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  <title>DSpace Collection:</title>
  <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/3549" />
  <subtitle />
  <id>http://localhost:8080/xmlui/handle/123456789/3549</id>
  <updated>2026-06-23T06:32:46Z</updated>
  <dc:date>2026-06-23T06:32:46Z</dc:date>
  <entry>
    <title>A Musical Composition Assistant System  using LSTM</title>
    <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/3551" />
    <author>
      <name>Fegade, Poonam G.</name>
    </author>
    <author>
      <name>Jagtap, Trupti</name>
    </author>
    <id>http://localhost:8080/xmlui/handle/123456789/3551</id>
    <updated>2023-01-03T08:35:16Z</updated>
    <published>2022-07-15T00:00:00Z</published>
    <summary type="text">Title: A Musical Composition Assistant System  using LSTM
Authors: Fegade, Poonam G.; Jagtap, Trupti
Abstract: Universally, music is one of the elements that create harmony in this world. Traditionally playing music &#xD;
or creating it has always been seen as a manual task. Music can be created with the help of instruments, voices and &#xD;
sounds. In the Modern Era of technology and AI, we can automate this process. For this, we must understand the basic &#xD;
structure of how music forms and view it scientifically. For the proposed system, we are using RNN-LSTM algorithm &#xD;
to generate music from given sample inputs. A Musical Composition Assistant System (MCAS) involves &#xD;
transformation of music scores into time series representation, encoding the music. A model is designed to execute this &#xD;
algorithm where data is represented with the help of musical instrument digital interface (MIDI) file format for easier &#xD;
access and better understanding. Pre-processing of data before feeding it into the model, revealing methods to read, &#xD;
process and prepare MIDI files for input are used. This system creates good music pieces in MIDI format with given &#xD;
input. The proposed system uses Flask API to interact with the frontend</summary>
    <dc:date>2022-07-15T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Motions Detection of Senior Citizen Using Machine  Learning</title>
    <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/3550" />
    <author>
      <name>Bagal, V. C.</name>
    </author>
    <author>
      <name>Hanpode, Pratik</name>
    </author>
    <id>http://localhost:8080/xmlui/handle/123456789/3550</id>
    <updated>2023-01-03T08:32:00Z</updated>
    <published>2022-09-15T00:00:00Z</published>
    <summary type="text">Title: Motions Detection of Senior Citizen Using Machine  Learning
Authors: Bagal, V. C.; Hanpode, Pratik
Abstract: Recognizing human motions is important from security point of view at any level and scenario. As there are plenty of human &#xD;
motions in a fraction of second, so classification of each motion is challenging task in real world. A Human activity Recognition System &#xD;
recognizes the Shapes and or orientation depending on implementation to task the system into per forming some job. Movement is a &#xD;
form of nonverbal information. A person can make numerous movements at a time. The proposed work aims to detect the movement and&#xD;
actions of a person using image detection methodology. Human activity recognition (HAR) aims to recognize activities from a series of&#xD;
observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many &#xD;
applications including video surveillance, healthcare, and human-computer interaction (HCI). The proposed work is suitable to identify &#xD;
objectionable human motions of senior citizen who live alone at home.</summary>
    <dc:date>2022-09-15T00:00:00Z</dc:date>
  </entry>
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