Academic Articles & Research Papers

LeafNST: an improved data augmentation method for classification of plant disease using object-based neural style transfer

Abstract Plant diseases significantly threaten global agriculture, impacting crop yield and food security. Nearly 30% of the crop yield is lost due to plant diseases. Efficient identification and classification of plant diseases through com...

Distribution of Marbling Throughout the M. Longissimus Thoracis et Lumborum of Beef Carcasses Using an Instrument-Grading System

Beef Loin, Strip Loins (IMPS #180; n = 20) with marbling scores between Modest00 and Modest30 at the 12th and 13th rib interface of the M. longissimus thoracis et lumborum (LL) were collected. Each strip loin was fabricated into 6 samples t...

Wasserstein Generative Adversarial Network Based De-Blurring Using Perceptual Similarity

The de-blurring of blurred images is one of the most important image processing methods and it can be used for the preprocessing step in many multimedia and computer vision applications. Recently, de-blurring methods have been performed by ...

Two-Argument Activation Functions Learn Soft XOR Operations Like Cortical Neurons

Neurons in the brain are complex machines with distinct functional compartments that interact nonlinearly. In contrast, neurons in artificial neural networks abstract away this complexity, typically down to a scalar activation function of a...

Neural networks in geophysical applications

Abstract Neural networks are increasingly popular in geophysics. Because they are universal approximators, these tools can approximate any continuous function with an arbitrary precision. Hence, they may yield important contributions to fin...

Overview of Artificial Neural Networks

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to ...

Invariant representation of physical stability in the human brain

Successful engagement with the world requires the ability to predict what will happen next. Here, we investigate how the brain makes a fundamental prediction about the physical world: whether the situation in front of us is stable, and henc...

Neural networks

article Free Access Share on Neural networks: applications in industry, business and science Authors: Bernard Widrow Stanford Univ., Stanford, CA Stanford Univ., Stanford, CAView Profile , David E. Rumelhart Stanford Univ., Stanford, CA Sta...

Identifying urban villages using graph neural network and transfer learning in Southern Jiangsu, China

China’s rapid urbanization has led to the emergence of urban villages (UVs), whose governance is crucial for urban quality and built environment management. However, conventional identification methods rely on high-resolution remote sensing...

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...

Automated Website Fingerprinting through Deep Learning

Several studies have shown that the network traffic that is generated by a visit to a website over Tor reveals information specific to the website through the timing and sizes of network packets.By capturing traffic traces between users and...

A Sex-Specific Platelet Switch Controls Anti-Tumor Immunity in Glioblastoma.

Deep learning for universal linear embeddings of nonlinear dynamics

Identifying coordinate transformations that make strongly nonlinear dynamics approximately linear has the potential to enable nonlinear prediction, estimation, and control using linear theory. The Koopman operator is a leading data-driven e...

Applications of Shaped-Charge Learning

It is well known that deep learning (DNN) has strong limitations due to a lack of explainability and weak defense against possible adversarial attacks. These attacks would be a concern for autonomous teams producing a state of high entropy ...

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...

Analysis of Customer Churn Classification for Sinarmas Syariah Lhokseumawe Insurance Services Using Deep Learning Tabnet and Explainable AI

Customer churn is a major challenge in the insurance industry because it directly affects customer retention, business sustainability, and company profitability. Early identification of customers at risk of churn is therefore essential for ...

Deep learning in analytical chemistry

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...

Feature Augmenting Networks for Improving Depression Severity Estimation From Speech Signals

Depression disorder has become one of the major psychological diseases endangering human health. Researcher in the affective computing community is supporting the development of reliable depression severity estimation system, from multiple ...

Understanding Deep Learning Techniques for Image Segmentation

The machine learning community has been overwhelmed by a plethora of deep learning--based approaches. Many challenging computer vision tasks, such as detection, localization, recognition, and segmentation of objects in an unconstrained envi...