scholarly journals Predictive Analysis of Taxi Fare using Machine Learning

2020 ◽  
pp. 373-378
Author(s):  
Pallab Banerjee ◽  
Biresh Kumar ◽  
Amarnath Singh ◽  
Priyeta Ranjan ◽  
Kunal Soni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Comparative Analysis ◽  
Supervised Learning ◽  
Analysis Of Algorithms ◽  
Learning Technologies ◽  
Predictive Analysis ◽  
Prediction Problem ◽  
Prediction Modeling

This research aims to study the predictive analysis, which is a method of analysis in Machine Learning. Many companies like Ola, Uber etc uses Artificial Intelligence and machine learning technologies to find the solution of accurate fare prediction problem. We are proposing this paper after comparative analysis of algorithms like regression and classification, which are useful for prediction modeling to get the most accurate value. This research will be helpful to those, who are involved in fare forecasting. In previous era, the fare was only dependent on distance, but with the enhancement in technologies the cab’s fare is dependent on a lot of factors like time, location, number of passengers, traffic, number of hours, base fare etc. The study is based on Supervised learning whose one application is prediction, in machine learning.

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

scholarly journals Before and beyond trust: reliance in medical AI

2021 ◽  
pp. medethics-2020-107095
Author(s):  
Charalampia (Xaroula) Kerasidou ◽  
Angeliki Kerasidou ◽  
Monika Buscher ◽  
Stephen Wilkinson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
New Technology ◽  
Public Trust ◽  
Learning Technologies ◽  
Data Driven ◽  
Policy Makers ◽  
The Public ◽  
Regulatory Frameworks ◽  
Practice Of Medicine

Artificial intelligence (AI) is changing healthcare and the practice of medicine as data-driven science and machine-learning technologies, in particular, are contributing to a variety of medical and clinical tasks. Such advancements have also raised many questions, especially about public trust. As a response to these concerns there has been a concentrated effort from public bodies, policy-makers and technology companies leading the way in AI to address what is identified as a "public trust deficit". This paper argues that a focus on trust as the basis upon which a relationship between this new technology and the public is built is, at best, ineffective, at worst, inappropriate or even dangerous, as it diverts attention from what is actually needed to actively warrant trust. Instead of agonising about how to facilitate trust, a type of relationship which can leave those trusting vulnerable and exposed, we argue that efforts should be focused on the difficult and dynamic process of ensuring reliance underwritten by strong legal and regulatory frameworks. From there, trust could emerge but not merely as a means to an end. Instead, as something to work in practice towards; that is, the deserved result of an ongoing ethical relationship where there is the appropriate, enforceable and reliable regulatory infrastructure in place for problems, challenges and power asymmetries to be continuously accounted for and appropriately redressed.

Network Security Policy Automation

2022 ◽  
pp. 83-112
Author(s):  
Myo Zarny ◽  
Meng Xu ◽  
Yi Sun
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Network Security ◽  
Security Policy ◽  
Learning Technologies ◽  
Use Cases ◽  
Hybrid Cloud ◽  
Cloud Environments ◽  
Enterprise Security

Network security policy automation enables enterprise security teams to keep pace with increasingly dynamic changes in on-premises and public/hybrid cloud environments. This chapter discusses the most common use cases for policy automation in the enterprise, and new automation methodologies to address them by taking the reader step-by-step through sample use cases. It also looks into how emerging automation solutions are using big data, artificial intelligence, and machine learning technologies to further accelerate network security policy automation and improve application and network security in the process.

scholarly journals Automation of business processes of entrepreneurial activity in the field of real estate based on artificial intelligence technologies

2021 ◽  
Vol 18 (5) ◽  
pp. 150-159
Author(s):  
A. N. Asaul ◽  
◽  
G. F. Shcherbina ◽  
M. A. Asaul ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Business Processes ◽  
Learning Technologies ◽  
Entrepreneurial Activity ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Sector ◽  
Entrepreneurial Activities

The article refines the concept of «business process», the essence of business processes` automation in entrepreneurial activities is considered through the use of artificial intelligence and machine learning technologies for IT integration in the real estate sector. Based on the market analysis, the state of development of artificial intelligence and machine learning in Russia, its significance and prospects for implementation in business activities in the real estate sector are studied.

scholarly journals Data Analytics and Public Safety

2021 ◽  
Vol 3 (3) ◽  
pp. 59-62
Author(s):  
Mark Masongsong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
West Coast ◽  
Data Analytics ◽  
Public Safety ◽  
Learning Technologies ◽  
Key Points

On November 27, 2020, UrbanLogiq CEO Mark Masongsong spoke on the topic of Data Analytics and Public Safety at the 2020 CASIS West Coast Security Conference. The key points of discussion focused on the challenges of artificial intelligence and machine learning technologies and their utility towards public safety. 

scholarly journals A Supervised Learning Algorithm to Forecast Weather Conditions for Playing Cricket

2019 ◽  
Vol 9 (1) ◽  
pp. 1560-1565
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Supervised Learning ◽  
Learning Algorithm ◽  
Weather Conditions ◽  
Redundant Data ◽  
Classification Technique

Now days, Machine learning is considered as the key technique in the field of technologies, such as, Internet of things (IOT), Cloud computing, Big data and Artificial Intelligence etc. As technology enhances, lots of incorrect and redundant data are collected from these fields. To make use of these data for a meaningful purpose, we have to apply mining or classification technique in the real world. In this paper, we have proposed two nobel approaches towards data classification by using supervised learning algorithm

Regulation and ethics in artificial intelligence and machine learning technologies: Where are we now? Who is responsible? Can the information professional play a role?

2020 ◽  
Vol 37 (2) ◽  
pp. 60-68
Author(s):  
Denise Carter
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
White Paper ◽  
Learning Technologies ◽  
Public And Private ◽  
General Data Protection Regulation ◽  
Oecd Principles ◽  
Information Professional ◽  
Uk Government ◽  
The Uk

Artificial intelligence (AI) and machine learning (ML) technologies are rapidly maturing and proliferating through all public and private sectors. The potential for these technologies to do good and to help us in our everyday lives is immense. But there is a risk that unless managed and controlled AI can also cause us harm. Questions about regulation, what form it takes and who is responsible for governance are only just beginning to be answered. In May 2019, 42 countries came together to support a global governance framework for AI. The Organisation for Economic Co-operation and Development (OECD) Principles on Artificial Intelligence (OECD (2019) OECD principles on AI. Available at: https://www.oecd.org/going-digital/ai/principles/ (accessed 2 March 2020)) saw like-minded democracies of the world commit to common AI values of trust and respect. In Europe, the European Commission’s (EC) new president, Ursula von der Leyen has made calls for a General Data Protection Regulation style. As a first step the EC has published a white paper: ‘On Artificial Intelligence – A European Approach to Excellence and Trust’ (European Commission (2020) Report, Europa, February). In February 2020, the UK government has published a report on ‘Artificial Intelligence in the Public Sector’ (The Committee on Standards in Public Life (2020) Artificial intelligence and public standards. Report, UK Government, February). This article discusses some of the potential threats AI may hold if left unregulated. It provides a brief overview of the regulatory activities for AI worldwide, and in more detail the current UK AI regulatory landscape. Finally, the article looks at the role that the information professional might play in AI and ML.

Machine Learning and Data Mining in Bioinformatics

2012 ◽  
pp. 695-703
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

Machine Learning and Data Mining in Bioinformatics

2009 ◽  
pp. 612-621 ◽  
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

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