Design and Implementation of Network Transfer Protocol for Big Genomic Data

Genomic data is growing exponentially due to next generation sequencing technologies (NGS) and their ability to produce massive amounts of data in a short time. NGS technologies generate big genomic data that needs to be exchanged between different locations efficiently and reliably. The current network transfer protocols rely on Transmission Control Protocol (TCP) or User Datagram Protocol (UDP) protocols, ignoring data size and type. Universal application layer protocols such as HTTP are designed for wide variety of data types and are not particularly efficient for genomic data. Therefore, we present a new data-aware transfer protocol for genomic-data that increases network throughput and reduces latency, called Genomic Text Transfer Protocol (GTTP). In this paper, we design and implement a new network transfer protocol for big genomic DNA dataset that relies on the Hypertext Transfer Protocol (HTTP). Modification to content-encoding of HTTP has been done that would transfer big genomic DNA datasets using machine-to-machine (M2M) and client(s)-server topologies. Our results show that our modification to HTTP reduces the transmitted data by 75% of original data and still be able to regenerate the data at the client side for bioinformatics analysis. Consequently, the transfer of data using GTTP is shown to be much faster (about 8 times faster than HTTP) when compared with regular HTTP

Second language education website and APP Design

During the pandemic, schools are closed and it brought a great chance for JoJo Learning to expand and explore the new method of customer acquisition. With a combination method of educational robot and E-book, JoJo is providing his customers with a compound solution for summer second language learning for kids and toddlers. As part of the team, my main responsibility is to prototype and publish new UIUX design and using data analysis as a method to find the key channels of the business

Service Industry Sentience- CS 4267

Since Amazon GO opened its\u27 doors to employees in late 2016, businesses have been watching closely to see if a store without checkout can succeed in the current market. While self-checkout stations have been in use since 1986, businesses are looking for ways to enhance the convenience of shoppers. From online ordering, contactless payments, and same day pickup, to drone delivery, leaving packages inside your home, and now checkout free stores. In this presentation I explore the possibility of using YOLO and OpenCV to produce a checkout system based on object recognition which makes a system that is cheaper and more viable for small businesses. While it may be possible for a large corporation to afford a setup similar to Amazon GO small businesses need a way to handle the growing needs of the market. In the face of COVID-19 contactless payments have become necessary and businesses of all types have scrambled to find ways to continue business in these turbulent times. A checkout system without human contact which can be cheaply implemented would be helpful for businesses around the world as well as their customers. By utilizing YOLOV3 and OpenCV and training using Google Colab, it is possible to create and maintain a database of products and customers for easy, contactless, checkout

Facial Recognition and Object Detection-based Smart Cashier System- CS 4732

The topic of smart based cashier system. Professor Dr. Aledhari is the project advisor for CS4732 Machine Vision

Improving Policy Making with Informed Covid-19 Case Prediction-CS 4267

Covid-19 has necessitated many changes in the way life in the United States of America is lived. These include things such as new operational hours for retail and other service locations, mandatory and encouraged social distancing protocols on a state and federal level, and major changes to the way educational services are delivered in K-12 and higher education in both public and private contexts. The goal of this project is to produce a mathematical model with the express purpose of directly aiding pandemic response policy decision making by comparing predicted impacts of various logistical decisions and social distancing guidelines

Tracking High-Speed Chase Vehicles with Machine Learning​- CS 4732

From law enforcement officers, to fleeing suspects, to the general public, lengthy police pursuits poses a risk to all involved. With modern advances in machine vision, pursuits caught on camera can be analyzed with machine learning techniques. These techniques can then be used to produce datasets for future researchers to use in machine learning algorithms. This paper explores ways to produce those datasets using OpenCV, a machine vision library for Python, as well as GOTURN, a object tracker for OpenCV

Using Artificial Intelligence to Prescribe Medicine- CS 4732

With the current advancements being made in the Machine Learning field, utilizing Artificial Intelligence and Deep Learning techniques to analyze complex information such as genome information has become possible. By creating and building up a database of such data as well as compatible medicines, we suspect it is possible to prescribe personalized medicines to treat patients with a high percentage of success while also being able to take into account various other health conditions and pre-dispositions. This possibility depends heavily on the ability of the Neural Network or various other Machine Learning structure to correctly interpret the information from a medical staff worker’s observations. We plan to utilize labelled genome data sets in order to test and train our project’s Machine Learning architecture then make improvements based off those results. We will be testing out various techniques for Machine Learning in order to properly process the complex and extremely long observation notes supplied to us in the data sets. Our primary goal is to make predictions on genetic mutations through the usage of text scrubbing. This will be done using Logistic Regression to classify all of the training data. Once all of the data is classified based on commonalities the algorithms detect, we will create Confusion matrices as well as calculate Accuracy and Precision on the testing data. These results will better allow us to make improvements on the current implementation

Machine learning for network application security: Empirical evaluation and optimization

Machine learning (ML) has demonstrated great potential to revolutionize the networking field. In this paper, we present a large-scale empirical study to evaluate the effectiveness of state-of-the-art ML algorithms for network application security. In our experiments, six classical ML algorithms and three neural network algorithms are evaluated over three networking datasets, KDDCup 99, NSL-KDD, and ADFA IDS 2017. Measurements are made between the non-optimized and optimized versions of ML algorithms. Furthermore, various training and testing ratios are experimented to assess each algorithm\u27s optimal performance. The results revealed that optimizing ML algorithms could help achieve better performance in detecting networking attacks. In particular, the Decision Tree proved to be the most accurate and fastest algorithm in the classical ML while the Recurrent Neural Network achieved the best performance among neural network algorithms

Motion Prediction for Vehicles - CS 4732

The idea of a self-driving car is one that makes us think more of a science fiction movie than a plausible addition to real life. While we have not come close to perfecting street legal autonomous vehicles by any means, over the past few years significant progress has been made. Autonomous vehicles need to be able to constantly perceive what is happening in their environment and be able to react accordingly. This study aims to improve upon the accuracy of previous motion prediction models, thus allowing the autonomous vehicle to better understand its surroundings. Building upon Lyft\u27s baseline model, we will implement a model with multi-modal prediction that will generate possible paths that are likely to occur. After training the new model and reviewing the results, the multi-modal approach handles complex intersections well. The baseline model had some instances where a predicted path would ignore the geometry of the road or would cut across an intersection. With the new model, ResNet50 was also implemented. Being a form of deep residual learning, ResNet50 helps give context awareness to the model. The implementation of ResNet50 helped eliminate errors where the predicted path did not follow the geometry of the road, as well as predictions in intersections

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.Scopus2-s2.0-8494108524