A Novel Trimet Graph Optimization (TGO) Topology for Wireless Networks

Amiripalli, Shanmuk Srinivas; Bobba, Veeramallu; Potharaju, Sai Prasad

doi:10.1007/978-981-13-0617-4_8

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…Based on the results of Figure 4 it is shown that ISSA prevents early convergence and has rapid convergence compared to LS, 54 LOA, 55 TGO, 56 and main SSA. 57 Early iterations, due to improvement, have rapid steps to converge in the search space.…”

Section: Ta B L E 2 the Comparative Performance Of Suggested The Algorithmsmentioning

confidence: 97%

“…These algorithms include Locust Search (LS) algorithm, 54 Lion Optimization Algorithm (LOA), 55 Trimet Graph Optimization (TGO). 56 Also in this research, this improved SSA optimization model is compared with its version of the main SSA optimization algorithm 57 to analyze its advantage over the main SSA model. The parameter specifications of the algorithms are illustrated in Table 1.…”

Section: Improved Sparrow Search Algorithm (Issa)mentioning

confidence: 99%

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Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Wang

Gholinia

2021

Concurrency and Computation

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One of the most sensitive factors affecting hydropower generation is climate change.The objective of this study is to forecast the hydropower generation under the influence of climate change for the next 50 years. For this purpose, the GFDL-CM3 model is used under three scenarios: RCP2.6, RCP4.5, and RCP8.5 to predict precipitation and temperature. Any change in the inlet flow to the turbine will cause changes in the hydropower output. Therefore, the more accurately the flow is estimated, the hydropower generation is forecasted more accurately. In this research, a hydrological-neural network hybrid model for flow prediction is presented in which the Improved Sparrow Search Algorithm (ISSA) has been used. The results forecasting climate parameters showed that the average annual rainfall, runoff, and evaporation have a downward trend compared to the control period. The minimum and maximum annual temperatures have an increasing trend compared to the control period. To evaluate the hydropower generation Aras dam, the HEC-Ressim reservoir model is utilized. By comparing the average annual hydropower with the control period, it is known that the power generation will decrease in future years. The average annual electricity generated by hydropower under the scenario RCP2.6 and RCP4.5 and RCP8.

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Section: Ta B L E 2 the Comparative Performance Of Suggested The Algorithmsmentioning

confidence: 97%

Section: Improved Sparrow Search Algorithm (Issa)mentioning

confidence: 99%

Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Wang

Gholinia

2021

Concurrency and Computation

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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 proposed decision fusion was successful, and the dimensionality reduction was probable, according to the results. In research [10][11][12] proposes a speaker-independent approach for categorizing emotional vocal sounds. The treatment divided the mechanism of recognizing emotions into two sections.…”

Section: Literature Surveymentioning

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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A Novel Trimet Graph Optimization (TGO) Topology for Wireless Networks

Cited by 9 publications

References 4 publications

Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Forecasting hydropower generation byGFDL‐CM3climate model and hybrid hydrological‐Elman neural network model based on Improved Sparrow Search Algorithm (ISSA)

Mineral Rock Classification Using Convolutional Neural Network

A Study on Speech Emotion Recognitions on Machine Learning Algorithms

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