduced to describe the ITMS architecture and …activities; then the new evaluation indexes including intelligence, complexity and interactivity were proposed and an ITMS QE model was established; further through the measurement of metrics elements, the quality score of the indicators were calculated; finally a prototype tool was developed to verify the efficacy and practicability of the method. The results showed that the proposed method has the advantages of accurate problem tracking and decrease decision-making uncertainty. This is applicable to the ITMS QE in various operational scenarios. Show more

Keywords: Intelligent traffic management system, complex system, multi-system interaction, quality evaluation

DOI: 10.3233/JIFS-230182

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6193-6208, 2023

Authors: Saini, Monika | Maan, Vijay Singh | Kumar, Ashish | Saini, Dinesh Kumar

Article Type: Research Article

Abstract: Cloud infrastructure provides a real time computing environment to customers and had wide applicability in healthcare, medical facilities, business, and several other areas. Most of the health data recorded and saved on the cloud. But the cloud infrastructure is configured using several components and that makes it a complex structure. And the high value of availability and reliability is essential for satisfactory operation of such systems. So, the present study is conducted with the prominent objective of assessing the optimum availability of the cloud infrastructure. For this purpose, a novel stochastic model is proposed and optimized using dragonfly algorithm (DA) …and Grey Wolf optimization (GWO) algorithms. The Markovian approach is employed to develop the Chapman-Kolmogorov differential difference equations associate with the system. It is considered that all failure and repair rates are exponentially distributed. The repairs are perfect. The numerical results are derived to highlight the importance of the study and identify the best algorithm. The system attains its optimum availability 0.9998649 at population size 120 with iteration 700 by GWO. It is revealed that grey wolf optimization algorithm performed better than the Dragonfly algorithm in assessing the availability, best fitted parametric values and execution time. Show more

Keywords: Availability, cloud infrastructure, dragonfly algorithm, grey wolf optimization algorithm, markov process

DOI: 10.3233/JIFS-231513

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6209-6227, 2023

Authors: Liao, Yi | Ning, Kuangfeng

Article Type: Research Article

Abstract: Multi-source online transfer learning uses the tagged data from multiple source domains to enhance the classification performance of the target domain. For unbalanced data sets, a multi-source online transfer learning algorithm that can oversample in the feature spaces of the source domain and the target domain is proposed. The algorithm consists of two parts: oversampling multiple source domains and oversampling online target domains. In the oversampling phase of the source domain, oversampling is performed in the feature space of the support vector machine (SVM) to generate minority samples. New samples are obtained by amplifying the original Gram matrix through neighborhood …information in the source domain feature space. In the oversampling phase of the online target domain, minority samples from the current batch search for k-nearest neighbors in the feature space from multiple batches that have already arrived, and use the generated new samples and the original samples in the current batch to train the target domain function together. The samples from the source domain and the target domain are mapped to the same feature space through the kernel function for oversampling, and the corresponding decision function is trained using the data from the source domain and the target domain with relatively balanced class distribution, so as to improve the overall performance of the algorithm. Comprehensive experiments were conducted on four real datasets, and compared to other baseline algorithms on the Office Home dataset, the accuracy improved by 0.0311 and the G-mean value improved by 0.0702. Show more

Keywords: Multi-source transfer learning, online learning, imbalanced data, support vector machine (SVM), k-nearest neighbor

DOI: 10.3233/JIFS-232627

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6229-6245, 2023

Authors: Zhao, Zhengwei | Yang, Genteng | Li, Zhaowen | Yu, Guangji

Article Type: Research Article

Abstract: Outlier detection is an important topic in data mining. An information system (IS) is a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. People often encounter missing values during data processing. A RVIS with the miss values is an incomplete real-valued information system (IRVIS). Due to the presence of the missing values, the distance between two information values is difficult to determine, so the existing outlier detection rarely considered an IS with the miss values. This paper investigates outlier detection for an IRVIS via rough set …theory and granular computing. Firstly, the distance between two information values on each attribute of an IRVIS is introduced, and the parameter λ to control the distance is given. Then, the tolerance relation on the object set is defined according to the distance, and the tolerance class is obtained, which is regarded as an information granule. After then, λ-lower and λ-upper approximations in an IRVIS are put forward. Next, the outlier factor of every object in an IRVIS is presented. Finally, outlier detection method for IRVIS via rough set theory and granular computing is proposed, and the corresponding algorithms is designed. Through the experiments, the proposed method is compared with other methods. The experimental results show that the designed algorithm is more effective than some existing algorithms in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the proposed method. Show more

Keywords: RST, GrC, IRVIS, outlier detection, outlier factor

DOI: 10.3233/JIFS-230737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6247-6271, 2023

Authors: Fan, Jianping | Chai, Mingxuan | Wu, Meiqin

Article Type: Research Article

Abstract: The competition in the new energy vehicle industry has intensified with the rapid development of the industry. In order to create innovative products, many businesses are now seeking cooperation with their supply chain members. Previous research on the new energy vehicle supply chain has mainly focused on government policies, supply chain retailers and with consumer gaming issues. This manuscript examines the problem of cooperation decisions between members of the new energy vehicle supply chain, namely a battery manufacturer and vehicle producer. The benefits of the two members are analyzed by constructing two models, one with non-incentives and the other with …government incentives. The model uses the triangular fuzzy number (TFN) instead of parameters in numerical calculations, taking complete account of the influence of uncertain environmental factors and using the triangular structured element method. The numerical examples result that government incentives positively promote cooperation between the two players, but the incentives should be as equal as possible. Finally, we aim to encourage supply chain members to cooperate and promote the development of the new energy vehicle industry. This study has positive implications for future supply chain member cooperation issues. Show more

Keywords: Energy vehicle supply chain, triangular fuzzy number (TFN), nash equilibrium, triangular structured element method

DOI: 10.3233/JIFS-231521

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6273-6287, 2023

Authors: Princy Magdaline, P. | Ganesh Babu, T.R.

Article Type: Research Article

Abstract:  Computed tomography (CT) scan pictures are routinely employed in the automatic identification and classification of lung cancer. The texture distribution of lung nodules can vary widely over the CT scan space and requires accurate detection. The evaluation of discriminative information in this volume can tremendously aid the classification process. A convolutional neural network, the Attention Gate Residual U-Net model, and KNN classifiers are utilized to detect lung cancer. The dataset of 1097 computed tomography (CT) images utilized in this study was obtained from the Iraq-Oncology Teaching Hospital/National Centre for Cancer Diseases (IQ-OTH/NCCD) to segment and classify lung tumors from CT …images using the novel Attention Gate Residual U-Net model, i.e., AGResU-Net and CNN architecture. The initial step is applying CNN to detect normal, benign, and malignant patients in CT images. Second, use AGResU-Net to partition lung tumour areas. In the third section of the project, a KNN classifier is used to determine if an instance is malignant or benign. In the initial phase, CNN was proposed to classify three distinct regions. Three optimization strategies are used in this work: Adam, RMSP, and SGDM. The classifier’s accuracy is 97%, 85%, and 82%, respectively. When compared to the RMSP optimizer, the Adams optimizer predicts probability rates more accurately. In the second phase, AGResU-Net is used for schematic segmentation of the tumor region. In the third phase, a KNN classifier is used to classify benign and malignant tumor from the segmented tumor regions. A new segmentation of the lung tumor model is proposed. In this developed algorithm, the labelled classified data set and the segmented tumor output result provide the same accuracy. The study results demonstrate high tumour classification accuracy and high probability of detection in benign and malignant cases. Show more

Keywords: Lung cancer, CT images, convolutional neural network, AGResU-Net, KNN

DOI: 10.3233/JIFS-233787

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6289-6302, 2023

Authors: Rafikiran, Shaik. | Devadasu, G. | Rajendhar, P. | Likhitha, R. | Basha, CH Hussaian

Article Type: Research Article

Abstract: The fuel cell-dependent electric vehicle systems are giving an important role in the present automotive systems because their features are less air pollution, high flexibility, reduced oil dependency, and more reliability. However, the fuel stack delivers nonlinear output V-I characteristics. So, the extraction of peak power from the fuel source is very difficult. In this work, a Variable Step Size Radial Basis Functional Network-based Adaptive Fuzzy Logic Controller (VSSDE-AFLC) is proposed for tracking the peak power point of the fuel cell system. The merits of the proposed Maximum Power Point Tracking (MPPT) controller are high tracing speed of functioning point …of the fuel cell, more flexibility, high abundant, acceptable oscillations across MPP, and less dependency on modeling of the fuel stack. Also, the single switch converter is utilized for increasing the voltage supply of the fuel cell. The features of the proposed converter are wide input operation, less voltage stress, high supply voltage conversion ratio, and good dynamic response. The proposed fuel cell-dependent boost converter is implemented by utilizing the MATLAB/Simulink software, and the converter is tested successfully by using the desired programmable DC supply. Show more

Keywords: Boost converter, conversion ratio, duty cycle, fast tracing speed, high voltage gain, fewer voltage ripples, and fast dynamic response

DOI: 10.3233/JIFS-224007

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6303-6321, 2023

Authors: Shakunthala, M. | HelenPrabha, K.

Article Type: Research Article

Abstract: Stroke is a type of cerebrovascular disorder that has a significant impact on people’s lives and well-being. Quantitative investigation of MRI imaging of the brain plays a critical role in analyzing and identifying therapy for stroke. A block primarily provokes stroke in the brain’s blood supply. Deep learning algorithms can be used to identify strokes in patients in a short period. Proposed deep learning methods are used to classify strokes using magnetic resonance imaging (MRI) images. Early detection enhances treatment opportunities and saves lives, which is the primary motivation of the proposed work. Deep learning methods have emerged as significant …research trends in recent years, particularly for classifying different types of stroke such as ischemic and hemorrhagic stroke. A dataset of 13,850 MRI images of stroke patients was collected from various reliable sources, including Madras scans and labs, Radiopaedia, Kaggle datasets, and online databases. Among these images, 7,810 were identified as cases of ischemic stroke, while 6,040 represented hemorrhagic strokes. For training purposes, a total of 9,700 images were used, with 4,150 images employed for testing. A comparative analysis of ANN, SVM, NB, ELM, KNN and Enhanced CNN technique is carried out, and 98.4% of classification accuracy is obtained by using Enhanced CNN. Statistical analysis of parameters such as accuracy, precision, F1-score, and recall was conducted, demonstrating that the Enhanced CNN method outperformed SVM, NB,ELM, KNN and ANN classifiers. The Enhanced CNN method achieved an accuracy of 0.984, precision of 0.949, recall of 0.972, and an F1-score of 0.960 on the training dataset, which is significantly higher than the other classifiers. Furthermore, the Enhanced CNN algorithm’s ability to automatically learn features and efficiently process large datasets enhances its potential as a powerful tool for accurately classifying stroke lesions. Show more

Keywords: Magnetic Resonance Imaging (MRI), Enhanced-CNN, hemorrhagic stroke, ischemic stroke, deep learning

DOI: 10.3233/JIFS-230024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6323-6338, 2023

Authors: Al-shami, Tareq M. | Hosny, Rodyna A. | Abu-Gdairi, Radwan | Arar, Murad

Article Type: Research Article

Abstract: Our target in the present work, is presenting the idea of weakly soft preopen (ws -preopen) subsets and studying some of its characterizations. With the assistance of some elucidative examples, the interrelationships between ws -preopen sets and some extensions of soft open sets are studied. Under some conditions such as extended and hyperconnected soft topologies, several motivating results and relationships are acquired. The interior and closure operators that built through ws -preopen and ws -preclosed subsets are introduced. Their main features that construe the relations among them are established. Soft continuity with respect to theses classes of soft subsets are …studied and their substantial characteristics are investigated. Generally, the systematic relations and outcomes that are lost through the scope of this study are discussed. The proposed line in the current study will present new ways to discover novel concepts in the field of soft topology. Show more

Keywords: ws-preopen set, extended soft topology, ws-preinterior, ws-preclosure, ws-precontinuous function

DOI: 10.3233/JIFS-230191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6339-6350, 2023

Authors: Al-shami, Tareq M. | Arar, Murad | Abu-Gdairi, Radwan | Ameen, Zanyar A.

Article Type: Research Article

Abstract: This work introduces weakly soft β-open subsets, a new family of soft-open sets. By this family, we expand a soft topology to a soft structure which is neither supra-soft topology nor infra-soft topology. The connections between this class of soft sets and other celebrated classes via soft topology are examined with some elucidative examples. Also, it is established some relationships under conditions of extended and hyperconnected soft topologies. Furthermore, the interior and closure operators are structured along with weakly soft β-open and weakly soft β-closed sets. Finally, the class of weakly soft β-continuous functions is introduced and its main characterizations …are studied. It is investigated the systematic relationships and findings that are lost for this kind of soft continuity as well as it is shown the conditions required to maintain some of these relationships such as full, extended and hyperconnected soft topologies. Show more

Keywords: Extended soft topology, weakly soft β-open set, β-closure, weakly soft β-interior, and weakly soft β-continuous

DOI: 10.3233/JIFS-230858

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6351-6363, 2023

Authors: Tang, Zhong

Article Type: Research Article

Abstract: Architectural aesthetics improve the appearance and value of a building/construction structure based on shape, color, rigidity, etc., appealingly. It includes the maximum safety requirements, durability, structural ability, etc. Therefore the aesthetic implementation requires high-level data accumulation and analysis to satisfy the earlier constraints. This article develops a Selective Aesthetic Application Paradigm (SAAP) for meeting the user criteria in structural design for region-specific adaptability. The proposed paradigm gathers information on the region, people’s expectations, visibility, and structural performance for the aesthetic design application. The proportion considerations in the application are subject to vary according to the region’s adaptability and performance. The …proportion of the accumulated data influence in the application is determined using deep learning. In the learning paradigm, two-layered configurations for region-adaptability and performance measures are trained to provide aesthetic design application recommendations. Based on the suggestion and recommendation, the deep learning module is trained to rectify design errors. The training is independent of the previous two error and adaptability verification layers. It is performed using the qualified (selected) aesthetic design with a previous history of user satisfaction. Show more

Keywords: Architectural aesthetics, data analysis, deep learning, error detection

DOI: 10.3233/JIFS-231076

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6365-6379, 2023

Authors: Mohamed, Mohamed S. | Elzayady, Hossam | Badran, Khaled M. | Salama, Gouda I.

Article Type: Research Article

Abstract: The use of hateful language in public debates and forums is becoming more common. However, this might result in antagonism and conflicts among individuals, which is undesirable in an online environment. Countries, businesses, and educational institutions are exerting their greatest efforts to develop effective solutions to manage this issue. In addition, recognizing such content is difficult, particularly in Arabic, due to a variety of challenges and constraints. Long-tailed data distribution is often one of the most significant issues in actual Arabic hate speech datasets. Pre-trained models, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformers (GPT), have …become more popular in numerous natural language processing (NLP) applications in recent years. We conduct extensive experiments to address data imbalance issues by utilizing oversampling methods and a focal loss function in addition to traditional loss functions. Quasi-recurrent neural networks (QRNN) are employed to fine-tune the cutting-edge transformer-based models, MARBERTv2, MARBERTv1, and ARBERT. In this context, we suggest a new approach using ensemble learning that incorporates best-performing models for both original and oversampled datasets. Experiments proved that our proposed approach achieves superior performance compared to the most advanced methods described in the literature. Show more

Keywords: Text classification, Arabic hate speech, oversampling method, transformers, ensemble learning

DOI: 10.3233/JIFS-231151

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6381-6390, 2023

Authors: Bergamini, Mariane Gavioli | Oliveira, Gustavo H.C. | Ribeiro, Eduardo P. | Leandro, Gideon Villar

Article Type: Research Article

Abstract: Accurate modeling of electric power generating unit and its hydraulic turbine regulation systems provides support for the speed controller synthesis and stability analysis. It is however a difficult task due to the presence of many non-linear factors in this system. an approach to estimate the parameters of hydraulic turbine regulatory system models is to derive the physical representation of each component and, through simulation, to compare to compare their models, outputs with real data obtained from a hydroelectric plant located in Brazil. The objective of this paper is to find the best values that will represent the system under study …as a whole. This problem can be seen as an optimization problem. To find its feasible and optimal solution, this work proposes a new metaheuristics multi-objective based on the Lion Algorithm (LA), called the Multi-Objective Lion Algorithm (MOLA), and its application in the estimation of parameters of the system under study. In addition, the new metaheuristic proposed is validated by using a set of benchmark cases. The results have demonstrated that MOLA outperforms or at least performs similarly to Multi-objective Grey Wolf Optimizer (MOGWO), Multiple Objective Particle Swarm Optimization (MOPSO), Multi-objective Salp Swarm Algorithm (MSSA), Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Non-dominated Sorting Genetic Algorithm III (NSGA-III) in the optimization of multi-objective benchmark functions. These results, suggest that the proposed MOLA algorithm works efficiently. Show more

Keywords: Parameter estimation, hydraulic turbine regulator system, multi-objective optimization, metaheuristics, multi-objective lion optimization algorithm

DOI: 10.3233/JIFS-232155

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6391-6412, 2023

Authors: Zheng, Yunchao

Article Type: Research Article

Abstract: Traditional Chinese art is vast and profound, with various colors having rich meanings. The combination of colors can vividly and intuitively represent various characteristics of things. Fully reflecting the characteristics of traditional Chinese folk art in graphic design can achieve extremely strong expressive effects. In current graphic design, the artistic colors of traditional Chinese folk art have not yet been fully displayed, and there is a lack of understanding of the profound connotation of traditional Chinese art. The graphic design industry has a very broad development space. The comprehensive evaluation of graphic design effects based on color psychology is a …classical multiple attribute group decision making (MAGDM) problems. In this work, we shall present some novel Dice similarity measures (DSM) of T-spherical fuzzy sets(T-SFSs) and the generalized Dice similarity measures (GDSM) of and indicates that the DSM and asymmetric measures (projection measures) are the special cases of the GDSM in some parameter values. Then, we propose the GDSM-based MAGDM models with T-SFSs. Then, we apply the GDSMs between T-SFSs to MAGDM. Finally, an illustrative example for comprehensive evaluation of graphic design effects based on color psychology is given to demonstrate the efficiency of the GDSMs. The main contributions of this paper are summarized: (1) some novel Dice similarity measures (DSM) and the generalized Dice similarity measures (GDSMs) of T-spherical fuzzy sets(T-SFSs) are proposed; (2) The weighted Dice similarity measures (WDSM) and the weighted generalized Dice similarity measures (WGDSMs) of T-spherical fuzzy sets(T-SFSs) are proposed to solve the MAGDM; (3) an illustrative example for comprehensive evaluation of graphic design effects based on color psychology is given to demonstrate the efficiency of the WGDSM; (4) Some comparative analysis are used to show the effectiveness of the proposed Dice similarity measures. Show more

Keywords: Multiple attribute group decision making, Dice similarity measures (DSMs), generalized Dice similarity measures (GDSMs), T-spherical fuzzy sets, graphic design effects

DOI: 10.3233/JIFS-232296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6413-6427, 2023

Authors: Ramachandran, L. | Mohan, V. | Senthilkumar, S. | Ganesh, J.

Article Type: Research Article

Abstract: White Spot Syndrome Virus (WSSV) is a major virus found in shrimp that causes huge economic loss in shrimp farms. A selective diagnostic approach for WSSV is required for the early diagnosis and protection of farms. This work proposes a novel recognition method based on improved Convolutional Neural Network (CNN) namely Dense Inception Convolutional Neural Network (DICNN) for diagnoses of WSSV disease. Initially, the process of data acquisition and data augmentation is carried out. The Inception structure is then used to improve the performance of multi-dimensional feature extraction. As a result, the proposed work has the highest accuracy of 97.22% …when compared to other traditional models. The proposed work is targeted to Litopenaeus Vannamei (LV), and Penaeus Monodon (PM) diversities for major threats detection of White Spot Syndrome (WSS). Performance metrics related to accuracy have been compared with other traditional models, which demonstrate that our model will efficiently recognize shrimp WSSV disease. Show more

Keywords: Convolutional neural networks, disease identification, image augmentation, white spot syndrome virus

DOI: 10.3233/JIFS-232687

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6429-6440, 2023

Authors: Krishna Veni, K.S. | Senthil Kumar, N. | Srinivas, R.

Article Type: Research Article

Abstract: In the electrical energy transmission and distribution sector, power transformers play an important role. Early fault diagnosis and prognosis are essential to ensure continuous operation and also to prepare a proper maintenance schedule based on the requirements. The occurrence of a fault in the transformer will lead to the formation of various gases inside the transformer tank. For fault diagnosis in the transformer, Dissolved Gas Analysis (DGA) is an excellent method. An Artificial Intelligence (AI) based fault diagnosis and prognosis system using dissolved gases in transformer oil is helpful to predict the health state of the transformer well in advance. …Hence, based on the fault severity level, the remaining useful life of the transformer, fault type and current state of the transformer can be estimated effectively by imparting AI to the existing system. A Two-Tier Fuzzy Logic Controller (TTFLC) is proposed in this article to find the type of fault and health index (HI) of the transformer. For further fault prognosis, an effective Gated Recurrent Network (GRN) based deep learning enabled future learning estimator is used for predicting the Criticality Index (CI) of the Transformer. The performance of the proposed method is evaluated for both data from the IEEE data set and expert data collected from the southern Tamil Nadu region. The proposed system shows better results even in multivariate, complex process systems. The diagnosis accuracy of the proposed system is obtained as 95.28% and it compared with conventional methods such as Rogers Ratio Method (RRM), Duval Triangle Method (DTM) and Duval Pentagon Method (DPM) and other AI based methods such as Radial Basis Neural Network (RBNN), k-nearest neighbors (KNN). The diagnosis accuracy of other conventional and AI based methods are less than 90% for the collected dataset. Show more

Keywords: Transformer, dissolved gas analysis, two tier fuzzy logic controller, fault diagnosis, fault prognosis, gated recurrent network, health index, criticality index

DOI: 10.3233/JIFS-223592

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6441-6452, 2023

Authors: Du, Kang | Fan, Ruguo | Xue, Hu | Wang, Yitong | Bao, Xuguang

Article Type: Research Article

Abstract: The mechanism of promoting cooperation in the public goods game has always been concerned by scholars. However, most of the existing studies are based on the premise that participants are self-interested. In order to explore why some sellers on e-commerce platforms voluntarily maintain the platform’s reputation, we incorporate heterogeneous social preferences of sellers into the spatial public goods game. We find that heterogeneous social preferences can enhance cooperation by improving collective rationality. Specifically, the altruistic preference of sellers can greatly reduce free-riding behavior, while the inequality aversion preference has a little inhibitory effect. Interestingly, when the benefit of maintaining the …platform’s reputation is relatively high, the reciprocal preference can inhibit cooperation, but it can promote cooperation when the benefit is relatively small. This is due to the existence of some loosely connected but stable cooperative or defective clusters of sellers in e-commerce platforms. Furthermore, we propose a dynamic punishment mechanism to punish free riders. We observe that the dynamic punishment mechanism is more effective than the static punishment mechanism in solving the second-order free-riding problem faced by punishers. Increasing the enhancement factor of public goods is identified as a fundamental approach to mitigating this problem. Show more

Keywords: E-commerce platform, altruism, inequality aversion, reciprocity, spatial public goods game

DOI: 10.3233/JIFS-232322

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6453-6467, 2023

Authors: Thao, Le Quang | Diep, Nguyen Thi Bich | Bach, Ngo Chi | Linh, Le Khanh | Giang, Nguyen Do Hoang

Article Type: Research Article

Abstract: In this study, we introduce a new method to address the pressing issue of school violence using Artificial Intelligence (AI). School violence is a critical issue that affects the safety and well-being of students, teachers, and the school community as a whole. Violent behaviors, such as bullying, physical assaults, and weapon use, can have long-term effects on students’ psychological health and academic performance. To reduce these issues, we developed a lightweight Deep Learning model that can be integrated into a school’s surveillance camera system to quickly detect violent fighting behaviors for timely intervention by school staff. The proposed FightNet model …consists of three components: MobileNetV2 backbone, Feature Pyramid Network (FPN) neck, and Centernet Object as a Point (COaP) head. By optimizing the hyperparameters of the model to extract keypoints in image frames from the COCO dataset, we applied an LSTM model to determine the temporal dependence of actions and classify them as “fighting” or “normal” using the UBI-Fights dataset. The FightNet model achieved mAP@0.5 of 45.34% and mAP@0.95 of 55.89% in estimating keypoints, and 72.68% accuracy and 71.69% F1-score in predicting actions. Based on these results, we conclude that the proposed model can effectively address the issue of school violence. Show more

Keywords: School fighting violence, multi-keypoints, FightNet, light-weight model, LSTM

DOI: 10.3233/JIFS-232480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6469-6483, 2023

Authors: Javeed, M.D. | Nagaraju, Regonda | Chandrasekaran, Raja | Rajulu, Govinda | Tumuluru, Praveen | Ramesh, M. | Suman, Sanjay Kumar | Shrivastava, Rajeev

Article Type: Research Article

Abstract: The process of partitioning into different objects of an image is segmentation. In different major fields like face tracking, Satellite, Object Identification, Remote Sensing and majorly in medical field segmentation process is very important to find the different objects in the image. To investigate the functions and processes of human boy in radiology magnetic resonance imaging (MRI) will be used. MRI technique is using in many hospitals for the diagnosis purpose widely in finding the stage of a particular disease. In this paper, we proposed a new method for detecting the tumor with enhanced performance over traditional techniques such as …K-Means Clustering, fuzzy c means (FCM). Different research methods have been proposed by researchers to detect the tumor in brain. To classify normal and abnormal form of brain, a system for screening is discussed in this paper which is developed with a framework of artificial intelligence with deep learning probabilistic neural networks by focusing on hybrid clustering for segmentation on brain image and crystal contrast enhancement. Feature’s extraction and classification are included in the developing process. Performance in Simulation of proposed design has shown the superior results than the traditional methods. Show more

Keywords: Segmentation, brain tumor, probabilistic neural networks, feature extraction, classification

DOI: 10.3233/JIFS-232493

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6485-6500, 2023

Authors: Zhan, Huawei | Pei, Xinyu | Zhang, Tianhao | Zhang, Linqing

Article Type: Research Article

Abstract: A flame detection algorithm based on the improved SSD (Single Shot Multibox Detector) is proposed in response to the issues with the limited detection distance, delayed reaction, and high false alarm rate of previous flame detection systems. First, the ResNet-50-SPD model was added to the original backbone network to improve the detection of low resolution and tiny objects. After that, incorporate feature fusion between layers to improve the bond between contexts. Before the feature entered the prediction, the impact of channel number reduction was eliminated using the adaptive module AAM. According to experimental findings, the modified SSD algorithm’s mAP value …on on the random division dataset and K-fold verification dataset reaches 87.89% and 89.63%, respectively, which is 3.97% and 5.17% higher than the original SSD, while the FPS remains at 64.9 f/s. It is helpful to improve the time of the fire alarm, find the ignition point in time, and better meet the actual engineering needs of fire monitoring. Show more

Keywords: Flame detection, SSD, ResNet-50-SPD, feature fusion, AAM

DOI: 10.3233/JIFS-232645

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6501-6512, 2023

Authors: Zhang, Boqiang | Gao, Tianzhi | Chen, Yanbin | Jin, Xin | Feng, Tianpei | Chen, Xinming

Article Type: Research Article

Abstract: A large number of grain machinery and vehicle equipment are usually required in the raw grain storage phase, and these objects together form the path planning map environment for the unmanned grain transfer vehicle. After using LiDAR to build a map of the environment for path planning, these dense and cluttered obstacles tend to affect the path planning effect making the unmanned transfer vehicle create a crossing from the impenetrable dense obstacles. To address this problem, this paper firstly deals with obstacles by fusing the DBSCAN clustering algorithm and K-means clustering algorithm, clustering obstacles, and extracting the cluster centroid and …boundary points of each obstacle class to avoid the above situation. Secondly, the specific A* algorithm is improved, the search field way of the A* algorithm is optimized, and the optimized 5×5 field search way is used instead of the traditional 3×3 field search way of A* to improve the node search efficiency of the algorithm. Finally, the repulsion function of the artificial potential field algorithm is added to the A* heuristic function as a safety function to increase the obstacle avoidance capability of the A* algorithm. After verification, the improvement can operate better in the dense and cluttered obstacle environment. Show more

Keywords: Grain depot, food logistics, clustering algorithm, A* algorithm, artificial potential field, raster map

DOI: 10.3233/JIFS-232780

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6513-6533, 2023

Authors: Xiaozhen, Zheng | Le, Xuong

Article Type: Research Article

Abstract: Carbon dioxide is produced during the manufacture of normal Portland cement; however, this gas may be minimized by utilizing ground granulated blast furnace slag (GGBFS ). When planning and constructing concrete buildings, compressive strength (f c ), a crucial component of concrete mixtures, is a need. It is essential to assess this GGBFS -blended concrete property precisely and consistently. The major objective of this research is to provide a practical approach for a comprehensive evaluation of machine learning algorithms in predicting the f c of concrete containing GGBFS . The research used the Equilibrium optimizer (EO ) …to enhance and accelerate the performance of the radial basis function (RBF ) network (REO ) and support vector regression (SVR ) (SEO ) analytical methodologies. The novelty of this work is particularly attributed to the application of the EO , the assessment of f c including GGBFS , the comparison with other studies, and the use of a huge dataset with several input components. The combined SEO and REO systems demonstrated proficient estimation abilities, as evidenced by coefficient of determination (R 2 ) values of 0.9946 and 0.9952 for the SEO ’s training and testing components and 0.9857 and 0.9914 for the REO , respectively. The research identifies the SVR optimized with the EO algorithm as the most successful system for predicting the f c of GGBFS concrete. This finding has practical implications for the construction industry, as it offers a reliable method for estimating concrete properties and optimizing concrete mixtures. Show more

Keywords: Compressive strength, ground granulated blast furnace slag, prediction, equilibrium optimizer, support vector regression

DOI: 10.3233/JIFS-233428

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6535-6547, 2023

Authors: Umaamaheshvari, A. | Sivasankari, K. | Suguna, N. | Kshirsagar, Pravin R. | Tirth, Vineet | Rajaram, A.

Article Type: Research Article

Abstract: The optimization algorithms mimic the process of natural evolution. In watermarking, appropriate positions to insert the watermark is identified by the image that covers. These positions represent the populations of genetic algorithms. The major drawback in genetic algorithm are that it may get stuck-up at a local optimum while moving towards the best global solution and hence the result is poor when compared to other local optimization techniques. The proposed work based on Bandelet based biogeography firefly hybrid algorithms. The Number of pixels, Intensity of the pixel and contrast are considered for watermarking. The redundancy is reduced by Bandelet and …used to determine the best location to embed the information into an image both locally and globally. Results of these techniques are compared based on coefficient correlation, index structural similarity, and noise ratio from peak signal. Show more

Keywords: Biogeography firefly algorithm, genetic algorithm, optimization, peak signal to noise ratio

DOI: 10.3233/JIFS-224590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6549-6559, 2023

Authors: Birong, Zhang

Article Type: Research Article

Abstract: In this paper, a bi-objective mixed-integer linear programming model is constructed to manage the pharmaceutical supply chain of a hospital. The proposed model aims to concurrently reduce the overall cost of obtaining drugs from several vendors and choose the best suitable source. The suggested model takes into account supplier distance, inventory management, and multi-product and multi-period. The major assumptions of the proposed model are product storage for future periods of decreased demand and supplier capacity. The results indicate that the ideal approach can minimize hospital supply and pharmaceutical planning expenses. The Best-Worst and TOPSIS methods determine which pharmaceutical supplier should …be selected for future orders. The suggested model identifies human resource capability as an essential factor that might significantly affect the system’s total cost. The results of applying the model and the sensitivity analysis validate the efficacy and validity of the suggested mathematical model and solution strategy. Show more

Keywords: Optimization, pharma supply chain, uncertainty, robust programming

DOI: 10.3233/JIFS-230017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6561-6574, 2023

Authors: Arulselvan, G. | Rajaram, A.

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-231905

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6575-6590, 2023

Authors: Xiao, Huimin | Gao, Xiaosong | Yang, Peng | Wei, Meng

Article Type: Research Article

Abstract: In the face of multi-attribute decision problems in complex situations, most traditional multi-attribute group decision methods are based on the assumption that the decision maker is perfectly rational, while in the face of complex decision problems, the decision maker usually has the psychological characteristics of limited rationality and may use more than one linguistic term to describe the decision information when expressing the decision information To this end, this paper selects probabilistic language term sets to describe complex preference information. First, to address the problem that the current probabilistic linguistic term set correlation coefficient cannot appropriately measure the degree of …correlation among probabilistic linguistic term sets, this paper proposes a new probabilistic linguistic term set correlation coefficient from three characteristic factors of probabilistic linguistic term sets: mean, variance, and length rate. To integrate the attribute index weights, probabilistic linguistic term set weighted mixed correlation coefficients are proposed. Second, this paper introduces the TODIM method, which can consider the psychological behavior of decision makers, and proposes a TODIM multi-attribute decision making method based on probabilistic linguistic term sets with mixed correlation coefficients. Finally, through an empirical analysis of four Internet listed companies in a new first-tier city in China, this study verifies the rationality and validity of the proposed method. The results show that the mixed correlation coefficient can comprehensively measure the correlation between probabilistic linguistic term sets, which provides an important method for future multi-attribute decision making problems. Show more

Keywords: Multi-attribute decision making, probabilistic linguistic term sets, mixed correlation coefficient, TODIM method

DOI: 10.3233/JIFS-232042

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6591-6604, 2023

Authors: Suresh Babu, D. | Ramakrishnan, M.

Article Type: Research Article

Abstract: A severe problem that regularly affects cloud systems are intrusions. Ignore how the expansion of Internet of Things (IoT) devices will result in enormous intrusions. To distinguish intrusions from authorized network activity, detection is a crucial procedure. An Enhanced Lion Optimization Algorithm (ELOA) is utilized in this research, IoT intrusion detection system. Intrusions are classified using the Deep Belief Network (DBN) and an SDN controller technique. The proposed ELOA-based Intrusion Detection System uses the optimal weight in DBN to train the neurons to categorize the data in a network as normal and attacked during the training phase. In the testing …step that follows training, data from nodes are examined, and by contrasting the training results, they are categorized as normal and attacked data. By using the proposed ELOA and DBN algorithms, our intrusion detection system can successfully identify intrusions. Based on the creation of blacklists for detecting IoT intrusions, the (SDN) Software Defined Networking controller can effectively prohibit harmful devices. In order to demonstrate that the proposed ELOA finds network intrusions more successfully, its performance is compared to that of other existing techniques. The node sizes of the algorithms are run and evaluated for 1000, 2000, 3000, 4000, and 5000 respectively. At highest node 5000, the Proposed ELOA and DPN have precision, recall, f-score and accuracy becomes as 97.8, 96.22, 97.5 and 98.67 respectively. Show more

Keywords: Internet of Things, intrusion detection, Enhanced Lion Optimization Algorithm, deep belief network, SDN controller

DOI: 10.3233/JIFS-232532

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6605-6615, 2023

Authors: Suganya, S. | Selvamuthukumaran, S.

Article Type: Research Article

Abstract: Hadoop is a big data processing system that enables the distributed processing of massive data sets across multiple computers using straightforward programming techniques. Hadoop has been extensively investigated in many attacks as a result of its growing significance in industry. A company may learn about the actions of invaders as well as the weaknesses of the Hadoop cluster by examining a significant quantity of data from the log file. In a Big Data setting, the goal of the paper is to generate an analytical classification for intrusion detection. In this study, Hadoop log files were examined based on assaults that …were recorded in the log files. Prior to analysis, the log data is cleaned and improved using a Hadoop preprocessing tool. For feature extraction, the hybrid Improved Sparrow Search Algorithm with Mutual Information Maximization (H-ISSA-MIM). Then the CNN (Convolutional Neural Network) classifier will detect the intrusions. The implementation is performed using the MATLAB 2020a software. The performance metrics like accuracy, precision, F-score, recall, specificity, FPR, FNR are calculated for the proposed methodology and it is compared with the existing techniques like Decision Tree (DT), Principal Components Analysis (PCA)- K means, Long Short Time Memory (LSTM). The maximum value of accuracy finds out in the proposed method 98% . Show more

Keywords: Hadoop attacks, log file, intrusion detection, big data environment and feature extraction, convolutional neural networks

DOI: 10.3233/JIFS-233579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6617-6628, 2023

Authors: Baqer, Ihsan A. | Jaber, Alaa Abdulhady | Soud, Wafa A.

Article Type: Research Article

Abstract: Belt drive contamination is considered one of the most common failure modes that could be developed in the belts due to harsh operation conditions, high humidity, and sunlight exposure, reducing the belt’s performance. If the belt failure has not been detected early, a sudden shutdown may happen, producing safety and economic consequences. However, most maintenance personnel use their senses of sight, hearing, smell, and touch to identify the cause of the problem while diagnosing a belt drive condition. Hence, this research involves developing an intelligent contamination status detection system based on vibration signal analysis for a pulley-belt rotating system. Time-domain …signal analysis was employed to extract some suggestive features such as the root mean square, kurtosis, and skewness from the vibration data. An artificial neural network (ANN) model was built to detect the simulated different operating conditions. The vibration data was gathered with the help of two MEMS accelerometers (ADXL335) interfaced with an NI USB-6009 data acquisition device. A signal capture, analysis, and feature extraction system was developed using Matlab Simulink. The simulated operating conditions include clean, wet, and powder-contaminated belts. The results showed that the designed system could identify the pulley-belt operation conditions with 100% overall accuracy. Show more

Keywords: Condition monitoring, fault diagnosis, preventive maintenance, time-domain signal analysis, machine learning

DOI: 10.3233/JIFS-222438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6629-6643, 2023

Authors: Lin, Tao | Chen, Biao | Wang, Ruixia | Zhang, Yabo | Shi, Yu | Jiang, Nan

Article Type: Research Article

Abstract: Vision-based Continuous Sign Language Recognition (CSLR) is a challenging and weakly supervised task aimed at segmenting sign language from weakly annotated image stream sequences for recognition. Compared with Isolated Sign Language Recognition (ISLR), the biggest challenge of this work is that the image stream sequences have ambiguous time boundaries. Recent CSLR works have shown that the visual-level sign language recognition task focuses on image stream feature extraction and feature alignment, and overfitting is the most critical problem in the CSLR training process. After investigating the advanced CSLR models in recent years, we have identified that the key to this study …is the adequate training of the feature extractor. Therefore, this paper proposes a CSLR model with Multi-state Feature Optimization (MFO), which is based on Fully Convolutional Network (FCN) and Connectionist Temporal Classification (CTC). The MFO mechanism supervises the multiple states of each Sign Gloss in the modeling process and provides more refined labels for training the CTC decoder, which can effectively solve the overfitting problem caused by training, while also significantly reducing the training cost in time. We validate the MFO method on the popular CSLR dataset and demonstrate that the model has better performance. Show more

Keywords: Continuous sign language recognition, fully convolutional network, multi-state feature optimization, connectionist temporal classification, adequate training

DOI: 10.3233/JIFS-223601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6645-6654, 2023

Authors: Wang, Libo | Zhao, Jun | Guo, Shizhong

Article Type: Research Article

Abstract: Concrete is known as one of the most important materials in the world. Concrete composites consisting of cement, water, aggregates, and some additives are used to improve the properties of concrete. These concrete have a certain compressive strength that can be increased depending on the type of concrete. In line with these ideas, high-performance concrete (HPC) has been produced, which can have high compressive strength by adding materials such as fly ash, silica fume, etc. This type of concrete is used in bridges, dams, and special constructions. However, obtaining the mixture design of HPC is problematic and complex, for this …reason, the machine learning methods can make it easy to achieve the output by saving time and energy. This study has used support vector regression (SVR) to predict the compressive strength of HPC. Moreover, this study provided two meta-heuristic algorithms for obtaining suitable and optimized results, which are contained the artificial hummingbird algorithm (AHA) and Sine Cosine Algorithm (SCA). The model by coupling with algorithms created the hybrid method in the framework of SVR-AHA and SVR-SCA. Furthermore, some criteria indicators have been used for determining the most desirable hybrid model, which is included coefficient of correlation (R2 ), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and weight absolute percentage error (WAPE). As a result, the AHA algorithm could have a more satisfying association model with the SVR model, and the results were RMSE = 2.00 (MPa), R2  = 98.59%, MAE = 0.717 (MPa), MAPE = 1.22 (MPa), and WAPE = 0.114 (MPa). Show more

Keywords: High-performance concrete, sine cosine algorithm, artificial hummingbird algorithm, support vector regression, compressive strength

DOI: 10.3233/JIFS-230132

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6655-6666, 2023

Authors: Li, Jiacheng | Wang, Jianhua | Liu, Wenjie | Gao, Shengxia | Du, Shiqiang

Article Type: Research Article

Abstract: The Dunhuang murals, notably the paintings on the interior walls of China’s Dunhuang Grottoes, are considered international cultural treasure. The Dunhuang murals were ruined to varied degrees after a lengthy period of erosion. Deep learning networks were utilized to reconstruct broken parts of murals in order to better preserve their important historical and cultural values. Due to the presence of various damages, such as large peeling, mold and scratches, and multi-scale objects in the mural, a simple porting of existing working methods is suboptimal. In this paper, we propose a progressive Dunhuang murals inpainting (PDMI) based on recurrent feature reasoning …network to progressively infer the pixel values of hole centers by a progressive approach, aiming to obtain visually reasonable and semantically consistent inpainted results. PDMI consists mainly of the FFC-based recurrent feature reasoning (RFR) module and Multi-scale Knowledge Consistent Attention (MKCA) module. The RFR module first fills in the feature value at the feature map’s hole border, then utilizes the obtained feature value as a clue for further inference. The module steadily improved the limitation of hole centers, making the inpainted results more explicit; MKCA enables feature maps in RFR to handle richer background information from distant location information in a flexible manner while preventing misuse. After several round-robin inferences provide multiple feature maps, these feature maps are fused using an adaptive feature weighted fusion mechanism, then the fused feature maps decode back to RGB image. Experiments on a publicly available dataset and a self-made Dunhuang mural dataset reveal that the proposed method outperforms the comparison algorithm in both qualitative and quantitative aspects. Show more

Keywords: Image inpainting, Dunhuang murals, progressive inpainting, feature reasoning

DOI: 10.3233/JIFS-230320

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6667-6678, 2023

Authors: Du, Jinze | Wang, Chang

Article Type: Research Article

Abstract: Based on the quaternion system, we give a new representation of the complex vague soft set, and related logical operations. This new representation contains more information than before. Three quaternion distance measures are proposed and a decision model is established. The disease diagnosis of breast cancer is applied to the model to reflect the superiority of the model. By comparing the diagnostic errors under the different distance measures, the most suitable distance measure for this dataset is selected.

Keywords: Quaternion, vague soft set, complex vague soft set

DOI: 10.3233/JIFS-231270

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6679-6690, 2023

Authors: Mythily, M. | David, Beaulah | Venkatesan, R. | Joseph, Iwin Thanakumar

Article Type: Research Article

Abstract: Emerging daily, new devices and software-driven advancements pose challenges in software development, including errors, bugs, and evolving requirements. This leads to delays in delivery. Ensuring software security within the Software Development Life Cycle (SDLC) is crucial. To address this, the research focuses on incorporating security aspects early in the SDLC through model transformation. Platform-independent models with security attributes like Integrity, Privacy, Security Audit, non-repudiation, and authentication are generated. A template-based source code generator is utilized to create the structure of the source model. The Secure Business Process Model (SBPM) encompasses Unified Modeling Language (UML) artifacts, such as analysis level classes …and sequence diagrams, enriched with security attributes derived from the activity model. Security requirements are linked to elements extracted from the source model, and structural codes with security-enabled members are produced. Automation in software development is inevitable, though not complete, as it plays a vital role in addressing these challenges and improving the security of software applications. Show more

Keywords: Index Terms: Object-oriented modeling, software design, software safety, software reusability, software tools.

DOI: 10.3233/JIFS-231359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6691-6705, 2023

Authors: Yang, Ze | Jiang, Xianliang | Jin, Guang | Bai, Jie

Article Type: Research Article

Abstract: Accurate and fast pest detection is crucial for ensuring high crop yield and quality in modern agriculture. However, there are significant challenges in using deep learning for pest detection, such as the small proportion of pest individuals in the image area, complex backgrounds in light-trapped pest images, and an unbalanced distribution of pest species. To address these problems, we propose MFSPest, a multi-scale feature selection network for detecting agricultural pests in trapping scenes. We design a novel selective kernel spatial pyramid pooling structure (SKSPP) in the feature extraction stage to enhance the network’s feature extraction ability for key regions and …reduce its focus on irrelevant background information. Furthermore, we present the equalized loss to increase the loss weights of rare categories and improve the distribution imbalance among pest categories. Finally, we build LAPD, a light-trapping agricultural pest dataset containing nine pest categories. We conducted experiments on this dataset and demonstrated that our proposed method achieves state-of-the-art performance, with Accuracy, Recall, and mean Average Precision (mAP) of 89.9%, 92.8%, and 93.6%, respectively. Our method satisfies the requirements of pest detection applications in practical scenarios and has practical value and economic benefits for use in agricultural pest trapping and management. Show more

Keywords: Deep learning, object detection, agricultural light-trapped pests, pest detection

DOI: 10.3233/JIFS-231590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6707-6720, 2023

Authors: Duan, Qi | Zong, Yi

Article Type: Research Article

Abstract: It is worth exploring how “novices in academic entrepreneurship” can more clearly judge their performance in academic entrepreneurship process, self-diagnose the interaction effect between internal and external factors, and improve the effectiveness of entrepreneurial activities. This research takes Chinese academic entrepreneurs as the object, through the qualitative research method of grounded theory analysis, constructs the behavior map of academic entrepreneurship. The main stages of academic entrepreneurship chain are divided, including four stages: starting point, finding technology application, stabilizing technology application, and enterprise mature development. The common decision logic of academic entrepreneurs in each stage is explained. At the same time, …the map shows the main influencing factors of academic entrepreneurial behavior and the logic of these factors’ influence on academic entrepreneurial behavior. The above results not only enrich the research theory in the field of academic entrepreneurship process, but also have guiding significance for the practical activities of “novices in academic entrepreneurship”. Show more

Keywords: Academic entrepreneurship, behavior map, grounded theory analysis

DOI: 10.3233/JIFS-232240

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6721-6733, 2023