• Publications
  • Influence
Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework
TLDR
We develop a multi-agent multi-objective reinforcement learning (RL) traffic signal control framework that simulates the driver's behavior (acceleration/deceleration) continuously in space and time dimensions. Expand
  • 117
  • 1
Machine learning in computational docking
TLDR
We present the state-of-the-art machine learning (ML) techniques in computational docking which is the process of predicting the best pose (orientation + conformation) of a small molecule when bound to a target larger receptor molecule (protein) in order to form a stable complex molecule. Expand
  • 52
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Tokyo Virtual Living Lab: Designing Smart Cities Based on the 3D Internet
TLDR
The Tokyo Virtual Living Lab is an experimental space based on 3D Internet technology that lets researchers conduct controlled driving and travel studies, including those involving multiple users in the same shared space. Expand
  • 35
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Multi-objective traffic light control system based on Bayesian probability interpretation
TLDR
We develop a multiagent traffic light control system based on a multi-objective sequential decision making framework based on the Bayesian interpretation of probability. Expand
  • 28
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Comparative assessment of machine-learning scoring functions on PDBbind 2013
TLDR
Computational docking is the core process of computer-aided drug design; it aims at predicting the best orientation and conformation of a small molecule (drug ligand) when bound to a target large receptor molecule (protein) in order to form a stable complex molecule. Expand
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Freeway ramp-metering control based on Reinforcement learning
  • Ahmed Fares, W. Gomaa
  • Engineering, Computer Science
  • 11th IEEE International Conference on Control…
  • 18 June 2014
TLDR
Reinforcement learning based density control agent (RLCA) is designed based on Markovion modeling with an associated Q-learning algorithm in order to address the stochastic nature of the traffic situation. Expand
  • 21
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Efficient and Robust Skeleton-Based Quality Assessment and Abnormality Detection in Human Action Performance
TLDR
We develop and evaluate vision-based methods to detect and assess neuromusculoskeletal disorders manifested in common daily activities using three-dimensional skeletal data provided by the SDK of a depth camera (e.g., MS Kinect). Expand
  • 6
  • 1
  • PDF
Humanoids skill learning based on real-time human motion imitation using Kinect
TLDR
In this paper, a novel framework which enables humanoid robots to learn new skills from demonstration is proposed. Expand
  • 6
  • 1
An LSTM-based Descriptor for Human Activities Recognition using IMU Sensors
TLDR
We introduce a framework of ADL recognition by making various pre-processing steps based on statistical and physical features which we call AMED. Expand
  • 6
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