scholarly journals A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1719
Author(s):  
Akihiko Oka ◽  
Norihisa Ishimura ◽  
Shunji Ishihara
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Big Data ◽  
Early Detection Of Cancer ◽  
Daily Life ◽  
Big Data Analysis ◽  
Medical Field ◽  
Recent Developments ◽  
Near Future ◽  
Great Step

Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.

Taming Artificial Intelligence in Psychiatry and Pain Medicine

2020 ◽  
pp. 399-412
Author(s):  
Usef Faghihi ◽  
Sioui Maldonado-Bouchard ◽  
Mario Incayawar
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Health Care ◽  
Deep Learning ◽  
State Of The Art ◽  
Daily Life ◽  
Health Care Sector ◽  
Human Cells ◽  
Pain Medicine ◽  
Medical Field

Today, deep learning (DL) algorithms are intertwined with our daily life. This subdomain of artificial intelligence (AI) technology is used to unlock your phone by only detecting your face, find the best path from work to your home or vice versa, or detect anomalies in the human cells taken for lab tests. Yet, although AI technology is helping in many fields, whether it has done so in the medical field is debatable. DL lacks reasoning; it is unable to determine the causes of events. This is especially crucial when it comes to the health care sector. At this point, computers cannot help physicians with their duties. On the contrary, they are the cause of burnout in more than half of physicians in United States. One of the causes of burnout repeatedly pointed out by physicians is the digitalization of medicine. This chapter presents some of the AI approaches that could help physicians. It also discusses the current limitations and dangers inherent to many of today’s state-of-the-art AI systems. The authors provide some ideas about the future of AI in pain medicine and psychiatry.

scholarly journals Hemi- and Homonyms in the Big Data Era

Diversity ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 472
Author(s):  
Jorge Rubén Sánchez-González
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Biological Monitoring ◽  
Data Science ◽  
General Rule ◽  
Big Data Analysis ◽  
Species List ◽  
Statistical Software ◽  
Independence Principle ◽  
Near Future

The issue of hemi- and homonyms is an unsolved topic in the Big Data era, where informatics and technicians, rather than biologists or taxonomists, analyze huge datasets. Nowadays, taxonomic nomenclature is ruled by four independent international codes, and according to them, the existence of hemihomonyms and homonyms is accepted under some conditions as an exception to the general rule. This situation entails confusion, disagreements, and a plethora of problems whose consequences could worsen in the near future within the framework of the big data era. Moreover, the increasing use of big databases and analyses, data science, bioinformatics, biological monitoring, and bioassessment has shown such exceptions to be inconvenient, since these exceptions to homonyms are considered as duplicates by databases and statistical software, which are handled by non-taxonomist experts. International Codes of Nomenclature must change within the new context of big data analysis. This work aims to propose the elimination of any exception to the presence of homonyms and to evaluate whether the Independence Principle makes sense within this new context. Increasing coordination between several independent nomenclatural systems is essential and, perhaps, we must conduct our efforts towards a universal species list, finishing with the historical schism between Codes.

scholarly journals A Big Data Analysis on Distributed File Storage System

2019 ◽  
Vol 9 (2) ◽  
pp. 2383-2388
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Storage System ◽  
Real Life ◽  
File Systems ◽  
Big Data Analysis ◽  
Distributed File Systems ◽  
File Storage ◽  
Recent Developments ◽  
The Impact

Nowadays, the digital technologies and information systems (i.e. cloud computing and Internet of Things) generated the vast data in terabytes to extract the knowledge for making a better decision by the end users. However, these massive data require a large effort of researchers at multiple levels to analyze for decision making. To find a better development, researchers concentrated on Big Data Analysis (BDA), but the traditional databases, data techniques and platforms suffers from storage, imbalance data, scalability, insufficient accuracy, slow responsiveness and scalability, which leads to very less efficiency in Big Data (BD) context. Therefore, the main objective of this research is to present a generalized view of complete BD system that consists of various stages and major components of every stage to process the BD. In specific, the data management process describes the NoSQL databases and different Parallel Distributed File Systems (PDFS) and then, the impact of challenges, analyzed for BD with recent developments provides a better understanding that how different tools and technologies apply to solve real-life applications.

scholarly journals State of the Art in Computational Bioacoustics and Machine Learning: How far have we come?

2019 ◽  
Vol 3 ◽  
Author(s):  
Dan Stowell
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
State Of The Art ◽  
Learning Networks ◽  
Recent Developments ◽  
Audio Data ◽  
Ecological Applications ◽  
Near Future ◽  
Statistical Ecology

Terrestrial bioacoustics, like many other domains, has recently witnessed some transformative results from the application of deep learning and big data (Stowell 2017, Mac Aodha et al. 2018, Fairbrass et al. 2018, Mercado III and Sturdy 2017). Generalising over specific projects, which bioacoustic tasks can we consider "solved"? What can we expect in the near future, and what remains hard to do? What does a bioacoustician need to understand about deep learning? This contribution will address these questions, giving the audience a concise summary of recent developments and ways forward. It builds on recent projects and evaluation campaigns led by the author (Stowell et al. 2015, Stowell et al. 2018), as well as broader developments in signal processing, machine learning and bioacoustic applications of these. We will discuss which type of deep learning networks are appropriate for audio data, how to address zoological/ecological applications which often have few available data, and issues in integrating deep learning predictions with existing workflows in statistical ecology.

scholarly journals Data Mining, Machine Learning, Deep Learning, Chemometrics. Definitions, common points and Trends (Spoiler Alert: VALIDATE your models!)

2021 ◽  
Vol 8 (32) ◽  
pp. 22-38
Author(s):  
José Manuel Amigo
Keyword(s):  
Analytical Chemistry ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Daily Life ◽  
Big Data Analysis ◽  
Mining Machine ◽  
Learning Data ◽  
Made In

Concepts like Machine Learning, Data Mining or Artificial Intelligence have become part of our daily life. This is mostly due to the incredible advances made in computation (hardware and software), the increasing capabilities of generating and storing all types of data and, especially, the benefits (societal and economical) that generate the analysis of such data. Simultaneously, Chemometrics has played an important role since the late 1970s, analyzing data within natural science (and especially in Analytical Chemistry). Even with the strong parallelisms between all of the abovementioned terms and being popular with most of us, it is still difficult to clearly define or differentiate the meaning of Machine Learning, Data Mining, Artificial Intelligence, Deep Learning and Chemometrics. This manuscript brings some light to the definitions of Machine Learning, Data Mining, Artificial Intelligence and Big Data Analysis, defines their application ranges and seeks an application space within the field of analytical chemistry (a.k.a. Chemometrics). The manuscript is full of personal, sometimes probably subjective, opinions and statements. Therefore, all opinions here are open for constructive discussion with the only purpose of Learning (like the Machines do nowadays).

scholarly journals A Data-Driven Platform to Computer Performance Analysis and Recommendation using AI and Big Data Analysis

2021 ◽  
Author(s):  
Shuo Chen ◽  
Yu Sun
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Qualitative Evaluation ◽  
Cost Effective ◽  
Big Data Analysis ◽  
Data Driven ◽  
Computer Performance ◽  
Time And Energy ◽  
Artificial Intelligence Application ◽  
Intelligence Application

When I was assembling the computer, I found a problem. This problem is that we need to spend a lot of time and energy when we choose a desktop with a configuration and price that we are satisfied with [5]. Some computer websites will only recommend some ordinary desktops to users. Does not allow users to get what they really want, and some other shops that assemble computer mainframes use the characteristics of customers that do not understand computers to increase prices. So I wanted to create a software to help these people who need to assemble a computer to find the most suitable computer efficiently and in accordance with their requirements [6]. This program, according to the needs of users, artificial intelligence application crawler technology can help users find the most suitable computer parts based on big data, and help users get the most cost-effective self-assembled computer host. We applied our application to match a person in need of a computer host with My Platform and conducted a qualitative evaluation of the method [7]. The results showed that My Platform can efficiently and quality match the user's needs and find the best solution for the user.

scholarly journals Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
E. A. Huerta ◽  
Asad Khan ◽  
Edward Davis ◽  
Colleen Bushell ◽  
William D. Gropp ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Social Patterns ◽  
Computing Paradigm ◽  
Recent Developments ◽  
Optimization Schemes ◽  
Performance Computing

Abstract Significant investments to upgrade and construct large-scale scientific facilities demand commensurate investments in R&D to design algorithms and computing approaches to enable scientific and engineering breakthroughs in the big data era. Innovative Artificial Intelligence (AI) applications have powered transformational solutions for big data challenges in industry and technology that now drive a multi-billion dollar industry, and which play an ever increasing role shaping human social patterns. As AI continues to evolve into a computing paradigm endowed with statistical and mathematical rigor, it has become apparent that single-GPU solutions for training, validation, and testing are no longer sufficient for computational grand challenges brought about by scientific facilities that produce data at a rate and volume that outstrip the computing capabilities of available cyberinfrastructure platforms. This realization has been driving the confluence of AI and high performance computing (HPC) to reduce time-to-insight, and to enable a systematic study of domain-inspired AI architectures and optimization schemes to enable data-driven discovery. In this article we present a summary of recent developments in this field, and describe specific advances that authors in this article are spearheading to accelerate and streamline the use of HPC platforms to design and apply accelerated AI algorithms in academia and industry.

scholarly journals The Use of Technical Analysis in the US, European and Asian Stock Markets

2020 ◽  
Vol 8 ◽  
pp. 302-318
Author(s):  
Deimante Teresiene ◽  
Margarita Aleksynaite
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analysis ◽  
Stock Markets ◽  
Investment Decisions ◽  
Technical Analysis ◽  
Big Data Analysis ◽  
The Us

Technical analysis is a widely used tool in making investment decisions. Nowadays it becomes very popular in the context of big data analysis and artificial intelligence framework. Although the analysis of the results of indicators in certain markets often becomes the axis of technical analysis research, it is difficult to find articles aimed at applying and comparing this analysis in different markets. This paper attempts to answer the question of whether technical analysis indicators work in the same or different ways in the US, European, and Asian stock markets. For this purpose, 8 indicators are calculated, and their results are compared in three selected markets. The correlation between the indicators themselves in individual markets is also determined. It has been observed that the performance of technical analysis is similar in different markets so this type of analysis can be used in artificial intelligence framework.

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