Sentiment Mining and Analysis over Text Corpora via Complex Deep Learning Naural Architectures

2021 ◽  
Vol 2 (4) ◽  
pp. 448-461
Author(s):  
Teresa Alcamo ◽  
Alfredo Cuzzocrea ◽  
Giovanni Pilato ◽  
Daniele Schicchi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Deep Learning ◽  
Neural Systems ◽  
Italian Language ◽  
Text Corpora ◽  
Sentiment Mining ◽  
Computational Advantage ◽  
Computing Infrastructure

We analyze and compare five deep-learning neural architectures to manage the problem of irony and sarcasm detection for the Italian language. We briefly analyze the model architectures to choose the best compromise between performances and complexity. The obtained results show the effectiveness of such systems to handle the problem by achieving 93\% of F1-Score in the best case. As a case study, we also illustrate a possible embedding of the neural systems in a cloud computing infrastructure to exploit the computational advantage of using such an approach in tackling big data.

scholarly journals Penetration testing on cloud---case study with owncloud

2016 ◽  
Vol 5 (2) ◽  
pp. 87
Author(s):  
Wenjuan Xu ◽  
Brian Groves ◽  
Willson Kwok
Keyword(s):  
Cloud Computing ◽  
Work Process ◽  
Penetration Testing ◽  
Cloud Computing Security ◽  
Security Challenges ◽  
Set Up ◽  
Computing Infrastructure

<p><strong> </strong></p><p>The cloud computing techniques bring different security challenges. In this paper, we set up ownCloud as the example cloud computing infrastructure. Then we present our work process and results of a series of penetration testing performed on the ownCloud.  We also analyse these results and give key recommendations for addressing the identified vulnerabilities.</p><p> </p><p> Keywords: cloud computing, security, penetration tesing, owncloud</p>

Analysis of Genomic Data in a Cloud Computing Environment

Big Data ◽  
2016 ◽  
pp. 1129-1158
Author(s):  
Philip Groth ◽  
Gerhard Reuter ◽  
Sebastian Thieme
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Genomic Data ◽  
Security Requirements ◽  
Use Case ◽  
Shared Resources ◽  
Security Issues ◽  
Business Partners ◽  
Short Time

A new trend for data analysis in the life sciences is Cloud computing, enabling the analysis of large datasets in short time. This chapter introduces Big Data challenges in the genomic era and how Cloud computing can be one feasible approach for solving them. Technical and security issues are discussed and a case study where Clouds are successfully applied to resolve computational bottlenecks in the analysis of genomic data is presented. It is an intentional outcome of this chapter that Cloud computing is not essential for analyzing Big Data. Rather, it is argued that for the optimized utilization of IT, it is required to choose the best architecture for each use case, either by security requirements, financial goals, optimized runtime through parallelization, or the ability for easier collaboration and data sharing with business partners on shared resources.

Analysis of Genomic Data in a Cloud Computing Environment

2015 ◽  
pp. 186-214
Author(s):  
Philip Groth ◽  
Gerhard Reuter ◽  
Sebastian Thieme
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Genomic Data ◽  
Security Requirements ◽  
Use Case ◽  
Shared Resources ◽  
Security Issues ◽  
Business Partners ◽  
Short Time

A new trend for data analysis in the life sciences is Cloud computing, enabling the analysis of large datasets in short time. This chapter introduces Big Data challenges in the genomic era and how Cloud computing can be one feasible approach for solving them. Technical and security issues are discussed and a case study where Clouds are successfully applied to resolve computational bottlenecks in the analysis of genomic data is presented. It is an intentional outcome of this chapter that Cloud computing is not essential for analyzing Big Data. Rather, it is argued that for the optimized utilization of IT, it is required to choose the best architecture for each use case, either by security requirements, financial goals, optimized runtime through parallelization, or the ability for easier collaboration and data sharing with business partners on shared resources.

scholarly journals Using AWS Cloud resources in Operating Systems undergraduate class during the pandemic: a case study

2020 ◽  
Author(s):  
André Gradvohl
Keyword(s):  
Operating System ◽  
Cloud Computing ◽  
Operating Systems ◽  
Remote Education ◽  
Computational Resources ◽  
Computing Infrastructure ◽  
Undergraduate Class ◽  
Cloud Resources

Pandemic brought new forms of remote education into the discussion. However, facilitating students' access to good computing infrastructure is not a widespread task. This paper presents a report on the use of computing resources in the AWS cloud by students in the Operating Systems class during the pandemic, from March to July 2020. The use of these computational resources was essential to consolidate some of the concepts covered in the course and, at the same time, to complement the students' knowledge about cloud computing. The results of this survey were very positive. Students said they learned more about the resources available in the cloud, the potential of cloud computing, and how to use it. Besides, they were able to create their remote infrastructure to carry out the work proposed in the Operating System class.

scholarly journals MAPPING GLACIER CHANGES USING CLUSTERING TECHNIQUES ON CLOUD COMPUTING INFRASTRUCTURE

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 29-34
Author(s):  
V. Ayma ◽  
C. Beltrán ◽  
P. N. Happ ◽  
G. A. O. P. Costa ◽  
R. Q. Feitosa
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Cloud Computing ◽  
Big Data ◽  
Machine Learning Algorithms ◽  
Local Mode ◽  
Ground Truth Data ◽  
Computational Performance ◽  
Thematic Accuracy ◽  
Computing Infrastructure

<p><strong>Abstract.</strong> Climate change and its effects are taking more importance nowadays; and glaciers are one of the most affected ecosystems by that, considering that the energy of Earth’s surface and its temperature may be directly related to glacier temporal changes. Then, the comprehension of glaciers behaviour, by its retreating or melting critical conditions, can be achieved by the analysis of Remote Sensing data, but considering the unprecedented volumes of information currently provided by satellites sensors, we can refer to this analysis as a big data problem. Machine learning techniques have the potential to improve the analysis of this type of data; however, most current machine learning algorithms are unable to properly process such huge volumes of data. In the attempt to overcome the computational limitations related to Remote Sensing Big Data analysis, we implemented the K-Means and Expectation Maximization algorithms, as distributed clustering solutions, exploiting the capabilities of cloud computing infrastructure for processing very large datasets. The solution was developed over the InterCloud Data Mining Package, which is a suite of distributed classification methods, previously employed in hyperspectral image analysis. In this work we extended the functionalities of that package, by making it able to process multispectral images using the aforementioned clustering algorithms. To validate our proposal, we analysed the Ausangate glacier, located on the Andes Mountains, in Peru, by mapping the changes in such environment through a multi-temporal Remote Sensing analysis. Our results and conclusions are focused on the thematic accuracy and the computational performance achieved by our proposed solution. Thematic accuracy was assessed by comparing the automatically detected glacier areas by the clustering approaches against the manually selected ground truth data. We compared the computational load involved in executing the clustering processes sequentially and in a distributed fashion, using a local mode and cluster configuration over a cloud computing infrastructure.</p>

Applications of deep learning in big data analytics for aircraft complex system anomaly detection

2021 ◽  
pp. 1748006X2110019
Author(s):  
Shungang Ning ◽  
Jianzhong Sun ◽  
Cui Liu ◽  
Yang Yi
Keyword(s):  
Time Series ◽  
Big Data ◽  
Deep Learning ◽  
Fault Detection ◽  
Real World ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Commercial Aircraft ◽  
Aircraft System

Big data analytics with deep learning approach have attracted increasing attention in transportation engineering, involving operations, maintenance, and safety. In commercial aviation sectors, operational, and maintenance data produced on modern aircraft is increasing exponentially, and predictive analysis of these data is an exciting and promising field in aviation maintenance, which has a potential to revolutionize aerospace maintenance industry. This study illustrates the state-of-the-art applications of deep learning in big data analytics for predictive maintenance and a real-world case study for commercial aircraft. A Long Short-Term Memory network based Auto-Encoders (LSTM-AE) is proposed for complex aircraft system fault detection and classification, which makes use of the raw time-series data from heterogeneous sensors. The proposed method uses nominal time-series samples corresponding to healthy behavior of the system to learn a reconstruction model based on LSTM-AE framework. Then the system health index (HI) and fault feature vectors are derived from the reconstruction error matrix for fault detection and classification. The proposed method is demonstrated on a real-world data set from a commercial aircraft fleet. The typical PCV faults as well as the 390 F sensor and 450 F sensor faults due to sense line air leakage are successfully detected and distinguished based on the extracted features. The case study results show that the computed HI can effectively characterize the health state of the aircraft system and different fault types can be identified with high confidence, which is helpful for line fault troubleshooting.

scholarly journals Review various technologies used to manage Traffic Management System using IOT

2021 ◽  
Vol 4 (1) ◽  
pp. 235-239
Author(s):  
Prince Goyal ◽  
Shanky Goyal ◽  
Navleen Kaur
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Traffic Control ◽  
Traffic Management ◽  
Big Data Analysis ◽  
Embedded Computing ◽  
Traffic Management System ◽  
Control Management

Internet of things (IoT) is the network of the devices includes the updating in technology, various devices are using sensors, actuators, embedded computing and cloud computing. This type of system leads to smart architecture in the home, cities and smart world. IoT plays an important role in traffic controlling and managing. In this paper, we give an overview of the various methods of traffic control management. With the help of this IOT kit, which includes different sensors to collect the data and process it accordingly with the help of big data analysis and deep learning algorithms, most accurate and efficient results are obtained for traffic management.

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