roopa27 Goto Github PK

ai-and-soft-computing

Basics of AI including PyPlot tutorials, Fuzzy Logic, Genetic Algorithms, Bayesian Networks, Perceptrons and NN's.

ann-based-global-weather-monitoring-and-projection-system

Weather forecasting has been an important field of research in the last few decades. Weather forecast are made by collecting quantitative data about the present state of the atmosphere and using scientific understanding of atmospheric process to project how the atmosphere will evolve in the near future. Weather prediction is basically based upon the historical time series data. In initial days weather forecasting was done through implementation of statistical methods and physical simulations but now a days prediction are made by other predictive analytical processes which are more evolved in accuracy. Artificial Neural Networks (ANN) have been applied extensively to both regress and classify weather phenomena. As the data of forecast is nonlinear and follows some irregular trends and patterns. ANN has evolved out to be a better way to improve the accuracy and reliability. This project will depict the extent by which global parameter affects the havoc caused in local regions. It will also show that, how Global warming and climate change creates turmoil economically, socially as well as destruction to global flora and fauna. The weather forecast system needs to be intelligent so that one can easily read the statistical data and generate patterns and further trends to study and based on past data one can able to predict the future.

awesome-quantum-machine-learning

Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

backpropagationnn

Backpropagation neural network in python/numpy

backpropogationusinggradientdescent-python

This code implements Back propagation in Neural Networks using gradient descent in Python.

blockendance

A student attendance system based on Blockchain technology

character-recognition-by-neural-network

Back Propagation, Python

clrs

📚 Solutions to Introduction to Algorithms Third Edition

cnn-landcover

2D Convolutional Neural Network for land use and land cover classification of radar and hyperspectral images

digit-recognition-ann

Training an artificial neural network using back-propagation on MNIST dataset

featureselectionpython

Includes Filter based, wrapper based, Embedded and Hybrid techniques

fst-pso

A settings-free global optimization method based on PSO and fuzzy logic

hybridsn

A keras based implementation of Hybrid-Spectral-Net as in IEEE GRSL paper "HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification".

ics3206-assignment

Assignment for Machine Learning, Expert Systems and Fuzzy Logic (University of Malta) 2019/20 - Using Support Vector Machines to classify hand-written digits

intrusion-detection-big-data

intrusion-detection-spark-conv-lstm

Intrusion detection system with Apache Spark and deep learning

intrusion-detection-system-using-deep-learning

VGG-19 deep learning model trained using ISCX 2012 IDS Dataset

intrusion-detection-system-using-pyspark

Project that gives idea on how to use Pyspark on UNSW-NB datasets used for intrusion detection.

iot-network-intrusion-detection-and-classification-using-explainable-xai-machine-learning

The continuing increase of Internet of Things (IoT) based networks have increased the need for Computer networks intrusion detection systems (IDSs). Over the last few years, IDSs for IoT networks have been increasing reliant on machine learning (ML) techniques, algorithms, and models as traditional cybersecurity approaches become less viable for IoT. IDSs that have developed and implemented using machine learning approaches are effective, and accurate in detecting networks attacks with high-performance capabilities. However, the acceptability and trust of these systems may have been hindered due to many of the ML implementations being ‘black boxes’ where human interpretability, transparency, explainability, and logic in prediction outputs is significantly unavailable. The UNSW-NB15 is an IoT-based network traffic data set with classifying normal activities and malicious attack behaviors. Using this dataset, three ML classifiers: Decision Trees, Multi-Layer Perceptrons, and XGBoost, were trained. The ML classifiers and corresponding algorithm for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets proved to be very high-performing based on model performance accuracies. Thereafter, established Explainable AI (XAI) techniques using Scikit-Learn, LIME, ELI5, and SHAP libraries allowed for visualizations of the decision-making frameworks for the three classifiers to increase explainability in classification prediction. The results determined XAI is both feasible and viable as cybersecurity experts and professionals have much to gain with the implementation of traditional ML systems paired with Explainable AI (XAI) techniques.

iot-network-intrusion-detection-system-unsw-nb15

Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset

multiclassqsvm

A collection of Jupyter notebooks developed by the community showing how to use Qiskit

neural-network-optimized-by-pso

this project integrates - ● Neural Network and Particle Swarm Optimization together to reduce training time of neural network. this is a fun machine learning experiment. This project analyzes the performance of NN optimized by PSO Replacing Back propagation. This hybrid approach then employed on a disease dataset and classified the diasese successfully. For comparison Purpose other well known approaches have also been implemented here in this project to compare the accuracy as well as efficiency of our model. this project has been implemented in python.

nsl-kdd

PySpark solution to the NSL-KDD dataset: https://www.unb.ca/cic/datasets/nsl.html

pec

, we model the edge caching problem in MSN network as a reinforcement learning problemwith Asynchronous Actor-Critic Agent (A3C) algorithm, where UE requires contents by redundant requestsfollowing an optimal strategy to maximize users’ rewards.

plant-disease-detection-using-cnn

The aim is to detect the symptoms of the disease occurring in leaves in an accurate way.Once the captured image is pre-processed, the various properties of the plant leaf such as intensity, color and size are extracted and sent to SVM classifier with Back propagation Neural Network for classification. The experimental results obtained using 169 images have shown that the classification accuracy by ANN ranges between 88% and 92%.

prediction-using-backpropagation-pso

Final Project mata kuliah Swarm Intelligence

pymetaheuristic

A python library for: Adaptive Random Search, Ant Lion Optimizer, Arithmetic Optimization Algorithm, Artificial Bee Colony Optimization, Artificial Fish Swarm Algorithm, Bat Algorithm, Biogeography Based Optimization, Cross-Entropy Method, Cuckoo Search, Differential Evolution, Dispersive Flies Optimization, Dragonfly Algorithm, Firefly Algorithm, Flow Direction Algorithm, Flower Pollination Algorithm, Genetic Algorithm, Grasshopper Optimization Algorithm, Gravitational Search Algorithm, Grey Wolf Optimizer, Harris Hawks Optimization, Improved Grey Wolf Optimizer, Improved Whale Optimization Algorithm, Jaya, Jellyfish Search Optimizer, Memetic Algorithm, Moth Flame Optimization, Multiverse Optimizer, Particle Swarm Optimization, Random Search, Salp Swarm Algorithm, Simulated Annealing, Sine Cosine Algorithm, Whale Optimization Algorithm

qcnn

qml

Introductions to key concepts in quantum machine learning, as well as tutorials and implementations from cutting-edge QML research.

qml_computervision

Repo containing the materials for my master thesis in physics 'Hybrid quantum-classical approach to quantum machine learning for computer vision'