Offline Signature Verification based on Euclidean distance using Support Vector Machine

In this project, a support vector machine is developed for identity verification of offline signature based on the matrices derived through Euclidean distance. A set of signature samples are collected from 35 different people. Each person gives his 15 different copies of signature and then these signature samples are scanned to have softcopy of them to train SVM. These scanned signature images are then subjected to a number of image enhancement operations like binarization, complementation, filtering, thinning, edge detection and rotation. On the basis of 15 original signature copies from each individual, Euclidean distance is calculated. And every tested image is compared with the range of Euclidean distance. The values from the ED are fed to the support vector machine which draws a hyper plane and classifies the signature into original or forged based on a particular feature value

Optimization of enzymatic process for preparation of absorbent cotton

An ecofriendly process has been developed for preparation of absorbent cotton using short staple fibres of cotton. Thecrude enzyme extract of solid state fermentation has been employed for absorbent cotton preparation and the treatmentconditions are optimized. Five gram of short staple fibre is used in each treatment. Under optimized solid state fermentationconditions, such as fungal strain (P. flabellatus), substrate composition (banana pseudo stem, cottonseed hulls andcottonseed meal in the ratio of 60:30:10) and fermentation period (5 days), the absorbency recorded is found 7 s. In anotherexperiment, process parameters of single bath enzymatic scouring and bleaching process are also optimized. Underoptimized process conditions, such as enzyme extract (30 %), temperature (60ºC), time (40 min), pH (9.0) and wetting agent(0.1%), the absorbency is found 2 s and whiteness index is 31.5 (CIE method). The pectinase and laccase activity recordedin the enzyme extract is found to be 28.1 and 6 units per milliliter respectively. The enzymes remain active at differenttemperature and pH tested. The characterization using scanning electron microscope (SEM) reveals the fibre surfacemodification in the enzyme treated cotton

Analysis of User’s Opinion using Deep Neural Network Techniques

Through many research and discoveries it has been widely accepted that aspect-level sentiment classification is achieved effectively by using Long Short-Term Memory (LSTM) network combined with attention mechanism and memory module. As existing approaches widely depend on the modeling of semantic relatedness of an aspect, at the same time we ignore their syntactic dependencies which are already a part of that sentence. This will result in undesirably an aspect on textual words that are descriptive of other aspects. So, in this paper, to offer syntax free contexts as well as they should be aspect specific, so we propose a proximity-weighted convolution network. To be more precise, we have one way of determining proximity weight which is dependency proximity. The construction of the model includes bidirectional LSTM architecture along with a proximity-weighted convolution neural network

Analysis of User’s Opinion using Deep Neural Network Techniques

Through many research and discoveries it has been widely accepted that aspect-level sentiment classification is achieved effectively by using Long Short-Term Memory (LSTM) network combined with attention mechanism and memory module. As existing approaches widely depend on the modeling of semantic relatedness of an aspect, at the same time we ignore their syntactic dependencies which are already a part of that sentence. This will result in undesirably an aspect on textual words that are descriptive of other aspects. So, in this paper, to offer syntax free contexts as well as they should be aspect specific, so we propose a proximity-weighted convolution network. To be more precise, we have one way of determining proximity weight which is dependency proximity. The construction of the model includes bidirectional LSTM architecture along with a proximity-weighted convolution neural network

Prescribing quality in patients with chronic diseases at primary and secondary health care facilities using prescription quality index tool

Background: Prescribing quality is a matter of major concern worldwide. This study was carried out to determine the quality of prescribing in chronic diseases at primary health care (PHC) and secondary health care (SHC) settings using the new prescription quality index (PQI) tool.Methods: A cross-sectional observational study was carried out at four PHC and two SHC facilities in Anand district of India. Patients attending these facilities for at least 3 months were included. Complete medical history and prescriptions received were noted. Total and criteria wise PQI scores were derived for each prescription. Prescriptions were categorized as poor (score of ≤31), medium (score 32-33), and high quality (score 34-43) based on PQI total score. The internal consistency of PQI was measured using item total correlation and Cronbach’s α so as to validate it in our settings. Data were analyzed using Statistical Package for Social Science 20.Results: A total of 134 prescriptions were collected and evaluated for quality of prescribing. Mean age of patients was 60.6 ± 13.5 years. Mean PQI score was 23.60 ± 9.3 with 71.6% prescriptions being of poor quality. Quality of prescribing did not differ at PHC and SHC (P>0.05). Of 22 criteria, PQI score was strongly correlated with drug indication, drug effectiveness, evidence-based prescribing, unnecessary duplication, duration of therapy, and cost (P<0.01). PQI total score was negatively correlated to the number of drugs per prescription. Cronbach’s α for the entire 22 criteria were 0.90.Conclusion: PQI was found to be a reliable tool for assessment of prescribing quality in chronic diseases

Mechanism of APR-246 and Sensitization of Cells to Targeted Agents

https://openworks.mdanderson.org/sumexp21/1224/thumbnail.jp

Does explicit comparative advertising affect Indian consumers’ attitudes towards low and high-involvement product?

YesWith increasing use of explicit comparative advertisement to get share of consumers’ mind and influence their purchase decision in western context, the same is now used extensively in emerging markets like India. However, there has not been sufficient research to understand the effectiveness of explicit comparative advertisement in low and high-involvement product categories. Therefore, the purpose of this paper is to attempt to understand the effectiveness of explicit comparative advertising on consumers’ attitude and purchase intention (PI) towards high and low-involvement products. The study carried out experimental treatments with 2 × 2 factorial design among 200 Indian young consumers who were in the age group 18-25. The independent variables were product categories and type of advertising (comparative and non-comparative) and dependent variables were consumer attitude and PIs. It was found that the comparative form of advertisement developed favourable response towards the advertisement, rather than towards the brand or PI. The study found that comparative advertising is effective for high as well as low-involvement product category in changing the consumer’s attitude towards the advertisement. The research has used print media for conducting the experiment. It can be inferred that comparisons should be supplemented with additional information in the form of the unique features and associated emotions and feeling of the product in order to develop favourable attitude towards the brand and PI. Comparative advertising is a growing domain and there has been very little contribution by the researchers specially on high and low-involvement product categories