Essays about: "Sequential analysis"

Showing result 1 - 5 of 171 essays containing the words Sequential analysis.

  1. 1. Android Malware Detection Using Machine Learning

    University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Author : Rahul Sai Kesani; [2024]
    Keywords : Malware; Machine Learning; Random Forest; Sequential Neural Network.;

    Abstract : Background. The Android smartphone, with its wide range of uses and excellent performance, has attracted numerous users. Still, this domination of the Android platform also has motivated the attackers to develop malware. The traditional methodology which detects the malware based on the signature is unfit to discover unknown applications. READ MORE

  2. 2. Detection of insurance fraud using NLP and ML

    University essay from Lunds universitet/Matematisk statistik

    Author : Rasmus Bäcklund; Hampus Öhman; [2023]
    Keywords : Technology and Engineering;

    Abstract : Machine-Learning can sometimes see things we as humans can not. In this thesis we evaluated three different Natural Language Procces-techniques: BERT, word2vec and linguistic analysis (UDPipe), on their performance in detecting insurance fraud based on transcribed audio from phone calls (referred to as audio data) and written text (referred to as text-form data), related to insurance claims. READ MORE

  3. 3. From Plant Genetic Resource Conservation to Digital Biorepositories: A Coming Paradigm Shift in Ex-Situ Conservation?

    University essay from Lunds universitet/Ekonomisk-historiska institutionen

    Author : Christopher Keegan; [2023]
    Keywords : Bioinformatics; Genebanks; Genetic Engineering; Mixed-Methods; Multi-Disciplinary; Patent Analysis; Plant Genetic Resources; Synthetic Biology; Technological Trajectories; Business and Economics;

    Abstract : Plant Genetic Resource Conservation has been a staple of agricultural development for the past century. Recently, several prominent papers have raised alarm surrounding the development of synthetic biology techniques and their potential to render modern practices and methods of conservation in the global genebank network obsolete. READ MORE

  4. 4. 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

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

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

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

    Abstract : 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. READ MORE