scholarly journals Using Artificial Intelligence and Machine Learning to Create a Travel Planning System Based on Users' Preferences and Behaviours

2021 ◽  
Author(s):  
Trang Bui
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Large Datasets ◽  
Planning System ◽  
The Internet ◽  
Travel Planning ◽  
Travel And Tourism ◽  
Mode Of Transportation

Travel and tourism have become a worldwide trend, and the market for this industry continues to grow extensively. Although most people enjoy traveling, planning trips can take hours, weeks or even months to ensure the best place, the best itinerary, and the best price is found. With Artificial Intelligence (AI) and Machine Learning (ML), large datasets can be analyzed and an AI-infused travel system can be utilized to generate highly personalized suggestions. The main purpose of the project is to create a travel planning application to provide an efficient and highly personalized experience for users. The app will help users plan their trips, choose activities, restaurants, mode of transportation and destinations that fit their preferences, budgets, and schedules in minutes without having to spend hours researching on the Internet or downloading multiple travel apps.

scholarly journals Using Artificial Intelligence and Machine Learning to Create a Travel Planning System Based on Users' Preferences and Behaviours

2021 ◽  
Author(s):  
Trang Bui
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Large Datasets ◽  
Planning System ◽  
The Internet ◽  
Travel Planning ◽  
Travel And Tourism ◽  
Mode Of Transportation

Travel and tourism have become a worldwide trend, and the market for this industry continues to grow extensively. Although most people enjoy traveling, planning trips can take hours, weeks or even months to ensure the best place, the best itinerary, and the best price is found. With Artificial Intelligence (AI) and Machine Learning (ML), large datasets can be analyzed and an AI-infused travel system can be utilized to generate highly personalized suggestions. The main purpose of the project is to create a travel planning application to provide an efficient and highly personalized experience for users. The app will help users plan their trips, choose activities, restaurants, mode of transportation and destinations that fit their preferences, budgets, and schedules in minutes without having to spend hours researching on the Internet or downloading multiple travel apps.

scholarly journals IMPROVING INTERNET OF THINGS PARKING SYSTEMS

2021 ◽  
Vol 19 (3) ◽  
pp. 163
Author(s):  
Dušan Bogićević
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Information Needs ◽  
Vehicle Classification ◽  
The Internet ◽  
Parking Space ◽  
Automated Systems ◽  
The Internet Of Things

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

scholarly journals AIoT Application for Food Recommendation based on Emotion Analysis

2021 ◽  
pp. 44-53
Author(s):  
Shivangi Ruhela ◽  
Pragati Chaudhary ◽  
Rishija Shrivas ◽  
Deepti Chopra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Data Exchange ◽  
The Internet ◽  
Human Intervention ◽  
User Interactions ◽  
Emotion Analysis ◽  
Efficiency Performance ◽  
Analyze Data

Artificial Intelligence(AI) and Internet of Things(IoT) are popular domains in Computer Science. AIoT converges AI and IoT, thereby applying AI into IoT. When ‘things’ are programmed and connected to the Internet, IoT comes into place. But when these IoT systems, can analyze data and have decision-making potential without human intervention, AIoT is achieved. AI powers IoT through Decision-Making and Machine Learning, IoT powers AI through data exchange and connectivity. With the AI’s brain and IoT’s body, the systems can have shot-up efficiency, performance and learning from user interactions. Some studies show that, by 2022, AIoT devices such as drones to save rainforests or fully automated cars, would be ruling the computer industries. The paper discusses AIoT at a greater depth, focuses on few case studies of AIoT for better understanding on practical levels, and lastly, proposes an idea for a model which suggests food through emotion analysis.

scholarly journals Management of Software-Defined Networking Powered by Artificial Intelligence

2021 ◽  
Author(s):  
Jehad Ali ◽  
Byeong-hee Roh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Heterogeneous Networks ◽  
Software Defined Networking ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Underlying Network ◽  
And Control ◽  
The Internet Of Things

Separating data and control planes by Software-Defined Networking (SDN) not only handles networks centrally and smartly. However, through implementing innovative protocols by centralized controllers, it also contributes flexibility to computer networks. The Internet-of-Things (IoT) and the implementation of 5G have increased the number of heterogeneous connected devices, creating a huge amount of data. Hence, the incorporation of Artificial Intelligence (AI) and Machine Learning is significant. Thanks to SDN controllers, which are programmable and versatile enough to incorporate machine learning algorithms to handle the underlying networks while keeping the network abstracted from controller applications. In this chapter, a software-defined networking management system powered by AI (SDNMS-PAI) is proposed for end-to-end (E2E) heterogeneous networks. By applying artificial intelligence to the controller, we will demonstrate this regarding E2E resource management. SDNMS-PAI provides an architecture with a global view of the underlying network and manages the E2E heterogeneous networks with AI learning.

scholarly journals ASSESSMENT OF TRENDS IN THE APPLICATION OF DIGITAL MARKETING

10.23856/3303 ◽  
2019 ◽  
Vol 33 (2) ◽  
pp. 28-35 ◽  
Author(s):  
Inta Kotane ◽  
Daina Znotina ◽  
Serhii Hushko
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Applied Research ◽  
Business Models ◽  
Digital Marketing ◽  
The Internet ◽  
Sources Of Information ◽  
Scientific Publications ◽  
Method Analysis ◽  
Descriptive Method

One of the conditions for the future development of companies is the identification and use of digital capabilities. In recent years, the environment in which we live and work has changed radically. If the emergence of the Internet was revolutionary in the way we communicate and obtain information, currently the availability and mobility of technologies affects consumers' habits and promotes the transformation of classic business models. Aim of the study: to explore and learn about the development trends of digital marketing. Applied research methods: monographic descriptive method, analysis, synthesis, statistical method. The study based on scientific publications, statistics and other sources of information. The results of the study show that in 2019 digital marketing tools are most actively used: artificial intelligence / augmented reality / machine learning; video marketing; chatbots, virtual assistants.

A Review on Cyber Security Mechanisms Using Machine and Deep Learning Algorithms

2020 ◽  
pp. 23-41 ◽  
Author(s):  
Thiyagarajan P.
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Information Security ◽  
Cyber Security ◽  
Learning Algorithms ◽  
The Other ◽  
The Internet ◽  
Learning Techniques

Digitalization is the buzz word today by which every walk of our life has been computerized, and it has made our life more sophisticated. On one side, we are enjoying the privilege of digitalization. On the other side, security of our information in the internet is the most concerning element. A variety of security mechanisms, namely cryptography, algorithms which provide access to protected information, and authentication including biometric and steganography, provide security to our information in the Internet. In spite of the above mechanisms, recently artificial intelligence (AI) also contributes towards strengthening information security by providing machine learning and deep learning-based security mechanisms. The artificial intelligence (AI) contribution to cyber security is important as it serves as a provoked reaction and a response to hackers' malicious actions. The purpose of this chapter is to survey recent papers which are contributing to information security by using machine learning and deep learning techniques.

scholarly journals Technology and More-Than-Human Design

Design Issues ◽  
2020 ◽  
Vol 36 (4) ◽  
pp. 33-44 ◽  
Author(s):  
Elisa Giaccardi ◽  
Johan Redström
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
The Internet ◽  
User Centered Design ◽  
Human Centered Design ◽  
Design Methodologies ◽  
The Everyday ◽  
The Internet Of Things ◽  
User Centered

Are we reaching the limits of what human-centered and user-centered design can cope with? Developing new design methodologies and tools to unlock the potentials of data technologies such as the Internet of Things, Machine Learning and Artificial Intelligence for the everyday job of design is necessary but not sufficient. There is now a need to fundamentally question what happens when human-centered design is unable to effectively give form to technology, why this might be the case, and where we could look for alternatives.

scholarly journals Could a Conversational AI Identify Offensive Language?

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 418
Author(s):  
Daniela America da Silva ◽  
Henrique Duarte Borges Louro ◽  
Gildarcio Sousa Goncalves ◽  
Johnny Cardoso Marques ◽  
Luiz Alberto Vieira Dias ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Social Media ◽  
Language Processing ◽  
Unethical Behavior ◽  
Historical Data ◽  
Language Models ◽  
The Internet ◽  
Proof Of Concept ◽  
Offensive Language

In recent years, we have seen a wide use of Artificial Intelligence (AI) applications in the Internet and everywhere. Natural Language Processing and Machine Learning are important sub-fields of AI that have made Chatbots and Conversational AI applications possible. Those algorithms are built based on historical data in order to create language models, however historical data could be intrinsically discriminatory. This article investigates whether a Conversational AI could identify offensive language and it will show how large language models often produce quite a bit of unethical behavior because of bias in the historical data. Our low-level proof-of-concept will present the challenges to detect offensive language in social media and it will discuss some steps to propitiate strong results in the detection of offensive language and unethical behavior using a Conversational AI.

Hacked but Don’t Know It

2020 ◽  
pp. 261-271
Author(s):  
Frederick R. Chang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
The Internet

This chapter discusses cybersecurity in an era when everyone is constantly using the Internet. It highlights the importance of research to ensure that information stays safe while users are on the Internet. Artificial intelligence and machine learning are proposed as possible avenues to securing cyberspace. However, much is still unknown, and collaboration among individuals in different fields is important to advance the research in cybersecurity. The author calls for greater investment in the training of future cyberpractitioners at all levels of expertise.

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