The Role of Machine Learning in Building Image Interpretation Systems

1997 ◽  
Vol 11 (01) ◽  
pp. 143-168 ◽  
Author(s):  
Terry Caelli ◽  
Walter F. Bischof
Keyword(s):  
Machine Learning ◽  
Data Structures ◽  
Image Interpretation ◽  
Problem Formulation ◽  
Data Models ◽  
New Techniques ◽  
Relational Structures ◽  
Complex Scenes ◽  
Rule Evaluation

Machine learning has been applied to many problems related to scene interpretation. It has become clear from these studies that it is important to develop or choose learning procedures appropriate for the types of data models involved in a given problem formulation. In this paper, we focus on this issue of learning with respect to different data structures and consider, in particular, problems related to the learning of relational structures in visual data. Finally, we discuss problems related to rule evaluation in multi-object complex scenes and introduce some new techniques to solve them.

scholarly journals Evolutionary Machine Learning: A Survey

2022 ◽  
Vol 54 (8) ◽  
pp. 1-35
Author(s):  
Akbar Telikani ◽  
Amirhessam Tahmassebi ◽  
Wolfgang Banzhaf ◽  
Amir H. Gandomi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Optimization Problems ◽  
Ensemble Methods ◽  
Problem Formulation ◽  
Fitness Value ◽  
Future Work ◽  
Rule Optimization

Evolutionary Computation (EC) approaches are inspired by nature and solve optimization problems in a stochastic manner. They can offer a reliable and effective approach to address complex problems in real-world applications. EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their results. Evolutionary approaches can be used in all three parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting, membership functions, and neural network topology), and postprocessing (e.g., rule optimization, decision tree/support vectors pruning, and ensemble learning). This article investigates the role of EC algorithms in solving different ML challenges. We do not provide a comprehensive review of evolutionary ML approaches here; instead, we discuss how EC algorithms can contribute to ML by addressing conventional challenges of the artificial intelligence and ML communities. We look at the contributions of EC to ML in nine sub-fields: feature selection, resampling, classifiers, neural networks, reinforcement learning, clustering, association rule mining, and ensemble methods. For each category, we discuss evolutionary machine learning in terms of three aspects: problem formulation, search mechanisms, and fitness value computation. We also consider open issues and challenges that should be addressed in future work.

scholarly journals Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: radiology leading the way for future

2021 ◽  
Vol 13 ◽  
pp. 175628722110448
Author(s):  
B.M. Zeeshan Hameed ◽  
Gayathri Prerepa ◽  
Vathsala Patil ◽  
Pranav Shekhar ◽  
Syed Zahid Raza ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Error ◽  
Data Science ◽  
Image Interpretation ◽  
X Ray ◽  
X Ray Imaging ◽  
Potential Applications ◽  
Efficiency And Productivity

Over the years, many clinical and engineering methods have been adapted for testing and screening for the presence of diseases. The most commonly used methods for diagnosis and analysis are computed tomography (CT) and X-ray imaging. Manual interpretation of these images is the current gold standard but can be subject to human error, is tedious, and is time-consuming. To improve efficiency and productivity, incorporating machine learning (ML) and deep learning (DL) algorithms could expedite the process. This article aims to review the role of artificial intelligence (AI) and its contribution to data science as well as various learning algorithms in radiology. We will analyze and explore the potential applications in image interpretation and radiological advances for AI. Furthermore, we will discuss the usage, methodology implemented, future of these concepts in radiology, and their limitations and challenges.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

The Role of Computer Modeling in Combined Sewer Overflow Abatement Planning

1992 ◽  
Vol 26 (7-8) ◽  
pp. 1831-1840 ◽  
Author(s):  
L. A. Roesner ◽  
E. H. Burgess
Keyword(s):  
System Modeling ◽  
Cost Effective ◽  
Combined Sewer Overflows ◽  
Sewer System ◽  
New Techniques ◽  
Hydraulic Simulation ◽  
Continuous Simulation ◽  
Combined Sewer ◽  
The U.S

Increased concern regarding water quality impacts from combined sewer overflows (CSOs) in the U.S. and elsewhere has emphasized the role of computermodeling in analyzing CSO impacts and in planning abatement measures. These measures often involve the construction of very large and costly facilities, and computer simulation during plan development is essential to cost-effective facility sizing. An effective approach to CSO system modeling focuses on detailed hydraulic simulation of the interceptor sewers in conjunction with continuous simulation of the combined sewer system to characterize CSOs and explore storage-treatment tradeoffs in planning abatement facilities. Recent advances in microcomputer hardware and software have made possible a number of new techniques which facilitate the use of computer models in CSO abatement planning.

Role of machine learning algorithms over heart diseases prediction

2020 ◽  
Author(s):  
Siva Kumar Jonnavithula ◽  
Abhilash Kumar Jha ◽  
Modepalli Kavitha ◽  
Singaraju Srinivasulu
Keyword(s):  
Machine Learning ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

The role of machine learning analytics and metrics in retailing research

2020 ◽  
Author(s):  
Xin (Shane) Wang ◽  
Jun Hyun (Joseph) Ryoo ◽  
Neil Bendle ◽  
Praveen K. Kopalle
Keyword(s):  
Machine Learning ◽  
Learning Analytics

scholarly journals Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows

2021 ◽  
Author(s):  
Doris Xin ◽  
Eva Yiwei Wu ◽  
Doris Jung-Lin Lee ◽  
Niloufar Salehi ◽  
Aditya Parameswaran
Keyword(s):  
Machine Learning
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