scholarly journals Advanced Machine Learning Models to Handle Unifying Attacks in Images

2019 ◽  
Vol 8 (10) ◽  
pp. 4336-4339
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Digital Systems ◽  
Neural Systems ◽  
Learning Approach ◽  
Digital Environment ◽  
Machine Learning Approach ◽  
Different Types ◽  
Innovative Work ◽  
Machine Learning Models

Critical advancement has been made with profound neural systems as of late. Sharing prepared models of profound neural systems has been a significant in the fast advancement of innovative work of these frameworks. In digital environment, there are different types of applications face security related attack sequences from third parties. Most of the machine learning related approaches was introduced to describe security in wind and vulnerable attack sequences. Digital Watermarking is one of the approach to handle adversary related security approach to handle attacks appeared in digital environment. But it has some limitations to describe efficient security behind the web related applications appeared in real time environment. So that in this paper, we propose and implement advanced machine learning approach i.e Neural Network based Click Prediction (NNBCP) to handle web related attack sequences in real time environment. It uses Integrated CAPTCHA procedure to provide machine learning based captcha generation for user login and registration to handle different types of attacks in digital systems.

A Machine Learning Approach for Predicting Real-time Risk of Intraoperative Hypotension in Traumatic Brain Injury

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Shara I. Feld ◽  
Daniel S. Hippe ◽  
Ljubomir Miljacic ◽  
Nayak L. Polissar ◽  
Shu-Fang Newman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Real Time ◽  
Learning Approach ◽  
Intraoperative Hypotension ◽  
Machine Learning Approach

scholarly journals Efficient machine learning approach for volunteer eye-blink detection in real-time using webcam

2021 ◽  
pp. 116073
Author(s):  
Paulo Augusto de Lima Medeiros ◽  
Gabriel Vinícius Souza da Silva ◽  
Felipe Ricardo dos Santos Fernandes ◽  
Ignacio Sánchez-Gendriz ◽  
Hertz Wilton Castro Lins ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Learning Approach ◽  
Eye Blink ◽  
Machine Learning Approach ◽  
Blink Detection ◽  
Efficient Machine

Telugu News Data Classification Using Machine Learning Approach

2022 ◽  
pp. 181-194
Author(s):  
Bala Krishna Priya G. ◽  
Jabeen Sultana ◽  
Usha Rani M.
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Research Work ◽  
Learning Approach ◽  
Fake News ◽  
Learning Models ◽  
Machine Learning Classifiers ◽  
Proposed Model ◽  
Machine Learning Approach ◽  
Machine Learning Models

Mining Telugu news data and categorizing based on public sentiments is quite important since a lot of fake news emerged with rise of social media. Identifying whether news text is positive, negative, or neutral and later classifying the data in which areas they fall like business, editorial, entertainment, nation, and sports is included throughout this research work. This research work proposes an efficient model by adopting machine learning classifiers to perform classification on Telugu news data. The results obtained by various machine-learning models are compared, and an efficient model is found, and it is observed that the proposed model outperformed with reference to accuracy, precision, recall, and F1-score.

scholarly journals Redewiedergabe – Schritte zur automatischen Erkennung

2019 ◽  
Vol 47 (1) ◽  
pp. 216-248
Author(s):  
Annelen Brunner
Keyword(s):  
Machine Learning ◽  
Automatic Detection ◽  
Learning Approach ◽  
Manual Annotation ◽  
Narrative Texts ◽  
Rule Based ◽  
Machine Learning Approach ◽  
Different Types ◽  
Short Narrative ◽  
Over Time

Abstract This contribution presents a quantitative approach to speech, thought and writing representation (ST&WR) and steps towards its automatic detection. Automatic detection is necessary for studying ST&WR in a large number of texts and thus identifying developments in form and usage over time and in different types of texts. The contribution summarizes results of a pilot study: First, it describes the manual annotation of a corpus of short narrative texts in relation to linguistic descriptions of ST&WR. Then, two different techniques of automatic detection – a rule-based and a machine learning approach – are described and compared. Evaluation of the results shows success with automatic detection, especially for direct and indirect ST&WR.

Machine learning approach for multi-perspective volcanic eruption recognition using thermal infrared images

2020 ◽  
Author(s):  
Claudia Corradino ◽  
Gaetana Ganci ◽  
Giuseppe Bilotta ◽  
Annalisa Cappello ◽  
Ciro Del Negro
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Real Time ◽  
Mount Etna ◽  
Learning Approach ◽  
Bag Of Words ◽  
Infrared Images ◽  
Machine Learning Approach ◽  
Thermal Infrared Images ◽  
Thermal Cameras

<p>Detect, locate and characterize eruptions in real-time is fundamental to monitor volcanic activity. Here we present an automatic system able to discover and identify the main types of eruptive activities by exploiting infrared images acquired by the thermal cameras installed around Mount Etna volcano. The system, which employs the machine learning approach, is based on a decision tree tool and a bag of words-based classifier. The decision tree provides information on the visibility level of the monitored area, while the bag of words-based classifiers detects the onset of the eruptive activity and recognize the eruption type among either explosion and/or lava flow or plume. Thus, applied to each image of all thermal cameras over Etna in real-time, the proposed system provides two outputs, namely the visibility level and the recognized activity status. By merging the outcomes coming from each thermal camera, the monitored phenomena can be fully described from different perspectives getting deeper information in real-time and in an automatic way.   </p>

Sign in / Sign up

Export Citation Format

Share Document