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

Authors: Tan, Guimei | Yu, Yuehai | Yu, Xichang

Article Type: Research Article

Abstract: Due to the complexity of the real world, randomness and uncertainty are ubiquitous and interconnected in the real world. In order to measure the research objects that contain both randomness and uncertainty in practical problems, and extend the entropy theory of uncertain random variables, this paper introduces the arc entropy of uncertain random variables and the arc entropy of their functions. On this basis, the mathematical properties of arc entropy and two key formulas for calculating arc entropy are also studied and derived. Finally, two types of the mean variance entropy model with the risk and diversification are established, and …the corresponding applications to rare book selection for the rare book market are also introduced. Show more

Keywords: Uncertainty theory, chance theory, uncertain random variable, arc entropy

DOI: 10.3233/JIFS-230995

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1583-1595, 2024

Authors: Nandhini, S.S. | Kannimuthu, S.

Article Type: Research Article

Abstract: It is obvious that the problem of Frequent Itemset Mining (FIM) is very popular in data mining, which generates frequent itemsets from a transaction database. An extension of the frequent itemset mining is High Utility Itemset Mining (HUIM) which identifies itemsets with high utility from the transaction database. This gains popularity in data mining, because it identifies itemsets which have more value but the same was not identified as frequent by Frequent Itemset Mining. HUIM is generally referred to as Utility Mining. The utility of the items is measured based on parameters like cost, profit, quantity or any other measures …preferred by the users. Compared to high utility itemsets (HUIs) mining, high average utility itemsets (HAUIs) mining is more precise by considering the number of items in the itemsets. In state-of-the-art algorithms that mines HUIS and HAUIs use a single fixed minimum utility threshold based on which HAUIs are identified. In this paper, the proposed algorithm mines HAUIs from transaction databases using Artificial Fish Swarm Algorithm (AFSA) with computed multiple minimum average utility thresholds. Computing the minimum average utility threshold for each item with the AFSA algorithm outperforms other state-of-the-art HAUI mining algorithms with multiple minimum utility thresholds and user-defined single minimum threshold in terms of number of HAUIs. It is observed that the proposed algorithm outperforms well in terms of execution time, number of candidates generated and memory consumption when compared to the state-of-the-art algorithms. Show more

Keywords: Artificial fish swarm algorithm, data mining, frequent itemset mining, high average utility itemsets, itemset mining, utility mining

DOI: 10.3233/JIFS-231852

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1597-1613, 2024

Authors: Kumari, Ankita | Dutta, Sandip | Chakraborty, Soubhik

Article Type: Research Article

Abstract: M obile A d-Hoc Net works (MANET) are considered one of the significant and growing areas in today’s scenario of technological advancement. It is an infrastructure-less and dynamic ad-hoc network that requires a connection between nodes to deliver packets and data. However, its design adopts a connection-less approach, at the helm of which no monitoring node exists. Hence, the threat of maintaining the network’s security remains an uphill task. Many attacks have been attempted to breach the protection of the MANET. This paper discusses one of the most potent attacks in a MANET infrastructure, the Sinkhole Attack . We try …to minimize the possibility of a sinkhole attack using a Fuzzy Q-learning- based approach, a reinforcement learning technique. The results are encouraging, suggesting that sinkhole attacks can be minimized to a great extent after the adaption of the proposed approach. Show more

Keywords: Sinkhole attack, MANET, fuzzy Q –learning, security, cryptography

DOI: 10.3233/JIFS-232003

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1615-1626, 2024

Authors: Arif, Waqar | Khan, Waheed Ahmad | Khan, Asghar | Mahmood, Tariq | Rashmanlou, Hossein

Article Type: Research Article

Abstract: In this manuscript, we develop TOPSIS (Technique for order of preference by similarity to ideal solution) method in the setting of bipolar fuzzy environment which has the ability to deal the data while keeping in view the positive and negative aspects. By using bipolar fuzzy sets, we establish the novel concept of rating the numerous preferences of any object described through the connection number(CN) of set pair analysis(SPA). In this regard, we extend the TOPSIS method based on the connection number(CN) of set pair analysis(SPA) in the frame of bipolar fuzzy sets. For the sake of verification, effectiveness and superiority …of our method, we conduct the comparative study of some real life problem related to decision making theory. Moreover, we observe that our proposed method also fulfills the existing test criterions. Show more

Keywords: BFSs, SPA, CN, TOPSIS

DOI: 10.3233/JIFS-232838

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1627-1635, 2024

Authors: Yin, Liru | Yang, Zhiwei

Article Type: Research Article

Abstract: When the traditional evolution model studies the regional distribution of agricultural parks, the relationship between regions is not clear enough, which leads to the lack of generality of regional distribution. In order to solve this problem, this study adopts the methods of topological division and cluster analysis, establishes the model of regional diversity and evolution of farms, and clusters the spatial information of agricultural parks. According to the feature factors, the images are classified, and the image dimension values are reduced. The data space is divided into cellular space by the method of network topology structure division, and the effective …coefficient of each cell is calculated, and the spatial structure and characteristics of agricultural parks are extracted to reveal the similarities and differences between different parks. The experimental results show that the evolution model of agricultural parks constructed by topological division and clustering method shows obvious clustering characteristics in space, and the relationships among the factors are good. It is proved that the model can describe the spatial differences and evolution trends of agricultural parks more accurately, so as to provide more targeted suggestions for the planning, management and sustainable development of agricultural parks. Show more

Keywords: Network partition, cluster method, agricultural park, regional difference, evolutionary model, collaborative

DOI: 10.3233/JIFS-234165

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1637-1645, 2024

Authors: Zhu, Yimin | Gao, Qing | Shi, Hongyan | Liu, Jinguo

Article Type: Research Article

Abstract: Gestures have long been recognized as an interaction technique that can provide a more natural, creative, and intuitive way to communicate with computers. However, some existing difficulties include the high probability that the same type of movement done at different speeds will be recognized as a different category of movement; cluttered, occluded, and low-resolution backgrounds; and the near-impossibility of fusing different types of features. To this end, we propose a novel framework for integrating different scales of RGB and motion skeletons to obtain higher recognition accuracy using multiple features. Specifically, we provide a network architecture that combines a three-dimensional convolutional …neural network (3DCNN) and post-fusion to better embed different features. Also, we combine RGB and motion skeleton information at different scales to mitigate speed and background issues. Experiments on several gesture recognition public datasets show desirable results, validating the superiority of the proposed gesture recognition method. Finally, we do a human-computer interaction experiment to prove its practicality. Show more

Keywords: Multi-modal action recognition, body action, robot simulation

DOI: 10.3233/JIFS-234791

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1647-1661, 2024

Authors: Jia, Wanjun | Li, Changyong

Article Type: Research Article

Abstract: This study proposes a method to help people with different degrees of hearing impairment to better integrate into society and perform more convenient human-to-human and human-to-robot sign language interaction through computer vision. Traditional sign language recognition methods make it challenging to get good results on scenes with backgrounds close to skin color, background clutter, and partial occlusion. In order to realize faster real-time display, by comparing standard single-target recognition algorithms, we choose the best effect YOLOv8 model, and based on this, we propose a lighter and more accurate SLR-YOLO network model that improves YOLOv8. Firstly, the SPPF module is replaced …with RFB module in the backbone network to enhance the feature extraction capability of the network; secondly, in the neck, BiFPN is used to enhance the feature fusion of the network, and the Ghost module is added to make the network lighter; lastly, in order to introduce partial masking during the training process and to improve the data generalization capability, Mixup, Random Erasing and Cutout three data enhancement methods are compared, and finally the Cutout method is selected. The accuracy of the improved SLR-YOLO model on the validation sets of the American Sign Language Letters Dataset and Bengali Sign Language Alphabet Dataset is 90.6% and 98.5%, respectively. Compared with the performance of the original YOLOv8, the accuracy of both is improved by 1.3 percentage points, the amount of parameters is reduced by 11.31%, and FLOPs are reduced by 11.58%. Show more

Keywords: Machine vision, sign language recognition, YOLO, deep learning, lightweight

DOI: 10.3233/JIFS-235132

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1663-1680, 2024

Authors: Sun, Qiong | Sun, Yu | Jiang, Jingjing

Article Type: Research Article

Abstract: As an important choice of strategic transformation of energy enterprises, digital transformation has a profound impact on the stock price fluctuation of enterprises. From the perspective of dynamic capacity and environmental regulation, analyzes influences of digital transformation upon energy companies’ share movement volatility, constructs a theoretical model that considers digital transformation and stock price volatility as the primary effects, dynamic capabilities as the mediator, and environmental regulation as the moderator. In addition, the study employs data from China’s A-share listed energy enterprises from 2013 to 2020, utilizing a fixed-effect model to perform an empirical test. The findings demonstrate a significant …positive correlation between the digital transformation of energy enterprises and the volatility of stock prices, indicating that the greater the extent of digital transformation, the higher the volatility of enterprise stock prices. Among the dimensions of dynamic capability, only adaptability and innovation ability appears to mediate the relation between digital transformation and stock price fluctuation. Moreover, environmental regulation positively moderates the relationship between digital transformation and the learning ability dimension. Finally, from the macro and micro levels, this study puts forward the policies and supportive measures to stabilize the stock price of energy enterprises, and suggestions on how to implement the digital transformation strategy reasonably according to their own development status and characteristics to provide valuable insights for encouraging the digital transformation among energy firms. Show more

Keywords: Energy enterprises, digital transformation, dynamic capability, stock price fluctuation, environmental regulation

DOI: 10.3233/JIFS-232161

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1681-1695, 2024

Authors: Sumathi, S. | Balaji Ganesh, A.

Article Type: Research Article

Abstract: Arrhythmia disorders are the leading cause of death worldwide and are primarily recognized by the patient’s irregular cardiac rhythms. Wearable Internet of Things (IoT) devices can reliably measure patients’ heart rhythms by producing electrocardiogram (ECG) signals. Due to their non-invasive nature, ECG signals have been frequently employed to detect arrhythmias. The manual procedure, however, takes a long time and is prone to error. Utilizing deep learning models for early automatic identification of cardiac arrhythmias is a preferable approach that will improve diagnosis and therapy. Though ECG analysis using cloud-based methods can perform satisfactorily, they still suffer from security issues. It …is essential to provide secure data transmission and storage for IoT medical data because of its significant development in the healthcare system. So, this paper proposes a secure arrhythmia classification system with the help of effective encryption and a deep learning (DL) system. The proposed method mainly involved two phases: ECG signal transmission and arrhythmia disease classification. In the ECG signal transmission phase, the patient’s ECG data collected through the IoT sensors is encrypted using the optimal key-based elgamal elliptic curve cryptography (OKEGECC) mechanism, and the encrypted data is securely transmitted to the cloud. After that, in the arrhythmia disease classification phase, the system collects the data from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) database to perform training. The collected data is preprocessed by applying the continuous wavelet transform (CWT) to improve the quality of the ECG data. Next, the feature extraction is carried out by deformable attention-centered residual network 50 (DARNet-50), and finally, the classification is performed using butterfly-optimized Bi-directional long short-term memory (BOBLSTM). The experimental outcomes showed that the proposed system achieves 99.76% accuracy, which is better than the existing related schemes. Show more

Keywords: Internet of things, electrocardiogram, data security, arrhythmia disease classification, machine learning

DOI: 10.3233/JIFS-235885

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1697-1712, 2024

Authors: Zheng, Lina | Chen, Lijun | Wang, Yini

Article Type: Research Article

Abstract: Information amount has been shown to be one of the most efficient methods for measuring uncertainty. However, there has been little research on outlier detection using information amount. To fill this void, this paper provides a new unsupervised outlier detection method based on the amount of information. First, the information amount in a given information system is determined, which offers a thorough estimate of the uncertainty of this information system. Then, the relative information amount and the relative cardinality are proposed. Following that, the degree of outlierness and weight function are shown. Furthermore, the information amount-based outlier factor is constructed, …which determines whether an object is an outlier by its rank. Finally, a new unsupervised outlier detection method called the information amount-based outlier factor (IAOF) is developed. To validate the effectiveness and advantages of IAOF, it is compared to five existing outlier identification methods. The experimental results on real-world data sets show that this method is capable of addressing the problem of outlier detection in categorical information systems. Show more

Keywords: Outlier detection, CIS, Information amount, IAOF

DOI: 10.3233/JIFS-236518

Citation: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 1, pp. 1713-1734, 2024