Er. Prince Verma

Work place: CT Group of Institution/CSE, Jalandhar, 144041, India



Research Interests: Data Mining, Data Structures and Algorithms


Prince Verma, he received the B.Tech degree in Computer Science from MIMIT, Malout (Pb), India in 2008 and M.Tech degree in Computer Science in 2013 from DAVIET, Jalandhar (Pb), India. Currently, he is Assistant Professor in Computer Science Department of CTIEMT, Jalandhar (Pb), India. His research focuses on Data Mining, Algorithm optimisation techniques.

Author Articles
E-Mail Spam Detection Using Refined MLP with Feature Selection

By Harjot Kaur Er. Prince Verma

DOI:, Pub. Date: 8 Sep. 2017

Electronic Mail (E-mail) has established a significant place in information user’s life. E-Mails are used as a major and important mode of information sharing because emails are faster and effective way of communication. Email plays its important role of communication in both personal and professional aspects of one’s life. The rapid increase in the number of account holders from last few decades and the increase in the volume of emails have generated various serious issues too. Emails are categorised into ham and spam emails. From past decades spam emails are spreading at a tremendous rate. These spam emails are illegitimate and unwanted emails that may contain junk, viruses, malicious codes, advertisements or threat messages to the authenticated account holders. This serious issue has generated a need for efficient and effective anti-spam filters that filter the email into spam or ham email. Spam filters prevent the spam emails from getting into user’s inbox. Email spam filters can filter emails on content base or on header base. Various spam filters are labelled into two categories learning and non-machine learning techniques. This paper will discuss the process of filtering the emails into spam and ham using various techniques.

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Comparative Weka Analysis of Clustering Algorithm‘s

By Harjot Kaur Er. Prince Verma

DOI:, Pub. Date: 8 Aug. 2017

Data mining is a procedure of mining or obtaining a pertinent volume of data or information making the data available for understanding and processing. Data analysis is a common method across various areas like computer science, biology, telecommunication industry and retail industry. Data mining encompass various algorithms viz. association rule mining, classification algorithm, clustering algorithms. This survey concentrates on clustering algorithms and their comparison using WEKA tool. Clustering is the splitting of a large dataset into clusters or groups following two criteria ie. High intra-class similarity and low inter-class similarity. Every cluster or group must contain one data item and every data item must be in one cluster. Clustering is an unsupervised technique that is fairly applicable on large datasets with a large number of attributes. It is a data modelling technique that gives a concise view of data. This survey tends to explain all the clustering algorithms and their variant analysis using WEKA tool on various datasets.

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K-MLP Based Classifier for Discernment of Gratuitous Mails using N-Gram Filtration

By Harjot Kaur Er. Prince Verma

DOI:, Pub. Date: 8 Jul. 2017

Electronic spam is a highly concerning phenomenon over the internet affecting various organisations like Google, Yahoo etc. Email spam causes several serious problems like high utilisation of memory space, financial loss, degradation of computation speed and power, and several threats to authenticated account holders. Email spam allows the spammers to deceit as a legitimate account holder of the organisations to fraud money and other useful information from the victims. It is necessary to control the spreading of spam and to develop an effective and efficient mechanism for defence. In this research, we proposed an efficient method for characterising spam emails using both supervised and unsupervised approaches by boosting the algorithm’s performance. This study refined a supervised approach, MLP using a fast and efficient unsupervised approach, K-Means for the detection of spam emails by selecting best features using N-Gram technique. The proposed system shows high accuracy with a low error rate in contrast to the existing technique. The system also shows a reduction in vague information when MLP was combined with K-Means algorithm for selecting initial clusters. N-Gram produces 100 best features from the group of data. Finally, the results are demonstrated and the output of the proposed technique is examined in contrast to the existing technique.

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