PREDICTING ROAD ACCIDENT RISK USING GOOGLE MAPS IMAGES AND A CONVOLUTIONAL NEURAL NETWORK
Aarya Agarwal, Westwood High School, USA
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.
Deep Learning, Convolutional Neural Networks, Maps Images, Road Accidents
For More Details :
http://aircconline.com/ijaia/V10N6/10619ijaia05.pdf
An Application of Convolutional Neural Networks on Human Intention Prediction
Lin Zhang1, Shengchao Li2, Hao Xiong2, Xiumin Diao2 and Ou Ma1 , 1 University of Cincinnati, USA , 2 Purdue University, USA
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.
Human-robot Interaction, Intentions Prediction, Convolutional Neural Networks
For More Details :
http://aircconline.com/ijaia/V10N5/10519ijaia01.pdf
Transfer Learning With Convolutional Neural Networks For Iris Recognition
Maram.G Alaslni1 and Lamiaa A. Elrefaei1, 2 , 1King Abdulaziz University, Saudi Arabia , 2 Benha University, Egypt
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.
Biometrics, iris, recognition, deep learning, convolutional neural network (CNN), transfer learning.
For More Details :
http://aircconline.com/ijaia/V10N5/10519ijaia05.pdf
Motion Prediction Using Depth Information of Human Arm Based on Alexnet
JingYuan Zhu1, ShuoJin Li1, RuoNan Ma2, Jing Cheng1 , 1 Tsinghua University, China , 2 Tsinghua University, China
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.
Human Motion, Prediction, Convolutional Neural Network, Depth Information
For More Details :
http:/aircconline.com/ijaia/V10N4/10419ijaia02.pdf
Artificial Intelligence and its Impact on the Fourth Industrial Revolution: A Review
Gissel Velarde
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.
Artificial Intelligence, Fourth Industrial Revolution, Deep Machine Learning, Emerging Technology, Human Computer Interaction, Common Good.
For More Details :
http://aircconline.com/ijaia/V10N6/10619ijaia04.pdf
An Obnoxious Lacuna on Discourses and Counter Discourses Over Artificial Intelligence
Dr. Atindra Dahal , Kathmandu School of Law, Nepal
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..
Artificial Intelligence, Human Future, Economic Development, Job Market
For More Details :
http://aircconline.com/ijaia/V10N2/10219ijaia02.pdf
An IOT-Based Crowd Sourcing System for Object Tracking and Information Sharing
Mike Qu1, Yu Sun2 , 1Northwood High School, Irvine, , 2California State Polytechnic University, Pomona
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.
Beacon, Device Network, Crowd sourcing, Double-blind, Artificial Intelligence
For More Details :
http://aircconline.com/ijaia/V10N1/10119ijaia04.pdf