Journal of Intelligent & Fuzzy Systems - Volume 45, issue 3

Authors: Wang, Long | Fang, Zhigeng | Zhang, Qin | Liu, Sifeng

Article Type: Research Article

Abstract: Different preferences of the indicators would be showed in some situations. However, the preferences are not considered into the traditional possibility functions, which are always assumed to be the linear functions. It might not be proper to analyze all kinds of indicators with the traditional possibility functions. Therefore, the universal possibility functions are provided. Due to the multiple uncertain features of the indicators, then the universal possibility functions are extended for the generalized grey numbers. According to the importance of indicators and the time, the weights of indicators and the time are given respectively. Next, generalized grey dynamic clustering models …with preferences are proposed. At last, the effectiveness of the suggested methods is verified via the case illustration and comparative analysis. Show more

Keywords: Preferences, generalized universal possibility function, multiple uncertain features, grey dynamic clustering method

DOI: 10.3233/JIFS-230816

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3555-3565, 2023

Authors: Arthi, A. | Beno, A. | Sharma, S. | Sangeetha, B.

Article Type: Research Article

Abstract: Mobile ad hoc networks (MANET) have become one of the hottest research areas in computer science, including in military and civilian applications. Such applications have formed a variety of security threats, particularly in unattended environments. An Intrusion detection system (IDS) must be in place to ensure the security and reliability of MANET services. These IDS must be compatible with the characteristics of MANETs and competent in discovering the biggest number of potential security threats. In this work, a specialized dataset for MANET is implemented to identify and classify three types of Denial of Service (DoS) attacks: Blackhole, Grayhole and Flooding …Attack. This work utilized a cluster-based routing algorithm (CBRA) in MANET.A simulation to gather data, then processed to create eight attributes for creating a specialized dataset using Java. Mamdani fuzzy-based inference system (MFIS) is used to create dataset labelling. Furthermore, an ensemble classification technique is trained on the dataset to discover and classify three types of attacks. The proposed ensemble classification has six base classifiers, namely, C4.5, Fuzzy Unordered Rule Induction Algorithm (FURIA), Multilayer Perceptron (MLP), Multinomial Logistic Regression (MLR), Naive Bayes (NB) and Support Vector Machine (SVM). The experimental results demonstrate that MFIS with the Ensemble classification technique enables an enhancing security in MANET’s by modeling the interactions among a malicious node with number of legitimate nodes. This is suitable for future works on multilayer security problem in MANET. Show more

Keywords: Mobile ad hoc networks (MANET), intrusion detection system (IDS), cluster-based routing algorithm (CBRA), mamdani fuzzy-based inference system (MFIS)

DOI: 10.3233/JIFS-230161

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3567-3574, 2023

Authors: Wang, Xiaotian | Pan, Zhongjie | He, Ningxin | Gao, Tiegang

Article Type: Research Article

Abstract: Unmanned aerial vehicles (UAVs) play a crucial role in maritime search and rescue missions, capturing images of open water scenarios and assisting in object detection. Previous object detection models have mainly focused on general scenarios. However, existing object detection models have mainly focused on general scenarios, while images captured by UAVs in vast ocean scenarios often contain numerous small objects that significantly degrade the performance of the original models. To address this challenge, we propose a model that can automatically detect objects in images captured by UAVs during maritime search and rescue missions. Our approach involves designing a new detection …head with higher resolution feature maps and more comprehensive feature information to improve the detection of small objects. Additionally, we integrate Swin Transformer blocks into the small object detection head, which can improve the model’s ability to obtain abundant contextual information and thus improves the model’s ability to detect small objects. Moreover, we fuse the Convolutional Block Attention Model into the small object detection head to help the model focus on important features. Finally, we adopt a model ensemble strategy to further improve the mean average precision (mAP). Our proposed model achieves a 4.05% improvement in mAP compared to the baseline model. Furthermore, our model outperforms the previous state-of-the-art model on the SeaDronesSee dataset in terms of fewer parameters, lower training costs, and higher mAP. Show more

Keywords: Deep learning, object detection, YOLOv5, Swin Transformer, UAV

DOI: 10.3233/JIFS-230200

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3575-3586, 2023

Authors: Wang, Zeyuan | Cai, Qiang | Lu, Jianping | Wei, Guiwu

Article Type: Research Article

Abstract: Dual probabilistic linguistic term set (DPLTS) is a new proposed decision-making environment. It uses probabilistic form to represent the appraisal of the alternative from decision makers. There are few methods to deal with DPLTS according to the literature proposed up to now. The purpose of this article is to proposed a new improved Multi-Attribute Border Approximation Area Comparison (MABAC) method extended by cumulative prospect theory (CPT) and combined with DPLTS to address the multi-criteria group decision-making (MCGDM) problem of sustainable supplier selection. In order to make the decision procedure containing more fuzzy information, we also improved the equation of distance …between DPLTSs with system of rectangular coordinates. This new improved MABAC method is combined with CPT and it is semi-objective method. Not only in the procedure of calculating distance between alternatives and border approximation area, but also in the procedure of determining the weights of attributes. At the end of this paper, the comparison of this new method with other proposed DPLTS methods, such as Correlation Coefficient Method and DPLTS-TODIM-CRITIC Method, demonstrates the availability and difference. Show more

Keywords: Multi-Criteria Group Decision-Making (MCGDM), dual probabilistic linguistic term sets (DPLTSs), MABAC method, Cumulative prospect theory (CPT), entropy weight, fuzzy distance, sustainable supplier selection

DOI: 10.3233/JIFS-230410

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3587-3608, 2023

Authors: Tian, Jie | Hu, Qiu-Xia

Article Type: Research Article

Abstract: It is difficult to determine which apples have moldy cores just by looking at the outside of the apple. In the present study, we investigated identifying moldy cores using near-infrared transmittance spectra. First, input spectral features selected by noise adjusted principal component analysis (NAPCA) for back propagation artificial neural network (BP ANN) was used to reduce the dimensions of the original data. Then, four factors and five levels uniform design of the input nodes, training functions, transfer layer functions and output layer functions for NAPCA-BP ANN optimization is proposed. And the original data were input into NAPCA-BP ANN to obtain …the recognition accuracy and NAPCA-support vector machine (SVM) was as a comparative recognition model. The results showed that through the uniform design-based NAPCA-BP ANN optimization, the NAPCA method had higher identification accuracy, precision, recall and F1 score, than either full spectrum or principal component analysis. Being assessed by different ratio of model test, functions in the hidden layer and output layer of NAPCA-BP ANN, the proposed method achieved the best accuracy to 98.03%. The accuracy, precision, recall and F1 score based on NAPCA-BP ANN were 3.92%, 2.86%, 2.78% and 2.82% higher than those based on NAPCA-SVM, respectively. This method provides a theoretical basis for the development of on-line monitoring of the internal quality of apples. Show more

Keywords: Noise adjusted principal component analysis, transmittance spectroscopy, uniform design, moldy cores

DOI: 10.3233/JIFS-231222

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3609-3619, 2023

Authors: Tang, Lianyao | Chen, Rong

Article Type: Research Article

Abstract: With the continuous development of manufacturing industry, the application range of NC machining technology has been further expanded. The contour accuracy is strongly related to the NC machining quality as a key machine tool performance indicator. Its application efficiency is plainly low as the majority of offline compensation-based contour accuracy adjustments rely heavily on manual experience. Moreover, the isolated research on automatic error compensation and its combination with algorithms does not start with the characteristics of contour accuracy in data processing. Therefore, based on the advantages of strong the robustness of the fuzzy algorithm and the high effectiveness of parameter …adjustment, an automatic compensation method for NC machining contour error based on fuzzy control is proposed. The contour error prediction model is designed according to the machining path, and then the automatic compensation strategy for contour error under fuzzy control is designed based on the feed speed. The results showed that under this method, the contour error can reach a maximum of 0.06 and a minimum of 0.025, which was 0.015 lower than the minimum contour error of genetic algorithm. This indicated that the method greatly reduced the CNC machining contour error and improved the contour accuracy, as well as reducing the time cost of contour error compensation, improving the efficiency of contour error compensation, and realizing the automation of error compensation control capability. This is helpful for advancing CNC machining automation technology and supporting the intelligent development of machinery manufacturing. Show more

Keywords: CNC machine tools, fuzzy control, contouring errors, automation, compensation

DOI: 10.3233/JIFS-231307

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3621-3635, 2023

Authors: Vinston Raja, R. | Ashok Kumar, K.

Article Type: Research Article

Abstract: In India, around 7 million people depend on fishing for their livelihoods. They are assisted with a reliable and fast brief forecast for the areas of fish aggregations. Habitat mapping is critical in supporting strategic choices on fish usage and protection. In conjunction with techniques for machine learning, remote control has made comprehensive fish mapping on relevant scales possible. In machine learning, supervised algorithms are utilized to make forecasts from datasets, when data is accessible without relating output factors. In this research work, Ocean Surface Temperature (OST) and Satellite derived Chlorophyl material are the basic inputs to generating the information …of Potential Fishing Zone (PFZ). The 16 features and additional financial derivative features are used for accurate future prediction of PFZ. The unwanted and missing data are removed using effective pre-processing techniques. Among the various methods available for forecasting nonlinear phenomena, the Neural Network is the best and the efficient method to get a forecast. Therefore, the Function Fitting Neural Network (FFNN) technique is mainly used to predicting the Integrated Potential Fishing Zone (IPFZ). The practical analyses are performed by analysing the 80% -20%, 60% -40% and future data in terms of various parameters. From the results, it is proved that the suggested FFNN achieved 90% of accuracy, where the existing neural network achieved 86% of accuracy by implementing with financial derivative features for the 80% -20% of available dataset. Show more

Keywords: Fishing activities, function fitting neural network technique, future data prediction, machine learning, sea surface temperature

DOI: 10.3233/JIFS-231447

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3637-3649, 2023

Authors: Ramsanjay, S. A. | Sumathi, S.

Article Type: Research Article

Abstract: Image dehazing is a revolutionary technique for restoring images with hazy or foggy landscapes, that has gotten a lot of focus in recent years since it gained importance in a surveillance system. However, the image processing by the traditional defogging algorithm has difficulties in integrating the depth of image detail and the color of the image. Therefore, in this paper, a novel framework based on wavelet decomposition and optimized gamma correction is proposed for efficaciously retrieving the fog-free image. The foggy image is first divided into low and high frequency sub-images using SWT (Stationary Wavelet Transform), which has the advantages …of preserving temporal features so that information loss can be stopped. Then the low frequency and high frequency images are processed with defogging and denoising modules to remove fog and noise respectively. The DOGC (Dragonfly optimal Gamma Correction) algorithm in dehazing module dynamically enhanced the color detail information without human intervention so that observed scene contrast and visibility are well preserved. Lastly, fog-free image is reconstructed from sub-enhanced images. The experimental findings show that the proposed framework outperforms state-of-the-art methods in terms of both quantitative and qualitative assessment criteria using the established dataset. Furthermore, the proposed method efficiently removes fog while preserving the naturalness of fog images. Show more

Keywords: DOGC, SWT, illumination, reflection, image dehazing

DOI: 10.3233/JIFS-221179

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3651-3664, 2023

Authors: Gherbi, Tahar | Zeggari, Ahmed | Ahmed Seghir, Zianou | Hachouf, Fella

Article Type: Research Article

Abstract:  Evaluating the performance of Content-Based Image Retrieval (CBIR) systems is a challenging and intricate task, even for experts in the field. The literature presents a vast array of CBIR systems, each applied to various image databases. Traditionally, automatic metrics employed for CBIR evaluation have been borrowed from the Text Retrieval (TR) domain, primarily precision and recall metrics. However, this paper introduces a novel quantitative metric specifically designed to address the unique characteristics of CBIR. The proposed metric revolves around the concept of grouping relevant images and utilizes the entropy of the retrieved relevant images. Grouping together relevant images holds great …value from a user perspective, as it enables more coherent and meaningful results. Consequently, the metric effectively captures and incorporates the grouping of the most relevant outcomes, making it highly advantageous for CBIR evaluation. Additionally, the proposed CBIR metric excels in differentiating between results that might appear similar when assessed using other metrics. It exhibits a superior ability to discern subtle distinctions among retrieval outcomes. This enhanced discriminatory power is a significant advantage of the proposed metric. Furthermore, the proposed performance metric is designed to be straightforward to comprehend and implement. Its simplicity and ease of use contribute to its practicality for researchers and practitioners in the field of CBIR. To validate the effectiveness of our metric, we conducted a comprehensive comparative study involving prominent and well-established CBIR evaluation metrics. The results of this study demonstrate that our proposed metric exhibits robust discrimination power, outperforming existing metrics in accurately evaluating CBIR system performance. Show more

Keywords: Information retrieval, performance evaluation, precision, information theory, entropy

DOI: 10.3233/JIFS-223623

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3665-3677, 2023

Authors: Jenefa, A. | Edward Naveen, V.

Article Type: Research Article

Abstract: The Darknet is a section of the internet that is encrypted and untraceable, making it a popular location for illicit and illegal activities. However, the anonymity and encryption provided by the network also make identifying and classifying network traffic significantly more difficult. The objective of this study was to provide a comprehensive review of the latest advancements in methods used for classifying darknet network traffic. The authors explored various techniques and methods used to classify traffic, along with the challenges and limitations faced by researchers and practitioners in this field. The study found that current methods for traffic classification in …the Darknet have an average classification error rate of around 20%, due to the high level of anonymity and encryption present in the Darknet, which makes it difficult to extract features for classification. The authors analysed several quantitative values, including accuracy rates ranging from 60% to 97%, simplicity of execution ranging from 1 to 9 steps, real-time implementation ranging from less than 1 second to over 60 seconds, unknown traffic identification ranging from 30% to 95%, encrypted traffic classification ranging from 30% to 95%, and time and space complexity ranging from O(1) to O(2n ). The study examined various approaches used to classify traffic in the Darknet, including machine learning, deep learning, and hybrid methods. The authors found that deep learning algorithms were effective in accurately classifying traffic on the Darknet, but the lack of labelled data and the dynamic nature of the Darknet limited their use. Despite these challenges, the study concluded that proper traffic classification is crucial for identifying malicious activity and improving the security of the Darknet. Overall, the study suggests that, although significant challenges remain, there is potential for further development and improvement of network traffic classification in the Darknet. Show more

Keywords: Network communication, Artificial intelligence, Clustering algorithms, Semi-supervised models, Statistical analysis, Deep neural networks

DOI: 10.3233/JIFS-231099

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3679-3700, 2023