scholarly journals Study of Intelligent Search Engine of Energy Industry Based on BERT Semantic Model

2021 ◽  
Author(s):  
Jiayang Li ◽  
Hao Li ◽  
Ni Yan ◽  
Ziyun Chen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Text Classification ◽  
Template Matching ◽  
Heterogeneous Data ◽  
Search Method ◽  
Semantic Model ◽  
Extraction Technology ◽  
Intelligent Search ◽  
Intention Recognition

Based on a variety of heterogeneous data of energy enterprises, this paper provides an intelligent search method based on the Bert preprocessing model. This paper provides a method of intention recognition based on the combination of template matching and text classification, which involves machine learning, deep learning and other fields of artificial intelligence. First, the preprocessing model Bert is used to rewrite the natural language into a vector based on syntax. Then, the information extraction technology is used to extract the structured and machine understandable information from the problem to provide parameters for intention processing. The intention recognition technology used in this paper first uses template matching method, and then uses text classification method. Finally, the effects of various methods are compared through experimental examples.

Convergence of AI, ML, and DL for Enabling Smart Intelligence

2021 ◽  
pp. 180-195
Author(s):  
Revathi Rajendran ◽  
Arthi Kalidasan ◽  
Chidhambara Rajan B.
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Smart Grids ◽  
Web Applications ◽  
Heterogeneous Data ◽  
Learning From Experience ◽  
Digital Era ◽  
Huge Data ◽  
Viable Solution

The evolution of digital era and improvements in technology have enabled the growth of a number of devices and web applications leading to the unprecedented generation of huge data on a day-to-day basis from many applications such as industrial automation, social networking cites, healthcare units, smart grids, etc. Artificial intelligence acts as a viable solution for the efficient collection and analyses of the heterogeneous data in large volumes with reduced human effort at low time. Machine learning and deep learning subspaces of artificial intelligence are used for the achievement of smart intelligence in machines to make them intelligent based on learning from experience automatically. Machine learning and deep learning have become two of the most trending, groundbreaking technologies that enable autonomous operations and provide decision making support for data processing systems. The chapter investigates the importance of machine learning and deep learning algorithms in instilling intelligence and providing an overview of machine learning, deep learning platforms.

scholarly journals The Role of Artificial Intelligence in Managing Multimorbidity and Cancer

2021 ◽  
Vol 11 (4) ◽  
pp. 314
Author(s):  
Alfredo Cesario ◽  
Marika D’Oria ◽  
Riccardo Calvani ◽  
Anna Picca ◽  
Antonella Pietragalla ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Personalized Medicine ◽  
Population Level ◽  
Patient Characteristics ◽  
Heterogeneous Data ◽  
Care Needs ◽  
Individual Preferences ◽  
Multimorbid Patients

Traditional healthcare paradigms rely on the disease-centered approach aiming at reducing human nature by discovering specific drivers and biomarkers that cause the advent and progression of diseases. This reductive approach is not always suitable to understand and manage complex conditions, such as multimorbidity and cancer. Multimorbidity requires considering heterogeneous data to tailor preventing and targeting interventions. Personalized Medicine represents an innovative approach to address the care needs of multimorbid patients considering relevant patient characteristics, such as lifestyle and individual preferences, in opposition to the more traditional “one-size-fits-all” strategy focused on interventions designed at the population level. Integration of omic (e.g., genomics) and non-strictly medical (e.g., lifestyle, the exposome) data is necessary to understand patients’ complexity. Artificial Intelligence can help integrate and manage heterogeneous data through advanced machine learning and bioinformatics algorithms to define the best treatment for each patient with multimorbidity and cancer. The experience of an Italian research hospital, leader in the field of oncology, may help to understand the multifaceted issue of managing multimorbidity and cancer in the framework of Personalized Medicine.

The Impact of Deep Learning on the Semantic Machine Learning Representation

2021 ◽  
pp. 22-49
Author(s):  
Abdul Kader Saiod ◽  
Darelle van Greunen
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Heterogeneous Data ◽  
Levels Of Abstraction ◽  
Knowledge Based ◽  
Data Representations ◽  
High Level ◽  
The Impact

Deep learning (DL) is one of the core subsets of the semantic machine learning representations (SMLR) that impact on discovering multiple processing layers of non-linear big data (BD) transformations with high levels of abstraction concepts. The SMLR can unravel the concealed explanation characteristics and modifications of the heterogeneous data sources that are intertwined for further artificial intelligence (AI) implementations. Deep learning impacts high-level abstractions in data by deploying hierarchical architectures. It is practically challenging to model big data representations, which impacts on data and knowledge-based representations. Encouraged by deep learning, the formal knowledge representation has the potential to influence the SMLR process. Deep learning architecture is capable of modelling efficient big data representations for further artificial intelligence and SMLR tasks. This chapter focuses on how deep learning impacts on defining deep transfer learning, category, and works based on the techniques used on semantic machine learning representations.

REALIZATION OF IDEAS OF ARTIFICIAL INTELLIGENCE FOR THE FINDING OF HIERARCHY OF MOTIVES OF TRAINING

2018 ◽  
pp. 12-19
Author(s):  
M. G. Koliada ◽  
T. I. Bugayova
Keyword(s):  
Artificial Intelligence ◽  
Hierarchical System ◽  
Productive Activity ◽  
Large Role ◽  
Educational Work ◽  
Intelligent Search ◽  
Dynamic Mechanisms ◽  
Innovative Method ◽  
Effectiveness Of Training ◽  
Probabilistic Nature

The hierarchy of learning motives plays an extremely important role for a management of productive activity of learners, their activity and purposefulness. In the process of educational work, such a motivational hierarchy is formed, where some motives are dynamic mechanisms of other motives that are very difficult to identify at the intuitive level, especially considering the influence of each of them. Therefore, to determine the most significant hierarchical sequence of motives, an innovative method was proposed which is based on the ideas of artificial intelligence. As an example, the search was implemented based on the so-called algorithm of imitation roasting, which is capable to take into account the probabilistic nature of motivational indicators. The article highlights the main leading educational motives of students, on the basis of which the “mechanism” of finding their optimal hierarchical system is shown, and one that simultaneously takes into account the multifactorial influence of their driving causes, taking into account their interconnection, interaction and dynamism. A step-by-step realization of construction of such a hierarchical system of main educational motives in combination with casual, minor motives which are difficult for expecting or providing in advance is shown. Given their unpredictability and probabilistic nature of occurrence, the proposed system of intelligent search allows you to select exactly those sequences of motives that provide the highest productivity and effectiveness of training. The value of the proposed algorithm of imitation roasting is that the accuracy of the result is sacrificed, but the number of iteration cycles decreases, which plays a large role in processing a significant number of motivational indicators.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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