Showing results for HUMAN Product Vector
GitHub Repo
https://github.com/mylifemychoice/Sentiment-Analysis-of-Amazon-Reviews
mylifemychoice/Sentiment-Analysis-of-Amazon-Reviews
Online reviews play an important role in today’s eCommerce industry. Product comments, ratings, posts, etc. have become crucial for a product’s success. People tend to buy products that have more ratings and favorable comments. However, fake reviews can be used to mislead users. Malicious users can post fallacious ratings and comments to any product which may result in degrading its overall ratings and consequentially damaging the customer’s trust. Thus, detecting & classifying these ratings and comments as real or fake has become mandatory for the effectiveness of business opportunities associated with eCommerce industry. A lot of researchers have published different techniques primarily for the detection of fake reviews. Some suggest the use of linguistics while others suggest the use of behavioral analysis. In this report, we use the optimal method for classifying the human sentiments and later the classified the reviews as real or spam. We applied different machine learning algorithms like Naïve Bayes, Decision Tree, Support Vector Machine, Logistic Regression, and Neural Networks on our dataset. This application gave performance results of each algorithm that were measured on the basis of parameters like precision, recall, and f-measure. Finally, a web prototype was developed to showcase the results.
GitHub Repo
https://github.com/amitsingh024/shopping_gpt
amitsingh024/shopping_gpt
I built ShoppingGPT, an AI-powered ecommerce assistant that uses semantic search and LLMs to recommend products from natural language queries. The system generates embeddings for product metadata, retrieves relevant products using FAISS vector search, reranks results, and uses GPT to generate human-like shopping recommendations.
GitHub Repo
https://github.com/jashshah10/personal-ai-shopping-assistance
jashshah10/personal-ai-shopping-assistance
This project is a command-line interface (CLI) AI shopping assistant that provides personalized product recommendations based on user queries. It leverages a vector database (ChromaDB) for efficient semantic search and a local large language model (Ollama) to generate human-like recommendation text.
GitHub Repo
https://github.com/whoisaman752/Review-Detection-System.main
whoisaman752/Review-Detection-System.main
AI vs Human Review Detector is a machine learning web application that identifies whether a product review is AI-generated or human-written. It uses NLP techniques like TF-IDF vectorization and sentiment analysis with a Logistic Regression model. The system is built using Flask for the backend and HTML, CSS, and JavaScript for the frontend.
GitHub Repo
https://github.com/ihash22/pcf-bom-mapper
ihash22/pcf-bom-mapper
An intelligent mapping agent that automatically translates fuzzy, human-written product material text from a CSV BOM into rigid LCA emission factor nomenclature. By leveraging semantic search (vector embeddings) and an LLM validation step, this tool automates the PCF calculation process, outputting a clear, audit-ready footprint breakdown.
GitHub Repo
https://github.com/arpit3043/Extractive-Text-Summerization
arpit3043/Extractive-Text-Summerization
Summarization systems often have additional evidence they can utilize in order to specify the most important topics of document(s). For example, when summarizing blogs, there are discussions or comments coming after the blog post that are good sources of information to determine which parts of the blog are critical and interesting. In scientific paper summarization, there is a considerable amount of information such as cited papers and conference information which can be leveraged to identify important sentences in the original paper. How text summarization works In general there are two types of summarization, abstractive and extractive summarization. Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. It can be correlated to the way human reads a text article or blog post and then summarizes in their own word. Input document → understand context → semantics → create own summary. 2. Extractive Summarization: Extractive methods attempt to summarize articles by selecting a subset of words that retain the most important points. This approach weights the important part of sentences and uses the same to form the summary. Different algorithm and techniques are used to define weights for the sentences and further rank them based on importance and similarity among each other. Input document → sentences similarity → weight sentences → select sentences with higher rank. The limited study is available for abstractive summarization as it requires a deeper understanding of the text as compared to the extractive approach. Purely extractive summaries often times give better results compared to automatic abstractive summaries. This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which is relatively harder than data-driven approaches such as sentence extraction. There are many techniques available to generate extractive summarization. To keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Since we will be representing our sentences as the bunch of vectors, we can use it to find the similarity among sentences. Its measures cosine of the angle between vectors. Angle will be 0 if sentences are similar. All good till now..? Hope so :) Next, Below is our code flow to generate summarize text:- Input article → split into sentences → remove stop words → build a similarity matrix → generate rank based on matrix → pick top N sentences for summary.
GitHub Repo
https://github.com/sadaf-ali/Geometric-statistics-based-descriptor-for-3D-ear-recognition
sadaf-ali/Geometric-statistics-based-descriptor-for-3D-ear-recognition
Several feature keypoint detection and description techniques have been proposed in the literature for 3D shape recognition. These techniques work well in discriminating different classes of shapes; however, they fail when used for comparing highly similar objects such as 3D ear or face in biometric applications. In this paper, we propose an efficient feature keypoint detection and description technique using geometric statistics for representation and matching of highly similar 3D objects and demonstrate its effectiveness in 3D ear-based biometric recognition. To compute the descriptor, we first extract feature keypoints from the 3D data by making use of surface variations followed by defining a descriptor vector for each keypoint. The descriptor vector is generated using three components. To compute the first component, concentric spheres that divide the space around a keypoint into annular regions are considered. Points falling in the annular regions are projected onto a plane perpendicular to the oriented normal of the keypoint. Lower-order moments of the 2D histogram of the spatial distribution of these projected points for each annular region are computed and used to define the first component of the descriptor vector. Next, component of the descriptor vector is computed using histograms of the inner products of the normals of the keypoint and its neighbours. The third component of the descriptor vector encodes the signed distances of the neighbours of the keypoint from the projection plane. Before concatenating individual components of the descriptor vector, the values are normalized to a common scale. Experiments on University of Notre Dame public database-Collection J2 (UND-J2) have achieved a rank-1 and rank-2 identification rates of 98.60% and 100%, respectively, with an equal error rate of 1.50%. Comparative results show the superiority of the proposed descriptor in recognizing highly similar objects like human ear.
GitHub Repo
https://github.com/Awaisali36/AI-product-data-enrichment-pipeline
Awaisali36/AI-product-data-enrichment-pipeline
Automated AI pipeline that scrapes, enriches and vectorizes 196+ industrial sewing machine products using dual-AI validation (GPT-4o + Gemini), Pinecone RAG storage, and weekly n8n orchestration. 95%+ data accuracy. Zero human intervention.
GitHub Repo
https://github.com/smithmael/AL-Fetwa
smithmael/AL-Fetwa
Al-Fetwa is a mobile-first Islamic jurisprudence decision support application designed to help users ask sensitive Fiqh questions in a structured, trustworthy, and context-aware way. The product is inspired by a Human-in-the-Loop AI model where AI assists with research, synthesis, clarification, and structured comparison across the four Sunni Madha
GitHub Repo
https://github.com/whoisaman752/Review-Detection-System