Autonomous Vehicular Corridor using Artificial Intelligence

2021 ◽  
Vol 9 (VI) ◽  
pp. 1254-1258
Author(s):  
Aravind R Kashyap
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
Human Intervention ◽  
Macroscopic Level ◽  
Operational Impact

This project considers the operational impact of Autonomous Vehicles by creating a corridor using the latest network available. The behaviour of these vehicles entering the corridor is monitored at the macroscopic level by modifying the data which can be extracted from the vehicle. This data is made to learn using machine learning called the Time Series Neural Network and the data is used as a parameter to make the vehicles Autonomous. The project resolves the location, develops and demonstrates the collision avoidance of the vehicles using Artificial Intelligence. Autonomous means the vehicles will be able to learn to act accordingly without human intervention

Modeling of Psychomotor Reactions of a Person Based on Modification of the Tapping Test

2021 ◽  
pp. 190-200
Author(s):  
Lesia Mochurad ◽  
Yaroslav Hladun
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Real Data ◽  
Finger Tapping ◽  
Similar Distribution ◽  
Model Learning ◽  
Machine Learning Model ◽  
Finger Tapping Test

The paper considers the method for analysis of a psychophysical state of a person on psychomotor indicators – finger tapping test. The app for mobile phone that generalizes the classic tapping test is developed for experiments. Developed tool allows collecting samples and analyzing them like individual experiments and like dataset as a whole. The data based on statistical methods and optimization of hyperparameters is investigated for anomalies, and an algorithm for reducing their number is developed. The machine learning model is used to predict different features of the dataset. These experiments demonstrate the data structure obtained using finger tapping test. As a result, we gained knowledge of how to conduct experiments for better generalization of the model in future. A method for removing anomalies is developed and it can be used in further research to increase an accuracy of the model. Developed model is a multilayer recurrent neural network that works well with the classification of time series. Error of model learning on a synthetic dataset is 1.5% and on a real data from similar distribution is 5%.

Authorship in the Age of Machine Learning and Artificial Intelligence

2021 ◽  
Author(s):  
J.-M. Deltorn ◽  
Franck Macrez
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Musical Work ◽  
Musical Works ◽  
New Generation ◽  
Specific Contribution

A new generation of machine learning (ML) and artificial intelligence (AI) creative tools are now at the disposal of musicians, professionals and amateurs alike. These new technical intermediaries allow the production of unprecedented forms of compositions, from generating new works by mimicking a style or by mixing a curated ensemble of musical works to letting an algorithm complete one’s own creation in unexpected directions or by letting an artist interact with the parameters of a neural network to explore fresh musical avenues. Unsurprisingly, this new spectrum of algorithmic compositions question both the nature and the degree of involvement of the creator in the musical work. As a consequence, the issue of authorship and, in particular, the assessment of the specific contribution of a (human) creator through the algorithmic pipeline may require special scrutiny when AI and ML tools are used to produce musical works.

scholarly journals A Bayesian neural network predicts the dissolution of compact planetary systems

2021 ◽  
Vol 118 (40) ◽  
pp. e2026053118
Author(s):  
Miles Cranmer ◽  
Daniel Tamayo ◽  
Hanno Rein ◽  
Peter Battaglia ◽  
Samuel Hadden ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Time Series ◽  
Planetary System ◽  
Machine Learning Algorithms ◽  
Orbital Elements ◽  
Bayesian Neural Network ◽  
Inference Model ◽  
Link Type ◽  
Numerical Integrator

We introduce a Bayesian neural network model that can accurately predict not only if, but also when a compact planetary system with three or more planets will go unstable. Our model, trained directly from short N-body time series of raw orbital elements, is more than two orders of magnitude more accurate at predicting instability times than analytical estimators, while also reducing the bias of existing machine learning algorithms by nearly a factor of three. Despite being trained on compact resonant and near-resonant three-planet configurations, the model demonstrates robust generalization to both nonresonant and higher multiplicity configurations, in the latter case outperforming models fit to that specific set of integrations. The model computes instability estimates up to 105 times faster than a numerical integrator, and unlike previous efforts provides confidence intervals on its predictions. Our inference model is publicly available in the SPOCK (https://github.com/dtamayo/spock) package, with training code open sourced (https://github.com/MilesCranmer/bnn_chaos_model).

scholarly journals Artificial intelligence in artistic practices

2021 ◽  
pp. 41-50
Author(s):  
Asmati Chibalashvili
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Visual Arts ◽  
Human Condition ◽  
Human Intervention ◽  
Basic Principles ◽  
Generative Art ◽  
Works Of Art ◽  
Artificial Intelligence Technology ◽  
Emotionally Intelligent

The article considers methods of involving artificial intelligence in artistic practices. Based on the analysis of ways to use this technology in visual arts and music, the basic principles of working with artificial intelligence technology are identified, including: imitation of historical art, implemented in projects The Next Rembrandt and Choral; generative art, which is found in the works “Hyperbolic Composition І” and “Hyperbolic Composition ІІ” of S. Eaton and also in the AIVA program (Artificial Intelligence Virtual Artist). The importance of the mechanisms of neurobiology in the process of working with artificial intelligence on the example of the project “Neural Zoo” of S. Crespo, Iamus program, in which the development of musical material is based on the principle of evolution, is stated. In the application Endel and in the opera “Emotionally intelligent” Artificially Intelligent Brainwave Opera» of E. Perlman, a neural network is used to read information about the human condition and its further processing for modification into a sound landscape or image. The development of artificial intelligence and its use in artistic practices opens up new opportunities, expanding both the field of authors of artistic content and attracting new audience. This phenomenon provokes many issues, including: the ability to think artificially of artificial intelligence, the ability to create works of art without human intervention, as well as issues related to copyright.

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.

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.

Comprehensive Overview of Neural Networks and Its Applications in Autonomous Vehicles

2019 ◽  
pp. 159-173
Author(s):  
Jay Rodge ◽  
Swati Jaiswal
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning Algorithms ◽  
Activation Functions ◽  
Comprehensive Overview ◽  
Unit Component

Deep learning and Artificial intelligence (AI) have been trending these days due to the capability and state-of-the-art results that they provide. They have replaced some highly skilled professionals with neural network-powered AI, also known as deep learning algorithms. Deep learning majorly works on neural networks. This chapter discusses about the working of a neuron, which is a unit component of neural network. There are numerous techniques that can be incorporated while designing a neural network, such as activation functions, training, etc. to improve its features, which will be explained in detail. It has some challenges such as overfitting, which are difficult to neglect but can be overcome using proper techniques and steps that have been discussed. The chapter will help the academician, researchers, and practitioners to further investigate the associated area of deep learning and its applications in the autonomous vehicle industry.

Review and Applications of Machine Learning and Artificial Intelligence in Engineering

2020 ◽  
pp. 1-12
Author(s):  
Melda Yucel ◽  
Gebrail Bekdaş ◽  
Sinan Melih Nigdeli
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Development Process ◽  
Current Trend ◽  
Engineering Applications ◽  
The Past ◽  
Applications Of Machine Learning ◽  
Basic Features

This chapter presents a summary review of development of Artificial Intelligence (AI). Definitions of AI are given with basic features. The development process of AI and machine learning is presented. The developments of applications from the past to today are mentioned and use of AI in different categories is given. Prediction applications using artificial neural network are given for engineering applications. Usage of AI methods to predict optimum results is the current trend and it will be more important in the future.

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