Machine Learning and Algorithms
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The development and optimization of algorithms that allow AI systems to learn from data and make decisions, including supervised, unsupervised, and reinforcement learning techniques.
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Item Defining Functional Models of Artificial Intelligence Solutions to Create a Library that an Artificial General Intelligence can use to Increase General Problem Solving Ability(2020-04-27) Williams, AndyThe AI industry continues to enjoy robust growth. With the growing number of AI algorithms, the question becomes how to leverage all these models intelligently in a way that reliably converges on AGI. One approach is to gather all these models ingo a single library that a system of artificial intelligence might use to increase it's general problem solving ability. This paper explores the requirements for building such a library, the requirements for that library to be searchable for AI algorithms that might have the capacity to significantly increase impact on any given problem, and the requirements for the use of that library to reliably converge on AGI. This paper also explores the importance to such an effort of defining a common set of semantic functional building blocks that AI models can be represented in terms of. In particular, how that functional decomposition might be used to organize large scale cooperation to create such an AI library, where that cooperation has not yet proved possible otherwise. And how such collaboration, as well as how such a library, might significantly increase the impact of each AI and AGI researcher’s work.Item A Technical Evaluation of the Performance of Classical Artificial Intelligence (AI) and Methods Based on Computational Intelligence (CI) i.e Supervised Learning, Unsupervised Learning And Ensemble Algorithms in Intrusion Detection Systems(2016-11) Zvarevashe, Kudakwashe; Mapanga, Innocent; Kadebu, PrudenceThe emergence of new technologies in this dynamic information era has caused a tremendous increase in the rate at which data is being generated through interactive applications thereby increasing the movement of information and data on communication networks as individuals, organizations and business interact on a daily basis. Big Data is flooding our networks and storage devices stimulating a cause for concern in terms of processing, storage, access and security of large blocks of data in most networks. The facilitation of online research services is always under the risk of intruders and malicious activity. Most techniques used in today's Intrusion Detection Systems are not able to deal with the dynamic and complex nature of cyber-attacks on computer networks. Over the years, Intrusion Detection Systems .Various methods have been developed by many researchers to detect intrusions aimed at networks as well as standalone devices which are based on machine learning algorithms, neural networks, statistical methods etc. In this paper, we study several such schemes and compare their performance. The experiments are done using WEKA (Waikato Environment for Knowledge Analysis) and one of the most popular Intrusion Detection Systems datasets which is NSL-KDD99 so as to analyse the consistency of each algorithm. We divide the schemes into methods based on classical artificial intelligence (AI) and methods based on computational intelligence (CI) i.e supervised learning, unsupervised learning, ensemble and immune algorithms. We explain how various characteristics of CI techniques can be used to build efficient IDS. This paper will further evaluate the performance of the algorithms using the following parameters: accuracy, detection rate and false alarm.