An Exploration of a Virtual Connection for Researchers and Educators by Exploring Strategies Enterprise Information Systems Specialists Need to Integrate Novel Neural Network Algorithms Into an Imaging Application – A Design Science Study

Emma Quindazzi, Samuel Sambasivam
InSITE 2022  •  2022  •  pp. 024
Aim/Purpose: The problem statement in the proposed study focuses on what strategies enterprise information systems specialists need to integrate novel algorithms into an imaging application that had not yet been identified. The aim is to demonstrate that a cross-convolutional neural network can be implemented within the home laboratory – an exploration of a virtual connection for re-search. An analysis of the works provides the basis for future extensibility of the software application for ImageJ2.

Background: The study was guided by the research question: What strategies do enterprise information systems specialists need to integrate novel algorithms into an imaging application? This study demonstrates how to bring a lab-tested application online within a home laboratory to further build upon those findings.

Methodology: A conceptual analysis was utilized for the artifact’s creation within the umbrella of design science to aid in the data interpretation segment. A conceptual framework was developed to determine relevant subject matter, such as useful software applications and technological enhancements to an image application. A research sample was not used in this study.

Contribution: This research contributed to the body of knowledge by using a cross-convolutional neural network to explore novel algorithms as an enterprise information system specialist and set up the imaging application called Im-ageJ2 for development. The study’s setup is documented in a series of steps to demonstrate the how-to set up such a study.

Findings: The findings were that it is possible to implement an existing work from within the home laboratory, steps of which are outlined for those to follow. Future work can be extended from the baseline workings. Furthermore, an analysis of the existing code was determined to see if the existing PyTorch code could be developed within Java to act as an extension to ImageJ2 later. By examining the programming code and the cross-convolutional functionality, a determination was made on the best Java mapping. A set of highlights of the processes used are included.

Recommendations for Practitioners: The study intersects two differing realms: artificial intelligence and the enterprise information systems network. This study builds upon an AWS system and details the steps to implement an artificial intelligence system on the Amazon Web Service (AWS) platform. The researcher investigated existing soft-ware imaging Java applications and probed the areas of potential extensibility for implementing the artificial intelligence novel algorithms.

Recommendations for Researchers: The concept of being able to bring online a turnkey set of computer hard-ware off-the-shelf within the home laboratory may be an unexplored avenue to some researchers. The recommendation is to encourage researchers to see past the constraints that a lack of hardware may bring about for computer science research. The researcher no longer has to be within an office laboratory on campus to access power computers. The doors are open to exploring a whole new world. It explores how to move through the various linkage is-sues with old libraries and new systems. Other avenues this research can enhance are extending the ImageJ libraries as a Java plugin with the novel algorithms such as the cross-convolutional network implemented on the AWS platform.

Impact on Society: Expanding the accessibility to the researcher and practitioner field could be profound. No longer is the researcher or practitioner constrained to the office laboratory. This work shows how to move through the issues to invoke the software to produce and investigate existing software. The findings and guidance of this study are profound.

Future Research: Future research should focus on implementing the mappings table as deter-mined in previous research. A future design could be pursued from the analysis and implementation of a Java neural network algorithm as a basic implementation. Java can chain elements to begin replicating the algorithm by Xue et al. (2019). The cross-convolutional element should have further analysis to ensure the full replication within Java.
A cross-convolutional neural network (CCNN) Java algorithm could be implemented and trial run within the code; this would allow for the leverage of the heavy lifting of the program code to predict image motion. There seemed to be few approaches regarding predicting motion frames of a future image, and such predictions could be a giant leap for the health care world regarding microscopic and x-ray images.
artificial intelligence, artificial intelligence in medicine, butterfly recognition, CIBR, computer vision, content-based image retrieval systems and Java, convolutional neural network, cross-convolutional neural network, deep learning, digital imaging, future image prediction, image recognition, ImageJ, ImageJ2, recurrent neural network, video motion
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