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Visar resultat 1 - 5 av 60 uppsatser som matchar ovanstående sökkriterier.

1. 1. En undersökning av metoder förautomatiserad text ochparameterextraktion frånPDF-dokument med NaturalLanguage Processing

   M1-uppsats, KTH/Hälsoinformatik och logistik

   Författare :Alexander Värling; Emil Hultgren; [2024]
   Nyckelord :portable document format; faktura; digitalisering; IT-lösningar; optisk teckenigenkänning; textextraktion; naturlig språkbehandling; generative pre-trained transformer; portable document format; faktura; digitalisering; IT-lösningar; optisk teckenigenkänning; textextraktion; naturlig språkbehandling; generative pre-trained transformer;

   Sammanfattning : I dagens affärsmiljö strävar många organisationer efter att automatisera processen för att hämta information från fakturor. Målet är att göra hanteringen av stora mängder fakturor mer effektiv. Trots detta möter man utmaningar på grund av den varierande strukturen hos fakturor. LÄS MER

3. 2. Nested Noun Phrase Detection in English Text with BERT

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

   Författare :Shweta Misra; [2023]
   Nyckelord :Phrase detection; nested noun phrase identification; phrase structure identification; sentence parsing; transformer models; machine learning; natural language processing; Frasdetektering; kapslad substantivfrasidentifiering; frasstrukturidentifiering; meningsanalys; transformers-modeller; maskininlärning; naturlig språkbehandling;

   Sammanfattning : In this project, we address the task of nested noun phrase identification in English sentences, where a phrase is defined as a group of words functioning as one unit in a sentence. Prior research has extensively explored the identification of various phrases for language understanding and text generation tasks. LÄS MER

4. 3. EMONAS : Evolutionary Multi-objective Neuron Architecture Search of Deep Neural Network

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

   Författare :Jiayi Feng; [2023]
   Nyckelord :DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Embedded Systems; DNN Deep Neural Network ; NAS Neural Architecture Search ; EA Evolutionary Algorithm ; Multi-Objective Optimization; Binary One Optimization; Inbyggda system;

   Sammanfattning : Customized Deep Neural Network (DNN) accelerators have been increasingly popular in various applications, from autonomous driving and natural language processing to healthcare and finance, etc. However, deploying them directly on embedded system peripherals within real-time operating systems (RTOS) is not easy due to the paradox of the complexity of DNNs and the simplicity of embedded system devices. LÄS MER

5. 4. Automated Vulnerability Management

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

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

   Sammanfattning : 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. LÄS MER

7. 5. 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