Computer Engineering Commons^™

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). …

A Sample Weight And Adaboost Cnn-Based Coarse To Fine Classification Of Fruit And Vegetables At A Supermarket Self-Checkout, Khurram Hameed, Douglas Chai, Alexander Rassau

Research outputs 2014 to 2021

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The physical features of fruit and vegetables make the task of vision-based classification of fruit and vegetables challenging. The classification of fruit and vegetables at a supermarket self-checkout poses even more challenges due to variable lighting conditions and human factors arising from customer interactions with the system along with the challenges associated with the colour, texture, shape, and size of a fruit or vegetable. Considering this complex application, we have proposed a progressive coarse to fine classification technique to classify fruit and vegetables at supermarket checkouts. The image and weight of …

Proactive Content Caching In Future Generation Communication Networks: Energy And Security Considerations, Muhammad Ishtiaque Aziz Zahed

Theses: Doctorates and Masters

The proliferation of hand-held devices and Internet of Things (IoT) applications has heightened demand for popular content download. A high volume of content streaming/downloading services during peak hours can cause network congestion. Proactive content caching has emerged as a prospective solution to tackle this congestion problem. In proactive content caching, data storage units are used to store popular content in helper nodes at the network edge. This contributes to a reduction of peak traffic load and network congestion.

However, data storage units require additional energy, which offers a challenge to researchers that intend to reduce energy consumption up to 90% …

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 …

Impact Of Direction Parameter In Performance Of Modified Aodv In Vanet, Afsana Ahamed, Hamid Vakilzadian

Department of Electrical and Computer Engineering: Faculty Publications

A vehicular ad hoc network (VANET) is a technology in which moving cars are used as routers (nodes) to establish a reliable mobile communication network among the vehicles. Some of the drawbacks of the routing protocol, Ad hoc On-Demand Distance Vector (AODV), associated with VANETs are the end-to-end delay and packet loss. We modified the AODV routing protocols to reduce the number of route request (RREQ) and route reply (RREP) messages by adding direction parameters and two-step filtering. The two-step filtering process reduces the number of RREQ and RREP packets, reduces the packet overhead, and helps to select the stable …

Far-Field Transient Absorption Nanoscopy With Sub-50 Nm Optical Super-Resolution, Yali Bi, Chi Yang, Lei Tong, Haozheng Li, Boyu Yu, Shuai Yan, Guang Yang, Meng Deng, Yi Wang, Wei Bao, Lei Ye, Ping Wang

Department of Electrical and Computer Engineering: Faculty Publications

Nanoscopic imaging or characterizing is the mainstay of the development of advanced materials. Despite great progress in electronic and atomic force microscopies, label-free and far-field characterization of materials with deep sub- wavelength spatial resolution has long been highly desired. Herein, we demonstrate far-field super-resolution transient absorption (TA) imaging of two-dimensional material with a spatial resolution of sub-50 nm. By introducing a donut- shaped blue saturation laser, we effectively suppress the TA transition driven by near-infrared (NIR) pump–probe photons, and push the NIR-TA microscopy to sub-diffraction-limited resolution. Specifically, we demonstrate that our method can image the individual nano-grains in graphene with …

Precursor-Surface Interactions Revealed During Plasma-Enhanced Atomic Layer Deposition Of Metal Oxide Thin Films By In-Situ Spectroscopic Ellipsometry, Ufuk Kilic, Alyssa Mock, Derek Sekora, Simeon Gilbert, Shah R. Valloppilly, Natale J. Ianno, Marjorie Langell, Eva Schubert, Mathias Schubert

Department of Electrical and Computer Engineering: Faculty Publications

We find that a five-phase (substrate, mixed native oxide and roughness interface layer, metal oxide thin film layer, surface ligand layer, ambient) model with two-dynamic (metal oxide thin film layer thickness and surface ligand layer void fraction) parameters (dynamic dual box model) is sufficient to explain in-situ spectroscopic ellipsometry data measured within and across multiple cycles during plasma-enhanced atomic layer deposition of metal oxide thin films. We demonstrate our dynamic dual box model for analysis of in-situ spectroscopic ellipsometry data in the photon energy range of 0.7–3.4 eV measured with time resolution of few seconds over large numbers of cycles …

Transcriptional Analysis Of Cleft Palate In Tgfβ3 Mutant Mice, J. Liu, S.K. Chanumolu, K.M. White, M. Albahrani, Hasan H. Otu, A. Nawshad

Department of Electrical and Computer Engineering: Faculty Publications

Cleft palate (CP) is one of the most common craniofacial birth defects, impacting about 1 in 800 births in the USA. Tgf-β3 plays a critical role in regulating murine palate development, and Tgf-β3 null mutants develop cleft palate with 100% penetrance. In this study, we compared global palatal transcriptomes of wild type (WT) and Tgf-β3 −/− homozygous (HM) mouse embryos at the crucial palatogenesis stages of E14.5, and E16.5, using RNA-seq data. We found 1,809 and 2,127 differentially expressed genes at E16.5 vs. E14.5 in the WT and HM groups, respectively (adjusted p < 0.05; |fold change|> 2.0). We focused on the genes that …

Long-Term Tracking Of Group-Housed Livestock Using Keypoint Detection And Map Estimation For Individual Animal Identification, Eric T. Psota, Ty Schmidt, Benny Mote, Lance C. Pérez

Department of Electrical and Computer Engineering: Faculty Publications

Tracking individual animals in a group setting is a exigent task for computer vision and animal science researchers. When the objective is months of uninterrupted tracking and the targeted animals lack discernible differences in their physical characteristics, this task introduces significant challenges. To address these challenges, a probabilistic tracking-by-detection method is proposed. The tracking method uses, as input, visible keypoints of individual animals provided by a fully-convolutional detector. Individual animals are also equipped with ear tags that are used by a classification network to assign unique identification to instances. The fixed cardinality of the targets is leveraged to create a …

Ieee Access Special Section Editorial: Security And Privacy For Cloud And Iot, Kuo-Hui Yeh, Weizhi Meng, Sk Hafizul Islam, Kuan Zhang, Ennan Zhai

Department of Electrical and Computer Engineering: Faculty Publications

No abstract provided.

Ieee Access Special Section Editorial: Energy Management In Buildings, Kun Wang, Yi Qian, Joel J.P.C. Rodrigues, Fei Richard Yu, Peng Li

Department of Electrical and Computer Engineering: Faculty Publications

No abstract provided.

Designing Shared Control Strategies For Teleoperated Robots Across Intrinsic User Qualities, Nancy Pham

School of Computing: Dissertations, Theses, and Student Research

Accounting for variance in human behavior is an integral part of interacting with robotic systems that share control between users and robots in order to reduce errors, improve performance, and maintain safety. In this work we focus on the shared control of a telepresence robot and how individual user traits may affect a person's performance while navigating the robot. This requires understanding which user qualities impact performance and cause conflicts -- with the ultimate goal of building shared controllers that adapt to those qualities. Toward this goal, we develop novel adaptive shared controllers and integrate the study of intrinsic user …

Assessing The Severity Of Health States Based On Social Media Posts, Shweta Yadav, Joy Prakash Sain, Amit P. Sheth, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya

Publications

The unprecedented growth of Internet users has resulted in an abundance of unstructured information on social media including health forums, where patients request healthrelated information or opinions from other users. Previous studies have shown that online peer support has limited effectiveness without expert intervention. Therefore, a system capable of assessing the severity of health state from the patients’ social media posts can help health professionals (HP) in prioritizing the user’s post. In this study, we inspect the efficacy of different aspects of Natural Language Understanding (NLU) to identify the severity of the user’s health state in relation to two perspectives(tasks) …

Alone: A Dataset For Toxic Behavior Among Adolescents On Twitter, Thilini Wijesiriwardene, Hale Inan, Ugur Kursuncu, Manas Gaur, Valerie L. Shalin, Krishnaprasad Thirunarayan, Amit P. Sheth, I. Budak Arpinar

Publications

The convenience of social media has also enabled its misuse, potentially resulting in toxic behavior. Nearly 66% of internet users have observed online harassment, and 41% claim personal experience, with 18% facing severe forms of online harassment. This toxic communication has a significant impact on the well-being of young individuals, affecting mental health and, in some cases, resulting in suicide. These communications exhibit complex linguistic and contextual characteristics, making recognition of such narratives challenging. In this paper, we provide a multimodal dataset of toxic social media interactions between confirmed high school students, called ALONE (AdoLescents ON twittEr), along with descriptive …

Minding Morality: Ethical Artificial Societies For Public Policy Modeling, Saikou Y. Diallo, F. Leron Shults, Wesley J. Wildman

VMASC Publications

Public policies are designed to have an impact on particular societies, yet policy-oriented computer models and simulations often focus more on articulating the policies to be applied than on realistically rendering the cultural dynamics of the target society. This approach can lead to policy assessments that ignore crucial social contextual factors. For example, by leaving out distinctive moral and normative dimensions of cultural contexts in artificial societies, estimations of downstream policy effectiveness fail to account for dynamics that are fundamental in human life and central to many public policy challenges. In this paper, we supply evidence that incorporating morally salient …

The Artificial University: Decision Support For Universities In The Covid-19 Era, Wesley J. Wildman, Saikou Y. Diallo, George Hodulik, Andrew Page, Andreas Tolk, Neha Gondal

VMASC Publications

Operating universities under pandemic conditions is a complex undertaking. The Artificial University (TAU) responds to this need. TAU is a configurable, open-source computer simulation of a university using a contact network based on publicly available information about university classes, residences, and activities. This study evaluates health outcomes for an array of interventions and testing protocols in an artificial university of 6,500 students, faculty, and staff. Findings suggest that physical distancing and centralized contact tracing are most effective at reducing infections, but there is a tipping point for compliance below which physical distancing is less effective. If student compliance is anything …

Multi-Robot Informative Path Planning In Unknown Environments Through Continuous Region Partitioning, Amitabh Bhattacharya

UNF Graduate Theses and Dissertations

This research activity is primarily focused to obtain information from an environment with the help of a group of coordinated robots. Each robot is responsible to plan its path independently but the robots, as an overall system, have a common goal of maximum information collection. This domain of research is known as Multi-Robot Informative Path Planning (MIPP). MIPP is very motivating due to its challenging nature and numerous real-world applications. It has shown its presence from semiautomatic applications like robotic search and rescue to fully automatic applications like interplanetary missions.

We consider the NP-Hard problem of MIPP in an unknown …

Sorting By Strip Moves And Strip Swaps, Chandrika Pandurang Rao

UNF Graduate Theses and Dissertations

Genome rearrangement problems in computational biology [19, 29, 27] and zoning algorithms in optical character recognition [14, 4] have been modeled as combinatorial optimization problems related to the familiar problem of sorting, namely transforming arbitrary permutations to the identity permutation. The term permutation is used for an arbitrary arrangement of the integers 1, 2,···, n, and the term identity permutation for the arrangement of 1, 2,···, n in increasing order. When a permutation is viewed as the string of integers from 1 through n, any substring in it that is also a substring in the identity permutation will …

A Direct Data-Cluster Analysis Method Based On Neutrosophic Set Implication, Florentin Smarandache, Sudan Jha, Gyanendra Prasad Joshi, Lewis Nkenyereya, Dae Wan Kim

Branch Mathematics and Statistics Faculty and Staff Publications

Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters. A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets. This paper focuses on cluster analysis based on neutrosophic set implication, i.e., a k-means algorithm with a threshold-based clustering technique. This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm. To evaluate the validity of the proposed method, several validity measures and validity indices are applied to the Iris dataset (from the University of California, Irvine, Machine …

(Φ, Ψ)-Weak Contractions In Neutrosophic Cone Metric Spaces Via Fixed Point Theorems, Florentin Smarandache, Wadei F. Al-Omeri

Branch Mathematics and Statistics Faculty and Staff Publications

In this manuscript, we obtain common fixed point theorems in the neutrosophic cone metric space. Also, notion of (Φ, Ψ)-weak contraction is defined in the neutrosophic cone metric space by using the idea of altering distance function. Finally, we review many examples of cone metric spaces to verify some properties.

Bibliometric Analysis Of Bearing Fault Detection Using Artificial Intelligence, Pooja Kamat, Rekha Sugandhi Dr.

Library Philosophy and Practice (e-journal)

The new industrial revolution called Industry 4.0 is proliferating at its peak. The time is no longer away when the human race is going to witness a huge paradigm shift. Intelligent machines empowered by Artificial Intelligence (AI)will take over the presence of human workers in the industrial manufacturing sector with the target of achieving 100% automation. With the emergence of cut-throat price competition in the product market, it has become equally important to manufacture goods at minimal costs and with the highest quality. Predicting the decrease in machinery efficiency at an earlier stage to accomplish this objective helps to reduce …

Business Analytics/Business Intelligence And It Infrastructure : Impact On Organizational Agility, Xiaofeng Chen, Keng Siau

Research Collection School Of Computing and Information Systems

This is an empirical research investigating the impact of business analytics (BA) and business intelligence (BI) use, IT infrastructure flexibility, and their interactions on organizational agility. Synthesizing the systems theory and awareness-motivation-capability framework, the authors propose that BA-Use, IT infrastructure flexibility, and their interactions significantly influence organizational agility. The results show the significant association of BA use and IT infrastructure flexibility with organizational agility. The results also suggest that BA use may demand corporations to build a more flexible IT infrastructure. However, the data does not reveal the proposed interaction between the two drivers of organizational agility.

Modeling And Counteracting Exposure Bias In Recommender Systems, Sami Khenissi, Olfa Nasraoui

Faculty and Staff Scholarship

What we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated machine learned predictions. Similarly, the predictive accuracy of learning machines heavily depends on the feedback data that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknown biases which can be exacerbated after several iterations of machine learning predictions and user feedback. Machine-caused biases risk leading to undesirable social effects ranging from polarization to unfairness and filter bubbles. In this paper, we study the bias inherent in widely used recommendation strategies such as matrix factorization. Then we …

A Comprehensive And Modular Robotic Control Framework For Model-Less Control Law Development Using Reinforcement Learning For Soft Robotics, Charles Sullivan

Open Access Theses & Dissertations

Soft robotics is a growing field in robotics research. Heavily inspired by biological systems, these robots are made of softer, non-linear, materials such as elastomers and are actuated using several novel methods, from fluidic actuation channels to shape changing materials such as electro-active polymers. Highly non-linear materials make modeling difficult, and sensors are still an area of active research. These issues have rendered typical control and modeling techniques often inadequate for soft robotics. Reinforcement learning is a branch of machine learning that focuses on model-less control by mapping states to actions that maximize a specific reward signal. Reinforcement learning has …

Tram System Automation For Environmental Spectroscopy And Vegetation Monitoring, Enrique Anguiano Chavez

Open Access Theses & Dissertations

Spectroscopy is the science of studying the interactions of matter and electromagnetic radiation (EMR). In particular, field spectroscopy takes place in a natural environment with a natural source of EMR. The paper presents progress towards the development and automation of a tram cart system. The new system in development collects high resolution, hyperspectral images and data from a spectrometer. Alternatives for a sensor cover mechanism to provide cover for the sensors mounted while the system is not operating are discussed, analyzing and comparing the benefits and disadvantages. An implementation for a charging station in an environment isolated from the electric …

Technological Challenges And Innovations In Cybersecurity And Networking Technology Program, Syed R. Zaidi, Ajaz Sana, Aparicio Carranza

Publications and Research

This era is posing a unique challenge to the Cybersecurity and related Engineering Technology areas, stimulated by the multifaceted technological boom expressed in accelerated globalization, digital transformation, the cloud, mobile access apps, and the Internet of Things (IoT)—where more and more devices are connected to the Internet every day. As the use of new Internet-based technologies increase; so does the risk of theft and misuse of sensitive information. This demands the awareness of cyber-criminality and the need for cyber hygiene in corporations, small businesses, and the government. As the need for experienced cybersecurity specialists has skyrocketed in recent years and …

Remote Communication In Wilderness Search And Rescue: Implications For The Design Of Emergency Distributed-Collaboration Tools For Network-Sparse Environments, Brennan Jones, Anthony Tang, Carman Neustaedter

Research Collection School Of Computing and Information Systems

Wilderness search and rescue (WSAR) requires careful communication between workers in different locations. To understand the contexts from which WSAR workers communicate and the challenges they face, we interviewed WSAR workers and observed a mock-WSAR scenario. Our findings illustrate that WSAR workers face challenges in maintaining a shared mental model. This is primarily done through distributed communication using two-way radios and cell phones for text and photo messaging; yet both implicit and explicit communication suffer. WSAR workers send messages for various reasons and share different types of information with varying levels of urgency. This warrants the use of multiple communication …

Demand-Driven Execution Using Future Gated Single Assignment Form, Omkar Javeri

Dissertations, Master's Theses and Master's Reports

This dissertation discusses a novel, previously unexplored execution model called Demand-Driven Execution (DDE), which executes programs starting from the outputs of the program, progressing towards the inputs of the program. This approach is significantly different from prior demand-driven reduction machines as it can execute a program written in an imperative language using the demand-driven paradigm while extracting both instruction and data level parallelism. The execution model relies on an executable Single Assignment Form which serves both as the internal representation of the compiler as well as the Instruction Set Architecture (ISA) of the machine. This work develops the instruction set …

Dynamic Dependency Collapsing, Görkem Aşılıoğlu

Dissertations, Master's Theses and Master's Reports

In this dissertation, we explore the concept of dynamic dependency collapsing. Performance increases in computer architecture are always introduced by exploiting additional parallelism when the clock speed is fixed. We show that further improvements are possible even when the available parallelism in programs are exhausted. This performance improvement is possible due to executing instructions in parallel that would ordinarily have been serialized. We call this concept dependency collapsing. We explore existing techniques that exploit parallelism and show which of them fall under the umbrella of dependency collapsing. We then introduce two dependency collapsing techniques of our own. The first technique …

A Review Of Emotion Sensing: Categorization Models And Algorithms, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Sentiment analysis consists in the identification of the sentiment polarity associated with a target object, such as a book, a movie or a phone. Sentiments reflect feelings and attitudes, while emotions provide a finer characterization of the sentiments involved. With the huge number of comments generated daily on the Internet, besides sentiment analysis, emotion identification has drawn keen interest from different researchers, businessmen and politicians for polling public opinions and attitudes. This paper reviews and discusses existing emotion categorization models for emotion analysis and proposes methods that enhance existing emotion research. We carried out emotion analysis by inviting experts from …