Journal of Intelligent & Fuzzy Systems - Volume 44, issue 2

Authors: Ahmed, Dliouah | Dai, Binxiang | Mostafa Khalil, Ahmed

Article Type: Research Article

Abstract: This paper aims to introduce a new multiple attribute decision-making model named possibility Fermatean fuzzy soft set (PFFSS), which is a combination of the generalized fuzzy soft sets and Fermatean fuzzy sets. Some operations and properties of the new model, including complement, restricted union, and extended intersection are discussed. Further, an application of PFFSSs is modeled for multiple attribute decision-making and solved with the help of our newly launched algorithm, that is, the selection of the best eco-system model based on a computer simulation report. Finally, a comparative analysis between the initiated PFFSS model and some existing approaches is provided …to show its reliability over them. Show more

Keywords: Fermatean fuzzy set, possibility Fermatean fuzzy soft set, algorithm, multiple attribute decision-making

DOI: 10.3233/JIFS-221614

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1565-1574, 2023

Authors: Zhang, Bei | Zhou, Chang-Jie | Yao, Wei

Article Type: Research Article

Abstract: Let L be a commutative unital quantale. For every L -fuzzy relation E on a nonempty set X , we define an upper rough approximation operator on L X , which is a fuzzy extension of the classical Pawlak upper rough approximation operator. We show that this operator has close relation with the subsethood operator on X . Conversely, by an L -fuzzy closure operator on X , we can easily get an L -fuzzy relation. We show that this relation can be characterized by more smooth ways. Without the help of the lower approximation operator, L …-fuzzy rough sets can still be studied by means of constructive and axiomatic approaches, and L -fuzzy similarities and L -fuzzy closure operators are one-to-one corresponding. We also show that, the L -topology induced by the upper rough approximation operator is stratified and Alexandrov. Show more

Keywords: L-fuzzy rough set, commutative unital quantale, L-fuzzy similarity, upper rough approximation operator, L-fuzzy closure operator, stratified Alexandrov L-topology

DOI: 10.3233/JIFS-221896

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1575-1584, 2023

Authors: Jin, Ting | Zhu, Yuanguo | Shu, Yadong | Cao, Jing | Yan, Hongyan | Jiang, Depeng

Article Type: Research Article

Abstract:  This paper discusses an uncertain time optimal control problem by considering time efficiency, which is to optimize the objective function about the first hitting time subject to uncertain differential equations. According to the definition of the α-path, the uncertain time optimal control problem is transformed into an equivalent deterministic optimal control problem. Two kinds of time optimal control models are presented where optimistic value and reaching index are chosen as the optimality criteria, respectively. Applying the proposed uncertain optimal control model to a portfolio selection problem, we obtain the uncertainty distribution of the first hitting time (the investors’ first profit …time). Meanwhile, sufficient conditions of the optimal control strategy of such models are provided. Numerical simulations are provided which reveal the change for our optimal control strategy. Show more

Keywords: Uncertainty optimal control, first hitting time, portfolio selection, optimistic value, reaching index

DOI: 10.3233/JIFS-222041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1585-1599, 2023

Authors: Wang, Yan | Han, Jianfeng | Guo, Ziqi

Article Type: Research Article

Abstract: Automated micro-expression recognition has become a research highlight in the emotion recognition field. Recent works proposed an LCBP (Local Cube Binary Pattern) method for micro-expression recognition and made full use of spatiotemporal features to represent micro-expressions. Nevertheless, LCBP misses the features while ignoring the underlying discriminative information. In this paper, we present an LCBP-STGCN (Local Cube Binary Pattern Spatial-Temporal Graph Convolutional Network) to resolve the problems of LCBP. A new STGCN with the ability to handle non-Euclidean structure data is proposed to extract high-level features of the micro-expression. STGCN is composed of Spatial Graph Convolutional Network (SGCN) to obtain spatial …information and Temporal Convolutional Network (TCN) to capture temporal information of micro-expression. To validly establish the spatiotemporal graph structure of SGCN, we apply ROI (Region of Interest) as node position, LCBP features as node information. By the alternating convolution of SGCN and TCN, high-level spatiotemporal features can be obtained. The extensive experiments on four spontaneous micro-expression datasets of SMIC, CASME I, CASME II, and SAMM demonstrate the proposed LCBP-STGCN can effectively recognize micro-expressions and achieve better performance than some state-of-the-arts. Show more

Keywords: Micro-expression, LCBP, graph convolutional network (GCN), recognition

DOI: 10.3233/JIFS-213079

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1601-1611, 2023

Authors: Dai, Songsong | Zheng, Jianwei

Article Type: Research Article

Abstract: In this paper, we propose a partial ordering ⪯ on the set of ordered weighted averaging (OWA) operators. Based on this relation ⪯, we introduce the negation, conjunction and disjunction operations, and establish a bounded De Morgan lattice equipped with an involutive negation for OWA operators. Finally, we develop a similarity measure between OWA operators based on the ordering ⪯.

Keywords: OWA operators, orders, lattices

DOI: 10.3233/JIFS-213214

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1613-1623, 2023

Authors: Wei, Yuanyuan | Jiang, Nan | Zhang, Zheng | Zeng, Mengxiong | Yang, Zhenkai

Article Type: Research Article

Abstract: Agent-based combat simulation is an important research method in the field of military science and system simulation. Behaviour decision model plays the key role in the design of combat simulation agents. The behaviour tree (BT) designed by nonplayer characters (NPCs) in the game provides an efficient and concise method for the construction of combat simulation agents and has been widely used. Because the rationality of BT construction directly affects the rationality of agent decision logic, designing a reasonable BT has become a crucial step. The design of the operational agent BT not only relies on rich tactical experience but also …needs to repeatedly adjust and optimize the BT according to the operational deduction and simulation results. To avoid unreasonable BT design caused by lack of experience and eliminate the process of repeated debugging, a modelling method of a combat simulation agent that combines reinforcement learning and the BT method was proposed. This method not only makes the design process of BT more automatic but also simplifies the experience requirements of the combat simulation agent designers. Experiments show that RL-BT effectively integrates the reinforcement learning method and can endow combat simulation agents with battlefield learning ability while making independent decisions. The agent based on RL-BT for decision modelling can continuously adjust and optimize the decision process through experience accumulation, and its performance in combat simulation is significantly better than that of the agent using the original BT. Show more

Keywords: Behaviour tree, reinforcement learning, Q-learning, agent modelling, combat simulation

DOI: 10.3233/JIFS-213222

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1625-1636, 2023

Authors: AL-Hossain Ahmad, AL-Nashri | Altoum, Sami H. | Elamin, Mahjoub A. | Othman, Hakeem A.

Article Type: Research Article

Abstract: In this paper, we explore the improper integral with exponential function f  = x x is approached to infinite series, and also prove the convergence of these series. An improper integral converges if the limit defining it exists. We use Maple code to calculate the infinite series. The application of improper integral appear in several domain in science. As an application in this paper, three examples are given to illustrate the effectiveness of our main result.

Keywords: Improper integral, exponential function, infinite series

DOI: 10.3233/JIFS-220183

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1637-1644, 2023

Authors: Jiang, Jian | Ao, Li

Article Type: Research Article

Abstract: The Belt and Road Initiative is a major Initiative proposed by Chinese President Xi Jinping in 2013. Research on the risk prevention and control of China’s financial investment in countries along the Belt and Road has become a very hot topic in the world. This research focuses on the risk evaluation methods and prevention and control countermeasures of China’s foreign investment under the Belt and Road Initiative. First, based on the analysis of the existing studies on economic investment evaluation, an intuitionistic fuzzy multi-attribute evaluation method based on entropy method and G1 method is proposed. The essence of the proposed …method is to combine the intuitionistic fuzzy set theory with subjective and objective evaluation methods, which improves the disadvantage of the original evaluation method taking too much subjective factors into consideration. This study applies the proposed method to the economic risk evaluation of China’s outward foreign direct investment (OFDI), constructs a 17-indicator economic risk system, and uses this method to rank the importance of the 17 indicators. The more important contribution is that this paper not only achieves improvements at the theoretical level and innovation at the practical level, but also condenses the research conclusions into three pieces of countermeasures and suggestions on China’s investment in countries along the Belt and Road. This research can provide theoretical support for Chinese government to make financial investment decisions in countries along the Belt and Road, and can also help countries along the Belt and Road to actively integrate into the Belt and Road Initiative, and promote the high-quality social and economic development of the countries along the Belt and Road. Show more

Keywords: Belt and road, economic investment, risk evaluation, indicator importance, intuitionistic fuzzy set, entropy method-G1 method

DOI: 10.3233/JIFS-220709

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1645-1659, 2023

Authors: Abughazalah, Nabilah | Khan, Majid | Yaqoob, Naveed | Munir, Noor | Hussain, Iqtadar

Article Type: Research Article

Abstract: The reduction of constrained mathematical structures leads us to generalize any abstract structures. Using minimum conditions to construct a secure and robust component of the modern encryption algorithm is one crucial problem in multimedia security. With this understanding, we have proposed a new algebraic structure, namely monogenic semigroup, to construct a digital information authentication scheme. Authentication is always completed at the beginning of the application, before any throttling or approval checks are performed, and before any other code is allowed to begin running in the background. Many authentication schemes offer a complex structure for implementation in cryptosystems and applications. The …anticipated mechanism uses a simple mathematical structure having the least conditions as compared to other mathematical structures. The suggested scheme provides structures for the authentication of text messages and images. Show more

Keywords: Monogenic semigroup, authentication, modern ciphers

DOI: 10.3233/JIFS-220969

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1661-1671, 2023

Authors: Chitra, Singaram | Kannan, Samikannu | Sundar Raj, Annadurai

Article Type: Research Article

Abstract: Medical advancements are being made in order to extend the lifespan of mankind. In the medical field, the penetration of Wireless Sensor Networks (WSN) can aid doctors in diagnosing patients accurately and prescribing the medications accordingly. In recent times, several people have permanent implants such as face makers and it is threatening to life to keep altering this body enhancement as well as it is required to possess a system in place to improve the performance of the Wireless Body Sensors. Transmission loss and route loss are two important elements that will drag the battery energy and minimizes its life …span. This research proposes optimal clustering and path selection protocol to enhance the lifetime of wireless body sensor networks. Initially, the data is collected from each body sensor through a clustering method called Glow-worm Swarm Optimization (GSO) and the Fruit-fly technique is applied to find the best path. Here, the cluster head is selected with the help of GSO that minimizes the energy consumption as well as enhances the lifetime of WBSN. Further, the best path is identified by the FFO using the fitness value that is measured within the nodes on the basis of the distance. Since hybrid technology is used here, the routing accomplished is shown to be better. The results reveal that the proposed model has improved the sensor life term (95 sec) while compared with other existing methods like PSO with FFO (78 sec), ACO with FFO (77 sec), GA with FFO (76 sec), and LEACH (68 sec) algorithm for 500 nodes. Show more

Keywords: Wireless sensor network, body sensors, clustering, routing protocol, glow-worm swarm optimization

DOI: 10.3233/JIFS-221172

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1673-1690, 2023

Authors: Saraswathi, Meera | Meera, K.N.

Article Type: Research Article

Abstract:  A radio mean labeling l of G maps distinct vertices of G to distinct elements of ℤ + satisfying the radio mean condition that diam ( G ) + 1 - d G ( w , w ′ ) ≤ ⌈ l ( w ) + l ( w ′ ) 2 ⌉ , ∀ w , w ′ ∈ V ( G ) where d G  (w , w ′) is the smallest length of a …w , w ′- path in G and diam  (G ) = max {d G  (w , w ′) : w , w ′ ∈ V  (G )} is the diameter of G . The radio mean number of graph G is defined as rmn  (G ) = min {span  (l ) : l  isaradiomeanlabelingof G } where span (l ) is given by max {l  (w ) : w  ∈ V  (G )}. It has been proved in literature that |V  (G ) | ≤ rmn  (G ) ≤ |V  (G ) | + diam  (G ) -2. Cryptographic algorithms can exploit the unique radio mean number associated with a graph to generate keys. An exhaustive listing of all feasible radio mean labelings and their span is essential to obtain the radio mean number of a given graph. Since the radio mean condition depends on the distance between vertices and the graph’s diameter, as the order and diameter increase, finding a radio mean labeling itself is quite difficult and, so is obtaining the radio mean number of a given graph. Here we discuss the extreme values of the radio mean number of a given graph of order n . In this article we obtained bounds on the radio mean number of a graph G of order n and diameter d in terms of the radio mean number of its induced subgraph H where diam  (H ) = d and d H  (w , w ′) = d G  (w , w ′) for any w , w ′ ∈ V  (H ). The diametral path P d +1 is one such induced subgraph of G and hence we have deduced the limits of rmn  (G ) in terms of rmn  (P d +1 ). It is known that if d  = 1,  2 or 3, then rmn  (G ) = n . Here, we have given alternative proof for the same. The authors of this article have studied radio mean labeling of paths in another article. Using those results, we have improved the bounds on the radio mean number of a graph of order n and diameter d  ≥ 4. It is also shown that among all connected graphs on n vertices, the path P n of order n possesses the maximum radio mean number. This is the first article that has completely solved the question of maximum and minimum attainable radio mean numbers of graphs of order n . Show more

Keywords: Channel assignment problem, graph labeling, radio labeling, radio mean labeling, radio mean number, paths

DOI: 10.3233/JIFS-221595

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1691-1702, 2023

Authors: Zhou, Lixin | Zhou, Kexin | Liu, Chen

Article Type: Research Article

Abstract: Stance detection is the task of classifying user reviews towards a given topic as either supporting, denying, querying, or commenting (SDQC) . Most approaches for solving this problem use only the textual features, including the linguistic features and users’ vocabulary choice. A few approaches have shown that information from the network structure like graph model can add value, in addition to the textual features, by providing social connections and interactions that may be vital for the stance detection task. In this paper, we present a novel model that combines the text features with the network structure by (1) creating a …graph-structure model based on conversational structure towards specific topics and (2) constructing a tree-gated neural network model (TreeGGNN) to capture structure information among reviews. We evaluate our model on four baseline models, which shows that the combination of text and network can achieve an improvement of 2–6% over the state-of-the-art baselines. Show more

Keywords: Stance detection, gated graph neural network, deep learning, structure of conversation thread

DOI: 10.3233/JIFS-221953

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1703-1714, 2023

Authors: Krishnakumar, S. | Manivannan, K.

Article Type: Research Article

Abstract: The meningioma brain tumor detection is more important than the other tumor detection such as Glioma and Glioblastoma, due to its high severity level. The tumor pixel density of meningioma tumor is high and it leads to sudden death if it is not detected timely. The meningioma images are detected using Modified Empirical Mode Decomposition- Convolutional Neural Networks (MEMD-CNN) classification approach. This method has the following stages data augmentation, spatial-frequency transformation, feature computations, classifications and segmentation. The brain image samples are increased using data augmentation process for improving the meningioma detection rate. The data augmented images are spatially transformed into …frequency format using MEMD transformation method. Then, the external empirical mode features are computed from this transformed image and they are fed into CNN architecture to classify the source brain image into either meningioma or non-meningioma. The pixels belonging tumor category are segmented using morphological opening-closing functions. The meningioma detection system obtains 99.4% of Meningioma Classification Rate (MCR) and 99.3% of Non-Meningioma Classification Rate (NMCR) on the meningioma and non-meningioma images. This MEMD-CNN technique for meningioma identification attains 98.93% of SET, 99.13% of SPT, 99.18% of MSA, 99.14% of PR and 99.13% of FS. From the statistical comparative analysis of the proposed MEMD-CNN system with other conventional detection systems, the proposed method provides optimum tumor segmentation results. Show more

Keywords: Meningioma, tumor, transformation, features, classification rate

DOI: 10.3233/JIFS-222172

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1715-1726, 2023

Authors: Zhang, Shiguang | Yuan, Qiuyun | Yuan, Feng | Liu, Shiqin

Article Type: Research Article

Abstract: Twin proximal support vector regression is a new regression machine designed by using twin support vector machine and proximal support vector regression. In this paper, we use the above models framework to build a new regression model, called the twin proximal least squares support vector regression model based on heteroscedastic Gaussian noise (TPLSSVR-HGN). The least square method is introduced and the regularization terms b 1 2 and b 2 2 are added respectively. It transforms an inequality constraint problem into two simpler equality constraint problems, which not only …improves the training speed and generalization ability, but also effectively improves the forecasting accuracy. In order to solve the parameter selection problem of model TPLSSVR-HGN, the particle swarm optimization algorithm with fast convergence speed and good robustness is selected to optimize its parameters. In order to verify the forecasting performance of TPLSSVR-HGN, it is compared with the classical regression models on the artificial data set, UCI data set and wind-speed data set. The experimental results show that TPLSSVR-HGN has better forecasting effect than the classical regression models. Show more

Keywords: Least squares support vector regression, twin proximal support vector regression, heteroscedastic Gaussian noise, short-term wind-speed forecasting, equality constraint

DOI: 10.3233/JIFS-211631

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1727-1741, 2023

Authors: Guo, Jidong | Jiao, Heyan

Article Type: Research Article

Abstract: Rapid prediction of earthquake casualties is vital to improve the efficiency of emergency rescue and reduce social losses. Using the Delphi process, nine feature attributes affecting post-earthquake casualties are identified. Corresponding membership functions for the feature attributes are established based on fuzzy theory. The objective weights of feature attributes obtained from the entropy technology are applied to modify the subjective weights from Analytical Hierarchy Process (AHP). To expand the size of the case base, a new idea of collecting cases based on seismic intensity scenarios is proposed. A numerical experiment is carried out for all cases in the case base …along the proposed fuzzy Case-Based Reasoning technical route. The average prediction error is only 14.93%. Show more

Keywords: Post-earthquake casualty, fuzzy set, Case-Based Reasoning, prediction

DOI: 10.3233/JIFS-212183

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1743-1753, 2023

Authors: Gong, Shu | Hua, Gang

Article Type: Research Article

Abstract: Graphs and hypergraphs are popular models for data structured representation. For example, traffic data, weather data, and animal skeleton data are all described by graph structures. Interval-valued fuzzy sets change the membership function of general fuzzy sets from single value functions to interval-valued functions, and thus describe the fuzzy attributes of things in terms of fuzzy intervals, which is more in line with the characteristics of fuzzy objectives. This paper aims to define the bipolar interval-valued fuzzy hypergraph to reveal the inner relationship of fuzzy data, and give some characterizations of it. The characteristics of bipolar interval-valued intuitionistic fuzzy hypergraph …and bipolar interval-valued Pythagorean fuzzy hypergraph are studied. In addition, we discuss the characteristics of the bipolar interval-valued fuzzy threshold graph. Finally, some instances are presented as the applications of bipolar interval-valued fuzzy hypergraphs. Show more

Keywords: Hypergraph, bipolar fuzzy set, threshold graph, bipolar interval-valued fuzzy threshold graph

DOI: 10.3233/JIFS-212551

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1755-1767, 2023

Authors: Wang, Sheng | Shi, Yumei | Hu, Chengxiang | Yu, Chunyan | Chen, Shiping

Article Type: Research Article

Abstract: Nowadays, poverty-stricken college students have become a special group among college students and occupied a higher proportion in it. How to accurately identify poverty levels of college students and provide funding is a new problem for universities. In this study, a novel model, which incorporated Random Forest with Principle Components Analysis (RF-PCA), is proposed to predict poverty levels of college students. To establish this model, we collect some useful information is to construct the datasets which include 4 classes of poverty levels and 21 features of poverty-stricken college students. Furthermore, the feature dimension reduction consists of two steps: the first …step is to select the top 16 features with the ranking of feature, according to the Gini importance and Shapley Additive explanations (SHAP) values of features based on Random Forest (RF) model; the second step is to extract 11 dimensions by means of Principle Components Analysis (PCA). Subsequently, confusion metrics and receiver operating characteristic (ROC) curves are utilized to evaluate the promising performance of the proposed model. Especially the accuracy of the model achieves 78.61%. Finally, compared with seven states of the art classification algorithms, the proposed model achieves a higher prediction accuracy, which indicates that the results provide great potential to identify the poverty levels of college students. Show more

Keywords: RF-PCA, poverty levels, feature selection, feature extraction

DOI: 10.3233/JIFS-213114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1769-1779, 2023

Authors: Zhang, Yong | Chen, Tianzhen | Jiang, Yuqing | Wang, Jianying

Article Type: Research Article

Abstract: Clustering is widely used in data mining and machine learning. The possibilistic c-means clustering (PCM) method loosens the constraint of the fuzzy c-means clustering (FCM) method to solve the problem of noise sensitivity of FCM. But there is also a new problem: overlapping cluster centers are not suitable for clustering non-cluster distribution data. We propose a novel possibilistic c-means clustering method based on the nearest-neighbour isolation similarity in this paper. All samples are taken as the initial cluster centers in the proposed approach to obtain k sub-clusters iteratively. Then the first b samples farthest from the center of …each sub-cluster are chosen to represent the sub-cluster. Afterward, sub-clusters are mapped to the distinguishable space by using these selected samples to calculate the nearest-neighbour isolation similarity of the sub-clusters. Then, adjacent sub-clusters can be merged according to the presented connecting strategy, and finally, C clusters are obtained. Our method proposed in this paper has been tested on 15 UCI benchmark datasets and a synthetic dataset. Experimental results show that our proposed method is suitable for clustering non-cluster distribution data, and the clustering results are better than those of the comparison methods with solid robustness. Show more

Keywords: Clustering, nearest-neighbour isolation similarity, possibilistic c-means, K-means, merging strategy

DOI: 10.3233/JIFS-213502

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1781-1792, 2023

Authors: Wang, Juntao | Kang, Mengna | Fu, Xuesong | Li, Fei

Article Type: Research Article

Abstract: In this paper, we introduce the notion of state monadic residuated lattices and study some of their related properties. Then we prove that the relationship between state monadic algebras of substructural fuzzy logics completely maintains the relationship between corresponding monadic algebras. Moreover, we introduce state monadic filters of state monadic residuated lattice, giving a state monadic filter generated by a nonempty subset of a residuated lattice, and obtain some characterizations of maximal and prime state monadic filters. Finally, we give some characterization of special kinds of state monadic residuated lattices, including simple, semisimple and local state monadic residuated lattices by …state monadic filters. Show more

Keywords: Mathematical fuzzy logic, mondaic residuated lattice, state monadic residuated lattice, state monadic filter

DOI: 10.3233/JIFS-213527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1793-1805, 2023

Authors: Thangavel, Jayakumar | Chinnaraj, Gnanavel | Chandrasekaran, Gokul | Kumarasamy, Vanchinathan

Article Type: Research Article

Abstract: This paper presents the design and development of Modular Multilevel Inverter (MMI) to reduce Total harmonic distortion (THD) using intelligent techniques towards marine applications. Many researchers have described the additional advantage of inverter control challenges such as voltage imbalance, increasing the number of voltage levels, power quality issues, reducing the number of semiconductors switches and achieving higher efficiency. Under the intelligent techniques, the implementation is carried out with aid of Artificial Neural Networks (ANN), Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to calculate the modulation index (ma ) and switching angles (θ ) for MMI. Based on …the calculation, it is trained to form a mapping between inputs and outputs for obtaining reduced Total Harmonics Distortion (THD). The objective of the intelligent controller is to control the inverter for regulating the output voltage with lowest THD. The proposed control structure has been estimated and compared for better robustness in terms of switching angle and modulation index with least THD in the inverter. Simulations and prototype models are made to analyze the controller’s performance, for inverter output voltage and harmonics. This proposed system is designed for marine lighting load application. The FPGA performance with all intelligent methods are analyzed by in SPARTAN3E500 FPGA device. Show more

Keywords: Artificial Neural Networks (ANN), Fuzzy Logic Controller (FLC), Adaptive Neuro-Fuzzy Inference System (ANFIS), Modular Multilevel Inverter (MMI), Total Harmonics Distortion (THD)

DOI: 10.3233/JIFS-220190

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1807-1821, 2023

Authors: Yang, Liu | Li, Ao

Article Type: Research Article

Abstract: In order to investigate the impact of travelers’ adaptive adjustment behaviors on traffic network flow diversion under the assumption of bounded rationality, a multi-agent route choice model with individual interaction mechanism is established by using cumulative prospect theory and evolutionary cellular automata. In the model, travelers are divided into risk-seeking and risk-aversion ones. Based on the reliability of travel time and the idea of cellular genetic algorithm, the dynamic reference points and their evolution rules for travelers with heterogeneous characteristics are designed to enable individual travelers dynamically adjust their travel time budget according to the changes in the decision-making environment. …Finally, the evolution rule of multi-agent reference points is combined with the traditional method of successive average algorithm to design the multi-agent bounded rational route choice evolution algorithm for the solving the problem of traffic flow assignment in a road network. The research main contributions show that the evolution model has well inherited the characteristics of the route flow diversion in the traditional model. Show more

Keywords: Bounded rationality, route choice, cumulative prospect theory, cellular automata, dynamic reference point

DOI: 10.3233/JIFS-220600

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1823-1834, 2023

Authors: Xu, Lan | Yang, Long

Article Type: Research Article

Abstract: The lack of a scientific and complete service quality evaluation system for Medical Caring and Nursing Combined Institutions for the Aged is a critical factor that makes it difficult to improve the quality of their services. Based on the SERVQUAL model, the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged is constructed from tangibles, security, reliability, responsiveness, and empathy. Considering the ambiguity, randomness, grey characteristics, and the interaction between indicators in the service evaluation process of Medical Caring and Nursing Combined Institutions for the Aged, the interval Mahalanobis-Taguchi system (MTS) is introduced into …the grey cloud clustering model, and a service quality evaluation model of the interval MTS— grey cloud clustering is proposed. The Medical Caring and Nursing Combined Institutions for the Aged in four typical cities of Jiangsu Province are taken as examples in this study. Feasibility of the proposed method is verified, and targeted measures are thus proposed to provide stronger support and reference for improving the service quality of these institutions. Show more

Keywords: Service quality, medical caring and nursing combined institutions for the aged, interval Mahalanobis-Taguchi system, grey cloud clustering

DOI: 10.3233/JIFS-221358

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1835-1846, 2023

Authors: Samimi, Navid | Nejatian, Samad | Parvin, Hamid | Bagherifard, Karamollah | Rezaei, Vahideh

Article Type: Research Article

Abstract: Existing fuzzy clustering ensemble approaches do not consider dependability. This causes those methods to be fragile in dealing with unsuitable basic partitions. While many ensemble clustering approaches are recently introduced for improvement of the quality of the partitioning, but lack of a median partition based consensus function that considers more participate reliable clusters, remains unsolved problem. Dealing with the mentioned problem, an innovative weighting fuzzy cluster ensemble framework is proposed according to cluster dependability approximation. For combining the fuzzy clusters, a fuzzy co-association matrix is extracted in a weighted manner out of initial fuzzy clusters according to their dependabilities. The …suggested objective function is a constrained nonlinear objective function and we solve it by sparse sequential quadratic programming (SSQP). Experimentations indicate our method can outperform modern clustering ensemble approaches. Show more

Keywords: Fuzzy cluster ensemble, cluster dependability, consensus function, base clustering, sequential quadratic programming

DOI: 10.3233/JIFS-201950

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1847-1863, 2023

Authors: Xia, Fangfang

Article Type: Research Article

Abstract: For thousands of years, the Chinese people have accumulated and inherited profound cultural traditions. The uniqueness of this cultural tradition lies in its amazing creative wisdom and power. “The ideological and political education of the integration of Chinese regional culture into international students refers to the educative influence of excellent regional culture that can run through the entire international education management system, curriculum system and extracurricular practice system to achieve “all-round, full-process, full-staff” Education goals. The sustainable education value evaluation based on the integration of regional culture into international students’ ideological education is a classical multiple-attribute decision-making (MADM) issue. In …this paper, we extend the geometric Heronian mean (GHM) operator to fuzzy number intuitionistic fuzzy numbers (FNIFNs) to propose the fuzzy number intuitionistic fuzzy GHM (FNIFGHM) operator. Then, the multiple-attribute decision-making (MADM) methods are built on FNIFGHM operator. Finally, a numerical example for sustainable education value evaluation based on the integration of regional culture into international students’ ideological education and some comparative analysis are used to prove the built methods’ credibility and reliability. Show more

Keywords: Multiple-attribute decision-making (MADM), fuzzy number intuitionistic fuzzy numbers (FNIFNs), fuzzy number intuitionistic fuzzy GHM (FNIFGHM) operator, sustainable education value evaluation

DOI: 10.3233/JIFS-222651

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1865-1880, 2023

Authors: Nakshathram, Sajithra | Duraisamy, Ramyachitra

Article Type: Research Article

Abstract: Protein Remote Homology and fold Recognition (PRHR) is the most crucial task to predict the protein patterns. To achieve this task, Sequence-Order Frequency Matrix-Sampling and Deep learning with Smith-Waterman (SOFM-SDSW) were designed using large-scale Protein Sequences (PSs), which take more time to determine the high-dimensional attributes. Also, it was ineffective since the SW was only applied for local alignment, which cannot find the most matches between the PSs. Hence, in this manuscript, a rapid semi-global alignment algorithm called SOFM-SD-GlobalSW (SOFM-SDGSW) is proposed that facilitates the affine-gap scoring and uses sequence similarity to align the PSs. The major aim of this …paper is to enhance the alignment of SW algorithm in both locally and globally for PRHR. In this algorithm, the Maximal Exact Matches (MEMs) are initially obtained by the bit-level parallelism rather than to align the individual characters. After that, a subgroup of MEMs is obtained to determine the global Alignment Score (AS) using the new adaptive programming scheme. Also, the SW local alignment scheme is used to determine the local AS. Then, both local and global ASs are combined to produce a final AS. Further, this resultant AS is considered to train the Support Vector Machine (SVM) classifier to recognize the PRH and folds. Finally, the test results reveal the SOFM-SDGSW algorithm on SCOP 1.53, SCOP 1.67 and Superfamily databases attains an ROC of 0.97, 0.941 and 0.938, respectively, as well as, an ROC50 of 0.819, 0.846 and 0.86, respectively compared to the conventional PRHR algorithms. Show more

Keywords: PRHR, SOFM-SMSW, DCNN, local and global alignment, adaptive programming, maximal exact match, affine-gap scoring, SVM

DOI: 10.3233/JIFS-213522

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1881-1891, 2023

Authors: Zhang, Nian | Zhou, Yifan | Zhou, Qin | Wei, Guiwu

Article Type: Research Article

Abstract: In this paper, an integrated decision-making methodology is proposed to solve the subjectivity and fuzziness in the selection of cold chain logistics service providers (LSPs). Firstly, the social network analysis (SNA) method is applied to select the evaluation criteria of cold chain LSPs, which is based on the systematic literature analysis. Then, a novel combination weighting method that combines the advantages of entropy weight (EW) method and improved analytic hierarchy process (AHP) is constructed to calculate the weight of criteria. Further, the fuzzy comprehensive evaluation (FCE) method is utilized to generate a ranking order of providers and recommend the optimal …provider. Finally, the illustrative example and comparison analysis are provided to prove the validity and feasibility of the approach. In addition, a sensitivity analysis is presented to discuss the stability of the proposed method. In conclusion, this paper innovatively constructs an index system of cold chain LSPs evaluation and selection, and the process of evaluation and selection is also objective. Show more

Keywords: Cold chain logistics service provider, social network analysis, combination weighting method, fuzzy comprehensive evaluation

DOI: 10.3233/JIFS-220780

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1893-1905, 2023

Authors: Kodavali, Lakshminarayana | Kuppuswamy, Sathiyamurthy

Article Type: Research Article

Abstract: Ethereum is one of the popular Blockchain platform. The key component in the Ethereum Blockchain is the smart contract. Smart contracts (SC) are like normal computer programs which are written mostly in solidity high-level object-oriented programming language. Smart contracts allow completing transactions directly between two parties in the network without any middle man or mediator. Modification of the smart contracts are not possible once deployed into the Blockchain. Thus smart contract has to be vulnerable free before deploying into the Blockchain. In this paper, Bayesian Network Model was designed and constructed based on Bayesian learning concept to detect smart contract …security vulnerabilities which are Reentrancy, Tx.origin and DOS. The results showed that the proposed BNMC (Bayesian Network Model Construction) design is able to detect the severity of each vulnerability and also suggest the reasons for the vulnerability. The accuracy of the proposed BNMC results are improved (accuracy 8% increased for both Reentracy and Tx.origin, 6% increased for DOS), compared with traditional method LSTM. This proposed BNMS design and implementation is the first attempt to detect smart contract vulnerabilities using Bayesian Networks. Show more

Keywords: Blockchain, smart contracts, vulnerabilities, Ethereum, Bayesian network, expert knowledge

DOI: 10.3233/JIFS-221898

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1907-1920, 2023

Authors: Guo, Yingchun | Wang, Dan | Yan, Gang | Zhu, Ye

Article Type: Research Article

Abstract: With the increasing variety of display devices, image retargeting has become an indispensable technology for adjusting the aspect ratio of images to adapt to different display terminals. Since the retargeting operation would cause geometric distortion and content loss of the image, the image retargeting quality assessment (IRQA) is necessary to guide the retargeting algorithm’s optimization, selection, and design. Our paper mainly works for systematically reviewing the state-of-the-art technologies in IRQA. And then, this paper further discusses image registration algorithms for matching the original image and the retargeted image. Next, we investigate the feature measurement methods for image retargeting quality evaluation. …To facilitate the quantitative assessment of the IRQA methods, this paper gives a list of publicly open datasets and the performance of the mainstream methods. Finally, some promising research directions towards IRQA are pointed out. From this survey, engineers from the industry may find skills to improve their image retargeting systems, and researchers from academia may find ideas to conduct some innovative work. Show more

Keywords: Registration algorithm, image retargeting, quality assessment

DOI: 10.3233/JIFS-220456

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1921-1942, 2023

Authors: Chen, Junfeng | Zheng, Kaijun | Li, Qingwu | Ayush, Altangerel

Article Type: Research Article

Abstract: The traveling thief problem (TTP) is a typical combinatorial optimization problem that integrates the computational complexity of the traveling salesman problem (TSP) and the knapsack problem (KP). The interdependent and mutually restrictive relationship between these two sub-problems brings new challenges to the heuristic optimization algorithm for solving the TTP problem. This paper first analyzes the performance of three sub-component combined iterative algorithms: Memetic Algorithm with the Two-stage Local Search (MATLS), S5, and CS2SA algorithms, which all employ the Chained Lin-ighan (CLK) algorithm to generate the circumnavigation path. To investigate the influence of different traveling routes on the performance of TTP …solving algorithms, we propose a combinatorial iterative TTP solving algorithm based on the Ant Colony Optimization (ACO) and MAX-MIN Ant System (MMAS). Finally, the experimental investigations suggest that the traveling route generation method dramatically impacts the performance of TTP solving algorithms. The sub-component combined iterative algorithms based on the MMAS algorithm to generate the circumnavigation path has the best practical effect. Show more

Keywords: Traveling thief problem, traveling salesman problem, knapsack problem, ant colony optimization, MAX-MIN ant system

DOI: 10.3233/JIFS-221032

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1943-1956, 2023

Authors: Zhou, Yuqian | Wang, Dong | Li, Qing

Article Type: Research Article

Abstract: Motivated by Hema Freshs new-retail case, we noticed that an effective recommender system is a common way to attract the consumers’ purchasing behaviors and thus enlarge the profit of platform as well as retailers. With the aim of increasing the benefits of all parties in the platform, this paper focusing on not only increasing the effectiveness of the recommender platform but also the evaluation system of measuring the interests of consumer, retailers and platform. In this paper, the interests of the third-party platform are added into the evaluation system, the profit of the third-party platform as an evaluation index is …taken and a 0–1 integer programming model is established which sets the profit of the platform as the objective function. The result of the proposed model and algorithm indicate that: (1) The relevance of products has a significant impact on platform recommendation when the consumers are selecting products. When the correlations of the products are high, the algorithms of selecting the products will have a lower capacity of 1% compared with the algorithm without products correlations. (2) The evaluation of the target products from the target consumers is quite different from the heterogeneity assumptions. When the consumer presentation is taken into consideration, it is hard to evaluate the consumer presence because of the strictly requirement of data for the platform recommendation system. (3) The proposed two-stage solution for the platform recommendation system is optimized in time and space complexity. Total optimization of the proposed method is 30% higher than the greedy algorithms. The two stages are combined together to obtain the approximate solution, and finally provide a reasonable and feasible recommendation for the third-party platform. Show more

Keywords: Third-party platform, advertising recommendation, two-stage model, integer programming algorithm

DOI: 10.3233/JIFS-221236

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1957-1975, 2023

Authors: Kang, Xinhui | Nagasawa, Shin’ya | Wu, Yixiang | Xiong, Xingfu

Article Type: Research Article

Abstract: Bamboo furniture is made of green and environmentally friendly bamboo, there is a unique hand temperature and weaving beauty in addition to bamboo texture and characteristics. In the past, making bamboo furniture relied on the traditional experience of craftsmen, which had less change in appearance and lack of communication with customers, and could not meet the fashion and aesthetic needs of modern people. Therefore, this paper connects deep convolution neural network (DCNN) and deep convolution generative adversarial network (DCGAN) to generate bamboo furniture design that meets customers’ emotional needs. First, based on collecting 17856 bamboo furniture in the market, DCNN …builds product image recognition models and enhances image recognition performance, thereby optimizing computational efficiency and obtaining high-quality output. The optimal recognition rate of emotional data set throughout the chair product is 98.7%, of which the modern chair has a recognition rate of 99.2%, and the recognition rate of fashion bamboo chairs is 98.2%. Second, DCGAN learns a good intermediate feature from a large quantity of non-marked images and automatically generates product styling that arouses the emotional resonance of customers. Finally, the fashion designers use this creative picture as the source of inspiration, cooperate with individual characteristics and trends of the times, then design green sustainable bamboo chairs. These design plans have increased the variety of product modalities, which greatly enhances customers’ emotional satisfaction and increases product sales. The collaborative design method proposed in this paper provides new ideas for generating the emotional design of bamboo furniture, which can also expand to other industrial product designs. Show more

Keywords: Emotional design, artificial intelligence, deep convolution generative adversarial networks, deep convolution neural network, bamboo furniture

DOI: 10.3233/JIFS-221754

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1977-1989, 2023

Authors: Huang, Xiaoqian | Hu, Yanrong | Liu, Hongjiu

Article Type: Research Article

Abstract: Most methods for evaluating a company’s financial performance currently focus on scoring, when there is a large amount of data, it is difficult to distinguish the company’s financial status. To cluster and predict the financial performance of companies, a hybrid model based on the fuzzy C-means clustering algorithm (FCM) and convolutional neural network (CNN) is proposed in this paper. Pearson correlation analysis was first performed on the indicators to ensure that they are not correlated with each other and to avoid indicator redundancy. The entropy method determined the weight of each index and ensured the high validity of the selected …indicators. Then, FCM clustering was carried out, and the performance of each company was clustered according to the indexes after data preprocessing with clustering labels. The processed data and labels were introduced into CNN to predict the level. The empirical study showed that the FCM-CNN model was superior to other machine learning models, which proved that this model has better clustering and forecasting ability, and could be applied to the prediction of corporate financial performance. Show more

Keywords: Fuzzy C-means clustering, convolutional neural network, performance clustering and prediction

DOI: 10.3233/JIFS-221995

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1991-2006, 2023

Authors: Shi, Zhihu

Article Type: Research Article

Abstract: In order to improve the accuracy of cloud manufacturing service recommendation results, improve recommendation efficiency and user satisfaction, a cloud manufacturing service recommendation model based on GA-ACO and carbon emission hierarchy is proposed. According to the concept of cloud manufacturing, a cloud manufacturing platform including resource layer, service layer, operation layer and application layer is constructed, and then a cloud manufacturing service quality perception model is established; genetic algorithm is used to realize cloud manufacturing service selection, and ACO algorithm is used to optimize cloud manufacturing service portfolio; According to the selection and combination results of the constructed cloud manufacturing …platform and cloud manufacturing service, taking the carbon emission field as an example, a hierarchical hierarchical model is constructed, and this model is used to further construct a cloud manufacturing service recommendation model from coarse to fine, from global to local; Identify user demand scenarios and implement cloud manufacturing service recommendations. The experimental results show that the recommendation results of the proposed method have high accuracy and efficiency, and can be recognized by most users. Show more

Keywords: GA-ACO, carbon emission hierarchy, service recommendation, quality perception model, cloud manufacturing platform

DOI: 10.3233/JIFS-222386

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2007-2017, 2023

Authors: Gao, Mengyuan | Ma, Shunagbao | Zhang, Yapeng | Xue, Yong

Article Type: Research Article

Abstract: Automatic identification picking robot is an important research content of agricultural modernization development. In order to overcome the difficulty of picking robots for accurate visual inspection and positioning of apples in a complex orchard, a detection method based on an instance segmentation model is proposed. To reduce the number of model parameters and improve the detection speed, the backbone feature extraction network is replaced from the Resnet101 network to the lightweight GhostNet network. Spatial Pyramid Pooling (SPP) module is used to increase the receptive field to enhance the semantics of the output network. Compared with Resnet101, the parameter quantity of …the model is reduced by 90.90%, the detection speed is increased from 5 frames/s to 10 frames/s, and the detection speed is increased by 100%. The detection result is that the accuracy rate is 91.67%, the recall rate is 97.82%, and the mAP value is 91.68%. To solve the repeated detection of fruits due to the movement of the camera, the Deepsort algorithms was used to solve the multi-tracking problems. Experiments show that the algorithm can effectively detect the edge position information and categories of apples in different scenes. It can be an automated apple-picking robot. The vision system provides strong technical support. Show more

Keywords: Instance segmentation, apple detection, GhostNet, Spatial Pyramid Pooling

DOI: 10.3233/JIFS-213072

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2019-2029, 2023

Authors: Vanam, Harika | JebersonRetna Raj, R | Janga, Vijaykumar

Article Type: Research Article

Abstract: Blogs, internet forums, social networks, and micro-blogging sites are some of the growing number of places where users can voice their opinions. Opinions on any given product, issue, service, or idea are contained in data, making them a valuable resource in their own right. Popular social networking services like Twitter, Facebook, and Google+ allows expressing views on a variety of topics, participating in discussions, or sending messages to a global user. Twitter sentiment analysis has received a lot of attention recently.Sentiment analysis is finding how a person feels about a topic from their written response about it and it can …be separated into positive and negative through its use. Doing so enables to classify the tweets made by a user in to appropriate classification category based on which some decisions can be made. The literature proposed approaches to develop the classifiers on the Twitter datasets. Operations, including tokenization, stop-word removal, and stemming will be performed. NLP converts the text to a machine-readable representation. Artificial Intelligence (AI) combines NLP data to evaluate if a situation is positive or negative. The document’s subjectivity can be identified using ML and NLP techniques to categorize them in to positive, neutral, or negative. Performing sentiment analysis in Twitter data can be tedious due to limited size, unstructured nature, misspellings, slang, and abbreviations. For this task, a Tweet Analyzing Model for Cluster Set Optimization with Unique Identifier Tagging (TAM-CSO-UIT) was built using prospects to determine positive or negative sentiment in tweets obtained from Twitter. This approach assigns a +ve/-ve value to each entry in the Tweet database based on probability assignment using n-gram model. To perform this effectively the tweet dataset is considered as a sliding window of length L. The proposed model accurately analyses and classifies the tweets. Show more

Keywords: Sentiment analysis, tweet analysis, tweet classification, unique identifier tagging

DOI: 10.3233/JIFS-220033

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2031-2039, 2023

Authors: Jiang, Yirong | Qiu, Jianwei | Meng, Fangxiu

Article Type: Research Article

Abstract: In this article, we explore the question of existence and finite time stability for fuzzy Hilfer-Katugampola fractional delay differential equations. By using the generalized Gronwall inequality and Schauder’s fixed point theorem, we establish existence of the solution, and the finite time stability for the presented problems. Finally, the effectiveness of the theoretical result is shown through verification and simulations for an example.

Keywords: Finite time stability, fuzzy Hilfer-Katugampola fractional differential equations, delay

DOI: 10.3233/JIFS-220588

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2041-2050, 2023

Authors: Shanmugam, Gowri | Thanarajan, Tamilvizhi | Rajendran, Surendran | Murugaraj, Sadish Sendil

Article Type: Research Article

Abstract: Clustering plays a fundamental task in the process of data mining, which remains more demanding due to the ever-increasing dimension of accessible datasets. Big data is considered more populous as it has the ability to handle various sources and formats of data under numerous highly developed technologies. This paper devises a robust and effective optimization-based Internet of Things (IoT) routing technique, named Student Psychology Based Optimization (SPBO) -based routing for the big data clustering. When the routing phase is done, big data clustering is carried out using the Deep Fractional Calculus-Improved Invasive Weed Optimization fuzzy clustering (Deep FC-IIWO fuzzy clustering) …approach. Here, the Mapreduce framework is used to minimizing the over fitting issues during big data clustering. The process of feature selection is performed in the mapper phase in order to select the major features using Minkowski distance, whereas the clustering procedure is carried out in the reducer phase by Deep FC-IIWO fuzzy clustering, where the FC-IIWO technique is designed by the hybridization of Improved Invasive Weed Optimizer (IIWO) and Fractional Calculus (FC). The developed SPBO-based routing approach achieved effective performance in terms of energy, clustering accuracy, jaccard coefficient, rand coefficient, computational time and space complexity of 0.605 J, 0.935, 0.947, 0.954, 2100.6 s and 72KB respectively. Show more

Keywords: Internet of Things, routing, big data, big data clustering, student psychology based optimization

DOI: 10.3233/JIFS-221391

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2051-2063, 2023

Authors: Hou, Shuai | Yu, Junqi | Su, Yucong | Liu, Zongyi | Dai, Junwei

Article Type: Research Article

Abstract: An improved mayfly algorithm is proposed for the energy saving optimization of parallel chilled water pumps in central air conditioning system, with the minimum energy consumption of parallel pump units as the optimization objective and the speed ratio of each pump as the optimization variable for the solution. For the problem of uneven random initialization of mayflies, the variable definition method of Circle chaotic mapping is used to make the initial position of the population uniformly distributed in the solution space, and the mayfly fitness value and the optimal fitness value are incorporated into the calculation of the weight coefficient, …which better balances the global exploration and local exploitation of the algorithm. For the problem that the algorithm is easy to fall into the local optimum at the later stage, a multi-subpopulation cooperative strategy is proposed to improve the global search ability of the algorithm. Finally, the performance of the improved mayfly algorithm is tested with two parallel pumping system cases, and the stability and time complexity of the algorithm are verified. The experiments show that the algorithm can get a better operation strategy in solving the parallel water pump energy saving optimization problem, and can achieve energy saving effect of 0.72%  8.68% compared with other optimization algorithms, and the convergence speed and stability of the algorithm have been significantly improved, which can be better applied to practical needs. Show more

Keywords: Energy saving optimization, parallel water pump, improved mayfly algorithm, circle chaotic mapping, multi subpopulation cooperative strategy

DOI: 10.3233/JIFS-222783

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2065-2083, 2023

Authors: Zhang, Yun | Zhang, Yude | Yu, Shujuan | Wang, Xiumei | Zhao, Shengmei | Wang, Weigang | Liu, Yan | Ding, Keke

Article Type: Research Article

Abstract: The lack of training data in new domain is a typical problem for named entity recognition (NER). Currently, researchers have introduced “entity trigger” to improve the cost-effectiveness of the model. However, it still required the annotator to attach additional trigger label, which increases the workload of the annotator. Moreover, this trigger applies only to English text and lacks research into other languages. Based on this problem, we have proposed a more cost-effective trigger tagging method and matching network. The approach not only automatic tagging entity triggers based on the characteristics of Chinese text, but also adds mogrifier LSTM to the …matching network to reduce context-free representation of input tokens. Experiments on two public datasets show that our automatic trigger is effective. And it achieves better performances with automatic trigger than other state-of-the-art methods (The F1-scores increased by 1∼4). Show more

Keywords: Chinese NER, entity trigger, Mogrifier LSTM, TMN, m-TMN

DOI: 10.3233/JIFS-212824

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2085-2096, 2023

Authors: Liu, Jing | Tian, Shengwei | Yu, Long | Long, Jun | zhou, Tiejun | Wang, Bo

Article Type: Research Article

Abstract: Sarcasm is a way to express the thoughts of a person. The intended meaning of the ideas expressed through sarcasm is often the opposite of the apparent meaning. Previous work on sarcasm detection mainly focused on the text. But nowadays most information is multi-modal, including text and images. Therefore, the task of targeting multi-modal sarcasm detection is becoming an increasingly hot research topic. In order to better detect the accurate meaning of multi-modal sarcasm information, this paper proposed a multi-modal fusion sarcasm detection model based on the attention mechanism, which introduced Vision Transformer (ViT) to extract image features and designed …a Double-Layer Bi-Directional Gated Recurrent Unit (D-BiGRU) to extract text features. The features of the two modalities are fused into one feature vector and predicted after attention enhancement. The model presented in this paper gained significant experimental results on the baseline datasets, which are 0.71% and 0.38% higher than that of the best baseline model proposed on F1-score and accuracy respectively. Show more

Keywords: Multi-modal, sarcasm detection, Attention, ViT, D-BiGRU

DOI: 10.3233/JIFS-213501

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2097-2108, 2023

Authors: Chandnani, Neeraj | Verma, Kirti

Article Type: Research Article

Abstract: Smart gadgets have created a buzz in the market today; you will find everything smart today. Like a smartwatch, smart band, smart led, smart heater, etc., and transmitting data securely between all these devices is necessary as an outcome; IoT devices developed defenseless to numerous devices. Faith replicas were predictable, significant simultaneous approaches to defend a large communication system in contrast to evil virtual outbreaks. In this research paper, the various Type-II fuzzy logic models are evaluated, which provides enhanced data security for IoT devices. Also, compression is applied between all data encryption techniques based on the parameters like Reproduction …time (circles), Program series (m), Quantity of device nodes, Number of spiteful nodes, and Total interval. Show more

Keywords: Type-II fuzzy logic, internet of things, encryption

DOI: 10.3233/JIFS-220570

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2109-2116, 2023

Authors: Chen, Deguang | Zhou, Jie

Article Type: Research Article

Abstract: MobileBert is a generic lightweight model suffering from a large network depth and parameter cardinality. Therefore, this paper proposes a secondary lightweight model entitled LightMobileBert, which retains the bottom 12 Transformers structure of the pre-trained MobileBert and utilizes the tensor decomposition technique to process the model to skip pre-training and further reduce the parameters. At the same time, the joint loss function is constructed based on the improved Supervised Contrastive Learning loss function and the Cross-Entropy loss function to improve performance and stability. Finally, the LMBert_Adam optimizer, an improved Bert_Adam optimizer, is used to optimize the model. The experimental results …demonstrate that LightMobileBert has a comparatively higher performance than MobileBert and other popular models while requiring 57% fewer network parameters than MobileBert, confirming that LightMobileBert retains a higher performance while being lightweight. Show more

Keywords: Natural language processing, lightweight model, tensor decomposition, supervised contrastive learning

DOI: 10.3233/JIFS-221985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2117-2129, 2023

Authors: Jayachandran, Shana | Dumala, Anveshini

Article Type: Research Article

Abstract: The Corona virus pandemic has affected the normal course of life. People all over the world take the social media to express their opinions and general emotions regarding this phenomenon. In a relatively short period of time, tweets about the new Corona virus increased by an amount never before seen on the social networking site Twitter. In this research work, Sentiment Analysis of Social Media Data to Identify the Feelings of Indians during Corona Pandemic under National Lockdown using recurrent neural network is proposed. The proposed method is analyzed using four steps: that is Data collection, data preparation, Building sentiment …analysis model and Visualization of the results. For Data collection, the twitter dataset are collected from social networking platform twitter by application programming interface. For Data preparation, the input data set are pre-processed for removing URL links, removing unnecessary spaces, removing punctuations and numbers. After data cleaning or preprocessing entire particular characters and non-US characters from Standard Code for Information Interchange, apart from hash tag, are extracted as refined tweet text. In addition, entire behaviors less than three alphabets are not assumed at analysis of tweets, lastly, tokenization and derivation was carried out by Porter Stemmer to perform opinion mining. To authenticate the method, categorized the tweets linked to COVID-19 national lockdown. For categorization, recurrent neural method is used. RNN classify the sentiment classification as positive, negative and neutral sentiment scores. The efficiency of the proposed RNN based Sentimental analysis classification of COVID-19 is assessed various performances by evaluation metrics, like sensitivity, precision, recall, f-measure, specificity and accuracy. The proposed method attains 24.51%, 25.35%, 31.45% and 24.53% high accuracy, 43.51%, 52.35%, 21.45% and 28.53% high sensitivity than the existing methods. Show more

Keywords: COVID 19, sentiment analysis, data analytics, lockdown, classification, recurrent neural network

DOI: 10.3233/JIFS-221883

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2131-2146, 2023

Authors: Liu, Zhongpu | Liu, Jianjuan

Article Type: Research Article

Abstract: For the issues of the ant colony algorithm (ACO) to solving the problems in mobile robot path planning, such as the slow optimization speed and the redundant paths in planning results, a high-precision improved ant colony algorithm (IPACO) with fast optimization and compound prediction mechanism is proposed. Firstly, aiming at maximizing the possibility of optimal node selection in the process of path planning, a composite optimal node prediction model is introduced to improve the state transition function. Secondly, a pheromone model with initialize the distribution and “reward or punishment” update mechanism is used to updates the global pheromone concentration directionally, …which increases the pheromone concentration of excellent path nodes and the heuristic effect; Finally, a prediction-backward mechanism to deal with the “deadlock” problem in the ant colony search process is adopted in the IPACO algorithm, which enhance the success rate in the ACO algorithm path planning. Five groups of different environments are selected to compare and verify the performance of IPACO algorithm, ACO algorithm and three typical path planning algorithms. The experimental simulation results show that, compared with the ACO algorithm, the convergence speed and the planning path accuracy of the IPACO algorithm are improved by 57.69% and 12.86% respectively, and the convergence speed and the planning path accuracy are significantly improved; the optimal path length, optimization speed and stability of the IPACO algorithm are improved. Which verifies that the IPACO algorithm can effectively improve the environmental compatibility and stability of the ant colony algorithm path planning, and the effect is significantly improved. Show more

Keywords: Mobile robot, Path planning, Path prediction model, Ant colony optimization algorithm, Reward and punishment update

DOI: 10.3233/JIFS-222211

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2147-2162, 2023

Authors: Guan, Xuechong

Article Type: Research Article

Abstract: Soft separation axioms and their properties are popular topic in the research of soft topological spaces. Two types of separation axioms T i -I and T i -II (i  = 0, 1, ⋯ , 4) which take single point soft sets and soft points as separated objects have been given in [18 ] and [30 ] respectively. In this paper we show that a soft T 0 -II(T 1 -II, T 2 -II, and T 4 -II respectively) space is a soft T 0 -I(T 1 -I, T 2 -I, and T 4 -I respectively) space, if the initial universe …set X and the parameter set E are sets of two elements. Some examples are given to explain that a soft T i -I may not to be a soft T i -II space (i  = 0, 1, ⋯ , 4). Show more

Keywords: Soft set, soft topological space, single point soft set, soft point, separation axiom

DOI: 10.3233/JIFS-212432

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2163-2171, 2023

Authors: Marimuthu, Poorani | Vaidehi, V.

Article Type: Research Article

Abstract: Remote Health Monitoring (RHM) is an important research topic among the researchers, where many challenges are to be addressed with respect to communication, device, synchronization, data analysis, knowledge inferencing, database maintenance, security, timely notification etc. Among these multi challenges, personalization of health data and scheduling of alert generation have been focused on this work. Recognizing the regular health pattern of each individual helps in diagnosing the disease accurately (reduces the False Alarm Ratio (FAR)) and provides the necessary treatment earlier. Similarly, in real time, with multiple patients, the latency should be minimal for timely alert generation. To address these two …challenges, a Density-based K- means clustering (DbK-meansC) approach has been proposed in this work that personalize the vital health values. From the personalized health values the abnormalities in the health status of a person can be detected earlier. Here the health records are continuously updated with respect to health values that reflects in personalization of health records. If any abnormality noted in the health values, then the proposed work sends an alert message to the caretaker / the respective doctor using a dynamic preemptive priority scheduling scheme. The scheduling is done with respect to the severity levels of the vital health values of each individual respectively. The arrived results show that the proposed personalized abnormality detection RHM model generate alerts with minimum latency in terms of response and waiting time in a multi patient environment. With proper personalization, the obtained specificity and sensitivity are 91.56% and 92.87% respectively and the computational time is reduced as the degree of personalization increases. Show more

Keywords: Density based clustering, personalization, dynamic priority scheduler, latency, severity index

DOI: 10.3233/JIFS-220539

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2173-2190, 2023

Authors: Han, Chao-Qun | Zhang, Xiao-Hong | Ma, Hong-Wei | Hu, Zhi-Hua

Article Type: Research Article

Abstract: Since the tax of carbon emission is popular and consumers are exhibiting low-carbon preference, a manufacturer may invest to adopt carbon emission reduction (CER) technologies to produce green products. In face of high cost of CER investment and random yield in low carbon production processes for the manufacturer, this paper explores the incentive role of the contracts of revenue-sharing (RS) and cost-sharing with subsidy (CSS) offered by a retailer in a low-carbon supply chain. Theoretical analysis and numerical experiments show that both RS and CSS can promote the manufacturer’s Carbon Emission Reduction (CER) efforts and improve the efficiency of the …supply chain, and RS boosts these more than CSS. RS and CSS can also decrease firms’ profit losses due to yield uncertainty, and RS also decreases firms’ profit losses more than CSS. Moreover, to motivate manufacturer’s CER efforts, the government should levy the highest-possible carbon tax under RS, the medium-level carbon tax under CSS, and the lowest-possible carbon tax for the decentralized case, and levy the same carbon tax on the centralized case with that under RS. Show more

Keywords: Yield uncertainty, retailer-driven incentive, carbon emission reduction, carbon tax, revenue-sharing, cost-sharing with su

DOI: 10.3233/JIFS-220354

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2191-2206, 2023

Authors: Jin, Feifei | Jiang, Hao | Pei, Lidan

Article Type: Research Article

Abstract: Single-valued neutrosophic set is an important tool for describing fuzzy information and solving fuzzy decision problems. It is known that entropy can be applied to measure the degree of uncertainty of evaluation information and determine the important degree of objects, similarity is mainly used to capture the internal relationship of the evaluation objects. Therefore, single-valued neutrosophic entropy and single-valued neutrosophic similarity are two important topics in multi-attribute decision-making (MADM) problems. In this paper, some new single-valued neutrosophic entropy and similarity methods are first proposed to deal with uncertain and fuzzy decision problems with the help of exponential function. Then, the …proofs of exponential entropy and exponential similarity measures fit the definition of single-valued neutrosophic similarity and single-valued neutrosophic entropy are presented. Moreover, we apply these two measure methods to cope with the MADM problems, then a new MADM method is provided. Finally, the developed MADM method is applied to the practical example of investment decision, and comparisons with other methods are conducted to show the advantages and rationality of our method. Show more

Keywords: Single-valued neutrosophic set, entropy, similarity measure, multi-attribute decision-making

DOI: 10.3233/JIFS-220566

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2207-2216, 2023

Authors: Altinsoy, Ufuk | Aktepe, Adnan | Ersoz, Suleyman

Article Type: Research Article

Abstract: In today’s understanding, the universities are considered as service providers besides their institutional functions. Because the universities shape the future of the country via the services they provide, it is a necessity that their service quality must be assessed by using scientific analyses, and their service quality must be improved based on such scientific findings. The Generation Z, whose members are currently receiving university education carries unique features that distinguish them from the previous generations. When this fact is considered, it is understood that the constant research and monitoring of the learning environment of the Generation Z is important. In …this study, as a result of a detailed literature search, a scale consisting of 7 dimensions and 36 indicators was developed in order to measure the higher education service quality of the Z generation. The validity and reliability tests of this scale are completed via the convergent and divergent validity analyses, Exploratory Factor Analysis (EFA), and Confirmatory Factor Analysis (CFA). Because the answers provided to the surveys reflect the personal evaluation of the participants, the Fuzzy Logic is employed, and the study is conducted by using the fuzzy modelling and fuzzy ranking. As a result of this study, the General Satisfaction Index is created, and improving recommendations are carried out based on the scores. Show more

Keywords: Service quality, fuzzy logic, artificial intelligence, higher education, generation-z

DOI: 10.3233/JIFS-220985

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2217-2230, 2023

Authors: Han, Yongguang | Yan, Rong | Gou, Chang

Article Type: Research Article

Abstract: Today’s higher vocational colleges have already put innovation and entrepreneurship education at the top of vocational education, and integrated it into the entire education and teaching work, in order to continuously improve the innovation and entrepreneurship ability of students in higher vocational colleges and improve their job competition. strength, and improve the quality of education in higher vocational colleges. The quality evaluation of innovation and entrepreneurship education in vocational colleges is a classical multiple attribute decision making (MADM) problems. In this paper, we introduced some calculating laws on interval-valued intuitionistic fuzzy sets (IVIFSs), Hamacher sum and Hamacher product and further …propose the induced interval-valued intuitionistic fuzzy Hamacher power ordered weighted geometric (I-IVIFHPOWG) operator. Meanwhile, we also study some ideal properties of built operator. Then, we apply the I-IVIFHPOWG operator to deal with the MADM problems under IVIFSs. Finally, an example for quality evaluation of innovation and entrepreneurship education in vocational colleges is used to test this new approach. Show more

Keywords: Multiple attribute decision making (MADM), interval-valued intuitionistic fuzzy sets (IVIFSs), IOWG operator, I-IVIFHPOWG operator, innovation and entrepreneurship education

DOI: 10.3233/JIFS-221701

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2231-2249, 2023