Trends in covolutional neural network in 2020 - International Journal of Artificial Intelligence & Applications (IJAIA)

" Trends in Convolutional Neural Network
in 2020"
International Journal of Artificial Intelligence &
Applications (IJAIA)
ISSN: 0975-900X (Online); 0976-2191 (Print)
http://airccse.org/journal/ijaia/ijaia.html

PREDICTING ROAD ACCIDENT RISK USING GOOGLE
MAPS IMAGES AND A CONVOLUTIONAL NEURAL
NETWORK
Aarya Agarwal,
Westwood High School, Austin, USA
ABSTRACT
Location specific characteristics of a road segment such as road geometry as well as surrounding road
features can contribute significantly to road accident risk. A Google Maps image of a road segment
provides a comprehensive visual of its complex geometry and the surrounding features. This paper
proposes a novel machine learning approach using Convolutional Neural Networks (CNN) to accident
risk prediction by unlocking the precise interaction of these many small road features that work in
combination to contribute to a greater accident risk. The model has worldwide applicability and a very
low cost/time effort to implement for a new city since Google Maps are available in most places across
the globe. It also significantly contributes to existing research on accident prevention by allowing for the
inclusion of highly detailed road geometry to weigh in on the prediction as well as the new location based
attributes like proximity to schools and businesses.
KEYWORDS
Deep Learning, Convolutional Neural Networks, Maps Images, Road Accidents.
Full Text: http://aircconline.com/ijaia/V10N6/10619ijaia05.pdf

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AUTHORS
Aarya Agarwal is a student at Westwood High school, Austin and he is currently pursuing an
IB diploma. He has completed several machine learning projects and has submitted several of
these projects to prestigious science competitions. His achievements include 1st place at the
Texas State Science Fair and Austin Regional Science Fair, as well as 2nd place at the Texas
Junior Academy of Sciences for the category of Computer Science/Math

An Application of Convolutional Neural Networks
on Human Intention Prediction
Lin Zhang1
, Shengchao Li2
, Hao Xiong2
, Xiumin Diao2
and Ou Ma1
1
Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati, Cincinnati,
Ohio, USA
2
School of Engineering Technology, Purdue University, West Lafayette, Indiana, USA
ABSTRACT
Due to the rapidly increasing need of human-robot interaction (HRI), more intelligent robots are in
demand. However, the vast majority of robots can only follow strict instructions, which seriously restricts
their flexibility and versatility. A critical fact that strongly negates the experience of HRI is that robots
cannot understand human intentions. This study aims at improving the robotic intelligence by training it
to understand human intentions. Different from previous studies that recognizing human intentions from
distinctive actions, this paper introduces a method to predict human intentions before a single action is
completed. The experiment of throwing a ball towards designated targets are conducted to verify the
effectiveness of the method. The proposed deep learning based method proves the feasibility of applying
convolutional neural networks (CNN) under a novel circumstance. Experiment results show that the
proposed CNN-vote method out competes three traditional machine learning techniques. In current
context, the CNN-vote predictor achieves the highest testing accuracy with relatively less data needed.
KEYWORDS
Human-robot Interaction; Intentions Prediction; Convolutional Neural Networks;

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AUTHORS
Lin Zhang as Sr. Research Associate is currently working in Intelligent Robotics and Autonomous
System Lab at University of Cincinnati. His major research interest is reinforcement learning and
its application in robotics.
Shengchao Li as Ph.D student is doing research in DeePURobotics Lab at Purdue University.
His major research interest is deep neural networks and image processing.
Hao Xiong as Ph.D candidate is doing research in DeePURobotics Lab at Purdue University. His
major research interest is dynamics and control of cable-driven robot and its application in
rehabilitation robots.
Xiumin Diao as Assistant Professor is supervising and directing DeePURobotics Lab at Purdue
University. His research interests are dynamics and control of cable-driven robot and intelligent
robotics.
Ou Ma as Professor is supervising and directing Intelligent Robotics and Autonomous System Lab
at University of Cincinnati. His research interests are multibody dynamics and control, impact-
contact dynamics, intelligent control of robotics, autonomous systems, human-robot
interaction,etc..

Transfer Learning With Convolutional Neural
Networks For Iris Recognition
Maram.G Alaslni1
and Lamiaa A. Elrefaei 1, 2
1
Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz
University, Jeddah, Saudi Arabia
2
Electrical Engineering Department, Faculty of Engineering at Shoubra
, Benha University, Cairo, Egypt
ABSTRACT
Iris is one of the common biometrics used for identity authentication. It has the potential to recognize
persons with a high degree of assurance. Extracting effective features is the most important stage in the
iris recognition system. Different features have been used to perform iris recognition system. A lot of
them are based on hand-crafted features designed by biometrics experts. According to the achievement of
deep learning in object recognition problems, the features learned by the Convolutional Neural Network
(CNN) have gained great attention to be used in the iris recognition system. In this paper, we proposed
an effective iris recognition system by using transfer learning with Convolutional Neural Networks. The
proposed system is implemented by fine-tuning a pre-trained convolutional neural network (VGG-16) for
features extracting and classification. The performance of the iris recognition system is tested on four
public databases IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris-Interval.
The results show that the proposed system is achieved a very high accuracy rate.
KEYWORDS
Biometrics, iris, recognition, deep learning, convolutional neural network (CNN), transfer learning.

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AUTHORS
Maram G. Alaslani, female, received her B.Sc. degree in Computer Science with honors from King Abdulaziz
University in 2010. her M.Sc. in 2018 at King Abdulaziz University, Jeddah, Saudi Arabia. She works as
Teaching Assistant from 2011 to date at Faculty of Computers and Information Technology at King Abdulaziz
University, Rabigh, Saudi Arabia. She has a research interest in image processing, pattern recognition, and
neural network.
Lamiaa A. Elrefaei, female, received her B.Sc. degree with honors in Electrical Engineering (Electronics and
Telecommunications) in 1997, her M.Sc. in 2003 and Ph.D. in 2008 in Electrical Engineering (Electronics) from
faculty of Engineering at Shoubra, Benha University, Egypt. She held a number of faculty positions at Benha
University, as Teaching Assistant from 1998 to 2003, as an Assistant Lecturer from 2003 to 2008, and has been
a lecturer from 2008 to date. She is currently an Associate Professor at the faculty of Computing and
Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia. Her research interests include
computational intelligence, biometrics, multimedia security, wireless networks, and Nano networks. She is a
senior member of IEEE.

Motion Prediction Using Depth Information of
Human Arm Based on Alexnet
JingYuan Zhu1
, ShuoJin Li1
, RuoNan Ma2
, Jing Cheng1
1
School of Aerospace of Engineering, Tsinghua University, Beijing, China
2
School of Economics and Management, Tsinghua University, Beijing, China
ABSTRACT
The development of convolutional neural networks(CNN) has provided a new tool to make classification
and prediction of human's body motion. This project tends to predict the drop point of a ball thrown out
by experimenters by classifying the motion of their body in the process of throwing. Kinect sensor v2 is
used to record depth maps and the drop points are recorded by a square infrared induction module. Firstly,
convolutional neural networks are made use of to put the data obtained from depth maps in and get the
prediction of drop point according to experimenters' motion. Secondly, huge amount of data is used to
train the networks of different structure, and a network structure that could provide high enough accuracy
for drop point prediction is established. The network model and parameters are modified to improve the
accuracy of the prediction algorithm. Finally, the experimental data is divided into a training group and a
test group. The prediction results of test group reflect that the prediction algorithm effectively improves
the accuracy of human motion perception.
KEYWORDS
Human Motion, Prediction, Convolutional Neural Network, Depth Information
Full Text: http:/aircconline.com/ijaia/V10N4/10419ijaia02.pdf

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Artificial Intelligence and its Impact on the
Fourth Industrial Revolution: A Review
Gissel Velarde
ABSTRACT
Artificial Intelligence may revolutionize everything during the so-called fourth industrial revolution,
which carries several emerging technologies and could progress without precedents in human history due
to its speed and scope. Government, academia, industry, and civil society show interest in understanding
the multidimensional impact of the emerging industrial revolution; however, its development is hard to
predict. Experts consider emerging technologies could bring tremendous benefits to humanity; at the same
time, they could pose an existential risk. This paper reviews the development and trends in AI, as well as
the benefits, risks, and strategies in the field. During the course of the emerging industrial revolution, the
common good may be achieved in a collaborative environment of shared interests and the hardest work
will be the implementation and monitoring of projects at a global scale.
KEYWORDS
Artificial Intelligence, Fourth Industrial Revolution, Deep Machine Learning, Emerging Technology,
Human Computer Interaction, Common Good.

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AUTHOR
Gissel Velarde holds a PhD degree in Computer Science and Engineering from
Aalborg University. She participated as a research member of the European project,
“Learning to Create” (Lrn2Cre8) of the European Commission. She has developed
machine learning models for classification, structural analysis, pattern discovery,
representation learning and recommendation systems.

An Obnoxious Lacuna on Discourses and Counter
Discourses Over Artificial Intelligence
Dr. Atindra Dahal
Associate Professor, Kathmandu School of Law, Bhaktapur, Nepal
ABSTRACT
Artificial intelligence is the highest form of human development and sound outcome of human conscience
till the date. But the very development seems to be devastating to human future ahead and has been
heavily projected accordingly. More than it may be to decay and destroy the world, the negative and
chilling views on the prospective damages of AI that scholars are percolating to public are costing many
times on humans; and that is plunging human mindset into irreparable pessimism and negativity. This
article explores the way that AI is being depressingly explored and investigated to browbeat public. In
addition, this write-up highlights the serious lacuna, which the advanced academic engagement has still
grossly failed to fill up, of a great deal in course of mainstreaming views and discussions for noble cause
of human development and societal well-belling . Further, it unmasks the dire need in making
constructive, encouraging and optimistic mind-set building academic pursuits and writings then makes an
alarming call to the all prominent scholars to engage with due compliance of it. As a doctrinal qualitative
research based on extensive survey of secondary data and literature, methodologically, with adoption of
paradigm of descriptive interpretation, this research hypothesizes that the discussions and discourses over
AI are biased, hold a serious lacuna thus need to be reconstructed to make it balanced and build better
world than to browbeat people.
KEYWORDS
Artificial Intelligence, Human Future, Economic Development, Job Market

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An IOT-Based Crowd Sourcing System for Object
Tracking and Information Sharing
Mike Qu1
, Yu Sun2
1
Northwood High School, Irvine, CA 92602
2
California State Polytechnic University, Pomona, CA 91768
ABSTRACT
Technological advancements has offered many solutions to the important current issues such as the
growing numbers of runaway children, wandering Alzheimer’s patients and lost pets in the society, yet
most branches of current technologies are not capable of encompassing all of these key problems. My
research proposes a solution that is practical, durable and reliable -- a proximity sensor device powered by
other users in the area with a process known as “crowd sourcing”, by using their mobile devices as
receiving stations of the service, extensively increasing the effectiveness of this service in especially
urban and suburban areas where there is a high population density
KEYWORDS
Beacon, Device Network, Crowd sourcing, Double-blind, Artificial Intelligence.

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