Digital Commons Network^™

Can Algorithms Promote Fair Use?, Peter K. Yu

FIU Law Review

No abstract provided.

Data Driven Development: Analyzing Urban Change Through Construction, Demolition, And Renovation, Nathan Ron-Ferguson

Electronic Theses and Dissertations Archive

A communitys urban form is determined by the interaction of transportation networks and adjacent land uses. This relationship has been used to understand and measure a variety of development patterns including urban sprawl with its low population density, segregated land uses, and auto-centric street network. A deeper understanding of development patterns is critical to understanding how cities change and how that change may affect the people residing in those communities. Construction, demolition, and renovation are key indicators of this change and while varying measures of each have been used to gain insight into how communities are changing, they are typically …

Smart Green Communication Protocols Based On Several-Fold Messages Extracted From Common Sequential Patterns In Uavs, Iván García-Magariño, Geraldine Gray, Raquel Lacuesta, Jaime Lloret

Articles

Green communications can be crucial for saving energy in UAVs and enhancing their autonomy. The current work proposes to extract common sequential patterns of communications to gather each common pattern into a single several- fold message with a high-level compression. Since the messages of a pattern are elapsed from each other in time, the current approach performs a machine learning approach for estimating the elapsed times using off-line training. The learned predictive model is applied by each UAV during flight when receiving a several-fold compressed message. We have explored neural networks, linear regression and correlation analyses among others. The current …

Introduction: Intelligent Entertainment: Shaping Policies On The Algorithmic Generation And Regulation Of Creative Works, Hannibal Travis

FIU Law Review

No abstract provided.

Copyright Law’S Impact On Machine Intelligence In The United States And The European Union, Matthew Sag

FIU Law Review

No abstract provided.

Language Model Co-Occurrence Linking For Interleaved Activity Discovery, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

As ubiquitous computer and sensor systems become abundant, the potential for automatic identification and tracking of human behaviours becomes all the more evident. Annotating complex human behaviour datasets to achieve ground truth for supervised training can however be extremely labour-intensive, and error prone. One possible solution to this problem is activity discovery: the identification of activities in an unlabelled dataset by means of an unsupervised algorithm. This paper presents a novel approach to activity discovery that utilises deep learning based language production models to construct a hierarchical, tree-like structure over a sequential vector of sensor events. Our approach differs from …

Modelling Interleaved Activities Using Language Models, Eoin Rogers, Robert J. Ross, John D. Kelleher

Conference papers

We propose a new approach to activity discovery, based on the neural language modelling of streaming sensor events. Our approach proceeds in multiple stages: we build binary links between activities using probability distributions generated by a neural language model trained on the dataset, and combine the binary links to produce complex activities. We then use the activities as sensor events, allowing us to build complex hierarchies of activities. We put an emphasis on dealing with interleaving, which represents a major challenge for many existing activity discovery systems. The system is tested on a realistic dataset, demonstrating it as a promising …

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 …

An Univariable Approach For Forecasting Workload In The Maintenance Industry, Paulo Silva, Fernando Pérez Téllez, John Cardiff

Articles

The forecasting of the workload in the maintenance industry is of great value to improve human resources allocation and reduce overwork. In this paper, we discuss the problem and the challenges it pertains. We analyze data from a company operating in the industry and present the results of several forecasting models.

Use Of Machine Learning To Predict Ethical Drift In Law Enforcement, Ryan Mann

Walden Dissertations and Doctoral Studies

U.S. law enforcement agencies are facing a legitimacy crisis. Incidents of police misconduct are the subject of widespread media coverage. Officer conduct continues to be a problem despite effectiveness of candidate screening. Underlying causes of ethical drift must be understood to reduce police misconduct. The purpose of this nonexperimental quantitative study was to examine the relationship between police ethical drift and agency size, officer age, officer gender, and officer education level. Ethical drift was the conceptual framework. Archival secondary data from local law enforcement agencies and the Florida Department of Law Enforcement Criminal Justice Standards and Training Commission were obtained …

Applying Artificial Intelligence To Medical Data, Shaikh Shiam Rahman

College of Graduate Studies: Theses & Dissertations

Machine learning, data mining, and deep learning has become the methodology of choice for analyzing medical data and images. In this study, we implemented three different machine learning techniques to medical data and image analysis. Our first study was to implement different log base entropy for a decision tree algorithm. Our results suggested that using a higher log base for the dataset with mostly categorical attributes with three or more categories for each attribute can obtain a higher accuracy. For the second study, we analyzed mental health data tuning the parameters of the decision tree (splitting method, depth and entropy). …

Analyze Informant-Based Questionnaire For The Early Diagnosis Of Senile Dementia Using Deep Learning, Fubao Zhu, Xiaonan Li, Daniel Mcgonigle, Haipeng Tang, Zhuo He, Chaoyang Zhang, Guang-Uei Hung, Pai-Yi Chiu, Weihua Zhou

Michigan Tech Publications, Part 1

OBJECTIVE: This paper proposes a multiclass deep learning method for the classification of dementia using an informant-based questionnaire.

METHODS: A deep neural network classification model based on Keras framework is proposed in this paper. To evaluate the advantages of our proposed method, we compared the performance of our model with industry-standard machine learning approaches. We enrolled 6,701 individuals, which were randomly divided into training data sets (6030 participants) and test data sets (671 participants). We evaluated each diagnostic model in the test set using accuracy, precision, recall, and F1-Score.

RESULTS: Compared with the seven conventional machine learning algorithms, the DNN …

Wheat Growth Monitoring And Yield Estimation Based On Multi-Rotor Unmanned Aerial Vehicle, Zhaopeng Fu, Jie Jang, Yang Gao, Brian Krienke, Meng Wang, Kaitai Zhong, Qiang Cao, Yongchao Tian, Yan Zhu, Weixing Cao, Xiaojun Liu

Department of Agronomy and Horticulture: Faculty Publications

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the …

Special Section Guest Editorial: Machine Learning In Optics, Jonathan Howe, Travis Axtell, Khan Iftekharuddin

Electrical & Computer Engineering Faculty Publications

This guest editorial summarizes the Special Section on Machine Learning in Optics.

Improving M-Learners' Performance Through Deep Learning Techniques By Leveraging Features Weights, Muhammad Adnan, Asad Habib, Jawad Ashraf, Babar Shah, Gohar Ali

All Works

© 2013 IEEE. Mobile learning (M-learning) has gained tremendous attention in the educational environment in the past decade. For effective M-learning, it is important to create an efficient M-learning model that can identify the exact requirements of mobile learners (M-learners). M-learning model is composed of features that are generated during M-learners' interaction with mobile devices. For an adaptive M-learning model, not only learning features are required, but it is also important to determine how they differ for various M-learners, their weights, and interrelationship. This study proposes a robust and adaptive M-learning model that is based on machine learning and deep …

Hierarchical Clustering To Predict The Response Of Cardiac Resynchronization Therapy In Patients With Heart Failure, Rukayat Bukola Adeosun

Dissertations, Master's Theses and Master's Reports

The heterogeneous nature of today’s evolving health databases requires new techniques and approaches to process these data and extract clinically useful information. This relevant information obtained can be used to improve the response rate of cardiac resynchronization therapy (CRT) in patients with heart failure. Hierarchical clustering (HC) which is an unsupervised ML technique may uncover clusters within the bulk of data of patient population which is useful for strategies towards precision and personalized medicine. This study aims to investigate how HC can be used to automatically group a bulk of clinically acquired CRT data into clusters and subgroups that could …

Artificial Intelligence In Plasma Electrolytic Micro-Oxidation For Surface Hardening - Insights From Scholarly Citation Networks And Patents., Priya Jadhav, Dr.Arun Bongale, Dr.Satish Kumar, Dr.Amit Kumar Tiwari

Library Philosophy and Practice (e-journal)

Objective - The purpose of this article is to analyze the top work areas and patent domains in the field of surface hardening by micro-arc oxidation. Also, it is directed on the opportunities of data analysis by different machine learning tools. Material and methods - The www.lens.org database is used to collect articles from Elsevier, Trans tech publications, Springer New York, MDPI, etc. to review the relevant articles as well as patents related to the topic. The result - A total of 1057 articles were published in 60 different journals and 756 patents in the area of research under various …

Digging Into Algorithms: Legal Ethics And Legal Access, Carla L. Reyes, Jeff Ward

Faculty Scholarship

The current discussions around algorithms, legal ethics, and expanding legal access through technological tools gravitate around two themes: (1) protection of the integrity of the legal profession and (2) a desire to ensure greater access to legal services. The hype cycle often pits the desire to protect the integrity of the legal profession against the ability to use algorithms to provide greater access to legal services, as though they are mutually exclusive. In reality, the arguments around protecting the profession from the threats posed by algorithms represent an over-fit in relation to what algorithms can actually achieve, while the visions …

A Deep Learning Approach To Mapping Irrigation: U-Net Irrmapper, Thomas Henry Colligan Iv

Graduate Student Theses, Dissertations, & Professional Papers

Accurate maps of irrigation are essential for understanding and managing water resources in light of a warming climate. We present a new method for mapping irrigation and apply it to the state of Montana over the years 2000-2019. The method is based on an ensemble of convolutional neural networks that only rely on raw Landsat surface reflectance data. The ensemble of networks method learns to mask clouds and ignore Landsat 7 scan-line failures without supervision, reducing the need for preprocessing data or feature engineering. Unlike other approaches to mapping irrigation, the method doesn't use other mapping products like the Cropland …

Machine Learning Techniques For Quantification Of Knee Segmentation From Mri, Sujeet More, Jimmy Singla, Ahed Abugabah, Ahmad Ali Alzubi

All Works

© 2020 Sujeet More et al. Magnetic resonance imaging (MRI) is precise and efficient for interpreting the soft and hard tissues. Moreover, for the detailed diagnosis of varied diseases such as knee rheumatoid arthritis (RA), segmentation of the knee magnetic resonance image is a challenging and complex task that has been explored broadly. However, the accuracy and reproducibility of segmentation approaches may require prior extraction of tissues from MR images. The advances in computational methods for segmentation are reliant on several parameters such as the complexity of the tissue, quality, and acquisition process involved. This review paper focuses and briefly …

Security Techniques For Intelligent Spam Sensing And Anomaly Detection In Online Social Platforms, Monther Aldwairi, Lo'ai Tawalbeh

All Works

Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. The recent advances in communication and mobile technologies made it easier to access and share information for most people worldwide. Among the most powerful information spreading platforms are the Online Social Networks (OSN)s that allow Internet-connected users to share different information such as instant messages, tweets, photos, and videos. Adding to that many governmental and private institutions use the OSNs such as Twitter for official announcements. Consequently, there is a tremendous need to provide the required level of security for OSN users. However, there are many challenges due …

Using Machine Learning On An Imbalanced Cancer Dataset, James Ekow Arthur

Open Access Theses & Dissertations

With an estimated 1.4 million cancer diagnosis worldwide and the increasing death of cancer patients. It is prudent to investigate methods, approaches and smarter ways of predicting and diagnosing of cancer so that a holistic techniques can be used to curb or reduce false predictions , increase exact predictions and also meticulos prognosis information .

Can a feasible technique be developed for the general problem of prognosis and diagnosis of cancer be developed ?

We will show here that this problem of cancer prognosis and diagnosis can be efficiently tackled with the aid of machine learning techniques and the best, …

Comparing Predictive Performance Of Statistical Learning Models On Medical Data, Francis Biney

Open Access Theses & Dissertations

This work investigates the predictive performance of 10 Machine learning models on three medical data including Breast cancer, Heart disease and Prostate cancer. Furthermore, we use the models to identify risk factors that contribute significantly to these diseases.

The models considered include; Logistic regression with L1 and L_2 penalties, Principal component logistic regression(PCR-LR), Partial least squares logistic regression(PLS-LR), Multivariate adaptive regression splines(MARS), Support vector machine with Radial Basis Kernel (SVM-RBK), Random Forest(RF), Gradient Boosting Machines(GBM), Elastic Net (Enet) and Feedforward Neural Network(FFNN). The models were grouped according to their similarities and learning style; i) Linear regularized models: LR-Lasso, LR-Ridge and …

Disaster Damage Categorization Applying Satellite Images And Machine Learning Algorithm, Farinaz Sabz Ali Pour, Adrian Gheorghe

Engineering Management & Systems Engineering Faculty Publications

Special information has a significant role in disaster management. Land cover mapping can detect short- and long-term changes and monitor the vulnerable habitats. It is an effective evaluation to be included in the disaster management system to protect the conservation areas. The critical visual and statistical information presented to the decision-makers can help in mitigation or adaption before crossing a threshold. This paper aims to contribute in the academic and the practice aspects by offering a potential solution to enhance the disaster data source effectiveness. The key research question that the authors try to answer in this paper is how …

Vegetation Detection Using Deep Learning And Conventional Methods, Bulent Ayhan, Chiman Kwan, Bence Budavari, Liyun Kwan, Yan Lu, Daniel Perez, Jiang Li, Dimitrios Skarlatos, Marinos Vlachos

Electrical & Computer Engineering Faculty Publications

Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer …

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 …

Application Of Advanced Algorithms And Statistical Techniques For Weed-Plant Discrimination, Saman Akbar Zadeh

Theses: Doctorates and Masters

Precision agriculture requires automated systems for weed detection as weeds compete with the crop for water, nutrients, and light. The purpose of this study is to investigate the use of machine learning methods to classify weeds/crops in agriculture. Statistical methods, support vector machines, convolutional neural networks (CNNs) are introduced, investigated and optimized as classifiers to provide high accuracy at high vehicular speed for weed detection.

Initially, Support Vector Machine (SVM) algorithms are developed for weed-crop discrimination and their accuracies are compared with a conventional data-aggregation method based on the evaluation of discrete Normalised Difference Vegetation Indices (NDVIs) at two different …

Implementing User Rights For Research In The Field Of Artificial Intelligence: A Call For International Action, Sean Flynn, Michael W. Carroll

Scholarly Articles in Law Reviews & Journals

Last year, before the onset of a global pandemic highlighted the critical and urgent need for technology-enabled scientific research, the World Intellectual Property Organization (WIPO) launched an inquiry into issues at the intersection of intellectual property (IP) and artificial intelligence (AI). We contributed comments to that inquiry, with a focus on the application of copyright to the use of text and data mining (TDM) technology. This article describes some of the most salient points of our submission and concludes by stressing the need for international leadership on this important topic. WIPO could help fill the current gap on international leadership, …

Connected Multi-Domain Autonomy And Artificial Intelligence: Autonomous Localization, Networking, And Data Conformity Evaluation, Konstantinos Tountas

Electronic Theses and Dissertations 2020 - Present

The objective of this dissertation work is the development of a solid theoretical and algorithmic framework for three of the most important aspects of autonomous/artificialintelligence (AI) systems, namely data quality assurance, localization, and communications. In the era of AI and machine learning (ML), data reign supreme. During learning tasks, we need to ensure that the training data set is correct and complete. During operation, faulty data need to be discovered and dealt with to protect from -potentially catastrophic- system failures. With our research in data quality assurance, we develop new mathematical theory and algorithms for outlier-resistant decomposition of high-dimensional matrices …

Hpcc Based Platform For Copd Readmission Risk Analysis With Implementation Of Dimensionality Reduction And Balancing Techniques, Piyush Jain

Electronic Theses and Dissertations 2020 - Present

Hospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts. In this study, we will be proposing a framework on how the readmission analysis and other healthcare models could be deployed in …