An Optimal TGO Topology Method for a Scalable and Survivable Network in IOT Communication Technology

Amiripalli, Shanmuk Srinivas; Bobba, Veeramallu

doi:10.1007/s11277-019-06315-z

Cited by 18 publications

(8 citation statements)

References 23 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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“…The future scope is this is a comparative study not much is to be improved. Optimization can also be achieved by TGO model [11][12][13][14][15][16][17].…”

Section: Literature Surveymentioning

confidence: 99%

Mineral Rock Classification Using Convolutional Neural Network

Amiripalli

Rao

Behara

et al. 2021

Recent Trends in Intensive Computing

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The main aim of the research is to build a model that can effectively predict the type of mineral rocks. Rocks can be predicted by observing it is colour, shape and chemical composition. On-site technicians need to apply different techniques on rock sample in order to predict rock type. Technicians need to apply different techniques on rock samples, so it is a time-consuming process, and sometimes the predictions may be accurate, and sometimes predictions may be false. When predictions are false, it might show a negative impact in several ways for workers and organization as well. We considered an image dataset of rock types, namely Biotite, Bornite, Chrysocolla, Malachite, Muscovite, Pyrite, and Quartz. We applied CNN (Convolutional Neural Network) Algorithm to get a better prediction of different mineral rocks. Nowadays, CNN is mainly used for image classification and image recognition tasks.

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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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An Optimal TGO Topology Method for a Scalable and Survivable Network in IOT Communication Technology

Cited by 18 publications

References 23 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

Mineral Rock Classification Using Convolutional Neural Network

A Review: Music Feature Extraction from an Audio Signal

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