Showing results for Cost Vector Product
GitHub Repo
https://github.com/SalarBasiri/SALAR-Bio-Lab
SalarBasiri/SALAR-Bio-Lab
The SALAR-Bio-Lab (Smart and Low-cost Advanced Analysis and Research) is the product of over two years of intensive R&D at the Golestan Ischemic Disorder Lab. It represents an end-to-end open-access framework for extracting complex heart characteristics, ranging from real-time electrical time-domain markers to spatial conduction velocity vectors.
GitHub Repo
https://github.com/SanjanaSaxena04/GenAI-Scaled-into-Production
SanjanaSaxena04/GenAI-Scaled-into-Production
Production-ready GenAI chatbot using RAG and LLMs with Qdrant for vector search. Supports PDF, DOCX, PPTX, TXT uploads, with source-aware answers. Includes input guardrails, cost tracking, and modular, scalable architecture. Built during internship at NTT DATA.
GitHub Repo
https://github.com/shalikaprasad/Cloud-Provider-Selection-Recommendation-Using-Machine-Learning
shalikaprasad/Cloud-Provider-Selection-Recommendation-Using-Machine-Learning
Cloud computing (CC) has recently been receiving tremendous attention from the IT trade and educational researchers. CC leverages its distinctive services to cloud customers in a very pay-as-you-go, anytime and anyplace manner. As well as Cloud services offer dynamically scalable services on demand. Therefore, service supplying plays a key role in CC. Then, it is good opportunity for customers to find suitable and lowly cost service for their project. Specially, Customer must be able to select appropriate cloud service according to their needs and money. It is time-consuming task for consumers to collect the necessary information and analyze from all cloud service providers to make right decision. As well as it is also a highly demanding task from a computational perspective because multiple consumers who have similar requirements conduct same computations repeatedly. They provide all products you might need for moving your business to the cloud. But these product offerings differ in pricing as well as the naming of their services. Some Businessmen already may use on-premises infrastructure or think which infrastructure will use for my project. They may have more complex problems like how to choose a cloud service, which services want use and specially how many costs want to pay for monthly or yearly. Sometimes, someone already use a cloud services, they have lot of problems like more expensive, less flexibility, hard to use, overwhelming options of services, poor management of GUI and tool, complex price schema and other issues. However, they must spend more price and time as useless. Because they could not select best cloud service provider early to their business. For solving the cloud service selection problem, many researchers have proposed some approaches including multicriteria decision analysis (MCDA) and Brokerage-Based Approach. But we cannot see any machine learning prediction system for solving this issue. This system enables the user to choose from among a number of available choices. In this paper, we make a neural network with TensorFlow to service selection in CC. This system focuses on three main players in CC. There are Amazon Web Services, Microsoft Azure and Google Cloud Platform in the race for cloud services providers. I identify and synthesize several products relevant for web services in Cloud providers. There are Featured, Compute, Storage, Database, Networking, Operation, Identity & Access and Cost. As well as I focus on Small and medium-sized businesses (SMBs). Because these are most aggressive segment in cloud service. It is less-complex IT needs, fewer legacy applications and less IT support than larger enterprises. We use Support Vector Machine (SVM), Multiple linear regression (MLR) and Multiple-criteria decision analysis (MCDA). We develop efficient and flexible recommendation system for ranking cloud service providers. I prove accuracy and effectiveness of our approach through an experimental study with the real and synthetic Cloud data.
GitHub Repo
https://github.com/himanshu-yadv/Square-Space
himanshu-yadv/Square-Space
"Square Space" is a C++ terminal-based shopping cart app where users can browse products, add or remove items from their cart, and see the total cost. It uses maps and vectors to manage products and cart data.
GitHub Repo
https://github.com/b05702057/Data-Structures-and-Algorithms-for-Criteo-Big-Data
b05702057/Data-Structures-and-Algorithms-for-Criteo-Big-Data
In this project, I implemented C++ stl vectors to store 6.4GB data containing 1.6M clicking information of the company named Criteo. There are 4 main functions. The first one is to get the information whether a user makes a purchase of the product he clicks at a specific time. The second one is to find the common purchase of two users. The third one is two know the conversion rate(purchases/clicks) of specific users. The last one is to know all the products a user buys. These operations can cost a lot of time because there are 1.6M pieces of data. My main work was to store these data in a specific way so that I could get information by implementing binary search.
GitHub Repo
https://github.com/anotidamafuvadze/LuxeFind
anotidamafuvadze/LuxeFind
AI-powered beauty discovery app using OpenAI embeddings and a vector database to perform semantic search over ~10K products, enriched with live pricing, ratings, images, and retailer links via an LLM-driven web pipeline, supporting user accounts, saved favorites, and cost- and latency-optimized caching for fast, personalized results.
GitHub Repo
https://github.com/Gabooreyy/product-inventory
Gabooreyy/product-inventory
Create an interface in HTML to manipulate an INVENTORY of products, to store the information we must use a vector (not use BDs or local storage) and limit it to a maximum of 20 products. The information of each product must allow to save the code, the name, the quantity and the cost, as well as how to calculate the value of the merchandise, which would be a value calculated by the quantity and the cost. In the interface (a single screen) have the inputs for each data, the buttons for the tasks to be carried out described below and a div to describe the activities to be carried out and their results. Buttons: Add new product Delete a product by code, you will need to return the product (if it exists) or null Search a product by code Retrieve all products // list Retrieve all products in reverse order of how they were entered // reverse list Insert a new product in a position (the position is also requested in an input), you can only insert it if the position exists, that is, if I have 5 products, I can't insert it from position 7 onwards.
GitHub Repo
https://github.com/RaffaToSpace/Returning-costumers-classifier
RaffaToSpace/Returning-costumers-classifier
Model that analyses data from an audiobook app to predict if past customers will return to purchase more products. Analysis made with a neural network (NN) and with support vector machine algorithm (SVM)..
GitHub Repo
https://github.com/amirulhazym/malay-qa-bot-rag
amirulhazym/malay-qa-bot-rag
Project 3: GenAI project which focus on mastering LLMs capabilities, limitations and cost for specific product and its use case. Also to mastering GenAI project lifecycle including RAG for real time knowledge update, vector database, and evaluation metrics for RAG chatbot. To experience self-hosted vs API-driven LLMs application.
GitHub Repo
https://github.com/Gabooreyy/ordered-product-inventory