scholarly journals Probabilistic Design of Retaining Wall Using Machine Learning Methods

2021 ◽  
Vol 11 (12) ◽  
pp. 5411
Author(s):  
Pratishtha Mishra ◽  
Pijush Samui ◽  
Elham Mahmoudi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reliability Index ◽  
Retaining Wall ◽  
Probabilistic Approach ◽  
Economical Design ◽  
Extensive Field ◽  
First Order Second Moment ◽  
Adaptive Regression ◽  
Almost All

Retaining walls are geostructures providing permanent lateral support to vertical slopes of soil, and it is essential to analyze the failure probability of such a structure. To keep the importance of geotechnics on par with the advancement in technology, the implementation of artificial intelligence techniques is done for the reliability analysis of the structure. Designing the structure based on the probability of failure leads to an economical design. Machine learning models used for predicting the factor of safety of the wall are Emotional Neural Network, Multivariate Adaptive Regression Spline, and SOS–LSSVM. The First-Order Second Moment Method is used for calculating the reliability index of the wall. In addition, these models are assessed based on the results they produce, and the best model among these is concluded for extensive field study in the future. The overall performance evaluation through various accuracy quantification determined SOS–LSSVM as the best model. The obtained results show that the reliability index calculated by the AI methods differs from the reference values by less than 2%. These methodologies have made the problems facile by increasing the precision of the result. Artificial intelligence has removed the cumbersome calculations in almost all the acquainted fields and disciplines. The techniques used in this study are evolved versions of some older algorithms. This work aims to clarify the probabilistic approach toward designing the structures, using the artificial intelligence to simplify the practical evaluations.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

FinTech Adoption in China

2021 ◽  
pp. 166-173
Author(s):  
Gagan Kukreja
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Financial Services ◽  
New Technologies ◽  
Large Population ◽  
Financial Inclusion ◽  
High Penetration ◽  
Digital Payments ◽  
Almost All

Almost all financial services (especially digital payments) in China are affected by new innovations and technologies. New technologies such as blockchain, artificial intelligence, machine learning, deep learning, and data analytics have immensely influenced all most all aspects of financial services such as deposits, transactions, billings, remittances, credits (B2B and P2P), underwriting, insurance, and so on. Fintech companies are enabling larger financial inclusion, changing in lifestyle and expenditure behavior, better and fast financial services, and lots more. This chapter covers the development, opportunities, and challenges of financial sectors because of new technologies in China. This chapter throws the light on opportunities that emerged because of the large population of 1.4 billion people, high penetration, and access to the latest and affordable technology, affordable cost of smartphones, and government policies and regulations. Lastly, this chapter portrays the untapped potentials of Fintech in China.

scholarly journals Thematic Dynamics of Internet of Things (IoT): Impact on Digital Personalized Healthcare (PHC)

2021 ◽  
Author(s):  
Bongs Lainjo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Successful Implementation ◽  
Daily Lives ◽  
Personalized Healthcare ◽  
Iot Platform ◽  
Iot Platforms ◽  
Almost All

Abstract Background: Information technology has continued to shape contemporary thematic trends. Advances in communication have impacted almost all themes ranging from education, engineering, healthcare, and many other aspects of our daily lives. Method: This paper attempts to review the different dynamics of the thematic IoT platforms. A select number of themes are extensively analyzed with emphasis on data mining (DM), personalized healthcare (PHC), and thematic trends of a select number of subjectively identified IoT-related publications over three years. In this paper, the number of IoT-related-publications is used as a proxy representing the number of apps. DM remains the trailblazer, serving as a theme with crosscutting qualities that drive artificial intelligence (AI), machine learning (ML), and data transformation. A case study in PHC illustrates the importance, complexity, productivity optimization, and nuances contributing to a successful IoT platform. Among the initial 99 IoT themes, 18 are extensively analyzed using the number of IoT publications to demonstrate a combination of different thematic dynamics, including subtleties that influence escalating IoT publication themes. Results: Based on findings amongst the 99 themes, the annual median IoT-related publications for all the themes over the four years were increasingly 5510, 8930, 11700, and 14800 for 2016, 2017, 2018, and 2019 respectively; indicating an upbeat prognosis of IoT dynamics. Conclusion: The vulnerabilities that come with the successful implementation of IoT systems are highlighted including the successes currently achieved by institutions promoting the benefits of IoT-related systems like the case study. Security continues to be an issue of significant importance.

scholarly journals OPPORTUNITIES FOR MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE IN NATIONAL MAPPING AGENCIES: ENHANCING ORDNANCE SURVEY WORKFLOW

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 185-189
Author(s):  
J. Murray ◽  
I. Sargent ◽  
D. Holland ◽  
A. Gardiner ◽  
K. Dionysopoulou ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Feature Extraction ◽  
Remote Sensing Data ◽  
Field Work ◽  
Remotely Sensed ◽  
Network Architectures ◽  
Remotely Sensed Data ◽  
Ordnance Survey ◽  
Extensive Field

Abstract. National Mapping agencies (NMA) are frequently tasked with providing highly accurate geospatial data for a range of customers. Traditionally, this challenge has been met by combining the collection of remote sensing data with extensive field work, and the manual interpretation and processing of the combined data. Consequently, this task is a significant logistical undertaking which benefits the production of high quality output, but which is extremely expensive to deliver. Therefore, novel approaches that can automate feature extraction and classification from remotely sensed data, are of great potential interest to NMAs across the entire sector. Using research undertaken at Great Britain’s NMA; Ordnance Survey (OS) as an example, this paper provides an overview of the recent advances at an NMA in the use of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) based applications. Examples of these approaches are in automating the process of feature extraction and classification from remotely sensed aerial imagery. In addition, recent OS research in applying deep (convolutional) neural network architectures to image classification are also described. This overview is intended to be useful to other NMAs who may be considering the adoption of similar approaches within their workflows.

scholarly journals Artificial Intelligence & Machine Learning: a model for a new judicial system?

2020 ◽  
pp. 81-91
Author(s):  
Paula C. Arias
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
System Integration ◽  
Judicial System ◽  
Modern Life ◽  
Technological Advances ◽  
The Creation ◽  
Almost All

Artificial Intelligence and Machine Learning are a result not only of technological advances but also of the exploitation of information or data, which has led to its expansion into almost all aspects of modern life, including law and its practice. Due to the benefits of these technologies, such as efficiency, objectivity, and transparency, the trend is towards the integration of Artificial Intelligence and Machine Learning in the judicial system. Integration that is advocated at all levels and, today, has been achieved mostly under the implementation of tools to assist the exercise of the judiciary. The "success" of this integration has led to the creation of an automated court or an artificially intelligent judge as a futuristic proposal.

Artificial Intelligence in Pharmaceutical Field – A Critical Review

2021 ◽  
Vol 18 ◽  
Author(s):  
Maithri Shanbhogue H ◽  
Shailesh Thirumaleshwar ◽  
Pramod Kumar TM ◽  
Hemanth Kumar S
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Development ◽  
Successful Implementation ◽  
Thought Process ◽  
Time Consumption ◽  
Research Technology ◽  
Human Thought ◽  
Almost All ◽  
Analyze Data

: Artificial intelligence is an emerging sector in almost all fields. It is not confined only to a particular category and can be used in various fields like research, technology, and health. AI mainly concentrates on how computers analyze data and mimic the human thought process. As drug development involves high R & D costs and uncertainty in time consumption, artificial intelligence can serve as one of the promising solutions to overcome all these demerits. Due to the availability of enormous data, there are chances of missing out on some crucial details. For solving these issues, algorithms like machine learning, deep learning, and other expert systems are being used. On successful implementation of AI in the pharmaceutical field, the delays in drug development, and failure at the clinical and marketing level can be reduced. This review comprises information regarding the development of AI, its subfields, its overall implementation, and its application in the pharmaceutical sector and provides insights on challenges and limitations concerning AI.

Probabilistic design of slopes

1987 ◽  
Vol 24 (4) ◽  
pp. 520-535 ◽  
Author(s):  
K. S. Li ◽  
P. Lumb
Keyword(s):  
Reliability Index ◽  
Slip Surface ◽  
Probabilistic Approach ◽  
Probabilistic Design ◽  
Performance Function ◽  
Stability Model ◽  
First Order Second Moment ◽  
The Stability ◽  
Derivatives Of ◽  
Slope Design

This paper discusses some improvements on the first-order second-moment (FOSM) probabilistic approach to slope design. The stability model by Morgenstern and Price is used for the formulation of the performance function, thus enabling the FOSM method to be applied to the probabilistic assessment of a general slip surface. A new solution scheme is also used herein for Morgenstern and Price's method. It does not require iterations for the calculation of the interslice forces and the derivatives of the performance function can be evaluated analytically. The reliability index βHL defined by Hasofer and Lind is used as an index of safety measure. It has the advantage of being "invariant," that is, its value does not depend on the format of the performance function, a property considered lacking in the conventional reliability index. Reference is also made to the probabilistic modelling of soil profiles. The importance of the correlation structure of soil properties is highlighted and its effect on the reliability index βHL is discussed. Key words: slope stability, safety factors, reliability index, probability of failure, general slip surface, rigorous stability model.

scholarly journals ARTificial intelligence raters: Neural networks for rating pictorial expression

2021 ◽  
Author(s):  
Thomas Gengenbach ◽  
Kerstin Schoch
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mean Squared Error ◽  
Quality Criterion ◽  
Learning Approaches ◽  
Human Expert ◽  
Machine Learning Approach ◽  
Professional Artists ◽  
Almost All

Previous studies on classification of fine art show that features of paintings can be captured and categorized using machine learning approaches. This progress can also benefit art psychology by facilitating data collection on artworks without the need to recruit experts as raters. In this study a machine learning approach is used to predict the ratings of RizbA, a Rating instrument for two-dimensional pictorial works. Based on a pre-trained model, the algorithm was fine-tuned via transfer learning on 886 pictorial works by contemporary professional artists and non-professionals. As quality criterion, artificial intelligence raters (ART) are compared with generic raters (GR) created from the real human expert raters, using error rate and mean squared error (MSE). ART ratings have been found to have the same error range as randomly chosen human ratings. Therefore, they can be seen as equivalent to real human expert raters for almost all items in RizbA. Further training with more data will close the gap to the human raters on all items.

scholarly journals Thematic Dynamics of Internet of Things (IoT): Impact on Digital Personalized Healthcare (PHC)

2021 ◽  
Vol 6 (3) ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Successful Implementation ◽  
Daily Lives ◽  
Personalized Healthcare ◽  
Iot Platform ◽  
Iot Platforms ◽  
Almost All

Information technology has continued to shape contemporary thematic trends. Advances in communication have impacted almost all themes ranging from education, engineering, healthcare, and many other aspects of our daily lives. This paper attempts to review the different dynamics of the thematic IoT platforms. A select number of themes are extensively analyzed with emphasis on data mining (DM), personalized healthcare (PHC), and thematic trends of a select number of subjectively identified IoT-related publications over three years. In this paper, the number of IoT-related-publications is used as a proxy representing the number of apps. DM remains the trailblazer, serving as a theme with crosscutting qualities that drive artificial intelligence (AI), machine learning (ML), and data transformation. A case study in PHC illustrates the importance, complexity, productivity optimization, and nuances contributing to a successful IoT platform. Among the initial 99 IoT themes, 18 are extensively analyzed using the number of IoT publications to demonstrate a combination of different thematic dynamics, including subtleties that influence escalating IoT publication themes. Based on findings amongst the 99 themes, the annual median IoT-related publications for all the themes over the four years were increasingly 5510, 8930, 11700, and 14800 for 2016, 2017, 2018, and 2019 respectively; indicating an upbeat prognosis of IoT dynamics. And finally, the vulnerabilities that come with the successful implementation of IoT systems are highlighted as part of the research. Security continues to be an issue of significant importance.

scholarly journals Machine Learning Techniques of Contact Aware Communication System: A Comprehensive Overview

2021 ◽  
Author(s):  
Mohsin Shahzad ◽  
Kashif Hussain ◽  
Muhammad Ali Qureshi ◽  
Fareeha Zahoor
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Communication System ◽  
Machine Learning Techniques ◽  
Attractive Alternative ◽  
Comprehensive Overview ◽  
Intelligent Behavior ◽  
Potential Applications ◽  
Almost All ◽  
Universal Communication

Machine Learning (ML) and Artificial Intelligence(AI) have revolutionized almost all fields that are linked to the acquisition of intelligent behavior in the real world. It is an attractive alternative for a researcher of artificial intelligence. Contrary to rule-based programming, ML is an algorithmic approach in which learning comes from existing data. The more data we have these computer systems look at, we say we’re ‘training’ the computer system, and as the computers begin to identify patterns in the data, identify abnormalities in the data from these abnormalities we improve the system architect according to the requirement. This article introduces the use of comprehensive concepts of machine learning, in general, particular, and their potential applications in communications. Furthermore, the current state and futuristic potentials of enabling universal communication with implications of machine learning methods have been explained. In this review paper, we offer a comprehensive talk on distinctive methods/techniques of information analytics, artificial intelligence (AI), and machine learning (ML) moved forward the contact aware communication system.

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