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.

/pdf/top-10-algorithms-in-data-mining-70qtv03lc6.pdf

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

The present study was undertaken to assess the magnitude of changes in soil properties and productivity of sugarcane with integration of inorganic (chemical fertilizer) and organic (vermicompost, neem cake and castor cake and biofertilizers) nutrient sources of fertilizer during 2016-18 in calcareous soil. Integration of organic and inorganic nutrient sources showed significant increase in organic carbon and respiration rate of soil after sugarcane harvest. The mean organic carbon (4.28-4.93g kg) and CO2 evolution (9.91-17.10 mg100gm soil day) varied significantly. Increased soil respiration rate indicating higher microbial activity and high rate of decomposition of organic matter in soil. The integrated nutrient management practices was also found effective in improving soil fertility in terms of available NPK content of post-harvest soil over control. The significant improvement in bulk density and porosity were observed with increasing proportion of various organic with inorganic fertilizer. Plant population, number of millable cane, cane length, single cane weight and cane yield were significantly affected due to integrated use of nutrients through inorganic and organic sources. The significantly higher cane yield (83.86t/ha) were recorded in treatment receiving 50% N through inorganics + 50% N through organics (Vermicompost + castor cake + neem cake) 1/3eah along with bio-fertilizer followed by treatment receiving 50% NPK+ 50% N (Vermicompost + castor cake) 1⁄2 each + biofertilizer (78.88 t ha). The juice quality parameters viz. brix, pol, purity and commercial cane sugar percent remains unaffected due to integrated use of organic and inorganic fertilizer however, juice recovery (61.2366.48%) and sugar yield (7.70-9.48 t/ha) varied significantly. Integration of chemical fertilizer with organic nutrient combinations of vermicompost and neem/castor oil cakes along with biofertilizers was more effective for sustainable sugarcane production and maintenance of soil health.

https://www.chemijournal.com/archives/2019/vol7issue6/PartG/SP-7-6-72-471.pdf

Soil properties, productivity and juice quality of sugarcane through integration of organic and inorganic nutrient sources in calcareous soil

Some new aspects of graph labeling in dental arch structure

Black scurf (Rhizoctonia solani) diseases of potato are becoming prominent in many potatoes growing state resulting in economic losses, since a decade. An experiment was conducted to find out more effective management approaches through chemicals, taking cv. Kufri Bahar with five treatments involving chemicals i. e. Penflufen 240 F S and Boric acid, application before planting. Based on 2 years pooled data revealed that all the treatments were significant over control in terms of yield. Maximum total tuber yield was obtained from treatment T-3(27.69t/h) and minimum (24.03t/h) was in treatment T1(Control). However, minimum percent incidence of disease and disease severity at harvest was observed in treatment T-3(Tuber dip treatment with Penflufen (0.083 %) for 10 min. before planting) followed by treatment T-2 (Tuber dip treatment with Penflufen (0.062 %) for 10 min. before planting)

https://www.chemijournal.com/archives/2017/vol5issue4/PartB/5-3-196-490.pdf

Management of black scurf disease of potato using chemicals Penflufen and Boric acid

https://link.springer.com/content/pdf/10.1007/s10854-021-07362-8.pdf

Vipin Kumar

Papers

Soil properties, productivity and juice quality of sugarcane through integration of organic and inorganic nutrient sources in calcareous soil

Some new aspects of graph labeling in dental arch structure

Management of black scurf disease of potato using chemicals Penflufen and Boric acid

Self-driven solar-blind ultraviolet photodetectors based on p-Zn1−xSbxO/n-Si− (x = 0.03, 0.05) heterojunction diodes

Growth and characterization of Cd0.8Zn0.2S thin films by spray pyrolysis method