scholarly journals Artificial intelligence and machine learning in design of mechanical materials

2021 ◽  
Author(s):  
Kai Guo ◽  
Zhenze Yang ◽  
Chi-Hua Yu ◽  
Markus J. Buehler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
State Of The Art ◽  
The State

This review revisits the state of the art of research efforts on the design of mechanical materials using machine learning.

scholarly journals Artificial Intelligence in Dermatopathology: New Insights and Perspectives

2021 ◽  
Vol 8 (3) ◽  
pp. 418-425
Author(s):  
Gerardo Cazzato ◽  
Anna Colagrande ◽  
Antonietta Cimmino ◽  
Francesca Arezzo ◽  
Vera Loizzi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Differential Diagnosis ◽  
State Of The Art ◽  
The State ◽  
Embryonic Stage ◽  
Future Perspectives ◽  
Review Of The Literature ◽  
Melanocytic Lesions ◽  
Modern Era

In recent years, an increasing enthusiasm has been observed towards artificial intelligence and machine learning, involving different areas of medicine. Among these, although still in the embryonic stage, the dermatopathological field has also been partially involved, with the attempt to develop and train algorithms that could assist the pathologist in the differential diagnosis of complex melanocytic lesions. In this article, we face this new challenge of the modern era, carry out a review of the literature regarding the state of the art and try to determine promising future perspectives.

Artificial Intelligence Approaches to Detect Neurodegenerative Disease From Medical Records

2021 ◽  
pp. 254-267
Author(s):  
Abhranil Gupta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neurodegenerative Disease ◽  
Medical Records ◽  
State Of The Art ◽  
The State ◽  
Learning Approaches ◽  
Brain Scans ◽  
The Future

This chapter gives a brief overview of the state of the art of machine learning approaches in detection of the neurodegenerative disease from medical records (brain scans, etc.). It starts with an understanding of the sub-field of artificial intelligence, machine learning, then goes to understand neurodegenerative disease, with a focus on four major diseases and then goes on to giving an overview of how such diseases are detected using machine learning. In the end, it discusses the future areas of research that needs to be done in order to improve the field of research.

scholarly journals New Business Models on Artificial Intelligence—The Case of the Optimization of a Blast Furnace in the Steel Industry by a Machine Learning Solution

2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Andrés Redchuk ◽  
Federico Walas Mateo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Business Model ◽  
Steel Industry ◽  
Business Models ◽  
State Of The Art ◽  
The State ◽  
Steel Manufacturing ◽  
New Business ◽  
New Business Models

This article took the case of the adoption of a Machine Learning (ML) solution in a steel manufacturing process through a platform provided by a Canadian startup, Canvass Analytics. The content of the paper includes a study around the state of the art of AI/ML adoption in steel manufacturing industries to optimize processes. The work aimed to highlight the opportunities that bring new business models based on AI/ML to improve processes in traditional industries. Methodologically, bibliographic research in the Scopus database was performed to establish the conceptual framework and the state of the art in the steel industry, then the case was presented and analyzed, to finally evaluate the impact of the new business model on the operation of the steel mill. The results of the case highlighted the way the innovative business model, based on a No-Code/Low-Code solution, achieved results in less time than conventional approaches of analytics solutions, and the way it is possible to democratize artificial intelligence and machine learning in traditional industrial environments. This work was focused on opportunities that arise around new business models linked to AI. In addition, the study looked into the framework of the adoption of AI/ML in a traditional industrial environment toward a smart manufacturing approach. The contribution of this article was the proposal of an innovative methodology to put AI/ML in the hands of process operators. It aimed to show how it was possible to achieve better results in a less complex and time-consuming adoption process. The work also highlighted the need for an important quantity of data from the process to approach this kind of solution.

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 What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask)

2018 ◽  
Vol 33 ◽  
Author(s):  
Mauro Vallati ◽  
Lukáš Chrpa ◽  
Thomas L. Mccluskey
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
The State ◽  
Strong Impact ◽  
Significant Progress ◽  
Planning Techniques ◽  
Artificial Intelligence Planning ◽  
The Future ◽  
International Planning ◽  
Planning Approaches

AbstractThe International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.

scholarly journals A Graph-based Evolutionary Algorithm for Automated Machine Learning

2020 ◽  
Author(s):  
Fei Qi ◽  
Zhaohui Xia ◽  
Gaoyang Tang ◽  
Hang Yang ◽  
Yu Song ◽  
...  
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Parameter Optimization ◽  
State Of The Art ◽  
The State ◽  
Complex Structures ◽  
Architecture Evolution ◽  
Automated Machine Learning ◽  
Art Performance

As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.

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