scholarly journals ChemVox: Voice-Controlled Quantum Chemistry

2020 ◽  
Author(s):  
Umberto Raucci ◽  
alessio Valentini ◽  
Elisa Pieri ◽  
Hayley Weir ◽  
Stefan Seritan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Quantum Chemistry ◽  
Computational Chemistry ◽  
Natural Sciences ◽  
Machine Learning Algorithms ◽  
Quantum Chemistry Calculations ◽  
Voice Control ◽  
Opening Up

Over the last decade, artificial intelligence has been propelled forward by advances in machine learning algorithms and computational hardware, opening up myriad new avenues for scientific research. Nevertheless, virtual assistants and voice control have yet to be widely utilized in the natural sciences. Here, we present ChemVox, an interactive Amazon Alexa skill that uses speech recognition to perform quantum chemistry calculations. This new application interfaces Alexa with cloud computing and returns the results through a capable device. ChemVox paves the way to making computational chemistry routinely accessible to the wider community

scholarly journals ChemVox: Voice-Controlled Quantum Chemistry

2020 ◽  
Author(s):  
Umberto Raucci ◽  
alessio Valentini ◽  
Elisa Pieri ◽  
Hayley Weir ◽  
Stefan Seritan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Quantum Chemistry ◽  
Computational Chemistry ◽  
Natural Sciences ◽  
Machine Learning Algorithms ◽  
Quantum Chemistry Calculations ◽  
Voice Control ◽  
Opening Up

Over the last decade, artificial intelligence has been propelled forward by advances in machine learning algorithms and computational hardware, opening up myriad new avenues for scientific research. Nevertheless, virtual assistants and voice control have yet to be widely utilized in the natural sciences. Here, we present ChemVox, an interactive Amazon Alexa skill that uses speech recognition to perform quantum chemistry calculations. This new application interfaces Alexa with cloud computing and returns the results through a capable device. ChemVox paves the way to making computational chemistry routinely accessible to the wider community

scholarly journals Research on innovation and practice of university-industry cooperation under AI environment

2021 ◽  
Vol 251 ◽  
pp. 02021
Author(s):  
Hainie Meng
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Great Success ◽  
Enterprise Integration ◽  
The Road ◽  
Intelligence Education ◽  
University Industry

Along with the deep learning represented by machine learning algorithms in machine vision and speech recognition in areas such as a great success,and cloud computing, big data provide a steady stream of data resources, such as artificial intelligence into the unprecedented rapid development, and is profoundly changing the from all walks of life, but the road of the artificial intelligence + education how to walk, it is lack of system, in view of this the paper together promote cooperation profound research of artificial intelligence, in order to inspire the thinking of artificial intelligence in university-enterprise cooperation promote the deepening of highervocational education and enterprise integration.

scholarly journals Significance of Artificial Intelligence and Machine Learning Techniques in Smart Cloud Computing: A Review

2019 ◽  
Vol 9 (3) ◽  
pp. 1-7
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Reinforcement Learning ◽  
Heuristic Algorithms ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Learning Techniques

Realization of the tremendous features and facilities provided by Cloud Computing by the geniuses in the world of digital marketing increases its demand. As customer satisfaction is the manifest of this ever shining field, balancing its load becomes a major issue. Various heuristic and meta-heuristic algorithms were applied to get optimum solutions. The current era is much attracted with the provisioning of self-manageable, self-learnable, self-healable, and self-configurable smart systems. To get self-manageable Smart Cloud, various Artificial Intelligence and Machine Learning (AI-ML) techniques and algorithms are revived. In this review, recent trend in the utilization of AI-ML techniques, their applied areas, purpose, their merits and demerits are highlighted. These techniques are further categorized as instance-based machine learning algorithms and reinforcement learning techniques based on their ability of learning. Reinforcement learning is preferred when there is no training data set. It leads the system to learn by its own experience itself even in dynamic environment.

Machine Learning Applications in Nanomedicine and Nanotoxicology

2019 ◽  
Vol 4 (1) ◽  
pp. 1-7
Author(s):  
Gerardo M. Casañola-Martin ◽  
Hai Pham-The
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Future Perspectives ◽  
Computational Tools ◽  
Machine Learning Applications ◽  
Nanoparticles Characterization

The development of machine learning algorithms together with the availability of computational tools nowadays have given an increase in the application of artificial intelligence methodologies in different fields. However, the use of these machine learning approaches in nanomedicine remains still underexplored in certain areas, despite the development in hardware and software tools. In this review, the recent advances in the conjunction of machine learning with nanomedicine are shown. Examples dealing with biomedical properties of nanoparticles, characterization of nanomaterials, text mining, and image analysis are also presented. Finally, some future perspectives in the integration of nanomedicine with cloud computing, deep learning and other techniques are discussed.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Artificial Intelligence, Machine Learning and Calculation of Intraocular Lens Power

2020 ◽  
Vol 237 (12) ◽  
pp. 1430-1437
Author(s):  
Achim Langenbucher ◽  
Nóra Szentmáry ◽  
Jascha Wendelstein ◽  
Peter Hoffmann
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Regression Model ◽  
Prediction Error ◽  
Learning Algorithms ◽  
Anterior Chamber Depth ◽  
Gaussian Process Regression ◽  
Machine Learning Algorithms ◽  
Effect Sizes ◽  
Axial Position

Abstract Background and Purpose In the last decade, artificial intelligence and machine learning algorithms have been more and more established for the screening and detection of diseases and pathologies, as well as for describing interactions between measures where classical methods are too complex or fail. The purpose of this paper is to model the measured postoperative position of an intraocular lens implant after cataract surgery, based on preoperatively assessed biometric effect sizes using techniques of machine learning. Patients and Methods In this study, we enrolled 249 eyes of patients who underwent elective cataract surgery at Augenklinik Castrop-Rauxel. Eyes were measured preoperatively with the IOLMaster 700 (Carl Zeiss Meditec), as well as preoperatively and postoperatively with the Casia 2 OCT (Tomey). Based on preoperative effect sizes axial length, corneal thickness, internal anterior chamber depth, thickness of the crystalline lens, mean corneal radius and corneal diameter a selection of 17 machine learning algorithms were tested for prediction performance for calculation of internal anterior chamber depth (AQD_post) and axial position of equatorial plane of the lens in the pseudophakic eye (LEQ_post). Results The 17 machine learning algorithms (out of 4 families) varied in root mean squared/mean absolute prediction error between 0.187/0.139 mm and 0.255/0.204 mm (AQD_post) and 0.183/0.135 mm and 0.253/0.206 mm (LEQ_post), using 5-fold cross validation techniques. The Gaussian Process Regression Model using an exponential kernel showed the best performance in terms of root mean squared error for prediction of AQDpost and LEQpost. If the entire dataset is used (without splitting for training and validation data), comparison of a simple multivariate linear regression model vs. the algorithm with the best performance showed a root mean squared prediction error for AQD_post/LEQ_post with 0.188/0.187 mm vs. the best performance Gaussian Process Regression Model with 0.166/0.159 mm. Conclusion In this paper we wanted to show the principles of supervised machine learning applied to prediction of the measured physical postoperative axial position of the intraocular lenses. Based on our limited data pool and the algorithms used in our setting, the benefit of machine learning algorithms seems to be limited compared to a standard multivariate regression model.

scholarly journals mSphere of Influence: the Rise of Artificial Intelligence in Infection Biology

mSphere ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Artur Yakimovich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Label Free ◽  
Edge Label ◽  
Bacterial Colony ◽  
Link Type ◽  
Colony Counting ◽  
Anthrax Spores ◽  
Infection Biology

ABSTRACT Artur Yakimovich works in the field of computational virology and applies machine learning algorithms to study host-pathogen interactions. In this mSphere of Influence article, he reflects on two papers “Holographic Deep Learning for Rapid Optical Screening of Anthrax Spores” by Jo et al. (Y. Jo, S. Park, J. Jung, J. Yoon, et al., Sci Adv 3:e1700606, 2017, https://doi.org/10.1126/sciadv.1700606) and “Bacterial Colony Counting with Convolutional Neural Networks in Digital Microbiology Imaging” by Ferrari and colleagues (A. Ferrari, S. Lombardi, and A. Signoroni, Pattern Recognition 61:629–640, 2017, https://doi.org/10.1016/j.patcog.2016.07.016). Here he discusses how these papers made an impact on him by showcasing that artificial intelligence algorithms can be equally applicable to both classical infection biology techniques and cutting-edge label-free imaging of pathogens.

scholarly journals Reviewing the relationship between machines and radiology: the application of artificial intelligence

2021 ◽  
Vol 10 (2) ◽  
pp. 205846012199029
Author(s):  
Rani Ahmad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Health Care Professionals ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Science ◽  
Computer Algorithms ◽  
Learning Models ◽  
Specificity And Sensitivity

Background The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. Purpose To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Material and Methods Studies published in 2010–2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. Results The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Conclusion Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.

scholarly journals Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

2021 ◽  
Vol 18 (11) ◽  
pp. 6099
Author(s):  
Joel Weijia Lai ◽  
Candice Ke En Ang ◽  
U. Rajendra Acharya ◽  
Kang Hao Cheong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Daily Living ◽  
Learning Algorithms ◽  
Health Condition ◽  
Machine Learning Algorithms ◽  
Human Cognition ◽  
Computer Algorithms ◽  
Large Sets ◽  
Prevention Methods

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.

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