Optimising online review inspired product attribute classification using the self-learning particle swarm-based Bayesian learning approach

Abstract The study explores the problems of reinforcement learning and finding non-obvious play strategies using reinforcement learning. Two approaches to agent training (blind and pattern-based) are considered and implemented. The advantage of the self-learning approach with reinforcement using patterns as applied to a specific game (tic-tac-toe five in a row) is shown. Recorded and analyzed the use of unusual strategies by an agent using a pattern-based approach.

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Blended Learning Case Study: “New to the Leadership Role”

International Journal of Advanced Corporate Learning (iJAC) ◽

10.3991/ijac.v12i2.11374 ◽

2019 ◽

Vol 12 (2) ◽

pp. 79

Author(s):

Ruben Weiser

Keyword(s):

Blended Learning ◽

Learning Experience ◽

Leadership Role ◽

The Self ◽

Working Environment ◽

Learning Approach ◽

Learning Program ◽

Leadership Program ◽

Face To Face ◽

Self Learning

The blended learning „Leadership Program“ is a clever combination of self-learning and face-to-face-teaching periods. Heart of the self-learning period are interactive exercises and multimedia eLearning videos. A moderator, who leads trough every section of the eLearning, ensures a strong involvement for the participants. With the help of valuable play scenes with real actors, the participants get encouraged to question their own behavior in daily business. In addition, there are graphically animated explainer videos and highly qualified interactive learning exercises.The eLearnings contain also transfer-exercises, which empower participants to adapt new learning contents easily to their working environment and gain valuable experiences.The blended learning “Leadership Program” contains, beside the eLearning videos, two face-to-face teaching periods. These periods are very important for the learning experience, because they put focus on self-reflection and refer to the transfer-exercises from the eLearnings. The trainer discusses with the participants their experiences, encourages them to share their findings from the self-learn period and provides feedback and advices. The face-to-face teaching is not about delivering knowledge, this happens during the self-learning period, it is about strengthen it.In the blended learning approach, the trainer is a learning companion, who guides participants through the different learning periods. Therefore, the trainer is always available for guidance during the self-learning period. The participants can get back to them over phone or email. All trainers have a special certificate, which enables them to teach with our blended learning approach. They know all eLearnings and transfer-exercises very well. Furthermore, a trainer guideline was specially developed for every blended learning program. This is possible due to our cooperation with the training company “Pawlik”, which gives us the opportunity to work with 150 specialized and highly certified trainers.Self-learning and face-to-face teaching periods are framed by webinars. In the first session the participants get a detailed overview about the blended learning approach and its structure. In addition, they have the opportunity to introduce their self and get to know each other. The blended learning journey ends with two coaching calls, where the trainer offers support to the participants, in case they struggle to put their learnings into practice.Our blended learning “Leadership Program” can be booked over Pink University. There are no extra bookings required for the trainers. It is structured in modules and can be adapted easily to customer needs.

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Deep learning-based question answering system for intelligent humanoid robot

Journal Of Big Data ◽

10.1186/s40537-020-00341-6 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Widodo Budiharto ◽

Vincent Andreas ◽

Alexander Agung Santoso Gunawan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Knowledge Base ◽

Humanoid Robot ◽

Question Answering ◽

The Self ◽

Learning Approach ◽

Learning Capability ◽

Question Answering Systems ◽

Self Learning

Abstract Background The development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. In this research, we would like to propose an Intelligent Humanoid Robot with the self-learning capability for accepting and giving responses from people based on Deep Learning and Big Data knowledge base. This kind of robot can be used widely in hotels, universities, and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action. Findings The question from the user will be processed using deep learning, and the result will be compared to the knowledge base on the system. We proposed our Deep Learning approach, based on Recurrent Neural Network (RNN) encoder, Convolution Neural Network (CNN) encoder, with Bidirectional Attention Flow (BiDAF). Conclusions Our evaluation indicates that using RNN based encoder with BiDAF gives a higher score, than CNN encoder with the BiDAF. Based on our experiment, our model get 82.43% F1 score and the RNN based encoder will give a higher EM/F1 score than using the CNN encoder.

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Deep Learning-Based Question Answering System for Intelligent Humanoid Robot

10.21203/rs.3.rs-26510/v2 ◽

2020 ◽

Author(s):

Widodo Budiharto ◽

Vincent Andreas ◽

Alexander Agung Santoso Gunawan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Knowledge Base ◽

Humanoid Robot ◽

Question Answering ◽

The Self ◽

Learning Approach ◽

Learning Capability ◽

Question Answering Systems ◽

Self Learning

Abstract Background- The development of Intelligent Humanoid Robot focuses on question answering systems that can interact with people is very limited. In this research, we would like to propose an Intelligent Humanoid Robot with the self-learning capability for accepting and giving responses from people based on Deep Learning and Big Data knowledge base. This kind of robot can be used widely in hotels, universities, and public services. The Humanoid Robot should consider the style of questions and conclude the answer through conversation between robot and user. In our scenario, the robot will detect the user’s face and accept commands from the user to do an action. Findings- The question from the user will be processed using deep learning, and the result will be compared to the knowledge base on the system. We proposed our Deep Learning approach, based on Recurrent Neural Network (RNN) encoder, Convolution Neural Network (CNN) encoder, with Bidirectional Attention Flow (BiDAF). Conclusions- Our evaluation indicates that using RNN based encoder with BiDAF gives a higher score, than CNN encoder with the BiDAF. Based on our experiment, our model get 82.43% F1 score and the RNN based encoder will give a higher EM / F1 score than using the CNN encoder.

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A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning

Mathematical Problems in Engineering ◽

10.1155/2019/9367093 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

En Lu ◽

Lizhang Xu ◽

Yaoming Li ◽

Zheng Ma ◽

Zhong Tang ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Path Planning ◽

Learning Strategies ◽

Learning Strategy ◽

Particle Swarm ◽

The Self ◽

Swarm Optimization ◽

Vehicle Path ◽

Improved Learning ◽

Self Learning

In order to balance the exploration and exploitation capabilities of the PSO algorithm to enhance its robustness, this paper presents a novel particle swarm optimization with improved learning strategies (ILSPSO). Firstly, the proposed ILSPSO algorithm uses a self-learning strategy, whereby each particle stochastically learns from any better particles in the current personal history best position (pbest), and the self-learning strategy is adjusted by an empirical formula which expresses the relation between the learning probability and evolution iteration number. The cognitive learning part is improved by the self-learning strategy, and the optimal individual is reserved to ensure the convergence speed. Meanwhile, based on the multilearning strategy, the global best position (gbest) of particles is replaced with randomly chosen from the top k of gbest and further improve the population diversity to prevent premature convergence. This strategy improves the social learning part and enhances the global exploration capability of the proposed ILSPSO algorithm. Then, the performance of the ILSPSO algorithm is compared with five representative PSO variants in the experiments. The test results on benchmark functions demonstrate that the proposed ILSPSO algorithm achieves significantly better overall performance and outperforms other tested PSO variants. Finally, the ILSPSO algorithm shows satisfactory performance in vehicle path planning and has a good result on the planned path.

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The Choice of Sample Size for Mortality Forecasting: A Bayesian Learning Approach

SSRN Electronic Journal ◽

10.2139/ssrn.2532408 ◽

2014 ◽

Cited By ~ 1

Author(s):

Hong Li ◽

Anja De Waegenaere ◽

Bertrand Melenberg

Keyword(s):

Sample Size ◽

Bayesian Learning ◽

Learning Approach ◽

Mortality Forecasting

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Artificial Intelligence in Biomedicine: A Legal Insight

BioTech ◽

10.3390/biotech10030015 ◽

2021 ◽

Vol 10 (3) ◽

pp. 15

Author(s):

Takis Vidalis

Keyword(s):

Artificial Intelligence ◽

Personal Data ◽

Health Data ◽

The Self ◽

Data Handling ◽

Direct Control ◽

Eu Legislation ◽

Attending Physicians ◽

Self Learning ◽

The Eu

The involvement of artificial intelligence in biomedicine promises better support for decision-making both in conventional and research medical practice. Yet two important issues emerge in relation to personal data handling, and the influence of AI on patient/doctor relationships. The development of AI algorithms presupposes extensive processing of big data in biobanks, for which procedures of compliance with data protection need to be ensured. This article addresses this problem in the framework of the EU legislation (GDPR) and explains the legal prerequisites pertinent to various categories of health data. Furthermore, the self-learning systems of AI may affect the fulfillment of medical duties, particularly if the attending physicians rely on unsupervised applications operating beyond their direct control. The article argues that the patient informed consent prerequisite plays a key role here, not only in conventional medical acts but also in clinical research procedures.

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