Cloud-Edge Network Data Processing based on User Requirements using Modify MapReduce Algorithm and Machine Learning Techniques

IoT requires data processing, which is provided by the cloud and fog computing. Fog computing shifts centralized data processing from the cloud data center to the edge, thereby supporting faster response due to reduced communication latencies. Its distributed architecture raises security and privacy issues; some are inherited from the cloud, IoT, and network whereas others are unique. Securing fog computing is equally important as securing cloud computing and IoT infrastructure. Security solutions used for cloud computing and IoT are similar but are not directly applicable in fog scenarios. Machine learning techniques are useful in security such as anomaly detection, intrusion detection, etc. So, to provide a systematic study, the chapter will cover fog computing architecture, parallel technologies, security requirements attacks, and security solutions with a special focus on machine learning techniques.

Download Full-text

Qmin: A machine learning-based application for mineral chemistry data processing and analysis

10.21203/rs.3.rs-629516/v1 ◽

2021 ◽

Author(s):

Guilherme Ferreira da Silva ◽

Marcos Vinicius Ferreira ◽

Iago Sousa Lima Costa ◽

Renato Borges Bernardes ◽

Carlos Eduardo Miranda Mota ◽

...

Keyword(s):

Machine Learning ◽

Data Processing ◽

Relevant Information ◽

Mineral Chemistry ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Max Planck ◽

Blind Test ◽

Chemistry Data ◽

Learning Techniques

Abstract Mineral chemistry analysis is a valuable tool in several phases of mineralogy and mineral prospecting studies. This type of analysis can point out relevant information, such as concentration of the chemical element of interest in the analyzed phase and, thus, the predisposition of an area for a given commodity. Due to this, considerable amount of data has been generated, especially with the use of electron probe micro-analyzers (EPMA), either in research for academic purposes or in a typical prospecting campaign in the mineral industry. We have identified an efficiency gap when manually processing and analyzing mineral chemistry data, and thus, we envisage this research niche could benefit from the versatility brought by machine learning algorithms. In this paper, we present Qmin, an application that assists in increasing the efficiency of mineral chemistry data processing and analysis stages through automated routines. Our code benefits from a hierarchical structure of classifiers and regressors trained by a Random Forest algorithm developed on a filtered training database extracted from the GEOROC (Geochemistry of Rocks of the Oceans and Continents) repository, maintained by the Max Planck Institute for Chemistry. To test the robustness of our application, we applied a blind test with more than 11,000 mineral chemistry analyses compiled for diamond prospecting within the scope of the Diamante Brasil Project of the Geological Survey of Brazil. The blind test yielded a balanced classifier accuracy of ca. 99% for the minerals known by Qmin. Therefore, we highlight the potential of machine learning techniques in assisting the processing and analysis of mineral chemistry data.

Download Full-text

Machine Learning Techniques for Speech Recognition using the Magnititudes

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e4945.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 1667-1671

Keyword(s):

Machine Learning ◽

Speech Recognition ◽

Data Processing ◽

Supervised Learning ◽

Current Trend ◽

Large Datasets ◽

Point Of View ◽

Machine Learning Techniques ◽

Supervised And Unsupervised Learning ◽

Learning Techniques

Speech is the most proficient method of correspondence between people groups. Discourse acknowledgment is an interdisciplinary subfield of computational phonetics that creates approaches and advances that empowers the acknowledgment and interpretation of communicated in language into content by PCs. It is otherwise called programmed discourse acknowledgment (ASR), PC discourse acknowledgment or discourse to content (STT). It consolidates information and research in the etymology, software engineering, and electrical building fields. This, being the best methodology of correspondence, could likewise be a helpful interface to speak with machines. Machine learning consists of supervised and unsupervised learning among which supervised learning is used for the speech recognition objectives. Supervised learning is that the data processing task of inferring a perform from labeled coaching information. Speech recognition is the current trend that has gained focus over the decades. Most automation technologies use speech and speech recognition for various perspectives. This paper offers a diagram of major innovative point of view and valuation for the fundamental advancement of speech recognitionand offers review method created in each phase of discourse acknowledgment utilizing supervised learning. The project will use ANN to recognize speeches using magnitudes with large datasets.

Download Full-text

Development of a Server for the Implementation of Data Processing Pipelines and ANN Training

Engineering Proceedings ◽

10.3390/engproc2021007038 ◽

2021 ◽

Vol 7 (1) ◽

pp. 38

Author(s):

Brais Galdo ◽

Daniel Rivero ◽

Enrique Fernandez-Blanco

Keyword(s):

Machine Learning ◽

Cloud Computing ◽

Data Processing ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Enormous Amount

Data processing and the use of machine learning techniques make it possible to solve a wide variety of problems. The great disadvantage of using this type of technology is the enormous amount of computation involved. This is why we have tried to develop an architecture that makes the best possible use of the resources available on each machine. The growth of cloud computing and the rise of virtualization techniques have led to a development that allows these tasks to be carried out in a more optimized way.

Download Full-text

5G Network Data Analysis using Machine Learning Techniques for Development of Smart Applications

Journal of Xidian University ◽

10.37896/jxu15.5/084 ◽

2021 ◽

Vol 15 (5) ◽

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Machine Learning Techniques ◽

Network Data ◽

Learning Techniques ◽

5G Network

Download Full-text

Depression detection from social network data using machine learning techniques

Health Information Science and Systems ◽

10.1007/s13755-018-0046-0 ◽

2018 ◽

Vol 6 (1) ◽

Cited By ~ 19

Author(s):

Md. Rafiqul Islam ◽

Muhammad Ashad Kabir ◽

Ashir Ahmed ◽

Abu Raihan M. Kamal ◽

Hua Wang ◽

...

Keyword(s):

Machine Learning ◽

Social Network ◽

Machine Learning Techniques ◽

Network Data ◽

Social Network Data ◽

Learning Techniques ◽

Depression Detection

Download Full-text

Mining Social Network Data for Predictive Personality Modelling by Employing Machine Learning Techniques

Computational Advancement in Communication Circuits and Systems - Lecture Notes in Electrical Engineering ◽

10.1007/978-981-13-8687-9_11 ◽

2019 ◽

pp. 113-127 ◽

Cited By ~ 1

Author(s):

Arjun Sengupta ◽

Anupam Ghosh

Keyword(s):

Machine Learning ◽

Social Network ◽

Machine Learning Techniques ◽

Network Data ◽

Social Network Data ◽

Learning Techniques

Download Full-text

Machine Learning for Clinical Data Processing

Advances in Digital Crime, Forensics, and Cyber Terrorism - Digital Forensics for the Health Sciences ◽

10.4018/978-1-60960-483-7.ch009 ◽

2011 ◽

pp. 193-215

Author(s):

Guo-Zheng Li

Keyword(s):

Machine Learning ◽

Data Processing ◽

Clinical Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Data Sets ◽

The Novel ◽

Real World Data ◽

Data Set ◽

Learning Techniques

This chapter introduces great challenges and the novel machine learning techniques employed in clinical data processing. It argues that the novel machine learning techniques including support vector machines, ensemble learning, feature selection, feature reuse by using multi-task learning, and multi-label learning provide potentially more substantive solutions for decision support and clinical data analysis. The authors demonstrate the generalization performance of the novel machine learning techniques on real world data sets including one data set of brain glioma, one data set of coronary heart disease in Chinese Medicine and some tumor data sets of microarray. More and more machine learning techniques will be developed to improve analysis precision of clinical data sets.

Download Full-text