Academic Articles & Research Papers

A Wilson–Cowan reservoir computer for interpretable spatiotemporal vision

Abstract We present a Wilson–Cowan reservoir computer (WC–RC) that treats a retinotopic excitatory–inhibitory neural field as a structured reservoir. Travelling waves and bounded oscillations provide an interpretable spatiotemporal basis, w...

Calibration and Orientation of Cameras in Computer Vision

AnimalTA: A highly flexible and easy‐to‐use program for tracking and analysing animal movement in different environments

Abstract Computer programs for video tracking of animal movement are evolving increasingly efficient, using complex algorithms or artificial intelligence systems. Despite the consequent progress in this field, researchers still face some fu...

Euclid Quick Data Release (Q1)

Strong gravitational lensing (SL) by galaxy clusters is a powerful probe of their inner mass distribution and a key test bed for cosmological models. However, the detection of SL events in wide-field surveys such as Euclid requires robust a...

Enhancing image caption generation through context-aware attention mechanism

Image captioning, the process of generating natural language descriptions based on image content, has garnered attention in AI research for its implications in scene understanding and human-computer interaction. While much prior research ha...

Identifying people’s energy information needs through Energy Citizen Personas

Abstract Energy Citizenship refers to individuals and communities participating, sometimes begrudgingly, in adopting energy solutions, whether sustainable or not. When enacted positively, it advances the green energy transition. There are s...

Thorax-Segment- and Leg-Segment-Specific Motor Control for Adaptive Behavior

We have just started to understand the mechanisms underlying flexibility of motor programs among segmental neural networks that control each individual leg during walking in vertebrates and invertebrates. Here, we investigated the mechanism...

ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning

COVID-19 has become a global pandemic that has affected not only the health sector but also economic, social, and psychological well-being. Individuals are using social media platforms to communicate their feelings and sentiments about the ...

Deep Learning

This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep...

A State-of-the-Art Survey on Deep Learning Theory and Architectures

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some ne...

A Survey on Deep Learning for Software Engineering

In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)” and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstr...

Rethinking Gated Recurrent Units for Rotating Machinery Prognostics: A Physics-Consistency Benchmark on the Mismatch Between Gating Mechanisms and Degradation Dynamics

Rotating machinery prognostics is essential for ensuring the reliability and operational safety of industrial systems. Although gated recurrent units (GRUs) have achieved competitive performance in remaining useful life (RUL) prediction, wh...

Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet

Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient...

A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images

Abstract Deep learning has significantly contributed to medical imaging and computer-aided diagnosis (CAD), providing accurate disease classification and diagnosis. However, challenges such as inter- and intra-class similarities, class imba...

Prediction of overall survival in acute myeloid leukemia using supervised machine learning.

Predicting Stock Price Movements with Combined Deep Learning Models and Two-Tier Metaheuristic Optimization Algorithm

Predictions on stock market prices are a noble task owing to huge complex, dynamic, and chaotic surroundings. Fast ups and downs arise in the stock market due to influences from foreign merchandise, such as sensitive political, stockholder,...

Machine learning in pain research

1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...

AN IMPROVED GRU BASED ON RECURRENT ATTENTION UNIT AND SELF- ATTENTION TECHNIQUE FOR TEXT SENTIMENT ANALYSIS

In text sentiment analysis, a crucial challenge is that conventional word vectors fail to capture lexical ambiguity. The Gated Recurrent Unit (GRU), an advanced variant of RNN, is extensively utilized in natural language processing tasks su...

Python 3 for Machine Learning

This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fun...

Using generalized random forests to characterize vulnerability to adverse health outcomes following wildfire smoke exposure in California

Background: As the health burden attributable to wildfire activity increases under climate change, it is crucial to determine which subgroups face heightened vulnerability to wildfire smoke. Marginalized communities may experience dispropor...