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Yu, Andrew SeohwanNBA ON-BALL SCREENS: AUTOMATIC IDENTIFICATION AND ANALYSIS OF BASKETBALL PLAYS
Master of Computer and Information Science, Cleveland State University, 2017, Washkewicz College of Engineering
The on-ball screen is a fundamental offensive play in basketball; it is often used to trigger a chain reaction of player and ball movement to obtain an effective shot. All teams in the National Basketball Association (NBA) employ the on-ball screen on offense. On the other hand, a defense can mitigate its effectiveness by anticipating the on-ball screen and its goals. In the past, it was difficult to measure a defender’s ability to disrupt the on-ball screen, and it was often described using abstract words like instincts, experience, and communication. In recent years, player motion-tracking data in NBA games has become available through the development of sophisticated data collection tools. This thesis presents methods to construct a framework which can extract, transform, and analyze the motion-tracking data to automatically identify the presence of on-ball screens. The framework also provides assistance for NBA players and coaches to adjust their game plans regarding the on-ball screen using trends from past games. With the help of support vector machines, the framework identifies on-ball screens with an accuracy of 85%, which shows considerable improvement from the current published results in existing literature.

Committee:

Sunnie Chung, Ph.D. (Committee Chair); Yongjian Fu, Ph.D. (Committee Member); Nigamanth Sridhar, Ph.D. (Committee Member)

Subjects:

Artificial Intelligence; Computer Science

Keywords:

NBA; Basketball; Basketball Analytics; NBA Analytics; Data Mining; Web Scraping; Machine Learning; Support Vector Machine; Classification;

Aring, Danielle CIntegrated Real-Time Social Media Sentiment Analysis Service Using a Big Data Analytic Ecosystem
Master of Computer and Information Science, Cleveland State University, 2017, Washkewicz College of Engineering
Big data analytics are at the center of modern science and business. Our social media networks, mobile devices and enterprise systems generate enormous volumes of it on a daily basis. This wide range of availability provides many organizations in every field opportunities to discover valuable intelligence for critical decision-making. However, traditional analytic architectures are insufficient to handle unprecedentedly big volume of data and complexity of data processing. This thesis presents an analytic framework to combat unprecedented scale of big data that performs data stream sentiment analysis effectively in real time. The work presents a Social Media Big Data Sentiment Analytics Service System (SMBDSASS). The architecture leverages Apache Spark stream data processing framework, coupled with a NoSQL Hive big data ecosystem. Two sentiment analysis models were developed; the first, a topic based model, given user provided topic or person of interest sentiment (opinion) analysis was performed on related topic sentences in a tweet stream. The second, an aspect (feature) based model given user provided product of interest and related product features aspect (feature) analysis was performed on reviews containing important feature terms. The experimental results of the proposed framework using real time tweet stream and product reviews show comparable improvements from the results of the existing literature, with 73% accuracy for topic-based sentiment model, and 74% accuracy for aspect (feature) based sentiment model. The work demonstrated that our topic and aspect based sentiment analysis models on the real time stream data processing framework using Apache Spark and machine learning classifiers coupled with a NoSQL big data ecosystem offer an efficient, scalable, real-time stream data-processing alternative for the complex multiphase sentiment analysis over common batch data mining frameworks.

Committee:

Sun Sunnie Chung, Ph.D. (Committee Chair); Yongjigan Fu, Ph.D. (Committee Member); Ifthkar Sikder, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

big data analytics, sentiment analysis, stream data-processing

Bhatt, Mrunal DipakkumarINTELLIGENT VOICE ACTIVATED HOME AUTOMATION (IVA)
Master of Computer and Information Science, Cleveland State University, 2016, Washkewicz College of Engineering
This thesis presents the design of an original Intelligent Home Automation Architecture. My work was divided in two phases. The first portion was dedicated to acquiring a thorough understanding of the most successful and diffused Home-Automation commercial architectures. During this phase, I intended to gain a deep appreciation for the variety of organizations, capabilities, limitations, and potential areas of growth of the existing Home-Automation leading systems. In order to acquire this knowledge, I had to use a reverse engineering approach. The reason for using this methodology arises from the fact that all the products considered in this study are commercially protected as industrial secrets. Consequently, it is not possible to obtain detailed descriptions of their 'real' architectures and internal operations. The second part of this thesis presents my personal contribution in the form of a prototype for a Smart-Home Architecture. My design, called IVA (short for Intelligent Voice Activated) home automation, is primarily driven by the processing of natural language voice commands. I argue that this approach should be attractive to seniors, and people with limited range of mobility. In addition, the hardware needed to implement the system is commonly available and inexpensive. The most sophisticated device in my model is a smartphone, which in most cases, is already own by the prospective user.

Committee:

Victor Matos, PhD (Committee Chair); Ben Blake, PhD (Committee Co-Chair); Sunnie Chung, PhD (Committee Member)

Subjects:

Computer Engineering; Computer Science; Information Technology; Technology

Keywords:

Home automation, smart home, smart home architecture, Domotic computing, technology, natural language speech controlled home automation, Android app, Arduino based hardware design