Intelligent Systems in Big Data, Semantic Web and Machine Learning

2021 ◽  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Semantic Web ◽  
Intelligent Systems

scholarly journals A Web-Based Framework for Classification and Integration of News Articles Big Data using Ontology

2019 ◽  
Vol 8 (3) ◽  
pp. 3257-3263
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Semantic Web ◽  
Social Networking ◽  
Social Networking Sites ◽  
Climate Information ◽  
Huge Amount ◽  
Web Based ◽  
Different Sources

Around 2.5 quintillion bytes of data have been created online: out of which most of the data has been generated in the last two years. To generate this huge amount of data from different sources, many devices are being utilized such as sensors to get the data about climate information, social networking sites, banking records, e-commerce records, etc. This data is known as Big Data. It mainly consists of three 3v’s volumes, velocity, and variety. Variety of data discusses about different formats of data originating from various data foundations. Hence, the big data variety’s issue is significant in explaining some genuine challenges. The semantic Web is utilized as an Integrator to join information from different sorts of data foundations like web services, social databases, and spreadsheets and so on and in various formats. The semantic Web is an all-encompassing type of the present web that gives simpler methods to look, reuse, join and offer the data. In this manner, it is along these lines seen as a combiner transversely over different things, information applications, and systems. This paper is an effort to uncover the nature of big data and a brief survey on the use of various semantic web-based methods and tools to add value to today’s big data. In addition, it discusses a case study on performing various machine learning functionalities on news articles and proposes a web-based framework for classification and integration of news articles big data using ontologies.

scholarly journals DIGITALIZATION TRENDS OF AGRICULTURAL ENTERPRISES IN UKRAINE

2021 ◽  
Vol 139 (5) ◽  
pp. 72-85
Author(s):  
TARASYUK Anton ◽  
GAMALIY Volodymyr
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Information And Communication Technologies ◽  
Intelligent Systems ◽  
Information Technologies ◽  
System Analysis ◽  
Agricultural Sector ◽  
Agricultural Enterprises ◽  
Current State ◽  
And Control

Background. Agriculture is a leader in the export of our country,butthere is no comprehensive systemic approach in Ukraine and in the world to the development of enterprises in this industry based on the use of information technology in terms of the concept of the Fourth Industrial Revolution. An analysis of recent research and publicationshas shown that there are some scientific achievements, but an important scientific and practical problem of a comprehensive strategy for digitalization of agricultural enterprises remains unresolved. The aim of the article is to study the current state of implementation of information technologies in Ukrainian agricultural enterprises, to identify unresolved problems of agricultural enterprises digitalization. Materials and methods. Methods of system analysis and synthesis, marketing researches, statistical and comparison were used in the paper. Results. Scientific hypotheses have been put forward regarding the need to create and implement a comprehensive concept of digitalization of agriculture – "Smart agricultural", which is a set of software and hardware that provides automated collection and transmission for processing all necessary data for management decisions in the agricultural sector. Based on the results of this study, the theoretical foundations for the development and application of intelligent systems in the agricultural sector and the use of automated workplaces in control systems have been developed. The main groups of hardware and software used for industry automation are considered. At detailed consideration of application of the specified technological directions, there are non-systematic application, absence of software for systematic fixing and control of parameters for the further analysis. Conclusion. The results of the development ways analysis of the "Agriculture 4.0" ("SmartFarm") concept for its application at the Ukrainian agricultural enterprises allowed to allocate four technological directions: aerospace technologies; Internet of Things (IoT); information and communication technologies; Big Data and Machine Learning. The main achievements in each technological direction, available developments and ways of their application are considered. We found out that technological and technical means are used to ensure the quality development of the agricultural sector, but most technologies are used for operational processes and control of the enterprise current state. The study demonstrates that the big data technology and machine learning, which are the most important for the creation of automated jobs are not developed completely. Keywords: management system, intelligent systems, machine learning, digitalization of agricultural sector.

scholarly journals Advanced Techniques in the Analysis and Prediction of Students’ Behaviour in Technology-Enhanced Learning Contexts

2020 ◽  
Vol 10 (18) ◽  
pp. 6178
Author(s):  
Juan A. Gómez-Pulido ◽  
Young Park ◽  
Ricardo Soto
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Intelligent Systems ◽  
Technology Enhanced Learning ◽  
Learning Tools ◽  
Special Issue ◽  
Learning Context ◽  
Learning Collaborative ◽  
Academic Field ◽  
Enhanced Learning

The development and promotion of teaching-enhanced learning tools in the academic field is leading to the collection of a large amount of data generated from the usual activity of students and teachers. The analysis of these data is an opportunity to improve many aspects of the learning process: recommendations of activities, dropout prediction, performance and knowledge analysis, resources optimization, etc. However, these improvements would not be possible without the application of computer science techniques that have demonstrated a high effectiveness for this purpose: data mining, big data, machine learning, deep learning, collaborative filtering, and recommender systems, among other fields related to intelligent systems. This Special Issue provides 17 papers that show advances in the analysis, prediction, and recommendation of applications propelled by artificial intelligence, big data, and machine learning in the teaching-enhanced learning context.

The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning

2020 ◽  
Author(s):  
Turan G. Bali ◽  
Amit Goyal ◽  
Dashan Huang ◽  
Fuwei Jiang ◽  
Quan Wen
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Corporate Bonds ◽  
Cross Sectional ◽  
The Cross

Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components

2019 ◽  
Vol 19 (25) ◽  
pp. 2301-2317 ◽  
Author(s):  
Ruirui Liang ◽  
Jiayang Xie ◽  
Chi Zhang ◽  
Mengying Zhang ◽  
Hai Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Growth Rate ◽  
Big Data ◽  
Human Genome Project ◽  
Genome Project ◽  
Support Vector ◽  
Successful Implementation ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Vector Machines

In recent years, the successful implementation of human genome project has made people realize that genetic, environmental and lifestyle factors should be combined together to study cancer due to the complexity and various forms of the disease. The increasing availability and growth rate of ‘big data’ derived from various omics, opens a new window for study and therapy of cancer. In this paper, we will introduce the application of machine learning methods in handling cancer big data including the use of artificial neural networks, support vector machines, ensemble learning and naïve Bayes classifiers.

Sign in / Sign up

Export Citation Format

Share Document