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Found 44 papers, 27 papers with code
Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images
Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition.
A semantic web approach to uplift decentralized household energy data
In a decentralized household energy system comprised of various devices such as home appliances, electric vehicles, and solar panels, end-users are able to dig deeper into the system's details and further achieve energy sustainability if they are presented with data on the electric energy consumption and production at the granularity of the device.
On the Relationship Between Ground- and Satellite- Based Global Horizontal Irradiance
Global horizontal irradiance (GHI) plays a significant role in maintaining the earth's ecological balance and generating electricity in photovoltaic systems.
Frequency-centroid features for word recognition of non-native English speakers
The objective of this work is to investigate complementary features which can aid the quintessential Mel frequency cepstral coefficients (MFCCs) in the task of closed, limited set word recognition for non-native English speakers of different mother-tongues.
Analyzing the impact of feature selection on the accuracy of heart disease prediction
In order to prevent patients from further damage, an accurate diagnosis of heart disease on time is an essential factor.
DMCNet: Diversified Model Combination Network for Understanding Engagement from Video Screengrabs
This provides a good learning experience for the students.
A predictive analytics approach for stroke prediction using machine learning and neural networks
Therefore, it is vital to study the interdependency of these risk factors in patients' health records and understand their relative contribution to stroke prediction.
Monitoring Atmospheric Pollutants From Ground-based Observations
Remote sensing analysts continuously monitor the amount of pollutants in the atmosphere.
Identifying Stroke Indicators Using Rough Sets
We propose a novel rough-set based technique for ranking the importance of the various EHR records in detecting stroke.
Detecting Blurred Ground-based Sky/Cloud Images
To the best of our knowledge, our approach is the first of its kind in the automatic identification of blurred images for ground-based sky/cloud images.
Analysis of French Phonetic Idiosyncrasies for Accent Recognition
However, there is a scope of improvement in speech recognition systems in identifying the nuances and accents of a speaker.
Graph-based Local Climate Classification in Iran
To the best of our knowledge, our proposed method is the first graph-based climate classification system.
Using GANs to Augment Data for Cloud Image Segmentation Task
While cloud/sky image segmentation has extensive real-world applications, a large amount of labelled data is needed to train a highly accurate models to perform the task.
Stereo Matching Based on Visual Sensitive Information
The area of computer vision is one of the most discussed topics amongst many scholars, and stereo matching is its most important sub fields.
A Clustering Framework for Residential Electric Demand Profiles
The availability of residential electric demand profiles data, enabled by the large-scale deployment of smart metering infrastructure, has made it possible to perform more accurate analysis of electricity consumption patterns.
Validating Clustering Frameworks for Electric Load Demand Profiles
Clustering analysis of these profiles is important to further analyze and comment on electricity consumption patterns.
An Advert Creation System for 3D Product Placements
Over the past decade, the evolution of video-sharing platforms has attracted a significant amount of investments on contextual advertising.
Forecasting Precipitable Water Vapor Using LSTMs
Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time.
Subjective Quality Assessment of Ground-based Camera Images
Image quality assessment is critical to control and maintain the perceived quality of visual content.
Estimating Solar Irradiance Using Sky Imagers
Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement.
Identifying Candidate Spaces for Advert Implantation
Virtual advertising is an important and promising feature in the area of online advertising.
The ALOS Dataset for Advert Localization in Outdoor Scenes
The rapid increase in the number of online videos provides the marketing and advertising agents ample opportunities to reach out to their audience.
CloudSegNet: A Deep Network for Nychthemeron Cloud Image Segmentation
In the existing literature, however, analysis of daytime and nighttime images is considered separately, mainly because of differences in image characteristics and applications.
The CASE Dataset of Candidate Spaces for Advert Implantation
With the advent of faster internet services and growth of multimedia content, we observe a massive growth in the number of online videos.
Multi-label Cloud Segmentation Using a Deep Network
Different empirical models have been developed for cloud detection.
TEST: A Terminology Extraction System for Technology Related Terms
This tool is used to automatically detect the existence of new technologies and tools in text, and extract terms used to describe these new technologies.
ADNet: A Deep Network for Detecting Adverts
Online video advertising gives content providers the ability to deliver compelling content, reach a growing audience, and generate additional revenue from online media.
Analyzing Solar Irradiance Variation From GPS and Cameras
The total amount of solar irradiance falling on the earth's surface is an important area of study amongst the photo-voltaic (PV) engineers and remote sensing analysts.
High-Dynamic-Range Imaging for Cloud Segmentation
It is thus difficult to capture the details of an entire scene with a regular camera in a single shot.
Correlating Satellite Cloud Cover with Sky Cameras
The role of clouds is manifold in understanding the various events in the atmosphere, and also in studying the radiative balance of the earth.
Analyzing Cloud Optical Properties Using Sky Cameras
It is important to study the various cloud properties, as it impacts the total solar irradiance falling on the earth's surface.
Study of Clear Sky Models for Singapore
The estimation of total solar irradiance falling on the earth's surface is important in the field of solar energy generation and forecasting.
Nighttime sky/cloud image segmentation
An accurate segmentation of sky/cloud images is already challenging because of the clouds' non-rigid structure and size, and the lower and less stable illumination of the night sky increases the difficulty.
Design of low-cost, compact and weather-proof whole sky imagers for high-dynamic-range captures
Ground-based whole sky imagers are popular for monitoring cloud formations, which is necessary for various applications.
Cloud Radiative Effect Study Using Sky Camera
weather reporting, climate forecasting, and solar energy generation.
Systematic study of color spaces and components for the segmentation of sky/cloud images
Sky/cloud imaging using ground-based Whole Sky Imagers (WSI) is a cost-effective means to understanding cloud cover and weather patterns.
Rough Set Based Color Channel Selection
Color channel selection is essential for accurate segmentation of sky and clouds in images obtained from ground-based sky cameras.
Detecting Rainfall Onset Using Sky Images
Ground-based sky cameras (popularly known as Whole Sky Imagers) are increasingly used now-a-days for continuous monitoring of the atmosphere.
Short-term prediction of localized cloud motion using ground-based sky imagers
Fine-scale short-term cloud motion prediction is needed for several applications, including solar energy generation and satellite communications.
Color-based Segmentation of Sky/Cloud Images From Ground-based Cameras
Sky/cloud images captured by ground-based cameras (a. k. a.
Machine Learning Techniques and Applications For Ground-based Image Analysis
Ground-based whole sky cameras have opened up new opportunities for monitoring the earth's atmosphere.
Estimation of solar irradiance using ground-based whole sky imagers
Ground-based whole sky imagers (WSIs) can provide localized images of the sky of high temporal and spatial resolution, which permits fine-grained cloud observation.
WAHRSIS: A Low-cost, High-resolution Whole Sky Imager With Near-Infrared Capabilities
Cloud imaging using ground-based whole sky imagers is essential for a fine-grained understanding of the effects of cloud formations, which can be useful in many applications.
