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Your search keyword '"Kanumuri, Chalapathiraju"' showing total 9 results
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9 results on '"Kanumuri, Chalapathiraju"'

3. Machine Learning Approach for Predicting the Maladies of Humans

Author

Kanumuri, Chalapathiraju, Torthi, Ravichandra, Sriramam, Yadavalli S. S., Swamy, D. Kumara, Maganti, Pavani, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Narayana, V. A., editor, Suganthan, P. N., editor, Anguera, Jaume, editor, Koppula, Vijaya Kumar, editor, and Gupta, Suneet Kumar, editor

Published
2023
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5. Stock market price prediction using neural networks (LSTM) and technical indicators.

Author

Varma P, Krishna Kanth, Kanumuri, Chalapathiraju, Devi, S. Sushma, and Sontenam, Tataji

Subjects
*ARTIFICIAL neural networks, *INVESTORS, *STOCK prices, *MARKET prices, *MOVING average process
Abstract

In recent years there has been a growing interest in stock market investment as it helps to accumulate wealth for investors, which tends to beat the returns obtained by traditional investment instruments (Fixed deposits, PPF etc.). But investors are not aware of the stock market behavior i.e., they do not know which stocks to buy or to sell to gain profit. Considering this problem and the opportunity to use Neural Networks for prediction, in this project we have developed a neural network model using LSTM and trained the model with different stock market indicators data as input (for various stocks like TCS, INFOSYS, HDFC, and DIVISLAB) for an accurate prediction of the stock prices. The indicators used are RSI and Moving Averages. This model now predicts the closing price of the next day which helps the investors to select the stocks to invest in, that bring profit. Also, a comparison is made to identify the best indicator, giving more accurate results. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Heart Disease Prediction Using Random Forest Based Hybrid Optimization Algorithms.

Author

Torthi, Ravichandra, Marapatla, Ajay Dilip Kumar, Mande, Soumya, Gadiraju, Harish Kumar Varma, and Kanumuri, Chalapathiraju

Subjects
OPTIMIZATION algorithms, PARTICLE swarm optimization, RANDOM forest algorithms, HEART diseases, RADIAL basis functions, FORECASTING
Abstract

Nowadays, heart diseases have become a leading cause of mortality worldwide and it affects a huge number of individuals. The early and accurate prediction of heart disease risk factors plays a crucial role in preventing opposing results. Additionally, it is necessary to recognize heart disease quickly and accurately by analyzing patient's data. This paper proposed a novel approach for predicting heart disease through machine learning techniques. The proposed Bat Algorithm (BA) and Particle Swarm Optimization (PSO) based Random Forest (RF), named BAPSO-RF is utilized for selecting optimum features that can enhance the heart-disease prediction accuracy. The proposed BAPSO-RF is evaluated on UCI heart disease dataset which contains 14 attributes and 270 records. The proposed BAPSO-RF model attains better results by utilizing metrics like accuracy, precision, recall, and f1-score values of about 98.71%, 98.67%, 98.23%, and 98.45% correspondingly which ensures early and accurate prediction of heart disease compared to existing techniques like hybrid of Genetic Algorithm and Particle Swarm Optimization (PSO) with Random Forest (GAPSO-RF), stacked Genetic Algorithm (GA) and Genetic Algorithm with Radial Basis Function (GA-RBF). [ABSTRACT FROM AUTHOR]

Published
2024
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7. A survey on detection of COVID 19 with the assist of machine learning (ML), deep learning (DL) and artificial intelligence (AI) approaches.

Author

Kanumuri, Chalapathiraju and Renu Madhavi, C. H.

Subjects
*MACHINE learning, *COVID-19, *DEEP learning, *REVERSE transcriptase polymerase chain reaction, *ARTIFICIAL intelligence
Abstract

The COVID-19 (Corona virus disease 2019) outbreak induced millions of human beings the loss of life, still spread of the disease was unstoppable and was declared a pandemic with the aid of the WHO (World Health Organization). Therefore, human beings across the world are nevertheless getting contaminated every day. Reverse Transcription Polymerase Chain Reaction (RT-PCR) to identify COVID-19 is not feasible in terms of both cost and time of identification, which might cost the patient's life. Therefore, to make identification economical and feasible, researchers were attempting to use clinical images (x-ray and CT etc.,) to detect COVID-19 with the assist of Machine Learning (ML) and Artificial Intelligence (AI) approaches to aid in automating the identification of the pandemic. This paper helps in understanding some ML and AI approaches for detecting COVID-19 from clinical lung images. The amassed records about accessible research sources and inspected a complete of 30 research papers until august 2021. This paper includes the exploration and analysis of data sets, pre-processing methods, segmentation approaches, feature extraction, classification, and test effects, which can be useful for discovering future lookup instructions in the area of automated analysis of COVID-19 sickness the usage of AI-based frameworks. [ABSTRACT FROM AUTHOR]

Published
2023
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