Sanjeev Sharma

Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, Madhya Pradesh, India

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Publications (43)4.22 Total impact

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    ABSTRACT: Data mining is the process of determining new, unanticipated, valuable patterns from existing databases by considering historical and recent developments in statistics, artificial intelligence, and machine learning. It can help companies focus on the most important information in their data warehouses. Association rule mining is one of the most highly researched and popular data mining techniques for finding associations between items in a set. It is frequently used in marketing, advertising, and inventory control. Typically, association rules only consider items in transactions (positive association rules). They do not consider items that do not occur together, which can be used to create rules that are also useful for market basket analysis. Also, existing algorithms often generate too many candidate itemsets when mining the data and scan the database multiple times. To resolve these issues in association rule mining algorithms, we propose SARIC (set particle swarm optimization for association rules using the itemset range and correlation coefficient). Our method uses set particle swarm optimization to generate association rules from a database and considers both positive and negative occurrences of attributes. SARIC applies the itemset range and correlation coefficient so that we do not need to specify the minimum support and confidence, because it automatically determines them quickly and objectively. We verified the efficiency of SARIC using two differently sized databases. Our simulation results demonstrate that SARIC generates more promising results than Apriori, Eclat, HMINE, and a genetic algorithm.
    No preview · Article · Nov 2015 · Knowledge and Information Systems
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    Pallav Sharm · Varsha Sharma · Sanjeev Sharma · Jitendra Agrawal

    Preview · Article · Aug 2015
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    ABSTRACT: The DBSCALE algorithms are used for clustering of very large database. The clustering techniques are very proficient and also the rate of correctness is increases, but these algorithms suffered from noise and outlier problem. The noise data and outlier decreases the performance. For the minimization of noise and outlier we modified DBSCALE algorithm using Naïve's Baye's theorem. Naïve's Baye's Theorem is basically a probability based function. This function estimate the outlier cluster data and increase the correctness rate of algorithm on according to threshold value. According to this techniques, it compute maximum posterior hypothesis for the outlier data.
    No preview · Conference Paper · Apr 2014
  • Pooja Yadav · Nishchol Mishra · Sanjeev Sharma
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    ABSTRACT: Need of hiding information from intruders has been around since ancient times. Nowadays Digital media is getting advanced like text, image, audio, video etc. To maintain the secrecy of information, different methods of hiding have been evolved. One of them is Steganography, which means hiding information under some other information without noticeable change in cover information. Recently Video Steganography has become a boon for providing large amount of data to be transferred secretly. Video is simply a sequence of images, hence much space is available in between for hiding information. In proposed scheme video steganography is used to hide a secret video stream in cover video stream. Each frame of secret video will be broken into individual components then converted into 8-bit binary values, and encrypted using XOR with secret key and encrypted frames will be hidden in the least significant bit of each frames using sequential encoding of Cover video. To enhance more security each bit of secret frames will be stored in cover frames following a pattern BGRRGBGR.
    No preview · Conference Paper · Dec 2013
  • Mansi Sharma · Nishchol Mishra · Sanjeev Sharma
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    ABSTRACT: Online social network is a platform which makes each individual to be connected with their friends and colleagues. This connectivity encourages the users to share their private information all the way through social networking sites but sharing of personal information on this open platform leads to concern of privacy issues. Personal information available in social networking sites makes the adversary to take undue advantage of data and harm the user by embarrassing them or ruining their reputation. Traffic Analysis is one of the privacy issues which mean pilfering the personal communication between two users. Friend in the Middle (FiM) approach focuses on providing privacy awake social network architecture and extends the current OSNs in such a way that it restricts unnecessary access to the information but it is prone to traffic analysis attacks. In the proposed scheme, dummy traffic approach is used to prevent traffic analysis attack in Friend in the Middle (FiM). Comparison between the existing FiM and the proposed FiM is made using various parameters and enhanced accuracy of the proposed approach with the existing technique is achieved.
    No preview · Conference Paper · Dec 2013
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    ABSTRACT: Classification is a machine learning procedure that tags data instances into predefined class labels which are used to predict the data according to those Classes. Many classification algorithms in data mining have been stated, such as: C4.5, Apriori, Genetic algorithm, and Fuzzy set approaches which mainly uses heuristic or greedy search to get frequent sets in data for classification, resulting in high error ratio. Recently, a new method for classification has been proposed, called the Classification Using Association also known as associative classification. The main purpose for this is to mine class-association rules. Associative Classification has more advantages than the heuristic and greedy method, as it easily removes noise and higher accuracy is obtained. It additionally generates a rule set, that are more complete than traditional classification methods. This paper presents a meticulous survey on various Associative classification techniques. Moreover, a comparative analysis of accuracy and efficiency of those methods is presented.
    No preview · Conference Paper · Dec 2013
  • Seema Sharma · Jitendra Agrawal · Shikha Agarwal · Sanjeev Sharma
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    ABSTRACT: Data mining (DM) is a most popular knowledge acquisition method for knowledge discovery. Classification is one of the data mining (machining learning) technique that maps the data into the predefined class and group's. It is used to predict group membership for data instance. There are many areas that adapt Data Mining techniques such as medical, marketing, telecommunications, and stock, health care and so on. This paper presents the various classification techniques including decision tree, Support vector Machine, Nearest Neighbor etc. This survey provides a comparative Analysis of various classification algorithms.
    No preview · Conference Paper · Dec 2013
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    Parul Mishra · Nishchol Mishra · Sanjeev Sharma · Ravindra Patel
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    ABSTRACT: Region duplication forgery detection is a special type of forgery detection approach and widely used research topic under digital image forensics. In copy move forgery, a specific area is copied and then pasted into any other region of the image. Due to the availability of sophisticated image processing tools, it becomes very hard to detect forgery with naked eyes. From the forged region of an image no visual clues are often detected. For making the tampering more robust, various transformations like scaling, rotation, illumination changes, JPEG compression, noise addition, gamma correction, and blurring are applied. So there is a need for a method which performs efficiently in the presence of all such attacks. This paper presents a detection method based on speeded up robust features (SURF) and hierarchical agglomerative clustering (HAC). SURF detects the keypoints and their corresponding features. From these sets of keypoints, grouping is performed on the matched keypoints by HAC that shows copied and pasted regions.
    Preview · Article · Nov 2013 · The Scientific World Journal
  • Seema Sharma · Jitendra Agrawal · Sanjeev Sharma

    No preview · Article · Nov 2013 · International Journal of Computer Applications
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    ABSTRACT: One of the key issues in cellular mobile communication is to find the current location of mobile terminal (MT) to deliver the services, which is called location management (LM). Much research has been done on dynamic LM that reduced the LM cost up to a large extent. In movement based dynamic LM scheme, the location area is defined in the form of ring of cells for individual user. Whenever an MT visits a cell outside of its current location area (LA), it triggers location update (LU). For this purpose, network must inform the mobile terminal about ID of all the cells present in its current location area. In this paper, a simple way of cell-ID assignment is proposed under which, network sends only the ID of center cell of LA ring to MT and then MT can compute IDs of all other cells in its location area. This saves a significant amount of wireless bandwidth by minimizing the signaling traffic at VLR level and thus reduces the mobility management overhead.
    No preview · Article · Sep 2013 · Telecommunication Systems
  • Yugchhaya Dhote · Nishchol Mishra · Sanjeev Sharma
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    ABSTRACT: Online Social Network has tremendously captured the attention of users in recent years. Some social networking sites are explicitly designed for social interaction, while some of them are application based providing content sharing along with social communication. Link prediction is a new interdisciplinary research direction in which, existing links are analyzed and future links are predicted among millions of users of social network. Traditional link prediction methods had focused on the use of graph metrics to determine, where new links are likely to arise. A small amount of work has been done on analyzing longterm graph trends. This paper does the survey and analysis of temporal evolution of link prediction. It has been found out that the earlier graph generation models were unrealistic in their prediction and can be complemented through the use of temporal metrics, resulting in a highly accurate link prediction. In addition, the paper is concluded with the proposed framework which exploits temporal feature along with local similarity attributes.
    No preview · Conference Paper · Aug 2013
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    Tuhin Shukla · Nishchol Mishra · Sanjeev Sharma

    Preview · Article · Apr 2013 · International Journal of Computer Applications
  • Gourav Jain · Nishchol Mishra · Sanjeev Sharma

    No preview · Article · Apr 2013 · International Journal of Computer Applications
  • Praneet Saurabh · Bhupendra Verma · Sanjeev Sharma
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    ABSTRACT: Exponential growth of internet acted as a centrifugal force in the development of a whole new array of applications and services which drives the e- business/ commerce globally. Now days businesses and the vital services are increasingly dependent on computer networks and the Internet which is vulnerable to the evolving and ever growing threats, due to this the users who are participating in various activities over internet are exposed to many security gaps which can be explored to take advantage. These alarming situations gave rise to the concern about security of computer systems/ networks which resulted in the development of various security concepts and products but unfortunately all these systems somehow fail to provide the desired level of security against ever-increasing threats. Later on it has been observed that there lies a huge analogy between the human immune system (HIS) and computer security systems as the previous protects the body from various external and internal threats very effectively. This paper proposes a general immunity inspired security framework which uses the concepts of HIS in order to overcome the ever growing complex security challenges.
    No preview · Conference Paper · Jan 2013
  • Arpit Jain · Shikha Agrawal · Jitendra Agrawal · Sanjeev Sharma

    No preview · Article · Jan 2013
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    ABSTRACT: The progress in the field of Computer Networks & Internet is increasing with tremendous volume in recent years. This raises important issues with regards to security. Several solutions emerged in the past which provide security at the host or network level. These traditional solutions like antivirus, firewall, spyware & authentication mechanism provide security to some extends but they still face the challenges of inherent system flaws & social engineering attacks. Some interesting solution emerged like Intrusion Detection & Prevention Systems but these too have some problems like detecting & responding in real time & discovering novel attacks. Several Machine Learning techniques like Neural Network, Support Vector Machine, Rough Set etc. Were proposed for making an efficient and Intelligent Network Intrusion Detection System. Also Particle Swarm Optimization is currently attracting considerable interest from the research community, being able to satisfy the growing demand of reliable & intelligent Intrusion Detection System (IDS). Recent development in the field of IDS shows that securing the network with a single technique proves to be insufficient to cater ever increasing threats, as it is very difficult to cope with all vulnerabilities of today’s network. So there is a need to combine all security technologies under a complete secure system that combines the strength of these technologies under a complete secure system that combines the strength of these technologies & thus eventually provide a solid multifaceted well against intrusion attempts. This paper gives an insight into how Particle Swarm Optimization and its variants can be combined with various Machine Learning techniques used for Anomaly Detection in Network Intrusion Detection System by researchers so as to enhance the performance of Intrusion Detection System.
    No preview · Chapter · Jan 2013
  • Gourav Jain · Nishchol Mishra · Sanjeev Sharma
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    ABSTRACT: A system that suggests list of most popular items to a set of users on the basis of their interest is named as recommendation system. Recommendation system filters the unnecessary information by applying knowledge discovery techniques for online users and has become the most powerful and admired tools in E-Business. ERPM is one of the easiest movie recommendation method, which overcomes the limitations of scalability and sparsity of recommendation system, but it generates predictions on the basis of probability model, which are less accurate and requires more time for calculations. This article presents a novel method named CRLRM (Category based Recommendation using Linear Regression Model) which is based on linear regression model that improves the prediction accuracy and speed up the calculations. Performance of proposed method is evaluated on the basis of MAE (Mean Absolute Error) comparison, and result obtained is far much better than ERPM and shows improvement in 30-40% of user ratings.
    No preview · Conference Paper · Jan 2013
  • Sitanshu Singh · Sanjeev Sharma · Santosh Sahu

    No preview · Article · Jun 2012 · International Journal of Computer Applications
  • Ghanshyam Raghuwanshi · Nishchol Mishra · Sanjeev Sharma

    No preview · Article · Apr 2012 · International Journal of Computer Applications
  • Narendra SinghBagri · Sanjeev Sharma · Santosh Sahu

    No preview · Article · Feb 2012 · International Journal of Computer Applications