Research on network design and analysis of TGO topology

Amiripalli, Shanmuk Srinivas; Bobba, Veeramallu

doi:10.1504/ijnvo.2018.10015031

Cited by 6 publications

(4 citation statements)

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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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“…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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“…We took 3 different datasets, namely RAVDESS, SAVEE and TESS, which consist of different emotions. We found the accuracies of the ten different classifiers Logistic Regression, Naïve Baye's, Stochastic Gradient Descent, KNN, Decision Tree, Random Forest, Support Vector Machine, MLPC, XG Boost and Light GBM, for each dataset and compared them to know which classifier have more accuracy [13][14][15][16]. The accuracy was calculated before and after masking of the datasets.…”

Section: Proposed Classification Algorithmmentioning

confidence: 99%

A Study on Speech Emotion Recognitions on Machine Learning Algorithms

Amiripalli

Patnaik

et al. 2021

Recent Trends in Intensive Computing

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Speech emotion detection has been extremely relevant in today’s digital culture in recent years. RAVDESS, TESS, and SAVEE Datasets were used to train the model in our project. To determine the precision of each algorithm with each dataset, we looked at ten separate Machine Learning Algorithms. Following that, we cleaned the datasets by using the mask feature to eliminate unnecessary background noise, and then we applied all 10 algorithms to this clean speech dataset to improve accuracy. Then we look at the accuracies of all ten algorithms and see which one is the greatest. Finally, by using the algorithm, we could calculate the number of sound files correlated with each of the emotions described in those datasets.

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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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Research on network design and analysis of TGO topology

Cited by 6 publications

References 0 publications

Mineral Rock Classification Using Convolutional Neural Network

Mineral Rock Classification Using Convolutional Neural Network

A Study on Speech Emotion Recognitions on Machine Learning Algorithms

A Review: Music Feature Extraction from an Audio Signal

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