Journal of Intelligent & Fuzzy Systems - Volume 45, issue 1

Authors: Liang, Baohua | Lu, Zhengyu

Article Type: Research Article

Abstract: Attribute reduction is a widely used technique in data preprocessing, aiming to remove redundant and irrelevant attributes. However, most attribute reduction models only consider the importance of attributes as an important basis for reduction, without considering the relationship between attributes and the impact on classification results. In order to overcome this shortcoming, this article firstly defines the distance between samples based on the number of combinations formed by comparing the samples in the same sub-division. Secondly, from the point of view of clustering, according to the principle that the distance between each point in the cluster should be as small …as possible, and the sample distance between different clusters should be as large as possible, the combined distance is used to define the importance of attributes. Finally, according to the importance of attributes, a new attribute reduction mechanism is proposed. Furthermore, plenty of experiments are done to verify the performance of the proposed reduction algorithm. The results show that the data sets reduced by our algorithm has a prominent advantage in classification accuracy, which can effectively reduce the dimensionality of high-dimensional data, and at the same time provide new methods for the study of attribute reduction models. Show more

Keywords: Rough sets, attribute reduction, clustering, combined distance

DOI: 10.3233/JIFS-222666

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1481-1496, 2023

Authors: Sakthivel, S. | Vinotha, N.

Article Type: Research Article

Abstract: Concerns of security as well as privacy are the chief obstacles which have prevented the public cloud’s extensive adoption in Intel IT as well as across the industry. Generally, IT organizations are quite reluctant to store sensitive as well as valuable data in infrastructures which are out of their control. The technique of anonymization is employed by enterprises to raise the security of the public cloud’s data whilst facilitating the data’s analysis as well as application. The procedure of data anonymization will modify how the data is either employed or published in such a way that it will prevent the …key information’s identification. The privacy issues are addressed using k-anonymity. However, the issue of selecting the variables for anonymization and suppression of variables without the loss of knowledge is an optimization problem. To address the selection of variables for anonymization and suppression, metaheuristic algorithms are used. Diverse research groups have successfully utilized the River Formation Dynamics (RFD) metaheuristic to handle numerous problems of discrete combinatorial optimization. Even so, this metaheuristic has never been adapted for use in domains of continuous optimization. To mitigate the local minima problem, hybridization of the algorithms is proposed. In this work, a modified K-Anonymity technique’s proposal has been given by using the Modified Hill Climbing (MHC) optimization, the RFD-MHC optimization, the RFD-PSO optimization, the RFD-MHC suppression as well as the RFD-PSO suppression. Furthermore, proposal for a suppression technique has also been given in this work. Experiments demonstrated that the RFD-PSO optimization has higher classification accuracy in the range of 6.73% to 8.55% when compared to manual K-anonymization. The work has also given better trade off for security analysis and data utility effectiveness. Show more

Keywords: Privacy preservation, security, K-anonymity model, river formation dynamics (RFD) and particle swarm optimization (PSO) algorithm, modified hill climbing (MHC)

DOI: 10.3233/JIFS-223509

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1497-1512, 2023

Authors: Wang, Changjing | Jiang, Huiwen | Wang, Yuxin | Huang, Qing | Zuo, Zhengkang

Article Type: Research Article

Abstract: The smart contract, a self-executing program on the blockchain, is key to programmable finance. However, the rise of smart contract use has also led to an increase in vulnerabilities that attract illegal activity from hackers. Traditional manual approaches for vulnerability detection, relying on domain experts, have limitations such as low automation and weak generalization. In this paper, we propose a deep learning approach that leverages domain-specific features and an attention mechanism to accurately detect vulnerabilities in smart contracts. Our approach reduces the reliance on manual input and enhances generalization by continuously learning code patterns of vulnerabilities, specifically detecting various types …of vulnerabilities such as reentrancy, integer overflow, forced Ether injection, unchecked return value, denial of service, access control, short address attack, tx.origin, call stack overflow, timestamp dependency, random number dependency, and transaction order dependency vulnerabilities. In order to extract semantic information, we present a semantic distillation approach for detecting smart contract vulnerabilities. This approach involves using a syntax parser, Slither, to segment the code into smaller slices and word embedding to create a matrix for model training and prediction. Our experiments indicate that the BILSTM model is the best deep learning model for smart contract vulnerability detection task. We looked at how domain features and self-attentiveness mechanisms affected the ability to identify 12 different kinds of smart contract vulnerabilities. Our results show that by including domain features, we significantly increased the F1 values for 8 different types of vulnerabilities, with improvements ranging from 7.35% to 48.58%. The methods suggested in this study demonstrate a significant improvement in F1 scores ranging from 4.18% to 38.70% when compared to conventional detection tools like Oyente, Mythril, Osiris, Slither, Smartcheck, and Securify. This study provides developers with a more effective method of detecting smart contract vulnerabilities, assisting in the prevention of potential financial losses. This research provides developers with a more effective means of detecting smart contract vulnerabilities, thereby helping to prevent potential financial losses. Show more

Keywords: Smart contract, vulnerability detection, attention mechanism, domain features, recurrent neural network 2010 MSC: 00-01, 99-00

DOI: 10.3233/JIFS-224489

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1513-1525, 2023

Authors: Xu, Haiyan | Zhang, Hao | Zhu, Anfeng | Xu, Gang

Article Type: Research Article

Abstract: In order to improve the accuracy and security of encrypted holographic 3D geographic information data acquisition and improve the actual resolution of geographic information files, a blind watermarking algorithm for encrypted holographic 3D geographic information data based on mapping mechanism is proposed. According to the characteristics of the mapping mechanism, a mapping mechanism structure diagram is constructed; Under the mapping mechanism technology, blind watermark data is preprocessed. Then, a watermark embedding operation is performed to obtain the watermark information image, and then a blind watermark that encrypts the holographic three-dimensional geographic information data is extracted. Finally, using the blind watermark …signal as input, the blind watermark information is obtained by using the watermark strength, and the holographic 3D geographic data information is segmented and encrypted to complete blind watermark detection. The blind watermark algorithm for encrypting the holographic 3D geographic information data is studied. The results show that the maximum difference between the correlation coefficient of the algorithm in this paper and the correlation coefficient of the unaffected algorithm is only 0.04, which has better anti attack performance, high security, good terrain information collection ability, high data accuracy, and can achieve curvature repair of information data. Show more

Keywords: Mapping mechanism, encrypted holography, 3D geographic information data, blind watermarking algorithm

DOI: 10.3233/JIFS-230064

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1527-1537, 2023

Authors: Smitha, E.S. | Sendhilkumar, S. | Mahalakshmi, G.S.

Article Type: Research Article

Abstract: Multi-modal information outbreak is consistently increasing in social media. Classification of tweet sentiments using various information modalities will help the recommender systems to achieve success in digital marketing. Moreover, aspect-level sentiment analysis categorizes a target’s sentiment polarity in a specific environment. Using topic modelling in aspect-level sentiment analysis enables the identification of more accurate aspect-based tweet sentiments. The existing sentiment classification techniques used for the development of recommendation systems do not focus on the aspect-based approach modelled using deep learning classifier with temporal analysis on the social media data. Hence, this paper proposes an efficient sentiment classification model that highlights …the impact of topic modelling-based word feature embedding for improvising the classification of Twitter sentiments and product reviews based on temporal reasoning and analysis for performing predictive analysis. For tweets context analysis, Latent Dirichlet Allocation based topic modelling is used in this work which generates the topics. For each topic, the sentiment is calculated separately and the topic guided feature expansion is done using Senti-wordnet. Moreover, an extended deep learning classification algorithm called Long Short-Term Memory (LSTM) with word embedding and temporal reasoning(LSTMWTR) is proposed in this paper for improving the classification accuracy. Finally, the labelled data are classified using the existing machine learning algorithms namely Naïve Bayes, Support Vector Machines and also using the deep learning models such as Convolution Neural Network(CNN),LSTM, Recurrent Neural Networks (RNN) and the transformer model namelyBi-directional Encoder Representation from Transformers (BERT),Convolution Bi-directional Recurrent Neural Network (CBRNN) and the proposed deep learning algorithm namelyLSTMWTR. These sentiment classification algorithms have been evaluated with word embedding for tweet sentiment classification and product review classification. The results obtained from this work show that the proposed LSTMWTR algorithm emerges as the highly accurate model for tweet sentiment and product review classification. Show more

Keywords: Sentiment, classification, word embedding, temporal reasoning, NB, multinomial NB, SVM, LSTM, LSTMWTR, BERT, CNN, RNN, and CBRNN

DOI: 10.3233/JIFS-230246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1539-1565, 2023

Authors: Ali, Imran | Li, Yongming | Pedrycz, Witold

Article Type: Research Article

Abstract:  In literature granular computing and formal concept analysis algorithm use only single-value attributes to knowledge discovery for the data of spatio-temporal aspects. However, most of the datasets like forest fires and tornado storms involve multiscale values for attributes. The limitation of single-value attributes of the existing approaches indicates only the data related to event occurrence which may be missing the elicitation of important knowledge related to severity of event occurrence. Motivated by these limitations, this research article proposes a novel and generalized method which uses ordinal semantic weighted multiscale values for attributes in formal concept analysis with granular computing measures …especially when spatio-temporal attributes are not given. The originality of proposed methodology is using ordinal semantic weighted multiscale values for attributes that give complete information of event occurrences. Moreover, the use of ordinal semantic weighted multiscale values improves the results of granular computing measures. The significance of proposed approach is well explained by experimental evaluation performed on publicly available datasets on storm occurring in different States of America. Show more

Keywords: Formal concept analysis, granular computing, granulation measures, ordinal semantic weighted multiscales

DOI: 10.3233/JIFS-223764

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1567-1586, 2023

Authors: Hati, Santu

Article Type: Research Article

Abstract: In present world the major cause of global warming and climate change are emission of carbon and greenhouse gas. Governments and policymakers around the world want to put their best efforts to control the pollution and climate change to save our environment. To reduce greenhouse gas emissions Government and policymakers takes carbon tax policy on carbon emission. Also in real world uncertainty is a pervasive phenomenon. Humans have a significant ability to make logical decisions based on uncertain information. For this purpose, we are developing a pollution control fuzzy production inventory model with imperfect and break-ability items under preservation technology …investment and carbon tax policy. In this model, the break-ability rate is dependent on inventory level as the break-ability rate of breakable items depends on the collected stress of inventory stock level. Here the unit production cost is dependent on raw material cost, wear-tear cost and development cost. Carbon emission is controlled by investing in carbon reduction technology and a fraction of product items are imperfect. In this study demand of the product depends on selling price and inventory stock level of product. Finally, this optimal control problem solved by using Pontryagin Maximum principle and the optimal results are illustrated graphically and numerically using MATLAB software. Subsequently, some sensitivity analysis is investigated as the impact of parameters on total profit. Show more

Keywords: Break-ability, deteriorating items, preservation technology, environment pollution control, fuzzy granular differentiability, fuzzy optimal control production inventory

DOI: 10.3233/JIFS-224019

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1587-1601, 2023

Authors: Murugesan, Malathi | Jeyali Laseetha, T.S. | Sundaram, Senthilkumar | Kandasamy, Hariprasath

Article Type: Research Article

Abstract: Glaucoma is a condition of the eye that is caused by an increase in the eye’s intraocular pressure that, when it reaches its advanced stage, causes the patient to lose all of their vision. Thus, glaucoma screening-based treatment administered in a timely manner has the potential to prevent the patient from losing all of their vision. However, because glaucoma screening is a complicated process and there is a shortage of human resources, we frequently experience delays, which can lead to an increase in the proportion of people who have lost their eyesight worldwide. In order to overcome the limitations of …current manual approaches, there is a critical need to create a reliable automated framework for early detection of Optic Disc (OD) and Optic Cup (OC) lesions. In addition, the classification process is made more difficult by the high degree of overlap between the lesion and eye colour. In this paper, we proposed an automatic detection of Glaucoma disease. In this proposed model is consisting of two major stages. First approach is segmentation and other method is classification. The initial phase uses a Stacked Attention based U-Net architecture to identify the optic disc in a retinal fundus image and then extract it. MobileNet-V2 is used for classification of and glaucoma and non-glaucoma images. Experiment results show that the proposed method outperforms other methods with an accuracy, sensitivity and specificity of 98.9%, 95.2% and 97.5% respectively. Show more

Keywords: Medical image segmentation, classification, convolutional neural network, U-Net, MobileNet-V2

DOI: 10.3233/JIFS-230659

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1603-1616, 2023

Authors: Jebril, Akram H. | Rashid, Rozeha A.

Article Type: Research Article

Abstract: Low power wide area networks (LPWANs) are made to survive conditions of extensive installation. Technological innovations, including Global Network Operator, Long Range Wide Area Network (LoRaWAN), Narrowband Internet of Things (NB-IoT), Weightless, Sigfox, etc., have adopted LPWANs. LoRaWAN is currently regarded to be one of the most cutting-edge and intriguing technology for the widespread implementation of the IoT. Although LoRaWAN offers the best features that make it fit with Internet - of - things specifications, there are still certain technical issues to overcome, such as link coordination, resource allocation and reliable transmission. In LoRaWAN, End-devices transmit randomized uplink frames to …the gateways using un-slotted random-access protocol. This randomness with the restrictions placed on the gateways is a reason that leads to a considerable decline in network performance, in particular downlink frames. In this paper, we propose a new approach to increase Acknowledgement (ACK) messages throughput. The suggested method takes advantage of both class A and class B features to enhance and assist LoRaWAN’s reliability by ensuring that an ACK message is sent for every confirmed uplink while retaining the minimum energy level that is utilized by nodes. Show more

Keywords: Internet of Things, LoRaWAN, Downlink Frame, Differential Evolution optimization, Collision, Acknowledgement Message

DOI: 10.3233/JIFS-230730

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1617-1631, 2023

Authors: Prusty, Sashikanta | Das, Priti | Dash, Sujit Kumar | Patnaik, Srikanta | Prusty, Sushree Gayatri Priyadarsini

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-223265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1633-1652, 2023

Authors: Guan, Hao | Ejaz, Farukh | ur Rehman, Atiq | Hussain, Muhammad | Kosari, Saeed

Article Type: Research Article

Abstract:  In this paper, we have defined some fuzzy topological invariants for particular types of uniform fuzzy graph. Some particular useful types of uniform fuzzy graphs are Uniform Edge Fuzzy Graph, Uniform Vertex Fuzzy Graph, Uniform Vertex-Edge Fuzzy Graph and Totally Uniform Fuzzy Graph. For each particular type we have defined different kinds of degrees in a graph in accordance with the unique nature of it. In the end, we have applied all our output results to a cellular neural fuzzy graph as an example, to verify the predicting ability of topological invariants. The aim of this paper is to define …more significant fuzzy topological invariants in fuzzy graphs. Our ideas will help to create a link between fuzzy graph theory and simple (crisp) graph theory. Show more

Keywords: Uniform Edge Fuzzy Graphs, Fuzzy Topological Invariants, Fuzzy degrees

DOI: 10.3233/JIFS-223402

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1653-1662, 2023

Authors: Bao, Qingfeng | Zhang, Sen | Guo, Jin | Ding, Dawei | Zhang, Zhenquan

Article Type: Research Article

Abstract:  In order to improve the optimal setting temperature problem to achieve the global optimum of product performance, costs and benefits. In this article, a hierarchical structure optimal setting approach of production indexes for the rolling heating furnace temperature field (RHFTF) is proposed. It is composed of three layers with different functions to obtain the temperature control setting model of the RHFTF. In the first layer, the bi-feature Gaussian mixture model clustering (BFGMMC) algorithm of loading plan is proposed to optimize the setting of a limited number of slabs. In the second layer, the type-2 fuzzy rule interpolation (T2FRI) setting method …is developed to obtain the optimal setting curve. Meanwhile, an improved KH (Kóczy-Hirota) α-cut distance (IKHCD) algorithm is proposed to get the miss information between any two adjacent interpolation points. In the third layer, knowledge feedforward compensation of rule matrices (KFCRM) algorithm is presented to improve the anti-interference ability of the setting model. The results of the study can demonstrate that the proposed method improves the accuracy of the model and optimizes the control strategy. Furthermore, the experimental results show that the proposed method meets the process technical requirements. Show more

Keywords: Hierarchical structure, bi-feature Gaussian mixture model clustering (BFGMMC), type-2 fuzzy rules interpolation (T2FRI), improved KH (Kóczy-Hirota) α-cut distance (IKHCD), knowledge feedforward compensation of rule matrices(KFCRM)

DOI: 10.3233/JIFS-223441

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1663-1681, 2023

Authors: Vasavi, J. | Abirami, M.S.

Article Type: Research Article

Abstract: Latent Lip groove application is been a notable topic in forensic applications like crime and other investigations. The detection of lip movement is been a challenging task since it is a smaller integral part of the human face. The conventional models operate on the available public or private dataset but it is constrained to the large population and unconstrained environment. The study aims at developing a deep learning model in a multimodal system using the deep U-Net Convolutional Neural Network architecture. It also aims at improving biometric authentication through a deep pattern recognition that involves the feature extraction of grooves …present in the human lips. An examination of grooves present in the input lip image is conducted by the present system to check the authenticity of the person entering the cyber-physical systems. The lip images are collected from the public security cameras via high-definition cameras in crowded areas that help the proposed method in forensic investigation and further, it considers various unconstrained scenarios to improve the efficacy of the system. The study involves initially pre-processing of lip image, and feature extraction of lip grooves to improve the efficacy of the lip trait. The simulation is conducted on the MATLAB tool to examine the efficacy of the model against various existing methods. Further, the study does not take into account the datasets available on the websites and lip images are only collected from a large set population in a real-time environment. The results of the simulation show that the proposed method achieves a higher degree of accuracy in extracting the grooves from the input lip images. Show more

Keywords: Biometric authentication, lip pattern, U-Net, grooves, multimodal

DOI: 10.3233/JIFS-223488

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1683-1693, 2023

Authors: Gao, Yu | Zhang, Qinghua | Zhao, Fan | Gao, Man

Article Type: Research Article

Abstract:  Fuzzy sets provide an effective method for dealing with uncertain and imprecise problems. For data of intermediate fuzzy distribution, membership degrees of objects whose attribute values are larger or smaller than the normal value would be the same and carried out the same decision. However, objects with different values mean that the information they contain is different for the decision-making problem. The decision process of calculating membership degrees in fuzzy set will lose the information of data itself. Therefore, bilateral fuzzy sets and their three-way decisions are proposed. First, the deviation degree is proposed in order to distinguish these objects. …Compared with the membership degree, the deviation degree extends the mapping range from [0, 1] to [- 1, 1]. For six typical membership functions, their corresponding deviation functions are discussed and deduced. Second, the concept of bilateral fuzzy sets is proposed and the corresponding operation rules are analyzed and proved. Then, three-way decisions and approximations based on bilateral fuzzy sets are constructed. Next, for the optimization of threshold, principle of least cost is extended to the three-way decisions model based on bilateral fuzzy sets, and theoretical derivation is carried out. Finally, based on probability statistics, the principle based on confidence interval is proposed, which provides a new perspective for threshold calculation. Show more

Keywords: Fuzzy sets, three-way decisions, confidence interval, Bilateral fuzzy sets

DOI: 10.3233/JIFS-230638

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1695-1715, 2023

Authors: Komala, C.R. | Velmurugan, V. | Maheswari, K. | Deena, S. | Kavitha, M. | Rajaram, A.

Article Type: Research Article

Abstract: Internet of Things (IoT) technologies increasingly integrate unmanned aerial vehicles (UAVs). IoT devices that are becoming more networked produce massive data. The process and memory of this enormous volume of data at local nodes, particularly when utilizing artificial intelligence (AI) algorithms to collect and utilize useful information, have been declared vital issues. In this paper, we introduce UAV computing to solve greater energy consumption, delay difficulties using task offload and clustered approaches, and make cloud computing operations accessible to IoT devices. First, we present a clustering technique to group IoT devices for data transmission. After that, we apply the Q-learning …approach to accomplish task offloading and allocate the difficult tasks to UAVs that are not yet fully loaded. The sensor readings from the CHs are then collected using UAV path planning. Furthermore, We use a convolutional neural network (CNN) to achieve UAV route planning. In terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets, the effectiveness of the current study is finally compared with the existing techniques using UAVs. The results showed that the suggested strategy outperformed the current approaches in terms of coverage ratio, clustering efficiency, UAV motion, energy consumption, and the number of collected packets. Additionally, the proposed technique consumed less energy due to CNN-based route planning and dynamic positioning, which reduced UAV transmits power. Overall, the study concluded that the suggested approach is effective for improving energy-efficient and responsive data transmission in crises. Show more

Keywords: UAV computing, Internet of Things, clustering, energy reduction, task offloading, and UAV path planning

DOI: 10.3233/JIFS-231242

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1717-1730, 2023

Authors: Jayapriya, P. | Umamaheswari, K. | Kavitha, A. | Ahilan, A.

Article Type: Research Article

Abstract: In recent years, finger vein recognition has gained a lot of attention and been considered as a possible biometric feature. Various feature selection techniques were investigated for intrinsic finger vein recognition on single feature extraction, but their computational cost remains undesirable. However, the retrieved features from the finger vein pattern are massive and include a lot of redundancy. By using fusion methods on feature extraction approaches involving weighted averages, the error rate is minimized to produce an ideal weight. In this research, a novel combinational model of intelligent water droplets is proposed along with hybrid PCA LDA feature extraction for …improved finger vein pattern recognition. Initially, finger vein images are pre-processed to remove noise and improve image quality. For feature extraction, Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) are employed to identify the most relevant characteristics. The PCA and LDA algorithms combine features to accomplish feature fusion. A global best selection method using intelligent water drops (GBS-IWD) is employed to find the ideal characteristics for vein recognition. The K Nearest Neighbour Classifier was used to recognize finger veins based on the selected optimum features. Based on empirical data, the proposed method decreases the equal error rate by 0.13% in comparison to existing CNN, 3DFM, and JAFVNet techniques. The overall accuracy of the proposed GBSPSO-KNN is 3.89% and 0.85% better than FFF and GWO, whereas, the proposed GBSIWD-KNN is 4.37% and 1.35% better than FFF and GWO respectively. Show more

Keywords: Principle component analysis, finger vein recognition, linear discriminant analysis, k-nearest neighbor, intelligent water drops

DOI: 10.3233/JIFS-222717

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1731-1742, 2023

Authors: Jiang, Feng | Lin, Chunhua | Chen, Jing | Wu, Chutian

Article Type: Research Article

Abstract: New energy integration is thought to be one of the most potential solutions to support the power system with a sustainable energy infrastructure. However, new energy is an uncertain power generation resource, and the electricity generated by it has the characteristics of randomness, intermittency and reverse peak regulation. Its large-scale integration into the power grid makes the operation and reliability scheduling of the power system more challenging. It was important to build a wireless sensing and monitoring network to monitor the power and change trend of the new energy field (station) in real time. The energy consumption of wireless sensing …monitoring network is an important factor to improve the reliability of new energy scheduling. Based on the energy consumption of the wireless sensing monitoring network built by the new energy scheduling, the compression sensing technology was integrated and the network routing protocol (I-LEACH protocol) was optimized. The sampling data was transmitted by the cluster head node at the compression rate of 0.6, the improved OMP (Orthogonal Matching Pursuit) algorithm was reconstructed to achieve reliable data transmission, and the network energy consumption was further reduced. Compared with the I-LEACH routing protocol network, the experiments show that the network residual energy of the proposed method increased by 22% and the life cycle increased by about 30%. This method is helpful to improve the reliability of new energy power dispatching system and it can provide reference for realizing the reliability scheduling of new energy power system. Show more

Keywords: I-LEACH, cluster head node, OMP

DOI: 10.3233/JIFS-222980

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1743-1756, 2023

Authors: Yue, Guanli | Deng, Ansheng | Qu, Yanpeng | Cui, Hui | Liu, Jiahui

Article Type: Research Article

Abstract: Ensemble clustering helps achieve fast clustering under abundant computing resources by constructing multiple base clusterings. Compared with the standard single clustering algorithm, ensemble clustering integrates the advantages of multiple clustering algorithms and has stronger robustness and applicability. Nevertheless, most ensemble clustering algorithms treat each base clustering result equally and ignore the difference of clusters. If a cluster in a base clustering is reliable/unreliable, it should play a critical/uncritical role in the ensemble process. Fuzzy-rough sets offer a high degree of flexibility in enabling the vagueness and imprecision present in real-valued data. In this paper, a novel fuzzy-rough induced spectral ensemble …approach is proposed to improve the performance of clustering. Specifically, the significance of clusters is differentiated, and the unacceptable degree and reliability of clusters formed in base clustering are induced based on fuzzy-rough lower approximation. Based on defined cluster reliability, a new co-association matrix is generated to enhance the effect of diverse base clusterings. Finally, a novel consensus spectral function is defined by the constructed adjacency matrix, which can lead to significantly better results. Experimental results confirm that the proposed approach works effectively and outperforms many state-of-the-art ensemble clustering algorithms and base clustering, which illustrates the superiority of the novel algorithm. Show more

Keywords: Rough set, fuzzy-rough set, ensemble clustering, cluster reliability, spectral clustering

DOI: 10.3233/JIFS-223897

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1757-1774, 2023

Authors: Santhadevi, D. | Janet, B.

Article Type: Research Article

Abstract: Many Internet of Things (IoT) devices are susceptible to cyber-attacks. Attackers can exploit these flaws using the internet and remote access. An efficient Intelligent threat detection framework is proposed for IoT networks. This paper considers four key layout ideas while building a deep learning-based intelligent threat detection system at the edge of the IoT. Based on these concepts, the Hybrid Stacked Deep Learning (HSDL) model is presented. Raw IoT traffic data is pre-processed with spark. Deep Vectorized Convolution Neural Network (VCNN) and Stacked Long Short Term Memory Network build the classification model (SLSTM). VCNN is used for extracting meaningful features …of network traffic data, and SLSTM is used for classification and prevents the DL model from overfitting. Three benchmark datasets (NBaIoT-balanced, UNSW-NB15 & UNSW_BOT_IoT- imbalanced) are used to test the proposed hybrid technique. The results are compared with state-of-the-art models. Show more

Keywords: Hybrid stacked deep learning, stacked LSTM, Vectorized Convolutional Neural Network, IoT-network security, edge computing

DOI: 10.3233/JIFS-223246

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1775-1790, 2023

Authors: Li, Huan

Article Type: Research Article

Abstract: The difficulties in determining the compressive strength of concrete are inherited due to the various nonlinearities rooted in the mix designs. These difficulties raise dramatically considering the modern mix designs of high-performance concrete. Presents study tries to define a simple approach to link the input ingredients of concrete with the resulted compressive with a high accuracy rate and overcome the existing nonlinearity. For this purpose, the radial base function is defined to carry out the modeling process. The optimal results were obtained by determining the optimal structure of radial base function neural networks. This task was handled well with two …precise optimization algorithms, namely Henry’s gas solubility algorithm and particle swarm optimization algorithm. The results defined both models’ best performance earned in the training section. Considering the root mean square error values, the best value stood at 2.5629 for the radial base neural network optimized by Henry’s gas solubility algorithm, whereas the same value for the the radial base neural network optimized by particle swarm optimization was 2.6583 although both hybrid models provided acceptable output results, the radial base neural network optimized by Henry’s gas solubility algorithm showed higher accuracy in predicting high performance concrete compressive strength. Show more

Keywords: High-performance concrete, Henry’s gas solubility algorithm, particle swarm optimization algorithm, radial base function neural network

DOI: 10.3233/JIFS-221342

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1791-1803, 2023

Authors: Caroline Misbha, J. | Ajith Bosco Raj, T. | Jiji, G.

Article Type: Research Article

Abstract: The research aims to provide network security so that it can be protected from several attacks, especially DoS (Denial-of-Service) or DDoS (Distributed Denial-of-Service) attacks that could at some point render the server inoperable. Security is one of the main obstacles. There are a lot of network risks and attacks available today. One of the most common and disruptive attacks is a DDoS attack. In this study, upgraded deep learning Elephant Herd Optimization with random forest classifier is employed for early DDos attack detection. The DDoS dataset’s number of characteristics is decreased by the proposed IDN-EHO method for classifying data learning …that works with a lot of data. In the feature extraction stage, deep neural networks (DNN) approach is used, and the classified data packages are compared to return the DDoS attack traffic characteristics with a significant percentage. In the classification stage, the proposed deep learning Elephant Herd Optimization with random forest classifier used to classify the data learning which deal with a huge amount of data and minimise the number of features of the DDoS dataset. During the detection step, when the extracted features are used as input features, the attack detection model is trained using the improved deep learning Elephant Herd Optimization. The proposed framework has the potential to be a promising method for identifying unidentified DDoS attacks, according to experiments. 99% recall, precision, and accuracy can be attained using the suggested strategy, according on the findings of the experiments. Show more

Keywords: Effective fuzzy, elephant herd optimization, DDoS attack, hybrid deep learning method

DOI: 10.3233/JIFS-224149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1805-1816, 2023

Authors: Xue, Junxiao | Kong, Xiangyan | Wang, Gang | Dong, Bowei | Guan, Haiyang | Shi, Lei

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-211999

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1817-1831, 2023

Authors: Li, Hui

Article Type: Research Article

Abstract: The scientific research work of colleges and universities has attracted more and more social attention because of its large number of multidisciplinary scientific and technological talents, hardware facilities and good scientific research environment, and the quality of scientific and technological management work of colleges and universities directly affects the level of scientific and technological work of colleges and universities. Starting from the common problems of scientific research management in colleges and universities, this paper explores the ideas and methods to further promote scientific research work by improving the quality of scientific research management. The quality evaluation of scientific research management …in application-oriented universities is classical multiple attribute group decision making (MAGDM). Based on this, we extend the traditional CODAS method to the Pythagorean 2-tuple linguistic sets (P2TLSs) and propose the Pythagorean 2-tuple linguistic CODAS (P2TL-CODAS) method for quality evaluation of scientific research management in application-oriented universities. The P2TL-CODAS method is established and all computing steps are simply presented. Furthermore, we apply the P2TL-CODAS method to evaluate the quality evaluation of scientific research management in application-oriented universities. Show more

Keywords: Multiple attribute group decision making (MAGDM), Pythagorean 2-tuple linguistic sets (P2TLSs), CODAS method, P2TL-CODAS model, quality evaluation of scientific research management

DOI: 10.3233/JIFS-230629

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1833-1845, 2023

Authors: Agitha, T. | Sivarani, T.S.

Article Type: Research Article

Abstract: This research work focus on level control in quadruple tank systems based on proposed Deep Neural Fuzzy based Fractional Order Proportional Integral Derivative (DN-FFOPID) controller system. This is used for controlling the liquid level in these non- linear cylindrical systems. These model helps in identifying the dynamics of the tank system which gives the control signal feed forwarded from the reference liquid level. But, it fails to minimize the error and the system is also subjected to external disturbances. Hence, to minimize this drawback a novel controller must be introduced in it. The proposed Deep Neural model is a six …layered network which are optimized with the back-propagation algorithm. It effectively trains the system thus reducing the steady state error, offset model errors and unmeasured disturbances. This neural intelligent system maintains the liquid level which fulfils the required design criteria like time constant, no overshoot, less rise time and less settling time, which can be applied to various fields. MATLAB/simulink at FOMCON toolbox is used to perform the simulation. Real time liquid control experimental results and simulation results are demonstrated which proves the effectiveness and feasibility of the proposed methods for the quadruple tank system which finds applications in effluent treatment, petrochemical, pharmaceutical and aerospace fields. Show more

Keywords: Proposed deep neural fuzzy based fractional order proportional integral derivative controller, non- linear quadruple tank systems, back propagation, MATLAB/simulink –FOMCON toolbox

DOI: 10.3233/JIFS-221674

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1847-1861, 2023

Authors: Liu, Pingqing | Wang, Hongjun | Ning, Baoquan | Wei, Guiwu

Article Type: Research Article

Abstract: The recruitment of university researchers can be considered a multi-attribute group decision-making (MAGDM) problem. MAGDM is a familiar issue with uncertainty and fuzziness in the decision-making field. Generalized hesitation fuzzy numbers (GHFNs) as a new expanded form of hesitation fuzzy numbers (HFNs) can better express the uncertain information in MAGDM. The TODIM is a very classical and widely used method to deal with the MAGDM issue. In this paper, we integrate cumulative prospect theory (CPT) into TODIM to consider not only decision makers’ subjective risk preferences but also their confidence level to obtain more reasonable choices under risk conditions. Therefore, …we propose the GHF CPT-TODIM approach to tackle the MAGDM issue. Meanwhile, in the GHF environment, it is proposed to use the volatility of attribute information (entropy weighting method) to obtain the importance of attributes, obtain the unknown attribute weight, and enhance the rationality of weight information. Finally, the validity and usefulness of the technique are verified by applying the GHF CPT-TODIM technique to the recruitment of university researchers and comparing it with the existing GHF MAGDM method, which offers a new way to solve the MAGDM problem with GHFNs. Show more

Keywords: Multi-attribute group decision-making (MAGDM), generalized hesitant fuzzy numbers (GHFNs), TODIM, cumulative prospect theory (CPT), recruitment of university researchers

DOI: 10.3233/JIFS-224437

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1863-1880, 2023

Article Type: Retraction

DOI: 10.3233/JIFS-219328

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 1, pp. 1881-1882, 2023