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Accepted Papers
Mathematical Simulation Of Package Delivery Optimization Using A Combination Of Carriers

Valentyn M. Yanchuk, Andrii G. Tkachuk, Dmitry S. Antoniuk, Tetiana A. Vakaliuk and Anna A. Humeniuk, Zhytomyr Polytechnic State University, Zhytomyr 10005, Ukraine

ABSTRACT

A variety of goods and services in the contemporary world requires permanent improvement of services e-commerce platform performance. Modern society is so deeply integrated with mail deliveries, purchasing of goods and services online, that makes competition between service and good providers a key selection factor. As long as logistic and timely and cost-effective delivery plays important part authors decided to analyze possible ways of improvements in the current field, especially for regions distantly located from popular distribution centers. Considering both: fast and lazy delivery the factor of costs is playing an important role for each end-user. Given work proposes a simulation that analyses the current cost of delivery for e-commerce orders in the context of delivery by the Supplier Fleet, World-Wide delivery service fleet and possible vendor drop-ship and checks of the alternative ways can be used to minimize the costs. The main object of investigation is focused around mid and small businesses living far from big distribution centers (except edge cases like lighthouses, edge rocks with very limited accessibility) but actively using e-commerce solutions for daily activities fulfillment. Authors analyzed and proposed a solution for the problem of cost optimization for packages delivery for long-distance deliveries using a combination of paths delivered by supplier fleets, world-wide and local carriers. Data models and Add-ons of contemporary Enterprise Resource Planning systems were used, and additional development is proposed in the perspective of the flow selection change. The experiment is based on data sources of the United States companies using a wide range of carriers for delivery services and uses the data sources of the real companies; however, it applies repetitive simulations to analyze variances in obtained solutions.

KEYWORDS

Simulation, Customer Behavior, Optimization, E-commerce.


An Internet-of-things Application to Assist the Detection of Falling to the Ground

Yifei Yu1, Yu Sun2 and Fangyan Zhang3, 1Sage Hill School, Newport Coast, CA, 92657, 2California State Polytechnic University, Pomona, CA, 91768, 3ASML, San Jose, CA, 95131

ABSTRACT

As people get old, the risk of them falling increases; the fall will impact senior citizens more negatively than younger people. My grandmother once fell and hit her when she was alone at home, and she instantly became unconscious. Frequently, senior citizens are unable to help themselves after they fall, even if they remain conscious. However, there isn’t a product that senior citizens can use to notify their relatives right away if they fall, and this leads to the question of how we can bring immediate aid to all senior citizens after they fall. This paper brings forward the product and software that can solve this problem. The product is a small wristband that detects any falls or collisions and notifies relatives right away. The software is an accompanying app that shows the data recorded from those falls or collisions, specifically designed for family members to keep track of their elders. We applied our application during our test sessions and conducted a qualitative evaluation of the approach. The results show that this experiment is a great solution to our problem, but with a few limitations and weaknesses.

KEYWORDS

Detection of falling, wristband, iOS, Android.


Decoder Based Pseudo Examples Generation and Release of Urdumnist Dataset

Wisal Khan1, Teerath Kumar2 and Bin Luo1, 1School of Computer and Technology, Anhui University, Hefei 230039, Peoples Republic of China, 2Kyung Hee University, South Korea

ABSTRACT

Pseudo examples generation has shown an impressive performance on image classi?cation tasks. Pseudo examples generation is useful when we have data in a few amounts that are used for semi-supervised learning or few-shot learning. Previous work used autoencoder architecture to improve the classi?cation performance in semisupervised learning, and pseudo examples generation and its optimization have improved performance in few-shot learning. In this paper, we propose a new way of pseudo examples generation using only a generator (Decoder) based approach to generate the pseudo examples for each class that is effective for both semi-supervised learning and few-shot learning. In our approach, we ?rst train Decoder for each class using random noise as input and examples as output. Once training is done, we generate a different number of samples using trained Decoders. To check the effectiveness of our approach, we use semi-supervised learning and few-shot learning techniques on famous datasets MNIST and FMNIST for the different numbers of sample selection. Our generator based approach outperforms previous semi-supervised learning and few-shot learning approaches. Secondly, we are the ?rst to release the UrduMNIST dataset consists of 10000 images, including 8000 training and 2000 test images collected through three different methods to include diversity. We also check the effectiveness of our methods on our UrduMNIST dataset.

KEYWORDS

Autoencoder, Generator, Semi-Supervised learning, few-shot learning.


Unsupervised Clustering for Distorted Image with Denoising Feature Learning

Qihao Lin, Jinyu Cai and Genggeng Liu, College of Mathematics and Computer Science,Fuzhou university, Fuzhou, 350116, China

ABSTRACT

High-dimensional of image data is an obstacle for clustering. One of methods to solve it is feature representation learning. However, if the image is distorted or suffers from the influence of noise, the extraction of effective features may be difficult. In this paper, an end-to-end feature learning model is proposed to extract denoising low-dimensional representations from distorted images, and these denoising features are evaluated by comparing with several feature representation methods in clustering task. First, some related works about classical dimensionality reduction are introduced. Then the architecture and working mechanism of denoising feature learning model are presented. As the structural characteristics of this model, it can obtain essential information from image to decrease reconstruction error. When facing with corrupted data, it also runs a better clustering result. Finally, extensive experiments demonstrate that the obtained feature representations by the proposed model are effective on eight standard image datasets.

KEYWORDS

Unsupervised Learning, Feature Representation, Auto-encoder, Clustering.


An Automated Recommendation Engine For College Selection And Loan Option Using Machine Learning And Big Data Analysis

Bill Zheng1, Yu Sun2, Fangyan Zhang3, 1Web School of California, Claremont, CA 91711, 2California State Polytechnic University, Pomona, CA, 91768, 3ASML, San Jose, CA, 95131

ABSTRACT

In the current political climate, mass media was depicted as highly divisive and inaccurate while many cannot efficiently identify its bias presented in the news. Using research regarding keywords in the current political environment, we have designed an algorithm that detects and quantifies political, opinion, and satirical biases present in current day articles. Our algorithm makes use of SciPy’s SK-Learn linear regression model and multiple regression model to automatically identify the bias of a news article based on a scale of 0 to 3 (-3 to 3 in political bias detection) to automatically detect the bias presented in a news source. The usage of this algorithm on all three segments, politics, opinion, and satire has been proven effective, and it enables an average reader to accurately evaluate the bias in a news source.

KEYWORDS

Mass media, political bias, machine learning, linear regression, regression model.


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