Master of Science, University of Akron, 2008, Computer Science

Microarray is a technology that has been widely used by the biologists to probe the presence of genes in a sample of DNA or RNA. Using the technology, the oligonucleotide probes can be massively parallel immobilized on a microarray chip. It allows the biologists to check the expression levels of thousands of genes together. This thesis develops a software system that includes a database repository to store different microarray datasets and a microarray data analysis tool for analyzing the stored data. The repository currently allows datasets of GenepixPro format to be deposited, although it can be expanded to include datasets of other formats. The user interface of the repository allows users conveniently upload data files and perform preferred data preprocessing and analysis. The analysis methods implemented includes the traditional k-nearest neighbor (kNN) methods and two new kNN methods developed in this study. Additional analysis methods can be added by future developers. The system was tested using a set of microRNA gene expression data. The design and implementation of the software tool are presented in the thesis along with the testing results from the microRNA dataset. The results indicate that the new weighted kNN method proposed in this study outperforms the traditional kNN method and the proposed mean method. We conclude that the system developed in the thesis effectively provides a structured microarray data repository, a flexible graphical user interface, and rational data mining methods.

Committee: Dr. Zhong-Hui Duan PhD (Advisor) Subjects: Bioinformatics; Computer Science

Bachelor of Arts, Wittenberg University, 2023, Computer Science

This paper aims to present a study on developing a program that assists nonprofit organizations in determining the ideal location for building their facilities based on community needs, thus maximizing their potential for success. The study highlights the importance of location in the success of nonprofit organizations, and the challenges they face in identifying suitable areas for their operations. The paper reviews existing literature on nonprofit organizations, location analysis, and data analysis techniques, and proposes a methodology for developing the program. The methodology involves data collection and analysis, and machine learning algorithms to predict community needs. The program provides a user-friendly interface for nonprofit organizations to access and analyze the data and offers recommendations for suitable locations based on their criteria. The study concludes that the proposed program can be a valuable tool for nonprofit organizations to make informed decisions about their location and maximize their potential for success in serving their communities.

Committee: Tyler Highlander (Advisor); Adam Parker (Committee Member); Kevin Steidel (Committee Member) Subjects: Business Administration; Computer Science; Geography; Management; Operations Research; Social Work

Master of Science, The Ohio State University, 2021, Food, Agricultural and Biological Engineering

According to the United States Department of Agriculture, projections show that food production needs to increase by 70-100% from 2010 – 2050 due to population growth in addition to other socioeconomic pressures. New methods are needed to increase the productivity and efficiency of agricultural systems and are critical for mitigating climate change and ensuring food security. Remote sensing (RS) and Unmanned Aerial Systems (UAS) have the potential to allow agricultural researchers to better manage and monitor these complex systems. Foliage temperature is a key variable in biophysical vegetative modeling and has been well documented to be an indicator of crop water stress. The ability to monitor subtle changes in foliage temperature using a calibrated thermal infrared (TIR) camera mounted on a UAS would open avenues for field-based stress monitoring at scales not possible without using airborne systems. However, current approaches to process thermal image data are time-consuming, inaccurate, or not well suited for foliage in field environments. And importantly, methods to extract foliage pixels from the background (i.e. soil, weeds, etc) are needed to remove the influence of background elements that can have dramatically different temperatures from the surrounding plant tissue. This study aims to train and validate a Supervised Machine Learning (SML) algorithm using a dual-camera system to extract foliage temperature in a complex field environment. A UAS campaign focused on a set of maize treatments was conducted at Waterman Farm throughout the summer of 2020, spanning diurnal acquisitions across the growing season. In-situ tower-based sensors were deployed to provide validation of the airborne data. Remotely sensed images, which included red, green, blue, and thermal infrared bands, were used to train an SML algorithm. Our results show that the combination of these four bands can be used to accurately identify foliage pixels within complex field scenes with an accu (open full item for complete abstract)

Committee: Darren Drewry (Advisor); Scott Shearer (Committee Member) Subjects: Agricultural Engineering; Computer Science

PhD, University of Cincinnati, 2020, Engineering and Applied Science: Mechanical Engineering

Cloud manufacturing is an emerging concept that enables the sharing of manufacturing resources, while pressing issues, such as trust, safety, and payment, remain as challenges. In this regard, the blockchain technology provides a potentially viable solution because of its unique strengths in decentralization and security. This research proposes a blockchain-based cloud additive manufacturing (BBCAM) framework and adopts the simulation approach to study the system efficiency and security of the BBCAM. The research consists of four interconnected tasks: (1) the construction of BBCAM framework, (2) the investigation of system operation efficiency and pricing strategy of providers using a game-theoretic approach, (3) a security conception of blockchain common service request architecture (BCSRA), and (4) the investigation of two protocols of BCSRA which include a privacy protection protocol by machine learning method, and a double-chain structure Byzantine fault-tolerant (DcS-BFT) protocol. In the proposed framework, consensus-oriented mechanisms generate the operating standards for BBCAM. A consortium or federated blockchain is adopted, and Proof-of-Authority (PoA) is employed as the consensus algorithm of block generation. Based on the proposed framework, a simulation study is conducted to investigate the efficiency, revenue, and machine utilization of the BBCAM system under the scenario of metal additive manufacturing service. Thereafter, a game-theoretic approach is adopted to investigate the pricing strategies of providers, and a fuzzy algorithm is employed in the pricing models. Two pricing scenarios are considered. One is that the information of players is unknown to each other, and the other is that the information of players who are within a limit of Euclidean space is known to each other. The effects of system load and weights of KNN recommendation algorithm are also studied. The BBCAM model requires an architecture that can provide security services i (open full item for complete abstract)

Committee: Jing Shi Ph.D. (Committee Chair); Manish Kumar Ph.D. (Committee Member); Jay Lee Ph.D. (Committee Member); Kumar Vemaganti Ph.D. (Committee Member); Boyang Wang (Committee Member) Subjects: Mechanical Engineering

PhD, University of Cincinnati, 2006, Engineering : Computer Science and Engineering

The objective of supervised learning is to estimate unknowns based on labeled training samples. For example, one may have aerial spectrographic readings for a large field planted in corn. Based on spectrographic observation, one would like to determine whether the plants in part of the field are weeds or corn. Since the unknown to be estimated is categorical or discrete, the problem is one of classification. If the unknown to be estimated is continuous, the problem is one of regression or numerical estimation. For example, one may have samples of ozone levels from certain points in the atmosphere. Based on those samples, one would like to estimate the ozone level at other points in the atmosphere. Algorithms for supervised learning are useful tools in many areas of agriculture, medicine, and engineering, including estimation of proper levels of nutrients for cows, prediction of malignant cancer, document analysis, and speech recognition. A few general references on supervised learning include [1], [2], [3], and [4]. Two recent reviews of the supervised learning literature are [5] and [6]. In general, univariate learning tree algorithms have been particularly successful in classification problems, but they can suffer from several fundamental difficulties, e.g., "a representational limitation of univariate decision trees: the orthogonal splits to the feature's axis of the sample space that univariate tree rely on" [8] and overfit [17]. In this thesis, we present a classification procedure for supervised classification that consists of a new univariate decision tree algorithm (Margin Algorithm) and two other related algorithms (Hyperplane and Box Algorithms). The full algorithm overcomes all of the usual limitations of univariate decision trees and is called the Paired Planes Classification Procedure. The Paired Planes Classification Procedure is compared to Support Vector Machines, K-Nearest Neighbors, and decision trees. The Hyperplane Algorithm allows direct user in (open full item for complete abstract)

Committee: Dr. Anca Ralescu (Advisor) Subjects:

Master of Science in Electrical Engineering, University of Toledo, 2012, College of Engineering

Interaction of birds with wind turbines has become an important public policy issue. Acoustic monitoring of birds in the vicinity of wind turbines can address this important public policy issue. The identification of nocturnal bird flight calls is also important for various applications such as ornithological studies and acoustic monitoring to prevent the negative effects of wind farms, human made structures and devices on birds. Wind turbines may have negative impact on bird population. Therefore, the development of an acoustic monitoring system is critical for the study of bird behavior. This work can be employed by wildlife biologist for developing mitigation techniques for both on-shore/off-shore wind farm applications and to address bird strike issues at airports. Acoustic monitoring involves preprocessing, feature extraction and classification. A novel Spectrogram-based Image Frequency Statistics (SIFS) feature extraction algorithm has been developed and was compared against traditional feature extraction techniques such as Discrete Wavelet Transform (DWT) and Mel Frequency Cepstral Coefficients (MFCC). Unlike traditional MFCC, signals were first cleaned with wavelet-denoising during the preprocessing stage. Additionally, a mixed MFCC-SIFS (MMS) technique was also developed. Features extracted from proposed algorithms were then combined with various classification algorithms such as k-NN, Multilayer Perceptron (MLP) and Hidden Markov Models (HMM) and Evolutionary Neural Network (ENN). SIFS and MMS algorithms, combined with ENN and MLP, provided the most accurate results. Proposed algorithms were tested with real data collected during the spring migration, around Lake Erie in Ohio, of five nocturnally migrating bird species native to Northwest Ohio. Also, sparrows, warblers and thrushes passing over the University of Toledo, the Ottawa National Wildlife Refuge and Ohio State University's Stone Lab between April 20 – May 29 were calculated. Quantification of cla (open full item for complete abstract)

Committee: Mohsin Jamali PhD (Advisor); Junghwan Kim PhD (Committee Member); Sonmez Sahutoglu PhD (Committee Member) Subjects: Electrical Engineering

Master of Science in Engineering, University of Akron, 2007, Biomedical Engineering

Numerous genome projects have produced a large and ever increasing amount of genomic sequence data. However, the biological functions of many proteins encoded by the sequences remain unknown. Protein function annotation and prediction become an essential and challenging task of post-genomic research. In this research, we present an automated protein function prediction system based on a set of proteins of known biological functions. The functions of the proteins are characterized with Gene Ontology (GO) annotations. The prediction system uses a novel measure to calculate the pair-wise overall similarity between protein sequences. The protein function prediction is performed based on the GO annotations of similar sequences using a weighted k-nearest neighbor method. We show the prediction accuracies obtained using the model organism yeast (Sacchyromyces cerevisiae). The results indicate that the weighted k-nearest neighbor method significantly outperforms the regular k-nearest neighbor method for protein biological function prediction.

Committee: Dale Mugler (Advisor) Subjects: