Role of Artificial Intelligence (AI) in Thermal Science and Engineering

2007 ◽  
Author(s):  
Kwang-Tzu Yang
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Science And Engineering ◽  
Recent Advances ◽  
Real World Applications ◽  
Critical Problems

The use of artificial intelligence methodologies in a variety of real-world applications has been around for some time. However, the application of such methodologies to thermal science and engineering is relatively new, but is receiving ever-increasing attention in the published literature since the mid 1990s. Such attention is due essentially to special requirements and needs of the field of thermal science and Engineering (TSE) in terms of its increasing complexity and the recognition that it is not feasible to approach many critical problems in this field by the use of traditional analysis. The purpose of the present brief review is to point out the recent advances in the artificial intelligence (AI) field and the successes of such methodologies to the current problems in thermal science and engineering. Some shortfalls and prospect for future applications will also be indicated.

Artificial Neural Networks (ANNs): A New Paradigm for Thermal Science and Engineering

2008 ◽  
Vol 130 (9) ◽  
Author(s):  
Kwang-Tzu Yang
Keyword(s):  
Future Prospects ◽  
Hybrid Analysis ◽  
New Paradigm ◽  
Science And Engineering ◽  
Special Requirement ◽  
Recent Advances ◽  
Real World Applications ◽  
Critical Problems ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The use of artificial neural network (ANN), as one of the artificial intelligence methodologies, in a variety of real-world applications has been around for some time. However, the application of ANN to thermal science and engineering is still relatively new, but is receiving ever-increasing attention in recent published literature. Such attention is due essentially to special requirement and needs of the field of thermal science and engineering in terms of its increasing complexity and the recognition that it is not always feasible to deal with many critical problems in this field by the use of traditional analysis. The purpose of the present review is to point out the recent advances in ANN and its successes in dealing with a variety of important thermal problems. Some current ANN shortcomings, the development of recent advances in ANN-based hybrid analysis, and its future prospects will also be indicated.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

Real World Applications

2016 ◽  
pp. 113-132
Author(s):  
Ibibia K. Dabipi ◽  
Judy A. Perkins ◽  
Tierney Moore
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Real World ◽  
Chain Structure ◽  
Total Asset ◽  
Chain Management ◽  
Real World Applications ◽  
End To End ◽  
Supply Chain Structure

Over the years the supply chain industry has been transforming to improve the end-to-end (production to delivery) process. Supply chain management (SCM) allows various industries to oversee and better handle how their product is manufactured and delivered. It allows them to track and identify the location of the product and to be more efficient in delivery. Integrating total asset visibility (TAV) technology into the supply chain structure can provide excellent visibility of a product. This kind of visibility complemented with various packaging schemes can assist in accommodating optimization strategies for visualizing the movement of a product throughout the entire supply chain pipeline. The chapter will define SCM, discuss TAV, review how transportation as well as optimization impacts SCM and TAV, and examine the role of packaging in the context of SCM and TAV.

Towards next-generation label-free biosensors: recent advances in whispering gallery mode sensors

Lab on a Chip ◽  
2017 ◽  
Vol 17 (7) ◽  
pp. 1190-1205 ◽  
Author(s):  
Eugene Kim ◽  
Martin D. Baaske ◽  
Frank Vollmer
Keyword(s):  
Real World ◽  
Whispering Gallery Mode ◽  
Label Free ◽  
Next Generation ◽  
Whispering Gallery ◽  
Recent Advances ◽  
Real World Applications

We review recent advances achieved in the field of optical whispering gallery mode biosensors. We discuss major challenges that these label-free sensors are faced with on their way towards future real-world applications and introduce different approaches suggested to overcome these issues. We furthermore highlight their potential future applications.

scholarly journals Thedlvhexsystem for knowledge representation: recent advances (system description)

2016 ◽  
Vol 16 (5-6) ◽  
pp. 866-883 ◽  
Author(s):  
CHRISTOPH REDL
Keyword(s):  
Knowledge Representation ◽  
Real World ◽  
Answer Set Programming ◽  
Current Status ◽  
System Description ◽  
Recent Advances ◽  
Real World Applications ◽  
External Sources ◽  
Answer Set

AbstractThedlvhexsystem implements thehex-semantics, which integrates answer set programming (ASP) with arbitrary external sources. Since its first release ten years ago, significant advancements were achieved. Most importantly, the exploitation of properties of external sources led to efficiency improvements and flexibility enhancements of the language, and technical improvements on the system side increased user's convenience. In this paper, we present the current status of the system and point out the most important recent enhancements over early versions. While existing literature focuses on theoretical aspects and specific components, a bird's eye view of the overall system is missing. In order to promote the system for real-world applications, we further present applications which were already successfully realized on top ofdlvhex.

ON SOME RECENT ADVANCES IN COMPLEX SOFTWARE NETWORKS: MODELING, ANALYSIS, EVOLUTION AND APPLICATIONS

2012 ◽  
Vol 22 (02) ◽  
pp. 1250024 ◽  
Author(s):  
HONGCHUN WANG ◽  
KEQING HE ◽  
BING LI ◽  
JINHU LÜ
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Point Of View ◽  
Future Research ◽  
Software Systems ◽  
Recent Advances ◽  
Modeling Analysis ◽  
Real World Applications

Complex software networks, as a typical kind of man-made complex networks, have attracted more and more attention from various fields of science and engineering over the past ten years. With the dramatic increase of scale and complexity of software systems, it is essential to develop a systematic approach to further investigate the complex software systems by using the theories and methods of complex networks and complex adaptive systems. This paper attempts to briefly review some recent advances in complex software networks and also develop some novel tools to further analyze complex software networks, including modeling, analysis, evolution, measurement, and some potential real-world applications. More precisely, this paper first describes some effective modeling approaches for characterizing various complex software systems. Based on the above theoretical and practical models, this paper introduces some recent advances in analyzing the static and dynamical behaviors of complex software networks. It is then followed by some further discussions on potential real-world applications of complex software networks. Finally, this paper outlooks some future research topics from an engineering point of view.

A review on the role of artificial intelligence in stem cell therapy: An initiative for modern medicines

2020 ◽  
Vol 21 ◽  
Author(s):  
Pravin Shende ◽  
Nikita P. Devlekar
Keyword(s):  
Artificial Intelligence ◽  
Individual Cell ◽  
Home Automation ◽  
Science And Engineering ◽  
Prediction Of Mortality ◽  
Wide Range ◽  
Potential Applications ◽  
Applications Of Ai

: Stem cells (SCs) show a wide range of applications in the treatment of numerous diseases including neurodegenerative diseases, diabetes, cardiovascular diseases, cancer, etc. SC related research has gained popularity owing to the unique characteristics of self-renewal and differentiation. Artificial intelligence (AI), an emerging field of computer science and engineering has shown potential applications in different fields like robotics, agriculture, home automation, healthcare, banking, and transportation since its invention. This review aims to describe the various applications of AI in SC biology including understanding the behavior of SCs, recognizing individual cell type before undergoing differentiation, characterization of SCs using mathematical models and prediction of mortality risk associated with SC transplantation. This review emphasizes the role of neural networks in SC biology and further elucidates the concepts of machine learning and deep learning and their applications in SC research.

Business Analytics for Business Analysts in Manufacturing

2015 ◽  
pp. 97-105
Author(s):  
Coleen Wilder ◽  
Ceyhun Ozgur
Keyword(s):  
Competitive Advantage ◽  
Real World ◽  
Skill Level ◽  
Business Analytics ◽  
Real World Applications ◽  
Business Analyst ◽  
Successful Business

Many of the skills that define analytics are not new. Nonetheless, it has become a new source of competitive advantage for many corporations. Today's workforce, therefore, must be cognizant of its power and value to effectively perform their jobs. In this chapter, the authors differentiate the role of a business analyst by defining the appropriate skill level and breadth of knowledge required for them to be successful. Business analysts fill the gap between the experts (data scientists) and the day-to-day users. Finally, the section on Manufacturing Analytics provides real-world applications of Analytics for companies in a production setting. The ideas presented herein argue in favor of a dedicated program for business analysts.

Artificial intelligence unifies knowledge and actions in drug repositioning

2021 ◽  
Author(s):  
Zheng Yin ◽  
Stephen T. C. Wong
Keyword(s):  
Artificial Intelligence ◽  
Experimental Data ◽  
Clinical Trials ◽  
Real World ◽  
Drug Repositioning ◽  
Safety Testing ◽  
Drug Candidates ◽  
Speed Up ◽  
Medical Indications

Drug repositioning aims to reuse existing drugs, shelved drugs, or drug candidates that failed clinical trials for other medical indications. Its attraction is sprung from the reduction in risk associated with safety testing of new medications and the time to get a known drug into the clinics. Artificial Intelligence (AI) has been recently pursued to speed up drug repositioning and discovery. The essence of AI in drug repositioning is to unify the knowledge and actions, i.e. incorporating real-world and experimental data to map out the best way forward to identify effective therapeutics against a disease. In this review, we share positive expectations for the evolution of AI and drug repositioning and summarize the role of AI in several methods of drug repositioning.

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