Context based Document Indexing and Retrieval using Big Data Analytics - A Review

In past few years it is observed that the internet usage is been grown wider all over the world, hence, the data generation and usage is been increased rapidly by the users, the data generated in different forms may or may not be structured. The usage of internet by individuals and organizations have been grown so, there is increasing quantity and diversity of digital data in the form of documents, became available to the end users. The Storage, Maintenance and organization of such huge data in databases is a challenging task. So, there is a great need of efficient and effective retrieval technique which focuses on improving the accuracy of document retrieval. In this paper we are going to discuss about document retrieval using context based indexing approach. Here lexical association between terms is used to separate content carrying terms and other-terms. Content carrying terms are used as they give idea about theme of the document. Indexing weight calculation is done for content carrying terms. Lexical association measure is used to calculate indexing weight of terms. The term having higher indexing weight is considered as important and sentence which contains these terms is also important. When user enters search query, the important terms are matched with the terms with higher weights in order to retrieve documents. The explicit semantic relation or frequent co-occurrence of terms is been considered in this context based indexing

Optimized Ensemble Approach for Multi-model Event Detection in Big data

Event detection acts an important role among modern society and it is a popular computer process that permits to detect the events automatically. Big data is more useful for the event detection due to large size of data. Multimodal event detection is utilized for the detection of events using heterogeneous types of data. This work aims to perform for classification of diverse events using Optimized Ensemble learning approach. The Multi-modal event data including text, image and audio are sent to the user devices from cloud or server where three models are generated for processing audio, text and image. At first, the text, image and audio data is processed separately. The process of creating a text model includes pre-processing using Imputation of missing values and data normalization. Then the textual feature extraction using integrated N-gram approach. The Generation of text model using Convolutional two directional LSTM (2DCon_LSTM). The steps involved in image model generation are pre-processing using Min-Max Gaussian filtering (MMGF). Image feature extraction using VGG-16 network model and generation of image model using Tweaked auto encoder (TAE) model. The steps involved in audio model generation are pre-processing using Discrete wavelet transform (DWT). Then the audio feature extraction using Hilbert Huang transform (HHT) and Generation of audio model using Attention based convolutional capsule network (Attn_CCNet). The features obtained by the generated models of text, image and audio are fused together by feature ensemble approach. From the fused feature vector, the optimal features are trained through improved battle royal optimization (IBRO) algorithm. A deep learning model called Convolutional duo Gated recurrent unit with auto encoder (C-Duo GRU_AE) is used as a classifier. Finally, different types of events are classified where the global model are then sent to the user devices with high security and offers better decision making process. The proposed methodology achieves better performances are Accuracy (99.93%), F1-score (99.91%), precision (99.93%), Recall (99.93%), processing time (17seconds) and training time (0.05seconds). Performance analysis exceeds several comparable methodologies in precision, recall, accuracy, F1 score, training time, and processing time. This designates that the proposed methodology achieves improved performance than the compared schemes. In addition, the proposed scheme detects the multi-modal events accurately

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

Kaposin-B Enhances the PROX1 mRNA Stability during Lymphatic Reprogramming of Vascular Endothelial Cells by Kaposi's Sarcoma Herpes Virus

Kaposi's sarcoma (KS) is the most common cancer among HIV-positive patients. Histogenetic origin of KS has long been elusive due to a mixed expression of both blood and lymphatic endothelial markers in KS tumor cells. However, we and others discovered that Kaposi's sarcoma herpes virus (KSHV) induces lymphatic reprogramming of blood vascular endothelial cells by upregulating PROX1, which functions as the master regulator for lymphatic endothelial differentiation. Here, we demonstrate that the KSHV latent gene kaposin-B enhances the PROX1 mRNA stability and plays an important role in KSHV-mediated PROX1 upregulation. We found that PROX1 mRNA contains a canonical AU-rich element (ARE) in its 3′-untranslated region that promotes PROX1 mRNA turnover and that kaposin-B stimulates cytoplasmic accumulation of the ARE-binding protein HuR through activation of the p38/MK2 pathway. Moreover, HuR binds to and stabilizes PROX1 mRNA through its ARE and is necessary for KSHV-mediated PROX1 mRNA stabilization. Together, our study demonstrates that kaposin-B plays a key role in PROX1 upregulation during lymphatic reprogramming of blood vascular endothelial cells by KSHV

Manganese-doped polyaniline electrodes as high-performance supercapacitors with superior energy density and prolonged shelf life

The rising energy ultimatum has urged a paradigm shift from conventional to non-conventional, green, and clean energy sources to indulge supply and demand. In light of this, manganese-doped polyaniline was synthesized via the In-situ oxidative polymerization method and was employed as material for supercapacitor electrodes. The doping of PANI by manganese was confirmed from EDX and XPS analysis. Electrochemical studies uncovered an areal capacitance of 776 mF/cm2 at a current density of 1 mA/cm2, gravimetric capacitance of 995 F/g at 1 A/g, 99% coulombic efficiency, and a capacitive retention of 86.5% was observed after 20,000 cycles at a current density of 35 mA/cm2 by the fabricated coin cell device. The shelf life performance of the coin cell was analyzed after 400 days and underwent a long run for one lakh cycles, which revealed a capacitive retention of 71% at 1 mA/cm2 and a stable coulombic efficiency of 96% throughout the cycling. From the performance analysis, the manganese-doped PANI claims to serve as an excellent electrode for supercapacitor applications

Comparative evaluation of fracture resistance of simulated immature teeth restored with apical plugs of mineral trioxide aggregate, Biodentine, and bone cement: An in vitro study

Aim: This in vitro study aimed to compare the fracture resistance of simulated immature permanent teeth restored with apical plugs of mineral trioxide aggregate (MTA), Biodentine, and bone cement. Methods: Forty-eight single-rooted human maxillary central incisors were selected and decoronated 6 mm above and 9 mm below the cementoenamel junction to simulate the immature teeth. Based on weight and homogeneity, the samples were distributed into three experimental groups (n = 12) and one control group (n = 12). In all the experimental group samples, a peeso reamer size 5 was stepped out 1 mm beyond the apex to enlarge the apices to a diameter of 1.5 mm. Apical plugs of MTA Plus (Prevest DenPro Limited, India), Biodentine (Septodont, France), and Bone cement (Surgical Simplex P, Stryker, Australia) were placed to 4 mm, and obturation was done using gutta-percha and AH Plus® sealer (Dentsply DeTrey, Konstanz, Germany). The force was applied at 45° angulation until fracture, using the universal testing machine. The results were analyzed using a one-way analysis of variance followed by Tukey's post hoc test at a 95% confidence level. Results: The Biodentine group showed a statistically higher fracture resistance value than the MTA Plus and bone cement group (P = 0.014 and P = 0.016, respectively). No statistically significant difference was reported between MTA Plus and the bone cement group. Conclusion: Within the limitations of this study, using Biodentine as an apical plug increases the fracture resistance of immature teeth. Bone cement can be used as a viable alternative to MTA