scholarly journals MACHINE LEARNING IN THE FIELD OF MANUFACTURING

10.6036/10197 ◽  
2021 ◽  
Vol 96 (6) ◽  
pp. 600-604
Author(s):  
GAIZKA GOMEZ ESCUDERO ◽  
PABLO FERNANDEZ DE LUCIO ◽  
HAIZEA GONZALEZ BARRIO ◽  
AMAIA CALLEJA OCHOA ◽  
IZARO AYESTA REMENTERIA ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Scientific Community ◽  
Manufacturing Sector ◽  
Evolutionary Analysis ◽  
Artificial Intelligence Techniques ◽  
Left Behind ◽  
Current State

Artificial intelligence is already a pulsating reality in society, and all major companies are trying to apply its use to their products. In this context, the manufacturing sector could not be left behind. The industry has more and more alternatives to choose from when it comes to implementing artificial intelligence techniques. In this aspect, Machine Learning can be of great use when it comes to solving the challenges that arise in manufacturing. Therefore, this article presents a brief context of Machine Learning, followed by its current applications in the manufacturing sector together with a historical evolutionary analysis of the publications related to Machine Learning in the manufacturing sector. It ends with some conclusions regarding the current state of Machine Learning in the scientific community and in companies. Keywords: Machine Learning, Review, Manufacturing.

Facial Expression Recognition with Gender Identification

2020 ◽  
pp. 57-63
Author(s):  
admin admin ◽  
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...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Human Face ◽  
Gender Identification ◽  
Artificial Intelligence Techniques ◽  
The Mind

The human facial emotions recognition has attracted interest in the field of Artificial Intelligence. The emotions on a human face depicts what’s going on inside the mind. Facial expression recognition is the part of Facial recognition which is gaining more importance and need for it increases tremendously. Though there are methods to identify expressions using machine learning and Artificial Intelligence techniques, this work attempts to use convolution neural networks to recognize expressions and classify the expressions into 6 emotions categories. Various datasets are investigated and explored for training expression recognition models are explained in this paper and the models which are used in this paper are VGG 19 and RESSNET 18. We included facial emotional recognition with gender identification also. In this project we have used fer2013 and ck+ dataset and ultimately achieved 73% and 94% around accuracies respectively.

Recommender Systems in Digital Libraries Using Artificial Intelligence and Machine Learning

2021 ◽  
pp. 162-178
Author(s):  
Namik Delilovic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Digital Libraries ◽  
Full Text ◽  
Text Search ◽  
Full Text Search ◽  
Artificial Intelligence Techniques ◽  
Search Results ◽  
Advanced Search ◽  
Search Field

Searching for contents in present digital libraries is still very primitive; most websites provide a search field where users can enter information such as book title, author name, or terms they expect to be found in the book. Some platforms provide advanced search options, which allow the users to narrow the search results by specific parameters such as year, author name, publisher, and similar. Currently, when users find a book which might be of interest to them, this search process ends; only a full-text search or references at the end of the book may provide some additional pointers. In this chapter, the author is going to give an example of how a user could permanently get recommendations for additional contents even while reading the article, using present machine learning and artificial intelligence techniques.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2021 ◽  
pp. 358-386
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

APPLICATION OF ROBUST M-ESTIMATIONS FOR MACHINE LEARNING IN VANETs

2020 ◽  
Author(s):  
А.Р. Абделлах ◽  
А. Мутханна ◽  
А.Е. Кучерявый
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Ad Hoc Networks ◽  
Communication Systems ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Telecommunication Networks ◽  
Artificial Intelligence Techniques ◽  
Hoc Networks

Исследования в области сетей и систем связи пятого и последующих поколений требуют применения новых технологических решений. Представлены методы искусственного интеллекта, которые в последнее время все чаще используются при решении разнообразных задач в области сетей и систем связи. Предлагается и исследуется эффективность применения робастных М-оценок для машинного обучения в сетях транспортных средств VANET (Vehicular Ad Hoc Networks). Investigations in the field of telecommunication networks and systems of the fifth and beyond generations require the use of new technological solutions. Artificial intelligence techniques, which have recently been increasingly used in solving various problems in the field of networks and communication systems, are presented. The paper proposes and investigates the effectiveness of applying robust M-estimations for machine learning in vehicular ad hoc networks (VANET).

scholarly journals A Compilation of Methods and Datasets for Group and Crowd Action Recognition

2018 ◽  
pp. 2025-2041
Author(s):  
Luis Felipe Borja ◽  
Jorge Azorin-Lopez ◽  
Marcelo Saval-Calvo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Machine Learning Techniques ◽  
Challenging Problem ◽  
Artificial Intelligence Techniques ◽  
Group Behaviour ◽  
Learning Techniques ◽  
Methods And Techniques ◽  
Different Levels

The human behaviour analysis has been a subject of study in various fields of science (e.g. sociology, psychology, computer science). Specifically, the automated understanding of the behaviour of both individuals and groups remains a very challenging problem from the sensor systems to artificial intelligence techniques. Being aware of the extent of the topic, the objective of this paper is to review the state of the art focusing on machine learning techniques and computer vision as sensor system to the artificial intelligence techniques. Moreover, a lack of review comparing the level of abstraction in terms of activities duration is found in the literature. In this paper, a review of the methods and techniques based on machine learning to classify group behaviour in sequence of images is presented. The review takes into account the different levels of understanding and the number of people in the group.

scholarly journals Survey on computational intelligence based image encryption techniques

2020 ◽  
Vol 19 (3) ◽  
pp. 1428
Author(s):  
Thirumalaimuthu Ramanathan ◽  
Md. Jakir Hossen ◽  
Md. Shohel Sayeed ◽  
Joseph Emerson Raja
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Image Encryption ◽  
Computational Intelligence ◽  
Visual Cryptography ◽  
Machine Learning Algorithms ◽  
Artificial Intelligence Techniques ◽  
Cryptographic Keys

Image encryption is an important area in visual cryptography that helps in protecting images when shared through internet. There is lot of cryptography algorithms applied for many years in encrypting images. In the recent years, artificial intelligence techniques are combined with cryptography algorithms to support image encryption. Some of the benefits that artificial intelligence techniques can provide are prediction of possible attacks on cryptosystem using machine learning algorithms, generation of cryptographic keys using optimization algorithms, etc. Computational intelligence algorithms are popular in enhancing security for image encryption. The main computational intelligence algorithms used in image encryption are neural network, fuzzy logic and genetic algorithm. In this paper, a review is done on computational intelligence-based image encryption methods that have been proposed in the recent years and the comparison is made on those methods based on their performance on image encryption.

scholarly journals A Compilation of Methods and Datasets for Group and Crowd Action Recognition

2017 ◽  
Vol 7 (3) ◽  
pp. 40-53 ◽  
Author(s):  
Luis Felipe Borja ◽  
Jorge Azorin-Lopez ◽  
Marcelo Saval-Calvo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Challenging Problem ◽  
Artificial Intelligence Techniques ◽  
Group Behaviour ◽  
Learning Techniques ◽  
Methods And Techniques ◽  
Different Levels

The human behaviour analysis has been a subject of study in various fields of science (e.g. sociology, psychology, computer science). Specifically, the automated understanding of the behaviour of both individuals and groups remains a very challenging problem from the sensor systems to artificial intelligence techniques. Being aware of the extent of the topic, the objective of this paper is to review the state of the art focusing on machine learning techniques and computer vision as sensor system to the artificial intelligence techniques. Moreover, a lack of review comparing the level of abstraction in terms of activities duration is found in the literature. In this paper, a review of the methods and techniques based on machine learning to classify group behaviour in sequence of images is presented. The review takes into account the different levels of understanding and the number of people in the group.

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