Search Results for author:
Found 51 papers, 10 papers with code
Using Deep Autoencoders for Facial Expression Recognition
Feature descriptors involved in image processing are generally manually chosen and high dimensional in nature.
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.
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.
q-LMF: Quantum Calculus-based Least Mean Fourth Algorithm
The proposed $q$-least mean fourth ($q$-LMF) is an extension of least mean fourth (LMF) algorithm and it is based on the $q$-calculus which is also known as Jackson derivative.
Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages
Cross-lingual speech emotion recognition is an important task for practical applications.
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
Chaotic Time Series Prediction using Spatio-Temporal RBF Neural Networks
The proposed RBF architecture is explored for the prediction of Mackey-Glass time series and results are compared with the standard RBF.
AFP-CKSAAP: Prediction of Antifreeze Proteins Using Composition of k-Spaced Amino Acid Pairs with Deep Neural Network
Antifreeze proteins (AFPs) are the sub-set of ice binding proteins indispensable for the species living in extreme cold weather.
Heart Segmentation From MRI Scans Using Convolutional Neural Network
Heart is one of the vital organs of human body.
A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)
However, MCML metrics based on model counting show that the performance can degrade substantially when tested against the entire (bounded) input space, indicating the high complexity of precisely learning these properties, and the usefulness of model counting in quantifying the true performance.
Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning
The proposed technique can be segregated into two stages, at the first stage, it takes a 2-D ROI containing the nodule as input and it performs patch-wise investigation along the axial axis with a novel adaptive ROI strategy.
Security and Privacy in IoT Using Machine Learning and Blockchain: Threats & Countermeasures
In this paper, we provide a summary of research efforts made in the past few years, starting from 2008 to 2019, addressing security and privacy issues using ML algorithms and BCtechniques in the IoT domain.
A Systematic Survey of Regularization and Normalization in GANs
Although a handful number of regularization and normalization methods have been proposed for GANs, to the best of our knowledge, there exists no comprehensive survey that primarily focuses on objectives and development of these methods, apart from some in-comprehensive and limited scope studies.
AFP-SRC: Identification of Antifreeze Proteins Using Sparse Representation Classifier
In this research, we propose a computational framework for the prediction of AFPs which is essentially based on a sample-specific classification method using the sparse reconstruction.
3D Object Localization Using 2D Estimates for Computer Vision Applications
A technique for object localization based on pose estimation and camera calibration is presented.
Graph-Based Generative Representation Learning of Semantically and Behaviorally Augmented Floorplans
We present a floorplan embedding technique that uses an attributed graph to represent the geometric information as well as design semantics and behavioral features of the inhabitants as node and edge attributes.
The Diabetic Buddy: A Diet Regulator andTracking System for Diabetics
In this regard, there is a need to build automatic tools to monitor the blood glucose levels of diabetics and their daily food intake.
NEUROSPF: A tool for the Symbolic Analysis of Neural Networks
This paper presents NEUROSPF, a tool for the symbolic analysis of neural networks.
Compliance Requirements in Large-Scale Software Development: An Industrial Case Study
Regulatory compliance is a well-studied area, including research on how to model, check, analyse, enact, and verify compliance of software.
Software Engineering
NNrepair: Constraint-based Repair of Neural Network Classifiers
We present novel strategies to enable precise yet efficient repair such as inferring correctness specifications to act as oracles for intermediate layer repair, and generation of experts for each class.
Envisioning security control in renewable dominated power systems through stochastic multi-period AC security constrained optimal power flow
To address the latter issue, this paper envisions N-1 security control in RES dominated power systems through stochastic multi-period AC security constrained optimal power flow (SCOPF).
Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques
The inherent flexibility of our approach allows easy adaptation to various tasks, thus making it highly relevant to many applications of the semiconductor industry.
q-RBFNN:A Quantum Calculus-based RBF Neural Network
In this research a novel stochastic gradient descent based learning approach for the radial basis function neural networks (RBFNN) is proposed.
Semi-supervised Learning with Missing Values Imputation
SSCFlow explicitly utilizes the label information to facilitate the imputation and classification simultaneously by estimating the conditional distribution of incomplete instances with a novel semi-supervised normalizing flow.
A scalable and fast artificial neural network syndrome decoder for surface codes
Surface code error correction offers a highly promising pathway to achieve scalable fault-tolerant quantum computing.
QuantifyML: How Good is my Machine Learning Model?
The efficacy of machine learning models is typically determined by computing their accuracy on test data sets.
AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks
For correction, we propose an input correction technique that uses a differential analysis to identify the trigger in the detected poisoned images, which is then reset to a neutral color.
Multiplier with Reduced Activities and Minimized Interconnect for Inner Product Arrays
Synthesis results of the proposed designs have been presented and compared with the non-pipelined online multiplier, pipelined online multiplier with full working precision and conventional serial-parallel and array multipliers.
VPN: Verification of Poisoning in Neural Networks
Neural networks are successfully used in a variety of applications, many of them having safety and security concerns.
Optimizing Indoor Navigation Policies For Spatial Distancing
In this paper, we focus on the modification of policies that can lead to movement patterns and directional guidance of occupants, which are represented as agents in a 3D simulation engine.
Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm
However, the impact of coreset selection on the performance of quantum K-Means clustering has not been explored.
Automated Quantum Circuit Design with Nested Monte Carlo Tree Search
Quantum algorithms based on variational approaches are one of the most promising methods to construct quantum solutions and have found a myriad of applications in the last few years.
An Overview of Structural Coverage Metrics for Testing Neural Networks
Deep neural network (DNN) models, including those used in safety-critical domains, need to be thoroughly tested to ensure that they can reliably perform well in different scenarios.
Dual-Stage Deeply Supervised Attention-based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans
In this paper, we propose a novel dual-stage deep learning-based scheme for the automatic segmentation of the mandibular canal.
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).
MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection
Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i. e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10 adjacent slices to feed into self-distillation-based Multi-Encoders Network (MEDS-Net).
Benchmarking Adversarially Robust Quantum Machine Learning at Scale
Machine learning (ML) methods such as artificial neural networks are rapidly becoming ubiquitous in modern science, technology and industry.
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
A subclass of QML methods is quantum generative adversarial networks (QGANs) which have been studied as a quantum counterpart of classical GANs widely used in image manipulation and generation tasks.
MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan
By employing a novel adaptive hard attention mechanism, MESAHA-Net iteratively performs slice-by-slice 2D segmentation of lung nodules, focusing on the nodule region in each slice to generate 3D volumetric segmentation of lung nodules.
Low-Latency Online Multiplier with Reduced Activities and Minimized Interconnect for Inner Product Arrays
Serial nature of the online algorithm and gradual increment/decrement of active slices minimize the interconnects and signal activities resulting in overall reduction of area and power consumption.
LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge Platforms
The results demonstrate the robustness, generalizability, and high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool for accurate and rapid medical image segmentation in diverse clinical applications, particularly on IoT and edge platforms.
Automating Microservices Test Failure Analysis using Kubernetes Cluster Logs
Kubernetes is a free, open-source container orchestration system for deploying and managing Docker containers that host microservices.
Towards quantum enhanced adversarial robustness in machine learning
Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm - remains a serious challenge.
A Comprehensive Overview of Large Language Models
Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks and beyond.
Efficacy of mannan-oligosaccharide and live yeast feed additives on performance, rumen morphology, serum biochemical parameters and muscle morphometric characteristics in buffalo calves
Histomorphological evaluation of rumen mucosal epithelium showed a significant improvement in the mixed-supplemented group (P<0. 05) as compared to the yeast-supplemented and control groups.
Pragmatic Comparison Analysis of Alternative Option Pricing Models
In this paper, we price European Call three different option pricing models, where the volatility is dynamically changing i. e. non constant.
DSLOT-NN: Digit-Serial Left-to-Right Neural Network Accelerator
We propose a Digit-Serial Left-tO-righT (DSLOT) arithmetic based processing technique called DSLOT-NN with aim to accelerate inference of the convolution operation in the deep neural networks (DNNs).
The Importance of Multimodal Emotion Conditioning and Affect Consistency for Embodied Conversational Agents
We have conducted a user study with 199 participants to assess how the average person judges the affects perceived from multimodal behaviors that are consistent and inconsistent with respect to a driving affect.
Clustering by Contour coreset and variational quantum eigensolver
In this work, we propose solving the k-means clustering problem with the variational quantum eigensolver (VQE) and a customised coreset method, the Contour coreset, which has been formulated with specific focus on quantum algorithms.
Radio Signal Classification by Adversarially Robust Quantum Machine Learning
Our extensive simulation results present that attacks generated on QVCs transfer well to CNN models, indicating that these adversarial examples can fool neural networks that they are not explicitly designed to attack.
Blue and Green-Mode Energy-Efficient Chemiresistive Sensor Array Realized by Rapid Ensemble Learning
The rapid advancement of Internet of Things (IoT) necessitates the development of optimized Chemiresistive Sensor (CRS) arrays that are both energy-efficient and capable.
