POSTER PAPER. This paper proposes an approach of indexing and retrieving degraded handwritten documents. We present a modified version of the popular Vector Model in information retrieval (IR). Our model incorporates top n candidates from a HR system into the scheme of calculating the term frequency (tf) and the inverted document frequency (idf). Standardized IR Tests show that the proposed app...

Volunteered geographic information (VGI) plays an increasingly crucial role in flash floods. However, topic classification and spatiotemporal analysis are complicated by the various expressions lengths of social media textual data. This paper conducted applicability on bidirectional encoder representation from transformers (BERT) four traditional methods, TextRank, term frequency–inverse docume...

As the amount of data available in a repository increases, content retrieval from the huge data stored in the repository becomes a tedious task. Though Content Management System helps us to manage the data, yet searching the relevant data is still a daunting task. For that, we need efficient Search Algorithms for maximizing the correlation between data required and data returned by semantic sea...

Text classification is one of the areas where machine learning algorithms are used. The size dataset and methods used for converting textual words into vectors play a major role in classifying them. This paper proposes heuristic based approach to classify documents using Genetic Algorithm aided Support Vector Machines (SVM) Ensemble Learning approach. real valued representation data done on app...

Improving the quality of cold chain logistics services is a great challenge for fresh food e-commerce companies. Traditionally, evaluation service has been carried out mainly through questionnaire surveys and expert groups, which are time-consuming laborious. Unlike previous studies, in this study, we used latent Dirichlet allocation model to explore using customer reviews on ‘fresh food’ categ...

We introduce the relative rank differential statistic which is a non-parametric approach to document and dialog analysis based on word frequency rank-statistics. We also present a simple method to establish semantic saliency in dialog, documents, and dialog segments using these word frequency rank statistics. Applications of our technique include the dynamic tracking of topic and semantic evolu...

Based on actual safety management difficulties and needs, this paper aims to screen extract the key accident potential factors of personal injuries deaths within electric power industry provide a reference for companies’ prevention effort. First, document sorts out analyzes all causes influencing elements that may lead occurrence deaths, based which rough are initially identified combined with ...

Increasing progress in numerous research fields and information technologies, led to an increase the publication of papers. Therefore, researchers take a lot time find interesting papers that are close their field specialization. Consequently, this paper we have proposed documents classification approach can cluster text into meaningful categories which contain similar scientific field. Our pre...

Traditional searchable encryption schemes construct document vectors based on the term frequency-inverse frequency (TF-IDF) model. Such are not only high-dimensional and sparse but also ignore semantic information of documents. The Sentence Bidirectional Encoder Representations from Transformers (SBERT) model can be used to train containing realize semantic-aware multi-keyword search. In this p...

Lateral genetic transfer (LGT) plays an important role in the evolution of microbes. Existing computational methods for detecting genomic regions of putative lateral origin scale poorly to large data. Here, we propose a novel method based on TF-IDF (Term Frequency-Inverse Document Frequency) statistics to detect not only regions of lateral origin, but also their origin and direction of transfer...