IoT Data Management System for Rapid Development of Machine Learning Models

2019 ◽  
Author(s):  
Keith Grueneberg ◽  
Bongjun Ko ◽  
David Wood ◽  
Xiping Wang ◽  
Dean Steuer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Management ◽  
Management System ◽  
Rapid Development ◽  
Data Management System ◽  
Learning Models ◽  
Machine Learning Models

Design of Hadoop-Based Massive Intelligence Data Management System

2014 ◽  
Vol 608-609 ◽  
pp. 598-602
Author(s):  
Guo Lin Zhao ◽  
Zi Yan Shi ◽  
Qiao Lin Hu
Keyword(s):  
Data Management ◽  
Computer Technology ◽  
Management System ◽  
Rapid Development ◽  
Data Management System ◽  
Massive Data ◽  
Network Resources ◽  
Performance Requirements

With the rapid development of computer technology, especially with the popularity of the network, there are more requirements of the information in the battlefield. The increasing amount of intelligence data should be handled. This paper is discussing how to store and handle the massive intelligence data, which is very massive, complicate, correlative, isomerism, and etc. Storage resources, computing resources, network resources, and so the performance requirements should be improved. This research focuses on the massive data management and the efficiently accessing.

scholarly journals A Comparative Study on the Privacy Risks of Face Recognition Libraries

2021 ◽  
Author(s):  
István Fábián ◽  
Gábor György Gulyás
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Rapid Development ◽  
Face Image ◽  
Human Face ◽  
Learning Models ◽  
Demographic Attributes ◽  
Computational Resources ◽  
Privacy Risks ◽  
Machine Learning Models

The rapid development of machine learning and the decreasing costs of computational resources has led to a widespread usage of face recognition. While this technology offers numerous benefits, it also poses new risks. We consider risks related to the processing of face embeddings, which are floating point vectors representing the human face in an identifying way. Previously, we showed that even simple machine learning models are capable of inferring demographic attributes from embeddings, leading to the possibility of re-identification attacks. This paper examines three popular Python libraries for face recognition, comparing their face detection performance and inspecting how much risk each library's embeddings pose regarding the aforementioned data leakage. Our experiments were conducted on a balanced face image dataset of different sexes and races, allowing us to discover biases in our results.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Risk Management on Information Security in ABC Company

2017 ◽  
Vol 4 (1) ◽  
pp. 62-66
Author(s):  
Luyen Ha Nam
Keyword(s):  
Risk Management ◽  
Information Security ◽  
Data Management ◽  
Management System ◽  
Risk Mitigation ◽  
Data Management System ◽  
Data Centre ◽  
Long Time ◽  
Mitigation Action ◽  
Better Than

From long, long time ago until nowadays information still takes a serious position for all aspect of life, fromindividual to organization. In ABC company information is somewhat very sensitive, very important. But how wekeep our information safe, well we have many ways to do that: in hard drive, removable disc etc. with otherorganizations they even have data centre to save their information. The objective of information security is to keep information safe from unwanted access. We applied Risk Mitigation Action framework on our data management system and after several months we have a result far better than before we use it: information more secure, quickly detect incidents, improve internal and external collaboration etc.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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