DSpace Collection:http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/35492024-02-26T23:37:30Z2024-02-26T23:37:30ZClustering Techniques in Machine LearningSingh, VatsalBharti, Monika [Guided by]http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/103062024-02-08T06:29:36Z2023-01-01T00:00:00ZTitle: Clustering Techniques in Machine Learning
Authors: Singh, Vatsal; Bharti, Monika [Guided by]
Abstract: In today’s era data generated by scientific applications and corporate environment has grown
rapidly not only in size but also in variety. This data collected is of huge amount and there is
a difficulty in collecting and analyzing such big data. Data mining is the technique in which
useful information and hidden relationship among data is extracted, but the traditional data
mining approaches cannot be directly used for big data due to their inherent complexity.
Data Clustering is one of the most important issues in data mining and machine learning.
Clustering is a task of discovering homogenous groups of the studied objects. Recently, many
researchers have a significant interest in developing clustering algorithms. The most problem
in clustering is that we do not have prior information knowledge about the given dataset.
Moreover, the choice of input parameters such as the number of clusters, number of nearest
neighbors and other factors in these algorithms make the clustering more challengeable topic.
Thus any incorrect choice of these parameters yields bad clustering results. Furthermore, these
algorithms suffer from unsatisfactory accuracy when the dataset contains clusters with
different complex shapes, densities, sizes, noise, and outliers. In this project, we propose a
new approach for unsupervised clustering task. Our approach consists of three phases of
operations. In the first phase we use the Genetic algorithm for finding first initial cluster
centroid. In genetic algorithm we use a crossover and mutation of the dataset. The second
phase, takes these initial cluster centroid produced by genetic algorithm for finding clusters
using K-means clustering. From the second phase we obtain a set of clusters of the given
dataset. Hence, the third phase considers these clusters for evaluation of cluster based on
Davies Bouldin Index. This new algorithm is named as Genetic K-means Algorithm (GKA).
We present experiments that provide the strength of our new proposed algorithm in
discovering clusters with different non-convex shapes, sizes, densities, noise, outliers and
higher accuracy. These experiments show the superiority of our proposed algorithm when
comparing with K-means algorithm.2023-01-01T00:00:00ZZopstore Web AppSingh, PiyushSehgal, Vivek Kumar [Guided by]http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/102522023-10-07T10:55:20Z2023-01-01T00:00:00ZTitle: Zopstore Web App
Authors: Singh, Piyush; Sehgal, Vivek Kumar [Guided by]
Abstract: Making a web application is very simple, but testing, organising, cleaning up, and
maintaining the code is difficult. We adhere to the Three-Layered Architecture and the Go
programming language to address this.
The handler, service, and datastore layers are independent of one another. The handler layer
parses the request body after receiving it to extract any pertinent data. After calling the
service layer, where the program's whole logic is specified, the response is subsequently
written to the response writer. This layer also communicates with the datastore layer. After
obtaining what it needs from the handler layer, it invokes the datastore layer. The datastore
layer holds all of the data.
Description: Enrollment No. 1912762023-01-01T00:00:00ZZopstore Web App Built using Three Layered ArchitectureBharota, RishabhHooda, Diksha [Guided by]http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/102512023-10-07T10:53:52Z2023-01-01T00:00:00ZTitle: Zopstore Web App Built using Three Layered Architecture
Authors: Bharota, Rishabh; Hooda, Diksha [Guided by]
Abstract: A web application may be made rather easily, but testing, structuring, cleaning, and maintaining the code is a barrier. To address this, we use the Go language and adhere to the Three Layered Architecture.
The three layers—handler, service, and datastore—are separate from one another. After receiving the request body, the handler layer parses it for any necessary information. The response is then written to the response writer after the service layer, where the program's entire logic is defined, has been called. Additionally, this layer converses with the datastore layer. It invokes the datastore layer after taking what it requires from the handler layer. All of the data is kept in the datastore layer. Any data storage device can be used. The only layer that interacts with the datastore is the use case layer. This is how we test each layer separately to make sure they don't affect one another.
Description: Enrollment No. 1912372023-01-01T00:00:00ZZopstore Web App Built using Three Layered Architecture in Java SpringbootRohan, SushantKumar, Amit [Guided by]http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/102502023-10-07T10:51:08Z2023-01-01T00:00:00ZTitle: Zopstore Web App Built using Three Layered Architecture in Java Springboot
Authors: Rohan, Sushant; Kumar, Amit [Guided by]
Abstract: Sprint boot is one of the popular Java frameworks to create reliable and
scalable web applications.. The three tiered architecture of spring boot consists
of a presentation/controller layer, a layer for managing business logic(service
layer) and a Data Transport Object(DTO) layer. The job of the presentation
layer is to take appropriate response bodies, query parameters and so on from
the client(web app, web page etc.) and to send responses accompanied by
appropriate headers and status codes. Data layer or DTO or Repository layer’s
main task is to communicate with the database and perform one of the core
functionalities like persisting an entity, reading from the database, updating
and deleting entries from the database. Sitting between these two layers is the
service layer. Service layer is responsible for handling business logic like
validation, preparing responses and so on.
Description: Enrollment No. 1912672023-01-01T00:00:00Z