Exclusive: Chainguard extends Repository scanning and policies to Java, Python and containers

Secure software supply chain solution provider Chainguard Inc. today expanded its Chainguard Repository product with malware ...
Tech Xplore

Brain-inspired AI architecture could compute faster while using far less power

Spiking neural networks (SNNs) are artificial intelligence (AI) models inspired by how biological neurons communicate with ...
The Tech Edvocate

How to create neural network

Spread the love“`html Understanding how to create a neural network can be a game-changer in the fields of artificial intelligence and machine learning. As industries increasingly rely on data-driven ...
IEEE

Graph Neural Network for Source Code Defect Prediction

Abstract: Predicting defective software modules before testing is a useful operation that ensures that the time and cost of software testing can be reduced. In recent years, several models have been ...
GitHub

Heterogeneous Directed Hypergraph Neural Network (HDHGN)

Our experiment is done in Ubuntu 18.04.6 LTS and in python 3.8. We implement our model by pytorch 1.10 and torch geometric 2.1.0. We train our model on a RTX 3090. The required environments are listed ...
Microsoft

AFGNN: API Misuse Detection using Graph Neural Networks and Clustering

Application Programming Interfaces (APIs) are crucial to software development, enabling integration of existing systems with new applications by reusing tried and tested code, saving development time ...
Scientific Research Publishing

Graph Neural Networks for Anomaly Detection in Cloud Infrastructure ()

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...
GitHub

Novel learning framework for building vulnerability detection models

Using graph neural networks and open-source repositories to detect code vulnerabilities. This is an implementation of the model described in: "Combining Graph-based Learning with Automated Data ...
Frontiers

Deriving equivalent symbol-based decision models from feedforward neural networks

Artificial intelligence (AI) has emerged as a transformative force across industries, driven by advances in deep learning and natural language processing, and fueled by large-scale data and computing ...
C&EN

MCNN_MC: Computational Prediction of Mitochondrial Carriers and Investigation of Bongkrekic Acid Toxicity Using Protein Language Models and Convolutional Neural Networks

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Mitochondrial carriers (MCs) are essential proteins that transport metabolites across ...