scholarly journals Is it possible to programmatically generate Vaporwave?

2020 ◽  
Author(s):  
Navan Chauhan
Keyword(s):  
Machine Learning ◽  
Slowing Down ◽  
Musical Genre ◽  
Internet Chat

Vaporwave is an internet mediated musical genre which emerged as an ironical variant of chillwave on internet chat groups. Even though vaporwave started in the early 2000s, it was not until the 2010s when it started gaining momentum. It is defined by its slowing down samples of 1980s songs, excessive use of reverb and choppy nature. This article deals with the blueprint for creating a vaporwave track and concludes with three generated vaporwave tracks. The approach taken in this articles differs from traditional machine learning oriented approaches as vaporwave heavily relies on remixing rather than creating original content.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine

10.2196/16607 ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. e16607 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

scholarly journals Unlocking the Power of Artificial Intelligence and Big Data in Medicine (Preprint)

2019 ◽  
Author(s):  
Christian Lovis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Decision Support ◽  
Data Science ◽  
Global Environment ◽  
Data Driven ◽  
Cross Linking ◽  
Science And Society ◽  
Slowing Down

UNSTRUCTURED Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound, with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.

Adaptive Constraint Handling in Optimization of Complex Structures by Using Machine Learning

2021 ◽  
Author(s):  
Yuecheng Cai ◽  
Jasmin Jelovica
Keyword(s):  
Machine Learning ◽  
Pareto Front ◽  
Search Algorithms ◽  
Constraint Handling ◽  
Computational Time ◽  
Complex Structures ◽  
Computationally Efficient ◽  
Nsga Ii ◽  
Slowing Down ◽  
Handling Methods

Abstract Optimization of complex systems requires robust and computationally efficient global search algorithms. Constraints make this a very difficult task, significantly slowing down an algorithm, and can even prevent finding the true Pareto front. This study continues the development of a recently proposed repair approach that exploits infeasible designs to increase computational efficiency of a prominent genetic algorithm, and to find a wider spread of the Pareto front. This paper proposes adaptive and automatized discovery of sensitivity of constraints to variables, i.e. the link, which needed direct designer’s input in the previous version of the repair approach. This is achieved by using machine learning in the form of artificial neural networks (ANN). A surrogate model is afterwards utilized in optimization based on ANN. The proposed approach is used for the recently proposed constraint handling implemented into NSGA-II optimization algorithm. The proposed framework is compared with two other constraint handling methods. The performance is analyzed on a structural optimization of a 178 m long chemical tanker which needs to fulfil class society’s criteria for strength. The results show that the proposed framework is competitive in terms of convergence and spread of the front. This is achieved while discovering the link automatically using ANN, without an input from a user. In addition, computational time is reduced by 60%.

scholarly journals Fuel Economy Improvement of Urban Buses with Development of an Eco-Drive Scoring Algorithm Using Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4471
Author(s):  
Kibok Kim ◽  
Jinil Park ◽  
Jonghwa Lee
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Fuel Economy ◽  
Engine Speed ◽  
Accelerator Pedal ◽  
Monitoring Method ◽  
Slowing Down ◽  
Vehicle Acceleration ◽  
Fuel Economy Improvement ◽  
Set Up

Eco-drive is a widely used concept. It can improve fuel economy for different driving behaviors such as vehicle acceleration or accelerator pedal operation, deceleration or coasting while slowing down, and gear shift timing difference. The feasibility of improving the fuel economy of urban buses by applying eco-drive was verified by analyzing data from drivers who achieved high fuel efficiencies in urban buses with a high frequency of acceleration/deceleration and frequent operation. The items that were monitored for eco-drive were: rapid take-off/acceleration/deceleration, accelerator pedal gradient, coasting rate, shift indicator violation, average engine speed, over speed, and gear shifting under low-end engine speed. The monitoring method for each monitored item was set up, and an index was produced using driving data. A fuel economy prediction model was created using machine learning to determine the contribution of each index to the fuel economy. Furthermore, the contribution of each monitoring item was analyzed using the prediction model explainer. Accordingly, points (defined as the eco-drive score) were allocated for each monitoring item. It was verified that this score can represent the eco-drive characteristics based on the relationship between the score and fuel economy. In addition, it resulted in an average annual fuel economy improvement of 12.1%.

Dance to your own drum: Identification of musical genre and individual dancer from motion capture using machine learning

2020 ◽  
Vol 49 (2) ◽  
pp. 162-177
Author(s):  
Emily Carlson ◽  
Pasi Saari ◽  
Birgitta Burger ◽  
Petri Toiviainen
Keyword(s):  
Machine Learning ◽  
Motion Capture ◽  
Musical Genre

scholarly journals Music Genre Classifier using Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 1698-1709
Author(s):  
Sparsh Nagpal
Keyword(s):  
Machine Learning ◽  
Data Extraction ◽  
Learning Algorithms ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Musical Genre ◽  
User Data ◽  
Audio Data ◽  
Music Genre

Audio data extraction and analysis is important and less explored compared to other forms of data. Here we use an Audio dataset (GTZan) to extract musical information and categorize the musical genre based on the parameters of audio. We compared the study on seven Machine learning algorithms and tested on unvisited user data to see the model performance seeing the algorithms’ accuracy ranging from 45% to 87%.

scholarly journals Digital Biomarkers of Physical Frailty and Frailty Phenotypes Using Sensor-Based Physical Activity and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5289
Author(s):  
Catherine Park ◽  
Ramkinker Mishra ◽  
Jonathan Golledge ◽  
Bijan Najafi
Keyword(s):  
Machine Learning ◽  
Older Adults ◽  
Daily Living ◽  
Recursive Feature Elimination ◽  
Physical Frailty ◽  
Slowing Down ◽  
Percentage Time ◽  
Chronic Stressors ◽  
Digital Biomarkers ◽  
Frailty Criteria

Remote monitoring of physical frailty is important to personalize care for slowing down the frailty process and/or for the healthy recovery of older adults following acute or chronic stressors. Taking the Fried frailty criteria as a reference to determine physical frailty and frailty phenotypes (slowness, weakness, exhaustion, inactivity), this study aimed to explore the benefit of machine learning to determine the least number of digital biomarkers of physical frailty measurable from a pendant sensor during activities of daily living. Two hundred and fifty-nine older adults were classified into robust or pre-frail/frail groups based on the physical frailty assessments by the Fried frailty criteria. All participants wore a pendant sensor at the sternum level for 48 h. Of seventeen sensor-derived features extracted from a pendant sensor, fourteen significant features were used for machine learning based on logistic regression modeling and a recursive feature elimination technique incorporating bootstrapping. The combination of percentage time standing, percentage time walking, walking cadence, and longest walking bout were identified as optimal digital biomarkers of physical frailty and frailty phenotypes. These findings suggest that a combination of sensor-measured exhaustion, inactivity, and speed have potential to screen and monitor people for physical frailty and frailty phenotypes.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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