Academic Articles & Research Papers

Detection of short-term peaks in VLF/LF signals around the time of gamma-ray burst detections using deep learning

Gamma-ray bursts (GRBs) are recognized as the most energetic and luminous astrophysical phenomena whose high-energy photons can ionize atmospheric particles and thus affect electrical conductivity. Consequently, electromagnetic waves ...

A Review of Deep Learning Applications in Intrusion Detection Systems: Overcoming Challenges in Spatiotemporal Feature Extraction and Data Imbalance

In the rapid development of the Internet of Things (IoT) and large-scale distributed networks, Intrusion Detection Systems (IDS) face significant challenges in handling complex spatiotemporal features and addressing data imbalance issues. T...

SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron

Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient. Unlike the non-spiking counterparts, most of...

Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions

In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance...

A review on deep learning in UAV remote sensing

Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, ...

Quantum Leap in Healthcare: A Comparative Analysis of AI/ML and Quantum Computing for Healthcare opportunities of the new era

The healthcare sector is undergoing rapid transformation driven by advances in artificial intelligence and machine learning, with quantum computing emerging as the next major breakthrough. This paper presents a comparative analysis of class...

Condensing AI-Based Attitude Control Using Kolmogorov–Arnold Networks for Memory Efficiency

Artificial Intelligence (AI) is rapidly transforming engineering fields, from robotics to aerospace, with applications in control systems for UAVs and satellites. This work builds on a previously developed AI attitude controller for the Inn...

MMSplice: modular modeling improves the predictions of genetic variant effects on splicing

Abstract Predicting the effects of genetic variants on splicing is highly relevant for human genetics. We describe the framework MMSplice (modular modeling of splicing) with which we built the winning model of the CAGI5 exon skipping predic...

Gene–gene interaction detection with deep learning

An open-source framework combines deep learning and permutations of gene interaction neural networks to detect complex gene–gene interactions and their significance in contributions to phenotypes....

On the Origin of Deep Learning

This paper is a review of the evolutionary history of deep learning models. It covers from the genesis of neural networks when associationism modeling of the brain is studied, to the models that dominate the last decade of research in deep ...

Scalable and accurate deep learning with electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variabl...

Neural Networks and Deep Learning

Deep learning: A philosophical introduction

Abstract Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been larg...

Deep Learning in Microscopy Image Analysis: A Survey

Computerized microscopy image analysis plays an important role in computer aided diagnosis and prognosis. Machine learning techniques have powered many aspects of medical investigation and clinical practice. Recently, deep learning is emerg...

Deep learning in robotics: a review of recent research

Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least 30 papers published on the subject between 2014 and the present. This re...

Review of deep learning for photoacoustic imaging

Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began...

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...

Face recognition in dense crowd using deep learning approaches with IP camera

A facial recognition system is a biometric security and surveillance system that can identify and monitor individuals in a crowded area. Manually monitoring a crowded environment is a difficult and error-prone task. Therefore, in such conte...

Compilation of training datasets for use of convolutional neural networks supporting automatic inspection processes in industry 4.0 based electronic manufacturing

Ensuring the highest quality standards at competitive prices is one of the greatest challenges in the manufacture of electronic products. The identification of flaws has the uppermost priority in the field of automotive electronics, particu...

Speaker recognition based on deep learning: An overview