Automatic Knowledge Extraction from OCR Documents Using Hierarchical Document Analysis

Industries can improve their business efficiency by analyzing and extracting relevant knowledge from large numbers of documents. Knowledge extraction manually from large volume of documents is labor intensive, unscalable and challenging. Consequently, there have been a number of attempts to develop intelligent systems to automatically extract relevant knowledge from OCR documents. Moreover, the automatic system can improve the capability of search engine by providing application-specific domain knowledge. However, extracting the efficient information from OCR documents is challenging due to highly unstructured format. In this paper, we propose an efficient framework for a knowledge extraction system that takes keywords based queries and automatically extracts their most relevant knowledge from OCR documents by using text mining techniques. The framework can provide relevance ranking of knowledge to a given query. We tested the proposed framework on corpus of documents at GE Power where document consists of more than hundred pages in PDF

Carbon Dynamics in Rewetted Tropical Peat Swamp Forests

Degraded and drained peat swamp forests (PSFs) are major sources of carbon emissions in the forestry sector. Rewetting interventions aim to reduce carbon loss and to enhance the carbon stock. However, studies of rewetting interventions in tropical PSFs are still limited. This study examined the effect of rewetting interventions on carbon dynamics at a rewetted site and an undrained site. We measured aboveground carbon (AGC), belowground carbon (BGC), litterfall, heterotrophic components of soil respiration (Rh), methane emissions (CH4), and dissolved organic carbon (DOC) concentration at both sites. We found that the total carbon stock at the rewetted site was slightly lower than at the undrained site (1886.73 ± 87.69 and 2106.23 ± 214.33 Mg C ha−1, respectively). The soil organic carbon (SOC) was 1685 ± 61 Mg C ha−1 and 1912 ± 190 Mg C ha−1 at the rewetted and undrained sites, respectively, and the carbon from litterfall was 4.68 ± 0.30 and 3.92 ± 0.34 Mg C ha−1 year−1, respectively. The annual average Rh was 4.06 ± 0.02 Mg C ha−1 year−1 at the rewetted site and was 3.96 ± 0.16 Mg C ha−1 year−1 at the undrained site. In contrast, the annual average CH4 emissions were −0.0015 ± 0.00 Mg C ha−1 year−1 at the rewetted site and 0.056 ± 0.000 Mg C ha−1 year−1 at the undrained site. In the rewetted condition, carbon from litter may become stable over a longer period. Consequently, carbon loss and gain mainly depend on the magnitude of peat decomposition (Rh) and CH4 emissions

Pengujian Aplikasi Sistem Informasi Akademik Berbasis Website Menggunakan Teknik Equivalence Partitioning dan Metode Black Box

This academic information system still has shortcomings in the data validation process, which will cause the data stored in the database not to match the desired data. Then it is proposed to test using the Black Box method with Equivalence Partitioning Techniques as a whole regarding the use, benefits, and results obtained from using the software. Black-box testing is a very important testing technique because it can identify errors in functions, interfaces, sample data, and access to external data sources. in implementation there are sometimes problems with testers who are not sure whether the software actually passes the test. in this implementation, the software that will be tested using black box testing is a website-based academic information system. Equivalence Partitioning Techniques discusses testing in the validation aspect of input data in terms of valid classes. From this research, it can be concluded that testing the level of accuracy of academic issues system software can provide the right solution for the school