Physical Sciences and Mathematics Commons^™

Machine Learning In Manufacturing: Review, Synthesis, And Theoretical Framework, Ajit Sharma, Zhibo Zhang, Rahul Rai

Business Administration Faculty Research Publications

There has been a paradigmatic shift in manufacturing as computing has transitioned from the programmable to the cognitive computing era. In this paper we present a theoretical framework for understanding this paradigmatic shift in manufacturing and the fast evolving role of artificial intelligence. Policy, Strategic and Operational implications are discussed. Implications for the future of strategy and operations in manufacturing are also discussed. Future research directions are presented.

Expectations Of Artificial Intelligence And The Performativity Of Ethics: Implications For Communication Governance, Aphra Kerr, Marguerite Barry, John D. Kelleher

Articles

This article draws on the sociology of expectations to examine the construction of expectations of ‘ethical AI’ and considers the implications of these expectations for communication governance. We first analyse a range of public documents to identify the key actors, mechanisms and issues which structure societal expectations around artificial intelligence (AI) and an emerging discourse on ethics. We then explore expectations of AI and ethics through a survey of members of the public. Finally, we discuss the implications of our findings for the role of AI in communication gover- nance. We find that, despite societal expectations that we can design …

Renewable Energy Integration In Distribution System With Artificial Intelligence, Yi Gu

Electronic Theses and Dissertations

With the increasing attention of renewable energy development in distribution power system, artificial intelligence (AI) can play an indispensiable role. In this thesis, a series of artificial intelligence based methods are studied and implemented to further enhance the performance of power system operation and control.

Due to the large volume of heterogeneous data provided by both the customer and the grid side, a big data visualization platform is built to feature out the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. An open source cluster calculation framework with Apache Spark is used to discover big data …

Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani

Electronic Theses and Dissertations

Automated Facial Expression Recognition (FER) has been a topic of study in the field of computer vision and machine learning for decades. In spite of efforts made to improve the accuracy of FER systems, existing methods still are not generalizable and accurate enough for use in real-world applications. Many of the traditional methods use hand-crafted (a.k.a. engineered) features for representation of facial images. However, these methods often require rigorous hyper-parameter tuning to achieve favorable results.

Recently, Deep Neural Networks (DNNs) have shown to outperform traditional methods in visual object recognition. DNNs require huge data as well as powerful computing units …

Diota: Decentralized Ledger Based Framework For Data Authenticity Protection In Iot Systems, Lei Xu, Lin Chen, Zhimin Gao, Xinxin Fan, Taeweon Suh, Weidong Shi

Computer Science Faculty Publications

It is predicted that more than 20 billion IoT devices will be deployed worldwide by 2020. These devices form the critical infrastructure to support a variety of important applications such as smart city, smart grid, and industrial internet. To guarantee that these applications work properly, it is imperative to authenticate these devices and data generated from them. Although digital signatures can be applied for these purposes, the scale of the overall system and the limited computation capability of IoT devices pose two big challenges. In order to overcome these obstacles, we propose DIoTA, a novel decentralized ledger-based authentication framework for …

Deep Neural Networks For Sentiment Analysis In Tweets With Emoticons, Mutharasu Narayanaperumal

CCAC Theses and Dissertations

Businesses glean meaningful feedback in regard to products and services from social media posts in order to improve the quality of products and services, as well as to meet customer expectations. Sentiment analysis is increasingly being used to help businesses by assigning positive or negative polarity to such posts. Although methods currently exist to determine the polarity of sentiments, such methods are unreliable when posts contain terms that are not typically part of a standard dictionary used for sentiment analysis, such as slang and informal language. This dissertation has aimed to empirically investigate alternative methods to improve the classification accuracy …

Exploring Artificial Intelligence-Mediated Communication (Aimc) As A Sub-Field Of Communication Studies. A Textual Examination, Md Nurul Karim Bhuiyan

All Graduate Theses, Dissertations, and Other Capstone Projects

From the book "Speaking into the Air: A History of the Idea of Communication," written by John Durham Peters, we understand a notion about developing one’s destiny; people have the freedom to choose multiple paths to follow (Peters, 2012). If we reject this idea, it is also easy for people to come up with distinct explanations. Even though the meaning of the same issues might vary subject to who is interpreting them, the primary concepts can be interpreted as more or less the same. If we study these two--"artificial intelligence" and "communication"- simultaneously, we can assume some characteristics. Thus, this …

Intelligent Cinematic Camera Control For Real-Time Graphics Applications, Ian Harris Meeder

Master's Theses

E-sports is currently estimated to be a billion dollar industry which is only growing in size from year to year. However the cinematography of spectated games leaves much to be desired. In most cases, the spectator either gets to control their own freely-moving camera or they get to see the view that a specific player sees. This thesis presents a system for the generation of cinematically-pleasing views for spectating real-time graphics applications. A custom real-time engine has been built to demonstrate the effect of this system on several different game modes with varying visual cinematic constraints, such as the rule …

Artificial Intelligence-Based Models For The Qualitative And Quantitative Prediction Of Aphytochemical Compound Using Hplc Method, Abdullahi Garba Usman, Seli̇n Işik, Sani Isah Abba, Fi̇li̇z Meri̇çli̇

Turkish Journal of Chemistry

Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile, Annona squamosal, Camellia sinensis (tea), and coriander (Coriandrum sativum L.). It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to …

Artificial Intelligence-Enhanced Decision Support For Informing Global Sustainable Development: A Human-Centric Ai-Thinking Approach, Meng-Leong How, Sin Mei Cheah, Yong-Jiet Chan, Aik Cheow Khor, Eunice Mei Ping Say

CCX Research

Sustainable development is crucial to humanity. Utilization of primary socio-environmental data for analysis is essential for informing decision making by policy makers about sustainability in development. Artificial intelligence (AI)-based approaches are useful for analyzing data. However, it was not easy for people who are not trained in computer science to use AI. The significance and novelty of this paper is that it shows how the use of AI can be democratized via a user-friendly human-centric probabilistic reasoning approach. Using this approach, analysts who are not computer scientists can also use AI to analyze sustainability-related EPI data. Further, this human-centric probabilistic …

A Hierarchical Temporal Memory Sequence Classifier For Streaming Data, Jeffrey Barnett

CCAC Theses and Dissertations

Real-world data streams often contain concept drift and noise. Additionally, it is often the case that due to their very nature, these real-world data streams also include temporal dependencies between data. Classifying data streams with one or more of these characteristics is exceptionally challenging. Classification of data within data streams is currently the primary focus of research efforts in many fields (i.e., intrusion detection, data mining, machine learning). Hierarchical Temporal Memory (HTM) is a type of sequence memory that exhibits some of the predictive and anomaly detection properties of the neocortex. HTM algorithms conduct training through exposure to a stream …

Adaptive Batch Size Selection In Active Learning For Regression, Anthony L. Faulds

CCAC Theses and Dissertations

Training supervised machine learning models requires labeled examples. A judicious choice of examples is helpful when there is a significant cost associated with assigning labels. This dissertation aims to improve upon a promising extant method - Batch-mode Expected Model Change Maximization (B-EMCM) method - for selecting examples to be labeled for regression problems. Specifically, it aims to develop and evaluate alternate strategies for adaptively selecting batch size in B-EMCM, named adaptive B-EMCM (AB-EMCM).

By determining the cumulative error that occurs from the estimation of the stochastic gradient descent, a stop criteria for each iteration of the batch can be specified …

Detecting And Protecting Against Ai-Synthesized Faces, Yuezun Li

Legacy Theses & Dissertations (2009 - 2024)

The recent advances in deep learning and the availability of vast volume of online personal images and videos have drastically improved the reality of synthesized faces in images and videos. While there are interesting and creative applications of the AI face synthesis systems, they can also be weaponized, as it can create the illusions of a person's presence and activities that do not occur in reality, which results in serious political, social, financial, and legal consequences. Therefore, it is of great importance to develop effective method to expose the AI-synthesized faces. In this thesis, a set of our recent efforts …

A Holistic Review Of Cybersecurity And Reliability Perspectives In Smart Airports, Nickolaos Koroniotis, Nour Moustafa, Francesco Schiliro, Praveen Gauravaram, Helge Janicke

Research outputs 2014 to 2021

Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence of smart airports. Services and systems powered by the IoT enable smart airports to have enhanced robustness, efficiency and control, governed by real-time monitoring and analytics. Smart sensors control the environmental conditions inside the airport, automate passenger-related actions and support airport security. However, these augmentations and automation introduce security threats to network systems of smart airports. Cyber-attackers demonstrated the susceptibility of IoT systems and networks to Advanced Persistent Threats (APT), due to hardware constraints, software flaws or IoT misconfigurations. With the increasing complexity of attacks, …

Machine Learning? In My Election? It's More Likely Than You Think: Voting Rules Via Neural Networks, Daniel Firebanks-Quevedo

Honors Papers

Impossibility theorems in social choice have represented a barrier in the creation of universal, non-dictatorial, and non-manipulable voting rules, highlighting a key trade-off between social welfare and strategy-proofness. However, a social planner may be concerned with only a particular preference distribution and wonder whether it is possible to better optimize this trade-off. To address this problem, we propose an end-to-end, machine learning-based framework that creates voting rules according to a social planner's constraints, for any type of preference distribution. After experimenting with rank-based social choice rules, we find that automatically-designed rules are less susceptible to manipulation than most existing rules, …

Superconducting Radio-Frequency Cavity Fault Classification Using Machine Learning At Jefferson Laboratory, Chris Tennant, Adam Carpenter, Tom Powers, Anna Shabalina Solopova, Lasitha Vidyaratne, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

We report on the development of machine learning models for classifying C100 superconducting radio-frequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. CEBAF is a continuous-wave recirculating linac utilizing 418 SRF cavities to accelerate electrons up to 12 GeV through five passes. Of these, 96 cavities (12 cryomodules) are designed with a digital low-level rf system configured such that a cavity fault triggers waveform recordings of 17 rf signals for each of the eight cavities in the cryomodule. Subject matter experts are able to analyze the collected time-series data and identify which of the …

Remark On Artificial Intelligence, Humanoid And Terminator Scenario: A Neutrosophic Way To Futurology, Victor Christianto, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This article is an update of our previous article in this SGJ journal, titled: On Gödel's Incompleteness Theorem, Artificial Intelligence & Human Mind [7]. We provide some commentary on the latest developments around AI, humanoid robotics, and future scenario. Basically, we argue that a more thoughtful approach to the future is "technorealism."

Empowering Qualitative Research Methods In Education With Artificial Intelligence, Luca Longo

Conference papers

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches …

Ai And Machine Learning Usage In Actuarial Science, Joanna Riley

Williams Honors College, Honors Research Projects

Some people in the world work hard and do whatever it takes in order to get a job that they love. There are others that don’t care about their jobs and solely perform them in order to make money. So, there are individuals or groups that wouldn’t care if a machine or computer were to replace them in their job, but others would be devastated. The question for this paper is: Can actuaries be completely replaced by computers, or do we need the human mind in order to make proper decisions and judgements?

Key words and phrases: actuarial science, artificial …

Searching For Needles In The Cosmic Haystack, Thomas Ryan Devine

Graduate Theses, Dissertations, and Problem Reports (ETD)

Searching for pulsar signals in radio astronomy data sets is a difficult task. The data sets are extremely large, approaching the petabyte scale, and are growing larger as instruments become more advanced. Big Data brings with it big challenges. Processing the data to identify candidate pulsar signals is computationally expensive and must utilize parallelism to be scalable. Labeling benchmarks for supervised classification is costly. To compound the problem, pulsar signals are very rare, e.g., only 0.05% of the instances in one data set represent pulsars. Furthermore, there are many different approaches to candidate classification with no consensus on a best …

An Automated Method For Detecting Water Levels Using Computer Vision And Artificial Intelligence, Priyanjani Chowdary Chandra

Graduate Research Theses & Dissertations

Flooding is one of the most dangerous weather events today. Between 2015-2019, on average, it has caused more than 130 deaths every year in the USA alone. World Health Organization has reported that, between 1998-2017, floods have affected more than 2 billion people worldwide. The devastating nature of flood necessitates the continuous monitoring of water level in the rivers and streams in flood-prone areas to detect the incoming flood. In this thesis, we have designed and implemented a computer vision and AI-based system that continuously detect the water level in the creek. Our solution employs an effective template matching algorithm …

Implementation Considerations For Mitigating Bias In Supervised Machine Learning, Bardia Bijani Aval

CSB and SJU Distinguished Thesis

Machine Learning (ML) is an important component of computer science and a mainstream way of making sense of large amounts of data. Although the technology is establishing new possibilities in different fields, there are also problems to consider, one of which is bias. Due to the inductive reasoning of ML algorithms in creating mathematical models, the predictions and trends found by the models will never necessarily be true – just more or less probable. Knowing this, it is unreasonable for us to expect the applied deductive reasoning of these models to ever be fully unbiased. Therefore, it is important that …

Ai Techniques For Covid-19, Adedoyin Ahmed Hussain, Ouns Bouachir, Fadi Al-Turjman, Moayad Aloqaily

All Works

© 2013 IEEE. Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the …

Could A Robot Be Your Psychotherapist?, Benjamin Huston

Graduate School of Professional Psychology: Doctoral Papers and Masters Projects

As technology has advanced over the years, it has been integrated into psychotherapy and changed the way that people receive mental health care (Schopp, Demiris, & Glueckauf, 2006). Many of these advances, such as telehealth practices, were seen as unsustainable until the public Internet offered broader access to technology-based care in the 1990s (Schopp, Demiris, & Glueckauf, 2006). These technology-based practices have since grown in popularity and with a recent increase in telehealth practices, text-based therapies, and applications to aid in mental health practices, modern therapy looks very different than it did even ten years ago (Fiske, Henningsen, & Buyx, …

Augmenting Education: Ethical Considerations For Incorporating Artificial Intelligence In Education, Dana Remian

Instructional Design Capstones Collection

Artificial intelligence (AI) has existed in theory and practice for decades, but applications have been relatively limited in most domains. Recent developments in AI and computing have placed AI-enhanced applications in various industries and a growing number of consumer products. AI platforms and services aimed at enhancing educational outcomes and taking over administrative tasks are becoming more prevalent and appearing in more and more classrooms and offices. Conversations about the disruption and ethical concerns created by AI are occurring in many fields. The development of the technology threatens to outpace academic discussion of its utility and pitfalls in education, however. …

“Where’S The I-O?” Artificial Intelligence And Machine Learning In Talent Management Systems, Manuel F. Gonzalez, John F. Capman, Frederick L. Oswald, Evan R. Theys, David L. Tomczak

Personnel Assessment and Decisions

Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; …

What Do You Mean? Research In The Age Of Machines, Arthur J. Boston

Faculty & Staff Research and Creative Activity

What Do You Mean?” was an undeniable bop of its era in which Justin Bieber explores the ambiguities of romantic communication. (I pinky promise this will soon make sense for scholarly communication librarians interested in artificial intelligence [AI].) When the single hit airwaves in 2015, there was a meta-debate over what Bieber meant to add to public discourse with lyrics like “What do you mean? Oh, oh, when you nod your head yes, but you wanna say no.” It is unlikely Bieber had consent culture in mind, but the failure of his songwriting team to take into account that some …

Realtime Object Detection Via Deep Learning-Based Pipelines, James G. Shanahan, Liang Dai

Information Systems and Analytics Department Faculty Conference Proceedings

Ever wonder how the Tesla Autopilot system works (or why it fails)? In this tutorial we will look under the hood of self-driving cars and of other applications of computer vision and review state-of-the-art tech pipelines for object detection such as two-stage approaches (e.g., Faster R-CNN) or single-stage approaches (e.g., YOLO/SSD). This is accomplished via a series of Jupyter Notebooks that use Python, OpenCV, Keras, and Tensorflow. No prior knowledge of computer vision is assumed (although it will be help!). To this end we begin this tutorial with a review of computer vision and traditional approaches to object detection such …

Experiences Of Using Intelligent Virtual Assistants By Visually Impaired Students In Online Higher Education, Michele R. Forbes

USF Tampa Graduate Theses and Dissertations

In today’s world, the attainment of higher education impacts the acquisition of competitive employment and, thus, quality of life. As a group, persons with disabilities continually fall behind others in such academic progress, requiring new efforts to support their earning of advanced credentials. Though highly beneficial for these individuals, obtaining a degree comes with elevated levels of stress. As enrollment of students with disabilities grows in all formats of higher education, those involved must understand the stress endured by these students and how to diminish it. Theories speculate that technology, such as intelligent virtual assistants, may be a viable tool …

Demonstration Of Visible And Near Infrared Raman Spectrometers And Improved Matched Filter Model For Analysis Of Combined Raman Signals, Alexander Matthew Atkinson

Electrical & Computer Engineering Theses & Dissertations

Raman spectroscopy is a powerful analysis technique that has found applications in fields such as analytical chemistry, planetary sciences, and medical diagnostics. Recent studies have shown that analysis of Raman spectral profiles can be greatly assisted by use of computational models with achievements including high accuracy pure sample classification with imbalanced data sets and detection of ideal sample deviations for pharmaceutical quality control. The adoption of automated methods is a necessary step in streamlining the analysis process as Raman hardware becomes more advanced. Due to limits in the architectures of current machine learning based Raman classification models, transfer from pure …