R. Venkata Ramana

Vignan's Foundation for Science, Technology & Research

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Shaik Sai Babu

Vignan's Foundation for Science, Technology & Research

M. Ram Mohan

National Geophysical Research Institute

V. Purnachandra Rao

Vignan's Foundation for Science, Technology & Research

Satyasree Nannapaneni

Vignan's Foundation for Science, Technology & Research

A. Prajith

National Centre for Earth Science Studies

V.S. Jeyakanthan

National Institute of Hydrology

Sabyasachi Swain

Indian Institute of Technology Roorkee

Saripoot Sawant

National Geophysical Research Institute

A. Keshav Krishna

National Geophysical Research Institute

Y. R. Satyaji Rao

National Institute of Hydrology

Cooperative Institutions

National Remote Sensing Centre

Indian Institute of Technology Kharagpur

Indian Space Research Organisation

Council of Scientific and Industrial Research

Banaras Hindu University

Indian Institute of Remote Sensing

National Institute of Hydrology

Indian Agricultural Research Institute

Academy of Scientific and Innovative Research

Vignan's Foundation for Science, Technology & Research

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Skin Disease Detection Using Deep Learning

International Journal of Scientific Research in Computer Science Engineering and Information Technology (2024)

V. Rakesh Dinisha Abhishek O. Earni Sai Y. S. H. S. Rohit R. Venkata Ramana

Skin diseases are a major public health problem worldwide, requiring effective and timely diagnosis for effective treatment. In this paper, we present a new approach to automatically detect skin diseases using deep learning technology. The model we propose uses a Convolutional Neural Network (CNN) to analyze dermatological images with high accuracy, providing reliable and fast diagnosis. The system was trained on a variety of datasets to provide reliable performance across a variety of skin conditions. Experimental results show that the proposed model outperforms existing methods, demonstrating its potential for integration into clinical settings. Implementation of this deep learning-based skin disease detection system has the potential to revolutionize dermatological diagnostics and provide a cost-effective and scalable solution to improve patient care.

10.32628/cseit2410134

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Citations (2)

Assessment of Pollution in Ground Water in Krishna Delta

International Journal of Innovative Technology and Exploring Engineering (2019)

R. Venkata Ramana

The maintenance of groundwater quality is more necessary due to increasing demand and need for various purposes such as drinking and cultivation of crops etc. Over- exploitation without proper planning decreases groundwater quality which affects adversely the health aspects of living beings and decrease in agricultural output.. Hence a detailed survey conducted for examine groundwater pollution in Doppalapudi (Latitude: 15.97410 N; Longitude: 80.50700 E) and Mannava (Latitude: 16.48100 N; Longitude: 80.57870 E) regions. The formations in the study area belong to sand and clay of Recent Era. Groundwater is analyzed for various physico-chemical parameters. Most of these parameters values are excess over prescribed values for drinking water. Values of quality methods such as Percent Sodium etc., suggest groundwater not suited to cultivation. Present attempt is made to examine the various controlling factors. This study is useful for similar studies throughout the world. Appropriate remedial measures are suggested for development of ground water quality to maintain environmental balance in study area.

Groundwater Pollution

10.35940/ijitee.h6915.078919

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Rare Earth Elements of Sediments in Rivers and Estuaries of the East Coast of India

Current Science (2021)

Shaik Sai Babu R. Venkata Ramana V. Purnachandra Rao M. Ram Mohan S. S. Sawant

The rare earth elements (REE) in the clay fraction of sediments in 15 rivers and their estuaries along the east coast of India were analysed in this study.The total REE content (∑REE) varied from 130.98 to 289.85 μg/g and from 70.89 to 352.61 μg/g in rivers and estuaries respectively.The ∑REEs of estuarine clays (except the Brahmani and Baitarani) was lower than in rivers.The Post-Archean average Australian Shale-normalized REE patterns in rivers and estuaries were similar and categorized into three types.The REE patterns reflect the composition of dominant geological formations in river basins and extent of sediment mixing from different sources during transport.Hydrodynamic conditions controlled the abundance and fractionation of REE in the estuaries.The Sm/Nd ratios of clays were largely controlled by mineral composition and Y/Ho ratios were affected by sedimentary processes in the estuaries.

East coast

10.18520/cs/v120/i3/519-537

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Citations (14)

Data Analytics Environment in Smart Industries using ML Strategies

International Journal of Recent Technology and Engineering (IJRTE) (2020)

T.S. Sandeep Himanshu Kumar Diwedi R. Venkata Ramana M. Prabhakar S M Shahariar Rafi -

Production is confronting significant difficulties to meet client prerequisites, always continually evolving. Along these lines, items must be made with productive procedures, insignificant interferences, and low asset utilizations. To accomplish this objective, immense measures of information produced by mechanical hardware should be overseen and investigated by current innovations. Since the large information period in assembling industry is still at a beginning period, there is a requirement for a reference design that joins huge information and AI advancements and lines up with the Industrie 4.0 guidelines and prerequisites. Right now, for planning an adaptable investigation stage for mechanical information are gotten from Industrie 4.0 measures and writing. In light of these prerequisites, a reference enormous information design for mechanical AI applications is projected and contrasted with linked facility. At long last, the proposed design is executed in the Lab Big Data Analytics at the Xblodes BigD and their versatility and execution has to be assessed on equal calculation of a modern PACA form. The benefits that are anticipated design is directly versatile and versatile AI use cases and will help with improving the modern computerization forms underway frameworks.

10.35940/ijrte.f8852.038620

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List of contributors

Elsevier eBooks (2025)

Dibyendu Adhikari Faizan Ahmad Roniki Anjaneyulu K. Athira Ram Avtar

10.1016/b978-0-443-14072-3.00033-2

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Monthly Rainfall Prediction Using Wavelet Neural Network Analysis

Water Resources Management (2013)

R. Venkata Ramana B. Krishna Sudhir Kumar N. G. Pandey

Rainfall is one of the most significant parameters in a hydrological model. Several models have been developed to analyze and predict the rainfall forecast. In recent years, wavelet techniques have been widely applied to various water resources research because of their time-frequency representation. In this paper an attempt has been made to find an alternative method for rainfall prediction by combining the wavelet technique with Artificial Neural Network (ANN). The wavelet and ANN models have been applied to monthly rainfall data of Darjeeling rain gauge station. The calibration and validation performance of the models is evaluated with appropriate statistical methods. The results of monthly rainfall series modeling indicate that the performances of wavelet neural network models are more effective than the ANN models.

Representation

10.1007/s11269-013-0374-4

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Citations (238)

Sleep detection for PC using machine learning model

AIP conference proceedings (2022)

V. R. Prakash Deepak Borse R. Venkata Ramana K. Yagnavallikasai

Non intrusive methods based on active remote IR illumination for eye tracking is important for many applications of vision-based man –machine interaction. In this project, we present a new real-time eye detection and tracking methodology that works under variable and realistic lighting conditions. Based on combining the bright-pupil effect resulted from IR light and the conventional appearance-based object recognition technique . Our method can robustly track eyes when the pupils are not very bright due to significant external illumination interferences . The appearance model is incorporated in both eyes detection and tracking via the use of support vector machine and the mean shift tracking. Additional improvement is achieved from modifying the image acquisition apparatus including the illuminator and the camera.

Tracking (education)

Active appearance model

10.1063/5.0116495

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Geospatial technology for coastal water resources management

Elsevier eBooks (2025)

Sabyasachi Swain Y. R. Satyaji Rao V.S. Jeyakanthan R. Venkata Ramana

10.1016/b978-0-443-14072-3.00001-0

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Hydrodynamic Modeling of Urban Flooding and Its Management: A Case Study of Patna, India