Search results for “Bayesian Networks.”

Published Subscription 29 May 2023

By Sejal Sharma, Sanyam Jain, Shivani Devi, Pulkit Tank, Neha Mahala

Abstract: The proposed project puts forward a systematic implementation of internet of things (IoT). Since, the technology is becoming more advanced day-by-day. Therefore, manual systems are being replaced by the automatic system. Today, the internet has become an integral part of life. The idea that devices can be controlled remotely across the world is made possible by IoT—the interconnection of devices via a global network. This paper deals with the home …

Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 11, Issue 1, 2023 · pp. 24–30 Read article

Published Subscription Review Article 20 Mar 2026

Machine Learning Assisted Optimization of Nanoscale MOSFET Parameters Using TCAD Simulation

By Amit Pandhare, Aditya Kumbhar, Vaibhav Godase

Abstract: This paper presents a machine learning (ML) assisted framework for the multi-objective optimization of nanoscale bulk n-channel metal-oxide-semiconductor field-effect transistors (nMOSFETs) with a 10 nm physical gate length, high-k HfO₂ gate dielectric, and TiN metal gate. Technology computer-aided design (TCAD) simulations employing drift-diffusion transport, Shockley-Read-Hall recombination, Lombardi mobility degradation, and density- gradient quantum correction models are used to generate a parametric dataset of 2,400 device configurations spanning gate length (L₝), …

Published in Journal of Microelectronics and Solid State Devices · Vol. 13, Issue 1, 2026 · pp. 10–19 Read article

Published Subscription 18 Jan 2021

Utilization of Statistical Learning Algorithms for Prediction of Elastic Modulus of Jointed Rock Mass

By Pijush Samui

Abstract: This study uses two statistical learning algorithms for the prediction of elastic modulus (Ej) of jointed rockmass. The first algorithm uses support vector machine (SVM) that is firmly based on the theory of statisticallearning and uses regression technique by introducing -insensitive loss functionhas been adopted. Thesecond algorithm uses relevance vector machine (RVM). It is based on a Bayesian formulation of a linearmodel with an appropriate prior that results in a …

Published in Recent Trends in Civil Engineering & Technology · Vol. 1, Issue 1-3, 2011 · pp. 81–94 Read article