Artificial Intelligence and Robotics Commons^™

Modeling And Verification Of Scene Of C3+Ato System Based On Timed Automata, Zhenhai Zhang, Yao Jie

Journal of System Simulation

Abstract: The C3+ATO system is in the experimental stage in our country and has the characteristics of high automation degree and high safety requirement at present. In order to confirm whether the specific function of the high-speed railway C3+ATO system meets the corresponding technical specification under the specific scene, a formal modeling and verification method based on the timed automata is proposed. The station automatic departure scene is selected as the modeling object. The functional requirements of C3+ATO system specification are extracted and the timed automata model of the scene is established. The message sequence chart of the corresponding process …

Study On Virtual And Real Entity Configuration Of Army Units Lvc Tactical Training, Gao Ang, Zhiming Dong, Qisheng Guo, Guohui Zhang

Journal of System Simulation

Abstract: Aiming at the problem that “real soldiers” in the LVC system cannot observe “virtual soldiers” and cannot completely meet the army's tactical training needs, the research on the configuration under the condition of limited interaction between virtual and real entities is carried out. Taking equipment is as the node, taking the interaction between the equipment that conforms to the objective facts of combat as the edge. An undirected graph description example of a virtual-real node interaction is build. All the complete subgraphs in the undirected graph are all the configuration modes of virtual and real entities, under which any …

The Whole Is Greater Than Its Parts: Ensembling Improves Protein Contact Prediction, Wendy M. Billings, Connor J. Morris, Dennis Della Corte

Faculty Publications

The prediction of amino acid contacts from protein sequence is an important problem, as protein contacts are a vital step towards the prediction of folded protein structures. We propose that a powerful concept from deep learning, called ensembling, can increase the accuracy of protein contact predictions by combining the outputs of different neural network models. We show that ensembling the predictions made by different groups at the recent Critical Assessment of Protein Structure Prediction (CASP13) outperforms all individual groups. Further, we show that contacts derived from the distance predictions of three additional deep neural networks—AlphaFold, trRosetta, and ProSPr—can be substantially …

Prediction Of Days-On-Market For Single-Family Homes In The Housing Market Of Savannah, Keagan Galbraith

Honors College Theses

The number of days that a home stays on the housing market (Days-On-Market—DOM) provides crucial information about the real estate market’s behavior that affects the buyer’s/seller’s decision (at the micro-level) and indicates the level of risk associated with real estate investments and identifies the housing bubbles (at the macro level). Housing data has a mixture of simple and complex attributes. A complex attribute in contrast with a simple attribute, has an array of values for a real estate property, which creates a major challenge in prediction of DOM. DOM is a binary attribute with values of “short” (£ six months) …

Ethics Of Ai In Education: Towards A Community-Wide Framework, Wayne Holmes, Kaska Poraysa-Pomsta, Ken Holstein, Emma Sutherland, Toby Baker, Simon Buckingham Shum, Olga C. Santos, Ma. Mercedes T. Rodrigo, Mutlu Cukurova, Ig Ibert Bittencourt, Kenneth R. Koedinger

Department of Information Systems & Computer Science Faculty Publications

While Artificial Intelligence in Education (AIED) research has at its core the desire to support student learning, experience from other AI domains suggest that such ethical intentions are not by themselves sufficient. There is also the need to consider explicitly issues such as fairness, accountability, transparency, bias, autonomy, agency, and inclusion. At a more general level, there is also a need to differentiate between doing ethical things and doing things ethically, to understand and to make pedagogical choices that are ethical, and to account for the ever-present possibility of unintended consequences. However, addressing these and related questions is far …

Lecture 00: Opening Remarks: 46th Spring Lecture Series, Tulin Kaman

Mathematical Sciences Spring Lecture Series

Opening remarks for the 46th Annual Mathematical Sciences Spring Lecture Series at the University of Arkansas, Fayetteville.

The Present And Future Of Artificial Intelligence In Ophthalmology, Robert Abishek, Elliot Cherkas

inSIGHT

Dr. Ravi Goel is a comprehensive ophthalmologist and cataract surgeon at Wills Eye Hospital, with a specific interest in finding ways that AI can help ophthalmologists improve their clinical care and treat more patients. Dr. Goel also publishes a daily blog, Protecting Sight, where he discusses a variety of topics ranging from advances in cataract surgery to medical education. One common thread throughout his blog is the burgeoning impact of AI on the field of ophthalmology, such as the utility of deep learning algorithms for diagnosing various diseases and the impact that improved intra-ocular lens (IOL) power calculations will have …

Taiger Ai: Saas Bundling And Unbundling, Singapore Management University

Perspectives@SMU

Software companies bundle support services with their products as standard practice. Is it possible to be different…and profitable?

The Power Of The "Internet Of Things" To Mislead And Manipulate Consumers: A Regulatory Challenge, Kate Tokeley

Notre Dame Journal on Emerging Technologies

The “Internet of Things” revolution is on its way, and with it comes an unprecedented risk of unregulated misleading marketing and a dramatic increase in the power of personalized manipulative marketing. IoT is a term that refers to a growing network of internet-connected physical “smart” objects accumulating in our homes and cities. These include “smart” versions of traditional objects such as refrigerators, thermostats, watches, toys, light bulbs, cars, and Alexa-style digital assistants. The corporations who develop IoT are able to utilize a far greater depth of data than is possible from merely tracking our web browsing in regular online environments. …

An Education Theory Of Fault For Autonomous Systems, William D. Smart, Cindy M. Grimm, Woodrow Hartzog

Notre Dame Journal on Emerging Technologies

Automated systems like self-driving cars and “smart” thermostats are a challenge for fault-based legal regimes like negligence because they have the potential to behave in unpredictable ways. How can people who build and deploy complex automated systems be said to be at fault when they could not have reasonably anticipated the behavior (and thus risk) of their tools? Part of the problem is that the legal system has yet to settle on the language for identifying culpable behavior in the design and deployment for automated systems. In this article we offer an education theory of fault for autonomous systems—a new …

An Automated Framework For Connected Speech Evaluation Of Neurodegenerative Disease: A Case Study In Parkinson's Disease, Sai Bharadwaj Appakaya

USF Tampa Graduate Theses and Dissertations

Neurodegenerative diseases affect millions of people around the world. The progressive degeneration worsens the symptoms, heavily impacting the quality of life of the patients as well as the caregivers. Speech production is one of the physiological processes affected by neurodegenerative diseases like Alzheimer’s disease, amyotrophic lateral sclerosis (ALS) and Parkinson’s disease (PD). Speech is the most basic form of communication, and the effect of neurodegeneration degrades speech production, thereby reducing social interaction and mental well-being. PD is the second most common neurodegenerative disease affecting speech production in 90% of the diagnosed individuals. Speech analysis methods for PD in clinical methods …

Using Machine Learning For Detection Of Covid-19, Justin Rickert

Honors Projects

Currently, the most widely used diagnostic tool for COVID-19 is the RT-PCR nasal swab test recommended by the CDC. However, some studies have shown that chest CT scans have the potential to be more accurate and are also capable of detecting the virus in its earlier stages. Unfortunately, CT results are not instantaneously available as it may be days before a radiologist can review the scan. This delay is one of the factors preventing the widespread use of CT scans for COVID detection. To address the delay, this project investigated Convolutional Neural Networks, an advanced form of machine learning used …

Learning And Simulation Algorithms For Constraint Physical Systems, Shuqi Yang

Dartmouth College Master’s Theses

This thesis explores two computational approaches to learn and simulate complex physical systems exhibiting constraint characteristics. The target applications encompass both solids and fluids. On the solid side, we proposed a new family of data-driven simulators to predict the behaviors of an unknown physical system by learning its underpinning constraints. We devised a neural projection operator facilitated by an embedded recursive neural network to interactively enforce the learned underpinning constraints and to predict its various physical behaviors. Our method can automatically uncover a broad range of constraints from observation point data, such as length, angle, bending, collision, boundary effects, and …

An Analysis Of The Interpretability Of Neural Networks Trained On Magnetic Resonance Imaging For Stroke Outcome Prediction, Esra Zihni, John D. Kelleher, Bryony Mcgarry

Conference papers

Applying deep learning models to MRI scans of acute stroke patients to extract features that are indicative of short-term outcome could assist a clinician’s treatment decisions. Deep learning models are usually accurate but are not easily interpretable. Here, we trained a convolutional neural network on ADC maps from hyperacute ischaemic stroke patients for prediction of short-term functional outcome and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of a bad outcome. Although highly accurate, the model’s predictions were not based on aspects of the ADC maps related to stroke pathophysiology.

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 …

Ai Use In Claims Processing And Utilization Review, Robert Rosenthal Dds

The Journal of the Michigan Dental Association

This paper investigates the use of artificial intelligence (AI) in claims processing and utilization review in the dental industry. This article aims to explore the potential benefits of AI in this area, such as increased efficiency, accuracy, and fraud detection. The paper begins by providing an overview of the current state of claims processing and utilization review in the dental industry. It then discusses the potential applications of AI in this area, such as automated claims adjudication, predictive analytics, and image recognition. The paper then presents a case study of P&R Dental Strategies, LLC, a leading business intelligence solutions provider …

The Emergence Of Artificial Intelligence In Dental Care Delivery, 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 …

10-Minute Ebd: Artificial Intelligence In Orthodontics, Jayne Kessel Dds

The Journal of the Michigan Dental Association

This Ten-Minute Evidence-Based Dentistry Article provides an example of the implementation of the EBD search process with trusted search engines for the identification of the best literature through critical appraisal to answer a clinical question. "For patients receiving orthodontic care, is an AI-generated treatment plan as likely to achieve acceptable outcomes?" Orthodontic treatment planning is a complex and time-consuming process that requires a high degree of expertise. Artificial intelligence (AI) has the potential to assist orthodontists in this process by automating some of the tasks involved, such as cephalometric analysis, surgery decisions, extraction decisions, and anchorage decisions.

A recent systematic …

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.

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:

1. 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.
2. 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.
3. Evidence-Based Dentistry: As part of …