Journal of Intelligent & Fuzzy Systems - Volume 46, issue 1

Authors: Sathish, E. | Muthukumar, R.

Article Type: Research Article

Abstract: In agriculture, selecting an “appropriate plant for an appropriate soil” is a crucial stage for all sorts of lands. There are different types of soil found in India. It is necessary to understand the features of the soil type to predict the types of crops cultivated in a particular soil. This leads to significant inconsistencies and errors in large-scale soil mapping. However, manually analyzing the soil type in the laboratory is cost-effective and time-consuming, yet it produces an inaccurate classification result. To overcome these challenges, a novel AQU-FRC Net (Aquila – Faster Regional Convolutional Neural Neural) is proposed for the …automatic prediction of soil and recommending suitable crops based on a soil-crop relationship database. The soil images were pre-processed using a Scalable Range-based Adaptive Bilateral Filter (SCRAB) for eliminating the noise artifacts from the images. The pre-processed images were classified using Faster-RCNN, which utilized MobileNet as a feature extraction network. The classification results were optimized by the Aquila optimization (AQU) algorithm that normalizes the parameters of the network to achieve better results. The proposed AQU-FRC Net achieves a high accuracy of 98.16% for predicting soil. The experimental results demonstrate that the model successfully predicts the soil when compared to other meta-heuristic-based methods. Show more

Keywords: MobileNet, Aquila – Faster RCNN, Faster-RCNN, meta-heuristic, aquila optimization

DOI: 10.3233/JIFS-230408

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 167-180, 2024

Authors: Meena, Rakesh | Joshi, Sunil | Raghuwanshi, Sandeep

Article Type: Research Article

Abstract: Rice is a staple meal that helps people worldwide access sufficient food. However, this crop has several illnesses, significantly lowering its production and quality. Because of this, it is imperative to conduct early disease detection to halt the spread of infections. Because of this, it is desirable to develop an automatic system that will help agronomists, pathologists, and indeed growers in directly diagnosing rice diseases. This would allow for preventative measures to be done as quickly as feasible. In this day and age of artificial intelligence, researchers have experimented with various learning approaches to discover diseases that can affect rice …plants. Deep learning has recently seen considerable use in many computer vision and image analysis fields, becoming one of the most prominent machine learning algorithms. Deep learning has also recently found substantial usage in many computer vision and picture analysis fields. On the other hand, deep learning methods have seen very little application in plant disease recognition, except for some ongoing research centered on the problem and using a public dataset of pictures magnified to show plant leaves. Because of their high computational complexity, which requires a huge memory cost, and the complexity of experimental materials’ backgrounds, which makes it difficult to train an efficient model, deep learning methods have only seen limited use in plant disease recognition. This is due to several factors, including the following: The Inception module was improved to recognise and detect rice plant illnesses in this research by substituting the original convolutions with architecture based on modified-Xception (M-Xception). In addition, ResNet extracts features by prioritising logarithm calculations over softmax calculations to get more consistent classification outcomes. The model’s training utilised a two-stage transfer learning process to produce an effective model. The results of the experiments reveal that the suggested approach can achieve the specified level of performance, with an average recognition fineness of 99.73% on the public dataset and 98.05% on the domestic dataset, respectively. Our proposed work is better as per existing methods and models. Show more

Keywords: Deep learning, rice crop, disease detection, feature extraction, M-Xception model

DOI: 10.3233/JIFS-230655

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 181-198, 2024

Authors: Li, Mengyang | Wang, Nan | Fu, Zhumu | Tao, Fazhan | Zhou, Tao

Article Type: Research Article

Abstract: In this paper, the robust stability of nonlinear system with unknown perturbation is considered combining operator-based right coprime factorization and fuzzy control method from the input-output view of point. In detail, fuzzy logic system is firstly combined with operator-based right coprime factorization method to study the uncertain nonlinear system. By using the operator-based fuzzy controller, the unknown perturbation is formulated, and a sufficient condition of guaranteeing robust stability is given by systematic calculation, which reduces difficulties in designing controller and calculating inverse of Bezout identity. Implications of the results related to former results are briefly compared and discussed. Finally, a …simulation example is shown to confirm effectiveness of the proposed design scheme of this paper. Show more

Keywords: Nonlinear systems, coprime factorization, robust stability, unknown perturbation, fuzzy control, robust control

DOI: 10.3233/JIFS-231879

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 199-207, 2024

Authors: Wang, Jia | Zhang, Ke | Li, Jingyuan

Article Type: Research Article

Abstract: Awareness of Network Security Situation (abbreviated as NSS for short) technology is in a period of vigorous development recently. NSS technology means network security situational awareness technology. It refers to the technology of collecting, processing, and analyzing various real-time information in the network to understand and evaluate the current network security status. It can not only find network security threats, but also reflect the NSS in the system security metrics, and provide users with targeted security protection measures. Based on data mining methods, this paper analyzed and models perceived threats and security events with data mining algorithms, and improved information …security measurement methods based on association analysis. This paper proposed network security information analysis and NSS based on data mining, and analyzed the experimental results of network awareness of NSS information security measurement. The experimental results showed that when the Timer was 8, the accuracy of the awareness of NSS information security measurement method based on data mining can reach 92.89%. The data mining model had the highest accuracy of 93.14% in situation understanding and evaluation of KDDCup-99 dataset. The results showed that the model can accurately predict the NSS. When Timer was 6, the highest accuracy of the model was 92.71%. In general, the NSS prediction mining model based on KDDCup-99 can better understand, evaluate and predict the situation. Show more

Keywords: Network security situation, data mining, information security, situation awareness

DOI: 10.3233/JIFS-233390

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 209-219, 2024

Authors: Lv, Zhenzhe | Liu, Qicheng

Article Type: Research Article

Abstract: In the era of big data, the complexity of data is increasing. Problems such as data imbalance and class overlap pose challenges to traditional classifiers. Meanwhile, the importance of imbalanced data has become increasingly prominent, it is necessary to find appropriate methods to enhance classification performance of classifiers on such datasets. In response, this paper proposes a mixed sampling method (ISODF-ENN) based on iterative self-organizing (ISODATA) denoising diffusion algorithm and edited nearest neighbors (ENN) data cleaning algorithm. The algorithm first uses iterative self-organizing clustering algorithm to divide minority class into different sub-clusters, then it uses denoising diffusion algorithm to generate …new minority class data for each sub-cluster, and finally it uses ENN algorithm to preprocess majority class data to remove the overlap with the minority class data. Each sub-cluster is oversampled according to sampling ratio, so that the oversampled minority class data also conforms to the distribution of original minority class data. Experimental results on keel datasets demonstrate that the proposed method outperforms other methods in terms of F-value and AUC, effectively addressing the issues of class imbalance and class overlap. Show more

Keywords: Imbalanced data, diffusion model, mixed-sampling, ISODATA, ENN

DOI: 10.3233/JIFS-233886

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 221-235, 2024

Authors: Jiang, Le | Liu, Hongbin

Article Type: Research Article

Abstract: Some risky multi-criteria group decision making problems include payoff and probability information. To deal with these problems, this study introduces a large scale multi-criteria group decision making model based on focus theory of choice. In this model, a group of experts’ linguistic evaluations on multiple criteria are first collected to form linguistic distributions. The positive foci of the linguistic distributions are computed and aggregated into the alternatives’ scores. It is noted that in this process the linguistic terms and probabilities are aggregated by using different rules. The positive foci of the alternatives’ scores are computed and the optimal alternative is …selected. A pollution treatment evaluation problem is solved by using the proposed model, and simulation experiments and comparative analysis are given. Show more

Keywords: Focus theory of choice, linguistic distribution, multi-criteria group decision making, positive foci

DOI: 10.3233/JIFS-234310

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 237-246, 2024

Authors: Dai, Songsong | Song, Haifeng | Xu, Yingying | Du, Lei

Article Type: Research Article

Abstract: This paper introduces the concept of (O , N )-difference, for an overlap function O and a fuzzy negation N . (O , N )-differences are weaker than fuzzy difference constructed from positive and continuous t-norms and fuzzy negations, in the sense that (O , N )-differences do not necessarily satisfy certain properties, as the right neutrality principle, but only weaker versions of these properties. This paper analyzes the main properties satisfied by (O , N )-differences, and provides a characterization of (O , N )-difference.

Keywords: Fuzzy conjunction, fuzzy difference, overlap function, t-norm

DOI: 10.3233/JIFS-234501

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 247-255, 2024

Authors: Jahanpanah, Sirus | Hamidi, Mohammad

Article Type: Research Article

Abstract: Fuzzy graphs as labeled graphs (fuzzy vertex labeling and fuzzy edge labeling) have many applications in real life such as complex networks, coding theory, medical sciences, communication networks, and management sciences. Also, triangular norms as a special class of functions, have many applications in fuzzy set theory, probability and statistics, and other areas. This paper considers the notations of an inverse fuzzy graph and triangular norms to introduce the new type of graphs as valued-inverse Dombi fuzzy graphs. The valued-inverse Dombi fuzzy graphs are a generalization of inverse fuzzy graphs and are dual to Dombi fuzzy graphs. For any given …greater than or equal to one real number, we construct a type of Dombi inverse fuzzy graph and investigate some conditions such that the product and union of Dombi inverse fuzzy graphs be a Dombi inverse fuzzy graph. Show more

Keywords: Fuzzy subset, Dombi triangular operator, valued-Dombi inverse fuzzy graph, Mathematics Subject Classification: 03E72, 05C72

DOI: 10.3233/JIFS-231535

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 257-268, 2024

Authors: Dandugala, Lakshmi Srinivasulu | Vani, Koneru Suvarna

Article Type: Research Article

Abstract: Big data analytics (BDA) is a systematic way to analyze and detect various patterns, relationships, and trends in vast amounts of data. Big data analysis and processing require significant effort, techniques, and equipment. The Hadoop framework software uses the MapReduce approach to do large-scale data analysis using parallel processing in order to generate results as soon as possible. Due to the traditional algorithm’s longer execution time and difficulty in processing big amounts of data, this is one of the main issues. Clusters are highly correlated inside each other but are not highly correlated with one another. The technique of effectively …allocating limited resources is known as an optimization algorithm for clustering. For processing large amounts of data with several dimensions, the conventional optimization approach is insufficient. By using a fuzzy method, this can be prevented. In this paper, we proposed Fuzzy based energy efficient clustering approach to enhance the clustering mechanism. In summary, Fuzzy based energy efficient clustering introduces a function that measures the distance between the cluster center and the instance, which aids in improved clustering, and we then present the MobileNet V2 model to improve efficiency and speed up computation. To enhance the method’s performance and reduce its time complexity, the distributed database simulates the shared memory space and parallelizes on the MapReduce framework on the Hadoop cloud computing platform. The proposed approach is evaluated using performance metrics such as Accuracy, Precision, Adjusted Rand Index (ARI), Recall, F1-Score, and Normalized Mutual Information (NMI). The experimental findings indicate that the proposed approach outperforms the existing techniques in terms of clustering accuracy. Show more

Keywords: Big data analytics (BDA), Hadoop, cloud computing, fuzzy based energy efficient clustering, MobileNet V2, MapReduce

DOI: 10.3233/JIFS-230387

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 269-284, 2024

Authors: Wang, Yibo

Article Type: Research Article

Abstract: With the development of digital creative industry and the use of more emerging digital technologies, the forms of digital cultural and creative design products are also increasingly diversified. Unlike traditional cultural and creative design products, digital cultural and creative design products are no longer limited to physical products, but appear more in the field of exhibition, virtual reality and product visualization. At the initial stage of the combination of digital information technology and cultural and creative content, digital cultural and creative design products, unlike ordinary cultural and creative design products, opened a new vision for users. The design quality evaluation …of digital cultural and creative design products is viewed as a multi-criteria group decision-making (MCGDM). The single-value neutrosophic sets (SVNSs) concept and its interval-valued version (Interval-valued neutrosophic sets, IVNSs) are within the recent rapid developments for managing the uncertain representation problem in MCGDM. In SVNSs, decision makers (DMs) could portray membership, non-membership and hesitancy. IVNSs expands this useful feature through portraying intervals to these three information decision degrees. In this manner, the uncertainty, ambiguity and vagueness hidden in human judgements could be quantified more efficiently. IVNSs have been widely employed and researched in MCGDM. The main purpose of this paper is to proposed the Interval-valued neutrosophic number MABAC (IVNN-MABAC) technique based on prospect theory (PT) to address the MCGDM. Eventually, an example for design quality evaluation of digital cultural and creative design products and some comparative analysis was employed to demonstrate the superiority of the designed technique. Show more

Keywords: MCGDM, IVNSs, MABAC technique, design quality evaluation, digital cultural, creative product

DOI: 10.3233/JIFS-230520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 285-296, 2024