Document Indexing Strategies in Big Data A Survey

From past few years, the operations of the Internet have a significant growth and individuals, organizations were unaware of this data explosion. Because of the increasing quantity and diversity of digital documents available to end users, mechanism for their effective and efficient retrieval is given highest importance. One crucial aspect of this mechanism is indexing, which serves to allow documents to be located quickly. The problem is that users want to retrieve on the basis of context, and individual words provide unreliable evidence about the contextual topic or meaning of a document. Hence, the available solutions cannot meet the needs of the growing heterogeneous data in terms of processing. This results in inefficient information retrieval or search query results. The design of indexing strategies that can support this need is required. There are various indexing strategies which are utilized for solving Big Data management issues, and can also serve as a base for the design of more efficient indexing strategies. The aim is to explore document indexing strategy for Big Data manageability. The existing systems like, Latent Semantic Indexing , Inverted Indexing, Semantic indexing and Vector Space Model has their own challenges such as, Demands high computational performance, Consumes more memory Space, Longer data processing time, Limits the search space, will not produce the exact answer, Can present wrong answers due to synonyms and polysemy, approach makes use of formal ontology. This paper will describe and compare the various Indexing techniques and presents the characteristics and challenges involved

A Hybrid Machine Learning Model to Recognize and Detect Plant Diseases in Early Stages

This paper presents an improved Inception module to recognise and detect plant illnesses substituting the original convolutions with architecture based on modified-Xception (m-Xception). In addition, ResNet extracts features by prioritising logarithm calculations over softmax calculations to get more consistent classification outcomes. The model’s training utilised a two-stage transfer learning process to produce an effective model. The results of the experiments reveal that the suggested approach is capable of achieving the specified level of performance, with an average recognition fineness of 99.73 on the public dataset and 98.05 on the domestic dataset, respectively

A VOS analysis of LSTM Learners Classification for Recommendation System

In response to the growing popularity of social web apps, much research has gone into analyzing and developing an AI-based responsive suggestion system. Machine learning and neural networks come in many forms that help online students choose the best texts for their studies. However, when training recommendation models to deal with massive amounts of data, traditional machine learning approaches require additional training models. As a result, they are deemed inappropriate for the personalized recommender generation of learning systems. In this paper, we examine LSTM-based strategies in order to make useful recommendations for future research

3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics

This volume contains contributed articles presented in the conference NCICCNDA 2018, organized by the Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Mysore, Karnataka (India) on 28th April 2018