International Journal of Trend in Scientific Research and Development

The purpose of “Image to Image Encoder” is to hide an image inside another image. Using the LSB method, which facilitates data hiding in an image. It works with JPEG and PNG formats for the cover image and always creates tiff encoded image due to its compression. Least Significant Bit Embeddings LSB are the general steganographic procedure that might be utilized to install information into a variety of digital media, the most studied applications are utilizing LSB embedding to hide one picture inside another. The security to keep up secrecy of message is accomplished by making it infeasible for a third individual to distinguish and recover the secret message. This method can be used in various fields for data security like for secret communication and data transfer via networks, for storing and transferring sensitive data and information in the defense sector. As the normal viewers can not identify the slight difference in the encoded image without the reference of the original cover image, this method ensures more security than cryptographic methods. 

by Samthomas Raphael | Dr. Ganesh D "Image to Image Encoder using Least Significant Bit" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, 

URL: https://www.ijtsrd.compapers/ijtsrd41253.pdf 

Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41253/image-to-image-encoder-using-least-significant-bit/samthomas-raphael

callforpaperlifesciences, lifesciencesjournal, researchpapers

What if apart of aircraft could let you know when the aircraft component needed to be replaced or repaired It can be done with continuous data collection, monitoring, and advanced analytics. In the aviation industry, predictive maintenance promises increased reliability as well as improved supply chain and operational performance. The main goal is to ensure that the engines work correctly under all conditions and there is no risk of failure. If an effective method for predicting failures is applied, maintenance may be improved. The main source of data regarding the health of the engines is measured during the flights. Several variables are calculated, including fan speed, core speed, quantity and oil pressure and, environmental variables such as outside temperature, aircraft speed, altitude, and so on. Sensor data obtained in real time can be used to model component deterioration. To predict the maintenance of an aircraft engine, LSTM networks is used in this paper. A sequential input file is dealt with by the LSTM model. The training of LSTM networks was carried out on a high performance large scale processing engine. Machines, data, ideas, and people must all be brought together to understand the importance of predictive maintenance and achieve business results that matter. 

by Nitin Prasad | Dr. A Rengarajan "Predicting the Maintenance of Aircraft Engines using LSTM" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, 

URL: https://www.ijtsrd.compapers/ijtsrd41288.pdf 

Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41288/predicting-the-maintenance-of-aircraft-engines-using-lstm/nitin-prasad

internationaljournalofscience, openaccessjournalofscience, ugcapprovedjournalsforscience 

Music genre classification has been a toughest task in the area of music information retrieval MIR . Classification of genre can be important to clarify some genuine fascinating issues, such as, making songs references, discovering related songs, finding societies who will like that particular song. The inspiration behind the research is to find the appropriate machine learning algorithm that predict the genres of music utilizing k nearest neighbor k NN and Support Vector Machine SVM . GTZAN dataset is the frequently used dataset for the classification music genre. The Mel Frequency cepstral coefficients MFCC is utilized to extricate features for the dataset. From results we found that k NN classifier gave more exact results compared to support vector machine classifier. If the training data is bigger than number of features, k NN gives better outcomes than SVM. SVM can only identify limited set of patterns. KNN classifier is more powerful for the classification of music genre.

by Seethal V | Dr. A. Vijayakumar "Music Genre Classification using Machine Learning" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, 

URL: https://www.ijtsrd.compapers/ijtsrd41263.pdf 

Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41263/music-genre-classification-using-machine-learning/seethal-v

callforpapereconomics, economicsjournal

Credit card plays a very vital role in todays economy and the usage of credit cards has dramatically increased. Credit card has become one of the most common method of payment for both online and offline as well as for regular purchases of a common man. It is very necessary to distinguish fraudulent credit card transactions by the credit card organizations so their clients are not charged for the purchases that they didn’t make. Despite the fact that using credit card gives huge benefits when used responsibly carefully and however significant credit and financial damages could be caused by fraudulent activities as well. Numerous methods have been proposed to stop these fraudulent activities. The project illustrates the model of a dataset to predict fraud transactions using machine learning. The model then detects if it is a fraudulent or a genuine transaction. The model also analyses and pre processes the dataset along with deployment of multiple anomaly detection using algorithms such as Local forest outlier and Isolation forest. 

by Nikitha Pradeep | Dr. A Rengarajan "Credit Card Fraud Detection" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, 

URL: https://www.ijtsrd.compapers/ijtsrd41289.pdf 

Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep

ugcapprovedmanagementjournal, openaccessjournalofmanagement, paperpublicationinmanagement

Sentimental analysis is a context based mining of text, which extracts and identify subjective information from a text or sentence provided. Here the main concept is extracting the sentiment of the text using machine learning techniques such as LSTM Long short term memory . This text classification method analyses the incoming text and determines whether the underlined emotion is positive or negative along with probability associated with that positive or negative statements. Probability depicts the strength of a positive or negative statement, if the probability is close to zero, it implies that the sentiment is strongly negative and if probability is close to1, it means that the statement is strongly positive. Here a web application is created to deploy this model using a Python based micro framework called flask. Many other methods, such as RNN and CNN, are inefficient when compared to LSTM. 

by Dirash A R | Dr. S K Manju Bargavi "LSTM Based Sentiment Analysis" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, 

URL: https://www.ijtsrd.compapers/ijtsrd42345.pdf 

Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42345/lstm-based-sentiment-analysis/dirash-a-r

callforpaperlanguages, languagesjournal, bestjournal

Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. 

by Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, 

URL: https://www.ijtsrd.compapers/ijtsrd42372.pdf 

Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh

callforpapermedicalscience, medicalsciencejournal

In this paper, we look at how supervised machine learning techniques can be used to forecast car prices in India. Data from the online marketplace quikr was used to make the predictions. The predictions were made using a variety of methods, including multiple linear regression analysis, Random forest regressor and Randomized search CV. The predictions are then analyzed and compared to determine which ones provide the best results. A seemingly simple problem turned out to be extremely difficult to solve accurately. All of the strategies yielded similar results. In the future, we want to make predictions using more advanced technologies. 

by Ravi Shastri | Dr. A Rengarajan "Prediction of Car Price using Linear Regression" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, 

URL: https://www.ijtsrd.compapers/ijtsrd42421.pdf 

Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42421/prediction-of-car-price-using-linear-regression/ravi-shastri

callforpaperlifesciences, lifesciencesjournal, researchpapers