Akhilesh Tiwari

Work place: Department of CSE & IT, Madhav Institute of Technology and Science, Gwalior (M.P), 474005, India

E-mail: atiwari.mits@gmail.com


Research Interests: Computer systems and computational processes, Data Mining, Data Structures and Algorithms


Dr. Akhlesh Tiwari has received the Ph.D. degree in Information Technology from Rajiv Gandhi Technological University, Bhopal (M.P.), India. He is currently working as Associate Professor in the Department of CSE & IT, Madhav Institute of Technology & Science (MITS), Gwalior (M.P.), India. He has guided several theses at Master and Under Graduate level. His area of current research includes Knowledge Discovery in Databases and Data Mining, Wireless Networks. He has published more than 20 research papers in the journals and conferences of international repute. He is also acting as a reviewer & member in the editorial board of various international journals. He is having the memberships of various Academic/ Scientific societies including IETE, CSI, GAMS, IACSIT, and IAENG.

Author Articles
On the Use of Rough Set Theory for Mining Periodic Frequent Patterns

By Manjeet Samoliya Akhilesh Tiwari

DOI: https://doi.org/10.5815/ijitcs.2016.07.08, Pub. Date: 8 Jul. 2016

This paper presents a new Apriori based approach for mining periodic frequent patterns from the temporal database. The proposed approach utilizes the concept of rough set theory for obtaining reduced representation of the initially considered temporal database. In order to consider only the relevant items for analyzing seasonal effects, a decision attribute festival has been considered. It has been observed that the proposed approach works fine for the analysis of the seasonal impact on buying behavior of customers. Considering the capability of approach for the analysis of seasonal profitability concern, decision making, and future marketing may use it for the important decision-making process for the uplifting of sell.

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Rough Set and Genetic Based Approach for Max-imization of Weighted Association Rules

By Shrikant Brajesh Sagar Akhilesh Tiwari

DOI: https://doi.org/10.5815/ijmecs.2016.03.07, Pub. Date: 8 Mar. 2016

The present paper proposes a new approach for the effective weighted association rule mining. The proposed approach utilizes the power of Rough Set Theory for obtaining reduct of the targeted dataset. Additionally, approach takes the benefit for weighted measures and the Genetic Algorithm for the generation of the desired set of rules. Enough analysis of proposed approach has been done and observed that the approach works as per the expectation and will be beneficial in situation when there is a requirement for the consideration of hidden rules(maximizing generated rules) in decision-making process.

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Efficient Algorithm for Destabilization of Terrorist Networks

By Nisha Chaurasia Akhilesh Tiwari

DOI: https://doi.org/10.5815/ijitcs.2013.12.03, Pub. Date: 8 Nov. 2013

The advisory feasibility of Social Network Analysis (SNA) to study social networks have encouraged the law enforcement and security agencies to investigate the terrorist network and its behavior along with key players hidden in the web. The study of the terrorist network, utilizing SNA approach and Graph Theory where the network is visualized as a graph, is termed as Investigative Data Mining or in general Terrorist Network Mining. The SNA defined centrality measures have been successfully incorporated in the destabilization of terrorist network by deterring the dominating role(s) from the network. The destabilizing of the terrorist group involves uncovering of network behavior through the defined hierarchy of algorithms. This paper concerning the destabilization of terrorist network proposes a pioneer algorithm which seems to replace the already available hierarchy of algorithms. This paper also suggests use of the two influential centralities, PageRank Centrality and Katz Centrality, for effectively neutralizing of the network.

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A New Model for Intrusion Detection based on Reduced Error Pruning Technique

By Mradul Dhakar Akhilesh Tiwari

DOI: https://doi.org/10.5815/ijcnis.2013.11.07, Pub. Date: 8 Sep. 2013

The increasing counterfeit of the internet usage has raised concerns of the security agencies to work very hard in order to diminish the presence of the abnormal users from the web. The motive of these illicit users (called intruders) is to harm the system or the network either by gaining access to the system or prohibiting genuine users to access the resources. Hence in order to tackle the abnormalities Intrusion Detection System (IDS) with Data Mining has evolved as the most demanding approach. On the one end IDS aims to detect the intrusions by monitoring a given environment while on the other end Data Mining allows mining of these intrusions hidden among genuine users. In this regard, IDS with Data Mining has been through several revisions in consideration to meet the current requirements with efficient detection of intrusions. Also several models have been proposed for enhancing the system performance. In context to improved performance, the paper presents a new model for intrusion detection. This improved model, named as REP (Reduced Error Pruning) based Intrusion Detection Model results in higher accuracy along with the increased number of correctly classified instances.

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