scholarly journals A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3314
Author(s):  
Zeinab Shahbazi ◽  
Yung-Cheol Byun
Keyword(s):  
Data Analysis ◽  
Large Scale ◽  
Intelligent Transportation System ◽  
Short Term Memory ◽  
Historical Data ◽  
Access Point ◽  
Machine Learning Techniques ◽  
Experimental Result ◽  
Real Time System ◽  
Demand Service

The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers. The majority of research has focused on forecasting the pick-up part of demand service and specifying the interconnection of spatial and temporal correlations. In this study, the main focus is to overcome the access point of non-registered users for having fake transactions using taxi services and predicting taxi demand pick-up and drop-off information. The integration of machine learning techniques and blockchain framework is considered a possible solution for this problem. The blockchain technique was selected as an effective technique for protecting and controlling the real-time system. Historical data analysis was processed by extracting the three higher related sections for the intervening time, namely closeness and trend. Next, the pick-up and drop-off taxi prediction task was processed based on constructing the components of multi-task learning and spatiotemporal feature extraction. The combination of feature embedding performance and Long Short-Term Memory (LSTM) obtain the pick-up and drop-off correlation by fusing the historical data spatiotemporal features. Finally, the taxi demand pick-up and drop-off prediction were processed based on the combination of the external factors. The experimental result is based on a real dataset in Jeju Island, South Korea, to show the proposed system’s efficacy and performance compared with other state-of-art models.

scholarly journals Interoperable and scalable data analysis with microservices: Applications in Metabolomics

2017 ◽  
Author(s):  
Payam Emami Khoonsari ◽  
Pablo Moreno ◽  
Sven Bergmann ◽  
Joachim Burman ◽  
Marco Capuccini ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Large Scale ◽  
Metabolite Identification ◽  
Access Point ◽  
Scientific Discipline ◽  
Resonance Spectroscopy ◽  
Magnetic Resonance Spectroscopy Study ◽  
Analysis Workflow ◽  
Computational Resources

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed in parallel using the Kubernetes container orchestrator. The access point is a virtual research environment which can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and established workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry studies, one nuclear magnetic resonance spectroscopy study and one fluxomics study, showing that the method scales dynamically with increasing availability of computational resources. We achieved a complete integration of the major software suites resulting in the first turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, multivariate statistics, and metabolite identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.

scholarly journals Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Noor Afiza Mat Razali ◽  
Nuraini Shamsaimon ◽  
Khairul Khalil Ishak ◽  
Suzaimah Ramli ◽  
Mohd Fahmi Mohamad Amran ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Intelligent Transportation System ◽  
Short Term Memory ◽  
Smart Cities ◽  
Experimental Studies ◽  
Machine Learning Techniques ◽  
Traffic Flow Prediction ◽  
Flow Prediction

AbstractThe development of the Internet of Things (IoT) has produced new innovative solutions, such as smart cities, which enable humans to have a more efficient, convenient and smarter way of life. The Intelligent Transportation System (ITS) is part of several smart city applications where it enhances the processes of transportation and commutation. ITS aims to solve traffic problems, mainly traffic congestion. In recent years, new models and frameworks for predicting traffic flow have been rapidly developed to enhance the performance of traffic flow prediction, alongside the implementation of Artificial Intelligence (AI) methods such as machine learning (ML). To better understand how ML implementations can enhance traffic flow prediction, it is important to inclusively know the current research that has been conducted. The objective of this paper is to present a comprehensive and systematic review of the literature involving 39 articles published from 2016 onwards and extracted from four main databases: Scopus, ScienceDirect, SpringerLink and Taylor & Francis. The extracted information includes the gaps, approaches, evaluation methods, variables, datasets and results of each reviewed study based on the methodology and algorithms used for the purpose of predicting traffic flow. Based on our findings, the common and frequent machine learning techniques that have been applied for traffic flow prediction are Convolutional Neural Network and Long-Short Term Memory. The performance of their proposed techniques was compared with existing baseline models to determine their effectiveness. This paper is limited to certain literature pertaining to common databases. Through this limitation, the discussion is more focused on (and limited to) the techniques found on the list of reviewed articles. The aim of this paper is to provide a comprehensive understanding of the application of ML and DL techniques for improving traffic flow prediction, contributing to the betterment of ITS in smart cities. For future endeavours, experimental studies that apply the most used techniques in the articles reviewed in this study (such as CNN, LSTM or a combination of both techniques) can be accomplished to enhance traffic flow prediction. The results can be compared with baseline studies to determine the accuracy of these techniques.

El Niño Index forecasting using machine learning techniques 

2021 ◽  
Author(s):  
Wanjiao Song ◽  
Wenfang Lu ◽  
Qing Dong
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Large Scale ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Time Series Dataset ◽  
The Pacific ◽  
Forecast Efficiency ◽  
Multiple Variables ◽  
Lag Times

<p>El Niño is a large-scale ocean-atmospheric coupling phenomenon in the Pacific. The interaction among marine and atmospheric variables over the tropical Pacific modulate the evolution of El Niño. The latest research shows that machine learning and neural network (NN) have appeared as effective tools to achieve meaningful information from multiple marine and atmospheric parameters. In this paper, we aim to predict the El Niño index more accurately and increase the forecast efficiency of El Niño events. Here, we propose an approach combining a neural network technique with long short-term memory (LSTM) neural network to forecast El Niño phenomenon. The attributes of model are resulted from physical explanation which are tested with the experiments and observations. The neural network represents the connection among multiple variables and machine learning creates models to identify the El Niño events. The preliminary experimental results exhibit that training NN-LSTM model on network metrics time series dataset provides great potential for predicting El Niño phenomenon at lag times of up to more than 6 months.  </p>

Predicting Medical Resources Required to be Dispatched After Earthquake and Flood, Using Historical Data and Machine Learning Techniques

2018 ◽  
Vol 8 (2) ◽  
pp. 13-35
Author(s):  
Homer Papadopoulos ◽  
Antonis Korakis
Keyword(s):  
Machine Learning ◽  
Web Services ◽  
Large Scale ◽  
Historical Data ◽  
Machine Learning Techniques ◽  
Medical Resources ◽  
Simulation Tools ◽  
Medical Response ◽  
Emergency Medical ◽  
Learning Techniques

This article presents a method to predict the medical resources required to be dispatched after large-scale disasters to satisfy the demand. The historical data of past incidents (earthquakes, floods) regarding the number of victims requested emergency medical services and hospitalisation, simulation tools, web services and machine learning techniques have been combined. The authors adopted a twofold approach: a) use of web services and simulation tools to predict the potential number of victims and b) use of historical data and self-trained algorithms to “learn” from these data and provide relative predictions. Comparing actual and predicted victims needed hospitalisation showed that the proposed models can predict the medical resources required to be dispatched with acceptable errors. The results are promoting the use of electronic platforms able to coordinate an emergency medical response since these platforms can collect big heterogeneous datasets necessary to optimise the performance of the suggested algorithms.

Predicting Medical Resources Required to be Dispatched After Earthquake and Flood, Using Historical Data and Machine Learning Techniques

2020 ◽  
pp. 38-66
Author(s):  
Homer Papadopoulos ◽  
Antonis Korakis
Keyword(s):  
Machine Learning ◽  
Web Services ◽  
Large Scale ◽  
Historical Data ◽  
Machine Learning Techniques ◽  
Medical Resources ◽  
Simulation Tools ◽  
Medical Response ◽  
Emergency Medical ◽  
Learning Techniques

This article presents a method to predict the medical resources required to be dispatched after large-scale disasters to satisfy the demand. The historical data of past incidents (earthquakes, floods) regarding the number of victims requested emergency medical services and hospitalisation, simulation tools, web services and machine learning techniques have been combined. The authors adopted a twofold approach: a) use of web services and simulation tools to predict the potential number of victims and b) use of historical data and self-trained algorithms to “learn” from these data and provide relative predictions. Comparing actual and predicted victims needed hospitalisation showed that the proposed models can predict the medical resources required to be dispatched with acceptable errors. The results are promoting the use of electronic platforms able to coordinate an emergency medical response since these platforms can collect big heterogeneous datasets necessary to optimise the performance of the suggested algorithms.

Vehicle-Classification Based on Edge Extraction and Background Difference

2011 ◽  
Vol 128-129 ◽  
pp. 1109-1113
Author(s):  
Chan Yang ◽  
Zhong Jian Dai
Keyword(s):  
Real Time ◽  
Intelligent Transportation System ◽  
Weather Conditions ◽  
Extraction Process ◽  
Detection Algorithm ◽  
Vehicle Classification ◽  
Experimental Result ◽  
Support Vector ◽  
Real Time System ◽  
Background Difference

The real-time vehicle classification plays an important role in Intelligent Transportation System (ITS). How to effectively improve the accuracy rate and the speed of the vehicle classification is still a hot research issue, the classification algorithm has to be effective but simple. In this paper, a vehicle detection algorithm based on edge-based background difference and region-based background difference is proposed. This algorithm can extract the moving vehicle completely, eliminate vehicle shadow effectively, and it is still significant despite the variations of illumination and weather conditions. The algorithm is simple with low computation quantity and suitable for real-time system. In the feature extraction process, the feature vector can be obtained in short time. Support vector machine (SVM) is also discussed in the classification process. The experimental result shows that the system can accurately recognize the vehicles.

scholarly journals Machine Learning for Scientific Data Analysis

2022 ◽  
pp. 115-126
Author(s):  
Gabriele Scalia
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Large Scale ◽  
Model Development ◽  
Scientific Data ◽  
Machine Learning Techniques ◽  
Scientific Model ◽  
Large Scale Analysis ◽  
Scientific Data Analysis ◽  
System Properties

AbstractOver the last few years, machine learning has revolutionized countless areas and fields. Nowadays, AI bears promise for analyzing, extracting knowledge, and driving discovery across many scientific domains such as chemistry, biology, and genomics. However, the specific challenges posed by scientific data demand to adapt machine learning techniques to new requirements. We investigate machine learning-driven scientific data analysis, focusing on a set of key requirements. These include the management of uncertainty for complex data and models, the estimation of system properties starting from low-volume and imprecise collected data, the support to scientific model development through large-scale analysis of experimental data, and the machine learning-driven integration of complementary experimental technologies.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Teaching Computer Based Historical Data Analysis

1994 ◽  
Vol 6 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Charles Anderson ◽  
Robert J. Morris
Keyword(s):  
Data Analysis ◽  
Social History ◽  
Historical Data ◽  
Good Practice ◽  
Computer Assisted ◽  
Historical Data Analysis ◽  
University Of Edinburgh ◽  
Computer Based ◽  
The University

A case study ofa third year course in the Department of Economic and Social History in the University of Edinburgh isusedto considerandhighlightaspects of good practice in the teaching of computer-assisted historical data analysis.

MSHE-PWM Control of Modular Multilevel Direct Current Transmission System

2015 ◽  
Vol 9 (1) ◽  
pp. 553-559
Author(s):  
HU Xin-xin ◽  
Chen Chun-lan
Keyword(s):  
Pulse Width Modulation ◽  
Reactive Power ◽  
Balance Control ◽  
Electric Energy ◽  
Access Point ◽  
Experimental Result ◽  
Switching Frequency ◽  
Pwm Control ◽  
Harmonic Elimination

In order to optimize the electric energy quality of HVDC access point, a modular multilevel selective harmonic elimination pulse-width modulation (MSHE-PWM) method is proposed. On the basis of keeping the minimum action frequency of the power device, MSHE-PWM method can meet the requirement for accurately eliminating low-order harmonics in the output PWM waveform. Firstly, establish the basic mathematical model of MMC topology and point out the voltage balance control principle of single modules; then, analyze offline gaining principle and realization way of MSHEPWM switching angle; finally, verify MSHE-PWM control performance on the basis of MMC reactive power compensation experimental prototype. The experimental result shows that the proposed MSHE-PWM method can meet such performance indexes as low switching frequency and no lower-order harmonics, and has verified the feasibility and effectiveness thereof for optimizing the electric energy quality of HVDC access point.

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