rhish9h's Projects
Mini erp system to manage the inventory of apace.
the backend files of apace inventory in htdocs/api
A cycling training plan builder.
mini project of glass classification, done with sklearn and deployed with django
A poetic programming language in Old English, where code and literature intertwine in harmonious verse.
This is a research paper based on anomaly based intrusion detection systems used in iot systems. This surveys similar technologies used along with details of system proposed by Shadi A et. al. This system helps in automatically identifying suspicious IOT devices connected to the network. It consist of the training phase where a profile of normal behaviors is built and testing phase where current traffic is classified as attack or normal with the profile created in the training phase. Machine learning ensemble model has been used, including several classifiers including J48, Meta Pagging, Random Forest, REPTree, AdaBoostM1, Decision Stump and NaΓ―ve Bayes. It is trained on the popular dataset of NSL-KDD.
3d cube in javascript
intrusion detection - classification between normal vs anomalous - based on ds2os dataset
A company has hosted a web application behind a Apache HTTPD Server. The DevOPS team needs to monitor the web traffic and take actions related to security and application performance improvements. The team needs the following information i. Top client ip addresses by number of requests ii. Top urlsby number of requests iii. Top urls by size of response iv. Top http request methods by number of requests v. Top Content types by number of request vi. Trend of rate of http request per 5 mins vii. Total data transfer - uploaded and downloaded. This information should be easily accessible to the DevOPS team at anytime and any place.
React portfolio website to display projects and information
Backend for the portfolio website
Spring Boot part of "Spring Boot + Angular full stack reddit clone" - (practice) following the tutorial of free code camp https://www.youtube.com/watch?v=DKlTBBuc32c
This is a tool for getting youtube video like count prediction. A Random Forest model was used for training on a large dataset of ~200,000 videos. Feature engineering, Data cleaning, Data selection and many other techniques were used for this task.