Sökning: "Djupinlärning"

Visar resultat 11 - 15 av 421 uppsatser innehållade ordet Djupinlärning.

1. 11. Analysis of speaking time and content of the various debates of the presidential campaign : Automated AI analysis of speech time and content of presidential debates based on the audio using speaker detection and topic detection

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Axel Valentin Maza; [2023]
   Nyckelord :Artificial Intelligence; Speaker detection; Speaker recognition; Speaker diarization; Speaker identification; Debate; Politics; Deep Learning; Artificiell intelligens; talardetektion; talarigenkänning; talardiarisering; talaridentifiering; debatt; politik; djupinlärning;

   Sammanfattning : The field of artificial intelligence (AI) has grown rapidly in recent years and its applications are becoming more widespread in various fields, including politics. In particular, presidential debates have become a crucial aspect of election campaigns and it is important to analyze the information exchanged in these debates in an objective way to let voters choose without being influenced by biased data. LÄS MER

3. 12. AI/ML Development for RAN Applications : Deep Learning in Log Event Prediction

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Yuxin Sun; [2023]
   Nyckelord :LSTM; Anomaly Detection; Failure Prediction; Log Mining; Deep Learning; LSTM; Anomali Detection; Failure Prediction; Log Mining; Deep Learning;

   Sammanfattning : Since many log tracing application and diagnostic commands are now available on nodes at base station, event log can easily be collected, parsed and structured for network performance analysis. In order to improve In Service Performance of customer network, a sequential machine learning model can be trained, test, and deployed on each node to learn from the past events to predict future crashes or a failure. LÄS MER

4. 13. Extraction of Global Features for enhancing Machine Learning Performance

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Abyel Tesfay; [2023]
   Nyckelord :Machine Learning; Deep Learning; Feature Extraction; Global Features; Time-series data; Bioprocessing; Maskininlärning; Djupinlärning; Funktionsextraktion; Globala Funktioner; Tidsserie data; Biobearbetning;

   Sammanfattning : Data Science plays an essential role in many organizations and industries to become data-driven in their decision-making and workflow, as models can provide relevant input in areas such as social media, the stock market, and manufacturing industries. To train models of quality, data preparation methods such as feature extraction are used to extract relevant features. LÄS MER

5. 14. Metod för ett automatiserat frågebesvarande i det svenska språket

   M1-uppsats, KTH/Hälsoinformatik och logistik

   Författare :Kristian Penna; [2023]
   Nyckelord :question answering; natural language processing; artificial intelligence; machine learning; deep learning; artificial neural networks; transformer; BERT; sentence transformers; semantic textual similarity; frågebesvarande; språkteknologi; artificiell intelligens; maskininlärning; djupinlärning; artificiella neurala nätverk; transformer; BERT; sentence transformers; semantisk textlikhet;

   Sammanfattning : I ärendehanteringssystem utgör avslutade ärenden en värdefull datamängd bestående av par av frågor och svar som organisationer med rätt metoder kan dra nytta av för att utvinna fördelar. I denna studie har en Sentence Transformers-modell blivit finjusterad för question answering som tillsammans med en datamängd från ett ärendehanteringssystem automatiskt kan besvara organisationsspecifika frågor i det svenska språket. LÄS MER

7. 15. The Impact of the Retrieval Text Set for Text Sentiment Classification With the Retrieval-Augmented Language Model REALM

   Master-uppsats, KTH/Matematik (Inst.)

   Författare :Oscar Blommegård; [2023]
   Nyckelord :The Impact of the Retrieval Text Set for Text Sentiment Classification With the Retrieval-Augmented Language Model REALM; Hämtningsförstärkta språkmodeller; Natural Language Processing; Transformers; Djupinlärning; Textklassificering;

   Sammanfattning : Large Language Models (LLMs) have demonstrated impressive results across various language technology tasks. By training on large corpora of diverse text collections from the internet, these models learn to process text effectively, allowing them to acquire comprehensive world knowledge. LÄS MER