Essays about: "long short term memory"

Showing result 11 - 15 of 335 essays containing the words long short term memory.

  1. 11. The Effects of Aerobic Exercise on The Neural Basis of Memory Functions in Elderly Individuals : A Systematic Review

    University essay from Högskolan i Skövde/Institutionen för biovetenskap

    Author : Mariam Sharif Osman; Suzan Almostafa; [2023]
    Keywords : Aerobic exercise; memory function; healthy older adults; neuroimaging; cognitive aging;

    Abstract : This systematic review aims to investigate the effects of aerobic exercise on the neuroal basis of memory functions in healthy elderly individuals. The search was conducted in accordance with PRISMA and covered three electronic databases, namely PubMed, Scopus, and Web of Science, for peer-reviewed published, and original research. READ MORE

  3. 12. Modelling Proxy Credit Cruves Using Recurrent Neural Networks

    University essay from KTH/Matematisk statistik

    Author : Lucas Fageräng; Hugo Thoursie; [2023]
    Keywords : Deep Neural Networks; Credit Risk; Financial Modelling; LSTM; Credit Default Swaps; Credit Valuation Adjustment; Djupa Neurala Nätverk; Kreditrisk; Finansiell Modellering; LSTM; Kreditswappar; Kreditvärderingsjustering;

    Abstract : Since the global financial crisis of 2008, regulatory bodies worldwide have implementedincreasingly stringent requirements for measuring and pricing default risk in financialderivatives. Counterparty Credit Risk (CCR) serves as the measure for default risk infinancial derivatives, and Credit Valuation Adjustment (CVA) is the pricing method used toincorporate this default risk into derivatives prices. READ MORE

  4. 13. Predicting Cryptocurrency Prices with Machine Learning Algorithms: A Comparative Analysis

    University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Author : Harsha Nanda Gudavalli; Khetan Venkata Ratnam Kancherla; [2023]
    Keywords : Bitcoin; Cryptocurrency; Machine Learning;

    Abstract : Background: Due to its decentralized nature and opportunity for substantial gains, cryptocurrency has become a popular investment opportunity. However, the highly unpredictable and volatile nature of the cryptocurrency market poses a challenge for investors looking to predict price movements and make profitable investments. READ MORE

  5. 14. Exploration and Evaluation of RNN Models on Low-Resource Embedded Devices for Human Activity Recognition

    University essay from KTH/Mekatronik och inbyggda styrsystem

    Author : Helgi Hrafn Björnsson; Jón Kaldal; [2023]
    Keywords : Recurrent Neural Networks; Long Short-Term Memory Networks; Embedded Systems; Human Activity Recognition; Edge AI; TensorFlow Lite Micro; Recurrent Neural Networks; Long Short-Term Memory Networks; Innbyggda systyem; Mänsklig aktivitetsigenkänning; Edge AI; TensorFlow Lite Micro;

    Abstract : Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. READ MORE

  6. 15. Customer churn prediction in a slow fashion e-commerce context : An analysis of the effect of static data in customer churn prediction

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

    Author : Luca Colasanti; [2023]
    Keywords : Survival Analysis; Time To Event prediction; Churn retention; Machine Learning; Deep Learning; Customer Clustering; E-commerce; Analisi di sopravvivenza; Previsione del tempo a evento; Ritenzione dall’abbandono dei clienti; Apprendimento automatico; Apprendimento profondo; Segmentazione della clientela; Commercio elettronico; Överlevnadsanalys; Tid till händelseförutsägelse; Churn Prediction; Maskininlärning; Djuplärning; Kundkluster; E-handel;

    Abstract : Survival analysis is a subfield of statistics where the goal is to analyse and model the data where the outcome is the time until the occurrence of an event of interest. Because of the intrinsic temporal nature of the analysis, the employment of more recently developed sequential models (Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)) has been paired with the use of dynamic temporal features, in contrast with the past reliance on static ones. READ MORE