Essays about: "Long Short-Term Memory Networks LSTM"

Showing result 1 - 5 of 111 essays containing the words Long Short-Term Memory Networks LSTM.

1. 1. Insurance Fraud Detection using Unsupervised Sequential Anomaly Detection

   University essay from Linköpings universitet/Institutionen för datavetenskap

   Author : Anton Hansson; Hugo Cedervall; [2022]
   Keywords : Insurance Fraud Detection; Anomaly Detection; Long Short-Term Memory Networks LSTM ; Unsupervised Learning; Autoencoder AE ; Variational Autoencoder VAE ; Interpretable Machine Learning; Feature Engineering; Feature Selection; Feature Importance;

   Abstract : Fraud is a common crime within the insurance industry, and insurance companies want to quickly identify fraudulent claimants as they often result in higher premiums for honest customers. Due to the digital transformation where the sheer volume and complexity of available data has grown, manual fraud detection is no longer suitable. READ MORE

3. 2. Time Dependencies Between Equity Options Implied Volatility Surfaces and Stock Loans, A Forecast Analysis with Recurrent Neural Networks and Multivariate Time Series

   University essay from KTH/Matematik (Avd.)

   Author : Simon Wahlberg; [2022]
   Keywords : RNN; LSTM; GRU; vector autoregression; implied volatility surface; stock loan; equity options; multivariate time-series analysis; financial mathematics.; Rekursiva neurala nätverk; LSTM; GRU; VAR; implicerade volatilitetsytor; aktielån; aktieoptioner; multidimensionell tidsserieanalys; finansiell matematik.;

   Abstract : Synthetic short positions constructed by equity options and stock loan short sells are linked by arbitrage. This thesis analyses the link by considering the implied volatility surface (IVS) at 80%, 100%, and 120% moneyness, and stock loan variables such as benchmark rate (rt), utilization, short interest, and transaction trends to inspect time-dependent structures between the two assets. READ MORE

4. 3. Investigating Machine Learning for verification of AMBA APB protocol.

   University essay from Lunds universitet/Institutionen för elektro- och informationsteknik

   Author : Abhiram Srisai Kishore; Mohammed Wasim; [2022]
   Keywords : Machine learning; SOC Verification; AMBA; Neural Networks; Deep Learning; Assertions.; Technology and Engineering;

   Abstract : It is a well-known fact that in any Application Specific Integrated Circuit (ASIC) design, verification consumes most time and resources. And when it comes to huge designs, finding bugs can be tedious given the area and the complexity. As per Moore’s law, the design complexity is increasing exponentially due to the growing demand for performance. READ MORE

5. 4. Cooperative security log analysis using machine learning : Analyzing different approaches to log featurization and classification

   University essay from Linköpings universitet/Databas och informationsteknik

   Author : Fredrik Malmfors; [2022]
   Keywords : Machine learning; word embeddings; deep learning; LSTM; CNN; auto encoder; NLP; natural language processing; intrusion detection; log analysis; logs; log classification; anomaly detection; supervised learning; unsupervised learning;

   Abstract : This thesis evaluates the performance of different machine learning approaches to log classification based on a dataset derived from simulating intrusive behavior towards an enterprise web application. The first experiment consists of performing attacks towards the web app in correlation with the logs to create a labeled dataset. READ MORE

6. 5. Energy-Efficient Private Forecasting on Health Data using SNNs

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Davide Di Matteo; [2022]
   Keywords : Spiking neural networks; differential privacy; synthetic data generation; smart health care; fitness trackers.; Spikande neurala nätverk; differentiell integritet; syntetisk datagenerering; smart hälsovård; träningsspårare.;

   Abstract : Health monitoring devices, such as Fitbit, are gaining popularity both as wellness tools and as a source of information for healthcare decisions. Predicting such wellness goals accurately is critical for the users to make informed lifestyle choices. READ MORE