Journal of Intelligent & Fuzzy Systems - Volume 45, issue 4

Authors: Gao, Kelian | Yu, Fangqing

Article Type: Research Article

Abstract: With the emergence of various new teaching concepts and teaching models in colleges and universities, the original evaluation system can no longer fully meet the requirements of the current development of physical education curriculum teaching in colleges and universities. Thus, reducing the judgment and guidance of evaluation on physical education curriculum teaching shall result in the development of physical education curriculum teaching quality evaluation lagging behind the development of teaching and being in a passive position. Therefore, it is particularly important to establish a new evaluation standard system for the teaching quality of physical education courses. The teaching quality evaluation …of college physical education (PE) is viewed as multiple attribute decision-making (MADM). In this paper, an enhanced probabilistic simplified neutrosophic grey relational analysis (PSN-GRA) method is designed for MADM. Then, in the environment of probabilistic simplified neutrophil set (PSNSs), the PSN-GRA method and CRITIC method are combined to rank the alternative schemes, and a numerical example of college physical education teaching quality evaluation proves the practicability of the new method and compares it with other methods. The results show that this method is simple, effective and simple in calculation. Show more

Keywords: Multiple attributes decision making (MADM), probabilistic simplified neutrosophic sets (PSNSs), GRA method, Teaching quality evaluation

DOI: 10.3233/JIFS-231728

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5199-5210, 2023

Authors: Chang, Yung-Chia | Chang, Kuei-Hu | Chen, Wei-Ting

Article Type: Research Article

Abstract: In vehicle leasing industry which presents a great business opportunity, information completed by applicants was assessed and judged by leasing associates manually in most cases; therefore, assessment results would be affected by their personal experience of leasing associates and decisions would be further affected accordingly. There are few researches on applicant credit risk assessment due to not easy to obtain of vehicle leasing data. Further, the difficulty in vehicle leasing risk assessment is increased due to class imbalance problems in vehicle leasing data. In order to address such issue, a research on credit risk assessment in vehicle leasing industry was …conducted in this study. The great disparity in the ratio of high risk and low risk data was addressed by applying synthetic minority over-sampling technique (SMOTE). Then, classification effect of risk assessment model was improved by applying logistic regression in a two-phase manner. In the section of empirical analysis, the feasibility and effectiveness of the approach proposed in this study was validated by using data of actual vehicle leasing application cases provided by a financial institution in Taiwan. It is found that the proposed approach provided a simple yet effective way to build a credit risk assessment model for companies that provide vehicle leasing. Show more

Keywords: Credit risk assessment model, logistic regression, synthetic minority over-sampling technique, category asymmetry

DOI: 10.3233/JIFS-231344

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5211-5222, 2023

Authors: Jayagopalan, Santhosh | Alkhouli, Mahmoud | Aruna, R.

Article Type: Research Article

Abstract: Nowadays the existing legacy management-based healthcare system maintains and processes a large amount of health-related data. The widespread adoption of the Internet of Things (IoT) and its progressive development have promised the way for the development of IoT-enabled healthcare with impressive data processing and big data storage capabilities. Intelligent medical healthcare intends to offer a framework to remotely monitor users’ health-related data as the Industrial Internet of Things (IIoT) develops. Because they are stored on a cloud server, the data are still susceptible to manipulation and privacy breaches. The Keras Xception Deep Learning System (KX-DLS) with Dynamic Searchable Symmetric Encryption …(DSSE) scheme is a revolutionary IoT-based deep learning intelligent privacy-preserving system that is advantageous for digital healthcare and its functionalities to handle security-related challenges. The dataset is being used to pre-train the system, and users’ personal information is kept separate in a secure location. Without knowing any personal information about the users, we analyse health-related data stored in the cloud and build a sophisticated security framework based on a deep learning model. With the most extensive collection of security features, our framework for learning intelligent privacy preservation optimizes the system to guarantee high data integrity and few privacy breaches. As a result, it may be useful in situations where users employ mobile devices with limited resources to engage a healthcare cloud system for extensive virtual health services, and the results of this research show that it has been a better-secured model in comparison with state-of-the-art previous techniques. Show more

Keywords: Privacy-preserving, internet of things, cloud storage, keras xception deep learning system, dynamic searchable symmetric encryption scheme

DOI: 10.3233/JIFS-231713

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5223-5238, 2023

Authors: Xu, Xiao-Peng | Wang, Li

Article Type: Research Article

Abstract: Fermentation engineering is a technology that uses engineering technology to produce useful biological products for human beings by using certain functions of organisms (mainly microorganisms) and active isolated enzymes, or to directly control certain industrial production processes with the participation of microorganisms. With the progress of science and technology, fermentation technology has also developed greatly, and has entered the stage of modern fermentation engineering that can artificially control and modify microorganisms to make these microorganisms produce products for human beings. As an important part of modern biotechnology, modern fermentation engineering has a broad application prospect. The grain fermentation process quality …evaluation is a classical MADM issues. In such paper, the generalized weighted Bonferroni mean (WBM) operator is constructed for MADM with single-valued neutrosophic sets (SVNSs). Then, the generalized single-valued neutrosophic number WBM (GSVNNWBM) operator is built and then the MADM decision methods are proposed based on the GSVNNWBM operator. Finally, an example about grain fermentation process quality comprehensive evaluation and some comparative analysis were given to demonstrate the GSVNNWBM method. Show more

Keywords: Multiple attribute decision making (MADM), single-valued neutrosophic sets (SVNSs), weighted Bonferroni mean (WBM) operator, maximizing deviation method, grain fermentation process quality evaluation

DOI: 10.3233/JIFS-231978

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5239-5249, 2023

Authors: Lv, Bailin | Wang, Sijia | Xia, Kaijian | Jiang, Yizhang

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-223202

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5251-5264, 2023

Authors: Sreekrishna, M. | Prem Jacob, T.

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-231625

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5265-5276, 2023

Authors: Wang, Jingyang

Article Type: Research Article

Abstract: Scientific research management is an important component of university management and an important guarantee for the sustainable operation of universities. A mature scientific research management system is a sharp tool to promote the discipline construction and comprehensive strength improvement of universities. Therefore, colleges and universities should actively explore new concepts, models, and mechanisms for scientific research management while continuously improving their own educational quality. Based on the analysis of the current situation of university scientific research management in China, it is of great significance to explore effective ways to improve the quality of university scientific research management in the new …era. The quality evaluation of higher education scientific research management in entrepreneurship undergraduate colleges is a classical MAGDM problems. Recently, the TODIM and VIKOR method has been used to cope with MAGDM issues. The interval-valued Pythagorean fuzzy sets (IVPFSs) are used as a tool for characterizing uncertain information during the quality evaluation of higher education scientific research management in entrepreneurship undergraduate colleges. In this manuscript, the interval-valued Pythagorean fuzzy TODIM-VIKOR (IVPF-TODIM-VIKOR) method is built to solve the MAGDM under IVPFSs. In the end, a numerical case study for quality evaluation of higher education scientific research management in entrepreneurship undergraduate colleges is given to validate the proposed method. Show more

Keywords: Multiple attribute group decision making (MAGDM), IVPFSs, TODIM, VIKOR, Higher education scientific research management

DOI: 10.3233/JIFS-232621

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5277-5289, 2023

Authors: Wang, Guodong | Chen, Zhen | Wang, Guowei

Article Type: Research Article

Abstract: The art curriculum is a compulsory course for students in the compulsory education stage, which is beneficial in cultivating students’ sentiments, cultivating their temperament and improving their intelligence. Since the new curriculum reform, the art curriculum has been implemented for nearly twenty years, and it has made great breakthroughs and gains. However, at the same time, there are many problems in the process of implementing the art curriculum. The art teaching effectiveness evaluation of primary schools is viewed as the multi-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic number cross-entropy (TFNN-CE) method is designed with help of cross-entropy …and triangular fuzzy neutrosophic sets (TFNSs). Furthermore, Then, TFNN-CE method is built to solve the MADM. Finally, a numerical example for art teaching effectiveness evaluation of primary schools is given and some comparisons are conducted to r illustrate advantages of the designed method. Show more

Keywords: Multiple attribute decision making (MADM) problems, triangular fuzzy neutrosophic sets (TFNSs), cross-entropy method, 2TLNN-CE method, art teaching effectiveness evaluation

DOI: 10.3233/JIFS-232638

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5291-5301, 2023

Authors: Li, Eryang | Feng, Xiangqian | Wei, Cuiping

Article Type: Research Article

Abstract: Internet of Things (IoT) technology now has a new purpose and relevance as a result of the digitalization wave. In this setting, businesses start to plan how they will use IoT technology. But some critical factors can prevent the successful deployment of IoT, and businesses must get beyond these critical factors if they want to do so. The literature review, system literature review, and Delphi technique are used to identify 15 critical factors. These critical factors are then divided into four categories: organization, technology, process, and environment. The PFN-weighted power harmonic operator is proposed with the aim of more effectively …obtaining assessment data from experts and lessening the inaccuracy of outcomes caused by information loss. The best and worst method (BWM) is used to determine the ideal weight of critical factors. Results indicate that the primary critical factors to the effective adoption of the Internet of Things are talent, resource limitations, integration complexity, technical operations, equipment power consumption, technical dependability, and data governance. This research will benefit corporate managers in recognizing the significance of the effective deployment of the Internet of Things, identifying major critical factors to this achievement, and making decisions to remove these factors. Thus, an organization may support the effective adoption of the animal Internet of Things. Show more

Keywords: Internet of things, critical factors, PFN, weighted power harmonic operator, BWM

DOI: 10.3233/JIFS-231023

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5303-5323, 2023

Authors: Karamat, Tahira | Ullah, Kifayat | Pamucar, Dragan | Akram, Maria

Article Type: Research Article

Abstract: Prioritization is usually required in problems involving multi-attribute group decision-making (MAGDM). Several strategies and procedures have been introduced in fuzzy systems to apply prioritization. This study examines the MAGDM problem in a Pythagorean fuzzy (PF) setting with varying amounts of demand for specialists and attributes. We regard the novel Aczel Alsina aggregation operators (AOs) as the most addition to fuzzy mathematics that can deal with uncertainties significantly. We suggest a few PF AOs based on Aczel Alsina t-norm and t-conorm, including the PF-prioritized Aczel Alsina averaging (PFPAAA) and PF-prioritized Aczel Alsina geometric (PFPAAG) operators. It is proven that these AOs …fulfil the aggregation criteria by investigating the properties of monotonicity, boundedness, and idempotency. The weights for prioritization are derived from the knowledge of experts, and the proposed operators can capture the phenomenon of prioritization among the aggregated arguments. The proposed AOs are then applied to assess fire extinguishers using a MAGDM technique. The importance of PFPAAA and PFPAAG operators is verified by comparing the proposed AOs with other well-known AOs. Show more

Keywords: Pythagorean fuzzy sets, prioritized aggregation operators, Aczel-Alsina t-norm and t-conorm, decision-making methods, fire extinguishers

DOI: 10.3233/JIFS-231876

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5325-5351, 2023

Authors: Kirthika, K.M. | Paulraj, M.P. | Hema, C.R.

Article Type: Research Article

Abstract: The EEG-based HTR utilizing AEP responses of both group of participants with normal hearing and abnormal hearing are managed with the objective of detecting hearing sensitivity level using Chebyshev Recurrence Polynomial and Dempster Convolutional Neural Network (CRP-DCNN) is designed. The CRP-DCNN method is split into three sections. They are preprocessing using Chebyshev Recurrence Polynomial Filter, feature extraction by employing Orthogonalized Singular Value and Median Skewed Wavelet. Here, both Orthogonalized Singular Value Decomposition-based parametric and Median Skewness-based non-parametric modeling techniques are employed for first obtaining the hearing threshold factors and then extracting statistical features for further processing. Finally Dempster Convolutional Neural …Network-based Classification for detecting hearing sensitivity level is presented. Hence, the objective to determine the significant correlations between the brain dynamics and the auditory responses and detect the hearing sensitivity level of the group of participants with normal hearing and with the group of participants with hearing loss are designed on accordance with the features of EEG signals. Simulations are performed in MATLAB to validate the features of EEG signals. Show more

Keywords: Electroencephalogram, hearing threshold response, auditory evoked potential, chebyshev recurrence polynomial, orthogonalized singular value decomposition, median skewness, dempster convolutional neural network

DOI: 10.3233/JIFS-231794

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5353-5366, 2023

Authors: Vaigandla, Karthik Kumar | Benita, J.

Article Type: Research Article

Abstract: Filter Bank Multicarrier(FBMC)is considered as one of the most standardized waveform for fifth generation (5G) mobile communication system application FBMC endures lot of nonlinear effects which occurs because of high Peak Average Power Ratio (PAPR). High value of PAPR due to the large dynamic range of multicarrier signal is one of the most significant issues in FBMC multicarrier based modulation technique. This paper presents one investigated PAPR reduction technique named as Selected Mapping (SLM) to minimize high PAPR by utilizing the complex signal divide into real and imaginary parts and then select minimum PAPR signal based on Modified Forest Optimization …Algorithm (MFOA)to achieve good PAPR which can maintain the FBMC based system performance with a required Bit Error Rate (BER). The associated method was produced with the aim of optimize the phase factors so that the phase rotation operation is accomplished to minimize PAPR by fixing the MFOA into the conventional SLM. The simulation results demonstrate that the proposed technique gives better performance in terms of BER and PAPR compared to other SLM based optimization techniques. Show more

Keywords: Bit error rate, filter bank multicarrier, modified forest optimization algorithm, selected mapping, peak average power ratio

DOI: 10.3233/JIFS-222090

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5367-5381, 2023

Authors: Mansoor, J. Shafiq | Subramaniam, Kamalraj

Article Type: Research Article

Abstract: The usage of cloud-based grid computing services and Internet of Things (IoT) devices in medical diagnoses is increasing enormously. The cloud service provider’s data centers store vast amounts of data without processing it. This big data need some intelligent technique to analyze and classify heart disease from the considerable volume of data; it is a challenging task. Many deep learning techniques are introduced earlier for heart disease diagnosis in the literature study. Still, all other classification techniques failed to achieve the minimum loss in heart disease classification with the highest accuracy and faster performance. This research introduces a new classification …approach to overcome these issues: elephant herding optimizer turned restricted Boltzmann machine EHO-RBM network. The optimizer is used in this network to optimize the number of neuron utilization during the learning process by updating the network weight without compromising the loss. The previous research proves that the optimizer is performed well in reaching global minima efficiently. Therefore, the new classifier incorporates the optimizers instead of the classical stochastic gradient descent optimizer to improve the network performance by minimizing the global minima faster with less loss in predicting heart disease. The simulation result of the new heart disease classification framework shows that the elephant herding optimizer-trained classification model has reduced the loss rate and maximized the accuracy rate up to 0.0027 then the comparison method. As a result, the new classifier has obtained a maximum accuracy of up to 99.96% . Show more

Keywords: Cloud computing, grid computing, IoT devices, elephant search optimizer turned restricted Boltzmann machine network, big data analytics, heart disease

DOI: 10.3233/JIFS-224275

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5383-5399, 2023

Authors: Vennam, Preethi | Mouleeswaran, S.K.

Article Type: Research Article

Abstract: Wireless Sensor Networks (WSNs) are a group of devices/sensors which are connected as a network for transferring and receiving the data observed from the environment through intermediate links. Energy efficiency and security during data broadcasting are considered challenging tasks in the WSN. These challenging tasks are considered as a motivation of this research and the Multi-Objective - Trust Aware Average Inertia Weighted Cat Swarm Optimization (MO-TAIWCSO) is proposed for achieving secure reliable transmission over the WSN. Due to an effective velocity update of searching process, the AIWCSO is selected for discovering an optimal solutions. The developed MO-TAIWCSO is optimized by …using the trust, energy ratio, communication cost, and degree of SCH. This MO-TAIWCSO performs optimal Secure Cluster Head (SCH) and secure path discovery for the secure transmission of data under malicious attacks. The main objective of this MO-TAIWCSO is to improve the data delivery while minimizing the energy usage of the nodes. The performance of the MO-TAIWCSO method is analyzed by using the throughput, Packet Delivery Ratio (PDR), energy consumption, network lifetime, Normalized Routing Load (NRL) and End to end delay (EED). The existing researches namely ETOR and TBSEER are used to evaluate the MO-TAIWCSO. The PDR of MO-TAIWCSO for 100 nodes is 99.97%, which is high when compared to the ETOR and TBSEER. Show more

Keywords: Energy efficiency, malicious Attacks, multi objective-trust aware average Inertia weighted cat swarm optimization, wireless sensor networks

DOI: 10.3233/JIFS-230564

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5401-5408, 2023

Authors: Zhao, Shulin | Sun, Xiaoting | Gai, Lingyun

Article Type: Research Article

Abstract: Plant diseases and pests are primary factors that can negatively affect crop yield, quality, and profitability. Therefore, the accurate and automatic identification of pests is crucial for the agricultural industry. However, traditional methods of pest classification are limited, as they face difficulties in identifying pests with subtle differences and dealing with sample imbalances. To address these issues, we propose a pest classification model based on data enhancement and multi-feature learning. The model utilizes Mobile Inverted Residual Bottleneck Convolutional Block (MBConv) modules for multi-feature learning, enabling it to learn diverse and rich features of pests. To improve the model’s ability to …capture fine-grained details and address sample imbalances, data enhancement techniques such as random mixing of pictures and mixing after region clipping are used to augment the training data. Our model demonstrated excellent performance not only on the large-scale pest classification IP102 dataset but also on smaller pest datasets. Show more

Keywords: Data enhancement, Multi-feature fusion, Pest classification, Convolution neural network

DOI: 10.3233/JIFS-230606

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5409-5421, 2023

Authors: Saurabh, | Sharma, Chirag | Khan, Shakir | Mahajan, Shubham | Alsagri, Hatoon S. | Almjally, Abrar | Alabduallah, Bayan Ibrahimm | Ansari, Asrar Ahmad

Article Type: Research Article

Abstract: With the ever-increasing demand for IoT Devices which enable all objects to connect and exchange information in applications such as healthcare applications, Industry 4.0, smart cities and smart homes, etc. IoT devices play a crucial role in our day-to-day life like homes, offices, healthcare, wearable, and agriculture. With the development of IoT devices, securing device-to-device communication has attracted more and more attention and we need to ensure the privacy and security of data amongst these IoT devices. User authentication has emerged as a major security concern while connecting IoT devices and the cloud. Many authentication schemes like mutual authentication, group …authentication have been proposed to ensure only authenticated users and with very high confidence we can rely on the decision-making process. Symmetric key based as well as Asymmetric key-based solutions have been proposed but due to the resource constraint nature of the IoT devices designing lightweight, robust, provably secure authentication schemes is a big challenge. This paper discusses the various authentication techniques designed for low-powered IoT devices and proposes a lightweight authentication scheme for IoT. Show more

Keywords: IoT, authentication, lightweight, Industry 4.0, and security

DOI: 10.3233/JIFS-232388

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5423-5439, 2023

Authors: Zheng, Yulan

Article Type: Research Article

Abstract: In marketing, customer segmentation is a very critical element. This paper focuses on clustering algorithms. First, the commonly used K-means algorithm was introduced, and then, it was optimized using the improved Lion Swarm Optimization (ILSO) algorithm and the Calinski-Harabasz (CH) index. The results of the experiment for the UCI dataset showed that the CH indicator obtained an accurate number of clusters, and the clustering accuracy of the ILSO-K-means algorithm was higher, both above 90%. Then, in customer segmentation, the customers of an enterprise were divided into four groups using the ILSO-K-means algorithm, and different marketing suggestions were given. The experimental …analysis proves the usability of the ILSO-K-means algorithm in customer segmentation, which can be further applied in practice. Show more

Keywords: Clustering algorithm, marketing, customer segmentation, lion cluster optimization algorithm, marketing methods

DOI: 10.3233/JIFS-232589

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5441-5448, 2023

Authors: Huang, Kai | Wang, Jian

Article Type: Research Article

Abstract: Demand forecasting of auto parts is an essential part of inventory control in the automotive supply chain. Due to non-stationarity, strong randomness, local mutation, and non-linearity in short-term auto parts demand data, and it is difficult to predict accurately. In this regard, this paper proposes a combination prediction model based on EEMD-CNN-BiLSTM-attention. First, the model uses the ensemble empirical mode decomposition method to decompose the original data into a series of eigenmode functions and a residual item to extract more feature information. And then uses the CNN-BiLSTM-attention model to analyze each mode separately. The components are predicted, and the prediction …results are summed to obtain the final prediction result. The attention mechanism is introduced to automatically assign corresponding weights to the BiLSTM hidden layer states to distinguish the importance of different time load sequences, which can effectively reduce the loss of historical information and highlight the input of critical historical time points. Finally, the final auto parts demand prediction results are output through the fully connected layer. Then, we conduct an experimental analysis of the collected short-term demand data for auto parts. Finally, the experimental results show that the prediction model proposed in this paper has more minor errors, higher prediction accuracy, and the model prediction performance is better than the other nine comparison models, thus verifying the EEMD-CNN-BiLSTM-attention model for short-term parts demand forecasting effectiveness. Show more

Keywords: Demand forecasting, EEMD, BiLSTM, short-term demand, auto parts

DOI: 10.3233/JIFS-224222

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5449-5465, 2023

Authors: Sivakami, K. | Vijayalakshmi, P.

Article Type: Research Article

Abstract: WSNs(Wireless Sensor Networks) has been developed with applications in many domains including agriculture, telecommunication, manufacturing industry, healthcare, and surveillance. More specifically, WSN plays a pivotal role in IoT (Internet of Things). The IoT sensors provide information about the physical phenomena in the deployed fields. As the sensors contain only limited resources, the factors like data processing, power consumption, transmission, and storage capabilities adversely affect the efficiency. Thus, the process of routing is necessary for network longevity. The data from IoT-based sensors is routed to the destination through a multi-hop routing system. The Energy aware Routing is motivated by the nature …inspired Fuzzy Butterfly Optimization (E2RFBOA). Further a new data aggregation method is introduced in this article customized for IoT based WSN to acquaint higher crop yield in precision farming. Nevertheless, the scalability becomes a primary concern when deployed in larger and denser networks. This is due to the fact that all nodes in IoT and WSN are mostly alive depending on higher usage of bandwidth and power. The primal aim is to build a novel routing protocol developed for IoT-WSN. Apart from this, an Energy aware Clustered Routing that is motivated by Adaptive Elephant Herding Optimization (E2CR-AEHO) is proposed, which sensors collect data and find a group of Cluster Heads (CHs). In the AEHO Algorithm, the formed CH is rotated depending on power consumption. This also prevents frequent re-clustering; at the same time it can effectively adapt to the changes in network topology. According to the AEHOA, the node population comprises of nodes that can choose its CHs among the other nodes. This algorithm takes into account a number of criteria, including power consumption, residual power of Sensor Nodes (SN), network reliability, and data reliability. The suggested approach can efficiently represent the network environment, allowing the routing algorithm to avoid passing over marked zones. Network-specific performances measures including PDRs (Packet Delivery Ratios), NLs (Network Lifetimes), PLRs (Packet Loss Ratios), and AE2E (Average End To End) delay are used to evaluate simulation outcomes. This proposed framework aggregates IoT, which can gradually reduce the amount of data, hence extending network lifetime. Show more

Keywords: Internet of things, swarm intelligence, information fusion, integer linear programming, adaptive elephant herding optimization, wireless sensor networks

DOI: 10.3233/JIFS-224251

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5467-5479, 2023

Authors: Luo, Jiangnan | Cai, Jinyu | Li, Jianping | Gao, Jiuhua | Zhou, Feng | Chen, Kailang | Liu, Lei | Hao, Mengda

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-232162

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5481-5492, 2023

Authors: Chen, Liuxin | Wang, Yutai | Liu, Jinyuan

Article Type: Research Article

Abstract: In the emergency decision-making process, decision-makers usually cannot give rational evaluations, and existing decision-making methods do not adequately consider the risk attitude of decision-makers either. To solve these problems, a combined method based on the prospect theory and the multi-attributive ideal-real comparative analysis (MAIRCA) method is put forward in the picture fuzzy environment. Firstly, the optimal aggregation (OA) model is proposed to obtain the ideal evaluations with the least disagreement among decision-makers. Regarding the evaluations as reference systems, the OA-based prospect theory is put forward, which could calculate the prospect matrix more reasonably. Secondly, considering the prospect matrix and alternative …preference, the improved MAIRCA method is proposed, which overcomes the defects of theory and has the better ranking ability. Then, the OA-based prospect theory-MAIRCA method is further put forward to effectively complete the decision-making process with risk attitudes. Finally, an illustrative example of earthquake emergency assessment and a series of comparative experiments are presented. The analyses of results show that the proposed method has great guiding significance in the field of emergency decision-making management. Show more

Keywords: Picture fuzzy set, optimal aggregation model, prospect theory, MAIRCA method

DOI: 10.3233/JIFS-223279

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5493-5507, 2023

Authors: Gautam, Devendra | Dixit, Anurag | Banda, Latha | Goyal, S.B. | Verma, Chaman | Kumar, Manoj

Article Type: Research Article

Abstract: In recent generations of the digital world medical data in Recommender Systems. Health Care Recommender System (HCRS) analyses the medical data and then predicts the user’s or patient’s illness. Nowadays, healthcare data is used by various users or patients in recommendation systems which are useful for everyone. Analysing and predicting medical data provides awareness to users and these data predictions may be enriched using various techniques of RS. Machine learning techniques are used to make sure that health data is reliable and of high quality. In every RS the issues are targeted such as scalability, sparsity and cold start problems. …In many social networking applications, these issues are resolved using ML algorithms. However, there is a significant gap between IT systems and medical diagnosis. The fuzzy genetic method is used in HCRS in order to bridge the gap between IT and healthcare applications. Through the use of the mutation and crossover operators, a real-value genetic method is used in this to compute similarity. With the user’s extra personalized information, fuzzy rules are later generated for the database. The Hybrid fuzzy-genetic method, also known as this situation, combines both techniques to improve recommendation quality. Utilizing this method will improve the quality of the recommendation process by discovering the most precise similarity measures among different users. Six factors are subjected to fuzzification, including age, gender, employment, height, weight, and region. Genre-interesting measure weights are then used, including Very Light, Light, Average, Heavy, and Very Heavy. Finally, the evaluation metrics used MAE and RMSE to evaluate the recommendation accuracy which showed the best results in comparison with baseline approaches such as Convolutional Neural Networks and Restricted Boltzman Machine. Show more

Keywords: Recommender system, confidentiality, deep learning, convolutional neural networks, fuzzy logics

DOI: 10.3233/JIFS-224257

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5509-5522, 2023

Authors: Qian, Zhuoyi | Guo, Peng | Wang, Yifan | Xiao, Fangcheng

Article Type: Research Article

Abstract: Self-driving cars are expected to replace human drivers shortly, bringing significant benefits to society. However, they have faced opposition from various organizations that argue it is challenging to respond to instances involving unavoidable personal injury. In situations involving deadly collisions, self-driving cars must make decisions that balance life and death. This paper investigates the ethical and moral decision-making challenges for self-driving cars from an algorithmic perspective. To address this issue, we introduce the accident-prioritized replay mechanism to the Deep Q-Networks (DQN) algorithm based on early humanities research. The mechanism quantifies a reward function that takes priority into account. RGB (red, …green, blue) images obtained by the camera installed in front of the self-driving cars are fed into the Xception network for training. To evaluate our approach, we compare it to the conventional DQN algorithm. The simulation results indicate that the Rawlsian DQN algorithm has superior stability and interpretability in decision-making. Furthermore, the majority of respondents to our survey accept the final decision made by our algorithm. Our experiment demonstrates that it is possible to incorporate ethical considerations into self-driving car decision-making, providing a solution for rational decision-making in emergency and dilemma circumstances. Show more

Keywords: Rawlsian maximin principle, carla simulator, depth-wise separable convolution, deep Q-network, ethical decision-making

DOI: 10.3233/JIFS-224553

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5523-5540, 2023

Authors: Shan, Yuxiang | Lu, Hailiang | Lou, Weidong

Article Type: Research Article

Abstract: Exploiting dynamic spatial and temporal features of location information for robot modeling is of great importance in many real applications. It has gained increasing attention in the era of the Internet of Things (IoT). However, successful modeling and accurate localization for robot in indoor environment is still a challenge, where the environment factors are complex and unpredictable, such as signal noise, obstacles and spare fingerprints. Existing studies usually employ data driven and learning based models to capture spatial and temporal features for robot location estimation, modeling dynamics of robot and make robot decision. However, the modeling and localization performance is …not satisfied. In this paper, to address above challenges, a novel deep learning framework called multi-faceted deep learning based dynamics modeling and robot localization learning (DMLoc) method is proposed. Specifically, a localization attention module is designed to capture the features from original fingerprints and optimized fingerprints information. Then, a multi-faceted localization module is proposed, which integrates extraction model and optimized model with long short-term memory (LSTM) and gate recurrent unit (GRU). Moreover, a multi-feature fusion layer is designed to fuse the extracted features and generate localization results. Extensive simulation results show the efficiency of the proposed DMLoc. Show more

Keywords: Robot localization, dynamics modeling, learning-based robot decision

DOI: 10.3233/JIFS-230895

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5541-5550, 2023

Authors: Jeyabalan, Saranya Devi | Yesudhas, Nancy Jane | Sathyanarayanan, Jayashree | Harichandran, Khanna Nehemiah

Article Type: Research Article

Abstract: Coronavirus disease 2019 (Covid-19) is a contagious pandemic illness characterized by severe acute respiratory syndrome. The daily rise of Covid-19 instances and fatalities has resulted in worldwide lockdowns, quarantines and social distancing. Researchers have been working incredibly to develop precisely focused strategies to warfare the Covid-19 pandemic. This study aims to develop a cyclical learning rate optimized stacked generalization computational models (CLR-SGCM) for predicting Covid-19 pandemic outbreaks. Stacked generalization framework performs hierarchical two-phase prediction. In the first phase, deep learning models namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and statistical model Auto Regressive Integrated Moving Average (ARIMA) …are used as sub models to create pooled datasets (PDS). Cyclical learning rate (CLR) optimizer is used to enhance learning rate of ensemble deep learning models namely LSTM and GRU. In the second phase, meta learner is trained on dataset PDS using four different regression algorithms such as linear regression, polynomial regression, lasso regression and ridge regression to perform the final predictions. Time series data from India, Brazil, and the United States were utilized to forecast the Covid-19 pandemic outbreak. According to experimental finding, the presented stacking ensemble model outpaces the individual learners in terms of accuracy and error rate. Show more

Keywords: Covid-19, forecasting, time series prediction, stacked generalization, CLR optimization, deep learning models

DOI: 10.3233/JIFS-231229

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5551-5566, 2023

Authors: Ge, Hongping | Liu, Huaying | Luo, Yun

Article Type: Research Article

Abstract: Aiming at the troubles of difficult extraction of fault features and low fault recognition rate in rotating equipment fault detection approach, a new technique for intelligent diagnosis based on modified hierarchical diversity entropy (MHDE) and extension theory (ET) is proposed in the thesis. Firstly, MHDE employs to comprehensively describe the fault information of the given signals. Secondly, the MHDE feature sets are regarded as the characteristic parameters of the extension matter element model, and the matter element model in various states is established. Finally, the testing datasets are fed into the matter element model for each operating conditions, and the …correlation function is used to compute the comprehensive correlation between the testing datasets and the various conditions of the rotating machinery, so as to realize the qualitative and quantitative identification of the testing datasets. The reliability and superiority of the proposed new approach is validated by real experimental datasets on various rotating machinery types. The analysis results show that the proposed novel technology can effectively excavate the feature information and accurately identify various fault conditions of rotating machinery. In addition, compared with other combined model technology in the paper, the proposed intelligent fault diagnosis technology has better classification performance. Show more

Keywords: Rotating machinery, modified hierarchical diversity entropy, extension theory, correlation function, fault diagnosis technology

DOI: 10.3233/JIFS-231363

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5567-5586, 2023

Authors: Peng, Peng | Ni, Zhiwei | Zhu, Xuhui | Chen, Qian

Article Type: Research Article

Abstract: A framework for spatial crowdsourcing task allocation based on centralized differential privacy is proposed for addressing the problem of worker’s location privacy leakage. Firstly, by combining two stages of differential privacy noise addition and clustering matching, a spatial crowdsourcing worker dataset with high differential privacy protection can be obtained; Secondly, the dynamic problem of spatial crowdsourcing task allocation is transformed into a static combinatorial optimization problem by dividing the spatiotemporal units and the “delay matching” strategy; Finally, the improved discrete glowworm swarm optimization algorithm is used to calculate the results of spatial crowdsourcing task allocation. It has been demonstrated that, …compared to the direct differential privacy noise-adding assignment method and the discrete glowworm swarm optimization assignment method, the proposed method achieves better task assignment results, with the total travel distance reduced by 12.42% and 3.56%, respectively, and the task assignment success rate increased by 11.75% and 3.34%, respectively. Show more

Keywords: Differential privacy, k-means clustering, space crowdsourcing, task allocation, the glowworm swarm optimization algorithm

DOI: 10.3233/JIFS-230734

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5587-5600, 2023

Authors: Zhu, Xuemin | Liu, Sheng | Zhu, Xuelin | You, Xiaoming

Article Type: Research Article

Abstract: An enhancing sparrow optimization algorithm with hybrid multi-strategy (EGLTA-SSA) is proposed, to improve the defects of the sparrow search algorithm (SSA), which is easy to fall into local optimum. Firstly, the elite backward learning strategy is introduced to initialize the sparrow population, to generate high-quality initial solutions. Secondly, the leader position is updated by fusing multi-strategy mechanisms. On one hand, the high distributivity of arithmetic optimization algorithm operators are used to deflate the target position, and enhance the ability of SSA to jump out of the local optimum. On the other hand, the leader position is perturbed by adopting the …golden levy flight method and the t-distribution perturbation strategy to improve the shortcoming of SSA in the late iteration when the population diversity decreases. Further, a probability factor is added for random selection to achieve more effective communication among leaders. Finally, to verify the effectiveness of EGLTA-SSA, CEC2005 and CEC2019 functions are tested and compared with state-of-the-art algorithms, and the experimental results show that EGLTA-SSA has a better performance in terms of convergence rate and stability. EGLTA-SSA is also successfully applied to three practical engineering problems, and the results demonstrate the superior performance of EGLTA-SSA in solving project optimization problems. Show more

Keywords: Sparrow optimization algorithm (SSA), arithmetic optimization algorithm, golden levy flight distribution, t-distribution perturbation, engineering design problems

DOI: 10.3233/JIFS-231114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5601-5632, 2023

Authors: Bai, Xiaojun | Pan, Zhaofeng | Meng, Gong | Wang, Shenhang | Fu, Yanfang

Article Type: Research Article

Abstract: Hard disk is the main storage device for cloud service, and there always contain massive disks deployed in a data center. Disk failure occur frequently in data center, which may lead to data loss and other disasters, so there have urgent needs for a failure prediction method of hard disk so as to ensure service reliability. This paper proposes a temporal prediction model based on LSTM. Firstly, the SMART data of the disk is analyzed, and the Pearson correlation coefficient is used to analyze the correlation between the monitoring time series data of the faulty disk and the normal disk, …and the monitoring index with the lowest correlation is selected as the fault feature; secondly, for the problem of serious imbalance of positive and negative samples in the SMART dataset, the SMOTEENN algorithm is introduced for oversampling to obtain a balanced dataset of positive and negative samples. The proposed method improves accuracy by 8.268% and F1-score by 8.657% compared to the conventional method. Show more

Keywords: Hard disk drives, failure prediction, association analysis, long-short term memory, SSA

DOI: 10.3233/JIFS-231268

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5633-5645, 2023

Authors: Suo, Jiafeng | Han, Dongchen | Zhao, Hui

Article Type: Research Article

Abstract: In the entity extraction task, there are some complex extraction problems, such as nested entity, entity boundary recognition, context ambiguity, and multi-instance entity recognition. Entity nesting is an important challenge in relational extraction. The main reason of entity nesting problem is that the boundary information between entities is not clear. In order to solve the entity nesting problem at the fragment level, while preserving the relationship between fragments with the same characteristics and improving efficiency, we proposed a brand new fragment annotation method. On the basis of traditional fragment annotation method, combined with pointer annotation method, we designed an annotation …method of "ergodic enumeration + group mapping". On the basis of this method, an entity extraction model is designed: Span-Extraction Based Entity Extraction Model (LMA). Our model underwent a series of validations in the English data sets New York Times(NYT) and WEBNLG, showing significant improvements over the baseline model F1. It can effectively alleviate the above problems. Show more

Keywords: Entity extraction, relational extraction, nested entity, context ambiguity

DOI: 10.3233/JIFS-231766

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5647-5657, 2023

Authors: Jia, Lijuan | Hou, Fang

Article Type: Research Article

Abstract: The evaluation of physical education teaching effectiveness is an important component of physical education teaching, and plays a multifaceted role in the process of physical education teaching. The information provided by it can control and regulate the progress of physical education teaching activities as a whole, ensuring that physical education teaching activities develop towards predetermined goals. With the development of the popularization of physical education, people’s requirements for the quality of physical education continue to improve, and the role and position of evaluation in teaching has become increasingly evident. Evaluation of physical education teaching effectiveness has become an indispensable process …in teaching activities. The college physical education teaching effect evaluation can be regarded as a multiple attribute decision making (MADM). Thus, this paper collected information in probabilistic hesitant fuzzy sets (PHFSs) and using CRITIC method to obtain the unknown weight among attributes. Further, a novel probabilistic hesitant fuzzy QUALIFLEX (PHF-QUALIFLEX) method was constructed for MADM. Finally, a numerical case for college physical education teaching effect evaluation was illustrated with this proposed model and other methods were utilized to compare with PHF-QUALIFLEX method to verify the feasibility and applicability. Show more

Keywords: Multiple attributes decision making (MADM), probabilistic hesitant fuzzy sets (PHFSs), QUALIFLEX method, CRITIC method, teaching effect evaluation

DOI: 10.3233/JIFS-231769

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5659-5670, 2023

Authors: Zhang, Yunlai

Article Type: Research Article

Abstract: This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433 .

DOI: 10.3233/JIFS-232226

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5671-5683, 2023

Authors: Shen, Haiyang | Huo, Kui | Qiao, Xin | Li, Chongzhi

Article Type: Research Article

Abstract: In order to solve the problems with the traditional aircraft target type recognition algorithm, such as difficulty in feature selection, weak generalization ability, slow recognition speed, and low recognition accuracy, this paper put forward a new method that could detect and recognize aircraft targets in aerial images quickly and accurately. The aircraft targets in the images were detected rapidly and located through YOLOv3-tiny, and after image denoising, shadow detection, and positioning, then we used the Sobel operator to calculate the edge gradient of the target; the image of the aircraft target was segmented by using the region growth method, and …then the principal component analysis (PCA)was used to obtain the central axis of the aircraft target. The projected distance from the edge contour to the central axis was sampled at equal intervals along the direction of the central axis, and its ratio to the length of the central axis was calculated to construct the feature vector. Finally, the Spearman rank correlation method was used to match the feature vectors to realize the recognition of the aircraft type. Experiments showed that the proposed method had strong adaptability and small computation and could quickly detect and accurately recognize aircraft targets in aerial images. Show more

Keywords: Deep learning, aircraft identification, principal component analysis, spearman rank correlation

DOI: 10.3233/JIFS-232239

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5685-5696, 2023

Authors: Gong, Meng

Article Type: Research Article

Abstract: With the increasing maturity and widespread application of computer multimedia technology, many universities have attempted to use multimedia technology for English teaching in order to solve some of the difficulties and contradictions faced in current college English teaching practices. Practice has proven that multimedia teaching of college English not only increases the amount of information in classroom teaching, but also improves the effectiveness of classroom teaching. At the same time, due to deviations in understanding, lack of conditions, and improper operation in work, the normal functioning of multimedia teaching is also restricted, which affects the effectiveness of multimedia teaching in …college English. How to carry out multimedia teaching of college English is indeed an important topic that needs further research. The fuzzy comprehensive evaluation of multimedia teaching effectiveness in college English is a classical multiple attribute decision making (MADM) problems. Recently, the TODIM and GRA method has been used to cope with MADM issues. The double-valued neutrosophic sets (DVNSs) are used as a tool for characterizing uncertain information during the fuzzy comprehensive evaluation of multimedia teaching effectiveness in college English. In this manuscript, the double-valued neutrosophic number Exponential TODIM-GRA (DVNN-ExpTODIM-GRA) method is built to solve the MADM under DVNSs. In the end, a numerical case study for fuzzy comprehensive evaluation of multimedia teaching effectiveness in college English is given to validate the proposed method. Show more

Keywords: Multiple attribute decision making (MADM), double-valued neutrosophic sets (DVNSs), ExpTODIM method, GRA method, multimedia teaching effectiveness

DOI: 10.3233/JIFS-233116

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5697-5707, 2023

Authors: Zhang, Qianhong | Pan, Bairong | Ouyang, Miao | Lin, Fubiao

Article Type: Research Article

Abstract: The article is concerned with large time behavior of solution to second-order fractal difference equation with positive fuzzy parameters x n + 1 = A + x n B + x n - 1 ,     n = 1 , 2 , ⋯ , here the initial values x i  (i  = -1, 0) and the parameters A , B are positive fuzzy numbers. Utilizing a generalization of division (g-division) of fuzzy numbers, one presents large time behaviors of positive fuzzy solution including persistence, boundedness, …global convergence. Moreover, two numerical examples verify the effectiveness of the qualitative analysis. Show more

Keywords: Second- order fractal difference equation, g-division, large time behavior, positive fuzzy parameter

DOI: 10.3233/JIFS-224099

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5709-5721, 2023

Authors: Xu, Huajie | Zhou, Yanping | Chen, Huiying | Kou, Yuanyuan

Article Type: Research Article

Abstract: In the era of the knowledge economy, how integrating into the network of collaborative innovation and promoting technology sharing has become the key to enhancing the competitiveness of enterprises. It is well known that inter-organizational trust is essential to technology sharing. Firstly, this paper discussed how inter-organizational trust plays a role in technology-sharing behavior. Secondly, based on “organization is bounded rational”, we established an evolutionary game model to analyze the influencing factors of technology sharing. Finally, we used the numerical simulation method to verify the model. Research shows that affective trust facilitates technology acquisition and cognitive trust facilitates technology sharing. …The synergetic benefit distribution coefficient influences the evolutionary equilibrium strategy of technology sharing, and there is an optimal synergistic benefit distribution coefficient that maximizes the willingness of both enterprises to share technology. Technology transfer cost and technology leakage risk negatively affect technology-sharing behavior. The degree of technology complementarity, trust coefficient, incentive coefficient, and the ability of shared technologies to transform into synergistic benefits positively influence technology-sharing behavior. The research provides a new way to solve the practical problem of collaborative innovation technology sharing among enterprises. Show more

Keywords: Collaborative innovation, inter-organizational trust, technology sharing, technology acquisition, evolutionary game

DOI: 10.3233/JIFS-231898

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5723-5738, 2023

Authors: Huang, Peixin | Luo, Qifang | Wei, Yuanfei | Zhou, Yongquan

Article Type: Research Article

Abstract: Data clustering is a machine learning method for unsupervised learning that is popular in the two areas of data analysis and data mining. The objective is to partition a given dataset into distinct clusters, aiming to maximize the similarity among data objects within the same cluster. In this paper, an improved honey badger algorithm called DELHBA is proposed to solve the clustering problem. In DELHBA, to boost the population’s diversity and the performance of global search, the differential evolution method is incorporated into algorithm’s initial step. Secondly, the equilibrium pooling technique is included to assist the standard honey badger algorithm …(HBA) break free of the local optimum. Finally, the updated honey badger population individuals are updated with Levy flight strategy to produce more potential solutions. Ten famous benchmark test datasets are utilized to evaluate the efficiency of the DELHBA algorithm and to contrast it with twelve of the current most used swarm intelligence algorithms and k-means. Additionally, DELHBA algorithm’s performance is assessed using the Wilcoxon rank sum test and Friedman’s test. The experimental results show that DELHBA has better clustering accuracy, convergence speed and stability compared with other algorithms, demonstrating its superiority in solving clustering problems. Show more

Keywords: Cluster analysis, k-means, equilibrium honey badger algorithm, differential evolution, metaheuristic optimization

DOI: 10.3233/JIFS-231922

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5739-5763, 2023

Authors: Yang, Taoli | Li, Jinjin | Li, Zhaowen | Zhou, Yinfeng | Feng, Danlu

Article Type: Research Article

Abstract: Knowledge and learning assessment is a popular topic. In existing models for constructing the knowledge structure of an individual, it is often considered whether an individual has mastered the skills to solve the corresponding item. However, the relationship between the number of skills an individual has mastered and the item is ignored. It is not reasonable to explain the phenomenon that individuals solve the same item but have different knowledge structures behind it. This paper introduces the concept of skill inclusion degree and constructs the variable precision α-models to delineate knowledge structures. The skill inclusion degree takes into account an …individual’s mastery of the number of skills assigned to each item. Firstly, the concept of the skill inclusion degree is given, and some of its properties are discussed. Then, the variable precision α-model is constructed. Moreover, the relationship between knowledge structures delineated via the variable precision α-models by a skill map is studied, and the algorithm of knowledge structures delineated via these models by a skill map is designed. Finally, the experimental results on a real dataset demonstrate the feasibility and effectiveness of the proposed algorithm. Show more

Keywords: Knowledge structure, skill map, skill inclusion degree, disjunctive model, conjunctive model, variable precision α-model

DOI: 10.3233/JIFS-222149

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5765-5781, 2023

Authors: Li, Kun | Tian, Shengwei | Yu, Long | Zhou, Tiejun | Wang, Bo | Wang, Fun

Article Type: Research Article

Abstract: In recent years multimodal sentiment analysis (MSA) has been devoted to developing effective fusion mechanisms and has made advances, however, there are several challenges that have not been addressed adequately: the models make insufficient use of important information (inter-modal relevance and independence information) resulting in additional noise, and the traditional ternary symmetric architecture cannot well solve the problem of uneven distribution of task-related information among modalities. Thus, we propose Mutual Information Maximization and Feature Space Separation and Bi-Bimodal Modality Fusion (MFSBF)framework which effectively alleviates these problems. To alleviate the problem of underutilization of important information among modalities, a mutual information …maximization module and a feature space separation module have been designed. The mutual information module maximizes the mutual information between two modalities to retain more relevance (modality-invariant) information, while the feature separation module separates fusion features to prevent the loss of independence(modality-specific) information during the fusion process. As different modalities contribute differently to the model, a bimodal fusion architecture is used, which involves the fusion of two bimodal pairs. The architecture focuses more on the modality that contains more task-ralated information and alleviates the problem of uneven distribution of useful information among modalities. The experiment results of our model on two publicly available datasets (CUM-MOSI and CUM-MOSEI) achieved better or comparable results than previous models, which demonstrate the efficacy of our method. Show more

Keywords: Multimodal sentiment analysis, mutual information, feature separation, modality fusion

DOI: 10.3233/JIFS-222189

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5783-5793, 2023

Authors: Xie, Ying | Hu, Fanchao | Liu, Xuewei | Zhai, Lirong

Article Type: Research Article

Abstract: In the actual production process, time-varying and nonlinear problems are numerous important problems to be considered, in view of these problems, a process monitoring approach based on locally weighted probabilistic kernel principal component analysis (LWPKPCA) is proposed. First, the method selects the normal process data with a high similarity to the test samples as training data of the local model, and continuously updates the local model according to the test samples to build an accurate time-varying model. Second, by weighting the data of different importance, the role of data similar to test samples in the modeling process is strengthened. Third, …the LWPKPCA model is applied to process monitoring, the monitoring indicators are established in a high-dimensional space and used to detect faults. Finally, on the basis of LWPKPCA, the penicillin fermentation process (PFP) is taken to evaluate the monitoring performance of the proposed methods. According to the comparison of the experiment results, the detection rate and accuracy rate of the LWPKPCA method is considerably better than those of probabilistic principal component analysis and probabilistic kernel principal component analysis methods. The results demonstrate that the proposed method is suitable for processing time-varying data with nonlinear characteristics, and the LWPKPCA process monitoring method is effective for improving the performance of fault detection. Show more

Keywords: Locally weighted probabilistic kernel principal component analysis, process monitoring, fault detection

DOI: 10.3233/JIFS-224383

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5795-5805, 2023

Authors: Liu, Baokai | He, Fengjie | Du, Shiqiang | Li, Jiacheng | Liu, Wenjie

Article Type: Research Article

Abstract: Small object detection has important application value in the fields of autonomous driving and drone scene analysis. As one of the most advanced object detection algorithms, YOLOv3 suffers some challenges when detecting small objects, such as the problem of detection failure of small objects and occluded objects. To solve these problems, an improved YOLOv3 algorithm for small object detection is proposed. In the proposed method, the dilated convolutions mish (DCM) module is introduced into the backbone network of YOLOv3 to improve the feature expression ability by fusing the feature maps of different receptive fields. In the neck network of YOLOv3, …the convolutional block attention module (CBAM) and multi-scale fusion module are introduced to select the important information for small object detection in the shallow network, suppress the uncritical information, and use the fusion module to fuse the feature maps of different scales, so as to improve the detection accuracy of the algorithm. In addition, the Soft-NMS and Complete-IOU (ClOU) strategies are applied to candidate frame screening, which improves the accuracy of the algorithm for the detection of occluded objects. The experimental results on MS COCO2017, VOC2007, VOC2012 datasets and the ablation experiments on MS COCO2017 datasets demonstrate the effectiveness of the proposed method.The experimental results show that the proposed method achieves better accuracy in small object detection than the original YOLOv3 model. Show more

Keywords: Small object detection, Dilated convolutions mish, Fusion module, Soft-NMS

DOI: 10.3233/JIFS-224530

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5807-5819, 2023

Authors: Jiang, Minghua | Wang, Yulin | Yu, Feng | Peng, Tao | Hu, Xinrong

Article Type: Research Article

Abstract: Forest fires can pose a serious threat to the survival of living organisms, and wildfire detection technology can effectively reduce the occurrence of large forest fires and detect them faster. However, the unpredictable and diverse appearance of smoke and fire, as well as interference from objects that resemble smoke and fire, can lead to the overlooking of small objects and detection of false positives that resemble the objects in the detection results. In this work, we propose UAV-FDN, a forest fire detection network based on the perspective of an unmanned aerial vehicle (UAV). It performs real-time wildfire detection of various …forest fire scenarios from the perspective of UAVs. The main concepts of the framework are as follows: 1) The framework proposes an efficient attention module that combines channel and spatial dimension information to improve the accuracy and efficiency of model detection under complex backgrounds. 2) It also introduces an improved multi-scale fusion module that enhances the network’s ability to learn objects details and semantic features, thus reducing the chances of small objects being false negative during inspection and false positive issues. 3) Finally, the framework incorporates a multi-head structure and a new loss function, which aid in boosting the network’s updating speed and convergence, enabling better adaptation to different objects scales. Experimental results demonstrate that the UAV-FDN achieves high performance in terms of average precision (AP), precision, recall, and mean average precision (mAP). Show more

Keywords: Forest fire, wildfire detection, unmanned aerial vehicle, deep learning, attention mechanism, multi-scale feature fusion

DOI: 10.3233/JIFS-231550

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5821-5836, 2023

Authors: Guo, An | Sun, Kaiqiong | Wang, Meng

Article Type: Research Article

Abstract: While deep learning based object detection methods have achieved high accuracy in fruit detection, they rely on large labeled datasets to train the model and assume that the training and test samples come from the same domain. This paper proposes a cross-domain fruit detection method with image and feature alignments. It first converts the source domain image into the target domain through an attention-guided generative adversarial network to achieve the image-level alignment. Then, the knowledge distillation with mean teacher model is fused in the yolov5 network to achieve the feature alignment between the source and target domains. A contextual aggregation …module similar to a self-attention mechanism is added to the detection network to improve the cross-domain feature learning by learning global features. A source domain (orange) and two target domain (tomato and apple) datasets are used for the evaluation of the proposed method. The recognition accuracy on the tomato and apple datasets are 87.2% and 89.9%, respectively, with an improvement of 10.3% and 2.4%, respectively, compared to existing methods on the same datasets. Show more

Keywords: Domain adaptation, deep learning, knowledge distillation, fruit detection

DOI: 10.3233/JIFS-232104

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5837-5851, 2023

Authors: Liu, Junhui | Li, Guozhu | Gao, Chen

Article Type: Research Article

Abstract: In this study, we are concerned with the optimization of fuzzy clustering (Fuzzy C-Means) on the basis of a collection of distributed datasets without violating data confidentiality and security. The optimization of fuzzy clusters is realized using the differential evolution algorithm in a federated learning environment. Fuzzy clustering plays an important role in revealing the underlying structure of a given dataset. However, traditional iterative method is easy to get stuck at local optimum. With the growing concerning on data confidentiality and security, how to reveal the underlying structure of the data that are stored locally across different sites is becoming …an urgent problem. In order to overcome these two obstacles, we propose a federated differential evolution algorithm to realize fuzzy clustering. We augment the well-known differential evolution algorithm such that it can work in a federated learning environment to ensure local data privacy. The design practice of the federated differential evolution is elaborated on by highlighting its effectiveness in finding the optimal fuzzy clusters on the basis of distributed datasets. The performance of the proposed method is compared with traditional fuzzy clustering algorithm. Experimental studies completed on a series of real-world datasets coming from machine learning repository are reported to demonstrate the superiority of the proposed algorithm. Show more

Keywords: Differential evolution, horizontal federated learning, fuzzy clustering, global optimization

DOI: 10.3233/JIFS-232709

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5853-5860, 2023

Authors: Wang, Yajun

Article Type: Research Article

Abstract: In order to improve the detection accuracy of high-voltage dense channel satellite image, a satellite target detection algorithm based on deep learning is proposed. The convolution neural network is selected to extract the feature map of high-voltage dense channel satellite image, and the extracted feature map is input into the optimized deformation convolution neural network. The value of each sampling point and the corresponding position authority of block convolution kernel are weighted by using the regular region sampling feature map. The feature map output by the convolution operation of pooling layer is used to obtain the depth features of the …same dimension. The depth feature is input into the full connection layer to obtain the full connection feature of candidate target area, and the target detection in high-voltage dense channel satellite image is realized. The experimental results show that the target detection accuracy of the method is higher than 99% and the false alarm rate and false alarm rate are lower than 1.4%. Show more

Keywords: Deep learning, high voltage dense channel, satellite, target detection algorithm, convolution neural network, regular region

DOI: 10.3233/JIFS-223936

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5861-5869, 2023

Authors: Che, Gaofeng | Yu, Zhen

Article Type: Research Article

Abstract: In this work, the output-feedback fault-tolerant tacking control issue for underactuated autonomous underwater vehicle (AUV) with actuators faults is investigated. Firstly, an output-feedback error tacking system is constructed based on the theoretical model of underactuated AUV with actuators faults. Then, an adaptive dynamic programming (ADP) based fault-tolerant control controller is developed. In our proposed control scheme, a neural-network observer is designed to approximate the system states with actuators faults. An online policy iteration algorithm is designed with critic network and action network in order to improve the tracking accuracy. Based on Lyapunov stability theorem, the stability of the error tracking …system is guaranteed by the proposed controller. At last, the simulation results show that the underactuated AUV achieves better tracking performance. Show more

Keywords: Adaptive dynamic programming (ADP), fault-tolerant tracking control, actuators faults, neural network observer, autonomous underwater vehicle (AUV)

DOI: 10.3233/JIFS-223976

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5871-5883, 2023

Authors: Xu, Fei | Wang, Peng | Xu, Huimin

Article Type: Research Article

Abstract:  Deep convolutional neural networks (DCNNs) have shown remarkable performance in image classification tasks in recent years. In the network structure of DPRN, as the network depth increases, the number of convolutional kernels also increases linearly or nonlinearly. On the one hand, in the DPRN block, the size of the receptive field is only 3 × 3, which results in insufficient network ability to extract feature map information of different filter sizes. On the other hand, the number of convolution kernels in the second 1x1 convolution will be multiplied by a coefficient relative to the first convolution, which can cause overfitting to some …extent. In order to overcome these weaknesses, we introduce the inception-like structure on the basis of the DPRN network which is called by pyramid inceptional residual networks (PIRN). In addition, we also discuss the performance of PIRN network with squeeze and excitation (SE) mechanism and regularization term. Furthermore, some results in network performance are discussed when adding a stochastic depth networkto the PIRN model. Compared to DPRN, PIRN achieved better results on the CIFAR10, CIFAR100, and Mini-ImageNet datasets. In the case of using zero-padding, the multiplicative PIRN with SE mechanism achieves the best result of 95.01% on the CIFAR10 dataset. Meanwhile, on the CIFAR100 and Mini-ImageNet datasets, the additive PIRN network with a network depth of 92 achieves the best results of 76.06% and 65.86%, respectively. According to the experimental results, our method has achieved better accuray than that of DPRN with same network settings which demonstrate its effectiveness in generalization ability. Show more

Keywords: Convolution neural network, Deep pyramidal residual network, Squeeze and excitation mechanism, Pyramidal inceptional residual network, L2 regularization

DOI: 10.3233/JIFS-230569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5885-5906, 2023

Authors: Zhang, Dong | Liu, Jinzhu | Liu, Duo | Li, Guanyu

Article Type: Research Article

Abstract: Knowledge graphs exhibit a typical hierarchical structure and find extensive applications in various artificial intelligence domains. However, large-scale knowledge graphs need to be completed, which limits the performance of knowledge graphs in downstream tasks. Knowledge graph embedding methods have emerged as a primary solution to enhance knowledge graph completeness. These methods aim to represent entities and relations as low-dimensional vectors, focusing on handling relation patterns and multi-relation types. Researchers need to pay more attention to the crucial feature of hierarchical relationships in real-world knowledge graphs. We propose a novel knowledge graph embedding model called H ierarchy-Aware P aired R elation …Vectors Knowledge Graph E mbedding (HPRE) to bridge this gap. By leveraging the power of 2D coordinates, HPRE adeptly model relation patterns, multi-relation types, and hierarchical features in the knowledge graph. Specifically, HPRE employs paired relation vectors to capture the distinct characteristics of head and tail entities, facilitating a better fit for relational patterns and multi-relation scenarios. Additionally, HPRE employs angular coordinates to differentiate entities at various levels of the hierarchy, effectively representing the hierarchical nature of the knowledge graph. The experimental results show that the HPRE model can effectively learn the hierarchical features of the knowledge graph and achieve state-of-the-art experimental results on multiple real-world datasets for the link prediction task. Show more

Keywords: Knowledge graph completion, link prediction, knowledge graph embedding, knowledge graph representation

DOI: 10.3233/JIFS-230982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5907-5926, 2023

Authors: Wang, Hejin | He, Mingzhao | Zeng, Chengli | Qian, Lei | Wang, Jun | Pan, Wu

Article Type: Research Article

Abstract: Immersive virtual reality technology has been widely used in teaching and learning scenarios because of its unique visual and interactive experiences that bring learners a sense of immersive reality. However, how to better apply immersive virtual reality technology to learning environments to promote learning effectiveness is a direction that has been studied and explored by many scholars. Although a growing number of studies have concluded that immersive virtual reality technology can enhance learners’ attention in teaching and learning, few studies have directly linked both learning behaviors and attention to investigate the differences in behavioral performance across attention. In this study, …attention data monitored by EEG physiological brainwaves and a large number of videos recorded during learning were used to explore the differences in the sequence of high attention behaviors across performance levels in an immersive virtual reality environment using behavioral data mining techniques. The results found that there was a strong correlation between attention and performance in immersive virtual reality, that thinking and looking may be more conducive to learners’ concentration, and that high concentration behaviors in the high-performing group accompanied the test and appeared after the monitoring, while the action continued to be repeated after the high concentration behaviors in the low-performing group. Based on this, this study provides a reference method for the analysis of the learning process in this environment, and provides a theoretical basis and practical guidance for the improvement of participants’ attention and learning effectiveness. Show more

Keywords: Immersive virtual reality, EEG feedback, learning behaviour, data mining

DOI: 10.3233/JIFS-231383

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5927-5938, 2023

Authors: Chen, Fu

Article Type: Research Article

Abstract: How to guarantee the quality of college physical education (PE) teaching and reverse the declining trend of college students’ physique year by year has become a hot topic for the research of higher education and school PE workers. The quality assurance of higher education in China should give full play to the role of colleges in teaching quality assurance activities, constantly improve the level of school running and improve the efficiency of school running. Because colleges themselves are the main body of higher education and teaching activities, they have the most power, qualification and responsibility to explain the quality of …higher education. The classroom teaching quality (CTQ) evaluation of college badminton training is regarded as multi-attribute decision-making (MADM). The 2-tuple linguistic neutrosophic sets (2TLNSs) which the truth-membership, indeterminacy-membership and the falsity-membership are assessed by using the 2-tuple linguistic term sets is an appropriate form to express the indeterminate decision-making information in the classroom teaching quality (CTQ) evaluation of college badminton training. In this paper, the Hamy mean (HM) and the power average (PA) are connected with 2-tuple linguistic neutrosophic sets (2TLNSs) to propose the 2-tuple linguistic neutrosophic numbers weighted power HM (2TLNWPHM) operator. Then, use the 2TLNWPHM operator to handle MADM with 2TLNS. Finally, taking the CTQ evaluation of college badminton training as an example, the proposed method is explained. The main contributions of this study are summarized: the establishment of the 2TLNWPHM operator; (2) The 2TLNWPHM operator was developed to handle MADM with 2TLNS; (3) Through the empirical application of the CTQ evaluation of badminton training in universities, the proposed method is validated; (4) Some comparative studies have shown the rationality of the 2TLNWPHM operator. Show more

Keywords: Multi-attribute decision making (MADM), neutrosophic numbers, 2-tuple linguistic neutrosophic sets (2TLNSs), 2TLNWPHM operator, CTQ evaluation

DOI: 10.3233/JIFS-231731

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5939-5953, 2023