NATURE–INSPIRED INTELLIGENCE: A REVIEW OF SELECTED METHODS AND APPLICATIONS

2009 ◽  
Vol 18 (04) ◽  
pp. 487-516 ◽  
Author(s):  
VASSILIOS VASSILIADIS ◽  
GEORGIOS DOUNIAS
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Optimization Problems ◽  
Biological Systems ◽  
Artificial Immune Systems ◽  
Rapid Evolution ◽  
Successful Implementation ◽  
Human Immune System ◽  
Survey Paper ◽  
The Way

The successful handling of numerous real–world complex problems has increased the popularity of nature–inspired intelligent (NII) algorithms and techniques. Their successful implementation primarily on difficult and complicated optimization problems, stresses their upcoming importance in the broader area of artificial intelligence. NII techniques take advantage of the way that biological systems deal with real–world situations. Specifically, they simulate the way real biological systems, such as the human brain, ant colonies and human immune system work, when solving complex real–world situations. In this survey paper, we briefly present a number of selected NII approaches and we point particular suitable areas of application for each of them. Specifically, five major categories of nature inspired approaches are presented, namely, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), DNA computing, artificial immune systems and membrane computing. Applications include problems related to optimization (financial, industrial and medical), task scheduling, system design (optimization of the system's parameters), image processing and data processing (feature selection and classification). We also refer to collaboration between NII techniques and classical AI methodologies, such as neural networks, genetic algorithms, fuzzy logic, etc. The current survey states that NII techniques are likely to become the next step in the rapid evolution of artificial intelligence tools.

scholarly journals Simulation of energy consumption in urban areas

2020 ◽  
Vol 152 ◽  
pp. 02006
Author(s):  
Nikolay Garyaev
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Energy Consumption ◽  
Urban Areas ◽  
Energy Supply ◽  
Digital Data ◽  
Successful Implementation ◽  
Digital Models ◽  
Balanced Distribution ◽  
The Way

One of the problems that may arise in the way of successful implementation of energy supply in urban areas is the difficulty of analyzing and interpreting a large amount of digital data received from various sensors. This problem may adversely affect the performance of energy organizations. The purpose of this study is to study modern tools to solve the problem of processing big data using technologies of simulation and artificial intelligence. This study is dedicated to the development of innovative digital models for the balanced distribution of energy consumption in urban areas.

COVID-19 Contact Tracing & Privacy Protection, We Are on the Way (Preprint)

2020 ◽  
Author(s):  
Jing Fan
Keyword(s):  
Real World ◽  
Privacy Protection ◽  
Contact Tracing ◽  
Personal Privacy ◽  
The Way

UNSTRUCTURED Smartphone-based contact tracing is proven to be effective in epidemic containment. To maintain its utilization meanwhile ensure the protection of personal privacy, different countries came up with different practices, new exploratory solutions may come into real-world practice soon as well.

A Fitting Solution

2018 ◽  
Author(s):  
Kaori Kashimura ◽  
Takafumi Kawasaki Jr. ◽  
Nozomi Ikeya ◽  
Dave Randall
Keyword(s):  
Organizational Change ◽  
Power Plant ◽  
Augmented Reality ◽  
Real World ◽  
Work Practices ◽  
Plant Construction ◽  
Tightly Coupled ◽  
Complex Scenario ◽  
The Relationship ◽  
The Way

This chapter provides an ethnography of a complex scenario involving the construction of a power plant and, in so doing, tries to show the importance of a practice-based approach to the problem of technical and organizational change. The chapter reports on fieldwork conducted in a highly complex and tightly coupled environment: power plant construction. The ethnography describes work practices on three different sites and describes and analyses their interlocking dependencies, showing the difficulties encountered at each location and the way in which the delays that result cascade through the different sites. It goes on to describe some technological solutions that are associated with augmented reality and that are being designed in response to the insights gained from the fieldwork. The chapter also reflects more generally on the relationship between fieldwork and design in real-world contexts.

scholarly journals Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Albert T. Young ◽  
Kristen Fernandez ◽  
Jacob Pfau ◽  
Rasika Reddy ◽  
Nhat Anh Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Stress Testing ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Skin Lesions ◽  
Stress Tests ◽  
Performance Metric ◽  
Intelligence Models ◽  
Artificial Intelligence Models

AbstractArtificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

Review of Network Flow Algorithms David P. Williamson

2021 ◽  
Vol 52 (1) ◽  
pp. 12-15
Author(s):  
S.V. Nagaraj
Keyword(s):  
Graduate Students ◽  
Real World ◽  
Network Flow ◽  
Network Flows ◽  
Optimization Problems ◽  
Capacity Constraints ◽  
Incoming Flow ◽  
Network Flow Problems ◽  
Flow Problems ◽  
Real World Problems

This book is on algorithms for network flows. Network flow problems are optimization problems where given a flow network, the aim is to construct a flow that respects the capacity constraints of the edges of the network, so that incoming flow equals the outgoing flow for all vertices of the network except designated vertices known as the source and the sink. Network flow algorithms solve many real-world problems. This book is intended to serve graduate students and as a reference. The book is also available in eBook (ISBN 9781316952894/US$ 32.00), and hardback (ISBN 9781107185890/US$99.99) formats. The book has a companion web site www.networkflowalgs.com where a pre-publication version of the book can be downloaded gratis.

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.

scholarly journals Explainable AI: A Review of Machine Learning Interpretability Methods

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 18
Author(s):  
Pantelis Linardatos ◽  
Vasilis Papastefanopoulos ◽  
Sotiris Kotsiantis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Black Box ◽  
Learning Systems ◽  
Model Complexity ◽  
Learning Models ◽  
New Methods ◽  
Industrial Adoption ◽  
Machine Learning Models ◽  
The Way

Recent advances in artificial intelligence (AI) have led to its widespread industrial adoption, with machine learning systems demonstrating superhuman performance in a significant number of tasks. However, this surge in performance, has often been achieved through increased model complexity, turning such systems into “black box” approaches and causing uncertainty regarding the way they operate and, ultimately, the way that they come to decisions. This ambiguity has made it problematic for machine learning systems to be adopted in sensitive yet critical domains, where their value could be immense, such as healthcare. As a result, scientific interest in the field of Explainable Artificial Intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret machine learning models, has been tremendously reignited over recent years. This study focuses on machine learning interpretability methods; more specifically, a literature review and taxonomy of these methods are presented, as well as links to their programming implementations, in the hope that this survey would serve as a reference point for both theorists and practitioners.

“There is Nothing New in Cyberspace” a Motion for Debate

1992 ◽  
Vol 36 (14) ◽  
pp. 1049-1049 ◽  
Author(s):  
Maxwell J. Wells
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Virtual Reality ◽  
Human Computer Interaction ◽  
Cold Fusion ◽  
Major Advance ◽  
Junk Bonds ◽  
Exciting Development ◽  
Computer Interaction ◽  
The Way

Cyberspace is the environment created during the experience of virtual reality. Therefore, to assert that there is nothing new in cyberspace alludes to there being nothing new about virtual reality. Is this assertion correct? Is virtual reality an exciting development in human-computer interaction, or is it simply another example of effective simulation? Does current media interest herald a major advance in information technology, or will virtual reality go the way of artificial intelligence, cold fusion and junk bonds? Is virtual reality the best thing since sliced bread, or is it last week's buns in a new wrapper?

scholarly journals A Survey of Robots in Healthcare

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 8 ◽  
Author(s):  
Maria Kyrarini ◽  
Fotios Lygerakis ◽  
Akilesh Rajavenkatanarayanan ◽  
Christos Sevastopoulos ◽  
Harish Ram Nambiappan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
The Elderly ◽  
Robotic Systems ◽  
Care Hospital ◽  
Robotic Technology ◽  
Persons With Disabilities ◽  
Survey Paper ◽  
Recent Advances ◽  
Art Research

In recent years, with the current advancements in Robotics and Artificial Intelligence (AI), robots have the potential to support the field of healthcare. Robotic systems are often introduced in the care of the elderly, children, and persons with disabilities, in hospitals, in rehabilitation and walking assistance, and other healthcare situations. In this survey paper, the recent advances in robotic technology applied in the healthcare domain are discussed. The paper provides detailed information about state-of-the-art research in care, hospital, assistive, rehabilitation, and walking assisting robots. The paper also discusses the open challenges healthcare robots face to be integrated into our society.

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