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Found 38 papers, 7 papers with code
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data.
Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery
We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain.
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19
Design of new drug compounds with target properties is a key area of research in generative modeling.
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates
We examine a pair of graph generative models for the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins.
An Effective Meaningful Way to Evaluate Survival Models
One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by the model and the true event time, over all subjects.
Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks
These models are further adapted for regression tasks to predict experimental binding affinities and pIC50 is crucial for drugs potency and efficacy.
Systematically designing better instance counting models on cell images with Neural Arithmetic Logic Units
These numerically biased units are added in the form of residual concatenated layers to original architectures and a comparative experimental study is done with these newly proposed changes.
Deep Learning-Based Image Kernel for Inductive Transfer
We propose a method to classify images from target classes with a small number of training examples based on transfer learning from non-target classes.
Uncertainty Estimation in Cancer Survival Prediction
Survival models are used in various fields, such as the development of cancer treatment protocols.
FeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware
Ever increasing number of Android malware, has always been a concern for cybersecurity professionals.
Blockchain based Attack Detection on Machine Learning Algorithms for IoT based E-Health Applications
Hence, in this article, we have proposed blockchain based solution to secure the datasets generated from IoT devices for E-Health applications.
Efficient Power-Splitting and Resource Allocation for Cellular V2X Communications
This article aims to further push the state-of-the-art of cellular V2X communications by providing an optimization framework for wireless charging, power allocation, and resource block assignment.
Mitigating the Impact of Adversarial Attacks in Very Deep Networks
In it, a Defensive Feature Layer (DFL) is integrated with a well-known DNN architecture which assists in neutralizing the effects of illegitimate perturbation samples in the feature space.
Multi Modal Adaptive Normalization for Audio to Video Generation
The multi-modal adaptive normalization uses the various features of audio and video such as Mel spectrogram, pitch, energy from audio signals and predicted keypoint heatmap/optical flow and a single image to learn the respective affine parameters to generate highly expressive video.
Few Shot Adaptive Normalization Driven Multi-Speaker Speech Synthesis
High quality multi-speaker speech synthesis while considering prosody and in a few shot manner is an area of active research with many real-world applications.
Robust One Shot Audio to Video Generation
High-quality video generation with expressive facial movements is a challenging problem that involves complex learning steps for generative adversarial networks.
Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective
Domain-aware machine learning (ML) models have been increasingly adopted for accelerating small molecule therapeutic design in the recent years.
One Shot Audio to Animated Video Generation
We propose a novel method OneShotAu2AV to generate an animated video of arbitrary length using an audio clip and a single unseen image of a person as an input.
A Provably Secure and Efficient Identity-Based Anonymous Authentication Scheme for Mobile Edge Computing
Mobile edge computing (MEC) allows one to overcome a number of limitations inherent in cloud computing, although achieving the broad range of security requirements in MEC settings remains challenging.
Phase transitions in D-dimensional Gauss-Bonnet-Born-Infeld AdS black holes
In this paper, we have investigated the phase transition in black holes when Gauss-Bonnet corrections to the spacetime curvature and Born-Infeld extension in stress-energy tensor of electromagnetic field are considered in a negative cosmological constant background.
General Relativity and Quantum Cosmology
FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia
Under the FL framework and Differentially Private thinking, we propose a FederatedDifferentially Private Generative Adversarial Network (FedDPGAN) to detectCOVID-19 pneumonia for sustainable smart cities.
Security in Next Generation Mobile Payment Systems: A Comprehensive Survey
The usage of mobile phones is pretty ordinary in this present era.
Few Shot Activity Recognition Using Variational Inference
To the best of our knowledge, we are the first to explore variational inference along with householder transformations to capture the full rank covariance matrix of posterior distribution, for few shot learning in activity recognition.
Learning from learning machines: a new generation of AI technology to meet the needs of science
We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery.
Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm
Based on the above two problems faced by ICPSs, we propose a virtual network embedded (VNE) algorithm with computing, storage resources and security constraints to ensure the rationality and security of resource allocation in ICPSs.
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning
Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics.
Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method
Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN's heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm.
A Reliable Data-transmission Mechanism using Blockchain in Edge Computing Scenarios
With the advent of the Internet of things (IoT) era, more and more devices are connected to the IoT.
IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode
This paper proposes a bandwidth aware multi domain virtual network embedding algorithm (BA-VNE).
EEG based Emotion Recognition: A Tutorial and Review
Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic.
De novo design of protein target specific scaffold-based Inhibitors via Reinforcement Learning
Efficient design and discovery of target-driven molecules is a critical step in facilitating lead optimization in drug discovery.
An Overview of Violence Detection Techniques: Current Challenges and Future Directions
The Big Video Data generated in today's smart cities has raised concerns from its purposeful usage perspective, where surveillance cameras, among many others are the most prominent resources to contribute to the huge volumes of data, making its automated analysis a difficult task in terms of computation and preciseness.
Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction
Neural Networks (GNNs) have revolutionized the molecular discovery to understand patterns and identify unknown features that can aid in predicting biophysical properties and protein-ligand interactions.
KL Regularized Normalization Framework for Low Resource Tasks
A lot of normalization techniques have been proposed but the success of normalization in low resource downstream NLP and speech tasks is limited.
Scaffold-Based Multi-Objective Drug Candidate Optimization
In therapeutic design, balancing various physiochemical properties is crucial for molecule development, similar to how Multiparameter Optimization (MPO) evaluates multiple variables to meet a primary goal.
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications.
Distraction-free Embeddings for Robust VQA
The generation of effective latent representations and their subsequent refinement to incorporate precise information is an essential prerequisite for Vision-Language Understanding (VLU) tasks such as Video Question Answering (VQA).
Style Description based Text-to-Speech with Conditional Prosodic Layer Normalization based Diffusion GAN
In this paper, we present a Diffusion GAN based approach (Prosodic Diff-TTS) to generate the corresponding high-fidelity speech based on the style description and content text as an input to generate speech samples within only 4 denoising steps.
