COVID-19 Spread Prediction Using Prophet and Data Fusion Algorithm

2022 ◽  
pp. 1-16
Author(s):  
Sangeetha V. ◽  
Evangeline D. ◽  
Sinthuja M.
Keyword(s):  
Longitudinal Data ◽  
Data Fusion ◽  
Predictive Analytics ◽  
New Technology ◽  
Vital Role ◽  
Healthcare Industry ◽  
Fusion Algorithm ◽  
Sparse Regularization ◽  
Novel Approach ◽  
Proposed Model

Today, technology plays a vital role in the healthcare industry. In the traditional way, physicians' minds were predicting the unknown disease based on their expertise and experience. Use of new technology like predictive analytics is transforming the healthcare industry. Predictive analytics in healthcare uses historical data (demographic information, person's past medical history and behaviors) to make predictions about the future. In this chapter, a predictive model is proposed to predict COVID-19 using prophet algorithm. A novel approach based on longitudinal data fusion approach will maintain temporal data from time to time. Sparse regularization regression uses data source and feature level to predict the spread of virus. The proposed model designed using longitudinal data fusion offers better clinical insights. Predictions will be very beneficial to government and healthcare groups to provoke suitable measures in controlling coronavirus. It is also beneficial to pharmaceutical companies to fabricate pills at a quicker rate.

Automated monitoring and assessment of productivity in earthmoving projects

2018 ◽  
Vol 45 (11) ◽  
pp. 958-972 ◽  
Author(s):  
Ashraf Salem ◽  
Osama Moselhi
Keyword(s):  
Data Fusion ◽  
Continuous Monitoring ◽  
Research Work ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Cost Overruns ◽  
Knowledge Based ◽  
Monitoring Model ◽  
Multi Sensor Data Fusion ◽  
Proposed Model

Continuous monitoring of productivity and assessment of its variations are crucial processes that significantly contribute to success of earthmoving projects. Numerous factors may lead to productivity variations. However, these factors are subjectively identified using manual knowledge-based expert judgment. Such manual recognition process is not only subject to errors but also time-consuming. There is a lack of research work that focuses on near real-time assessment of productivity variation and its effect on cost, schedule and effective utilization of resources in earthmoving projects. This paper presents a customized multi-source automated data acquisition model that acquires data from a variety of wireless sensing technologies. The acquired multi-sensor data are transmitted to a central MySQL database. Then a newly developed data fusion algorithm is applied for truck state recognition, and hence the duration of each earthmoving state. Multi-sensor data fusion facilitates measurement of actual productivity, and consequently the assessment of productivity ratios that support continuous monitoring of productivity variation in earthmoving operations. The developed tracking and monitoring model generates an early warning that supports proactive decisions to avoid schedule delays, cost overruns, and inefficient depletion of resources. A case study is used to reveal the applicability of the proposed model in monitoring and assessing actual productivity and its deviations from planned productivity. Finally, results are discussed and conclusions are drawn highlighting the features of the proposed model.

Novel Approach of Improving Secondary Electron Detector in FIB System

2018 ◽  
Author(s):  
Steve Wang ◽  
Jim McGinn ◽  
Peter Tvarozek ◽  
Amir Weiss
Keyword(s):  
Integrated Circuit ◽  
Secondary Electron ◽  
Focused Ion Beam ◽  
Ion Beam ◽  
Vital Role ◽  
Electron Detector ◽  
System A ◽  
Novel Approach

Abstract Secondary electron detector (SED) plays a vital role in a focused ion beam (FIB) system. A successful circuit edit requires a good effective detector. Novel approach is presented in this paper to improve the performance of such a detector, making circuit altering for the most advanced integrated circuit (IC) possible.

Application of multi-resolution data fusion algorithm

2010 ◽  
Vol 30 (9) ◽  
pp. 2556-2558 ◽  
Author(s):  
Ming-bo SHI ◽  
Ji-hong CHEN ◽  
Zheng-zheng JIANG
Keyword(s):  
Data Fusion ◽  
Fusion Algorithm ◽  
Resolution Data

Soil Moisture based Automatic Irrigation System to Improve Water Productivity

2020 ◽  
Vol 7 (04) ◽  
Author(s):  
PRADEEP H K ◽  
JASMA BALASANGAMESHWARA ◽  
K RAJAN ◽  
PRABHUDEV JAGADEESH
Keyword(s):  
Soil Moisture ◽  
Soil Moisture Content ◽  
Irrigation System ◽  
Water Productivity ◽  
Vital Role ◽  
Agricultural Water Management ◽  
Water Management System ◽  
Proposed Model ◽  
Crop Growth Stage ◽  
Experimental Findings

Irrigation automation plays a vital role in agricultural water management system. An efficient automatic irrigation system is crucial to improve crop water productivity. Soil moisture based irrigation is an economical and efficient approach for automation of irrigation system. An experiment was conducted for irrigation automation based on the soil moisture content and crop growth stage. The experimental findings exhibited that, automatic irrigation system based on the proposed model triggers the water supply accurately based on the real-time soil moisture values.

Applications of blockchain in healthcare (Preprint)

2020 ◽  
Author(s):  
Pranav C
Keyword(s):  
Decision Making ◽  
Large Scale ◽  
Review Paper ◽  
New Technology ◽  
Personal Identification ◽  
Healthcare Industry ◽  
Healthcare Organizations ◽  
Paper Briefly ◽  
Blockchain Technology ◽  
Audit Trails

UNSTRUCTURED The word blockchain elicits thoughts of cryptocurrency much of the time, which does disservice to this disruptive new technology. Agreed, bitcoin launched in 2011 was the first large scale implementation of blockchain technology. Also, Bitcoin’s success has triggered the establishment of nearly 1000 new cryptocurrencies. This again lead to the delusion that the only application of blockchain technology is for the creation of cryptocurrency. However, the blockchain technology is capable of a lot more than just cryptocurrency creation and may support such things as transactions that require personal identification, peer review, elections and other types of democratic decision-making and audit trails. Blockchain exists with real world implementations beyond cryptocurrencies and these solutions deliver powerful benefits to healthcare organizations, bankers, retailers and consumers among others. One of the areas where blockchain technology can be used effectively is healthcare industry. Proper application of this technology in healthcare will not only save billions of money but also will contribute to the growth in research. This review paper briefly defines blockchain and deals in detail the applications of blockchain in various areas particularly in healthcare industry.

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

Food waste treatment option selection through spherical fuzzy AHP

2021 ◽  
pp. 1-11
Author(s):  
Aysu Melis Buyuk ◽  
Gul T. Temur
Keyword(s):  
Treatment Option ◽  
Food Waste ◽  
Waste Treatment ◽  
Fuzzy Ahp ◽  
Treatment Options ◽  
Analytic Hierarchy ◽  
Food Treatment ◽  
Novel Approach ◽  
Proposed Model ◽  
Option Selection

In line with the increase in consciousness on sustainability in today’s global world, great emphasis has been attached to food waste management. Food waste is a complex issue to manage due to uncertainties on quality, quantity, location, and time of wastes, and it involves different decisions at many stages from seed to post-consumption. These ambiguities re-quire that some decisions should be handled in a linguistic and ambiguous environment. That forces researchers to benefit from fuzzy sets mostly utilized to deal with subjectivity that causes uncertainty. In this study, as a novel approach, the spherical fuzzy analytic hierarchy process (SFAHP) was used to select the best food treatment option. In the model, four main criteria (infrastructural, governmental, economic, and environmental) and their thirteen sub-criteria are considered. A real case is conducted to show how the proposed model can be used to assess four food waste treatment options (composting, anaerobic digestion, landfilling, and incineration). Also, a sensitivity analysis is generated to check whether the evaluations on the main criteria can change the results or not. The proposed model aims to create a subsidiary tool for decision makers in relevant companies and institutions.

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

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