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

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

Authors: Chen, Haoying

Article Type: Research Article

Abstract: Big data is changing our lives and the way we understand the world, as well as the operational patterns of business and social organizations. Fully understanding the value of data and knowing how to use big data to provide a basis for business decision-making has gradually become the most basic thinking that business organizations should possess in the era of big data. Under the thinking mode of data-driven decision-making, many information science researchers have discussed the model, architecture, operation mechanism and other aspects of big data competitive intelligence system. At the same time, more and more enterprises, such as IBM, …Amazon, Google, Microsoft, Wal Mart, etc., have begun to attach importance to the development and construction of big data competitive intelligence software systems, and have achieved certain results. The enterprise competitive intelligence system evaluation in the context of big data is regarded as multi-attribute decision-making (MADM). 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 power HM (2TLNPHM) operator. Then, use the 2TLNPHM operator to handle MADM with 2TLNS. Finally, taking the enterprise competitive intelligence system evaluation in the context of big data as an example, the proposed method is explained. The main contributions of this study are summarized: the establishment of the 2TLNPHM operator; (2) The 2TLNPHM operator was developed to handle MADM with 2TLNS; (3) Through the empirical application of the enterprise competitive intelligence system evaluation, the proposed method is validated; (4) Some comparative studies have shown the rationality of the 2TLNPHM operator. Show more

Keywords: Multi-attribute decision making (MADM), neutrosophic numbers, 2-tuple linguistic neutrosophic sets (2TLNSs), 2TLNPHM operator, enterprise competitive intelligence system evaluation

DOI: 10.3233/JIFS-231768

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5955-5970, 2023

Authors: Wu, Huiyong | Yang, Tongtong | Wu, Harris | Li, Hongkun | Zhou, Ziwei

Article Type: Research Article

Abstract: Good air quality is one of the prerequisites for stable urban economic growth and sustainable development. Air quality is influenced by a range of environmental elements. In this study, seven common air pollutants and six kinds of meteorological data in a major city in China are studied. In this urban setting, the air quality index will be estimated based on a Long Short-term Memory (LSTM)model. To improve prediction accuracy, the Random Forest (RF) method is adopted to choose important features and pass them to the LSTM model as input, an improved sparrow search algorithm (ISSA) is used to optimize the …hyperparameters of the LSTM model. According to the experimental findings, the RF-ISSA-LSTM model demonstrates superior accuracy compared to both the basic LSTM model and the ISSA-LSTM fusion model. Show more

Keywords: Sustainable development, long short-term memory, sparrow search algorithm, random forest, air quality index

DOI: 10.3233/JIFS-232308

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5971-5985, 2023

Authors: Prabakaran, S. | Mary Praveena, S.

Article Type: Research Article

Abstract: Osteosarcomas are a type of bone tumour that can develop anywhere in the bone but most typically do so around the metaphyseal growth plates at the ends of long bones. Death rates can be lowered by early detection. Manual osteosarcoma identification can be difficult and requires specialised knowledge. With the aid of contemporary technology, medical photographs may now be automatically analysed and categorised, enabling quicker and more effective data processing. This paper proposes a novel hyperparameter-tuned deep learning (DL) approach for predicting osteosarcoma on histology images with effective feature selection mechanism which aims to improve the prediction accuracy of the …classification system for bone tumor detection. The proposed system mainly consists of ‘6’ phases: data collection, preprocessing, segmentation, feature extraction, feature selection, and classification. Firstly, the dataset of histology images is gathered from openly available sources. Then Median Filtering (MEF) is utilized as the preprocessing step that enhances the quality of the input images for accurate prediction by eliminating unwanted information from them. Afterwards, the pre-processed image was segmented using Harmonic Mean-based Otsu Thresholding (HMOTH) approach to obtain the tumor-affected regions from the pre-processed data. Then the features from the segmented tumor portions are extracted using the Self-Attention Mechanism-based MobileNet (SAMMNet) model. A Van der Corput sequence and Adaptive Inertia Weight included Reptile Search Optimization Algorithm (VARSOA) is used to select the more relevant features from the extracted features. Finally, a Hyperparameter-Tuned Deep Elman Neural Network (HTDENN) is utilized to diagnose and classify osteosarcoma, in which the hyperparameters of the neural network are obtained optimally using the VARSOA. The proposed HTDENN attains the higher accuracy of 0.9531 for the maximum of 200 epochs, whereas the existing DENN, MLP, RF, and SVM attains the accuracies of 0.9492, 0.9427, 0.9413, and 0.9387. Likewise, the proposed model attains the better results for precision (0.9511), f-measure (0.9423), sensitivity (0.9345) and specificity (0.9711) than the existing approaches for the maximum of 200 epochs. Simulation outcomes proved that the proposed model outperforms existing research frameworks for osteosarcoma prediction and classification. Show more

Keywords: Deep Elman Neural Network, osteosarcoma diagnosis, histology images, median filter, convolutional neural network

DOI: 10.3233/JIFS-233484

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 5987-6003, 2023

Authors: Ullah, Sami | Kashif, Muhammad | Aslam, Muhammad | Haider, Gulfam | AlAita, Abdulrahman | Saleem, Muhammad

Article Type: Research Article

Abstract: The application of classical statistical methods is not feasible given the presence of imprecise, fuzzy, uncertain, or undetermined observations in the underlying dataset. This is due to the existence of uncertainties pervading every aspect of real-life situations, which cannot always be accurately addressed by classical statistical approaches. In order to tackle this problem, a new methodology known as neutrosophic analysis of variance (NANOVA) has been developed as an extension of classical approaches to analyze datasets with uncertainty. The proposed approach can be applied regardless of the number of factors and replications. Moreover, NANOVA introduces a novel matrix-based approach to derive …the F_N-test in an uncertain environment. To assess the effectiveness of NANOVA, various real datasets have been employed, and research findings on single- and two-factor NANOVAs with measures of indeterminacy have been presented. According to our comparisons, NANOVA provides a more informative, efficient, flexible, and reliable approach to deal with uncertainties than classical statistical methods. Therefore, there is a need to go beyond conventional statistical techniques and adopt advanced methodologies that can effectively handle uncertainties. Show more

Keywords: Imprecise data, classical statistics, interval statistics, analysis of variance, F-test

DOI: 10.3233/JIFS-223636

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6005-6017, 2023

Authors: Patidar, Ritu | Patel, Sachin

Article Type: Research Article

Abstract: Many people have been severely affected by the COVID-19 outbreak, which has left them anxious, terrified, and other difficult feelings. Since the introduction of coronavirus vaccinations, people’s emotional spectrum has broadened and become more sophisticated.We want to observe and interpret their sentiments using deep learning techniques in this work. The most efficient way to convey one’s thoughts and feelings right now is via social media, and using Twitter may help one better understand what is popular and what is going through other people’s minds. Analyzing and visualization of data play a vital role in Data Science; as customers over e-commerce …increase, feedback/reviews shared by them increase significantly, and decisions by a new customer to buy a product or not rely on these reviews; reviews might falsely be displayed which may be involving in controlling if any products demand and supply so, reviews analyzing and visualizationto understand they are genuinely playing an important role over e-commerce nowadays. Our primary objective in conducting this study was to understand better the various perspectives individuals held on the vaccination process and reviews of products purchased online. As shown by the presented study, analysis and visualization approaches may be used to facilitate rapid and easy comprehension of e-commerce data, despite its high dimensionality.All correlation and non-correlation factors were mapped and examined, providing a comprehensive picture of the proposed data and its connection to other parameters.The proposed work provides an overview of sentiment observations across arguments and the relationships between parameters; it opens the door for modeling to extract some decision-making insights from the data, which can be used to improve the efficiency of application areas like product quality and customer satisfaction. Show more

Keywords: E-commerceproduct, COVID-19 vaccines, NLTK, CNN model, XLnet model, TextBlob

DOI: 10.3233/JIFS-230662

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6019-6034, 2023

Authors: Fan, Jianping | Tian, Ge | Wu, Meiqin

Article Type: Research Article

Abstract: Cross-efficiency in data envelopment analysis is widely used in production as an evaluation method that includes input and output indicators and allows for self-evaluation and mutual evaluation of decision making units (DMUs). However, as the application scenarios continue to expand, the traditional methods gradually fail to meet the needs. Many researchers have proposed improved methods and made great progress in weight determination, but the existing studies still have shortcomings in considering the psychological behavior of decision makers (DMs) and there is still relatively little research on cross-efficiency in fuzzy environments. In this paper, we proposed a method to apply CRITIC …to determine weights and introduce both prospect theory and regret theory into the evaluation method of cross-efficiency to obtain the prospect cross-efficiency matrix and regret cross-efficiency matrix respectively, and then applied the Pythagorean hesitant fuzzy operator to aggregate them to achieve the ranking of DMUs through the fraction function. This largely takes into account the subjective preference and regret avoidance psychology of DMs. The applicability of this paper’s method is also verified through an example of shopping for a new energy vehicle. Finally, the effectiveness of this paper’s method is verified by comparing three traditional methods with this paper’s method, which provides an effective method for considering risk preferences in the decision-making process. Show more

Keywords: Data envelopment analysis, cross-efficiency, CRITIC, prospect theory, regret theory, Pythagorean hesitant fuzzy set

DOI: 10.3233/JIFS-231371

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6035-6045, 2023

Authors: Ismail, Isaudin | Abd Mutalip, Fatin Noor Najihah | Jacob, Kavikumar

Article Type: Research Article

Abstract: The Copula concept has long been used in many applications, especially in the financial field. This concept was first used in 1959 by Sklar in his mathematical work and greatly assisted in the applications of financial and insurance areas. The copula functions have been widely used in dependence modeling. In this study, we look at how the copula began to develop from a basic form to a more advanced form through studies that previous researchers have made. Throughout this study, we find various types of the copula, and each exhibits its own characteristics lying under two main families, Elliptical and …Archimedean copulas. Our findings suggest that copula is vital in solving problems in statistical dependence measures and joint marginal distribution functions. This comprehensive study served as a review paper on the development of copulas from their initial existence to their latest evolution. Show more

Keywords: Copula, financial field, decision-making, insurance, marginal distribution

DOI: 10.3233/JIFS-223481

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6047-6062, 2023

Authors: Yu, Zhongliang

Article Type: Research Article

Abstract: The aerospace target tracking is difficult to achieve due to the dataset is intrinsically rare and expensive, and the complex space background, and the large changes of the target in the size. Meta-learning can better train a model when the data sample is insufficient, and tackle the conventional challenges of deep learning, including the data and the fundamental issue of generalization. Meta-learning can quickly generalize a tracker for new task via a few adapt. In order to solve the strenuous problem of object tracking in aerospace, we proposed an aerospace dataset and an information fusion based meta-learning tacker, and named …as IF-Mtracker. Our method mainly focuses on reducing conflicts between tasks and save more task information for a better meta learning initial tracker. Our method was a plug-and-play algorithms, which can employ to other optimization based meta-learning algorithm. We verify IF-Mtracker on the OTB and UAV dataset, which obtain state of the art accuracy than some classical tracking method. Finally, we test our proposed method on the Aerospace tracking dataset, the experiment result is also better than some classical tracking method. Show more

Keywords: Aerospace tracking dataset, meta learning, information fusion, aerospace tracking dataset

DOI: 10.3233/JIFS-230265

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6063-6075, 2023

Authors: Ramaswamy, Srividhya Lakshmi | Chinnappan, Jayakumar

Article Type: Research Article

Abstract: The deep learning revolution in the current decade has transformed the artificial intelligence industry. Eventually, deep learning techniques have become essential for many computational modeling tasks. Nevertheless, deep neural models provide a high degree of automation for natural language processing (NLP) applications. Deep neural models are extensively used to decode public reviews subjective to specific products, services, and other social activities. Further, to improve sentiment classification accuracy, several neural architectures have been developed. Convolutional neural networks (CNN) and Long-short term memory (LSTM) are the popular deep models employed in ensemble architectures for sentiment classification tasks. This review article extensively compares …the competence of CNN and LSTM-based ensemble models to improve the sentiment accuracy for online review datasets. Further, this article also provides an empirical study on various ensemble models concerning the position of LSTM and CNN for efficient sentiment classification. This empirical study provides deep learning researchers with insights into building effective multilayer LSTM and CNN models for many sentiment analysis tasks. Show more

Keywords: Sentiment analysis, convolutional neural network, long-short term memory, multilayer ensemble architectures, review dataset

DOI: 10.3233/JIFS-230917

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6077-6105, 2023

Authors: Jhansi Rani, Challapalli | Devarakonda, Nagaraju

Article Type: Research Article

Abstract: The study addresses the challenges of human action recognition and analysis in computer vision, with a focus on classifying Indian dance forms. The complexity of these dance styles, including variations in body postures and hand gestures, makes classification difficult. Deep learning models require large datasets for good performance, so standard data augmentation techniques are used to increase model generalizability. The study proposes the Indian Classical Dance Generative Adversarial Network (ICD-GAN) for augmentation and the quantum-based Convolutional Neural Network (QCNN) for classification. The research consists of three phases: traditional augmentation, GAN-based augmentation, and a combination of both. The proposed QCNN is …introduced to reduce computational time. Different GAN variants DC-GAN, CGAN, MFCGAN are employed for augmentation, while transfer learning-based CNN models VGG-16, VGG-19, MobileNet-v2, ResNet-50, and new QCNN are implemented for classification. The study demonstrates that GAN-based augmentation outperforms traditional methods, and QCNN reduces computational complexity while improving prediction accuracy. The proposed method achieves a precision rate of 98.7% as validated through qualitative and quantitative analysis. It provides a more effective and efficient approach compared to existing methods for Indian dance form classification. Show more

Keywords: Quantum convolution neural network, data augmentation, generative adversarial network, Indian classical dance, transfer learning

DOI: 10.3233/JIFS-231183

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6107-6125, 2023

Authors: Zhang, Chaoqin | Li, Ting | Yin, Yifeng | Ma, Jiangtao | Gan, Yong | Zhang, Yanhua | Qiao, Yaqiong

Article Type: Research Article

Abstract: With the continuous development of knowledge graph completion (KGC) technology, the problem of few-shot knowledge graph completion (FKGC) is becoming increasingly prominent. Traditional methods for KGC are not effective in addressing this problem due to the lack of sufficient data samples. Therefore, completing the task of knowledge graph with few-shot data has become an urgent issue that needs to be addressed and solved. This paper first presents a concise introduction to FKGC, which covers relevant definitions and highlights the advantages of FKGC techniques. We then categorize FKGC methods into meta-learning-based, metric-based, and graph neural network-based methods, and analyze the unique …characteristics of each model. We also introduced the research on FKGC in a specific domain - Temporal Knowledge Graph Completion (TKGC). Subsequently, we summarized the commonly used datasets and evaluation metrics in existing methods and evaluated the completion performance of different models in TKGC. Finally, we presented the challenges faced by FKGC and provided directions for future research. Show more

Keywords: Knowledge graph, few-shot learning, knowledge graph completion, temporal knowledge graph completion

DOI: 10.3233/JIFS-232260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6127-6143, 2023

Authors: Marimuthu, M. | Mohanraj, G. | Karthikeyan, D. | Vidyabharathi, D.

Article Type: Research Article

Abstract: Web browsers have become an integral part of our daily lives, granting us access to vast information and services. However, this convenience significantly risks personal information and data security. One common source of this risk is browser extensions, which users often employ to add new features to their browsers. Unfortunately, these extensions can also pose a security threat, as malicious ones may access and steal sensitive information such as passwords, credit card details, and personal data. The vulnerability of web browsers to malicious extensions is a significant challenge that effectively tackles through robust defence mechanisms. To address this concern, Secure …Vault – API is proposed and designed to safeguard confidential web page content from malicious extensions. The Web Crypto API provides cryptographic functions that protect data during transmission and storage. The Secure Vault encompasses a Sentinel extension responsible for parsing the web page’s Document Object Model (DOM) content and querying for all “vault” elements. The extension then verifies that the DOM content has not been tampered with by any malicious extension by calculating the SHA512 hash value of the concatenated vault elements as a string, with no whitespace between them. With its encryption, hashing, and isolation techniques, the Secure Vault effectively protects confidential web page content from malicious extensions. It provides a secure environment for storing and processing sensitive data, reducing the risk of data breaches caused by malicious extensions. The proposed approach offers significant advantages over existing strategies in terms of protecting confidential web page content from malicious extensions. This not only improves the efficiency and effectiveness of the browser extensions but also ensures compatibility, interoperability and performance across different web browsers with respect to the load time of HTML elements. Users can browse the web and carry out sensitive transactions with peace of mind, knowing their data is safeguarded against theft or manipulation by malicious extensions. Show more

Keywords: Browser security, chrome extensions, secure browsing, Web Crypto API, malicious extension

DOI: 10.3233/JIFS-233122

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6145-6160, 2023

Authors: Sundarakumar, M.R. | Sharma, Ravi | Fathima, S.K. | Gokul Rajan, V. | Dhayanithi, J. | Marimuthu, M. | Mohanraj, G. | Sharma, Aditi | Johny Renoald, A.

Article Type: Research Article

Abstract: For large data, data mining methods were used on a Hadoop-based distributed infrastructure, using map reduction paradigm approaches for rapid data processing. Though data mining approaches are established methodologies, the Apriori algorithm provides a specific strategy for increasing data processing performance in big data analytics by applying map reduction. Apriori property is used to increase the efficiency of level-wise creation of frequent itemsets by minimizing the search area. A frequent itemset’s subsets must also be frequent (Apriori property). If an itemset is rarely, then all of its supersets are infrequent as well. We refined the apriori approach by varying the …degree of order in locating frequent item sets in large clusters using map reduction programming. Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC) is a classical algorithm which are used for data processing from the huge datasets but their accuracy is not up to the mark. In this article, updated Apriori algorithms such as multiplied-fixed-pass combined counting (MFPC) and average time-based dynamic combined counting (ATDFC) are used to successfully achieve data processing speed. The proposed approaches are based on traditional Apriori core notions in data mining and will be used in the map-reduce multi-pass phase by ignoring pruning in some passes. The optimized-MFPC and optimized-ATDFC map-reduce framework model algorithms were also presented. The results of the experiments reveal that MFPC and ATDFC are more efficient in terms of execution time than previously outmoded approaches such as Fixed Pass Combined Counting (FPC) and Dynamic Pass Combined Counting (DPC). In a Hadoop multi-node cluster, this paradigm accelerates data processing on big data sets. Previous techniques were stated in terms of reducing execution time by 60–80% through the use of several passes. Because of the omitted trimming operation in data pre-processing, our proposed new approaches will save up to 84–90% of that time. Show more

Keywords: Algorithms, pruning, data mining, hadoop cluster, map reduce

DOI: 10.3233/JIFS-232048

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6161-6177, 2023

Authors: Zhang, Hang | Liu, Yongli | Chao, Hao

Article Type: Research Article

Abstract: The density peak clustering algorithm (DPC) quickly divides each cluster based on high-density peak points and shows better clustering performance. In order to address the issue that the local density is constrained by the preset cut-off distance in DPC and the Euclidean distance cannot capture the possible correlation between different features, a DPC algorithm based on improved dung beetle optimization (IDBO) and Mahalanobis metric is proposed, called IDBO-MDDPC. The IDBO algorithm enhances the ball dung beetle individual by incorporating nonlinear dynamic factors to increase the search and development capabilities of the algorithm and by incorporating an adaptive cosine wave inertial …weight strategy to more precisely determine the optimal position of the thief dung beetle in order to improve the convergence speed and accuracy of the algorithm. The IDBO algorithm is simulated on eight benchmark functions, and the results demonstrate that it is superior to other comparison algorithms in terms of convergence speed and accuracy. In the DPC algorithm, the Mahalanobis metric is used to capture the correlation between features to improve clustering performance. The IDBO algorithm is integrated with the DPC algorithm, and the F-Measure evaluation index is used to design the objective function so that the optimal value of the cut-off distance can be automatically selected. In order to evaluate the efficiency of the algorithm, three sets of artificially synthesized datasets and five sets of UCI standard datasets were chosen for studies. Experimental results show that the IDBO-MDDPC algorithm can automatically determine a better cut-off distance value and ensure higher clustering accuracy. Show more

Keywords: Density peak clustering, nonlinear dynamic factor, adaptive cosine wave inertia weight, mahalanobis metric

DOI: 10.3233/JIFS-232334

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6179-6191, 2023

Authors: Cheng, Chen | Li, Bixin | Chen, Dong

Article Type: Research Article

Abstract: Intelligent Traffic Management System (ITMS) is a complex and intelligent cyber-physical system (CPS) with multi-subsystem interaction, which plays a significant role in traffic safety. However, the quality evaluation requirements of ITMS, particularly its running quality, cannot be satisfied by the current quality evaluation metrics. Moreover, the present ITMS evaluation techniques are arbitrary. The effectiveness of road traffic is impacted because ITMS quality cannot be adequately assured. To fill this gap, this paper proposes a quality evaluation (QE) methodology based on the ITMS business data flow. First, the ITMS QE dimension extraction process was introduced to describe the ITMS architecture and …activities; then the new evaluation indexes including intelligence, complexity and interactivity were proposed and an ITMS QE model was established; further through the measurement of metrics elements, the quality score of the indicators were calculated; finally a prototype tool was developed to verify the efficacy and practicability of the method. The results showed that the proposed method has the advantages of accurate problem tracking and decrease decision-making uncertainty. This is applicable to the ITMS QE in various operational scenarios. Show more

Keywords: Intelligent traffic management system, complex system, multi-system interaction, quality evaluation

DOI: 10.3233/JIFS-230182

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6193-6208, 2023

Authors: Saini, Monika | Maan, Vijay Singh | Kumar, Ashish | Saini, Dinesh Kumar

Article Type: Research Article

Abstract: Cloud infrastructure provides a real time computing environment to customers and had wide applicability in healthcare, medical facilities, business, and several other areas. Most of the health data recorded and saved on the cloud. But the cloud infrastructure is configured using several components and that makes it a complex structure. And the high value of availability and reliability is essential for satisfactory operation of such systems. So, the present study is conducted with the prominent objective of assessing the optimum availability of the cloud infrastructure. For this purpose, a novel stochastic model is proposed and optimized using dragonfly algorithm (DA) …and Grey Wolf optimization (GWO) algorithms. The Markovian approach is employed to develop the Chapman-Kolmogorov differential difference equations associate with the system. It is considered that all failure and repair rates are exponentially distributed. The repairs are perfect. The numerical results are derived to highlight the importance of the study and identify the best algorithm. The system attains its optimum availability 0.9998649 at population size 120 with iteration 700 by GWO. It is revealed that grey wolf optimization algorithm performed better than the Dragonfly algorithm in assessing the availability, best fitted parametric values and execution time. Show more

Keywords: Availability, cloud infrastructure, dragonfly algorithm, grey wolf optimization algorithm, markov process

DOI: 10.3233/JIFS-231513

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6209-6227, 2023

Authors: Liao, Yi | Ning, Kuangfeng

Article Type: Research Article

Abstract: Multi-source online transfer learning uses the tagged data from multiple source domains to enhance the classification performance of the target domain. For unbalanced data sets, a multi-source online transfer learning algorithm that can oversample in the feature spaces of the source domain and the target domain is proposed. The algorithm consists of two parts: oversampling multiple source domains and oversampling online target domains. In the oversampling phase of the source domain, oversampling is performed in the feature space of the support vector machine (SVM) to generate minority samples. New samples are obtained by amplifying the original Gram matrix through neighborhood …information in the source domain feature space. In the oversampling phase of the online target domain, minority samples from the current batch search for k-nearest neighbors in the feature space from multiple batches that have already arrived, and use the generated new samples and the original samples in the current batch to train the target domain function together. The samples from the source domain and the target domain are mapped to the same feature space through the kernel function for oversampling, and the corresponding decision function is trained using the data from the source domain and the target domain with relatively balanced class distribution, so as to improve the overall performance of the algorithm. Comprehensive experiments were conducted on four real datasets, and compared to other baseline algorithms on the Office Home dataset, the accuracy improved by 0.0311 and the G-mean value improved by 0.0702. Show more

Keywords: Multi-source transfer learning, online learning, imbalanced data, support vector machine (SVM), k-nearest neighbor

DOI: 10.3233/JIFS-232627

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6229-6245, 2023

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

Article Type: Research Article

Abstract: Outlier detection is an important topic in data mining. An information system (IS) is a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. People often encounter missing values during data processing. A RVIS with the miss values is an incomplete real-valued information system (IRVIS). Due to the presence of the missing values, the distance between two information values is difficult to determine, so the existing outlier detection rarely considered an IS with the miss values. This paper investigates outlier detection for an IRVIS via rough set …theory and granular computing. 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 relation on the object set is defined according to the distance, and the tolerance class is obtained, which is regarded as an information granule. After then, λ-lower and λ-upper approximations in an IRVIS are put forward. Next, the outlier factor of every object in an IRVIS is presented. Finally, outlier detection method for IRVIS via rough set theory and granular computing is proposed, and the corresponding algorithms is designed. Through the experiments, the proposed method is compared with other methods. The experimental results show that the designed algorithm is more effective than some existing algorithms in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the proposed method. Show more

Keywords: RST, GrC, IRVIS, outlier detection, outlier factor

DOI: 10.3233/JIFS-230737

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6247-6271, 2023

Authors: Fan, Jianping | Chai, Mingxuan | Wu, Meiqin

Article Type: Research Article

Abstract: The competition in the new energy vehicle industry has intensified with the rapid development of the industry. In order to create innovative products, many businesses are now seeking cooperation with their supply chain members. Previous research on the new energy vehicle supply chain has mainly focused on government policies, supply chain retailers and with consumer gaming issues. This manuscript examines the problem of cooperation decisions between members of the new energy vehicle supply chain, namely a battery manufacturer and vehicle producer. The benefits of the two members are analyzed by constructing two models, one with non-incentives and the other with …government incentives. The model uses the triangular fuzzy number (TFN) instead of parameters in numerical calculations, taking complete account of the influence of uncertain environmental factors and using the triangular structured element method. The numerical examples result that government incentives positively promote cooperation between the two players, but the incentives should be as equal as possible. Finally, we aim to encourage supply chain members to cooperate and promote the development of the new energy vehicle industry. This study has positive implications for future supply chain member cooperation issues. Show more

Keywords: Energy vehicle supply chain, triangular fuzzy number (TFN), nash equilibrium, triangular structured element method

DOI: 10.3233/JIFS-231521

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6273-6287, 2023

Authors: Princy Magdaline, P. | Ganesh Babu, T.R.

Article Type: Research Article

Abstract:  Computed tomography (CT) scan pictures are routinely employed in the automatic identification and classification of lung cancer. The texture distribution of lung nodules can vary widely over the CT scan space and requires accurate detection. The evaluation of discriminative information in this volume can tremendously aid the classification process. A convolutional neural network, the Attention Gate Residual U-Net model, and KNN classifiers are utilized to detect lung cancer. The dataset of 1097 computed tomography (CT) images utilized in this study was obtained from the Iraq-Oncology Teaching Hospital/National Centre for Cancer Diseases (IQ-OTH/NCCD) to segment and classify lung tumors from CT …images using the novel Attention Gate Residual U-Net model, i.e., AGResU-Net and CNN architecture. The initial step is applying CNN to detect normal, benign, and malignant patients in CT images. Second, use AGResU-Net to partition lung tumour areas. In the third section of the project, a KNN classifier is used to determine if an instance is malignant or benign. In the initial phase, CNN was proposed to classify three distinct regions. Three optimization strategies are used in this work: Adam, RMSP, and SGDM. The classifier’s accuracy is 97%, 85%, and 82%, respectively. When compared to the RMSP optimizer, the Adams optimizer predicts probability rates more accurately. In the second phase, AGResU-Net is used for schematic segmentation of the tumor region. In the third phase, a KNN classifier is used to classify benign and malignant tumor from the segmented tumor regions. A new segmentation of the lung tumor model is proposed. In this developed algorithm, the labelled classified data set and the segmented tumor output result provide the same accuracy. The study results demonstrate high tumour classification accuracy and high probability of detection in benign and malignant cases. Show more

Keywords: Lung cancer, CT images, convolutional neural network, AGResU-Net, KNN

DOI: 10.3233/JIFS-233787

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6289-6302, 2023

Authors: Rafikiran, Shaik. | Devadasu, G. | Rajendhar, P. | Likhitha, R. | Basha, CH Hussaian

Article Type: Research Article

Abstract: The fuel cell-dependent electric vehicle systems are giving an important role in the present automotive systems because their features are less air pollution, high flexibility, reduced oil dependency, and more reliability. However, the fuel stack delivers nonlinear output V-I characteristics. So, the extraction of peak power from the fuel source is very difficult. In this work, a Variable Step Size Radial Basis Functional Network-based Adaptive Fuzzy Logic Controller (VSSDE-AFLC) is proposed for tracking the peak power point of the fuel cell system. The merits of the proposed Maximum Power Point Tracking (MPPT) controller are high tracing speed of functioning point …of the fuel cell, more flexibility, high abundant, acceptable oscillations across MPP, and less dependency on modeling of the fuel stack. Also, the single switch converter is utilized for increasing the voltage supply of the fuel cell. The features of the proposed converter are wide input operation, less voltage stress, high supply voltage conversion ratio, and good dynamic response. The proposed fuel cell-dependent boost converter is implemented by utilizing the MATLAB/Simulink software, and the converter is tested successfully by using the desired programmable DC supply. Show more

Keywords: Boost converter, conversion ratio, duty cycle, fast tracing speed, high voltage gain, fewer voltage ripples, and fast dynamic response

DOI: 10.3233/JIFS-224007

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6303-6321, 2023

Authors: Shakunthala, M. | HelenPrabha, K.

Article Type: Research Article

Abstract: Stroke is a type of cerebrovascular disorder that has a significant impact on people’s lives and well-being. Quantitative investigation of MRI imaging of the brain plays a critical role in analyzing and identifying therapy for stroke. A block primarily provokes stroke in the brain’s blood supply. Deep learning algorithms can be used to identify strokes in patients in a short period. Proposed deep learning methods are used to classify strokes using magnetic resonance imaging (MRI) images. Early detection enhances treatment opportunities and saves lives, which is the primary motivation of the proposed work. Deep learning methods have emerged as significant …research trends in recent years, particularly for classifying different types of stroke such as ischemic and hemorrhagic stroke. A dataset of 13,850 MRI images of stroke patients was collected from various reliable sources, including Madras scans and labs, Radiopaedia, Kaggle datasets, and online databases. Among these images, 7,810 were identified as cases of ischemic stroke, while 6,040 represented hemorrhagic strokes. For training purposes, a total of 9,700 images were used, with 4,150 images employed for testing. A comparative analysis of ANN, SVM, NB, ELM, KNN and Enhanced CNN technique is carried out, and 98.4% of classification accuracy is obtained by using Enhanced CNN. Statistical analysis of parameters such as accuracy, precision, F1-score, and recall was conducted, demonstrating that the Enhanced CNN method outperformed SVM, NB,ELM, KNN and ANN classifiers. The Enhanced CNN method achieved an accuracy of 0.984, precision of 0.949, recall of 0.972, and an F1-score of 0.960 on the training dataset, which is significantly higher than the other classifiers. Furthermore, the Enhanced CNN algorithm’s ability to automatically learn features and efficiently process large datasets enhances its potential as a powerful tool for accurately classifying stroke lesions. Show more

Keywords: Magnetic Resonance Imaging (MRI), Enhanced-CNN, hemorrhagic stroke, ischemic stroke, deep learning

DOI: 10.3233/JIFS-230024

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6323-6338, 2023

Authors: Al-shami, Tareq M. | Hosny, Rodyna A. | Abu-Gdairi, Radwan | Arar, Murad

Article Type: Research Article

Abstract: Our target in the present work, is presenting the idea of weakly soft preopen (ws -preopen) subsets and studying some of its characterizations. With the assistance of some elucidative examples, the interrelationships between ws -preopen sets and some extensions of soft open sets are studied. Under some conditions such as extended and hyperconnected soft topologies, several motivating results and relationships are acquired. The interior and closure operators that built through ws -preopen and ws -preclosed subsets are introduced. Their main features that construe the relations among them are established. Soft continuity with respect to theses classes of soft subsets are …studied and their substantial characteristics are investigated. Generally, the systematic relations and outcomes that are lost through the scope of this study are discussed. The proposed line in the current study will present new ways to discover novel concepts in the field of soft topology. Show more

Keywords: ws-preopen set, extended soft topology, ws-preinterior, ws-preclosure, ws-precontinuous function

DOI: 10.3233/JIFS-230191

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6339-6350, 2023

Authors: Al-shami, Tareq M. | Arar, Murad | Abu-Gdairi, Radwan | Ameen, Zanyar A.

Article Type: Research Article

Abstract: This work introduces weakly soft β-open subsets, a new family of soft-open sets. By this family, we expand a soft topology to a soft structure which is neither supra-soft topology nor infra-soft topology. The connections between this class of soft sets and other celebrated classes via soft topology are examined with some elucidative examples. Also, it is established some relationships under conditions of extended and hyperconnected soft topologies. Furthermore, the interior and closure operators are structured along with weakly soft β-open and weakly soft β-closed sets. Finally, the class of weakly soft β-continuous functions is introduced and its main characterizations …are studied. It is investigated the systematic relationships and findings that are lost for this kind of soft continuity as well as it is shown the conditions required to maintain some of these relationships such as full, extended and hyperconnected soft topologies. Show more

Keywords: Extended soft topology, weakly soft β-open set, β-closure, weakly soft β-interior, and weakly soft β-continuous

DOI: 10.3233/JIFS-230858

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6351-6363, 2023

Authors: Tang, Zhong

Article Type: Research Article

Abstract: Architectural aesthetics improve the appearance and value of a building/construction structure based on shape, color, rigidity, etc., appealingly. It includes the maximum safety requirements, durability, structural ability, etc. Therefore the aesthetic implementation requires high-level data accumulation and analysis to satisfy the earlier constraints. This article develops a Selective Aesthetic Application Paradigm (SAAP) for meeting the user criteria in structural design for region-specific adaptability. The proposed paradigm gathers information on the region, people’s expectations, visibility, and structural performance for the aesthetic design application. The proportion considerations in the application are subject to vary according to the region’s adaptability and performance. The …proportion of the accumulated data influence in the application is determined using deep learning. In the learning paradigm, two-layered configurations for region-adaptability and performance measures are trained to provide aesthetic design application recommendations. Based on the suggestion and recommendation, the deep learning module is trained to rectify design errors. The training is independent of the previous two error and adaptability verification layers. It is performed using the qualified (selected) aesthetic design with a previous history of user satisfaction. Show more

Keywords: Architectural aesthetics, data analysis, deep learning, error detection

DOI: 10.3233/JIFS-231076

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6365-6379, 2023

Authors: Mohamed, Mohamed S. | Elzayady, Hossam | Badran, Khaled M. | Salama, Gouda I.

Article Type: Research Article

Abstract: The use of hateful language in public debates and forums is becoming more common. However, this might result in antagonism and conflicts among individuals, which is undesirable in an online environment. Countries, businesses, and educational institutions are exerting their greatest efforts to develop effective solutions to manage this issue. In addition, recognizing such content is difficult, particularly in Arabic, due to a variety of challenges and constraints. Long-tailed data distribution is often one of the most significant issues in actual Arabic hate speech datasets. Pre-trained models, such as bidirectional encoder representations from transformers (BERT) and generative pre-trained transformers (GPT), have …become more popular in numerous natural language processing (NLP) applications in recent years. We conduct extensive experiments to address data imbalance issues by utilizing oversampling methods and a focal loss function in addition to traditional loss functions. Quasi-recurrent neural networks (QRNN) are employed to fine-tune the cutting-edge transformer-based models, MARBERTv2, MARBERTv1, and ARBERT. In this context, we suggest a new approach using ensemble learning that incorporates best-performing models for both original and oversampled datasets. Experiments proved that our proposed approach achieves superior performance compared to the most advanced methods described in the literature. Show more

Keywords: Text classification, Arabic hate speech, oversampling method, transformers, ensemble learning

DOI: 10.3233/JIFS-231151

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6381-6390, 2023

Authors: Bergamini, Mariane Gavioli | Oliveira, Gustavo H.C. | Ribeiro, Eduardo P. | Leandro, Gideon Villar

Article Type: Research Article

Abstract: Accurate modeling of electric power generating unit and its hydraulic turbine regulation systems provides support for the speed controller synthesis and stability analysis. It is however a difficult task due to the presence of many non-linear factors in this system. an approach to estimate the parameters of hydraulic turbine regulatory system models is to derive the physical representation of each component and, through simulation, to compare to compare their models, outputs with real data obtained from a hydroelectric plant located in Brazil. The objective of this paper is to find the best values that will represent the system under study …as a whole. This problem can be seen as an optimization problem. To find its feasible and optimal solution, this work proposes a new metaheuristics multi-objective based on the Lion Algorithm (LA), called the Multi-Objective Lion Algorithm (MOLA), and its application in the estimation of parameters of the system under study. In addition, the new metaheuristic proposed is validated by using a set of benchmark cases. The results have demonstrated that MOLA outperforms or at least performs similarly to Multi-objective Grey Wolf Optimizer (MOGWO), Multiple Objective Particle Swarm Optimization (MOPSO), Multi-objective Salp Swarm Algorithm (MSSA), Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Non-dominated Sorting Genetic Algorithm III (NSGA-III) in the optimization of multi-objective benchmark functions. These results, suggest that the proposed MOLA algorithm works efficiently. Show more

Keywords: Parameter estimation, hydraulic turbine regulator system, multi-objective optimization, metaheuristics, multi-objective lion optimization algorithm

DOI: 10.3233/JIFS-232155

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6391-6412, 2023

Authors: Zheng, Yunchao

Article Type: Research Article

Abstract: Traditional Chinese art is vast and profound, with various colors having rich meanings. The combination of colors can vividly and intuitively represent various characteristics of things. Fully reflecting the characteristics of traditional Chinese folk art in graphic design can achieve extremely strong expressive effects. In current graphic design, the artistic colors of traditional Chinese folk art have not yet been fully displayed, and there is a lack of understanding of the profound connotation of traditional Chinese art. The graphic design industry has a very broad development space. The comprehensive evaluation of graphic design effects based on color psychology is a …classical multiple attribute group decision making (MAGDM) problems. In this work, we shall present some novel Dice similarity measures (DSM) of T-spherical fuzzy sets(T-SFSs) and the generalized Dice similarity measures (GDSM) of and indicates that the DSM and asymmetric measures (projection measures) are the special cases of the GDSM in some parameter values. Then, we propose the GDSM-based MAGDM models with T-SFSs. Then, we apply the GDSMs between T-SFSs to MAGDM. Finally, an illustrative example for comprehensive evaluation of graphic design effects based on color psychology is given to demonstrate the efficiency of the GDSMs. The main contributions of this paper are summarized: (1) some novel Dice similarity measures (DSM) and the generalized Dice similarity measures (GDSMs) of T-spherical fuzzy sets(T-SFSs) are proposed; (2) The weighted Dice similarity measures (WDSM) and the weighted generalized Dice similarity measures (WGDSMs) of T-spherical fuzzy sets(T-SFSs) are proposed to solve the MAGDM; (3) an illustrative example for comprehensive evaluation of graphic design effects based on color psychology is given to demonstrate the efficiency of the WGDSM; (4) Some comparative analysis are used to show the effectiveness of the proposed Dice similarity measures. Show more

Keywords: Multiple attribute group decision making, Dice similarity measures (DSMs), generalized Dice similarity measures (GDSMs), T-spherical fuzzy sets, graphic design effects

DOI: 10.3233/JIFS-232296

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6413-6427, 2023

Authors: Ramachandran, L. | Mohan, V. | Senthilkumar, S. | Ganesh, J.

Article Type: Research Article

Abstract: White Spot Syndrome Virus (WSSV) is a major virus found in shrimp that causes huge economic loss in shrimp farms. A selective diagnostic approach for WSSV is required for the early diagnosis and protection of farms. This work proposes a novel recognition method based on improved Convolutional Neural Network (CNN) namely Dense Inception Convolutional Neural Network (DICNN) for diagnoses of WSSV disease. Initially, the process of data acquisition and data augmentation is carried out. The Inception structure is then used to improve the performance of multi-dimensional feature extraction. As a result, the proposed work has the highest accuracy of 97.22% …when compared to other traditional models. The proposed work is targeted to Litopenaeus Vannamei (LV), and Penaeus Monodon (PM) diversities for major threats detection of White Spot Syndrome (WSS). Performance metrics related to accuracy have been compared with other traditional models, which demonstrate that our model will efficiently recognize shrimp WSSV disease. Show more

Keywords: Convolutional neural networks, disease identification, image augmentation, white spot syndrome virus

DOI: 10.3233/JIFS-232687

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6429-6440, 2023

Authors: Krishna Veni, K.S. | Senthil Kumar, N. | Srinivas, R.

Article Type: Research Article

Abstract: In the electrical energy transmission and distribution sector, power transformers play an important role. Early fault diagnosis and prognosis are essential to ensure continuous operation and also to prepare a proper maintenance schedule based on the requirements. The occurrence of a fault in the transformer will lead to the formation of various gases inside the transformer tank. For fault diagnosis in the transformer, Dissolved Gas Analysis (DGA) is an excellent method. An Artificial Intelligence (AI) based fault diagnosis and prognosis system using dissolved gases in transformer oil is helpful to predict the health state of the transformer well in advance. …Hence, based on the fault severity level, the remaining useful life of the transformer, fault type and current state of the transformer can be estimated effectively by imparting AI to the existing system. A Two-Tier Fuzzy Logic Controller (TTFLC) is proposed in this article to find the type of fault and health index (HI) of the transformer. For further fault prognosis, an effective Gated Recurrent Network (GRN) based deep learning enabled future learning estimator is used for predicting the Criticality Index (CI) of the Transformer. The performance of the proposed method is evaluated for both data from the IEEE data set and expert data collected from the southern Tamil Nadu region. The proposed system shows better results even in multivariate, complex process systems. The diagnosis accuracy of the proposed system is obtained as 95.28% and it compared with conventional methods such as Rogers Ratio Method (RRM), Duval Triangle Method (DTM) and Duval Pentagon Method (DPM) and other AI based methods such as Radial Basis Neural Network (RBNN), k-nearest neighbors (KNN). The diagnosis accuracy of other conventional and AI based methods are less than 90% for the collected dataset. Show more

Keywords: Transformer, dissolved gas analysis, two tier fuzzy logic controller, fault diagnosis, fault prognosis, gated recurrent network, health index, criticality index

DOI: 10.3233/JIFS-223592

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6441-6452, 2023

Authors: Du, Kang | Fan, Ruguo | Xue, Hu | Wang, Yitong | Bao, Xuguang

Article Type: Research Article

Abstract: The mechanism of promoting cooperation in the public goods game has always been concerned by scholars. However, most of the existing studies are based on the premise that participants are self-interested. In order to explore why some sellers on e-commerce platforms voluntarily maintain the platform’s reputation, we incorporate heterogeneous social preferences of sellers into the spatial public goods game. We find that heterogeneous social preferences can enhance cooperation by improving collective rationality. Specifically, the altruistic preference of sellers can greatly reduce free-riding behavior, while the inequality aversion preference has a little inhibitory effect. Interestingly, when the benefit of maintaining the …platform’s reputation is relatively high, the reciprocal preference can inhibit cooperation, but it can promote cooperation when the benefit is relatively small. This is due to the existence of some loosely connected but stable cooperative or defective clusters of sellers in e-commerce platforms. Furthermore, we propose a dynamic punishment mechanism to punish free riders. We observe that the dynamic punishment mechanism is more effective than the static punishment mechanism in solving the second-order free-riding problem faced by punishers. Increasing the enhancement factor of public goods is identified as a fundamental approach to mitigating this problem. Show more

Keywords: E-commerce platform, altruism, inequality aversion, reciprocity, spatial public goods game

DOI: 10.3233/JIFS-232322

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6453-6467, 2023

Authors: Thao, Le Quang | Diep, Nguyen Thi Bich | Bach, Ngo Chi | Linh, Le Khanh | Giang, Nguyen Do Hoang

Article Type: Research Article

Abstract: In this study, we introduce a new method to address the pressing issue of school violence using Artificial Intelligence (AI). School violence is a critical issue that affects the safety and well-being of students, teachers, and the school community as a whole. Violent behaviors, such as bullying, physical assaults, and weapon use, can have long-term effects on students’ psychological health and academic performance. To reduce these issues, we developed a lightweight Deep Learning model that can be integrated into a school’s surveillance camera system to quickly detect violent fighting behaviors for timely intervention by school staff. The proposed FightNet model …consists of three components: MobileNetV2 backbone, Feature Pyramid Network (FPN) neck, and Centernet Object as a Point (COaP) head. By optimizing the hyperparameters of the model to extract keypoints in image frames from the COCO dataset, we applied an LSTM model to determine the temporal dependence of actions and classify them as “fighting” or “normal” using the UBI-Fights dataset. The FightNet model achieved mAP@0.5 of 45.34% and mAP@0.95 of 55.89% in estimating keypoints, and 72.68% accuracy and 71.69% F1-score in predicting actions. Based on these results, we conclude that the proposed model can effectively address the issue of school violence. Show more

Keywords: School fighting violence, multi-keypoints, FightNet, light-weight model, LSTM

DOI: 10.3233/JIFS-232480

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6469-6483, 2023

Authors: Javeed, M.D. | Nagaraju, Regonda | Chandrasekaran, Raja | Rajulu, Govinda | Tumuluru, Praveen | Ramesh, M. | Suman, Sanjay Kumar | Shrivastava, Rajeev

Article Type: Research Article

Abstract: The process of partitioning into different objects of an image is segmentation. In different major fields like face tracking, Satellite, Object Identification, Remote Sensing and majorly in medical field segmentation process is very important to find the different objects in the image. To investigate the functions and processes of human boy in radiology magnetic resonance imaging (MRI) will be used. MRI technique is using in many hospitals for the diagnosis purpose widely in finding the stage of a particular disease. In this paper, we proposed a new method for detecting the tumor with enhanced performance over traditional techniques such as …K-Means Clustering, fuzzy c means (FCM). Different research methods have been proposed by researchers to detect the tumor in brain. To classify normal and abnormal form of brain, a system for screening is discussed in this paper which is developed with a framework of artificial intelligence with deep learning probabilistic neural networks by focusing on hybrid clustering for segmentation on brain image and crystal contrast enhancement. Feature’s extraction and classification are included in the developing process. Performance in Simulation of proposed design has shown the superior results than the traditional methods. Show more

Keywords: Segmentation, brain tumor, probabilistic neural networks, feature extraction, classification

DOI: 10.3233/JIFS-232493

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6485-6500, 2023

Authors: Zhan, Huawei | Pei, Xinyu | Zhang, Tianhao | Zhang, Linqing

Article Type: Research Article

Abstract: A flame detection algorithm based on the improved SSD (Single Shot Multibox Detector) is proposed in response to the issues with the limited detection distance, delayed reaction, and high false alarm rate of previous flame detection systems. First, the ResNet-50-SPD model was added to the original backbone network to improve the detection of low resolution and tiny objects. After that, incorporate feature fusion between layers to improve the bond between contexts. Before the feature entered the prediction, the impact of channel number reduction was eliminated using the adaptive module AAM. According to experimental findings, the modified SSD algorithm’s mAP value …on on the random division dataset and K-fold verification dataset reaches 87.89% and 89.63%, respectively, which is 3.97% and 5.17% higher than the original SSD, while the FPS remains at 64.9 f/s. It is helpful to improve the time of the fire alarm, find the ignition point in time, and better meet the actual engineering needs of fire monitoring. Show more

Keywords: Flame detection, SSD, ResNet-50-SPD, feature fusion, AAM

DOI: 10.3233/JIFS-232645

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6501-6512, 2023

Authors: Zhang, Boqiang | Gao, Tianzhi | Chen, Yanbin | Jin, Xin | Feng, Tianpei | Chen, Xinming

Article Type: Research Article

Abstract: A large number of grain machinery and vehicle equipment are usually required in the raw grain storage phase, and these objects together form the path planning map environment for the unmanned grain transfer vehicle. After using LiDAR to build a map of the environment for path planning, these dense and cluttered obstacles tend to affect the path planning effect making the unmanned transfer vehicle create a crossing from the impenetrable dense obstacles. To address this problem, this paper firstly deals with obstacles by fusing the DBSCAN clustering algorithm and K-means clustering algorithm, clustering obstacles, and extracting the cluster centroid and …boundary points of each obstacle class to avoid the above situation. Secondly, the specific A* algorithm is improved, the search field way of the A* algorithm is optimized, and the optimized 5×5 field search way is used instead of the traditional 3×3 field search way of A* to improve the node search efficiency of the algorithm. Finally, the repulsion function of the artificial potential field algorithm is added to the A* heuristic function as a safety function to increase the obstacle avoidance capability of the A* algorithm. After verification, the improvement can operate better in the dense and cluttered obstacle environment. Show more

Keywords: Grain depot, food logistics, clustering algorithm, A* algorithm, artificial potential field, raster map

DOI: 10.3233/JIFS-232780

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6513-6533, 2023

Authors: Xiaozhen, Zheng | Le, Xuong

Article Type: Research Article

Abstract: Carbon dioxide is produced during the manufacture of normal Portland cement; however, this gas may be minimized by utilizing ground granulated blast furnace slag (GGBFS ). When planning and constructing concrete buildings, compressive strength (f c ), a crucial component of concrete mixtures, is a need. It is essential to assess this GGBFS -blended concrete property precisely and consistently. The major objective of this research is to provide a practical approach for a comprehensive evaluation of machine learning algorithms in predicting the f c of concrete containing GGBFS . The research used the Equilibrium optimizer (EO ) …to enhance and accelerate the performance of the radial basis function (RBF ) network (REO ) and support vector regression (SVR ) (SEO ) analytical methodologies. The novelty of this work is particularly attributed to the application of the EO , the assessment of f c including GGBFS , the comparison with other studies, and the use of a huge dataset with several input components. The combined SEO and REO systems demonstrated proficient estimation abilities, as evidenced by coefficient of determination (R 2 ) values of 0.9946 and 0.9952 for the SEO ’s training and testing components and 0.9857 and 0.9914 for the REO , respectively. The research identifies the SVR optimized with the EO algorithm as the most successful system for predicting the f c of GGBFS concrete. This finding has practical implications for the construction industry, as it offers a reliable method for estimating concrete properties and optimizing concrete mixtures. Show more

Keywords: Compressive strength, ground granulated blast furnace slag, prediction, equilibrium optimizer, support vector regression

DOI: 10.3233/JIFS-233428

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6535-6547, 2023

Authors: Umaamaheshvari, A. | Sivasankari, K. | Suguna, N. | Kshirsagar, Pravin R. | Tirth, Vineet | Rajaram, A.

Article Type: Research Article

Abstract: The optimization algorithms mimic the process of natural evolution. In watermarking, appropriate positions to insert the watermark is identified by the image that covers. These positions represent the populations of genetic algorithms. The major drawback in genetic algorithm are that it may get stuck-up at a local optimum while moving towards the best global solution and hence the result is poor when compared to other local optimization techniques. The proposed work based on Bandelet based biogeography firefly hybrid algorithms. The Number of pixels, Intensity of the pixel and contrast are considered for watermarking. The redundancy is reduced by Bandelet and …used to determine the best location to embed the information into an image both locally and globally. Results of these techniques are compared based on coefficient correlation, index structural similarity, and noise ratio from peak signal. Show more

Keywords: Biogeography firefly algorithm, genetic algorithm, optimization, peak signal to noise ratio

DOI: 10.3233/JIFS-224590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6549-6559, 2023

Authors: Birong, Zhang

Article Type: Research Article

Abstract: In this paper, a bi-objective mixed-integer linear programming model is constructed to manage the pharmaceutical supply chain of a hospital. The proposed model aims to concurrently reduce the overall cost of obtaining drugs from several vendors and choose the best suitable source. The suggested model takes into account supplier distance, inventory management, and multi-product and multi-period. The major assumptions of the proposed model are product storage for future periods of decreased demand and supplier capacity. The results indicate that the ideal approach can minimize hospital supply and pharmaceutical planning expenses. The Best-Worst and TOPSIS methods determine which pharmaceutical supplier should …be selected for future orders. The suggested model identifies human resource capability as an essential factor that might significantly affect the system’s total cost. The results of applying the model and the sensitivity analysis validate the efficacy and validity of the suggested mathematical model and solution strategy. Show more

Keywords: Optimization, pharma supply chain, uncertainty, robust programming

DOI: 10.3233/JIFS-230017

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6561-6574, 2023

Authors: Arulselvan, G. | Rajaram, A.

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-231905

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6575-6590, 2023

Authors: Xiao, Huimin | Gao, Xiaosong | Yang, Peng | Wei, Meng

Article Type: Research Article

Abstract: In the face of multi-attribute decision problems in complex situations, most traditional multi-attribute group decision methods are based on the assumption that the decision maker is perfectly rational, while in the face of complex decision problems, the decision maker usually has the psychological characteristics of limited rationality and may use more than one linguistic term to describe the decision information when expressing the decision information To this end, this paper selects probabilistic language term sets to describe complex preference information. First, to address the problem that the current probabilistic linguistic term set correlation coefficient cannot appropriately measure the degree of …correlation among probabilistic linguistic term sets, this paper proposes a new probabilistic linguistic term set correlation coefficient from three characteristic factors of probabilistic linguistic term sets: mean, variance, and length rate. To integrate the attribute index weights, probabilistic linguistic term set weighted mixed correlation coefficients are proposed. Second, this paper introduces the TODIM method, which can consider the psychological behavior of decision makers, and proposes a TODIM multi-attribute decision making method based on probabilistic linguistic term sets with mixed correlation coefficients. Finally, through an empirical analysis of four Internet listed companies in a new first-tier city in China, this study verifies the rationality and validity of the proposed method. The results show that the mixed correlation coefficient can comprehensively measure the correlation between probabilistic linguistic term sets, which provides an important method for future multi-attribute decision making problems. Show more

Keywords: Multi-attribute decision making, probabilistic linguistic term sets, mixed correlation coefficient, TODIM method

DOI: 10.3233/JIFS-232042

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6591-6604, 2023

Authors: Suresh Babu, D. | Ramakrishnan, M.

Article Type: Research Article

Abstract: A severe problem that regularly affects cloud systems are intrusions. Ignore how the expansion of Internet of Things (IoT) devices will result in enormous intrusions. To distinguish intrusions from authorized network activity, detection is a crucial procedure. An Enhanced Lion Optimization Algorithm (ELOA) is utilized in this research, IoT intrusion detection system. Intrusions are classified using the Deep Belief Network (DBN) and an SDN controller technique. The proposed ELOA-based Intrusion Detection System uses the optimal weight in DBN to train the neurons to categorize the data in a network as normal and attacked during the training phase. In the testing …step that follows training, data from nodes are examined, and by contrasting the training results, they are categorized as normal and attacked data. By using the proposed ELOA and DBN algorithms, our intrusion detection system can successfully identify intrusions. Based on the creation of blacklists for detecting IoT intrusions, the (SDN) Software Defined Networking controller can effectively prohibit harmful devices. In order to demonstrate that the proposed ELOA finds network intrusions more successfully, its performance is compared to that of other existing techniques. The node sizes of the algorithms are run and evaluated for 1000, 2000, 3000, 4000, and 5000 respectively. At highest node 5000, the Proposed ELOA and DPN have precision, recall, f-score and accuracy becomes as 97.8, 96.22, 97.5 and 98.67 respectively. Show more

Keywords: Internet of Things, intrusion detection, Enhanced Lion Optimization Algorithm, deep belief network, SDN controller

DOI: 10.3233/JIFS-232532

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6605-6615, 2023

Authors: Suganya, S. | Selvamuthukumaran, S.

Article Type: Research Article

Abstract: Hadoop is a big data processing system that enables the distributed processing of massive data sets across multiple computers using straightforward programming techniques. Hadoop has been extensively investigated in many attacks as a result of its growing significance in industry. A company may learn about the actions of invaders as well as the weaknesses of the Hadoop cluster by examining a significant quantity of data from the log file. In a Big Data setting, the goal of the paper is to generate an analytical classification for intrusion detection. In this study, Hadoop log files were examined based on assaults that …were recorded in the log files. Prior to analysis, the log data is cleaned and improved using a Hadoop preprocessing tool. For feature extraction, the hybrid Improved Sparrow Search Algorithm with Mutual Information Maximization (H-ISSA-MIM). Then the CNN (Convolutional Neural Network) classifier will detect the intrusions. The implementation is performed using the MATLAB 2020a software. The performance metrics like accuracy, precision, F-score, recall, specificity, FPR, FNR are calculated for the proposed methodology and it is compared with the existing techniques like Decision Tree (DT), Principal Components Analysis (PCA)- K means, Long Short Time Memory (LSTM). The maximum value of accuracy finds out in the proposed method 98% . Show more

Keywords: Hadoop attacks, log file, intrusion detection, big data environment and feature extraction, convolutional neural networks

DOI: 10.3233/JIFS-233579

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6617-6628, 2023

Authors: Baqer, Ihsan A. | Jaber, Alaa Abdulhady | Soud, Wafa A.

Article Type: Research Article

Abstract: Belt drive contamination is considered one of the most common failure modes that could be developed in the belts due to harsh operation conditions, high humidity, and sunlight exposure, reducing the belt’s performance. If the belt failure has not been detected early, a sudden shutdown may happen, producing safety and economic consequences. However, most maintenance personnel use their senses of sight, hearing, smell, and touch to identify the cause of the problem while diagnosing a belt drive condition. Hence, this research involves developing an intelligent contamination status detection system based on vibration signal analysis for a pulley-belt rotating system. Time-domain …signal analysis was employed to extract some suggestive features such as the root mean square, kurtosis, and skewness from the vibration data. An artificial neural network (ANN) model was built to detect the simulated different operating conditions. The vibration data was gathered with the help of two MEMS accelerometers (ADXL335) interfaced with an NI USB-6009 data acquisition device. A signal capture, analysis, and feature extraction system was developed using Matlab Simulink. The simulated operating conditions include clean, wet, and powder-contaminated belts. The results showed that the designed system could identify the pulley-belt operation conditions with 100% overall accuracy. Show more

Keywords: Condition monitoring, fault diagnosis, preventive maintenance, time-domain signal analysis, machine learning

DOI: 10.3233/JIFS-222438

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6629-6643, 2023

Authors: Lin, Tao | Chen, Biao | Wang, Ruixia | Zhang, Yabo | Shi, Yu | Jiang, Nan

Article Type: Research Article

Abstract: Vision-based Continuous Sign Language Recognition (CSLR) is a challenging and weakly supervised task aimed at segmenting sign language from weakly annotated image stream sequences for recognition. Compared with Isolated Sign Language Recognition (ISLR), the biggest challenge of this work is that the image stream sequences have ambiguous time boundaries. Recent CSLR works have shown that the visual-level sign language recognition task focuses on image stream feature extraction and feature alignment, and overfitting is the most critical problem in the CSLR training process. After investigating the advanced CSLR models in recent years, we have identified that the key to this study …is the adequate training of the feature extractor. Therefore, this paper proposes a CSLR model with Multi-state Feature Optimization (MFO), which is based on Fully Convolutional Network (FCN) and Connectionist Temporal Classification (CTC). The MFO mechanism supervises the multiple states of each Sign Gloss in the modeling process and provides more refined labels for training the CTC decoder, which can effectively solve the overfitting problem caused by training, while also significantly reducing the training cost in time. We validate the MFO method on the popular CSLR dataset and demonstrate that the model has better performance. Show more

Keywords: Continuous sign language recognition, fully convolutional network, multi-state feature optimization, connectionist temporal classification, adequate training

DOI: 10.3233/JIFS-223601

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6645-6654, 2023

Authors: Wang, Libo | Zhao, Jun | Guo, Shizhong

Article Type: Research Article

Abstract: Concrete is known as one of the most important materials in the world. Concrete composites consisting of cement, water, aggregates, and some additives are used to improve the properties of concrete. These concrete have a certain compressive strength that can be increased depending on the type of concrete. In line with these ideas, high-performance concrete (HPC) has been produced, which can have high compressive strength by adding materials such as fly ash, silica fume, etc. This type of concrete is used in bridges, dams, and special constructions. However, obtaining the mixture design of HPC is problematic and complex, for this …reason, the machine learning methods can make it easy to achieve the output by saving time and energy. This study has used support vector regression (SVR) to predict the compressive strength of HPC. Moreover, this study provided two meta-heuristic algorithms for obtaining suitable and optimized results, which are contained the artificial hummingbird algorithm (AHA) and Sine Cosine Algorithm (SCA). The model by coupling with algorithms created the hybrid method in the framework of SVR-AHA and SVR-SCA. Furthermore, some criteria indicators have been used for determining the most desirable hybrid model, which is included coefficient of correlation (R2 ), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and weight absolute percentage error (WAPE). As a result, the AHA algorithm could have a more satisfying association model with the SVR model, and the results were RMSE = 2.00 (MPa), R2  = 98.59%, MAE = 0.717 (MPa), MAPE = 1.22 (MPa), and WAPE = 0.114 (MPa). Show more

Keywords: High-performance concrete, sine cosine algorithm, artificial hummingbird algorithm, support vector regression, compressive strength

DOI: 10.3233/JIFS-230132

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6655-6666, 2023

Authors: Li, Jiacheng | Wang, Jianhua | Liu, Wenjie | Gao, Shengxia | Du, Shiqiang

Article Type: Research Article

Abstract: The Dunhuang murals, notably the paintings on the interior walls of China’s Dunhuang Grottoes, are considered international cultural treasure. The Dunhuang murals were ruined to varied degrees after a lengthy period of erosion. Deep learning networks were utilized to reconstruct broken parts of murals in order to better preserve their important historical and cultural values. Due to the presence of various damages, such as large peeling, mold and scratches, and multi-scale objects in the mural, a simple porting of existing working methods is suboptimal. In this paper, we propose a progressive Dunhuang murals inpainting (PDMI) based on recurrent feature reasoning …network to progressively infer the pixel values of hole centers by a progressive approach, aiming to obtain visually reasonable and semantically consistent inpainted results. PDMI consists mainly of the FFC-based recurrent feature reasoning (RFR) module and Multi-scale Knowledge Consistent Attention (MKCA) module. The RFR module first fills in the feature value at the feature map’s hole border, then utilizes the obtained feature value as a clue for further inference. The module steadily improved the limitation of hole centers, making the inpainted results more explicit; MKCA enables feature maps in RFR to handle richer background information from distant location information in a flexible manner while preventing misuse. After several round-robin inferences provide multiple feature maps, these feature maps are fused using an adaptive feature weighted fusion mechanism, then the fused feature maps decode back to RGB image. Experiments on a publicly available dataset and a self-made Dunhuang mural dataset reveal that the proposed method outperforms the comparison algorithm in both qualitative and quantitative aspects. Show more

Keywords: Image inpainting, Dunhuang murals, progressive inpainting, feature reasoning

DOI: 10.3233/JIFS-230320

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6667-6678, 2023

Authors: Du, Jinze | Wang, Chang

Article Type: Research Article

Abstract: Based on the quaternion system, we give a new representation of the complex vague soft set, and related logical operations. This new representation contains more information than before. Three quaternion distance measures are proposed and a decision model is established. The disease diagnosis of breast cancer is applied to the model to reflect the superiority of the model. By comparing the diagnostic errors under the different distance measures, the most suitable distance measure for this dataset is selected.

Keywords: Quaternion, vague soft set, complex vague soft set

DOI: 10.3233/JIFS-231270

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6679-6690, 2023

Authors: Mythily, M. | David, Beaulah | Venkatesan, R. | Joseph, Iwin Thanakumar

Article Type: Research Article

Abstract: Emerging daily, new devices and software-driven advancements pose challenges in software development, including errors, bugs, and evolving requirements. This leads to delays in delivery. Ensuring software security within the Software Development Life Cycle (SDLC) is crucial. To address this, the research focuses on incorporating security aspects early in the SDLC through model transformation. Platform-independent models with security attributes like Integrity, Privacy, Security Audit, non-repudiation, and authentication are generated. A template-based source code generator is utilized to create the structure of the source model. The Secure Business Process Model (SBPM) encompasses Unified Modeling Language (UML) artifacts, such as analysis level classes …and sequence diagrams, enriched with security attributes derived from the activity model. Security requirements are linked to elements extracted from the source model, and structural codes with security-enabled members are produced. Automation in software development is inevitable, though not complete, as it plays a vital role in addressing these challenges and improving the security of software applications. Show more

Keywords: Index Terms: Object-oriented modeling, software design, software safety, software reusability, software tools.

DOI: 10.3233/JIFS-231359

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6691-6705, 2023

Authors: Yang, Ze | Jiang, Xianliang | Jin, Guang | Bai, Jie

Article Type: Research Article

Abstract: Accurate and fast pest detection is crucial for ensuring high crop yield and quality in modern agriculture. However, there are significant challenges in using deep learning for pest detection, such as the small proportion of pest individuals in the image area, complex backgrounds in light-trapped pest images, and an unbalanced distribution of pest species. To address these problems, we propose MFSPest, a multi-scale feature selection network for detecting agricultural pests in trapping scenes. We design a novel selective kernel spatial pyramid pooling structure (SKSPP) in the feature extraction stage to enhance the network’s feature extraction ability for key regions and …reduce its focus on irrelevant background information. Furthermore, we present the equalized loss to increase the loss weights of rare categories and improve the distribution imbalance among pest categories. Finally, we build LAPD, a light-trapping agricultural pest dataset containing nine pest categories. We conducted experiments on this dataset and demonstrated that our proposed method achieves state-of-the-art performance, with Accuracy, Recall, and mean Average Precision (mAP) of 89.9%, 92.8%, and 93.6%, respectively. Our method satisfies the requirements of pest detection applications in practical scenarios and has practical value and economic benefits for use in agricultural pest trapping and management. Show more

Keywords: Deep learning, object detection, agricultural light-trapped pests, pest detection

DOI: 10.3233/JIFS-231590

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6707-6720, 2023

Authors: Duan, Qi | Zong, Yi

Article Type: Research Article

Abstract: It is worth exploring how “novices in academic entrepreneurship” can more clearly judge their performance in academic entrepreneurship process, self-diagnose the interaction effect between internal and external factors, and improve the effectiveness of entrepreneurial activities. This research takes Chinese academic entrepreneurs as the object, through the qualitative research method of grounded theory analysis, constructs the behavior map of academic entrepreneurship. The main stages of academic entrepreneurship chain are divided, including four stages: starting point, finding technology application, stabilizing technology application, and enterprise mature development. The common decision logic of academic entrepreneurs in each stage is explained. At the same time, …the map shows the main influencing factors of academic entrepreneurial behavior and the logic of these factors’ influence on academic entrepreneurial behavior. The above results not only enrich the research theory in the field of academic entrepreneurship process, but also have guiding significance for the practical activities of “novices in academic entrepreneurship”. Show more

Keywords: Academic entrepreneurship, behavior map, grounded theory analysis

DOI: 10.3233/JIFS-232240

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6721-6733, 2023

Authors: Thao, Le Quang | Diep, Nguyen Thi Bich | Bach, Ngo Chi | Cuong, Duong Duc | Linh, Le Khanh | Linh, Nguyen Viet | Linh, Tran Ngoc Bao

Article Type: Research Article

Abstract: Vietnamese students are facing significant academic pressure due to societal and familial expectations, which leads to an unfavorable learning environment. We aim to employ a temporary spatial-temporal stress monitoring system. Using Wireless Sensor Network (WSN) technology, it collects data on students’ emotional states and incorporates a prediction model, “Reduce Students’ Stress in School” (R3 S), to detect students’ emotional states across school premises. The integration of R3 S and WSN is conducted in three stages. Initially, sensor nodes are deployed in schools to collect emotional data. Subsequently, we introduce a novel hybrid model combining a one-dimensional Convolutional Neural Network with Long Short-Term …Memory networks (1D-CNN-LSTM) to generate a predictive emotional map. This model’s performance, evaluated using RMSE and MAE metrics, shows exceptional precision compared to other LSTM models. When predicting the “stress” condition, the R3 S model achieved a Mean Absolute Error (MAE) of 10.30 and a Root Mean Square Error (RMSE) of 0.041. Lastly, we generate a comprehensive map of cumulative emotional conditions, serving as a guide for school counselors. This map aids in fostering a healthy, conducive learning environment. Show more

Keywords: Monitor student emotion, wireless sensor network, LSTM, 1DCNN, prediction stress

DOI: 10.3233/JIFS-232256

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6735-6749, 2023

Authors: Arun Kumar, A. | Manikandan, B.V. | Kannan, S. | Bhuvanesh, A.

Article Type: Research Article

Abstract: This paper proposed a multi-objective-based Generation Expansion Planning (GEP) for the real-word power generation system of Tamil Nadu, an Indian state. GEP aims to solve numerous conflicting problems for constructing new power plants. The proposed approaches are Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) and Circle Search algorithm. The key objectives of the proposed method is to reduce budget, to maximize reliability and to minimize the pollutant discharge. Therefore, the apt formulations are modeled and solved to establish the conflicting facets of GEP problem. This paper implements MOCLPSO algorithm to solve Multi-Objective GEP (MOGEP) problem for 7-year and 14-year planning …horizon. By then, the proposed model is implemented at MATLAB/Simulink platform and the implementation is calculated. The proposed method shows better results in all approaches like Seagull Optimization Algorithm (SOA), Particle Swarm Optimization (PSO) and Cuckoo Search algorithm. The outcomes establish the competence of MOCLPSO and Circle Search Algorithm to offer good-ranged Pareto optimal non-dominated solutions. Show more

Keywords: CLPSO, recuperation, GEP, Tamil Nadu, power station, utility

DOI: 10.3233/JIFS-232909

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6751-6766, 2023

Authors: Nisha, A.S. | Siva Rani, T.S.

Article Type: Research Article

Abstract: The process of fusing different images from various imaging modalities into a single, fused image that contains a wealth of information and improves the usability of medical images in real-world applications is known as medical image fusion. The most useful features from data can be automatically extracted by deep learning models. In the recent past, the field of image fusion has been preparing to introduce a deep learning model. In this work we can achieve the multi-Focus medical image fusion by hybrid deep learning models. Here the relevant health care data are collected from database (CT & MRI brain images). …Following the input images are pre-processed using sliding window and the abnormal data is eliminated using distribution map method. Further the proposed work comprises 3 steps, 1) the proposed method is used to extract the features from the input image using the modified Tetrolet transform (MMT), which uses a brain image as an input image. This model is capable of identifying anomalous trends in time series data and automatically deriving from the input data characteristics that characterise the system state.2) Propose a novel hybrid model based on CNN with Bi-LSTM (Bi-directional Short Term Memory) multi-focus image fusion method to overcome the difficulty faced by the existing fusion methods. 3) This hybrid model are used to predict the brain tumor present in the fused image. Finally, experimental results are evaluated using a variety of performance measures. From the results, we can see that our suggested model contributes to an increase in predictive performance while also lowering the complexity in terms of storage and processing time. Show more

Keywords: CNN with Bi-LSTM, hierarchical data fusion, deep learning, health care applications, sliding window, modified tetrolet transform, multi-focus image fusion

DOI: 10.3233/JIFS-224439

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6767-6783, 2023

Authors: Thomas, Julia T. | Kumar, Mahesh

Article Type: Research Article

Abstract: In industry, for the quality inspection processes, acceptance sampling plans proved to be economically viable, but the unpredictability of the plan’s characteristics made the use of conventional acceptance sampling plans less reliable. The generalized fuzzy multiple deferred state sampling plan (GFMDSSP) is suggested in this study for qualities that consider the difficulty in calculating the precise value of the percentage of defectives in a batch. The strategy is created with a minimal average sample size in mind and the performance measures have already been determined. An analysis of the current fuzzy acceptance sampling plans for characteristics is conducted, and an …important conclusion is drawn regarding the effectiveness of the proposed scheme. Analysis of the impact of inspection errors on the sampling process reveals a decline in plan acceptance standards that is correlated with escalating inspection errors. Finally, some numerical examples are provided to support the findings. Show more

Keywords: Fuzzy acceptance sampling plans, average sample number, acceptable quality level, limiting quality level, inspection errors

DOI: 10.3233/JIFS-224487

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6785-6796, 2023

Authors: Samy, V.S. | Thenkanidiyoor, Veena

Article Type: Research Article

Abstract: Due to the unpredictable nature of the weather and the complexity of atmospheric movement, extreme weather has always been a significant and challenging meteorological concern. Meteorological problems and the complexity of how the atmosphere moves have made it necessary to find a technological solution. Deep learning techniques can automatically learn and train from vast quantities of data to provide enhanced feature expression. This is frequently used in computer vision, natural language processing, and other domains to enhance the performance of numerous real-time problems. The purpose of this research is to propose a deep learning-based approach for effectively predicting extreme weather …events such as blizzards. To recognize weather patterns and forecast blizzards, the proposed deep learning-based method primarily employs RNN with LSTM. Real-time datasets from the Polar Regions were used to test the proposed approach’s accuracy, and tests were conducted to compare it to existing weather forecasting models. The accuracy of the model is 49.60% (univariate) and 55.19% (bivariate) using bivariate attributes of wind speed and air pressure based on the calculated RMSE values such as 0.0023 and 0.0021. Show more

Keywords: Weather patterns analytics, machine learning, deep learning, extreme prediction and weather forecasting

DOI: 10.3233/JIFS-224543

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6797-6812, 2023

Authors: Thao, Le Quang | Bach, Ngo Chi | Cuong, Duong Duc | Linh, Le Khanh

Article Type: Research Article

Abstract: Babies who can’t communicate through language use crying as a way to express themselves. By identifying the unique characteristics of their cries, parents can quickly meet their needs and ensure their health. This study aimed to create a lightweight deep learning model called Bbcry to classify the cries of babies and determine their needs, such as hunger, pain, normal, deafness, or asphyxia. The model was trained using the Chillanto dataset and underwent three stages of development. Initially, the Wav2Vec 2.0 model was utilized as a teacher for the Knowledge Distillation (KD) method and applied to the transformer and prediction layers …to reduce the number of required parameters. Then, a projection head layer was added and linked to the transformer layers to control their impact on the Wav2Vec 2.0 model. This resulted in the first version of the Bbcry model with an accuracy of 93.39% and an F1-score of 87.60%. Finally, the number of transformer layers was reduced to create the Bbcry-v4 model with only 9.23 million parameters, which used only 10% of the parameters of Wav2Vec 2.0 while only slightly reducing accuracy and F1-score. The study concludes with a software demonstration that shows the proposed model’s ability to accurately recognize and determine the needs of infants based on their cries. Show more

Keywords: Dunstan baby language, infant cry classification, knowledge distillation, Wav2Vec

DOI: 10.3233/JIFS-232118

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6813-6824, 2023

Authors: Fan, Jianping | Yuan, Jiu | Wu, Meiqin

Article Type: Research Article

Abstract: This paper studies a large-scale group decision-making method (LSGMD) based on incomplete hesitant fuzzy linguistic preference relations (IHFLPRs) and proposes an improved model for additive consistency of hesitant fuzzy linguistic preference relations (HFLPRs). Additionally, consistency control and fuzzy C-means (FCM) clustering are utilized to enhance efficiency and reliability. Firstly, a model is proposed to address the issues of missing elements in IHFLPRs and insufficient additive consistency in HFLPRs, aiming to more accurately reflect decision makers’ preference relationships towards candidate alternatives. Subsequently, the FCM method is employed to cluster decision experts’ preference information and obtain the overall preference information. Finally, the …rationality and accuracy of our proposed method are demonstrated through a case study and comparative analysis. Show more

Keywords: Incomplete hesitant fuzzy linguistic preference relations, consistency control, large-scale group decision making, Fuzzy C-means clustering

DOI: 10.3233/JIFS-232615

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6825-6836, 2023

Authors: Lin, Pao-Ching | Huang, Jui-Chan | Ho, Ping-Tsan

Article Type: Research Article

Abstract: In recent years, tourism has developed rapidly and made great contributions to the economic enhancement of various regions; While tourism environment carrying ability assessment is the key to tourism sustainable development. The randomness and fuzziness of the traditional multi-level fuzzy comprehensive tourism environmental carrying ability assessment model cannot be combined effectively. In view of this, to construct a reasonable and objective assessment model, this study improves the multi-level fuzzy comprehensive tourism environmental carrying ability assessment model based on cloud model. The results indicate that the unimproved model judges that this tourism environment carrying ability level corresponds to level 2 for …selecting tourism destination. And it is in a suitable load state. The evaluation results on the foundation of cloud model improved multi-level fuzzy comprehensive tourism environmental carrying ability assessment show that its Ex is 5.748, En is 1,296 and He is 0.1, which is between moderate to slightly overloaded, and the overall state is moderate, but there is a tendency to develop towards slightly overloaded. The evaluation results of the improved model are more intuitive in showing the carrying capacity of the tourism environment, and these evaluation results are more objective and reliable, which verifies the applicability of the research model. This research model provides a theoretical basis and data support for the study of tourism environment carrying capacity. Show more

Keywords: Tourism, environmental carrying ability, cloud model, fuzzy integrated assessment, assessment model

DOI: 10.3233/JIFS-232982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6837-6847, 2023

Authors: Li, Kunpeng | Xu, Junjie | Zhao, Huimin | Deng, Wu

Article Type: Research Article

Abstract: Most of the flight accident data have uneven distribution of categories. When the traditional classifier is applied to this data, it will pay less attention to the minority class data. Synthetic Minority Over-sampling Technique (SMOTE), and its improvements are well-known methods to address this imbalance problem at the data level. However, traditional algorithms still have the problems in blurring the boundary of positive and negative classes and changing the distribution of original data. In order to overcome these problems and accurately predict flight accidents, a new Clustered Biased Borderline SMOTE(CBB-SMOTE) is proposed for Quick Access Recorder (QAR) Go-Around data. It …generates more obvious positive and negative class boundaries by using K-means for boundary minority class data and safety minority class data respectively, and maintains the original data distribution to the greatest extent through a biased oversampling method. Experiments were carried out on a group of QAR Go-Around data. The data set is balanced by CBB-SMOTE, SMOTE, Cluster-SMOTE algorithm respectively, and the random forest algorithm is used to predict the new data set. The experimental results show that CBB-SMOTE outperforms the SMOTE in terms of G-means value, Recall and AUC. Show more

Keywords: Imbalanced learning, oversampling, SMOTE, QAR Go-Around data, data generation

DOI: 10.3233/JIFS-233548

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6849-6862, 2023

Authors: Suresh Kumar, P. | Barkathulla, A.

Article Type: Research Article

Abstract: A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed …in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively. Show more

Keywords: Wireless sensor network, cluster head selection, energy efficiency, clustering, network lifetime

DOI: 10.3233/JIFS-221370

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 4, pp. 6863-6873, 2023