Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

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Anomaly detection: A survey

Soil Chemical Analysis

Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering.

Data clustering: 50 years beyond K-means

This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

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Top 10 algorithms in data mining

The practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms in to taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping objects according to measured or perceived intrinsic characteristics. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes cluster analysis (unsupervised learning) from discriminant analysis (supervised learning). The objective of cluster analysis is to simply find a convenient and valid organization of the data, not to establish rules for separating future data into categories.

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Data Clustering: 50 Years Beyond K-means

Soil Carbon Fractions as Influenced by Long-Term Integrated Nutrient Management under Rice-Wheat Cropping System in Calcareous Soil of North Bihar

In this study a taste masked orodispersible tablets of Tramadol Hydrochloride were prepared and evaluated for comparative in vitro dissolution profile with commercially available market product. The bioequivalence of Test and Reference product of Tramadol Hydrochloride tablets using in vitro multipoint dissolution profile study by UV-Visible spectrophotometry were performed. Dissolution medium were purified water & three different buffer solutions of different pH like pH 1.2 (hydrochloric acid solution), pH 4.5 (acetate buffer solution), and pH 6.8 (phosphate buffer solution). The Test and Reference tablets were also evaluated to general quality test of dispersible tablets like weight variation, hardness, friability, disintegration time or dispersion time water absorption ratio, wetting time and assay. Both Test & Reference product were complied with the official specification for uniformity of weight, friability and disintegration time and Assay. The dissolution profiles showed there are no significant change drug release of Test and Reference product of Tramadol Hydrochloride Tablets. The tablets were further analysed for similarity between the dissolution profile of Test and Reference product through determination of difference factor (f1), similarity factor (f2) by calculating the dissolution results of both Test and Reference product at multi time point after 05, 10, 15, 30, 45 minutes. These results similarity factor shows that the both Test and Reference product tablets in this study were bioequivalent.

Formulation of Taste Masked Orodispersible Tablets of Tramadol Hydrochloride and Comparative In Vitro Dissolution Profile With Market Product

The larvae of catfish, Clarias batrachus (av. wt. 15 ± 2 mg) were exposed to five concentrations of thyroxine (T4) with three feeding regimes. Five media 1.3 mcg L−1 (T-1), 2.2 mcg L−1 (T-2), 3.6 mcg L−1 (T-3), 6.0 mcg L−1 (T-4), 10.0 mcg L−1 (T-5) of T4 and control with three feeding regimes 1 day (once a day), 2 days (twice a day) and 3 days (thrice a day) were maintained in triplicate set in 54, troughs containing 2-L water. The larvae reared in media (T1, T2, T-3, T-4 & T-5) with feeding regimes 1 day, 2 days & 3 days showed 100 % survival which was significantly high (p < 0.05) in comparison to control (60.5 ± 0.7, 64.5 ± 0.7 and 71.0 ± 1.4 % respectively with regimes 1 day, 2 days and 3 days). Net gain in biomass was found significantly higher (p < 0.05) in feeding regimes 3 days (C, 244.7 %; T-1, 305.7 %; T-2, 217.5 %; T-3, 369.9 %; T-4, 371.4 %; T-5, 298.3 %) and 2 days (C, 146.5 %; T-1, 186.6 %; T-2, 122.7 %; T-3, 243.6 %, T-4, 200 %, T5, 123.4 %) in comparison to 1 day. The synergistic effects of T4, feeding regimes and their interaction evaluated through two-way ANOVA. The color of the fish-body was more darker in the case of experimental fishes in comparison to the control groups. The findings suggest that thyroxine enriched ambient water and feeding regimes had a significant role in improved survival and growth on C. batrachus larvae, a prioritized fish for aquaculture.

Synergistic Effects of Thyroxine and Feeding Regimes on Early Survival and Biomass Gain in Asian Catfish, Magur ( Clarias batrachus , Linn.)

The progress in tissue engineering and regenerative medicines has made organ replacement or regeneration easier, and its demand has increased rapidly in recent years. Bio-printing of human organs or tissues has become possible only because of the successful development of the bio-ink used in three-dimensional (3D) printing technology. Owing to the unique attributes of 3D printing, it can create an object of any complexity, including tissues with highly customized requirements for the subject (i.e., patient) specific applications. Development in smart materials, such as thermoplastics, powdered plastics, and photopolymers, enabled 3D printing to create objects with customized mechanical properties to mimic human organ models accurately. It brings new possibilities to create bionic tissues or organs, and it becomes even more desirable where the donor shortage is a severe problem. Despite cell printing, the effort remains to be made to accomplish the higher objectives of the in-vitro manufacturing of tissues or organs. This chapter sheds light on the progress and development of 3D printing technology to create organs or tissues. Also, the current state-of-the-art of the materials that can be processed, designed, is discussed comprehensively. The potential and major limitations of 3D printing technology in the field of bio-printing and related medical applications are discussed in brief.

Vipin Kumar

Papers

Soil Carbon Fractions as Influenced by Long-Term Integrated Nutrient Management under Rice-Wheat Cropping System in Calcareous Soil of North Bihar

Formulation of Taste Masked Orodispersible Tablets of Tramadol Hydrochloride and Comparative In Vitro Dissolution Profile With Market Product

Synergistic Effects of Thyroxine and Feeding Regimes on Early Survival and Biomass Gain in Asian Catfish, Magur ( Clarias batrachus , Linn.)

Introduction to 3D Printing Technology for Biomedical Applications

Studies on correlation and path coefficient analysis for quantitative characters in pumpkin (cucurbita moschata L.)