Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods

2017 ◽  
Vol 27 (1) ◽  
pp. 5-13 ◽  
Author(s):  
Renguang Zuo ◽  
Yihui Xiong
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Geochemical Anomalies

scholarly journals Application of machine learning methods in big data analytics at management of contracts in the construction industry

2018 ◽  
Vol 170 ◽  
pp. 01106 ◽  
Author(s):  
Marina Valpeters ◽  
Ivan Kireev ◽  
Nikolay Ivanov
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Construction Industry ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Prognostic Models ◽  
Problem Analysis ◽  
Learning Methods ◽  
Machine Learning Methods

The number of experts who realize the importance of big data continues to increase in various fields of the economy. Experts begin to use big data more frequently for the solution of their specific objectives. One of the probable big data tasks in the construction industry is the determination of the probability of contract execution at a stage of its establishment. The contract holder cannot guarantee execution of the contract. Therefore it leads to a lot of risks for the customer. This article is devoted to the applicability of machine learning methods to the task of determination of the probability of a successful contract execution. Authors try to reveal the factors influencing the possibility of contract default and then try to define the following corrective actions for a customer. In the problem analysis, authors used the linear and non-linear algorithms, feature extraction, feature transformation and feature selection. The results of investigation include the prognostic models with a predictive force based on the machine learning algorithms such as logistic regression, decision tree, randomize forest. Authors have validated models on available historical data. The developed models have the potential for practical use in the construction organizations while making new contracts.

Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

2019 ◽  
Vol 19 (25) ◽  
pp. 2301-2317 ◽  
Author(s):  
Ruirui Liang ◽  
Jiayang Xie ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Hai Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Big Data ◽  
Human Genome Project ◽  
Genome Project ◽  
Support Vector ◽  
Successful Implementation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

Biases in machine learning models and big data analytics: The international criminal and humanitarian law implications

2020 ◽  
Vol 102 (913) ◽  
pp. 199-234
Author(s):  
Nema Milaninia
Keyword(s):  
Machine Learning ◽  
Human Rights ◽  
Big Data ◽  
Data Analytics ◽  
International Criminal ◽  
Big Data Analytics ◽  
International Criminal Law ◽  
Gender Inequalities ◽  
Humanitarian Law ◽  
Mass Graves

AbstractAdvances in mobile phone technology and social media have created a world where the volume of information generated and shared is outpacing the ability of humans to review and use that data. Machine learning (ML) models and “big data” analytical tools have the power to ease that burden by making sense of this information and providing insights that might not otherwise exist. In the context of international criminal and human rights law, ML is being used for a variety of purposes, including to uncover mass graves in Mexico, find evidence of homes and schools destroyed in Darfur, detect fake videos and doctored evidence, predict the outcomes of judicial hearings at the European Court of Human Rights, and gather evidence of war crimes in Syria. ML models are also increasingly being incorporated by States into weapon systems in order to better enable targeting systems to distinguish between civilians, allied soldiers and enemy combatants or even inform decision-making for military attacks.The same technology, however, also comes with significant risks. ML models and big data analytics are highly susceptible to common human biases. As a result of these biases, ML models have the potential to reinforce and even accelerate existing racial, political or gender inequalities, and can also paint a misleading and distorted picture of the facts on the ground. This article discusses how common human biases can impact ML models and big data analytics, and examines what legal implications these biases can have under international criminal law and international humanitarian law.

Crowdsourcing dermatology: DataDerm, big data analytics, and machine learning technology

2018 ◽  
Vol 78 (3) ◽  
pp. 643-644 ◽  
Author(s):  
Andrew J. Park ◽  
Justin M. Ko ◽  
Robert A. Swerlick
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Learning Technology
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