Gunwant Mankar

ABSTRACT: Project Under Development…. PROJECT OUTPUT VIDEO: SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS: System : Pentium i3 Processor. Hard Disk : 500 GB. Monitor : 15“ LED Input Devices : Keyboard, Mouse. Ram : 4 GB SOFTWARE REQUIREMENTS: Operating system : Windows 10/11. Coding Language : JAVA Frontend : JSP, HTML, CSS, JavaScript. IDE Tool : Apache…

ABSTRACT: Unsupervised Cross-domain Sentiment Classification is the task of adapting a sentiment classifier trained on a particular domain (source domain), to a different domain (target domain), without requiring any labeled data for the target domain. By adapting an existing sentiment classifier to previously unseen target domains, we can avoid the cost for manual data annotation…

ABSTRACT: Online delivery systems have seen an unprecedented evolution since the COVID-19 Pandemic outbreak and the ensuing lockdowns. Despite the fact that when we think of food delivery, technology is not the first thing that comes to mind, it is becoming an increasingly important aspect of the online food delivery sector. Numerous opportunities are emerging…

ABSTRACT: In this project, we develop Online Book Buying and Selling Portalin Java. This project is like an e- bookstore website where books can be bought from the comfort of home through the Internet. A user can buy the books or can upload their books to resell them on the online. An online bookstore is…

ABSTRACT: Mining opinion targets and opinion words from online reviews are important tasks for fine-grained opinion mining, the key component of which involves detecting opinion relations among words. To this end, this paper proposes a novel approach based on the partially-supervised alignment model, which regards identifying opinion relations as an alignment process. Then, agraph-based co-ranking…

ABSTRACT: The Web creates excellent opportunities for businesses to provide personalized online services to their customers. Recommender systems aim to automatically generate personalized suggestions of products/services to customers (businessesor individuals). Although recommend-er systems have been well studied, there are still two challenges in the development of a recommend-er system, particularly in real-world B2B e-services:1) items…

ABSTRACT: We propose TrustSVD, a trust-based matrix factorization technique for recommendations. Trust SVD integrates multiple information sources into the recommendation model in order to reduce the data sparsity and cold start problems and their degradation of recommendation performance. An analysis of social trust data from four real-world data sets suggests that not only the explicit…