scholarly journals Network effects govern the evolution of maritime trade

2020 ◽  
Vol 117 (23) ◽  
pp. 12719-12728 ◽  
Author(s):  
Zuzanna Kosowska-Stamirowska
Keyword(s):  
Global Economy ◽  
Socioeconomic Development ◽  
Network Effects ◽  
Trade Flows ◽  
Machine Learning Techniques ◽  
Maritime Trade ◽  
Trade Volume ◽  
Learning Techniques ◽  
The World ◽  
Underlying Network

Maritime transport accounts for over 80% of the world trade volume and is the backbone of the global economy. Global supply chains create a complex network of trade flows. The structure of this network impacts not only the socioeconomic development of the concerned regions but also their ecosystems. The movements of ships are a considerable source of CO2emissions and contribute to climate change. In the wake of the announced development of Arctic shipping, the need to understand the behavior of the maritime trade network and to predict future trade flows becomes pressing. We use a unique database of daily movements of the world fleet over the period 1977–2008 and apply machine learning techniques on network data to develop models for predicting the opening of new shipping lines and for forecasting trade volume on links. We find that the evolution of this system is governed by a simple rule from network science, relying on the number of common neighbors between pairs of ports. This finding is consistent over all three decades of temporal data. We further confirm it with a natural experiment, involving traffic redirection from the port of Kobe after the 1995 earthquake. Our forecasting method enables researchers and industry to easily model effects of potential future scenarios at the level of ports, regions, and the world. Our results also indicate that maritime trade flows follow a form of random walk on the underlying network structure of sea connections, highlighting its pivotal role in the development of maritime trade.

scholarly journals Comparison of Machine Learning algorithm for COVID-19 Death Risk Prediction

2021 ◽  
Author(s):  
Praveeen Anandhanathan ◽  
Priyanka Gopalan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Decision Tree Algorithm ◽  
The Past ◽  
Random Forest Method ◽  
Learning Techniques ◽  
The World

Abstract Coronavirus disease (COVID-19) is spreading across the world. Since at first it has appeared in Wuhan, China in December 2019, it has become a serious issue across the globe. There are no accurate resources to predict and find the disease. So, by knowing the past patients’ records, it could guide the clinicians to fight against the pandemic. Therefore, for the prediction of healthiness from symptoms Machine learning techniques can be implemented. From this we are going to analyse only the symptoms which occurs in every patient. These predictions can help clinicians in the easier manner to cure the patients. Already for prediction of many of the diseases, techniques like SVM (Support vector Machine), Fuzzy k-Means Clustering, Decision Tree algorithm, Random Forest Method, ANN (Artificial Neural Network), KNN (k-Nearest Neighbour), Naïve Bayes, Linear Regression model are used. As we haven’t faced this disease before, we can’t say which technique will give the maximum accuracy. So, we are going to provide an efficient result by comparing all the such algorithms in RStudio.

scholarly journals Sentiment Analysis in Social Media using Machine Learning Techniques

2020 ◽  
pp. 193-201 ◽  
Author(s):  
Hayder A. Alatabi ◽  
Ayad R. Abbas
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Sentiment Analysis ◽  
Machine Learning Techniques ◽  
Great Success ◽  
Social Media Data ◽  
Learning Techniques ◽  
The World ◽  
Analysis System ◽  
Media Data

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

Machine Learning Techniques and Risk Management

2021 ◽  
pp. 185-200
Author(s):  
Christos Floros ◽  
Panagiotis Ballas
Keyword(s):  
Machine Learning ◽  
Financial Institutions ◽  
Business Practice ◽  
Machine Learning Techniques ◽  
Accounting Disclosure ◽  
Future Performance ◽  
Learning Techniques ◽  
The World ◽  
Unstable Environment

Crises around the world reveal a generally unstable environment in the last decades within which banks and financial institutions operate. Risk is an inherent characteristic of financial institutions and is a multifaceted phenomenon. Everyday business practice involves decisions, which requires the use of information regarding various types of threats involved together with an evaluation of their impact on future performance, concluding to combinations of types of risks and projected returns for decision makers to choose from. Moreover, financial institutions process a massive amount of data, collected either internally or externally, in an effort to continuously analyse trends of the economy they operate in and decode global economic conditions. Even though research has been performed in the field of accounting and finance, the authors explore the application of machine learning techniques to facilitate decision making by top management of contemporary financial institutions improving the quality of their accounting disclosure.

Educational Software Based on Matlab GUIs for Neural Networks Courses

2016 ◽  
pp. 333-358 ◽  
Author(s):  
Pablo Díaz-Moreno ◽  
Juan José Carrasco ◽  
Emilio Soria-Olivas ◽  
José M. Martínez-Martínez ◽  
Pablo Escandell-Montero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Educational Software ◽  
Machine Learning Techniques ◽  
Teaching Tool ◽  
Neural Models ◽  
Learning Techniques ◽  
The World ◽  
Programming Skills

Neural Networks (NN) are one of the most used machine learning techniques in different areas of knowledge. This has led to the emergence of a large number of courses of Neural Networks around the world and in areas where the users of this technique do not have a lot of programming skills. Current software that implements these elements, such as Matlab®, has a number of important limitations in teaching field. In some cases, the implementation of a MLP requires a thorough knowledge of the software and of the instructions that train and validate these systems. In other cases, the architecture of the model is fixed and they do not allow an automatic sweep of the parameters that determine the architecture of the network. This chapter presents a teaching tool for the its use in courses about neural models that solves some of the above-mentioned limitations. This tool is based on Matlab® software.

scholarly journals Machine learning versus logistic regression methods for 2-year mortality prognostication in a small, heterogeneous glioma database

2018 ◽  
Author(s):  
Sandip S Panesar ◽  
Rhett N D’Souza ◽  
Fang-Cheng Yeh ◽  
Juan C Fernandez-Miranda
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Techniques ◽  
World Health ◽  
Support Vector ◽  
Molecular Characteristics ◽  
Regression Methods ◽  
Learning Techniques ◽  
The World ◽  
Health Organization

AbstractBackgroundMachine learning (ML) is the application of specialized algorithms to datasets for trend delineation, categorization or prediction. ML techniques have been traditionally applied to large, highly-dimensional databases. Gliomas are a heterogeneous group of primary brain tumors, traditionally graded using histopathological features. Recently the World Health Organization proposed a novel grading system for gliomas incorporating molecular characteristics. We aimed to study whether ML could achieve accurate prognostication of 2-year mortality in a small, highly-dimensional database of glioma patients.MethodsWe applied three machine learning techniques: artificial neural networks (ANN), decision trees (DT), support vector machine (SVM), and classical logistic regression (LR) to a dataset consisting of 76 glioma patients of all grades. We compared the effect of applying the algorithms to the raw database, versus a database where only statistically significant features were included into the algorithmic inputs (feature selection).ResultsRaw input consisted of 21 variables, and achieved performance of (accuracy/AUC): 70.7%/0.70 for ANN, 68%/0.72 for SVM, 66.7%/0.64 for LR and 65%/0.70 for DT. Feature selected input consisted of 14 variables and achieved performance of 73.4%/0.75 for ANN, 73.3%/0.74 for SVM, 69.3%/0.73 for LR and 65.2%/0.63 for DT.ConclusionsWe demonstrate that these techniques can also be applied to small, yet highly-dimensional datasets. Our ML techniques achieved reasonable performance compared to similar studies in the literature. Though local databases may be small versus larger cancer repositories, we demonstrate that ML techniques can still be applied to their analysis, though traditional statistical methods are of similar benefit.

scholarly journals Assessment of Machine Learning Techniques in IoT-Based Architecture for the Monitoring and Prediction of COVID-19

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1834
Author(s):  
Abdullah Aljumah
Keyword(s):  
Neural Network ◽  
Real World ◽  
Short Term Memory ◽  
Herd Immunity ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Sensitive Information ◽  
K Nearest Neighbor ◽  
Learning Techniques ◽  
The World

From the end of 2019, the world has been facing the threat of COVID-19. It is predicted that, before herd immunity is achieved globally via vaccination, people around the world will have to tackle the COVID-19 pandemic using precautionary steps. This paper suggests a COVID-19 identification and control system that operates in real-time. The proposed system utilizes the Internet of Things (IoT) platform to capture users’ time-sensitive symptom information to detect potential cases of coronaviruses early on, to track the clinical measures adopted by survivors, and to gather and examine appropriate data to verify the existence of the virus. There are five key components in the framework: symptom data collection and uploading (via communication technology), a quarantine/isolation center, an information processing core (using artificial intelligent techniques), cloud computing, and visualization to healthcare doctors. This research utilizes eight machine/deep learning techniques—Neural Network, Decision Table, Support Vector Machine (SVM), Naive Bayes, OneR, K-Nearest Neighbor (K-NN), Dense Neural Network (DNN), and the Long Short-Term Memory technique—to detect coronavirus cases from time-sensitive information. A simulation was performed to verify the eight algorithms, after selecting the relevant symptoms, on real-world COVID-19 data values. The results showed that five of these eight algorithms obtained an accuracy of over 90%. Conclusively, it is shown that real-world symptomatic information would enable these three algorithms to identify potential COVID-19 cases effectively with enhanced accuracy. Additionally, the framework presents responses to treatment for COVID-19 patients.

scholarly journals Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yar Muhammad ◽  
Mohammad Dahman Alshehri ◽  
Wael Mohammed Alenazy ◽  
Truong Vinh Hoang ◽  
Ryan Alturki
Keyword(s):  
Machine Learning ◽  
Medical Image Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
X Ray ◽  
Expert Radiologist ◽  
Learning Techniques ◽  
Effective Manner ◽  
The World ◽  
Chest X Ray

Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease.

scholarly journals Automated Detection of COVID-19 using Deep Learning

2021 ◽  
Vol 10 (2) ◽  
pp. , 824-830
Keyword(s):  
Public Health ◽  
Deep Learning ◽  
Global Economy ◽  
Imaging Techniques ◽  
Automated Detection ◽  
Diagnostic Tools ◽  
Radiological Imaging ◽  
Daily Lives ◽  
Learning Techniques ◽  
The World

Corona virus 2019 (COVID-2019), has first appeared in Wuhan, China in December 2019, spread around the world rapidly causing thousands of fatalities. It is caused a devastating result in our daily lives, public health, and also the global economy. It is important to sight the positive cases as early as possible therefore forestall any unfoldment of this epidemic and to quickly treat affected patients. The necessity for auxiliary diagnostic tools has increased as there aren't any accurate automated toolkits available. Recent findings obtained using radiology imaging techniques suggest that such images contain salient information about the COVID-19 virus. Coupling deep learning techniques with radiological imaging may end up within the accurate detection of this disease. This assistance will help to beat the matter of an absence of specialized physicians in the remote villages.

scholarly journals Prediction of Bike Sharing Demand

2017 ◽  
Vol 10 (1) ◽  
pp. 219-226 ◽  
Author(s):  
Purnima Sachdeva ◽  
K N Sarvanan
Keyword(s):  
New York ◽  
Linear Combination ◽  
Random Forests ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Machine Learning Techniques ◽  
Health Consciousness ◽  
Learning Techniques ◽  
The World ◽  
Bike Sharing

Bike sharing systems have been gaining prominence all over the world with more than 500 successful systems being deployed in major cities like New York, Washington, London. With an increasing awareness of the harms of fossil based mean of transportation, problems of traffic congestion in cities and increasing health consciousness in urban areas, citizens are adopting bike sharing systems with zest. Even developing countries like India are adopting the trend with a bike sharing system in the pipeline for Karnataka. This paper tackles the problem of predicting the number of bikes which will be rented at any given hour in a given city, henceforth referred to as the problem of ‘Bike Sharing Demand’. In this vein, this paper investigates the efficacy of standard machine learning techniques namely SVM, Regression, Random Forests, Boosting by implementing and analyzing their performance with respect to each other.This paper also presents two novel methods, Linear Combination and Discriminating Linear Combination, for the ‘Bike Sharing Demand’ problem which supersede the aforementioned techniques as good estimates in the real world.

scholarly journals Detection of Fake Doctored Images used to Circulate Fake News

2020 ◽  
Vol 9 (7) ◽  
pp. 1031-1036
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Neural Network Models ◽  
The Public ◽  
Learning Techniques ◽  
The World ◽  
Classification Of Images

Over the few years the world has seen a surge in fake news and some people are even calling it an epidemic. Misleading false articles are sold as news items over social media, whatsapp etc where no proper barrier is set to check the authenticity of posts. And not only articles but news items also contain images which are doctored to mislead the public or cause sabotage. Hence a proper barrier to check for authenticity of images related to news items is absolutely necessary. And hence classification of images(related to news items) on the basis of authenticity is imminent. This paper discusses the possibilities of identifying fake images using machine learning techniques. This is an introduction into fake news detection using the latest evolving neural network models

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