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Recommendation systems have been used to predict the next place a user is willing to visit. However, these methods commonly achieve low hit rates because they aim to recommend exact locations among many possibilities. A higher-level approach that is ...
An intelligent combination of the Internet of Things (IoT) and approaches to modeling and simulation is one of the most challenging endeavors for future cities, manufacturing industries, and predictive maintenance. Digital Twins take on a unique role ...
In today's cities, it is increasingly normal to see different systems based on Artificial Intelligence (AI) that help citizens and government institutions in their daily lives. This is possible thanks to the Internet of Things (IoT). In this paper we ...
To improve the speed and accuracy in human detection in Search and Rescue (SAR) operations, this paper presents a novel and highly efficient machine learning empowered system by extending the You Only Look Once (YOLO) algorithm, which is designed and ...
The recent works on automated vehicle make and model recognition (VMMR) have embraced the use of advanced deep learning models such as convolutional neural networks. In this work, we introduce an adversarial attack against such VMMR systems through ...
The role of connected vehicular networks has become vital for the future of smart and modern cities, as it can be envisioned as a stand alone connected network or a bridge between various networks, and end-users. Vehicular networks design provides ...
Future fifth generation (5G) networks are envisioned to provide improved Quality-of-Experience (QoE) for applications by means of higher data rates, low and ultra-reliable latency and very high reliability. Proving increasing beneficial for mobile ...
In recent years, deep neural networks have revolutionized the development of intelligent systems and applications in many areas. Despite their numerous advantages and potentials, these intelligent models still suffer from several issues. Among them, the ...
The Internet of Things (IoT) is a relatively new concept with a number of potential uses in agriculture. In this work we propose a system based on IoT for early detection of wine moth infestation, as well as monitoring the number of pests at a ...
In modern society, vehicles have become an indispensable means of transportation to ensure people's travel and the circulation of social production materials and living materials. However, while bringing us convenience in life, with the increasing ...
In recent years, in the face of the increasingly complicated traffic environment caused by the significant increase in the number of motor vehicles, in order to improve road traffic safety, autonomous driving technology has become the focus of research, ...
Crowd monitoring and management is an important application of Mobile Crowdsensing (MCS). The emergence of COVID-19 pandemic has made the modeling and simulation of community infection spread a vital activity in the battle against the disease. This ...
To meet the needs for energy savings in Internet of Things (IoT) and mobile systems, solar energy has been increasingly exploited to serve as a green and renewable source to allow systems to better operate in an energy-efficient way. In this respect, ...
The 5th generation of the cellular mobile communication system (5G) is in the meantime stepwise being deployed in mobile carriers' infrastructure. Various standardization tracks as well as research activity are investigating the exploitation of the very ...
Machine learning is a highly promising tool to design the physical layer of wireless communication systems, but it usually requires that a channel model is known. As data rates increase and wireless transceivers become more complex, the wireless channel,...
Some recent works showed that several machine learning algorithms, such as square-root Lasso, Support Vector Machines, and regularized logistic regression, among many others, can be represented exactly as distributionally robust optimization (DRO) ...
Distributionally Robust Optimization (DRO) has been shown to provide a flexible framework for decision making under uncertainty and statistical estimation. For example, recent works in DRO have shown that popular statistical estimators can be ...
We study the problem of coordination control of multiple traffic signals to mitigate traffic congestion. The parameters we optimize are the coordination pattern and offsets. A coordination pattern indicates which traffic signals are coordinated. Offsets ...
This paper aims to propose a novel deep learning-integrated framework for deriving reliable simulation input models through incorporating multi-source information. The framework sources and extracts multi-source data generated from construction ...
Earth moving operations are a critical component of construction and mining industries with a lot of potential for optimization and improved productivity. In this paper we combine discrete event simulation with reinforcement learning (RL) and neural ...