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AURAK hosts 16th Asian Conference on Intelligent Information and Database Systems

AURAK hosts 16th Asian Conference on Intelligent Information and Database Systems
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United States , Ravil Mussabayev , Ngoc Thanh Nguyen , Khouloud Salameh , Ngoc Nha Vi Tran , Urszula Boryczka , Ras Al Khaimah , Stephen Wilhite , Michalis Vazirgiannis , Leszek Rutkowski , Rustam Mussabayev , David Camacho , Schahram Dustdar , Engineering Department , University Of Science , Wroclaw University Of Science , Training Deep Neural Networks , American University Of Ras Al Khaimah , American University , Asian Conference , Intelligent Information , Database Systems , Computer Analysis , Social Networks , Machine Learning Algorithms , Natural Language Processing Models ,

Toshiba Introduces New Anomaly Detection AI for Large-scale Industrial Plants at ICDM2021 LITSA

TOKYO Toshiba Corporation (TOKYO: 6502) has developed a new anomaly detection AI that enables large-scale industrial plants to overcome a widespread and growing challenge: using a small workforce to achieve constant and effective monitoring of thousands of sensors, and the accurate detection of anomalous signals hidden among small variations ....

Japan General , Susumu Naito , Toshiba Corporation , Toshiba Corporate Research Development Center , Toshiba Energy Systems Solutions Corporation , Ariake Corporation , International Conference , Data Mining , Corporate Research , Development Center , Water Distribution , Toshiba Energy Systems , Solutions Corporation , Mikawa Plant , Cyber Physical Systems , Smart Water Networks , Unsupervised Anomaly Detection , Multivariate Time Series , Consumer Electronics ,

"Recognizing diseases with multivariate physiological signals by a Deep" by Jun Liao, Dandan Liu et al.

The usage of multivariate time series to identify diseases plays an important role in the medical field, as it can help medical staff to improve diagnose accuracy and reduce medical costs. Current research shows that deep Convolutional Neural Networks (CNN) can automatically capture features from raw data and Long Short-Term Memory (LSTM) networks can manage and learn temporal dependence between time series data such as physiological signals. In this work, we propose a deep learning framework called DeepCNN-LSTM by combining the CNN and LSTM to leverage their respective advantages for disease recognition, allowing itself to characterize complex temporal varieties with multiple autoencoded features. In particular, we use stationary wavelet transform together with median filter to preprocess low-frequency signal data, and introduce sliding window to segment physiological time series before model training for performance improvement on the training speed as well as the accuracy for recogn ....

Convolutional Neural Networks , Long Short Term Memory , Deepcnn Lstm , Disease Recognition , Multivariate Time Series , Hysiological Signal , நீண்டது குறுகிய கால நினைவு ,