Jaypee Institute of Information Technology

About: Jaypee Institute of Information Technology is a education organization based out in Noida, Uttar Pradesh, India. It is known for research contribution in the topics: Computer science & Cluster analysis. The organization has 2136 authors who have published 3435 publications receiving 31458 citations. The organization is also known as: JIIT Noida.

Twitter sentiment analysis using hybrid cuckoo search method

Abstract: A hybrid cuckoo search method (CSK) has been presented for Twitter sentiment analysis.CSK modifies the random initialization of population in cuckoo search (CS) by K-means to resolve the problem of random initialization.The proposed algorithm has outperformed five popular algorithms.The statistical analysis has been done to validate the performance of the proposed algorithm. Sentiment analysis is one of the prominent fields of data mining that deals with the identification and analysis of sentimental contents generally available at social media. Twitter is one of such social medias used by many users about some topics in the form of tweets. These tweets can be analyzed to find the viewpoints and sentiments of the users by using clustering-based methods. However, due to the subjective nature of the Twitter datasets, metaheuristic-based clustering methods outperforms the traditional methods for sentiment analysis. Therefore, this paper proposes a novel metaheuristic method (CSK) which is based on K-means and cuckoo search. The proposed method has been used to find the optimum cluster-heads from the sentimental contents of Twitter dataset. The efficacy of proposed method has been tested on different Twitter datasets and compared with particle swarm optimization, differential evolution, cuckoo search, improved cuckoo search, gauss-based cuckoo search, and two n-grams methods. Experimental results and statistical analysis validate that the proposed method outperforms the existing methods. The proposed method has theoretical implications for the future research to analyze the data generated through social networks/medias. This method has also very generalized practical implications for designing a system that can provide conclusive reviews on any social issues.

Escherichia coli biofilm: development and therapeutic strategies

Abstract: Summary Escherichia coli biofilm consists of a bacterial colony embedded in a matrix of extracellular polymeric substances (EPS) which protects the microbes from adverse environmental conditions and results in infection. Besides being the major causative agent for recurrent urinary tract infections, E. coli biofilm is also responsible for indwelling medical device-related infectivity. The cell-to-cell communication within the biofilm occurs due to quorum sensors that can modulate the key biochemical players enabling the bacteria to proliferate and intensify the resultant infections. The diversity in structural components of biofilm gets compounded due to the development of antibiotic resistance, hampering its eradication. Conventionally used antimicrobial agents have a restricted range of cellular targets and limited efficacy on biofilms. This emphasizes the need to explore the alternate therapeuticals like anti-adhesion compounds, phytochemicals, nanomaterials for effective drug delivery to restrict the growth of biofilm. The current review focuses on various aspects of E. coli biofilm development and the possible therapeutic approaches for prevention and treatment of biofilm-related infections.