Journal of Intelligent & Fuzzy Systems - Volume 45, issue 6

Authors: Gao, Caiwen | Zhang, Zhiqiang | Liu, Baoliang

Article Type: Research Article

Abstract: This paper extends the traditional multifactor population model from a probabilistic background to an uncertain background, allowing us to better account for the fact that the birth and death rates are sometimes affected by different, independent uncertain noises. The theory of multifactor uncertain differential equation is applied to analyze the novel model. First, a multifactor uncertain population model is developed and the solution of the model is obtained. Secondly, the stability in measure and stability in distribution of the multifactor uncertain population model are studied respectively, and the relationship between the two kinds of stability is further discussed. Then, a …method of moment estimation based on residuals is proposed for the unknown parameters in the multifactor uncertain population model. Finally, an example is given to illustrate the validity and rationality of the parameter estimation method. Show more

Keywords: Parameter estimation, residual, multifactor differential equation, stability

DOI: 10.3233/JIFS-232020

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10295-10303, 2023

Authors: Mu, Yongan | Liu, Wei | Lu, Tao | Li, Juan | Gao, Sheng | Wang, Zihao

Article Type: Research Article

Abstract: The self-adaptive multi-agent system requires adaptive adjustments based on the dynamic environment during its runtime. Heterogeneous agent can accomplish different task goals, enhance the efficiency of system operation, but its complex collaboration problem poses new challenges to the study of verification of adaptive policies for heterogeneous multi-agents. This paper proposes a runtime verification method for self-adaptive multi-agent systems using probabilistic timed automata. The method constructs a probabilistic timed automaton model by formally describing the functional characteristics of heterogeneous agents and integrating random factors in the environment to simulate the operation process of the self-adaptive multi-agent system. Regarding the collaboration logic …among heterogeneous agents, security constraints are established to ensure the security of state transition processes during system operation. Combining model checking with runtime quantitative verification methods to conduct experiment and applying it in the case of an intelligent unmanned parking system. Experimental results manifest the correctness of the cooperation logic between agents can effectively ensure the stability of the system at runtime. Significant improvement in system uptime and efficiency compared to the initial system without runtime quantitative validation. Show more

Keywords: Self-adaptive system, heterogeneous multi-agent, probabilistic timed automata, agent cooperation logic, runtime quantitative verification

DOI: 10.3233/JIFS-232397

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10305-10322, 2023

Authors: Xu, Zan | Lu, TongWei

Article Type: Research Article

Abstract: Some anomaly detection methods are based on CNN to fuse spatial and channel-wise information together within local receptive fields. However, the correlation between feature channels has not been fully utilized. Channel attention has been shown to model the interdependence between convolution feature channels and improve network representation. It is possible to introduce channel attention into anomaly detection. We attempt to directly embed the SE(Squeeze and Excitation) module into the convolutional layer but reduced anomaly detection performance. Therefore, we propose a lightweight channel attention module C-SE(Current Squeeze and Excitation) suitable for anomaly detection. C-SE module not only improves the representation ability …of depth convolutional neural network but also has a significant effect on texture anomaly detection. C-SE module body is constructed by average pooling and maximum pooling branches, which ensure that local salient features of the image are not lost. Then reduce the negative impact of feature calibration through a long connection. In addition, the improvement of classifier plays an important role. Experimental results have shown that the proposed method outperforms the Patch SVDD methods by 3% in image-level AUROC and 0.7% in pixel-level AUROC on the MVTec benchmark. The higher AUROC score and the faster rate of convergence prove the effectiveness of the method. Show more

Keywords: Anomaly detection, channel attention, feature calibration, texture, MVTec

DOI: 10.3233/JIFS-232677

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10323-10334, 2023

Authors: Manivannan, K. | Sathiamoorthy, S.

Article Type: Research Article

Abstract: In the last decades, Tuberculosis (TB) can be considered a serious illness affecting people over the globe and it leads to mortality when left untreated. Chest X-Ray (CXR) is the topmost selection for the recognition of pulmonary diseases in hospitals since it can be cost-efficient and easily available in many nations. But, manual CXR image screening is a huge load for radiologists, which results in a maximum inter-observer discrepancy rate. At present, Computer-Aided Detection (CAD) is a powerful imaging equipment for detecting and screening dangerous ailments. In recent times, Deep Learning (DL) based CAD schemes have demonstrated positive outcomes in …the recognition of TB diseases. This study introduces an Egret Swarm Optimization Algorithm with Deep Feature Fusion based Tuberculosis Classification (ESOA-DFFTC) technique on CXR Images. The presented ESOA-DFFTC technique utilizes feature fusion and tuning processes for the classification of TB. To accomplish this, the ESOA-DFFTC model first exploits the Gaussian Filtering (GF) approach for image denoising purposes. Next, the ESOA-DFFTC model performs a feature fusion process using three DL models namely ResNeXt-50, MobileNetv2, and Xception. To enhance the achievement of the DL models, the ESOA-based hyperparameter optimizer is implemented in the study. For TB classification, the ESOA-DFFTC methodology uses an Arithmetic Optimization Algorithm (AOA) with Weight-Dropped Long Short-Term Memory (WDLSTM) methodology. The investigational output of the ESOA-DFFTC system was examined on a benchmark medical imaging dataset. A wide comparative investigation stated the greater achievement of the ESOA-DFFTC system over other current algorithms. Show more

Keywords: Computer-aided diagnosis, machine learning, Tuberculosis, chest X-Ray images, feature fusion, Metaheuristics

DOI: 10.3233/JIFS-233975

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10335-10347, 2023

Authors: Fang, Yuhua | Xie, Zhijun | Chen, Kewei | Huang, Guangyan | Zarei, Roozbeh | Xie, Yuntao

Article Type: Research Article

Abstract: Traditional Simultaneous Localization and Mapping application in dynamic situations is constrained by static assumptions. However, the majority of well-known dynamic SLAM systems use deep learning to identify dynamic objects, which creates the issue of trade-offs between accuracy and real-time. To tackle this issue, this work suggests a unique dynamic semantics method(DYS-SLAM) for semantic simultaneous localization and mapping that strikes a trade-off between high accuracy and high real-time performance. We propose M-LK, an enhanced Lucas-Kanade(LK) optical flow method. This technique keeps the continuous motion and greyscale consistency assumptions from the original method while switching out the spatial consistency assumption for a …motion consistency assumption, reducing sensitivity to image gradients to identify dynamic feature points and regions efficiently. In order to increase segmentation accuracy while maintaining real-time performance, we develop a segmentation refinement scheme that projects 3D point cloud segmentation results into 2D object detection zones. A dense semantic octree graph is built in the interim to expedite the high-level process. Compared to the four equivalent dynamic SLAM approaches, experiments on the publicly available TUM RGB-D dataset demonstrate that the DYS-SLAM method offers competitive localization accuracy and satisfactory real-time performance in both high and low-dynamic scenarios. Show more

Keywords: Visual SLAM, object detection, dynamic environment, deep learning for visual perception

DOI: 10.3233/JIFS-234235

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10349-10367, 2023

Authors: Mao, Yaxin

Article Type: Research Article

Abstract: The process of attempting to estimate the future prices of particular stocks by utilizing historical data and various analytical tools, including deep learning algorithms, is called stock price prediction. Insurance providers’ overall approach and decisions to manage their risks, enhance their profitability, and give value to their policyholders are referred to as the insurance strategy. It requires various things to be considered, including underwriting procedures, pricing strategies, product creation, risk analysis, claims administration, and investment choices. This study proposed optimizing an insurance strategy and predicting securities prices using a deep learning algorithm. Initially, the real stock data sources for Microsoft …Corporation (MSFT) were gathered from Ping An Insurance Company of China (PAICC) and the Shanghai-based National Association of Securities Dealers Automated Quotation (NASDAQ). Normalization is the procedure used to preprocess data for the raw data. We suggest an Enhanced dragonfly-optimized deep neural network (EDODNN) with stock price forecasting and insurance. The outcomes demonstrate that the proposed model outperforms the current methodology and achieves accuracy, precision, recall, F1 score, R 2 , and RMSE. To display the effectiveness of the suggested system, its performance is compared to more established methods to obtain the highest level of efficiency for the research. Show more

Keywords: Deep learning, stock price prediction, insurance strategy, microsoft corporation (MSFT), enhanced dragonfly optimized deep neural network (EDODNN)

DOI: 10.3233/JIFS-234292

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10369-10379, 2023

Authors: Yuan, Jinhai | Li, Sisi | Fan, Xin

Article Type: Research Article

Abstract: Educators across different fields disseminate their knowledge and utilize digital technologies to improve student skills for their careers and sustainability. Students’ skills that are improved are verified based on assessment and knowledge application over different circumstances. The article investigates the impact of the effective educator’s knowledge assessment and their role in student skill development. The motivation for the research arises from the realization that teachers’ knowledge and their capacity for transferring skills and information to students successfully play a significant role in the quality of education. The goal of the study is to develop a GA2M that has been verified …and can be used by educators to improve their performance, enhance student’s results, and eventually progress educational practices to use Fuzzy methods for reasoning and to include new rules for improving knowledge to bridge the knowledge gap between educators and students’ skill growth. It requires a great effect by the educator to enhance their ability over successive performance improvement. This article analyzes the ability for better improvement using the proposed Guided Ability Assessment Model (GA2M). The proposed model discards the negative impact of the ability on students’ skill deterioration. Besides, the ratio of skill improvement across various new abilities and exposures is analyzed using Fuzzy inference. This analysis frames the interference using knowledge rules required for different circumstances. These rules are framed using existing skill implications and problem-solving ability. This proposed model proposes new rules for development of various abilities of educators. Based on their current ability, further training process for the educator’s skill development is prescribed. Therefore, the inference for fuzzification is performed for the positive impact on students’ skill development. If the inference succeeds, the assessment is leveraged between skill guidance and ability. Show more

Keywords: Digital assessment, educator ability, fuzzy model, student skill

DOI: 10.3233/JIFS-231074

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10381-10395, 2023

Authors: Luo, Zhi-Yong | Chen, Ya-Nan | Liu, Xin-Tong

Article Type: Research Article

Abstract: In cloud computing, optimizing task scheduling is crucial for improving overall system performance and resource utilization. To minimize cloud service costs and prevent resource wastage, advanced techniques must be employed to efficiently allocate cloud resources for executing tasks. This research presents a novel multi-objective task scheduling method, BSSA, which combines the Backtracking Search Optimization Algorithm (BSA) and the Sparrow Search Algorithm (SSA). BSA enhances SSA’s convergence accuracy and global optimization ability in later iterations, improving task scheduling results. The proposed BSSA is evaluated and compared against traditional SSA and other algorithms using a set of 8 benchmark test functions. Moreover, …BSSA is tested for task scheduling in cloud environments and compared with various metaheuristic scheduling algorithms. Experimental results demonstrate the superiority of the proposed BSSA, validating its effectiveness and efficiency in cloud task scheduling. Show more

Keywords: Cloud computing, task scheduling, multi-objective optimization, sparrow search algorithm

DOI: 10.3233/JIFS-232527

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10397-10409, 2023

Authors: Khan, Muhammad Ishfaq | Almazrooei, Abdullah Eqal | Yanhong, Li | Ibrar, Muhammad | Nazif, Fatima | Latif, Abdul

Article Type: Research Article

Abstract: Disaster Management Program plays a critical role in coordinating and implementing strategies to address emergencies and disasters, ranging from natural events like hurricanes, earthquakes, and wildfire to human-made incidents such as industrial accidents or terrorist attacks. Simultaneously, it is widely studied as a typical multi-attribute decision-making (MADM) problem. This paper investigates the concept of complex picture fuzzy sets (CPFS), an extension of picture fuzzy sets (PFS), achieved by the inclusion of a phase term. The existence of phase terms expand the scope of CPFS from real line to a complex plane of unit disc and highlight its originality by demonstrating …its capacity to handle both vagueness and periodicity simultaneously. In this paper, the complex picture fuzzy Hamy mean operator (CPFHM) and complex picture fuzzy dual Hamy mean operator (CPFDHM) is studied. The reason of selecting complex picture fuzzy Hamy mean operator (CPFHMO) is that it can find interrelationship among multi-input variables. Then the various properties of CPFHM and CPFDHM operator are described in depth. A multi-attributes group decision-making (MAGDM) technique for solving group decision-making problems is proposed based on these operators. The validity of the present technique is demonstrated by analyzing a disaster management problem. Furthermore we check the sensitivity of parameter k and apply the validity test on our proposed technique. Finally, a comprehensive comparison is provided between the proposed model and specific existing approaches, illustrating that the suggested decision model is superior and more advantageous than the existing employed methodologies. Show more

Keywords: Picture fuzzy set, complex picture fuzzy set, Hamy mean operator, multi-attribute group decision making, evaluation the disaster management program

DOI: 10.3233/JIFS-232529

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10411-10436, 2023

Authors: Feng, Qingyuan

Article Type: Research Article

Abstract: China’s education has entered the era of quality, and in order for higher vocational colleges to develop better, effective measures must be taken to improve the quality of education. Facing the continuous innovation of education in higher vocational colleges, it is necessary to strengthen institutional construction and constrain educational management with institutional constraints, which is the key to ensuring the education quality. The higher vocational education quality evaluation in the new era is regarded as multiple attribute decision-making (MADM). Recently, the EDAS and CRITIC model has been employed to solve MAGDM. The triangular fuzzy neutrosophic sets (TFNSs) are constructed as …an efficient tool for portraying the uncertain information during the higher vocational education quality evaluation in the new era. In this paper, the triangular fuzzy neutrosophic number EDAS (TFNN-EDAS) model based on the Hamming distance and Euclid distance is constructed to solve the MADM under TFNSs. The CRITIC method is utilized to obtain the weight information based on the Hamming distance and Euclid distance under INNSs. Finally, a numerical example of higher vocational education quality evaluation in the new era is constructed and some efficient comparisons are founded to verify the TFNN-EDAS method. Show more

Keywords: Multiple attribute decision making (MADM), triangular fuzzy neutrosophic sets (TFNSs), EDAS method, higher vocational education quality evaluation

DOI: 10.3233/JIFS-234044

Citation: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 10437-10450, 2023