Medicine in New Era with Artificial Intelligence and Systems Biology

2018 ◽  
Vol 74 (11) ◽  
pp. 1343-1351
Author(s):  
Yoshiyuki Asai ◽  
Takeshi Abe ◽  
Takahide Hayano
Keyword(s):  
Artificial Intelligence ◽  
Systems Biology ◽  
New Era

scholarly journals How Do Machines Learn? Artificial Intelligence as a New Era in Medicine

2021 ◽  
Vol 11 (1) ◽  
pp. 32
Author(s):  
Oliwia Koteluk ◽  
Adrian Wartecki ◽  
Sylwia Mazurek ◽  
Iga Kołodziejczak ◽  
Andrzej Mackiewicz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Medical Data ◽  
General Process ◽  
New Era ◽  
Automated Evaluation ◽  
Machine Learning Applications ◽  
And Training ◽  
Current Standards

With an increased number of medical data generated every day, there is a strong need for reliable, automated evaluation tools. With high hopes and expectations, machine learning has the potential to revolutionize many fields of medicine, helping to make faster and more correct decisions and improving current standards of treatment. Today, machines can analyze, learn, communicate, and understand processed data and are used in health care increasingly. This review explains different models and the general process of machine learning and training the algorithms. Furthermore, it summarizes the most useful machine learning applications and tools in different branches of medicine and health care (radiology, pathology, pharmacology, infectious diseases, personalized decision making, and many others). The review also addresses the futuristic prospects and threats of applying artificial intelligence as an advanced, automated medicine tool.

scholarly journals Comparative study of cloud platforms to develop a Chatbot

2017 ◽  
Vol 6 (3) ◽  
pp. 57 ◽  
Author(s):  
Amit Patil ◽  
Marimuthu K ◽  
Nagaraja Rao A ◽  
Niranchana R
Keyword(s):  
Artificial Intelligence ◽  
Comparative Study ◽  
Time Complexity ◽  
Completion Time ◽  
Setup Time ◽  
Human Being ◽  
Cloud Platform ◽  
New Era ◽  
Ibm Watson ◽  
Aws Lambda

Before chatbots there were simply bots: The invention of a chatbot brought us to the new era of technology, the era of conversation service. A chatbot is a virtual person that can effectively talk to any human being with the help of interactive conversion textual skill. Now a days there are many cloud-based platforms available for developing and deploying the chatbot such as Microsoft bot framework, IBM Watson, Kore, AWS lambda, Microsoft Azure bot service, Chatfuel, Heroku and many more but all those techniques has some drawbacks such as built-in Artificial Intelligence, NLP, conversion service, programming etc. This paper represents the comparison between all cloud-based chatbot technologies with some constraint such as built-in AI, setup time, completion time, complexity etc. Finally, by the comparison, we will get to know that which cloud platform is efficient and suitable for developing chatbot.

scholarly journals Performance Evaluation of Artificial Intelligence on Soil Property Detection

2018 ◽  
Vol 4 (10) ◽  
pp. 5
Author(s):  
Smriti Singhatiya ◽  
Dr. Shivnath Ghosh
Keyword(s):  
Artificial Intelligence ◽  
Precision Agriculture ◽  
Crop Production ◽  
Nutrient Status ◽  
Soil Testing ◽  
Support Vector ◽  
Soil Test ◽  
New Era ◽  
Agriculture Soil ◽  
Historical Role

Now-a-days there is a need to study the nutrient status in lower horizons of the soil. Soil testing has played historical role in evaluating soil fertility maintenance and in sustainable agriculture. Soil testing shall also play its crucial role in precision agriculture. At present there is a need to develop basic inventory as per soil test basis and necessary information has to be built into the system for translating the results of soil test to achieve the crop production goal in new era. To achieve this goal artificial intelligence approach is used for predicting the soil properties.  In this paper for analysing these properties support vector regression (SVR), ensembled regression (ER) and neural network (NN) are used. The performance is evaluated with respect to MSE and RMSE and it is observed that ER outperforms better with respect to SVR and NN.

scholarly journals Artificial Intelligence & Popular Music: SKYGGE, Flow Machines, and the Audio Uncanny Valley

Arts ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 130 ◽  
Author(s):  
Melissa Avdeeff
Keyword(s):  
Artificial Intelligence ◽  
Popular Music ◽  
Markov Models ◽  
Uncanny Valley ◽  
New Era ◽  
Music Production ◽  
Music Generation

This article presents an overview of the first AI-human collaborated album, Hello World, by SKYGGE, which utilizes Sony’s Flow Machines technologies. This case study is situated within a review of current and emerging uses of AI in popular music production, and connects those uses with myths and fears that have circulated in discourses concerning the use of AI in general, and how these fears connect to the idea of an audio uncanny valley. By proposing the concept of an audio uncanny valley in relation to AIPM (artificial intelligence popular music), this article offers a lens through which to examine the more novel and unusual melodies and harmonization made possible through AI music generation, and questions how this content relates to wider speculations about posthumanism, sincerity, and authenticity in both popular music, and broader assumptions of anthropocentric creativity. In its documentation of the emergence of a new era of popular music, the AI era, this article surveys: (1) The current landscape of artificial intelligence popular music focusing on the use of Markov models for generative purposes; (2) posthumanist creativity and the potential for an audio uncanny valley; and (3) issues of perceived authenticity in the technologically mediated “voice”.

scholarly journals The combination of artificial intelligence and systems biology for intelligent vaccine design

2020 ◽  
Vol 15 (11) ◽  
pp. 1267-1281
Author(s):  
Giulia Russo ◽  
Pedro Reche ◽  
Marzio Pennisi ◽  
Francesco Pappalardo
Keyword(s):  
Artificial Intelligence ◽  
Systems Biology ◽  
Vaccine Design

scholarly journals A Practical Approach to Artificial Intelligence in Plastic Surgery

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Akash Chandawarkar ◽  
Christian Chartier ◽  
Jonathan Kanevsky ◽  
Phaedra E Cress
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Plastic Surgery ◽  
Aesthetic Surgery ◽  
Practical Approach ◽  
Quality Data ◽  
Surgical Innovation ◽  
Mathematical Basis ◽  
New Era ◽  
Quantitative Studies

Abstract Understanding the intersection of technology and plastic surgery has been and will be essential to positioning plastic surgeons at the forefront of surgical innovation. This account of the current and future applications of artificial intelligence (AI) in reconstructive and aesthetic surgery introduces us to the subset of issues amenable to support from this technology. It equips plastic surgeons with the knowledge to navigate technical conversations with peers, trainees, patients, and technical partners for collaboration and to usher in a new era of technology in plastic surgery. From the mathematical basis of AI to its commercially viable applications, topics introduced herein constitute a framework for design and execution of quantitative studies that will better outcomes and benefit patients. Finally, adherence to the principles of quality data collection will leverage and amplify plastic surgeons’ creativity and undoubtedly drive the field forward.

scholarly journals Containing COVID-19 in China: AI and the robotic restructuring of future cities

2020 ◽  
Vol 10 (2) ◽  
pp. 238-241 ◽  
Author(s):  
Bei Chen ◽  
Simon Marvin ◽  
Aidan While
Keyword(s):  
Artificial Intelligence ◽  
Management Strategies ◽  
Autonomous Systems ◽  
New Era ◽  
Technological Affordances

COVID-19 has generated interest in the potential of urban robotics and automation to manage and police physical distancing and quarantine. This commentary examines the intersection between COVID-19 management strategies and the technological affordances of robotics, autonomous systems, and artificial intelligence (AI) in urban pandemic control. Examples from China illustrate the possibilities for urban robotics and automation in a new era of urban bio-(in)security.

scholarly journals Systems Biology and Synthetic Biology: A New Epoch for Toxicology Research

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Mark T. Mc Auley ◽  
Hyunok Choi ◽  
Kathleen Mooney ◽  
Emily Paul ◽  
Veronica M. Miller
Keyword(s):  
Systems Biology ◽  
Synthetic Biology ◽  
Aromatic Hydrocarbons ◽  
Personalised Medicine ◽  
Adverse Birth Outcomes ◽  
New Era ◽  
Advantages And Disadvantages ◽  
The Future ◽  
Polycyclic Aromatic ◽  
Innovative Techniques

Systems biology and synthetic biology are emerging disciplines which are becoming increasingly utilised in several areas of bioscience. Toxicology is beginning to benefit from systems biology and we suggest in the future that is will also benefit from synthetic biology. Thus, a new era is on the horizon. This review illustrates how a suite of innovative techniques and tools can be applied to understanding complex health and toxicology issues. We review limitations confronted by the traditional computational approaches to toxicology and epidemiology research, using polycyclic aromatic hydrocarbons (PAHs) and their effects on adverse birth outcomes as an illustrative example. We introduce how systems toxicology (and their subdisciplines, genomic, proteomic, and metabolomic toxicology) will help to overcome such limitations. In particular, we discuss the advantages and disadvantages of mathematical frameworks that computationally represent biological systems. Finally, we discuss the nascent discipline of synthetic biology and highlight relevant toxicological centred applications of this technique, including improvements in personalised medicine. We conclude this review by presenting a number of opportunities and challenges that could shape the future of these rapidly evolving disciplines.

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