AIMC Topic: Memory, Short-Term

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A novel hybrid model for emotion detection in text through sequential and transformer-based approaches: LSTM enhanced RoBERTa (LER).

Scientific reports
Text emotion detection is an essential task in Natural Language Processing (NLP), with applications in customer support automation, diagnosing mental health, and social media analysis. Yet, precise emotion detection is a difficult problem as human em...

Reducing Artifact Preprocessing in Heart Rate Variability-Based Personalized Psychosis Prediction Using Adaptive Long Short-Term Memory Models.

International journal of neural systems
This research looks at the use of long-short-term memory (LSTM) networks to predict psychosis, in patients within the schizophrenia spectrum, based on Heart Rate Variability (HRV) data acquired from wearable devices. Our primary objective is to test ...

Bi-directional ConvLSTM networks for early recognition of human activities and action prediction.

Scientific reports
Early detection of human activity is essential in domains including robotics, entertainment, surveillance, and healthcare. Early detection that is accurate enables prompt decision-making, enhancing system responsiveness and overall effectiveness. Con...

IV3TM: Inception V3 enabled bidirectional long short-term memory network for brain tumor classification.

PloS one
A brain tumor is one of the life-threatening neurological conditions affecting millions of people worldwide. Early diagnosis and classification of brain tumor types facilitate prompt treatment, thereby increasing the patient's chances of survival. Th...

Explainable machine learning algorithm predicting working memory performance in Parkinson's disease using task-fMRI.

Journal of neurology
BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder that affects both motor and cognitive functions, particularly working memory (WM). Machine learning offers an advantage for decoding complex brain activity patterns, but its applica...

Behavioral Timing of Interictal Spikes, But Not Rate, Correlates with Impaired Working Memory Performance.

The Journal of neuroscience : the official journal of the Society for Neuroscience
In temporal lobe epilepsy, interictal spikes (IS)-hyper-synchronous bursts of network activity-occur at high rates in between seizures. We sought to understand the influence of IS on working memory by recording hippocampal local field potentials from...

Prediction of CO concentration in mushroom greenhouse via optimized long and short term memory algorithm.

Scientific reports
To increase the accuracy as well as effectiveness of predicting the level of CO in mushroom cultivating greenhouses, two optimized prediction models of long and short term memory neural networks (VMD-SSA-LSTM and VMD-DBO-LSTM) are proposed. To start ...

Enhanced epileptic seizure detection using CNNs with convolutional block attention and short-term memory networks.

Behavioural brain research
Analyzing the electroencephalography (EEG) signals of epilepsy patients can monitor the condition, detect and intervene in epileptic seizures in time. To enhance the lives of these patients, it is necessary to develop accurate methods to detect epile...

From mazes to automation: Modernizing working memory research in animal models.

Behavioural brain research
Working memory (WM) is a core cognitive mechanism necessary for adaptive behavior. In the last few decades, scientists have studied WM using rodent models through traditional and time-consuming approaches, such as the Radial Arm Maze and the T-Maze. ...

Improved state refinement for LSTM determined 3D CAISR-LSTM model for automatic myocardial infarction detection.

Physiological measurement
Electrocardiograms (ECGs) contain valuable information in the clinical diagnosis of myocardial infarction (MI). However, its interpretation process is dependent on cardiologists with extensive clinical experience and expertise. The issue not only cau...