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The purpose of the Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology is to foster advancements of knowledge and help disseminate results concerning recent applications and case studies in the areas of fuzzy logic, intelligent systems, and web-based applications among working professionals and professionals in education and research, covering a broad cross-section of technical disciplines.
The journal will publish original articles on current and potential applications, case studies, and education in intelligent systems, fuzzy systems, and web-based systems for engineering and other technical fields in science and technology. The journal focuses on the disciplines of computer science, electrical engineering, manufacturing engineering, industrial engineering, chemical engineering, mechanical engineering, civil engineering, engineering management, bioengineering, and biomedical engineering. The scope of the journal also includes developing technologies in mathematics, operations research, technology management, the hard and soft sciences, and technical, social and environmental issues.
Authors: Deng, Yu
Article Type: Research Article
Abstract: The competitiveness evaluation of regional financial centers is frequently looked as the multiple attribute group decision-making (MAGDM) problem. Based on the TODIM method and fuzzy number intuitionistic fuzzy sets (FNIFS), this paper proposes a new FNIF-TODIM method to evaluate the competitiveness of regional financial centers. First, some basic theories related to FNIFS are briefly introduced. In addition, the weights of the attributes are obtained objectively using the CRITIC weighting method. Then, the traditional TODIM method is extended to FNIFS to obtain the final order of alternatives. As a result, all alternatives can be ranked and the best one for the …competitiveness assessment of regional financial centers can be identified. Finally, an example for competitiveness evaluation of regional financial centers and some decision comparative analysis is listed. The results show that the established algorithmic approach is useful. The main works of this work are: (1) the paper constructs the FNIF-TODIM method for the evaluation of the competitiveness of regional financial centers; (2) the established method is illustrated by a case study for competitiveness evaluation of regional financial centers; and (3) some comparisons prove the rationality and advantages. Show more
Keywords: Multiple attribute group decision making (MAGDM), fuzzy number intuitionistic fuzzy sets (FNIFSs), TODIM method, CRITIC method, Competitiveness evaluation
DOI: 10.3233/JIFS-221247
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7045-7057, 2023
Authors: Giri, Sourav Kumar | Garai, Totan | Islam, Sahidul
Article Type: Research Article
Abstract: It is challenging for a decision-maker to decide a proper decision in severe situations of multi-aspirated real-life problems.So there is always an ambiguity in the mind of decision maker. Keeping such vagueness in mind, this paper aims to incorporate some situation parameters imprecise in nature. The imprecise parameters are taken in single-valued bipolar neutrosophic environments. Different arithmetic operations on the single-valued bipolar neutrosophic number using the (α , β) cut method are proposed in this paper. Using this we have calculated the possibility mean of single valued bipolar neutrosophic numbers. A multi-item economic production quantity model with one time only …discount is considered here with some parameters in single valued bipolar neutrosophic number as a case study of our proposed work. A possibilistic mean de-fuzzification technique is used here using possibility measures. Finally, numerical illustration and sensitivity analysis is done for different variables to emphasize the excellence of our proposed work. Show more
Keywords: Possibilistic mean, bipolar neutrosophic number, multi-item inventory model, one time only discount
DOI: 10.3233/JIFS-222752
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7059-7072, 2023
Authors: Satyanarayana Murthy, N. | Venkata Subbaiah, G.
Article Type: Research Article
Abstract: The minimal energy sensor nodes are critical to the long-term viability of any wireless sensor network WSN). Clustering is used for this purpose. Choosing an efficient Cluster Head (CH) is critical in such cluster-based networks, as they are accountable for aggregating and transmitting data from their associate nodes to the base station (BS). A Generative Adversarial Networks (GAN) is proposed in this work to improve the selection of CH. As part of the fitness function, nodes’ residual energy, average energy, and inter-cluster distance are all considered. In an effort to further reduce energy consumption, a GAN routing method is proposed …for use at the base station level for Efficient Energy. Simulations are used to evaluate the proposed ideal.The WSNs which require long life time with minimum cost sensors demand the proposed work. The research about the human unattainable places can be fit to necessitate this work. This method supports the maintenance of mines and petroleum refineries. In terms of energy consumption and network life expectancy, the results demonstrate a substantial improvement. And also, the proposed technique is analyzed and compared along with the existing approaches as Low-Energy Adaptive Clustering Hierarchy (Security based (S-LEACH), Cluster based (C-LEACH, More Energy Efficient-LEACH) (ME-LEACH) schemes. The proposed method detects the best location of storage-nodes for the sensor network. There is no need of agitation on battery drain up of storage-nodes (because of wireless recharge) which is a highly energy spending unit. The proposed method improves the network lifetime by a significant level. The proposed method is best fit to mines, petroleum refineries, forest department and military. The proposed method behaves as not only better storage scheme but also best fit to retrieval schemes. Show more
Keywords: Energy efficiency, generative adversarial networks, clustering, cluster heads, network life time, hierarchical routing
DOI: 10.3233/JIFS-223442
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7073-7082, 2023
Authors: Shi, Zhengqi | Xie, Shurui | Li, Lingqiang
Article Type: Research Article
Abstract: The generalized neighborhood system-based rough set is an important extension of Pawlak’s rough set. The rough sets based on generalized neighborhood systems include two basic models: optimistic and pessimistic rough sets. In this paper, we give a further study on pessimistic rough sets. At first, to regain some properties of Pawlak’s rough sets that are lost in pessimistic rough sets, we introduce the mediate, transitive, positive (negative) alliance conditions for generalized neighborhood systems. At second, some approximation operators generated by special generalized neighborhood systems are characterized, which include serial, reflexive, symmetric, mediate, transitive, and negative alliance generalized neighborhood systems and …their combinations (e.g. reflexive and transitive). At third, we discuss the topologies generated by the upper and lower approximation operators of the pessimistic rough sets. Finally, combining practical examples, we apply pessimistic rough sets to rule extraction of incomplete information systems. Particularly, we prove that different decision rules can be obtained when different neighborhood systems are chosen. This enables decision makers to choose decisions based on personal preferences. Show more
Keywords: Rough set, multi-granulation rough sets, neighborhood system, approximation operator, axiomatic characterizations, information system
DOI: 10.3233/JIFS-222021
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7083-7097, 2023
Authors: Ganapathy, Revathy | Rajendran, Velayutham
Article Type: Research Article
Abstract: In current years, increased number of cyberspace users cause rapid ascends of network traffics. For instance: probability of receiving network traffic ever since software technologies that linked with devices produced massive amounts of data which are unable to accommodate through conventional schemes port based, payload based and machine learning approaches. Simultaneously SDN technology can alleviate problems of conventional method in classifying network traffic as malicious and benign, resources allocation, network monitoring along with enhancement in overall network performance via activist methods. This research work analyzed the net traffic metadata of 1,04,345 samples gathered from RYU-SDN controller, an OpenFlow controller using …mininet emulator with 23 features then performed encrypted metadata categorization into three classes namely TCP, UDP and ICMP attacks through deep CNN with two layers LSTM, CNN-two layers GRU and ConvNet Bidirectional with two layers GRU approaches with hyper parameters tuning appropriate for better network convergence, performance, optimization too. The proposed experimental outcomes reveals that deep based CB-GRU method fulfill traffic classification in SDN environment and accomplished significance enhancement in terms of accuracy 99.97%, and loss rate 0.01. Other evaluation criterias precision, recall, area under curve, were calculated for performance identification in net data traffic classification than conventional methods. Show more
Keywords: Software defined network (SDN), artificial intelligence (AI), ConvNet (CNN), long short term memory (LSTM), stochastic gradient descent (SGD) optimizer
DOI: 10.3233/JIFS-220051
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7099-7111, 2023
Authors: You, Yanlin | Wang, Zhenyu
Article Type: Research Article
Abstract: Point-of-interest (POI) recommendation has become one of the research highlights in the field of recommender systems due to the prosperity of location-based social networks in recent years. Various techniques have been proposed to improve the performance of the personalized recommendation service. Embedding-based methods have shown promising effect and attracted great attention for their flexibility and efficiency. Bayesian Personalized Ranking (BPR), as a famous optimization algorithm, has been widely used to learning the parameters of Embedding-based models in the recommendation scenario. However, existing Bayesian Personalized Ranking and its follow-up methods ignore the unique user preference when constructing the positive and negative …samples, leading a suboptimal performance. To overcome this limitation, we propose a novel method named preference-aware Bayesian Personalized Ranking (PABPR) according to empirical analyses on real-world datasets. The empirical analyses show that a user tend to visit a POI with categories which have been visited before. Thus, the key idea of PABPR is to introduce such user behaviors into the sample constructing process. PABPR is a general method which could be used for training various Embedding methods. Extensive experiments show that PABPR can lead a superior model performance compare to BPR and its variant methods. Show more
Keywords: Point-of-interest recommendation, user preference, Bayesian Personalized Ranking, embedding
DOI: 10.3233/JIFS-222705
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7113-7119, 2023
Authors: Xu, Aoqi | Tian, Man-Wen | Kausar, Nasreen | Mohammadzadeh, Ardashir | Pamucar, Dragan | Ozbilge, Ebru
Article Type: Research Article
Abstract: The financial systems have complicated dynamics and are perturbed by various uncertainties and disturbances. Chaos theory provides a practical approach to analyzing financial systems. The chaotic systems have unpredictable random characteristics that help to analyze the financial systems better. Recently, type-3 (T3) fuzzy logic systems (FLSs) have been developed for high-uncertain systems. T3-FLSs provide a reliable tool to cope with high-noisy environments. In T3-FLSs, the upper/lower bounds of uncertainties are fuzzy values. This property results in a strong tool to model more levels of uncertainties. Control, modeling, and forecasting accuracy in financial systems are so important. Then, better systems with …higher accuracy are required. In this paper, a new T3-FLS based controller is introduced for chaotic financial systems. By solving a Riccati equation, sufficient conditions are concluded for optimality and robustness. T3-FLSs are learned to minimize the error and stabilize the whole system. A new optimal learning rules are extracted for T3-FLSs. Various benchmark chaotic model of financial systems are considered for examining the efficacy of the introduced approach, and the excellent response and superiority of the suggested approach is verified. Also, a comparison with other methods demonstrates the better efficiency of the suggested scheme. Show more
Keywords: Fuzzy logic, financial systems, chaotic systems, optimal fuzzy control
DOI: 10.3233/JIFS-223396
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7121-7134, 2023
Authors: Jianfeng, Li | Xin, Chen | Hua, Guo | Guiling, Sun | Yuhua, Xu | Naizhen, Zhang
Article Type: Research Article
Abstract: The long-term maintenance of good condition for equipment is the basis of carrying out combat missions under the high technology and fast pace of modern war. However, the knowledge in the health management field at present has the characteristics of distribution, multi-source, heterogeneity and uncertainty, which seriously affects the efficiency of knowledge sharing and reuse. In order to improve the utilization of health management knowledge, an ontology-based knowledge representation method is proposed to describe knowledge in a unified and standardized way, and the classical ontology is extended to express the uncertain knowledge in the field of health management. In addition, …to improve the maintenance and knowledge updating efficiency, a global ontology model and a hierarchy, time and activity (HTA) ontology model are constructed. This paper takes the guidance subsystem of a missile as an example to illustrate the process of knowledge modeling. The results show this method realizes knowledge sharing in the health management field and can provide decision support for health management of equipment. Show more
Keywords: Knowledge representation, fuzzy ontology, HTA model, knowledge modeling
DOI: 10.3233/JIFS-224151
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7135-7152, 2023
Authors: Zhou, Jia-Jia | Zhu, Yi-An | Li, Lian | Shi, Xian-Chen
Article Type: Research Article
Abstract: The existing researchers generalize the decision-theoretic rough sets (DTRSs) model from the viewpoint of the cost function, whether the information system is complete, and so on. Few of them consider multiple different strategies to rank the expected losses. Furthermore, under the circumstance of Pythagorean fuzzy, we can’t directly define the partition of the objects set by employing equivalence relation, there is a need for constructing the general binary relation. Aiming at these problems, in present paper, we propose the similarity measure-based three-way decisions (3WD) in Pythagorean fuzzy information systems, both the binary relation and the similarity neighborhood are induced by …similarity measure between objects. Each object has its own losses, different strategies are designed to rank the expected losses. Further, the similarity measure-based DTRSs dealing with crisp concept and the similarity measure-based Pythagorean fuzzy DTRSs dealing with Pythagorean fuzzy concept are developed to establish the three regions of similarity measure-based 3WD. Finally, the proposed models are used to make decisions for classifying the network nodes of flying ad-hoc networks (FANETs) into normal nodes also called safe nodes, suspicious nodes, and malicious nodes also called unsafe nodes under the evaluation of Pythagorean fuzzy information. Show more
Keywords: Pythagorean fuzzy information systems, DTRSs, similarity measure-based 3WD, FANETs
DOI: 10.3233/JIFS-221424
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7153-7168, 2023
Authors: Leena Rosalind Mary, G. | Deepa, G.
Article Type: Research Article
Abstract: The first Zagreb index is equal to the sum of the squares of the degrees at each vertex of G. In this study, we create four distinct types of fuzzy transformation graphs and investigate the fundamental characteristics shared by them. Additionally, upper bounds on the first Zagreb index of fuzzy transformation graphs in terms of fuzzy graph G elements have been discovered.
Keywords: Fuzzy graph, fuzzy transformation graph, first Zagreb index
DOI: 10.3233/JIFS-221781
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7169-7180, 2023
Authors: Xu, Meiling | Fu, Yongqiang | Tian, Boping
Article Type: Research Article
Abstract: The fraud problem has drastically increased with the rapid growth of online lending. Since loan applications, approvals and disbursements are operated online, deceptive borrowers are prone to conceal or falsify information to maliciously obtain loans, while lenders have difficulty in identifying fraud without direct contacts and lack binding force on customers’ loan performance, which results in the frequent occurrence of fraud events. Therefore, it is significant for financial institutions to apply valuable data and competitive technologies for fraud detection to reduce financial losses from loan scams. This paper combines the advantages of statistical methods and ensemble learning algorithms to design …the grouped trees and weighted ensemble algorithm (GTWE), and establishes fraud prediction models for online loans based on mobile application usage behaviors(App behaviors) by logistic regression, extreme gradient boosting (XGBoost), long short-term memory (LSTM) and the GTWE algorithm, respectively. The experimental results show that the fraud prediction model based on the GTWE algorithm achieves outstanding classification effect and stability with satisfactory interpretability. Meanwhile, the fraud probability of customers detected by the fraud prediction model is as high as 84.19%, which indicates that App behaviors have a considerable impact on predicting fraud in online loan application. Show more
Keywords: Fraud prediction, mobile application usage behaviors, extreme gradient boosting, long short-term memory, grouped trees, weighted ensemble algorithm
DOI: 10.3233/JIFS-222405
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7181-7194, 2023
Authors: Zhang, Ruifan | Wang, Hao | Yang, Gongping
Article Type: Research Article
Abstract: Embedding similarity-based methods obtained good results in unsupervised anomaly detection (AD). This kind of method usually used feature vectors from a model pre-trained by ImageNet to calculate scores by measuring the similarity between test samples and normal samples. Ultimately, anomalous regions are localized based on the scores obtained. However, this strategy may lead to a lack of sufficient adaptability of the extracted features to the detection of anomalous patterns for industrial anomaly detection tasks. To alleviate this problem, we design a novel anomaly detection framework, MFFA, which includes a pseudo sample generation (PSG) block, a local-global feature fusion perception (LGFFP) …block and an anomaly map compensation (AMC) block. The PSG block can make the pre-trained model more suitable for real-world anomaly detection tasks by combining the CutPaste augmentation. The LGFFP block aggregates shallow and deep features on different patches and inputs them to CaiT (Class-attention in image Transformers) to guide self-attention, effectively interacting local and global information between different patches, and the AMC block can compensate each other for the two anomaly maps generated by the nearest neighbor search and multivariate Gaussian fitting, improving the accuracy of anomaly detection and localization. In experiments, MVTec AD dataset is used to verify the generalization ability of the proposed method in various real-world applications. It achieves over 99.1% AUROCs in detection and 98.4% AUROCs in localization, respectively. Show more
Keywords: Anomaly detection, pseudo sample, feature fusion, transformer, anomaly map compensation
DOI: 10.3233/JIFS-222595
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7195-7210, 2023
Authors: Jain, Manish | Kumar, Sanjay | Park, Choonkil
Article Type: Research Article
Abstract: The question of relaxing the compatible hypothesis of the pair of mappings in fixed point theory has always been remained an open problem. We address such an open problem raised by Choudhury et al. [4 ] and also explicitly settles the issue of monotone and continuity hypotheses of the involved mappings in coupled coincidence point results. Moreover, we state a gap in an example given in [3 ] and repair it. Application to the dynamic programming problem shows the usability of present work. Finally, we also propose an open problem for further investigation.
Keywords: GV-fuzzy metric space,φ-contractions, Hadɘić type t-norm, mixed monotone property, coupled coincidence point 2010 Mathematics Subject Classification. 47H10, 54H25.
DOI: 10.3233/JIFS-222637
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7211-7223, 2023
Authors: Angel, A. Sheeba | Jayaparvathy, R.
Article Type: Research Article
Abstract: Despite the numerous risks that high-rise buildings face, fire accidents happen most frequently. Studying fire accidents in high-rise buildings is crucial because they can result in harm to people’s health, fatalities, property damage, and pollution. The number of accidental fires in buildings is very large since it is difficult to isolate a single cause and all processes and control measures are not appropriately implemented. This paper proposes a fuzzy-bow tie approach to evaluating the risk of fire accidents by taking into account the various fire sources and effects. The fourteen-floor high-rise residential building is used as a case study for …the proposed fuzzy bow tie approach. The fuzzy fault tree approach estimates that there is a 0.0968% risk of a fire accident occurring in that high-rise building, with a possibility for 9 out of 100 accidental fires annually. The fuzzy event tree model predicts that loss of life and loss of property are the most likely consequences of an accidental fire. Accordingly, mitigation strategies can be developed by building officials and fire safety practitioners. Show more
Keywords: Risk assessment, fire alarm and control system, safety, fault tree, sensitivity analysis
DOI: 10.3233/JIFS-223307
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7225-7242, 2023
Authors: Li, Runya | Pang, Ling
Article Type: Research Article
Abstract: Remote sensing image technology is of great significance for dynamic management and monitoring of ground buildings. In order to improve the data fusion ability of remote sensing image of ground buildings, a data fusion method of remote sensing image of ground buildings based on multi-level fuzzy evaluation is proposed. This method constructs a remote sensing image acquisition model of ground buildings, and uses image enhancement methods to realize the gray information analysis and image enhancement of the remote sensing image rate of ground buildings. Finally, combining the remote sensing image data fusion method and the fuzzy region reconstruction method, it …reconstructs the pixels of the dynamically changed ground buildings. The simulation results show that the remote sensing image data fusion accuracy of ground buildings is good, and the remote sensing feature extraction accuracy of ground buildings is high. The dynamic real-time monitoring of remote sensing image of ground buildings is realized. Show more
Keywords: Multistage fuzzy evaluation, remote sensing image, data fusion, enhancement, building image
DOI: 10.3233/JIFS-223434
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7243-7255, 2023
Authors: Prabhu, Akshatha | Shobha Rani, N. | Basavaraju, H.T.
Article Type: Research Article
Abstract: One of the most essential factors in classifying and qualitatively evaluating mangoes for various industrial uses is weight. To meet grading requirements during industrial processing, this paper presents an orientation-independent weight estimation method for the mango cultivar “Alphonso.” In this study, size and geometry are considered as key variables in estimating weight. Based on the visual fruit geometry, generalized hand-crafted local and global features, and conventional features are calculated and subjected to the proposed feature selection methodology for optimal feature identification. The optimal features are employed in regression analysis to estimate the predicted weight. Four regression models –MLR, Linear SVR, …RBF SVR, and polynomial SVR—are used during the experimental trials. A self-collected mango database with two orientations per sample is obtained using a CCD camera. Three different weight estimation techniques are used in the analysis concerning orientation 1, orientation 2, and combining both orientations. The SVR RBF kernel yields a higher correlation between predicted and actual weights, and experiments demonstrate that orientation 1 is symmetric to orientation 2. By exhibiting a correlation coefficient of R2 = 0.99 with SVR-RBF for weight estimation using both orientations as well as individual orientations, it is observed that the correlation between predicted and estimated weights is nearly identical Show more
Keywords: Mass estimation, computer vision, mango processing, Alphonso mangoes, automated weight estimation
DOI: 10.3233/JIFS-223510
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7257-7275, 2023
Authors: Sahoo, Amit Kumar | Mishra, Sudhansu Kumar | Acharya, Deep Shekhar | Sahu, Sitanshu Sekhar | Paul, Sanchita | Gupta, Vikash Kumar
Article Type: Research Article
Abstract: System identification techniques have proved to be the most effective methodologies for the modeling highly non-linear and system. For the purpose of real-time parameter estimation of a Maglev system, a Teaching Learning Based Optimization (TLBO) for updating the weights of Functional Link Artificial Neural Network (FLANN) model is proposed and implemented in this research. Moreover, we proposed a one & two-Degree of Freedom (one-DOF & two-DOF) Fractional Order PID (FOPID) controller, where the parameters are optimized by using the Teaching Learning Based Optimization (TLBO) and the recently proposed Black Widow Optimization (BWO) algorithm. To investigate the robustness of the proposed …controller, a pulse signal disturbance is added at equal intervals of the output of the identified model of the Maglev system. It is found that the suggested two-DOF FOPID controller with TLBO performs better than its counterpart in terms of both in time domain specifications (i.e., maximum overshoot = 1.2648%, settling time = 1.3884 sec and rise time = 0.8685 sec) and in robustness analysis (i.e., system is sufficiently robust, because the infinity norms of the sensitivity and the complementary sensitivity functions of the system are less than two). The TLBO algorithm has performed better for both identification and optimization of controller parameter due to very less number of algorithmic parameter is as compared to other algorithm. Show more
Keywords: System identification, fractional calculus, FOPID, IOPID, MAGLEV system
DOI: 10.3233/JIFS-222238
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7277-7289, 2023
Authors: Cui, Wanqiu | Wang, Dawei | Feng, Wengang
Article Type: Research Article
Abstract: Image semantic learning techniques are crucial for image understanding and classification. In social networks, image data is widely disseminated thanks to convenient acquisition and intuitive expression characteristics. However, due to the freedom of users to publish information, the image has apparent context dependence and semantic fuzziness, which brings difficulties to image representation learning. Fortunately, social attributes such as hashtags carry rich semantic relations, which can be conducive to understanding the meaning of images. Therefore, this paper proposes a new method named Social Heterogeneous Graph Networks (SHGN) for image semantic learning in social networks. First, a heterogeneous graph is built to …expand image semantic relations by social attributes. Then the consistent semantic space is reconstructed through cross-media feature alignment. Finally, an image semantic extended learning network is designed to capture and integrate the social semantics and visual feature, which obtains a rich semantic representation of images from a social context. The experiments demonstrate that SHGN can achieve efficient image representation, and favorably against many baseline algorithms. Show more
Keywords: Social networks image, representation learning, heterogeneous graph, social semantic aggregation
DOI: 10.3233/JIFS-222981
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7291-7304, 2023
Authors: Manikandan, N.K. | Kavitha, M.
Article Type: Research Article
Abstract: The e-learning is necessary in this fast internet world, especially during this pandemic situation, to continue education without any interruption and it is used reduce the educational cost significantly when reduces the energy loss. Generally, machine learning and deep learning algorithms are used to identify patterns that facilitate learning and help learners understand concepts easily. Many content recommendation systems are available for assisting learners as e-learning applications by providing the required study materials. Despite the fact that existing recommendation systems struggle to provide precise content to e-learners due to the availability of a massive volume of data on the internet …and other repositories. For this purpose, we propose a new content recommendation system for recommending suitable content to learners according to their interests and learning capabilities. The proposed content recommendation system employs a newly proposed semantic-aware hybrid feature optimizer that incorporates new optimization algorithms such as the Enhanced Personalized Best Cuckoo Search Algorithm (EpBestCSA) and the Enhanced Harris Hawks Optimization Algorithm (EHHOA) for selecting suitable features that aid in improving prediction accuracy, as well as a newly proposed Deep Semantic Structure Model (DSSM) that incorporates Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). According to the experimental results, the proposed model outperforms other recommendation systems in terms of precision, recall, f-measure, and prediction accuracy. The ten-fold cross validation is done to test the performance of the proposed methodology. Show more
Keywords: Semantic analysis, hybrid feature optimizer, Cuckoo search, Harris Hawks Optimization, deep semantic structure, and content recommendation system
DOI: 10.3233/JIFS-213422
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7305-7318, 2023
Authors: Ge, Liang | Jia, Yixuan | Li, Qinhong | Ye, Xiaofeng
Article Type: Research Article
Abstract: Traffic speed prediction is a crucial task of the intelligent traffic system. However, due to the highly nonlinear temporal patterns and non-static spatial dependence of traffic data, timely and accurate traffic forecasting remains a challenge. The existing methods usually use a static adjacency matrix to model spatial dependence while ignoring the spatial dynamic characteristics of the road network.Meanwhile, the dynamic influence of different time steps on the prediction target is ignored. Thus, we propose a dynamic multi-graph convolution recurrent neural network (DMGCRNN), which models the dynamic correlations of road networks over time based on various information of road network. Dynamic …correlation is an essential factor for accurate traffic prediction, because it reflects the change of the traffic conditions in real-time. In this model, we design a dynamic graph construction method, which utilizes the local temporal and spatial characteristics of each road segment to construct dynamic graphs. Then, a dynamic multi-graph convolution fusion module is proposed, which considers the dynamic characteristics of spatial correlations and global information to model the dynamic trend of spatial dependence. Moreover, by combing the global context information, temporal attention is provided to capture the dynamic temporal dependence among different time steps. The experimental results from two real-world traffic datasets demonstrate that our method outperforms the state-of-the-art baselines. Show more
Keywords: Traffic speed prediction, dynamic graph construction, Spatio-temporal dependence
DOI: 10.3233/JIFS-222857
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7319-7332, 2023
Authors: Simin, Wang | Lulu, Qin | Chunmiao, Ma | Weiguo, Wu
Article Type: Research Article
Abstract: With the rapid development of cloud computing, there are more and more large-scale data centers, which makes the energy management of data centers more complex. In order to achieve better energy-saving effect, it is necessary to solve the problems of concurrent management and interdependence of IT, refrigeration, storage, and network equipment. Reinforcement learning learns by interacting with the environment, which is a good way to realize the independent management of the data center. In this paper, a overall energy consumption method for data center based on deep reinforcement learning is proposed to achieve collaborative energy saving of data center task …scheduling and refrigeration equipment. A new multi-agent architecture is proposed to separate the training process from the execution process, simplify the interaction process during system operation and improve the operation effect. In the deep learning stage, a hybrid deep Q network algorithm is proposed to optimize the joint action value function of the data center and obtain the optimal strategy. Experiments show that compared with other reinforcement learning methods, our method can not only reduce the energy consumption of the data center, but also reduce the frequency of hot spots. Show more
Keywords: Energy consumption, data center, job scheduling, cooling system, deep reinforcement learning, multi-agent system
DOI: 10.3233/JIFS-223769
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7333-7349, 2023
Authors: Hu, Wujin | Shao, Yi | Liu, Yefei
Article Type: Research Article
Abstract: With the successful promotion of the new round of basic education curriculum reform, China’s physical education (PE) teaching ideology and PE teaching mode have undergone profound changes, and these changes urgently require schools to establish a PE teaching quality (PETQ) evaluation system that is compatible with them, and urgently resolve the contradiction between theory and practice. The evaluation of teaching quality is not only a value judgment of teachers’ teaching ability and teaching effect, but also a value judgment of students’ learning ability and learning achievement changes. Therefore, it is an important issue of higher education research to construct a …university PE teaching quality evaluation system and actively promote the healthy development of university PE teaching evaluation. The PETQ evaluation is viewed as the multi-attribute decision-making (MADM). In order to take the full use of power average (PA) operator and Heronian mean (HM) operator, in this article, we combine the generalized Heronian mean (GHM) operator and PA with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose the generalized 2-tuple linguistic neutrosophic power weighted HM (G2TLNPWHM) operator. The G2TLNPWHM could relieve the influence of the awkward data through power weights and it could also consider the relationships between the attributes, and it can give more accurate ranking order then the existing methods. The new MADM method is built on G2TLNPWHM operators. Finally, an example for PETQ evaluation in is used to show the proposed methods. Show more
Keywords: Multi-attribute decision making (MADM), neutrosophic numbers, 2-tuple linguistic neutrosophic numbers set (2TLNSs), G2TLNPWHM operator, PE teaching quality (PETQ)
DOI: 10.3233/JIFS-224539
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7351-7365, 2023
Authors: Zhang, Yu | Liu, Fan | Hu, Yupeng | Li, Xiaoli | Dong, Xiangjun | Cheng, Zhiyong
Article Type: Research Article
Abstract: Cross-domain recommendation aims to alleviate the target domain’s data sparsity problem by leveraging source domain knowledge. Existing GCN-based approaches perform graph convolution operations in each domain separately. However, the direct effect of item feature and topological structure information in the source domain are neglected for user preference modeling in the target domain. In this paper, we propose a novel Dual Attentive Graph Convolutional Network for Cross-Domain Recommendation (DAG4CDR). Specifically, we integrate the source and target domain’s interaction data to construct a unified user-item bipartite graph and then perform GCN propagation on the graph to learn user and item embeddings. Over …the unified graph, the interaction data from both domains can be leveraged to learn user and item embeddings via information propagation. In the embedding aggregation phase, the messages passed from different items of two domains to users are weighted by a designed dual attention mechanism, which considers the contributions of different items from both node- and domain-level. We conducted extensive experiments to validate the effectiveness of our method on several publicly available datasets, and the results demonstrate the superiority of our model on preference modeling for both common and non-common users. Show more
Keywords: Cross-domain recommendation, graph convolutional network, attention mechanism
DOI: 10.3233/JIFS-222411
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7367-7378, 2023
Authors: Yousif, Majeed A. | Hamasalh, Faraidun K.
Article Type: Research Article
Abstract: In this paper, a novel numerical scheme is developed using a new construct by non-polynomial spline for solving the time fractional Generalize Fisher equation. The proposed models represent bacteria, epidemics, Brownian motion, kinetics of chemicals and fuzzy systems. The basic concept of the new approach is constructing a non-polynomial spline with different non-polynomial trigonometric and exponential functions to solve fractional differential equations. The investigated method is demonstrated theoretically to be unconditionally stable. Furthermore, the truncation error is analyzed to determine the or-der of convergence of the proposed technique. The presented method was tested in some examples and compared graphically with …analytical solutions for showing the applicability and effectiveness of the developed numerical scheme. In addition, the present method is compared by norm error with the cubic B-spline method to validate the efficiency and accuracy of the presented algorithm. The outcome of the study reveals that the developed construct is suitable and reliable for solving nonlinear fractional differential equations. Show more
Keywords: Non-polynomial spline, generalize fisher equation, truncation error, stability analysis
DOI: 10.3233/JIFS-222445
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7379-7389, 2023
Authors: Tuo, Meimei | Yang, Wenzhong
Article Type: Research Article
Abstract: In today’s big data era, there are a large number of unstructured information resources on the web. Natural language processing researchers have been working hard to figure out how to extract useful information from them. Entity Relation Extraction is a crucial step in Information Extraction and provides technical support for Knowledge Graphs, Intelligent Q&A systems and Intelligent Retrieval. In this paper, we present a comprehensive history of entity relation extraction and introduce the relation extraction methods based on Machine Mearning, the relation extraction methods based on Deep Learning and the relation extraction methods for open domains. Then we summarize the …characteristics and representative results of each type of method and introduce the common datasets and evaluation systems for entity relation extraction. Finally, we summarize current entity relation extraction methods and look forward to future technologies. Show more
Keywords: Information extraction, relation extraction, natural language processing, machine learning, deep learning
DOI: 10.3233/JIFS-223915
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7391-7405, 2023
Authors: Sun, Quan | Yang, Lichen | Li, Hongsheng | Sun, Guodong
Article Type: Research Article
Abstract: Aluminum electrolytic capacitor (AEC) is one of the most pivotal components that affect the reliability of power electronic systems. The electrolyte evaporation and dielectric degradation are the two main reasons for the parametric degradation of AEC. Remaining useful life (RUL) prediction for AEC is beneficial for obtaining the health state in advance and making reasonable maintenance strategies before the system suffers shutdown malfunction, which can increase the reliability and safety. In this paper, a hybrid machine learning (ML) model with GRU and PSO-SVR is proposed to realize the RUL prediction of AEC. The GRU is used for the recursive multi-step …prediction of AEC to model the times series of AEC, SVR optimized by PSO for hyper-parameters is applied for error compensation caused by recursive GRU. Finally, the proposed model is validated by two kinds of data sets with accelerated degradation experiments. Compared with the other methods, the results show that the proposed scheme can obtain greater prediction performance index of RUL under different prediction time points, which can support the technology of health management for power electronic system. Show more
Keywords: Aluminum electrolytic capacitor, remaining useful life, machine learning, error compensation
DOI: 10.3233/JIFS-220866
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7407-7417, 2023
Authors: Dhumras, Himanshu | Bajaj, Rakesh Kumar
Article Type: Research Article
Abstract: Systematic assessment of insufficiencies and inexactness in the information along with parametrization of multi-sub attributes is one of the substantial features in the field of decision-making. In the present communication, a new way of defining Picture Fuzzy Hypersoft Set (PFHSS) has been presented which contains an additional capacity of accommodating the components of neutral membership (abstain) and refusal compared to Intuionistic Fuzzy Hypersoft Set (IFHSS). The main objective of the present study is to establish the novelty of PFHSS with some of the basic operations and introduce various important aggregation operators. Some of the important properties and operational laws related …to the introduced picture fuzzy hypersoft weighted average/ordered weighted average operator (PFHSWA/PFHSOWA) and weighted geometric/ordered weighted geometric operator (PFHSWG/PFHSOWG) have been proved in detail. On the basis of these aggregation operators and obtained results, a new algorithm for solving a decision-making problem, involving the multi-sub attributes and their parametrization in the shade of abstain and refusal feature, has been proposed. A numerical example of the selection process of employees for a company has been solved in order to suitably ensure and validate the implementation of the proposed methodology. Some of the advantageous features of the proposed notions and algorithm have been listed along with the comparative analysis in contrast with the existing literature. Finally, the efficacy of the proposed notion and methodology has been duly concluded with the scope for future work. Show more
Keywords: Picture fuzzy set, soft set, hypersoft set, aggregation operators, decision-making
DOI: 10.3233/JIFS-222437
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7419-7447, 2023
Authors: Shamia, D. | Balasamy, K. | Suganyadevi, S.
Article Type: Research Article
Abstract: Security, secrecy, and authenticity problems have arisen as a result of the widespread sharing of medical images in social media. Copyright protection for online photo sharing is becoming a must. In this research, a cutting-edge method for embedding encrypted watermarks into medical images is proposed. The proposed method makes use of fuzzy-based ROI selection and wavelet-transformation to accomplish this. In the first step of the process, a fuzzy search is performed on the original picture to locate relevant places using the center region of interest (RoI) and the radial line along the final intensity. The suggested method takes a digital …picture and divides it into 4×4 non-overlapping blocks, with the intent of selecting low information chunks for embedding in order to maximize invisibility. By changing the coefficients, a single watermark bit may be inserted into both the left and right singular SVD matrices. The absence of false positives means the suggested technique can successfully integrate a large amount of data. Watermarks are encrypted using a pseudorandom key before being embedded. Discrete wavelet transform saliency map, block mean method, and cosine functions are used to construct an adaptively-generated pseudo-random key from the cover picture. Images uploaded to social media platforms must have a high degree of invisibility and durability. These watermarking features, however, come with a price. The optimal scaling factor is used to strike a balance between the two in the proposed system. Furthermore, the suggested scheme’s higher performance is confirmed by comparison with the latest state-of-the-art systems. Show more
Keywords: Watermarking, key component, wavelet transform, Fuzzy ROI, encryption
DOI: 10.3233/JIFS-222618
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7449-7457, 2023
Authors: Guo, Xiaobin | Chen, Ying | Zhuo, Quanxiu
Article Type: Research Article
Abstract: In paper the generalized real eigenvalue and fuzzy eigenvector of a crisp real symmetric matrix with respect to another real symmetric matrix is studied. The original generalized fuzzy eigen problem is extended into a crisp generalized eigen problem of a real symmetric matrix with high orders using the arithmetic operation of LR fuzzy matrix and vector. Two cases are analysed: (a) the unknown eigenvalue λ is a non negative real number; (b) the unknown eigenvalue λ is a negative real number. Two computing models are established and an algorithm for finding the generalized fuzzy eigenvector of a real symmetric matrix …is derived. Moreover, a sufficient condition for the existence of a strong generalized fuzzy eigenvector is given. Some numerical examples are shown to illustrated our proposed method. Show more
Keywords: Fuzzy numbers, fuzzy eigenvectors, matrix computation, fuzzy linear systems
DOI: 10.3233/JIFS-222641
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7459-7467, 2023
Authors: Li, Xiaoning | Yu, Qiancheng | Yang, Yufan | Tang, Chen | Wang, Jinyun
Article Type: Research Article
Abstract: This paper proposes an evolutionary ensemble model based on a Genetic Algorithm (GAEEM) to predict the transmission trend of infectious diseases based on ensemble again and prediction again. The model utilizes the strong global optimization capability of GA for tuning the ensemble structure. Compared with the traditional ensemble learning model, GAEEM has three main advantages: 1) It is set to address the problems of information leakage in the traditional Stacking strategy and overfitting in the Blending strategy. 2) It uses a GA to optimize the combination of base learners and determine the sub. 3) The feature dimension of the data …used in this layer is extended based on the optimal base learner combination prediction information data, which can reduce the risk of underfitting and increase prediction accuracy. The experimental results show that the R2 performance of the model in the six cities data set is higher than all the comparison models by 0.18 on average. The MAE and MSE are lower than 42.98 and 42,689.72 on average. The fitting performance is more stable in each data set and shows good generalization, which can predict the epidemic spread trend of each city more accurately. Show more
Keywords: Evolutionary ensemble, genetic algorithm, ensemble strategy, epidemics transmission prediction
DOI: 10.3233/JIFS-222683
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7469-7481, 2023
Authors: Nath, Sudarshan | Das Gupta, Suparna | Saha, Soumyabrata
Article Type: Research Article
Abstract: Skin disease is currently considered to be one of the most common diseases in the globe. Most of the human population has experienced it at some point but not all skin illnesses are as severe as others. There are some diseases that are symptomless or show fewer symptoms. Skin cancer is a potentially fatal outcome of serious skin illnesses that might develop if they are not detected in time. Due to the fact that medical professionals aren’t always quick or reliable enough to make a proper diagnosis. There is a hefty price tag attached to employing sophisticated equipment. Therefore, we …propose a system capable of classifying skin diseases using deep learning approaches, such as CNN architecture and six preset models including MobileNet, VGG19, ResNet, EfficientNet, Inception, and DenseNet. Acne, blisters, cold sores, psoriasis, and vitiligo are some of the most often seen skin conditions, thus we scoured the web resources for relevant photographs of these conditions. We have applied data augmentation methods to extend the size of the dataset and include more image variations. In the validation dataset, we achieved an accuracy rate of approx 99 percent, while in the test dataset; we achieved an accuracy rate of approx 90 percent. Our proposed method would help to diagnose skin diseases in a faster and more cost-effective way. Show more
Keywords: Skin disease, deep learning, CNN, MobileNet, VGG19, ResNet, EfficientNet, Inception, DenseNet, Acne, blisters, cold sore, psoriasis, vitiligo
DOI: 10.3233/JIFS-222773
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7483-7499, 2023
Authors: Shen, Dong | Fang, Haoyu | Li, Qiang | Liu, Jiale | Guo, Sheng
Article Type: Research Article
Abstract: Visual Simultaneous Localization and Mapping (SLAM) is one of the key technologies for intelligent mobile robots. However, most of the existing SLAM algorithms have low localization accuracy in dynamic scenes. Therefore, a visual SLAM algorithm combining semantic segmentation and motion consistency detection is proposed. Firstly, the RGB images are segmented by SegNet network, the prior semantic information is established and the feature points of high-dynamic objects are removed; Secondly, motion consistency detection is carried out, the fundamental matrix is calculated by the improved Random Sample Consistency (RANSAC) algorithm, the abnormal feature points are output by the epipolar geometry method, and …the feature points of low-dynamic objects are eliminated by combining the prior semantic information. Thirdly, the static feature points are used for pose estimation. Finally, the proposed algorithm is tested on the TUM dataset, the algorithm in this paper reduces the average RMSE of ORB-SLAM2 by 93.99% in highly dynamic scenes, which show that the algorithm can effectively improve the localization accuracy of the visual SLAM system in dynamic scenes. Show more
Keywords: Simultaneous localization and mapping (SLAM), semantic segmentation, motion consistency detection, dynamic feature points
DOI: 10.3233/JIFS-222778
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7501-7512, 2023
Authors: Chen, Yong | Long, Feiyu | Kuang, Wei | Zhang, Tianbao
Article Type: Research Article
Abstract: Blast-induced ground vibration is highly possible to result in serious losses such as destroyed buildings. The crucial parameter of the mentioned vibration is peak particle velocity (PPV). Many equations have been developed to predict PPV, however, worse performance has been reported by multiple literatures. This paper developed a method for predicting PPV based on Mamdani Fuzzy Inference System. Firstly, Minimum Redundancy Maximum Relevance was employed to identify the blasting design parameters which significantly contribute to the PPV induced by blasting. Secondly, K-means method was applied to determine the value ranges of the selected parameters. The selected parameters and corresponding value …ranges were combined to input into Mamdani Fuzzy Inference System for obtaining predicted PPV. Totally, 280 samples were collected from a blasting site. 260 out of them were used to train the proposed method and 20 were assigned for test. The proposed method was tested in the comparison with empirical equation USBM, multiple linear regression analysis, pure Mamdani Fuzzy Inference System in terms of the difference between predicted PPV and measured PPV, coefficient of correlation, root-mean-square error, and mean absolute error. The results from that showed that the proposed method has the better performance in PPV prediction. Show more
Keywords: Blasting, peak particle velocity, parameter selection, k-means method, Mamdani Fuzzy Inference System
DOI: 10.3233/JIFS-223195
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7513-7522, 2023
Authors: Praveen, R. | Pabitha, P.
Article Type: Research Article
Abstract: The Internet of Medical Things (IoMT) is a network of medical devices, hardware infrastructure, and software that allows healthcare information technology to be communicated over the web. The IoMT sensors communicate medical data to server for the quick diagnosis. As, it handles private and confidential information of a user, security is the primary objective. The existing IoT authentication schemes either using two-factor(Username, password) or multi-factor (username, password, biometric) to authenticate a user. Typically the structural characteristics-based biometric trait like Face, Iris, Palm print or finger print is used as a additional factor. There are chances that these biometrics can be …fabricated. Thus, these structural biometrics based authentication schemes are fail to provide privacy, security, authenticity, and integrity. The biodynamic-based bioacoustics signals are gained attention in the era of human-computer interactions to authenticate a user as it is a unique feature to each user. So, we use a frequency domain based bio-acoustics as a biometric input. Thus, this work propose a Secure Lightweight Bioacoustics based User Authentication Scheme using fuzzy embedder for the Internet of Medical Things applications. Also, the IoT sensors tends to join and leave the network dynamically, the proposed scheme adopts chinese remainder technique for generate a group secret key to protect the network from the attacks of former sensor nodes. The proposed scheme’s security is validated using the formal verification tool AVISPA(Automated Validation of Internet Security Protocols and Applications). The system’s performance is measured by comparing the proposed scheme to existing systems in terms of security features, computation and communication costs. It demonstrates that the proposed system outperforms existing systems. Show more
Keywords: e-Healthcare, internet of medical things security, remote patient monitoring, user authentication, bioacoustics, fuzzy embedder
DOI: 10.3233/JIFS-223617
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7523-7542, 2023
Authors: Xu, Baohua | Chen, Jiayu | Li, Zhi | Yang, Tao
Article Type: Research Article
Abstract: In recent years, with the continuous occurrence of natural disasters, people have gradually realized the importance of improving emergency response capability, and the weight of time constraints for rational allocation of emergency materials has gradually increased. Therefore, a high-dimensional collaborative allocation method of disaster materials with time window constraints is studied. A high-dimensional collaborative distribution model of disaster materials with time window constraints is constructed by combining four dimensional decision-making indexes: maximizing the satisfaction of material demand, fairness of material distribution and minimizing the total cost of expected emergency response; Build SPEA2 + SDE hybrid algorithm, solve the model and output the …optimal solution set. The simulation results show that this method can have the ability of high-dimensional distribution of disaster materials, obtain the output of the optimal distribution scheme set of disaster materials, and the material satisfaction is more than 0.70. Under the condition of minimum distribution cost, the distribution of disaster materials can be completed. Show more
Keywords: Time window constraint, disaster materials, high dimensional collaborative allocation, multi-objective constraints, decision index
DOI: 10.3233/JIFS-224428
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7543-7552, 2023
Authors: Singh, Varsha | Agrawal, Prakhar | Tiwary, Uma Shanker
Article Type: Research Article
Abstract: Generating natural language description for visual content is a technique for describing the content available in the image(s). It requires knowledge of both the domains of computer vision and natural language processing. For this, various models with different approaches are suggested. One of them is encoder-decoder-based description generation. Existing papers used only objects for descriptions, but the relationship between them is equally essential, requiring context information. Which required techniques like Long Short-Term Memory (LSTM). This paper proposes an encoder-decoder-based methodology to generate human-like textual descriptions. Dense-LSTM is presented for better description as a decoder with a modified VGG19 encoder to …capture information to describe the scene. Standard datasets Flickr8K and Flickr30k are used for testing and training purposes. BLEU (Bilingual Evaluation Understudy) score is used to evaluate the generated text. For the proposed model, a GUI (Graphical User Interface) is developed, which produces the audio description of the output received and provides an interface for searching the related visual content and query-based search. Show more
Keywords: Convolutional neural network (CNN), dense-long short-term memory (Dense-LSTM), bilingual evaluation understudy score (BLEU), textual description generation
DOI: 10.3233/JIFS-222358
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7553-7565, 2023
Authors: Gao, Feng | Ahmadzade, Hamed | Gao, Rong | Zou, Zezhou
Article Type: Research Article
Abstract: Gini coefficient is a device to characterize dispersion of uncertain variables. In order to measure variation of uncertain variables, the concept of Gini coefficient for uncertain variables is proposed. By invoking inverse uncertainty distribution, we obtain a formula for calculating Gini coefficient for uncertain variables. As an application of Gini coefficient, portfolio selection problems for uncertain returns are solved via mean-Gini models. For better understanding, several examples are provided.
Keywords: Uncertain variables, monte-carlo simulation, inverse uncertainty distribution, portfolio optimization, Gini coefficient
DOI: 10.3233/JIFS-222762
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7567-7575, 2023
Authors: Gao, Bin | Zhang, Naiwen
Article Type: Research Article
Abstract: For urban, the quantity and quality of talent is often an important measure of their development potential. Based on the theory of talent and environment comfort degree and the theory of urban comfort degree, this paper constructs the Evaluation Index System of urban talent development environment. This paper selects 7 coastal urban in Shandong province as the research objects, and uses entropy Weight-TOPSIS and cluster analysis to measure the talent development environment. The research results show that: (1) The talent development environment of seven coastal urban presents the phenomenon of “siphoning” and “distinctness” of talents. On the whole, Qingdao, Weifang, …and Yantai have certain advantages in the talent development environment. (2) Qingdao is the leading city, Weifang, Yantai, Weihai and Dongying are follow-up urban, and Binzhou and Rizhao are backward urban. (3) The environment of the eight first-level indicators forms a “magnetic field” for the development of talents. Only by fully releasing the “magnetic field effect” of the talent development environment can urban ensure that talents are “attracted, retained and used well". This paper puts forward some suggestions to optimize the talent de-velopment environment in coastal urban, which will help to stimulate the vitality and creativity of all kinds of talents in coastal urban. Show more
Keywords: Coastal urban, talent development environment, measurement, entropy weight-topsis method
DOI: 10.3233/JIFS-222889
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7577-7587, 2023
Authors: Wei, Pingping | Zhang, Xin
Article Type: Research Article
Abstract: This paper proposes a robust autoencoder with Wasserstein distance metric to extract the linear separability features from the input data. To minimize the difference between the reconstructed feature space and the original feature space, using Wasserstein distance realizes a homeomorphic transformation of the original feature space, i.e., the so-called the reconstruction of feature space. The autoencoder is used for features extraction of linear separability in the reconstructed feature space. Experiment results on real datasets show that the proposed method reaches up 0.9777 and 0.7112 on the low-dimensional and high-dimensional datasets in extracted accuracies, respectively, and also outperforms competitors. Results also …confirm that compared with feature metric-based methods and deep network architectures-based method, the linear separabilities of those features extracted by distance metric-based methods win over them. More importantly, the linear separabilities of those features obtained by evaluating distance similarity of the data are better than those obtained by evaluating feature importance of data. We also demonstrate that the data distribution in the feature space reconstructed by a homeomorphic transformation can be closer to the original data distribution. Show more
Keywords: Autoencoder, distance measure, feature extraction, linear separability
DOI: 10.3233/JIFS-223017
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7589-7598, 2023
Authors: Ahmed Seghir, Zianou | Djezzar, Meriem | Hemam, Mounir | Zeggari, Ahmed | Hachouf, Fella
Article Type: Research Article
Abstract: The application of 3D technology is rapidly expanding, and stereoscopic imagery is typically used to display 3D data. However, compression, transmission, and other necessary processes may reduce the quality of these images. Stereo image quality assessment (SIQA) has gained more attention to guarantee that customers have a positive watching experience. In order to provide the highest level of experience, it is necessary to develop a quality evaluation mechanism for stereoscopic content that is both dependable and precise. A full-reference method for SIQA is presented in this paper. Compared to previous measures, this method gives users more freedom to use distorted …pixel metrics and edge similarity. The binocular summation map is calculated by adding the left and right images for a stereo pair. Improved gradient similarity based distorted pixel measure (SGSDM) is used to calculate the quality of binocular summation. The scored 3D LIVE IQA database is used to evaluate the correlation of the proposed metric with the DMOS subjective score given by the database. The proposed method’s efficacy is demonstrated by experimental comparisons. Show more
Keywords: Gradient similarity, quality assessment, test image, distorted pixel measure, SIQA
DOI: 10.3233/JIFS-223375
Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 5, pp. 7599-7611, 2023
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
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