Enhanced Document Clustering using K-Means with Support Vector Machine (SVM) Approach

Today’s digital world consists of a large amount of data. Volume of data in the digital world is increasing continuously. Dealing with such important, complex and unstructured data is important. These files consist of data in unstructured text, whose analysis by computer examiners is difficult to be performed. In forensic analysis, experts have to spend a lot of time as well as efforts, to identify criminals and related evidence for investigation. However crime investigation process needs to be faster and efficient. As large amount of information is collected during crime investigation, data mining is an approach which can be useful in this perspective. Data mining is a process that extracts useful information from large amount of crime data so that possible suspects of the crime can be identified efficiently. Algorithms for clustering documents can provide the learning of knowledge from the documents under analysis. This can be done by applying different clustering algorithms to different datasets. Clustering algorithms indeed tends to induce clusters formed by either relevant or irrelevant documents, further extending work by using Clustering Technique Cascaded with Support Vector Machine, thus contributing to enhance the experts job and investigation process can be speed up. DOI: 10.17762/ijritcc2321-8169.150612

Pattern of oral cancer registered at a tertiary care teaching hospital in rural Western Maharashtra

Non-communicable diseases including cancer are emerging as major public health problems in India.Cancer usually means malignancy, has become one of the ten leading cause of death in India. The leading sites of cancer vary from country to country. Oral cancer ranks in the top three of all cancers in India, accounting for over 30% of all cancers reported in the country and its control is quickly becoming a global health priority. The present study was conducted to find out the contribution of different type of oral cancer in a tertiary care teaching hospital of western Maharashtra, India. A retrospective hospital record based study was carried out for the period of 2007-2011 in the department of Radiotherapy of Pravara Rural Hospital, Loni, Maharashtra, India. A total of 5879 patients who were diagnosed with cancer, of them 633 (10.76%) patients had oral cancer. Data was collected on the basis of the patient’s record in the hospital and analyzed in the form of percentage and proportions whenever appropriate. A total of 633 oral cancer patients were screened, of which 411 (64.93%) were males and 222 (35.07%) were females. Among oral cancer, buccal mucosa was highest (37.12%); followed by tongue (36.80%), oropharynx (4.74%) and lip and palate (3.15%). Oral cancer is one of the common malignancies in developing countries like India. It is common in males compared to females and is usually seen after middle age

Secure Mobile Based E-Voting System

The E-Voting means the voting process in election by using electronic device. In this proposed system described how the android mobile phones are efficient and can be used for voting. The android platform is used to develop an application. Our system support simultaneous voting due to the distributed nature of the database. During election electronic device is used for voting process. A voter may only need to register only once for a particular election and that does all, voter need to cast his /her vote without actually have to present at the voting cell. The registration process must be done at Booth application for once then voter is been given a facility to vote from his/her Android mobile phone irrespective of his/her location. This proposed system suppose to propose a new e-voting system, which ensures voter confidentiality and voting accuracy, thus providing an important framework that based on unique identification ADHAAR ID (U-ID) number. An online solution is very useful as the information about the voters and the election committee is also made available to the people in this system

Survey on Dynamic Query Forms for Database Queries

The databases used in today’s scientific research and web handle huge volumes of data. Such databases contain hundreds or even thousands of complex relations and attributes. The proposed system that implements dynamic query forms for non-relational data. The DQF captures a user’s preference and ranks query form components which assists the user in making decisions. Query form generation is an iterative process and initially requires user guidance in the form of feedback .The system automatically generates ranking list of form components, at each iteration and the desired form components are added by the user in Query forms. The ranking of form components depend on the captured user preference. The query results can be viewed at each iteration by the user after filling and submitting the query form. In order to measure the quality of the results generated by the Query form, a probabilistic model has been developed

Simultaneous estimation of simvastatin and labetalol in bulk and solid dosage form

A simple, accurate, precise, sensitive, and highly selective ultra violet spectrometer method has been developed for the simultaneous estimation of simvastatin and labetalol in bulk and solid dosage form. The estimation of simvastatin was carried out at 239 nm while labetalol was estimated at 222.4 nm. The developed method was validated for linearity range, precision, recovery studies and interference study for mixture, all these parameter showed the adaptability of the method for the method quality analysis of the drug in bulk and combination formulation. Keywords: Simvastatin, Labetalol, UV Spectrophotometric, Dosage form

Mapping cropland extent and areas of Australia at 30-m resolution using multi-year time-series Landsat data and Random Forest machine learning algorithm through Google Earth Engine (GEE) Cloud Computing

Mapping accurate, precise, and consistent cropland products is crucial for global food security analysis. Mapping croplands, including fallow areas, is an important measure to determine the quantity of food that is produced, where it is produced, and when it is produced (which season). Satellite based earth observation provides the best opportunity for globally consistent, spatially explicit, cost effective, objective, and efficient way to map croplands at various spatial resolutions. However, mapping cropland extent at finer (e.g., 30-m or finer) spatial resolution over very large areas such as continental, and global extent is challenging. This study developed a precise Landsat 30-m cropland extent map and calculated cropland areas of the Australian continent for the nominal year 2015 using a random forest (RF) machine learning algorithm (MLA) through Google Earth Engine (GEE) cloud computing platform. The process involved the use of 8 bands (blue, green, red, NIR, SWIR1, temp, NDVI and NDWI) of Landsat-8 every 16-day data for the years 2014 and 2015. Each band was a composited over 2-4 time-period using mean value compositing. Overall, there was a 48-layer data-cube on which we generated knowledge of croplands versus non-croplands, coded the knowledge into RF MLA and run on the GEE cloud to obtain cropland extent for all of Australia. An external independent evaluation team conducted an accuracy assessment using an independent validation data set collected from field survey and sub-meter to 5-m very high spatial resolution imagery. Results showed an overall accuracy of 97.56% with high producer’s accuracy of 98.7% and user’s accuracy of 89.0% for the cropland class. The study also determined how the cropland areas change with spatial resolution of imagery at 30-m, 250-m, and 1-km. We established that cropland location precision and map accuracies were significantly higher for 30-m. We also established that areas determined using 30-m were much more precise and reliable

NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 30 m V001

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Australia, New Zealand, China, and Mongolia for nominal year 2015 at 30 meter resolution (GFSAD30AUNZCNMOCE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30AUNZCNMOCE data product uses the pixel-based supervised classifier Random Forest (RF) to retrieve cropland extent from a combination of Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Each GFSAD30AUNZCNMOCE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10⁰ by 10⁰ area

NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security Support Analysis Data (GFSAD) Crop Mask 2010 Global 1 km V001

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security Support Analysis Data (GFSAD) Crop Mask Global 1 kilometer (km) dataset was created using multiple input data including: remote sensing such as Landsat, Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation and Moderate Resolution Imaging Spectrometer (MODIS); secondary elevation data; climate 50-year precipitation and 20-year temperature data; reference sub-meter to 5-meter resolution ground data and country statistics data. The GFSAD1KCM provides spatial distribution of a disaggregated five class global cropland extent map derived for nominal 2010 at 1-km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). The GFSAD1KCM nominal 2010 product is based on data ranging from years 2007 through 2012

Global Cropland Area Database (GCAD) derived from Remote Sensing in Support of Food Security in the Twenty-first Century: Current Achievements and Future Possibilities

The precise estimation of the global agricultural cropland- extents, areas, geographic locations, crop types, cropping intensities, and their watering methods (irrigated or rainfed; type of irrigation) provides a critical scientific basis for the development of water and food security policies (Thenkabail et al., 2012, 2011, 2010). By year 2100, the global human population is expected to grow to 10.4 billion under median fertility variants or higher under constant or higher fertility variants (Table 1) with over three quarters living in developing countries, in regions that already lack the capacity to produce enough food. With current agricultural practices, the increased demand for food and nutrition would require in about 2 billion hectares of additional cropland, about twice the equivalent to the land area of the United States, and lead to significant increases in greenhouse gas productions (Tillman et al., 2011). For example, during 1960-2010 world population more than doubled from 3 billion to 7 billion. The nutritional demand of the population also grew swiftly during this period from an average of about 2000 calories per day per person in 1960 to nearly 3000 calories per day per person in 2010..

The Genome of the Stick Insect Medauroidea extradentata Is Strongly Methylated within Genes and Repetitive DNA

BACKGROUND: Cytosine DNA methylation has been detected in many eukaryotic organisms and has been shown to play an important role in development and disease of vertebrates including humans. Molecularly, DNA methylation appears to be involved in the suppression of initiation or of elongation of transcription. Resulting organismal functions are suggested to be the regulation of gene silencing, the suppression of transposon activity and the suppression of initiation of transcription within genes. However, some data concerning the distribution of methylcytosine in insect species appear to contradict such roles. PRINCIPAL FINDINGS: By comparison of MspI and HpaII restriction patterns in genomic DNA of several insects we show that stick insects (Phasmatodea) have highly methylated genomes. We isolated methylated DNA fragments from the Vietnamese Walking Stick Medauroidea extradentata (formerly known as Baculum extradentatum) and demonstrated that most of the corresponding sequences are repetitive. Bisulfite sequencing of one of these fragments and of parts of conserved protein-coding genes revealed a methylcytosine content of 12.6%, mostly found at CpG, but also at CpT and CpA dinucleotides. Corresponding depletions of CpG and enrichments of TpG and CpA dinucleotides in some highly conserved protein-coding genes of Medauroidea reach a similar degree as in vertebrates and show that CpG methylation has occurred in the germline of these insects. CONCLUSIONS: Using four different methods, we demonstrate that the genome of Medauroidea extradentata is strongly methylated. Both repetitive DNA and coding genes appear to contain high levels of methylcytosines. These results argue for similar functions of DNA methylation in stick insects as those already known for vertebrates