International Journal of Modeling, Simulation, and Scientific Computing

Mumps is the most common disease for children, which causes hearing loss in all auditory systems. WHO initiated compulsory vaccination for children who are at age of 12 months to 12 years, since 1967. Children must take two doses of vaccination for mumps. The dynamics of the system describing the mumps transmission is studied in this work. The main aim is to elucidate the nonlinear behavior of the system which can provide insights into the long-term effects of Mumps infection on auditory function. As the fractional-order derivative preserves the impact of system memory, we employ the Caputo fractional derivative as the dynamical system describing modeling to simulate hearing loss in children brought on by the mumps virus. The boundedness existence of the solution and stability analysis of the dynamical system are derived in the framework. The result is derived using a well-known numerical method to capture the corresponding consequences and predict the future consequence of the dynamical system associated with parameters. To testify to the importance of the generalizing classical model with a fractional operator, we plot 2D graphs. This study helps to researcher beginners to understand the essential and fundamental tools while investigating real-world problems.

Nonlinear state-space models (SSMs) are widely used to model actual industrial processes. System identification is an important method to reduce the uncertainty of the simulation model. In recent years, system identification has been greatly improved with the rise of machine learning. However, there are a few reviews on the latest identification methods based on machine learning. Therefore, this paper focuses on the latest development of identification methods for nonlinear SSM in recent years. In particular, this paper comprehensively compares the identification methods based on traditional methods and machine learning. In addition, according to the type of uncertainty, we divided the paper into the parameter’s identification and the identification of unknown parts of the model. Compared with the classification of other reviews, our classification method is clearer. Briefly, this paper organizes the review according to the classification of uncertainty. Each type is extended from offline identification to online identification. Specifically, interval identification and point estimation methods are reviewed for offline parameter identification. For online parameter identification, point estimation methods are reviewed. In the case that the model is partially unknown or black-box, the modeling methods and identification methods are mainly reviewed. In addition to the traditional methods, this paper focuses on the latest progress in the application of machine learning in system recognition. Finally, at the end of the paper, this paper summarizes the existing methods and points out the key problems that still need to be solved.

With the rapid advancement of metaverse technology, the military field has gradually begun to pay attention to the potential applications of metaverse technology. The military metaverse is a new and comprehensive military ecosystem that integrates the virtual world and the real world, unlocking new possibilities in areas such as military training, education, exercises, combat support, and equipment research and development. This paper analyzes the concept and connotation of the military metaverse and presents its main features and key technologies, as well as the role it plays in the military field. Furthermore, it proposes the primary application scenarios of the military metaverse and provides an application architecture. Finally, the paper offers a forward-looking perspective on the future development of military metaverse.

Relation extraction (RE) is a crucial step for knowledge graph construction, which aims to extract meaningful relations between entity pairs in plain texts. Very few works have been studied on Chinese relation extraction (CRE) in the military field. Moreover, recent deep neural network-based methods have achieved considerable performance but still suffer from three inherent limitations, including overlapping of entities, imbalanced data and the ambiguity. Therefore, this work investigates a novel Multi-Grained Lattice Transformer (MGLT), which leverages external information of lexicon and word sense tailored for CRE. In MGLT, self-matched lexicon words and related word senses are fused through a cross-transformer mechanism to alleviate the ambiguity in texts. The finally enriched sequence representation in MGLT captures the relatedness between the head entity and the tail one, which is helpful to alleviate the overlapping of entities. Experimental results on two benchmark datasets and a self-developed dataset constructed from online military news show that the proposed MGLT achieves state-of-the-art (SOTA) performance. Compared with other typical baselines, MGLT achieves better area under curve (AUC) and $F1$-score by up to 10.46% and 6.90%, respectively. We further demonstrate the effectiveness of using ensemble learning to fully exploit complementary information from multiple MGLT-based base learners to improve the overall performance for imbalanced data classification on the military dataset. Such results indicate that the proposed ensemble learning model is effective and robust to be applied in practical applications.

Using an artificial neural network and the Bayesian Regularization Technique (NNs-BRT), the stochastic method’s strength is used to analyze the differential system, illustrating a nonlinear smoke epidemic differential model (NSED). This allows for a more precise, dependable, cost-effective, dynamic calculating approach. In addition to experiments utilizing nonlinear mathematical structures through five distinct classes of differential operators-smokers contemplating quitting, infrequent smokers, regular smokers, those who have temporarily abstained from smoking, and those who have permanently quit smoking the NSED framework has been established. By allocating 25% of the data for testing and validation and 75% for training, the proposed NNs-BRT can determine the estimated solutions of five different examples based on the numerical computation of the NSED system employing Adams techniques. To prove the accuracy of the given NNs-BRTs, a comparison of the results from the dataset obtained using the Adam method for different scenarios reflecting variance in recruitment rate, effective contact rate between C and A, natural death rate, how quickly occasional smokers become regular smokers, contact rate between smokers, and temporary quitters, number of smokers quitting, and percentage of smokers who are still leaving for good is made. The use of error histograms, mean square error, regression, correlation, and state transitions is also examined in numerical replications of the NNs-BRTs to verify their competence, dependability, accuracy, consistency, and proficiency.

Designing intelligent diagnosis of prostate diseases in intelligent medical assistance systems has gradually become a research hotspot. However, rectal ultrasound (TRUS) as the main diagnostic tool for prostate diseases remains a challenging issue. (1) Due to limited prostate TRUS imaging data, it is difficult to train a robust deep learning model. (2) In terms of visual features, ultrasound images of prostate cancer are similar to TRUS images of other tissues and organs, so it is difficult for a single neural network model to accurately learn the feature representation of the disease. To address the above problems, we first establish a high-quality dataset for prostate TRUS imaging, and then design multi teacher knowledge distillation to achieve accurate disease recognition. The experimental results show that, compared with knowledge distillation without a teacher model and a single teacher model, knowledge distillation using multiple teacher models can significantly improve the accuracy of prostate TRUS image cancer prediction. As the number of teacher models increases, the accuracy rate is further improved, which verifies the effectiveness of this method in intelligent systems.

Aiming at the problems of information disconnection and business interruption in supply chain networks caused by uncertainties such as trade wars and COVID-19, a projection-transformation-based model for predicting possible collaborative relationship links in bimodal supply chain networks is designed from the perspective of network science. The model first transforms the bimodal network structure of supply chain into a corresponding unimodal projection network by projection transformation; then screens the candidate collaborative links in the network; finally assigns weights to the similarity links according to different influencing factors, and calculates the possibility of potential collaborative links in the bimodal supply chain network by this. The experimental results show that the model can achieve higher quality prediction in real bimodal supply chain networks compared with other baseline link prediction models, which provides theoretical and methodological support for better formulation of business contingency plans and risk defense measures.

Interactive scientific visualization applications have been more and more used to observe, analyze and understand the complex phenomena studied in several scientific disciplines such as (nano-sciences, fluid simulations, etc.). To facilitate the coupling of the different computation, visualization and exploration codes, which are often heterogeneous and difficult to cooperate, scientists must be able to use and reuse the different parts of the applications already built in order to interchange and enrich certain parts without being experts. In this context, a middleware called FlowVR has been developed by the scientific community leading to make applications divided into set of components exchanging messages. Moreover, this middleware enables to encapsulate existing codes in components, interconnect them via data links, and deploy them on distributed computing resources. Nevertheless, FlowVR does not offer any capability to check the functional correctness of new developed component-based applications. This paper presents a framework called Model and Component-based framework for Scientific Applications (MCSA) based on a interplay of model-driven and component-based software engineering. On the one hand, we provide a domain specific language dedicated to physics/chemistry scientists in order to built their MCSA applications. This language also allows to capitalize efforts by proposing existing components that have been already developed. On the other hand, we propose a model transformation to check the correctness of instantiated models, for this, we use a new subclass of Petri net called strict Colored FIFO Nets (sCFN). Our approach has been backed by a case study relies on a molecular dynamics application.

Knowledge graph completion is one of the steps of knowledge graph construction. It aims to complete the incomplete triple data in the initially constructed knowledge graph and make the data in the knowledge graph more abundant and complete. At present, there are few studies on knowledge graph completion in the equipment field, and the attribute and relationship samples in the equipment knowledge graph are prone to uneven distribution, while the traditional knowledge graph completion method is difficult to solve the problem of uneven distribution of attribute and relationship samples. Therefore, this paper proposes an internal instance type completion method of equipment knowledge graph based on EP2TP-TRT. First, the TransE model is used to embed the relationship and attributes of the equipment instance, respectively. Then, the EP2TP model is used to map the instance attributes, and the TRT model is used to map the type relationship. Finally, the scores of the EP2TP and TRT models are integrated by designing different weights, and the training prediction is carried out to enhance the representation ability of instance information and type information. Compared with the mainstream advanced models, this method improves the MRR and HITS @ 1 indicators by about 0.89% and 2.1%, respectively.

Big Data is a massive collection of data that require secure information transmission in the network. Hence, various existing Cryptography techniques have been used to improve information security in big data. However, the existing techniques cause data extrusion due to truncated transformation and diffusion properties in encryption and key generation processes that cause data leakage and loss. Hence, a novel Enhanced Encryption with Quantum-based Neuro Cryptography technique is introduced in which encryption is performed using logical shift and modulo operation based transformation that removes data extrusion and due to stream of photons in Quantum Cryptography Key has been generated and converted to the secret key via neuro cryptography. This process enhanced diffusion property and removed leakage and data loss. Moreover, malicious activities are happening due to inflexible encryption processes thus reducing confusion property that leads to delay and attack on nonresistance. Hence, a novel Inherited process with cyclic crossover operation has been developed in which one-point cyclic crossover and bit string mutation are achieved to combine ciphertext and key that increase confusion property and eliminate timing attack. The proposed system increases throughput and reduces encryption and decryption time with high diffusion and confusion properties.

Existing classification models for traditional Chinese paintings mostly ignore shallow detail features, which leads to the imprecise classification of styles. To address the above problems, this paper proposes a Chinese traditional painting style automatic classification model based on dual-channel feature fusion with multi-attention mechanism. First, the spatial attention mechanism is introduced to enhance the Swin-Transformer framework to obtain the salient features of Chinese ancient painting images. Second, a dual-channel attention mechanism is constructed to extract global semantic features and local features of Chinese ancient painting images. Finally, the extracted features are fused and categorized based on the softmax classifier. To verify the feasibility and validity of the proposed model, this paper performs simulations on the Chinese painting dataset and compares it with existing algorithms.The average classification accuracy of the proposed model is 90.6$\%$, with an improvement of 3.14$\%$, which is better than the existing model in both visual effects and objective data comparisons.

In the modern world, traffic accidents frequently result in fatalities and serious injuries. The ability of machine learning to foretell the severity of road traffic accidents has shown great promise. The classification of traffic accidents has shown to be a good application for algorithms like random forest. In this paper, performance on a specific dataset has been evaluated using random forest and other models. The dataset used for the analysis came from a publicly accessible source and contained information on several variables like the type of road, the time of day, and the weather. In order to analyze the severity of accidents, a number of algorithms were applied to the dataset, including decision tree, random forest classifier, and logistic regression algorithm. Each model was evaluated on parameters such as model accuracy, precision and recall of the model, and F1 score. The random forest classifier outperformed the other models, achieving an accuracy of 98.48%. The study concludes that machine learning algorithms can accurately predict the severity of road traffic accidents, which could help to reduce the number of accidents and fatalities on the road.

The most fatal disease on the earth is thought to be illness of the heart. There are a lot of features that change the heart’s composition or functionality. In most cases, it is hard for doctors to make a diagnosis accurately and quickly. This study’s objective is to determine critical factors and methods of data mining which can improve the accuracy for prediction of heart disease. Further, it is essential to make use of automatic technologies in diagnosing heart diseases as early as possible. To develop a new prediction technique for heart disease that comprises four phases such as “(a) Pre-processing, (b) Feature extraction, (c) Feature selection and (d) Classification”. The initial stage of pre-processing is when the incoming data is treated to the elimination of redundant and missed numbers. Then, from the initial stage of data, the higher-order statistical and statistical characteristics, chi-squared features and symmetrical uncertainty attributes are derived. However, when working with a greater number of characteristics, the curse of dimensionality was a severe issue. Hence, the characteristics of optimal features are planned from the overall set of features. A novel Hybrid Bull and Elephant Algorithm (HB-EA) is introduced for the selection of optimal features. Consequently, the selected set of features is subjected to various classifiers as an ensemble model that contains “Naïve Bayes (NB), Decision Tree (DT), Neural Network (NN), Support Vector Machine (SVM), Optimized Recurrent Neural Network (RNN) and Linear Regression (LR)”. The final step is to log off efficiency for outputs obtained from the group of classifiers and determine the outcome. The RNN weights are ideally tuned by the suggested HB-EA technique to boost the system’s accuracy. The proposed model is finally evaluated against existing techniques to determine its superiority. The suggested technique for dataset 1 achieved maximum accuracy (0.916), and it is 15.24%, 8.76%, 7.56%, 4.09% and 1.89% better than convolution schemes like Random Forest (RF), Deep Belief Network (DBN), SVM, $K$-Nearest Neighbor (KNN) and Elephant Herding Optimization (EHO) models.

The state of the environment and human behavior today contributes to a wide range of diseases that affect people. Medical professionals often find it challenging to detect disorders by themselves appropriately; it is crucial to recognize and anticipate them early on. This paper aims to detect and predict people with more widespread chronic illnesses. To prevent such diseases from worsening, this research proposes a new deep learning-based technique to predict chronic diseases. Initially, patient data will be collected using Internet of Things (IoT) devices. Then, the missing values from input data are eliminated, and categorical data encoding, outlier detection, and data transformation are performed in the pre-processing stage. After that, the necessary attributes are selected to optimize the performance by eliminating unnecessary features using Binary Grasshopper Whale Optimization Algorithm (BGWOA), which combines the benefits of the Binary Grasshopper Optimization Algorithm (BGOA) and Binary Whale Optimization Algorithm (BWOA) algorithms. Then, the disease can be classified as chronic or not, utilizing a three-layer stacked bidirectional long short-term memory (TLSBLSTM) technique. The performance is evaluated on two chronic disease datasets that are publicly available. It successfully obtained good results by preparing the dataset on heart disease and comparing the findings using the most recent state-of-the-art approaches. According to the experimental findings, the proposed approach performs better in evaluating performance measures than the existing approaches. The observed accuracy of the proposed method is 99.87% and 99.84% for chronic kidney disease dataset and cardiovascular disease dataset, respectively.

Kang Feng proposed the symplectic geometric algorithms for Hamiltonian systems at Beijing “International Symposium on Differential Geometry and Differential Equations” in August 1984, opening up a new field of research. Over the past 40 years, geometric numerical integration for dynamical systems has been irreplaceably applied in numerous fields, including celestial mechanics, plasma physics, fluid mechanics, biochemistry, meteorological forecasting, seismic exploration, data science and artificial intelligence. This paper mainly summarizes the achievements of geometric numerical integration for dynamical systems in China over the past 40 years.

Several approaches have been proposed to remedy the dimensionality problem that reinforcement learning (RL) suffers from. Among the solutions, hierarchical reinforcement learning (HRL) consists of dividing an RL problem into sub-problems called options or abstract actions. Discovering abstract actions or options for HRL is challenging, multiple approaches are proposed. In this paper, we present a new approach, an agent with direction sense for an automatic option discovery. Our agent uses its direction sense to discover shortcuts and shortest paths between states that he has already visited, he detects bottlenecks for building termination conditions and initiation states for options. Thus, at the learning step, the agent uses his previous experience of exploration in parallel with an intrinsically motivated learning. The options discovered are task-independent and could be used for new tasks. Experimental results on maze problems and Tic-tac-toe game indicate better results compared with flat RL and another RL approach in general and special cases.

With the development of Model-Based Systems Engineering (MBSE), model reuse technology is an important method to improve the modeling efficiency and credibility, and the research difficulties and hotspots in the field of complex system simulation. However, model reuse still faces challenges due to the existence of text structure bias and feature mismatch. To this end, this paper presents a structure-based model similarity quantization algorithm to obtain the similarities among models. In the first stage, the properties of the structure between the models are obtained by the structural vectorization method, and different module data are accounted. In the second stage, weights are assigned to the module data using the AHP–CRITIC assignment method. The experiment is based on the circuit amplifier model as a case study, and the experimental results verify the feasibility of structure-based model similarity quantification.

Image correction and deblurring have always posed significant challenges in the field of image processing, and the results of license plate image recognition with low structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) will significantly affect the use of vehicles. To address these problems, we propose a real-time algorithm based on deep learning, which can simultaneously perform image spatial correction and deblurring in the same network, known as $4\times \mathrm{\text{STN}}+\mathrm{\text{DSC}}15+\mathrm{\text{DSK}}$. First, a 15-layer depthwise separable convolutional neural network is designed as the basic network for high-speed image deblurring, and four spatial transformer network (STN) modules are integrated into the basic network to achieve image spatial correction capability. Second, the network is divided into three dense blocks, in which dense residual connections are used to improve the PSNR. Finally, a bottleneck structure is created to improve the real-time performance of image reconstruction that can simultaneously perform image space correction and deblurring. The test results on the CCPD dataset demonstrate that the $4\times \mathrm{\text{STN}}+\mathrm{\text{DSC}}15+\mathrm{\text{DSK}}$ algorithm is advanced, with PSNR reaching 30.14dB, SSIM reaching 0.911, and image reconstruction only 0.12s. The correction and deblurring modules can promote each other, jointly improving the image reconstruction ability of the network, that is, the network achieves more advanced image reconstruction performance while maintaining high real-time performance.