Bhasker Pant

Cluster head selection by randomness with data recovery in WSN

Wireless sensor networks are potentially used in the field of surveillance and monitoring since last few years. In such applications, clustering play an important role in enhancement of the life span and scalability of the network. Each cluster contains a cluster head that controls the whole cluster working, in which various researcher focuses on the good selection of the cluster head that can improve the life of the WSN. Previous works on cluster head selection lacks in data recovery. In this paper we are proposing Cluster Head Selection by Randomness with Data Recovery in WSN (CHSRDR) method for selecting the cluster head inside the cluster with data recovery. The proposed method, CHSRDR, considers the heterogeneity in power and maintains a cluster of vice-heads on the basis of randomness inside the cluster; these vice-heads can work as a head in future, when the main head come to end of power. The headship circulates among the vice-heads of the cluster. We have simulated the method and got the enhancement in throughput.

Association Rule Mining to Deduce the Most Frequently Occurring Amino Acid Patterns in HIV

HIV is one of the most dreaded diseases of the century. Throughout the world efforts are underway to develop new vaccines and design new drugs so as to combat this viral menace. In an effort to probe deeper into the functioning of these viruses we present association based rules formulation so as to decipher the most frequently occurring amino acids in these viruses. This is a novel attempt of its kind since we are attempting to find put the most informative association rules using Apriori algorithm implemented through WEKA. The information generated can be of great use to molecular biologists and drug designers since the associated amino acids can be a very good drug targets. Our findings suggest that L-Selenocysteine and L-Pyrrolysine are most frequently associated amino acids in the 4 classes of virulent proteins analyzed for association rules and Cyteine and Arginine show the strongest association in one of the class analyzed i.e. Gp41. Hence these can be potential drug candidates. General Terms Association Rule Mining, HIV, Apriori Algorithm.

Naïve Bayes Classifier for Classification of Plant and Animal miRNA

MicroRNAs (miRNA) are single-stranded RNA molecules of about 21-23 nucleotides in length. MicroRNAs (miRNAs) constitute a large family of non coding RNAs that function to regulate gene expression. Till today wet lab experiments have been used to classify the miRNA of plants and animals. The wet lab techniques are highly expensive, labour intensive and time consuming. Thus there arises a need for computational approach for classification of plants and animal miRNA. These computational approaches are fast and economical as compared to wet lab techniques. In view of above a machine learning models has been developed for classification of plant and animal miRNA using Naive Bayes classifier. The model has been tested on available data and it gives results with 85.71% accuracy.

ProCoS: Protein composition server.

ProCoS is a free online tool for computing different combinations of peptide compositions. It is developed as an applet and a server with a capability to handle multiple FASTA sequences. The generalized algorithm for computing poly-amino acid composition forms the core of ProCoS. It produces output in different formats for easy visualization of results. It also allows composition analysis of sequences in full or in specific parts. Thus, ProCoS is user-friendly, flexible and unique.

Opinion Mining of Real Time Twitter Tweets

is a real-time information network and micro- blogging service that allows users to post updates. The service rapidly gained worldwide popularity that connects to the latest stories, ideas, opinions, and news. It is a powerful tool for real-time way of communicating with people by combining messages that are quick to write, easy to read, public and accessible anywhere. On Twitter anyone can read, write and share messages or tweets. Opinion mining is a type of natural language processing for tracking the mood of the public about a particular product. Opinion mining, which is also called sentiment analysis, involves building a system to collect and examine opinions about the product made in blog posts, comments, reviews or tweets. Social media plays an important role in inferring the opinion of the authors. In this paper we focused on tweets that will result in analyzing the view of the public on generally discussed topics. A tweets puller is developed that automatically collects random opinions and classifier tool that performs classifications on that corpus collected from Twitter. Our classification is based on features extracted and classified into POSITIVE, NEGATIVE and NEUTRAL. The results further evaluated and concluded to infer the performance of the classification through SVM.

A Model for Detection, Classification and Identification of Spam Mails Using Decision Tree Algorithm

Spam mails are unsolicited bulk mails which are meant to fulfill some malicious purpose of the sender. They may cause economical, emotional and time losses to the recipients. Hence there is a need to understand their characteristics and distinguish them from normal in box mails. Decision tree classifier has been trained with the major characteristics of spam mails and results obtained with more then 86.7437% accuracy. This classifier can be a valuable strategy for software developers who are trying to combat this ever growing problem.

Latent Class Analysis (LCA) Based Approach for Finding Best Hotels

Researchers shows a huge interest in Latent Class Analysis (LCA) in various domains over the last two decades. We proposed a new Latent Class Data Analysis using Statistical modeling approach to categorize better and worst Hotel to Stay. The main objective of this study was to find the unobserved classes in the Trap Advisor dataset. The results allow to identify new entry of the Hotel and detects whether it lies in Good Hotel category or in worst Hotel category. For evaluation and demonstration purpose freely, available Trip Advisor dataset is used.

Study and Analysis of Demonetization Move Taken by Indian Prime Minister Mr. Narendra Modi

Demonetization is the process of stopping currency in the monetary standard. Mr. Narendra Modi shocked the whole country at live telecast on November 8, 2016 at 8:15 pm by announcing demonetization of 500 and 1000 rupee notes from November 9, 2016, to handle the threat of black money, terrorist funding, and fake currency. In this article, we study and analyzed what the people of India feel about this decision of the government on demonetization and analyzed the effect on the business community. The results show that 51.02% people are supported this while the 34.69% are against it, rest 14.28% are neutral.

Optimized Location-Specific Movie-Reviewing System Using Tweets

Day-by-day, the density of the mobile phone is increasing throughout the world, and consequently, the social media become more powerful than before. People share almost everything on the social media and this data can be further used to make various kinds of recommendation, prediction, and data analysis task. One of the main domains in this scenario is reviewing system. In this article, we review currently running movies based on dynamically collected tweets. Tweets are collected based on specific location so that the system can review the movie status on that location. The experimental results show that the system performs well and the same system can be applicable to other similar topics like song-reviewing system and product-reviewing system.

Energy efficient task distribution with efficient deployment and scheduling of sensor nodes in WSN

Lifetime of the network plays a vital role in working wireless sensor networks. Network lifetime can be improved by the efficient use of present sensor nodes in wireless sensor nodes. In this paper, our objective is to deploy and schedule the sensor nodes in such a manner that improves the lifetime and provides a way to distribute the tasks among the sensor nodes that completes the task on the basis of priority and accuracy. Thus, the overall objective of this paper is to calculate the optimised location of sensor nodes contain a fix sensing range and schedule the sensor nodes that maximises the lifetime. The upper bound of network lifetime can be pre-calculated mathematically, we are applying this knowledge to find the locations of sensor nodes that improves the lifetime. Further, the nodes are scheduled and task is distributed among the sensor nodes that achieve this upper bound. In this paper, we are applying teaching learning algorithm to find the deployment location and schedule followed by an algorithm for task distribution. The comparative study shows that our method outperforms as compared to naive methods.