M_M's repositories
applied-ml
đź“š Papers by organizations sharing their work on applied data science & machine learning.
arduino_robot_arm
ROS packages to control an Arduino robot arm by using Moveit
awesome-in-arabic
A collection of awesome developer accounts (Twitter, Facebook,...) 👨‍💻 that enrich Arabic content, podcasts, articles, Youtube channels and Some advises and guidelines.
Bringing-Old-Photos-Back-to-Life
Bringing Old Photo Back to Life (CVPR 2020 oral)
camel_tools
A suite of Arabic natural language processing tools developed by the CAMeL Lab at New York University Abu Dhabi.
DADAM
DADAM: A Consensus-based Distributed Adaptive Gradient Method for Online Optimization
design-resources-for-developers
Curated list of design and UI resources from stock photos, web templates, CSS frameworks, UI libraries, tools and much more
Exercise_Sparse-Autoencoder-Ufldl
Exercise_Sparse Autoencoder-Ufldl
first-order-model
This repository contains the source code for the paper First Order Motion Model for Image Animation
Intrusion-Detection-Systems
This is the repo of the research paper, "Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security".
Iot-Cyber-Security-with-Machine-Learning-Research-Project
IoT networks have become an increasingly valuable target of malicious attacks due to the increased amount of valuable user data they contain. In response, network intrusion detection systems have been developed to detect suspicious network activity. UNSW-NB15 is an IoT-based network traffic data set with different categories for normal activities and malicious attack behaviors. UNSW-NB15 botnet datasets with IoT sensors' data are used to obtain results that show that the proposed features have the potential characteristics of identifying and classifying normal and malicious activity. Role of ML algorithms is for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets is possible. The ML model metrics using the UNSW-NB15 dataset revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets’ attacks and their tracks.
KATE
Code & data accompanying the KDD 2017 paper "KATE: K-Competitive Autoencoder for Text"
MBGD_RDA
Matlab source code of the paper "D. Wu, Y. Yuan, J. Huang and Y. Tan*, Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA), IEEE Trans. on Fuzzy Systems, 28(5), pp. 1003-1015, 2020."
monk_v1
Monk is a low code Deep Learning tool and a unified wrapper for Computer Vision.
pubmed2doc
Write PubMed search results with two display options (citation or listview) to PDF or Word
pytorch_warmup
Learning Rate Warmup in PyTorch
SCUT-FBP5500-Database-Release
A diverse benchmark database for multi-paradigm facial beauty prediction
SGDLibrary
MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.19
Slime-Mould-Algorithm-A-New-Method-for-Stochastic-Optimization-
In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is proposed based upon the oscillation mode of slime mould in nature. The proposed SMA has several new features with a unique mathematical model that uses adaptive weights to simulate the process of producing positive and negative feedback of the propagation wave of slime mould based on bio-oscillator to form the optimal path for connecting food with excellent exploratory ability and exploitation propensity. The proposed SMA is compared with up-to-date metaheuristics in an extensive set of benchmarks to verify the efficiency. Moreover, four classical engineering structure problems are utilized to estimate the efficacy of the algorithm in optimizing engineering problems. The results demonstrate that the algorithm proposed benefits from competitive, often outstanding performance on different search landscapes. The source codes and info of SMA are publicly available at: http://www.alimirjalili.com/SMA.html
Stochastic-Optimization-Algorithms
The aim of the course is for the students to attain basic knowledge of new methods in computer science inspired by evolutionary processes in nature, such as genetic algorithms, genetic programming, and artificial life. These are both relevant to technical applications, for example in optimization and design of autonomous systems, and for understanding biological systems, e.g., through simulation of evolutionary processes.
widget-importer-exporter
A WordPress plugin for importing and exporting widgets.