scholarly journals Augmented Realities, Artificial Intelligence, and Machine Learning: Clinical Implications and How Technology Is Shaping the Future of Medicine

2020 ◽  
Vol 9 (12) ◽  
pp. 3811 ◽  
Author(s):  
Gaby N. Moawad ◽  
Jad Elkhalil ◽  
Jordan S. Klebanoff ◽  
Sara Rahman ◽  
Nassir Habib ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Life ◽  
Narrative Review ◽  
Advanced Technology ◽  
Medical Community ◽  
Clinical Implications ◽  
Medical Providers ◽  
Medical Specialties ◽  
Medical Advances

Technology has been integrated into every facet of human life, and whether it is completely advantageous remains unknown, but one thing is for sure; we are dependent on technology. Medical advances from the integration of artificial intelligence, machine learning, and augmented realities are widespread and have helped countless patients. Much of the advanced technology utilized by medical providers today has been borrowed and extrapolated from other industries. There remains no great collaboration between providers and engineers, which may be why medicine is only in its infancy of innovation with regards to advanced technologic integration. The purpose of this narrative review is to highlight the different technologies currently being utilized in a variety of medical specialties. Furthermore, we hope that by bringing attention to one shortcoming of the medical community, we may inspire future innovators to seek collaboration outside of the purely medical community for the betterment of all patients seeking care.

scholarly journals THE FINANCIAL ASPECT OF IoT PROCESS AUTOMATION IN CREATING NEW VALUE FOR THE ECONOMY

2020 ◽  
Vol 18 (2) ◽  
Author(s):  
Nedeljko Šikanjić ◽  
Zoran Ž. Avramović ◽  
Esad Jakupović
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Scientific Paper ◽  
General Purpose ◽  
Advanced Technology ◽  
Process Automation ◽  
Advanced Technologies ◽  
Financial Benefits ◽  
Financial Aspect

In today’s world, devices with possibility to communicate, are emerging and growing daily. This advanced technology is bringing ideas of how to use these devices, in order to gain financial benefits for enterprises, business and economy in general. Purpose of research in this scientific paper is to discover, what are the trends in connecting these devices, called internet of things (IoT), what are financial aspects of implementing IoT solutions and how leaders in area of cloud computing and IoT, are implementing additional advanced technologies such as machine learning and artificial intelligence, to improve processes and gain increase in revenue, while bringing automation in place for the end users. Development of informational society is not only bringing innovation to everyday life, but is also providing effect on the economy. This effect reflects on various business platforms, companies and organizations while increasing the quality of the end product or service that is being provided.

scholarly journals Redistribution and Rekognition

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Sarah Myers West
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Community ◽  
Statistical Methods ◽  
Gender Discrimination ◽  
Human Life ◽  
Human Interaction ◽  
The People ◽  
Computer Scientists ◽  
Global Population

Computer scientists, and artificial intelligence researchers in particular, have a predisposition for adopting precise, fixed definitions to serve as classifiers (Agre, 1997; Broussard, 2018). But classification is an enactment of power; it orders human interaction in ways that produce advantage or suffering (Bowker & Star, 1999). In so doing, it obscures the messiness of human life, masking the work of the people involved in training machine learning systems, and hiding the uneven distribution of its impacts on communities (Taylor, 2018; Gray, 2019; Roberts, 2019). Feminist scholars, and particularly feminist scholars of color, have made powerful critiques of the ways in which artificial intelligence systems formalize, classify, and amplify historical forms of discrimination and act to reify and amplify existing forms of social inequality (Eubanks, 2017; Benjamin, 2019; Noble, 2018). In response, the machine learning community has begun to address claims of algorithmic bias under the rubric of fairness, accountability, and transparency. But in doing so, it has largely dealt with these issues in familiar terms, using statistical methods aimed at achieving parity and deploying fairness ‘toolkits’. Yet actually existing inequality is reflected and amplified in algorithmic systems in ways that exceed the capacity of statistical methods alone. This article outlines a feminist critique of extant methods of dealing with algorithmic discrimination. I outline the ways in which gender discrimination and erasure are built into the field of AI at a foundational level; the product of a community that largely represents a small, privileged, and male segment of the global population (Author, 2019). In so doing, I illustrate how a situated mode of inquiry enables us to more closely examine a feedback loop between discriminatory workplaces and discriminatory systems.

scholarly journals An Introduction on Interpretable Machine Learning

2020 ◽  
Vol 9 (7S) ◽  
pp. 107-111
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Life ◽  
Accuracy Score ◽  
Learning Models ◽  
Ethical Practices ◽  
Interpretable Machine Learning ◽  
Machine Learning Model ◽  
Machine Learning Applications ◽  
Machine Learning Models

As Artificial Intelligence penetrates all aspects of human life, more and more questions about ethical practices and fair uses arise, which has motivated the research community to look inside and develop methods to interpret these Artificial Intelligence/Machine Learning models. This concept of interpretability can not only help with the ethical questions but also can provide various insights into the working of these machine learning models, which will become crucial in trust-building and understanding how a model makes decisions. Furthermore, in many machine learning applications, the feature of interpretability is the primary value that they offer. However, in practice, many developers select models based on the accuracy score and disregarding the level of interpretability of that model, which can be chaotic as predictions by many high accuracy models are not easily explainable. In this paper, we introduce the concept of Machine Learning Model Interpretability, Interpretable Machine learning, and the methods used for interpretation and explanations.

scholarly journals Modern technologies of marketing research and market analysis

2021 ◽  
Vol 2021 (2) ◽  
pp. 29-39
Author(s):  
Oleh Duma ◽  
◽  
M. Melnyk ◽  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Human Life ◽  
Marketing Research ◽  
Research Process ◽  
Target Market ◽  
Advantages And Disadvantages ◽  
Marketing Activities ◽  
Modern Technologies

Nowadays, marketing research is increasingly important for the success of enterprises. Conducting marketing research reduces the risk of making wrong decisions in the analysis and development of marketing strategies, planning and control of marketing activities. The article provides an overview of the emergence of marketing research, explores the latest methods of marketing research, their advantages and disadvantages, the possibility of its application at different stages of marketing activities. Scientific approaches to the interpretation of the concepts "marketing research", "methods of marketing research" are systematized. The latest methods of marketing research that widely use AI, Big Data, ML, TRI * M, have been studied. The technologies of mobile advertising, areas of use of artificial intelligence, the essence and features of the formation of Big Data and machine learning were researched in the article. The benefits of using artificial intelligence, big data and machine learning to conduct marketing research were researched in the article. Analytical materials are confirmed by cases from the practice of marketing research. All research outcomes were proved by cases of Independent Media, TNS Ukraine, British Council, Chat fuel and Coca - Cola. The scheme of the marketing research process is supplemented by the possibilities of applying the latest technologies, which are grouped by stages. Any marketing research is a sequence of steps. Each of them uses a set of tools that provide collection, processing and analysis of data about the target market, customers, or economic processes. Each of these stages can be implemented using the modern technologies that are widely used in various spheres of human life. The directions of application the artificial intelligence, Big data, machine learning for carrying out office researches, field researches, pilot researches and a method of focus groups are offered. The analysis of realization of methods of marketing researches on the basis of Big Data, AI, ML is carried out.

scholarly journals Application of Various Machine Learning Techniques in Sentiment Analysis for Depression Detection

2019 ◽  
Vol 8 (10S) ◽  
pp. 292-296
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Mental Disorder ◽  
Human Life ◽  
Geographic Location ◽  
Machine Learning Techniques ◽  
World Health ◽  
Detection Algorithms ◽  
Learning Techniques ◽  
Depression Detection

Depression is the world’s fourth leading disease and will be in the second in 2020 according to the statistics of World Health Organization.Depression affects many people irrespective of their age, geographic location, demographic or social position and more commonly affects females than males.Depression is a mental disorder which can impair many facets of human life. Though not easily detected it has intense and wide-ranging impressions. Although many researchers explored numerous techniques in predicting depression, still there is no improvement and the generations are facing higher rate of depression. It is believed that the depression detection algorithms can be more accurate and their performance can be better if they rely on artificial intelligence. On considering these factors, it is planned to perform a survey on the application of various machine learning techniques that have been used in the domain of sentimental analysis for depression detection.

Machine Learning and Artificial Intelligence in Marketing Applications During COVID-19 Pandemic

2020 ◽  
pp. 21-31
Author(s):  
L. Kuladeep Kumar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Real World ◽  
Current Situation ◽  
Advanced Technology ◽  
The Novel ◽  
Model Design ◽  
Economic Activities

Since the outbreak of the novel SARS-CoV-2, machine learning and artificial intelligence (ML/AI) have become the powerful marketing tools to mitigate economic activities during COVID-19 pandemic. The goal of ML/AI technology is to provide data and insights so that brands can understand what’s working and what’s not. This will help marketers understand and anticipate what sort of communications work and how to deliver them. Therefore, these are such promising methods employed by various marketing providers. AI uses machine learning to adapt and make changes which impact marketing in real time. The exact impact of events such as the COVID-19 pandemic is hard to predict, but AI will help us track and anticipate these circumstances, as well as provide us with the data needed to proceed. This chapter deals with recent studies that use such advanced technology to increase researchers from different perspectives, address problems and challenges by using such an algorithm to assist marketing experts in real-world issues. This chapter also discusses suggestions conveying researchers on ML/AI-based model design, marketing experts, and policymakers on few errors encountered in the current situation while tackling the current pandemic.

scholarly journals Human-Centric AI: The Symbiosis of Human and Artificial Intelligence

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 332
Author(s):  
Davor Horvatić ◽  
Tomislav Lipic
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Life ◽  
Data Driven ◽  
Learning Approaches ◽  
Second Wave

Well-evidenced advances of data-driven complex machine learning approaches emerging within the so-called second wave of artificial intelligence (AI) fostered the exploration of possible AI applications in various domains and aspects of human life, practices, and society [...]

scholarly journals Future Service in Industry 5.0

2019 ◽  
Vol 2 (1) ◽  
pp. 67-79 ◽  
Author(s):  
Umar Al Faruqi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Industry 4.0 ◽  
Industrial Revolution ◽  
Human Life ◽  
Rapid Development ◽  
The Internet ◽  
Fourth Industrial Revolution ◽  
Develop Technology ◽  
The Internet Of Things

With the rapid development of technology in the digitalization era, Industry 4.0 became a terminology that became a reference for research and development in the field of technology in various sectors. This continues to trigger all people to develop technology to enable better utilization in facilitating human life. Society 5.0 is an idea that explains the revolution in people's lives with the development of the fourth industrial revolution. The concept that wants to be presented is how there is a revolution in society that both utilizing technology and also considering humanities aspects. Some sectors of work and needs are beginning to enter digitalization that utilizes Artificial Intelligence, Big Data, Robotics, Automation, Machine Learning, and the Internet of Things.

scholarly journals Machine Learning for Healthcare Diagnostics

2019 ◽  
Vol 8 (6S2) ◽  
pp. 999-1001
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Genetic Disease ◽  
World Wide ◽  
Early Stage ◽  
Human Life ◽  
The Real ◽  
Initial Stage

Presently machine learning and artificial intelligence is playing one of the most important role in diagnose many genetic and non genetic disease. So that the rapid inventions in machine learning can save thousands of life’s as it can diagnose the early stage of many serious diseases. In this research the datasets for such diseases is studied and it will be analyzed that how such deep machine learning will impact to a human life. The problem with such methodology is that it is not possible to get accurate results in the initial stage of research. The reason is every human have different immunity power and stamina. There are many diagnostics center who are fully dependent on the equipments which are fully based on machine learning. In order to boost this process it is necessary to collect the real time patient’s data from different hospitals, states and countries. So that it will be beneficial for world wide.

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