Results 271 to 280 of about 464,627 (312)

Graph Theory for Dimensionality Reduction: A Case Study to Prognosticate Parkinson's

IEEE Annual Information Technology, Electronics and Mobile Communication Conference, 2020
In the present world, the commotion centering Big Data is somewhat obscuring the craft of mining information from smaller samples. Populations with limited examples but huge dimensionality are a common phenomenon, otherwise known as the curse of ...
Shithi Maitra, Tonmoy Hossain, Khan Md Hasib, Fairuz Shadmani Shishir   +3 more
semanticscholar   +1 more source

Application of the graph theory and matrix methods to contractor ranking

International Journal of Project Management, 2009
Abstract The most important element in construction procurement is the contractor selection, which can result from contractor’s ranking. Contractor prequalification is essential in most construction projects, and the process has been performed by many different methods in practice.
Maryam Darvish, Mehrdad Yasaei, Azita Saeedi   +2 more
openaire   +1 more source

Reducing unnecessary handovers and improving ranking abnormality based on multi‐attribute decision making graph theory and matrix approach with Euclidean distance in heterogeneous wireless networks

Concurrency and Computation: Practice and Experience, 2023
SummaryNew mobile devices offer multiple network interfaces to allow the users to connect to the best available network. The heterogeneous networks can provide better internet connectivity to the users by means of vertical handover. The handover must be triggered at a suitable point of time to avoid mobility issues such as unnecessary handovers and ...
Gaganpreet Kaur, Raman Kumar Goyal, Rajesh Mehta   +2 more
openaire   +1 more source

Graph-based rank aggregation: a deep-learning approach

International Journal of Web Information Systems
Purpose This study aims to introduce a novel rank aggregation algorithm that leverages graph theory and deep-learning to improve the accuracy and relevance of aggregated rankings in metasearch scenarios, particularly when faced with inconsistent and low-
A. Keyhanipour
semanticscholar   +1 more source

LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models

arXiv.org
The evolving capabilities of large language models are accompanied by growing sizes and deployment costs, necessitating effective inference optimisation techniques.
David Hoffmann, Kailash Budhathoki, Matthaeus Kleindessner   +2 more
semanticscholar   +1 more source

Graph theory application and web page ranking for website link structure improvement

Behaviour & Information Technology, 2009
Since the web is always developing, and users' needs are constantly changing, organisations in recent decades have increasingly focused on developing information and communication technologies (ICTs). To introduce new e-services to their customers, they have largely invested in web development and promotional activities.
Babak Abedin, Babak Sohrabi
openaire   +1 more source

Fast DCT+: A Family of Fast Transforms Based on Rank-One Updates of the Path Graph

IEEE International Conference on Acoustics, Speech, and Signal Processing
This paper develops fast graph Fourier transform (GFT) algorithms with O(nlogn) runtime complexity for rank-one updates of the path graph. We first show that several commonly-used audio and video coding transforms belong to this class of GFTs, which we ...
Samuel Fern'andez-Menduina, Eduardo Pavez, Antonio Ortega   +2 more
semanticscholar   +1 more source

Column and row subset selection using nuclear scores: algorithms and theory for Nyström approximation, CUR decomposition, and graph Laplacian reduction

arXiv.org
Column selection is an essential tool for structure-preserving low-rank approximation, with wide-ranging applications across many fields, such as data science, machine learning, and theoretical chemistry.
M. Fornace, Michael Lindsey
semanticscholar   +1 more source

Applying Genetic Algorithms to Validate a Conjecture in Graph Theory: The Minimum Dominating Set Problem

Mexican International Conference on Artificial Intelligence, 2023
J. Cervantes-Ojeda, María del Carmen Gómez Fuentes, Julián Fresán-Figueroa   +2 more
semanticscholar   +1 more source

A novel graph-based multiple kernel learning framework for hyperspectral image classification

International Journal of Remote Sensing
Multiple kernel learning (MKL) is an efficient way to improve hyperspectral image classification with few training samples by integrating spectral and spatial features.
Shirin Hassanzadeh, H. Danyali, A. Karami, M. Helfroush   +3 more
semanticscholar   +1 more source