The statistical significance obtained for the trail formulations experimentally proved its suitability for this dosage form design to achieve desired level of drug release.
In the current era, we are facing a new problem of parking of vehicles. It is a major problem in urban cities. The problem is more tough because of continues growing number of vehicle and also size of vehicles. Car parking is not just a major problem in India but also in all over the world. We know that one million vehicles burn oil every day. In this paper, we propose an automatic and real-time system for automated car parking. This system would be implemented by the use of internet of things (IOTs). IOT refers as any physical thing that is connected to internet or exchanging information or data between internet and physical device. Arduino Uno is a microcontroller used in IOT. It is used for building digital devices, and interactive objects that can sense and control physical devices. Our smart parking will be implementing using Arduino Uno board for car parking and Ethernet shield to connect parking area with web or internet. By using our automated parking system, a user can save much time for searching free parking space. An infrared sensor has to be employed in each slot to check that a particular plot is vacant or not. Searching smart parking system helps people to search parking space accessible with the ease of IOT automation by supplying parking slot free information. The user can book in advance the parking slot and update the information to the server. Every user has a unique id and password. In case a car in stolen and enter the parking IOT the server checks the database and inform the police.
Eladi Gutika is a polyherbal formulation official in "Ayurvedic formulary of India" and used for dry cough and throat infection. A simple, specific and precise highperformance thin-layer chromatography (HPTLC) method has been developed for quantification of piperine and 18-β glycyrrhetinic acid in Eladi Gutika. We report the extraction and estimation of these compounds in a laboratory prepared sample of Eladi Gutika and two of its marketed formulations. The compounds were chromatographed on precoated silica gel G 60 254 plates in the mobile phase comprising of toluene, ethyl acetate, and glacial acetic acid. Under the optimized chromatographic conditions, the calibration plot was found to be linear in the range of 0.2-1 μg mL −1 with a correlation coefficient R 2 = 0.9902 for piperine and 0.9904 for 18-β glycyrrhetinic acid. Mean recovery for piperine was 99.75% w/w and for 18-β glycyrrhetinic acid was 101.36% w/w.
Recommendation systems are obtaining more attention in various application fields especially e-commerce, social networks and tourism etc. The top items are recommended based on the ability of recommender system which predict the future preference out of the available items. Because of the internet, the people in the current society has too many options that’s why the recommendation system is very essential. The recommendation is achieved by the particular users who predict the ratings for numerous items and recommend those items to other users. Majorly, content and collaborative filtering techniques are employed in typical recommendation systems to find user preferences and provide final recommendations. But, these systems commonly lacks to take growing user preferences in various contextual factors. Context aware recommendation systems consider various contextual parameters into account and attempt to catch user preferences appropriately. The majority of the work in the recommender system domain focuses on increasing the recommendation accuracy by employing several proposed approaches where the main motive remains to maximize the accuracy of recommendations while ignoring other design objectives, such as a user’s an item’s context. Therefore, in this paper an effective deep learning based context aware recommendation model is proposed which can be act as an efficient recommender system by showing minimum error during recommendation. Initially, the dataset is pre-processed using Natural Language Tool Kit (NLTK) in Python platform. After pre-processing, the TF–IDF and word embedding model is used for every pre-processed reviews to extract the features and contextual information. The extracted feature is considered as an input of density based clustering to group the negative, neutral and positive sentiments of user reviews. Finally, deep recurrent neural Network (DRNN) is employed to get the most preferable user from every cluster. The recurrent neural network model parameter values are initialized through the fitness computation of Bald Eagle Search (BES) algorithm. The proposed model is implemented using NYC Restaurant Rich Dataset using Python programming platform and performance is evaluated based on the metrics of accuracy, precision, recall and compared with existing models. The proposed recommendation model achieves 99.6% accuracy which is comparatively higher than other machine learning models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.