Polarity Classification of Twitter Data using Sentiment Analysis

Crowd source information is of vital importance these days, since we relay much on information available from internet. Thus, Sentiment analysis or opinion mining becomes one of the major tasks of NLP (Natural Language Processing) and has gained much attention in recent years. Sentiment analysis is the process of computationally identifying and categorizing opinions expressed in a piece of text, specifically to determine whether the user’s attitude towards a specific area or product in case of ecommerce, etc. is positive, negative, or neutral. Sentiment analysis application are broad and powerful. It can be helpful in many ways like it helps marketers to evaluate the success of an ad campaign, in new product launch, to determine which versions of a product or service are popular and it also identifies which demographics like or dislike product features. This paper evaluates two classifiers, one is linear and other is probabilistic for sentiment polarity categorization. Data used in this study are the tweets collected from twitter.com. We further represent a comparative study of three different algorithms, Naïve Bayes, SVM (Support Vector Machines), and Logistic regression and how they vary on the same data set

Cosmological constant as Hyper Geometric function in fivedimensional space-time

In this paper, we have studied the Einstein Field equation in Kaluza-Klein space-time in five dimension with the metric dS^2 = dt^2 − A^2(dx^2 + dy^2 + dz^2)− B^2dψ^2 under the assumption that B = αt, where α = Constant and scale factor satisfying the relation R^4 = A^3B with perfect fluid having energy momentum tensor Tij = (ρ + p)vivj − pgij In this paper, we have assumed that G = 1/t and we have found the value of cosmological constant Λ is a function time t in terms of hyper geometric function

Synthesis of quinazolinones derivatives an antiproliferative agent against human lung carcinoma cells

2,3‐Dihydroquinazolin‐4(1H)‐one derivatives (3a-p) were synthesized in excellent yields. These compounds were screened for antiproliferative activity against A549 cells and were found as potent cytotoxicity. Compounds A4, A8, A10 found to be more promising antiproliferative against the lung carcinoma A549 cells. IC50 values for compounds A4, A8 and A10 were found to be 8.6, 8.9 and 8.1 μg/L against A549 cells, respectively

Design and Implementation of Deep Learning Method for Disease Identification in Plant Leaf

In the whole agriculture plays a very important in country’s economic condition specially in Indian agriculture has a crucial role for raising the Indian economic structure and its level. India’s frequent changing climatic situation, various bacterial disease is much normal that drastically decreases the productivity of crop productivity. Most of the researcher is moving towards into this topic to find the early detection technique to identify the disease in small green leaves plants. A single, micro bacterial infectious disease can destroy all the agricultural small green leaves plants get damaged overnight and hence must be prevented and cured as earliest as possible so that agriculture production. In this research work, we had tried to developed a green small green leaves plants bacterial disease early detection system based on the deep learning network system which will detect the disease at very earlier state of symptoms observed. Deep learning technique is has various algorithms to detect the earliest stage of any of the procedural processing of any bacterial infections or disease. This paper consists of investigations and analysis of latest deep learning techniques. Initially we will explore the deep learning architecture, its various source of data and different types of image processing method that can be used for processing the images captured of leaf for data processing. Different DL architectures with various data visualization’s tools has recently developed to determine symptoms and classifications of different type of plant-based disease. We had observed some issue that was un identified in previous research work during our literature survey and their technique to resolve that issue in order to handle the functional auto-detection system for identifying the certain plant disease in the field where massive growth of green small green leaves plants production is mostly done. Recently various enhancement has been done in techniques in CNN (convolution neural network) that generates much accurate images classification of any object. Our research work is based on deep learning network that will observe and identifies the symptoms generated in leaflet of plant and identifies the type of bacterial infection in progress in that with the help of plant classification stated in the plant dataset. Our research work represents the implementation DCGAN and Hybrid Net Model using Deep learning algorithm for early-stage identification of green plant leaves disease in various environmental condition. Our result obtained shows that it has DCGAN accuracy 96.90% when compared withHybrid Net model disease detection methodologies

A unique influenza A (H5N1) virus causing a focal poultry outbreak in 2007 in Manipur, India

<p>Abstract</p> <p>Background</p> <p>A focal H5N1 outbreak in poultry was reported from Manipur, a north-eastern state, of India, in 2007. The aim of this study was to genetically characterize the Manipur isolate to understand the relationship with other H5N1 isolates and to trace the possible source of introduction of the virus into the country.</p> <p>Results</p> <p>Characterization of the complete genome revealed that the virus belonged to clade 2.2. It was distinctly different from viruses of the three EMA sublineages of clade 2.2 but related to isolates from wild migratory waterfowl from Russia, China and Mongolia. The HA gene, had the cleavage site GERRRRKR, earlier reported in whooper swan isolates from Mongolia in 2005. A stop codon at position 29 in the PB1-F2 protein could have implications on the replication efficiency. The acquisition of polymorphisms as seen in recent isolates of 2005–07 from distinct geographical regions suggests the possibility of transportation of H5N1 viruses through migratory birds.</p> <p>Conclusion</p> <p>Considering that all eight genes of the earlier Indian isolates belonged to the EMA3 sublineage and similar strains have not been reported from neighbouring countries of the subcontinent, it appears that the virus may have been introduced independently.</p

Simultaneous Determination of Rofecoxib and Tizanidine by HPTLC

Abstract: An innovative high performance thin layer chromatography method was developed and validated for simultaneous determination of rofecoxib and tizanidine from tablet dosage form. Rosiglitazone maleate was used as an internal standard. The separation was achieved using HPTLC plates (Merck #5548) precoated with silica gel 60F 254 on aluminum sheets and a mobile phase comprising of toluene: ethyl acetate: methanol: triethyl amine in volume ratio of 6:3:0.5:0.1 (v/v/v/v), with chamber saturation of 15 min. The plate was developed up to 8 cm and air dried. The plate was then scanned and quantified at 235 nm. The linearity of rofecoxib and tizanidine were in the range of 3.75 µg/spot to 11.25 µg/spot and 0.30 µg/spot to 0.90 µg/spot respectively. The limit of detection for rofecoxib and tizanidine was found to be 45.00 ng/spot and 30.00 ng/spot respectively. The limit of quantification for rofecoxib and tizanidine was found to be 135.00 ng/spot and 90.00 ng/spot respectively. The percentage assay was found between the range of 99.58% to 103.21% for rofecoxib and 98.73% to 101.55% for tizanidine respectively, whereas recovery was found between 99.97% to 100.43% for rofecoxib and 100.00% to 101.00% for tizanidine by standard addition method. The proposed method is accurate, precise and rapid for the simultaneous determination of rofecoxib and tizanidine in dosage form

An avian influenza A(H11N1) virus from a wild aquatic bird revealing a unique Eurasian-American genetic reassortment

Influenza surveillance in different wild bird populations is critical for understanding the persistence, transmission and evolution of these viruses. Avian influenza (AI) surveillance was undertaken in wild migratory and resident birds during the period 2007–2008, in view of the outbreaks of highly pathogenic AI (HPAI) H5N1 in poultry in India since 2006. In this study, we present the whole genome sequence data along with the genetic and virological characterization of an Influenza A(H11N1) virus isolated from wild aquatic bird for the first time from India. The virus was low pathogenicity and phylogenetic analysis revealed that it was distinct from reported H11N1 viruses. The hemagglutinin (HA) gene showed maximum similarity with A/semipalmatedsandpiper/Delaware/2109/2000 (H11N6) and A/shorebird/Delaware/236/2003(H11N9) while the neuraminidase (NA) gene showed maximum similarity with A/duck/Mongolia/540/2001(H1N1). The virus thus possessed an HA gene of the American lineage. The NA and other six genes were of the Eurasian lineage and showed closer relatedness to non-H11 viruses. Such a genetic reassortment is unique and interesting, though the pathways leading to its emergence and its future persistence in the avian reservoir is yet to be fully established

Ensuring nutritional security in India through wheat biofortification: A review

Undernourishment of nutrients, also known as hidden hunger, affects over 2 billion populace globally. Even though stunting among children below five years of age has decreased in India in the last ten years, India is home to roughly thirty percent of the world's population of stunted pre-schoolers. A significant improvement has been witnessed in the targeted development and deployment of biofortified crops; approximately 20 million farm households from developing counties benefit from cultivating and consuming biofortified crops. There is ample scope for including biofortified varieties in the seed chain, ensuring nutritional security. Wheat is a dietary staple in India, typically consumed as wholemeal flour in the form of flatbreads such as chapatti and roti. Wheat contributes to nearly one fifth of global energy requirements and can also provide better amounts of iron (Fe) and zinc (Zn). As a result, biofortified wheat can serve as a medium for delivery of essential micronutrients such as Fe and Zn to end users. This review discusses wheat biofortification components such as Fe and Zn dynamics, its uptake and movement in plants, the genetics of their buildup, and the inclusion of biofortified wheat varieties in the seed multiplication chain concerning India

Characterization of the Influenza A H5N1 Viruses of the 2008-09 Outbreaks in India Reveals a Third Introduction and Possible Endemicity

Widespread infection of highly pathogenic avian influenza A H5N1 was reported from backyard and commercial poultry in West Bengal (WB), an eastern state of India in early 2008. Infection gradually spread to Tripura, Assam and Sikkim, the northeastern states, with 70 outbreaks reported between January 2008 and May 2009. Whole genome sequence analysis of three isolates from WB, one isolate from Tripura along with the analysis of hemagglutinin (HA) and neuraminidase (NA) genes of 17 other isolates was performed during this study. In the HA gene phylogenetic tree, all the 2008-09 Indian isolates belonged to EMA3 sublineage of clade 2.2. The closest phylogenetic relationship was found to be with the 2007-09 isolates from Bangladesh and not with the earlier 2006 and 2007 Indian isolates implying a third introduction into the country. The receptor-binding pocket of HA1 of two isolates from WB showed S221P mutation, one of the markers predicted to be associated with human receptor specificity. Two substitutions E119A (2 isolates of WB) and N294S (2 other isolates of WB) known to confer resistance to NA inhibitors were observed in the active site of neuraminidase. Several additional mutations were observed within the 2008-09 Indian isolates indicating genetic diversification. Overall, the study is indicative of a possible endemicity in the eastern and northeastern parts of the country, demanding active surveillance specifically in view of the critical mutations that have been observed in the influenza A H5N1 viruses