A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Rashid, M. A.
- The Gir Lion
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Source
Indian Forester, Vol 91, No 2 (1965), Pagination: 108-114Abstract
no abstract
- A Note on Hardboard Manufacture in Australia
Authors
Source
Indian Forester, Vol 93, No 11 (1967), Pagination: 763-764Abstract
no abstract- The Role of Public Relations in Indian Forestry
Authors
Source
Indian Forester, Vol 97, No 5 (1971), Pagination: 258-260Abstract
no abstract- Chemical and Microscopic Standardization of Poly-Herbal Unani Drug Sufoof-E-Hazim
Authors
1 Drug Standardization Research Institute (Unani), CCRUM PLIM Building, Kamla Nehru Nagar, Ghaziabad, U.P, IN
Source
Asian Journal of Research in Chemistry, Vol 4, No 8 (2011), Pagination: 1265-1268Abstract
The use of herbal drugs for health care has increased tremendously during the past few decades. The medicines in Unani system now have a wider reach among people world over. In order to ensure the reliability and acceptability of Unani medicines there is an urgent need of developing standard operative procedures and pharmacopoeial standardization of various formulations and ingredients.
Work on pharmacopoeial standardization including SOP for its preparation has been carried out on the drug Sufoof-e-Hazim which is a powder drug of high therapeutic efficacy, used to cure flatulence of stomach and indigestion. SOP for the drug has been developed by preparing the drug on laboratory scale, using the standard ingredients and following the prescribed procedures. In order to work out the diagnostic features, the formulation was studied on the basis of pharmacopoeial parameters such as microscopy, physico-chemical analysis, thin layer chromatography, level of microbial and pesticide contaminations, aflatoxins and the heavy metals.
Keywords
SOP, Pharmacopoeial Standardization, Microscopy, TLC, HPTLC, U V Spectroscopy, Heavy Metals.- Face Detection for Behaviour Analysis using Deep Learning
Authors
1 Electronics & Telecommunication Engineering Department, Northeastern University, Boston, US
Source
Digital Signal Processing, Vol 12, No 7-9 (2020), Pagination:Abstract
The smart classroom of the future we envision will greatly enhance the learning experience and achieve seamless communication between students and teachers through real-time detection and machine intelligence. Additionally, facial recognition can capture student emotions such as happiness, sadness, neutrality, anger, nausea, surprise, and more. From this sentiment we analyze it and in the analysis derive the final overall student behavior of a particular speech. So, you can also get results in the form of teacher feedback and student feedback from student behavior. The three main parts of the student attendance system are then described in detail using two deep learning facial recognition algorithms. Behavioral analysis model based on facial recognition neural network or Haar classifier. iii) Automatic teacher feedback based on student behavior analysis.