What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Statistics
- Operating system
Sally McClean mainly focuses on Markov model, Data mining, Artificial intelligence, Machine learning and Statistics.
Her Markov model research is multidisciplinary, incorporating elements of Poisson distribution, Stochastic modelling, Duration and Resource allocation.
She studies Knowledge extraction, a branch of Data mining.
Sally McClean works mostly in the field of Artificial intelligence, limiting it down to concerns involving Gait analysis and, occasionally, Gait, Principal component analysis and Pattern recognition.
Her research integrates issues of Text mining, Robustness and Benchmark data in her study of Machine learning.
Her Statistics research incorporates themes from Differential effects and Geriatrics.
Her most cited work include:
- Optimal Placement of Accelerometers for the Detection of Everyday Activities (229 citations)
- Smart City Architecture and its Applications Based on IoT (203 citations)
- Evidential fusion of sensor data for activity recognition in smart homes (196 citations)
What are the main themes of her work throughout her whole career to date?
Sally McClean spends much of her time researching Artificial intelligence, Data mining, Computer network, Machine learning and Quality of service.
Her Artificial intelligence research includes themes of Natural language processing, Computer vision and Pattern recognition.
The various areas that Sally McClean examines in her Data mining study include Cluster analysis, Distributed database, Database and Sensor fusion.
As part of her studies on Computer network, she frequently links adjacent subjects like Next-generation network.
Machine learning connects with themes related to Probabilistic logic in her study.
In her research on the topic of Quality of service, Cloud computing is strongly related with Distributed computing.
She most often published in these fields:
- Artificial intelligence (19.79%)
- Data mining (14.04%)
- Computer network (10.00%)
What were the highlights of her more recent work (between 2016-2021)?
- Artificial intelligence (19.79%)
- Machine learning (8.94%)
- Process (3.40%)
In recent papers she was focusing on the following fields of study:
Sally McClean mostly deals with Artificial intelligence, Machine learning, Process, Distributed computing and Cloud computing.
Sally McClean combines subjects such as Layer, Computer vision and Natural language processing with her study of Artificial intelligence.
Her studies deal with areas such as Preference, Group decision-making, Outlier and Measure as well as Machine learning.
The Process study combines topics in areas such as Event, Risk analysis and Markov model.
Her Distributed computing research is multidisciplinary, incorporating perspectives in Quality of service, Network monitoring, Energy and Cloud server.
Her work carried out in the field of Markov chain brings together such families of science as Discrete time and continuous time, Process, Data mining and Markov process.
Between 2016 and 2021, her most popular works were:
- Unmanned Aerial Vehicles for Disaster Management (17 citations)
- Characterization of Received Signal Strength Perturbations Using Allan Variance (11 citations)
- Special issue – communications in statistics – theory and methods 4th stochastic modeling techniques and data analysis international conference (9 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Operating system
Her primary areas of study are Artificial intelligence, Computer security, Search and rescue, Emergency management and Domain.
Her work deals with themes such as Graph database, Information retrieval and Graph based, which intersect with Artificial intelligence.
The study incorporates disciplines such as Situation analysis, Enterprise system and Identification in addition to Computer security.
Her Domain course of study focuses on Predictive analytics and Wireless sensor network and Activity recognition.
Her Activity recognition study combines topics in areas such as Image processing and Computer vision.
Her biological study deals with issues like Data collection, which deal with fields such as Sensor fusion, Automatic identification and data capture and Feature.
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