scholarly journals From Tabulated Data to Knowledge Graph: A Novel Way of Improving the Performance of the Classification Models in the Healthcare Data

2021 ◽  
Author(s):  
Nazar Zaki ◽  
Elfadil Abdalla Mohamed ◽  
Tetiana Habuza
Keyword(s):  
Machine Learning ◽  
Data Structure ◽  
Data Analysis ◽  
Knowledge Graph ◽  
Classification Models ◽  
Effective Technique ◽  
Graph Data ◽  
Healthcare Data ◽  
Suggested Technique ◽  
Tabulated Data

In sectors like healthcare, having classification models that are both reliable and accurate is vital. Regrettably, contemporary classification techniques employing machine learning disregard the correlations between instances within data. This research, to rectify this, introduces a basic but effective technique for converting tabulated data into data graphs, incorporating structural correlations. Graphs have a unique capacity to capture structural correlations between data, allowing us to gain a deeper insight in comparison to carrying out isolated data analysis. The suggested technique underwent testing once the integration of graph data structure-related elements had been carried out and returned superior results to testing solely employing original features. The suggested technique achieved validity by returning significantly improved levels of accuracy.

scholarly journals 1P059 Interactive and systematic NMR data analysis based on graph data structure on the integrated NMR date analysis platform, KUJIRA(Proteins-methodology,Poster Presentations)

2007 ◽  
Vol 47 (supplement) ◽  
pp. S38
Author(s):  
Naohiro Kobayashi ◽  
Naoya Tochio ◽  
Tadashi Tomizawa ◽  
Seizo Koshiba ◽  
Takanori Kigawa ◽  
...  
Keyword(s):  
Data Structure ◽  
Data Analysis ◽  
Graph Data ◽  
Nmr Data ◽  
Date Analysis ◽  
Poster Presentations ◽  
Analysis Platform

Machine Learning Based Predictive Action on Categorical Non-Sequential Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1020-1030
Author(s):  
Pradeep S. ◽  
Jagadish S. Kallimani
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Categorical Data ◽  
Numerical Data ◽  
Processing Technique ◽  
Machine Learning Algorithms ◽  
Sequential Data ◽  
Industry Standard ◽  
Robust Model ◽  
Future Work

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

Machine learning based accident prediction in secure IoT enable transportation system

2021 ◽  
pp. 1-13 ◽  
Author(s):  
Bhabendu Kumar Mohanta ◽  
Debasish Jena ◽  
Niva Mohapatra ◽  
Somula Ramasubbareddy ◽  
Bharat S. Rawal
Keyword(s):  
Machine Learning ◽  
Smart City ◽  
Information Exchange ◽  
Secure Communication ◽  
Intelligent Transportation System ◽  
Transportation System ◽  
Information And Communications Technology ◽  
Classification Models ◽  
Architecture Model ◽  
Accident Severity

Smart city has come a long way since the development of emerging technology like Information and communications technology (ICT), Internet of Things (IoT), Machine Learning (ML), Block chain and Artificial Intelligence. The Intelligent Transportation System (ITS) is an important application in a rapidly growing smart city. Prediction of the automotive accident severity plays a very crucial role in the smart transportation system. The main motive behind this research is to determine the specific features which could affect vehicle accident severity. In this paper, some of the classification models, specifically Logistic Regression, Artificial Neural network, Decision Tree, K-Nearest Neighbors, and Random Forest have been implemented for predicting the accident severity. All the models have been verified, and the experimental results prove that these classification models have attained considerable accuracy. The paper also explained a secure communication architecture model for secure information exchange among all the components associated with the ITS. Finally paper implemented web base Message alert system which will be used for alert the users through smart IoT devices.

Feasibility of Longest Prefix Matching using Learned Index Structures

2021 ◽  
Vol 48 (4) ◽  
pp. 45-48
Author(s):  
Shunsuke Higuchi ◽  
Junji Takemasa ◽  
Yuki Koizumi ◽  
Atsushi Tagami ◽  
Toru Hasegawa
Keyword(s):  
Machine Learning ◽  
Data Structure ◽  
Index Structures ◽  
Packet Forwarding ◽  
Fundamental Idea ◽  
Search Operation ◽  
Longest Prefix Matching ◽  
Ip Packet ◽  
Computation Speed

This paper revisits longest prefix matching in IP packet forwarding because an emerging data structure, learned index, is recently presented. A learned index uses machine learning to associate key-value pairs in a key-value store. The fundamental idea to apply a learned index to an FIB is to simplify the complex longest prefix matching operation to a nearest address search operation. The size of the proposed FIB is less than half of an existing trie-based FIB while it achieves the computation speed nearly equal to the trie-based FIB. Moreover, the computation speed of the proposal is independent of the length of IP prefixes, unlike trie-based FIBs.

Sign in / Sign up

Export Citation Format

Share Document