COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

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Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

A–Z survey of Internet of Things: Architectures, protocols, applications, recent advances, future directions and recommendations

Computer vision is one of the most frontier technologies in computer science. It is used to build artificial systems to extract valuable information from images and has a broad range of applications in various areas such as agriculture, business, and healthcare. Convolutional neural networks represent the key algorithms in computer vision, and in recent years, they have attained notable advances in many real-world problems. The accuracy of the network for a particular task profoundly relies on the hyperparameters’ configuration. Obtaining the right set of hyperparameters is a time-consuming process and requires expertise. To approach this concern, we propose an automatic method for hyperparameters’ optimization and structure design by implementing enhanced metaheuristic algorithms. The aim of this paper is twofold. First, we propose enhanced versions of the tree growth and firefly algorithms that improve the original implementations. Second, we adopt the proposed enhanced algorithms for hyperparameters’ optimization. First, the modified metaheuristics are evaluated on standard unconstrained benchmark functions and compared to the original algorithms. Afterward, the improved algorithms are employed for the network design. The experiments are carried out on the famous image classification benchmark dataset, the MNIST dataset, and comparative analysis with other outstanding approaches that were tested on the same problem is conducted. The experimental results show that both proposed improved methods establish higher performance than the other existing techniques in terms of classification accuracy and the use of computational resources.

Optimizing Convolutional Neural Network Hyperparameters by Enhanced Swarm Intelligence Metaheuristics

Cloud computing is an emerging computer technology, that provides distributed, scalable, elastic computer resources to the end-user over the Internet. One of the most challenging tasks in the cloud computing environment is task scheduling. The main objectives of the task scheduling are to identify the appropriate resources for scheduling a specific task on time, utilize the resources more efficiently, and reduce the total completion time of all input tasks to be executed. The task scheduling problem belongs to the class NP-hard. Since metaheuristic algorithms are proven to be efficient in the NP hard optimization, in this paper, we propose a task scheduling algorithm using metaheuristics approach. The proposed scheduler is based on the grey wolf optimizer nature-inspired algorithm. The experimental results prove the quality and robustness of the proposed method.

Task Scheduling in Cloud Computing Environment by Grey Wolf Optimizer

We have recently witnessed the rapid development of several emerging technologies, including the internet of things, which lead to a high interest in wireless sensor networks. Tiny sensor nodes are now important parts of a large number of complex systems, with numerous applications including military, environment monitoring, surveillance and body area sensor networks. One of the biggest challenges each wireless sensor network has to handle is the network lifetime maximization. To achieve this, numerous clustering algorithms have been created, with the goal to improve energy consumption throughout the network by balancing the energy consumption overall nodes. All clustering algorithms incorporate load balancing to achieve energy efficiency. One of the basic and most important algorithms in use is LEACH. Swarm intelligence metaheuristics have already been applied in solving numerous problems of wireless sensor networks, including lifetime optimization, localization and many other NP hard problems with promising results, as can be seen in the literature overview. In the research proposed in this paper, an improved version of the firefly algorithm has been applied to improve the network lifetime. The firefly algorithm was used to help in forming the clusters and selection of the cluster head. Additionally, we have evaluated the performance of the improved firefly algorithm by comparing it to the LEACH, basic firefly algorithm and particle swarm optimization, that were all tested on the same network infrastructure model. Conducted simulations have proven that our proposed metaheuristic achieves better and more consistent performance than other algorithms.

Wireless Sensor Networks Life Time Optimization Based on the Improved Firefly Algorithm.

This paper presents firefly algorithm framework for designing convolutional neural network architecture. Convolutional neural networks can be classified as a special category of deep neural networks that in most cases consist of several convolution, fully connected (dense) and pooling layers. Wide set of image classification tasks and problems from the computer vision domain were successfully tackled by convolutional neural networks. One of the most challenging tasks from this domain is to find the convolutional neural network architecture that obtains the best performance for the specific application. The values of network's hyper-parameters have significant influence on the overall network performance. Research shown in this paper deals with convolutional neural network hyper-parameters optimization that define the network's architecture and structure. The hyper-parameters that were taken into account for this research include the number of convolutional and dense layers, the number of kernels per layer and the kernel size. We performed hyper-parameters optimization by the well-known firefly algorithm that belongs to the group of swarm intelligence metaheuristis. Solution's quality, robustness and performance of our proposed framework was tested against the MNIST dataset. Obtained empirical results showed that the proposed framework achieves promising performance in this domain.

Designing Convolutional Neural Network Architecture by the Firefly Algorithm

A novel type of coronavirus, now known under the acronym COVID-19, was initially discovered in the city of Wuhan, China. Since then, it has spread across the globe and now it is affecting over 210 countries worldwide. The number of confirmed cases is rapidly increasing and has recently reached over 14 million on July 18, 2020, with over 600,000 confirmed deaths. In the research presented within this paper, a new forecasting model to predict the number of confirmed cases of COVID-19 disease is proposed. The model proposed in this paper is a hybrid between machine learning adaptive neuro-fuzzy inference system and enhanced genetic algorithm metaheuristics. The enhanced genetic algorithm is applied to determine the parameters of the adaptive neuro-fuzzy inference system and to enhance the overall quality and performances of the prediction model. Proposed hybrid method was tested by using realistic official dataset on the COVID-19 outbreak in the state of China. In this paper, proposed approach was compared against multiple existing state-of-the-art techniques that were tested in the same environment, on the same datasets. Based on the simulation results and conducted comparative analysis, it is observed that the proposed hybrid approach has outperformed other sophisticated approaches and that it can be used as a tool for other time-series prediction.

Miodrag Zivkovic

Papers

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

Wireless Sensor Networks Life Time Optimization Based on the Improved Firefly Algorithm.

Task Scheduling in Cloud Computing Environment by Grey Wolf Optimizer

Designing Convolutional Neural Network Architecture by the Firefly Algorithm

Hybrid Genetic Algorithm and Machine Learning Method for COVID-19 Cases Prediction