scholarly journals Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3258 ◽  
Author(s):  
Bai ◽  
Sun ◽  
Zang ◽  
Zhang ◽  
Shen ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Event Identification ◽  
Short Period ◽  
Grid Monitoring ◽  
Low Efficiency ◽  
Event Based ◽  
Power Dispatching

Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low efficiency of monitoring alarm information, this paper proposes a method based on natural language processing (NLP) and a hybrid model that combines long short-term memory (LSTM) and convolutional neural network (CNN) for the identification of grid monitoring alarm events. Firstly, the characteristics of the alarm information text were analyzed and induced and then preprocessed. Then, the monitoring alarm information was vectorized based on the Word2vec model. Finally, a monitoring alarm event identification model based on a combination of LSTM and CNN was established for the characteristics of the alarm information. The feasibility and effectiveness of the method in this paper were verified by comparison with multiple identification models.

scholarly journals EventEpi–A Natural Language Processing Framework for Event-Based Surveillance

2019 ◽  
Author(s):  
Auss Abbood ◽  
Alexander Ullrich ◽  
Rüdiger Busche ◽  
Stéphane Ghozzi
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Application ◽  
Fine Tuning ◽  
Entity Recognition ◽  
World Health ◽  
Support Vector ◽  
Event Based ◽  
Processing Framework

AbstractAccording to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of epidemiologists sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the event it describes relevant, we developed a natural-language-processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles’ key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We trained a naive Bayes classifier to find the single most likely one using RKI’s EBS database as labels. Then, for relevance scoring, we defined two classes to which any article might belong: The article is relevant if it is in the EBS database and irrelevant otherwise. We compared the performance of different classifiers, using document and word embeddings. Two of the tested algorithms stood out: The multilayer perceptron performed best overall, with a precision of 0.19, recall of 0.50, specificity of 0.89, F1 of 0.28, and the highest tested index balanced accuracy of 0.46. The support-vector machine, on the other hand, had the highest recall (0.88) which can be of higher interest for epidemiologists. Finally, we integrated these functionalities into a web application called EventEpi where relevant sources are automatically analyzed and put into a database. The user can also provide any URL or text, that will be analyzed in the same way and added to the database. Each of these steps could be improved, in particular with larger labeled datasets and fine-tuning of the learning algorithms. The overall framework, however, works already well and can be used in production, promising improvements in EBS. The source code is publicly available at https://github.com/aauss/EventEpi.

scholarly journals COVID-19 ChatBot

2021 ◽  
Vol 9 (11) ◽  
pp. 44-49
Author(s):  
Satish Tirumalapudi
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
And Control ◽  
Prediction Problems

Abstract: Chat bots are software applications that help users to communicate with the machine and get the required result, this is where Natural Language Processing (NLP) comes into the picture. Natural language processing is based on deep learning that enables computers to acquire meaning from inputs given by the users. Natural language processing techniques can make possible the use of natural language to express ideas, thus drastically increasing accessibility. NLP engines rely on the elements of intent, utterance, entity, context, and session. Here in this project, we will be using Deep learning techniques which will be trained on the dataset which contains categories, patterns, and responses. Long Short-Term Memory (LSTM) is a Recurrent Neural Network that is capable of learning order dependence in sequence prediction problems. One of the most popular RNN approaches is LSTM to identify and control a dynamic system. We use an RNN to classify the category user’s message belongs to and then will give a response from the list of responses. Keywords: NLP – Natural Language Processing, LSTM – Long Short Term Memory, RNN – Recurrent Neural Networks.

scholarly journals Penerapan Convolutional Long Short-Term Memory untuk Klasifikasi Teks Berita Bahasa Indonesia

2021 ◽  
Vol 10 (4) ◽  
pp. 354-361
Author(s):  
Yudi Widhiyasana ◽  
Transmissia Semiawan ◽  
Ilham Gibran Achmad Mudzakir ◽  
Muhammad Randi Noor
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
Learning Rate ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Bahasa Indonesia

Klasifikasi teks saat ini telah menjadi sebuah bidang yang banyak diteliti, khususnya terkait Natural Language Processing (NLP). Terdapat banyak metode yang dapat dimanfaatkan untuk melakukan klasifikasi teks, salah satunya adalah metode deep learning. RNN, CNN, dan LSTM merupakan beberapa metode deep learning yang umum digunakan untuk mengklasifikasikan teks. Makalah ini bertujuan menganalisis penerapan kombinasi dua buah metode deep learning, yaitu CNN dan LSTM (C-LSTM). Kombinasi kedua metode tersebut dimanfaatkan untuk melakukan klasifikasi teks berita bahasa Indonesia. Data yang digunakan adalah teks berita bahasa Indonesia yang dikumpulkan dari portal-portal berita berbahasa Indonesia. Data yang dikumpulkan dikelompokkan menjadi tiga kategori berita berdasarkan lingkupnya, yaitu “Nasional”, “Internasional”, dan “Regional”. Dalam makalah ini dilakukan eksperimen pada tiga buah variabel penelitian, yaitu jumlah dokumen, ukuran batch, dan nilai learning rate dari C-LSTM yang dibangun. Hasil eksperimen menunjukkan bahwa nilai F1-score yang diperoleh dari hasil klasifikasi menggunakan metode C-LSTM adalah sebesar 93,27%. Nilai F1-score yang dihasilkan oleh metode C-LSTM lebih besar dibandingkan dengan CNN, dengan nilai 89,85%, dan LSTM, dengan nilai 90,87%. Dengan demikian, dapat disimpulkan bahwa kombinasi dua metode deep learning, yaitu CNN dan LSTM (C-LSTM),memiliki kinerja yang lebih baik dibandingkan dengan CNN dan LSTM.

scholarly journals Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process

2021 ◽  
Author(s):  
Faisal Khalil ◽  
Gordon Pipa
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Short Term Memory ◽  
High Tech ◽  
Long Term Memory ◽  
Term Memory ◽  
Hourly Data ◽  
Analytic Models

AbstractThis study tries to unravel the stock market prediction puzzle using the textual analytic with the help of natural language processing (NLP) techniques and Deep-learning recurrent model called long short term memory (LSTM). Instead of using count-based traditional sentiment index methods, the study uses its own sum and relevance based sentiment index mechanism. Hourly price data has been used in this research as daily data is too late and minutes data is too early for getting the exclusive effect of sentiments. Normally, hourly data is extremely costly and difficult to manage and analyze. Hourly data has been rarely used in similar kinds of researches. To built sentiment index, text analytic information has been parsed and analyzed, textual information that is relevant to selected stocks has been collected, aggregated, categorized, and refined with NLP and eventually converted scientifically into hourly sentiment index. News analytic sources include mainstream media, print media, social media, news feeds, blogs, investors’ advisory portals, experts’ opinions, brokers updates, web-based information, company’ internal news and public announcements regarding policies and reforms. The results of the study indicate that sentiments significantly influence the direction of stocks, on average after 3–4 h. Top ten companies from High-tech, financial, medical, automobile sectors are selected, and six LSTM models, three for using text-analytic and other without analytic are used. Every model includes 1, 3, and 6 h steps back. For all sectors, a 6-hour steps based model outperforms the other models due to LSTM specialty of keeping long term memory. Collective accuracy of textual analytic models is way higher relative to non-textual analytic models.

scholarly journals Bi-LSTM Model to Increase Accuracy in Text Classification: Combining Word2vec CNN and Attention Mechanism

2020 ◽  
Vol 10 (17) ◽  
pp. 5841 ◽  
Author(s):  
Beakcheol Jang ◽  
Myeonghwi Kim ◽  
Gaspard Harerimana ◽  
Sang-ug Kang ◽  
Jong Wook Kim
Keyword(s):  
Neural Network ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Text Classification ◽  
Short Term Memory ◽  
User Behavior ◽  
Hybrid Approach ◽  
Attention Mechanism

There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. Large amounts of data are generated from various sources such as social media and websites. Text classification is a representative research topic in the field of natural-language processing that categorizes unstructured text data into meaningful categorical classes. The long short-term memory (LSTM) model and the convolutional neural network for sentence classification produce accurate results and have been recently used in various natural-language processing (NLP) tasks. Convolutional neural network (CNN) models use convolutional layers and maximum pooling or max-overtime pooling layers to extract higher-level features, while LSTM models can capture long-term dependencies between word sequences hence are better used for text classification. However, even with the hybrid approach that leverages the powers of these two deep-learning models, the number of features to remember for classification remains huge, hence hindering the training process. In this study, we propose an attention-based Bi-LSTM+CNN hybrid model that capitalize on the advantages of LSTM and CNN with an additional attention mechanism. We trained the model using the Internet Movie Database (IMDB) movie review data to evaluate the performance of the proposed model, and the test results showed that the proposed hybrid attention Bi-LSTM+CNN model produces more accurate classification results, as well as higher recall and F1 scores, than individual multi-layer perceptron (MLP), CNN or LSTM models as well as the hybrid models.

scholarly journals EventEpi—A natural language processing framework for event-based surveillance

2020 ◽  
Vol 16 (11) ◽  
pp. e1008277
Author(s):  
Auss Abbood ◽  
Alexander Ullrich ◽  
Rüdiger Busche ◽  
Stéphane Ghozzi
Keyword(s):  
Public Health ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Fine Tuning ◽  
Entity Recognition ◽  
World Health ◽  
Case Count ◽  
Event Based ◽  
Processing Framework

According to the World Health Organization (WHO), around 60% of all outbreaks are detected using informal sources. In many public health institutes, including the WHO and the Robert Koch Institute (RKI), dedicated groups of public health agents sift through numerous articles and newsletters to detect relevant events. This media screening is one important part of event-based surveillance (EBS). Reading the articles, discussing their relevance, and putting key information into a database is a time-consuming process. To support EBS, but also to gain insights into what makes an article and the event it describes relevant, we developed a natural language processing framework for automated information extraction and relevance scoring. First, we scraped relevant sources for EBS as done at the RKI (WHO Disease Outbreak News and ProMED) and automatically extracted the articles’ key data: disease, country, date, and confirmed-case count. For this, we performed named entity recognition in two steps: EpiTator, an open-source epidemiological annotation tool, suggested many different possibilities for each. We extracted the key country and disease using a heuristic with good results. We trained a naive Bayes classifier to find the key date and confirmed-case count, using the RKI’s EBS database as labels which performed modestly. Then, for relevance scoring, we defined two classes to which any article might belong: The article is relevant if it is in the EBS database and irrelevant otherwise. We compared the performance of different classifiers, using bag-of-words, document and word embeddings. The best classifier, a logistic regression, achieved a sensitivity of 0.82 and an index balanced accuracy of 0.61. Finally, we integrated these functionalities into a web application called EventEpi where relevant sources are automatically analyzed and put into a database. The user can also provide any URL or text, that will be analyzed in the same way and added to the database. Each of these steps could be improved, in particular with larger labeled datasets and fine-tuning of the learning algorithms. The overall framework, however, works already well and can be used in production, promising improvements in EBS. The source code and data are publicly available under open licenses.

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