Predictive Modeling of Insurance Claims Using Machine Learning Approach for Different Types of Motor Vehicles

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.

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Predictive modeling of wildfires: A new dataset and machine learning approach

Fire Safety Journal ◽

10.1016/j.firesaf.2019.01.006 ◽

2019 ◽

Vol 104 ◽

pp. 130-146 ◽

Cited By ~ 17

Author(s):

Younes Oulad Sayad ◽

Hajar Mousannif ◽

Hassan Al Moatassime

Keyword(s):

Machine Learning ◽

Predictive Modeling ◽

Learning Approach ◽

Machine Learning Approach

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HAMLET

Terminology ◽

10.1075/term.20017.rig ◽

2021 ◽

Author(s):

Ayla Rigouts Terryn ◽

Véronique Hoste ◽

Els Lefever

Keyword(s):

Machine Learning ◽

Language Processing ◽

Hybrid Approach ◽

Substantial Effect ◽

Training Data ◽

Supervised Machine Learning ◽

Learning Approach ◽

Term Extraction ◽

Machine Learning Approach ◽

Different Types

Abstract Automatic term extraction (ATE) is an important task within natural language processing, both separately, and as a preprocessing step for other tasks. In recent years, research has moved far beyond the traditional hybrid approach where candidate terms are extracted based on part-of-speech patterns and filtered and sorted with statistical termhood and unithood measures. While there has been an explosion of different types of features and algorithms, including machine learning methodologies, some of the fundamental problems remain unsolved, such as the ambiguous nature of the concept “term”. This has been a hurdle in the creation of data for ATE, meaning that datasets for both training and testing are scarce, and system evaluations are often limited and rarely cover multiple languages and domains. The ACTER Annotated Corpora for Term Extraction Research contain manual term annotations in four domains and three languages and have been used to investigate a supervised machine learning approach for ATE, using a binary random forest classifier with multiple types of features. The resulting system (HAMLET Hybrid Adaptable Machine Learning approach to Extract Terminology) provides detailed insights into its strengths and weaknesses. It highlights a certain unpredictability as an important drawback of machine learning methodologies, but also shows how the system appears to have learnt a robust definition of terms, producing results that are state-of-the-art, and contain few errors that are not (part of) terms in any way. Both the amount and the relevance of the training data have a substantial effect on results, and by varying the training data, it appears to be possible to adapt the system to various desired outputs, e.g., different types of terms. While certain issues remain difficult – such as the extraction of rare terms and multiword terms – this study shows how supervised machine learning is a promising methodology for ATE.

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Advanced Machine Learning Models to Handle Unifying Attacks in Images

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.j9830.0881019 ◽

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.

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A machine learning approach to identifying different types of uncertainty

Economics Letters ◽

10.1016/j.econlet.2018.07.003 ◽

2018 ◽

Vol 171 ◽

pp. 58-62 ◽

Cited By ~ 9

Author(s):

Bennett Saltzman ◽

Julieta Yung

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Machine Learning Approach ◽

Different Types

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Predictive modeling for wine authenticity using a machine learning approach

Artificial Intelligence in Agriculture ◽

10.1016/j.aiia.2021.07.001 ◽

2021 ◽

Author(s):

Nattane Luíza da Costa ◽

Leonardo A. Valentin ◽

Inar Alves Castro ◽

Rommel Melgaço Barbosa

Keyword(s):

Machine Learning ◽

Predictive Modeling ◽

Learning Approach ◽

Machine Learning Approach

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A Machine Learning Approach Predicts Tissue-Specific Drug Adverse Events

10.1101/288332 ◽

2018 ◽

Cited By ~ 4

Author(s):

Neel S. Madhukar ◽

Kaitlyn Gayvert ◽

Coryandar Gilvary ◽

Olivier Elemento

Keyword(s):

Machine Learning ◽

Adverse Events ◽

Drug Development ◽

Training Data ◽

Tissue Type ◽

Learning Approach ◽

Tissue Specific ◽

Machine Learning Approach ◽

Different Types ◽

Development Pipeline

ABSTRACTOne of the main causes for failure in the drug development pipeline or withdrawal post approval is the unexpected occurrence of severe drug adverse events. Even though such events should be detected by in vitro, in vivo, and human trials, they continue to unexpectedly arise at different stages of drug development causing costly clinical trial failures and market withdrawal. Inspired by the “moneyball” approach used in baseball to integrate diverse features to predict player success, we hypothesized that a similar approach could leverage existing adverse event and tissue-specific toxicity data to learn how to predict adverse events. We introduce MAESTER, a data-driven machine learning approach that integrates information on a compound’s structure, targets, and phenotypic effects with tissue-wide genomic profiling and our toxic target database to predict the probability of a compound presenting with different types of tissue-specific adverse events. When tested on 6 different types of adverse events MAESTER maintains a high accuracy, sensitivity, and specificity across both the training data and new test sets. Additionally, MAESTER scores could flag a number of drugs that were approved, but later withdrawn due to unknown adverse events – highlighting its potential to identify events missed by traditional methods. MAESTER can also be used to identify toxic targets for each tissue type. Overall MAESTER provides a broadly applicable framework to identify toxic targets and predict specific adverse events and can accelerate the drug development pipeline and drive the design of new safer compounds.

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