Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Named entity recognition (NER) is a subtask of the information extraction. NER system reads the text and highlights the entities. NER will separate different entities according to the project. NER is the process of two steps. The steps are detection of names and classifications of them. The first step is further divided into the segmentation. The second step will consist to choose an ontology which will organize the things categorically. Document summarization is also called automatic summarization. It is a process in which the text document with the help of software will create a summary by selecting the important points of the original text. In this chapter, the authors explain how document summarization is performed using named entity recognition. They discuss about the different types of summarization techniques. They also discuss about how NER works and its applications. The libraries available for NER-based information extraction are explained. They finally explain how NER is applied into document summarization.

Computer Science

School of Computer Science and Engineering

Chennai Campus

VIT Unitversity

Trends and Applications of Text Summarization Techniques Advances in Data Mining and Database Management

Named Entity Recognition in Document Summarization

With an 18 per cent of India's gross domestic production (GDP) and an employment to 50% of its workforce, agriculture is one of the most important sectors to the Indian Economy. The Indian food industry is rapidly growing ever after the green revolution and it has an increasing contribution to world food trade every year due to its immense potential for value addition. Various seasonal, economic and biological factors influence the crop production but unpredictable and incalculable changes in such factor's leads to huge losses in the market and to its stakeholders [1]. Thus, to minimize the loss, yield prediction is an important issue that needs to be addressed. Through this paper, we propose to predict various factors such as rainfall level, soil composition, weather and seasonal change values to an optimum accuracy to maximize the output of the crop production through simple neural network model. © 2020 Innovare Academics Sciences Pvt. Ltd. All rights reserved.

World University root node

Journal of Critical Reviews

Crop production prediction using artificial neural network

A WS provides the communication between heterogeneous systems. While performing this operation, we need to focus on QoS of consumer, provider and registry directory. There will be some parameters like WS selection, prediction and rank these are parameters need to consider while QoS implementation in web services. While performing integration in web services we need to focus on QoS requirements regarding server and network performance. Performance of WS is related to locations i.e the network distance and the Internet connections between consumer and provider. There will be more QoS approach which works on consumers collected QoS data, based on this data system can predict the QoS of WS. Throughput and response time are the QoS of WS. In this paper, we have proposed parallel XML parser, by which we can parse UDDI, WSDL and SOAP XML files parallel by which it will improve the response time and throughput of WS.

Fulltext

Indonesian Journal of Electrical Engineering and Computer Science

Parallelism in web services: design of parallel XML parser for web services

The large and wide range of information has become a tough time for crawlers and search engines to extract related information. This paper discusses about focused crawlers also called as topic specific crawler and variations of focused crawlers leading to distributed architecture, i.e., context aware notification architecture. To get the relevant pages from a huge amount of information available in the internet we use the focused crawler. This can bring out the relevant pages for the given topic with less number of searches in a short time. Here the input to the focused crawler is a topic specified using exemplary documents, but not using the keywords. Focused crawlers avoid the searching of all the web documents instead it searches over the links that are relevant to the crawler boundary. The Focused crawling mechanism helps us to save CPU time to large extent to keep the crawl up-to-date.

Focused crawling from the basic approach to context aware notification architecture

Mental health is nowadays a topic which is most frequently discussed when it comes to research but least frequently discussed when it comes to the personal life. The well-being of a person is the measure of mental health. The increasing use of technology will lead to a lifestyle of less physical work. Also, the constant pressure on an employee in any industry will make more vulnerable to mental disorder. These vulnerabilities consist of peer pressure, anxiety attack, depression, and many more. Here we have taken the dataset of the questionnaires which were asked to an IT industry employee. Based on their answers the result is derived. Here output will be that the person needs an attention or not. Different machine learning techniques are used to get the results. This prediction also tells us that it is very important for an IT employee to get the regular mental health check up to tract their health. The employers should have a medical service provided in their company and they should also give benefits for the affected employees. © BEIESP.

International Journal of Innovative Technology and Exploring Engineering

Prediction of mental disorder for employees in IT industry

In this paper, we propose a novel methodology to parse the argumentative structures. Firstly, the essays are of the type persuasive essays. They are divided into tokens. Here now we apply new model to detect the argumentative structures. Integer linear programming is used as the proposed model to optimize types of argument components and their argumentative relations. This model will increase the efficiency of the performance of base classifiers and also outperforms the heuristic baselines. Here the corpus is consisting of the persuasive essays having annotated schemes. The corpus is the open source and made available. © 2018 IEEE.

2018 International Conference on Recent Trends in Advance Computing (ICRTAC)

Argumentative Analysis on Persuasive Essays

The core ambition of this article is to provide benign, Smart and Sophisticated Life for all the households. Our ultimate aim is to make our client feel safe and secure in their houses. Our methodology uses wireless communication. Our System has an Arduino which acts as a 'Back Bone' to whole project, its focal functionality is to accumulate information from sensors and provide them appropriate response. Our complete Smart System is energy efficient and intelligent enough to handle real world circumstances in real time without daunting. © 2018 Asian Research Publishing Network (ARPN).

ARPN Journal of Engineering and Applied Sciences

Smart home using iot

As particulate matter in the air can cause several kinds of respiratory and cardiovascular diseases, the air quality information predicting attracts more and more attention. Knowing these information in advance is very important to protect human from health problems. With the development of computer technology, the data we can collect is increasingly becoming fine-grained. Most important of all, they need to be analyse in real-time. However, existing methods could not meet the demand of real-time analysis. In this paper, we predict air quality based on a Ipython implementation of random forest algorithm. First, a distributed random forest algorithm is implemented using Ipython on the basis of resilient distributed dataset and shared variable. Then, we build an air quality prediction model using the parallelized random forest algorithm. The proposed method is evaluated with real meteorology data obtained from IIT Madras. The experiment results show that the proposed method is fast in predicting concentration level of PM2.5. And the results also prove the effectiveness and scalability of our method when deal with big data. © IAEME Publication.

International Journal of Civil Engineering and Technology

Ensemble learning on forecasting fine grained pollutant levels in air using random forest, naive bayes, decision tree algorithms

In the current era, we are facing a new problem of parking of vehicles. It is a major problem in urban cities. The problem is more tough because of continues growing number of vehicle and also size of vehicles. Car parking is not just a major problem in India but also in all over the world. We know that one million vehicles burn oil every day. In this paper, we propose an automatic and real-time system for automated car parking. This system would be implemented by the use of internet of things (IOTs). IOT refers as any physical thing that is connected to internet or exchanging information or data between internet and physical device. Arduino Uno is a microcontroller used in IOT. It is used for building digital devices, and interactive objects that can sense and control physical devices. Our smart parking will be implementing using Arduino Uno board for car parking and Ethernet shield to connect parking area with web or internet. By using our automated parking system, a user can save much time for searching free parking space. An infrared sensor has to be employed in each slot to check that a particular plot is vacant or not. Searching smart parking system helps people to search parking space accessible with the ease of IOT automation by supplying parking slot free information. The user can book in advance the parking slot and update the information to the server. Every user has a unique id and password. In case a car in stolen and enter the parking IOT the server checks the database and inform the police.

Asian Journal of Pharmaceutical and Clinical Research

INTERNET OF THING BASED CAR PARKING SYSTEM

Department of Embedded Technology

School of Electronics Engineering

Vellore Campus

2017 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2)

Automatic traffic diversion system using traffic signals

Melioidosis is a serious infectious diseases affecting multi-organ system in humans with high mortality rate. The disease is caused by the bacterium, Burkholderia pseudomallei and it is intrinsically resistant to many antibiotics. Thus, there is an urgent need for protective vaccine against B. pseudomallei; which may reduce morbidity and mortality in endemic areas. The identification of peptides that bind to major histocompatibility complex II class helps in understanding the nature of immune response and identifying T-cell epitopes for the design of new vaccines. Previous studies indicate that, ompA, bipB, fliC and groEL proteins of B. pseudomallei stimulate CD4+ T-cell immune response and act as protective immunogens. However, the data for CD4+ T-cell epitopes of these immunogenic proteins are very limited. Hence, in this present study we attempted to identify CD4+ T-cell epitopes in B. pseudomallei immunogenic proteins using in silico approaches. We did population coverage analysis for these identified epitopic core sequences to identify individuals in endemic areas expected to respond to a given set of these epitopes on the basis of HLA genotype frequencies. We observed that eight epitopic core sequences, two from each immunogenic protein, were associated with the maximum number of HLA-DR binding alleles. These eight peptides are found to be immunogenic in more than 90% of population in endemic areas considered. Thus, these eight peptides containing epitopic core sequences may act as probable vaccine candidates and they may be considered for the development of epitope-based vaccines for melioidosis. © 2016 Elsevier Ltd.

Department of Bio-Sciences

School of Bio Sciences and Technology

Department of Biotechnology

Journal of Theoretical Biology

Identification of CD4+ T-cell epitope and investigation of HLA distribution for the immunogenic proteins of Burkholderia pseudomallei using in silico approaches – A key vaccine development strategy for melioidosis

The prevalence of e-commerce domain has resulted in the development of several comparison-shopping engines. These engines are specifically built on a vertical/segment or a niche and distinct from a general web search engines. These engines help consumers to filter and compare products based on pricing details, features, ratings and other criteria. They also provide subjective information such as reviews, ratings, recommendations and other ancillary products/services. The focus of the current solution is to enhance and develop a e-Commerce product evaluator that provides a higher level of personalization, relative ratings/ranking between competing products, scoring best fit match taking into consideration, the consumers propensity, profile and their past social media history and also gives the enough inputs for the business owners to improve their profits. © Research India Publications.