scholarly journals Big Data Analytics and Machine Learning of Harbour Craft Vessels to Achieve Fuel Efficiency: A Review

2021 ◽  
Vol 9 (12) ◽  
pp. 1351
Author(s):  
Zhi Yung Tay ◽  
Januwar Hadi ◽  
Favian Chow ◽  
De Jin Loh ◽  
Dimitrios Konovessis
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Fuel Efficiency ◽  
Big Data Analytics ◽  
Learning Systems ◽  
Learning System ◽  
Supervised Machine Learning ◽  
Maritime Industry ◽  
Unsupervised Machine Learning

The global greenhouse gas emitted from shipping activities is one of the factors contributing to global warming; thus, there is an urgent need to mitigate the adverse effect of climate change. One of the key strategies is to build a vibrant maritime industry with the use of innovation and digital technologies as well as intelligent systems. The digitization of the shipping industry not only provides a competitive edge to the shipping business model but also enhances ship operational and energy efficiency. This review paper focuses on the big data analytics and machine learning applied to harbour craft vessels with the aim to achieve fuel efficiency. The paper reviews the telemetry system requires for the digitalization of harbour craft vessels, its challenges in installation, the vessel monitoring and data transmission system. The commonly used methods for data cleaning are also presented. Last but not least, the paper considers two types of the machine learning systems, i.e., supervised and unsupervised machine learning systems. The multi-linear regression and hidden Markov model for supervised machine learning system and the artificial neural network, grey box model and long short-term memory model for unsupervised machine learning are discussed, and their pros and cons are presented.

Intelligent Big Data Analytics in Health

2022 ◽  
pp. 1663-1702
Author(s):  
Ebru Aydindag Bayrak ◽  
Pinar Kirci
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Early Diagnosis ◽  
Neurological Disorders ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Learning Systems ◽  
Learning System ◽  
Definition Of

Intelligent big data analytics and machine learning systems have been introduced to explain for the early diagnosis of neurological disorders. A number of scholarly researches about intelligent big data analytics in healthcare and machine learning system used in the healthcare system have been mentioned. The authors have explained the definition of big data, big data samples, and big data analytics. But the main goal is helping researchers or specialists in providing opinion about diagnosing or predicting neurological disorders using intelligent big data analytics and machine learning. Therefore, they focused on the healthcare systems using these innovative ways in particular. The information of platform and tools about big data analytics in healthcare is investigated. Numerous academic studies based on the detection of neurological disorders using both machine learning methods and big data analytics have been reviewed.

Intelligent Big Data Analytics in Health

2019 ◽  
pp. 252-291
Author(s):  
Ebru Aydindag Bayrak ◽  
Pinar Kirci
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Early Diagnosis ◽  
Neurological Disorders ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Systems ◽  
Learning Systems ◽  
Learning System ◽  
Definition Of

Intelligent big data analytics and machine learning systems have been introduced to explain for the early diagnosis of neurological disorders. A number of scholarly researches about intelligent big data analytics in healthcare and machine learning system used in the healthcare system have been mentioned. The authors have explained the definition of big data, big data samples, and big data analytics. But the main goal is helping researchers or specialists in providing opinion about diagnosing or predicting neurological disorders using intelligent big data analytics and machine learning. Therefore, they focused on the healthcare systems using these innovative ways in particular. The information of platform and tools about big data analytics in healthcare is investigated. Numerous academic studies based on the detection of neurological disorders using both machine learning methods and big data analytics have been reviewed.

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.

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