THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

The aim of any data mining technique is to build an efficient predictive or descriptive model of a large amount of data. Applications of evolutionary algorithms have been found to be particularly useful for automatic processing of large quantities of raw noisy data for optimal parameter setting and to discover significant and meaningful information. Many real-life data mining problems involve multiple conflicting measures of performance, or objectives, which need to be optimized simultaneously. Under this context, multiobjective evolutionary algorithms are gradually finding more and more applications in the domain of data mining since the beginning of the last decade. In this two-part paper, we have made a comprehensive survey on the recent developments of multiobjective evolutionary algorithms for data mining problems. In this paper, Part I, some basic concepts related to multiobjective optimization and data mining are provided. Subsequently, various multiobjective evolutionary approaches for two major data mining tasks, namely feature selection and classification, are surveyed. In Part II of this paper, we have surveyed different multiobjective evolutionary algorithms for clustering, association rule mining, and several other data mining tasks, and provided a general discussion on the scopes for future research in this domain.

A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I

Cold gas dynamic spray additive manufacturing today: Deposit possibilities, technological solutions and viable applications

Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.

/pdf/big-data-analytics-for-preventive-medicine-2oyqhfvqua.pdf

Big data analytics for preventive medicine

Magnesium-based composites and alloys for medical applications: A review of mechanical and corrosion properties

Evolutionary multi objective optimization (EMOO) systems are evolutionary systems which are used for optimizing various measures of the evolving system. Rule mining has gained attention in the knowledge discovery literature. The problem of discovering rules with specific properties is treated as a multi objective optimization problem. The objectives to be optimized being the metrics like accuracy, comprehensibility, surprisingness, novelty to name a few. There are a variety of EMOO algorithms in the literature. The performance of these EMOO algorithms is influenced by various characteristics including evolutionary technique used, chromosome representation, parameters like population size, number of generations, crossover rate, mutation rate, stopping criteria, Reproduction operators used, objectives taken for optimization, the fitness function used, optimization strategy, the type of data, number of class attributes and the area of application. This study reviews EMOO systems taking the above criteria into consideration. There are other hybridization strategies like use of intelligent agents, fuzzification, meta data and meta heuristics, parallelization, interactiveness with the user, visualization, etc., which further enhance the performance and usability of the system. Genetic Algorithms (GAs) and Genetic Programming (GPs) are two widely used evolutionary strategies for rule knowledge discovery in Data mining. Thus the proposed study aims at studying the various characteristics of the EMOO systems taking into consideration the two evolutionary strategies of Genetic Algorithm and Genetic programming.

Evolutionary multi objective optimization for rule mining: a review

A statistical prediction of density and hardness of biodegradable mechanically alloyed Mg–Zn alloy using fractional factorial design

Synthesis of Biodegradable Mg-Zn Alloy by Mechanical Alloying: Effect of Milling Time

The kinetics of chlorination of titanium nitride (TiN) was investigated in the temperature range of 523 K to 673 K (250 °C to 400 °C). The results showed that the extent of chlorination slightly increased with increasing temperature and decreasing particle size of titanium nitride at constant flow rate of N2-Cl2 gas mixture. At 523 K (250 °C), the extent of chlorination was 85.6 pct in 60 minutes whereas at 673 K (400 °C), it was 97.7 pct investigated by weight loss measurement and confirmed by ICP analyses. The experimental results indicated that a shrinking unreacted core model with mixed-control mechanism governed the chlorination rate. It was observed that the surface chemical reaction of chlorine gas on the surface of TiN particles was rate controlling in the initial stage and, during later stage, internal (pore) diffusion through the intermediate product layer was rate controlling step. Overall the process follows the mixed-control model incorporating both chemical reaction and internal diffusion control. The activation energy for the chlorination of TiN was found to be about 10.97 kJ mol−1. In processing TiCl4 from TiN and TiO0.02C0.13N0.85, the solids involved in the chlorination process were characterized by X-ray diffraction (XRD), Scanning electron microscopy (SEM) and Energy-dispersive X-ray spectrometer (EDX). The SEM/EDX results demonstrated the consumption of TiN particles with extent of chlorination that showed shrinking core behavior.

Chlorination Kinetics of Titanium Nitride for Production of Titanium Tetrachloride from Nitrided Ilmenite

Since its introduction in the 1990s the internet has proliferated in the life of human kind in many numbers of ways. The two by-products of the internet are intelligent agents and intrusions which are far away from each other in the intention of their creation while similar in their characteristics. With automated code roaming the network intruding the users on one side as worms, viruses, and Trojans and autonomous agents tending to help the users on the other side, the internet has given great research challenges to the computer scientists. The greatest challenge of the internet is intrusion, which has increased and never decreased. There are various security systems for the internet. As the Human Immune System protects human body from external attacks, these security systems tend to protect the internet from intruders. Thus the internet security systems are comparable with human immune systems in which autonomous cells move throughout the body to protect it while learning to tackle new threats and keeping them in their memory for the future. These properties are comparable with that of autonomous agents in the internet. Thus intelligent agent technology combined with ideas from human immune system is a great area of research which is still in its developing phase. In this paper, state of the art of security systems which use both these technologies of intelligent agents and artificial immune system i.e., Agent Based Artificial Immune System (ABAIS) for security are reviewed, paying special attention to features of human immune system used in the system, the role of the agents in the ABAIS and the security mechanisms provided against intrusions.

Sivakumar Ramakrishnan

Papers

Evolutionary multi objective optimization for rule mining: a review

A statistical prediction of density and hardness of biodegradable mechanically alloyed Mg–Zn alloy using fractional factorial design

Synthesis of Biodegradable Mg-Zn Alloy by Mechanical Alloying: Effect of Milling Time

Chlorination Kinetics of Titanium Nitride for Production of Titanium Tetrachloride from Nitrided Ilmenite

Intelligent agent based artificial immune system for computer security--a review