KOMPARASI ALGORITMA LR, K-NN DAN SVM UNTUK ESTIMASI AREA KEBAKARAN HUTAN

Kebakaran hutan menimbulkan berbagai permasalahan seperti asap yang dapat mengganggu sistem pernapasan, kerusakan lingkungan dan bencana lainnya. Kebakaran hutan juga dapat berdampak pada biaya yang akan dikeluarkan untuk menyelesaikan masalah yang timbul akibat kebakaran hutan, sehingga diperlukan penelitian untuk mengukur tingkat radiasi api pada area yang terbakar. Algoritma LR (Linear Regression), K-NN (K-Nearest Neighbor) dan SVM (Support Vector Machine) merupakan metode untuk regresi dan klasifikasi. Pada penelitian ini dilakukan perbandingan atau komparasi untuk mendapatkan algoritma terbaik dalam estimasi area kebakaran hutan

AVALIAÇÃO DE MODELOS DE PREDITIVOS DE REGRESSÃO PARA ESTIMAR ESFORÇO DE SOFTWARE

Effort estimation is a critical task in the software development life cycle. Inaccurate estimations might cause customer dissatisfaction and reduce product quality. In this paper, we evaluate the use of machine learning-based techniques to estimate effort for software tasks. We executed an empirical study based on the [Desharnais 1989] dataset and compared the predictions of three models: Linear Regression (LR), Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) algorithms. The results of our study show that some software metrics are more important to estimate software effort than others. Also based on the quadratic error, which calculates how close the distance of a square regression line is to a set of points, we can successfully estimate 76% of the software effort for the dataset studied, where the predictive models created show only 3% difference between them.A estimativa de esforço é uma tarefa crítica no ciclo de vida de desenvolvimento de software. Estimativas imprecisas podem causar insatisfação do cliente e reduzir a qualidade do produto. Neste artigo, avaliamos o uso de técnicas baseadas em aprendizado de máquina para estimar o esforço para tarefas de software. Executamos um estudo empírico baseado no conjunto de dados [Desharnais 1989] e comparamos as previsões de três modelos: Regressão Linear (LR), Support Vector Machine (SVM) e K-Nearest Neighbor (KNN). Os resultados do nosso estudo mostram que algumas métricas de software são mais importantes para estimar o esforço de software do que outras. Também com base no erro quadrático, que calcula quão próxima a distância de uma linha de regressão quadrada é de um conjunto de pontos, podemos estimar com sucesso 76% do esforço de software para o conjunto de dados estudado, onde os modelos preditivos criados mostram apenas 3% diferença entre eles

Identifikation von Beinahekollisionen in maritimen Verkehrsdaten als Ground-Truth für szenariobasiertes Testen

Diese Arbeit geht der Frage nach, wie sich validierungsrelevante Beinahekollisionssituationen aus historischen Verkehrsdaten detektieren und als Ground Truth nutzen lassen. Nach der Sichtung des Stands der Technik werden Anforderungen an die Datenerhebung, Datenspeicherung, sowie die Datenanalyse erhoben und ein entsprechendes Konzept erstellt. Zur Bestimmung von Beinahekollisionen werden zunächst die relevanten Einflussfaktoren hergeleitet und es folgt, gemäß der Definition, die Entwicklung mehrerer Methoden und Werkzeuge zur Identifikation von fahrerreaktionsbasierten, funktionsreaktionsbasierten, kontextbasierten und historienbasierten Auffälligkeiten. Als Vorbereitung auf die Evaluation schließt sich die Implementierung und Integration der Systemartefakte in das maritime Testfeld eMIR an. Es kann gezeigt werden, dass der Ansatz zur objektiven Erkennung von Beinahekollisionen geeignet ist und als Ground Truth für das szenariobasierte Testen eingesetzt werden kann

Context-aware mobility analytics and trip planning

The study of user mobility is to understand and analyse the movement of individuals in the spatial and temporal domains. Mobility analytics and trip planning are two vital components of user mobility that facilitate the end users with easy to access navigational support through the urban spaces and beyond. Mobility context describes the situational factors that can influence user mobility decisions. The context-awareness in mobility analytics and trip planning enables a wide range of end users to make effective mobility decisions. With the ubiquity of urban sensing technologies, various situational factors related to user mobility decisions can now be collected at low cost and effort. This huge volume of data collected from heterogeneous data sources can facilitate context-aware mobility analytics and trip planning through intelligent analysis of mobility contexts, mobility context prediction, mobility context representation and integration considering different user perspectives. In each chapter of this thesis such issues are addressed through the development of case-specific solutions and real-world deployments. Mobility analytics include prediction and analysis of many diverse mobility contexts. In this thesis, we present several real-world user mobility scenarios to conduct intelligent contextual analysis leveraging existing statistical methods. The factors related to user mobility decisions are collected and fused from various publicly available open datasets. We also provide future prediction of important mobility contexts which can be utilized for mobility decision making. The performance of context prediction tasks can be affected by the imbalance in context distribution. Another aspect of context prediction is that the knowledge from domain experts can enhance the prediction performance however, it is very difficult to infer and incorporate into mobility analytics applications. We present a number of data-driven solutions aiming to address the imbalanced context distribution and domain knowledge incorporation problems for mobility context prediction. Given an imbalanced dataset, we design and implement a framework for context prediction leveraging existing data mining and sampling techniques. Furthermore, we propose a technique for incorporating domain knowledge in feature weight computation to enhance the task of mobility context prediction. In this thesis, we address key issues related to trip planning. Mobility context inference is a challenging problem in many real-world trip planning scenarios. We introduce a framework that can fuse contextual information captured from heterogeneous data sources to infer mobility contexts. In this work, we utilize public datasets to infer mobility contexts and compute trip plans. We propose graph based context representation and query based adaptation techniques on top of the existing methods to facilitate trip planning tasks. The effectiveness of trip plans relies on the efficient integration of mobility contexts considering different user perspectives. Given a contextual graph, we introduce a framework that can handle multiple user perspectives concurrently to compute and recommend trip plans to the end user. This thesis contains efficient techniques that can be employed in the area of urban mobility especially, context-aware mobility analytics and trip planning. This research is built on top of the existing predictive analytics and trip planning techniques to solve problems of contextual analysis, prediction, context representation and integration in trip planning for real-world scenarios. The contributions of this research enable data-driven decision support for traveling smarter through urban spaces and beyond