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124 results on '"Veeramsetty, Venkataramana"'

7. Active Power Load Data Dimensionality Reduction Using Autoencoder

Author

Veeramsetty, Venkataramana, Kiran, Prabhu, Sushma, Munjampally, Babu, Amuda Mahesh, Rakesh, Rathlavath, Raju, Kunchala, Salkuti, Surender Reddy, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Salkuti, Surender Reddy, editor, Ray, Papia, editor, and Singh, Arvind R., editor

Published
2023
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10. Short Term Active Power Load Forecasting Using Machine Learning with Feature Selection

Author

Veeramsetty, Venkataramana, Rakesh Chandra, D., Salkuti, Surender Reddy, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Salkuti, Surender Reddy, editor, and Ray, Papia, editor

Published
2022
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11. Transient Stability Enhancement of Power System with Grid Connected DFIG Based Wind Turbine

Author

Rakesh Chandra, D., Salkuti, Surender Reddy, Veeramsetty, Venkataramana, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Salkuti, Surender Reddy, editor, and Ray, Papia, editor

Published
2022
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12. Spectacles prediction using machine learning algorithms.

Author

Amulya, Soma, Sudeeptha, K., Sathvika, G., and Veeramsetty, Venkataramana

Subjects
RANDOM forest algorithms, DECISION trees, LOGISTIC regression analysis, EYESTRAIN, REGRESSION analysis, MACHINE learning
Abstract

Eye defects in teenagers is becoming more prevalent these days, especially due to the pandemic situation. Digital eye strain due to excessive usage of gadgets can result in a prescription for spectacles, which is a basic test in any ophthalmology clinic, but due to the current situation of the pandemic, visiting a clinic might be risky. In this project, we propose an AI (ML) model for spectacles prediction based on a few input parameters through an app. AI machine learning models are used widely in the medical sector. Here, we applied three different machine learning models (logistic regression algorithm, decision tree algorithm and random forest algorithm) to extract maximum accuracy prediction using the dataset we collected from teenagers. We achieved highest accuracy of 97% using logistic regression model and made a website to predict spectacles. [ABSTRACT FROM AUTHOR]

Published
2024
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13. Campus placements predictor using machine learning algorithms.

Author

Reddy, Rithika, Srilatha, Koleti, Tahani, Nada, and Veeramsetty, Venkataramana

Subjects
REGRESSION trees, RANDOM forest algorithms, REGRESSION analysis
Abstract

These days, the students are worried about their placement opportunities in this competitive world. Getting placed in a company with good package is what students dream. In this paper, a machine learning model to predict the package with which the students will get placed in a company. If they have less package then they can try to improve their skills. Based on the marks they have scored in some technical subjects are used to predict the package of the student using machine learning model. In this paper, linear regression model, regression tree and random forest classifier models are used for p rediction. Among all these three models, random forest model is performing well with least error while prediction. [ABSTRACT FROM AUTHOR]

Published
2024
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15. Surveillance System for Monitoring Social Distance

Author

Jethani, Sahil, Jain, Ekansh, Thomas, Irene Serah, Pechetti, Harshitha, Pareek, Bhavya, Gupta, Priyanka, Veeramsetty, Venkataramana, Singal, Gaurav, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Wong, Kit, editor, Sarangapani, Jagannathan, editor, and Gupta, Suneet Kumar, editor

Published
2021
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27. Power quality disturbances classification using autoencoder and radial basis function neural network.

Author

Veeramsetty, Venkataramana, Dhanush, Aitha, Nagapradyullatha, Aluri, Krishna, Gundapu Rama, and Salkuti, Surender Reddy

Abstract

The classification of power quality (PQ) disturbances is a critical task for both utilities and industry. PQ issues cause power system equipment to fail. PQ disruptions also cause significant disruption in the paper and semiconductor industries, with significant financial implications as well as technological difficulties. Deep learning based approaches are used for automatic PQ disturbance classification, which requires huge amounts of data. A PQ disturbance dataset consisting of 12 PQ disturbances is developed using wavelet transform and MATLAB software. In this paper, an autoencoder is used to reduce the dimensionality of power quality disturbances data from higher dimensionality space, which consists of 72 input features, to lower dimensionality space, which consists of 21 input features. Based on data extracted from the autoencoder, a radial basis function neural network is used to identify the type of PQ disturbances. Based on the simulation results, it is observed that radial basis function neural network is able to distinguish the type of PQ disturbance with 92 % accuracy. [ABSTRACT FROM AUTHOR]

Published
2023
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28. Weather Forecasting Using Radial Basis Function Neural Network in Warangal, India.

Author

Veeramsetty, Venkataramana, Kiran, Prabhu, Sushma, Munjampally, and Salkuti, Surender Reddy

Subjects
RADIAL basis functions, MACHINE learning, WEATHER forecasting
Abstract

Weather forecasting is an essential task in any region of the world for proper planning of various sectors that are affected by climate change. In Warangal, most sectors, such as agriculture and electricity, are mainly influenced by climate conditions. In this study, weather (WX) in the Warangal region was forecast in terms of temperature and humidity. A radial basis function neural network was used in this study to forecast humidity and temperature. Humidity and temperature data were collected for the period of January 2021 to December 2021. Based on the simulation results, it is observed that the radial basis function neural network model performs better than other machine learning models when forecasting temperature and humidity. [ABSTRACT FROM AUTHOR]

Published
2023
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35. RoadWay

Author

Singal, Gaurav, primary, Singhal, Himanshu, additional, Kushwaha, Riti, additional, Veeramsetty, Venkataramana, additional, Badal, Tapas, additional, and Lamba, Sonu, additional

Published
2022
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36. ZERO CROSSING POINT DETECTION IN A DISTORTED SINUSOIDAL SIGNAL USING DECISION TREE CLASSIFIER.

Author

VEERAMSETTY, Venkataramana, JADHAV, Pravallika, RAMESH, Eslavath, SRINIVASULA, Srividya, and SALKUTI, Surender Reddy

Subjects
DECISION trees, STANDARD deviations, POWER electronics, SIGNAL detection
Abstract

Zero-crossing point detection in a sinusoidal signal is essential in the case of various power systems and power electronics applications like power system protection and power converters controller design. In this paper, 96 data sets are created from a distorted sinusoidal signal based on MATLAB simulation. Distorted sinusoidal signals are generated in MATLAB with various noise and harmonic levels. In this paper, a decision tree classifier is used to predict the zero crossing point in a distorted signal based on input features like slope, intercept, correlation and Root Mean Square Error (RMSE). Decision tree classifier model is trained and tested in the Google Colab environment. As per simulation results, it is observed that decision tree classifier is able to predict the zero-crossing points in a distorted signal with maximum accuracy of 98.3 % for noise signals and 100 % for harmonic distorted signals. [ABSTRACT FROM AUTHOR]

Published
2022
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37. A PLATFORM INDEPENDENT WEB-APPLICATION FOR SHORT-TERM ELECTRIC POWER LOAD FORECASTING ON A 33/11 KV SUBSTATION USING REGRESSION MODEL.

Author

VEERAMSETTY, Venkataramana, VAISHNAVI, Gudelli Sushma, KUMAR, Modem Sai Pavan, KIRAN, Prabhu, SUMANTH, Nagula, PRASANNA, Potharaboina, and SALKUTI, Surender Reddy

Subjects
ELECTRICAL load, REGRESSION analysis, ELECTRIC utilities, MACHINE learning, INDEPENDENT system operators, NETWORK operating system, ELECTRIC power failures, TECHNOLOGICAL forecasting
Abstract

Short-term electric power load forecasting is a critical and essential task for utilities of the electric power industry for proper energy trading and that enable the independent system operator to operate the network without any technical and economical issues. In this paper, machine learning model such as linear regression model is used to forecast the active power load one hour and one day ahead. Real time active power load data to train and test the machine learning model is collected from a 33/11 kV substation located in Telangana State, India. Based on the simulation results, it is observed that linear regression model can forecast the load with less mean absolute error i.e. 0.042 with training data and 0.045 with testing data in comparison with support vector regressor model for an hour ahead operation. Whereas in the case of the day ahead operation, linear regression model can forecast the load with less mean absolute error i.e. 0.055 with training data and 0.057 with testing data in comparison with support vector regressor model. A platform independent web application is developed to help the operators of the 33/11 kV substation which is located in Godishala, Telangana State, India. [ABSTRACT FROM AUTHOR]

Published
2022
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