International Journal of Modeling, Simulation, and Scientific Computing

Advances in computing allow for the construction of increasingly large and complex models and simulations. Exhaustive error checking of these intricate, large computational simulation models is daunting and potentially impractical. This paper explores an approach to error-checking simulation model components using an Acceptance Sampling methodology from the field of industrial manufacturing. We propose a systematic process in which a simulation inspector examines only a fraction of the computational model elements to measure the errors present. Our proposed process could support established verification processes by sampling the simulation components to identify whether the model is acceptably error free and which components require correcting. The proposed methodology relies on several statistical constraints but serves the interests of simulation professionals as part of the overall verification process. We illustrate the application and usefulness of our methodology through a real-world case study of a citywide microscopic transportation model.

Accurate forecast of spare parts demand is of great significance for modern enterprises to provide accurate support and improve market competitiveness. In most studies, mathematical laws are used to forecast, without enough consideration of the actual operation of equipment and the fact that the accuracy of spare parts demand forecasting is not high, which cannot adapt to the new characteristics of complex equipment use environment and fierce market competition in modern enterprises. The digital twin model can be used to forecast the demand for spare parts more accurately and guide modern enterprises to carry out accurate support. By analyzing the current spare parts demand of modern enterprises, the paper puts forward the forecasting ideas of spare parts demand based on the digital twin model by using the digital twin model of equipment maintenance management in modern enterprises. In the digital twin model, the theoretical demand forecasting model of spare parts based on life distribution of replaceable units is introduced, and the sensitivity coefficient system of spare parts demand of replaceable units to operation and environment is constructed. The digital twin model is used to feedback train the sensitivity coefficient to obtain the reliable spare parts demand rules. Based on the theoretical demand and sensitivity coefficient of spare parts, the forecasting method of spare parts demand is given, and the spare parts demand forecasting model is established. Through case analysis, the feasibility and accuracy of the forecasting model are verified.

Position tracking has become a critical key component for a huge variety of devices, ranging from mobile telephone location tracking to biodiversity monitoring. The majority of location-based services rely mostly on the user’s ongoing and prospective position, indicating a growing need of forecasting the user’s future location. Together with position prediction, forecasting the trajectories between two terminals is beneficial, because it enables to optimize the travel direction between them. This study tackles the problem of increasing prediction accuracy to its maximum level. The proposed work undergoes two major phases: feature extraction and prediction. Initially, antecedent and consequent features, spatio-temporal matching based features, and matching users based features can all be generated from the raw input data. For more precise prediction the most relevant features are extracted. The features will then be fed into the prediction algorithm, which will forecast user mobility. The prediction phase is constructed with an optimized convolutional neural network (CNN). Moreover, the weight of CNN is fine-tuned via a new improved butterfly optimization algorithm (IBOA), which is a conceptual improvement of standard BOA. At last, the supremacy of the presented approach is proved over other models with respect to varied measures. The accuracy of the proposed work is 18.33%, 26.67%, 33.33%, 55.2%, and 61.67% better than the existing models like HS–EH, GAF-WO, CNN, and GSTF.

In order to research the rescue path problem in the accident of passenger ships under tilt, this paper establishes a multi-objective rescue path optimization model under tilt effect. By analyzing the fuzzy time and fuzzy risk, the objective functions of this model are optimal satisfaction function and optimal risk function. Related constraints are also described mathematically. The PSO-GA (particle swarm and genetic) hybrid algorithm is used to solve the model when designing the algorithm. Two-level planning is incorporated in the algorithm, the best solution in the lower-level planning is assigned to the upper-level, and the upper-level plan feeds back the result to the lower level, and finally the global optimal Pareto solution is obtained. Decision makers can choose appropriate solutions based on their preference. The simulation experiment compares the multi-objective rescue path optimization model with the traditional time-optimal model. Among the three optimal solution sets, solution 1 decreases by 3.36% in risk and the satisfaction rate increases by 69.44%. Solution 2 rose by 13.96% in risk, but the satisfaction increased by 87.93%, and the risk of solution 3 decreased by 11.41%, while the satisfaction increased by 52.41%. The results show that the established model is reasonable and the algorithm is feasible.

Student performance calculation is an essential process in online learning scheme, which intends to afford students along with admittance to active learning. Student performance forecast is most concerning problem in education and training field, particularly in educational data mining (EDM). The prediction process provisions the students to choose courses and intend suitable training strategies for themselves. Furthermore, student performance calculation permits lecturers and educational supervisors to designate which students should be observed and maintained to finish their plans with finest outcomes. These provisions can decrease the official notices and exclusions from universities because of students’ poor performance. In this paper, Political Fractional Competitive Multi-verse Optimization enabled Deep Neuro fuzzy network (PFCMVO enabled DNFN) uses spark framework for student performance calculation. Moreover, Yeo–Johnson transformation is applied for transforming the input data for effectual student performance prediction. In addition, Damerau–Levenshtein (DL) distance is applied to select appropriate features. The DNFN classifier is utilized to execute student performance prediction where the classifier is trained by PFCMVO algorithm. The developed student performance prediction model outperforms than the other existing techniques with respect to Precision, Recall, $F$-measure, and Prediction accuracy of 0.9259, 0.9321, 0.9290, and 0.9372 for dataset-1 and 0.9126, 0.9271, 0.9198, and 0.9248 for dataset-2, respectively.

In this paper, a method which reconstructs an H(div)-conforming local equilibrated flux is presented for equilibrated flux-based a posteriori error estimate for the finite element method of the second-order elliptic problem. The flux is reconstructed in the lowest-order Raviart–Thomas spaces for finite element approximation. For a simplicial mesh, the reconstruction which performed on every element rather than on the patch of the elements of the mesh or on the dual mesh is achieved by solving a third (or fourth)-order linear equations on every element and a second-order linear equations on every edge or face. So, the amount of computational work is small. Numerical examples demonstratex the effectiveness and improvements of our method.

Information technology services for businesses and consumers can be delivered via the Internet using cloud computing (CC) because it is agile, cost-effective, and time-tested. For many real-world applications, the data are kept in the cloud by a third-party service and accessible through the Internet as needed through CC approaches. Risks associated with CC involve the data security and network security account for real-time systems. This paper discusses different security threats in CC and suggests a solution by designing a network security analysis scheme with machine learning (NSA-ML). The ML classifier predicts the network vulnerabilities and prevents insecure communication in a CC environment. The proposed NSA-ML presents a data authentication scheme with a novel encryption methodology to ensure data security. The experimental results show that the proposed NSA-ML outperforms the existing cloud security approaches by gaining an efficiency of 95.4%.

Determining and proving that a specific person or several persons may or may not be there at the Crime Scene (CS) in every criminal investigation are vital. Thus, in the law enforcement community, more often the physical evidence is collected, preserved, and analyzed. The accused cannot be predicted by normal people or judge just by looking at the evidence obtained at the analysis phase. So, research studies were undertaken on automated recognition as well as retrieval system aimed at forensic Crime Scene Investigation (CSI). A survey on the investigation of forensic CS evidence is depicted here. The main focus is rendered on the computer-centered automated investigation system. The latest research on the different evidence-centered Forensic Investigation (FI), such as the face, Finger-Print (FP), shoeprint, together with other Foot-Wear (FW) impressions, Machine Learning (ML) algorithm-centered FI, ML-centered pattern recognition, features of disparate evidence in forensic CSI, and various matching technique-centered FI, is surveyed here. Finally, centered on the accuracy and other two metrics, the methods’ performance for CSI is compared. Out of all the other methods, OLBP + LSSVM produced better results for precision and recall followed by CLSTM. In terms of accuracy, CLSTM produced better results than any other method.

Data Mining is a process of exploring the huge data in search of reliable patterns and methodical relationship among variables. As a result, the findings may be validated through applying the detected patterns to a novel subset of the data. In simple words, Data Mining is referred as extracting the useful information as large dataset and transforming into reliable structure for future use. Data Mining has shown its incredible performance in various fields to a greater extent, out of which, Educational Data Mining (EDM) is one among them. Many researchers have addressed huge number of problems in EDM and applied various techniques to reveal the useful and hidden information that helped in the process of decision making. Students getting employed during and after graduation are one of the important parts of their life. Students, based on their academic performances, are getting employed in companies they deserve. But still, the probability of getting employed is very less in this competitive world. In this paper, a real-time scenario has been chosen for analyzing various factors for getting employed/unemployed. Various clustering and classification techniques have been implemented and their performances are studied. A hybrid approach is presented in this paper that integrates the benefits of particle swarm optimization (PSO) and fuzzy clustering means (FCMs). The results obtained show that the proposed technique helps in obtaining higher accuracy to other clustering techniques. The proposed clustering algorithm PSO-FCM, accuracy is 34.4%, 36.45% and 28.45% higher than the existing method, time complexity shows 45%, 33% and 49% lower than the existing $K$-means clustering, Naïve Bayes clustering and SVM clustering algorithms, respectively.

Electric vehicles (EVs) are being introduced by different manufacturers as an eco-friendly alternative to vehicles with internal combustion engines, provided with several benefits. In the upcoming years, the count of EVs is expected to expand. However, the uncoordinated charging of these vehicles can put severe stress on the power grid. The problem of charge scheduling of EVs is an important issue and has seen significant research activity in the past few years. Thus, this survey paper intents to perform a review of 65 research works about this scenario that operate with different approaches under a diverse period of time. Particularly, this paper describes about the power flow model, scheduling control mechanism and type of charging followed in EVs. The performance measures recorded in each of the works are described here. Finally, a compact description on research gaps and challenges relying on this aspect is made that aids the researchers in doing future works.

Cancer is a deadly disease that affects millions of people all over the world, which stimulates unrestricted segmentation of the cell in the affected tissue. The prior determination of cancerous cells renders on-time healthcare service to the affected persons, which promotes easier life-saving. Hence, there is a need for automatic cancer classification methods. Though there are so many conventional classification methods to determine the cancer cells, these methods fail due to their training issues. In this research, a classification model is developed, named as Coyote–Wolf Optimization-based Deep Neural Network (CoWo-DNN), for the classification of cancerous cell from the normal tissues. The proposed CoWo-DNN for the classification of cancer cell utilizes gene expression data, which is log-transformed for effective processing. The comparative evaluation of the proposed CoWo-DNN with the conventional methods demonstrates the supremacy of the proposed method in terms of the performance parameters such as precision rate, accuracy, recall, $F$-measure and TRP. The proposed CoWo-DNN achieves the maximum accuracy of 91.8%, precision of 93.7%, recall of 89.2% and $F$-measure of 86.4% in the case of breast cancer, and the accuracy of 96.2%, precision of 93.33%, recall of 97.8% and $F$-measure of 98.8% in the case of colon cancer.

As software aging has been discovered in many fields, some time-series methods are utilized to find a better time to make a software system become more resilient from the degradation states. However, those existing methods cannot well solve the sequence relationship of the resource consumption series in the aging problems since data series contains linear and nonlinear characteristics. In this paper, we propose a method, recurrent neural network with self-organizing map, to enhance the prediction accuracy for resource consumption series of software aging problems. In order to smooth the resource consumption series, a pre-processing process and a self-organizing map are used. To make the prediction more accurate, a recurrent neural network is used. In the experiment, the proposed method is applied to two types of resource consumption series, and the result shows that our proposed method owns better performances in two aspects.

Predicting relative visual salient regions on three-dimensional (3D) meshes benefits many computer graphics applications. Most computation models for mesh saliency focus on geometrical information alone. Nevertheless, the ignored texture, lighting and material also provide more detailed appearance information, especially in the context of static scene rendering. In this paper, we propose a mesh saliency detection algorithm considering both geometrical and colorimetric information to address this challenge. Our model first computes the local curvature entropy at multi-scale to capture the geometrical details. Second, a set of images are projected onto the screen at several viewpoints with specified material and lighting model. Potentially salient regions on the rendered images are detected by fusion of multiple color difference maps measured with an approximated multi-scale Laplacian of Gaussian filter. A Gaussian distribution-based central bias model is applied to the image saliency map to emphasize the global rarity of salient regions. Third, the saliency maps of rendered images are projected back to the 3D mesh via the ray casting method. In the end, both saliency maps are combined linearly as the saliency map of 3D mesh. Experiment on the human fixation database demonstrates the performance of our method compared to the classic methods in terms of linear correlation coefficient and AUC.

Automatic modulation classification (AMC) became an important process in various communication systems including commercial, telecommunication and military applications. Further, the accuracy of AMC impacts the performance of these applications. Various machine learning approaches were developed to improve the performance of AMC. However, they failed to classify the different modulation schemes, which needs to satisfy all the spectrum requirements under multipath fading environment. Further, the conventional methods suffer with computational complexity in training to satisfy the real-time operational requirements. So, this paper focuses on implementation of extreme learning machine (ELM) for reduction of training complexities and improves the classification performance. Initially, deep leaning convolutional neural network (DLCNN) model is introduced for extracting the interdependent modulation features based on different modulation types. Then, red deer optimization algorithm (RDOA) is introduced for selecting the best features from DLCNN extracted features. Further, the hybrid ELM with bagging classifier (HELM-BC) is used to classify the various modulation types, i.e., families. The simulation results show that the performance of the proposed AMC system using RDOA-based HELM-BC approach is superior to the conventional AMC systems.

The growing number of automobiles on the road has now become a significant source of traffic, accidents, and pollution. Intelligent Transportation Systems (ITSs) could be the key to finding solutions that drastically reduce these issues. The linked vehicular networks channel is a fast-expanding topic for workflow management system and development. Traffic detection is a big issue on city streets. To make informed decisions in order to prevent traffic jams, one of the solutions is the Vehicular Ad-Hoc Network (VANET). We describe an approach for detecting traffic jams in both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, which provides vehicle drivers with multiple alternatives for determining the location of congestion, its size, and how to prevent becoming stuck in a traffic jam. The data are sent to the driver to assist him in making the appropriate selection. Delivering Quality of Service (QoS) for automotive networks is a difficult challenge due to the characteristics such as terms of transmission or high mobility, congested and fragmented channels, hardware defects, and a large number of vehicular devices. As a result, it is extremely desired to get and distribute resources efficiently. This research uses Reinforcement Learning with Decision-Making Model (RLDMM) method to enhance channel allocation. By using latency, Signal-to-Interference Ratio (SIR) and QoS, the available channel is initially determined. This method is used to determine suitable channel for platoon members. As a result, the proposed RLDMM achieves an SINR reduction of 29 Db, 69 kbps of throughput, 21.4% of collision probability with 4ms of latency.

In recent years, nonorthogonal multiple access (NOMA) has grasped the attention of all researchers in both industrial and academic fields because it has been regarded as an effective solution for 5G technologies to maximize spectral efficiency and connectivity of the system. Also, it has sufficient potential to maximize the performance of a network. Besides, the deployment of NOMA in heterogeneous networks (HetNets) satisfies the requirements of user’s explosive data traffic. However, the increasing demand for energy consumption of the wireless network provokes the researchers to establish an energy-efficient resource allocation scheme. The applications of NOMA provide better utilization of spectrum efficiency and minimize the cost of resource allocation. The resource allocation problem in wireless networks still remains a challenging task as the HetNets are suffered from mutual cross-tier interference. Hence, this research proposes a new effective hybrid optimization-based energy-efficient resource allocation scheme for NOMA HetNets by introducing a newly proposed method called Feedback Water Cycle Algorithm (FWCA). The method evaluates the user-pairing and sub-channel issue for reducing computational complexity. In addition to this, the network is analyzed for determining the power consumption and energy model with dynamic coefficients. Moreover, the developed FWCO obtained the maximum achievable rate of 25.992Mbps/Hz, maximum energy efficiency of 77.398%, maximum sum rate of 31.748Mbps/Hz, and maximum throughput of 8.888Mbps.

The world wide web acts as the dominant tool for data transmission due to access such as data retrieving and data transactions. The retrieval of data from the web is a complex procedure due to the large volume of web domain. The basic uses of the websites are described through web usage mining, which mines the weblog records to identify the pattern of accessing the web pages through the user. The web page prediction assists the web users in finding the plot and obtains the information as to their requirements. Several effective algorithms have been developed to mine association rules that make the predictive model more appropriate for web prediction. They can be commonly revised to ensure the changing feature of web access patterns. The Apriori algorithm involves extracting the recurrent itemset and interrelation rule that learns the relational data is commonly utilized for web page prediction. The Apriori algorithm remains the standard model for deriving the patterns and rules from the datasets in co-operative rule extraction. The Apriori algorithm thus generates large mines associated rules for web page prediction. Hence, to select the best rule, the proposed deer hunting rooster-based chicken swarm optimization algorithm is used by integrating the cockerel search agents’ dominating social search creatures’ hunting habits and their traits of looking for food. Further, the neural network (NN) is employed in this research for the prediction of web pages with minimum error. The trained NN is a technique of unsupervised learning that analyzes a dataset of input to produce the desired result, in which the effectiveness of the NN is enhanced by optimal tuning of weight by the adaptive deer hunting rooster-based chicken swarm optimization algorithm. The experimental analysis illustrates that the proposed adaptive deer hunting rooster-based chicken swarm optimization frameworks inherit lower error measures such as mean deviation = 139.89 and symmetric mean absolute percentage error$=$0.45579 for the FIFA dataset. The proposed web page prediction models’ L2 norm and infinity norm are 58.017 and 14, respectively, for the MSNBC_SPMF dataset.