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

Authors: Fathy, E. | Ammar, E.

Article Type: Research Article

Abstract: In this research, we use the harmonic mean technique to present an interactive strategy for addressing neutrosophic multi-level multi-objective linear programming (NMMLP) problems. The coefficients of the objective functions of level decision makers and constraints are represented by neutrosophic numbers. By using the interval programming technique, the NMMLP problem is transformed into two crisp MMLP problems, one of these problems is an MMLP problem with all of its coefficients being upper approximations of neutrosophic numbers, while the other is an MMLP problem with all of its coefficients being lower approximations of neutrosophic numbers. The harmonic mean method is then used …to combine the many objectives of each crisp problem into a single objective. Then, a preferred solution for NMMLP problems is obtained by solving the single-objective linear programming problem. An application of our research problem is how to determine the optimality the cost of multi-objective transportation problem with neutrosophic environment. To demonstrate the proposed strategies, numerical examples are solved. Show more

Keywords: Neutrosophic number, multi-level linear programming, multi-objective programming, harmonic mean technique, transportation problem

DOI: 10.3233/JIFS-211374

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2251-2267, 2023

Authors: Yang, Ruicheng | Wang, Pucong | Qi, Ji

Article Type: Research Article

Abstract: Categorical Boost (CatBoost) is a new approach in credit rating. In the process of classification and prediction using CatBoost, parameter tuning and feature selection are two crucial parts, which affect the classification accuracy of CatBoost significantly. This paper proposes a novel SSA-CatBoost model, which mixes Sparrow Search Algorithm (SSA) and CatBoost to improve classification and prediction accuracy for credit rating. In terms of parameter tuning, the SSA-CatBoost optimization obtains the most optimal parameters by iterating and updating the sparrow’s position, and utilize the optimal parameter to improve the accuracy of classification and prediction. In terms of feature selection, a novel …wrapping method called Recursive Feature Elimination algorithm is adopted to reduce the adverse impact of noise data on the results, and further improves calculation efficiency. To evaluate the performance of the proposed SSA-CatBoost model, P2P lending datasets are employed to assess the prediction results, then the interpretable Shap package is used to explain the reason why the proposed model considers a sample as good or bad. Consequently, the experimental results show that the SSA-CatBoost model has an ideal accuracy in classification and prediction for credit rating by comparing the SSA-CatBoost model with the CatBoost model and other well-known machine learning models. Show more

Keywords: CatBoost, sparrow search algorithm, parameter tuning, feature selection, credit rating

DOI: 10.3233/JIFS-221652

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2269-2284, 2023

Authors: Sophia, Sundar Singh Sheeba Jeya | Diwakaran, S.

Article Type: Research Article

Abstract: Glaucoma is an irreversible blindness that affects the people over the age of 40 years. Many approaches are proposed to detect glaucoma in image by dealing with its complex data. Redundancy is the major problem in medical image which could lead to increased false positive and false negative rates. This paper proposed a three-structure CNN optimized with Hybrid optimization approach for glaucoma detection and severity differentiation. The CNN structure is designed with three sub-groups to do attention prediction, segmentation and classification. The mathematical equation for Loss function is derived for the CNN structure with three hyper-parameters which is optimized with …Hybrid approach. Hybrid optimization approach consist of Muddy Electric fish Optimization and Grass hopper optimization algorithm for exploration and exploitation processes. The proposed method is designed in a Matlab and validated with LAG and Rim-One database. The proposed method achieved accuracy greater than 95% and other metrics like F2 and AUC has reached 98%. Show more

Keywords: Hybrid optimization, Glaucoma detection, image processing, convolutional neural network

DOI: 10.3233/JIFS-221262

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2285-2303, 2023

Authors: Tatar, Veysel | Yazicioglu, Osman | Ayvaz, Berk

Article Type: Research Article

Abstract: Work-related musculoskeletal disorders (WMSDs) are the most common occupational health problems in agriculture workers due to repetitive and excessive force movement activities involved in their job processes. The Fine-Kinney method has been commonly used as a quantitative evaluation method in risk assessment studies. Classically, the risk value via Fine–Kinney is calculated by the mathematical multiplication irrespective of the degree of importance of each risk parameter (probability (P), exposure (E), and consequence (C)). Hence, a novel risk management model was proposed based on integrating Fine-Kinney and spherical fuzzy AHP-TOPSIS. First, each risk parameter is weighted using the spherical fuzzy AHP (SF-AHP). …Second, the spherical fuzzy TOPSIS (SF-TOPSIS) method is used for hazard ranking. The proposed model is applied to evaluate risks in tea harvesting workers for work-related musculoskeletal disorders. Subsequently, a sensitivity analysis is carried out to test the proposed model. Finally, we compare the proposed model’s applicability and effectiveness with the spherical fuzzy COmbinative Distance-based ASsessment (SF-CODAS) method based on Fine-Kinney. The ranking similarity between the proposed Fine-Kinney-based SF-TOPSIS and SF-CODAS methods is checked by applying Spearman’s rank correlation coefficient, in which 92% of rankings are matched. Show more

Keywords: Risk assessment, Fine–Kinney method, Spherical fuzzy sets, Work-related musculoskeletal disorders (WMSDs), AHP-TOPSIS

DOI: 10.3233/JIFS-222652

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2305-2323, 2023

Authors: He, Mingjun | Che, Jinxing | Jiang, Zheyong | Zhao, Weihua | Wan, Bingrong

Article Type: Research Article

Abstract: Understanding and forecasting air quality index (AQI) plays a vital role in guiding the reduction of air pollution and helping social sustainable development. By combining fuzzy logic with decomposition techniques, ANFIS has become an important means to analyze the data resources, uncertainty and fuzziness. However, few studies have paid attention to the noise of decomposed subseries. Therefore, this paper presents a novel decomposition-denoising ANFIS model named SSADD-DE-ANFIS (Singular Spectrum Analysis Decomposition and Denoising-Differential Evolution-Adaptive Neuro-Fuzzy Inference System). This method uses twice SSA to decompose and denoise the AQI series, respectively, then fed the subseries obtained after the decomposition and denoising …into the constructed ANFIS for training and predicting, and the parameters of ANFIS are optimized using DE. To investigate the prediction performance of the proposed model, twelve models are included in the comparisons. The experimental results of four seasons show that: the RMSE of the proposed SSADD-DE-ANFIS model is 1.400628, 0.63844, 0.901987 and 0.634114, respectively, which is 19.38%, 21.27%, 20.43%, 21.27% and 87.36%, 88.12%, 88.97%, 88.71% lower than that of the single SSA decomposition and SSA denoising. Diebold-Mariano test is performed on all the prediction results, and the test results show that the proposed model has the best prediction performance. Show more

Keywords: Air quality index forecasting, decomposition-denoising, Adaptive Neuro-Fuzzy Inference System, singular spectrum analysis, differential evolution algorithm

DOI: 10.3233/JIFS-222920

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2325-2349, 2023

Authors: Nizarudeen, Shanu | Shunmugavel, Ganesh R.

Article Type: Research Article

Abstract: Intracerebral haemorrhage (ICH) is defined as bleeding occurs in the brain and causes vascular abnormality, tumor, venous Infarction, therapeutic anticoagulation, trauma property, and cerebral aneurysm. It is a dangerous disease and increases high mortality rate within the age of 15 to 24. It may be cured by finding what type of ICH is affected in the brain within short period with more accuracy. The previous method did not provide adequate accuracy and increase the computational time. Therefore, in this manuscript Detection and Categorization of Acute Intracranial Hemorrhage (ICH) subtypes using a Multi-Layer DenseNet-ResNet Architecture with Improved Random Forest Classifier (IRF) …is proposed to detect the subtypes of ICH with high accuracy, less computational time with maximal speed. Here, the brain CT images are collected from Physionet repository publicly dataset. Then the images are pre-processed to eliminate the noises. After that, the image features are extracted by using multi layer Densely Connected Convolutional Network (DenseNet) combined with Residual Network (ResNet) architecture with multiple Convolutional layers. The sub types of ICH (Epidural Hemorrhage (EDH), Subarachnoid Hemorrhage (SAH), Intracerebral Hemorrhage (ICH), Subdural Hemorrhage (SDH), Intraventricular Hemorrhage (IVH), normal is classified by using Improved Random Forest (IRF) Classifier with high accuracy. The simulation is activated in MATLAB platform. The proposed Multilayer-DenseNet-ResNet-IRF approach attains higher accuracy 23.44%, 31.93%, 42.83%, 41.9% compared with existing approaches, like Detection with classification of intracranial haemorrhage on CT images utilizing new deep-learning algorithm (ICH-DC-CNN), Detection with classification of intracranial haemorrhage on CT images utilizing new deep-learning algorithm (ICH-DC-CNN-ResNet-50), Shallow 3D CNN for detecting acute brain hemorrhage from medical imaging sensors (ICH-DC-S-3D-CNN), Convolutional neural network: a review of models, methods and applications to object detection (ICH-DC-CNN-AlexNet) respectively. Show more

Keywords: Acute Intracranial Hemorrhage (ICH), Computerized Tomography (CT), Residual Network (ResNet), Densely Connected Convolutional Networks (DenseNet), Extreme Gradient Boosting (XGBoost) Classifier

DOI: 10.3233/JIFS-221177

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2351-2366, 2023

Authors: Ma, Zhipeng | Guo, Hongyue | Wang, Lidong

Article Type: Research Article

Abstract: Forecasting trend and variation ranges for time series has been challenging but crucial in real-world modeling. This study designs a hybrid time series forecasting (FIGDS) model based on granular computing and dynamic selection strategy. Firstly, with the guidance of the principle of justifiable granularity, a collection of interval-based information granules is formed to characterize variation ranges for time series on a specific time domain. After that, the original time series is transformed into granular time series, contributing to dealing with time series at a higher level of abstraction. Secondly, the L 1 trend filtering method is applied to extract …trend series and residual series. Furthermore, this study develops hybrid predictors of the trend series and residual series for forecasting the variation range of time series. The ARIMA model is utilized in the forecasting task of the residual series. The dynamic selection strategy is employed to identify the ideal forecasting models from the pre-trained multiple predictor system for forecasting the test pattern of the trend series. Eventually, the empirical experiments are carried out on ten time series datasets with a detailed comparison for validating the effectiveness and practicability of the established hybrid time series forecasting method. Show more

Keywords: Granular computing, information granule, time series forecasting, dynamic selection, L1 trend filtering

DOI: 10.3233/JIFS-222746

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2367-2379, 2023

Authors: Riali, Ishak | Fareh, Messaouda | Ibnaissa, Mohamed Chakib | Bellil, Mounir

Article Type: Research Article

Abstract: Medical decisions, especially when diagnosing Hepatitis C, are challenging to make as they often have to be based on uncertain and fuzzy information. In most cases, that puts doctors in complex yet uncertain decision-making situations. Therefore, it would be more suitable for doctors to use a semantically intelligent system that mimics the doctor’s thinking and enables fast Hepatitis C diagnosis. Fuzzy ontologies have been used to remedy the shortcomings of classical ontologies by using fuzzy logic, which allows dealing with fuzzy knowledge in ontologies. Moreover, Fuzzy Bayesian networks are well-known and widely used to represent and analyze uncertain medical data. …This paper presents a system that combines fuzzy ontologies and Bayesian networks to diagnose Hepatitis C. The system uses a fuzzy ontology to represent sequences of uncertain and fuzzy data about patients and some features relevant to Hepatitis C diagnosis, enabling more reusable and interpretable datasets. In addition, we propose a novel semantic diagnosis process based on a fuzzy Bayesian network as an inference engine. We conducted an experimental study on 615 real cases to validate the proposed system. The experimentation allowed us to compare the results of existing machine learning algorithms for the Hepatitis C diagnosis with the results of our proposed system. Our solution shows promising results and proves effective for fast medical assistance. Show more

Keywords: Fuzzy ontology, medical diagnosis, semantic representation, fuzzy Bayesian networks, uncertainty, reasoning

DOI: 10.3233/JIFS-213563

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2381-2395, 2023

Authors: Jindaluang, Wattana

Article Type: Research Article

Abstract: A machine learning method is now considered capable of accurately segmenting images. However, one significant disadvantage of this strategy is that it requires a lengthy training phase and an extensive training dataset. This article uses an image segmentation by histogram thresholding approach that does not require training to overcome this difficulty. This article proposes straightforward and time-optimal algorithms, which are guaranteed by mathematical proofs. Furthermore, we experiment with the proposed algorithms using 100 images from a standard database. The results show that, while their performances are not significantly different, the two proposed methods are roughly 10 and 20 times faster …than the most simple and optimal method, Brute Force. They also show that the proposed algorithms can deal with bimodal images and images with various shapes of the image histogram. Because our proposed algorithms are the most efficient and effective. As a result, they can be used for real-time segmentations and as a pre-processing approach for multiple object segmentation. Show more

Keywords: Image segmentation, histogram thresholding, dynamic programming, optimization problem, time-optimal algorithm

DOI: 10.3233/JIFS-222259

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2397-2411, 2023

Authors: Zhang, Luyang | Wang, Huaibin | Wang, Haitao

Article Type: Research Article

Abstract: Unconstrained video face recognition is an extension of face recognition technology, and it is an indispensable part of intelligent security and criminal investigation systems. However, general face recognition technology cannot be directly applied to unconstrained video face recognition, because the video contains fewer frontal face image frames and a single image contains less face feature information. To address the above problems, this work proposes a Feature Map Aggregation Network (FMAN) to achieve unconstrained video face recognition by aggregating multiple face image frames. Specifically, an image group is used as the input of the feature extraction network to replace a single …image to obtain a multi-channel feature map group. Then a quality perception module is proposed to obtain quality scores for feature maps and adaptively aggregate image features from image groups at the feature map level. Finally, extensive experiments are conducted on the challenging face recognition benchmarks YTF, IJB-A and COX to evaluate the proposed method, showing a significant increase in accuracy compared to the state-of-the-art. Show more

Keywords: Video face recognition, Aggregation, Deep convolutional neural network, Feature map

DOI: 10.3233/JIFS-212382

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2413-2425, 2023

Authors: Yao, Linjie | Zhang, Guidong | Sheng, Yuhong

Article Type: Research Article

Abstract: Multi-dimensional uncertain differential equations (MUDEs) are often used to describe complex systems that vary with time. In this paper, the generalized moment estimation method is employed to estimate the MUDEs’ unknown parameters. A method to optimize parameters with multiple estimation results is proposed. The hypothesis test and α-path are proposed to verify the feasibility of the parameter results. Several examples of parameter estimation for MUDEs are given, as well as two numerical examples to verify the feasibility of the method.

Keywords: Uncertainty theory, multi-dimensional uncertain differential equation, generalized moment estimation, parameter estimation

DOI: 10.3233/JIFS-213503

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2427-2439, 2023

Authors: Sathish, S. | Kavitha, K. | Poongodi, J.

Article Type: Research Article

Abstract: The industrial world including the merits of Internet of Things (IoT) paradigm has wide opened the evolution of new digital technology to facilitate promising and revolutionizing dimensions in diversified industrial application. However, handling the deployment challenges of security awareness, energy consumption, resource optimization, service assurance and real-time big data analytics in Industrial IoT Networks is a herculean task. In this paper, Dantzig Wolfe Decomposition Algorithm-based Service Assurance and Parallel Optimization Algorithm (DWDA-SAPOA) is proposed for guaranteeing QoS in energy efficient Software-Defined Industrial IoT Networks. This DWDA-SAPOA is proposed for achieving minimized energy consumption on par with the competitive network routing …algorithms which fails in satisfying the strict requirements of heterogeneous Quality of Service (QoS) during the process of optimizing resources under industrial communications. It is proposed as a service assurance and centralized route optimization strategy using the programmability and flexibility characteristics facilitating by the significant Software Defined Networking (SDN) paradigm which is implemented over a multi-layer programmable industrial architecture. It supports bandwidth-sensitive service and ultra-reliable low-latency communication type of heterogeneous flows that represents a routing optimization problem which could be potentially modelled as a multi-constrained shortest path problem. It further adopts Dantzig Wolfe Decomposition Algorithm (DWDA) to handle the complexity of NP-hard involved in solving the multi-constrained shortest path problems. The simulation experiments of the proposed DWDA-SAPOA prove its predominance in minimizing energy consumption by 24.28%, flow violation by 19.21%, packet loss by 21.28%, and end-to-end delay by 29.82%, and bandwidth utilization by up to 26.22% on par with the benchmarked QoS provisioning and energy-aware routing problem. Show more

Keywords: Software defined networking, Dantzig Wolfe Decomposition algorithm, industrial internet of things networks, multi-constrained shortest path problem, centralized route optimization

DOI: 10.3233/JIFS-221776

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2441-2454, 2023

Authors: Akalya devi, C. | Karthika Renuka, D. | Pooventhiran, G. | Harish, D. | Yadav, Shweta | Thirunarayan, Krishnaprasad

Article Type: Research Article

Abstract: Emotional AI is the next era of AI to play a major role in various fields such as entertainment, health care, self-paced online education, etc., considering clues from multiple sources. In this work, we propose a multimodal emotion recognition system extracting information from speech, motion capture, and text data. The main aim of this research is to improve the unimodal architectures to outperform the state-of-the-arts and combine them together to build a robust multi-modal fusion architecture. We developed 1D and 2D CNN-LSTM time-distributed models for speech, a hybrid CNN-LSTM model for motion capture data, and a BERT-based model for text …data to achieve state-of-the-art results, and attempted both concatenation-based decision-level fusion and Deep CCA-based feature-level fusion schemes. The proposed speech and mocap models achieve emotion recognition accuracies of 65.08% and 67.51%, respectively, and the BERT-based text model achieves an accuracy of 72.60%. The decision-level fusion approach significantly improves the accuracy of detecting emotions on the IEMOCAP and MELD datasets. This approach achieves 80.20% accuracy on IEMOCAP which is 8.61% higher than the state-of-the-art methods, and 63.52% and 61.65% in 5-class and 7-class classification on the MELD dataset which are higher than the state-of-the-arts. Show more

Keywords: Emotion recognition, time-distributed models, CNN-LSTM, BERT, DCCA

DOI: 10.3233/JIFS-220280

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2455-2470, 2023

Authors: Han, Meng | Li, Ang | Gao, Zhihui | Mu, Dongliang | Liu, Shujuan

Article Type: Research Article

Abstract: In reality, the data generated in many fields are often imbalanced, such as fraud detection, network intrusion detection and disease diagnosis. The class with fewer instances in the data is called the minority class, and the minority class in some applications contains the significant information. So far, many classification methods and strategies for binary imbalanced data have been proposed, but there are still many problems and challenges in multi-class imbalanced data that need to be solved urgently. The classification methods for multi-class imbalanced data are analyzed and summarized in terms of data preprocessing methods and algorithm-level classification methods, and the …performance of the algorithms using the same dataset is compared separately. In the data preprocessing methods, the methods of oversampling, under-sampling, hybrid sampling and feature selection are mainly introduced. Algorithm-level classification methods are comprehensively introduced in four aspects: ensemble learning, neural network, support vector machine and multi-class decomposition technique. At the same time, all data preprocessing methods and algorithm-level classification methods are analyzed in detail in terms of the techniques used, comparison algorithms, pros and cons, respectively. Moreover, the evaluation metrics commonly used for multi-class imbalanced data classification methods are described comprehensively. Finally, the future directions of multi-class imbalanced data classification are given. Show more

Keywords: Classification, multi-class imbalance data, data preprocessing method, algorithm-level classification method

DOI: 10.3233/JIFS-221902

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2471-2501, 2023

Authors: Xiao, Yanjun | Zhao, Churui | Qi, Hao | Liu, Weiling | Meng, Zhaozong | Peng, Kai

Article Type: Research Article

Abstract: In the control system of a lithium battery rolling mill, the correction system was crucial. This was because the correction system had a significant impact on the performance of the lithium battery rolling mill, including high precision and efficient rolling quality. However, the non-linearity of the correction system and the uncertainty of the correction system made it a challenging problem to achieve a high precision correction control. The contribution and innovation of this paper was a genetic fuzzy PID control strategy based on Kalman filter, which was proposed and applied to the control of lithium battery rolling mill correction technology. …In order to achieve intelligent control of a high-precision electrode rolling mill correction system, an algorithm fusion control scheme was proposed. Firstly, a novel and detailed correction system model was presented. Next, the initial PID parameters of the correction were optimized by means of a genetic algorithm so that the PID parameters could be adapted to the correction control process and then optimized again by adding an extended Kalman filter. Finally, the lithium battery rolling mill correction control system was validated, tested and commissioned in the field. The results showed that the designed algorithm could meet the working requirements of the lithium battery rolling mill and that it improved the accuracy of the correction system. In the actual lithium battery rolling mill production process, the algorithm was compared with a conventional PID. Compared with the common single algorithm, the fusion algorithm proposed in this paper was a complete set of high precision correction control system algorithm to solve the high precision problem faced by the correction system in the actual lithium battery rolling mill correction system. Show more

Keywords: Pole piece rolling mill, deviation correction system, fuzzy PID, genetic algorithm, algorithm fusion, extended kalman filter

DOI: 10.3233/JIFS-221028

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2503-2523, 2023

Authors: Vo, Tham

Article Type: Research Article

Abstract: The wind power is considered as a potential renewable energy resource which requires less management cost and effort than the others like as tidal, geothermal, etc. However, the natural randomization and volatility aspects of wind in different regions have brought several challenges for efficiently as well as reliably operating the wind-based power supply grid. Thus, it is necessary to have centralized monitoring centers for managing as well as optimizing the performance of wind power farms. Among different management task, wind speed prediction is considered as an important task which directly support for further wind-based power supply resource planning/optimization, hence towards …power shortage risk and operating cost reductions. Normally, considering as traditional time-series based prediction problem, most of previous deep learning-based models have demonstrated significant improvement in accuracy performance of wind speed prediction problem. However, most of recurrent neural network (RNN) as well as sequential auto-encoding (AE) based architectures still suffered several limitations related to the capability of sufficient preserving the spatiotemporal and long-range time dependent information of complex time-series based wind datasets. Moreover, previous RNN-based wind speed predictive models also perform poor prediction results within high-complex/noised time-series based wind speed datasets. Thus, in order to overcome these limitations, in this paper we proposed a novel integrated convolutional neural network (CNN)-based spatiotemporal randomization mechanism with transformer-based architecture for wind speed prediction problem, called as: RTrans-WP. Within our RTrans-WP model, we integrated the deep neural encoding component with a randomized CNN learning mechanism to softy align temporal feature within the long-range time-dependent learning context. The utilization of randomized CNN component at the data encoding part also enables to reduce noises and time-series based observation uncertainties which are occurred during the data representation learning and wind speed prediction-driven fine-tuning processes. Show more

Keywords: Wind speed prediction, deep learning, transformer, randomization, nomenclatures

DOI: 10.3233/JIFS-222446

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2525-2541, 2023

Authors: Yu, Wenmei | Xia, Lina | Cao, Qiang

Article Type: Research Article

Abstract: With the development of big data, Internet finance, the digital economy is developing rapidly and has become an important force to drive the continuous transformation of the global economy and society. China has put forward plans for the development of digital economy from 2021 to 2025, requiring the number of core industries of digital economy to reach 10% of GDP by 2025, while continuously improving China’s digital economy to achieve high-quality development of China’s digital economy. Aiming at China’s digital economy, we use the adaptive lasso method and select feature variables based on quantitative and qualitative perspectives, so as to …predict the development trend of China’s digital economy from 2021 to 2025 based on the TDGM (1, 1, r) grey model optimized by the particle swarm algorithm. Meanwhile, we have added the comparative analyses with TDGM(1,1), Grey Verhulst, GM(1,1) models and evaluate the prediction results both Ex-ante and Ex-post, demonstrating the feasibility of the proposed model and the accuracy. Finally, we find that the future of China’s digital economy will meet the planned objectives in terms of quantity and quality, but the trend of digital economy development in quantity is faster, thanks to the development of digital technology application industry. Show more

Keywords: Digital economy development, adaptive lasso grey model, TDGM(1, 1, r) model, quantity and quality

DOI: 10.3233/JIFS-222520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2543-2560, 2023

Authors: Muthumanickam, Arunkumar | Balasubramanian, Gomathy | Chakrapani, Venkatesh

Article Type: Research Article

Abstract: The field of self-driving cars is one that is rapidly growing in popularity. The goal of autonomous vehicles has always been to avoid accidents. It has long been argued that human errors while driving are the primary cause of traffic accidents, and autonomous cars have the potential to remove this. An intelligent transportation system based on the Internet of Things (IoT) is required at some point for the vehicle to make an instant choice to evade accidents, regardless of the competence of a decent driver Mishaps on the road and in the weather are those that occur due to unfavourable …weather circumstances such as fog, gusts, snow, rain, slick pavement, sleet, etc. There are many factors that might cause a vehicle to lose control, including speed, weight, momentum, poor fleet maintenance. It has the potential to lessen the number of collisions caused by poor weather and deteriorating road circumstances. An IoT-based intelligent accident escaping system for poor weather and traffic circumstances is presented here. A variety of sensors are used to check the health of the vehicle. Data from sensors is processed by a microcontroller and displayed on the dashboard of a car after it has been received. The proposed model combines both an IoT system that monitors weather and road conditions and an intelligent system based on deep learning that learns the adverse variables that impact an accident in order to anticipate and prescribe a harmless speed to the driver. The experimental results show that the proposed deep learning technique achieved 94% of accuracy, where the existing LeNet model achieved 80% of accuracy for the prediction process. The proposed ResNet is more effective than LeNet, because identity mapping is used to solve the vanishing gradient problems. Show more

Keywords: Accidents-free driving, autonomous vehicles, deep learning, fleet management, internet of things, microcontroller, sensors

DOI: 10.3233/JIFS-222719

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2561-2576, 2023

Authors: Little Flower, X. | Poonguzhali, S.

Article Type: Research Article

Abstract: For real-time applications, the performance in classifying the movements should be as high as possible, and the computational complexity should be low. This paper focuses on the classification of five upper arm movements which can be provided as a control for human-machine interface (HMI) based applications. The conventional machine learning algorithms are used for classification with both time and frequency domain features, and k-nearest neighbor (KNN) outplay others. To further improve the classification accuracy, pretrained CNN architectures are employed which leads to computational complexity and memory requirements. To overcome this, the deep convolutional neural network (CNN) model is introduced with …three convolutional layers. To further improve the performance which is the key idea behind real-time applications, a hybrid CNN-KNN model is proposed. Even though the performance is high, the computation costs of the hybrid method are more. Minimum redundancy maximum relevance (mRMR), a feature selection method makes an effort to reduce feature dimensions. As a result, better performance is achieved by our proposed method CNN-KNN with mRMR which reduces computational complexity and memory requirement with a mean prediction accuracy of about 99.05±0.25% with 100 features. Show more

Keywords: Empirical mode decomposition, minimum redundancy maximum relevance, spectrogram representation, k-nearest neighbor, deep learning

DOI: 10.3233/JIFS-220811

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2577-2591, 2023

Authors: Annapandi, P. | Ramya, R. | Kotaiah, N.C. | Rajesh, P. | Subramanian, Arun

Article Type: Research Article

Abstract: This manuscript proposes an efficient hybrid strategy to obtain the optimal solution of operational cost reduction, size reduction of hybrid renewable energy sources and optimal power flow control for off-grid system. Here, off-grid is incorporated with photovoltaic array, wind turbine, Diesel generator, and battery energy storage system. The hybrid method is joint execution of Giza Pyramids Construction (GPC) and Billiards-inspired optimization algorithm (BOA) hence it is named GPC-BOA technique. The major purpose of proposed method is minimizing the operational cost as well as size of hybrid renewable energy sources and improves the power flow of system. In this energy management …system of off-grid provides cost reduction which includes the generation, replacement, operating and maintenance, cost of fuel consumption, cost of exchanged power with grid, and the penalty for emissions. Here, the GPC method is employed for forecasting the load requirement of system. The BOA technique optimizes the off-grid system through the deliberation of forecasted load requirement. At last, the proposed approach is performed on MATLAB platform and the performance is assessed using existing techniques. Show more

Keywords: Energy management system, cost, power flow, photovoltaic array, wind turbine, Diesel generator, battery energy storage system

DOI: 10.3233/JIFS-221176

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2593-2614, 2023

Authors: Cisneros, Luis | Rivera, Gilberto | Florencia, Rogelio | Sánchez-Solís, J. Patricia

Article Type: Research Article

Abstract: Business analytics refers to the application of sophisticated tools to obtain valuable information from a large dataset that is generated by a company. Among these tools, fuzzy optimisation stands out because it helps decision-makers to solve optimisation problems considering the uncertainty that commonly occurs in application domains. This paper presents a bibliometric analysis following the PRISMA statement on the Dimensions database to obtain publications related to fuzzy optimisation applied to business domains. The purpose of this analysis is to gather useful information that can help researchers in this area. A total of 2,983 publications were analysed using VOSviewer to identify …the trend in the number of publications per year, relationships in terms in both the title and abstract of these publications, most influential publications, and relationships among journals, authors, and institutions. Show more

Keywords: PRISMA statement, VOSviewer, bibliometric insights, scientific landscape, fuzzy optimisation, prescriptive analytics

DOI: 10.3233/JIFS-221573

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2615-2630, 2023

Authors: Duman, Ekrem

Article Type: Research Article

Abstract: The use of the social media (SM) has become more and more widespread during the last two decades, the companies started looking for insights for how they can improve their businesses using the information accumulating therein. In this regard, it is possible to distinguish between two lines of research: those based on anonymous data and those based on customer specific data. Although obtaining customer specific SM data is a challenging task, analysis of such individual data can result in very useful insights. In this study we take up this path for the customers of a bank, analyze their tweets and …develop three kinds of analytical models: clustering, sentiment analysis and product propensity. For the latter one, we also develop a version where, besides the text information, the structural information available in the bank databases are also used in the models. The result of the study is a considerably more efficient set of analytical CRM models. Show more

Keywords: Social media, banking, CRM, NLP, sentiment analysis

DOI: 10.3233/JIFS-221619

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2631-2642, 2023

Authors: Han, Yongguang | Zhang, Shanshan | Deng, Dexue

Article Type: Research Article

Abstract: Aiming at the multi-attribute group decision-making (MAGDM) problem with unclear index weights values, and thinking about the bounded rational behavior of decision makers (DMs), we proposed a new improved CPT-VIKOR decision method under intuitionistic fuzzy (IF-CPT-VIKOR). Due to the emergence of special cases in IFSs, a new IFS score function and distance formula are defined. Meanwhile, the use of entropy weight method to obtain the weight information of indicators enhances the objectivity of the model. Furthermore, CPT is integrated into the IFS environment, which fully reflects the psychological behavior of DMs, and take advantage of the VIKOR method to determine …the final sorting of the scheme. Finally, through the application cases of the commercial concrete supplier selection (CCSS) and the comparison with the existing authoritative methods to verify the feasibility and validity of the method. Show more

Keywords: Multiple attribute group decision making (MAGDM), cumulative prospect theory (CPT), VIKOR method, intuitionistic fuzzy sets (IFSs), Commercial concrete supplier selection (CCSS)

DOI: 10.3233/JIFS-221780

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2643-2654, 2023

Authors: Peng, Jinghong | Zhou, Jun | Liang, Guangchuan | Qin, Can | Peng, Cao | Chen, YuLin | Hu, Chengqiang

Article Type: Research Article

Abstract: Gas gathering pipeline network system is an important process facility for gas field production, which is responsible for collecting, transporting and purifying natural gas produced by wells. In this paper, an optimization model for the layout of star-tree gas gathering pipeline network in discrete space is established to find the most economical design scheme. The decision variables include valve set position, station position and pipeline connection relation. A series of equality and inequality constraints are developed, including node flow balance constraints, pipeline hydraulic constraints and pipeline structure constraints. A global optimization strategy is proposed and an improved genetic algorithm is …used to solve the model. To verify the validity of the proposed method, the optimization model is applied to a coalbed methane field gathering pipeline network in China. The results show that the global optimization scheme saves 1489.74×104 RMB (26.36%) in investment cost compared with the original scheme. In addition, the comparison between the global and hierarchical optimization scheme shows that the investment cost of the global optimization scheme is 567.22×104 RMB less than that of the hierarchical optimization scheme, which further proves the superiority of the global optimization method. Finally, the study of this paper can provide theoretical guidance for the design and planning of gas field gathering pipeline network. Show more

Keywords: Natural gas, pipeline network, layout design, global optimization, genetic algorithm

DOI: 10.3233/JIFS-222199

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2655-2672, 2023

Authors: Zhang, Qi | Su, Qian | Liu, Baosen | Pei, Yanfei | Zhang, Zongyu | Chen, De

Article Type: Research Article

Abstract: Effectively evaluating high-embankment deformation and stability is important for heavy-haul railway safety. An improved extension model with an attribute reduction algorithm was proposed for the comprehensive evaluation method. First, a hierarchical evaluation system for high embankments in heavy-haul railways was established using the attribute reduction algorithm, which includes the principal component analysis, maximum information coefficient, coefficient of variation, and improved Dempster-Shafer evidence theory. Furthermore, the improved extension model was used to evaluate high-embankment performance in heavy-haul railways. In this improved extension model, the combination weighting method, an asymmetric proximity function, and the maximum membership principle effectiveness verification were used. Finally, …three high embankments in a Chinese heavy-haul railway were studied. The results illustrate that the main influencing factors for high-embankment performance in a heavy-haul railway are annual rainfall, annual temperature, and 21 other indicators. The performance of the three embankments is level III (ordinary), level II (fine), and level III (ordinary), respectively, indicating that these embankments have generally unfavourable performance. The three embankments’ performance matches field measurements, and the proposed method outperforms the Fuzzy-AHP method, cloud model, and gray relational analysis. This study demonstrates the feasibility of the proposed method in assessing the high-embankment performance under heavy axle loads. Show more

Keywords: Heavy-haul railway, high embankment, comprehensive evaluation, improved extension model, attribute reduction

DOI: 10.3233/JIFS-222562

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2673-2692, 2023

Authors: Cetinkaya, Suleyman | Demir, Ali

Article Type: Research Article

Abstract: The purpose of this research is to establish the solution to the time-fractional initial value problem (TFIVP) in Caputo- Fabrizio sense by implementing a new integral transform called ARA transform together with the iterative method. The existence of the ARA transform is investigated. Moreover, it is shown that the ARA integral transform of order n of a continuous function well defined. First, TFIVP is reduced into a simpler problem by utilizing the ARA transform. Secondly, the truncated solution of the reduced problem is obtained through the iterative method. Finally, the application of inverse ARA transform allows us to construct …a truncated solution of TFIVP. The novelty of this study is that the first time the ARA transform is applied to obtain the solution of TFIVP in the Caputo-Fabrizio sense. Illustrative examples with the Fokker-Planck equation present that this method works better than other methods which is one of the strong points of this research. Show more

Keywords: Caputo-Fabrizio derivative, ARA transform, iterative method, time fractional initial value problem, Fokker-Planck equation

DOI: 10.3233/JIFS-223237

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2693-2701, 2023

Authors: Dai, Qinglong | Qin, Guangjun | Li, Jianwu | Zhao, Jun | Cai, Jifan

Article Type: Research Article

Abstract: Flink is regarded as a promising distributed data processing engine for unifying bounded data and unbounded data. Unbalanced workloads upon multiple workers/task managers/servers in the Flink bring congestion, which will lead to the quality of service (QoS) decreasing. The balanced load distribution could efficiently improve QoS. Besides, existing works are lagging behind the current Flink version. To distribute workloads upon workers evenly, a resource-oriented load balancing task scheduling (RoLBTS) mechanism for Flink is proposed. The capacities of CPU, memory, and bandwidth are taken into consideration. Based on the barrel principle, the memory, and the bandwidth are respectively selected to model …the resource occupancy ratio of the physical node and that of the physical link. On the based of modeled resource occupancy ratio, the data processing of load-balancing resource usage in Flink is formulated as a quadratic programming problem. Based on the self-recursive calling, a RoLBTS algorithm for scheduling task-needed resources is presented. Trough the numerical simulation, the superiority of our work is evaluated in terms of resource score, the number of possible scheduling solutions, and resource usage ratio. Show more

Keywords: Unbounded data, bounded data, integrated stream processing, Flink, load balancing

DOI: 10.3233/JIFS-222524

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2703-2713, 2023

Authors: Lina, Ma | Hao, Ma | Yang, Zhang | Iqbal, Najaf

Article Type: Research Article

Abstract: In the context of the strategic target of carbon emission peaking and carbon neutrality, industrial green technology innovation (GTI) has become the focus of discussion in academia these days. Based on the panel data of 30 provinces in China from 2011 to 2019, we construct Spatial Durbin Models to explore the spatial effects of capital enrichment (CE) on GTI by using the geographical distance matrix, the economic distance matrix and the adjacency matrix. The results reveal that: (1) The regional differences in the development of GTI are prominent, showing a higher level in the east and lower in the west. …(2) GTI exhibits the spatial characteristic of polarization. Its spatiotemporal evolutionary pattern reveals a phased feature of first strengthened and then weakened. (3) The CE has a significant inhibitory effect on GTI, which may be caused by the “rebound effect”, dominated by short-term economic interests and the ineffective capital allocation. This effect is more prominent in regions with unbalanced economies. (4) The spatial spillover effect of CE is significantly negative, indicating a “siphon effect”. Based on these findings, the suggestions for promoting GTI are put forward. Show more

Keywords: Capital enrichment, green technology innovation, spatio-temporal evolution, spatial spillover effect, low-carbon economy

DOI: 10.3233/JIFS-213565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2715-2727, 2023

Authors: Qiu, Yutan | Zhou, Qing

Article Type: Research Article

Abstract: Role-oriented network embedding aims to preserve the structural similarity of nodes so that nodes with the same role stay close to each other in the embedding space. Role-oriented network embeddings have wide applications such as electronic business and scientific discovery. Anonymous walk (AW) has a powerful ability to capture structural information of nodes, but at present, there are few role-oriented network embedding methods based on AW. Our main contribution is the proposal of a new framework named REAW, which can generate the role-oriented embeddings of nodes based on anonymous walks. We first partition a number of anonymous walks starting from …a node into the representative set and the non-representative set. Then, we leverage contrastive learning techniques to learn AW embeddings. We integrate the learned AW embeddings with AW’s empirical distribution to obtain the structural feature of the node, and finally we generate the node’s embedding through message passing operations. Extensive experiments on real network datasets demonstrate the effectiveness of our framework in capturing the role of nodes. Show more

Keywords: Network embedding, network structure, role-oriented, anonymous walk

DOI: 10.3233/JIFS-222712

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2729-2739, 2023

Authors: Song, Xudong | Wan, Xiaohui | Yi, Weiguo | Cui, Yunxian | Li, Changxian

Article Type: Research Article

Abstract: In recent years, the lack of thermal images and the difficulty of thermal feature extraction have led to low accuracy and efficiency in the fault diagnosis of circuit boards using thermal images. To address the problem, this paper presents a simple and efficient intelligent fault diagnosis method combined with computer vision, namely the bag-of-SURF-features support vector machine (BOSF-SVM). Firstly, an improved BOF feature extraction based on SURF is proposed. The preliminary fault features of the abnormally hot components are extracted by the speeded-up robust features algorithm (SURF). In order to extract the ultimate fault features, the preliminary fault features are …clustered into K clusters by K-means and substituted into the bag-of-features model (BOF) to generate a bag-of-SURF-feature vector (BOSF) for each image. Then, all of the BOSF vectors are fed into SVM to train the fault classification model. Finally, extensive experiments are conducted on two homemade thermal image datasets of circuit board faults. Experimental results show that the proposed method is effective in extracting the thermal fault features of components and reducing misdiagnosis and underdiagnosis. Also, it is economical and fast, facilitating savings in labour costs and computing resources in industrial production. Show more

Keywords: Thermal images, circuit boards, fault diagnosis, bag-of-features, support vector machine

DOI: 10.3233/JIFS-223093

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2741-2752, 2023

Authors: Rajalakshmi, R. | Sivakumar, P. | Prathiba, T. | Chatrapathy, K.

Article Type: Research Article

Abstract: In healthcare (HC), Internet of Things (IoT) integrated cloud computing provides various features and real-time applications. However, owing to the nature of IoT architecture, their types, various modes of communication and the density of data transformed in the network, security is currently a critical issue in the IoT healthcare (IoT-HC) field. This paper proposes a deep learning (DL) model, namely Adaptive Swish-based Deep Multi-Layer Perceptron (ASDMLP) that identifies the intrusions or attacks in the IoT healthcare (IoT-HC) platform. The proposed model starts by clustering the patients’ sensor devices in the network using the Probability-based Fuzzy C-Means (PFCM) model. After clustering …the devices, the cluster heads (CHs) among the cluster members are selected based on the energy, distance and degree of the sensor devices for aggregating the data sensed by the medical sensor devices. The base station (BS) sends the patient’s data collected by the CHs to the cloud server (CS). At the cloud end, the proposed model implements an IDS by applying training of the DL model in publicly available databases. The DL approach first performs preprocessing of the data and then selects optimal features from the dataset using the Opposition and Greedy Levy mutation-based Coyotes Optimization Algorithm (OGCOA). The ASDMLP trains these optimal features for the detection of HC data intrusions. The outcomes confirm that the proposed approach works well on real-time IoT datasets for intrusion detection (ID) without compromising the energy consumption (EC) and lifespan of the network. Show more

Keywords: Smart healthcare, Internet of Things (IoT), intrusion detection system, deep learning, healthcare security

DOI: 10.3233/JIFS-223166

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2753-2768, 2023

Authors: Lovelyn Rose, S. | Ravitha Rajalakshmi, N. | Sabari Nathan, M. | Suraj Subramanian, A. | Harishkumar, R.

Article Type: Research Article

Abstract: Recently computer vision and NLP based techniques have been employed for document layout analysis where different types of elements in the document and their relative position are identified. This process is trickier as there are blocks which are structurally similar but semantically different such as title, text etc. This works attempts to use region-based CNN architecture (F-RCNN) for determining five different sections in the scientific articles. To improve the performance of detection algorithm, reading order is used as an additional feature and this model is known as MF-RCNN. First, an algorithm is formulated to find the reading order in documents …which adopts Manhattan-layout using a color-coding scheme. Secondly, this information is fused with the input image without changing its shape. Experimental results show that MF-RCNN which uses the reading order performs better when compared with F-RCNN when tested on Publaynet dataset. Show more

Keywords: FRCNN, reading order, XY tree, multiple channels, manhattan layout

DOI: 10.3233/JIFS-220705

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2769-2778, 2023

Authors: Xu, Juan | Ma, Zhen Ming | Xu, Zeshui

Article Type: Research Article

Abstract: Heronian mean (HM) operators, which can capture the interrelationship between input arguments with the same importance, have been a hot research topic as a useful aggregation technique. In this paper, we propose the generalized normalized cross weighted HM operators on the unit interval which can not only capture the interrelationships between input arguments but also aggregate them with different weights, some desirable properties are derived. Then, generalized cross weighted HM operators are extended to real number set and applied to binary classification. We list the detailed steps of binary classification with the developed aggregation operators, and give a comparison of …the proposed method with the existing ones using the Iris dataset with 5-fold cross-validation (5-f cv), the accuracy of the proposed method for the training sets and the testing sets are both 100%. Show more

Keywords: Generalized cross weighted HM operator, cross weight vector, binary classification

DOI: 10.3233/JIFS-221152

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2779-2789, 2023

Authors: Osman, Mawia | Xia, Yonghui

Article Type: Research Article

Abstract: This paper proposes a method for solving fuzzy linear and nonlinear partial q -differential equations by the fuzzy q -differential transform. Further, we implemented the fuzzy fractional q -differential transform for solving some types of fuzzy fractional q -differential equations . The technique investigated is based on gH -differentiability, fuzzy q-derivative, and fuzzy q-fractional derivative. Various concrete problems have been tested by implementing the new method, and the results show great performance. The results also reveal that the method is a very effective and quite accurate mathematical tool for solving fuzzy fractional and integer q -differential equations. Finally, we …have provided some examples illustrating our method. Show more

Keywords: Fuzzy numbers, fuzzy-valued functions, fuzzy q-derivative; fuzzy q-fractional derivative, gH-differentiability, fuzzy q-differential transform method

DOI: 10.3233/JIFS-222567

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2791-2846, 2023

Authors: Wang, Biao | Wei, Hongquan | Li, Ran | Liu, Shuxin | Wang, Kai

Article Type: Research Article

Abstract: Spotting rumors from social media and intervening early has always been a daunting challenge. In recent years, Deep neural networks have begun to discover rumors by exploring the way of rumor propagation. The existing static graph models either only focus on the spatial structure information of rumor propagation or on time series propagation information but do not effectively combine them. This paper proposes the Static Spatiotemporal Model (SSM), which first extracts the textual semantic information and constructs undirected and directed propagation trees. Then obtains spatial structure information of rumor propagation through Graph Convolutional Network and extracts time series propagation information …through the Recurrent Neural Network. The extracted spatiotemporal information is enhanced using different source node information hopping. Finally, SSM uses a weighted connection ensemble to rumor classification. Experimentally validated on datasets such as Weibo and Twitter, the results show that the proposed method outperforms several state-of-the-art static graph models. To better apply SSM in early detection and characterize early concepts, this paper presents a new data collection index for early detection, which can detect events that spread faster and have more significant influence in a targeted manner. The experimental results on the new indicators further verify the superiority of SSM as it can extract sufficient information in early detection or events with fewer participants. Show more

Keywords: Rumor detection, deep learning, SSM, spatiotemporal information, early detection, data collection index

DOI: 10.3233/JIFS-220417

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2847-2862, 2023

Authors: Ramkumar, N. | Sadasivam, G. Sudha | Renuka, D. Karthika

Article Type: Research Article

Abstract: Multimodal analysis focuses on the internal and external manifestations of cancer cells to provide physicians, oncologists and surgeons with timely information on personalized diagnosis and treatment for patients. Decision fusion in multimodal analysis reduces manual intervention, and improves classification accuracy facilitating doctors to make quick decisions. Genetic characteristics extracted on biopsies do not, however, provide details on adjacent cells. Images can only provide external observable details of cancer cells. While mammograms can detect breast cancer, region wise details can be obtained from ultrasound images. Hence, different types of imaging techniques are used. Features are extracted using the SelectKbest method in …the Wisconsin Breast Cancer, Clinical and gene expression datasets. The features are extracted using Gray Level Co-occurrence Matrix from Histology, Mammogram and Sonogram images. For image datasets, the Convolution Neural Network (CNN) is used as a classifier. The combined features from clinical, gene expression and image datasets are used to train an Integrated Stacking Classifier. The integrated multimodal system’s effectiveness is shown by experimental findings. Show more

Keywords: Convolution neural networks, multimodal analysis, gray level co-occurrence matrix, histopathological, mammogram, sonogram and integrated stacking classifier

DOI: 10.3233/JIFS-220633

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2863-2880, 2023

Authors: Xie, Ying | Zhu, Yuan | Lu, Zhenjie

Article Type: Research Article

Abstract: In view of the large-scale and high-dimensional problems of industrial data and fault-tracing problems, a fault detection and diagnosis method based on multi-block probabilistic kernel partial least squares (MBPKPLS) is proposed. First, the process variables are divided into several blocks in a decentralized manner to address the large-scale and high-dimensional problems. The probabilistic characteristics and relationship between the corresponding process variables and the quality variables of each block are analyzed using latent variables, and the PKPLS model of each block is established separately. Second, the MBPKPLS model is applied to process monitoring, statistics of each block are established in a …high-dimensional space, and the monitoring indicators in each block are used to detect faults. Third, based on fault detection, the multi-block concept is further used to locate the cause of fault, thereby solving the problem of fault tracing. Finally, a numerical example and the penicillin fermentation process (PFP) are used to test the effectiveness of the MBPKPLS method. The results demonstrate that the proposed method is suitable for processing large-scale, high-dimensional data with strong nonlinear characteristics, and the MBPKPLS process monitoring method is effective for improving the performance of fault detection and diagnosis. Show more

Keywords: Large-scale industrial process, multi-block probabilistic kernel partial least squares, fault detection, fault diagnosis

DOI: 10.3233/JIFS-220605

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2881-2894, 2023

Authors: Ma, Yizhe | Yu, Long | Lin, Fangjian | Tian, Shengwei

Article Type: Research Article

Abstract:  In increasingly complex scenes, multi-scale information fusion becomes more and more critical for semantic image segmentation. Various methods are proposed to model multi-scale information, such as local to global, but this is not enough for the scene changes more and more, and the image resolution becomes larger and larger. Cross-Scale Sampling Transformer is proposed in this paper. We first propose that each scale feature is sparsely sampled at one time, and all other features are fused, which is different from all previous methods. Specifically, the Channel Information Augmentation module is first proposed to enhance query feature features, highlight part of …the response to sampling points and enhance image features. Next, the Multi-Scale Feature Enhancement module performs a one-time fusion of full-scale features, and each feature can obtain information about other scale features. In addition, the Cross-Scale Fusion module is used for cross-scale fusion of query feature and full-scale feature. Finally, the above three modules constitute our Cross-Scale Sampling Transformer(CSSFormer). We evaluate our CSSFormer on four challenging semantic segmentation benchmarks, including PASCAL Context, ADE20K, COCO-Stuff 10K, and Cityscapes, achieving 59.95%, 55.48%, 50.92%, and 84.72% mIoU, respectively, outperform the state-of-the-art. Show more

Keywords: Multi-scale fusion, Segmentation, Transformer

DOI: 10.3233/JIFS-220976

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2895-2907, 2023

Authors: Xu, Huili | Li, Bing

Article Type: Research Article

Abstract: In this paper, a class of Clifford-valued neutral fuzzy neural-type networks with proportional delay and D operator and whose self feedback coefficients are also Clifford numbers are considered. By using the Banach fixed point theorem and some differential inequality techniques, we directly study the existence and global asymptotic stability of pseudo almost periodic solutions by not decomposing the considered Clifford-valued systems into real-valued systems. Finally, two examples are given to illustrate our main results. Our results of this paper are new.

Keywords: Clifford-valued neural network, fuzzy neural network, proportional delay, D operator, pseudo almost periodic solution, global asymptotic stability.

DOI: 10.3233/JIFS-221017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2909-2925, 2023

Authors: Deepa, S. | Sridhar, K.P. | Baskar, S. | Mythili, K.B. | Reethika, A. | Hariharan, P.R.

Article Type: Research Article

Abstract: A smart healthcare network can use sensors and the Internet of Things (IoT) to enhance patient care while decreasing healthcare expenditures. It has become more difficult for healthcare providers to keep track and analyze the massive amounts of data it generates. Health care data created by IoT devices and e-health systems must be handled more efficiently. A wide range of healthcare industries can benefit from machine learning (ML) algorithms in the digital world. However, each of these algorithms has to be taught to anticipate or solve a certain problem. IoT-enabled healthcare data and health monitoring-based machine learning algorithms (IoT-HDHM-MLA ) …have been proposed to solve the difficulties faced by healthcare providers. Sensors and IoT devices are vital for monitoring an individual’s health. The proposed IoT-HDHM-MLA aims to deliver healthcare services via remote monitoring with experts and machine learning algorithms. In this system, patients are monitored in real-time for various key characteristics using a collection of small wireless wearable nodes. The health care business benefits from systematic data collection and efficient data mining. Thus, the experimental findings demonstrate that IoT-HDHM-MLA enhances efficiency in patient health surveillance. Show more

Keywords: Health monitoring, machine learning algorithms, IoT, smart healthcare

DOI: 10.3233/JIFS-221274

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2927-2941, 2023

Authors: Abolpour, Kh. | Zahedi, M.M. | Shamsizadeh, M.

Article Type: Research Article

Abstract:  The current study aims to investigate the L-valued tree automata theory based on t-norm/t-conorm and it further examines their algebraic and L-valued topological properties. Specifically, the concept of L-valued operators with t-norm/t-conorm is introduced and the existing relationships between them are also studied. Interestingly, we associate L-valued co-topologies/topologies for a given L-valued tree automaton, using them to characterize some algebraic concepts. Further, we introduce the concepts of Alexandroff L-graded co-topologies and Alexandroff L-graded topologies which correspond to the L-valued operators with t-norm and L-valued operators with t-conorm/implicator, respectively. In addition, we aim to specify the relationship between the L-graded co-topologies/topologies, …showing that the introduced L-graded co-topologies/topologies have some interesting consequences under homomorphism. Show more

Keywords: L-valued tree automaton, operator, L-valued topology, homomorphism

DOI: 10.3233/JIFS-221960

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2943-2955, 2023

Authors: Chhabra, Megha | Sharan, Bhagwati | Kumar, Manoj

Article Type: Research Article

Abstract:  The users of mobile phone are exponentially increasing. The applications are developed every day in a variety of domains to enhance the Quality of User Experience (QoUE) along with utility determinants. The design of the mobile application impacts the QoUE. QoUE in mobile applications is a measure that describes the appropriateness of the purpose of the application and the need for user retention. However, the challenge is to identify, understand, focus and interconnect the variety of determinants influencing the QoUE based on mobile application design. These determinants are based on the diversity of users and the related functional needs, user-specific …needs, and background functioning of the application. The modelling and analysis help mobile application developers to improve, increase and retain user engagement on the app based on improved QoUE. To do so, a qualitative analytical method is employed in the following steps. The first ever Fuzzy Cognitive Map (FCM) is proposed to show the causal-effect links of the interdependent determinants in mobile applications based on QoUE. In our model, the existence of relationships between determinants relies on a thorough literature review. The weight of these links is estimated by users of different ages and lines of work. This is performed by an empirical study based on a questionnaire filled by experts. The questionnaire is based on the formal utility and perceived QoUE-based topics. Finally, scenario-based analysis on formed FCM based on these inputs is performed. We show that small changes in cases using different direct determinants can be used to enhance QoUE. These changes can be studied before launching an application for the user, thereby limiting the need to rework the improvements based on QoUE and providing useful guidance for the possible increase in user base and behaviour change. Show more

Keywords: User experience, fuzzy cognitive maps, modelling, quality experience, mobile applications

DOI: 10.3233/JIFS-222111

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2957-2979, 2023

Authors: Tian, Xiaoyan | Chen, Xinzhang | Feng, Linlin

Article Type: Research Article

Abstract: As the latest and hottest concept in the international arena, the metaverse concept has attracted the attention of various industries including information, economy, art, management, education and teaching for its application and technology integration research, but whether to define metaverse as a technology or a scenario has not yet reached a unified understanding in the academic and scientific communities. We believe that metaverse should be used as a key concept and emerging theory in building the future intelligent field. Therefore, we introduce the concept of metaverse in future film and animation teaching as a novel, strategic and disruptive teaching field …with great potential, and the constructed metaverse self-directed learning field will become an important part of school education resource optimization. In this study, we focus on the quality improvement path of film and animation teaching in the context of metaverse, and conduct a study on the assessment method of teaching quality after the introduction of metaverse concept. Specifically, we discuss the quality improvement measures in the future teaching of film and animation, construct a teaching field of film and animation based on the metaverse, and propose a related teaching quality assessment model and establish an index system for the quality assessment of film and animation teaching in the context of the metaverse. The index system is composed of 3 primary indicators, 9 secondary indicators and 27 tertiary indicators, and the quantitative calculation is carried out by the Analytic Hierarchy Process (AHP) in fuzzy mathematics, and the weighting results of the indicators are calculated. The research goal of combining quantitative analysis and qualitative research was achieved. What can be seen through our research is that the metaverse online classroom built with virtual reality and other technologies will have more advantages than the traditional teaching classroom. In the future, similar learning devices can be introduced in film and animation teaching, and diversified learning modules can be established. Not only can the learning efficiency of offline classroom be improved, but also more learning space can be opened for online classroom. This study bridges the gap in the theory of quality assessment of film and animation teaching after the introduction of the future metaverse concept, innovates the analysis of the new concept and the improvement of the old method, builds a new scenario of organic combination of new technology and traditional education teaching, and provides a new idea for international and domestic future education research. Show more

Keywords: Teaching quality assessment, teaching film and animation, metaverse, metaverse field architecture, fuzzy mathematical theory

DOI: 10.3233/JIFS-222779

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2981-2997, 2023

Authors: Gokiladevi, M. | Santhoshkumar, Sundar

Article Type: Research Article

Abstract: Early identification of chronic kidney disease (CKD) becomes essential to reduce the severity level and mortality rate. Since medical diagnoses are equipped with latest technologies such as machine learning (ML), data mining, and artificial intelligence, they can be employed to diagnose the disease and aid decision making process. Since the accuracy of the classification model greatly depends upon the number of features involved, the feature selection (FS) approaches are developed which results in improved accuracy. With this motivation, this study designs a novel chaotic binary black hole based feature selection with classification model for CKD diagnosis, named CBHFSC-CKD technique. The …proposed CBHFSC-CKD technique encompasses the design of chaotic black hole based feature selection (CBH-FS) to choose an optimal subset of features and thereby enhances the diagnostic performance. In addition, the bacterial colony algorithm (BCA) with kernel extreme learning machine (KELM) classifier is applied for the identification of CKD. Moreover, the design of BCA to optimally adjust the parameters involved in the KELM results in improved classification performance. A comprehensive set of simulation analyses is carried out and the results are inspected interms of different aspects. The simulation outcome pointed out the supremacy of the CBHFSC-CKD technique compared to other recent techniques interms of different measures. Show more

Keywords: Chronic kidney disease, data classification, feature selection, machine learning, metaheuristics, disease diagnosis

DOI: 10.3233/JIFS-220994

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 2999-3010, 2023

Authors: Öztunç, Simge | İhtiyar, Sultan

Article Type: Research Article

Abstract: In this paper the concept of soft continuity is focused on for digital images by using soft sets which is defined on κ - adjacent digital images. Also the definitions of digital soft isomorphism and digital soft retraction are given. Some theorems are obtained deal with soft isomorphism and soft retraction for digital images and some numerical examples are presented in dimension 2. Finally digital soft retraction is obtained as a soft topological invariant.

Keywords: Digital image, soft set, soft continuous function, soft retraction

DOI: 10.3233/JIFS-221213

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3011-3021, 2023

Authors: Zhao, Zhengwei | Yang, Genteng | Li, Zhaowen

Article Type: Research Article

Abstract: Outlier detection is a process to find out the objects that have the abnormal behavior. It can be applied in many aspects, such as public security, finance and medical care. An information system (IS) as a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. A RVIS with missing values is an incomplete real-valued information system (IRVIS). The notion of inner boundary comes from the boundary region in rough set theory (RST). This paper conducts experiments directly in an IRVIS and investigates outlier detection in an IRVIS …based on inner boundary. Firstly, the distance between two information values on each attribute of an IRVIS is introduced, and the parameter λ to control the distance is given. Then, the tolerance relations on the object set are defined according to the distance, by the way, the tolerance classes, the λ-lower and λ-upper approximations in an IRVIS are put forward. Next, the inner boundary under each conditional attribute in an IRVIS is presented. The more inner boundaries an object belongs to, the more likely it is to be an outlier. Finally, an outlier detection method in an IRVIS based on inner boundary is proposed, and the corresponding algorithm (DE) is designed, where DE means degree of exceptionality. Through the experiments base on UCI Machine Learning Repository data sets, the DE algorithm is compared with other five algorithms. Experimental results show that DE algorithm has the better outlier detection effect in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the DE algorithm. Show more

Keywords: RST, IRVIS, Outlier detection, Inner boundary

DOI: 10.3233/JIFS-222777

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3023-3041, 2023

Authors: Azimifar, Maryam | Nejatian, Samad | Parvin, Hamid | Bagherifard, Karamollah | Rezaei, Vahideh

Article Type: Research Article

Abstract: We introduce a semi-supervised space adjustment framework in this paper. In the introduced framework, the dataset contains two subsets: (a) training data subset (space-one data (SOD )) and (b) testing data subset (space-two data (STD )). Our semi-supervised space adjustment framework learns under three assumptions: (I) it is assumed that all data points in the SOD are labeled, and only a minority of the data points in the STD are labeled (we call the labeled space-two data as LSTD ), (II) the size of LSTD is very small comparing to the size of SOD , and (III) …it is also assumed that the data of SOD and the data of STD have different distributions. We denote the unlabeled space-two data by ULSTD , which is equal to STD  - LSTD . The aim is to map the training data, i.e., the data from the training labeled data subset and those from LSTD (note that all labeled data are considered to be training data, i.e., SOD  ∪ LSTD ) into a shared space (ShS ). The mapped SOD , ULSTD , and LSTD into ShS are named MSOD , MULSTD , and MLSTD , respectively. The proposed method does the mentioned mapping in such a way that structures of the data points in SOD and MSOD , in STD and MSTD , in ULSTD and MULSTD , and in LSTD and MLSTD are the same. In the proposed method, the mapping is proposed to be done by a principal component analysis transformation on kernelized data. In the proposed method, it is tried to find a mapping that (a) can maintain the neighbors of data points after the mapping and (b) can take advantage of the class labels that are known in STD during transformation. After that, we represent and formulate the problem of finding the optimal mapping into a non-linear objective function. To solve it, we transform it into a semidefinite programming (SDP ) problem. We solve the optimization problem with an SDP solver. The examinations indicate the superiority of the learners trained in the data mapped by the proposed approach to the learners trained in the data mapped by the state of the art methods. Show more

Keywords: Semi-supervised domain adaptation, non-linear optimization, local-preserving domain adaptation, semidefinite programming, kernel learning, principal component analysis

DOI: 10.3233/JIFS-200224

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3043-3057, 2023

Authors: Dhamodharavadhani, S. | Rathipriya, R.

Article Type: Research Article

Abstract: This paper aims to develop the methodology for enhancing the regression models using Cluster based sampling techniques (CST) to achieve high predictive accuracy and can also be used to handle large datasets. Hard clustering (KMeans Clustering) or Soft clustering (Fuzzy C-Means) to generate samples called clusters, which in turn is used to generate the Local Regression Models (LRM) for the given dataset. These LRMs are used to create a Global Regression Model. This methodology is known as Enhanced Regression Model (ERM). The performance of the proposed approach is tested with 5 different datasets. The experimental results revealed that the proposed …methodology yielded better predictive accuracy than the non-hybrid MLR model; also, fuzzy C-Means performs better than the KMeans clustering algorithm for sample selection. Thus, ERM has potential to handle data with uncertainty and complex pattern and produced a high prediction accuracy rate. Show more

Keywords: Clustering, KMeans, fuzzy c-means, multiple linear regression, regression, sampling methods

DOI: 10.3233/JIFS-211736

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3059-3069, 2023

Authors: Lekha, A. | Parvathy, K.S.

Article Type: Research Article

Abstract:  Let G  = (V , μ , σ ) be a fuzzy graph on a finite set V . A fuzzy subset μ ′ of μ is called a fuzzy dominating set of G  if, μ ′ ( v ) + ∑ x ∈ V ( σ ( x , v ) ∧ μ ′ ( x ) ) ≥ μ ( v ) for every v ∈ V . Fuzzy domination number γ fz is defined accordingly. In this paper we …initiate a study of this parameter. Some properties of fuzzy dominating sets are studied and fuzzy domination number γ fz is determined for some graphs. Show more

Keywords: Fuzzy Graph, Fuzzy Dominating Sets, Fuzzy Domination Number

DOI: 10.3233/JIFS-220987

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3071-3077, 2023

Authors: Ayidzoe, Mighty Abra | Yu, Yongbin | Mensah, Patrick Kwabena | Cai, Jingye | Baagyere, Edward Yellakuor | Bawah, Faiza Umar

Article Type: Research Article

Abstract: Colorectal cancer is the third most diagnosed malignancy in the world. Polyps (either malignant or benign) are the primary cause of colorectal cancer. However, the diagnosis is susceptive to human error, less effective, and falls below recommended levels in routine clinical procedures. In this paper, a Capsule network enhanced with radon transforms for feature extraction is proposed to improve the feasibility of colorectal cancer recognition. The contribution of this paper lies in the incorporation of the radon transforms in the proposed model to improve the detection of polyps by performing efficient extraction of tomographic features. When trained and tested with …the polyp dataset, the proposed model achieved an overall average recognition accuracy of 94.02%, AUC of 97%, and an average precision of 96%. In addition, a posthoc analysis of the results exhibited superior feature extraction capabilities comparable to the state-of-the-art and can contribute to the field of explainable artificial intelligence. The proposed method has a considerable potential to be adopted in clinical trials to eliminate the problems associated with the human diagnosis of colorectal cancer. Show more

Keywords: Capsule network, colorectal polyp, convolutional neural network, explainable artificial intelligence

DOI: 10.3233/JIFS-212168

Citation: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 3079-3091, 2023