GAS CHROMATOGRAPHY AND MASS SPECTROMETRY ANALYSIS OF BIOACTIVE COMPOUNDS OF DRYOPTERIS HIRTIPES (BLUMZE) KUNTZE

Objective: The objective of this study was to analyze GC–MS analysis of whole plant methanolic extract of Dryopteris hirtipes from Dryopteridaceae family. Methods: Gas chromatography and mass spectrometry analysis of whole plant extract was carried out with instrument GC–MS. Results: The methanolic extract of D. hirtipes reveals to identify more known and unknown bioactive compounds. In this study, seven major bioactive compounds were identified such as Stigmast-5-en-3-ol(56.65%), Phytol (5.39%), Lanost -8-en-3-ol-(3 β)(3.18%), Neophytadiene(2.68%), Tri-o-trimethylsilyl N-heptaflurobutryl derivative of terbutaline(2.19%), 1H-Imidazole 2-methanol(1.28%), and 8A-(2,4-Dimethyl-1-nitrilo-pent-2-yl) dioxy)tocopherone(1.0%) and low concentrations of compounds like hexadecanoic acid(0.6%). Conclusion: These identified compounds are having active pharmacological properties such as antimicrobial property, hypotension, anti-inflammatory, anti-tumor, anti-cancer, anti-hepatitis, analgesic, and antipyretic properties. However, D. hirtipes is a rare pteridophyte and used to cure many diseases, and so there need further studies to isolate and identify the specific active compounds present in it

Cryptanalysis of Simplified-AES using Particle Swarm Optimisation

Particle swarm optimisation (PSO) based cryptanalysis has gained much attention due to its fast convergence rate. This paper proposes a PSO-based cryptanalysis scheme for breaking the key employed in simplified-advance encryption standard (S-AES). The cost function is derived using letter frequency analysis. The novelty in our approach is to apply ciphertext-only attack for an S-AES encryption system, where we obtained the key in a minimum search space compared to the Brute-Force attack. Experimental results prove that PSO can be used as an effective tool to attack the key used in S-AES.Defence Science Journal, 2012, 62(2), pp.117-121, DOI:http://dx.doi.org/10.14429/dsj.62.77

Effect of Aerobic Training on Percentage of Body Fat and Resting Heart Rate among College Obese Women

The aim of the present research was to determine the effect of aerobic training on percentage body fat and resting heart rate among obese women. For this purpose twenty (20) female obese samples (age 17-25) were selected. The subjects were given endurance training for only one session in the morning between 6 am and 7 am for three alternate days a week for six weeks. To analyse the collected data,„t? ratio was used at 0.05level of confidence. The results showed that there was a significant decrease in the percentage of body fat but no changes is elicited on resting heart rate. It was concluded that the aerobic training is widely believed to induce changes in the percentage body fat among obese women

Flood Susceptibilty Prediction using LSTM Algorithm

This Floods are one of the most frequent and severe natural disasters. Especially in India, where about 60% of the flood damage occurs due to river floods and 40% due to heavy rainfalls and cyclones. The main reasons for these intense flooding's are severe deforestation, development, and infrastructure in flood-prone areas, impermeable surfaces, and heavy rainfall. The impact of flooding includes loss of human life, damage to property, destruction of crops, loss of livestock, and deterioration of health conditions owing to waterborne diseases. This project helps in predicting the susceptibility of flooding of a particular area based on not only on the rainfall level given by the meteorological department but also based on the data such as previous rainfall levels, River flow. The data obtained are trained by using special recurrent network algorithm called LSTM (Long Short-Term Memory networks)

Viable mass production method for cotton pink bollworm, Pectinophora gossypiella (Saunders)

AbstractCotton seed based artificial diet has been standardized for continuous rearing of pink bollworm Pectinophora gossypiella (Saunders) at the Central Institute for Cotton Research, Regional Station, Coimbatore. The ingredients of the diet are easily available and are cost effective. Basic ingredients of the diet are cotton seed flour (processed) and chick pea flour, Carbohydrate, Protein, Fat sources, multi vitamin, antimicrobial agents and agar as thickening agent are used as other ingredients. Micro centrifuge tubes with lid were used as rearing containers. Individual neonate larvae were released on each piece of the diet inside the micro centrifuge tube and the lids were closed. This prevented larval escape, retaining them inside the tubes and also prevented diet dehydration. The recovery of insect reared on diet was recorded as 95.56%. Egg hatchability and adult emergence were 100% while pupal malformation was nil. Eggs, larval and pupal periods were recorded as 4.8±0.632, 25.10±0.994 and 7.9±0.88days, respectively. Larval and pupal weights were recorded as 21.40mg±3.63, 18.00mg±2.73, respectively

Boosting a Hybrid Model Recommendation System for Sparse Data using Collaborative Filtering and Deep Learning

499-502The exponential increase in the volume of online data has generated a confront of overburden of data for online users, which slow down the suitable access to products of pursuit on the Web. This contributed to the need for recommendation systems. Recommender system is a special form of intelligent technique that takes advantage of past user transactions on products to give recommendations of products. Collaborative filtering has turn out to be the commonly adopted method of providing users with customized services, except that it endures the problem of sparsely rated inputs. For collaborative filtering, we introduce a deep learning-based architecture which evaluates a discrete factorisation of vectors from sparse inputs. The characteristics of the products are retrieved using a deep learning model, denoising auto encoders. The traditional collaborative filtering algorithm that predicts and uses the past history of consumer interest and product characteristics are updated with the characteristics obtained by deep learning model for sparse rated inputs. The results of sparse data problem tested on MovieLens data set will greatly enhance the user and product transaction

Effective recommendation model using social network for linking user pursuit to product content

40-45The ongoing advancement of data innovation and the rapid development of the internet has encouraged a blast of data which has highlighted the issue of data overload. In reaction to this issue, recommender programs have evolved and helped users find their fascinating content. With the progressively entangled social setting, how to satisfy customized demands effectively has become another development in customized proposal administration contemplates. To mitigate the sparse issue of recommendation systems, we suggest a new recommendation approach based on fuzzy theory to improve their consistency and flexibility in diverse contexts. The proposed method also employs social network to reflect multifaceted factors of users. In this strategy, we group clients and consider about assortment of complex variables. The results on amazon dataset indicate that the proposed method achieves better efficiency over current methods

Ai-Driven Prediction Models For Medical Image Enhancement And Analysis Using Deep Learning

Thyroid diseases, including cancer, demand for accurate medical imaging for diagnosis and treatment direction. Important MRI imaging of the thyroid occasionally suffers with poor resolution and noise. Conventional imaging enhancement techniques may not be able to effectively solve noise or capture small features required for a successful diagnosis, therefore lowering the image quality and diagnostic accuracy. For feature extraction and classification, we provide an artificial intelligence prediction model on multi-scale deep convolutional neural network (CNN). Our method reduces noise and solves resolution enhancement, hence improving thyroid MRI images. For this work we used a bespoke CNN architecture and a 500 thyroid MRI image collection. Not only considerably outperforming current techniques, our model generated a Structural Similarity Index (SSIM) of 0.89 and a Peak Signal-to---- Noise Ratio (PSNR) of 32.5 dB. The improvements brought diagnosis accuracy 15% above more traditional techniques