Blar i NTNU Open på forfatter "Ramampiaro, Heri"

Viser treff 21-40 av 50

    • Exploring Decomposition for Solving Pattern Mining Problems 

      Djenouri, Youcef; Lin, Jerry Chun-Wei; Nørvåg, Kjetil; Ramampiaro, Heri; Yu, Philip S. (Peer reviewed; Journal article, 2021)
      This article introduces a highly efficient pattern mining technique called Clustering-based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in the transaction ...

    • Exploring the Efficiency of Zero-Cost Proxies in NAS for Human Action Recognition 

      Imran, Zaim ul-Abrar; Schau, Max Torre (Master thesis, 2023)
      Både Neural Architecture Search (NAS) og Graph Convolutional Networks (GCNs) er to felter innen maskinlæring som har gjennomgått en stor utvik- ling de seneste årene. Det å finne en GCN-arkitektur som gir gode resultater ...

    • Exploring the Efficiency of Zero-Cost Proxies in NAS for Human Action Recognition 

      Imran, Zaim ul-Abrar; Schau. Max Torre (Master thesis, 2023)
      Både Neural Architecture Search (NAS) og Graph Convolutional Networks (GCNs) er to felter innen maskinlæring som har gjennomgått en stor utvikling de seneste årene. Det å finne en GCN-arkitektur som gir gode resultater kan ...

    • Extracting news events from microblogs 

      Øystein, Repp; Ramampiaro, Heri (Journal article; Peer reviewed, 2018)
      Twitter stream has become a large source of information, but the magnitude of tweets posted and the noisy nature of its content makes harvesting of knowledge from Twitter has challenged researchers for long time. Aiming ...

    • Fast and accurate group outlier detection for trajectory data 

      Djenouri, Youcef; Nørvåg, Kjetil; Ramampiaro, Heri; Lin, Jerry Chun-Wei (Peer reviewed; Journal article, 2020)
      Previous approaches to solve the trajectory outlier detection problem exclusively examine single outliers. However, anomalies in trajectory data may often occur in groups. This paper introduces a new problem, group trajectory ...

    • Forecasting Intra-Hour Imbalances in Electric Power Systems 

      Saleh Salem, Tárik; Kathuria, Karan; Ramampiaro, Heri; Langseth, Helge (Journal article; Peer reviewed, 2019)
      Keeping the electricity production in balance with the actual demand is becoming a difficult and expensive task in spite of an involvement of experienced human operators. This is due to the increasing complexity of the ...

    • Graph Convolutional Networks for Predicting Cerebral Palsy in Infants 

      Haukeland, Andreas; Aubert, Sindre Aarnes (Master thesis, 2021)

    • High Utility Drift Detection in Quantitative Data Streams 

      Duong, Quang-Huy; Ramampiaro, Heri; Nørvåg, Kjetil; Fournier-Viger, Philippe; Dam, Thu-Lan (Journal article; Peer reviewed, 2018)
      This paper presents an efficient algorithm for detecting changes (drifts) in the utility distributions of patterns, named High Utility Drift Detection in Transactional Data Stream (HUDD-TDS). The algorithm is specifically ...

    • Highly Efficient Pattern Mining Based on Transaction Decomposition 

      Djenouri, Youcef; Lin, Jerry Chun-Wei; Nørvåg, Kjetil; Ramampiaro, Heri (Chapter; Peer reviewed, 2019)
      This paper introduces a highly efficient pattern mining technique called Clustering-Based Pattern Mining (CBPM). This technique discovers relevant patterns by studying the correlation between transactions in transaction ...

    • Human Pose Estimation Using a Two-Staged Convolutional Neural Network 

      Araya, Mattis; Reime, Eivind (Master thesis, 2020)
      Human pose estimation, metoden for å lokalisere menneskelige kroppsdeler, har i de siste årene blitt et populært forskningsfelt grunnet sitt brede applikasjonsdomene. Til tross for denne populariteten er metoden fortsatt ...

    • Human Pose Estimation Using a Two-Staged Convolutional Neural Network 

      Araya, Mattis; Reime, Eivind (Master thesis, 2020)
      Human pose estimation, metoden for å lokalisere menneskelige kroppsdeler, har i de siste årene blitt et populært forskningsfelt grunnet sitt brede applikasjonsdomene. Til tross for denne populariteten er metoden fortsatt ...

    • Locality-adapted kernel densities of term co-occurrences for location prediction of tweets 

      Özdikis, Özer; Ramampiaro, Heri; Nørvåg, Kjetil (Journal article; Peer reviewed, 2019)
      While geographical metadata referring to the originating locations of tweets provides valuable information to perform effective spatial analysis in social networks, scarcity of such geotagged tweets imposes limitations on ...

    • Location prediction using neural networks 

      Løkken, Erlend; Waatevik, Endre (Master thesis, 2019)
      For å kunne studere brukeres bevegelser og handlinger er det viktig å identifisere lokasjonen en tweet er skrevet fra eller omhandler. Denne typen informasjon kan brukes i en rekke applikasjoner som avhenger av geografisk ...

    • Machine Learning in Financial Market Surveillance: A Survey 

      Tiwari, Shweta; Ramampiaro, Heri; Langseth, Helge (Peer reviewed; Journal article, 2021)
      The use of machine learning for anomaly detection is a well-studied topic within various application domains. However, the detection problem for market surveillance remains challenging due to the lack of labeled data and ...

    • Multiple Dense Subtensor Estimation with High Density Guarantee 

      Duong, Quang-Huy; Ramampiaro, Heri; Nørvåg, Kjetil (Chapter, 2020)
      Dense subtensor detection is a well-studied area, with a wide range of applications, and numerous efficient approaches and algorithms have been proposed. Existing algorithms are generally efficient for dense subtensor ...

    • New Insights into Content Production in Social Platforms 

      Kusmierczyk, Tomasz (Doctoral theses at NTNU;2018:173, Doctoral thesis, 2018)
      Social media influences to a great extent everyday life of millions of people, and therefore the understanding of certain patterns relating to online platforms and communities, and the ability to model and predict them, ...

    • Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles 

      Saleh Salem, Tárik; Langseth, Helge; Ramampiaro, Heri (Peer reviewed; Journal article, 2020)
      Prediction intervals are a machine- and human-interpretable way to represent predictive uncertainty in a regression analysis. In this paper, we present a method for generating prediction intervals along with point estimates ...

    • Ranking Streaming Data With Continuous Queries 

      Skarshaug, Sandra Marie (Master thesis, 2019)
      En økende mengde data genereres som en del av digitaliseringen av samfunnet vårt. Forskning fra 2013 viser at 90% av all dataen generert i verden frem til det tidspunktet, ble generert i løpet av de to foregående årene. ...

    • Semantic Matching: Dynamic Composition of Matcher Ensembles for Ontology Alignment 

      Vennesland, Audun (Doctoral theses at NTNU;2020:247, Doctoral thesis, 2020)
      Semantic matching is a computational process that aims to automatically identify the semantic relationship between elements represented in different graph-like sources. Typically, this is a process that involves human ...

    • Skeleton Based Cerebral Palsy Diagnosis using Deep Learning and Attention 

      Vold, Martin (Master thesis, 2020)
      Dyp læring har i de siste årene oppnådd gode resultater innen forskningsfelt som datasyn og menneskelig aktivitets gjenkjenning. Innen medisin har disse gjennombruddene åpnet nye dører for hvordan problemer blir løst og ...