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Found 50 papers, 4 papers with code
MedISure: Towards Assuring Machine Learning-based Medical Image Classifiers using Mixup Boundary Analysis
Machine learning (ML) models are becoming integral in healthcare technologies, presenting a critical need for formal assurance to validate their safety, fairness, robustness, and trustworthiness.
Multivessel Coronary Artery Segmentation and Stenosis Localisation using Ensemble Learning
This study introduces an end-to-end machine learning solution developed as part of our solution for the MICCAI 2023 Automatic Region-based Coronary Artery Disease diagnostics using x-ray angiography imagEs (ARCADE) challenge, which aims to benchmark solutions for multivessel coronary artery segmentation and potential stenotic lesion localisation from X-ray coronary angiograms.
Ranked #3 on
Coronary Artery Segmentation
on ARCADE
Adversarial Machine Learning for Social Good: Reframing the Adversary as an Ally
This paper encompasses a taxonomy that highlights the emergence of AdvML4G, a discussion of the differences and similarities between AdvML4G and AdvML, a taxonomy covering social good-related concepts and aspects, an exploration of the motivations behind the emergence of AdvML4G at the intersection of ML4G and AdvML, and an extensive summary of the works that utilize AdvML4G as an auxiliary tool for innovating pro-social applications.
Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey
In this paper, we explore various privacy challenges that future metaverses are expected to face, given their reliance on AI for tracking users, creating XR and MR experiences, and facilitating interactions.
R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild
Semantic understanding of roadways is a key enabling factor for safe autonomous driving.
Membership Inference Attacks on DNNs using Adversarial Perturbations
Secondly, we utilize the framework to propose two novel attacks: (1) Adversarial Membership Inference Attack (AMIA) efficiently utilizes the membership and the non-membership information of the subjects while adversarially minimizing a novel loss function, achieving 6% TPR on both Fashion-MNIST and MNIST datasets; and (2) Enhanced AMIA (E-AMIA) combines EMIA and AMIA to achieve 8% and 4% TPRs on Fashion-MNIST and MNIST datasets respectively, at 1% FPR.
Robust Surgical Tools Detection in Endoscopic Videos with Noisy Data
In this paper, we propose a systematic methodology for developing robust models for surgical tool detection using noisy data.
Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers
In this paper, we propose a Multimodal Dual Attention Transformer (MDAT) model to improve cross-language SER.
Motion Comfort Optimization for Autonomous Vehicles: Concepts, Methods, and Techniques
We also discuss the technical details related to the automatic driving comfort system, the response time of the AV driver, the comfort level of the AV, motion sickness, and related optimization technologies.
Can We Revitalize Interventional Healthcare with AI-XR Surgical Metaverses?
Recent advancements in technology, particularly in machine learning (ML), deep learning (DL), and the metaverse, offer great potential for revolutionizing surgical science.
Transformers in Speech Processing: A Survey
The remarkable success of transformers in the field of natural language processing has sparked the interest of the speech-processing community, leading to an exploration of their potential for modeling long-range dependencies within speech sequences.
Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models
Although many works have studied these adversarial perturbations in general, the adversarial vulnerabilities of deep crowd-flow prediction models in particular have remained largely unexplored.
Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation
Data distribution shift is a common problem in machine learning-powered smart city applications where the test data differs from the training data.
Secure and Trustworthy Artificial Intelligence-Extended Reality (AI-XR) for Metaverses
Metaverse is expected to emerge as a new paradigm for the next-generation Internet, providing fully immersive and personalised experiences to socialize, work, and play in self-sustaining and hyper-spatio-temporal virtual world(s).
Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques
Building operations represent a significant percentage of the total primary energy consumed in most countries due to the proliferation of Heating, Ventilation and Air-Conditioning (HVAC) installations in response to the growing demand for improved thermal comfort.
District Wise Price Forecasting of Wheat in Pakistan using Deep Learning
Such arrangements can be made more effective if a dynamic analysis is carried out to estimate the future yield based on certain current factors.
Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection
Breast cancer is one of the most common cause of deaths among women.
Sentiment Analysis of Users' Reviews on COVID-19 Contact Tracing Apps with a Benchmark Dataset
In this work, we propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding on the development and training of AI models for automatic sentiment analysis of users' reviews.
Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge
Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency).
Fake Visual Content Detection Using Two-Stream Convolutional Neural Networks
The proposed detector has demonstrated significant performance improvement compared to the current state-of-the-art fake content detectors and fusing the frequency and spatial domain streams has also improved generalization of the detector.
An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification
In recent years, deep learning (DL) techniques have provided state-of-the-art performance on different medical imaging tasks.
Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning
The anticipated increase in the count of IoT devices in the coming years motivates the development of efficient algorithms that can help in their effective management while keeping the power consumption low.
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges
In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical (data and algorithmic) challenges to a successful deployment of AI in human-centric applications, with a particular emphasis on the convergence of these concepts/challenges.
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction
One of the roadblocks for accurate seizure prediction is scarcity of epileptic seizure data.
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning
In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process.
Particle Swarm Optimized Federated Learning For Industrial IoT and Smart City Services
Most of the research on Federated Learning (FL) has focused on analyzing global optimization, privacy, and communication, with limited attention focusing on analyzing the critical matter of performing efficient local training and inference at the edge devices.
Examining Machine Learning for 5G and Beyond through an Adversarial Lens
Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e. g., resource allocation problems), there is currently tremendous excitement in the mobile networks domain around the transformative potential of data-driven AI/ML based network automation, control and analytics for 5G and beyond.
Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services
With Machine Learning (ML) services now used in a number of mission-critical human-facing domains, ensuring the integrity and trustworthiness of ML models becomes all-important.
Artificial Intelligence-Assisted Energy and Thermal Comfort Control for Sustainable Buildings: An Extended Representation of the Systematic Review
Recent works have been directed towards more advanced control strategies, based mainly on artificial intelligence which has the ability to imitate human behavior.
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals.
Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in quickly adapting to the surrounding environments.
Secure and Robust Machine Learning for Healthcare: A Survey
Recent years have witnessed widespread adoption of machine learning (ML)/deep learning (DL) techniques due to their superior performance for a variety of healthcare applications ranging from the prediction of cardiac arrest from one-dimensional heart signals to computer-aided diagnosis (CADx) using multi-dimensional medical images.
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends
Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from the task of designing efficient machine learning (ML) models to make prediction and classification decisions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Black-box Adversarial ML Attack on Modulation Classification
We have evaluated the robustness of two famous such modulation classifiers (based on the techniques of convolutional neural networks and long short term memory) against adversarial machine learning attacks in black-box settings.
The Adversarial Machine Learning Conundrum: Can The Insecurity of ML Become The Achilles' Heel of Cognitive Networks?
We also provide some guidelines to design secure ML models for cognitive networks that are robust to adversarial attacks on the ML pipeline of cognitive networks.
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward
Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services.
Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey
In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics.
Motion Corrected Multishot MRI Reconstruction Using Generative Networks with Sensitivity Encoding
The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis.
Generative Adversarial Network
Motion Correction In Multishot Mri
+1
Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages
Cross-lingual speech emotion recognition is an important task for practical applications.
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness
Deep learning has undoubtedly offered tremendous improvements in the performance of state-of-the-art speech emotion recognition (SER) systems.
Automating Motion Correction in Multishot MRI Using Generative Adversarial Networks
Multishot Magnetic Resonance Imaging (MRI) has recently gained popularity as it accelerates the MRI data acquisition process without compromising the quality of final MR image.
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning
Deep neural networks (DNN)-based machine learning (ML) algorithms have recently emerged as the leading ML paradigm particularly for the task of classification due to their superior capability of learning efficiently from large datasets.
SDN Flow Entry Management Using Reinforcement Learning
Modern information technology services largely depend on cloud infrastructures to provide their services.
Using Deep Autoencoders for Facial Expression Recognition
Feature descriptors involved in image processing are generally manually chosen and high dimensional in nature.
Soft Computing Techniques for Dependable Cyber-Physical Systems
Cyber-Physical Systems (CPS) allow us to manipulate objects in the physical world by providing a communication bridge between computation and actuation elements.
Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection
Our choice of RNNs is motivated by the great success of deep learning in medical applications and by the observation that RNNs represent the deep learning configuration most suitable for dealing with sequential or temporal data even in the presence of noise.
Transfer Learning for Improving Speech Emotion Classification Accuracy
The majority of existing speech emotion recognition research focuses on automatic emotion detection using training and testing data from same corpus collected under the same conditions.
Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study
Inspired by this, we propose VAEs for deriving the latent representation of speech signals and use this representation to classify emotions.
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges
We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking.
Artificial Intelligence Based Cognitive Routing for Cognitive Radio Networks
Cognitive routing protocols are envisioned as routing protocols that fully and seamless incorporate AI-based techniques into their design.
