scholarly journals Analysis of the possibilities for using machine learning algorithms in the Unity environment

2021 ◽  
Vol 20 ◽  
pp. 197-204
Author(s):  
Karina Litwynenko ◽  
Małgorzata Plechawska-Wójcik
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Performance ◽  
Imitation Learning ◽  
Policy Optimization

Reinforcement learning algorithms are gaining popularity, and their advancement is made possible by the presence of tools to evaluate them. This paper concerns the applicability of machine learning algorithms on the Unity platform using the Unity ML-Agents Toolkit library. The purpose of the study was to compare two algorithms: Proximal Policy Optimization and Soft Actor-Critic. The possibility of improving the learning results by combining these algorithms with Generative Adversarial Imitation Learning was also verified. The results of the study showed that the PPO algorithm can perform better in uncomplicated environments with non-immediate rewards, while the additional use of GAIL can improve learning performance.

Machine Learning and Data Mining in Bioinformatics

2012 ◽  
pp. 695-703
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

Machine Learning and Data Mining in Bioinformatics

2009 ◽  
pp. 612-621 ◽  
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

scholarly journals Solving problems of the oil and gas sector using machine learning algorithms

2021 ◽  
pp. 327-337
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Oil And Gas ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Oil Production ◽  
Machine Learning Algorithms ◽  
Classification Problems ◽  
Well Placement ◽  
Oil And Gas Sector

The article describes the tasks of the oil and gas sector that can be solved by machine learning algorithms. These tasks include the study of the interference of wells, the classification of wells according to their technological and geophysical characteristics, the assessment of the effectiveness of ongoing and planned geological and technical measures, the forecast of oil production for individual wells and the total oil production for a group of wells, the forecast of the base level of oil production, the forecast of reservoir pressures and mapping. For each task, the features of building machine learning models and examples of input data are described. All of the above tasks are related to regression or classification problems. Of particular interest is the issue of well placement optimisation. Such a task cannot be directly solved using a single neural network. It can be attributed to the problems of optimal control theory, which are usually solved using dynamic programming methods. A paper is considered where field management and well placement are based on a reinforcement learning algorithm with Markov chains and Bellman's optimality equation. The disadvantages of the proposed approach are revealed. To eliminate them, a new approach of reinforcement learning based on the Alpha Zero algorithm is proposed. This algorithm is best known in the field of gaming artificial intelligence, beating the world champions in chess and Go. It combines the properties of dynamic and stochastic programming. The article discusses in detail the principle of operation of the algorithm and identifies common features that make it possible to consider this algorithm as a possible promising solution for the problem of optimising the placement of a grid of wells.

Enhanced Prediction for Observed Peptide Count in Protein Mass Spectrometry Data by Optimally Balancing the Training Dataset

2017 ◽  
Vol 31 (12) ◽  
pp. 1750040 ◽  
Author(s):  
Anoop Kumar Tiwari ◽  
Abhigyan Nath ◽  
Karthikeyan Subbiah ◽  
Kaushal Kumar Shukla
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Mass Spectrometry Data ◽  
Learning Performance ◽  
Training Dataset ◽  
Imbalanced Dataset ◽  
Mass Spectrometry Experiment ◽  
Protein Mass

Imbalanced dataset affects the learning of classifiers. This imbalance problem is almost ubiquitous in biological datasets. Resampling is one of the common methods to deal with the imbalanced dataset problem. In this study, we explore the learning performance by varying the balancing ratios of training datasets, consisting of the observed peptides and absent peptides in the Mass Spectrometry experiment on the different machine learning algorithms. It has been observed that the ideal balancing ratio has yielded better performance than the imbalanced dataset, but it was not the best as compared to some intermediate ratio. By experimenting using Synthetic Minority Oversampling Technique (SMOTE) at different balancing ratios, we obtained the best results by achieving sensitivity of 92.1%, specificity value of 94.7%, overall accuracy of 93.4%, MCC of 0.869, and AUC of 0.982 with boosted random forest algorithm. This study also identifies the most discriminating features by applying the feature ranking algorithm. From the results of current experiments, it can be inferred that the performance of machine learning algorithms for the classification tasks can be enhanced by selecting optimally balanced training dataset, which can be obtained by suitably modifying the class distribution.

Machine learning in cardiovascular medicine: are we there yet?

Heart ◽  
2018 ◽  
Vol 104 (14) ◽  
pp. 1156-1164 ◽  
Author(s):  
Khader Shameer ◽  
Kipp W Johnson ◽  
Benjamin S Glicksberg ◽  
Joel T Dudley ◽  
Partho P Sengupta
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Complex Disease ◽  
Clinical Care ◽  
Learning Algorithms ◽  
Cognitive Learning ◽  
Data Depth ◽  
Machine Learning Algorithms ◽  
Patient Specific ◽  
Cardiovascular Medicine

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.

scholarly journals Machine Learning Combinatorial Frameworks for Architecture

2021 ◽  
Vol 7 (2) ◽  
pp. 20-29
Author(s):  
Joshua Lye ◽  
Alisa Andrasek
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Imitation Learning ◽  
Learning Models ◽  
Learning Agent ◽  
Policy Optimization ◽  
Machine Learning Models ◽  
Reinforcement Learning Algorithm

This paper investigates the application of machine learning for the simulation of larger architectural aggregations formed through the recombination of discrete components. This is primarily explored through establishing hardcoded assembly and connection logics which are used to form the framework of architectural fitness conditions for machine learning models. The key machine learning models researched are a combination of the deep reinforcement learning algorithm proximal policy optimization (PPO) and Generative Adversarial Imitation Learning (GAIL) in the Unity Machine Learning Agent asset toolkit. The goal of applying these machine learning models is to train the agent behaviours (discrete components) to learn specific logics of connection. In order to achieve assembled architectural `states that allow for spatial habitation through the process of simulation.

scholarly journals Quantius: Generic, high-fidelity human annotation of scientific images at 105-clicks-per-hour

2017 ◽  
Author(s):  
Alex J. Hughes ◽  
Joseph D. Mornin ◽  
Sujoy K. Biswas ◽  
David P. Bauer ◽  
Simone Bianco ◽  
...  
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Image Annotation ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Performance ◽  
Computational Algorithms ◽  
High Fidelity ◽  
Scientific Images

We describe Quantius, a crowd-based image annotation platform that provides an accurate alternative to task-specific computational algorithms for difficult image analysis problems. We use Quantius to quantify a variety of computationally challenging medium-throughput tasks with ~50x and 30x savings in analysis time and cost respectively, relative to a single expert annotator. We show equivalent deep learning performance for Quantius- and expert-derived annotations, bridging towards scalable integration with tailored machine-learning algorithms.

scholarly journals A Hybrid Teaching Mode Based on Machine Learning Algorithm

2020 ◽  
Vol 6 (1) ◽  
pp. 22-28
Author(s):  
Jinjin Liang ◽  
Yong Nie
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Scientific Evidence ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Performance ◽  
Support Vector ◽  
Teaching Process ◽  
Teaching Mode ◽  
Hybrid Teaching

Background: Hybrid teaching mode is a new trend under the Education Informatization environment, which combines the advantages of educators’ supervision offline and learners’ self-regulated learning online. Capturing learners’ learning behavior data becomes easy both from the traditional classroom and online platform. Methods: If machine learning algorithms can be applied to mine valuable information underneath those behavior data, it will provide scientific evidence and contribute to wise decision making as well as effective teaching process designing by educators. Results: This paper proposed a hybrid teaching mode utilizing machine learning algorithms, which uses clustering analysis to analyze the learner’s characteristics and introduces a support vector machine to predict future learning performance. The hybrid mode matches the predicted results to carry out the offline teaching process. Conclusion: Simulation results on about 356 students’ data on one specific course in a certain semester demonstrate that the proposed hybrid teaching mode performs very well by analyzing and predicting the learners’ performance with high accuracies.

Transfer Learning

2010 ◽  
pp. 242-264 ◽  
Author(s):  
Lisa Torrey ◽  
Jude Shavlik
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Learning Community ◽  
Transfer Learning ◽  
Inductive Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Task Mapping ◽  
Transfer Of Knowledge ◽  
Open Problems

Transfer learning is the improvement of learning in a new task through the transfer of knowledge from a related task that has already been learned. While most machine learning algorithms are designed to address single tasks, the development of algorithms that facilitate transfer learning is a topic of ongoing interest in the machine-learning community. This chapter provides an introduction to the goals, settings, and challenges of transfer learning. It surveys current research in this area, giving an overview of the state of the art and outlining the open problems. The survey covers transfer in both inductive learning and reinforcement learning, and discusses the issues of negative transfer and task mapping.

scholarly journals An Overview of Reinforcement Learning Algorithms for Handover Management in 5G Ultra-Dense Small Cell Networks

2022 ◽  
Vol 12 (1) ◽  
pp. 426
Author(s):  
Jawad Tanveer ◽  
Amir Haider ◽  
Rashid Ali ◽  
Ajung Kim
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Small Cells ◽  
High Data ◽  
Fifth Generation ◽  
Learning Techniques ◽  
Machine Type Communications ◽  
Cell Networks

The fifth generation (5G) wireless technology emerged with marvelous effort to state, design, deployment and standardize the upcoming wireless network generation. Artificial intelligence (AI) and machine learning (ML) techniques are well capable to support 5G latest technologies that are expected to deliver high data rate to upcoming use cases and services such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). These services will surely help Gbps of data within the latency of few milliseconds in Internet of Things paradigm. This survey presented 5G mobility management in ultra-dense small cells networks using reinforcement learning techniques. First, we discussed existing surveys then we are focused on handover (HO) management in ultra-dense small cells (UDSC) scenario. Following, this study also discussed how machine learning algorithms can help in different HO scenarios. Nevertheless, future directions and challenges for 5G UDSC networks were concisely addressed.

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