874 research outputs found

    Development of a sustainable evolutionary-inspired artificial intelligent system for municipal water demand modelling.

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    Doctoral Degree. University of KwaZulu-Natal. Durban.This study presents the development of a differential evolution (DE)-inspired artificial neural network (ANN) that incorporates climate and socioeconomic information for a more accurate and reliable water demand forecasting. The study also addresses the limitations of ANN. Multiple feature selection techniques were employed to identify the minimal subset of features for optimal learning. The performance of the feature selection techniques was validated and compared to a baseline scenario comprising a full set of data covering potential casual variables including weather, socio-economic and historical water consumption data. The performance of the models was evaluated based on accuracy. Results show that all the feature selection techniques outperformed the baseline scenario. More importantly, the subset of features obtained from the Pearson correlation technique produced the most superior model in terms of model accuracy. Findings from the study suggests that inclusion of weather and socioeconomic variables in water demand modelling could enhance the accuracy of forecasts and cater for the impacts of climate and socioeconomic variations in water demand planning and management. The performance of the optimal DE-inspired model was thereafter compared to those developed via conventionally-used multiple linear regression and standard time series technique – exponential smoothing as well as other prominent soft computing techniques, namely support vector machines (SVM) and conjugate-gradient (CG)-trained multilayer perceptron (MLP). Results show that the DE-inspired ANN model was superior to the four other techniques for both the baseline scenario and optimal subset of features. DE showcased robustness in fine-tuning algorithm parameter values thereby producing good performance in terms of the solution efficiency and quality. Generally, this study demonstrates that water demand models can account for the impacts of weather and socioeconomic variations by incorporating explanatory variables based on weather and socioeconomic factors. This study also suggests that the synergetic use of feature selection techniques, DE algorithm and an early stopping criterion could be used in addressing the limitations of ANN and developing an improved and more reliable water demand forecasting model. This work goes further to propose for a novel and more comprehensive integrated water demand and management modelling framework (IWDMMF) that is capable of syncing conventional evolutionary computation techniques and social aspects of society. The methodologies, principles and techniques behind this study fosters sustainable development and thus could be adopted in planning and management of water resources.Publications from this thesis can be found on page v

    Immersive Telepresence: A framework for training and rehearsal in a postdigital age

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    Recent Advances in Social Data and Artificial Intelligence 2019

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    The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

    An intelligent destination recommendation system for tourists.

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    Choosing a tourist destination from the information available is one of the most complex tasks for tourists when making travel plans, both before and during their travel. With the development of a recommendation system, tourists can select, compare and make decisions almost instantly. This involves the construction of decision models, the ability to predict user preferences, and interpretation of the results. This research aims to develop a Destination Recommendation System (DRS) focusing on the study of machine-learning techniques to improve both technical and practical aspects in DRS. First, to design an effective DRS, an intensive literature review was carried out on published studies of recommendation systems in the tourism domain. Second, the thesis proposes a model-based DRS, involving a two-step filtering feature selection method to remove irrelevant and redundant features and a Decision Tree (DT) classifier to offer interpretability, transparency and efficiency to tourists when they make decisions. To support high scalability, the system is evaluated with a huge body of real-world data collected from a case-study city. Destination choice models were developed and evaluated. Experimental results show that our proposed model-based DRS achieves good performance and can provide personalised recommendations with regard to tourist destinations that are satisfactory to intended users of the system. Third, the thesis proposes an ensemble-based DRS using weight hybrid and cascade hybrid. Three classification algorithms, DT, Support Vector Machines (SVMs) and Multi- Layer Perceptrons (MLPs), were investigated. Experimental results show that the bagging ensemble of MLP classifiers achieved promising results, outperforming baseline learners and other combiners. Lastly, the thesis also proposes an Adaptive, Responsive, Interactive Model-based User Interface (ARIM-UI) for DRS that allows tourists to interact with the recommended results easily. The proposed interface provides adaptive, informative and responsive information to tourists and improves the level of the user experience of the proposed system

    Computation in Complex Networks

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    Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

    Multi-Agent Systems

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    This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

    When in doubt ask the crowd : leveraging collective intelligence for improving event detection and machine learning

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