Multi-level Iterative Interdependency Clustering of Diabetic Data Set for Efficient Disease Prediction

2018 ◽  
pp. 83-93
Author(s):  
B. V. Baiju ◽  
K. Rameshkumar
Keyword(s):  
Disease Prediction ◽  
Data Set ◽  
Multi Level

scholarly journals Real Time Symptomatic Analysis for Efficient Disease Prediction and Recommendation Generation Using Multi Level Symptom Similarity Measure

2018 ◽  
Vol 7 (4.5) ◽  
pp. 40
Author(s):  
Sathish Kumar.P.J ◽  
Dr R.Jagadeesh Kan
Keyword(s):  
Similarity Measure ◽  
Disease Prediction ◽  
Data Set ◽  
Second Stage ◽  
Class Level ◽  
Clustering And Classification ◽  
Multi Level ◽  
Noticeable Rate ◽  
High Dimensional Clustering

The problem of high dimensional clustering and classification has been well studied in previous articles. Also, the recommendation generation towards the treatment based on input symptoms has been considered in this research part. Number of approaches has been discussed earlier in literature towards disease prediction and recommendation generation. Still, the efficient of such recommendation systems are not up to noticeable rate. To improve the performance, an efficient multi level symptom similarity based disease prediction and recommendation generation has been presented. The method reads the input data set, performs preprocessing to remove the noisy records. In the second stage, the method performs Class Level Feature Similarity Clustering. The classification of input symptom set has been performed using MLSS (Multi Level Symptom Similarity) measure estimated between different class of samples. According to the selected class, the method selects higher frequent medicine set as recommendation using drug success rate and frequency measures. The proposed method improves the performance of clustering, disease prediction with higher efficient medicine recommendation.  

Abstiegserfahrungen in der Kommune und der Aufstieg Unabhängiger Wählergemeinschaften. Neue Befunde aus einem Mehrebenen-Kommunalwahlpanel

2021 ◽  
Vol 52 (1) ◽  
pp. 59-77
Author(s):  
Christina-Marie Juen ◽  
Markus Tepe ◽  
Michael Jankowski
Keyword(s):  
Panel Data ◽  
Voter Turnout ◽  
Party System ◽  
Local Politics ◽  
Tax Revenue ◽  
Data Set ◽  
Political Fragmentation ◽  
Electoral Success ◽  
Party Positions ◽  
Multi Level

In Germany, Independent Local Lists (UWG) have become an integral part of local politics in recent decades . Despite their growing political importance, the reasons for their electoral rise have hardly been researched . Recent studies argue that Independent Local Lists pursue anti-party positions, which makes them attractive to voters who are dissatisfied with the party system . Assuming that a decline of confidence in established parties corresponds with the experience of local deprivation, this contribution uses a multi-level panel data set to investigate how socio-economic (emigration, aging, declining tax revenue) and political­cultural (turnout, fragmentation) deprivation processes affect the electoral success of Inde­pendent Local Lists . The empirical findings suggest that Independent Local Lists are more successful in municipalities where voter turnout has fallen and political fragmentation has increased .

scholarly journals Implementation of Liver Disease Prediction Using Machine Learning

2021 ◽  
pp. 606-616
Author(s):  
Boggarapu Sai Surya ◽  
Nitesh Kumar Singh ◽  
S Sasi Rekha
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Disease Prediction ◽  
Medical Field ◽  
Data Set ◽  
Classification Techniques

This work Liver Disease Prediction Using Machine Learning is a machine learning application. In this project, you predict whether the patient contain a liver disease or not using python Jupyter Notebook. To predict presence of liver disease we apply some of the classification techniques. It gives an idea of how machine learning helps in medical field and how classification techniques going to predict liver disease using liver disease data set.

scholarly journals Symptoms Based Multiple Disease Prediction Model using Machine Learning Approach

2021 ◽  
Vol 10 (9) ◽  
pp. 67-72
Author(s):  
Talasila Bhanuteja ◽  
◽  
Kilaru Venkata Narendra Kumar ◽  
Kolli Sai Poornachand ◽  
Chennupati Ashish ◽  
...  
Keyword(s):  
Machine Learning ◽  
Well Being ◽  
Local Area ◽  
Learning Approach ◽  
Disease Prediction ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Tree Classifier ◽  
Machine Learning Approach ◽  
Disease Forecast

The turn of events and misuse of a few noticeable Data mining strategies in various genuine application regions (for example Trade, Medical management and Natural science) has induced the usage of such methods in Machine Learning (ML) constrains, to distinct helpful snippets of information of the predefined information in medical services networks, biomedical fields and so forth The exact examination of clinical data set advantages in early illness expectation, patient consideration and local area administrations. The methodology of Machine Learning (ML) has been effectively utilized in grouped technologies including Disease forecast. The objective of generating classifier framework utilizing Machine Learning (ML) models is to massively assist with addressing the well-being related issues by helping the doctors to foresee and analyze illnesses at a beginning phase. Sample information of 4920 patient’s records determined to have 41 illnesses was chosen for examination. A reliant variable was made out of 41 sicknesses. 95 of 132 autonomous variables (symptoms) firmly identified with infections were chosen and advanced. This examination work completed shows the illness expectation framework created utilizing Machine learning calculations like Random Forest, Decision Tree Classifier and LightGBM. The paper confers the relative investigation of the consequences of the above-mentioned algorithms are utilized efficiently.

scholarly journals Heart Disease Prediction

2018 ◽  
Vol 7 (3.12) ◽  
pp. 750
Author(s):  
S Vinothini ◽  
Ishaan Singh ◽  
Sujaya Pradhan ◽  
Vipul Sharma
Keyword(s):  
Heart Disease ◽  
Decision Tree ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Disease Prediction ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Logistics Regression ◽  
Learning Technique ◽  
Work Integration

Machine learning algorithm are used to produce new pattern from compound data set. To cluster the patient heart condition to check whether his /her heart normal or stressed or highly stressed k-means clustering algorithm is applied on the patient dataset. From  the results of clustering ,it is hard to elucidate and to obtain the required conclusion from these clusters. Hence another algorithm, the decision tree, is used for the exposition of the clusters of . In this work, integration of decision tree with the help of k-means algorithm is aimed. Another learning technique such as SVM and Logistics regression is used. Heart disease prediction results from SVM and Logistics regression were compared. 

scholarly journals Constructing a Lightweight Key-Value Store Based on the Windows Native Features

2019 ◽  
Vol 9 (18) ◽  
pp. 3801 ◽  
Author(s):  
Hyuk-Yoon Kwon
Keyword(s):  
State Of The Art ◽  
Main Idea ◽  
Real Data ◽  
Data Sets ◽  
Parameter Setting ◽  
Data Set ◽  
Multi Level ◽  
Windows Registry ◽  
Best Parameter ◽  
Better Than

In this paper, we propose a method to construct a lightweight key-value store based on the Windows native features. The main idea is providing a thin wrapper for the key-value store on top of a built-in storage in Windows, called Windows registry. First, we define a mapping of the components in the key-value store onto the components in the Windows registry. Then, we present a hash-based multi-level registry index so as to distribute the key-value data balanced and to efficiently access them. Third, we implement basic operations of the key-value store (i.e., Get, Put, and Delete) by manipulating the Windows registry using the Windows native APIs. We call the proposed key-value store WR-Store. Finally, we propose an efficient ETL (Extract-Transform-Load) method to migrate data stored in WR-Store into any other environments that support existing key-value stores. Because the performance of the Windows registry has not been studied much, we perform the empirical study to understand the characteristics of WR-Store, and then, tune the performance of WR-Store to find the best parameter setting. Through extensive experiments using synthetic and real data sets, we show that the performance of WR-Store is comparable to or even better than the state-of-the-art systems (i.e., RocksDB, BerkeleyDB, and LevelDB). Especially, we show the scalability of WR-Store. That is, WR-Store becomes much more efficient than the other key-value stores as the size of data set increases. In addition, we show that the performance of WR-Store is maintained even in the case of intensive registry workloads where 1000 processes accessing to the registry actively are concurrently running.

scholarly journals Multi-Level Deep Cascade Trees for Conversion Rate Prediction in Recommendation System

2019 ◽  
Vol 33 ◽  
pp. 338-345 ◽  
Author(s):  
Hong Wen ◽  
Jing Zhang ◽  
Quan Lin ◽  
Keping Yang ◽  
Pipei Huang
Keyword(s):  
Conversion Rate ◽  
Recommendation System ◽  
Feature Learning ◽  
Feature Representation ◽  
Gradient Boosting ◽  
Data Set ◽  
Rate Prediction ◽  
Clear Explanation ◽  
Cascade Structure ◽  
Multi Level

Developing effective and efficient recommendation methods is very challenging for modern e-commerce platforms. Generally speaking, two essential modules named “ClickThrough Rate Prediction” (CTR) and “Conversion Rate Prediction” (CVR) are included, where CVR module is a crucial factor that affects the final purchasing volume directly. However, it is indeed very challenging due to its sparseness nature. In this paper, we tackle this problem by proposing multiLevel Deep Cascade Trees (ldcTree), which is a novel decision tree ensemble approach. It leverages deep cascade structures by stacking Gradient Boosting Decision Trees (GBDT) to effectively learn feature representation. In addition, we propose to utilize the cross-entropy in each tree of the preceding GBDT as the input feature representation for next level GBDT, which has a clear explanation, i.e., a traversal from root to leaf nodes in the next level GBDT corresponds to the combination of certain traversals in the preceding GBDT. The deep cascade structure and the combination rule enable the proposed ldcTree to have a stronger distributed feature representation ability. Moreover, inspired by ensemble learning, we propose an Ensemble ldcTree (E-ldcTree) to encourage the model’s diversity and enhance the representation ability further. Finally, we propose an improved Feature learning method based on EldcTree (F-EldcTree) for taking adequate use of weak and strong correlation features identified by pretrained GBDT models. Experimental results on off-line data set and online deployment demonstrate the effectiveness of the proposed methods.

Effect of Word-Line Bias on Linearity of Multi-Level Conductance Steps for Multi-Layer Neural Networks Based on NAND Flash Cells

2020 ◽  
Vol 20 (7) ◽  
pp. 4138-4142
Author(s):  
Sung-Tae Lee ◽  
Suhwan Lim ◽  
Nagyong Choi ◽  
Jong-Ho Bae ◽  
Dongseok Kwon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Flash Memory ◽  
Storage Capacity ◽  
High Density ◽  
Nand Flash ◽  
Nand Flash Memory ◽  
Data Set ◽  
Source Line ◽  
In Series ◽  
Multi Level

NAND flash memory which is mature technology has great advantage in high density and great storage capacity per chip because cells are connected in series between a bit-line and a source-line. Therefore, NAND flash cell can be used as a synaptic device which is very useful for a high-density synaptic array. In this paper, the effect of the word-line bias on the linearity of multi-level conductance steps of the NAND flash cell is investigated. A 3-layer perceptron network (784×200×10) is trained by a suitable weight update method for NAND flash memory using MNIST data set. The linearity of multi-level conductance steps is improved as the word line bias increases from Vth −0.5 to Vth +1 at a fixed bit-line bias of 0.2 V. As a result, the learning accuracy is improved as the word-line bias increases from Vth −0.5 to Vth+1.

scholarly journals The impact of open-border organization culture and employees’ knowledge, attitudes, and rewards with regards to open innovation: an empirical study

2020 ◽  
Vol 24 (9) ◽  
pp. 2273-2297 ◽  
Author(s):  
Deemah Alassaf ◽  
Marina Dabić ◽  
Dara Shifrer ◽  
Tugrul Daim
Keyword(s):  
Organizational Culture ◽  
Knowledge Management ◽  
Open Innovation ◽  
Management Practices ◽  
Mediating Effect ◽  
Data Set ◽  
Content Type ◽  
Industrial Sectors ◽  
Multi Level ◽  
The Impact

Purpose The purpose of this paper is to fill a significant research gap in academic literature pertaining to open innovation (OI). To do so, this paper empirically tests the impact of organizational culture, employees’ knowledge, attitudes and rewards as antecedents and mediators of OI adoption in organizations, facilitating a more thorough understanding by using an empirical multi-level approach. Design/methodology/approach This paper analyzes the results of the “Identification of Industrial Needs for Open Innovation Education in Europe” survey through a quantitative analysis using logistic regression models. This survey includes 528 employees working in 28 different industrial sectors in 37 countries, most of which are in Europe. Findings The results suggest a positive impact of organizational characteristics on the adoption of OI (i.e. including the adoption of outside-in and inside-out OI activities in participating organizations), showing that the openness of an organization’s culture increases its likelihood of adopting an OI paradigm. More importantly, the results highlight the positive mediating effect of employees’ knowledge and rewards on this relationship. Research limitations/implications The data set that was the basis of this paper was generated in European countries, the results of the analysis are limited and appropriate for this region and may vary when applied to other regions of the world. Practical implications The proposed multi-level approach offers new insight into organizational knowledge. It enables the improvement of OI and knowledge management practices in organizations by assisting practitioners and academics in recognizing the relationship between organizational culture; employees’ knowledge, attitudes and rewards; and the adoption of the OI paradigm. Social implications This paper offers a possible explanation on why open-border cultures are more likely to have a successful OI adoption, by relating it to factors that advance in the presence of an open-border culture, such as active participation of OI relative departments in knowledge sourcing and knowledge exchange, and rewarding employees for OI activities. Originality/value This paper presents a new framework which links organizational culture to OI, moving on from merely examining culture in terms of its positive or negative impact on OI adoption. It contributes to research on the OI paradigm and knowledge management by highlighting the significance of antecedents and mediators from a multi-level perspective using multiple units of analysis. Most previous studies focus on a single unit of analysis.

scholarly journals Bayesian Multilevel Models for Count Data

2021 ◽  
pp. 224-233
Author(s):  
Olumide Sunday Adesina
Keyword(s):  
Count Data ◽  
Multilevel Models ◽  
Geometric Model ◽  
Real Life ◽  
Simulated Data ◽  
Data Set ◽  
K Value ◽  
Real Life Data ◽  
Level Model ◽  
Multi Level

The traditional Poisson regression model for fitting count data is considered inadequate to fit over-or under-dispersed count data and new models have been developed to make up for such inadequacies inherent in the model. In this study, Bayesian Multi-level model was proposed using the No-U-Turn Sampler (NUTS) sampler to sample from the posterior distribution. A simulation was carried out for both over-and under-dispersed data from discrete Weibull distribution. Pareto k diagnostics was implemented, and the result showed that under-dispersed and over-dispersed simulated data has all its k value to be less than 0.5, which indicate that all the observations are good. Also all WAIC were the same as LOO-IC except for Poisson in the over-dispersed simulated data. Real-life data set from National Health Insurance Scheme (NHIS) was used for further analysis. Seven multi-level models were f itted and the Geometric model outperformed other model. 

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