Academic Articles & Research Papers

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...

Fundamentals of Deep Learning

This chapter deals with the basic understanding of the deep learning model. It starts with a view of artificial intelligence, machine learning, and deep learning. It also represents how these three concepts are related to each other. The en...

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 ...

Deep learning improves antimicrobial peptide recognition

Motivation: Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by w...

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

Detection of Atrial Fibrillation Using a Machine Learning Approach

The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocar...

Acoustic Impedance Inversion from Seismic Imaging Profiles Using Self Attention U-Net

Seismic impedance inversion is a vital way of geological interpretation and reservoir investigation from a geophysical perspective. However, it is inevitably an ill-posed problem due to the noise or the band-limited characteristic of seismi...

Automatic Identification of Brain Injury Mechanism Based on Deep Learning

ObjectiveTo apply the convolutional neural network （CNN） Inception_v3 model in automatic identification of acceleration and deceleration injury based on CT images of brain, and to explore the application prospect of deep learning technology...

Deep Learning-Based Prediction of the Axial Capacity of CFRP-Strengthened Concrete Columns

Fiber-reinforced polymer (FRP) composites are widely used to strengthen reinforced concrete (RC) columns due to their high strength, durability, and ease of installation. Accurate prediction of the axial capacity of CFRP-strengthened concre...

Improved sports image classification using deep neural network and novel tuna swarm optimization

Abstract Sports image classification is a complex undertaking that necessitates the utilization of precise and robust techniques to differentiate between various sports activities. This study introduces a novel approach that combines the de...

A Simple Baseline for Bayesian Uncertainty in Deep Learning

We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning. Stochastic Weight Averaging (SWA), which computes the first moment of stochastic gradient desc...

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...