다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Statistical Analysis with Missing Data

Catriona Mackenzie and Jacqui Poltera’s discussion of narrative integration and identity takes up a wide range of issues. It advances objections against Galen Strawson’s critique of narrative identity and defends both the claim that some narrative conception of identity can capture descriptively the ways in which human agents experience and make sense of their lives and also the claim that some form of narrative self-interpretation is required for a fulfilling, well-lived life. In defending these claims, Mackenzie and Poltera appeal, in part, to Elyn Saks’s autobiographical account of her long, on-going struggle with schizophrenia and the significance of this struggle for her selfunderstanding (Saks 2007). According to the authors,

/pdf/on-narrative-identity-4e5q4c50sx.pdf

On narrative identity

Sensor-based and vision-based human activity recognition: A comprehensive survey

Time in language.

Motor Imagery Brain Computer Interface (MI-BCI) provides a non-muscular channel for communication to those who are suffering from neuronal disorders. The designing of an accurate and reliable MI-BCI system requires the extraction of informative and discriminative features. Common Spatial Pattern (CSP) has been potent and is widely used in BCI for extracting features in motor imagery tasks. The classifiers translate these features into device commands. Many classification algorithms have been devised, among those Support Vector Machine (SVM) and Linear Discriminate Analysis (LDA) have been widely used. In recent studies, the researchers are using deep neural networks for the classification of motor imagery tasks. This paper provides a comprehensive review of dominant feature extraction methods and classification algorithms in brain-computer interface for motor imagery tasks. Authors discuss existing challenges in the domain of motor imagery brain-computer interface and suggest possible research directions.

Signal processing techniques for motor imagery brain computer interface: A review

In the era of MOOCs, online exams are taken by millions of candidates, where scoring short answers is an integral part. It becomes intractable to evaluate them by human graders. Thus, a generic automated system capable of grading these responses should be designed and deployed. In this paper, we present a fast, scalable, and accurate approach towards automated Short Answer Scoring (SAS). We propose and explain the design and development of a system for SAS, namely AutoSAS. Given a question along with its graded samples, AutoSAS can learn to grade that prompt successfully. This paper further lays down the features such as lexical diversity, Word2Vec, prompt, and content overlap that plays a pivotal role in building our proposed model. We also present a methodology for indicating the factors responsible for scoring an answer. The trained model is evaluated on an extensively used public dataset, namely Automated Student Assessment Prize Short Answer Scoring (ASAP-SAS). AutoSAS shows state-of-the-art performance and achieves better results by over 8% in some of the question prompts as measured by Quadratic Weighted Kappa (QWK), showing performance comparable to humans.

/pdf/get-it-scored-using-autosas-an-automated-system-for-scoring-14y00dlgh4.pdf

Get IT Scored Using AutoSAS — An Automated System for Scoring Short Answers

Review of Machine Learning Techniques for EEG Based Brain Computer Interface

Database store datasets that are not always complete. They contain missing fields inside some records, that may occur due to human or system error involved in a data collection task. Data imputation is the process of filling in the missing value to generate complete records. Complete databases can be analyzed more accurately in comparison to incomplete databases. This paper proposes a 2-stage hybrid model for filling in the missing values using fuzzy c-means clustering and multilayer perceptron (MLP) working in sequence and compares it with k -means imputation and fuzzy c -means (FCM) imputation. The accuracy of the model is checked using Mean Absolute Percentage Error (MAPE). The MAPE value obtained shows that the proposed model is more accurate in filling multiple values in a record compared to stage 1 alone.

Hybrid model for data imputation: Using fuzzy c means and multi layer perceptron

Predicting likelihood of psychological disorders in PlayerUnknown's Battlegrounds (PUBG) players from Asian countries using supervised machine learning.

Swati Aggarwal

Papers

Signal processing techniques for motor imagery brain computer interface: A review

Get IT Scored Using AutoSAS — An Automated System for Scoring Short Answers

Review of Machine Learning Techniques for EEG Based Brain Computer Interface

Hybrid model for data imputation: Using fuzzy c means and multi layer perceptron

Predicting likelihood of psychological disorders in PlayerUnknown's Battlegrounds (PUBG) players from Asian countries using supervised machine learning.