What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Database
Data mining, Artificial intelligence, Machine learning, Cluster analysis and Information retrieval are her primary areas of study.
She is interested in Association rule learning, which is a field of Data mining.
Reda Alhajj interconnects Domain, Numerical digit and Pattern recognition in the investigation of issues within Artificial intelligence.
Her studies in Machine learning integrate themes in fields like Process, Identification, Fuzzy classification, Principal component analysis and Multimodal biometrics.
She works mostly in the field of Cluster analysis, limiting it down to topics relating to Set and, in certain cases, Determining the number of clusters in a data set, k-means clustering, Representation, Iris flower data set and Database index, as a part of the same area of interest.
Her Information retrieval study combines topics in areas such as Sentiment analysis, Sentiment score and Social network.
Her most cited work include:
- Genetic algorithm based framework for mining fuzzy association rules (116 citations)
- Efficient Periodicity Mining in Time Series Databases Using Suffix Trees (106 citations)
- Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening (93 citations)
What are the main themes of her work throughout her whole career to date?
Her scientific interests lie mostly in Data mining, Artificial intelligence, Machine learning, Cluster analysis and Information retrieval.
Reda Alhajj is interested in Association rule learning, which is a branch of Data mining.
Her Artificial intelligence research is multidisciplinary, relying on both Set and Pattern recognition.
Her study on Machine learning is mostly dedicated to connecting different topics, such as Process.
Reda Alhajj has included themes like XML database, XML validation, Document Structure Description and XML Schema Editor in her Information retrieval study.
Her Fuzzy classification study integrates concerns from other disciplines, such as Defuzzification and Neuro-fuzzy.
She most often published in these fields:
- Data mining (48.26%)
- Artificial intelligence (34.55%)
- Machine learning (21.82%)
What were the highlights of her more recent work (between 2015-2021)?
- Artificial intelligence (34.55%)
- Data mining (48.26%)
- Data science (11.90%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Artificial intelligence, Data mining, Data science, Machine learning and Recommender system.
Artificial intelligence is closely attributed to Pattern recognition in her study.
Her Data mining study incorporates themes from Social network analysis, Topic model, Correlation coefficient, Data point and Elastic net regularization.
Her Machine learning research incorporates elements of Classifier, Drug discovery and Social network.
Her studies deal with areas such as Outcome and Feature as well as Recommender system.
Her research in Information retrieval intersects with topics in Sentiment analysis and Sentiment score.
Between 2015 and 2021, her most popular works were:
- Emotion detection from text and speech: a survey (53 citations)
- Emotion and sentiment analysis from Twitter text (45 citations)
- Emotion and sentiment analysis from Twitter text (45 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Her main research concerns Artificial intelligence, Social network, Recommender system, Process and Data science.
Her biological study spans a wide range of topics, including Machine learning, Graph and Pattern recognition.
Her study in Machine learning is interdisciplinary in nature, drawing from both Link and Network model.
Her Network model study is concerned with the larger field of Data mining.
Specifically, her work in Data mining is concerned with the study of Knowledge extraction.
Reda Alhajj interconnects Sentiment analysis, Sentiment score and Information retrieval in the investigation of issues within Social network.
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