Significant of Gradient Boosting Algorithm in Data Management System

2021 ◽  
Vol 9 (2) ◽  
pp. 85-100
Author(s):  
Md Saikat Hosen ◽  
Ruhul Amin
Keyword(s):  
Data Management ◽  
Text Classification ◽  
Learning Process ◽  
Learning Algorithm ◽  
Error Function ◽  
Data Management System ◽  
Gradient Boosting ◽  
Response Parameter ◽  
Boosting Algorithms ◽  
Boosting Algorithm

Gradient boosting machines, the learning process successively fits fresh prototypes to offer a more precise approximation of the response parameter. The principle notion associated with this algorithm is that a fresh base-learner construct to be extremely correlated with the “negative gradient of the loss function” related to the entire ensemble. The loss function's usefulness can be random, nonetheless, for a clearer understanding of this subject, if the “error function is the model squared-error loss”, then the learning process would end up in sequential error-fitting. This study is aimed at delineating the significance of the gradient boosting algorithm in data management systems. The article will dwell much the significance of gradient boosting algorithm in text classification as well as the limitations of this model. The basic methodology as well as the basic-learning algorithm of the gradient boosting algorithms originally formulated by Friedman, is presented in this study. This may serve as an introduction to gradient boosting algorithms. This article has displayed the approach of gradient boosting algorithms. Both the hypothetical system and the plan choices were depicted and outlined. We have examined all the basic stages of planning a specific demonstration for one’s experimental needs. Elucidation issues have been tended to and displayed as a basic portion of the investigation. The capabilities of the gradient boosting algorithms were examined on a set of real-world down-to-earth applications such as text classification.

scholarly journals Construction of cardiac arrhythmia prediction model using deep learning and gradient boosting

2021 ◽  
Vol 13 (3) ◽  
pp. 114-119
Author(s):  
Dhanar Bintang Pratama ◽  
Favian Dewanta ◽  
Syamsul Rizal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Medical Staff ◽  
Heart Attack ◽  
Learning Algorithm ◽  
Gradient Boosting ◽  
Ecg Signal ◽  
Deep Learning Algorithm ◽  
Boosting Algorithm

Arrhythmia is a condition in which the rhythm of heartbeat becomes irregular. This condition in extreme cases can lead to fatal heart attack accidents. In order to reduce heart attack risk, appropriate early treatments should be conducted right after getting results of Arrhythmia condition, which is generated by electrocardiography ECG tools. However, reading ECG results should be done by qualified medical staff in order to diagnose the existence of arrhythmia accurately. This paper proposes a deep learning algorithm method to classify and detect the existence of arrhythmia from ECG reading. Our proposed method relies on Convolutional Neural Network (CNN) to extract feature from a single lead ECG signal and also Gradient Boosting algorithm to predict the final outcome of single lead ECG reading. This method achieved the accuracy of 96.18% and minimized the number of parameters used in CNN Layer.

scholarly journals Robust Intelligent Malware Detection using Light GBM Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 1253-1263
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Gradient Boosting ◽  
Machine Learning Algorithm ◽  
Suggested Approach ◽  
Light Gradient ◽  
Boosting Algorithm ◽  
Iot Devices ◽  
The Internet Of Things

Attackers take advantage of every second that the anti- vendor delays identifying the attacking malware signature and to provide notifications. In addition, the longer the detection period delayed, the greater the damage to the host device. To put it another way, the lack of ability to detect attacks early complicates the problem and rises serious harm. Consequently, this research intends to develop a knowledgeable anti-malware system capable of immediately detecting and terminating malware actions, rather than waiting for anti-malware updates. The research concentrates in its scope on the detection of malware on the Internet of Things (IoT), based on Machine Learning (ML) techniques. A latest open source ML algorithm called the Light Gradient Boosting Algorithm (LightGBM) has been used to develop our instant host and network layer antimalware approach without any human intervention. For examination reasons, the suggested approach serves the LightGBM machine learning algorithm to adopt datasets obtained from real IoT devices using the LightGBM machine learning algorithm. The results indicate a successful method to detecting and classifying high accuracy malware at both network and host levels based on the Holdout method of cross-validation. Additionally, this result is better than many prior related studies which used different algorithms of Machine Learning and Deep Learning. Though, an old study which used the same dataset was the best among the literature. However, it still slightly less than what this study achieved, besides the complexity which deep learning adds. Lastly, the results show the ability of the proposed approach to detect IoT botnet attacks fast, which is a vital feature to end botnet activity before spreading to any new network device.

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.

Benchmarking and Analyzing the Energy Consumption of Cloud Data Management System

2014 ◽  
Vol 36 (7) ◽  
pp. 1485-1499 ◽  
Author(s):  
Jie SONG ◽  
Tian-Tian LI ◽  
Zhi-Liang ZHU ◽  
Yu-Bin BAO ◽  
Ge YU
Keyword(s):  
Energy Consumption ◽  
Data Management ◽  
Management System ◽  
Data Management System ◽  
Cloud Data ◽  
Cloud Data Management

The UNC Graph Server: A Distributed Hypermedia Data Management System

1991 ◽  
Author(s):  
Douglas E. Shackelford ◽  
John B. Smith ◽  
Joan Boone ◽  
Barry Elledge
Keyword(s):  
Data Management ◽  
Management System ◽  
Data Management System

Clinical Trial Data Management Software: A Review of the Technical Features

2019 ◽  
Vol 14 (3) ◽  
pp. 160-172 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Clinical Trial Data ◽  
Trial Data ◽  
Data Management System ◽  
Management Systems ◽  
Data Management Systems ◽  
Technical Features

Background:Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems.Methods:Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study.Results:Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development.Conclusion:It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.

A Review of Clinical Data Management Systems Used in Clinical Trials

2019 ◽  
Vol 14 (1) ◽  
pp. 10-23 ◽  
Author(s):  
Aynaz Nourani ◽  
Haleh Ayatollahi ◽  
Masoud Solaymani Dodaran
Keyword(s):  
Clinical Trials ◽  
Data Management ◽  
Clinical Data ◽  
Data Management System ◽  
Management Systems ◽  
Management Process ◽  
Data Management Systems ◽  
Ovid Medline ◽  
Research Findings ◽  
Accuracy Of Results

Background:A clinical data management system is a software supporting the data management process in clinical trials. In this system, the effective support of clinical data management dimensions leads to the increased accuracy of results and prevention of diversion in clinical trials. The aim of this review article was to investigate the dimensions of data management in clinical data management systems.Methods:This study was conducted in 2017. The used databases included Web of Science, Scopus, Science Direct, ProQuest, Ovid Medline and PubMed. The search was conducted over a period of 10 years from 2007 to 2017. The initial number of studies was 101 reaching 19 in the final stage. The final studies were described and compared in terms of the year, country and dimensions of the clinical data management process in clinical trials.Results:The research findings indicated that none of the systems completely supported the data management dimensions in clinical trials. Although these systems were developed for supporting the clinical data management process, they were similar to electronic data capture systems in many cases. The most significant dimensions of data management in such systems were data collection or entry, report, validation, and security maintenance.Conclusion:Seemingly, not sufficient attention has been paid to automate all dimensions of the clinical data management process in clinical trials. However, these systems could take positive steps towards changing the manual processes of clinical data management to electronic processes.

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