Application of Artificial Intelligence Techniques in Drilling System Design and Operations: A State of the Art Review and Future Research Pathways

2016 ◽  
Author(s):  
Opeyemi Bello ◽  
Catalin Teodoriu ◽  
Tanveer Yaqoob ◽  
Joachim Oppelt ◽  
Javier Holzmann ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
System Design ◽  
State Of The Art ◽  
Future Research ◽  
Artificial Intelligence Techniques ◽  
Drilling System

scholarly journals Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

2020 ◽  
Vol 9 (2) ◽  
pp. 21 ◽  
Author(s):  
Martins O. Osifeko ◽  
Gerhard P. Hancke ◽  
Adnan M. Abu-Mahfouz
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Energy Efficient ◽  
State Of The Art ◽  
Future Internet ◽  
Future Research ◽  
Research Directions ◽  
Efficient Data ◽  
Future Research Directions ◽  
Efficient Data Collection

Smart, secure and energy-efficient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage. One such solution, termed cognitive sensing (CS) describes the use of smart sensors to intelligently perceive inputs from the environment. Further, CS has been proposed for use in FIoT in order to facilitate smart, secure and energy-efficient data collection processes. In this article, we provide a survey of different Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for different FIoT applications. We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions. This survey contributes to a better understanding of AI techniques deployed for cognitive sensing in FIoT as well as future research directions in this regard.

scholarly journals Co-authorship prediction in academic social network

2019 ◽  
Author(s):  
William Takahiro Maruyama ◽  
Luciano Antonio Digiampietri
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Social Network ◽  
State Of The Art ◽  
The State ◽  
Artificial Intelligence Techniques ◽  
Academic Social Network ◽  
Academic Social Networks

The prediction of relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating the most promising partnerships. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks.

scholarly journals A Compilation of Methods and Datasets for Group and Crowd Action Recognition

2017 ◽  
Vol 7 (3) ◽  
pp. 40-53 ◽  
Author(s):  
Luis Felipe Borja ◽  
Jorge Azorin-Lopez ◽  
Marcelo Saval-Calvo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Challenging Problem ◽  
Artificial Intelligence Techniques ◽  
Group Behaviour ◽  
Learning Techniques ◽  
Methods And Techniques ◽  
Different Levels

The human behaviour analysis has been a subject of study in various fields of science (e.g. sociology, psychology, computer science). Specifically, the automated understanding of the behaviour of both individuals and groups remains a very challenging problem from the sensor systems to artificial intelligence techniques. Being aware of the extent of the topic, the objective of this paper is to review the state of the art focusing on machine learning techniques and computer vision as sensor system to the artificial intelligence techniques. Moreover, a lack of review comparing the level of abstraction in terms of activities duration is found in the literature. In this paper, a review of the methods and techniques based on machine learning to classify group behaviour in sequence of images is presented. The review takes into account the different levels of understanding and the number of people in the group.

scholarly journals State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1030 ◽  
Author(s):  
Syed Saqib Ali ◽  
Bong Jun Choi
Keyword(s):  
Artificial Intelligence ◽  
Smart Grid ◽  
Power System ◽  
Energy Management ◽  
Smart Grids ◽  
State Of The Art ◽  
Energy Resources ◽  
Distributed Energy ◽  
Artificial Intelligence Techniques ◽  
Home Energy Management

The power system worldwide is going through a revolutionary transformation due to the integration with various distributed components, including advanced metering infrastructure, communication infrastructure, distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future power system. These components are becoming more tightly integrated with IoT. They are expected to generate a vast amount of data to support various applications in the smart grid, such as distributed energy management, generation forecasting, grid health monitoring, fault detection, home energy management, etc. With these new components and information, artificial intelligence techniques can be applied to automate and further improve the performance of the smart grid. In this paper, we provide a comprehensive review of the state-of-the-art artificial intelligence techniques to support various applications in a distributed smart grid. In particular, we discuss how artificial techniques are applied to support the integration of renewable energy resources, the integration of energy storage systems, demand response, management of the grid and home energy, and security. As the smart grid involves various actors, such as energy produces, markets, and consumers, we also discuss how artificial intelligence and market liberalization can potentially help to increase the overall social welfare of the grid. Finally, we provide further research challenges for large-scale integration and orchestration of automated distributed devices to realize a truly smart grid.

scholarly journals Artificial intelligence techniques over the fifth generation mobile networks: a review

2021 ◽  
Vol 24 (1) ◽  
pp. 317
Author(s):  
Ashwaq N. Hassan ◽  
Sarab Al-Chlaihawi ◽  
Ahlam R. Khekan
Keyword(s):  
Artificial Intelligence ◽  
Mobile Networks ◽  
Human Error ◽  
Smart Cities ◽  
Mobile Network ◽  
Fourth Generation ◽  
Future Research ◽  
Artificial Intelligence Techniques ◽  
Fifth Generation ◽  
Sixth Generation

<span>A well Fifth generation (5G) mobile networks have been a common phrase in recent years. We have all heard this phrase and know its importance. By 2025, the number of devices based on the fifth generation of mobile networks will reach about 100 billion devices. By then, about 2.5 billion users are expected to consume more than a gigabyte of streamed data per month. 5G will play important roles in a variety of new areas, from smart homes and cars to smart cities, virtual reality and mobile augmented reality, and 4K video streaming. Bandwidth much higher than the fourth generation, more reliability and less latency are some of the features that distinguish this generation of mobile networks from previous generations.  Clearly, at first glance, these features may seem very impressive and useful to a mobile network, but these features will pose serious challenges for operators and communications companies. All of these features will lead to considerable complexity. Managing this network, preventing errors, and minimizing latency are some of the challenges that the 5th generation of mobile networks will bring. Therefore, the use of artificial intelligence and machine learning is a good way to solve these challenges. in other say, in such a situation, proper management of the 5G network must be done using powerful tools such as artificial intelligence. Various researches in this field are currently being carried out. Research that enables automated management and servicing and reduces human error as much as possible. In this paper, we will review the artificial intelligence techniques used in communications networks. Creating a robust and efficient communications network using artificial intelligence techniques is a great incentive for future research.</span><span> The importance of this issue is such that the sixth generation (6G) of cellular communications; There is a lot of emphasis on the use of artificial intelligence.</span>

Sign in / Sign up

Export Citation Format

Share Document