Informatics Publishing Limited

Shailesh Kumar ¹, Kumkum Dubey ¹, P. K. Singh ¹

Affiliations
1 Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur - 273016, Uttar Pradesh, IN

Abstract

The requirement for storage performance and capacity are increasing rapidly. NAND flash-based SSDs have been proposed as a reliable and speedy and low power consumption storage device. An important part of each SSDs is its flash translation layers (FTL). Flash translation layer is highly impact overall performance and it manages the internal data layout for storage. There are many different trade-offs involved in FTL implementation. This survey focuses on address translation technologies and provides a broad overview of existing schemes. In flash memory, flash translation layer is a very important structure and so many techniques have been proposed.

Full Text

   

A. K. Ghorai ¹, D. K. Kundu ¹, Shailesh Kumar ¹, A. Shamna ¹, Debarati Datta ¹

Affiliations
1 ICAR-Central Research Institute for Jute and Allied Fibres, Barrackpore 700 120, IN

Abstract

Crop diversification in waterlogged rice field using gunny bag based soil columns produced 3–4 tonnes of kharif rice, 4.5–5.4 tonnes of rabi rice along with other vegetable crops worth Rs 0.5–3.0 lakh/ha with higher B : C ratio for cucurbits. Crop diversification in rice field using gunny bag/hessian based soil columns increased cropping intensity by 100–200 %, generated additional returns and increased employment opportunities. In this process, nearly 1500 to 5000 number of gunny bags (capacity 50 kg) can be used per hectare rice land in an economical manner. Even if 1.0% (0.4 M ha) of the total rice acreage in India and Bangladesh (40 M ha) is diversified, about 10 lakh tonnes jute fibre will be utilized. It will increase the marketing opportunities of raw jute fibre, provide nutritional and livelihood security to resource-poor farmers.

Keywords

Crop Diversification, Economic Benefit, Gunny Bag, Vegetable Production, Waterlogged Rice Field.

Full Text

   

Sumit Govil ¹, Sandesh Tripathi ², Amit Kumar ¹, Divya Shrivastava ¹, Shailesh Kumar ³

Affiliations
1 School of Life Sciences, Jaipur National University, Jaipur - 302025, Rajasthan, IN
2 Birla Institute of Applied Sciences, Bhimtal, Nainital - 263136, Uttarakhand, IN
3 National Centre for Cell Science, NCCS Complex, Pune University Campus, Pune - 411007, Maharashtra, IN

Abstract

In the drug discovery path, most drug candidates failed at the early stages due to their pharmacokinetic behavior in the system. Early prediction of pharmacokinetic properties and screening methods can reduce the time and investment for lead discoveries. Plasma protein binding is one of these properties which has a vital role in drug discovery and development. The focus of the current study is to develop a computational model for the classification of Low Plasma Protein Binding (LPPB) and High Plasma Protein Binding (HPPB) drugs using machine learning methods for early screening of molecules through WEKA. Plasma protein binding drugs data was collated from the Drug Bank database where 617 drug candidates were found to interact with plasma proteins, out of which an equal proportion of high and low plasma protein binding drugs were extracted to build a training set of ~300 drugs. The machine learning algorithms were trained with a training set and evaluated by a test set. We also compared various machine learning-based classification algorithms i.e., the Naïve Bayes algorithm, Instance-Based Learner (IBK), multilayer perceptron, and random forest to determine the best model based on accuracy. It was observed that the random forest algorithm-based model outperforms with an accuracy of 99.67% and 0.9933 kappa value on training set and on test set as compared to other classification methods and can predict drug plasma binding capacity in the given data set using the WEKA tool.

Keywords

Drug Discovery, Machine Learning, Multilayer Perceptron, Pharmacokinetic Plasma Protein Binding, Random Forest

Full Text