Machine Learning Approaches for Temporal and Spatio-Temporal Covid-19 Forecasting: A Brief Review and a Contribution

2021 ◽  
pp. 333-357
Author(s):  
Ana Clara Gomes da Silva ◽  
Clarisse Lins de Lima ◽  
Cecilia Cordeiro da Silva ◽  
Giselle Machado Magalhães Moreno ◽  
Eduardo Luiz Silva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Spatio Temporal

scholarly journals Epileptic Seizure Prediction Through Machine Learning and Spatio-Temporal Features Based Time Series Analysis of Intracranial Electroencephalogram Data

2019 ◽  
Vol 9 (1) ◽  
pp. 4763-4770
Keyword(s):  
Machine Learning ◽  
Temporal Dynamics ◽  
Class Imbalance ◽  
Training Data ◽  
Learning Approaches ◽  
Seizure Prediction ◽  
Buffer Time ◽  
Temporal Features ◽  
Emergency Medication ◽  
Spatio Temporal

Epilepsy is a group of neurological disorders identifiable by infrequent but recurrent seizures. Seizure prediction is widely recognized as a significant problem in the neuroscience domain. Developing a Brain-Computer Interface (BCI) for seizure prediction can provide an alert to the patient, providing a buffer time to get the necessary emergency medication or at least be able to call for help, thus improving the quality of life of the patients. A considerable number of clinical studies presented evidence of symptoms (patterns) before seizure episodes and thus, there is large research on seizure prediction, however, there is very little existing literature that illustrates the use of structured processes in machine learning for predicting seizures. Limited training data and class imbalance (EEG segments corresponding to preictal phase, the duration just before the seizure, to about an hour prior to the episode, are usually in a tiny minority) are a few challenges that need to be addressed when employing machine learning for this task. In this paper we present a comparative study of various machine learning approaches that can be used for classification of EEG signals into preictal and interictal (Interictal is the time between seizures) using the features extracted from the intracranial EEG. Publicly available data has been used for this purpose for both human and canine subjects. After data pre-processing and extensive feature extraction, different models are trained and are effectively used to analyze the temporal dynamics of the brain (interictal and preictal) in affected subjects. We present the improved results for various classification algorithms, with AUROC values of best classification models at 0.99.

scholarly journals Spatio-Temporal Resolution of Irradiance Samples in Machine Learning Approaches for Irradiance Forecasting

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 51518-51531 ◽  
Author(s):  
Annette Eschenbach ◽  
Guillermo Yepes ◽  
Christian Tenllado ◽  
Jose I. Gomez-Perez ◽  
Luis Pinuel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Temporal Resolution ◽  
Learning Approaches ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Classification and Prediction of Land Use and Land Cover Change for the Vembanad Lake System, Kerala – a Machine Learning Approach

2021 ◽  
Author(s):  
Parthasarathy Kulithalai Shiyam Sundar ◽  
Paresh Chandra Deka
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Google Earth ◽  
Anthropogenic Activity ◽  
Support Vector ◽  
Learning Approaches ◽  
Barren Land ◽  
Lake System ◽  
Spatio Temporal

Abstract Land use and land cover (LULC) change has become a critical issue for decision planners and conservationists due to inappropriate growth and its effect on natural ecosystems. As a result, the goal of this study is to identify the LULC for the Vembanad Lake System (VLS), Kerala in the short term, i.e., within a decade, utilizing two standard machine learning approaches, Random Forest (RF) and Support Vector Machines (SVM), on the Google Earth Engine (GEE) platform. When comparing the two techniques, SVM is classified at an average accuracy of around 84.5%, while RF is classified at 89%. The RF outperformed the SVM in almost identical spectral classes such as barren land and built-up areas. As a result, RF classified LULC is considered to predict the Spatio-temporal distribution of LULC transition analysis for 2035 and 2050. The study was conducted in Idrisi TerrSet software using the Cellular Automata (CA)-Markov chain analysis. The model's efficiency is evaluated by comparing the projected 2019 image to the actual 2019 classified image. The efficiency was good with more than 94% accuracy for the classes except for barren land, which might have resulted from the recent natural calamities and the accelerated anthropogenic activity in the area.

Multiple vehicles detection and tracking for intelligent transport systems using machine learning approaches

2019 ◽  
Vol 70 (3) ◽  
pp. 214-224
Author(s):  
Bui Ngoc Dung ◽  
Manh Dzung Lai ◽  
Tran Vu Hieu ◽  
Nguyen Binh T. H.
Keyword(s):  
Machine Learning ◽  
Gaussian Mixture ◽  
Research Field ◽  
Transport Systems ◽  
Learning Approaches ◽  
Subtraction Method ◽  
Intelligent Transport Systems ◽  
Intelligent Transport ◽  
Detection And Tracking ◽  
Multiple Vehicles

Video surveillance is emerging research field of intelligent transport systems. This paper presents some techniques which use machine learning and computer vision in vehicles detection and tracking. Firstly the machine learning approaches using Haar-like features and Ada-Boost algorithm for vehicle detection are presented. Secondly approaches to detect vehicles using the background subtraction method based on Gaussian Mixture Model and to track vehicles using optical flow and multiple Kalman filters were given. The method takes advantages of distinguish and tracking multiple vehicles individually. The experimental results demonstrate high accurately of the method.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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