Implementation of Data-Driven Machine Learning Models for Design and Performance Optimization of Evacuated U-Tube Solar Collector

2021 ◽  
Author(s):  
B. Kiran Naik ◽  
Astarag Mohapatra ◽  
P. K. S. Tejes ◽  
Chatur Gembali
Keyword(s):  
Machine Learning ◽  
Performance Optimization ◽  
Solar Collector ◽  
Data Driven ◽  
Learning Models ◽  
And Performance ◽  
Machine Learning Models

A Survey on Data-driven Network Intrusion Detection

2022 ◽  
Vol 54 (9) ◽  
pp. 1-36
Author(s):  
Dylan Chou ◽  
Meng Jiang
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Real World ◽  
Data Driven ◽  
Network Intrusion Detection ◽  
Large Network ◽  
Learning Models ◽  
Simulated Environments ◽  
Network Intrusion ◽  
Machine Learning Models

Data-driven network intrusion detection (NID) has a tendency towards minority attack classes compared to normal traffic. Many datasets are collected in simulated environments rather than real-world networks. These challenges undermine the performance of intrusion detection machine learning models by fitting machine learning models to unrepresentative “sandbox” datasets. This survey presents a taxonomy with eight main challenges and explores common datasets from 1999 to 2020. Trends are analyzed on the challenges in the past decade and future directions are proposed on expanding NID into cloud-based environments, devising scalable models for large network data, and creating labeled datasets collected in real-world networks.

scholarly journals Modeling and Optimization of Anaerobic Digestion: A Review

2021 ◽  
Vol 229 ◽  
pp. 01022
Author(s):  
Fatima Walid ◽  
Sanaa El Fkihi ◽  
Houda Benbrahim ◽  
Hicham Tagemouati
Keyword(s):  
Mathematical Modeling ◽  
Machine Learning ◽  
Anaerobic Digestion ◽  
Biogas Production ◽  
Data Driven ◽  
Management Technique ◽  
Learning Models ◽  
Process Dynamics ◽  
Metaheuristic Techniques ◽  
Machine Learning Models

Anaerobic digestion is recognized as being an advantageous waste management technique representing a source of clean and renewable energy. However, biogas production through such practice is complex and it relies on the interaction of several factors including changes in operating and monitoring parameters. Enormous researchers have focused and gave their full attention to mathematical modeling of anaerobic digestion to get good insights about process dynamics, aiming to optimize its efficiency. This paper gives an overview of the different approaches applied to tackle this challenge including mechanistic and data-driven models. This review has led us to conclude that neural networks combined with metaheuristic techniques has the potential to outperform mechanistic and classical machine learning models.

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