Machine-Learning
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Crop Monitoring System Using Image Processing and Environmental Data
Crop Monitoring System Using Image Processing and Environmental Data Shadman Rabbi - 18341015 Ahnaf Shabik - 13201013 Supervised by: Dr. Md. Ashraful Alam Department of Computer Science and Engineering BRAC University Declaration We, hereby declare that this thesis is based on the results found by ourselves. Materials of work found by other researcher are mentioned by reference. This thesis, neither in whole or in part, has been previously submitted for any degree. ______________________ Signature of Supervisor (Dr. Md. Ashraful Alam) ___________________ Signature of Author (Shadman Rabbi) ___________________ Signature of Author (Ahnaf Shabik) i Acknowledgement Firstly we would like to thank the almighty for enabling us to initiate our research, to put our best efforts and successfully conclude it. Secondly, we offer our genuine and heartiest appreciation to our regarded Supervisor Dr. Md. Ashraful Alam for his contribution, direction and support in leading the research and preparation of the report. His involvement, inclusion and supervision have inspired us and acted as a huge incentive all through our research. Last but not the least, we are grateful to the resources, seniors, companions who have been indirectly but actively helpful with the research. Especially Monirul Islam Pavel, Ekhwan Islam, Touhidul Islam and Tahmidul Haq has propelled and motivated us through this journey. We would also like to acknowledge the help we got from various assets over the internet; particularly from fellow researchers’ work. ii Table of Contents Declaration -
Effect of Distance Measures on Partitional Clustering Algorithms
Sesham Anand et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 6 (6) , 2015, 5308-5312 Effect of Distance measures on Partitional Clustering Algorithms using Transportation Data Sesham Anand#1, P Padmanabham*2, A Govardhan#3 #1Dept of CSE,M.V.S.R Engg College, Hyderabad, India *2Director, Bharath Group of Institutions, BIET, Hyderabad, India #3Dept of CSE, SIT, JNTU Hyderabad, India Abstract— Similarity/dissimilarity measures in clustering research of algorithms, with an emphasis on unsupervised algorithms play an important role in grouping data and methods in cluster analysis and outlier detection[1]. High finding out how well the data differ with each other. The performance is achieved by using many data index importance of clustering algorithms in transportation data has structures such as the R*-trees.ELKI is designed to be easy been illustrated in previous research. This paper compares the to extend for researchers in data mining particularly in effect of different distance/similarity measures on a partitional clustering algorithm kmedoid(PAM) using transportation clustering domain. ELKI provides a large collection of dataset. A recently developed data mining open source highly parameterizable algorithms, in order to allow easy software ELKI has been used and results illustrated. and fair evaluation and benchmarking of algorithms[1]. Data mining research usually leads to many algorithms Keywords— clustering, transportation Data, partitional for similar kind of tasks. If a comparison is to be made algorithms, cluster validity, distance measures between these algorithms.In ELKI, data mining algorithms and data management tasks are separated and allow for an I. INTRODUCTION independent evaluation. -
Scikit-Learn
Scikit-Learn i Scikit-Learn About the Tutorial Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python. This library, which is largely written in Python, is built upon NumPy, SciPy and Matplotlib. Audience This tutorial will be useful for graduates, postgraduates, and research students who either have an interest in this Machine Learning subject or have this subject as a part of their curriculum. The reader can be a beginner or an advanced learner. Prerequisites The reader must have basic knowledge about Machine Learning. He/she should also be aware about Python, NumPy, Scipy, Matplotlib. If you are new to any of these concepts, we recommend you take up tutorials concerning these topics, before you dig further into this tutorial. Copyright & Disclaimer Copyright 2019 by Tutorials Point (I) Pvt. Ltd. All the content and graphics published in this e-book are the property of Tutorials Point (I) Pvt. Ltd. The user of this e-book is prohibited to reuse, retain, copy, distribute or republish any contents or a part of contents of this e-book in any manner without written consent of the publisher. We strive to update the contents of our website and tutorials as timely and as precisely as possible, however, the contents may contain inaccuracies or errors. Tutorials Point (I) Pvt. Ltd. provides no guarantee regarding the accuracy, timeliness or completeness of our website or its contents including this tutorial. -
BETULA: Numerically Stable CF-Trees for BIRCH Clustering?
BETULA: Numerically Stable CF-Trees for BIRCH Clustering? Andreas Lang[0000−0003−3212−5548] and Erich Schubert[0000−0001−9143−4880] TU Dortmund University, Dortmund, Germany fandreas.lang,[email protected] Abstract. BIRCH clustering is a widely known approach for clustering, that has influenced much subsequent research and commercial products. The key contribution of BIRCH is the Clustering Feature tree (CF-Tree), which is a compressed representation of the input data. As new data arrives, the tree is eventually rebuilt to increase the compression. After- ward, the leaves of the tree are used for clustering. Because of the data compression, this method is very scalable. The idea has been adopted for example for k-means, data stream, and density-based clustering. Clustering features used by BIRCH are simple summary statistics that can easily be updated with new data: the number of points, the linear sums, and the sum of squared values. Unfortunately, how the sum of squares is then used in BIRCH is prone to catastrophic cancellation. We introduce a replacement cluster feature that does not have this nu- meric problem, that is not much more expensive to maintain, and which makes many computations simpler and hence more efficient. These clus- ter features can also easily be used in other work derived from BIRCH, such as algorithms for streaming data. In the experiments, we demon- strate the numerical problem and compare the performance of the orig- inal algorithm compared to the improved cluster features. 1 Introduction The BIRCH algorithm [23,24,22] is a widely known cluster analysis approach, that won the 2006 SIGMOD Test of Time Award. -
Implementation of Multivariate Data Set by Cart Algorithm
International Journal of Information Technology and Knowledge Management July-December 2010, Volume 2, No. 2, pp. 455-459 IMPLEMENTATION OF MULTIVARIATE DATA SET BY CART ALGORITHM Sneha Soni Data mining deals with various applications such as the discovery of hidden knowledge, unexpected patterns and new rules from large Databases that guide to make decisions about enterprise to products and services competitive. Basically, data mining is concerned with the analysis of data and the use of software techniques for finding patterns and regularities in sets of data. Data Mining, which is known as knowledge discovery in databases has been defined as the nontrivial extraction of implicit, previous unknown and potentially useful information from data. In this paper CART Algorithm is presented which is well known for classification task of the datamining.CART is one of the best known methods for machine learning and computer statistical representation. In CART Result is represented in the form of Decision tree diagram or by flow chart. This paper shows results of multivariate dataset Classification by CART Algorithm. Multivariate dataset Encompassing the Simultaneous observation and analysis of more than one statistical variable. Keywords: Data Mining, Decision Tree, Multivariate Dataset, CART Algorithm 1. INTRODUCTION 2. CART ALGORITHM In data mining and machine learning different classifier are CART stands for Classification and Regression Trees a used for classifying different dataset for finding optimal classical statistical and machine learning method introduced classification. In this paper Classification and Regression by Leo Breiman, Jerome Friedman, Richard Olsen and Tree or CART Algorithm is implanted on multivariate Charles Stone in 1984.it is a data mining procedure to present dataset. -
B. Gnana Priya Assistant Professor Department of Computer Science and Engineering Annamalai University
A COMPARISON OF VARIOUS MACHINE LEARNING ALGORITHMS FOR IRIS DATASET B. Gnana Priya Assistant Professor Department of Computer Science and Engineering Annamalai University ABSTRACT Machine learning techniques are applications includes video surveillance, e- used for classification to predict group mail spam filtering, online fraud detection, membership of the data given. In this virtual personal assistance like Alexa, paper different machine learning automatic traffic prediction using GPS and algorithms are used for Iris plant many more. classification. Plant attributes like petal and sepal size of the Iris plant are taken Machine learning algorithms are and predictions are made from analyzing broadly classified into supervised and unsupervised algorithms. Supervised the pattern to find the class of Iris plant. Algorithms such as K-nearest neigbour, learning is a method in which we train the Support vector machine, Decision Trees, machine using data which are well labelled Random Forest and Naive Bayes classifier or the problem for which answers are well were applied to the Iris flower dataset and known. Then the machine is provided with were compared. new set of examples and the supervised learning algorithm analyses the training KEYWORDS: Machine learning, KNN, data and comes out with predictions and SVM, Naive Bayes, Decision Trees, produces an correct outcome from labelled Random Forest data. Unsupervised learning is the training of machine using information that is 1. INTRODUCTION neither classified nor labelled and allowing Machine learning is employed in the algorithm to act on that information almost every field nowadays. Starting without guidance. Reinforcement learning from the recommendations based on our approach is based on observation. -
Research Techniques in Network and Information Technologies, February
Tools to support research M. Antonia Huertas Sánchez PID_00185350 CC-BY-SA • PID_00185350 Tools to support research The texts and images contained in this publication are subject -except where indicated to the contrary- to an Attribution- ShareAlike license (BY-SA) v.3.0 Spain by Creative Commons. This work can be modified, reproduced, distributed and publicly disseminated as long as the author and the source are quoted (FUOC. Fundació per a la Universitat Oberta de Catalunya), and as long as the derived work is subject to the same license as the original material. The full terms of the license can be viewed at http:// creativecommons.org/licenses/by-sa/3.0/es/legalcode.ca CC-BY-SA • PID_00185350 Tools to support research Index Introduction............................................................................................... 5 Objectives..................................................................................................... 6 1. Management........................................................................................ 7 1.1. Databases search engine ............................................................. 7 1.2. Reference and bibliography management tools ......................... 18 1.3. Tools for the management of research projects .......................... 26 2. Data Analysis....................................................................................... 31 2.1. Tools for quantitative analysis and statistics software packages ...................................................................................... -
The Iris Dataset Revisited... Informatica 44 (2020) 35–44 37
https://doi.org/10.31449/inf.v44i1.2715 Informatica 44 (2020) 35–44 35 The Iris Dataset Revisited – a Partial Ordering Study Lars Carlsen Awareness Center, Linkøpingvej 35, Trekroner, DK-4000 Roskilde4, Denmark E-mail: [email protected] Rainer Bruggemann Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Department: Ecohydrology D-92421 Schwandorf, Oskar - Kösters-Str. 11, Germany E-mail: [email protected] Keywords: IRIS data set, partial ordering, separability, dominance, classification Received: March 11, 2019 The well-known Iris data set has been studied applying partial ordering methodology. Previous studies, e.g., applying supervision learning such as neural networks (NN) and support-vector machines (SVM) perfectly distinguish between the three Iris subgroups, i.e., Iris Setosa, Iris Versicolour and Iris Virginica, respectively, in contrast to, e.g., K-means clustering that only separates the full Iris data set in two clusters. In the present study applying partial ordering methodology further discloses the difference between the different classification methods. The partial ordering results appears to be in perfect agreement with the results of the K-means clustering, which means that the clear separation in the three Iris subsets applying NN and SVM is neither recognized by clustering nor by partial ordering methodology. Povzetek: Analizirana je znana baza učnih domen Iris s poudarkom na nekaterih metodah, recimo gručenju. 1 Introduction One of the most often applied datasets in machine discipline with the methodological components of learning studies test cases is the Iris dataset [1, 2]. This combinatorics, algebra and graph theory, can be dataset includes 150 entries comprising 3 x 50 entries for attributed to the work of Birkhoff [5] and Hasse [6]. -
Machine Learning for Improved Data Analysis of Biological Aerosol Using the WIBS
Atmos. Meas. Tech., 11, 6203–6230, 2018 https://doi.org/10.5194/amt-11-6203-2018 © Author(s) 2018. This work is distributed under the Creative Commons Attribution 4.0 License. Machine learning for improved data analysis of biological aerosol using the WIBS Simon Ruske1, David O. Topping1, Virginia E. Foot2, Andrew P. Morse3, and Martin W. Gallagher1 1Centre of Atmospheric Science, SEES, University of Manchester, Manchester, UK 2Defence, Science and Technology Laboratory, Porton Down, Salisbury, UK 3Department of Geography and Planning, University of Liverpool, Liverpool, UK Correspondence: Simon Ruske ([email protected]) Received: 19 April 2018 – Discussion started: 18 June 2018 Revised: 15 October 2018 – Accepted: 26 October 2018 – Published: 19 November 2018 Abstract. Primary biological aerosol including bacteria, fun- The lowest classification errors were obtained using gra- gal spores and pollen have important implications for public dient boosting, where the misclassification rate was between health and the environment. Such particles may have differ- 4.38 % and 5.42 %. The largest contribution to the error, in ent concentrations of chemical fluorophores and will respond the case of the higher misclassification rate, was the pollen differently in the presence of ultraviolet light, potentially al- samples where 28.5 % of the samples were incorrectly clas- lowing for different types of biological aerosol to be dis- sified as fungal spores. The technique was robust to changes criminated. Development of ultraviolet light induced fluores- in data preparation provided a fluorescent threshold was ap- cence (UV-LIF) instruments such as the Wideband Integrated plied to the data. Bioaerosol Sensor (WIBS) has allowed for size, morphology In the event that laboratory training data are unavailable, and fluorescence measurements to be collected in real-time. -
Principal Component Analysis
02.01.2019 Prncpal component analyss - Wkpeda Principal component analysis Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called principal components. If there are observations with variables, then the number of distinct principal components is . This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors (each being a linear combination of the variables and containing n PCA of a multivariate Gaussian observations) are an uncorrelated orthogonal basis set. PCA is sensitive to distribution centered at (1,3) with a the relative scaling of the original variables. standard deviation of 3 in roughly the (0.866, 0.5) direction and of 1 in PCA was invented in 1901 by Karl Pearson,[1] as an analogue of the the orthogonal direction. The principal axis theorem in mechanics; it was later independently developed vectors shown are the eigenvectors of the covariance matrix scaled by and named by Harold Hotelling in the 1930s.[2] Depending on the field of the square root of the corresponding application, it is also named the discrete Karhunen–Loève transform eigenvalue, and shifted so their tails (KLT) in signal processing, the Hotelling transform in multivariate quality are at the mean. -
A Study on Adoption of Data Mining Tools and Collision of Predictive Techniques
ISSN(Online): 2319-8753 ISSN (Print): 2347-6710 International Journal of Innovative Research in Science, Engineering and Technology (An ISO 3297: 2007 Certified Organization) Website: www.ijirset.com Vol. 6, Issue 8, August 2017 A Study on Adoption of Data Mining Tools and Collision of Predictive Techniques Lavanya.M1, Dr.B.Jagadhesan2 M.Phil Research Scholar, Dhanraj Baid Jain College (Autonomous), Thoraipakkam, Chennai, India1 Associate Professor, Dhanraj Baid Jain College (Autonomous), Thoraipakkam, Chennai, India2 ABSTRACT: Data mining, also known as knowledge discovery from databases, is a process of mining and analysing enormous amounts of data and extracting information from it. Data mining can quickly answer business questions that would have otherwise consumed a lot of time. Some of its applications include market segmentation – like identifying characteristics of a customer buying a certain product from a certain brand, fraud detection – identifying transaction patterns that could probably result in an online fraud, and market based and trend analysis – what products or services are always purchased together, etc. This article focuses on the various open source options available and their significance in different contexts. KEYWORDS: Data Mining, Clustering, Mining Tools. I. INTRODUCTION The development and application of data mining algorithms requires the use of powerful software tools. As the number of available tools continues to grow, the choice of the most suitable tool becomes increasingly difficult. Some of the common mining tasks are as follows. • Data Cleaning − The noise and inconsistent data is removed. • Data Integration − Multiple data sources are combined. • Data Selection − Data relevant to the analysis task are retrieved from the database. -
A Comparative Study on Various Data Mining Tools for Intrusion Detection
International Journal of Scientific & Engineering Research Volume 9, Issue 5, May-2018 1 ISSN 2229-5518 A Comparative Study on Various Data Mining Tools for Intrusion Detection 1Prithvi Bisht, 2Neeraj Negi, 3Preeti Mishra, 4Pushpanjali Chauhan Department of Computer Science and Engineering Graphic Era University, Dehradun Email: {1prithvisbisht, 2neeraj.negi174, 3dr.preetimishranit, 4pushpanajlichauhan}@gmail.com Abstract—Internet world is expanding day by day and so are the threats related to it. Nowadays, cyber attacks are happening more frequently than a decade before. Intrusion detection is one of the most popular research area which provides various security tools and techniques to detect cyber attacks. There are many ways to detect anomaly in any system but the most flexible and efficient way is through data mining. Data mining tools provide various machine learning algorithms which are helpful for implementing machine- learning based IDS. In this paper, we have done a comparative study of various state of the art data mining tools such as RapidMiner, WEKA, EOA, Scikit-Learn, Shogun, MATLAB, R, TensorFlow, etc for intrusion detection. The specific characteristics of individual tool are discussed along with its pros & cons. These tools can be used to implement data mining based intrusion detection techniques. A preliminary result analysis of three different data-mining tools is carried out using KDD’ 99 attack dataset and results seem to be promising. Keywords: Data mining, Data mining tools, WEKA, RapidMiner, Orange, KNIME, MOA, ELKI, Shogun, R, Scikit-Learn, Matlab —————————— —————————— 1. Introduction These patterns can help in differentiating between regular activity and malicious activity. Machine-learning based IDS We are living in the modern era of Information technology where most of the things have been automated and processed through computers.