Richard Millham

Bio: Richard Millham is an academic researcher from Durban University of Technology. The author has contributed to research in topics: Search algorithm & Legacy system. The author has an hindex of 10, co-authored 89 publications receiving 435 citations. Previous affiliations of Richard Millham include Catholic University College of Ghana & De Montfort University.

Review of Big Data Analytics, Artificial Intelligence and Nature-Inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing.

Abstract: The emergence of the 2019 novel coronavirus (COVID-19) which was declared a pandemic has spread to 210 countries worldwide. It has had a significant impact on health systems and economic, educational and social facets of contemporary society. As the rate of transmission increases, various collaborative approaches among stakeholders to develop innovative means of screening, detecting and diagnosing COVID-19's cases among human beings at a commensurate rate have evolved. Further, the utility of computing models associated with the fourth industrial revolution technologies in achieving the desired feat has been highlighted. However, there is a gap in terms of the accuracy of detection and prediction of COVID-19 cases and tracing contacts of infected persons. This paper presents a review of computing models that can be adopted to enhance the performance of detecting and predicting the COVID-19 pandemic cases. We focus on big data, artificial intelligence (AI) and nature-inspired computing (NIC) models that can be adopted in the current pandemic. The review suggested that artificial intelligence models have been used for the case detection of COVID-19. Similarly, big data platforms have also been applied for tracing contacts. However, the nature-inspired computing (NIC) models that have demonstrated good performance in feature selection of medical issues are yet to be explored for case detection and tracing of contacts in the current COVID-19 pandemic. This study holds salient implications for practitioners and researchers alike as it elucidates the potentials of NIC in the accurate detection of pandemic cases and optimized contact tracing.

Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets

Abstract: Due to the high-dimensional characteristics of dataset, we propose a new method based on the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed approach uses the natural strategy established by Charles Darwin; that is, ‘It is not the strongest of the species that survives, but the most adaptable’. This means that in the evolution of a swarm, the elitists are motivated to quickly obtain more and better resources. The memory function helps the proposed method to avoid repeat searches for the worst position in order to enhance the effectiveness of the search, while the binary strategy simplifies the feature selection problem into a similar problem of function optimisation. Furthermore, the wrapper strategy gathers these strengthened wolves with the classifier of extreme learning machine to find a sub-dataset with a reasonable number of features that offers the maximum correctness of global classification models. The experimental results from the six public high-dimensional bioinformatics datasets tested demonstrate that the proposed method can best some of the conventional feature selection methods up to 29% in classification accuracy, and outperform previous WSAs by up to 99.81% in computational time.

Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges

Abstract: Sustainable energy development consists of design, planning, and control optimization problems that are typically complex and computationally challenging for traditional optimization approaches. However, with developments in artificial intelligence, bio-inspired algorithms mimicking the concepts of biological evolution in nature and collective behaviors in societies of agents have recently become popular and shown potential success for these issues. Therefore, we investigate the latest research on bio-inspired approaches for smart energy management systems in smart homes, smart buildings, and smart grids in this paper. In particular, we give an overview of the well-known and emerging bio-inspired algorithms, including evolutionary-based and swarm-based optimization methods. Then, state-of-the-art studies using bio-inspired techniques for smart energy management systems are presented. Lastly, open challenges and future directions are also addressed to improve research in this field.

Swarm Search Methods in Weka for Data Mining

Abstract: Building a good prediction from high-dimensional data model in data mining is a challenging endeavor. One key step in data pre-processing is feature selection (FS) which is about finding the right feature subset for effective supervised learning. FS has two parts: feature evaluators and search methods to find the appropriate features in the search space. In this paper we introduce a collection of search methods that implement metaheuristics search which is also known as swarm search (SS). SS has the advantage over conventional search such as local search, that SS has the facility to explore global optima by a group of autonomous search agents. We have recently added nine new methods to the Weka machine learning workbench. The objective of these nine swarm search methods is to supplement the existing search methods in Weka for providing efficient and effective FS in data mining. We have carried out two experiments using synthetic data and medical data. The results show that in general SS has certain advantages over the conventional search methods. The SS methods can be found in the Weka Package Manager as open source code. Researchers and Weka users are encouraged to enhance data mining performance using these free swarm search programs.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Automated Demand Response from Home Energy Management System under Dynamic Pricing and Power and Comfort Constraints

Abstract: This paper presents a comprehensive and general optimization-based home energy management controller, incorporating several classes of domestic appliances including deferrable, curtailable, thermal, and critical ones. The operations of the appliances are controlled in response to dynamic price signals to reduce the consumer�€™s electricity bill whilst minimizing the daily volume of curtailed energy and therefore considering the user�€™s comfort level. To avoid shifting most portion of consumer demand towards the least price intervals, which could create network issues due to loss of diversity, higher prices are applied when the consumer�€™s demand goes beyond a power threshold level. The arising mixed integer nonlinear optimization problem is solved in an iterative manner rolling throughout the day to follow the changes in the anticipated price signals and the variations in the controller inputs while information is updated. The results from different realistic case studies show the effectiveness of the proposed controller to minimize the household�€™s daily electricity bill while preserving comfort level as well as preventing creation of new least-price peaks.

Handbook Of Biometrics

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