muralikrishna26 Goto Github PK

classification-of-b-cell-epitopes-using-deep-learning

In this competitions, one had to develop models using TensorFlow (Python Library) to classify B-cell epitopes and Non-epitope. Developed model is clearly explained in "MT19132_Readme.pdf" file

codejudge

computer-security

data_structures_and_algorithm-coding

diabetes-prediction-using-ensemble-techniques

Diabetes is one of the most commonly known chronic diseases, leading to complications in health if it is unidentified and not diagnosed. Implemented various machine learning algorithms on the data collected from PIMA Indian Diabetes Database, which is sourced from the UCI Machine learning repository. applied machine learning techniques such as K Nearest Neighbors, Logistic regression, Naive Bayes, Decision trees, Gaussian process, Linear SVM, RBF SVM, Xgboost, Gradient boost, AdaBoost and Random forest. All these mentioned algorithms are applied to the normalized data. The performance comparison of the model is discussed based on the accuracy as an evaluation metric, along with a brief description of how every model is implemented in this paper. The voting classifier is applied on top of the best models from the above machine learning techniques listed.

implementation-of-truth-table-and-disjunctive-normal-form

Written a Python program that can take as input any given propositional formula and puts out i) the corresponding truth table, and ii) the corresponding disjunctive normal form of the given formula.

information-retrieval

Contains various methods and models that makes retriving of informtion more simplier.

network-science

Play with Graphs and Networks

opinion-and-fact-classification

In the present-day technology huge amount of data is being generated every day. So, it’s turning out to be a challenging task to handle text-based data. In the world of text-based sentences it is not that simple to differentiate between fact and opinions. So, This project is to build the model that classifies/identifies facts from/and opinions in the given text by using various machine learning and deep learning techniques.

patient-billing-system

This system can be used to maintain location of each patient . Information about the patient and the charges to be paid is also stored .

prediction-of-dna-binders

Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation, as these proteins play a crucial role in gene-regulation. We have developed an model using various machine learning techniques to for predicting DNA-binding domains and proteins.

prediction-of-dna-interacting-residues

Given DNA Interacting residues, we have created model using various machine learning techniques to predict whether it is protine contained DNA or not.

prediction-of-high-risk-cancer-patients

Classification of high risk and low risk cancer patients using Convolutional Neural Network and Mutli-Layer Perceptron.

rdflib

RDFLib is a Python library for working with RDF, a simple yet powerful language for representing information.

stage-classification-of-liver-cancer

Stage classification of liver cancer patients from thier gene expression profile. Models used are : KNN, Naive Bayes, SVM, LSTM

survey

Created a simple website to conduct a survey on any general issue where both the teachers and students will vote.

terinary-classification-based-on-relevance-score

Ranking based on Relevance Score. The objective of the project is to predict the relevance score {0, 1, 2} for a given (query, document) pair feature vector in the test set.