BrianDehlinger's repositories
DCB-Dynamic-Codon-Biaser
A tool for dynamically calculating the codon usage bias in bacterial genomes and querying of a database for Codon Bias statistical analysis
Stand-Arrow
The official repository for the Stand Arrow discord bot written in discord.py.
ArmadilloCord
A discord bot that allows users to play a multiplayer nature-themed adventure game.
bpadictionary
Data dictionary for the Blood PAC Commons
build-your-own-x
🤓 Build your own (insert technology here)
cdis-manifest
Manifests tracking service versions to release to each environment
cloud-automation
Automation for standing up Gen3 commons on AWS, GCP, Azure, and on-prem
CVE-2021-44228_scanner
Scanners for Jar files that may be vulnerable to CVE-2021-44228
detect-secrets
An enterprise friendly way of detecting and preventing secrets in code.
Distributed-Systems
Developing access for Front/Back Implementation Using Socket Communication
Distributed-Systems-Mobile
Mobile Application for Distributed Systems Testing Application
Flare-Cogs
Assortment of cogs.
InSilicoSeq
:rocket: A sequencing simulator
KotlinBully
A Kotlin Implementation of the Bully Algorithm
local-log4j-vuln-scanner
Simple local scanner for vulnerable log4j instances
SparkSentiment
Apache Spark and Sentiment Anaylsis.
WASIS
WASIS (Wildlife Animal Sound Identification System) is a public-domain software that recognizes animal species based on their sounds. From a partnership between Laboratory of Information Systems (LIS) and Fonoteca Neotropical Jacques Vielliard (FNJV) of the Institute of Biology of the University of Campinas (UNICAMP), the main goal of this project is to design a tool which supports multiple algorithms to help scientists and general public on the identification of species. And why is it important? Besides the curiosity itself of knowing which species is calling, we can possibly identify invasive species in a certain area, help on establish migratory patterns from sounds of different locations during a specific period of time, as well as support long duration recording analysis. The software architecture was designed to support multiple audio feature techniques that extract meaningful information of animal sounds, and classification algorithms that use these extracted data to match against respective audio information stored in the software data repository. The main purpose of these algorithms is to design a classification scheme that can best predict the classes/labels for unseen data (an audio file that we want to identify), process similar to the human brain ability to differentiate among a wide range of sounds and to assign them to previously heard sounds.