An Image Analysis Environment for Species Identification of Food Contaminating Beetles
Authors: Daniel Martin, Hongjian Ding, Leihong Wu, Howard Semey, Amy Barnes, Darryl Langley, Su Inn Park, Zhichao Liu, Weida Tong, Joshua Xu
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | we have developed in collaboration with FDA food analysts some image analysis-based machine intelligence to achieve species identification with up to 90% accuracy. The current project is a continuation of this development effort. Here we present an image analysis environment that allows practical deployment of the machine intelligence on computers with limited processing power and memory. |
| Researcher Affiliation | Collaboration | 1School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA 2ARL Chemistry Lab1, Arkansas Regional Laboratory, Office of Regulatory Affairs, US Food and Drug Administration, AR 72079, USA 3Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA 4Data Science Team, System Technology, Samsung Austin Semiconductor LLC, Austin, TX 78754, USA |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions that the MATLAB Compiler Runtime (MCR) is free but does not state that their own implementation code is open source or provide a link. |
| Open Datasets | Yes | This paper is a follow-up to previous research by Park et al. (preprint), which can be accessed in the references. ... Park, S.; Bisgin, H.; Ding, H.; Semey, H.; Langley, D.; Tong, W.; and Xu, J. Species Identification of Food Contaminating Beetles by Recognizing Patterns in Microscopic Images of Elytra Fragments. Preprint, submitted September 9, 2015. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning was provided. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments, only mentioning the target environment of 'computers with limited processing power and memory'. |
| Software Dependencies | No | The paper mentions that the environment was implemented in MATLAB and uses MATLAB Compiler Runtime (MCR) and GUIDE, but it does not provide specific version numbers for any of these software components. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as hyperparameter values, training configurations, or system-level settings. |