Journal of Intelligent & Fuzzy Systems - Volume 44, issue 5

Authors: Saxena, Arti | Dubey, Y.M. | Kumar, Manish

Article Type: Research Article

Abstract: Models prediction is done for accurately anticipating metal removal rate (MRR), machine power (MP), and estimated tool life (ETL), which are vital in the industrial setup for better precision and higher speed. Cutting speed (CS) and feed rate (FR) were employed as controlling parameters for machining of P8 material on the SBCNC 60. By maintaining one of the two parameters constant at the mid-level, data from drilling experiments are sampled and examined. Application of ANOVA yields that the feed rate is 52.61 percent significant and the cutting speed is 46.49 percent significant for MRR, while cutting speed contributes 57.59 percent …and feed rate contributes 41.77 percent to the machine power, and the same cutting speed contributes 83 percent to ETL’s output. The analysis results that CS at 190 m/min and FR at 0.3 mm/rev are optimal combinations of input control parameters for all output of drilling operations. The development of prediction models is done by fuzzy and its comparison is carried out with classical regression method for the achievement of optimum MRR, MP and ETL. Numerical parameters for establishing the optimum model are calculated for MAPE, RMSE, MAD, and correlation coefficient between experimental values and the values obtained from regression, and fuzzy logic predictions. MAPE, RMSE, MAD, and correlation coefficient calculated 1.27%, 2.43, 1.89, and 0.99 for MRR,0.97%,0.10, 0.09 and 0.997 for MP and 5.12%,1.01,0.67 and 0.99 for ETL respectively. Hence, the proposed fuzzy logic rules effectively predict the MRR, MP, and ETL on P8 material with optimized performance. Show more

Keywords: ANOVA, Correlation coefficient (R), Estimated Tool Life, Fuzzy Logic, Mean Absolute difference (MAD), Mean Absolute Percentage Error (MAPE), Root mean square error (RMSE), Machine Power, Metal Removal Rate

DOI: 10.3233/JIFS-222768

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7613-7627, 2023

Authors: Venkatesh Kumar, M. | Lakshmi, C.

Article Type: Research Article

Abstract: Because significantly complex crypto procedures such as holomorphic encryption are robotically applied, despite the fact that consumer gadgets under our software circumstances are not, computational overhead is outrageously high. Simply hiding customers with the aid of nameless communications to act to protect the server and adversaries from linking suggestions made with the aid of the same customer makes the traditional method, which computes with the aid of any server based on the amount of provided services, impossible, and customers with charge features widely publicised with the aid of the server cause additional security concerns, impossible. To overcome the above existing …drawbacks, this research study presents a Privacy Preservation Data Collection and Access Control Using Entropy-Based Conic Curve. To safeguard the identity of clients and their requests, EBCC employs a unique group signature technic and an asymmetric cryptosystem. First, we ought to implement our EBCC method for data acquisition while maintaining privacy. Second, we consider looking at the properties of secure multiparty computation. EBCC employs lightweight techniques in encryption, aggregation, and decryption, resulting in little computation and communication overhead. Security research suggests that the EBCC is safe, can withstand collision attacks, and can conceal consumer distribution, which is required for fair balance checks in credit card payments. Finally, the results are analysed to illustrate the proposed method performance in addition to the more traditional ABC, AHRPA, ECC, and RSA methods. The proposed work should be implemented in JAVA. Show more

Keywords: Entropy-based conic curve, data mining, privacy-preserving, key generation, encryption, decryption

DOI: 10.3233/JIFS-223141

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7629-7642, 2023

Authors: Chen, Xue-gang | Sohn, Moo Young | Ma, De-xiang

Article Type: Research Article

Abstract: In real-life scenarios, both the vertex weight and edge weight in a network are hard to define exactly. We can incorporate the fuzziness into a network to handle this type of uncertain situation. Here, we use triangular fuzzy number to describe the vertex weight and edge weight of a fuzzy network G . In this paper, we consider weighted k -domination problem in fuzzy network. The weighted k -domination (WKD) problem is to find a k dominating set D which minimizes the cost f  (D ) : = ∑u ∈D w  (u ) + ∑v ∈V \D  min {∑u ∈S w … (uv ) |S  ⊆ N  (v ) ∩ D , |S | = k }. First, we put forward an integer linear programming model with a polynomial number of constrains for the WKD problem. If G is a cycle, we design a dynamic algorithm to determine its exact weighted 2-domination number. If G is a tree, we give a label algorithm to determine its exact weighted 2-domination number. Combining a primal-dual method and a greedy method, we put forward an approximation algorithm for general fuzzy network on the WKD problem. Finally, we describe an application of the WKD problem to police camp problem. Show more

Keywords: Fuzzy network, triangular fuzzy number, weighted k-domination, algorithm

DOI: 10.3233/JIFS-213120

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7643-7651, 2023

Authors: Punarselvam, E.

Article Type: Research Article

Abstract: Parkinson’s disease is neurological degenerative disorder cause by deficient dopamine production which in turn harms the motor functionality and speech. With latest IoT advancement in the health care era, we propose intelligent and smart Parkinson’s disease detection system based on voice signal analysis. Addition to PDs detection, we propose remote health monitoring feature that keep on monitoring and diagnosing PD person activity. To perform all tasks efficiently we divide our propose model in three phases: monitoring, diagnosing and analysis. During monitoring phase, PDs person voice signal is monitored and captured via IoT sensor enabled Smartphone device. This voices signal is …further processed for PD detection over MEC server during diagnosing phase. We use Tunable Q factor wavelet transform (TQWT) for extracting feature from voice sample, these extracted feature are reduced FRS methods. For feature reduction PCA and LDA are used. Theses processed feature are then applied to hybrid case-based reasoning neuro-fuzzy (ANFIS) classification system to detect Parkinson’s disease. On the detection of PDs abnormality, the proposed healthcare monitoring system immediately generates notification to the patient simultaneously send detection report to centralized healthcare cloud system. This PDs detection report is further analyzed and stored at cloud server during analysis phase where report is analyzed by professional health expert and send the appropriate treatment and medication to PD infected person or care taker. For experimentation and performance evaluation benchmark baseline UCI dataset of PDs are used. We analyzed our proposed hybrid ANFIS-CBR classifier with existing classifiers over the accuracy, sensitivity and specificity parameter. Based on the result analysis, it is observed that proposed hybrid classifier maximum accuracy, sensitivity, and specificity of 98.23%, 99.1%, and 95.3% in comparison to other classifier. Show more

Keywords: Parkinson’s Disease (PDs), Internet of things (IoT), Tunable Q-factor wavelet transform, Feature reduction and selection (FRS), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Forward feature selection (FFS), Backward feature selection (BFS), Adaptive Neuro-fuzzy interference System (ANFIS), Case-Based Reasoning (CBR), MEC (Mobile edge computing), Cloud computing

DOI: 10.3233/JIFS-220941

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7653-7668, 2023

Authors: Glukhikh, Igor | Chernysheva, Tatyana | Glukhikh, Dmitry

Article Type: Research Article

Abstract: The case-based reasoning method has a high potential for solving tasks of intelligence decision-support. To implement it, it is necessary to solve the problem of comparing situations and selecting the one that is most similar to the current situation in the knowledge base. The problem arises in the case of heterogeneous objects and situations with many different types of parameters and their possible uncertainty. In this paper, an approach based on machine (deep) learning is investigated for this task. It is proposed to carry out the process of selecting situations and solutions from the knowledge base in two stages: recognition …of the states of the elements of a complex object and the relationships between them, then the formation of a representation of the situation in the state space and its use for comparing situations using neural networks. An ensemble neural network model based on a multi-layer network is proposed. It successfully simulates the cognitive functions of a human (expert), correctly selects similar situations and ranks them according to the similarity parameter. Proposed neural network models provide the implementation of a hybrid-CBR approach for decision-making on complex objects. Show more

Keywords: Artificial intelligence, decision support systems, case-based reasoning, similarity assessment, neural network models, urban infrastructure

DOI: 10.3233/JIFS-221335

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7669-7682, 2023

Authors: Shi, Xiaolong | Kosari, Saeed | Rashmanlou, Hossein | Broumi, Said | Satham Hussain, S.

Article Type: Research Article

Abstract: The interval-valued quadripartitioned neutrosophic set is represented by the partition of the interval-valued neutrosophic set’s indeterminacy function into contradiction and ignorance parts. This article introduces the properties of interval quadripartitioned single valued neutrosophic graph. The properties like complementary, self-complementary, strong and complete interval-valued quadripartitioned neutrosophic graphs are investigated. The finest illustration of locating a climate conducive to apricot cultivation in Ladakh is provided by the notion that has been offered. The model gives us details on the location that should be chosen for apricot farming. Using the proposed concepts, we highlight potential applications of the usual apricot plant that thrives …in extremely cold climates and is appropriate for higher production. The adopted approach makes a superior fit to consider the problems in application viewpoint. Show more

Keywords: Interval quadripartitioned neutrosophic graph, properties on graphs, complement of interval quadripartitioned neutrosophic graph

DOI: 10.3233/JIFS-222572

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7683-7697, 2023

Authors: Wang, Chu | Zhao, Xuefeng | Wang, Bin | Deng, Chao | Feng, Junlan

Article Type: Research Article

Abstract: Tabular data is a widely used data form in many fields such as product marketing. In some cases, the domain shift between source and target domain of tabular data may occur with the changing of collection conditions such as time. The extant methods on tabular data mainly consist of neural-network-based methods and tree-based methods. They both meet challenges induced by domain shift on tabular data. First, neural-network-based methods are lack of effective mechanism to extract the features of tabular data and the performance may not be higher than tree-based models. Second, tree-based methods are lack of effective feature representations to …model the associations between source domain and target domain. To improve the performance of tree-based methods for domain shift, a novel pseudo-label based domain adaptation method is proposed for the tree-based method called Xgboost. The proposed method consists of pseudo-label generation and selection strategies. The pseudo-label generation strategy can control the effects of pseudo-labels on Xgboost in a more flexible way by setting proper values of pseudo-labels. The pseudo-label selection strategy can select the pseudo-labels with high confidences under a consistency condition based on the outputs of Xgboost. The quality of pseudo-labels for the data in target domain is improved and so does the performance of Xgboost trained by the data in both source domain and target domain. In the experiment, several UCI datasets and 5G terminal datasets are used to show that the proposed methods can effectively improve the performance of Xgboost. Show more

Keywords: Domain adaptation, Pseudo-label, Tabular data, Xgboost

DOI: 10.3233/JIFS-223118

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7699-7708, 2023

Authors: Kang, Xinhui | Nagasawa, Shin’ya

Article Type: Research Article

Abstract: The automobile shows try to convey a clear product or service message to the audience in a short period of time. Therefore, the steps of materials, shape, display and other aspects need to be carefully designed to provide an important display platform for the business. However, most exhibitors depend on their subjective preferences to decide the size and planning of the booth, which fails to attract the attention of customers. In this paper, the evaluation grid method (EGM) and support vector regression (SVR) are combined to design the automobile booth, which provides an innovation process for booth planning and improves …the visual appeal of the booth. Firstly, the EGM is used to interview ten highly involved groups, thus obtaining the evaluation grid diagram of the connecting line among the upper emotional needs, the median design items, and the lower specific elements. Secondly, the importance ranking of upper emotional needs is determined by the grey relational analysis. Finally, the SVR is used to establish a mapping model between key emotional needs and lower design elements, thus obtaining the best combination of booth design features preferred by customers. The verification results show that the proposed method can significantly improve the emotional satisfaction of customers and provide clear trade exhibition guidance for exhibitors. Show more

Keywords: Evaluation grid method, support vector regression, grey relational analysis, automobile trade booth design

DOI: 10.3233/JIFS-223364

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7709-7722, 2023

Authors: Liu, Sijia | Guo, Zixue

Article Type: Research Article

Abstract: The digital economy based on the new generation of information technology has increasingly become an important driving force for economic development, and it is of great practical significance to study the evaluation of the development level of the digital economy. On the basis of summarizing the connotation of the digital economy, the evaluation index system of digital economy development level is firstly constructed from four dimensions of digital infrastructure, digital industry, digital application level and digital innovation ability. Secondly, the combination weighting method of CRITIC-entropy method is used to weight the indicators. Thirdly, the evaluation model of digital economy development …level based on grey correlation-VIKOR method is constructed, and the relevant data of 30 provinces in China in 2020 are taken as samples for empirical research. The results show that there is significant regional heterogeneity in the development level of digital economy in China. The development level of digital economy in eastern China is much higher than that in western China. The most important factor affecting the development level of China’s digital economy is the development of software industry. At the same time, digital innovation ability is also an important index to distinguish the development level of digital economy. Finally, corresponding policy suggestions are put forward in response to the problems in the development of China’s digital economy. Show more

Keywords: Digital economy, combination weighting method, improved VIKOR method, regional economy

DOI: 10.3233/JIFS-223567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7723-7738, 2023

Authors: Priyadharshini, P. | Pavalarajan, S.

Article Type: Research Article

Abstract: The Internet of Things (IoT) is a system of machines, computing devices, electronic equipment, and different sensors. It forms a network, where the transmission of device-related data can be accomplished. The devices in the IoT are connected to each other through wireless links and form ad-hoc networks. In IoT based applications, the lifetime of the communicating nodes is a greater concern. The network lifetime can be maximized by introducing energy efficient data transmission in the network. Therefore, a traffic and delay-aware energy-efficient routing (TADEER) protocol for IoT-based networks are proposed in this work. The proposed technique assigns delay for transmitting …data based on the criticality level of data and traffic rate at the forwarding nodes. Fixing delays for data transmission helps to avoid unnecessary transmissions. The route selection process is implemented using an optimization algorithm. A Fuzzy logic (FL) based biogeography-based optimization (BBO) algorithm is presented in this work. Thus, the number of data transmission and energy consumption can be reduced. The performance of the proposed method is evaluated by analyzing transmission delay, network lifetime, and energy consumption. By comparing the simulation results to the existing methods TEAR and ETASA, the simulation results are validated. Show more

Keywords: Internet of Things, smart home, energy management, demand response, energy consumption, wireless sensor network

DOI: 10.3233/JIFS-220399

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7739-7752, 2023