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

Cu2ZnSnS4 (CZTS) is deemed to be one of the most promising photovoltaic materials due to its properties such as tunable band gap energy of 1.4–1.6 eV and an absorption coefficient greater than 104 cm−1. In the present work, CZTS films were deposited onto glass substrate by a less complex and cost efficient screen-printing method and thereafter sintered. The structural, morphological, optical and electrical properties of CZTS films were studied. The x-ray diffraction and Raman studies show that the screen-printed CZTS films were indexed to a single phase kesterite structure. The morphology of the films was examined through scanning electron microscopy. The EDAX analysis indicates the presence of Cu, Zn, Sn, and S elements in the films. Direct type band gap of 1.51 eV was found for the films, which is quite close to the optimum band gap, i.e., between 1.4 eV and 1.5 eV required for a solar absorber layer. The dark electrical resistivity of films was found to be of the order of 102 Ω-cm as evaluated by the two probe method.

Fabrication and Characterization of Screen-Printed Cu 2 ZnSnS 4 Films for Photovoltaic Applications

Plant based traditional and indigenous medicines are used globally for the treatment of various diseases. About 80% of the world population relies on plants and their products for primary health care. The plant Alangium salviifolium has been in use traditionally for treatment of various ailments. Almost every part of this plant including roots, leaves, stem and bark are used in the Ayurveda and Siddha system of medicines for treatment of various diseases. In modern scientific literatures, the plant has been reported to have potential efficacy against hypertension, diabetes, epilepsy, cancer, inflammation, etc. It is reported to contain various biologically active phytochemicals such as alangine, ankorine, tubulosine, alangicine, salsoline etc. Aims: The present review highlights traditional uses of different parts of Alangium salviifolium, its phytochemical constituents with therapeutic activity, translational research on the plant and the evidence based studies on various pharmacological effects of the plant. Place and Duration of Study: Value Addition Research and Development, Division of Human Review Article Panara et al.; EJMP, 12(4): 1-15, 2016; Article no.EJMP.23899 2 Health, National Innovation Foundation-India and Society for Research and Initiatives for Sustainable Technologies and Institutions (SRISTI), Ahmedabad, India between October, 2015 to December, 2015. Methodology: A review of literature was carried out using several resources through online internet searches, including scientific databases such as Pubmed, Science Direct, Google Scholar, etc. Results: Alangium salviifolium is a widely distributed plant with a number of reported traditional uses. Several phytochemicals including alangine, ankorine, tubulosine, alangicine, salsolin etc. have been characterized in different parts. The phytoconstituents isolated from the plant have been shown to be associated with a number of biological effects demonstrating the therapeutic potential. Conclusion: Due to amplified acceptance and use of traditional plants, stringent scientific validation studies are being carried out worldwide, to develop evidence based phyto-medications. Alangium salviifolium is an excellent medicinal plant which holds numerous bioactive phytochemicals. Evidence based scientific studies have been reported against hypertension, diabetes, epilepsy, cancer, inflammation, ulcer, etc. Various plant parts have been found to possess biological activity more specifically towards overcoming metabolic ailments. Mere scientific evidences of activity of the extracts will not create the solutions, rather studies should focus on developing contemporary formulations after extensive analysis of its bioactivity, pharmacokinetics and pharmacodynamics, safety, etc. using appropriate animal models followed by clinical trials. Substantial research has already been conducted on this plant during last few decades, which can be used by scientists in developing useful therapeutic solution from Alangium salviifolium.

Vipin Kumar

Papers

Fabrication and Characterization of Screen-Printed Cu 2 ZnSnS 4 Films for Photovoltaic Applications

Importance of Alangium salviifolium and Its Pharmacological Update

Single channel tunable wavelength demultiplexer using nonlinear one dimensional defect mode photonic crystal

Challenges in regulating interfacial-chemistry of the sodium-metal anode for room-temperature sodium-sulfur batteries

Nonlinear transmission and reflection characteristics of plasma/polystyrene one dimensional photonic crystal