Multiclass Classification of Cardiac Rhythms on Short Single Lead ECG Recordings using Bidirectional Long Short-Term Memory Networks

AbstractImage analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultimate objective of the image analysis. Here, the time-frequency time-space long short-term memory network (TF-TS LSTM) developed for classification of time series is applied for classifying histopathological images. The deep learning is empowered by the use of sequential time-frequency and time-space features extracted from the images. Furthermore, unlike conventional classification practice, a strategy for class modeling is designed to leverage the learning power of the TF-TS LSTM. Tests on several datasets of histopathological images of haematoxylin-and-eosin and immunohistochemistry stains demonstrate the strong capability of the artificial intelligence (AI)-based approach for producing very accurate classification results. The proposed approach has the potential to be an AI tool for robust classification of histopathological images.

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Random forest and long short-term memory based machine learning models for classification of ion mobility spectrometry spectra

Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XXII ◽

10.1117/12.2585829 ◽

2021 ◽

Author(s):

Patrick C. Riley ◽

Samir V. Deshpande ◽

Brian S. Ince ◽

Brian C. Hauck ◽

Kyle P. O'Donnell ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Ion Mobility ◽

Short Term Memory ◽

Learning Models ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Machine Learning Models

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Ensemble-Based Feature Selection With Long Short-Term Memory for Classification of Network Intrusion

Advances in Social Networking and Online Communities - E-Collaboration Technologies and Strategies for Competitive Advantage Amid Challenging Times ◽

10.4018/978-1-7998-7764-6.ch008 ◽

2021 ◽

pp. 228-245

Author(s):

Preethi D. ◽

Neelu Khare

Keyword(s):

Feature Selection ◽

Performance Metrics ◽

Short Term Memory ◽

Short Term ◽

Chi Square ◽

Term Memory ◽

Network Intrusion ◽

Proposed Model ◽

Long Short Term Memory

This chapter presents an ensemble-based feature selection with long short-term memory (LSTM) model. A deep recurrent learning model is proposed for classifying network intrusion. This model uses ensemble-based feature selection (EFS) for selecting the appropriate features from the dataset and long short-term memory for the classification of network intrusions. The EFS combines five feature selection techniques, namely information gain, gain ratio, chi-square, correlation-based feature selection, and symmetric uncertainty-based feature selection. The experiments were conducted using the standard benchmark NSL-KDD dataset and implemented using tensor flow and python. The proposed model is evaluated using the classification performance metrics and also compared with all the 41 features without any feature selection as well as with each individual feature selection technique and classified using LSTM. The performance study showed that the proposed model performs better, with 99.8% accuracy, with a higher detection and lower false alarm rates.

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