scholarly journals Neural Networks and Its Application in Engineering

10.28945/3317 ◽  
2009 ◽  
Author(s):  
Oludele Awodele ◽  
Olawale Jegede
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Process ◽  
Future Research ◽  
Control Engineering ◽  
Research Directions ◽  
Agricultural Engineering ◽  
Recent Advances ◽  
Future Research Directions ◽  
Network Application

Neural Network (NN) has emerged over the years and has made remarkable contribution to the advancement of various fields of endeavor. The purpose of this work is to examine neural networks and their emerging applications in the field of engineering, focusing more on Controls. In this work, we have examined the various architectures of NN and the learning process. The needs for neural networks, training of neural networks, and important algorithms used in realizing neural networks have also been briefly discussed. Neural network application in control engineering has been extensively discussed, whereas its applications in electrical, civil and agricultural engineering were also examined. We concluded by identifying limitations, recent advances and promising future research directions.

MetaLearning with Graph Neural Networks

2021 ◽  
Vol 23 (2) ◽  
pp. 13-22
Author(s):  
Debmalya Mandal ◽  
Sourav Medya ◽  
Brian Uzzi ◽  
Charu Aggarwal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Graph Problems ◽  
Meta Learning ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Graph Neural Networks

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

Landscape Models of Complex Change

2021 ◽  
pp. 554-580
Author(s):  
Daniel Albert ◽  
Martin Ganco
Keyword(s):  
Organizational Change ◽  
Future Research ◽  
Research Directions ◽  
Traditional Form ◽  
Nk Model ◽  
Complex Change ◽  
Landscape Models ◽  
Recent Advances ◽  
Complex Landscape ◽  
Future Research Directions

This chapter reviews recent advances in the NK modeling literature conceptualizing organizational change and innovation as a search over a complex landscape. It discusses both strengths and limitations of this perspective and delineates potential for future research directions. The key argument is that the NK model in its traditional form may be exhausting the theoretical insights that it can provide to the field. However, substantial modifications and extensions of the NK model or new classes of landscape models may provide fresh perspectives. Specifically, we consider the modeling efforts that endogenize the landscape construction as the next frontier in this literature. We also discuss several recent studies that incorporate various extensions of the NK model and allow for agent-driven changes to the landscape.

scholarly journals CubeSat Communications: Recent Advances and Future Challenges

2020 ◽  
Author(s):  
Nasir Saeed ◽  
Ahmed Elzanaty ◽  
Heba Almorad ◽  
Hayssam Dahrouj ◽  
Tareq Y. Al-Naffouri ◽  
...  
Keyword(s):  
Channel Modeling ◽  
Future Research ◽  
New Paradigm ◽  
Earth Remote Sensing ◽  
Research Directions ◽  
Space Industry ◽  
Recent Advances ◽  
Wide Range ◽  
Future Challenges ◽  
Future Research Directions

<pre><pre>Given the increasing number of space-related applications, research in the emerging space industry is becoming more and more attractive. One compelling area of current space research is the design of miniaturized satellites, known as CubeSats, which are enticing because of their numerous applications and low design-and-deployment cost. </pre><pre>The new paradigm of connected space through CubeSats makes possible a wide range of applications, such as Earth remote sensing, space exploration, and rural connectivity.</pre><pre>CubeSats further provide a complementary connectivity solution to the pervasive Internet of Things (IoT) networks, leading to a globally connected cyber-physical system.</pre><pre>This paper presents a holistic overview of various aspects of CubeSat missions and provides a thorough review of the topic from both academic and industrial perspectives.</pre><pre>We further present recent advances in the area of CubeSat communications, with an emphasis on constellation-and-coverage issues, channel modeling, modulation and coding, and networking.</pre><pre>Finally, we identify several future research directions for CubeSat communications, including Internet of space things, low-power long-range networks, and machine learning for CubeSat resource allocation.</pre></pre>

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

Main Features and Types of Educational Use of Wiki Technology

2021 ◽  
pp. 213-228
Author(s):  
Nikolaos Karipidis ◽  
Jim Prentzas
Keyword(s):  
Learning Communities ◽  
Learning Process ◽  
Future Research ◽  
Learning Approaches ◽  
Research Directions ◽  
Online Learning Communities ◽  
Wiki Technology ◽  
History Of ◽  
Future Research Directions ◽  
The Web

Wiki technology has become very popular during the last years and is used in many fields. It enables the collaborative creation and management of content retaining the history of changes. There is abundant wiki-based content on the web covering a large number of subjects. A significant contribution of wikis involves education. Under certain conditions, technology may enhance the learning process due to the unique features it encompasses. In this context, wikis may prove very helpful as they provide the infrastructure for collaborative learning approaches and the development of online learning communities. This chapter discusses main features of wikis, wiki features specifically required in education, and typical uses of wikis in education. Representative examples of successful wikis are presented. Future research directions are also outlined.

Recent advances in supramolecular block copolymers for biomedical applications

2020 ◽  
Vol 8 (36) ◽  
pp. 8219-8231
Author(s):  
Wumaier Yasen ◽  
Ruijiao Dong ◽  
Aliya Aini ◽  
Xinyuan Zhu
Keyword(s):  
Block Copolymers ◽  
Biomedical Applications ◽  
Future Research ◽  
Biological Applications ◽  
Research Directions ◽  
Recent Advances ◽  
Future Research Directions ◽  
Reversible Nature

Supramolecular block copolymers with a dynamically reversible nature and hierarchical microphase-separated structures can greatly enrich the library of pharmaceutical carriers and outline future research directions for biological applications.

scholarly journals Graph Neural Networks: Taxonomy, Advances, and Trends

2022 ◽  
Vol 13 (1) ◽  
pp. 1-54
Author(s):  
Yu Zhou ◽  
Haixia Zheng ◽  
Xin Huang ◽  
Shufeng Hao ◽  
Dengao Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real World ◽  
Research Community ◽  
Future Research ◽  
Research Directions ◽  
Comprehensive Review ◽  
Future Research Directions ◽  
Graph Neural Networks ◽  
Low Dimensional

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-dimensional spaces according to specific tasks. Up to now, there have been several surveys on this topic. However, they usually lay emphasis on different angles so that the readers cannot see a panorama of the graph neural networks. This survey aims to overcome this limitation and provide a systematic and comprehensive review on the graph neural networks. First of all, we provide a novel taxonomy for the graph neural networks, and then refer to up to 327 relevant literatures to show the panorama of the graph neural networks. All of them are classified into the corresponding categories. In order to drive the graph neural networks into a new stage, we summarize four future research directions so as to overcome the challenges faced. It is expected that more and more scholars can understand and exploit the graph neural networks and use them in their research community.

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