D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 54 Citations 12,200 624 World Ranking 2247 National Ranking 60

Research.com Recognitions

Awards & Achievements

2020 - IEEE Fellow For contributions to haptically-enabled robotic systems

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Mechanical engineering

His primary scientific interests are in Artificial intelligence, Artificial neural network, Prediction interval, Data mining and Machine learning. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Computer vision and Pattern recognition. Saeid Nahavandi usually deals with Artificial neural network and limits it to topics linked to Control theory and Linear form and Control.

The concepts of his Prediction interval study are interwoven with issues in Simulated annealing, Electric power system, Minification, Uncertainty quantification and Coverage probability. His work in Data mining covers topics such as Cluster analysis which are related to areas like Unsupervised learning. In his study, Feedforward neural network and Control engineering is strongly linked to Fuzzy logic, which falls under the umbrella field of Machine learning.

His most cited work include:

  • Dynamic nanofin heat sinks (767 citations)
  • Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals (306 citations)
  • Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances (294 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Artificial intelligence, Computer vision, Control theory, Artificial neural network and Simulation. His biological study spans a wide range of topics, including Machine learning and Pattern recognition. His research in Computer vision is mostly focused on Segmentation.

The concepts of his Control theory study are interwoven with issues in Control engineering and Kinematics. His Artificial neural network study which covers Prediction interval that intersects with Coverage probability. He has included themes like Virtual training, Virtual reality, Human–computer interaction and Robot, Teleoperation in his Haptic technology study.

He most often published in these fields:

  • Artificial intelligence (37.39%)
  • Computer vision (14.02%)
  • Control theory (13.83%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (37.39%)
  • Machine learning (10.32%)
  • Artificial neural network (13.92%)

In recent papers he was focusing on the following fields of study:

Artificial intelligence, Machine learning, Artificial neural network, Deep learning and Control theory are his primary areas of study. His research integrates issues of Computer vision and Pattern recognition in his study of Artificial intelligence. He is interested in Feature selection, which is a branch of Machine learning.

His studies deal with areas such as Uncertainty quantification, Prediction interval and Transfer of learning as well as Artificial neural network. Saeid Nahavandi combines subjects such as Robot and Mechanism with his study of Control theory. His study focuses on the intersection of Mechanism and fields such as Kinematics with connections in the field of Workspace.

Between 2017 and 2021, his most popular works were:

  • Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications (152 citations)
  • A Classifier Graph Based Recurring Concept Detection and Prediction Approach. (136 citations)
  • Synchronization of an Inertial Neural Network With Time-Varying Delays and Its Application to Secure Communication (123 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Mechanical engineering

His primary areas of investigation include Artificial intelligence, Machine learning, Deep learning, Control theory and Workspace. Saeid Nahavandi interconnects Computer vision and Pattern recognition in the investigation of issues within Artificial intelligence. His Machine learning research integrates issues from Baseline and CAD.

The various areas that Saeid Nahavandi examines in his Deep learning study include Transfer of learning and Reinforcement learning. His work carried out in the field of Control theory brings together such families of science as Robot, Mechanism and Synchronization. His Workspace research includes elements of Motion, Kinematics, Model predictive control, Simulation and Algorithm.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Dynamic nanofin heat sinks

Pyshar Yi;Khashayar Khoshmanesh;Adam F. Chrimes;Jos L. Campbell.
Advanced Energy Materials (2014)

767 Citations

Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances

A. Khosravi;S. Nahavandi;D. Creighton;A. F. Atiya.
IEEE Transactions on Neural Networks (2011)

399 Citations

Lower Upper Bound Estimation Method for Construction of Neural Network-Based Prediction Intervals

A Khosravi;S Nahavandi;D Creighton;A F Atiya.
IEEE Transactions on Neural Networks (2011)

375 Citations

Dielectrophoretic platforms for bio-microfluidic systems.

Khashayar Khoshmanesh;Saeid Nahavandi;Sara Baratchi;Arnan Mitchell.
Biosensors and Bioelectronics (2011)

363 Citations

Comments on 'Information measure for performance of image fusion'

M. Hossny;S. Nahavandi;D. Creighton.
Electronics Letters (2008)

299 Citations

A Review of Vision-Based Gait Recognition Methods for Human Identification

Jin Wang;Mary She;Saeid Nahavandi;Abbas Kouzani.
digital image computing: techniques and applications (2010)

247 Citations

A Classifier Graph Based Recurring Concept Detection and Prediction Approach.

Yange Sun;Yange Sun;Zhihai Wang;Yang Bai;Honghua Dai.
Computational Intelligence and Neuroscience (2018)

202 Citations

Construction of Optimal Prediction Intervals for Load Forecasting Problems

Abbas Khosravi;Saeid Nahavandi;Doug Creighton.
IEEE Transactions on Power Systems (2010)

196 Citations

Prediction Intervals for Short-Term Wind Farm Power Generation Forecasts

A. Khosravi;S. Nahavandi;D. Creighton.
IEEE Transactions on Sustainable Energy (2013)

191 Citations

Efficient Road Detection and Tracking for Unmanned Aerial Vehicle

Hailing Zhou;Hui Kong;Lei Wei;Douglas Creighton.
IEEE Transactions on Intelligent Transportation Systems (2015)

185 Citations

Best Scientists Citing Saeid Nahavandi

Abbas Khosravi

Abbas Khosravi

Deakin University

Publications: 33

Arnan Mitchell

Arnan Mitchell

RMIT University

Publications: 28

Hieu Trinh

Hieu Trinh

Deakin University

Publications: 22

Kourosh Kalantar-zadeh

Kourosh Kalantar-zadeh

UNSW Sydney

Publications: 21

Enrico Zio

Enrico Zio

Politecnico di Milano

Publications: 19

U. Rajendra Acharya

U. Rajendra Acharya

Ngee Ann Polytechnic

Publications: 15

Abbas Z. Kouzani

Abbas Z. Kouzani

Deakin University

Publications: 15

Dan Zhang

Dan Zhang

York University

Publications: 14

Hamid R. Tizhoosh

Hamid R. Tizhoosh

University of Waterloo

Publications: 14

Jinde Cao

Jinde Cao

Southeast University

Publications: 13

Qingsong Xu

Qingsong Xu

University of Macau

Publications: 12

Witold Pedrycz

Witold Pedrycz

University of Alberta

Publications: 12

Junzhi Yu

Junzhi Yu

Peking University

Publications: 11

Erik Blasch

Erik Blasch

United States Air Force Research Laboratory

Publications: 10

Zhigang Zeng

Zhigang Zeng

Huazhong University of Science and Technology

Publications: 9

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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