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Basics of AI including PyPlot tutorials, Fuzzy Logic, Genetic Algorithms, Bayesian Networks, Perceptrons and NN's.
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.
A curated list of awesome resources related to capsule networks
Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web
Backpropagation neural network in python/numpy
This code implements Back propagation in Neural Networks using gradient descent in Python.
A student attendance system based on Blockchain technology
Back Propagation, Python
📚 Solutions to Introduction to Algorithms Third Edition
2D Convolutional Neural Network for land use and land cover classification of radar and hyperspectral images
Training an artificial neural network using back-propagation on MNIST dataset
A list of all public EEG-datasets
Includes Filter based, wrapper based, Embedded and Hybrid techniques
A settings-free global optimization method based on PSO and fuzzy logic
A keras based implementation of Hybrid-Spectral-Net as in IEEE GRSL paper "HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification".
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 system with Apache Spark and deep learning
VGG-19 deep learning model trained using ISCX 2012 IDS Dataset
Project that gives idea on how to use Pyspark on UNSW-NB datasets used for intrusion detection.
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.
Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset
A collection of Jupyter notebooks developed by the community showing how to use Qiskit
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.
PySpark solution to the NSL-KDD dataset: https://www.unb.ca/cic/datasets/nsl.html
, 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.
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%.
Final Project mata kuliah Swarm Intelligence
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
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.