Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates.

BACKGROUND: Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research. RESULTS: The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575. CONCLUSIONS: We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

Automated splitting into batches for observational biomedical studies with sequential processing.

Experimental design usually focuses on the setting where treatments and/or other aspects of interest can be manipulated. However, in observational biomedical studies with sequential processing, the set of available samples is often fixed, and the problem is thus rather the ordering and allocation of samples to batches such that comparisons between different treatments can be made with similar precision. In certain situations, this allocation can be done by hand, but this rapidly becomes impractical with more challenging cohort setups. Here, we present a fast and intuitive algorithm to generate balanced allocations of samples to batches for any single-variable model where the treatment variable is nominal. This greatly simplifies the grouping of samples into batches, makes the process reproducible, and provides a marked improvement over completely random allocations. The general challenges of allocation and why good solutions can be hard to find are also discussed, as well as potential extensions to multivariable settings.

Energy Efficiency Optimisation of Joint Computational Task Offloading and Resource Allocation Using Particle Swarm Optimisation Approach in Vehicular Edge Networks.

With the progression of smart vehicles, i.e., connected autonomous vehicles (CAVs), and wireless technologies, there has been an increased need for substantial computational operations for tasks such as path planning, scene recognition, and vision-based object detection. Managing these intensive computational applications is concerned with significant energy consumption. Hence, for this article, a low-cost and sustainable solution using computational offloading and efficient resource allocation at edge devices within the Internet of Vehicles (IoV) framework has been utilised. To address the quality of service (QoS) among vehicles, a trade-off between energy consumption and computational time has been taken into consideration while deciding on the offloading process and resource allocation. The offloading process has been assigned at a minimum wireless resource block level to adapt to the beyond 5G (B5G) network. The novel approach of joint optimisation of computational resources and task offloading decisions uses the meta-heuristic particle swarm optimisation (PSO) algorithm and decision analysis (DA) to find the near-optimal solution. Subsequently, a comparison is made with other proposed algorithms, namely CTORA, CODO, and Heuristics, in terms of computational efficiency and latency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating an 8% and a 5% increase in energy efficiency.

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm.

The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.

A Novel Simulated Annealing-Based Hyper-Heuristic Algorithm for Stochastic Parallel Disassembly Line Balancing in Smart Remanufacturing.

Remanufacturing prolongs the life cycle and increases the residual value of various end-of-life (EoL) products. As an inevitable process in remanufacturing, disassembly plays an essential role in retrieving the high-value and useable components of EoL products. To disassemble massive quantities and multi-types of EoL products, disassembly lines are introduced to improve the cost-effectiveness and efficiency of the disassembly processes. In this context, disassembly line balancing problem (DLBP) becomes a critical challenge that determines the overall performance of disassembly lines. Currently, the DLBP is mostly studied in straight disassembly lines using single-objective optimization methods, which cannot represent the actual disassembly environment. Therefore, in this paper, we extend the mathematical model of the basic DLBP to stochastic parallel complete disassembly line balancing problem (DLBP-SP). A novel simulated annealing-based hyper-heuristic algorithm (HH) is proposed for multi-objective optimization of the DLBP-SP, considering the number of workstations, working load index, and profits. The feasibility, superiority, stability, and robustness of the proposed HH algorithm are validated through computational experiments, including a set of comparison experiments and a case study of gearboxes disassembly. To the best of our knowledge, this research is the first to introduce gearboxes as a case study in DLBP which enriches the research on disassembly of industrial equipment.

Comparison of Heuristic Algorithms in Identification of Parameters of Anomalous Diffusion Model Based on Measurements from Sensors.

In recent times, fractional calculus has gained popularity in various types of engineering applications. Very often, the mathematical model describing a given phenomenon consists of a differential equation with a fractional derivative. As numerous studies present, the use of the fractional derivative instead of the classical derivative allows for more accurate modeling of some processes. A numerical solution of anomalous heat conduction equation with Riemann-Liouville fractional derivative over space is presented in this paper. First, a differential scheme is provided to solve the direct problem. Then, the inverse problem is considered, which consists in identifying model parameters such as: thermal conductivity, order of derivative and heat transfer. Data on the basis of which the inverse problem is solved are the temperature values on the right boundary of the considered space. To solve the problem a functional describing the error of the solution is created. By determining the minimum of this functional, unknown parameters of the model are identified. In order to find a solution, selected heuristic algorithms are presented and compared. The following meta-heuristic algorithms are described and used in the paper: Ant Colony Optimization (ACO) for continous function, Butterfly Optimization Algorithm (BOA), Dynamic Butterfly Optimization Algorithm (DBOA) and Aquila Optimize (AO). The accuracy of the presented algorithms is illustrated by examples.

Pairing Optimization via Statistics: Algebraic Structure in Pairing Problems and Its Application to Performance Enhancement.

Fully pairing all elements of a set while attempting to maximize the total benefit is a combinatorically difficult problem. Such pairing problems naturally appear in various situations in science, technology, economics, and other fields. In our previous study, we proposed an efficient method to infer the underlying compatibilities among the entities, under the constraint that only the total compatibility is observable. Furthermore, by transforming the pairing problem into a traveling salesman problem with a multi-layer architecture, a pairing optimization algorithm was successfully demonstrated to derive a high-total-compatibility pairing. However, there is substantial room for further performance enhancement by further exploiting the underlying mathematical properties. In this study, we prove the existence of algebraic structures in the pairing problem. We transform the initially estimated compatibility information into an equivalent form where the variance of the individual compatibilities is minimized. We then demonstrate that the total compatibility obtained when using the heuristic pairing algorithm on the transformed problem is significantly higher compared to the previous method. With this improved perspective on the pairing problem using fundamental mathematical properties, we can contribute to practical applications such as wireless communications beyond 5G, where efficient pairing is of critical importance. As the pairing problem is a special case of the maximum weighted matching problem, our findings may also have implications for other algorithms on fully connected graphs.

Meta-Heuristic Device-Free Localization Algorithm under Multiple Path Effect.

In the scenario of device-free localization under multiple effects, the accuracy of localization based on compressed sensing theory is severely affected. Most existing localization techniques directly ignore multiple path effects. However, it is not practical to ignore the multiple path effect due to its high signal strength, which can provide localization information. In this paper, we formulate the sensing matrix optimization problem in compressed sensing for device-free localization scenarios based on multiple reflections. To solve this problem, we model it as a constrained combinatorial optimization problem and propose a hybrid meta-heuristic algorithm. First, smart reflection surfaces and virtual node models are used to construct the desired communication links. Second, we iteratively improve the properties of the measurement matrix by using K-means clustering to obtain reasonable thresholds, and use a meta-heuristic algorithm to optimize the sensing matrix. Finally, the simulation results show that the proposed method efficiently optimizes the sensing matrix and achieves fast and high-precision localization while conserving communication resources.

Scientific planning of urban cordon sanitaire for desired queuing time.

The outbreak of the COVID-19 pandemic disrupted ofur normal life. Many cities enforced a cordon sanitaire as a countermeasure to protect densely inhabited areas. Travelers can only cross the cordon after being checked. To minimize the waiting time in the queue, this paper proposes a method to determine the scientific planning of urban cordon sanitaire for desired queuing time, which is a significant problem that has not been explored. A novel two-stage optimization model is proposed where the first stage is the transportation system equilibrium problem to predict traffic inflow, and the second stage is the queuing network design problem to determine the allocation of test stations. This method aims to minimize the total health infrastructure investment for the desired maximum queuing time. Note that queuing theory is used to represent the queuing phenomenon at each urban entrance. A heuristic algorithm is designed to solve the proposed model where the Method of Successive Averages (MSA) is adopted for the first stage, and the Genetic Algorithm (GA) with elite strategy is adopted for the second stage. An experimental study with sensitivity analysis is conducted to demonstrate the effectiveness of the proposed methods. The results show that the methods can find a good heuristic optimal solution. This research is helpful for policymakers to determine the optimal investment and planning of cordon sanitaire for disease prevention and control, as well as other criminal activities such as drunk driving, terrorists, and smuggling.

A novel hybrid hunger games algorithm for intrusion detection systems based on nonlinear regression modeling.

Along with the advancement of online platforms and significant growth in Internet usage, various threats and cyber-attacks have been emerging and become more complicated and perilous in a day-by-day base. Anomaly-based intrusion detection systems (AIDSs) are lucrative techniques for dealing with cybercrimes. As a relief, AIDS can be equipped with artificial intelligence techniques to validate traffic contents and tackle diverse illicit activities. A variety of methods have been proposed in the literature in recent years. Nevertheless, several important challenges like high false alarm rates, antiquated datasets, imbalanced data, insufficient preprocessing, lack of optimal feature subset, and low detection accuracy in different types of attacks have still remained to be solved. In order to alleviate these shortcomings, in this research a novel intrusion detection system that efficiently detects various types of attacks is proposed. In preprocessing, Smote-Tomek link algorithm is utilized to create balanced classes and produce a standard CICIDS dataset. The proposed system is based on gray wolf and Hunger Games Search (HGS) meta-heuristic algorithms to select feature subsets and detect different attacks such as distributed denial of services, Brute force, Infiltration, Botnet, and Port Scan. Also, to improve exploration and exploitation and boost the convergence speed, genetic algorithm operators are combined with standard algorithms. Using the proposed feature selection technique, more than 80 percent of irrelevant features are removed from the dataset. The behavior of the network is modeled using nonlinear quadratic regression and optimized utilizing the proposed hybrid HGS algorithm. The results show the superior performance of the hybrid algorithm of HGS compared to the baseline algorithms and the well-known research. As shown in the analogy, the proposed model obtained an average test accuracy rate of 99.17%, which has better performance than the baseline algorithm with 94.61% average accuracy.

The Efficient Mobile Management Based on Metaheuristic Algorithm for Internet of Vehicle.

With the low latency, high transmission rate, and high reliability provided by the fifth-generation mobile communication network (5G), many applications requiring ultra-low latency and high reliability (uRLLC) have become a hot research topic. Among these issues, the most important is the Internet of Vehicles (IoV). To maintain the safety of vehicle drivers and road conditions, the IoV can transmit through sensors or infrastructure to maintain communication quality and transmission. However, because 5G uses millimeter waves for transmission, a large number of base stations (BS) or lightweight infrastructure will be built in 5G, which will make the overall environment more complex than 4G. The lightweight infrastructure also has to be considered together. For these reasons, in 5G, there are two mechanisms for handover, horizontal, and vertical handover; hence, it must be discussed how to handle handover to obtain the best performance for the whole network. In this paper, to address handover selection, we consider delay time, energy efficiency, load balancing, and energy consumption and formulate it as a multi-objective optimization (MOO) problem. At the same time, we propose the handover of the mobile management mechanism based on location prediction combined with heuristic algorithms. The results show that our proposed mechanism is better than the distance-based one for energy efficiency, load, and latency. It optimizes by more than about 20% at most.

An Observation Scheduling Approach Based on Task Clustering for High-Altitude Airship.

Airship-based Earth observation is of great significance in many fields such as disaster rescue and environment monitoring. To facilitate efficient observation of high-altitude airships (HAA), a high-quality observation scheduling approach is crucial. This paper considers the scheduling of the imaging sensor and proposes a hierarchical observation scheduling approach based on task clustering (SA-TC). The original observation scheduling problem of HAA is transformed into three sub-problems (i.e., task clustering, sensor scheduling, and cruise path planning) and these sub-problems are respectively solved by three stages of the proposed SA-TC. Specifically, a novel heuristic algorithm integrating an improved ant colony optimization and the backtracking strategy is proposed to address the task clustering problem. The 2-opt local search is embedded into a heuristic algorithm to solve the sensor scheduling problem and the improved ant colony optimization is also implemented to solve the cruise path planning problem. Finally, extensive simulation experiments are conducted to verify the superiority of the proposed approach. Besides, the performance of the three algorithms for solving the three sub-problems are further analyzed on instances with different scales.

Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories.

Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows. Traditional routing algorithms are not applicable for this novel type of network, as they cause unnecessary congestion and latency. Therefore, this study examined the classification of TSN flows in smart factories, converted the routing problem into two graphical problems, and proposed two heuristic optimization algorithms, namely GATTRP and AACO, to find the optimal solution. The experiments showed that the algorithms proposed in this paper could provide a more reasonable routing arrangement for various TSN flows with different time sensitivities. The algorithms could effectively reduce the overall delay by up to 74% and 41%, respectively, with promising operating performances.

Visible Particle Series Search Algorithm and Its Application in Structural Damage Identification.

Identifying structural damage is an essential task for ensuring the safety and functionality of civil, mechanical, and aerospace structures. In this study, the structural damage identification scheme is formulated as an optimization problem, and a new meta-heuristic optimization algorithm, called visible particle series search (VPSS), is proposed to tackle that. The proposed VPSS algorithm is inspired by the visibility graph technique, which is a technique used basically to convert a time series into a graph network. In the proposed VPSS algorithm, the population of candidate solutions is regarded as a particle series and is further mapped into a visibility graph network to obtain visible particles. The information captured from the visible particles is then utilized by the algorithm to seek the optimum solution over the search space. The general performance of the proposed VPSS algorithm is first verified on a set of mathematical benchmark functions, and, afterward, its ability to identify structural damage is assessed by conducting various numerical simulations. The results demonstrate the high accuracy, reliability, and computational efficiency of the VPSS algorithm for identifying the location and the extent of damage in structures.

Computational Methods for Parameter Identification in 2D Fractional System with Riemann-Liouville Derivative.

In recent times, many different types of systems have been based on fractional derivatives. Thanks to this type of derivatives, it is possible to model certain phenomena in a more precise and desirable way. This article presents a system consisting of a two-dimensional fractional differential equation with the Riemann-Liouville derivative with a numerical algorithm for its solution. The presented algorithm uses the alternating direction implicit method (ADIM). Further, the algorithm for solving the inverse problem consisting of the determination of unknown parameters of the model is also described. For this purpose, the objective function was minimized using the ant algorithm and the Hooke-Jeeves method. Inverse problems with fractional derivatives are important in many engineering applications, such as modeling the phenomenon of anomalous diffusion, designing electrical circuits with a supercapacitor, and application of fractional-order control theory. This paper presents a numerical example illustrating the effectiveness and accuracy of the described methods. The introduction of the example made possible a comparison of the methods of searching for the minimum of the objective function. The presented algorithms can be used as a tool for parameter training in artificial neural networks.

Optimal Task Allocation Algorithm Based on Queueing Theory for Future Internet Application in Mobile Edge Computing Platform.

For 5G and future Internet, in this paper, we propose a task allocation method for future Internet application to reduce the total latency in a mobile edge computing (MEC) platform with three types of servers: a dedicated MEC server, a shared MEC server, and a cloud server. For this platform, we first calculate the delay between sending a task and receiving a response for the dedicated MEC server, shared MEC server, and cloud server by considering the processing time and transmission delay. Here, the transmission delay for the shared MEC server is derived using queueing theory. Then, we formulate an optimization problem for task allocation to minimize the total latency for all tasks. By solving this optimization problem, tasks can be allocated to the MEC servers and cloud server appropriately. In addition, we propose a heuristic algorithm to obtain the approximate optimal solution in a shorter time. This heuristic algorithm consists of four algorithms: a main algorithm and three additional algorithms. In this algorithm, tasks are divided into two groups, and task allocation is executed for each group. We compare the performance of our proposed heuristic algorithm with the solution obtained by three other methods and investigate the effectiveness of our algorithm. Numerical examples are used to demonstrate the effectiveness of our proposed heuristic algorithm. From some results, we observe that our proposed heuristic algorithm can perform task allocation in a short time and can effectively reduce the total latency in a short time. We conclude that our proposed heuristic algorithm is effective for task allocation in a MEC platform with multiple types of MEC servers.

Optimization of geographical space of ecosystem service areas and land-use planning, Iran.

This study provides a framework for optimizing ecosystem service zones based on landscape patterns. Ecosystem service zoning is a tool for making more informed land-use decisions and integrating ecosystem services into land development strategies. Accordingly, a meta-heuristic algorithm (simulated annealing-genetic hybrid) was applied to optimize zones of three ecosystem services, including aesthetic value, recreation value, and noise pollution reduction service. The goal was to select ecosystem service zones in terms of area, percentage of landscape, shape, and number of patches in two land-use types, including the built-up and green cover. The results indicated that optimum zones based on green infrastructure comprised more extent and average suitability than human infrastructure. The western and southern parts of the target area showed the highest values of ecosystem services for the two land-use classes. The convergence curve of the objective functions indicated the good performance of the employed algorithm in optimizing ecosystem service zones. Overall, the results indicated that optimizing ecosystem service zones based on landscape patterns improved the land-use management framework in the study area. Moreover, the used algorithm allowed for integrating different parameters into ecosystem services zone configurations.

EGFAFS: A Novel Feature Selection Algorithm Based on Explosion Gravitation Field Algorithm.

Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data.

An Opposition-Based Learning CRO Algorithm for Solving the Shortest Common Supersequence Problem.

As a non-deterministic polynomial hard (NP-hard) problem, the shortest common supersequence (SCS) problem is normally solved by heuristic or metaheuristic algorithms. One type of metaheuristic algorithms that has relatively good performance for solving SCS problems is the chemical reaction optimization (CRO) algorithm. Several CRO-based proposals exist; however, they face such problems as unstable molecular population quality, uneven distribution, and local optimum (premature) solutions. To overcome these problems, we propose a new approach for the search mechanism of CRO-based algorithms. It combines the opposition-based learning (OBL) mechanism with the previously studied improved chemical reaction optimization (IMCRO) algorithm. This upgraded version is dubbed OBLIMCRO. In its initialization phase, the opposite population is constructed from a random population based on OBL; then, the initial population is generated by selecting molecules with the lowest potential energy from the random and opposite populations. In the iterative phase, reaction operators create new molecules, where the final population update is performed. Experiments show that the average running time of OBLIMCRO is more than 50% less than the average running time of CRO_SCS and its baseline algorithm, IMCRO, for the desoxyribonucleic acid (DNA) and protein datasets.

An integrated approach based on artificial intelligence and novel meta-heuristic algorithms to predict demand for dairy products: a case study.

This research specifically addresses the prediction of dairy product demand (DPD). Since dairy products have a short consumption period, it is important to have accurate information about their future demand. The main contribution of this research is to provide an integrated framework based on statistical tests, time-series neural networks, and improved MLP, ANFIS, and SVR with novel meta-heuristic algorithms in order to obtain the best prediction of DPD in Iran. At first, a series of economic and social indicators that seemed to be effective in the demand for dairy products is identified. Then, the ineffective indices are eliminated by using the Pearson correlation coefficient, and statistically significant variables are determined. Since the regression relation is not able to predict this demand properly, the artificial intelligence tools including MLP, ANFIS, and SVR are implemented and improved with the help of novel meta-heuristic algorithms such as grey wolf optimization (GWO), invasive weed optimization (IWO), cultural algorithm (CA), and particle swarm optimization (PSO). The designed hybrid method is used to predict the DPD in Iran by using data from 2013 to 2017. The high accurate results confirm that the proposed hybrid methods have the ability to improve the prediction of the demand for various products.