SENTIMENT ANALYSIS FOR SEARCH ENGINE

The chief purpose of this study is to detect and eliminate the sentiment bias in a search engine. Sentiment bias means a bias induced in the search results based on the sentiment of the user’s search query. As people increasing depend on search engines for information, it is important to understand the quality of results produced by the search engines. This study does not try to build a search engine but leverage the existing search engines to provide better results to the user. In this study, only the queries that have high sentiment polarity are analyzed and the machine learning models are used to predict the sentiment polarity of the input query, sentiment polarity of the documents produced by the search engine for the given query and also to change the sentiment polarity of the input query to its opposite sentiment. This project proposes an end-to-end system that eliminates the search engine bias by producing results that align with the query sentiment as well as the opposite sentiment. The system comprising of three models for document level sentiment analysis, aspect level sentiment analysis and sentiment style transfer. The document level sentiment analyzer is an LSTM based model that uses GloVe word embeddings to analyze the sentiment of the documents produced by the search engine. The aspect level sentiment analyzer uses deep memory network with attention and auxiliary memory to analyze the sentiment of each search query. In order to obtain the iv documents of the opposite polarity, the sentiment of the search query is reversed using the sentiment style transfer model that uses a bi-directional LSTM. The results are analyzed to determine the sentiment bias of the search engine based on the input query. In our experiments, we observed that positive sentiment queries yielded 67% documents with positive sentiment and negative sentiment queries yielded 70% documents with negative sentiment. The proposed system eliminates this bias by providing the users with two sets of result, one with positive sentiment and one with negative sentiment

A study of hough transform for weld extraction

The process of joining metals is called welding. At times, selecting a poor quality material or improper usage of welding technologies may cause defects in welded joints. Some of these welded joints have to be tested nondestructively, because their failure can cause lot of damage, for instance in power plants. Radiography is a very common method for non-destructive testing of welds. It is done by certified weld inspectors who have knowledge about weld flaws, looking at the radiograph of the welded joint with naked eye. The judgment of the weld inspector can be biased; subjective, because it is dependent on his/her experience. This manual method can also become very time consuming. Many researches were exploring computer aided examination of radiographic images in early 1990’s. With much advancement in computer vision and image processing technologies, they are being used to find more effective ways of automatic weld inspection. These days, fuzzy based methods are being widely used in this area too. The first step in automatic weld inspection is to locate the welds or find a Region of Interest (ROI) in the radiographic image [7]. In this thesis, a Standard Hough Transform (SHT) based methodology is developed for weld extraction. Firstly, we have done binarization of image to remove the background and non-welds. For binarization, optimal binary threshold is found by a metaheuristic –Simulated annealing. Secondly, we use SHT to generate the Hough Transform matrix of all non-zero points in the binary image. Thirdly, we have explored two different paths to find a meaningful set of lines in the binarized image that are welds. Finally, these lines are verified as weld using a weld-peak detection procedure. Weld-peak detection is also helpful to remove any non-welds that were remaining. We have used 25 digitized radiographic images containing 100 welds to test the method in terms of true detection and false alarm rate

The boundary element method applied to static and dynamic crack problems using hypersingular boundary integral equations

The need for hypersingular boundary integral equations in crack problems is motivated through acoustic and elastic wave scattering from a thin screen and crack. By integrating over a small (not necessarily symmetric) neighborhood about the singular point, and the rest of the boundary, and identifying terms from the integrals over the two surfaces which cancel each other, the finite-part of the hypersingular integral is defined for curved surfaces in both two and three dimensions. Stokes\u27 theorem is used to regularize the hypersingular integrals to a form conducive to simple numerical integration techniques. With no prior assumptions on the discretizasion or integration by parts, this method results in integrals which are at most weakly singular. The equivalence of this approach to the finite-part of the hypersingular integral is established;The necessary condition on the density function for the hypersingular integral equation to have meaning and the consequences on the solution of not satisfying the necessary conditions is discussed. This new formulation places restrictions on the choice of shape functions and the possible location of the collocation points within elements due to the smoothness requirement on the density function. Such restrictions for regular boundary integral equations with Cauchy principal value integrals are also discussed. The different kinds of integrals encountered in a hypersingular boundary integral equation such as weakly singular integrals, nearly singular integrals and regular area and line integrals are studied. Discretization considerations for precise and efficient numerical computation of these integrals in the context of the boundary element method is established and the influence of discretization on the solution is highlighted through numerical examples. Examples are chosen from problems of acoustic and elastic wave scattering from thin screens and cracks in three dimensions

Linalool prevents oxidative stress activated protein kinases in single UVB-exposed human skin cells

Ultraviolet-B radiation (285–320 nm) elicits a number of cellular signaling elements. We investigated the preventive effect of linalool, a natural monoterpene, against UVB-induced oxidative imbalance, activation of mitogen-activated protein kinase (MAPK) and nuclear factor kappa-B (NF-κB) signaling in HDFa cells. We observed that linalool treatment (30 μM) prevented acute UVB-irradiation (20 mJ/cm 2) mediated loss of activities of antioxidant enzymes in HDFa cells. The comet assay results illustrate that linalool significantly prevents UVB-mediated 8-deoxy guanosine formation (oxidative DNA damage) rather than UVB-induced cyclobutane pyrimidine (CPD) formation. This might be due to its ability to prevent UVB-induced ROS formation and to restore the oxidative imbalance of cells. This has been reflected in UVB-induced overexpression of MAPK and NF-κB signaling. We observed that linalool inhibited UVB-induced phosphorylation of ERK1, JNK and p38 proteins of MAPK family. Linalool inhibited UVB-induced activation of NF-κB/p65 by activating IκBa. We further observed that UVB-induced expression of TNF-α, IL6, IL-10, MMP-2 and MMP-9 was modulated by linalool treatment in HDFa cells. Thus, linalool protects the human skin cells from the oxidative damages of UVB radiation and modulates MAPK and NF-κB signaling in HDFa cells. The present findings substantiate that linalool may act as a photoprotective agent against UVB-induced skin damages

PROTECTIVE ROLE OF PTEROSTILBENE ON PLASMA AND TISSUE GLYCOPROTEIN COMPONENTS IN HIGH-FAT DIET-FED AND STREPTOZOTOCIN-INDUCED TYPE 2 DIABETIC MICE

Objective: The aim of the current study is to evaluate the effect of pterostilbene (PTS), on deranged plasma and tissue glycoprotein components in high-fat-diet (HFD) and streptozotocin (STZ)-induced type 2 diabetic mice. Methods: The experimental duration was 16 weeks. The C57BL6/J mice were fed a normal diet, normal diet with PTS, HFD, and STZ injection (10th week only), and diabetic mice with PTS for the past 6 weeks. Results: A significant increase in glycoprotein components such as hexose, hexosamine, fucose, and sialic acid (SA) in plasma was observed in diabetic mice. In hepatic and renal tissues, a significant decrease in SA with an increase in other glycoprotein components was detected in diabetic mice when compared with control mice. Oral administration of PTS significantly improved the glycoprotein levels in plasma and tissues of diabetic mice to near normal level. Conclusion: In this study, we resolved that PTS improves disturbed glycoprotein metabolism in HFD and STZ-induced type 2 diabetic mice

An Effcient Algorithm for Mining Association Rules In Massive Datasets

Data mining, also known as Knowledge Discovery in Databases (KDD) is one of the most important and interesting research areas in 21st century. Frequent pattern discovery is one of the important techniques in data mining. The application includes Medicine, Telecommunications and World Wide Web. Nowadays frequent pattern discovery research focuses on finding co-occurrence relationships between items. Apriori algorithm is a classical algorithm for association rule mining. Lots of algorithms for mining association rules and their mutations are proposed on the basis of Apriori algorithm. Most of the previous algorithms Apriori-like algorithm which generates candidates and improving algorithm strategy and structure but at the same time many of the researchers not concentrate on the structure of database. In this research paper, it has been proposed an improved algorithm for mining frequent patterns in large datasets using transposition of the database with minor modification of the Apriori-like algorithm. The main advantage of the proposed method is the database stores in transposed form and in each iteration database is filtered and reduced by generating the transaction id for each pattern. The proposed method reduces the huge computing time and also decreases the database size. Several experiments on real-life data show that the proposed algorithm is very much faster than existing Apriori-like algorithms. Hence the proposed method is very much suitable for the discovering frequent patterns from large datasets

Privacy Preserving Physical Layer Authentication Scheme for LBS based Wireless Networks

With the fast development in services related to localisation, location-based service (LBS) gains more importance amongst all the mobile wireless services. To avail the service in the LBS system, information about the location and identity of the user has to be provided to the service provider. The service provider authenticates the user based on their identity and location before providing services. In general, sharing location information and preserving the user’s privacy is a highly challenging task in conventional authentication techniques. To resolve these challenges in authenticating the users, retaining users’ privacy, a new SVD (singular value decomposition) based Privacy Preserved Location Authentication Scheme (SPPLAS) has been proposed. In this proposed method, physical layer signatures such as channel state information (CSI) and carrier frequency offset (CFO) are used for generating secret key required for encrypting the user’s location and identity information, and thus encrypted user’s information is sent to service provider for authentication. Secret key is generated by applying SVD on CSI vector. The proposed scheme aids in authenticating the user through location information while protecting the user’s privacy. The performance of the proposed method is evaluated in terms of bit mismatch, leakage and bit error rate performance of receiver and adversary. The simulation results show that the proposed scheme achieves better robustness and security than the existing location-based authentication techniques

6-Meth­oxy-2,3,4,9-tetra­hydro-1H-carbazol-1-one

The carbazole unit of the title mol­ecule, C13H13NO2, is not planar. The dihedral angle between the benzene ring and the pyrrole ring is 1.69 (6)°. The cyclo­hexene ring adopts an envelope conformation. Inter­molecular C—H⋯O and N—H⋯O hydrogen bonds are present in the crystal structure. A C—H⋯π inter­action, involving the benzene ring, is also found in the crystal structure

MICROBIOLOGICAL EVALUATION OF NITROGEN GAS IN PHARMACEUTICAL INDUSTRY

Nitrogen gases are used at different stages of the pharmaceutical manufacturing process for several applications. Nitrogen gas sampling for microorganisms plays vital part in the contamination control. The aim of the work was to evaluate the aerobic and anaerobic microbial count in nitrogen gas during drug manufacturing process. The Nitrogen samples were collected at two different sites about 1000 liters of air sampled by using M-Air- T air sampler and petri plates containing soya bean casein digest agar used for sampling. The processed plates were incubated for aerobic microbial count at 20-25° C for 3 days for fungal count, followed by 30-35° C for 3-5 days for bacterial count. Incubated anaerobic microbial count at 30-35°C for 3 days using anaerobic jar and anaerobic gas pack. After incubation the plates were evaluated for microbial count. The results observed during the study met the acceptance limits as per the ISPE guideline, the microbial count for non-sterile applications limit is to be established based on the product bioburden limits. Typical level NMT 5 cfu/m3 and for sterile applications as per the viable particle requirements for grade area where the product is exposed to the compressed gas (eg.Grade A, Grade A/B, Grade B or Grade C). The pharmaceutical manufacturing industries are supposed to set up the limit and acceptance criteria based on the periodic evaluation. Keywords: Nitrogen gas, Aerobic, Anaerobic microbial count, Sterile, Non-sterile Pharmaceutical industr