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

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