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13 results on '"Ramachandra, A.C."'

5. Multimedia Multilevel Security by Integrating Steganography and Cryptography Techniques

Author

Satish E.G., Sreenivasa N., Naresh E., Ramesh Naidu P., and Ramachandra A.C.

Subjects
steganography, cryptography, lsb, psnr, steno image, aes., Information technology, T58.5-58.64
Abstract

Multimedia-based Steganography is famous for its security purpose, These steganography techniques were used by our ancestors and it is still carried out now with better and vast technology. The aim is to provide better security in an effective and secure manner where the original data is covered and hidden by some of the media covers like video, audio, image, text, etc. so that only the sender who sends it and the receiver who receives it can only see the secret data inside it. In this project, we are combining all the steganography types and giving the user the advantage of selecting his/her own choice of multimedia cover to hide the real content. The options of the different algorithms will be provided so that the users who are comfortable with that algorithm have the privilege to select it and the file will be processed with that algorithm. This steganography technique is used by the government to send information that supports national security, The company uses it for digital copyrighted media, and military services also use this technology to send it to the army who are on a mission. We aim to provide the same with more security and a rich user experience. In this survey, a variety of methods is used to provide the same facility and recent research in the field.

Published
2023
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6. Prediction of Loan Approval in Banks using Machine Learning Approach

Author

Viswanatha V, Ramachandra A.C, Vishwas K N, Adithya G, Viswanatha V, Ramachandra A.C, Vishwas K N, and Adithya G

Abstract

Due to significant technology advancements, people's needs have expanded. As a result, there have been more requests for loan approval in the banking sector. A few qualities, taken for consideration, when choosing a candidate for loan approval in order to, determine loan's status. Banks face a major challenge; when it, comes to assessing loan applications and lowering the risks associated with potential borrower defaults. Since they must thoroughly evaluate each borrower's eligibility for a loan, banks find this process to be particularly challenging. This research proposes combining machine learning (ML) models and ensemble learning approaches to find the probability of accepting individual loan requests. This tactic can increase the accuracy with which qualified candidates are selected from a pool of applicants. As a result, this method can be used to address the problems with loan approval processes outlined above. Both the loan applicants and the bank employees profit from the strategy's dramatic reduction in sanctioning time. Because of the banking industry's expansion, more people were applying to loans at banks. In order to predict the accuracy of loan approval status for applied person, we used four different algorithms namely Random Forest, Naive Bayes, Decision Tree, and KNN. By using these, we obtained better accuracy of 83.73% with Naïve Bayes algorithm as best one.

Published
2023

7. Implementation of Decision Tree Algorithm for Prediction of Rheumatoid Arthritis Disease

Author

Viswanatha V, Ramachandra A.C, Krupa J, Viswanatha V, Ramachandra A.C, and Krupa J

Abstract

A very challenging and difficult part of finding a cure for Rheumatoid Arthritis ( RA) disease is the same as finding a cure for an autoimmune disease. In the case of autoimmune diseases, it is very difficult to find the cause. RA is also one of the autoimmune diseases that causes inflammation in joints. It does not only limit to joints but also can spread or occur in various parts of the body like skin, lungs, and many others. Many features like age, sex, and previous history of certain diseases affect the occurrence of RA. The proposed ML model uses a decision tree algorithm to analyze a comprehensive set of clinical and laboratory variables collected from RA patients. These variables include age, ID, time, treatment, gender, and other baseline features. The ML model employs feature selection techniques to identify the most relevant predictors that contribute to disease progression. By training the decision tree algorithm on a large dataset of RA patients, the model generates an accurate predictive model for assessing disease severity and progression. The model is able to detect RA disease with almost 80% accuracy with the given training dataset. RA can have many other symptoms and characteristics. This data was trained on a basic dataset available from the Kaggle website. The dataset is in CSV format and has 6 features. The model was trained using a decision tree algorithm, which categorizes the data into different categories and then checks for specific data when needed in Jupyter Notebook. The study explored detecting basic symptoms and characteristics of RA disease. The Decision tree model has given accuracy of 86.5% and 74.4% on training and testing respectively. So Accuracy of the model is pretty good for the dataset used.

Published
2023

8. Implementation of IoT in Agriculture: A Scientific Approach for Smart Irrigation

Author

V Viswanatha, Ramachandra A.C, Venkata Siva Reddy R, Ashwini Kumari P, Srinivasa Murthy R, and Sathisha B M

Abstract

Digital technologies empower the transformation into data-driven, intelligent, agile, and autonomous farm operations and are typically considered a key to addressing the grand challenges in agriculture. To avoid unscientific water supply for plantation as well as to save the water and also yield the better crop, therefore, to increase production efficiency out of smart irrigation and to send the status of irrigation at standard environmental conditions, The Internet of Things (IoT) based prototype is designed and implemented. The prototype automatically turns ON /OFF the motor pump based on the moisture level of the soil by taking the temperature and humidity of the environment near the plantation into consideration (In India, the standard parameters for watering the vegetable plantation are Humidity>60%, Temperature < 25°C and Humidity

Published
2022
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9. Bidirectional DC-DC Converter Circuits and Smart Control Algorithms: A Review

Author

Viswanatha V, Ramachandra A.C, and Venkata Siva Reddy R

Abstract

The entire article has been dedicated to cover the current state-of-the-art in Bidirectional DC-DC converter topologies and its smart control algorithms, identified the research gaps and concluded with the motivation for taking up the work. It covers the literature survey of Bidirectional buck-boost DC-DC converters and control schemes are carried out on two aspects, one is on topology perspective and another one is on control schemes. Different topologies with and without transformers of bidirectional DC - DC converters are discussed. Non-isolated converters establish the DC path between input and output sides while transformer based converters cancels the DC path in between input and output sides since it introduces AC line between two DC lines just like in flyback converter. Transformer less converter is preferred when there is no much protection needed for load from high voltage levels also these converters are used in high power applications. The Bidirectional DC-DC converter can switch the power between two DC sources and the load. To do so, it has to use proper control schemes and control algorithms. It can store the excess energy in batteries or in super capacitors. In contrast, isolated topologies contain transformers in their circuits. Due to this, it offers advantages like safeguarding sensitive loads from high power which is at input side. In addition to it, multiple input and output ports can be established. With the isolation in DC-DC converters, input and output sections are separated from electrical stand point of view. With isolation, both input and output sections will not be having common ground point .The DC path is removed with isolation due to usage of transformer in DC-DC converters. In contrast to its features, it is capable to be used in low power applications since transformer is switching at high frequency, the size of the coil reduces and hence it can handle limited rate of current. The bidirectional DC-DC converters are categorized based on isolation property so called isolated bidirectional converters. Features and applications of each topology is presented. Comparative analysis w.r.t research gaps between all the topologies is presented. Also the scope of control schemes with artificial intelligence is discussed. Pros and cons of each control scheme i.e. research gaps in control schemes and impact of control scheme for bidirectional DC-DC converters are also presented.

Published
2022
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