scholarly journals In-silico Predictive Mutagenicity Model Generation Using Supervised Learning Approaches

2011 ◽  
Author(s):  
Anurag Passi ◽  
◽  
Abhik Seal ◽  
UC Abdul Jaleel
Keyword(s):  
Supervised Learning ◽  
In Silico ◽  
Model Generation ◽  
Learning Approaches

scholarly journals In-silico predictive mutagenicity model generation using supervised learning approaches

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
Abhik Seal ◽  
◽  
Anurag Passi ◽  
UC Abdul Jaleel ◽  
David J Wild
Keyword(s):  
Supervised Learning ◽  
In Silico ◽  
Model Generation ◽  
Learning Approaches

scholarly journals In-silico Predictive Mutagenicity Model Generation Using Supervised Learning Approaches

2011 ◽  
Author(s):  
Abhik Seal ◽  
Anurag Passi ◽  
Abhik Seal ◽  
OSDD Consortium ◽  
UC Abdul Jaleel
Keyword(s):  
Supervised Learning ◽  
In Silico ◽  
Model Generation ◽  
Learning Approaches

Unsupervised and Supervised Learning Approaches Together for Microarray Analysis

2011 ◽  
Vol 106 (1) ◽  
pp. 45-73 ◽  
Author(s):  
Indrajit Saha ◽  
Ujjwal Maulik ◽  
Sanghamitra Bandyopadhyay ◽  
Dariusz Plewczynski
Keyword(s):  
Supervised Learning ◽  
Microarray Analysis ◽  
Learning Approaches

Emergent Languages from Pretrained Embeddings Characterize Latent Concepts in Dynamic Imagery

2020 ◽  
Vol 14 (03) ◽  
pp. 357-373
Author(s):  
James R. Kubricht ◽  
Alberto Santamaria-Pang ◽  
Chinmaya Devaraj ◽  
Aritra Chowdhury ◽  
Peter Tu
Keyword(s):  
Unsupervised Learning ◽  
Supervised Learning ◽  
Semantic Analysis ◽  
Single Agent ◽  
Learning Approach ◽  
Learning Approaches ◽  
Physical Motion ◽  
Semantic Concepts ◽  
Dynamic Imagery ◽  
Motion Types

Recent unsupervised learning approaches have explored the feasibility of semantic analysis and interpretation of imagery using Emergent Language (EL) models. As EL requires some form of numerical embedding as input, it remains unclear which type is required in order for the EL to properly capture key semantic concepts associated with a given domain. In this paper, we compare unsupervised and supervised approaches for generating embeddings across two experiments. In Experiment 1, data are produced using a single-agent simulator. In each episode, a goal-driven agent attempts to accomplish a number of tasks in a synthetic cityscape environment which includes houses, banks, theaters and restaurants. In Experiment 2, a comparatively smaller dataset is produced where one or more objects demonstrate various types of physical motion in a 3D simulator environment. We investigate whether EL models generated from embeddings of raw pixel data produce expressions that capture key latent concepts (i.e. an agent’s motivations or physical motion types) in each environment. Our initial experiments show that the supervised learning approaches yield embeddings and EL descriptions that capture meaningful concepts from raw pixel inputs. Alternatively, embeddings from an unsupervised learning approach result in greater ambiguity with respect to latent concepts.

scholarly journals Supervised Learning-Based Fast, Stealthy, and Active NAT Device Identification Using Port Response Patterns

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1444
Author(s):  
Seungwoon Lee ◽  
Si Jung Kim ◽  
Jungtae Lee ◽  
Byeong-hee Roh
Keyword(s):  
Supervised Learning ◽  
Response Patterns ◽  
Learning Approaches ◽  
Security Issue ◽  
Ip Address ◽  
Device Identification ◽  
Address Translation ◽  
Proposed Model ◽  
Network Operations ◽  
Network Administrators

Although network address translation (NAT) provides various advantages, it may cause potential threats to network operations. For network administrators to operate networks effectively and securely, it may be necessary to verify whether an assigned IP address is using NAT or not. In this paper, we propose a supervised learning-based active NAT device (NATD) identification using port response patterns. The proposed model utilizes the asymmetric port response patterns between NATD and non-NATD. In addition, to reduce the time and to solve the security issue that supervised learning approaches exhibit, we propose a fast and stealthy NATD identification method. The proposed method can perform the identification remotely, unlike conventional methods that should operate in the same network as the targets. The experimental results demonstrate that the proposed method is effective, exhibiting a F1 score of over 90%. With the efficient features of the proposed methods, we recommend some practical use cases that can contribute to managing networks securely and effectively.

Sign in / Sign up

Export Citation Format

Share Document