ABSTRACT Artificial Neural network (ANN) is an area of computing that is modeled after the neural network of the biological brain and over the last few decades, has experienced huge success in its application in areas such as business, Medicine, Industry, Automotive, Astronomy, Finance, etc. Since Neural Networks are inherently parallel architectures, there have been several earlier researches to build custom ASIC based systems that include multiple parallel processing units. However, these ...
Aqueous crude extract of Desmodium adscendens, as dispensed at the Centre for Scientific Research into Plant Medicine (CSRPM), was freeze-dried into a powder and used in this study to evaluate its toxicological effects in rats. For acute toxicity studies, high doses of the freeze-dried extract, were administered orally as single doses to rats, and the behaviours of the animals and organ toxicity were noted. Low, medium and high doses of the plant extract, representing 3, 10, and 30% of ...
ABSTRACT Recommender systems are web-based platforms or software that use various machine learning methods to propose useful items to users. Several techniques have been used to develop such a system for generating a list of recommendations. Multi-criteria is a new technique that recommends items based on multiple characteristics or attributes of the items. This technique has been used to solve many recommendation problems and its predictive performance has been tested and proven to be more ...
ABSTRACT After many years of rigorous research and development in wireless sensor network (WSN) technology with numerous responses to innovative applications, WSNs still have some interesting unanswered questions. In this thesis we explain the challenges of the state of art in WSN for environmental monitoring applications using open-source hardware platforms, Arduino UNO, DHT11 temperature-humidity sensor, XBee and Raspberry Pi. The system is not only low cost but scalable enough to accept m...
ABSTRACT Research in computer vision and machine learning is a significant part of research in computer science departments of many leading institutions resulting in ideas and products that have direct applications in different industries such as medical image segmentation in the medical industry, and face recognition and tracking in the entertainment and security industry. Face recognition is a significant part of research in computer vision and machine learning and has a wide range of appl...
ABSTRACT The telecommunication industry has a lot of data related to households, individuals and devices. Advertisers pay a premium to ensure they advertise to their target audience. To ensure that content is personalized, it is necessary to accurately predict who is using a device in real time. A probabilistic matching algorithm to determine the profile of an individual based on behavioural analytics is developed and implemented. Two datasets ‘People data’ and ‘Device data’ were lin...
Abstract We have used ab initio density functional theory to study the coadsorption of N and O on Pt(100) and Pt(111) surfaces. Our calculations show that on Pt(100) the most favourable site is the hollow site for N and the bridge site for O, while for Pt(111) there are different possible N and O coadsorption geometries. On both surfaces the interaction between N and O is repulsive in nature.
ABSTRACT In this thesis, we consider the problem of approximating solution of generalized equilibrium problems and common fixed point of finite family of strict pseudocontractions. The result obtained is applied in approximation of solution of generalized mixed equilibrium problems and common fixed point of finite family of strict pseudocontractions. Our theorems improve and unify some existing results that were recently announced by several authors. Corollaries obtained and our method of pr...
ABSTRACT Let H be a real Hilbert space and A : D(A) ⊂ H → H be an unbounded, linear, self-adjoint, and maximal monotone operator. The aim of this thesis is to solve u 0 (t) + Au(t) = 0, when A is linear but not bounded. The classical theory of differential linear systems cannot be applied here because the exponential formula exp(tA) does not make sense, since A is not continuous. Here we assume A is maximal monotone on a real Hilbert space, then we use the Yosida approximation to solve. A...
Coastal erosion has become a major problem to coastal dwellers worldwide as its effects of destruction to properties, flooding and inundations continue to render populations homeless. Coastal erosion is defined as the wearing away of material from a coastal profile due to imbalance in the supply and export of material from a certain section (Marchand, 2010). Coastal erosion is noted to have burdened most nations‘ financial budgets in the mitigation and management of this quandary. Mar...
ABSTRACT Density functional theory (DFT) is a useful theoretical and computational tool for electronic structure calculations, which form the basis for the classification of materials into conductors, semiconductors or insulators. DFT started with a crude approximation by Thomas and Fermi (TF theory) which calculated the kinetic energy of electrons using the so-called local density approximation (LDA). Although TF is computationally inexpensive, it provides a poor numerical result due to a la...
ABSTRACT Text analysis is a branch of data mining that deals with text documents. This project brings to light the classification of texts into their various categories. The structured and unstructured data seems to on a high rise in this era. Thus, to be able to classify this data is important. Classification however starts from collection, preprocessing, and feature extraction. There are several techniques that can be used for text classification, but machine learning algorithms will be em...
ABSTRACT In this thesis, we implemented Point Distribution Model and basic Active Shape Model algorithm and contributed this to the AUST Computer Vision and Machine Learning code library. We applied the Active Shape Model to segmenting lateral ventricles of 2D brain images and used machine learning – specifically K-Nearest Neighbour algorithm- to improve segmentation results. A statistical shape model is created from a training dataset which is used to search for an object of interest in a...
INTRODUCTION Variational methods have proved to be very important in the study of optimal shape, time, velocity, volume or energy. Laws existing in mechanics, physics, astronomy, economics and other fields of natural sciences and engineering obey variational principles. The main objective of variational method is to obtain the solutions governed by these principles. Fermat postulated that light follows a part of least possible time, this is a subject in finding minimizers of a given functiona...
Abstract We attempt the design and development of an educational game based on the Input-ProcessOutcome model. This tool helps students and other professionals to learn and appreciate the decision-making processes carried out by geophysicists and petroleum engineers, concerned with the activity of well placement, to maximize production from oil fields. It also stimulates learning and application of technology to support decision making.