Crop Coverage Data Classification using Support Vector Machine

A statistical tool which can be used in various applications ranging from medical science to agricultural science is support vector machines. The proposed methodology used is support vector machine and it isused to classify a raster map. The dataset used herein is of Gujarat state agriculture map. The proposed approach is used to classify raster map into groups based on crop coverage of various crops. One group represents rice crop coverageand the othermillets crop coverage and yet another that of cotton crop coverage.Various statistical parameters are used to measure the efficacy of the proposed methodology employed

A Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting

This paper presents an approach to classify remote sensed data using a hybrid classifier. Random forest, Support Vector machines and boosting methods are used to build the said hybrid classifier. The central idea is to subdivide the input data set into smaller subsets and classify individual subsets. The individual subset classification is done using support vector machines classifier. Boosting is used at each subset to evaluate the learning by using a weight factor for every data item in the data set. The weight factor is updated based on classification accuracy. Later the final outcome for the complete data set is computed by implementing a majority voting mechanism to the individual subset classification outcomes

Supervised Classification of Remote Sensed Data using Support Vector Machine

Support vector machines have been used as a classification method in various domains including and not restricted to species distribution and land cover detection Support vector machines offer many key advantages like its capacity to handle huge feature spaces and its flexibility in selecting a similarity function In this paper the support vector machine classification method is applied to remote sensed data Two different formats of remote sensed data is considered for the same The first format is a comma separated value format wherein a classification model is developed to predict whether a specific bird species belongs to Darjeeling area or any other region The second format used is raster format which contains image of Andhra Pradesh state in India Support vector machine classification method is used herein to classify the raster image into categories One category represents land and the other water wherein green color is used to represent land and light blue color is used to represent water Later the classifier is evaluated using kappa statistics and accuracy parameter

Data-driven classification of low-power communication signals by an unauthenticated user using a software-defined radio

Many large-scale distributed multi-agent systems exchange information over low-power communication networks. In particular, agents intermittently communicate state and control signals in robotic network applications, often with limited power over an unlicensed spectrum, prone to eavesdropping and denial-of-service attacks. In this paper, we argue that a widely popular low-power communication protocol known as LoRa is vulnerable to denial-of-service attacks by an unauthenticated attacker if it can successfully identify a target signal's bandwidth and spreading factor. Leveraging a structural pattern in the LoRa signal's instantaneous frequency representation, we relate the problem of jointly inferring the two unknown parameters to a classification problem, which can be efficiently implemented using neural networks.Comment: Accepted for presentation at Asilomar Conference on Signals, Systems, and Computers, 202

A case report of Stevens-Johnson syndrome and toxic epidermal necrolysis due to diclofenac sodium

Stevens-Johnson syndrome (SJS) is a severe skin reaction most often triggered by particular drugs in most of the cases. A more severe form of the condition is called toxic epidermal necrolysis (TEN) which involves more than 30% of the skin surface and extensive damage to the mucous membranes. SJS and TEN previously were thought to be separate conditions, but they are now considered part of a disease spectrum. The main drugs which induce SJS were anti-gout drugs, anti-epileptics, analgesics, NSAIDs and antibiotics. Diclofenac which is a NSAID and phenyl acetic acid derivative that rarely causes SJS. Although diclofenac induced Stevens-Johnson syndrome is reported very rare among adults, it shouldn’t be neglected. In this report we mentioned about the Stevens-Johnson syndrome (SJS) which was later developed into TEN due to usage diclofenac sodium, in a 65 years old female patient

Crop Coverage Data Classification using Support Vector Machine

A statistical tool which can be used in various applications ranging from medical science to agricultural science is support vector machines. The proposed methodology used is support vector machine and it isused to classify a raster map. The dataset used herein is of Gujarat state agriculture map. The proposed approach is used to classify raster map into groups based on crop coverage of various crops. One group represents rice crop coverageand the othermillets crop coverage and yet another that of cotton crop coverage.Various statistical parameters are used to measure the efficacy of the proposed methodology employed

A Hybrid Random Forest based Support Vector Machine Classification Supplemented by Boosting

This paper presents an approach to classify remote sensed data using a hybrid classifier. Random forest, Support Vector machines and boosting methods are used to build the said hybrid classifier. The central idea is to subdivide the input data set into smaller subsets and classify individual subsets. The individual subset classification is done using support vector machines classifier. Boosting is used at each subset to evaluate the learning by using a weight factor for every data item in the data set. The weight factor is updated based on classification accuracy. Later the final outcome for the complete data set is computed by implementing a majority voting mechanism to the individual subset classification outcomes

FORMULATION AND EVALUATION OF TAFLUPROST OPHTHALMIC SOLUTION

The aim of the present study was to formulate, develop and evaluate ophthalmic solution containing Tafluprost. The selected prostaglandin analogue belongs to BCS - II. So to increase the solubility of the Tafluprost in WFI Beta Cyclodextrin was used by performing various trials variables of the experiments procedure Stirring time and speed were optimized to enhance the solubility. From the experimental procedure with different trails 20mg/mL of Cyclodextrins was fixed for the optimized formula. The product were characterized for appearance, physical state, colour and odour of the drug characteristics. The prepared formulations were evaluated for pH, osmolality and assay and found to be in acceptable ranges. Stability study was carried out for optimized formulation at 40°C±2°C /NMT 25%RH and 30°C ±2°C/ 65%±5% RH for 3 months, were evaluated for pH ,osmolality ,assay found to be within  acceptable limits. Finally it can be concluded that the in house product Tafluprost ophthalmic solution was met with in the specification

Tectono-Thermal History of the Neoarchean Balehonnur Shear Zone, Western Dharwar Craton (Southern India)

AbstractA widely spaced Neoarchean shear zone network traverses the granite-greenstone terrains of the Western Dharwar craton (WDC). The NNW-SSE trending Balehonnur shear zone traverses the largest part of the preserved tilted Archean crustal ensemble in the Western Dharwar craton (WDC) from the amphibolite-granulite transition in the south to greenschist facies in the north and eventually concealed under Deccan lava flows. Published tectonic fabrics data and kinematic analysis, with our data reveal a sinistral sense of shearing that effectuate greenstone sequences, Tonalite-Trondhjemite-Granodiorite Gneisses (TTG), and Koppa granite as reflected in variable deformation and strain localization. A profound increase of strain towards the core of the shear zone in the ca. 2610 Ma Koppa granite is marked by a transition from weak foliation outside the shear zone through the development of C-S structures and C-prime fabrics, mylonite to ultramylonite. The mineral assemblages in the Koppa granite and adjoining greenstone indicate near peak P-T conditions of 1.2 Gpa, 775-800°C following a slow cooling path of 1.0 GPa and 650°C. Field-based tectonic fabrics data together with U-Pb zircon ages reveal that the Koppa granite emplaced along the contact zone of Shimoga-Bababudan basin ca. 2610 Ma, coinciding with the emplacement of ca. 2600 Ma Arsikere-Banavara, Pandavpura, and Chitradurga granites further east which mark the stabilization of WDC. Significant variation in major element oxide (SiO2 = 56-69 wt.%) together with high content of incompatible elements (REE, Nb, Zr, and Y) and high zircon crystallization temperatures (~1000°C) of Koppa granite suggests derivation by partial melting of composite sources involving enriched uppermost mantle and lower crust. The development of widely spaced shear zones is probably linked to the assembly of eastern and western blocks through westward convergence of hot oceanic lithosphere against already cratonized thick colder western block leading to the development of strain heterogeneities between greenstone and TTGs due to their different mineral assemblages leading to rheological contrast in the cratonic lithologies