560 skills found · Page 6 of 19
KalyanM45 / Hand Tracking Using Opencvhis Python script utilizes OpenCV and MediaPipe to perform real-time hand tracking using a webcam. The code captures video input from the default camera, processes the frames to detect and track hand landmarks using the MediaPipe Hands module, and subsequently visualizes the landmarks on the live feed.
open-power-sdk / CurtCompute processor utilization and system call processing metrics based on "perf" trace data
stupid-cooh / Metal Multiaxial Fatigue Life Prediction Using Deep LearningThis repository contains code for predicting multiaxial fatigue life of metals using deep learning models (CNN, LSTM, and GRU) combined with fully connected layers. It processes a dataset published on Materials Cloud, utilizing high-quality data to train and evaluate the models effectively.
manvirchakal / LearnAIAWS Breaking Barriers Hackathon WINNER: A personalized learning platform powered by Claude Haiku (A foundational model on AWS Bedrock) which processes educational documents (textbooks, pdfs) and generates tailored educational content utilizing RAG with built in accessibility and inclusion features.
andrewandrepowell / Zybo PetalinuxSmall projects intended to run on the Digilent Zybo development board, utilizing PetaLinux on the Zynq's ARM processor.
pulsar123 / Macro ScriptsA set of BASH scripts and C++ programs representing a complete workflow for processing macro focus stacking photographs. Utilizes other open source packages (dcraw, Hugin, ImageMagick) internally. The Deadpixels script (automatic detection of dead and hot pixels in raw photographs) can be of use for general photography, not just for macro photography.
scriptlabs-cc / Discord Joiner And Boost ToolThis project is designed to automate the process of joining Discord servers and boosting them using multiple tokens. It utilizes the 'discord.js-selfbot-v13' library for Discord interactions, '2captcha'for solving captchas, and proxies for managing multiple accounts.
jjordanbaird / EmailVectorDBThis project demonstrates how to parse emails, process them using OpenAI's GPT-3.5, and load the data into a Weaviate vector database for enhanced search capabilities. Utilizing few-shot prompts and parallel processing, it showcases the power of combining NLP techniques with vector search.
Andrew-Tsegaye / Project AI Summarizer AppThe Project AI Summarizer App is a powerful tool that harnesses the capabilities of AI to provide efficient and accurate text summarization. It utilizes advanced natural language processing models to analyze the input text and generate a condensed summary that captures the essential information.
phmz / Revu Clirevu: An advanced tool for code review utilizing GPT-4 and the GitHub API. It streamlines the review process of pull requests, local changes, and individual files, and additionally proposes commit messages based on local diffs and commit history.
coderiekelt / UnityCefSharpAn implementation of CefSharp for Unity, utilizing a secondary process to facilitate rendering.
qg5 / Go Solana TpuA simple and efficient TPU (Transaction Processing Unit) client for Solana, utilizing the QUIC protocol for data transmission.
tienle / Docsplit Paperclip ProcessorThis gem is Paperclip processor, utilizing Docsplit in order to convert uploaded files to pdf and extract information/thumbnails.
Anandsavran / Graphical Simulator For Resource Allocation Graphs RAG The Graphical Simulator for Resource Allocation Graphs (RAG) is a software application designed to visually simulate and analyze resource allocation scenarios in a computer system. It helps users understand and detect deadlocks, resource contention, and inefficient resource utilization using a graphical representation of processes and resources.
bytehide / CSharp ChatBot GPTThis repository contains a simple C# chatbot powered by OpenAI’s ChatGPT. The chatbot utilizes the RestSharp and Newtonsoft.Json libraries to interact with the ChatGPT API and process user input.
elhajuojy / FoxranderFoxrander is a micro front-end framework for PHP that utilizes object-oriented programming to simplify the process of creating HTML elements. With Foxrander, you can easily render HTML and CSS with features such as routing, PSR-4 autoloading, and integration with Tailwind CSS
salmanyam / Jitrop NativeThe project collects the gadgets and records the time to obtain gadgets from a process by utilizing an attack technique called Just-In-Time Return-Oriented Programming (JIT-ROP). We utilize the JIT-ROP technique to evaluate different fine-grained address space layout randomization (ASLR) schemes and measure the upper bound of effective re-randomization intervals. Our evaluation and measurements have been published in ACM CCS 2020. We implement a native version of the JIT-ROP technique.
manyasrinivas2021 / AI BASED FACIAL EMOTION DETECTION USING DEEP LEARNING“AI Based Facial Emotion Detection”, developed using many machine learning algorithms including convolution neural networks (CNN) for a facial expression recognition task. The goal is to classify each facial image into one of the seven facial emotion categories considered in this study.Trained CNN models with different depth using gray-scale images from the Kaggle website.CNN models are developed in Pytorch and exploited Graphics Processing Unit (GPU) computation in order to expedite the training process. In addition to the networks performing based on raw pixel data,Hybrid feature strategy is employed by which trained a novel CNN model with the combination of raw pixel data and Histogram of Oriented Gradients (HOG) features. To reduce the over fitting of the models,different techniques are utilized including dropout and batch normalization in addition to L2 regularization. Cross validation is applied to determine the optimal hyper-parameters and evaluated the performance of the developed models by looking at their training histories. Visualization of different layers of a network is presented to show what features of a face can be learned by CNN models. Based on the emotion the program recommends the music for the user to up flit the mood.
ananya2001gupta / Bitcoin Price Prediction Using AI ML.Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.
tahmed11 / DeepScanA simple shell script which utilizes nmap, nikto, dirb, enum4linux and other open source goodies to automate enumeration process.
