Artificial Intelligence and Robotics Commons^™

Integrating The Spatial Pyramid Pooling Into 3d Convolutional Neural Networks For Cerebral Microbleeds Detection, Andre Accioly Veira

CCAC Theses and Dissertations

Cerebral microbleeds (CMB) are small foci of chronic blood products in brain tissues that are critical markers for cerebral amyloid angiopathy. CMB increases the risk of symptomatic intracerebral hemorrhage and ischemic stroke. CMB can also cause structural damage to brain tissues resulting in neurologic dysfunction, cognitive impairment, and dementia. Due to the paramagnetic properties of blood degradation products, CMB can be better visualized via susceptibility-weighted imaging (SWI) than magnetic resonance imaging (MRI).CMB identification and classification have been based mainly on human visual identification of SWI features via shape, size, and intensity information. However, manual interpretation can be biased. Visual screening …

Utilizing Machine Learning In Healthcare In An Ethical Fashion, Nishka Ayyar

CMC Senior Theses

This thesis paper explores the ethical considerations surrounding the use of machine learning (ML) solutions in healthcare. The background section discusses the basics of machine learning techniques and algorithms, and the increasing interest in their utilization in the healthcare sector. The paper then reviews and critically analyzes four studies that highlight concerns related to using ML in healthcare, including issues of bias, privacy, accountability, and transparency. Based on the analysis of these studies, the paper presents several recommendations for addressing these concerns. The paper concludes with a discussion on the potential benefits of using machine learning technology in healthcare. Ultimately, …

Murder On The Vr Express: Studying The Impact Of Thought Experiments At A Distance In Virtual Reality, Andrew Kissel, Krzysztof J. Rechowicz, John B. Shull

Philosophy Faculty Publications

Hypothetical thought experiments allow researchers to gain insights into widespread moral intuitions and provide opportunities for individuals to explore their moral commitments. Previous thought experiment studies in virtual reality (VR) required participants to come to an on-site laboratory, which possibly restricted the study population, introduced an observer effect, and made internal reflection on the participants’ part more difficult. These shortcomings are particularly crucial today, as results from such studies are increasingly impacting the development of artificial intelligence systems, self-driving cars, and other technologies. This paper explores the viability of deploying thought experiments in commercially available in-home VR headsets. We conducted …

Defending Ai-Based Automatic Modulation Recognition Models Against Adversarial Attacks, Haolin Tang, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Yanxiao Zhao

Engineering Technology Faculty Publications

Automatic Modulation Recognition (AMR) is one of the critical steps in the signal processing chain of wireless networks, which can significantly improve communication performance. AMR detects the modulation scheme of the received signal without any prior information. Recently, many Artificial Intelligence (AI) based AMR methods have been proposed, inspired by the considerable progress of AI methods in various fields. On the one hand, AI-based AMR methods can outperform traditional methods in terms of accuracy and efficiency. On the other hand, they are susceptible to new types of cyberattacks, such as model poisoning or adversarial attacks. This paper explores the vulnerabilities …

A Hybrid Deep Learning Approach For Crude Oil Price Prediction, Hind Aldabagh, Xianrong Zheng, Ravi Mukkamala

Computer Science Faculty Publications

Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model …

Charged Track Reconstruction With Artificial Intelligence For Clas12, Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos, Nikos Chrisochoides

Computer Science Faculty Publications

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run …

A Structure-Aware Generative Adversarial Network For Bilingual Lexicon Induction, Bocheng Han, Qian Tao, Lusi Li, Zhihao Xiong

Computer Science Faculty Publications

Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. However, most previous methods treat word embeddings as isolated entities and fail to jointly consider both the intra-space and inter-space topological relations between words. This limitation makes it challenging to align words from embedding spaces with distinct topological structures, especially when the assumption of isomorphism may not hold. To this end, we propose a novel approach called the Structure-Aware Generative Adversarial Network (SA-GAN) model to explicitly capture multiple topological structure information to achieve accurate …

Identifying The Serious Clinical Outcomes Of Adverse Reactions To Drugs By A Multi-Task Deep Learning Framework, Haochen Zhao, Peng Ni, Qichang Zhao, Xiao Liang, Di Ai, Shannon Erhardt, Jun Wang, Yaohang Li, Jiianxin Wang

Computer Science Faculty Publications

Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing computational methods primarily focus on predicting whether or not the drug is associated with an adverse reaction and do not consider the core issue of drug benefit-risk assessment-whether the treatment outcome is serious when adverse drug reactions occur. To this end, we categorize serious clinical outcomes caused by adverse reactions to drugs into seven distinct classes and present a deep learning framework, so-called GCAP, for predicting the …

Dfhic: A Dilated Full Convolution Model To Enhance The Resolution Of Hi-C Data, Bin Wang, Kun Liu, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Hi-C technology has been the most widely used chromosome conformation capture(3C) experiment that measures the frequency of all paired interactions in the entire genome, which is a powerful tool for studying the 3D structure of the genome. The fineness of the constructed genome structure depends on the resolution of Hi-C data. However, due to the fact that high-resolution Hi-C data require deep sequencing and thus high experimental cost, most available Hi-C data are in low-resolution. Hence, it is essential to enhance the quality of Hi-C data by developing the effective computational methods.

Results: In this work, we propose …

Mitigating Anomalous Electricity Consumption In Smart Cities Using An Ai-Based Stacked-Generalization Technique, Arshid Ali, Laiq Khan, Nadeem Javaid, Safdar Hussain Bouk, Abdulaziz Aldegheishem, Nabil Alrahjeh

Computer Science Faculty Publications

Energy management and efficient asset utilization play an important role in the economic development of a country. The electricity produced at the power station faces two types of losses from the generation point to the end user. These losses are technical losses (TL) and non-technical losses (NTL). TLs occurs due to the use of inefficient equipment. While NTLs occur due to the anomalous consumption of electricity by the customers, which happens in many ways; energy theft being one of them. Energy theft majorly happens to cut down on the electricity bills. These losses in the smart grid (SG) are the …

Claimdistiller: Scientific Claim Extraction With Supervised Contrastive Learning, Xin Wei, Md Reshad Ul Hoque, Jian Wu, Jiang Li

Computer Science Faculty Publications

The growth of scientific papers in the past decades calls for effective claim extraction tools to automatically and accurately locate key claims from unstructured text. Such claims will benefit content-wise aggregated exploration of scientific knowledge beyond the metadata level. One challenge of building such a model is how to effectively use limited labeled training data. In this paper, we compared transfer learning and contrastive learning frameworks in terms of performance, time and training data size. We found contrastive learning has better performance at a lower cost of data across all models. Our contrastive-learning-based model ClaimDistiller has the highest performance, boosting …

An Approach To Developing Benchmark Datasets For Protein Secondary Structure Segmentation From Cryo-Em Density Maps, Thu Nguyen, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

More and more deep learning approaches have been proposed to segment secondary structures from cryo-electron density maps at medium resolution range (5--10Å). Although the deep learning approaches show great potential, only a few small experimental data sets have been used to test the approaches. There is limited understanding about potential factors, in data, that affect the performance of segmentation. We propose an approach to generate data sets with desired specifications in three potential factors - the protein sequence identity, structural contents, and data quality. The approach was implemented and has generated a test set and various training sets to study …

General-Purpose Planning Algorithms In Partially-Observable Stochastic Games, Bryan Mckenney

Honors Theses and Capstones

Partially observable stochastic games (POSGs) are difficult domains to plan in because they feature multiple agents with potentially opposing goals, parts of the world are hidden from the agents, and some actions have random outcomes. It is infeasible to solve a large POSG optimally. While it may be tempting to design a specialized algorithm for finding suboptimal solutions to a particular POSG, general-purpose planning algorithms can work just as well, but with less complexity and domain knowledge required. I explore this idea in two different POSGs: Navy Defense and Duelyst.

In Navy Defense, I show that a specialized algorithm framework, …

Rage Against The Machine: Who Is Responsible For Regulating Generative Artificial Intelligence In Domestic And Cross-Border Litigation?, S. I. Strong

Faculty Articles

In 2023, ChatGPT—an early form of generative artificial intelligence (AI) capable of creating entirely new content—took the world by storm. The first shock came when ChatGPT demonstrated its ability to pass the U.S. bar exam. Soon thereafter, the world learned that ChatGPT was being used by both lawyers and judges in actual litigation.

Some within the legal community find the use of generative AI in civil and criminal litigation entirely unproblematic. Others find generative AI troubling as a matter of due process and procedural fairness due to its propensity not only to misinterpret legitimate legal authorities but to create fictitious …

A Review Of Piezoelectric Footwear Energy Harvesters: Principles, Methods, And Applications, Bingqi Zhao, Feng Qian, Alexander Hatfield, Lei Zuo, Tian-Bing Xu

Mechanical & Aerospace Engineering Faculty Publications

Over the last couple of decades, numerous piezoelectric footwear energy harvesters (PFEHs) have been reported in the literature. This paper reviews the principles, methods, and applications of PFEH technologies. First, the popular piezoelectric materials used and their properties for PEEHs are summarized. Then, the force interaction with the ground and dynamic energy distribution on the footprint as well as accelerations are analyzed and summarized to provide the baseline, constraints, potential, and limitations for PFEH design. Furthermore, the energy flow from human walking to the usable energy by the PFEHs and the methods to improve the energy conversion efficiency are presented. …

Deep Learning-Based Technique For The Perception Of The Cervical Cancer, Aya Haraz, Hossam El-Din Moustafa, Abeer Twakol Khaleel, Ahmed H. Eltanboly

Mansoura Engineering Journal

In third-world countries, cervical cancer is the most prevalent and leading cause of death. It is affected by a variety of factors, including smoking, poor nutritional status, immunological inadequacy, and prolonged use of contraception. The Pap smear test, which is intended to prevent cervical cancer, finds preneoplastic changes in cervical epithelial cells. This study framework classified cervical cancer cells from Pap smears into five specified cell types using machine learning-based classification algorithms. The SIPaKMeD database is used in this investigation. This public dataset, which was manually cropped from 966 cluster cell images taken from Pap smear slides, has 4045 isolated …

Artificial Intelligence-Enabled Exploratory Cyber-Physical Safety Analyzer Framework For Civilian Urban Air Mobility, Md. Shirajum Munir, Sumit Howlader Dipro, Kamrul Hasan, Tariqul Islam, Sachin Shetty

VMASC Publications

Urban air mobility (UAM) has become a potential candidate for civilization for serving smart citizens, such as through delivery, surveillance, and air taxis. However, safety concerns have grown since commercial UAM uses a publicly available communication infrastructure that enhances the risk of jamming and spoofing attacks to steal or crash crafts in UAM. To protect commercial UAM from cyberattacks and theft, this work proposes an artificial intelligence (AI)-enabled exploratory cyber-physical safety analyzer framework. The proposed framework devises supervised learning-based AI schemes such as decision tree, random forests, logistic regression, K-nearest neighbors (KNN), and long short-term memory (LSTM) for predicting and …

Automating Intersection Marking Data Collection And Condition Assessment At Scale With An Artificial Intelligence-Powered System, Kun Xie, Huiming Sun, Xiaomeng Dong, Hong Yang, Hongkai Yu

Civil & Environmental Engineering Faculty Publications

Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with …

Design Of Robust Blockchain-Envisioned Authenticated Key Management Mechanism For Smart Healthcare Applications, Siddhant Thapiyal, Mohammad Wazid, Devesh Pratap Singh, Ashok Kumar Das, Sachin Shetty

VMASC Publications

The healthcare sector is a very crucial and important sector of any society, and with the evolution of the various deployed technologies, like the Internet of Things (IoT), machine learning and blockchain it has numerous advantages. However, in this section, the data is much more vulnerable than others, because the data is strictly private and confidential, and it requires a highly secured framework for the transmission of data between entities. In this article, we aim to design a blockchain-envisioned authentication and key management mechanism for the IoMT-based smart healthcare applications (in short, we call it SBAKM-HS). We compare the various …

Machine-Learning-Based Vulnerability Detection And Classification In Internet Of Things Device Security, Sarah Bin Hulayyil, Shancang Li, Li Da Xu

Information Technology & Decision Sciences Faculty Publications

Detecting cyber security vulnerabilities in the Internet of Things (IoT) devices before they are exploited is increasingly challenging and is one of the key technologies to protect IoT devices from cyber attacks. This work conducts a comprehensive survey to investigate the methods and tools used in vulnerability detection in IoT environments utilizing machine learning techniques on various datasets, i.e., IoT23. During this study, the common potential vulnerabilities of IoT architectures are analyzed on each layer and the machine learning workflow is described for detecting IoT vulnerabilities. A vulnerability detection and mitigation framework was proposed for machine learning-based vulnerability detection in …

Practical Ai Value Alignment Using Stories, Md Sultan Al Nahian

Theses and Dissertations--Computer Science

As more machine learning agents interact with humans, it is increasingly a prospect that an agent trained to perform a task optimally - using only a measure of task performance as feedback--can violate societal norms for acceptable behavior or cause harm. Consequently, it becomes necessary to prioritize task performance and ensure that AI actions do not have detrimental effects. Value alignment is a property of intelligent agents, wherein they solely pursue goals and activities that are non-harmful and beneficial to humans. Current approaches to value alignment largely depend on imitation learning or learning from demonstration methods. However, the dynamic nature …

Novel Architectures And Optimization Algorithms For Training Neural Networks And Applications, Vasily I. Zadorozhnyy

Theses and Dissertations--Mathematics

The two main areas of Deep Learning are Unsupervised and Supervised Learning. Unsupervised Learning studies a class of data processing problems in which only descriptions of objects are known, without label information. Generative Adversarial Networks (GANs) have become among the most widely used unsupervised neural net models. GAN combines two neural nets, generative and discriminative, that work simultaneously. We introduce a new family of discriminator loss functions that adopts a weighted sum of real and fake parts, which we call adaptive weighted loss functions. Using the gradient information, we can adaptively choose weights to train a discriminator in the direction …

Machine Learning For Advancing Automation And Quality Control In Robotic Welding, Joseph Kershaw

Theses and Dissertations--Mechanical and Aerospace Engineering

Welding is a vital manufacturing process across a diverse range of industries to permanently join metallic components into needed assemblies. Though widely used and studied, the process is not fully understood due to the myriad of interconnected physics that present themselves during the welding process. Electrical, thermal, structural, and fluidic mechanics are simultaneously at work influencing one another to achieve the bonding that manufacturers desire. In addition to general manufacturing issues, such as tool wear or misalignment, micro-level changes to the material structure and composition can have cascading impacts on the final weld. This complexity makes it difficult, if not …

Generalizable And Adaptable Data-Driven Methods For Overcoming Barriers To Practical Industrial Condition Monitoring, Matthew B. Russell

Theses and Dissertations--Electrical and Computer Engineering

The future of smart manufacturing relies on predictive maintenance systems that intelligently minimize expensive downtime through timely assessment of machine condition. Deep Learning (DL) has achieved excellent performance in industrial condition monitoring experiments, but the constraints of the manufacturing environment prevent many algorithms from being practically deployed on the factory floor. Ubiquitous sensing from online machines generates high velocity data streams that require new techniques for efficient transmission and storage. Despite these ever-increasing data lakes, many applications still lack the data needed for training DL fault diagnosis and wear tracking models since most data is unlabeled and only from nominal …

Biomarker Identification For Breast Cancer Types Using Feature Selection And Explainable Ai Methods, David E. La Rosa Giraud

Honors Undergraduate Theses

This paper investigates the impact the LASSO, mRMR, SHAP, and Reinforcement Feature Selection techniques on random forest models for the breast cancer subtypes markers ER, HER2, PR, and TN as well as identifying a small subset of biomarkers that could potentially cause the disease and explain them using explainable AI techniques. This is important because in areas such as healthcare understanding why the model makes a specific decision is important it is a diagnostic of an individual which requires reliable AI. Another contribution is using feature selection methods to identify a small subset of biomarkers capable of predicting if a …

Determining Child Sexual Abuse Posts Based On Artificial Intelligence, Susan Mckeever, Christina Thorpe, Vuong Ngo

Conference papers

The volume of child sexual abuse materials (CSAM) created and shared daily both surface web platforms such as Twitter and dark web forums is very high. Based on volume, it is not viable for human experts to intercept or identify CSAM manually. However, automatically detecting and analysing child sexual abusive language in online text is challenging and time-intensive, mostly due to the variety of data formats and privacy constraints of hosting platforms. We propose a CSAM detection intelligence algorithm based on natural language processing and machine learning techniques. Our CSAM detection model is not only used to remove CSAM on …

The European Commission And Ai: Guidelines, Acts And Plans Impacting The Teaching Of Ai And Teaching With Ai, Keith Quille, Brett A. Becker, Lidia Vidal-Meliá

Academic Posters Collection

Recent developments, guidelines, and acts by the European Commission have started to frame policy for AI and related areas such as ML and data, not only for the broader community, but in the context of education specifically. This poster presents a succinct overview of these developments. Specifically, we look to bring together all publications that might impact the teaching of AI (for example, teacher expectations in the coming years around AI competencies) and publications that affect the use of AI in the classroom. We mean using tools and systems that incorporate both ‘Good Old Fashioned’ AI and those that can …

Enhancing Early-Stage Xai Projects Through Designer-Led Visual Ideation Of Ai Concepts, Helen Sheridan, Emma Murphy, Dympna O'Sullivan

Academic Posters Collection

The pervasive use of artificial intelligence (AI) in processing users’ data is well documented with the use of AI believed to profoundly change users’ way of life in the near future. However, there still exists a sense of mistrust among users who engage with AI systems some of this stemming from lack of transparency, including users failing to understand what AI is, what it can do and its impact on society. From this, the emerging discipline of explainable artificial intelligence (XAI) has emerged, a method of designing and developing AI where a systems decisions, processes and outputs are explained and …

Unlocking The Black Box: Evaluating Xai Through A Mixed Methods Approach Combining Quantitative Standardised Scales And Qualitative Techniques, Helen Sheridan, Dympna O'Sullivan, Emma Murphy

Academic Posters Collection

In 1950 when Alan Turing first published his groundbreaking paper, computing machinery and intelligence and asked “Can machines think?” a new era of research exploring the intelligence of digital computers and their ability to deceive and/or imitate a human was ignited. From these first explorations of AI to modern day artificial intelligence and machine learning systems many advances, breakthroughs and improved algorithms have been developed usually advancing at an exponential pace. This has resulted in the pervasive use of AI systems in the processing of data. Concerns have been expressed related to biased decisions by AI systems around the processing …

Knowledge Discovery On The Integrative Analysis Of Electrical And Mechanical Dyssynchrony To Improve Cardiac Resynchronization Therapy, Zhuo He

Dissertations, Master's Theses and Master's Reports

Cardiac resynchronization therapy (CRT) is a standard method of treating heart failure by coordinating the function of the left and right ventricles. However, up to 40% of CRT recipients do not experience clinical symptoms or cardiac function improvements. The main reasons for CRT non-response include: (1) suboptimal patient selection based on electrical dyssynchrony measured by electrocardiogram (ECG) in current guidelines; (2) mechanical dyssynchrony has been shown to be effective but has not been fully explored; and (3) inappropriate placement of the CRT left ventricular (LV) lead in a significant number of patients.

In terms of mechanical dyssynchrony, we utilize an …