Search results for "590"
showing 10 items of 644 documents
Deep Hybrid Neural Networks on Multi-temporal Satellite Data: Predicting Farm-scale Crop Yields
2021
Master's thesis in Information- and communication technology (IKT590) Accurate farm-scale crop yield predictions can enable farmers to improve their yield per decare and inform subsequent sectors of the availability of grains sooner. Existing research on yield predictions is limited to regional analytics, which often fails to capture local yield variations influenced by farm management decisions and field conditions. Farm-scale crop yield predictions require precise ground-truth prediction targets, which are not always available. It takes substantial manual labor to create large and suitable datasets of high-resolution per-farm samples. This thesis introduces a hybrid multi-temporal deep ne…
Machine Learning Techniques to Predict Pandemic from Social Media
2018
Master's thesis Information- and communication technology IKT590 - University of Agder 2018 In recent years, there has been a particular focus on improving public health through the means of prediction and preparedness of pandemic diseases. Early detection, prediction, and analysis of disease outbreaks allow the authority agencies to mitigate the side effects of Pandemic and immune the people. Nowadays, social media such as Twitter or Facebook play a vital role in the crisis situation. By means of social media, people from all over the world can be aware of the recent pandemic outbreaks. In fact, the mainstream adoption of social media in people daily life has caused a paradigm shift in how…
Deep Reinforcement Learning using Capsules in Advanced Game Environments
2017
Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Reinforcement Learning (RL) is a research area that has blossomed tremendously in recent years and has shown remarkable potential for arti cial intelligence based opponents in computer games. This success is primarily due to vast capabilities of Convolutional Neural Networks (ConvNet), enabling algorithms to extract useful information from noisy environments. Capsule Network (CapsNet) is a recent introduction to the Deep Learning algorithm group and has only barely begun to be explored. The network is an architecture for image classi cation, with superior performance for classi cation of the MNIST da…
A Generative Adversarial Approach for Packet Manipulation Detection
2018
Master's thesis Information- and communication technology IKT590 - University of Agder 2018 Over the years, machine learning has been used together with intrusion detection systems to protect networks against different threats. The evolution of machine learning has exploded and there are new types of of machine learning algorithms being studied on different fields. Networks security is not one these fields that have the most research, and with the continuous change in the way attacks are appearing, machine learning in network security is more alluring than ever. The intention of this thesis is to present a solution that shows that using machine learning in intrusion detection domain is a wa…
Prediction of Electricity Usage Using Convolutional Neural Networks
2017
Master's thesis Information- and communication technology IKT590 - University of Agder 2017 Convolutional Neural Networks are overwhelmingly accurate when attempting to predict numbers using the famous MNIST-dataset. In this paper, we are attempting to transcend these results for time- series forecasting, and compare them with several regression mod- els. The Convolutional Neural Network model predicted the same value through the entire time lapse in contrast with the other models, while the Multi-Layer Perception through Machine Learning model performed overall best. Temperature variables are directly related to power consumption, but the weights from the power consumption values from 1, 2…
An Encoder-Decoder based Deep Learning Approach for Anonymization of Visual Surveillance Media with Preservation of Utility
2020
Master's thesis in Information- and communication technology (IKT590) The field of computer vision has seen significant progress recently fol-lowing innovations in deep learning neural networks. Activity can be identified from surveillance cameras. Automatic detection of unwanted incidents would enable police to act quickly with appropriate resources. Activity detecting machine learning algorithms need many examples in its learning phase. However, videos from surveillance cameras may contain privacy-sensitive information. The videos are not allowed to be used outside of the police unless anonymized. However, traditional anonymization techniques remove visual information, reducing utility as…
Expanding on the end-to-end memory network for goal-oriented dialogue
2019
Master's thesis Information- and communication technology IKT590 - University of Agder 2019 A series of end-to-end models have been proposed in order to satisfy therequirements of the Dialog System Technology Challenge: building an end-to-end dialog system for goal-oriented applications. While these modelshave proven to be a good solution for such tasks, they perform worse whendealing with out-of-vocabulary tasks and none-synthetic data. Additionally,they rely heavily on the use of an underlying knowledge base to achieve goodresults.We propose two new models that build on the end-to-end memory net-work architecture. The goal of these two models is to better handle out-of-vocabulary tasks an…
A Machine Learning Approach for Intrusion Detection
2020
Master's thesis in Information- and communication technology (IKT590) Securing networks and their confidentiality from intrusions is crucial, and for this rea-son, Intrusion Detection Systems have to be employed. The main goal of this thesis is to achieve a proper detection performance of a Network Intrusion Detection System (NIDS). In this thesis, we have examined the detection efficiency of machine learning algorithms such as Neural Network, Convolutional Neural Network, Random Forestand Long Short-Term Memory. We have constructed our models so that they can detect different types of attacks utilizing the CICIDS2017 dataset. We have worked on identifying 15 various attacks present in CICI…
Computer Vision based Auxiliary System for Computer Assembly: System Design and implementation
2019
Master's thesis Information- and communication technology IKT590 - University of Agder 2019 This thesis proposes a solution that employs AI in assisting a human withlittle or no technical background for computer assembly in real-time with-out any form of other assistance. To achieve this goal, a state-of-the-artobject detection, namely Lighthead R-CNN is adopted as foundation. Al-terations and modifications of the algorithm are carried out to achieve theoptimal trade-o↵between accuracy loss and speed gain. In more details,it is expected to reduce the complexity of the algorithm in order to makethe solution applicable in a computer with limited computational capacity.Numerical results show t…
Blockchain-based Smart Contracts for Consent Management in eHealth
2021
Master's thesis in Information- and communication technology (IKT590) Since the introduction of Bitcoin by Satoshi Nakamoto in a white paper in 2008, Blockchain has gathered considerable attention because of its ability to be decentralized and immutable. Blockchain is still considered as a new and experimental technology, and the state-of-the-art literature review was conducted to identify various use cases for the Blockchain technology for the healthcare industry. Consent management is one of the most critical components in healthcare because of the constantly evolving eHealth services requiring access to personal data, and of corresponding privacy laws, such as the European General Data P…