Test Data-Driven Machine Learning Models for Reliable Quantum Circuit Output

2021 ◽  
Author(s):  
Vedika Saravanan ◽  
Samah Mohamed Saeed
Keyword(s):  
Machine Learning ◽  
Test Data ◽  
Quantum Circuit ◽  
Data Driven ◽  
Learning Models ◽  
Circuit Output ◽  
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.

Data Driven Machine Learning Models for Shale Gas Adsorption Estimation

2020 ◽  
Author(s):  
Lei Wang ◽  
Mingliang Liu ◽  
Arlybek Altazhanov ◽  
Bekassyl Syzdykov ◽  
Jiang Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Shale Gas ◽  
Gas Adsorption ◽  
Data Driven ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals The Future of PHM Could be Tiny under Cloud: Exploring Potential Application Patterns of TinyML in PHM Scenarios

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Xingyu Zhou ◽  
Zhuangwei Kang ◽  
Robert Canady ◽  
Shunxing Bao ◽  
Daniel Allen Balasubramanian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Learning Models ◽  
Level Data ◽  
Data Source ◽  
Single Data ◽  
The Impact ◽  
Machine Learning Models

Deep learning has shown impressive performance acrosshealth management and prognostics applications. Nowadays, an emerging trend of machine learning deployment on resource constraint hardware devices like micro-controllers(MCU) has aroused much attention. Given the distributed andresource constraint nature of many PHM applications, using tiny machine learning models close to data source sensors for on-device inferences would be beneficial to save both time andadditional hardware resources. Even though there has beenpast works that bring TinyML on MCUs for some PHM ap-plications, they are mainly targeting single data source usage without higher-level data incorporation with cloud computing.We study the impact of potential cooperation patterns betweenTinyML on edge and more powerful computation resources oncloud and how this would make an impact on the application patterns in data-driven prognostics. We introduce potential ap-plications where sensor readings are utilized for system health status prediction including status classification and remaining useful life regression. We find that MCUs and cloud com-puting can be adaptive to different kinds of machine learning models and combined in flexible ways for diverse requirement.Our work also shows limitations of current MCU-based deep learning in data-driven prognostics And we hope our work can

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