scholarly journals Machine learning accelerates the technological development of brain science

2021 ◽  
Vol 1948 (1) ◽  
pp. 012039
Author(s):  
Mengya Chen ◽  
Longfei Li
Keyword(s):  
Machine Learning ◽  
Technological Development ◽  
Brain Science

scholarly journals A Machine Learning-Based Study on Brain Science in Aviation

2021 ◽  
Vol 168 ◽  
pp. S197
Author(s):  
Z.R. Tang ◽  
Xu-Yi Qiu ◽  
Xizhong Yang ◽  
Edmond Q. Wu
Keyword(s):  
Machine Learning ◽  
Brain Science

scholarly journals Prediction Model of Football World Cup Championship Based on Machine Learning and Mobile Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanyang Bai ◽  
Xuesheng Zhang
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Clustering Algorithm ◽  
Technological Development ◽  
Rapid Development ◽  
Evidence Theory ◽  
Predictive Analysis ◽  
Learning Technology ◽  
World Cup ◽  
Python Programming

With the technological development and change of the times in the current era, with the rapid development of science and technology and information technology, there is a gradual replacement in the traditional way of cognition. Effective data analysis is of great help to all societies, thereby drive the development of better interests. How to expand the development of the overall information resources in the process of utilization, establish a mathematical analysis–oriented evidence theory system model, improve the effective utilization of the machine, and achieve the goal of comprehensively predicting the target behavior? The main goal of this article is to use machine learning technology; this article defines the main prediction model by python programming language, analyzes and forecasts the data of previous World Cup, and establishes the analysis and prediction model of football field by K-mean and DPC clustering algorithm. Python programming is used to implement the algorithm. The data of the previous World Cup football matches are selected, and the built model is used for the predictive analysis on the Python platform; the calculation method based on the DPC-K-means algorithm is used to determine the accuracy and probability of the variables through the calculation results, which develops results in specific competitions. Research shows how the machine wins and learns the efficiency of the production process, and the machine learning process, the reliability, and accuracy of the prediction results are improved by more than 55%, which proves that mobile algorithm technology has a high level of predictive analysis on the World Cup football stadium.

scholarly journals Detection and Classification of Aircraft Fixation Elements during Manufacturing Processes Using a Convolutional Neural Network

2020 ◽  
Vol 10 (19) ◽  
pp. 6856 ◽  
Author(s):  
Leandro Ruiz ◽  
Manuel Torres ◽  
Alejandro Gómez ◽  
Sebastián Díaz ◽  
José M. González ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Technological Development ◽  
Critical Issue ◽  
Machine Learning Techniques ◽  
Manufacturing Processes ◽  
Advanced Manufacturing ◽  
Vision Systems ◽  
Development Capacity

The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility.

Cultural heritage monument complex monitoring data analyses using machine learning algorithms

2020 ◽  
Author(s):  
David Kvavadze ◽  
Giorgi Basilaia ◽  
Tea Munchava ◽  
Giorgi Laluashvili ◽  
Mikheil Elashvili
Keyword(s):  
Machine Learning ◽  
Cultural Heritage ◽  
Technological Development ◽  
Complex Structure ◽  
Active Tectonics ◽  
Machine Learning Algorithms ◽  
Critical Conditions ◽  
Runoff Water ◽  
Complex Monitoring ◽  
The Impact

<p>Cultural heritage monuments, that were created by mankind for centuries are scattered throughout the world. Most of them are experiencing impacts coming from nature and humans each year that result in damage and changing their common state. Many of the monuments are facing critical conditions and require diagnostics, study and planning and management of conservation/rehabilitation works. Due to the impact of environmental factors such as temperature, humidity, precipitation, the existence of complex structure of cracks, infiltrated water and runoff water streams, together with active tectonics in the region, Uplistsikhe and Vardzia rock-cut city monuments located in Georgia face problems and permanent destruction.</p><p>We have developed continuous monitoring systems that are installed in Vardzia and Uplistsikhe.</p><p>These systems are generating large amounts of data and it is almost impossible to analyze this data using conventional methods. In parallel with technological development, it is now possible to analyze big data using machine learning. We decided to use machine learning to address our problem. This approach gave us some interesting results. We were able to detect correlations between different sensors, see anomalies in data that gave us some clues about hazard zones. Additionally  models and predictions about the monument's condition were made.</p><p>Our work shows that machine learning could be used to estimate conditions make predictions about monuments state.</p><p>This work was supported by Shota Rustaveli National Science Foundation of Georgia (SRNS) [Grant Number fr17_90]</p>

scholarly journals Machine Learning Applied in the Financial Industry

2021 ◽  
Vol 9 (4) ◽  
pp. 239
Author(s):  
Yuntao Sun
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Technological Development ◽  
Financial Industry ◽  
Process Data ◽  
Learning Methods ◽  
Security Issues ◽  
Machine Learning Methods ◽  
Fraudulent Activity ◽  
Financial Losses

<p>Technological development provides industries and spheres with numerous benefits, particularly availability of new progressive methods that contribute to increase efficiency and enhance performance. Thus, machine learning methods may contribute to financial industry that is involved in processing of a large volume of data. Machine learning methods facilitate to process data faster and efficiently with the minimal intervention of humans. In addition, it helps to</p><div>predict possible risks for financial business and minimize risks related to the fraudulent activity or financial losses. Furthermore, application of machine learning methods contributes to enhance the work with clients and targeted groups, as well as provide them with appropriate services. The major risks of machine learning methods applications within the financial sphere relate to unpredictability and cyber security issues.</div>

scholarly journals Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

2021 ◽  
Vol 35 (3) ◽  
pp. 243-253
Author(s):  
Olivier Debauche ◽  
Meryem Elmoulat ◽  
Saïd Mahmoudi ◽  
Jérôme Bindelle ◽  
Frédéric Lebeau
Keyword(s):  
Machine Learning ◽  
Animal Species ◽  
Technological Development ◽  
Welfare Analysis ◽  
Farm Animals ◽  
Learning Approaches ◽  
Current Trends ◽  
Animal Category

Numerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors.

scholarly journals The Teleological Nature of Digital Aesthetics – the New Aesthetic in Advance of Artificial Intelligence

2019 ◽  
pp. 105
Author(s):  
Scott Contreras-Koterbay
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Media Studies ◽  
Technological Development ◽  
Aesthetic Experiences ◽  
Real Possibility ◽  
Digital Aesthetics ◽  
Curatorial Practice ◽  
Digital Objects ◽  
The Aesthetic

If aesthetic and teleological judgments are equally reflective, then it can be argued that such judgments can be applied concurrently to digital objects, specifically those that are products of the rapidly developing sophisticated forms of artificial intelligence (AI). Evidence of the aesthetic effects of technological development are observable in more than just experienceable objects; rooted in inscrutable machine learning, AI’s complexity is a problem when it is presented as an aesthetic authority, particularly when it comes to automated curatorial practice or as a progressively determinative aesthetic force originating in an independent agency that is internally self-consistent.Rooted in theories of the post-digital and the New Aesthetic, this paper examines emerging new forms of art and aesthetic experiences that appear to reveal these capabilities of AI. While the most advanced forms of AI barely qualify for a ‘soft’ description at this point, it appears inevitable that a ‘hard’ form of AI is in the future. Increased forms of technological automation obscure the increasingly real possibility of genuine products of the imagination and the creativity of autonomous digital agencies as independent algorithmic entities, but such obfuscation is likely to fade away under the evolutionary pressures of technological development. It’s impossible to predict the aesthetic products of AI at this stage but, if the development of AI is teleological, then it might be possible to predict some of the foreseeable associated aesthetic problems. Article received: April 10, 2019; Article accepted: July 6, 2019; Published online: October 15, 2019; Original scholarly paperHow to cite this article: Contreras-Koterbay, Scott. "The Teleological Nature of Digital Aesthetics – the New Aesthetic in Advance of Artificial Intelligence." AM Journal of Art and Media Studies 20 (2019): 105-112. doi: 10.25038/am.v0i20.326.

Machine learning techniques applied to industrial engineering: a multi criteria approach

2019 ◽  
Author(s):  
Fabio De Felice ◽  
Marta Travaglioni ◽  
Giuseppina Piscitelli ◽  
Raffaele Cioffi ◽  
Antonella Petrillo
Keyword(s):  
Machine Learning ◽  
Industry 4.0 ◽  
Technological Development ◽  
Predictive Maintenance ◽  
Industrial Engineering ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
The World ◽  
The Subject ◽  
To Receive

With the Industry 4.0 (I4.0) beginning, the world is witnessing an important technological development. The success of I4.0 is linked to the implementation of enabling technologies, including Machine Learning, which focuses on the machines’ ability to receive a series of data and learn on their own. The present research aims to systematically analyze the existing literature on the subject in various aspects, including publication year, authors, scientific sector, country, institution and keywords. Understanding and analyzing the existing literature on Machine Learning applied to predictive maintenance is preparatory to recommend policy on the subject.

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