International Journal of Advanced Networking and Applications

Gabriel Kabanda ¹

Affiliations
1 Adjunct Professor of Machine Learning Woxsen School of Business, Woxsen University, Hyderabad, IN

Abstract

A facial recognition system matches a human face from a digital image or a video frame against an authentic repository of faces or Eigenfaces subject to algorithmic performance and detection accuracy. Dimensionality reduction is a type of unsupervised learning for which input is images of higher-dimensional data and these images are represented with a lower-dimensional space. The purpose of the research paper is to evaluate the performance of Dimensionality Reduction algorithms for face recognition using different approaches of Machine Learning (ML). The research uses the Interpretivist Paradigm characterised by a subjectivist epistemology, relativist ontology, naturalist methodology, and a balanced axiology. The quantitative methodology with an experimental research design was used. The results of the experiment show that only selecting the top M eigenfaces reduces the dimensionality of the data, and that too few eigenfaces results in too much information loss, and hence less discrimination between faces. With increasing dimensionality, the amount of training instances needed rises exponentially (i.e., kd). The performance of the Dimensionality Reduction Algorithm is benchmarked against the Clustering, Bayesian, Genetic, Reinforcement Q-Learning and Reinforcement A3C Algorithms. The outcome of the research makes significant value-adding contributions to the future of advances in Big Data Analytics and ML.

Keywords

Dimensionality Reduction Algorithms, Cybersecurity, Artificial Intelligence, Machine Learning, Deep Learning, Big Data Analytics, Facial Recognition

Full Text

 

Gabriel Kabanda ¹, Tendeukai Chipfumbu ², Tinashe Chingoriwo ³

Affiliations
1 Adjunct Professor of Machine Learning Woxsen School of Business, Woxsen University, Hyderabad, IN
2 Department of Information and Marketing Sciences, Midlands State University Faculty of Business Sciences, IN
3 DPhil (Information Technology) Candidate Faculty of Technology, Zimbabwe Open University, ZW

Abstract

The purpose of the research is to explore and develop Deep Reinforcement Learning and Q-Learning algorithms in order to improve Ethereum cybersecurity in contract vulnerabilities, the smart contract market and research leadership in the area. Deep Reinforcement Learning (Deep RL) is gaining popularity among AI researchers due to its ability to handle complex, dynamic, and particularly high-dimensional cyber protection problems. The benchmark of RL is goal-oriented behavior that increases rewards and decreases penalties or losses, and enhances real-time interaction between an agent and its surroundings. The research paper examines the three major cryptocurrencies (Bitcoin, Litecoin and Ethereum) and the role played by cyber-attacks.The Design Science Research Paradigm as applied in Information Systems research was used in this research, as it is hinged on the idea that information and understanding of a design problem and its solution are attained in the crafting of an artefact. The proposed constructs were in the form of Deep Reinforcement Learning and Q-Learning algorithms designed to improve Ethereum cybersecurity. Smart contracts on the Ethereum blockchain can automatically enforce contracts made between two unknown parties. Blockchain (BC) and artificial intelligence (AI) are used together to strengthen one another's skills and complement one another. Consensus algorithms (CAs) of BC and deep reinforcement learning (DRL) in ETS were thoroughly reviewed. In order to integrate many DCRs and provide grid services, this article suggests an effective incentive-based autonomous DCR control and management framework. This framework simultaneously adjusts the grid's active power with accuracy, optimizes DCR allocations, and increases profits for all prosumers and system operators. The best incentives in a continuous action space to persuade prosumers to reduce their energy consumption were found using a model-free deep deterministic policy gradient-based strategy. Extensive experimental experiments were carried out utilizing real-world data to show the framework's efficacy.

Keywords

Reinforcement Learning, DRL, Double Q-Learning, Blockchain, Ethereum Blockchain, Cryptocurrencies, ECC, DNS.

Full Text