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ATU Annual Student Research Symposium
No full texthttps://orc.library.atu.edu/homepage_slideshow/1011/thumbnail.jp
Would It Be More Cost Effective for Arkansas Tech to Plant Winter Crop to Extend Grazing Periods Using No-Till Practices?
Get PDFWith prices rising, farmers are looking to cut costs in any way possible. Modern farming methods can aid these farmers in their operation costs. Nutrition being the direct relation to meat yields our focus turns to feed consumption. Feed being the largest expense farmers face, they must find cost effective ways to feed their cattle. Grazing forages make up most of the Cows\u27 diet, for profits to go up we must look down. Cattles’s grazing period depend a lot on the weather, with native warm season grasses are dormant during the winter the nutritional benefits go down with it as well. Winter crop introduction helps promote a longer grazing period. The longer the grazing period, the less a farmer spends on importing feed for their cattle, plus giving them live green forages. When used with modern practices such as No-Till farming, live winter plants can carry beneficial factors for the soil as well. Some of the benefits from No-Till practices with winter grasses can include improved drainage and better topsoil conservation
League of Learning: Deep Learning for Soccer Action Video Classification
Get PDFThe field of sports video analysis using deep learning is rapidly advancing. Proper classification and analysis of sports videos are essential to manage the growing sports media content. It offers numerous benefits for the media, advertising, analytics, and education sectors. Soccer, also known as football, worldwide, is among the most popular sports. This research study used a deep learning-based approach for soccer action detection. Deep learning has become a popular machine learning technique, especially for image and video classification. We have used the SoccerAct dataset, which consists of ten soccer actions like corner, foul, freekick, goal kick, long pass, on target, penalty, short pass, substitution, and throw-in. Our study analyzes ConvLSTM and LRCN, two different methods for soccer action detection in video clips. ConvLSTM is an extension of the Long Short-Term Memory (LSTM) architecture that allows the modeling of both spatial and temporal dependencies in sequential data by integrating convolutional operations into recurrent neural networks (RNNs).On the other hand, convolutional neural networks (CNNs) and recurrent units -usually LSTM or GRU-combine in Long-term Recurrent Convolutional Networks (LRCN) to capture temporal relationships through RNNs and spatial characteristics through CNNs. The ConvLSTM model achieved 70%, and the LRCN model achieved 71% accuracy on its first iteration. The model\u27s performance is enhanced through fine-tuning the pre-trained model, incorporating batch normalization to mitigate overfitting, and conducting hyperparameter tuning. Our study highlights how deep learning techniques can improve soccer action detection systems significantly. A soccer action detection model could be used in various real-world contexts, including sports performance analysis, journalism and reporting, broadcasting, and many more
Use of Deep Learning in Content-Based Image Retrieval (CBIR)
Get PDFIn the world of computer vision and data retrieval, a crucial task is finding images within a database based on their visual content. This is known as content-based image retrieval (CBIR). As the number of digital images explodes across fields like online shopping, healthcare, and social media, the need for powerful and precise CBIR systems becomes ever more critical. Early CBIR methods depended on features crafted by hand, like color distributions, texture descriptions, and shape characteristics. However, these techniques often have difficulty capturing the true meaning of an image and might not handle very large datasets effectively. With the rise of deep learning, CBIR has undergone a significant shift. Deep convolutional neural networks (CNNs) are now used to automatically learn distinctive features directly from the raw image data. In this study, we investigate the effectiveness of pre-trained CNN models, specifically ResNet, GoogleNet, and AlexNet, for CBIR tasks on the STL-10 dataset. These models have been trained on massive image datasets like ImageNet, allowing them to learn rich, layered representations of visual features. By utilizing these models, we can extract high-level features from images and use them to find similar images based on these characteristics. Despite the progress made in deep learning based CBIR systems, challenges persist. A major hurdle is the gap between the low-level features extracted from images and the high-level semantic concepts they represent. Additionally, the choice of pre-trained model architecture, its hyperparameters, and the metrics used to measure similarity can significantly impact a CBIR system\u27s performance. By examining how different pre-trained models perform on the STL-10 dataset, we aim to gain insights into the strengths and weaknesses of each model for CBIR tasks. We will also assess the accuracy of the retrieved images and explore potential avenues for improving CBIR effectiveness in real-world applications
Adapt Laser Shaping
Get PDFInvestigation into producing a machine learning algorithm that allows a He-Ne laser to classify whether a produced beam shape is uniformly gaussian or not, in order to adaptively move the laser to consistently target the encoded interference pattern area. This will then result in continuous uniform beam shapes of the desired output
Learning Statistics With R: A Tutorial for Psychology Students and Other Beginners
Get PDFReview of OER Statistics textbook by Danielle Navarro, available at https://open.umn.edu/opentextbooks/textbooks/learning-statistics-with-r-a-tutorial-for-psychology-students-and-other-beginner
Teaching Methods and Practices
Get PDFReview of OER Education textbook by Jason Proctor, available at https://open.ocolearnok.org/teachingmethods
How Arguments Work: A Guide to Writing and Analyzing Texts in College
Get PDFReview of OER Composition textbook by Anna Mills, available at https://open.umn.edu/opentextbooks/textbooks/how-arguments-work-a-guide-to-writing-and-analyzing-texts-in-colleg
The Great Gatsby Dock
No full textThis is a design of a book cover of The Great Gatsby by F. Scott Fitzgerald.https://orc.library.atu.edu/bookart_2024/1020/thumbnail.jp
Journey to the Center of the Earth
No full textThis is a design of a book cover for book Journey to the Center of the Earth by Jules Verne.https://orc.library.atu.edu/bookart_2024/1003/thumbnail.jp