shubh-1612

shubh-1612's Stars

• ankitpriyarup/Coding_Notes

  1.6k820

• MysteryVaibhav/leetcode_company_wise_questions

  This is a repository containing the list of company wise questions available on leetcode premium

  7.1k2.6k

• mister0/How-to-prepare-for-google-interview-SWE-SRE

  This repository includes resources which are more than sufficient to prepare for google interview if you are applying for a software engineer position or a site reliability engineer position

  656176

• HugoBlox/theme-academic-cv

  🎓 无需编写任何代码即可轻松创建漂亮的学术网站 Easily create a beautiful academic résumé or educational website using Hugo and GitHub. No code.

  Language:TeX3.9k6.3k

• Sanbongawa/binary_swarm_intelligence

  Libraries of binary swarm intelligence mainly used for obtaining optimal solution of feature selection

  Language:Python2712

• dipakkr/A-to-Z-Resources-for-Students

  ✅ Curated list of resources for college students

  17.4k4.5k

• prakash-chakraborty/free-tshirts-stickers-and-swag-for-developers

  List of free tshirts, stickers and swags available for developers

  1.1k131

• getify/You-Dont-Know-JS

  A book series on JavaScript. @YDKJS on twitter.

  179k33.5k

• Vamsicse/CheatPreventionVisualCryptogaphy

  Visual Cryptography (VC) is a method of encrypting a secret image in to n shares such that stacking a sufficient number of shares reveals the secret image. Unlike conventional cryptographic methods, VC needs no complicated computation for recovering the secret. The act of decryption is to stack shares which is the bitwise OR operation of pixels in the shares. Most of the previous research work on VisualCryptography focuses on improving two parameters Pixel expansion and contrast. This paper considers the attacks of malicious adversaries who may deviate from thescheme in any way. There are three cheating methods that are applied on existing Visual Cryptography schemes. This paper improves one of the cheat-preventing schemes. A generic method that converts a Visual Cryptography scheme to another Visual Cryptography scheme that has the property of cheating prevention is proposed. The overhead of the conversion is near optimal in both contrast degression and pixel expansion.

  Language:Java1

• daQuincy/Image-Steganography-using-LSB-and-XOR-Operation-on-MSB

  Python implementation of the research paper "Simple and Secure Image Steganography using LSB and Triple XOR Operation on MSB"

  Language:Jupyter Notebook73

• poncovka/particle-swarm-optimization-demo

  The application demonstrates the principal of the Particle Swarm Optimization.

  Language:C++1

• kkentzo/pso

  Particle Swarm Optimization (PSO) in C

  Language:C11263

• adisa-arc/ThreatDetection

  This is the Particle Swarm for Cyber Threat and Cyber Attack Prediction

  Language:HTML4

• pushkar1393/tsunami-epicentre-detection-using-Particle-Swarm-Optimization

  Developed a Particle Swarm Optimization algorithm based project that optimized its solution iteratively. Deployed self-learning bots over a susceptible area to narrow down on the epicentre based on flock location

  Language:Java2

• tberg1234/Swarm

  Swarm Final Project about Communication and Object Shape Detection in GPS Denied Environments

  Language:C++1

• dipankarsk/Feature-Selection-Hybrid

  Intrusion Detection is a technique to identify the abnormal behavior of system due to attack. The unusual behavior of the environment is then identified and steps are taken and methods are formed to classify and recognize attacks. Data set containing a number of records sometimes may decrease the classifiers performance due to redundancy of data. The other problems may include memory requirements and processing power so we need to either reduce the number of data or the number of records. Feature Selection techniques are used to reduce the vertical largeness of data set. This project makes a comparative study of Particle Swarm Optimization, Genetic Algorithm and a hybrid of the two where we see that PSO being simpler swarm algorithm works for feature selection problems but since it is problem dependent and more over its stochastic approach makes it less efficient in terms of error reduction compared to GA. In standard PSO, the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on sub optimal solutions that are not even guaranteed to be local optimum. A further drawback is that stochastic approaches have problem-dependent performance. This dependency usually results from the parameter settings in each algorithm. The different parameter settings for a stochastic search algorithm result in high performance variances. In this project the modification strategies are proposed in PSO using GA. Experimental results show that GA performs better than PSO for the feature selection in terms of error reduction problems whereas hybrid outperforms both the model in terms of error reduction.

  Language:Matlab255

• Anukool97/INTRUSION-DETECTION-SYSTEM-ON-DOCKER-SWARM

  This projects take a simple Network Intrusion Detection System, make it running on the docker swarm containers. Hence taking the received packets from the client to the server in realtime. Then applying the ML Model on the packet traffic in order to detect the Intrusion. There are 4 attack-class which this model can alert to. Namely: 1> DOS attack 2>R2L(Remote to Local) 3>U2R (User to Root) 4> Probe Attack.

  Language:Jupyter Notebook5

• ashishgupta1350/Hackerrank-GFG-Practice-Codes

  Codes for revision.

  Language:C++31

• reactjs/react-gradual-upgrade-demo

  Demonstration of how to gradually upgrade an app to a new version of React

  Language:JavaScript42226