Physical Sciences and Mathematics Commons^™

Music Popularity, Diffusion And Recommendation In Social Networks: A Fusion Analytics Approach, Jing Ren

Dissertations and Theses Collection (Open Access)

Streaming music and social networks offer an easy way for people to gain access to a massive amount of music, but there are also challenges for the music industry to design for promotion strategies via the new channels. My dissertation employs a fusion of machine-based methods and explanatory empiricism to explore music popularity, diffusion, and promotion in the social network context.

An Investigation Into The Effects Of Multiple Kernel Combinations On Solutions Spaces In Support Vector Machines, Paul Kelly, Luca Longo

Conference papers

The use of Multiple Kernel Learning (MKL) for Support Vector Machines (SVM) in Machine Learning tasks is a growing field of study. MKL kernels expand on traditional base kernels that are used to improve performance on non-linearly separable datasets. Multiple kernels use combinations of those base kernels to develop novel kernel shapes that allow for more diversity in the generated solution spaces. Customising these kernels to the dataset is still mostly a process of trial and error. Guidelines around what combinations to implement are lacking and usually they requires domain specific knowledge and understanding of the data. Through a brute …

Detecting Rip Currents From Images, Corey C. Maryan

LSU New Orleans Theses and Dissertations

Rip current images are useful for assisting in climate studies but time consuming to manually annotate by hand over thousands of images. Object detection is a possible solution for automatic annotation because of its success and popularity in identifying regions of interest in images, such as human faces. Similarly to faces, rip currents have distinct features that set them apart from other areas of an image, such as more generic patterns of the surf zone. There are many distinct methods of object detection applied in face detection research. In this thesis, the best fit for a rip current object detector …

A Machine Learning Approach To Predict First-Year Student Retention Rates At University Of Nevada, Las Vegas, Aditya Rajuladevi

UNLV Theses, Dissertations, Professional Papers, and Capstones

First-year student retention rates for a four-year institution refers to the percentage of First-time Full-time students from the previous fall who return to the same institution for the following fall. First-year retention rates act as an important indicator of the student satisfaction as well as the performance of the university. Moreover, universities with low retention rates may face a decline in the admissions of talented students with a notable loss of tuition fees and contributions from alumni. Therefore, it is important for universities to formulate strategies to identify students who are at risk of not being retained and take necessary …

Improving Swarm Performance By Applying Machine Learning To A New Dynamic Survey, John Taylor Jackson

Master's Theses

A company, Unanimous AI, has created a software platform that allows individuals to come together as a group or a human swarm to make decisions. These human swarms amplify the decision-making capabilities of both the individuals and the group. One way Unanimous AI increases the swarm’s collective decision-making capabilities is by limiting the swarm to more informed individuals on the given topic. The previous way Unanimous AI selected users to enter the swarm was improved upon by a new methodology that is detailed in this study. This new methodology implements a new type of survey that collects data that is …

Improving The Efficacy Of Context-Aware Applications, Jon C. Hammer

Graduate Theses and Dissertations

In this dissertation, we explore methods for enhancing the context-awareness capabilities of modern computers, including mobile devices, tablets, wearables, and traditional computers. Advancements include proposed methods for fusing information from multiple logical sensors, localizing nearby objects using depth sensors, and building models to better understand the content of 2D images.

First, we propose a system called Unagi, designed to incorporate multiple logical sensors into a single framework that allows context-aware application developers to easily test new ideas and create novel experiences. Unagi is responsible for collecting data, extracting features, and building personalized models for each individual user. We demonstrate the …

Improving Asynchronous Advantage Actor Critic With A More Intelligent Exploration Strategy, James B. Holliday

Graduate Theses and Dissertations

We propose a simple and efficient modification to the Asynchronous Advantage Actor Critic (A3C)

algorithm that improves training. In 2016 Google’s DeepMind set a new standard for state-of-theart

reinforcement learning performance with the introduction of the A3C algorithm. The goal of

this research is to show that A3C can be improved by the use of a new novel exploration strategy we

call “Follow then Forage Exploration” (FFE). FFE forces the agents to follow the best known path

at the beginning of a training episode and then later in the episode the agent is forced to “forage”

and explores randomly. In …

A Home Security System Based On Smartphone Sensors, Michael Mahler

Graduate Theses and Dissertations

Several new smartphones are released every year. Many people upgrade to new phones, and their old phones are not put to any further use. In this paper, we explore the feasibility of using such retired smartphones and their on-board sensors to build a home security system. We observe that door-related events such as opening and closing have unique vibration signatures when compared to many types of environmental vibrational noise. These events can be captured by the accelerometer of a smartphone when the phone is mounted on a wall near a door. The rotation of a door can also be captured …

Multimodal Depression Detection: An Investigation Of Features And Fusion Techniques For Automated Systems, Michelle Renee Morales

Dissertations, Theses, and Capstone Projects

Depression is a serious illness that affects a large portion of the world’s population. Given the large effect it has on society, it is evident that depression is a serious health issue. This thesis evaluates, at length, how technology may aid in assessing depression. We present an in-depth investigation of features and fusion techniques for depression detection systems. We also present OpenMM: a novel tool for multimodal feature extraction. Lastly, we present novel techniques for multimodal fusion. The contributions of this work add considerably to our knowledge of depression detection systems and have the potential to improve future systems by …

A Continuous Space Generative Model, Erzen Komoni

Graduate Theses and Dissertations

Generative models are a class of machine learning models capable of producing digital images with plausibly realistic properties. They are useful in such applications as visualizing designs, rendering game scenes, and improving images at higher magnifications. Unfortunately, existing generative models generate only images with a discrete predetermined resolution. This paper presents the Continuous Space Generative Model (CSGM), a novel generative model capable of generating images as a continuous function, rather than as a discrete set of pixel values. Like generative adversarial networks, CSGM trains by alternating between generative and discriminative steps. But unlike generative adversarial networks, CSGM uses only one …

Artificial Intelligence: A Study On Governance, Policies, And Regulations, Weiyu Wang, Keng Siau

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) is displacing jobs and creating an upheaval in the world. It will change the way we work and the way we live. What should be the AI governance, policies, and regulations? How can AI governance, policies, and regulations mitigate and alleviate the negative aspects of AI advancement? How will AI governance, policies, and regulations impact the future of work and the future of humanity? This longitudinal multiple case studies research will study the evolution and revolution of AI governance, policies, and regulations, and how governance, policies and regulations impact AI advancement and are impacted by AI advancement. …

Standard Machine Learning Techniques In Audio Beehive Monitoring: Classification Of Audio Samples With Logistic Regression, K-Nearest Neighbor, Random Forest And Support Vector Machine, Prakhar Amlathe

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Honeybees are one of the most important pollinating species in agriculture. Every three out of four crops have honeybee as their sole pollinator. Since 2006 there has been a drastic decrease in the bee population which is attributed to Colony Collapse Disorder (CCD). The bee colonies fail/ die without giving any traditional health symptoms which otherwise could help in alerting the Beekeepers in advance about their situation.

Electronic Beehive Monitoring System has various sensors embedded in it to extract video, audio and temperature data that could provide critical information on colony behavior and health without invasive beehive inspections. Previously, significant …

Walknet: A Deep Learning Approach To Improving Sidewalk Quality And Accessibility, Andrew Abbott, Alex Deshowitz, Dennis Murray, Eric C. Larson

SMU Data Science Review

This paper proposes a framework for optimizing allocation of infrastructure spending on sidewalk improvement and allowing planners to focus their budgets on the areas in the most need. In this research, we identify curb ramps from Google Street View images using traditional machine learning and deep learning methods. Our convolutional neural network approach achieved an 83% accuracy and high level of precision when classifying curb cuts. We found that as the model received more data, the accuracy increased, which with the continued collection of crowdsourced labeling of curb cuts will increase the model’s classification power. We further investigated a model …

A Legal Perspective On The Trials And Tribulations Of Ai: How Artificial Intelligence, The Internet Of Things, Smart Contracts, And Other Technologies Will Affect The Law, Iria Giuffrida, Fredric Lederer, Nicolas Vermeys

Faculty Publications

No abstract provided.

Detecting Speakers In Video Footage, Michael Williams

Master's Theses

Facial recognition is a powerful tool for identifying people visually. Yet, when the end goal is more specific than merely identifying the person in a picture problems can arise. Speaker identification is one such task which expects more predictive power out of a facial recognition system than can be provided on its own. Speaker identification is the task of identifying who is speaking in video not simply who is present in the video. This extra requirement introduces numerous false positives into the facial recognition system largely due to one main scenario. The person speaking is not on camera. This paper …

Using Autoencoder To Reduce The Length Of The Autism Diagnostic Observation Schedule (Ados), Sara Hussain Daghustani

Electronic Theses, Projects, and Dissertations

This thesis uses autoencoders to explore the possibility of reducing the length of the Autism Diagnostic Observation Schedule (ADOS), which is a series of tests and observations used to diagnose autism spectrum disorders in children, adolescents, and adults of different developmental levels. The length of the ADOS, directly and indirectly, causes barriers to its access for many individuals, which means that individuals who need testing are unable to get it. Reducing the length of the ADOS without significantly sacrificing its accuracy would increase its accessibility. The autoencoders used in this thesis have specific connections between layers that mimic the sectional …

Multimodal Sensing And Data Processing For Speaker And Emotion Recognition Using Deep Learning Models With Audio, Video And Biomedical Sensors, Farnaz Abtahi

Dissertations, Theses, and Capstone Projects

The focus of the thesis is on Deep Learning methods and their applications on multimodal data, with a potential to explore the associations between modalities and replace missing and corrupt ones if necessary. We have chosen two important real-world applications that need to deal with multimodal data: 1) Speaker recognition and identification; 2) Facial expression recognition and emotion detection.

The first part of our work assesses the effectiveness of speech-related sensory data modalities and their combinations in speaker recognition using deep learning models. First, the role of electromyography (EMG) is highlighted as a unique biometric sensor in improving audio-visual speaker …

Support Vector Machines For Image Spam Analysis, Aneri Chavda, Katerina Potika, Fabio Di Troia, Mark Stamp

Faculty Publications, Computer Science

Email is one of the most common forms of digital communication. Spam is unsolicited bulk email, while image spam consists of spam text embedded inside an image. Image spam is used as a means to evade text-based spam filters, and hence image spam poses a threat to email-based communication. In this research, we analyze image spam detection using support vector machines (SVMs), which we train on a wide variety of image features. We use a linear SVM to quantify the relative importance of the features under consideration. We also develop and analyze a realistic “challenge” dataset that illustrates the limitations …

Integrated Strategies For Sustainable Wastewater-Based Algal Biofuel Production And Environmental Mitigation In The Us, Javad Roostaei

Wayne State University Dissertations

Integration of algae cultivation with wastewater treatment has received increasing interest as a cost-effective strategy for biofuel production. However, there has been no full assessment of algal biofuel production with wastewater on macro-scale by taking into account wastewater resources, land availability, CO2 emission resources, and geographic variation. This research addressed and evaluated the use of wastewater for algae cultivation, in terms of modeling and laboratory experiments. The first goal of this research was to develop a spatially explicit lifecycle model, by integrating life cycle assessment (LCA), and Geographic Information Systems (GIS) analysis, for the evaluation of the environmental and economic …

Iterative Matrix Factorization Method For Social Media Data Location Prediction, Natchanon Suaysom

HMC Senior Theses

Since some of the location of where the users posted their tweets collected by social media company have varied accuracy, and some are missing. We want to use those tweets with highest accuracy to help fill in the data of those tweets with incomplete information. To test our algorithm, we used the sets of social media data from a city, we separated them into training sets, where we know all the information, and the testing sets, where we intentionally pretend to not know the location. One prediction method that was used in (Dukler, Han and Wang, 2016) requires appending one-hot …

Data-Driven Predictive Framework For Modeling Complex Multi-Physics Engineering Applications, Arturo Schiaffino Bustamante

Open Access Theses & Dissertations

Computational models are often encountered in multiple engineering application, such as structural design, material science, heat transfer and fluid dynamics. These simulations offer the engineers the capability of understanding complex physical situations before putting them to practice, either through experimentation or prototyping. The current advances in computational sciences, hardware architecture, software development and big data technology, have allowed the construction of sturdy predicting frameworks for analyzing a wide array of natural phenomena across different disciplines, either through the implementation of statistical methods, such as big data, and uncertainty quantification, or through high performance computing of a numerical model. The objective …

Generating Diverse And Meaningful Captions: Unsupervised Specificity Optimization For Image Captioning, Annika Lindh, Robert J. Ross, Abhijit Mahalunkar, Giancarlo Salton, John D. Kelleher

Conference papers

Image Captioning is a task that requires models to acquire a multi-modal understanding of the world and to express this understanding in natural language text. While the state-of-the-art for this task has rapidly improved in terms of n-gram metrics, these models tend to output the same generic captions for similar images. In this work, we address this limitation and train a model that generates more diverse and specific captions through an unsupervised training approach that incorporates a learning signal from an Image Retrieval model. We summarize previous results and improve the state-of-the-art on caption diversity and novelty.

We make our …

Probabilistic Clustering Ensemble Evaluation For Intrusion Detection, Steven M. Mcelwee

CCAC Theses and Dissertations

Intrusion detection is the practice of examining information from computers and networks to identify cyberattacks. It is an important topic in practice, since the frequency and consequences of cyberattacks continues to increase and affect organizations. It is important for research, since many problems exist for intrusion detection systems. Intrusion detection systems monitor large volumes of data and frequently generate false positives. This results in additional effort for security analysts to review and interpret alerts. After long hours spent reviewing alerts, security analysts become fatigued and make bad decisions. There is currently no approach to intrusion detection that reduces the workload …

Machine Learning Methods For Septic Shock Prediction, Aiman A. Darwiche

CCAC Theses and Dissertations

Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. Building on this body of literature, this dissertation develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify high-risk patients. A robust prediction model …

Application Of Machine Learning On Fracture Interference, Dennis Wayne Chamberlain Jr.

Graduate Theses, Dissertations, and Problem Reports (ETD)

A method has been developed that locates and determines well-to-well hydraulic fracture interference (frac-hit) in shale plays using hard data. This method uses Artificial Neural Networks (ANN) with designated parameters and target outputs in conjunction with graphs of gas flowrate, tubing pressure, and cumulative gas prediction. The method was created to address the significant increase in frac-hit occurrences due to the infill wells being completed in shale plays. The production data of the well is first cleaned to eliminate outliers in the initial timeframe of the well and periods of no production so that the ANN model can be accurately …

Scalable Feature Selection And Extraction With Applications In Kinase Polypharmacology, Derek Jones

Theses and Dissertations--Computer Science

In order to reduce the time associated with and the costs of drug discovery, machine learning is being used to automate much of the work in this process. However the size and complex nature of molecular data makes the application of machine learning especially challenging. Much work must go into the process of engineering features that are then used to train machine learning models, costing considerable amounts of time and requiring the knowledge of domain experts to be most effective. The purpose of this work is to demonstrate data driven approaches to perform the feature selection and extraction steps in …

Deep Neural Networks For Multi-Label Text Classification: Application To Coding Electronic Medical Records, Anthony Rios

Theses and Dissertations--Computer Science

Coding Electronic Medical Records (EMRs) with diagnosis and procedure codes is an essential task for billing, secondary data analyses, and monitoring health trends. Both speed and accuracy of coding are critical. While coding errors could lead to more patient-side financial burden and misinterpretation of a patient’s well-being, timely coding is also needed to avoid backlogs and additional costs for the healthcare facility. Therefore, it is necessary to develop automated diagnosis and procedure code recommendation methods that can be used by professional medical coders.

The main difficulty with developing automated EMR coding methods is the nature of the label space. The …

Classification Of Eeg Signals Of User States In Gaming Using Machine Learning, Chandana Mallapragada

Masters Theses

"In this research, brain activity of user states was analyzed using machine learning algorithms. When a user interacts with a computer-based system including playing computer games like Tetris, he or she may experience user states such as boredom, flow, and anxiety. The purpose of this research is to apply machine learning models to Electroencephalogram (EEG) signals of three user states -- boredom, flow and anxiety -- to identify and classify the EEG correlates for these user states. We focus on three research questions: (i) How well do machine learning models like support vector machine, random forests, multinomial logistic regression, and …

Tracking Topical Evolution In Large Document Collections, Sheikh Motahar Naim

Open Access Theses & Dissertations

A large document collection that builds up over time usually contains a number of different themes. All of these themes or topics are not equally important at the same time. One topic might have high probabilities in some years due to some relevant events, and low probabilities in other years. Analyzing the evolution of such topics has useful applications in a variety of domains, for example, helping researchers to quickly see the changes of research topics in an area, assisting intelligence agents in tracking the activities of a terrorist group, or monitoring damages caused by a natural disaster. In this …

Non-Linear Machine Learning With Active Sampling For Mox Drift Compensation, Tamara Matthews, Muhammad Iqbal, Horacio Gonzalez-Velez

Conference papers

Abstract—Metal oxide (MOX) gas detectors based on SnO2 provide low-cost solutions for real-time sensing of complex gas mixtures for indoor ambient monitoring. With high sensitivity under ideal conditions, MOX detectors may have poor longterm response accuracy due to environmental factors (humidity and temperature) along with sensor aging, leading to calibration drifts. Finding a simple and efficient solution to correct such calibration drifts has been the subject of numerous studies but remains an open problem. In this work, we present an efficient approach to MOX calibration using active and transfer sampling techniques coupled with non-linear machine learning algorithms, namely neural networks, …