A Hybrid Theory Of Power Theft Detection

A Hybrid Theory Of Power Theft Detection
A HYBRID APPROACH TO POWER THEFT DETECTION Abstract:– Now a day's electricity
theft is a major issue face by all electricity companies. Since electricity theft directly affect the profit
made by electricity companies, detection and prevention of electricity theft is necessary. In this
paper we are proposing a hybrid approach to detect the electricity theft. We will use SVM and ELM
for our approach. Introduction:– As we know electricity theft is a major problem for all electricity
companies. This problem is not related to Indian companies only; other country's electricity
companies also face this problem. Electricity companies losses money every year due to theft. There
are two types of losses namely transmission loss and non–transmission loss, some research papers
uses term technical loss and non–technical loss respectively. Transmission loss occurs while
transmitting energy form generation side to consumer's side. Non–Transmission losses occur due to
wrong billing, false meter reading, electricity theft, etc. First two losses can be prevented by taking
proper meter reading and calculating accurate bill for electricity consume, but electricity theft is
hard to prevent since no one predict about which consumer is honest or dishonest. Still losses due to
electricity theft can be reduce by detecting theft or fraud consumer and taking actions accordingly.
Figure 1. Ration of Electricity losses [1] Theft detection is done manually by inspecting consumers.
This is
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Text Analytics And Natural Language Processing
IV. SENTIMENT ANALYSIS A. The Sentiment analysis process i) Collection of data ii)
Preparation of the text iii) Detecting the sentiments iv) Classifying the sentiment v) Output i)
Collection of data: the first step in sentiment analysis involves collection of data from user. These
data are disorganized, expressed in different ways by using different vocabularies, slangs, context of
writing etc. Manual analysis is almost impossible. Therefore, text analytics and natural language
processing are used to extract and classify[11]. ii) Preparation of the text : This step involves
cleaning of the extracted data before analyzing it. Here non–textual and irrelevant content for the
analysis are identified and discarded iii) Detecting the sentiments: All the extracted sentences of the
views and opinions are studied. From this sentences with subjective expressions which involves
opinions, beliefs and view are retained back whereas sentences with objective communication i.e
facts, factual information are discarded iv) Classifying the sentiment: Here, subjective sentences are
classified as positive, negative, or good, bad or like, dislike[1] v) Output: The main objective of
sentiment analysis is to convert unstructured text into meaningful data. When the analysis is
finished, the text results are displayed on graphs in the form of pie chart, bar chart and line graphs.
Also time can be analyzed and can be graphically displayed constructing a sentiment time line with
the chosen

Nt1310 Unit 4 Test Report
The training data contained both labeled data D_la={〖x_i,y_i}〗_(i=1)^kl and unlabeled data
D_un= {〖x_j}〗_(j=kl+1)^(kl+u) where x_(i ) is the feature descriptor of image I and y_i={1,...,k}
is its label .k is the number of categories. l is the number of labeled data in each category, and u is
the number of unlabeled data. Our method aims to learn a high–level image representation S by
exploiting the few labeled data D_land great quantities of unlabeled ones, which is then fed into
different classifiers to obtain final classification results. The procedure of semisupervised feature
learning by SSEP is shown in Fig. 1. First, a new sampling algorithm based on GNA [19] is
proposed to produce T WT sets P^t={(〖s_i^t,c_i^t)}〗_(i=1)^kp , t ∈{1,.....,T}

Classification And Novel Class Detection Approaches Of...
A Survey On Various Classification And Novel Class Detection Approaches Of Feature Evolving
Data Stream Abstract: The classification of data stream is challenging task for data mining
community. Dynamic changing nature of data stream has some difficulties such as feature evolution,
concept evolution, concept drift and infinite length. As we know that the data streams are huge in
amount, it is impractical to store and use all the data for training. Concept drift occurs when
underlying concept changes. Concept–evolution occurs as a result of new classes evolving in the
stream. Another important characteristic of data streams, namely, feature evolution, in data stream
new features emerge as stream advancement. In this paper we discuss the ... Show more content on
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Various techniques have been proposed to address this problem. In order to deal with concept drift,
classification model must be updated with recent data. Another characteristic of data stream is
concept evolution, when new classes evolve in data concept evolution occurs. In order to deal with
this problem classification model must be able to detect novel classes when they appear. For
example intrusion detection in a network traffic. Most important characteristic is feature evolution in
which new features (words) emerge and old features fade away. Ensemble techniques have been
more popular than single model [1]. In this technique more than one classifier is used for
classification with higher efficiency. Each classifier in the classification model is trained on different
data chunks. With the help of advanced data streaming technologies [2], we are now able to collect
large volume of data for different application domains. For example credit card transaction, network
traffic monitoring etc. the presence of irrelevant and redundant data slows down the learning
algorithms [3] [4]. By removing or ignoring irrelevant and redundant feature, prediction
performance and computational efficiency can be improved. Multiclass miner works with dynamic
feature vector and detects novel classes. It is a combination of OLINDDA and FAE approach.
OLINDDA and FAE are used to detect novel classes and to classify data chunks

Evaluation And Workflow Design And Quality Assessment
Although crowdsourcing has been successfully applied in many fields in the past decades,
challenges still exist especially in task/workflow design and quality assessment. We take a deeper
look at crowdsourcing classification tasks, and explore how task and workflow design can impact
the answer quality. Our research is intended to use large knowledge base and citizen science projects
as examples and investigate the workflow design considerations and its impact on worker
performance as well as overall quality outcome based on statistical, probabilistic, or machine
learning models for quality answer prediction, such that optimal workflow design principles can be
recommended and applied in other citizen science projects or other human–computer ... Show more
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However, challenges still remain no matter it is via volunteered activities
(cite{Lease2011,Newman2012}) or paid–microtask platforms
(cite{Kittur2013a,Demartini2015,Bernaschina2015}). Some of the most important challenges
include: task and workflow design, and quality assessment. As cite{Kittur2013a} point out, though
there are some initial research in complex workflow, we have little knowledge of the broader design
space of workflow and it is impossible to simply aggregate multiple independent judgements for
complex tasks which may have dependencies between microtasks. Task and workflow design
(cite{little2010turkit,Kittur2011,demartini2012zencrowd}) are crucial in ensuring question is
properly understood, mitigating the chance of spam and keeping user engaged. They are essential to
obtain high quality and quantity of answers. Quality assessment on the fly

Classification Between The Objects Is Easy Task For Humans
Classification between the objects is easy task for humans but it has proved to be a complex
problem for machines. The raise of high–capacity computers, the availability of high quality and
low–priced video cameras, and the increasing need for automatic video analysis has generated an
interest in object classification algorithms. A simple classification system consists of a camera fixed
high above the interested zone, where images are captured and consequently processed.
Classification includes image sensors, image preprocessing, object detection, object segmentation,
feature extraction and object classification. Classification system consists of database that contains
predefined patterns that compares with detected object to classify in to proper category. Image
classification is an important and challenging task in various application domains, including
biomedical imaging, biometry, video surveillance, vehicle navigation, industrial visual inspection,
robot navigation, and remote sensing. Fig. 1.1 Steps for image classification Classification process
consists of following steps a) Pre–processing– atmospheric correction, noise removal, image
transformation, main component analysis etc. b) Detection and extraction of a object– Detection
includes detection of position and other characteristics of moving object image obtained from
camera. And in extraction, from the detected object estimating the trajectory of the object in the
image plane. c) Training: Selection of the

Classification Accuracy Of Statistical Software...
The Comparisons of Classification Accuracy of Statistical Software Performance for Default of
Credit Card Clients
Meixian Wang
University of New Hampshire
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1 Abstract . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3 1.3 Literature Review on Seven Data Mining Techniques . . . . . . . . . . . . . . . . . 6 1.4 Methods for
Classification Assessment and Comparison . . . . . . . . . . . . . . . 8
2 Classification Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.1 Description of the
Software and Data . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Classification Results . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . 12 2.3 Accuracy Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.4 Conclusion . .

Neural Stack Essay
Neural stack is a type of data structure. Neural network helps in learning push and pull the neural
network by using the back propagation. There are some of the pre–requisite of this understanding of
neural network in general. It is better if we understand how neural networks will help to push the
stack on sequences and pull off it in a reverse order. It is better to have a sequence to be pushed over
6 numbers by popping 6 times and pushing it over 6 times and reverse the list in correct sequence.
Here, neural stack comes into existence by accepting the clear inputs and transforming it to the
pattern over the learned data. Neural stacks help in inputting and accepting the data as well popping
and pushing it accordingly so that it will ... Show more content on Helpwriting.net ...
Some researchers are skeptical about the success of deep learning.
STATE BEFORE DEEP LEARNING
Deep learning is also called as machine learning it is a technique where the computers do naturally
likewise humans. If consider the driverless car deep learning and machine learning is a reason
behind it. Deep learning is also a reason behind the recognition of stop sign, voice control over the
stop sign and hands free speakers etc. deep learning success was seen later it was impossible without
the pervious strengths that adapted deep learning. Before the deep learning, machine learning came
into existence and was a part of machine learning. Deep learning is just a part of machine learning
algorithms it used many layers and processing of nonlinear to units its feature for transformation and
extension. These algorithms have been an important supervision of applications that includes of
pattern and classification and it involves multiple layers of data that helps in representation of
certain features. These definitions are one of the common layers that is used in non–liner processing
over the generative models that includes of hidden layers

Nt1310 Unit 1 Literature Review
2.2. RELATED WORK 2. 2.1. SECURE k–NEAREST NEIGHBOR TECHNIQUES Retrieving the
k–Nearest Neighbors to a given query (q) is one of the most fundamental problems in many
application domains such as similarity search, pattern recognition, and data mining. In the literature,
many techniques have been proposed to address the SkNN problem, which can be classified into
two categories based on whether the data are encrypted or not: centralized and distributed.
Centralized Methods: In the centralized methods, the data owner is assumed to outsource his/her
database and DBMS functionalities (e.g., kNN query) to an untrusted external service provider,
which manages the data on behalf of the data owner, where only the trusted users are allowed to
query the ... Show more content on Helpwriting.net ...
In the past decade, a number of PPDM techniques have been proposed to facilitate users in
performing data mining tasks in privacy–sensitive environments. Agrawal and Srikant [3], as well as
Lindell and Pinkas [63], were the first to introduce the notion of privacy–preserving under data
mining applications. Existing PPDM techniques can be classified into two broad categories: data
perturbation and data distribution. Data Perturbation Methods: With these methods, values of
individual data records are perturbed by adding random noise in such a way that the distribution of
the perturbed data look very deferent from that of the actual data. After such a transformation, the
perturbed data is sent to the Miner to perform the desired data mining tasks. Agrawal and Srikant [3]
proposed the first data perturbation technique that could be used to build a decision–tree classifier. A
number of randomization–based methods were later proposed [6, 33, 34, 73, 104]. Data perturbation
techniques are not, however, applicable to semantically– secure encrypted data. They also fail to
produce accurate data mining results due to the addition of statistical noises to the data. Data
Distribution Methods: These methods assume that the dataset is partitioned
eitherhorizontallyorverticallyanddistributedacrossdiﬀerentparties. The parties

Character Recognition By Machines, An Innovative Way By...
Abstract–Character Recognition by machines is an innovative way by which the dependence on
manpower is reduced. Character recognition provides a reliable alternative of converting manual
text into digitized format. Now–a–days, as technology becomes integral part of human life, many
applications have enabled the incorporation of English OCR for real time inputs. The advantages
that the English alphabet has is its simplicity offered by less number of letters i.e. 26 and easier
classification due to the concept of lowercase and uppercase. If we consider Devnagari script in this
scenario, we will come across myriad hurdles because this script lacks the simplicity of English. The
concept of fused letters, modifiers, shirorekha and spitting similarities in some letters make
recognition difficult. Also, character recognition for handwritten text is far more complex than that
for machine printed characters. This is because of the versatility and different writing techniques
adopted by people. The direction of strokes, pressure applied on writing equipments, quality of
writing equipment and the mentality of the writer itself highly affects the written text. These
problems when combined with the intricate details of Devnagari script, the complications in
constructing a HCR of this script are increased. The proposed system focuses on these two issues by
adopting Hough transform for detecting features from lines and curves. Further, for classification,
SVM is used. These two methods

Predictive Analytics And The Health Care Industry
Before proceeding to review a range of predictive analytic algorithms, it is important to know how
critical predictive analytics is to the health care industry. The growth rate of US healthcare
expenditures, increasing annually by nearly 5% in real terms over the last decade and a major
contributor to the high national debt levels projected over the next two decades. McKinsey estimates
that Big Data can enable more than $300 billion savings per year in US healthcare, with two–thirds
of that through reductions of around 8% to national healthcare expenditures. Imagine if there were
health care analytics in the middle ages. The black plague could have been avoided saving millions
of lives of people as it would have been easy to single out the ... Show more content on
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It could consist of patient–related data, data from healthcare devices like monitors and sensors,
hospital records, application data measuring health metrics and everything including social media
posts, webpages, emergency correspondence, research data from genomics to innovative drugs,
advertisement data, newsfeeds and articles in medical journals. As much as there is scope for finding
out patterns among these data, it is not easy to implement predictive analytics in healthcare industry
because of the limitations like hand–written prescriptions, scanned images and medical records
which comprise of unstructured and disintegrated data. Moreover, medical data is involved with
legal and privacy issues. The adoption rate of analytics in healthcare industry is quite slow making it
more challenging. The Why of applying predictive analytics in healthcare: If predictive analytics is
applied extensively to the rapidly growing healthcare industry, limitless advantages can be realized.
Some of the advantages are: 1) Improved real–time decisions about treatment and support, consumer
commitment 2) Effortless revenue management with focus on global as well as local markets 3)
Standardized clinical processes, guidelines and protocols greatly improving operational efficiency 4)
Reduction in fraud claims, security threats greatly helping insurance companies 5) Mining for
unknown variables that determine quality such as "hidden" re–admission factors or finding out

A Machine Learning Approach For Emotions Classification
A machine learning approach for emotions classification in Micro blogs ABSTRACT Micro
blogging today has become a very popular communication tool among Internet users. Millions of
users share opinions on different aspects of life every day. Therefore micro blogging web–sites are
rich sources of data for opinion mining and sentiment analysis. Because micro blogging has
appeared relatively recently, there are a few research works that are devoted to this topic.In this
paper, we are focusing on using Twitter, which is an amazing microblogging tool and an
extraordinary communication medium for text and social web analyses.We will try to classify the
emotions in to 6 basic discrete emotional categories such as anger, disgust, fear, joy, sadness and
surprise. Keywords : Emotion Analysis; Sentiment Analysis; Opinion Mining; Text Classification 1.
INTRODUCTION Sentiment analysis or opinion mining is the computational study of opinions,
sentiments and emotions expressed in text. Sentiment analysis refers to the general method to
extract subjectivity and polarity from text.It uses a machine learning approach or a lexicon based
approach to analyse human sentiments about a topic..The challenge for sentimental analysis lies in
identifying human emotions expressed in these text. The classification of sentiment analysis goes as
follows: Machine Learning is the field of study that gives computer the ability to learn without being
explicitly programmed. Machine learning explores the

Questions On Deep Learning Technique Essay
1.4.3 Deep Learning Technique
Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it,
and then make a determination or prediction about something in the world. So rather than hand–
coding software routines with a specific set of instructions to accomplish a particular task, the
machine is "trained" using large amounts of data and algorithms that give it the ability to learn how
to perform the task [12]. Deep learning is another Machine Learning (ML) algorithm. Deep learning
is essentially a set of techniques that help you to parameterize deep neural network structures, neural
networks with many, many layers and parameters. Deep Learning breaks down tasks in ways that
makes all kinds of machine assists seem possible, even likely. The confusion matrix, in Figure 8
shows that the accuracy of this model is (90.80) with weighted average precision (91.37) greater
than recall (91.11) and F1–score (91.24). From the above results, it appears that Deep Learning
classifier achieve higher accuracy, precision, recall, and F1–score. Figure 11: Clustering accuracy
using Deep Learning Technique
1.5 Results Comparison
Table 2: Performance Measures Comparison
Model Decision Trees Naïve Bays Deep Learning
Domain precision recall precision recall precision recall food 100.00 25.93 58.06 66.67 46.55
100.00 communication 63.77 95.65 88.89 86.96 100.00 100.00 education 83.54 88.26 88.65 88.26
90.22 88.26 medical 61.67 62.71

Data Extraction Of Knowledge From High Volume Of Data Essay
Introduction:
Data mining is extraction of knowledge from high volume of data. In this data stream mining
experiment, I have used "sorted.arff" dataset contains 540888 instances and 22 attributes. I have
tried two single algorithms and two ensemble algorithms, tested the accidents on road for last 15
years.
Weka: Data Mining Software
Weka ("Waikato Environment for knowledge Analysis") is a collection of algorithms and tools used
for data analysis. The algorithms can be applied directly or it can be called using java code, an
object oriented programming language. It contains tools for pre–processing, classification,
regression, clustering, associating, select attributes and visualization on given dataset. The
advantages of using WEKA software is, it is freely available and platform independent. It is simple
tool and it can be used by non–specialist of data mining. For testing, it doesn't need any
programming code at all.
WEKA can identify .arff file format. It can classify the dataset present in .arff file. First open the file
sorted.arff, second, test the file with few algorithms with respect to accuracy and finally predict the
value of D1 factor. The screenshot 1 is the pre–processing of 22 attributes in Weka and last attribute
D1 factor is analysed using algorithms.
Screenshot 1: Graphs of pre–processed data
Algorithms Considered:
There are different types of machine learning logarithms available to solve the classification
problems. To carry out this experiment

Innovations in Handwriting Recognition Essay
Emergence networks mimics biological nervous system unleash generations of inventions and
discoveries in the artificial intelligent field. These networks have been introduced by McCulloch and
Pitts and called neural networks. Neural network's function is based on principle of extracting the
uniqueness of patterns through trained machines to understand the extracted knowledge. Indeed,
they gain their experiences from collected samples for known classes (patterns). Quick development
of neural networks promotes concept of the pattern recognition by proposing intelligent systems
such as handwriting recognition, speech recognition and face recognition. In particular, Problem of
handwriting recognition has been considered significantly during ... Show more content on
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The first study discusses the basic operations of erosion and dilation and present a system to
recognize six handwritten digits. For this purpose, a novel method to recognize cursive and
degraded text has been found by Badr & Haralick (1994 ) through using technology of OCR. Parts
of symbols (primitives) are detected to interpret the symbols with this method (KUMAR et al.
2010). The study involves mathematical morphology operations to perform the recognition process.
Cun et al. (1998) challenge this problem by designing a neural based classifier to discriminate
handwritten numeral. This study achieved a reliable system with very high accuracy (over 99%) on
the MINIST database. Moreover, the gradient and curvature of the grey character image have been
taken into consideration by Shi et al. (2002 ) to enhance the accuracy of handwriting digits
recognition. Uniquely, Teow & Loe (2002 ) identify new idea to solve this problem based on
biological vision model with excellent results and very low error rate (0.59%). The discoveries and
development have been continuing through innovating new algorithms and learning rules. Besides
efficiency of using these rules individually in the machine learning, some researchers have going
further in developing the accuracy and performance of the learning by mixing several rules to
support one

Social Values: What Is A Personal Value?
What is a personal value?
A personal value is an individual's absolute or relative and ethical value, the assumption of which
can be the basis for ethical action. A value system is a set of consistent values and measures. A
principle value is a foundation upon which other values and measures of integrity are based. Some
values are physiologically determined and are normally considered objective, such as a desire to
avoid physical pain or to seek pleasure. Other values are considered subjective, vary across
individuals and cultures, and are in many ways aligned with belief and belief systems. Types of
values include ethical/moral values, doctrinal/ideological (religious, political) values, social values,
and aesthetic values. It is debated whether some values that are not clearly physiologically
determined, such as altruism, are intrinsic, and whether some, such as acquisitiveness, should be ...
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The first are personal life value priorities – Determining candidates most important current values
(e.g., money, location, service to others, time with family), rank–ordering and deciding which will
trade off if faced with a contradiction (e.g., the job you want not being available in the location you
want). As said earlier, many people keep themselves in a state of continual agitation by refusing to
make focused value decisions.
The second are personal job–content objectives – Identifying what specific combination of skills or
competencies (e.g., intellectual, technical, interpersonal, physical, artistic, mathematical, etc.)
candidates want to develop and exercise in their future on–the–job activities. These objectives
become their criteria for judging the content of potential employee, if a potential opening involves
doing a lot of financial or technical analysis by their self with no opportunity for interacting with
others, some candidates will avoid that job even if it is a

Standardized Databases And Benchmarks For Experiment
In order to evaluate which approach is better in this field, some standardized databases and
benchmarks for experiment are designed. Many databases are designed for different kinds of
methods, owing that different methods may have different assumptions on shapes. A commonly used
database is 99shapes, by Kimia et al. It contains ninety nine planar shapes which classified into nine
classes, with eleven shapes in each. Shapes in the same class are in different variant form, including
occluded, noised, rotated, etc. Other databases including MPEG–7 Shape Dataset [5], Articulated
Dataset, Swedish Leaf Dataset and Brown Dataset are used to have further experiments. Similar to
[13], Precision and Recall is used for benchmark for the reason of fair comparisons. C. Results and
Discussion Table I shows the optimal result from test on 99shape dataset. The numbers of points we
sampled from the shapes are 50, 50 and 25 for RSD, RAD and TF respectively. For the articulated
dataset, 45, 35 and 45 points are sample for RSD, RAD and TF. Retrieval result on articulated
dataset was presented in Table I. We have noticed that result on 99shape from RAD is slightly better
than RSD, while on articulated dataset RSD performs slightly better than RAD. During the above
experiment, we tried to normalize the descriptors and found that experiment on 99 shapes received
little influence from normalization while result from articulated dataset has some improvement.
From the Table I, our

The Analysis On The System
First, data is prepared for being processed through sequential subsequent phases. First for every
image per subject is loaded in the gray scale mode. Next, vein image is adjusted using a threshold
detected adaptive for each image. The vein image is segmented to recognize the region of interest,
vein region. For the vein region a feature extractor is used to extract the most power features exists.
These features are stored labeled for the subject name for father classification and recognition.
Dataset Splitter The Dataset stacking stage is the procedure in charge of dividing the dataset into
two sections got from the preprocessing module. Holdout system is utilized to part the dataset into
two sections where given information are ... Show more content on Helpwriting.net ...
Histogram of Gradient, HOG, is a filter based on a moveable window that is playing important role
of quantity and quality of the features extracted from the vein shape. In finger vein of any person,
the vein contain thick sharp lines in horizontal and vertical direction which represents constrains
over the process of feature extraction. In the used dataset, vein image is directed in horizontal
direction that leads to modify the window of the HOG filter to be adaptive over the vein lines. The
window of the filter start scans from left to right at the top towards bottom. The window used for
extracting features based on mining the rectangle region bounded by the window circumstance. The
set of features is adaptive incremental to get all over the features of the vein for being vectored.
HOG is effective feature extraction mechanism for getting feature in gray level color schema based
on gradient operator. HOG is proposed by using a rectangular cell moving over the pixels of image
regardless of direction or histogram of the image. The proposed modification of the HOG cell leads
to gain the pros of both ordinary HOG approach besides tracking line feature that track the direction
of line. That is achieved by dedicating the vein direction in horizontal followed by a directed
window. On other hand of the proposed recognition system, another feature extraction is modified
and optimized to be used known as

Data Mining Information About Data
Abstract– Data Mining extracts useful information about data. In other words, Data Mining extracts
the knowledge or interesting information from large set of structured data that are from different
sources. Data mining applications are used in a range of areas such as it is used for financial data
analysis, retail and telecommunication industries, banking, health care and medicine. In health care,
the data mining is mainly used for disease prediction. In data mining, there are several techniques
have been developed and used for predicting the diseases that includes data preprocessing,
classification, clustering, association rules and sequential patterns. This paper analyses the
performance of two classification techniques such as Bayesian ... Show more content on
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The medical data processing has the high potential in medical domain for extracting the hidden
patterns within the dataset [15]. These patterns are used for clinical diagnosis and prognosis. The
medical data are generally distributed, heterogeneous and voluminous in nature. An important
problem in medical analysis is to achieve the correct diagnosis of certain important information.
This paper describes classification algorithms and it is used to analyze the performance of these
algorithms. The accuracy measures are True Positive (TP) rate, F Measure, Receiver Operating
Characteristics (ROC) area and Kappa Statistics. The error measures are Mean Absolute Error
(M.A.E), Root Mean Squared Error (R.M.S.E), Relative Absolute Error (R.A.E) and Relative Root
Squared Error (R.R.S.E) [5]. Section 2 explains the literature review; Section 3 describes the
classification algorithms. Experimental results are analyzed in section 4 and section 5 illustrates the
conclusion of this paper.
II. LITERATURE REVIEW Dr. S.Vijayarani et al., [11] determined the performance of various
classification techniques in data mining for predicting the heart disease from the heart disease
dataset. The classification algorithms is used and tested in this work. The performance factors
evaluate the efficiency of algorithms, clustering accuracy and error rate. The result illustrates
LOGISTICS classification function efficiency is better than multilayer perception and sequential

Definition And Application Of Pattern
Review on Pattern Recognitions –Rajat B.
Abstract
Pattern recognition is a technique to differentiate different pattern into classes through the help of
supervised or unsupervised technique. We have developed highly sophisticated skills for sensing the
environment and taking actions according to what we observe. This sensing and understanding is
mostly dependent on ability to differentiate between patterns. The pattern recognition ability if with
the help of machine learning can be applied in machine. The machine ability to make decision like
human being will be enhanced. Many applications such as data mining, web searching, face
recognition etc has already been in uses which are based on the pattern recognitions. The objective
of this review paper is to summarize and compare some of the well–known methods and application
used in pattern recognition system.
Keywords–
Pattern recognition, classification, clustering, machine learning, error estimation, neural networks.
Introduction
A pattern is an entity, that could be given a name and pattern recognition is the study of how
machines can observe the environment and make sense of it by differentiating between patterns.
Humans are best pattern recognizers but we do not understand how we recognize patterns. Why we
need the pattern recognition? The answer is more relevant patterns at our disposal, the better
decisions we can take. The challenge in pattern recognition is that the

Boosted Decision Tree Essay
chapter{Multivariate Analysis For Particle Identification}
Multivariate data analysis and machine learning become a useful tool in high–energy physics. The
need of more sophisticated data analysis algorithms arose with the increased complexity of the
classification problem.
In T2K, selecting a neutrino interaction event is like picking the needle from the haystack, due to the
tiny neutrino cross–section and a large number of background events.
Nevertheless, increasing the selection purity and efficiency is crucial for precision measurement of
neutrino cross–section.
In this thesis, a machine learning algorithm called Boosted Decision Tree (BDT) is used as a particle
identification (PID) classifier.
Information gained from the ND280 ... Show more content on Helpwriting.net ...
To illustrate this idea, ~cref{fig:MVA_KIT} shows the signal and the background distribution for
two measured variables, var0 and var1, of a toy example.
Using tradition cuts on var0 or var1 will result in very poor efficiency. However, visualising the
two–dimensional distribution of var0 and var1, one can find a better decision boundary to separate
signal from background.
begin{figure}[H]
centering
includegraphics[scale = 0.55]{./Include/MVAdv.jpg}
caption{Single and multivariate cut effects on correlated data. Signal (in blue) and background (in
red) normalised probability distribution for var0 and var1 of a toy example are shown at the left and
the centre plots respectively.
A better decision boundary, using variables, correlation, is shown (in green) in the right plot. Figure
courtesy of ~cite{MVA_KIT}. } label{fig:MVA_KIT}
end{figure}
The usage of variables correlation increases the efficiency and purity of the selection.
It may be possible to visualise such relationship for two– or three–dimensional problems, yet, a
computer algorithm will be needed to optimise the decision boundary in higher–dimensional feature
spaces.
section{Event Classification}
Each event, signal or background, has ``D'' measured variables that construct a D–dimensional
feature space, for instance, the features used in the positron selection are given in

cref{table:BDT_InputVariables}.
A machine learning algorithm is a map from the D–dimensional

Markov Random Fields ( 3D ) Microstructural Map Of Materials
I. Abstract: The objective of this proposal is to develop an open–source code to generate validated
three–dimensional (3D) microstructural map of materials by coupling Markov Random Fields
(MRFs) with targeted experimental sampling. MRF is a mathematical model, in which state of a
voxel can be modeled by knowing the state of its neighbors. In this work, microstructures will be
generated synthetically based on an adaptively measured set of experimental micrographs. These
microstructures will be used to fill in gaps in information at the component–scale level. Currently
available methods for microstructure synthesis in advanced aerospace alloys, such as Aluminum–
Lithium (Al–Li) and Titanium (Ti), run into various difficulties when modeling ... Show more
Yet, new computational algorithms are needed to achieve microstructural reconstruction at the scale
of entire engineering components. In this work, MRF models will be integrated with advanced data
acquisition techniques to investigate large scale adaptive computational microstructure
reconstruction.
IV. Proposed Work: The work will utilize an autonomous optical microstructure measurement
platform, called Robo–Met 3D® (shown in Fig 1), for autonomous sampling and validation of MRF
computational modeling. This system, currently available at the University of Michigan,
sequentially polishes away layers of material with high accuracy and enables metallographic etching
and imaging of the microstructure of materials. Post–processing reassembles these two–dimensional
(2D) images, into 3D models [4]. In this work, robotic microscopy and the MRF algorithm will be
used together to computationally reconstruct microstructures at adaptive locations. The MRF
algorithm utilizes an iterated convergence criterion that minimizes the differences between the
neighbors of a 3D voxel and the corresponding 2D experimental images. In this project, the iterative
process will be carried out in a multiscale fashion, starting with a coarse voxel to a finer mesh, once
the coarser 3D image has converged to a local minimum. The next step in this project will be to
generate a montage of the 3D microstructures

The Sentiment Analysis Review
Abstract– Sentiment analysis is the computational study of opinions, sentiments, subjectivity,
evaluations, attitudes, views and emotions expressed in text. Sentiment analysis is mainly used to
classify the reviews as positive or negative or neutral with respect to a query term. This is useful for
consumers who want to analyse the sentiment of products before purchase, or viewers who want to
know the public sentiment about a new released movie. Here I present the results of machine
learning algorithms for classifying the sentiment of movie reviews which uses a chi–squared feature
selection mechanism for training. I show that machine learning algorithms such as Naive Bayes and
Maximum Entropy can achieve competitive accuracy when trained using features and the publicly
available dataset. It analyse accuracy, precision and recall of machine learning classification
mechanisms with chi–squared feature selection technique and plot the relationship between number
of ... Show more content on Helpwriting.net ...
Feature Selection
The next step in the sentiment analysis is to extract and select text features. Here feature selection
technique treat the documents as group of words (Bag of Words (BOWs)) which ignores the position
of the word in the document.Here feature selection method used is Chi–square (x2).
A chi–square test also referred to as a statistical hypothesis test in which the sampling distribution of
the test statistic is a chi–square distribution when the null hypothesis is true. The chi–square test is
used to determine whether there is a significant difference between the expected frequencies and the
observed frequencies in one or more categories.
Assume n be the total number of documents in the collection, pi(w) be the conditional probability of
class i for documents which contain w, Pi be the global fraction of documents containing the class i,
and F(w) be the global fraction of documents which contain the word w. Then, the x2–statistic of the
word between word w and class i is defined[1]

Literature Review On Seven Data Mining Techniques Essay
1.3 Literature Review on Seven Data Mining Techniques 1.3.1 K–Nearest Neighbor Classifiers
(KNN) Given a positive number K and an unknown sample, a KNN classifier searches the K closest
observations in training set to the unknown sample. It then classifies the unknown sample into the
class with the smallest distance. The advantage of KNN is that it does not need to estimate the
relationship between the response and the predictors (Shmueli, et al. 2016), while this method is
dramatically affected by the number of Nearest Neighbors (James, et al. 2013). 1.3.2 Logistic
Regression (LR) LR shares a similar idea with linear regression except its response is a categorical
variable. It estimates the probability that an unknown observation in the training set belongs to one
of the classes (James, et al. 2013). The major disadvantage of LR is that it poorly deals with the
model that exists multicollinearity issue (Shmueli, et al. 2016). However, it provides a
straightforward classification with probability. 1.3.3 Classification Trees (CT) CT estimate a
probability for each class in each node to classify a qualitative response. It does not require any
variable subset selections and variable transformation. But the tree structure has an inherent
weakness which is that it is unstable and is highly affected by a small change in the data (Shmueli,
et al. 2016). 1.3.4 Random Forests (RF) RF first draws multiple random samples with replacement
from the training

International Statistical Classification Of Diseases And...
In today's technological world patients are choosing where they receive their care based on research
and public access to hospitals quality of care numbers. Hospitals are competing with other hospitals
for patients. In order to attract patients hospitals are improving their quality of care by providing
safe and efficient care. Advancements in Medical Technology has made it possible for Health care
providers to better diagnose and treat their patients, one of those medical advancements is
conversion of International Statistical Classification of Diseases and Related Health Problems 9th
edition (ICD–9) to International Statistical Classification of Diseases and Related Health Problems
10th edition (ICD–10).
ICD–10 a medical classification list went live on October 1, 2015 for U.S healthcare industry after
experiencing many lengthy. The adoption of ICD–10 codes offer many more relegation options
compared to ICD–9 (Rouse, 2015). With ICD–10 codes, healthcare officials properly document
diseases on patient's charts, government agencies track epidemiology trends, and insurance carriers
assist in medical reimbursement decisions. ICD–10 codes are developed by The World Health
Organization (WHO) and are adopted by the rest of the healthcare system in the United States.
ICD–9 was introduced in 1979. With the advancement in medicine and the direction healthcare has
gone into a new set of coding was needed that supported advances in modern technology and
medical devices.ICD–10 is

The Test On 99shape Dataset
which classified into nine classes, with eleven shapes in each. Shapes in the same class are in
different variant form, including occluded, noised, rotated, etc. Other databases including MPEG–7
Shape Dataset [5], Articulated Dataset, Swedish Leaf Dataset and Brown Dataset are used to have
further experiments. Similar to [13], Precision and Recall is used for benchmark for the reason of
fair comparisons. C. Results and Discussion Table I shows the optimal result from test on 99shape
dataset. The numbers of points we sampled from the shapes are 50, 50 and 25 for RSD, RAD and
TF respectively. For the articulated dataset, 45, 35 and 45 points are sample for RSD, RAD and TF.
Retrieval result on articulated dataset was presented in Table I. We have noticed that result on
99shape from RAD is slightly better than RSD, while on articulated dataset RSD performs slightly
better than RAD. During the above experiment, we tried to normalize the descriptors and found that
experiment on 99 shapes received little influence from normalization while result from articulated
dataset has some improvement. From the Table I, our algorithm has a almost 100% correct classified
rate for Human and Wrench. We noticed that the Airplanes class is of the lowest correct rate except
for the top 3 ranks. And the hit rate declined rapidly which make it singled out from the Table I. The
matching distance in this class is carefully investigated and the distance revealed that our descriptors

Voting Based Neural Network: Extreme Learning Machine Essay
Extreme learning Machine (ELM) [1] is a single hidden layer feed forward network (SLFN)
introduced by G. B. Huang in 2006. In ELM, the weights between input and hidden neurons and the
bias for each hidden neuron are assigned randomly. The weight between output neurons and hidden
neurons are generated using the Moore Penrose Generalized Inverse [18]. This makes ELM a fast
learning classifier. It surmounts various traditional gradient based learning algorithms [1] such as
Back Propagation (BP) and well known classifier Support Vector Machine (SVM) .
In order to improve the performance various variants of the ELM came over time such as Enhanced
Incremental ELM (EI–ELM)[2], Optimal Pruned ELM (OP–ELM) [3], Convex Incremental ELM
(CI–ELM)[4], ... Show more content on Helpwriting.net ...
Mainly ensemble pruning [12] approaches are categorized into three types.
a). Ordering Based Pruning: In this pruning approach the classifiers are arranged using some criteria
and some of the top classifiers are selected as a Pruned Ensemble (PE). Some of the Ordering Based
Pruning approaches are as follows: Kappa Pruning [12], Reduce Error Pruning [12], Minimum
Distance Minimization Pruning(MDP) [12], Pruning via Individual Contribution Ordering [13],
Ensemble Pruning Using Spectral Coefficient [14].
b) Optimization based pruning is a pruning approach which uses evolutionary techniques for
pruning such as Genetic Algorithm (GA). A fitness function is genetically optimized to get a subset
of classifiers which minimizes the error. Various variants of genetic based ensemble pruning have
been proposed such as Genetic Algorithm based Selective Neural Network Ensemble (GASEN)
[15], GAB: EPA [16]. Objective of GASEN is to select the best PE and maximize the accuracy of
the PE by assigning the best weight to the classifiers of the PE. It uses fitness function, which is
function of the generalization error minimized by genetic algorithm. GAB:EPA [16] was proposed
for handling multiclass imbalanced data sets, diversity factor was also incorporated in fitness
function to improve the performance.
c) Cluster Based Pruning Technique: In such type of pruning technique many clusters of the
component classifiers are made and from

When Popularity Of Machine Learning Models Increased, A...
When popularity of machine learning models increased, a number of automated trading systems
were build around these models. But rst, let 's take a look at the history of machine learning
models in the eld of nancial predictions. At rst, White (1988) applied arti cial neural
networks
(ANN) to reveal nonlinear regularities in the IBM stock price movements. Subsequently, Kamijo
and Tanigawa (1990) used a recurrent neural network for the recognition of price patterns in the
Japanese market. Cheng, Wagner, and Lin (1996) used an ANN to predict the weekly price direction
of the 30–year U.S. treasury bonds and averaged an annualized return on investment of
17:3%. Later, A.–S. Chen, Leung, and Daouk (2003) predicted the direction of the return on the
Taiwan Stock Exchange Index and showed that the ANN based strategies outperformed the random
walk model and the generalized methods of moments with Kalman lter. Despite the reported
success, many researchers have shown that neural networks have some limitations. For instance,
Schoneburg (1990) pointed out that the performance of a neural network is very sensitive to its
design. Lee, Oh, and Kim (1991) highlighted the slow convergence of the backpropagation learning
algorithm (common method to train a neural network) and its convergence to local minimum due to
the non–linear dimensionality of market data. Nevertheless, neural networks and its various
extensions are still widely used.
Probably one of the most popular methods for

Comparison Of Customization Using Human Analysis And Avior...
exploitation human based mostly behavior analysis. Then the malwares are classified into malware
families Worms and Trojans. The limitation of this work is that customization using human analysis
isn't potential for today's real time traffic that is voluminous and having a range of threats.
Table 1
Comparison of malware detection techniques with focus on ransomware
Authors
Technique
Limitation
Advantages
Nolen Scaife et. al. [18]
CryptoDrop – It is a alert system which work on the concept of blocking the process which deals
with alteration of large amount of users data.
CryptoDrop is unable to determine the intent of the changes it inspects.
For example, it cannot distinguish whether the user or ransomware is encrypting a set of ... Show
more content on Helpwriting.net ...
The limitation is that an attacker will adopt countermeasures to beat the system as a result of this
technique uses global image based mostly options
Scheme uses real time datasets for classification prupose.
Rieck et al. [9] projected a framework for automatic analysis of malware behavior using machine
learning. This framework collected sizable amount of malware samples and monitored their
behavior employing a sandbox environment. By embedding the determined behavior in an
exceedingly vector space, they apply the training algorithms. Clustering is used to spot the novel
categories of malware with similar behavior. Assigning unknown malware to those discovered
categories is completed by classification. supported each, bunch and classification, an progressive
approach is employed for behavior–based analysis, capable of process the behavior of thousands of
malware binaries on routine.
Anderson et al. [10] given a malware detection rule supported the analysis of graphs created from
dynamically collected instruction traces. A changed version of Ether malware analysis framework
[13] is employed to gather information. the strategy uses 2–grams to condition the transition
probabilities of a Markoff process (treated as a graph). Machinery of graph kernels is employed to
construct a similarity matrix between instances within the training set. Kernel matrix is made by
exploitation 2 distinct measures of similarity: a Gaussian kernel, that measures native

A Research Study On Machine Learning Community, The...
Many researchers have proposed various methodologies for finding best solution. J. Ross Quinlan.
In machine learning community, the decision tree algorithms, Quinlan's ID3 and its successor C4.5:
Programs for machine learning are probably the most popular. The various issues related to decision
tree are discussed from the initial state of building a tree to methods of pruning, converting trees into
rules and handling other problems such as missing attribute values. Apart from that, Quinlan
discusses limitations of programs for machine learning, such as its bias in favour of rectangular
regions along with ideas for extending the abilities of algorithm. [1]
Mohamed Medhat Gaber, Arkady Zaslavsky and Shonali Krishnaswamy. Illustrated that the
theoretical foundations of data stream analysis discussed. Mining data stream systems, techniques
are critically reviewed. Finally, the research problems in streaming mining field of study are
discussed. These research issues should be addressed in order to realize robust systems that are
capable of fulfilling the needs of data stream mining applications. The main aim is to explore the
data for testing a specific hypothesis. The machine learning field came into existence with
advancement in computing power. So, the goal is to achieve efficient solutions to data analysis
problems. There are some issues regarding data stream mining discussed such as 'Handling the
continuous flow of data streams.', 'Unbounded

Hyperspectral Image Classification
Classification is a principle technique in hyperspectral images (HSI) analysis, where a label is
assigned to each pixel based on its characteristics. Applying machine learning techniques to these
datasets need special consideration, since the hyperspectral images are typically represented by
features vectors of extremely high dimensions. A robust HSI classification requires a prudent
combination of deep feature extractor and powerful classifier. In the last one decade, extensive
classification methods are designed in hyperspectral objects, but these approaches prone to learn the
correlated features, and usually fail to consider the structural information in the feature space. Most
of CNN methods extract image features at the last layer ... Show more content on Helpwriting.net ...
Keywords: High dimensionality, Hyperspectral image, classification, deep learning, feature
extraction, Introduction In the last decade, hyperspectral image (HSI) classification has been a very
active research discipline. Hyperspectral image contains ultra–high dimensional data with hundreds
spectral channels, which leads to highly correlated features and the noises that presented in adjacent
bands. However, the classification results are affected by these redundant and correlated features.
Therefore, in processing hyperspectral images, the classification approaches has been proposed
jointly by dimensionality reduction. Several feature extraction methods (see Section 2) have been
developed to solve the classification problem in hyperspectral images. Feature extraction based on
HSI aims to reduce the dimensionality of the data while preserving the discriminative information
(spectral–spatial information) as possible. However, there is no universal feature extraction which
perform well in all type of application and thus, it remain an active research area in advancing HSI
analysis for the foreseeable future. Moreover the complexity, diversity, dimensionality and quantity
of HSI are all increasing day by day and thus, the existing learning tasks are computationally
inapplicable and incompetent for analyzing and modeling the HSI data [9]. There are several
challenges in the hyperspectral data classification: (1) ultra–high dimensionality of data, (2) limited

Measuring A Computational Prediction Method For Fast And...
In general, the gap is broadening rapidly between the number of known protein sequences and the
number of known protein structural classes. To overcome this crisis, it is essential to develop a
computational prediction method for fast and precisely determining the protein structural class.
Based on the predicted secondary structure information, the protein structural classes are predicted.
To evaluate the performance of the proposed algorithm with the existing algorithms, four datasets,
namely 25PDB, 1189, D640 and FC699 are used. In this work, an Improved Support Vector
Machine (ISVM) is proposed to predict the protein structural classes. The comparison of results
indicates that Improved Support Vector Machine (ISVM) predicts more accurate protein structural
class than the existing algorithms.
Keywords–Protein structural class, Support Vector Machine (SVM), Naïve Bayes, Improved
Support Vector Machine (ISVM), 25PDB, 1189, D640 and FC699.
I. INTRODUCTION (HEADING 1)
Usually, the proteins are classified into one of the four structural classes such as, all–α, all–β, α+β,
α/β. So far, several algorithms and efforts have been made to deal with this problem. There are two
steps involved in predicting protein structural classes. They are, i) Protein feature representation and
ii) Design of algorithm for classification. In earlier studies, the protein sequence features can be
represented in different ways such as, Functional Domain Composition (Chou And Cai, 2004),
Amino Acids

Disadvantages Of Support Vector Machine
ABSTRACT
This term paper includes the learning and study of Support Vector Machine and its various different
variations. The task of Support Vector Machine map data to a higher dimensional space and helps to
find out the maximal marginal hyperplane to separate the data.
In this paper, a learning method, Support vector Machine, is applied on the different datasets for
getting more enhanced results. SVM is introduced in the early 90's, and they led to an explosion of
interest in machine learning. SVM have been developed by Vapnik and are gaining popularity in the
field of machine learning due to many advance functioning and efficient performance.
In this paper, we will implement the concept of Support Vector Machine and its ... Show more
The advantages of using LSVM are that it makes equality constrain disappear in its dual and makes
the objective function convex. Furthermore, it seems to be faster than SMO in terms of classifying
datasets with millions of data in several minutes. Moreover, it provides better generalization
capability. The disadvantage is that it doesn't able to scale up for large problems. [7]
Proximal SVM: The key idea of proximal SVM is that it classifies points which are closer to the two
parallel planes and try to push them apart .The advantage of PSVM is that it overcomes the
limitation of LSVM. It is able to handle the large data sets. Its performance is comparable with
standard SVM. The disadvantage is that it is designed for linear kernel SVM. [8]
Reduced SVM: The reduced SVM preselects a subset of n–examples and termed them as support
vector candidates. The advantage is that it proves to be fruitful for larger problems and problems
with many support vectors. The disadvantage of RSVM is it is suited for large scale nonlinear kernel
SVM.

Nt1310 Unit 1 Data Analysis
Based on Chapter 2, Neural Network Method (NN) will be chosen for voice–based command
recognition method because it can handle bigger databased. For Neural Network to implement
pattern recognition is quite common, and beneficial to use is backpropagation. Supervised learning
that starts by inputting the training data through the network is a form of this method. When the data
is put in the network, it will generate propagation output activations and then propagated backwards
through the neural network, and generating a delta value for all hidden and output neuron. The
weights of the network are then update by calculated delta values that generate by neural network,
which increase the speech and quality of the learning process. Backpropagation ... Show more
δ^k (i)= –∝∂V/(∂n^k (i) ) Equation 3.9 Now it can be shown by using equation 3.1, equation 3.6 and
equation 3.9, that is shown in equation 3.10 and equation 3.11. ∂V/(∂∆w^k (i,j) ) = ∂V/(∂∆n^k (i) )
∂V/(∂∆w^k (i,j) ) = δ^k (i)a^(k–1) (j) Equation 3.10 ∂V/(∂b^k (i) )= ∂V/(∂∆n^k (i) ) (∂n^k (i))/(∂b^k
(i) )= δ^k (i) Equation 3.11 It can also be shown that the sensitivities satisfy the following
recurrence relation in equation 3.12 δ^k= F^M 〖(n〗^M)W^( 〖k+1〗^T ) δ^(k+1) Equation 3.12
Where equation 3.13 and equation 3.14 Equation 3.13 f^k (n)= (df^k (n))/dn Equation 3.14 This
recurrence relation is initialized at the final layer shown in equation 3.15. δ^M= 〖–F〗^M 〖(n〗
^M)(t_q–a_q) Equation 3.15 The overall learning algorithm now proceed as follows; first, propagate
the input forward using equation 3.3 and equation 3.4; next, propagate the sensitivities back using
equation 3.15 and equation 3.12; and lastly, update the weights and offset using equation 3.7,
equation 3.8, equation 3.10 and equation 3.11. (Murphy,

A Brief Note On Random Forest Tree Based Approach It Uses...
COMPARISON OF CLASSIFIERS
CLASSIFIER CATEGORY DESCRIPTION REFERENCE
Naive Bayes Probability based classifier This classifier is derived from Naïve Bayes conditional
probability. This is suitable for datasets having less number of attributes. [5]
Bayesian Net Probability based classifier Network of nodes based on Naïve Bayes classifier is
termed as Bayesian Net. This can be applied to larger datasets as compared to Naïve Bayes. [9]
Decision Tree (J48) Tree based approach It is enhanced version of C 4.5 algorithm and used ID3.
[15]
Random Forest Tree based approach It is also a decision tree based approach but have more
accuracy as compared to J48. [15]
Random Tree Tree based approach It generates a tree by randomly selecting branches from ... Show
more content on Helpwriting.net ...
a similar development leads rules extraction techniques to make poorer sets of rules. DT algorithms
perform a variety method of nominal attributes and can 't handle continuous ones directly. As result,
an outsized variety of ml and applied math techniques will solely be applied to information sets
composed entirely of nominal variables. However, an awfully giant proportion of real information
sets embrace continuous variables: that 's variables measured at the interval or magnitude relation
level. One answer to the present drawback is to partition numeric variables into variety of sub–
ranges and treat every such sub–range as a class. This method of partitioning continuous variables
into classes is sometimes termed discretization. Sadly, the quantity of how to discretize a continual
attribute is infinite. Discretization could be a potential long bottleneck, since the variety of attainable
discretization is exponential within the number of interval threshold candidates at intervals the
domain [14]. The goal of discretization is to seek out a collection of cut points to partition the range
into a little variety of intervals that have sensible category coherence, that is sometimes measured by
an analysis operate. Additionally to the maximization of reciprocality between category labels and
attribute values, a perfect discretization technique

The Relationship Between Physicochemical Properties And...
Introduction and Motivation
There is a big wine market in the world, as it plays a pivotal role in many social gatherings. Because
of this, it is absolutely essential for the wine industry to be able to determine what physicochemical
properties are essential for wine to be given a good rating and overall, increase its market value.
Hence there is a need to investigate the influence of these properties for both wine manufacturing
and selling purposes. The relationship between physicochemical properties and sensory analysis is
not easy to understand[3], which makes wine classification a difficult task, as rating is based mainly
on taste preference. Hence we were motivated to come up with a model that could predict wine
preferences solely ... Show more content on Helpwriting.net ...
The physicochemical tests include 11 continuous variables, such as determination of alcohol,
sulfates or pH values.
Research Questions
The goal of this paper is to classify wine quality based on physicochemical and sensory analysis.
For this purpose, this study uses the Neural Nets toolbox, SVM (Support Vector Machine) library,
and Logistic Regression in Matlab, and Multivariable Regression in R to analyse and classify wine
quality.
This paper determines the most influential features for red wine quality ratings and answer the
question of which machine learning algorithm performs the best in classifying wine quality.
Experiment
4.1 Neural Nets
We are using the Neural Nets Toolbox which is already implemented in Matlab to check the
accuracy of quality classification for our wine data set. To use this toolbox, we needed to input the
training data matrix, which is a 1599x11 matrix (with 11 features) and a target matrix made up of
our quality outputs ranging from 3 to 8.
The target matrix has the same number of rows as there are samples of wine. Hence it has 1599
rows. Each row gives the binary label vector for one of the classifications. The number of columns
depends on how many classes there are. Each column representing one class. For each wine sample,
if the class is n, the nth column would have a value of 1 and every other column would have a value
of 0. So if there were 6 classes (as quality ratings ranges from 3 to 8), for

A Report On The Data
Based on the objectives of the experiment, it is important to describe the credit datasets, the
classi ers, the combination techniques of the classi ers, and lastly the software used in carrying
out the experiments.. 3.1 Datasets To access the prediction accuracy of the four classi ers and their
combinations in the two class classi cation problem analysis, two real life datasets taken from the
University of California, Irvine(UCI) repository were used. These datasets are described below [28].
3.1.1 German Credit Datasets This is a nancial dataset which is made up of 1,000 instances. This
dataset records 700 cases of creditworthy applicants and 300 cases of applicants who are not credit–
worthy. It contains categorical and symbolic attributes. The German credit dataset is of two forms
[26] [29]. The original dataset german.data consists of 20 attributes of which,7 are numerical
and 13 categorical). The german.data–numeric an edited copy of the original dataset which
consist of 24 numeric attributes. The german.data–numeric which has 24 input variable basically
represents 19 attributes with 4 of these attributes changed to dummy variables. The 20 attributes in
the German.data or the 24 attributes in the german.data–numeric are some basic information
about applicants needed in creating a score card which will be used to predict if an applicant will
default or not. This information is seen in the table below. Attribute Information Attribute 1 Status of
existing

Social And Social Data Analysis
Abstract–Social data analysis could be a kind of analysis during Which individuals add a social,
cooperative context to form sense of knowledge Social data analysis includes 2 main constituent
parts: 1) knowledge generated from social networking sites (or through social applications), and 2)
refined analysis of that knowledge, in several cases requiring period (or close to real–time)
knowledge analytic, measurements that perceive and suitably weigh factors like influence, reach,
and contentedness, AN understanding of the context of the information being analyzed, and also the
inclusion of your time horizon concerns. In short, social knowledge analytics involves the analysis
of social media so as to know and surface insights that is embedded inside the information. Geo–
location is that the identification of the real–world geographic location of an object, like a
microwave radar supply, mobile or Internet–connected computer terminal. Geo–location could ask
the apply of assessing the location, or to the particular assessed location. Geo–location is closely
associated with the utilization of positioning systems however is also distinguished from it by a
larger stress on determinative a significant location (e.g. a street address) instead of simply a group
of geographic coordinates. Keywords–Location–based services, query processing, group
queries,social constraints. I. INTRODUCTION Social knowledge refers to knowledge people
produce that's wittingly and voluntarily shared

Prediction Of Age, Gender And Personality Traits Using...
Prediction of Age, Gender and Personality Traits Using Facebook Data
Manali Bhalgat
Introduction
In the last decade, social networks like Facebook [1] have emerged as popular medium of social
interaction and information dissemination. From a social web data mining perspective, Facebook
stores a wealth of data about people and their interests. As more and more users are creating their
own content on Facebook, there is a growing interest to mine this data for use in personalized
information access services, recommender systems, tailored advertisements, and other applications
that can benefit from personalization.
Research studies leverage on data about status updates, pages liked, number of friends, number of
groups joined and other ... Show more content on Helpwriting.net ...
Related Work
In this section, we discuss the recent research work related to techniques for predicting age, gender
and personality based on the information available on social networking sites. Most studies predict
personality from the language used by a person to update status or chat in social network.
According to recent works based on study of function words such as pronouns, conjunctions, articles
and prepositions, the elderly use more future tense words and pronouns in their plural forms. The
same studies show that males use more articles and females make heavy use of first person singular
pronouns. In [7], the authors state that even with its challenges, text categorization is a reliable
approach to identify the age and gender of people in social network communication. Goldbeck et al.,
in [3], show that people of different age groups talked about different topics. For example, those
within the age group of 13 to 18 mostly discussed activities related to school, 19 to 22 year olds
talked about university/college.
Many efforts have been made by researchers to analyze the words used by humans to understand
their psychology [2]. Public information of a group of Facebook users was collected by the authors
of [6]. They were able to predict the Big–five personality traits of the users using this data within
89% accuracy.
Methodology
1. Problem Definition
To survey the machine learning techniques for

A Hybrid Theory Of Power Theft Detection

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