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2022 ◽  
Vol 54 (8) ◽  
pp. 1-34
Fuqiang Gu ◽  
Mu-Huan Chung ◽  
Mark Chignell ◽  
Shahrokh Valaee ◽  
Baoding Zhou ◽  

Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods.

Ali Saeed ◽  
Rao Muhammad Adeel Nawab ◽  
Mark Stevenson

Word Sense Disambiguation (WSD), the process of automatically identifying the correct meaning of a word used in a given context, is a significant challenge in Natural Language Processing. A range of approaches to the problem has been explored by the research community. The majority of these efforts has focused on a relatively small set of languages, particularly English. Research on WSD for South Asian languages, particularly Urdu, is still in its infancy. In recent years, deep learning methods have proved to be extremely successful for a range of Natural Language Processing tasks. The main aim of this study is to apply, evaluate, and compare a range of deep learning methods approaches to Urdu WSD (both Lexical Sample and All-Words) including Simple Recurrent Neural Networks, Long-Short Term Memory, Gated Recurrent Units, Bidirectional Long-Short Term Memory, and Ensemble Learning. The evaluation was carried out on two benchmark corpora: (1) the ULS-WSD-18 corpus and (2) the UAW-WSD-18 corpus. Results (Accuracy = 63.25% and F1-Measure = 0.49) show that a deep learning approach outperforms previously reported results for the Urdu All-Words WSD task, whereas performance using deep learning approaches (Accuracy = 72.63% and F1-Measure = 0.60) are low in comparison to previously reported for the Urdu Lexical Sample task.

2022 ◽  
Vol 167 ◽  
pp. 108765
Zixiao Yang ◽  
Peng Xu ◽  
Biao Zhang ◽  
Chuanlong Xu ◽  
Liming Zhang ◽  

2022 ◽  
Vol 73 ◽  
pp. 103414
Alexander Chikov ◽  
Nikolay Egorov ◽  
Dmitry Medvedev ◽  
Svetlana Chikova ◽  
Evgeniy Pavlov ◽  

2028 ◽  
Vol 4 (2) ◽  
pp. 10-14
Abdul Kabir Aineka ◽  
Muhammad Rusdi Rasyid

This research is motivated by the low learning outcomes of students of class VIII MTs Al-Akbar Sorong City on Jurisprudence subjects caused by Jurisprudence teachers in presenting subject matter which is sometimes monotonous. Teachers are more likely to use the lecture method in learning so as to make students bored. Therefore the researcher chose one of the Articulation learning methods to improve student learning outcomes. This method uses a chain message delivery system, which is from the teacher to students and is passed from one student to another student. This study aims to improve student learning outcomes in Jurisprudence subjects using Articating learning methods for students of class VIII MTs Al-Akbar Sorong City. This type of research is classroom action research (CAR). The subject was students in class VIII MTs Al-Akbar Sorong City in the odd semester of 2016/2017 academic year totaling 38 people. This research was conducted in 2 cycles, namely the first cycle and the second cycle carried out as many as 4 meetings. Data retrieval is done by using test results of learning and observation. The collected data is analyzed quantitatively and qualitatively. Quantitative data is calculated using the SPSS 16.0 formula. The results obtained after the action are given, namely: (1) the activeness of students during the learning process in class has increased, (2) in the first cycle the average score of student learning outcomes tests on Jurisprudence subjects between the first and second meetings in the first cycle is 62, 89% and 74.47% and in the second cycle, the average test score of student learning outcomes in fiqh subjects has increased ie, 80.79% and 94.34%. From the results of this study, in general, it can be concluded that an increase in student learning outcomes in the subjects of Jurisprudence VIII MTs Al-Akbar Sorong after applying the Articulation method

2022 ◽  
Vol 307 ◽  
pp. 118251
Peipei Chen ◽  
Yi Wu ◽  
Honglin Zhong ◽  
Yin Long ◽  
Jing Meng

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