Cartographie et caractérisation floristique de la forêt marécageuse de Lokoli (Bénin)

La présente étude s’inscrit dans le cadre de l’évaluation des ressources biologiques de la forêt marécageuse de Lokoli en république du Bénin en vue de définir les stratégies pour sa conservation durable. Le SIG a permis de réaliser la carte de végétation de la forêt qui a servi de base pour la collecte de données floristiques et dendrométriques. La surface d’inventaire des placeaux est de 30 x 30 m. Au total, 125 espèces végétales sont recensées dont 30 espèces menacées de disparition au Bénin. La densité spécifique totale est de 53 espèces/ha et celle des individus de dbh ³ 10 cm est d’environ 15 espèces/ha. L’indice de diversité de Shannon (H’ = 3,24 bits) est moyen alors que l’équitabilité de Pielou (Eq = 0,63) est faible. Les espèces les plus dominantes écologiquement sont: Alstonia congensis, Xylopia rubescens, Syzygium owariense et Raphia hookeri. Les principales formes de pressions sont: coupe de bois, collecte de plantes médicinales et l’exploitation du raphia. Cependant, la forêt apparaît bien préservée avec environ 78% de couverture forestière.Trois principaux groupements végétaux sont décrits et la densité de raphia est proportionnelle au degré de dégradation des divers faciès végétaux.Mots clés: Bénin, forêt marécageuse de Lokoli, SIG, diversité floristique, conservation, Dahomey Gap

Classification in Very High Dimensional Problems with Handfuls of Examples

Abstract. Modern classification techniques perform well when the number of training examples exceed the number of features. If, however, the number of features greatly exceed the number of training examples, then these same techniques can fail. To address this problem, we present a hierarchical Bayesian framework that shares information between features by modeling similarities between their parameters. We believe this approach is applicable to many sparse, high dimensional problems and especially relevant to those with both spatial and temporal components. One such problem is fMRI time series, and we present a case study that shows how we can successfully classify in this domain with 80,000 original features and only 2 training examples per class.

Financial Fraud Detection and Data Mining of Imbalanced Databases using State Space Machine Learning

Risky decisions made by humans exhibit characteristics common to each decision. The related systems experience repeated abuse by risky humans and their actions collude to form a systemic behavioural set. Financial fraud is an example of such risky behaviour. Fraud detection models have drawn attention since the financial crisis of 2008 because of their frequency, size and technological advances leading to financial market manipulation. Statistical methods dominate industrial fraud detection systems at banks, insurance companies and financial marketplaces. Most efforts thus far have focused on anomaly detection problems and simple rules in the academic literature and industrial setting. There are unsolved issues in modeling the behaviour of risky agents in real-world financial markets using machine learning. This research studies the challenges posed by fraud detection, including the problem of imbalanced class distributions, and investigates the use of Reinforcement Learning (RL) to model risky human behaviour. Models have been developed to transform the relevant financial data into a state-space system. Reinforcement Learning agents uncover the decision-making processes by risky humans and derive an optimal path of behaviour at the end of the learning process. States are weighted by risk and then classified as positive (risky) or negative (not-risky). The positive samples are composed of features that represent the hidden information underlying the risky behaviour. Reinforcement Learning is implemented as unsupervised and supervised models. The unsupervised learning agent searches for risky behaviour without any previous knowledge of the data; it is not “trained” on data with true class labels. Instead, the RL learner relates samples through experience. The supervised learner is trained on a proportion (e.g. 90%) of the data with class labels. It derives a policy of optimal actions to be taken at each state during the training stage. One policy is selected from several learning agents and then the model is exposed to the other proportion (e.g. 10%) of data for classification. RL is hybridized with a Hidden Markov Model (HMM) in the supervised learning model to impose a probabilistic framework around the risky agent’s behaviour. We first study an insider trading example to demonstrate how learning algorithms can mimic risky agents. The classification power of the model is further demonstrated by applying it to a real-world based database for debit card transaction fraud. We then apply the models to two problems found in Statistics Canada databases: heart disease detection and female labour force participation. All models are evaluated using appropriate measures for imbalanced class problems: “sensitivity” and “false positive”. Sensitivity measures the number of correctly classified positive samples (e.g. fraud) as a proportion of all positive samples in the data. False positive counts the number of negative samples classified positive as a proportion of all negative samples in the data. The intent is to maximize sensitivity and minimize the false positive rate. All models show high sensitivity rates while exhibiting low false positive rates. These two metrics are ideal for industrial implementation because of high levels of identification at a low cost. Fraud detection rate is the focus with detection rates of 75-85% proving that RL is a superior method for data mining of imbalanced databases. By solving the problem of hidden information, this research can facilitate the detection of risky human behaviour and prevent it from happening

Adaptive Health Monitoring Using Aggregated Energy Readings from Smart Meters

Worldwide, the number of people living with self-limiting conditions, such as Dementia, Parkinson’s disease and depression, is increasing. The resulting strain on healthcare resources means that providing 24-hour monitoring for patients is a challenge. As this problem escalates, caring for an ageing population will become more demanding over the next decade, and the need for new, innovative and cost effective home monitoring technologies are now urgently required. The research presented in this thesis directly proposes an alternative and cost effective method for supporting independent living that offers enhancements for Early Intervention Practices (EIP). In the UK, a national roll out of smart meters is underway. Energy suppliers will install and configure over 50 million smart meters by 2020. The UK is not alone in this effort. In other countries such as Italy and the USA, large scale deployment of smart meters is in progress. These devices enable detailed around-the-clock monitoring of energy usage. Specifically, each smart meter records accurately the electrical load for a given property at 10 second intervals, 24 hours a day. This granular data captures detailed habits and routines through user interactions with electrical devices. The research presented in this thesis exploits this infrastructure by using a novel approach that addresses the limitations associated with current Ambient Assistive Living technologies. By applying a novel load disaggregation technique and leveraging both machine learning and cloud computing infrastructure, a comprehensive, nonintrusive and personalised solution is achieved. This is accomplished by correlating the detection of individual electrical appliances and correlating them with an individual’s Activities of Daily Living. By utilising a random decision forest, the system is able to detect the use of 5 appliance types from an aggregated load environment with an accuracy of 96%. By presenting the results as vectors to a second classifier both normal and abnormal patient behaviour is detected with an accuracy of 92.64% and a mean squared error rate of 0.0736 using a random decision forest. The approach presented in this thesis is validated through a comprehensive patient trial, which demonstrates that the detection of both normal and abnormal patient behaviour is possible