Artificial Intelligence and Robotics Commons^™

Segmentation Stability Of Human Head And Neck Cancer Medical Images For Radiotherapy Applications Under De-Identification Conditions: Benchmarking Data Sharing And Artificial Intelligence Use-Cases, Jaakko Sahlsten, Kareem A Wahid, Enrico Glerean, Joel Jaskari, Mohamed A Naser, Renjie He, Benjamin H Kann, Antti Mäkitie, Clifton D Fuller, Kimmo Kaski

Faculty, Staff and Student Publications

BACKGROUND: Demand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patient identifiers. Defacing, i.e., the removal of facial features from images, is often considered a reasonable compromise between data protection and re-usability for neuroimaging data. While defacing tools have been developed by the neuroimaging community, their acceptability for radiotherapy applications have not been explored. Therefore, this study systematically investigated the impact of available defacing algorithms on HNC organs at risk (OARs).

METHODS: A publicly available dataset …

Development Of Machine Learning Based Approach To Predict Fuel Consumption And Maintenance Cost Of Heavy-Duty Vehicles Using Diesel And Alternative Fuels, Sasanka Katreddi

Graduate Theses, Dissertations, and Problem Reports (ETD)

One of the major contributors of human-made greenhouse gases (GHG) namely carbon dioxide (CO2), methane (CH4), and nitrous oxide (NOX) in the transportation sector and heavy-duty vehicles (HDV) contributing to about 27% of the overall fraction. In addition to the rapid increase in global temperature, airborne pollutants from diesel vehicles also present a risk to human health. Even a small improvement that could potentially drive energy savings to the century-old mature diesel technology could yield a significant impact on minimizing greenhouse gas emissions. With the increasing focus on reducing emissions and operating costs, there is a need for efficient and …

Toward A Generative Modeling Analysis Of Clas Exclusive 2𝜋 Photoproduction, T. Alghamdi, Y. Alanazi, M. Battaglieri, Ł. Bibrzycki, A. V. Golda, A. N. Hiller Blin, E. L. Isupov, Y. Li, L. Marsicano, W. Melnitchouk, V. I. Mokeev, G. Montaña, A. Pilloni, N. Sato, A. P. Szczepaniak, T. Vittorini

Computer Science Faculty Publications

AI-supported algorithms, particularly generative models, have been successfully used in a variety of different contexts. This work employs a generative modeling approach to unfold detector effects specifically tailored for exclusive reactions that involve multiparticle final states. Our study demonstrates the preservation of correlations between kinematic variables in a multidimensional phase space. We perform a full closure test on two-pion photoproduction pseudodata generated with a realistic model in the kinematics of the Jefferson Lab CLAS g11 experiment. The overlap of different reaction mechanisms leading to the same final state associated with the CLAS detector’s nontrivial effects represents an ideal test case …

Machine Learning-Based Jet And Event Classification At The Electron-Ion Collider With Applications To Hadron Structure And Spin Physics, Kyle Lee, James Mulligan, Mateusz Płoskoń, Felix Ringer, Feng Yuan

Physics Faculty Publications

We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC energies, focusing on (i) identifying the flavor of the jet and (ii) identifying the underlying hard process of the event. We propose applications of our machine learning-based jet identification in the key research areas at the future EIC and current Relativistic Heavy Ion Collider program, including enhancing constraints on (transverse momentum dependent) parton distribution functions, improving experimental access to transverse spin asymmetries, studying photon structure, and quantifying the modification of hadrons and jets in …

Comments Of The Cordell Institute On Ai Accountability, Neil M. Richards, Woodrow Hartzog, Jordan Francis

Scholarship@WashULaw

These comments are a response to the National Telecommunications and Information Administration's 2023 request for comment on AI accountability (AI Accountability RFC, NTIA–2023–0005).

Responding to NTIA’s recent inquiry into AI assurance and accountability, we offer two main arguments regarding the importance of substantive legal protections. First, a myopic focus on concepts of transparency, bias mitigation, and ethics (for which procedural compliance efforts such as audits, assessments, and certifications are proxies) is insufficient when it comes to the design and implementation of accountable AI systems. We call rules built around transparency and bias mitigation “AI half-measures,” because they provide the appearance …

View Synthesis With Scene Recognition For Cross-View Image Localization, Uddom Lee, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

Image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with …

Security Of Internet Of Things (Iot) Using Federated Learning And Deep Learning — Recent Advancements, Issues And Prospects, Vinay Gugueoth, Sunitha Safavat, Sachin Shetty

Electrical & Computer Engineering Faculty Publications

There is a great demand for an efficient security framework which can secure IoT systems from potential adversarial attacks. However, it is challenging to design a suitable security model for IoT considering the dynamic and distributed nature of IoT. This motivates the researchers to focus more on investigating the role of machine learning (ML) in the designing of security models. A brief analysis of different ML algorithms for IoT security is discussed along with the advantages and limitations of ML algorithms. Existing studies state that ML algorithms suffer from the problem of high computational overhead and risk of privacy leakage. …

Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette

Electrical & Computer Engineering Faculty Publications

Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …

Adaptive Critic Network For Person Tracking Using 3d Skeleton Data, Joseph G. Zalameda, Alex Glandon, Khan M. Iftekharuddin, Mohammad S. Alam (Ed.), Vijayan K. Asari (Ed.)

Electrical & Computer Engineering Faculty Publications

Analysis of human gait using 3-dimensional co-occurrence skeleton joints extracted from Lidar sensor data has been shown a viable method for predicting person identity. The co-occurrence based networks rely on the spatial changes between frames of each joint in the skeleton data sequence. Normally, this data is obtained using a Lidar skeleton extraction method to estimate these co-occurrence features from raw Lidar frames, which can be prone to incorrect joint estimations when part of the body is occluded. These datasets can also be time consuming and expensive to collect and typically offer a small number of samples for training and …

Class Activation Mapping And Uncertainty Estimation In Multi-Organ Segmentation, Md. Shibly Sadique, Walia Farzana, Ahmed Temtam, Khan Iftekharuddin, Khan Iftekharuddin (Ed.), Weijie Chen (Ed.)

Electrical & Computer Engineering Faculty Publications

Deep learning (DL)-based medical imaging and image segmentation algorithms achieve impressive performance on many benchmarks. Yet the efficacy of deep learning methods for future clinical applications may become questionable due to the lack of ability to reason with uncertainty and interpret probable areas of failures in prediction decisions. Therefore, it is desired that such a deep learning model for segmentation classification is able to reliably predict its confidence measure and map back to the original imaging cases to interpret the prediction decisions. In this work, uncertainty estimation for multiorgan segmentation task is evaluated to interpret the predictive modeling in DL …

Transfer Learning Using Infrared And Optical Full Motion Video Data For Gender Classification, Alexander M. Glandon, Joe Zalameda, Khan M. Iftekharuddin, Gabor F. Fulop (Ed.), David Z. Ting (Ed.), Lucy L. Zheng (Ed.)

Electrical & Computer Engineering Faculty Publications

This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification.

Deep-Learning-Based Classification Of Digitally Modulated Signals Using Capsule Networks And Cyclic Cumulants, John A. Snoap, Dimitrie C. Popescu, James A. Latshaw, Chad M. Spooner

Electrical & Computer Engineering Faculty Publications

This paper presents a novel deep-learning (DL)-based approach for classifying digitally modulated signals, which involves the use of capsule networks (CAPs) together with the cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals, but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in the paper …

A Survey Of Using Machine Learning In Iot Security And The Challenges Faced By Researchers, Khawlah M. Harahsheh, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber …

Comparison Of Machine Learning Methods For Classification Of Alexithymia In Individuals With And Without Autism From Eye-Tracking Data, Furkan Iigin, Megan A. Witherow, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Alexithymia describes a psychological state where individuals struggle with feeling and expressing their emotions. Individuals with alexithymia may also have a more difficult time understanding the emotions of others and may express atypical attention to the eyes when recognizing emotions. This is known to affect individuals with Autism Spectrum Disorder (ASD) differently than neurotypical (NT) individuals. Using a public data set of eye-tracking data from seventy individuals with and without autism who have been assessed for alexithymia, we train multiple traditional machine learning models for alexithymia classification including support vector machines, logistic regression, decision trees, random forest, and multilayer perceptron. …

Special Section Editorial: Artificial Intelligence For Medical Imaging In Clinical Practice, Claudia Mello-Thoms, Karen Drukker, Sian Taylor-Phillips, Khan Iftekharuddin, Marios Gavrielides

Electrical & Computer Engineering Faculty Publications

This editorial introduces the JMI Special Section on Artificial Intelligence for Medical Imaging in Clinical Practice.

An Explainable Artificial Intelligence Framework For The Predictive Analysis Of Hypo And Hyper Thyroidism Using Machine Learning Algorithms, Md. Bipul Hossain, Anika Shama, Apurba Adhikary, Avi Deb Raha, K. M. Aslam Uddin, Mohammad Amzad Hossain, Imtia Islam, Saydul Akbar Murad, Md. Shirajum Munir, Anupam Kumur Bairagi

Electrical & Computer Engineering Faculty Publications

The thyroid gland is the crucial organ in the human body, secreting two hormones that help to regulate the human body's metabolism. Thyroid disease is a severe medical complaint that could be developed by high Thyroid Stimulating Hormone (TSH) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two critical conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Machine learning models can be used to precisely process the data generated from different medical sectors and to build a model to predict several diseases. In this paper, we use different machine-learning algorithms to …

Light Auditor: Power Measurement Can Tell Private Data Leakage Through Iot Covert Channels, Woosub Jung, Kailai Cui, Kenneth Koltermann, Junjie Wang, Chunsheng Xin, Gang Zhou

Electrical & Computer Engineering Faculty Publications

Despite many conveniences of using IoT devices, they have suffered from various attacks due to their weak security. Besides well-known botnet attacks, IoT devices are vulnerable to recent covert-channel attacks. However, no study to date has considered these IoT covert-channel attacks. Among these attacks, researchers have demonstrated exfiltrating users' private data by exploiting the smart bulb's capability of infrared emission.

In this paper, we propose a power-auditing-based system that defends the data exfiltration attack on the smart bulb as a case study. We first implement this infrared-based attack in a lab environment. With a newly-collected power consumption dataset, we pre-process …

Is Disclosure And Certification Of The Use Of Generative Ai Really Necessary?, Maura R. Grossman, Paul W. Grimm, Daniel G. Brown

Faculty Scholarship

No abstract provided.

Understanding Human-Centred Artificial Intelligence In The Banking Sector, Krishnaraj Arul Obuchettiar, Alan @ Ali Madjelisi Megargel

Research Collection Lee Kong Chian School Of Business

The advent of smart digital devices and social media has shaped how consumers interact and transact with their financial institutions. Consumers increasingly want hyperpersonalised interactions that are more frequent and proactive, while financial institutions have a growing need to cater to consumers’ new demands. Financial institutions, such as banks, continuously adapt to the latest technologies to keep pace with evolving customer behaviours, needs, and experiences. One such emerging technology is artificial intelligence (AI). Many organisations realise the potential of AI; however, a human-centred AI system must be capable of understanding human characteristics and making decisions like humans. This paper presents …

Directional Speaker Poster, Eugene Ng, Bryan Wong, Ruhaan Das

Student Works

Changi Airport is set to expand with a new terminal, Terminal 5. Currently, many of the airport's processes are manual, requiring a high dependence on staff. This proposal aims to incorporate automation and AI for a smoother passenger experience.