What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- Statistics
Shahrokh Valaee mostly deals with Algorithm, Artificial intelligence, Vehicular ad hoc network, Compressed sensing and Speech recognition.
Shahrokh Valaee has included themes like Selection algorithm, Theoretical computer science and Array processing in his Algorithm study.
His Artificial intelligence study combines topics in areas such as Field and Computer vision.
His work is dedicated to discovering how Vehicular ad hoc network, Mobile radio are connected with Vehicular communication systems, Simulation, Atomic broadcast, Telecommunications network and Broadcasting and other disciplines.
His work deals with themes such as Computer network, Distributed computing, Routing protocol and Cluster analysis, which intersect with Wireless ad hoc network.
His study looks at the relationship between Distributed computing and fields such as Communication channel, as well as how they intersect with chemical problems.
His most cited work include:
- Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing (402 citations)
- Parametric localization of distributed sources (297 citations)
- Vehicular Node Localization Using Received-Signal-Strength Indicator (173 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Computer network, Algorithm, Artificial intelligence, Real-time computing and Network packet.
His studies deal with areas such as Wireless, Wireless ad hoc network, Vehicular ad hoc network and Distributed computing as well as Computer network.
Shahrokh Valaee focuses mostly in the field of Vehicular ad hoc network, narrowing it down to matters related to Node and, in some cases, Communication channel and Wireless sensor network.
His Algorithm study also includes
- Mathematical optimization which intersects with area such as Estimation theory,
- Energy and related Ising model.
His studies in Artificial intelligence integrate themes in fields like Machine learning, Computer vision and Pattern recognition.
His Real-time computing study incorporates themes from Simulation and Reliability.
He most often published in these fields:
- Computer network (29.58%)
- Algorithm (24.58%)
- Artificial intelligence (22.08%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial neural network (9.17%)
- Algorithm (24.58%)
- Artificial intelligence (22.08%)
In recent papers he was focusing on the following fields of study:
Artificial neural network, Algorithm, Artificial intelligence, Regularization and Ising model are his primary areas of study.
The various areas that he examines in his Algorithm study include Ranging, Minification, Channel state information and Pruning.
His Channel state information research includes elements of Transmitter, Multipath propagation and Compressed sensing.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning, Computer vision and Pattern recognition.
His work in Network topology covers topics such as Scheduling which are related to areas like Wireless sensor network and Real-time computing.
He conducted interdisciplinary study in his works that combined Computer network and Mobile telephony.
Between 2018 and 2021, his most popular works were:
- Survey of Dropout Methods for Deep Neural Networks. (40 citations)
- Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks (32 citations)
- Indoor Localization Improved by Spatial Context—A Survey (31 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Statistics
Shahrokh Valaee focuses on Artificial intelligence, Inference, Artificial neural network, Computer vision and Machine learning.
Many of his research projects under Artificial intelligence are closely connected to Fingerprint recognition with Fingerprint recognition, tying the diverse disciplines of science together.
The study incorporates disciplines such as Regularization, MNIST database and Deep neural networks in addition to Inference.
His Regularization research incorporates themes from Ising model and Pattern recognition.
His work carried out in the field of Computer vision brings together such families of science as Visualization, Spatial analysis and Robustness.
Shahrokh Valaee interconnects Ensemble learning and Feature extraction in the investigation of issues within Recurrent neural network.
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