1,721,134 research outputs found
Smart Geo Expo, Seoul Korea 2019
No full textThis data is collected during Smart Geo Expo which was held in COEX center Seoul, Korea in 2019. It contains Wi-Fi data from Galaxy S8 and LG G6 which can be used to test indoor positioning techniques
Catastrophic factors involved in road accidents: Underlying causes and descriptive analysis
No full textIt contains the road accidents data for South Korea including various factors
Catastrophic factors involved in road accidents: Underlying causes and descriptive analysis
No full textIt contains the road accidents data for South Korea including various factors
Smart Geo Expo, Seoul Korea 2019
No full textThis data is collected during Smart Geo Expo which was held in COEX center Seoul, Korea in 2019. It contains Wi-Fi data from Galaxy S8 and LG G6 which can be used to test indoor positioning techniques
Student academic success prediction in multimedia-supported virtual learning system using ensemble learning approach
No full textETCNN: Extra Tree and Convolutional Neural Network-based Ensemble Model for COVID-19 Tweets Sentiment Classification
No full textPandemics influence people negatively and people experience fear and disappointment. With the global outspread of COVID-19, the sentiments of the general public are substantially influenced, and analyzing their sentiments could help to devise corresponding policies to alleviate negative sentiments. Often the data collected from social media platforms is unstructured leading to low classification accuracy. This study brings forward an ensemble model where the benefits of handcrafted features and automatic feature extraction are combined by machine learning and deep learning models. Unstructured data is obtained, preprocessed, and annotated using TextBlob and VADER before training machine learning models. Similarly, the efficiency of Word2Vec, TF, and TF-IDF features is also analyzed. Results reveal the better performance of the extra tree classifier when trained with TF-IDF features from TextBlob annotated data. Overall, machine learning models perform better with TF-IDF and TextBlob. The proposed model obtains superior performance using both annotation techniques with 0.97 and 0.95 scores of accuracy using TextBlob and VADER respectively with Word2Vec features. Results reveal that use of machine learning and deep learning models together with a voting criterion tends to yield better results than other machine learning models. Analysis of sentiments indicates that predominantly people possess negative sentiments regarding COVID-19
An improved skin lesion detection solution using multi-step preprocessing features and NASNet transfer learning model
No full textImproving the review classification of Google apps using combined feature embedding and deep convolutional neural network model
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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