Impact of trimet graph optimization topology on scalable networks

Amiripalli, Shanmuk Srinivas; Bobba, Veeramallu

doi:10.3233/jifs-169954

Cited by 20 publications

(8 citation statements)

References 16 publications

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“…As the Naïve Bayes classifier requires a small dataset to predict there will be a loss of accuracy which is the disadvantage of the naïve Bayes classifier. Where in the decision tree and random forest algorithm requires less effort for prediction as it is in the tree-like structure [8] [9]. Using a decision tree and random forest interpretation of a complex decision tree model can be simplified by its appearance.…”

Section: Proposed Methodologymentioning

confidence: 99%

Usage of Data Mining Techniques in Predicting the Heart Diseases Decision Tree & Random Forest Algorithm

Rao*,

Anitha

2019

IJITEE

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Nowadays, heart disease is the main cause of several deaths among all other diseases. Due to the lack of resources in the medical field, the prediction of heart diseases becomes a major problem. For early diagnosis and treatment, some classification algorithms such as Decision Tree and Random Forest Algorithm are used. The data mining techniques compare the accuracy of the algorithm and predict heart diseases. The main aim of this paper is to predict heart disease based on the dataset values. In this paper we are comparing the accuracy of above two algorithms. To implement these methods the following steps are used. In first phase, a dataset of 13 attributes is collected and it was applied on classification techniques using the Decision tree and Random Forest Algorithms. Finally, the accuracy is collected for both the algorithms. In this paper we observed that random forest is generating better results than decision tree in prediction of heart diseases.

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Section: Proposed Methodologymentioning

confidence: 99%

Usage of Data Mining Techniques in Predicting the Heart Diseases Decision Tree & Random Forest Algorithm

Rao*,

Anitha

2019

IJITEE

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“…On the other hand, pitch and harmonic features are suitable for song similarity retrieval and cover song detection [49], [58], [59], [60], [61], [62], [63], [64] at a melodic level.…”

Section: Overview Of Mid-level Featuresmentioning

confidence: 99%

A Review: Music Feature Extraction from an Audio Signal

Jitendra¹

2020

IJATCSE

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In recent years, the revenue earned through digital music stood at a billion-dollar market and the US remained the most profitable market for Digital music. Due to the digital shift, today people have access to millions of music clips from online music applications through their smart phones. In this context, there are some issues identified between the music listeners, music search engine by querying and retrieving music clips from a large collection of music data set. Classification is one of the fundamental problems in music information retrieval (MIR). Still, there are some hurdles according to their listener's preferences regarding music collections and their categorization. In this paper, different music extraction features are addressed, which can be used in various tasks related to music classification like a listener's mood, instrument recognition, artist identification, genre, query-by-humming, and music annotation. This review illustrates various features that can be used for addressing the research challenges posed by music mining.

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“…Kahramanli et al [32] presented a novel methodology using ANN and fuzzy Neural Network. Temurtas et al [33] presented a comparative research on PIMA Indian diabetes classification techniques. This work uses multilayer NNthat is trained using Levenberg-Marquardt (LM) scheme and a probabilistic NN.…”

Section: Literature Surveymentioning

confidence: 99%

Logistic regression and Random forest-based hybrid classifier with recursive feature elimination technique for diabetes classification

L¹

2020

IJATCSE

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Diabetes mellitus is a chronic metabolic ailment being considered as one of the deadliest diseases in the world. Millions of cases and deaths have been reported due to diabetes. Initial forecast of diabetes condition helps in reducing the death rate significantly that happen due to it. Current advancements in biomedical techniques have facilitated to store the electronic health record datasets which can be analyzed for better diagnosis. In order to explore these datasets, data mining techniques are considered as promising techniques which examines the data rapidly and provides a desired outcome. In this work, the data mining technique was adopted for diabetes classification using machine learning techniques. The proposed approach comprises of several steps such as missing value imputation, attribute selection and classification which are performed using mean missing value imputation, logistic regression &Recursive Feature Elimination for feature selection and random forest for classification, respectively. The evaluation results demonstrate that projected methods achieve97.39% prediction accuracy which shows a significant improvement in contrast to prevailing current approaches.

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Impact of trimet graph optimization topology on scalable networks

Cited by 20 publications

References 16 publications

Usage of Data Mining Techniques in Predicting the Heart Diseases Decision Tree & Random Forest Algorithm

Usage of Data Mining Techniques in Predicting the Heart Diseases Decision Tree & Random Forest Algorithm

A Review: Music Feature Extraction from an Audio Signal

Logistic regression and Random forest-based hybrid classifier with recursive feature elimination technique for diabetes classification

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