Combining machine learning and computational chemistry for predictive insights into chemical systems
Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …
by dramatically accelerating computational algorithms and amplifying insights available from …
Deep learning for time series classification: a review
Abstract Time Series Classification (TSC) is an important and challenging problem in data
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
mining. With the increase of time series data availability, hundreds of TSC algorithms have …
[HTML][HTML] Assessing behavioral data science privacy issues in government artificial intelligence deployment
JR Saura, D Ribeiro-Soriano… - Government Information …, 2022 - Elsevier
In today's global culture where the Internet has established itself as the main tool for
communication and commerce, the capability to massively analyze and predict citizens' …
communication and commerce, the capability to massively analyze and predict citizens' …
Time series forecasting of petroleum production using deep LSTM recurrent networks
Time series forecasting (TSF) is the task of predicting future values of a given sequence
using historical data. Recently, this task has attracted the attention of researchers in the area …
using historical data. Recently, this task has attracted the attention of researchers in the area …
Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
Addressing binary classification over class imbalanced clinical datasets using computationally intelligent techniques
Nowadays, healthcare is the prime need of every human being in the world, and clinical
datasets play an important role in developing an intelligent healthcare system for monitoring …
datasets play an important role in developing an intelligent healthcare system for monitoring …
Simple Behavioral Analysis (SimBA)–an open source toolkit for computer classification of complex social behaviors in experimental animals
Aberrant social behavior is a core feature of many neuropsychiatric disorders, yet the study
of complex social behavior in freely moving rodents is relatively infrequently incorporated …
of complex social behavior in freely moving rodents is relatively infrequently incorporated …
Adversarial attacks on deep neural networks for time series classification
Time Series Classification (TSC) problems are encountered in many real life data mining
tasks ranging from medicine and security to human activity recognition and food safety. With …
tasks ranging from medicine and security to human activity recognition and food safety. With …
A review of unsupervised feature learning and deep learning for time-series modeling
This paper gives a review of the recent developments in deep learning and unsupervised
feature learning for time-series problems. While these techniques have shown promise for …
feature learning for time-series problems. While these techniques have shown promise for …
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
Training classifiers with datasets which suffer of imbalanced class distributions is an
important problem in data mining. This issue occurs when the number of examples …
important problem in data mining. This issue occurs when the number of examples …