Engineering Commons^™

Asset Cueing Nuclear Radiation Anomaly Detection Using An Embedded Neural Network Resource, April Inamura

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Nuclear radiation detection is inherently a challenging task, coupled with a high background variation or increase in anomalies, the accuracy for detection can plummet. A key factor in the success of nuclear detection hinges on the sensor’s ability to generalize its model and directly leads to the model’s robustness. The goal of this project is to develop algorithms suitable for use on the University of Nebraska-Lincoln’s Pingora chip, a low-power, system-on-chip device with an active neural processing unit (NPU) made for nuclear radiation detection. The thesis aims to improve Pingora’s overall generalization ability in nuclear radiation source detection. A multiphase …

Estimating Crop Stomatal Conductance Through High-Throughput Plant Phenotyping, Junxiao Zhang

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

During photosynthesis and transpiration, crops exchange carbon dioxide and water with the atmosphere through stomata. When a crop experiences water stress, stomata are closed to reducing water loss. However, the closing of stomata also negatively affects the photosynthetic efficiency of the crop and leads to lower yields. Stomatal conductance (gs) quantifies the degree of stomatal opening and closing by using the rate of gas exchange between the crop and the atmosphere, which helps to understand the water status of the crop for better irrigation management. Unfortunately, gs measurement typically requires contact measuring instruments and manual collection in the field, which …

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Learnfca: A Fuzzy Fca And Probability Based Approach For Learning And Classification, Suraj Ketan Samal

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Formal concept analysis(FCA) is a mathematical theory based on lattice and order theory used for data analysis and knowledge representation. Over the past several years, many of its extensions have been proposed and applied in several domains including data mining, machine learning, knowledge management, semantic web, software development, chemistry ,biology, medicine, data analytics, biology and ontology engineering.

This thesis reviews the state-of-the-art of theory of Formal Concept Analysis(FCA) and its various extensions that have been developed and well-studied in the past several years. We discuss their historical roots, reproduce the original definitions and derivations with illustrative examples. Further, we provide …

Prediction Of Meltpool Depth In Laser Powder Bed Fusion Using In-Process Sensor Data, Part-Level Thermal Simulations, And Machine Learning, Grant King

Department of Mechanical and Materials Engineering: Dissertations, Theses, and Student Research

The goal of this thesis is the prevention of flaw formation in laser powder bed fusion additive manufacturing process. As a step towards this goal, the objective of this work is to predict meltpool depth as a function of in-process sensor data, part-level thermal simulations, and machine learning. As motivated in NASA's Marshall Space Flight Center specification 3716, prediction of meltpool depth is important because: (1) it can serve as a surrogate to estimate process status without the need for expensive post-process characterization, and (2) the meltpool depth provides an avenue for rapid qualification of microstructure evolution. To achieve the …

Seabem: An Artificial Intelligence Powered Web Application To Predict Cover Crop Biomass, Aime Christian Tuyishime, Andrea Basche

Honors Theses

SEABEM, the Stacked Ensemble Algorithms Biomass Estimator Model, is a web application with a stacked ensemble of Machine Learning (ML) algorithms running on the backend to predict cover crop biomass for locations in Sub-Saharan. The SEABEM model was developed using a previously developed database of crop growth and yield that included site characteristics such as latitude, longitude, soil texture (sand, silt, and clay percentages), temperature, and precipitation. The goal of SEABEM is to provide global farmers, mainly small-scale African farmers, the knowledge they need before practicing and benefiting from cover crops while avoiding the expensive and time-consuming operations that come …

Using Contextual Bandits To Improve Traffic Performance In Edge Network, Aziza Al Zadjali

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Edge computing network is a great candidate to reduce latency and enhance performance of the Internet. The flexibility afforded by Edge computing to handle data creates exciting range of possibilities. However, Edge servers have some limitations since Edge computing process and analyze partial sets of information. It is challenging to allocate computing and network resources rationally to satisfy the requirement of mobile devices under uncertain wireless network, and meet the constraints of datacenter servers too. To combat these issues, this dissertation proposes smart multi armed bandit algorithms that decide the appropriate connection setup for multiple network access technologies on the …

Bibliometric Study On Analysing Impact Of Newly Launched Products Over Existing Ones Through Ai, Dipak Sharma, Vageesh Devrath, Abhinav Rajput, Gouranga Jyoti Kataky, Priyanka Tupe-Waghmare, Ismail Akbani

Library Philosophy and Practice (e-journal)

Different analysis models like Conditional Mean Analysis, Trend Analysis, Correlation Analysis helps us to analyse the delicate equilibrium between businesses that gets impacted when a new product is launched in a cluster. This paper shows a statistical report of research done on the businesses in a cluster based on ongoing trends and current customer needs . There is surplus data present on various platforms related to every product following the ongoing trends in the form of customer reviews.The research mainly speculates mainly how the businesses get impacted with change in consumer needs, wants and demands. With the help of datasets …

Bibliometric Review Of Predictive Maintenance Using Vibration Analysis, Aashna Midha Ms., Ishita Maheshwari Ms., Kaushik Ojha Mr., Kritika Gupta Ms., Shripad V. Deshpande Mr.

Library Philosophy and Practice (e-journal)

Every day the world is depending more and more on machines in almost every aspect of life. With the increasing use of machines, there also needs to be an evolution in the maintenance of these machines. Predictive maintenance is a process used to monitor the equipment and machinery during its operation to detect any damages and/or deteriorations and enable the required maintenance plan in advance, resulting in reduced operational costs and full utilization of tools and parts. The fundamental goal of this bibliometric review paper is a comprehension of the extent and sources of the literature available for predictive maintenance …

Real-Time Monitoring Of Fdm 3d Printer For Fault Detection Using Machine Learning: A Bibliometric Study, Vaibhav Kisan Kadam, Satish Kumar, Arunkumar Bongale

Library Philosophy and Practice (e-journal)

Additive Manufacturing has wide application range including healthcare, Fashion, Manufacturing, Prototypes, Tooling etc. AM techniques are subjected to various defects that may be printing defects or anomalies in machine. There is gap between current AM techniques and smart manufacturing since current AM lacks in build sensors necessary for process monitoring and fault detection. Both of these issues can be solved by incorporating real-time monitoring into AM. So the study is carried out to identify recent work done in AM to improve current system. For this bibliometric study Scopus database is used, study is kept limited to year 2010-2021 and English …

Bibliometric Survey On Flood Prediction Using Machine Learning, Seema Patil Prof., Daksh Khurana Mr., Kartik Rao Mr, Priyanshu Meena Mr, Shivendra Singh Mr

Library Philosophy and Practice (e-journal)

Floods are one of the most devastating natural hazards, and modelling them is extremely difficult. Flood prediction model advancement study led to factors such as loss of human and animal life, property damage, and risk mitigation. The focus of this bibliometric survey is to recognise the few studies which have upheld on the factors affecting the floods. The analysis is done based on 254 documents such as articles, conference papers, article reviews and some reviews and notes. India contributes to the maximum number of documents followed by China and the United States of America. This bibliometric survey is conducted using …

Prediction Of Stocks And Stock Price Using Artificial Intelligence : A Bibliometric Study Using Scopus Database, Priyanka Tupe-Waghmare, Priyanka Tupe-Waghmare

Library Philosophy and Practice (e-journal)

Prediction of stocks and the prices of the stock is one of the most crucial points of discussion amongst the researchers and analysts in the financial domain to date. Every stakeholder and most importantly the investor desires to earn higher profit for his investment in the market and try to use several different strategies to invest their money. There are numerous methods to predict and analyse the movement of the stock prices. They are broadly divided into – statistical and artificial intelligence-based methods. Artificial intelligence is used to predict the futuristic prices of stocks and use wide range of algorithms …

Field Demonstration Of Gpr And Uav Technologies For Evaluation Of Two Us 75/77 Bridges, Sepehr Pashoutani, Jinying Zhu, Chungwook Sim, Ji-Yong Lee

Nebraska Department of Transportation: Research Reports

Two Nebraska bridges with asphalt overlay were selected for nondestructive testing and evaluation (NDT/NDE). Three NDT techniques were conducted on these two bridges, including Ground Penetrating Radar (GPR), Half-Cell Potential (HCP) and Unmanned Aerial Vehicle (UAV) imaging. NDT data were collected during three construction stages of the bridges: (1) before repair on existing asphalt overlay; (2) on bare concrete after asphalt removal; (3) and after repairing delaminated concrete.

A machine learning technique, autoencoder, was used to build quantitative relationships between different NDT datasets. On bare concrete, the GPR amplitude and HCP voltage show a strong linear relationship. Then a threshold …

Time Series Data Analysis Using Machine Learning-(Ml) Approach, Mvv Prasad Kantipudi Dr., Pradeep Kumar N.S Dr., S.Sreenath Kashyap Dr., Ss Anusha Vemuri Ms

Library Philosophy and Practice (e-journal)

Healthcare benefits related to continuous monitoring of human movement and physical activity can potentially reduce the risk of accidents associated with elderly living alone at home. Based on the literature review, it is found that many studies focus on human activity recognition and are still active towards achieving practical solutions to support the elderly care system. The proposed system has introduced a joint approach of machine learning and signal processing technology for the recognition of human's physical movements using signal data generated by accelerometer sensors. The framework adopts the concept of DSP to select very descriptive feature sets and uses …

Diabetes Prediction Using Machine Learning : A Bibliometric Analysis, Vijayshri Nitin Khedkar, Sina Patel

Library Philosophy and Practice (e-journal)

Diabetes Mellitus is a chronic disease which can be deadly if undetected for longer time. Artificial intelligence is helping in healthcare industry to a great extent by helping professionals to derive useful information and patterns from data available in various formats: Survey data, electronic health records, laboratory data.. Diabetes, if predicted at an early stage can help many people to save lives and cost for healthcare. Decision-making, diagnosing and predicting diabetes have become an increasing trend in recent years. There are numerous publications in diabetes prediction and yet it’s an ongoing research topic with availability of new data and methods. …

A Literature Survey And Bibliometric Analysis Of Application Of Artificial Intelligence Techniques On Wireless Mesh Networks, Smita R. Mahajan Mrs., Harikrishnan R Dr., Ketan Kotecha Dr.

Library Philosophy and Practice (e-journal)

Recent years have seen a surge in the use of technology for executing transactions in both online and offline modes. Various industries like banking, e-commerce, and private organizations use networks for the exchange of confidential information and resources. Network security is thus of utmost importance, with the expectation of effective and efficient analysis of the network traffic. Wireless Mesh Networks are effective in communicating information over a vast span with minimal costs. A network is evaluated based on its security, accessibility, and extent of interoperability. Artificial Intelligence techniques like machine learning and deep learning have found widespread use to solve …

Representational Learning Approach For Predicting Developer Expertise Using Eye Movements, Sumeet Maan

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

The thesis analyzes an existing eye-tracking dataset collected while software developers were solving bug fixing tasks in an open-source system. The analysis is performed using a representational learning approach namely, Multi-layer Perceptron (MLP). The novel aspect of the analysis is the introduction of a new feature engineering method based on the eye-tracking data. This is then used to predict developer expertise on the data. The dataset used in this thesis is inherently more complex because it is collected in a very dynamic environment i.e., the Eclipse IDE using an eye-tracking plugin, iTrace. Previous work in this area only worked on …

A Bibliometric Survey Of Smart Wearable In The Health Insurance Industry, Apeksha Shah, Swati Ahirrao, Shraddha Phansalkar, Ketan Kotecha

Library Philosophy and Practice (e-journal)

Smart wearables help real-time and remote monitoring of health data for effective diagnostic and preventive health care services. Wearable devices have the ability to track and monitor healthcare vitals such as heart rate, physical activities, BMI (Body Mass Index), blood pressure, and keeps an individual notified about the health status. Artificial Intelligence-enabled wearables show an ability to transform the health insurance sector. This would not only enable self-management of individual health but also help them focus from treatments to the preventions of health hazards. With this customer-centric approach to health care, it will enable the insurance companies to track the …

Routing Optimization In Heterogeneous Wireless Networks For Space And Mission-Driven Internet Of Things (Iot) Environments, Sara El Alaoui

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

As technological advances have made it possible to build cheap devices with more processing power and storage, and that are capable of continuously generating large amounts of data, the network has to undergo significant changes as well. The rising number of vendors and variety in platforms and wireless communication technologies have introduced heterogeneity to networks compromising the efficiency of existing routing algorithms. Furthermore, most of the existing solutions assume and require connection to the backbone network and involve changes to the infrastructures, which are not always possible -- a 2018 report by the Federal Communications Commission shows that over 31% …

Advanced Techniques To Detect Complex Android Malware, Zhiqiang Li

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Android is currently the most popular operating system for mobile devices in the world. However, its openness is the main reason for the majority of malware to be targeting Android devices. Various approaches have been developed to detect malware.

Unfortunately, new breeds of malware utilize sophisticated techniques to defeat malware detectors. For example, to defeat signature-based detectors, malware authors change the malware’s signatures to avoid detection. As such, a more effective approach to detect malware is by leveraging malware’s behavioral characteristics. However, if a behavior-based detector is based on static analysis, its reported results may contain a large number of …

Personality Prediction Through Curriculam Vitae Analysis Involving Password Encryption And Prediction Analysis, Gagandeep Kaur, Shruti Maheshwari

Library Philosophy and Practice (e-journal)

A recruitment process requires an eligibility check, an aptitude evaluation and a psychometric analysis of prospective candidates. The work puts forward an application where the system allows employers to post new job offerings and registered candidates can apply. The application estimates applicant’s emotional aptitude through a psychometric analysis based on a test whereas the professional standard is verified via a technical aptitude test. OCEAN Model is used to assess emotional quotient and predict the personality traits. Machine learning techniques such as Logistic Regression are used for modelling the personality predictor. The details of the candidates are kept secure by using …

Bibliometric Survey On Incremental Clustering Algorithms, Archana Chaudhari, Rahul Raghvendra Joshi, Preeti Mulay, Ketan Kotecha, Parag Kulkarni

Library Philosophy and Practice (e-journal)

For clustering accuracy, on influx of data, the parameter-free incremental clustering research is essential. The sole purpose of this bibliometric analysis is to understand the reach and utility of incremental clustering algorithms. This paper shows incremental clustering for time series dataset was first explored in 2000 and continued thereafter till date. This Bibliometric analysis is done using Scopus, Google Scholar, Research Gate, and the tools like Gephi, Table2Net, and GPS Visualizer etc. The survey revealed that maximum publications of incremental clustering algorithms are from conference and journals, affiliated to Computer Science, Chinese lead publications followed by India then United States. …

A Comprehensive Framework To Replicate Process-Level Concurrency Faults, Supat Rattanasuksun

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Concurrency faults are one of the most damaging types of faults that can affect the dependability of today’s computer systems. Currently, concurrency faults such as process-level races, order violations, and atomicity violations represent the largest class of faults that has been reported to various Linux bug repositories. Clearly, existing approaches for testing such faults during software development processes are not adequate as these faults escape in-house testing efforts and are discovered during deployment and must be debugged.

The main reason concurrency faults are hard to test is because the conditions that allow these to occur can be difficult to replicate, …

Assessing The Relationship Between Groundwater Nitrate Concentrations And Environmental Variables Through Repeat Sampling And Statistical Machine Learning: Dutch Flats, Nebraska, Martin Wells

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Nitrate-contaminated aquifers are common in landscapes dominated by agricultural land use. Health concerns related to consuming nitrate-contaminated groundwater are well documented and continued research aimed at decreasing concentrations is critical. A 1990s U.S. Geological Survey (USGS) study focused on groundwater characteristics in the Dutch Flats area of western Nebraska. Agricultural-related practices were determined to largely influence groundwater recharge and nitrate concentrations ([NO₃^-]). Since the conclusion of the USGS study, a transition to more efficient irrigation technology has been observed in this region. The emphasis of this 2016 study was to resample several well nests examined in 1998 …

Significant Permission Identification For Android Malware Detection, Lichao Sun

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

A recent report indicates that a newly developed malicious app for Android is introduced every 11 seconds. To combat this alarming rate of malware creation, we need a scalable malware detection approach that is effective and efficient. In this thesis, we introduce SigPID, a malware detection system based on permission analysis to cope with the rapid increase in the number of Android malware. Instead of analyzing all 135 Android permissions, our approach applies 3-level pruning by mining the permission data to identify only significant permissions that can be effective in distinguishing benign and malicious apps. Based on the identified significant …

Towards Building An Intelligent Integrated Multi-Mode Time Diary Survey Framework, Hariharan Arunachalam

Department of Computer Science and Engineering: Dissertations, Theses, and Student Research

Enabling true responses is an important characteristic in surveys; where the responses are free from bias and satisficing. In this thesis, we examine the current state of surveys, briefly touching upon questionnaire surveys, and then on time diary surveys (TDS). TDS are open-ended conversational surveys of a free-form nature with both, the interviewer and the respondent, playing a part in its progress and successful completion. With limited research available on how intelligent and assistive components can affect TDS respondents, we explore ways in which intelligent systems such as Computer Adaptive Testing, Intelligent Tutoring Systems, Recommender Systems, and Decision Support Systems …