Physical Sciences and Mathematics Commons^™

Anomaly Detection In Sequential Data: A Deep Learning-Based Approach, Jayesh Soni

FIU Electronic Theses and Dissertations

Anomaly Detection has been researched in various domains with several applications in intrusion detection, fraud detection, system health management, and bio-informatics. Conventional anomaly detection methods analyze each data instance independently (univariate or multivariate) and ignore the sequential characteristics of the data. Anomalies in the data can be detected by grouping the individual data instances into sequential data and hence conventional way of analyzing independent data instances cannot detect anomalies. Currently: (1) Deep learning-based algorithms are widely used for anomaly detection purposes. However, significant computational overhead time is incurred during the training process due to static constant batch size and learning …

Integrating Cultural Knowledge Into Artificially Intelligent Systems: Human Experiments And Computational Implementations, Anurag Acharya

FIU Electronic Theses and Dissertations

With the advancement of Artificial Intelligence, it seems as if every aspect of our lives is impacted by AI in one way or the other. As AI is used for everything from driving vehicles to criminal justice, it becomes crucial that it overcome any biases that might hinder its fair application. We are constantly trying to make AI be more like humans. But most AI systems so far fail to address one of the main aspects of humanity: our culture and the differences between cultures. We cannot truly consider AI to have understood human reasoning without understanding culture. So it …

Intelligent Data Analytics Using Deep Learning For Data Science, Maria E. Presa Reyes

FIU Electronic Theses and Dissertations

Nowadays, data science stimulates the interest of academics and practitioners because it can assist in the extraction of significant insights from massive amounts of data. From the years 2018 through 2025, the Global Datasphere is expected to rise from 33 Zettabytes to 175 Zettabytes, according to the International Data Corporation. This dissertation proposes an intelligent data analytics framework that uses deep learning to tackle several difficulties when implementing a data science application. These difficulties include dealing with high inter-class similarity, the availability and quality of hand-labeled data, and designing a feasible approach for modeling significant correlations in features gathered from …

An Exploration Of Controlling The Content Learned By Deep Neural Networks, Liqun Yang

FIU Electronic Theses and Dissertations

With the great success of the Deep Neural Network (DNN), how to get a trustworthy model attracts more and more attention. Generally, people intend to provide the raw data to the DNN directly in training. However, the entire training process is in a black box, in which the knowledge learned by the DNN is out of control. There are many risks inside. The most common one is overfitting. With the deepening of research on neural networks, additional and probably greater risks were discovered recently. The related research shows that unknown clues can hide in the training data because of the …

Interpretability Of Ai In Computer Systems And Public Policy, Farzana Beente Yusuf

FIU Electronic Theses and Dissertations

Advances in Artificial Intelligence (AI) have led to spectacular innovations and sophisticated systems for tasks that were thought to be capable only by humans. Examples include playing chess and Go, face and voice recognition, driving vehicles, and more. In recent years, the impact of AI has moved beyond offering mere predictive models into building interpretable models that appeal to human logic and intuition because they ensure transparency and simplicity and can be used to make meaningful decisions in real-world applications. A second trend in AI is characterized by important advancements in the realm of causal reasoning. Identifying causal relationships is …

Development Of A Real-Time Single-Lead Single-Beat Frequency-Independent Myocardial Infarction Detector, Harold Martin

FIU Electronic Theses and Dissertations

The central aim of this research is the development and deployment of a novel multilayer machine learning design with unique application for the diagnosis of myocardial infarctions (MIs) from individual heartbeats of single-lead electrocardiograms (EKGs) irrespective of their sampling frequencies over a given range. To the best of our knowledge, this design is the first to attempt inter-patient myocardial infarction detection from individual heartbeats of single-lead (lead II) electrocardiograms that achieves high accuracy and near real-time diagnosis. The processing time of 300 milliseconds to a diagnosis is just at the time range in between extremely fast heartbeats of around 300 …

Correlating Water Quality And Profile Data In The Florida Keys Using Machine Learning Methods, Alejandro M. Torres Castellanos

FIU Electronic Theses and Dissertations

Water quality is a very active subject of research in the water science field, where its importance includes maintaining the environment, managing wastewater, and securing fresh water. However, the increase of human development has led to problems that are affecting the ecosystem. Motivated by these problems, this research aims to find a solution for understanding the coastal water of the Florida Keys. The research used machine learning methods to find a correlation between water quality dataset and profile measurements dataset. To achieve this objective, the research first went through cleaning, rescuing, and structuring a readable dataset of the profile measurements …

An Angle-Based Stochastic Gradient Descent Method For Machine Learning: Principle And Application, Chongya Song

FIU Electronic Theses and Dissertations

In deep learning, optimization algorithms are employed to expedite the resolution to accurate models through the calibrations of the current gradient and the associated learning rate. A major shortcoming of these existing methods is the manner in which the calibration terms are computed, only utilizing the previous gradients during their computations. Because the gradient is a time-sensitive variable computed at a specific moment in time, it is possible that older gradients can introduce significant deviation into the calibration terms. Although most algorithms alleviate this situation by combining the exponential moving average of the previous gradients, we found that this method …

Causality In Microbiomes, Md Musfiqur Rahman Sazal

FIU Electronic Theses and Dissertations

No abstract provided.

Automatic Learning Of Document Section Structure For Ontology-Based Semantic Search, Deya Banisakher

FIU Electronic Theses and Dissertations

Modeling natural human behavior in understanding written language is crucial for developing true artificial intelligence. For people, words convey certain semantic concepts. While documents represent an abstract concept---they are collections of text organized in some logical structure, that is, sentences, paragraphs, sections, and so on. Similar to words, these document structures, are used to convey a logical flow of semantic concepts. Machines however, only view words as spans of characters and documents as mere collections of free-text, missing any underlying meanings behind words and the logical structure of those documents.

Automatic semantic concept detection is the process by which the …

Reputation-Aware Trajectory-Based Data Mining In The Internet Of Things (Iot), Samia Tasnim

FIU Electronic Theses and Dissertations

Internet of Things (IoT) is a critically important technology for the acquisition of spatiotemporally dense data in diverse applications, ranging from environmental monitoring to surveillance systems. Such data helps us improve our transportation systems, monitor our air quality and the spread of diseases, respond to natural disasters, and a bevy of other applications. However, IoT sensor data is error-prone due to a number of reasons: sensors may be deployed in hazardous environments, may deplete their energy resources, have mechanical faults, or maybe become the targets of malicious attacks by adversaries. While previous research has attempted to improve the quality of …

Context-Aware Personalized Point-Of-Interest Recommendation System, Ramesh Raj Baral

FIU Electronic Theses and Dissertations

The increasing volume of information has created overwhelming challenges to extract the relevant items manually. Fortunately, the online systems, such as e-commerce (e.g., Amazon), location-based social networks (LBSNs) (e.g., Facebook) among many others have the ability to track end users' browsing and consumption experiences. Such explicit experiences (e.g., ratings) and many implicit contexts (e.g., social, spatial, temporal, and categorical) are useful in preference elicitation and recommendation. As an emerging branch of information filtering, the recommendation systems are already popular in many domains, such as movies (e.g., YouTube), music (e.g., Pandora), and Point-of-Interest (POI) (e.g., Yelp).

The POI domain has many …

A Model-Based Ai-Driven Test Generation System, Dionny Santiago

FIU Electronic Theses and Dissertations

Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 elements. …

Multi-Robot Coordination And Scheduling For Deactivation & Decommissioning, Sebastian A. Zanlongo

FIU Electronic Theses and Dissertations

Large quantities of high-level radioactive waste were generated during WWII. This waste is being stored in facilities such as double-shell tanks in Washington, and the Waste Isolation Pilot Plant in New Mexico. Due to the dangerous nature of radioactive waste, these facilities must undergo periodic inspections to ensure that leaks are detected quickly. In this work, we provide a set of methodologies to aid in the monitoring and inspection of these hazardous facilities. This allows inspection of dangerous regions without a human operator, and for the inspection of locations where a person would not be physically able to enter.

First, …

Automatic Extraction Of Narrative Structure From Long Form Text, Joshua Daniel Eisenberg

FIU Electronic Theses and Dissertations

Automatic understanding of stories is a long-time goal of artificial intelligence and natural language processing research communities. Stories literally explain the human experience. Understanding our stories promotes the understanding of both individuals and groups of people; various cultures, societies, families, organizations, governments, and corporations, to name a few. People use stories to share information. Stories are told –by narrators– in linguistic bundles of words called narratives.

My work has given computers awareness of narrative structure. Specifically, where are the boundaries of a narrative in a text. This is the task of determining where a narrative begins and ends, a …

A Mathematical Framework On Machine Learning: Theory And Application, Bin Shi

FIU Electronic Theses and Dissertations

The dissertation addresses the research topics of machine learning outlined below. We developed the theory about traditional first-order algorithms from convex opti- mization and provide new insights in nonconvex objective functions from machine learning. Based on the theory analysis, we designed and developed new algorithms to overcome the difficulty of nonconvex objective and to accelerate the speed to obtain the desired result. In this thesis, we answer the two questions: (1) How to design a step size for gradient descent with random initialization? (2) Can we accelerate the current convex optimization algorithms and improve them into nonconvex objective? For application, …

Augmented Terrain-Based Navigation To Enable Persistent Autonomy For Underwater Vehicles In Gps-Denied Environments, Gregory M. Reis

FIU Electronic Theses and Dissertations

Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), play a major role in the study of ocean processes that require long-term sampling efforts and commonly perform navigation via dead-reckoning using an accelerometer, a magnetometer, a compass, an IMU and a depth sensor for feedback. However, these instruments are subjected to large drift, leading to unbounded uncertainty in location. Moreover, the spatio-temporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. To add to this, the interesting features are themselves spatio-temporally dynamic, and effective …

User-Centric Privacy Preservation In Mobile And Location-Aware Applications, Mingming Guo

FIU Electronic Theses and Dissertations

The mobile and wireless community has brought a significant growth of location-aware devices including smart phones, connected vehicles and IoT devices. The combination of location-aware sensing, data processing and wireless communication in these devices leads to the rapid development of mobile and location-aware applications. Meanwhile, user privacy is becoming an indispensable concern. These mobile and location-aware applications, which collect data from mobile sensors carried by users or vehicles, return valuable data collection services (e.g., health condition monitoring, traffic monitoring, and natural disaster forecasting) in real time. The sequential spatial-temporal data queries sent by users provide their location trajectory information. The …

Efficient Mission Planning For Robot Networks In Communication Constrained Environments, Md Mahbubur Rahman

FIU Electronic Theses and Dissertations

Many robotic systems are remotely operated nowadays that require uninterrupted connection and safe mission planning. Such systems are commonly found in military drones, search and rescue operations, mining robotics, agriculture, and environmental monitoring. Different robotic systems may employ disparate communication modalities such as radio network, visible light communication, satellite, infrared, Wi-Fi. However, in an autonomous mission where the robots are expected to be interconnected, communication constrained environment frequently arises due to the out of range problem or unavailability of the signal. Furthermore, several automated projects (building construction, assembly line) do not guarantee uninterrupted communication, and a safe project plan is …

Large Scale Data Mining For It Service Management, Chunqiu Zeng

FIU Electronic Theses and Dissertations

More than ever, businesses heavily rely on IT service delivery to meet their current and frequently changing business requirements. Optimizing the quality of service delivery improves customer satisfaction and continues to be a critical driver for business growth. The routine maintenance procedure plays a key function in IT service management, which typically involves problem detection, determination and resolution for the service infrastructure.

Many IT Service Providers adopt partial automation for incident diagnosis and resolution where the operation of the system administrators and automation operation are intertwined. Often the system administrators' roles are limited to helping triage tickets to the processing …

Learning Data-Driven Models Of Non-Verbal Behaviors For Building Rapport Using An Intelligent Virtual Agent, Reza Amini

FIU Electronic Theses and Dissertations

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight …

Mobimed: Framework For Rapid Application Development Of Medical Mobile Apps, Frank Hernadez

FIU Electronic Theses and Dissertations

In the medical field images obtained from high definition cameras and other medical imaging systems are an integral part of medical diagnosis. The analysis of these images are usually performed by the physicians who sometimes need to spend long hours reviewing the images before they are able to come up with a diagnosis and then decide on the course of action. In this dissertation we present a framework for a computer-aided analysis of medical imagery via the use of an expert system. While this problem has been discussed before, we will consider a system based on mobile devices.

Since the …