NUST Institutions Library Catalogue catalog › Details for: SWIMS A Sentiment Analysis Approach with Semi-supervised Feature Weighting and Intelligent Model Selection/

Normal view MARC view ISBD view

SWIMS A Sentiment Analysis Approach with Semi-supervised Feature Weighting and Intelligent Model Selection/ Farhan Hassan

TextPublisher: Rawalpindi; NUST College of EME 2016Description: 105,p; ill; 30,cmSubject(s): PhD-Computer Software EngineeringDDC classification: 200 Online resources: Click here to access online

Tags from this library: No tags from this library for this title. Log in to add tags.

Item type	Current location	Home library	Shelving location	Call number	Status	Date due	Barcode	Item holds
Soft Copy	Central Library (CL)	Central Library (CL)	Thesis	200 (Browse shelf)	Available		CLPT-172
Reference	College of Electrical & Mechanical Engineering (CEME)	College of Electrical & Mechanical Engineering (CEME)	Thesis	200 THE (Browse shelf)	Not for loan		TH-SOF-1296

Total holds: 0

Previous	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	No cover image available	Next
Previous	200 Healthcare Benefit Optimization and Fraud Detection using Adaptive Learning and Data Driven Modeling/	200 A Framework for Mapping Crime Data on Sociological Hypotheses/	200 IntelliHealth An intelligent medical decision support system using a novel multi-layer classifier ensemble framework based on enhanced bagging approach with multi-objective optimized weighted voting scheme/	200 SWIMS A Sentiment Analysis Approach with Semi-supervised Feature Weighting and Intelligent Model Selection/	200 Efficient Multi-Channel Medium Access Control Protocol for SDR-based Tactical Wireless Networks/	200 Machine Learning Using Approximate Computing/	200 Close Loop Decision Aiding Framework for Rehabilitation of Neurological Disorders based on Neurotransmission Cognitive Theory/	Next

There are no comments on this title.

to post a comment.

Click on an image to view it in the image viewer