Data Mining - Concepts and Techniques.

It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

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Computational Thinking 計算論的思考

Machine learning for reliability engineering and safety applications: Review of current status and future opportunities

Machine learning (ML) pervades an increasing number of academic disciplines and industries. Its impact is profound, and several fields have been fundamentally altered by it, autonomy and computer vision for example; reliability engineering and safety will undoubtedly follow suit. There is already a large but fragmented literature on ML for reliability and safety applications, and it can be overwhelming to navigate and integrate into a coherent whole. In this work, we facilitate this task by providing a synthesis of, and a roadmap to this ever-expanding analytical landscape and highlighting its major landmarks and pathways. We first provide an overview of the different ML categories and sub-categories or tasks, and we note several of the corresponding models and algorithms. We then look back and review the use of ML in reliability and safety applications. We examine several publications in each category/sub-category, and we include a short discussion on the use of Deep Learning to highlight its growing popularity and distinctive advantages. Finally, we look ahead and outline several promising future opportunities for leveraging ML in service of advancing reliability and safety considerations. Overall, we argue that ML is capable of providing novel insights and opportunities to solve important challenges in reliability and safety applications. It is also capable of teasing out more accurate insights from accident datasets than with traditional analysis tools, and this in turn can lead to better informed decision-making and more effective accident prevention.

Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities

One ring to rule them all

Machine learning is currently identified as one of the major parts of the research in Robotics. However the advanced concept of machine learning plus optimization reported effective for developing learning systems. This article considers the novel integration of machine learning and optimization for the complex and dynamic context of Robot learning. Further the proposed case study presents an effective framework for learning and solving the global optimization problem within the context of Robotics and learning.

Learning in Robotics

The paper represents the state-of-the-art review of the machine learning methods for hate speech detection. This paper reviews novel applications of machine learning algorithms in hate speech. The machine learning based three algorithms i.e., Long-Short Term Memory, random forest, convolution neural network found to be most useful in hate speech detection. These algorithms are found to be most useful for twitter, Facebook, and other social platforms. This paper briefly surveys the most usable deep learning algorithms for detecting the hate speech in Arabic, English, Hindi, and other languages. The review result shows that the mentioned machine learning algorithms give an excellent results over other deep learning algorithm. Therefore, these three algorithms are widely acceptable for the evaluation of hate speech.

Annamaria Varkonyi

Papers

Learning in Robotics

A Review on Emotion Based Harmful Speech Detection Using Machine Learning