scholarly journals Viral reverse engineering using Artificial Intelligence and big data COVID-19 infection with Long Short-term Memory (LSTM)

2021 ◽  
Vol 22 ◽  
pp. 101531
Author(s):  
Ahmad M. Abu Haimed ◽  
Tanzila Saba ◽  
Ayman Albasha ◽  
Amjad Rehman ◽  
Mahyar Kolivand
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Reverse Engineering ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Pemanfaatan Asynchronous Advantage Actor-Critic Dalam Pembuatan AI Game Bot Pada Game Arcade

2019 ◽  
Vol 1 (2) ◽  
pp. 74-84
Author(s):  
Evan Kusuma Susanto ◽  
Yosi Kristian
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Trial And Error ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

Asynchronous Advantage Actor-Critic (A3C) adalah sebuah algoritma deep reinforcement learning yang dikembangkan oleh Google DeepMind. Algoritma ini dapat digunakan untuk menciptakan sebuah arsitektur artificial intelligence yang dapat menguasai berbagai jenis game yang berbeda melalui trial and error dengan mempelajari tempilan layar game dan skor yang diperoleh dari hasil tindakannya tanpa campur tangan manusia. Sebuah network A3C terdiri dari Convolutional Neural Network (CNN) di bagian depan, Long Short-Term Memory Network (LSTM) di tengah, dan sebuah Actor-Critic network di bagian belakang. CNN berguna sebagai perangkum dari citra output layar dengan mengekstrak fitur-fitur yang penting yang terdapat pada layar. LSTM berguna sebagai pengingat keadaan game sebelumnya. Actor-Critic Network berguna untuk menentukan tindakan terbaik untuk dilakukan ketika dihadapkan dengan suatu kondisi tertentu. Dari hasil percobaan yang dilakukan, metode ini cukup efektif dan dapat mengalahkan pemain pemula dalam memainkan 5 game yang digunakan sebagai bahan uji coba.

scholarly journals Enormous Information Examination using Big Data in a Distributed Environment with Profound Learning of Next Generation Interruption Identification Framework Enhancement

2019 ◽  
Vol 9 (1) ◽  
pp. 1779-1784
Keyword(s):  
Big Data ◽  
Short Term Memory ◽  
Neural System ◽  
Distributed Environment ◽  
Short Term ◽  
Term Memory ◽  
Identification Rate ◽  
Long Short Term Memory ◽  
Future Vision ◽  
Spark Framework

With the developing utilization of data innovation in all life areas, hacking has turned out to be more contrarily powerful than any other time in recent memory. Additionally, with creating advances, assaults numbers are developing exponentially like clockwork and become progressively refined so conventional I.D.S ends up wasteful recognizing them. We accomplish those outcomes by utilizing Networking Chabot, a profound intermittent neural system: Long Short Term Memory (L.S.T.M) [2]over Apache Spark Framework that has a contribution of stream traffic and traffic conglomeration and the yield is a language of two words, typical or strange. The new and proposed blending ideas of the language are preparing, relevant examination, circulated profound adapting, huge information, and oddity discovery of stream investigation. We propose a model that portrays the system dynamic typical conduct from an arrangement of a great many parcels inside their unique circumstance and examines them in close to constant to identify point, aggregate and relevant inconsistencies. The examination shows lower false positive, higher identification rate and better point abnormalities location. With respect to demonstrate of relevant and aggregate oddities identification, we talk about our case and the explanation for our speculation. Be that as it may, the investigation is done on arbitrary little subsets of the dataset as a result of equipment restrictions, so we offer examination and our future vision musings as we wish that full demonstrate will be done in future by other intrigued specialists who have preferable equipment foundation over our own..

scholarly journals Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 10 ◽  
Author(s):  
Rabiya Khalid ◽  
Nadeem Javaid ◽  
Fahad A. Al-zahrani ◽  
Khursheed Aurangzeb ◽  
Emad-ul-Haq Qazi ◽  
...  
Keyword(s):  
Big Data ◽  
Short Term Memory ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Term Memory ◽  
Proposed Model ◽  
Multiple Variables ◽  
Long Short Term Memory

In the smart grid (SG) environment, consumers are enabled to alter electricity consumption patterns in response to electricity prices and incentives. This results in prices that may differ from the initial price pattern. Electricity price and demand forecasting play a vital role in the reliability and sustainability of SG. Forecasting using big data has become a new hot research topic as a massive amount of data is being generated and stored in the SG environment. Electricity users, having advanced knowledge of prices and demand of electricity, can manage their load efficiently. In this paper, a recurrent neural network (RNN), long short term memory (LSTM), is used for electricity price and demand forecasting using big data. Researchers are working actively to propose new models of forecasting. These models contain a single input variable as well as multiple variables. From the literature, we observed that the use of multiple variables enhances the forecasting accuracy. Hence, our proposed model uses multiple variables as input and forecasts the future values of electricity demand and price. The hyperparameters of this algorithm are tuned using the Jaya optimization algorithm to improve the forecasting ability and increase the training mechanism of the model. Parameter tuning is necessary because the performance of a forecasting model depends on the values of these parameters. Selection of inappropriate values can result in inaccurate forecasting. So, integration of an optimization method improves the forecasting accuracy with minimum user efforts. For efficient forecasting, data is preprocessed and cleaned from missing values and outliers, using the z-score method. Furthermore, data is normalized before forecasting. The forecasting accuracy of the proposed model is evaluated using the root mean square error (RMSE) and mean absolute error (MAE). For a fair comparison, the proposed forecasting model is compared with univariate LSTM and support vector machine (SVM). The values of the performance metrics depict that the proposed model has higher accuracy than SVM and univariate LSTM.

scholarly journals Air Pollution and Cardiorespiratory Hospitalization, Modeling and Analysis Using Artificial Intelligence Techniques

2021 ◽  
Author(s):  
Raja Sher Afgun Usmani ◽  
Thulasyammal Ramiah Pillai ◽  
Ibrahim Abaker Targio Hashem ◽  
Mohsen Marjani ◽  
Rafiza Shaharudin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Air Pollution ◽  
Short Term Memory ◽  
Short Term ◽  
Human Welfare ◽  
Term Memory ◽  
Modeling And Analysis ◽  
Klang Valley ◽  
Long Short Term Memory ◽  
Better Than

Abstract Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the Artificial Intelligence (AI) techniques. We propose the Enhanced Long Short-Term Memory (ELSTM) model and provide a comparison with other AI techniques, i.e., Long Short-Term Memory (LSTM), Deep Learning (DL), and Vector Autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.

PREDIKSI RATA-RATA ZAT BERBAHAYA DI DKI JAKARTA BERDASARKAN INDEKS STANDAR PENCEMAR UDARA MENGGUNAKAN METODE LONG SHORT-TERM MEMORY

2021 ◽  
Vol 26 (1) ◽  
pp. 41-55
Author(s):  
Anisa Oktaviani ◽  
Hustinawati
Keyword(s):  
Artificial Intelligence ◽  
Air Quality ◽  
World Health Organization ◽  
Quality Index ◽  
Short Term Memory ◽  
World Health ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Health Organization

Indonesia menempati peringkat ke-6 dari 98 negara paling berpolusi di dunia pada tahun 2019. Di tahun tersebut, rata-rata AQI (Air Quality Index) sebesar 141 dan rata-rata konsentrasi PM2.5 sebesar 51.71 μg/m3 yang lima kali lipat diatas rekomendasi World Health Organization (WHO). Salah satu kota penyumbang polusi udara yaitu Jakarta. Berdasarkan data ISPU (Indeks Standar Pencemar Udara) yang diambil dari SPKU (Stasiun Pemantau Kualitas Udara) Dinas Lingkungan Hidup DKI Jakarta melampirkan pada tahun 2019, Jakarta memiliki kualitas udara sangat tidak sehat. Oleh karena itu perlu adanya model Artificial Intelligence dalam memperdiksi rata-rata tingkat zat berbahaya pada udara di DKI Jakarta. Salah satu algoritma yang dapat diterapkan dalam membuat model prediksi dengan menggunakan data timeseries adalah Long Short-Term Memory (LSTM). Tujuan dari penelitian ini membangun model prediksi rata-rata ISPU di DKI Jakarta menggunakan metode LSTM yang berguna bagi para pemangku kepentingan dibidang lingkungan hidup khususnya mengenai polusi udara. Penelitian mengenai prediksi rata-rata ISPU di DKI Jakarta menggunakan metode LSTM, menghasilkan nilai evaluasi MAPE 12.28%. Berdasarkan hasil evaluasi MAPE yang diperoleh, model LSTM yang digunakan untuk prediksi rata-rata ISPU di DKI Jakarta masuk kedalam kategori akurat.

Abstract P502: Deep Learning-powered Wearable Electrocardiogram Big Data Monitoring for Precision Cardiac Health

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Qingxue Zhang
Keyword(s):  
Big Data ◽  
Short Term Memory ◽  
Linear Regression Method ◽  
Ecg Monitoring ◽  
Short Term ◽  
Term Memory ◽  
Health Technologies ◽  
Cardiac Health ◽  
Skin Contact ◽  
Long Short Term Memory

Smart health technologies are bringing exciting possibilities to the cardiac healthcare area. Wearable electrocardiogram (ECG) monitoring is expected to establish cardiac big data towards precision cardiac health. However, there are two key obstacles here. Firstly, how to conveniently measure the standard 12-lead ECG in our daily lives is an open question, since the traditional 12-lead ECG is mainly used in clinics or hospitals. The Holter ECG monitor is actually not convenient and comfortable enough for daily and long-term use. The Apple Watch only provides finger-touch-based single lead ECG measurement, neither supporting 12-lead ECG nor continuous tracking. In this study, a long short-term memory neural network-based ECG monitoring system is proposed, which can generate the remaining 9-lead ECG from only 3-lead ECG, offering a very high wearabilty, usability and convenience. Secondly, how to maintain a high ECG quality even when the user has different physical activities is another critical challenge. Usually, the ECG morphology may be contaminated by diverse motions artifacts induced by sensor-to-skin contact variations. This has to be addressed to guarantee the obtained ECG is usable and interpretable. We have introduced bidirectional long short-term memory to deal with these noisy fluctuations, by learning the temporal consistent dynamics among 3-lead ECG. The system has been evaluated on ten human subjects to demonstrate the effectiveness. Compared with the ground truth, the reconstructed 12-lead ECG has a correlation as high as 0.88 and a root mean square error of 0.059 mV, far superior to the traditional linear regression method. The proposed novel monitor is expected to greatly advance precision cardiac health.

scholarly journals Using Artificial Intelligence for Pattern Recognition in a Sports Context

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3040 ◽  
Author(s):  
Ana Cristina Nunes Rodrigues ◽  
Alexandre Santos Pereira ◽  
Rui Manuel Sousa Mendes ◽  
André Gonçalves Araújo ◽  
Micael Santos Couceiro ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Positional Data

Optimizing athlete’s performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding of the game and, consequently, providing the opportunity to improve the athletic performance. Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition. This research paper combines the use of physiological and positional data as sequential features of different artificial intelligence approaches for action recognition in a real match context, adopting futsal as its case study. The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification method. The methods were used to process all data sequences, which allowed to determine, based on the balance between precision and recall, that Dynamic Bayesian Mixture Model presents a superior performance, with an F1 score of 80.54% against the 33.31% achieved by the Long Short-Term Memory Network and 14.74% achieved by ANN.

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