Special Issue on Cutting Edge of Reinforcement Learning and its Hybrid Methods

2017 ◽  
Vol 21 (5) ◽  
pp. 833-833
Author(s):  
Keiki Takadama ◽  
Kazuteru Miyazaki
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Evolutionary Computation ◽  
Hybrid Methods ◽  
Cutting Edge ◽  
Large Contribution ◽  
Special Issue ◽  
Wide Range ◽  
Multiagent Environments

Machine learning has been attracting significant attention again since the potential of deep learning was recognized. Not only has machine learning been improved, but it has also been integrated with “reinforcement learning,” revealing other potential applications, e.g., deep Q-networks (DQN) and AlphaGO proposed by Google DeepMind. It is against this background that this special issue, “Cutting Edge of Reinforcement Learning and its Hybrid Methods,” focuses on both reinforcement learning and its hybrid methods, including reinforcement learning with deep learning or evolutionary computation, to explore new potentials of reinforcement learning.Of the many contributions received, we finally selected 13 works for publication. The first three propose hybrids of deep learning and reinforcement learning for single agent environments, which include the latest research results in the areas of convolutional neural networks and DQN. The fourth through seventh works are related to the Learning Classifier System, which integrates evolutionary computation and reinforcement learning to develop the rule discovery mechanism. The eighth and ninth works address problems related to goal design or the reward, an issue that is particularly important to the application of reinforcement learning. The last four contributions deal with multiagent environments.These works cover a wide range of studies, from the expansion of techniques incorporating simultaneous learning to applications in multiagent environments. All works are on the cutting edge of reinforcement learning and its hybrid methods. We hope that this special issue constitutes a large contribution to the development of the reinforcement learning field.

Special Issue on New Trends in Reinforcement Learning

2009 ◽  
Vol 13 (6) ◽  
pp. 599-599
Author(s):  
Kazuteru Miyazaki ◽  
◽  
Keiki Takadama ◽  
Keyword(s):  
Reinforcement Learning ◽  
Single Agent ◽  
Large Contribution ◽  
Special Issue ◽  
Learning Classifier System ◽  
Wide Range ◽  
Important Approach ◽  
Directed Learning ◽  
Multi Agent ◽  
Institute Of Technology

Recently, the tailor-made system that grants an individual request has been recognized as the important approach. Such a system requires the ggoal-directed learningh through interaction between user and system, which is mainly addressed in greinforcement learningh domain. This special issue on gNew Trends in Reinforcement Learningh called for papers on the cuttingedge research exploring the goal-directed learning, which represents reinforcement learning. Many contributions were forthcoming, but we finally selected 12 works for publication. Although greinforcement learningh is included in the title of this special issue, the research works do not necessarily have to be on reinforcement learning itself, so long as the theme coincides with that of this special issue. In making our final selections, we gave special consideration to the kinds of research which can actively lead to new trends in reinforcement learning. Of the 12 papers in this special issue, the first four mainly deal with the expansion of the reinforcement learning method in single agent environments. These cover a broad range of research, from works based on dynamic programming to exploitation-oriented methods. The next two works deal with the Learning Classifier System (LCS), which applies the rule discovery mechanism to reinforcement learning. LCS is a technique with a long history, but for this issue, we were able to publish two theoretical works. We are also grateful to Prof. Toshio Fukuda, Nagoya University, and Prof. Kaoru Hirota, Tokyo Institute of Technology, the editors-in-chief, and the NASTEC 2008 conference staff for inviting us to guest-edit this Journal. The next four papers mainly deal with multi agent environments. We were able to draw from a wide range of research: from measuring interaction, through the expansion of techniques incorporating simultaneous learning, to research leading to application in multi agent environments. The last two contributions mainly deal with application. We publish one paper on exemplar generalization and another detailing the successful application to government bond trading. Each of these researches can be considered to be at the cutting-edge of reinforcement learning. We would like to end by saying that we hope this special issue constitutes a large contribution to the development of the field while holding a wide international appeal.

scholarly journals Special Issue “Advances in Machine Learning and Deep Learning Based Machine Fault Diagnosis and Prognosis”

Processes ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 532
Author(s):  
Mohand Djeziri ◽  
Marc Bendahan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Special Issue ◽  
Maintenance Strategies ◽  
Corrective Maintenance ◽  
Diagnosis And Prognosis ◽  
Machine Fault Diagnosis ◽  
Failure Prognosis ◽  
Fault Diagnosis And Prognosis

Fault diagnosis and failure prognosis aim to reduce downtime of the systems and to optimise their performance by replacing preventive and corrective maintenance strategies with predictive or conditional ones [...]

scholarly journals Share Market Prediction using Deep Neural Network

2019 ◽  
Vol 8 (3) ◽  
pp. 8619-8622
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Stock Market ◽  
Deep Neural Network ◽  
Learning Technology ◽  
Financial Investment ◽  
The Future ◽  
Future Prediction

People, due to their complexity and volatile actions, are constantly faced with challenges in understanding the situation in the market share and the forecast for the future. For any financial investment, the stock market is a very important aspect. It is necessary to study while understanding the price fluctuations of the stock market. In this paper, the stock market prediction model using the Recurrent Digital natural Network (RDNN) is described. The model is designed using two important machine learning concepts: the recurrent neural network (RNN), multilayer perceptron (MLP) and reinforcement learning (RL). Deep learning is used to automatically extract important functions of the stock market; reinforcement learning of these functions will be useful for future prediction of the stock market, the system uses historical stock market data to understand the dynamic market behavior when you make decisions in an unknown environment. In this paper, the understanding of the dynamic stock market and the deep learning technology for predicting the price of the future stock market are described.

Deep Neural Networks for Hierarchical Taxonomic Fossil Classification of Carbonate Skeletal Grains

2021 ◽  
Author(s):  
Sidhant Idgunji ◽  
Madison Ho ◽  
Jonathan L. Payne ◽  
Daniel Lehrmann ◽  
Michele Morsilli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Single Class ◽  
Deep Learning Algorithm ◽  
Human Ability ◽  
Wide Range ◽  
Potential Applications ◽  
Animal Phyla

<p>The growing digitization of fossil images has vastly improved and broadened the potential application of big data and machine learning, particularly computer vision, in paleontology. Recent studies show that machine learning is capable of approaching human abilities of classifying images, and with the increase in computational power and visual data, it stands to reason that it can match human ability but at much greater efficiency in the near future. Here we demonstrate this potential of using deep learning to identify skeletal grains at different levels of the Linnaean taxonomic hierarchy. Our approach was two-pronged. First, we built a database of skeletal grain images spanning a wide range of animal phyla and classes and used this database to train the model. We used a Python-based method to automate image recognition and extraction from published sources. Second, we developed a deep learning algorithm that can attach multiple labels to a single image. Conventionally, deep learning is used to predict a single class from an image; here, we adopted a Branch Convolutional Neural Network (B-CNN) technique to classify multiple taxonomic levels for a single skeletal grain image. Using this method, we achieved over 90% accuracy for both the coarse, phylum-level recognition and the fine, class-level recognition across diverse skeletal grains (6 phyla and 15 classes). Furthermore, we found that image augmentation improves the overall accuracy. This tool has potential applications in geology ranging from biostratigraphy to paleo-bathymetry, paleoecology, and microfacies analysis. Further improvement of the algorithm and expansion of the training dataset will continue to narrow the efficiency gap between human expertise and machine learning.</p>

Selected Papers from NaBIC 2010

2011 ◽  
Vol 15 (9) ◽  
pp. 1299-1299
Author(s):  
Yusuke Nojima ◽  
Mario K?ppen
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Evolutionary Computation ◽  
Machine Intelligence ◽  
Darwinian Evolution ◽  
Special Issue ◽  
Interactive Evolutionary Computation ◽  
Biologically Inspired ◽  
Fitness Value ◽  
Wide Range

The Second World Congress on Nature and Biologically Inspired Computing (NaBIC2010) was held at the Kitakyushu International Conference Center December 15-17, 2010, in Kitakyushu, Japan. NaBIC2010 provided a forum for researchers, engineers, and students from worldwide to discuss state-of-the-art machine intelligence and to address issues related to building human-friendly machines by learning from nature. NaBIC2010 covered a wide range of studies ? from theoretical and algorithmic studies on nature and biologically inspired computing techniques to their real-world applications. Top researchers presenting papers at NaBIC2010 were invited to contribute to this special issue. Through a fair peer review process, four extended papers have been accepted ? an acceptance rate of 50%. The first paper entitled gA Study on Computational Efficiency and Plasticity in Baldwinian Learningh by Liu and Iba analyzes Baldwinian evolution efficiency by comparing it to alternatives such as standard Darwinian evolution with no learning, Lamarckian evolution, and Baldwinian evolution with different learning and plasticity evolution. The second paper entitled gExperimental Study of a Structured Differential Evolution with Mixed Strategiesh by Ishimizu and Tagawa proposes island-based DE with ring or torus networks. The authors examine the performance of the proposed DE with the effects of different strategies. The third paper entitled gMulti-Space Competitive DGA for Model Selection and its Application to Localization of Multiple Signal Sourcesh by Ishikawa, Misawa, Kubota, Tokiwa, Horio, and Yamakawa proposes a distributed genetic algorithm in which each subpopulation searches for a solution in different decision space. Subpopulations change size based on search progress. The fourth paper entitled gAn Extended Interactive Evolutionary Computation Using Heart Rate Variability as Fitness Value for Composing Music Chord Progressh by Fukumoto, Nakashima, Ogawa, and Imai uses heart-rate variability instead of direct human evaluations in an interactive evolutionary computation framework. As guest editors of this special issue, we would like to thank the authors for their unique and interesting contributions and the reviewers for their careful checking and invaluable comments.

scholarly journals ARTIFICIAL INTELLIGENCE AND NEXT GENERATION PATHOLOGY: TOWARDS PERSONALIZED MEDICINE

2021 ◽  
Vol 65 (2) ◽  
Author(s):  
Oleksandr Dudin ◽  
◽  
Ozar Mintser ◽  
Oksana Sulaieva ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Analysis ◽  
Deep Learning ◽  
Personalized Medicine ◽  
Digital Pathology ◽  
Automated Image Analysis ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Wide Range

Introduction. Over the past few decades, thanks to advances in algorithm development, the introduction of available computing power, and the management of large data sets, machine learning methods have become active in various fields of life. Among them, deep learning possesses a special place, which is used in many spheres of health care and is an integral part and prerequisite for the development of digital pathology. Objectives. The purpose of the review was to gather the data on existing image analysis technologies and machine learning tools developed for the whole-slide digital images in pathology. Methods: Analysis of the literature on machine learning methods used in pathology, staps of automated image analysis, types of neural networks, their application and capabilities in digital pathology was performed. Results. To date, a wide range of deep learning strategies have been developed, which are actively used in digital pathology, and demonstrated excellent diagnostic accuracy. In addition to diagnostic solutions, the integration of artificial intelligence into the practice of pathomorphological laboratory provides new tools for assessing the prognosis and prediction of sensitivity to different treatments. Conclusions: The synergy of artificial intelligence and digital pathology is a key tool to improve the accuracy of diagnostics, prognostication and personalized medicine facilitation

scholarly journals The general design of the automation for multiple fields using reinforcement learning algorithm

2022 ◽  
Vol 25 (1) ◽  
pp. 481
Author(s):  
Vijaya Kumar Reddy Radha ◽  
Anantha N. Lakshmipathi ◽  
Ravi Kumar Tirandasu ◽  
Paruchuri Ravi Prakash
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Optimization Methods ◽  
Graph Representation ◽  
Machine Learning Technique ◽  
Learning Society ◽  
Learning Technique ◽  
Learning Concept

<p>Reinforcement learning is considered as a machine learning technique that is anxious with software agents should behave in particular environment. Reinforcement learning (RL) is a division of deep learning concept that assists you to make best use of some part of the collective return. In this paper evolving reinforcement learning algorithms shows possible to learn a fresh and understable concept by using a graph representation and applying optimization methods from the auto machine learning society. In this observe, we stand for the loss function, it is used to optimize an agent’s parameter in excess of its knowledge, as an imputational graph, and use traditional evolution to develop a population of the imputational graphs over a set of uncomplicated guidance environments. These outcomes in gradually better RL algorithms and the exposed algorithms simplify to more multifaceted environments, even though with visual annotations.</p>

scholarly journals Anti-CRISPR RNAs: designing universal riboregulators with deep learning of Csy4-mediated RNA processing

2020 ◽  
Author(s):  
Haotian Guo ◽  
Xiaohu Song ◽  
Ariel B. Lindner
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Rna Processing ◽  
Binding Sites ◽  
Transcriptional Networks ◽  
Promising Alternative ◽  
Wide Range ◽  
Flanking Sequences ◽  
Structural Commonality

AbstractRNA-based regulation offers a promising alternative of protein-based transcriptional networks. However, designing synthetic riboregulators with desirable functionalities using arbitrary sequences remains challenging, due in part to insufficient exploration of RNA sequence-to-function landscapes. Here we report that CRISPR-Csy4 mediates a nearly all-or-none processing of precursor CRISPR RNAs (pre-crRNAs), by profiling Csy4 binding sites flanked by > 1 million random sequences. This represents an ideal sequence-to-function space for universal riboregulator designs. Lacking discernible sequence-structural commonality among processable pre-crRNAs, we trained a neural network for accurate classification (f1-score ≈ 0.93). Inspired by exhaustive probing of palindromic flanking sequences, we designed anti-CRISPR RNAs (acrRNAs) that suppress processing of pre-crRNAs via stem stacking. We validated machine-learning-guided designs with >30 functional pairs of acrRNAs and pre-crRNAs to achieve switch-like properties. This opens a wide range of plug-and-play applications tailored through pre-crRNA designs, and represents a programmable alternative to protein-based anti-CRISPRs.

scholarly journals Network Intrusion Detection using a Deep Learning Approach

2020 ◽  
Vol 9 (3) ◽  
pp. 59-64
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Research Work ◽  
Computer Hardware ◽  
Classification Model ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Wide Range

At present situation network communication is at high risk for external and internal attacks due to large number of applications in various fields. The network traffic can be monitored to determine abnormality for software or hardware security mechanism in the network using Intrusion Detection System (IDS). As attackers always change their techniques of attack and find alternative attack methods, IDS must also evolve in response by adopting more sophisticated methods of detection .The huge growth in the data and the significant advances in computer hardware technologies resulted in the new studies existence in the deep learning field, including ID. Deep Learning (DL) is a subgroup of Machine Learning (ML) which is hinged on data description. The new model based on deep learning is presented in this research work to activate operation of IDS from modern networks. Model depicts combination of deep learning and machine learning, having capacity of wide range accurate analysis of traffic network. The new approach proposes non-symmetric deep auto encoder (NDAE) for learning the features in unsupervised manner. Furthermore, classification model is constructed using stacked NDAEs for classification. The performance is evaluated using a network intrusion detection analysis dataset, particularly the WSN Trace dataset. The contribution work is to implement advanced deep learning algorithm consists IDS use, which are efficient in taking instant measures in order to stop or minimize the malicious actions

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