scholarly journals Modelling of Traffic Flows and Supply Chains Based on Geospatial Knowledge

2021 ◽  
Vol 2068 (1) ◽  
pp. 012042
Author(s):  
A Kolesnikov ◽  
P Kikin ◽  
E Panidi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supply Chains ◽  
Inventory Management ◽  
Traffic Flows ◽  
Machine Learning Methods ◽  
Customer Interaction ◽  
Long Term Impact ◽  
Artificial Intelligence Systems

Abstract The field of logistics and transport operates with large amounts of data. The transformation of such arrays into knowledge and processing using machine learning methods will help to find additional reserves for optimizing transport and logistics processes and supply chains. This article analyses the possibilities and prospects for the application of machine learning and geospatial knowledge in the field of logistics and transport using specific examples. The long-term impact of geospatial-based artificial intelligence systems on such processes as procurement, delivery, inventory management, maintenance, customer interaction is considered.

A Short Introduction to the Ethics of Artificial Intelligence

2020 ◽  
pp. 1-42
Author(s):  
S. Matthew Liao
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intelligent Systems ◽  
Ethical Issues ◽  
Black Box ◽  
Human Beings ◽  
Key Issues ◽  
Long Term Impact ◽  
Bad Data

This introduction outlines in section I.1 some of the key issues in the study of the ethics of artificial intelligence (AI) and proposes ways to take these discussions further. Section I.2 discusses key concepts in AI, machine learning, and deep learning. Section I.3 considers ethical issues that arise because current machine learning is data hungry; is vulnerable to bad data and bad algorithms; is a black box that has problems with interpretability, explainability, and trust; and lacks a moral sense. Section I.4 discusses ethical issues that arise because current machine learning systems may be working too well and human beings can be vulnerable in the presence of these intelligent systems. Section I.5 examines ethical issues arising out of the long-term impact of superintelligence such as how the values of a superintelligent AI can be aligned with human values. Section I.6 presents an overview of the essays in this volume.

Artificial Intelligence in the Hospitality Sector

2021 ◽  
pp. 257-278
Author(s):  
Debdutta Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Changing Roles ◽  
Hospitality Sector ◽  
Long Term Impact ◽  
The Cost ◽  
And Robotics

Hospitality is one of the most important sectors of the economy and offers employment to thousands of people. The recent advances in technology has seen that quite a few of the players in this industry have successfully deployed artificial intelligence, machine learning, and robotics. This chapter delves into the details of such deployment in the various processes in this sector and discusses the short-term, medium-term, and long-term impact of these technologies on all the major stakeholders of this industry. The author also looks at the cost benefit analysis of this technologies and concludes that most players sooner, rather than later would be forced by competition to strongly adopt them. The chapter also briefly discusses the changing roles of human employees in this scenario.

TRAFFIC FLOWS FORECASTING BASED ON MACHINE LEARNING

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Machine Learning ◽  
Traffic Flows ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Packet Arrival ◽  
Entire Flow ◽  
The Mean ◽  
Active Flow

The article aims to develop a model for forecasting the characteristics of traffic flows in real-time based on the classification of applications using machine learning methods to ensure the quality of service. It is shown that the model can forecast the mean rate and frequency of packet arrival for the entire flow of each class separately. The prediction is based on information about the previous flows of this class and the first 15 packets of the active flow. Thus, the Random Forest Regression method reduces the prediction error by approximately 1.5 times compared to the standard mean estimate for transmitted packets issued at the switch interface.

RESEARCHING METHODS FOR PROCESSING TEXT INFORMATION AND REVIEWING THE STAGES OF AN ARTIFICIAL INTELLIGENCE MODEL CREATION AT PRODUCING CHATBOTS

2021 ◽  
Vol 2021 (2) ◽  
pp. 19-23
Author(s):  
Anastasiya Ivanova ◽  
Aleksandr Kuz'menko ◽  
Rodion Filippov ◽  
Lyudmila Filippova ◽  
Anna Sazonova ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Text Processing ◽  
Text Format ◽  
Machine Learning Methods ◽  
Methods And Techniques ◽  
Text Information ◽  
Processing Techniques ◽  
Machine Processing

The task of producing a chatbot based on a neural network supposes machine processing of the text, which in turn involves using various methods and techniques for analyzing phrases and sentences. The article considers the most popular solutions and models for data analysis in the text format: methods of lemmatization, vectorization, as well as machine learning methods. Particular attention is paid to the text processing techniques, after their analyzing the best method was identified and tested.

An artificial intelligence-enabled industry classification and its interpretation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Daejin Kim ◽  
Hyoung-Goo Kang ◽  
Kyounghun Bae ◽  
Seongmin Jeon
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Classification System ◽  
Standard Industrial Classification ◽  
American Industry ◽  
Content Type ◽  
Global Industry ◽  
Machine Learning Methods ◽  
Industry Classification ◽  
Traditional Industry

PurposeTo overcome the shortcomings of traditional industry classification systems such as the Standard Industrial Classification Standard Industrial Classification, North American Industry Classification System North American Industry Classification System, and Global Industry Classification Standard Global Industry Classification Standard, the authors explore industry classifications using machine learning methods as an application of interpretable artificial intelligence (AI).Design/methodology/approachThe authors propose a text-based industry classification combined with a machine learning technique by extracting distinguishable features from business descriptions in financial reports. The proposed method can reduce the dimensions of word vectors to avoid the curse of dimensionality when measuring the similarities of firms.FindingsUsing the proposed method, the sample firms form clusters of distinctive industries, thus overcoming the limitations of existing classifications. The method also clarifies industry boundaries based on lower-dimensional information. The graphical closeness between industries can reflect the industry-level relationship as well as the closeness between individual firms.Originality/valueThe authors’ work contributes to the industry classification literature by empirically investigating the effectiveness of machine learning methods. The text mining method resolves issues concerning the timeliness of traditional industry classifications by capturing new information in annual reports. In addition, the authors’ approach can solve the computing concerns of high dimensionality.

The Usage Analysis of Machine Learning Methods for Intrusion Detection in Software-Defined Networks

2021 ◽  
pp. 959-975
Author(s):  
Derya Yiltas-Kaplan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Network Architectures ◽  
Software Defined Networks ◽  
Learning Approaches ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Network Problems ◽  
Usage Analysis

This chapter focuses on the process of the machine learning with considering the architecture of software-defined networks (SDNs) and their security mechanisms. In general, machine learning has been studied widely in traditional network problems, but recently there have been a limited number of studies in the literature that connect SDN security and machine learning approaches. The main reason of this situation is that the structure of SDN has emerged newly and become different from the traditional networks. These structural variances are also summarized and compared in this chapter. After the main properties of the network architectures, several intrusion detection studies on SDN are introduced and analyzed according to their advantages and disadvantages. Upon this schedule, this chapter also aims to be the first organized guide that presents the referenced studies on the SDN security and artificial intelligence together.

Long term forecasting using machine learning methods

2018 ◽  
Author(s):  
Hossein Sangrody ◽  
Ning Zhou ◽  
Salih Tutun ◽  
Benyamin Khorramdel ◽  
Mahdi Motalleb ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Long Term Forecasting

scholarly journals Experiencing ProvLake to Manage the Data Lineage of AI Workflows

2020 ◽  
Author(s):  
Leonardo Guerreiro Azevedo ◽  
Renan Souza ◽  
Raphael Melo Thiago ◽  
Elton Soares ◽  
Marcio Moreno
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Management ◽  
Oil And Gas ◽  
Core Concept ◽  
Data Lineage ◽  
Oil And Gas Exploration ◽  
Provenance Data ◽  
Management Techniques ◽  
Artificial Intelligence Systems

Machine Learning (ML) is a core concept behind Artificial Intelligence systems, which work driven by data and generate ML models. These models are used for decision making, and it is crucial to trust their outputs by, e.g., understanding the process that derives them. One way to explain the derivation of ML models is by tracking the whole ML lifecycle, generating its data lineage, which may be accomplished by provenance data management techniques. In this work, we present the use of ProvLake tool for ML provenance data management in the ML lifecycle for Well Top Picking, an essential process in Oil and Gas exploration. We show how ProvLake supported the validation of ML models, the understanding of whether the ML models generalize respecting the domain characteristics, and their derivation.

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