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Comparing the Predictions of Convolutional Neural Networks, Random Forest Transfer Learning, and Support Vector Machines in the Image Processing of Neoplasms
No full textThis study investigated the efficacy of Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest Transfer Learning (RFTL) in the image processing of lung neoplasms for enhanced diagnostic accuracy. Leveraging the Lung Image Database Consortium Image Database Resource Initiative (LIDC-IDRI), chest CT images were analyzed to compare the three models\u27 performance. It was hypothesized that the most accurate predictions would result from the RFTL model because it would have the most efficient data processing. Results indicated that all three models demonstrated high accuracy, with CNNs excelling in precision, SVMs in recall, and RFTL showing a balanced performance. Precision-recall curves highlighted trade-offs between models, emphasizing the importance of considering diagnostic priorities. Comparisons with existing literature and models revealed similarities and differences, underlining the adaptability of these machine learning approaches to different medical imaging tasks. Despite notable achievements, the study acknowledged limitations, such as hardware constraints and environmental variations during training. Recommendations included the exploration of ensemble methods, hyperparameter tuning, and data augmentation for improved model performance. Future research avenues involved testing alternative CNN models, diverse machine learning algorithms, and the inclusion of clinical data for a more comprehensive analysis. In conclusion, this research contributed insights into the comparative strengths and trade offs of CNNs, SVMs, and RFTL in lung cancer image diagnosis. The findings underscored the models\u27 potential clinical relevance and provided a foundation for refining techniques in medical image processing, paving the way for improved early detection and patient outcomes in lung cancer diagnosis
Smart Dog Feeder
No full textIt is a known fact that different breeds and sizes of dogs have vastly different nutritional needs. According to Forbes, 35% of the 123.6 million households in America owned two or more dogs as of 2020. This automatic dog feeder solves the common problem of dogs eating out of the wrong food bowl, resulting in them not getting the correct nutrients. The main base of the feeder works like a typical automatic feeder, releasing more food into the bowl on a predetermined schedule. The feeder uses cameras to detect and identify which pet is in front of a particular bowl. A Google Teachable Machines program determines if the dog is in front of its correct bowl and, if so, signals the lid of the bowl to open. When the correct dog is no longer in view of the camera, the lid closes. The lid remains closed unless signaled to open. The bowl also includes a sensor that is able to detect if a part of the dog’s body is in the bowl while the lid is closing. If the sensor detects a foreign object inside the bowl, the lid will immediately reopen to ensure that your pet cannot be hurt by the lid mechanism. Once the sensor can no longer detect an obstruction, the lid securely closes back up
2024 Furman University Scholarship Reception Program
Get PDFOn February 16, 2024, the Libraries and the Office of the Provost hosted the Furman University Scholarship Reception. The reception showcased scholarly publications, creative works, and professional accomplishments of Furman faculty or staff from the 2024 calendar year. It highlighted Furman faculty and staff who completed a degree, received a grant from an external funding source totaling more than $1,000, and/or published books, book chapters, journal articles, exhibits, recordings, performances, films, or other works. The following Furman presenters provided four-minute speeches about their scholarly or creative works: Chris Alvin, Associate Professor of Computer Science Caroline Davis, Assistant Professor of Theatre Arts David Eubanks, Assistant Vice President for the Office of Institutional Assessment and Effectiveness Yang Gao, Assistant Professor of Sociology Eunice Kim, Assistant Professor of Classic
George Singleton: Finding The Absurd Gets Harder
Get PDFThe acclaimed writer has been crafting stories that make people laugh, and think, since 1983