Welcome to COMIT 2021

5th International Conference on
Computer Science and Information
Technology (COMIT 2021)

October 29 ~ 30, 2021, Vienna, Austria

Accepted Papers
Carenvision: A Data-Driven Machine Learning Framework for Automated Car Value Prediction

TianGe (Terence) Chen1, Angel Chang1, Evan Gunnell2, Yu Sun2, 1Rancho Cucamonga High School, Rancho Cucamonga, CA, 91701, 2California State Polytechnic University, Pomona, CA, 91768

ABSTRACT

When people want to buy or sell a personal car, they struggle to know when the timing is best in order to buy their favorite vehicle for the best price or sell for the most profit. We have come up with a program that can predict each car’s future values based on experts’ opinions and reviews. Our program extracts reviews which undergo sentiment analysis to become our data in the form of positive and negative sentiment. The data is then collected and used to train the Machine Learning model, which will in turn predict the car’s retail price.

KEYWORDS

Machine Learning, Polynomial Regression, Artificial Neural Network.


Very deep convolutional neural network for an automated image classification

M.Dhilsath Fathima and R Hariharan, Assistant Professor, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, India

ABSTRACT

An automated image classification is an essential task of the computer vision field. The tagging of images in to a set of predefined groups is referred to as image classification. A specific image is being classified into a large number of different cat-egories. The implementation of computer vision to automate image classification would be beneficial because manual image evaluation and identification can be time-consum-ing, particularly when there are many images of different classes. Deep learning ap-proaches are proven to overperform existing machine learning techniques in a number of fields in recent years, and computer vision is one of most notable examples. Com-puter vision utilizes many deep learning techniques to an automated image classifica-tion task. The very deep neural network is a powerful deep learning model for image classification, and this paper examines it briefly using following image datasets. MNIST hand-written digit dataset is used as typical image datasets in this proposed framework to prove the efficacy of very deep neural networks over other deep learning models. An objective of this proposed work is understanding a very deep neural net-work architecture to implement essential image classification tasks: handwritten digit recognition. This paper analyses a very deep neural network architecture to train Con-volutional neural network parameters on the two datasets mentioned above. The feasi-bility of the proposed model is evaluated using classifier performance metrics such as classifier accuracy, standard deviation, and entropy loss. The study results of the very deep neural network model are compared to Convolutional neural network and convo-lutional neural network with batch normalization. According to the results of the com-parison study, very deep neural networks achieve a high accuracy of 98.9% for hand-written datasets and 90.84% classification accuracy for fashion datasets. The outcome of the proposed work is used to interpret how well a very deep neural network performs when comparison to the other two models of deep neural network. This proposed ar-chitecture may be used to automate the classification of handwritten digits classifica-tion.

KEYWORDS

Very deep neural network, Convolutional neural network, batch normali-zation, hand-written digit classification.


Intelligent Speed Adaptive System using Image Regression Method for Highway and Urban Roads

Bhavesh Sharma1 and Junaid Ali2, 1Department of Electrical, Electronics and Communication Engineering, Engineering College, Ajmer, India, 2Department of Mechanical Engineering, Indian Institute of Technology, Madras, India

ABSTRACT

Intelligent Speed Adaptive System (ISAS) is an emerging technology in the field of autonomous vehicles. However, the public acceptance rate of ISAS is drastically low because of several downfalls i.e. reliability and low accuracy. Various researchers have contributed methodologies to enhance the traffic prediction scores and algorithms to improve the overall adaptability of ISAS. The literature is scarce for Image Regression in this range of application. Computer vision has proved its iota in stream of object detection in self-driving technology in which most of the models are assisted through the complex web of neural nets and live imaging systems. In this article, some major issues related to the present technology of the ISAS and discussed new methodologies to get higher prediction accuracy to control the speed of vehicle through Image Regression technique to develop a computer vision model to predict the speed of vehicle with each frame of live images.

KEYWORDS

Intelligent Systems, Self-Driving Vehicle, Image Processing, Image Regression, Computer Vision, Automotive.


Using Different Assessment Indicators in Supporting Online Learning

Yew Kee Wong,HuangHuai University, Zhumadian, Henan, China

ABSTRACT

The assessment outcome for many online learning methods are based on the number of correct answers and than convert it into one final mark or grade. We discovered that when using online learning, we can extract more detail information from the learning process and these information are useful for the assessor to plan an effective and efficient learning model for the learner. Statistical analysis is an important part of an assessment when performing the online learning outcome. The assessment indicators include the difficulty level of the question, time spend in answering and the variation in choosing answer. In this paper we will present the findings of these assessment indicators and how it can improve the way the learner being assessed when using online learning system. We developed a statistical analysis algorithm which can assess the online learning outcomes more effectively using quantifiable measurements. A number of examples of using this statistical analysis algorithm are presented.

KEYWORDS

Artificial Intelligence, Assessment Indicator, Online Learning, Statistical Analysis Algorithm.


Current Security Topics and Evolving Risk Mapping Leveraging LDA Machine Learning Models

Joshua Scarpino, Marymount University, Arlington, Virginia, 22207, USA

ABSTRACT

This is a study around the application of Genism’s LDA model toward identification of critical topics within cybersecurity by leveraging social media user feeds. The research was intended to advance focus around trending topics that are critical within security as the threat landscape evolves. This research provides an op-opportunity to expanded threat intelligence by leveraging security professionals as a critical source of intel. This can help to create focus for security awareness training materials and assist in the possible early identification of emerging threats.

KEYWORDS

Gensim Latent Dirichlet Allocation, Social Media, Security Awareness.