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Articles 1 - 30 of 39
Full-Text Articles in Physical Sciences and Mathematics
Energy Planning Model Design For Forecasting The Final Energy Consumption Using Artificial Neural Networks, Haidy Eissa
Energy Planning Model Design For Forecasting The Final Energy Consumption Using Artificial Neural Networks, Haidy Eissa
Theses and Dissertations
“Energy Trilemma” has recently received an increasing concern among policy makers. The trilemma conceptual framework is based on three main dimensions: environmental sustainability, energy equity, and energy security. Energy security reflects a nation’s capability to meet current and future energy demand. Rational energy planning is thus a fundamental aspect to articulate energy policies. The energy system is huge and complex, accordingly in order to guarantee the availability of energy supply, it is necessary to implement strategies on the consumption side. Energy modeling is a tool that helps policy makers and researchers understand the fluctuations in the energy system. Over the …
Natively Implementing Deep Reinforcement Learning Into A Game Engine, Austin Kincer
Natively Implementing Deep Reinforcement Learning Into A Game Engine, Austin Kincer
Undergraduate Honors Theses
Artificial intelligence (AI) increases the immersion that players can have while playing games. Modern game engines, a middleware software used to create games, implement simple AI behaviors that developers can use. Advanced AI behaviors must be implemented manually by game developers, which decreases the likelihood of game developers using advanced AI due to development overhead.
A custom game engine and custom AI architecture that handled deep reinforcement learning was designed and implemented. Snake was created using the custom game engine to test the feasibility of natively implementing an AI architecture into a game engine. A snake agent was successfully trained …
Reducing Kidney Discard With Artificial Intelligence Decision Support: The Need For A Transdisciplinary Systems Approach, Richard Threlkeld, Lirim Ashiku, Casey I. Canfield, Daniel Burton Shank, Mark A. Schnitzler, Krista L. Lentine, David A. Axelrod, Anil Choudary Reddy Battineni, Henry Randall, Cihan H. Dagli
Reducing Kidney Discard With Artificial Intelligence Decision Support: The Need For A Transdisciplinary Systems Approach, Richard Threlkeld, Lirim Ashiku, Casey I. Canfield, Daniel Burton Shank, Mark A. Schnitzler, Krista L. Lentine, David A. Axelrod, Anil Choudary Reddy Battineni, Henry Randall, Cihan H. Dagli
Engineering Management and Systems Engineering Faculty Research & Creative Works
Purpose of Review: A transdisciplinary systems approach to the design of an artificial intelligence (AI) decision support system can more effectively address the limitations of AI systems. By incorporating stakeholder input early in the process, the final product is more likely to improve decision-making and effectively reduce kidney discard.
Recent Findings: Kidney discard is a complex problem that will require increased coordination between transplant stakeholders. An AI decision support system has significant potential, but there are challenges associated with overfitting, poor explainability, and inadequate trust. A transdisciplinary approach provides a holistic perspective that incorporates expertise from engineering, social science, and …
Artificial Intelligence As Augmenting Automation: Implications For Employment, F. Ted Tschang, Esteve Almirall
Artificial Intelligence As Augmenting Automation: Implications For Employment, F. Ted Tschang, Esteve Almirall
Research Collection Lee Kong Chian School Of Business
There has been great concern in recent years that artificial intelligence (AI) may cause widespread unemployment, but proponents say that AI augments existing jobs. Both of these positions have substance, but there is a need is to articulate the mechanisms by which AI may actually do both, and in the process, transform work and business organizations alike. We use economic studies showing past transformations automation wrought on the structure of employment and skills (such as the favouring of nonroutine skills) to articulate a ground for discussion. We then use case evidence of AI and automation to show how AI is …
Incidence Matrix And Some Of Its Applications In Graph Theory, Hizer Leka, Faton Kabashi
Incidence Matrix And Some Of Its Applications In Graph Theory, Hizer Leka, Faton Kabashi
UBT International Conference
In this paper we will focus mainly on some basic concepts and definitions regarding incidence matrices and some examples of their application in graph theory. To give their clearest definition of the incidence matrix, we will first give the meaning of the incidence structure, then through it to define the incidence matrix. The structure of incidence is called the ordered triplet S=(P,B,I), where P∩B=ϕ, I⊆P×B and P,B while, are two non-empty sets and I a relation in between them, such that I⊂P×B. We call the elements of P community dots and we will mark them in lower case letters of, …
Aessa Young Professionals Forum Webinar “Technologies And Skills That Will Gearup The Aerospace Industry Post Pandemic” - A Global Perspective With An Emphasis On South Africa October 2021, Linda Vee Weiland
Publications
A webinar presentation for AeSSA Young Professionals.
A Framework To Study The Impact Of Interventions On Social Isolation During Pandemics Using Multi-Agent Simulation, Simranpreet Kaur
A Framework To Study The Impact Of Interventions On Social Isolation During Pandemics Using Multi-Agent Simulation, Simranpreet Kaur
Electronic Theses and Dissertations
The spread of Coronavirus, widely known as COVID-19, has posed detrimental effects worldwide, affecting almost every primary sector. Due to its asymptomatic behavior and non-early diagnosis, government and health organizations implemented interventions such as physical distancing, lockdown, and quarantine, to mitigate the spread of the virus. Studies have shown that a connection exists between social isolation and health risks experienced by individuals. Thus, this research proposes an agent-based model to address the impact of varying interventions in our society. For simulation purposes, the SEIR model is followed, and agents are categorized into two classes based on their pace of movement, …
Umaine Artificial Intelligence Webinar: Ai For Space And Aerospace Promotional Flyer, University Of Maine Artificial Intelligence, Institute Of Electrical And Electronics Engineers Maine Com/Cs Chapter, Vice President For Research And Dean Of The Graduate School
Umaine Artificial Intelligence Webinar: Ai For Space And Aerospace Promotional Flyer, University Of Maine Artificial Intelligence, Institute Of Electrical And Electronics Engineers Maine Com/Cs Chapter, Vice President For Research And Dean Of The Graduate School
General University of Maine Publications
Promotional flyer for the first webinar in the UMaine Artificial Intelligence 2021-2022 webinar series.
The University of Maine Artificial Intelligence Initiative (UMaine AI) is a unique Maine-based venture that brings together university, industry, government, and community collaborators from Maine and beyond to advance the field of artificial intelligence, and through development of innovative technologies and applications find transformative solutions to enhance human life and societal well-being in Maine and beyond.
Selecting Robust Strategies When Players Do Not Know Exactly What Game They Are Playing, Oscar Samuel Veliz
Selecting Robust Strategies When Players Do Not Know Exactly What Game They Are Playing, Oscar Samuel Veliz
Open Access Theses & Dissertations
Game theory is a tool for modeling multi-agent decision problems and has been used to great success in modeling and simulating problems such as poker, security, and trading agents. However, many real games are extremely large and complex with multiple agent interactions. One approach for solving these games is to use abstraction techniques to shrink the game to a form that can be solved by removing details and translating a solution back to the original.However, abstraction introduces error into the model. This research studies ways to analyze games, abstractions, and strategies that are robust to noise in the game.
Gaining …
Social Network Analysis: A Machine Learning Approach, Bonaventure Chidube Molokwu
Social Network Analysis: A Machine Learning Approach, Bonaventure Chidube Molokwu
Electronic Theses and Dissertations
Social Network Analysis (SNA) is an appealing research topic, within the domain of Artificial Intelligence (AI), owing to its widespread application in the real world. In this dissertation, we have proposed effective Machine Learning (ML) and Deep Learning (DL) approaches toward resolving these open problems with regard to SNA, viz: Breakup Prediction, Link Prediction, Node Classification, Event-based Analysis, and Trend/Pattern Analysis. SNA can be employed toward resolving several real-world problems; and ML as well as DL have proven to be very effective methodologies for accomplishing Artificial Intelligence (AI)- related goals. Existing literature have focused on studying the apparent and latent …
Using Contextual Bandits To Improve Traffic Performance In Edge Network, Aziza Al Zadjali
Using Contextual Bandits To Improve Traffic Performance In Edge Network, Aziza Al Zadjali
Department of Computer Science and Engineering: Dissertations, Theses, and Student Research
Edge computing network is a great candidate to reduce latency and enhance performance of the Internet. The flexibility afforded by Edge computing to handle data creates exciting range of possibilities. However, Edge servers have some limitations since Edge computing process and analyze partial sets of information. It is challenging to allocate computing and network resources rationally to satisfy the requirement of mobile devices under uncertain wireless network, and meet the constraints of datacenter servers too. To combat these issues, this dissertation proposes smart multi armed bandit algorithms that decide the appropriate connection setup for multiple network access technologies on the …
An Exploration Of Controlling The Content Learned By Deep Neural Networks, Liqun Yang
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 …
Trust In And Ethical Design Of Carebots: The Case For Ethics Of Care, Gary Kok Yew Chan
Trust In And Ethical Design Of Carebots: The Case For Ethics Of Care, Gary Kok Yew Chan
Research Collection Yong Pung How School Of Law
The paper has two main objectives: to examine the challenges arising from the use of carebots as well as to discuss how the design of carebots can deal with these challenges. First, it notes that the use of carebots to take care of the physical and mental health of the elderly, children and the disabled as well as to serve as assistive tools and social companions encounter a few main challenges. They relate to the extent of the care robots’ ability to care for humans, potential deception by robot morphology and communications, (over)reliance on or attachment to robots, and the …
A Framework To Study The Impact Of Interventions On Social Isolation During Pandemics Using Multi-Agent Simulation, Simranpreet Kaur
A Framework To Study The Impact Of Interventions On Social Isolation During Pandemics Using Multi-Agent Simulation, Simranpreet Kaur
Electronic Theses and Dissertations
The spread of Coronavirus, widely known as COVID-19, has posed detrimental effects worldwide, affecting almost every primary sector. Due to its asymptomatic behavior and non-early diagnosis, government and health organizations implemented interventions such as physical distancing, lockdown, and quarantine, to mitigate the spread of the virus. Studies have shown that a connection exists between social isolation and health risks experienced by individuals. Thus, this research proposes an agent-based model to address the impact of varying interventions in our society. For simulation purposes, the SEIR model is followed, and agents are categorized into two classes based on their pace of movement, …
Fine-Grained Detection Of Hate Speech Using Bertoxic, Yakoob Khan
Fine-Grained Detection Of Hate Speech Using Bertoxic, Yakoob Khan
Dartmouth College Undergraduate Theses
This thesis describes our approach towards the fine-grained detection of hate speech using deep learning. We leverage the transformer encoder architecture to propose BERToxic, a system that fine-tunes a pre-trained BERT model to locate toxic text spans in a given text and utilizes additional post-processing steps to refine the prediction boundaries. The post-processing steps involve (1) labeling character offsets between consecutive toxic tokens as toxic and (2) assigning a toxic label to words that have at least one token labeled as toxic. Through experiments, we show that these two post-processing steps improve the performance of our model by 4.16% on …
Lexical Complexity Prediction With Assembly Models, Aadil Islam
Lexical Complexity Prediction With Assembly Models, Aadil Islam
Dartmouth College Undergraduate Theses
Tuning the complexity of one's writing is essential to presenting ideas in a logical, intuitive manner to audiences. This paper describes a system submitted by team BigGreen to LCP 2021 for predicting the lexical complexity of English words in a given context. We assemble a feature engineering-based model and a deep neural network model with an underlying Transformer architecture based on BERT. While BERT itself performs competitively, our feature engineering-based model helps in extreme cases, eg. separating instances of easy and neutral difficulty. Our handcrafted features comprise a breadth of lexical, semantic, syntactic, and novel phonetic measures. Visualizations of BERT …
Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman
Automated Analysis Of Rfps Using Natural Language Processing (Nlp) For The Technology Domain, Sterling Beason, William Hinton, Yousri A. Salamah, Jordan Salsman
SMU Data Science Review
Much progress has been made in text analysis, specifically within the statistical domain of Term Frequency (TF) and Inverse Document Frequency (IDF). However, there is much room for improvement especially within the area of discovering Emerging Trends. Emerging Trend Detection Systems (ETDS) depend on ingesting a collection of textual data and TF/IDF to identify new or up-trending topics within the Corpus. However, the tremendous rate of change and the amount of digital information presents a challenge that makes it almost impossible for a human expert to spot emerging trends without relying on an automated ETD system. Since the U.S. Government …
Machine Learning In Stock Price Prediction Using Long Short-Term Memory Networks And Gradient Boosted Decision Trees, Carl Samuel Cederborg
Machine Learning In Stock Price Prediction Using Long Short-Term Memory Networks And Gradient Boosted Decision Trees, Carl Samuel Cederborg
Honors Projects
Quantitative analysis has been a staple of the financial world and investing for many years. Recently, machine learning has been applied to this field with varying levels of success. In this paper, two different methods of machine learning (ML) are applied to predicting stock prices. The first utilizes deep learning and Long Short-Term Memory networks (LSTMs), and the second uses ensemble learning in the form of gradient tree boosting. Using closing price as the training data and Root Mean Squared Error (RMSE) as the error metric, experimental results suggest the gradient boosting approach is more viable.
Honors Symposium: ML is …
S4e9: How Can We Get The Most Out Of Technology?, Ron Lisnet, Richard Corey, Nicholas Giudice, Caitlin Howell
S4e9: How Can We Get The Most Out Of Technology?, Ron Lisnet, Richard Corey, Nicholas Giudice, Caitlin Howell
The Maine Question
Refrigerators tell us when we’re out of juice. Digital assistants schedule appointments and alert us to the weather forecast. Driverless cars slide into tight parallel parking spaces. Today, many of us increasingly rely on devices, apps and artificial intelligence in our daily lives.
How can technology be designed to do the most good? How can scientists make it easy to use and put people, rather than the technology, in charge? This is the work of the University of Maine VEMI Lab. VEMI stands for Virtual Environment and Multimodal Interaction. This week, directors Rick Corey, Nick Giudice and Caitlin Howell talk …
The Impact Of Artificial Intelligence On Dental Care Delivery: A Comprehensive Review, Robert A. Faiella D.M.D., M.M.Sc., M.B.A., Shaju Puthussery M.S.
The Impact Of Artificial Intelligence On Dental Care Delivery: A Comprehensive Review, Robert A. Faiella D.M.D., M.M.Sc., M.B.A., Shaju Puthussery M.S.
The Journal of the Michigan Dental Association
This comprehensive review explores the transformative role of Artificial Intelligence (AI) in the evolution of dental care delivery. As oral health specialists, dentists continually seek to enhance their ability to prevent, diagnose, and manage oral diseases while maintaining and improving patient oral health. The integration of AI offers unprecedented opportunities to revolutionize dental practice and patient care.
AI is rapidly advancing in healthcare, including dental care, with a projected global healthcare AI market value of $45.2 billion by 2026. This technology can potentially revolutionize prevention, diagnosis, treatment planning, and treatment outcomes.
Aspects of AI in dentistry include:
· Diagnostic Accuracy …
J Mich Dent Assoc April 2021
The Journal of the Michigan Dental Association
In the April 2021 issue of the Journal of the Michigan Dental Association, we offer a comprehensive range of original feature content showcasing the latest developments in dental practice and knowledge, including:
- AI in Dental Care Delivery: Explore the groundbreaking role of Artificial Intelligence (AI) and Machine Learning in dental care, revolutionizing efficiency, safety, care outcomes, and treatment planning consistency.
- AI in Dental Claims Processing: Discover how AI is employed by third-party payers to streamline dental claims processing, resulting in cost containment and the proactive identification of potential fraud, waste, and abuse.
- Evidence-Based Dentistry: As part of …
Variable Autoencoders For Biosensor Data Augmentation, Solomon Kim
Variable Autoencoders For Biosensor Data Augmentation, Solomon Kim
Honors Theses
Over the past decade machine learning and artificial intelligence's resurgence spawned the desire to mimic human creative ability. Initially attempts to create images, music, and text flooded the community, though little has been learned regarding constrained, one-dimensional data generation. This paper demonstrates a variational autoencoder approach to this problem. By modeling biosensor current and concentration data we aim to augment the existing dataset. In training a multi-layer neural network based encoder and decoder we were able to generate realistic, original samples., These results demonstrate the ability to realistically augment datasets, improving training of machine learning models designed to predict concentration …
Powered By Ai, Christopher J. Smiley
Powered By Ai, Christopher J. Smiley
The Journal of the Michigan Dental Association
Artificial Intelligence (AI) is revolutionizing dental practice through its ability to process vast amounts of data, enhance diagnosis, and improve patient care. However, AI introduces the challenge of bias and ethical considerations. Dentists and dental benefit providers are utilizing AI for early disease detection and efficient data management, but transparency and fairness in AI algorithms are vital. The Rome Call for AI Ethics emphasizes ethical, non-biased AI development. In the broader context, AI-driven marketing and predictive behavior raise concerns about privacy and ethical data use. The dental community must embrace AI's power while upholding ethical standards and transparency.
Reviving Mozart With Intelligence Duplication, Jacob E. Galajda
Reviving Mozart With Intelligence Duplication, Jacob E. Galajda
Honors Undergraduate Theses
Deep learning has been applied to many problems that are too complex to solve through an algorithm. Most of these problems have not required the specific expertise of a certain individual or group; most applied networks learn information that is shared across humans intuitively. Deep learning has encountered very few problems that would require the expertise of a certain individual or group to solve, and there has yet to be a defined class of networks capable of achieving this. Such networks could duplicate the intelligence of a person relative to a specific task, such as their writing style or music …
Source Code Comment Classification Artificial Intelligence, Cole Sutyak
Source Code Comment Classification Artificial Intelligence, Cole Sutyak
Williams Honors College, Honors Research Projects
Source code comment classification is an important problem for future machine learning solutions. In particular, supervised machine learning solutions that have largely subjective data labels but are difficult to obtain the labels for. Machine learning problems are problems largely because of a lack of data. In machine learning solutions, it is better to have a large amount of mediocre data than it is to have a small amount of good data. While the mediocre data might not produce the best accuracy, it produces the best results because there is much more to learn from the problem.
In this project, data …
Personality And Emotion For Virtual Characters In Strong-Story Narrative Planning, Alireza Shirvani
Personality And Emotion For Virtual Characters In Strong-Story Narrative Planning, Alireza Shirvani
Theses and Dissertations--Computer Science
Interactive virtual worlds provide an immersive and effective environment for training, education, and entertainment purposes. Virtual characters are an essential part of every interactive narrative. The interaction of rich virtual characters can produce interesting narratives and enhance user experience in virtual environments. I propose models of personality and emotion that are highly domain independent and integrate those models into multi-agent strong-story narrative planning systems. I demonstrate the value of the strong-story properties of the model by generating story conflicts intelligently. My models of emotion and personality enable the narrative generation system to create more opportunities for players to resolve conflicts …
Understanding Artificial Intelligence Adoption, Implementation, And Use In Small And Medium Enterprises In India, Dipak Sadashiv Jadhav
Understanding Artificial Intelligence Adoption, Implementation, And Use In Small And Medium Enterprises In India, Dipak Sadashiv Jadhav
Walden Dissertations and Doctoral Studies
This quantitative cross-sectional correlational study involves understanding the impact of various factors on Artificial Intelligence (AI) adoption, implementation, and use in the small and medium enterprises (SME) sector in India. Increased AI use across industry sectors including SMEs makes it essential to analyze decisions involving AI adoption. The main research question and secondary research questions were used to help understand correlations between diffusion of innovation (DOI), the technology, organization, and environment (TOE) framework, and technology adoption model (TAM) and decisions involving AI adoption. I used prevalidated survey instruments and online surveys via the Survey Monkey platform as part of data …
Improving Space Efficiency Of Deep Neural Networks, Aliakbar Panahi
Improving Space Efficiency Of Deep Neural Networks, Aliakbar Panahi
Theses and Dissertations
Language models employ a very large number of trainable parameters. Despite being highly overparameterized, these networks often achieve good out-of-sample test performance on the original task and easily fine-tune to related tasks. Recent observations involving, for example, intrinsic dimension of the objective landscape and the lottery ticket hypothesis, indicate that often training actively involves only a small fraction of the parameter space. Thus, a question remains how large a parameter space needs to be in the first place — the evidence from recent work on model compression, parameter sharing, factorized representations, and knowledge distillation increasingly shows that models can be …
Values Of Trust In Ai In Autonomous Driving Vehicles, Ru Lian
Values Of Trust In Ai In Autonomous Driving Vehicles, Ru Lian
Masters Theses
“Automation with artificial intelligence technology is an emerging field and is widely used in various industries. With the increasing autonomy, learning, and adaptability of intelligent machines such as self-driving cars, it is difficult to regard them as simple tools in human hands. At the same time, a series of problems and challenges such as predictability, interpretability, and causality arise. Trust in self-driving technology will impact the adoption and utilization of autonomous driving technology. A qualitative research methodology, Value-Focused Thinking, is used to identify the values of trust in autonomous driving vehicles and analyze the relationship between these values”--Abstract, page iii.
Human-Robot Collaboration Using Commonsense Knowledge In Smart Manufacturing Contexts, Christopher Joseph Conti
Human-Robot Collaboration Using Commonsense Knowledge In Smart Manufacturing Contexts, Christopher Joseph Conti
Theses, Dissertations and Culminating Projects
Human-robot collaboration (HRC), where humans and robots work together on specific tasks, is a growing part of smart manufacturing that entails artificial intelligence (AI) techniques in manufacturing processes. Robots need to be able to dynamically understand their working environments and human partners both accurately and quickly, as inaccurate or slow predictions can be dangerous to humans and collaborative tasks. To handle challenging environments, robots need to utilize commonsense knowledge (CSK), which is everyday knowledge about fundamental concepts, such as how basic objects interact with each other, what their properties are, and how they are associated. Human beings utilize CSK regularly, …