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<title>01. VSC CSIT FP Journal Articles</title>
<link href="http://10.9.150.37:8080/dspace//handle/atmiyauni/511" rel="alternate"/>
<subtitle/>
<id>http://10.9.150.37:8080/dspace//handle/atmiyauni/511</id>
<updated>2026-05-10T15:03:11Z</updated>
<dc:date>2026-05-10T15:03:11Z</dc:date>
<entry>
<title>A study of recommendation system in E-commerce</title>
<link href="http://10.9.150.37:8080/dspace//handle/atmiyauni/966" rel="alternate"/>
<author>
<name>Gohel, Divyesh</name>
</author>
<author>
<name>Vanjara, Pratik</name>
</author>
<id>http://10.9.150.37:8080/dspace//handle/atmiyauni/966</id>
<updated>2023-05-16T05:36:24Z</updated>
<published>2022-01-01T00:00:00Z</published>
<summary type="text">A study of recommendation system in E-commerce
Gohel, Divyesh; Vanjara, Pratik
Recommendation Systems (RS) are commonly employed in the e-commerce business to deal &#13;
with the problem of information overload. Because there is so much information available &#13;
these days, users are having trouble discovering relevant product and service information &#13;
that matches their tastes and interests. The technique of obtaining relevant knowledge from &#13;
enormous databases is known as data mining (DM). DM's job is to describe and forecast data &#13;
so that information may be retrieved. Information retrieval (IR) is a subfield of RS, which is a &#13;
subfield of data mining (DM). Recommendation engines are essentially data filtering and &#13;
information retrieval tools that employ algorithms and data to suggest the most relevant item &#13;
to a given user. Content-based (CB) filtering, Collaborative Filtering (CF), and hybrid filtering &#13;
techniques are some of the strategies and methodologies employed by RS. This study explains &#13;
the function of data mining in recommendation systems and provides an RS process. Also &#13;
includes a methodological overview, RS difficulties, and a comparison of several e-commerce &#13;
website recommendation systems.
</summary>
<dc:date>2022-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>A survey: Cyber security facet for  machine learning algorithms</title>
<link href="http://10.9.150.37:8080/dspace//handle/atmiyauni/965" rel="alternate"/>
<author>
<name>Gohel, Amit M.</name>
</author>
<author>
<name>Vanjara, Pratik A.</name>
</author>
<id>http://10.9.150.37:8080/dspace//handle/atmiyauni/965</id>
<updated>2023-05-16T05:31:41Z</updated>
<published>2022-01-01T00:00:00Z</published>
<summary type="text">A survey: Cyber security facet for  machine learning algorithms
Gohel, Amit M.; Vanjara, Pratik A.
It is undeniably true that right now data is a really huge presence for all organizations or &#13;
associations. In this way ensuring its security is vital and the security models driven by &#13;
genuine datasets has become very significant. The activities dependent on military, &#13;
government, business and regular citizens are connected to the security and accessibility of &#13;
PC frameworks and organization. Starting here of safety, the organization security is a critical &#13;
issue on the grounds that the limit of assaults is constantly ascending throughout the long &#13;
term and they transform into be more modern and circulated. The target of this audit is to &#13;
clarify and look at the most usually utilized datasets. This paper centers cyber security aspect &#13;
to the various machine learning approaches such as Random Forest, SVM and KDD.
</summary>
<dc:date>2022-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Crop price data interpretation: A  comparison of machine learning</title>
<link href="http://10.9.150.37:8080/dspace//handle/atmiyauni/963" rel="alternate"/>
<author>
<name>Hirpara, Jignesh</name>
</author>
<author>
<name>Vanjara, Pratik</name>
</author>
<id>http://10.9.150.37:8080/dspace//handle/atmiyauni/963</id>
<updated>2023-05-16T05:25:41Z</updated>
<published>2022-01-01T00:00:00Z</published>
<summary type="text">Crop price data interpretation: A  comparison of machine learning
Hirpara, Jignesh; Vanjara, Pratik
Machine learning and its methodologies are used in agribusiness domains to predict edit costs &#13;
based on stock availability and generation. On a daily basis, a massive amount of data is &#13;
generated through the display of farming products. Horticulture has a large amount of data, &#13;
but unfortunately, much of it isn't able to find out inconspicuous details in information. Edit &#13;
cost estimates are more beneficial to agriculturists and the agriculture society since they &#13;
demand proper timing. Information mining procedures that have progressed play a critical &#13;
role in the discovery of hidden design in data. Following Designs, Cluster Analysis, and &#13;
visualization methodologies are used to provide a unique representation to predict the &#13;
horticultural edit cost. Past trim cost, climate, current advertise cost, stock accessibility, and &#13;
up and coming trim generation in current year or season are all used to compare information &#13;
mining procedure execution.Recently, the most often used programmer has been designed for &#13;
cost inquiry rather than cost determination. When compared to individual agriculturists in &#13;
various countries with stable environments, India's agribusiness generation is exceptionally &#13;
instable, and without appropriate MSP, it will not benefit agriculturists and farming crew. If &#13;
ranchers and agribusiness personnel are given the opportunity to appropriate alter costs, &#13;
destitution in India can be reduced.
</summary>
<dc:date>2022-01-01T00:00:00Z</dc:date>
</entry>
<entry>
<title>Hybrid machine learning in classification methods for HCR in gujarati language</title>
<link href="http://10.9.150.37:8080/dspace//handle/atmiyauni/960" rel="alternate"/>
<author>
<name>Doshi, Priyank D.</name>
</author>
<author>
<name>Vanjara, Pratik</name>
</author>
<id>http://10.9.150.37:8080/dspace//handle/atmiyauni/960</id>
<updated>2023-05-16T04:16:42Z</updated>
<published>2022-01-01T00:00:00Z</published>
<summary type="text">Hybrid machine learning in classification methods for HCR in gujarati language
Doshi, Priyank D.; Vanjara, Pratik
The problem of recognizing Gujarati Handwritten character with vowels opening new future &#13;
scope where one can use smart phone, website or any handy scanner to convert hand written &#13;
Gujarati Language into text. It will be very effective to give education in mother language at &#13;
primary level. Public, Private and Government sectors will be benefited when they get any &#13;
hand written Guajarati Script and they can directly convert it into softcopy or into text form. &#13;
There are many methods used to solve this problem.Using CNN we can improve new &#13;
algorithm depending on training data set, mathematical model and other intricacy. &#13;
Convolutional Neural Network or machine learning is very useful for this. Still there are more &#13;
chances for improvement and rising accuracy using Machine learning in combination of Deep &#13;
Learning as a hybrid model.
</summary>
<dc:date>2022-01-01T00:00:00Z</dc:date>
</entry>
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