International Journal of Modeling, Simulation, and Scientific Computing

It has become critical to develop explanatory models using partial differential equations (PDEs) for big data from interesting scientific phenomena, e.g., fluids, atmosphere, and cosmic phenomena. Big data are often measured at discrete spatiotemporal points. In this paper, we assume a PDE and consider a set of the PDE solution data at multiple discrete points as pseudo-measurement data. In a data-driven PDE derivation, it is assumed that the PDE is a linear regression model comprising partial temporal and spatial differential terms, where the coefficients are estimated using regression analysis techniques, and the PDE derivation accuracy is defined as the difference (i.e., error) between the exact and estimated coefficients. A spatiotemporal model is required to calculate the partial temporal and spatial differential terms; thus, we employ a spatiotemporal model based on a neural network (NN) obtained from big data. To develop the data-driven PDE derivation technique, we employ PDEs with exact solutions such that we can calculate the differential terms by differentiating them analytically at multiple discrete points. If we assume an activation function in the NN model, the partial differential term can be derived using a chain rule. The NN model accuracy is measured by the loss function and error between the exact and estimated partial differential terms. In addition, we clarify a requirement for an NN structure that can maximize the PDE derivation and NN model accuracy by varying the NN meta-parameters, i.e., the numbers of NN layers and neurons.

In recent times, many MAC protocols were implemented for boosting and improving energy efficiency (EE) in WSNs. Moreover, the cooperative MIMO method is found to be much capable of enhancing the EE of WSNs if configured properly. This paper intends to propose cross-layer design for multihop virtual MIMO system to enhance the end-to-end (ETE) reliability, EE, and QoS of the adopted WSN. The protocol is set here to focus on the energy utilization for transmission of data packets by optimal selection of transmission constraints for each node of the network. Moreover, the protocol’s ETE latency and throughput are also modeled as the dependent variables of BER performance of every link. To discover the improved BER criteria of each link that meets the ETE QoS requirement in reduced energy utilization, this paper employs a new hybrid optimization algorithm named Lion Mutated Dragonfly Algorithm (LM–DA) that is a hybrid variant of both Lion Algorithm (LA) and Dragonfly Algorithm (DA). Finally, the performance of the adopted scheme is validated over other state-of-the-art models. The results state that the energy consumed by the adopted LM–DA approach is about 2.65%, 1.77%, and 1.77% reduced over LA, PSO, and DA schemes, respectively.

In cloud computing, a lot of challenges like the server failures, loss of confidentiality, improper workloads, etc. are still bounding the efficiency of cloud systems in real-world scenarios. For this reason, many research works are being performed to overcome the shortcoming of existing systems. Among them, load balancing seems to be the most critical issue that worsen the performance of the cloud sector, and hence there necessitates the optimal load balancing with optimal task scheduling. With the intention of accomplishing optimal load balancing by effectual task deployment, this paper plans to develop an advanced load balancing model with the assistance acquired from the metaheuristic algorithms. Usually, handling of tasks in cloud system is an NP-hard problem and moreover, nonpreemptive independent tasks are crucial in cloud computing. This paper goes with the introduction of a new optimal load balancing model by considering three major objectives: minimum makespan, priority, and load balancing, respectively. Moreover, a new single-objective function is also defined that incorporates all the three objectives mentioned above. Furthermore, the deployment of tasks must be optimal and for this a new hybrid optimization algorithm referred as Firefly Movement insisted WOA (FM-WOA) is introduced. This FM-WOA is the conceptual amalgamation of standard Whale Optimization Algorithm (WOA) and Firefly (FF) algorithm. Finally, the performances of the proposed FM-WOA model is compared over the conventional models with the intention of proving its efficiency in terms of makespan, task completion (priority), and degree of imbalance as well.

The static workspace of the multi-robot collaborative towing system with floating base was analyzed. The spacial configuration of the system was built, and the system was divided into three types according to the actuation. The kinematic and dynamic models of the floating base and the towing system were established, respectively, by using the Newton-Euler equation. Then the static workspace of the system was analyzed, and the specific steps to solve the workspace with the improved Monte Carlo method were obtained. Finally, the static workspaces of three types of systems were solved though actual simulation calculation, the simulation results show that the improved Monte Carlo method can get a workspace with higher precision compare to the traditional Monte Carlo method. Results of this study lay a basis for the subsequent trajectory plan, stability analysis and configuration optimization of the system.

Due to the open nature of wireless data transmission, routing and data security pose an important research challenge in the Internet of Things (IoT)-enabled networks. Also, the characteristic features, like constrained resources, heterogeneity, uncontrolled environment, and scalability requirement, make the security issues even more challenging. Hence, an effective and secure routing protocol named modified Energy Harvesting Trust-aware Routing Algorithm (mod-EHTARA) is proposed to increase the energy efficiency and the lifespan of the nodes. The proposed mod-EHTARA is designed by adopting the Link Lifetime (LLT) model with the traditional EHTARA. The optimal secure routing path is effectively selected by the proposed mod-EHTARA using the cost metric, which considers the factors like delay, LLT, energy, and trust. The big data classification process is carried out at the Base Station (BS) using the MapReduce framework. Accordingly, the big data classification is progressed using a stacked autoencoder, which is trained by the Adaptive E-Bat algorithm. The Adaptive E-Bat algorithm is developed by integrating the adaptive concept with the Bat Algorithm (BA) and Exponential Weighted Moving Average (EWMA). The proposed mod-EHTARA showed better performance by obtaining a maximal energy of 0.9855.

This paper discusses the issues of computational efforts and the accuracy of solutions of differential equations using multilayer perceptron and Chebyshev polynomials-based functional link artificial neural networks. Some ordinary and partial differential equations have been solved by both these techniques and pros and cons of both these type of feed-forward networks have been discussed in detail. Apart from that, various factors that affect the accuracy of the solution have also been analyzed.

Background: Diabetes and hypertension are two of the commonest diseases in the world. As they unfavorably affect people of different age groups, they have become a cause of concern and must be predicted and diagnosed well in advance.

Objective: This research aims to determine the effectiveness of artificial neural networks (ANNs) in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases.

Sample: This work used two online datasets which consist of data collected from 768 individuals. We applied neural network algorithms to predict if the individuals have those two diseases based on some factors. Diabetes prediction is based on five factors: age, weight, fat-ratio, glucose, and insulin, while blood pressure prediction is based on six factors: age, weight, fat-ratio, blood pressure, alcohol, and smoking.

Method: A model based on the Multi-Layer Perceptron Neural Network (MLP) was implemented. The inputs of the network were the factors for each disease, while the output was the prediction of the disease’s occurrence. The model performance was compared with other classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). We used performance metrics measures to assess the accuracy and performance of MLP. Also, a tool was implemented to help diagnose the diseases and to understand the results.

Result: The model predicted the two diseases with correct classification rate (CCR) of 77.6% for diabetes and 68.7% for hypertension. The results indicate that MLP correctly predicts the probability of being diseased or not, and the performance can be significantly increased compared with both SVM and KNN. This shows MLPs effectiveness in early disease prediction.

In this paper, we study two fractional models in the Caputo–Fabrizio sense and Atangana–Baleanu sense, in which the effects of malaria infection on mosquito biting behavior and attractiveness of humans are considered. Using Lyapunov theory, we prove the global asymptotic stability of the unique endemic equilibrium of the integer-order model, and the fractional models, whenever the basic reproduction number $R_0$ is greater than one. By using fixed point theory, we prove existence, and conditions of the uniqueness of solutions, as well as the stability and convergence of numerical schemes. Numerical simulations for both models, using fractional Euler method and Adams–Bashforth method, respectively, are provided to confirm the effectiveness of used approximation methods for different values of the fractional-order $\gamma$.

In the big data platform, because of the large amount of data, the problem of load imbalance is prominent. Most of the current load balancing methods have problems such as high data flow loss rate and long response time; therefore, more effective load balancing method is urgently needed. Taking HBase as the research subject, the study analyzed the dynamic load balancing method of data flow. First, the HBase platform was introduced briefly, and then the dynamic load-balancing algorithm was designed. The data flow was divided into blocks, and then the load of nodes was predicted based on the grey prediction GM(1,1) model. Finally, the load was migrated through the dynamic adjustable method to achieve load balancing. The experimental results showed that the accuracy of the method for load prediction was high, the average error percentage was 0.93%, and the average response time was short; under 3000 tasks, the response time of the method designed in this study was 14.17% shorter than that of the method combining TV white space (TVWS) and long-term evolution (LTE); the average flow of nodes with the largest load was also smaller, and the data flow loss rate was basically 0%. The experimental results show the effectiveness of the proposed method, which can be further promoted and applied in practice.

This paper presents an unsupervised approach to cluster reviews of products collected from Amazon and then generates its labels of each cluster. Instead of using a complete review, this paper splits a review into sentences and considers all sentences from the reviews as inputs for Clustering. Hierarchical Agglomerative Clustering (HAC) is used to cluster sentences. The approaches of cluster labeling are also unsupervised. For labeling, three different methods have been used to find a limited number of essential words for each cluster. Extracted essential words are used to construct phrases. Constructed phrases are used as labels for each cluster. This paper compares the result of the labeling method with baseline labeling. In the result evaluation, all the labeling methods outperform the baseline method. The aim of this research is cluster labeling that makes a set of labels to describe a cluster content and distinguishes the labels from other cluster labels.

Heroin is considered as damage on the health of the individuals. In this paper, we consider a heroin epidemic model with treat age, where the inner rivalry in between the drug users for a small dose of drugs is investigated. The influence of treatment period for a drug consumer before quitting treatment is investigated, where it is obtained that the considered model can undergo backward bifurcation, which shows the possibility of having two endemic equilibriums, this type of bifurcation is discussed in terms of the basic reproduction number $R_0$. The bifurcation diagram is drown in the case of an age structured model. Also, we show the existence of Hopf bifurcation is shown under suitable conditions on the model parameters. The obtained mathematical results are confirmed numerically.

In animals, the command centers in the brain can drive the locomotion. However, it remains unclear how the brain modulates the locomotor central pattern generator (CPG). In this paper, a novel model is established to describe the relation between the brain and the CPG with time delay. The artificial recurrent neural network (RNN) consists of various computational modules that are used to model the brain. The brain synchronization under amplitude and frequency variations of the CPG and the effect of the RNN parameters variations on the CPG are investigated. In the paper, the excitatory neuron probability and average connections number are parameter space of RNN and the parameter space of CPG, which include frequency and amplitude. According to the simulation results, the best RNN synchronization could be obtained by finding the optimum parameters space between the RNN and the CPG. I propose that the parameter space of some CPGs is related to the parameter space of the brain. This leads to a brain load decrement that facilities the control action. The results are meaningful to investigate how to study the relationship between the brain and the locomotion.