Therapy of short‐term memory disorders in fluent aphasia: A single case study

Aphasiology ◽  
2007 ◽  
Vol 21 (5) ◽  
pp. 448-458 ◽  
Author(s):  
Monica Koenig‐Bruhin ◽  
Felix Studer‐Eichenberger
Keyword(s):  
Short Term Memory ◽  
Single Case ◽  
Memory Disorders ◽  
Single Case Study ◽  
Short Term ◽  
Term Memory ◽  
Fluent Aphasia

scholarly journals Arabic dialect sentiment analysis with ZERO effort. \\ Case study: Algerian dialect

2020 ◽  
Vol 23 (65) ◽  
pp. 124-135
Author(s):  
Imane Guellil ◽  
Marcelo Mendoza ◽  
Faical Azouaou
Keyword(s):  
Sentiment Analysis ◽  
Short Term Memory ◽  
State Of The Art ◽  
Short Term ◽  
Term Memory ◽  
Ongoing Work ◽  
Long Short Term Memory ◽  
Large Corpus ◽  
Unique Condition

This paper presents an analytic study showing that it is entirely possible to analyze the sentiment of an Arabic dialect without constructing any resources. The idea of this work is to use the resources dedicated to a given dialect \textit{X} for analyzing the sentiment of another dialect \textit{Y}. The unique condition is to have \textit{X} and \textit{Y} in the same category of dialects. We apply this idea on Algerian dialect, which is a Maghrebi Arabic dialect that suffers from limited available tools and other handling resources required for automatic sentiment analysis. To do this analysis, we rely on Maghrebi dialect resources and two manually annotated sentiment corpus for respectively Tunisian and Moroccan dialect. We also use a large corpus for Maghrebi dialect. We use a state-of-the-art system and propose a new deep learning architecture for automatically classify the sentiment of Arabic dialect (Algerian dialect). Experimental results show that F1-score is up to 83% and it is achieved by Multilayer Perceptron (MLP) with Tunisian corpus and with Long short-term memory (LSTM) with the combination of Tunisian and Moroccan. An improvement of 15% compared to its closest competitor was observed through this study. Ongoing work is aimed at manually constructing an annotated sentiment corpus for Algerian dialect and comparing the results

scholarly journals Prediksi Kunjungan Wisatawan Taman Nasional Gunung Merbabu dengan Time Series Forecasting dan LSTM

2020 ◽  
Vol 11 (2) ◽  
pp. 131
Author(s):  
Josua Manullang ◽  
Albertus Joko Santoso ◽  
Andi Wahju Rahardjo Emanuel
Keyword(s):  
Time Series ◽  
National Park ◽  
Short Term Memory ◽  
Lag Time ◽  
Time Series Forecasting ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract. Prediction of tourist visits of Mount Merbabu National Park (TNGMb) needs to be done to control the number of visitors and to preserve the national park. The combination of time series forecasting (TSF) and deep learning methods has become a new alternative for prediction. This case study was conducted to implement several methods combination of TSF and Long-Short Term Memory (LSTM) to predict the visits. In this case study, there are 18 modelling scenarios as research objects to determine the best model by utilizing tourist visits data from 2013 to 2018. The results show that the model applying the lag time method can improve the model's ability to capture patterns on time series data. The error value is measured using the root mean square error (RMSE), with the smallest value of 3.7 in the LSTM architecture, using seven lags as a feature and one lag as a label.Keywords: Tourist Visit, Taman Nasional Gunung Merbabu, Prediction, Recurrent Neural Network, Long-Short Term MemoryAbstrak. Prediksi kunjungan wisatawan Taman Nasional Gunung Merbabu (TNGMb) perlu dilakukan untul pengendalian jumlah pengunjung dan menjaga kelestarian taman nasional. Gabungan metode antara time series forecasting (TSF) dan deep learning telah menjadi alternatif baru untuk melakukan prediksi. Studi kasus ini dilakukan untuk mengimplementasi gabungan dari beberapa macam metode antara TSF dan Long-Short Term Memory (LSTM) untuk memprediksi kunjungan pada TNGMb. Pada studi kasus ini, terdapat 18 skenario pemodelan sebagai objek penelitian untuk menentukan model terbaik, dengan memanfaatkan data jumlah kunjungan wisatawan di TNGMb mulai dari tahun 2013 sampai dengan tahun 2018. Hasil prediksi menunjukkan pemodelan dengan menerapkan metode lag time dapat meningkatakan kemampuan model untuk menangkap pola pada data deret waktu. Besar nilai kesalahan diukur menggunakan root mean square error (RMSE), dengan nilai terkecil sebesar 3,7 pada arsitektur LSTM, menggunakan tujuh lag sebagai feature dan satu lag sebagai label. Kata Kunci: Kunjungan Wisatawan, Taman Nasional Gunung Merbabu, Prediksi, Recurrent Neural Network, Long-Short Term Memory

Peak Load Forecasting Using Long-Short Term Memory : Case Study of Jawa-Madura-Bali System

2021 ◽  
Author(s):  
Anindita Satria Surya ◽  
Musa Partahi Marbun ◽  
K.G.H. Mangunkusumo ◽  
Muhammad Ridwan
Keyword(s):  
Short Term Memory ◽  
Peak Load ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

MANAGING CHALLENGING BEHAVIOUR IN AN ADULT WITH LEARNING DISABILITIES: THE USE OF LOW AROUSAL APPROACH

1998 ◽  
Vol 26 (2) ◽  
pp. 163-171 ◽  
Author(s):  
Andrew McDonnell ◽  
Simon Reeves ◽  
Amanda Johnson ◽  
Alan Lane
Keyword(s):  
Learning Disabilities ◽  
Behaviour Change ◽  
Single Case ◽  
Challenging Behaviour ◽  
Single Case Study ◽  
Short Term ◽  
One Year ◽  
Challenging Behaviours

Behaviour change is often a desired outcome for carers and professionals who work with people with learning disabilities and challenging behaviours. Managing these behaviours in the short term is an important step towards this goal. This single case study presents the use of a low arousal approach in managing challenging behaviours in a young man labelled with severe challenging behaviour. This strategy focuses on the interaction of the carers with the client and how their approach has an important impact on the behaviour of the client. The study documents the incidents of challenging behaviour and shows a decline in their frequency from baseline over a one-year period. These gains were maintained at five-month follow-up. The implications for services of these findings are discussed.

Time Series Forecasting in Retail Sales Using LSTM and Prophet

2021 ◽  
pp. 241-262
Author(s):  
Clony Junior ◽  
Pedro Gusmão ◽  
José Moreira ◽  
Ana Maria M. Tome
Keyword(s):  
Time Series ◽  
Data Science ◽  
Short Term Memory ◽  
Special Kind ◽  
Time Series Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Use Of Time

Data science highlights fields of study and research such as time series, which, although widely explored in the past, gain new perspectives in the context of this discipline. This chapter presents two approaches to time series forecasting, long short-term memory (LSTM), a special kind of recurrent neural network (RNN), and Prophet, an open-source library developed by Facebook for time series forecasting. With a focus on developing forecasting processes by data mining or machine learning experts, LSTM uses gating mechanisms to deal with long-term dependencies, reducing the short-term memory effect inherent to the traditional RNN. On the other hand, Prophet encapsulates statistical and computational complexity to allow broad use of time series forecasting, prioritizing the expert's business knowledge through exploration and experimentation. Both approaches were applied to a retail time series. This case study comprises daily and half-hourly forecasts, and the performance of both methods was measured using the standard metrics.

A Developmental Phonological Short-Term Memory Deficit: A Case Study

2001 ◽  
Vol 45 (2) ◽  
pp. 164-188 ◽  
Author(s):  
Gerri Hanten ◽  
Randi C. Martin
Keyword(s):  
Short Term Memory ◽  
Memory Deficit ◽  
Short Term ◽  
Term Memory

scholarly journals Précis of From neuropsychology to mental structure

1991 ◽  
Vol 14 (3) ◽  
pp. 429-438 ◽  
Author(s):  
Tim Shallice
Keyword(s):  
Individual Differences ◽  
Short Term Memory ◽  
Single Case ◽  
Normal Function ◽  
Short Term ◽  
Cognitive Neuropsychology ◽  
Term Memory ◽  
Input And Output ◽  
Eclectic Approach ◽  
Cognitive Theories

AbstractNeuropsychological results are increasingly cited in cognitive theories although their methodology has been severely criticised. The book argues for an eclectic approach but particularly stresses the use of single-case studies. A range of potential artifacts exists when inferences are made from such studies to the organisation of normal function – for example, resource differences among tasks, premorbid individual differences, and reorganisation of function. The use of “strong” and “classical” dissociations minimises potential artifacts. The theoretical convergence between findings from fields where cognitive neuropsychology is well developed and those from the normal literature strongly suggests that the potential artifacts are not critical. The fields examined in detail in this respect are short-term memory, reading, writing, the organisation of input and output speech systems, and visual perception. Functional dissociation data suggest that not only are input systems organised modularly, but so are central systems. This conclusion is supported by findings on impairment of knowledge, visual attention, supervisory functions, memory, and consciousness.

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