Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

International Journal of Computer and Applications

8. Subset Selection in Regression (Monographs on Statistics and Applied Probability, no. 40). By A. J. Miller. ISBN 0 412 35380 6. Chapman and Hall, London, 1990. 240 pp. £25.00.

Subset Selection in Regression

Pharmaceutical applications of various natural gums, mucilages and their modified forms

Camels' milk and milk products.

Diorganotin(IV) derivatives of N-phthaloyl amino acids

Diorganotin(VI) complexes of N-acetylamino acids

The present study was undertaken to find out the potential of gum from Moringa oleifera to act as a binder and release retardant in tablet formulations. The effect of calcium sulphate dihydrate (water insoluble) and lactose (water soluble) diluent on the release of propranolol hydrochloride was studied. The DSC thermograms of drug, gum and mixture of gum/drug indicated no chemical interaction. Tablets (F1, F2, F3, and F4) were prepared containing calcium sulphate dihydrate as diluent, propranolol hydrochloride as model drug using 10%, 8%, 6% and 4% w/v of gum solution as binder. Magnesium stearate was used as lubricant. Physical and technological properties of granules and tablets like flow rate, Carr index, Hausner ratio, angle of repose, hardness, friability and disintegration time were determined and found to be satisfactory. Tablets were prepared by wet granulation method containing calcium sulphate dihydrate as excipient, propranolol hydrochloride as model drug using 10%, 20% and 30% of gum as release retardant, magnesium stearate was used as lubricant. Similarly tablets were prepared replacing lactose with calcium sulphate dihydrate. Despite of the widely varying physico-chemical characteristics of the excipients, the drug release profiles were found to be similar. The drug release increased with increasing proportions of the excipient and decreased proportion of the gum irrespective of the solubility characteristics of the excipient. The values of release exponent 'n' are between 0.37 and 0.54. This implies that the release mechanism is Fickian. There is no evidence that the dissolution or erosion of the excipient has got any effect on the release of the drug. The t(50%) values for tablets containing calcium sulphate dihydrate were on an average 10%-15% longer than the tablets containing lactose as excipient. These relatively small differences in t(50%) values suggest that the nature of excipient used appeared to play a minor role in regulating the release, while the gum content was a major factor.

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Evaluation of Gum of Moringa oleifera as a Binder and Release Retardant in Tablet Formulation

The present study was undertaken with an objective to find out the gelling potentials of a natural gum obtained from plant Moringa oleifera . The gum was extracted by using water as solvent and precipitated using acetone as non-solvent. Physical characteristics such as, solubility, swelling index, loss on drying, and pH were studied. Diclofenac sodium was used as model drug for the formulation of gels. Seven batches of drug loaded gels with concentration of mucilage ranging from 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, and 8.5 were formulated by using glycerin as plasticizer and methyl paraben as preservative. The pH, viscosity, and in vitro diffusion profiles were studied. The gels prepared with 8.0% of mucilage were found to be ideal and comparable with a commercial preparation.

/pdf/preparation-and-evaluation-of-gels-from-gum-of-moringa-2xegnsb8tl.pdf

Roopam Gupta

Papers

Camels' milk and milk products.

Diorganotin(IV) derivatives of N-phthaloyl amino acids

Diorganotin(VI) complexes of N-acetylamino acids

Evaluation of Gum of Moringa oleifera as a Binder and Release Retardant in Tablet Formulation

Preparation and evaluation of gels from gum of Moringa oleifera