What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Myra Spiliopoulou spends much of her time researching World Wide Web, Web mining, Data science, Data mining and Web standards.
She combines subjects such as Directed graph, Information retrieval and Database with her study of World Wide Web.
Her Web mining study combines topics from a wide range of disciplines, such as Social Semantic Web, Personalization, Web development and Web modeling.
Her Data science research incorporates elements of Bibliography, Data stream mining, The Internet, Knowledge extraction and Temporal data mining.
The various areas that Myra Spiliopoulou examines in her Data mining study include Machine learning, Cluster analysis and Artificial intelligence.
Myra Spiliopoulou has included themes like Web analytics, Web intelligence, Web navigation and Data Web in her Web standards study.
Her most cited work include:
- A survey of temporal knowledge discovery paradigms and methods (368 citations)
- A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis (300 citations)
- Web usage mining for Web site evaluation (289 citations)
What are the main themes of her work throughout her whole career to date?
Her primary scientific interests are in Artificial intelligence, World Wide Web, Information retrieval, Data mining and Machine learning.
Myra Spiliopoulou has researched Artificial intelligence in several fields, including Quality, Pattern recognition and Natural language processing.
Her World Wide Web study frequently draws connections between adjacent fields such as Data science.
Myra Spiliopoulou combines subjects such as Semantics, XML and Identification with her study of Information retrieval.
Her study brings together the fields of Cluster analysis and Data mining.
Her research investigates the connection between Web mining and topics such as Knowledge extraction that intersect with issues in Web usage analysis.
She most often published in these fields:
- Artificial intelligence (25.24%)
- World Wide Web (23.64%)
- Information retrieval (18.53%)
What were the highlights of her more recent work (between 2015-2021)?
- Artificial intelligence (25.24%)
- Machine learning (16.61%)
- Tinnitus (5.75%)
In recent papers she was focusing on the following fields of study:
Myra Spiliopoulou mainly focuses on Artificial intelligence, Machine learning, Tinnitus, Data science and mHealth.
Her Artificial intelligence research integrates issues from Quality, Pattern recognition and Natural language processing.
The concepts of her Machine learning study are interwoven with issues in Data mining and Identification.
Her Tinnitus study incorporates themes from Psychological intervention, Loudness and Distress, Clinical psychology.
Her work in Data science tackles topics such as Epidemiology which are related to areas like Visual analytics.
Her Recommender system study contributes to a more complete understanding of World Wide Web.
Between 2015 and 2021, her most popular works were:
- Editorial: Towards an Understanding of Tinnitus Heterogeneity. (45 citations)
- Prospective crowdsensing versus retrospective ratings of tinnitus variability and tinnitus–stress associations based on the TrackYourTinnitus mobile platform (27 citations)
- Outpatient Tinnitus Clinic, Self-Help Web Platform, or Mobile Application to Recruit Tinnitus Study Samples? (23 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Her primary areas of investigation include Tinnitus, Artificial intelligence, Crowdsensing, Machine learning and Recommender system.
Her Tinnitus research incorporates elements of Neuroscience and Cognitive neuroscience.
Her studies deal with areas such as Quality, Natural language processing, Relevance, Crowdsourcing and Study of Health in Pomerania as well as Artificial intelligence.
Her studies in Machine learning integrate themes in fields like Probabilistic logic and Identification.
Her study on Recommender system is covered under World Wide Web.
Her study in Collaborative filtering is interdisciplinary in nature, drawing from both Semi-supervised learning, Singular value decomposition, Throughput and Data mining.
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