nathan.msr's repositories
Automated-Gland-Segmentation-Leading-to-Cancer-Detection-for-Colorectal-Biopsy-Images
Glandular formation and morphology along with the architectural appearance of glands exhibit significant importance in the detection and prognosis of inflammatory bowel disease and colorectal cancer. The extracted glandular information from segmentation of histopathology images facilitate the pathologists to grade the aggressiveness of tumor. Manual segmentation and classification of glands is often time consuming due to large datasets from a single patient. We are presenting an algorithm that can automate the segmentation as well as classification of H and E (hematoxylin and eosin) stained colorectal cancer histopathology images. In comparison to research being conducted on cancers like prostate and breast, the literature for colorectal cancer segmentation is scarce. Inter as well as intra-gland variability and cellular heterogeneity has made this a strenuous problem. The proposed approach includes intensity-based information, morphological operations along with the Deep Convolutional Neural network (CNN) to evaluate the malignancy of tumor. This method is presented to outpace the traditional algorithms. We used transfer learning technique to train AlexNet for classification. The dataset is taken from MCCAI GlaS challenge which contains total 165 images in which 80 are benign and 85 are malignant. Our algorithm is successful in classification of malignancy with an accuracy of 90.40, Sensitivity 89% and Specificity of 91%. here is a copy of this project from a
facial-expression-recognition
Facial expression recognition deep learning examples
node-sqlite3
SQLite3 bindings for Node.js
NPLRuntime
NPL - Neural Parallel Language
Project-14.-Parkinson-s-Disease-Detection.ipynb
About this file Data Set Information: This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds to one of 195 voice recordings from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to the "status" column which is set to 0 for healthy and 1 for PD. Attribute Information: Matrix column entries (attributes): name - ASCII subject name and recording number MDVP:Fo(Hz) - Average vocal fundamental frequency MDVP:Fhi(Hz) - Maximum vocal fundamental frequency MDVP:Flo(Hz) - Minimum vocal fundamental frequency MDVP:Jitter(%) , MDVP:Jitter(Abs) , MDVP:RAP , MDVP:PPQ , Jitter:DDP - Several measures of variation in fundamental frequency MDVP:Shimmer , MDVP:Shimmer(dB) , Shimmer:APQ3 , Shimmer:APQ5 , MDVP:APQ , Shimmer:DDA - Several measures of variation in amplitude NHR , HNR - Two measures of ratio of noise to tonal components in the voice status - Health status of the subject (one) - Parkinson's, (zero) - healthy RPDE , D2 - Two nonlinear dynamical complexity measures DFA - Signal fractal scaling exponent spread1 , spread2 , PPE - Three nonlinear measures of fundamental frequency variation
supervised
supervised machine learning
blood.vue_firebase
bloodbank
CoderByte-Challenges-Solutions
CoderByte-Challenges-Solutions
Coding_ChallengeOptions
https://drive.google.com/file/d/15X00ZWBjla7qGOIW33j8865QdF89IyAk/view?usp=sharing\ The dataset is tabular and the features involved should be self-explanatory. We would like for you to come up with a specific problem yourself and solve it properly. This is an “open challenge,” mainly focusing on natural language processing. The problem could be either about predictive modeling or providing analytical insights for some business use cases. Note the problem should be treated as large-scale, as the dataset is large (e.g., >100GB) and will not fit into the RAM of your machine. Python is strongly recommended in terms of the coding language.
CaffeOnSpark
Distributed deep learning on Hadoop and Spark clusters.
Coursera_Big_Data_for_Data_Engineers
Assignments for Big Data for Data Engineers specialization on Coursera by Yandex.
docker-hadoop-spark-workbench
[EXPERIMENTAL] This repo includes deployment instructions for running HDFS/Spark inside docker containers. Also includes spark-notebook and HDFS FileBrowser.
FlutterChatAppTutorial
Fully Functioning Chat App with Flutter & Firebase
learning-hadoop-and-spark
Companion to Learning Hadoop and Learning Spark courses on Linked In Learning
learntools
Tools and tests used in Kaggle Learn exercises
migrate
Tool to help customers migrate artifacts between Databricks workspaces. This allows customers to export configurations and code artifacts as a backup or as part of a migration between a different workspace.
mmlspark
Microsoft Machine Learning for Apache Spark
nodejs.org
The Node.js website.
PyHive
Python interface to Hive and Presto. 🐝
spark
Apache Spark - A unified analytics engine for large-scale data processing
sparta
Real Time Analytics and Data Pipelines based on Spark Streaming
sqlitebrowser
Official home of the DB Browser for SQLite (DB4S) project. Previously known as "SQLite Database Browser" and "Database Browser for SQLite". Website at:
sqlmap
Automatic SQL injection and database takeover tool