PiCN: Python ICN and NFN by University of Basel

A Julia package for working differential mobility analyzers.

github action to run the lizard.You can continue to verify the complexity of your source code.

A virtual computer network design for an organization having 6 departments.

Contains all the experiments conducted in St. Francis Institute of Technology for Sem - VI of EXTC branch

MATLAB/GNU Octave code to predict the hygroscopicity parameter kappa for organic compounds from functional group composition.

TESS speech emotion recognition with log-mel CNNs, speaker-independent train (OAF) / test (YAF), SpecAugment-style augmentation, Grad-CAM explainability, and a small Whisper+TF‑IDF text baseline showing emotion isn’t in transcripts alone.

Code in various languages to relate the hygroscopicity parameter kappa with hygroscopic growth factor or critical supersaturation.

An extension for Visual Studio Code to run Lizard complexiy analysis.

Disaster Management Communication Network using Cisco Packet Tracer

CCN (Contents-Centric Networking) Simulator

🔬 This project aims to detect whether malaria cells are parasitized or uninfected using Convolutional Neural Networks (CNN). The project provides a Jupyter Notebook file and a pre-trained model file for the malaria cell detection task.

A custom simulator for CCN networks (https://en.wikipedia.org/wiki/Content_centric_networking)

A machine learning model to recognize emotion in speech.

The UI

Tool to show code coverage metrics, measured by PHP Unit in Cobertura format, in console and CI/CD pipeline

These classes introduce geographical locality for clients' requests for ccnsim simulator

I have provided solutions for all important labs held in ucp .In this repo i have shared some codes for Graded labs

This code implements 2lru (klru k=2) in ccnsim which is a simulator for Content Centric Networks

This project presents a multi-method AI pipeline for prioritizing exoplanet candidates from the NASA Kepler KOI dataset. Combining from-scratch implementations of Decision Trees, Naive Bayes, K-Means clustering, and Bayesian reasoning with a CNN baseline, the system ranks 1,979 unresolved signals by their likelihood of being genuine exoplanets.