Digital Commons Network^™

Monocular Depth Image Mark-Less Pose Estimation Based On Feature Regression, Chen Ying, Shen Li

Journal of System Simulation

Abstract: Monocular camera mark-less pose estimation system suffers low accuracy, robustness and efficiency due to variety of action, self-occlusion of human body. A method of feature exaction from point clouds was proposed, in which a single-to-multiple (S2M) feature regressor and a joint position regressor were designed to quickly and accurately predict the 3D positions of body joints from a single depth image without any temporal information. Experiment result shows that the estimation accuracy is superior to that of state-of-the-arts and multi-camera based methods.

Development Of The Algorithm For Supporting Decision-Making In Self-Government Bodies Using Machine Learning, X J. Raximboyev

Scientific-technical journal

The article deals with the problem of constructing a model and algorithm for decision support in self-government bodies using machine learning. The method of multiple linear regression for processing the training sample was chosen as a machine learning method. In the training sample, independent data consists of parametric estimates in numerical form of self-government bodies in three areas of activity, such as education, social environment and crime. And the dependent parameter consists of generalized expert assessments of self-government bodies, also in numerical form. The model and algorithm of the decision support process using the method of multiple linear regression are …

Critical Temperature Prediction Of Superconductors Based On Atomic Vectors And Deep Learning, Shaobo Li, Yabo Dan, Xiang Li, Tiantian Hu, Rongzhi Dong, Zhuo Cao, Jianjun Hu

Faculty Publications

In this paper, a hybrid neural network (HNN) that combines a convolutional neural network (CNN) and long short-term memory neural network (LSTM) is proposed to extract the high-level characteristics of materials for critical temperature (Tc) prediction of superconductors. Firstly, by obtaining 73,452 inorganic compounds from the Materials Project (MP) database and building an atomic environment matrix, we obtained a vector representation (atomic vector) of 87 atoms by singular value decomposition (SVD) of the atomic environment matrix. Then, the obtained atom vector was used to implement the coded representation of the superconductors in the order of the atoms in the chemical …

Wart Treatment Decision Support Using Support Vector Machine, Md. Mamunur Rahman, Yuan Zhou, Shouyi Wang, Jamie Rogers

Industrial, Manufacturing, and Systems Engineering Student Research - Archive

Warts are noncancerous benign tumors caused by the Human Papilloma Virus (HPV). The success rates of cryotherapy and immunotherapy, two common treatment methods for cutaneous warts, are 44% and 72%, respectively. The treatment methods, therefore, fail to cure a significant percentage of the patients. This study aims to develop a reliable machine learning model to accurately predict the success of immunotherapy and cryotherapy for individual patients based on their demographic and clinical characteristics. We employed support vector machine (SVM) classifier utilizing a dataset of 180 patients who were suffering from various types of warts and received treatment either by immunotherapy …

Artificial Intelligence & Machine Learning: A Model For A New Judicial System?, Paula C. Arias

Articles

Artificial Intelligence and Machine Learning are a result not only of technological advances but also of the exploitation of information or data, which has led to its expansion into almost all aspects of modern life, including law and its practice. Due to the benefits of these technologies, such as efficiency, objectivity, and transparency, the trend is towards the integration of Artificial Intelligence and Machine Learning in the judicial system. Integration that is advocated at all levels and, today, has been achieved mostly under the implementation of tools to assist the exercise of the judiciary. The «success» of this integration has …

Artificial Intelligence Techniques For Automated Diagnosis Of Neurological Disorders, U. Raghavendra, U. Rajendra Acharya, Hojjat Adeli

Open Access archive

Background: Authors have been advocating the research ideology that a computer-aided diagnosis (CAD) system trained using lots of patient data and physiological signals and images based on adroit integration of advanced signal processing and artificial intelligence (AI)/machine learning techniques in an automated fashion can assist neurologists, neurosurgeons, radiologists, and other medical providers to make better clinical decisions. Summary: This paper presents a state-of-the-art review of research on automated diagnosis of 5 neurological disorders in the past 2 decades using AI techniques: epilepsy, Parkinson's disease, Alzheimer's disease, multiple sclerosis, and ischemic brain stroke using physiological signals and images. Recent research articles …

Predicting Pilot Misperception Of Runway Excursion Risk Through Machine Learning Algorithms Of Recorded Flight Data, Edwin Vincent Odisho Ii

Doctoral Dissertations and Master's Theses

The research used predictive models to determine pilot misperception of runway excursion risk associated with unstable approaches. The Federal Aviation Administration defined runway excursion as a veer-off or overrun of the runway surface. The Federal Aviation Administration also defined a stable approach as an aircraft meeting the following criteria: (a) on target approach airspeed, (b) correct attitude, (c) landing configuration, (d) nominal descent angle/rate, and (e) on a straight flight path to the runway touchdown zone. Continuing an unstable approach to landing was defined as Unstable Approach Risk Misperception in this research. A review of the literature revealed that an …

Are The Code Snippets What We Are Searching For? A Benchmark And An Empirical Study On Code Search With Natural-Language Queries, Shuhan Yan, Hang Yu, Yuting Chen, Beijun Shen

Research Collection School Of Computing and Information Systems

Code search methods, especially those that allow programmers to raise queries in a natural language, plays an important role in software development. It helps to improve programmers' productivity by returning sample code snippets from the Internet and/or source-code repositories for their natural-language queries. Meanwhile, there are many code search methods in the literature that support natural-language queries. Difficulties exist in recognizing the strengths and weaknesses of each method and choosing the right one for different usage scenarios, because (1) the implementations of those methods and the datasets for evaluating them are usually not publicly available, and (2) some methods leverage …

Harnessing Artificial Intelligence Capabilities To Improve Cybersecurity, Sherali Zeadally, Erwin Adi, Zubair Baig, Imran A. Khan

Information Science Faculty Publications

Cybersecurity is a fast-evolving discipline that is always in the news over the last decade, as the number of threats rises and cybercriminals constantly endeavor to stay a step ahead of law enforcement. Over the years, although the original motives for carrying out cyberattacks largely remain unchanged, cybercriminals have become increasingly sophisticated with their techniques. Traditional cybersecurity solutions are becoming inadequate at detecting and mitigating emerging cyberattacks. Advances in cryptographic and Artificial Intelligence (AI) techniques (in particular, machine learning and deep learning) show promise in enabling cybersecurity experts to counter the ever-evolving threat posed by adversaries. Here, we explore AI's …

Innovative Identification Of Substance Use Predictors: Machine Learning In A National Sample Of Mexican Children, Alejandro L. Vázquez, Melanie M. Domenech Rodríguez, Tyson S. Barrett, Sarah E. Schwartz [email protected], Nancy G. Amador Buenabad, Marycarmen N. Bustos Gamiño, María De Lourdes Gutiérrez López, Jorge A. Villatoro Velázquez

Psychology Faculty Publications

Machine learning provides a method of identifying factors that discriminate between substance users and non-users potentially improving our ability to match need with available prevention services within context with limited resources. Our aim was to utilize machine learning to identify high impact factors that best discriminate between substance users and non-users among a national sample (N = 52,171) of Mexican children (i.e., 5th, 6th grade; Mage = 10.40, SDage = 0.82). Participants reported information on individual factors (e.g., gender, grade, religiosity, sensation seeking, self-esteem, perceived risk of substance use), socioecological factors (e.g., neighborhood quality, community type, peer influences, parenting), and …

Investigating Patterns In Convolution Neural Network Parameters Using Probabilistic Support Vector Machines, Yuqiu Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Artificial neural networks(ANNs) are recognized as high-performance models for classification problems. They have proved to be efficient tools for many of today's applications like automatic driving, image and video recognition and restoration, big-data analysis. However, high performance deep neural networks have millions of parameters, and the iterative training procedure thus involves a very high computational cost. This research attempts to study the relationships between parameters in convolutional neural networks(CNNs). I assume there exists a certain relation between adjacent convolutional layers and proposed a machine learning model(MLM) that can be trained to represent this relation. The MLM's generalization ability is evaluated …

Digital Libraries, Intelligent Data Analytics, And Augmented Description: A Demonstration Project, Elizabeth Lorang, Leen-Kiat Soh, Yi Liu, Chulwoo Pack

University of Nebraska-Lincoln Libraries: Faculty Publications

From July 16-to November 8, 2019, the Aida digital libraries research team at the University of Nebraska-Lincoln collaborated with the Library of Congress on “Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project.“ This demonstration project sought to (1) develop and investigate the viability and feasibility of textual and image-based data analytics approaches to support and facilitate discovery; (2) understand technical tools and requirements for the Library of Congress to improve access and discovery of its digital collections; and (3) enable the Library of Congress to plan for future possibilities. In pursuit of these goals, we focused our …

Learning-Guided Network Fuzzing For Testing Cyber-Physical System Defences, Yuqi Chen, Christopher M. Poskitt, Jun Sun, Sridhar Adepu, Fan Zhang

Research Collection School Of Computing and Information Systems

The threat of attack faced by cyber-physical systems (CPSs), especially when they play a critical role in automating public infrastructure, has motivated research into a wide variety of attack defence mechanisms. Assessing their effectiveness is challenging, however, as realistic sets of attacks to test them against are not always available. In this paper, we propose smart fuzzing, an automated, machine learning guided technique for systematically finding 'test suites' of CPS network attacks, without requiring any knowledge of the system's control programs or physical processes. Our approach uses predictive machine learning models and metaheuristic search algorithms to guide the fuzzing of …

Ai-Assisted Network-Slicing Based Next-Generation Wireless Networks, Xuemin Shen, Jie Gao, Wen Wu, Kangjia Lyu, Mushu Li, Weihua Zhuang, Xu Li, Jaya Rao

Electrical and Computer Engineering Faculty Research and Publications

The integration of communications with different scales, diverse radio access technologies, and various network resources renders next-generation wireless networks (NGWNs) highly heterogeneous and dynamic. Emerging use cases and applications, such as machine to machine communications, autonomous driving, and factory automation, have stringent requirements in terms of reliability, latency, throughput, and so on. Such requirements pose new challenges to architecture design, network management, and resource orchestration in NGWNs. Starting from illustrating these challenges, this paper aims at providing a good understanding of the overall architecture of NGWNs and three specific research problems under this architecture. First, we introduce a network-slicing based …

Deployment Of Deep Reinforcement Learning Algorithms On Physical Robotic Systems, P. Surya .

Manipal Institute of Technology, Manipal Theses and Dissertations

In the recent years, Reinforcement Learning (RL), has surfaced as an impressive tool for solving complex sequential decision-making problems in control theory. Its major grasp on control theory has been on Markov decision problems (MDP) and its variants. It overcomes the long observed problems of the curse of dimensionality and the curse of modeling. This outperforms the classical Dynamic Programming (DP) that falter under these curses. The work is to use this advantage in our favour to leverage autonomous navigation in a model learnable environment or otherwise. The work involves the use of a Meta OS called Robot Operating System …

Machine Learning In Manufacturing: Review, Synthesis, And Theoretical Framework, Ajit Sharma, Zhibo Zhang, Rahul Rai

Business Administration Faculty Research Publications

There has been a paradigmatic shift in manufacturing as computing has transitioned from the programmable to the cognitive computing era. In this paper we present a theoretical framework for understanding this paradigmatic shift in manufacturing and the fast evolving role of artificial intelligence. Policy, Strategic and Operational implications are discussed. Implications for the future of strategy and operations in manufacturing are also discussed. Future research directions are presented.

A Systematic Literature Survey Of Unmanned Aerial Vehicle Based Structural Health Monitoring, Sreehari Sreenath

Theses, Dissertations and Capstones

Unmanned Aerial Vehicles (UAVs) are being employed in a multitude of civil applications owing to their ease of use, low maintenance, affordability, high-mobility, and ability to hover. UAVs are being utilized for real-time monitoring of road traffic, providing wireless coverage, remote sensing, search and rescue operations, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. They are the next big revolution in technology and civil infrastructure, and it is expected to dominate more than $45 billion market value. The thesis surveys the UAV assisted Structural Health Monitoring or SHM literature over the last decade and categorize UAVs …

Mining The Harvard Caselaw Access Project, Felix B. Chang, Erin Mccabe, James Lee

Faculty Articles and Other Publications

This Essay illustrates how machine learning can disrupt legal scholarship through the algorithmic extraction and analysis of big data. Specifically, we utilize data from Harvard Law School’s Caselaw Access Project to model how courts tackle two thorny question in antitrust: the measure of market power and the balance between antitrust and regulation.

Facial Action Unit Detection With Deep Convolutional Neural Networks, Siddhesh Padwal

Electronic Theses and Dissertations

The facial features are the most important tool to understand an individual's state of mind. Automated recognition of facial expressions and particularly Facial Action Units defined by Facial Action Coding System (FACS) is challenging research problem in the field of computer vision and machine learning. Researchers are working on deep learning algorithms to improve state of the art in the area. Automated recognition of facial action units has man applications ranging from developmental psychology to human robot interface design where companies are using this technology to improve their consumer devices (like unlocking phone) and for entertainment like FaceApp. Recent studies …

Analysis Of Massive Online Medical Consultation Service Data To Understand Physicians’ Economic Return: Observational Data Mining Study, Jinglu Jiang, Ann-Frances Cameron, Ming Yang

Management and Accounting Faculty Scholarship

Background: Online health care consultation has become increasingly popular and is considered a potential solution to health care resource shortages and inefficient resource distribution. However, many online medical consultation platforms are struggling to attract and retain patients who are willing to pay, and health care providers on the platform have the additional challenge of standing out in a crowd of physicians who can provide comparable services. Objective: This study used machine learning (ML) approaches to mine massive service data to (1) identify the important features that are associated with patient payment, as opposed to free trial–only appointments; (2) explore the …

Renewable Energy Integration In Distribution System With Artificial Intelligence, Yi Gu

Electronic Theses and Dissertations

With the increasing attention of renewable energy development in distribution power system, artificial intelligence (AI) can play an indispensiable role. In this thesis, a series of artificial intelligence based methods are studied and implemented to further enhance the performance of power system operation and control.

Due to the large volume of heterogeneous data provided by both the customer and the grid side, a big data visualization platform is built to feature out the hidden useful knowledge for smart grid (SG) operation, control and situation awareness. An open source cluster calculation framework with Apache Spark is used to discover big data …

Fluorescent Sensor Arrays Can Predict And Quantify The Composition Of Multicomponent Bacterial Samples, Denis Svechkarev, Marat Sadykov, Lucas J. Houser, Kenneth W. Bayles, Aaron M. Mohs

Journal Articles: Pathology and Microbiology

Fast and reliable identification of infectious disease agents is among the most important challenges for the healthcare system. The discrimination of individual components of mixed infections represents a particularly difficult task. In the current study we further expand the functionality of a ratiometric sensor array technology based on small-molecule environmentally-sensitive organic dyes, which can be successfully applied for the analysis of mixed bacterial samples. Using pattern recognition methods and data from pure bacterial species, we demonstrate that this approach can be used to quantify the composition of mixtures, as well as to predict their components with the accuracy of ~80% …

Automated Recognition Of Facial Affect Using Deep Neural Networks, Behzad Hasani

Electronic Theses and Dissertations

Automated Facial Expression Recognition (FER) has been a topic of study in the field of computer vision and machine learning for decades. In spite of efforts made to improve the accuracy of FER systems, existing methods still are not generalizable and accurate enough for use in real-world applications. Many of the traditional methods use hand-crafted (a.k.a. engineered) features for representation of facial images. However, these methods often require rigorous hyper-parameter tuning to achieve favorable results.

Recently, Deep Neural Networks (DNNs) have shown to outperform traditional methods in visual object recognition. DNNs require huge data as well as powerful computing units …

Development Of Machine Learning Tutorials For R, John Pintar

All Undergraduate Theses and Capstone Projects

Machine learning (ML) techniques developed in computer science have revolutionized nearly every sector of industry. Despite the prevalence and usefulness of ML, students outside of computer science rarely receive training in ML. Students frequently receive training in statistical analysis, often using the software package R, which is free, open source, and has additional downloadable modules. A popular module is the ML package caret, which contains 238 different ML algorithms, each with 0-9 hyperparameters. caret is powerful, flexible, and provides consistent syntax across algorithms. In the hands of an experienced practitioner, this tunability is welcomed and can increase accuracy. However, when …

Predicting Depression Using Deep Learning And Ensemble Algorithms On Raw Twitter Data, Nisha P. Shetty, Balachandra Muniyal, Arshia Anand, Sushant Kumar

Open Access archive

Social network and microblogging sites such as Twitter are widespread amongst all generations nowadays where people connect and share their feelings, emotions, pursuits etc. Depression, one of the most common mental disorder, is an acute state of sadness where person loses interest in all activities. If not treated immediately this can result in dire consequences such as death. In this era of virtual world, people are more comfortable in expressing their emotions in such sites as they have become a part and parcel of everyday lives. The research put forth thus, employs machine learning classifiers on the twitter data set …

Artificial Intelligence Inventions & Patent Disclosure, Tabrez Y. Ebrahim

Faculty Scholarship

Artificial intelligence (“AI”) has attracted significant attention and has imposed challenges for society. Yet surprisingly, scholars have paid little attention to the impediments AI imposes on patent law’s disclosure function from the lenses of theory and policy. Patents are conditioned on inventors describing their inventions, but the inner workings and the use of AI in the inventive process are not properly understood or are largely unknown. The lack of transparency of the parameters of the AI inventive process or the use of AI makes it difficult to enable a future use of AI to achieve the same end state. While …

Predicting Escherichia Coli Loads In Cascading Dams With Machine Learning: An Integration Of Hydrometeorology, Animal Density And Grazing Pattern, Olufemi P. Abimbola, Aaron R. Mittelstet, Tiffany Messer, Elaine D. Berry, Shannon L. Bartelt-Hunt, Samuel Hansen

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the US Meat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy …

Revised Polyhedral Conic Functions Algorithm For Supervised Classification, Gürhan Ceylan, Gürkan Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

In supervised classification, obtaining nonlinear separating functions from an algorithm is crucial for prediction accuracy. This paper analyzes the polyhedral conic functions (PCF) algorithm that generates nonlinear separating functions by only solving simple subproblems. Then, a revised version of the algorithm is developed that achieves better generalization and fast training while maintaining the simplicity and high prediction accuracy of the original PCF algorithm. This is accomplished by making the following modifications to the subproblem: extension of the objective function with a regularization term, relaxation of a hard constraint set and introduction of a new error term. Experimental results show that …

Probabilistic Machine Learning Using Bayesian Inference, Mayank Pandey

Undergraduate Journal of Mathematical Modeling: One + Two

Machine Learning is a branch of AI (Artificial Intelligence) which expands on the idea of a computational system extending its knowledge about set methodical behaviors from the data that is fed to it to essentially develop analytical skills that can help in identifying patterns and making decisions with little to no participation of a real human being. Computer algorithms help in gaining experience to improve the facility over time for use by both consumers and corporations. In today’s technologically advanced world, Machine Learning has given us self-driving cars, speech recognition software, and AI agents like Siri and Google assistant. This …

Implementation Considerations For Mitigating Bias In Supervised Machine Learning, Bardia Bijani Aval

CSB and SJU Distinguished Thesis

Machine Learning (ML) is an important component of computer science and a mainstream way of making sense of large amounts of data. Although the technology is establishing new possibilities in different fields, there are also problems to consider, one of which is bias. Due to the inductive reasoning of ML algorithms in creating mathematical models, the predictions and trends found by the models will never necessarily be true – just more or less probable. Knowing this, it is unreasonable for us to expect the applied deductive reasoning of these models to ever be fully unbiased. Therefore, it is important that …