Advanced search

Showing result 31 - 35 of 1266 essays matching the above criteria.

  1. 31. Predicting user churn using temporal information : Early detection of churning users with machine learning using log-level data from a MedTech application

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

    Author : Love Marcus; [2023]
    Keywords : User churn; Customer attrition; Artificial neural networks; Log-level analysis; Random forests; Decision trees; Användarbortfall; Kundbortfall; Artificiella neurala nätverk; logganalys; Slumpskogar; Beslutsträd;

    Abstract : User retention is a critical aspect of any business or service. Churn is the continuous loss of active users. A low churn rate enables companies to focus more resources on providing better services in contrast to recruiting new users. READ MORE

  3. 32. Speech Classification using Acoustic embedding and Large Language Models Applied on Alzheimer’s Disease Prediction Task

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

    Author : Maryam Kheirkhahzadeh; [2023]
    Keywords : Speech classification; Alzheimer’s disease detection; GPT-3; BERT; Text embedding; Dementia; wav2vec2.0; Klassificering av tal; detektion av Alzheimer’s sjukdom; GPT-3; BERT; textinbäddning; demens; wav2vec2.0;

    Abstract : Alzheimer’s sjukdom är en neurodegenerativ sjukdom som leder till demens. Den kan börja tyst i de tidiga stadierna och fortsätta under åren till en allvarlig och obotlig fas. Språkstörningar uppstår ofta som ett av de tidiga symptomen och kan till slut leda till fullständig mutism i de avancerade stadierna av sjukdomen. READ MORE

  4. 33. Automatic compilation and summarization of documented Russian equipment losses in Ukraine : A method development

    University essay from Försvarshögskolan

    Author : Carl Zaff; [2023]
    Keywords : Russian Equipment loss; Russo-Ukrainian War; Method development; Machine learning; Tesseract OCR; Oryxspioenkop; OSINT; Ryssland; Materielförlust; Rysk-ukrainska kriget; Metodutveckling; Maskininlärning; Tesseract OCR; Oryxspioenkop; OSINT;

    Abstract : Since the Russian invasion of Ukraine on the 24th of February 2022 – most of the United Nations have, in one way or another, participated in the most significant war of many decades. The war is characterized by Russia’s atrocious war crimes, illegal annexations, terror, propaganda, and complete disrespect for international law. READ MORE

  5. 34. Automated Vulnerability Management

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

    Author : Yuhan Ma; [2023]
    Keywords : Software security; Machine learning; Automation; Vulnerability management; Natural language processing; Programvarusäkerhet; Maskininlärning; Automation; Sårbarhetshantering; Bearbetning av naturligt språk;

    Abstract : The field of software security is constantly evolving, and security must be taken into consideration throughout the entire product life cycle. This is particularly important in today’s dynamic security landscape, where threats and vulnerabilities constantly change. READ MORE

  6. 35. 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