Artificial Neural Networks And Predictive Policing

Artificial Neural Networks And Predictive Policing
"Sir, please come with me, we deny your access to this building based on the data we received, and
we need you to go through an interrogation with us." How would a person react if he/she is
suspected to commit a crime? How would that person feel if the police just randomly show up and
ask for the intention of whatever that makes him/her suspicious? This is what will happen,
frequently, if artificial neural networks are used as a mean for predictive policing. First, just to
clarify, predictive policing is seeking to prevent future harm and reduce crime rates by analyzing
information and patrolling areas based on the result. The police are able to predict when, where, and
what crime is likely to happen with the help of this emerging ... Show more content on
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People often think that if there's science that supports the systems, it would be accurate. However,
the information the systems take in is not just at all. In order to support my argument, I want to point
out that there are cases where colored people are in a disadvantage for more likely to be accused
even though the white are doing the same thing. For instance, the police has always targeted more
on black people than on white people on trafficking. Because the crimes that took place in the past
are not in favored of black people, there are more cases against them. Therefore, the systems sort
through the data and conclude that black people are more likely to commit the same type of crime,
and suggesting the police put more attention on them. If the read–in information is not bias–free,
how can the prediction be just to everyone? My second argument is that neural networks violate
people's privacy when they collect information from social media or any other means without their
consent. Although some predictive policing software only allow the police to predict the type, time,
and location of a crime, other police systems are used without protecting people's right for privacy.
Our data, posts, information, and pictures are being looked over by the predictive policing systems
without us knowing or agreeing upon it. Therefore, it is not fair for the police to search our
... Get more on HelpWriting.net ...

Disadvantages Of Deep Learning
Deep Learning which developed as a Machine Learning approach has become very popular
nowadays. It helps in dealing with complex problem with a greater understanding. Traditional
Machine Learning model used to solve problems successfully where final output was a simple
function of input data, whereas Deep Learning can capture composite relations. Deep Learning is
basically learning data representations which is all about making things and presenting to real
audiences. In order to master the various aspects of Deep Learning one requires proficiency of core
academic content, critical thinking, analytical thinking and self–directed learning. It consists of
mathematical models, which can be termed as composition of same type of functions wherein some
of the functions can be changed so as to predict the final value. Human–computer interactions are
based on the belief of ... Show more content on Helpwriting.net ...
Some of these models need input parameter from the user, thus are heuristically designed and hence
performance might depend on characteristic of parameters and input image. Some of the new
learning–based methods overcome above problems and give solutions to complex problems. It is for
this reason that deep neural networks have recently seen an impressive comeback. CNN
(Convolutional Neural Network) used in Deep learning for image restoration, works by averaging
out the output of various trained network to the same input. Neural Networks have numerous
application in several areas of image processing. It is used for classifying the image and the
mathematical analysis of CNN operates feature extraction first and then give the results to trainable
classifier. This model works by training the network to reconstruct high quality images from
degraded or blurred input images. The model gives promising results from the learned set of
denoisers and also can be used for low level applications to deliver high performance

A Literature Review On Biological Neural Network
Chapter–2: Literature Review
In this chapter, we discuss a brief introduction of neural network and biometrics . Traditionally, the
term neural network had been used to refer to a network or circuit of biological neurons. Neural
networks are inspired by our brains. The modern usage of the term often refers to artificial neural
networks, which are composed of artificial neurons or nodes. Thus, the term has two distinct usages:
2.1 Biological Neural Network generally, a biological neural network is consists of a set or sets of
chemically linked or functionally linked neurons. The human brain owns about 1014 synapses and
1011 neurons. A neuron consisting of a soma (cell body),dendrites (receive signal) and axons (send
signal). A synapses ... Show more content on Helpwriting.net ...
Each learing or training methods in supervised learing depends on the idea display data training in
front of the network in the form of a pair of forms input form and target form. (See Figure (2–5)) .
Fig( 2–5): Supervised Learning Rule [1]
2)Unsupervised learning Network works to calculate the output without a previous expectation,
Where we offer network only inputs and it is find target And working on a self–organizing data
Where it competes neurons to get a signal and the neuron Winner we get it on the output and this is
called "self–regulation of the network neurons" . (See Figure (2–6)). Fig(2–6):Unsupervised
Learning Rule [1]
.
2.3.3. Transfer Function
In behavior of an ANN depends in each of the weights and the input–output function (transfer
function) that is selected for the units. This function usually located into one of three types[8]:
Linear (or slope): The output activity is commensurate to the total weighted output (see Figure

Comparative Predictive Modeling On Cnx Nifty With...
Comparative Predictive Modeling on CNX Nifty with Artificial Neural Network
By
Bikramaditya Ghosh, First and Corresponding Author
Asst. Professor, ISME, Bangalore
Address
301, Raghav Harmony, S R layout
Off Wind Tunnel Road
Bangalore–560017
INDIA
E Mail– bikram77777@gmail.com
Phone– +919535015777
Dr. Padma Srinivasan
Assoc. Professor , Christ University, Bangalore
Abstract
CNX Nifty being an important barometer to indicate country's growth has always been followed
with lots of interest from both academia and industry. Now, CNX Nifty could be predicted or not on
a random basis gives rise to many a questions. This sounds redolent with any predictive modeling
though with a certain degree of accuracy in built in to the system. The major point of consideration
is that predictive modeling could be done by various measures and mechanisms. In predictive
modeling Multiple Adaptive Regression (MARS), Classification and Regression Trees (CART),
Logistic OLS or Non Linear OLS could be used. Here in this study the researcher has utilized
Neural Network as a "Predictive Modeler" to predict CNX Nifty closing on certain random time
zones under consideration.
Keywords
Neural Network, CNX Nifty, Predictive Modeling
Literature Review
Quite a few in depth articles were found in the said domain. However India specific studies were
missing. The author took the baton from his own research, where he did detect the presence of
sentiment in CNX Nifty deploying Probabilistic

Drawbacks Of Collaborative And Content Based Filtering...
Drawbacks of Collaborative and Content–Based Filtering Methods and the Advantages of Deep
Belief Networks in Recommender Systems Sayali Borkar*, Girija Godbole*, Amruta Kulkarni* and
Shruti Palaskar*
*Computer Engineering Department, Pune Institute of Computer Technology, India
Abstract–A large number of modern businesses are based on core idea of users consuming content in
a physical or digital form, from a catalogue. The catalogue is available for browsing through a web
site or mobile application. For example, in video or audio rental and streaming applications, the
content is a media file, in news applications, the content is in text and image based format. Although
the applications look diverse, the differences are only superficial. There are numerous common
factors. The basic content that forms a catalogue is dynamic in nature. The information is in the
form of high dimensional temporal/time series sequence.
In this paper, we present a survey of applications implementing collaborative filtering methods,
content–based filtering methods and deep neural networks with Restricted Boltzmann Machines
(RBMs) for generating recommendations. The drawbacks of collaborative and content–based
methods is analyzed and proof of concept is provided for the need of deep learning based
recommender systems. We introduce RBM algorithm and its applications for use in generic
recommendation generation and propose to implement a deep learning neural networks
algorithms(RBM) to create

Human Age Estimation from Facial Images Using Artificial...
Introduction
Face Images convey a significant amount of knowledge including information about identity,
emotional state, ethnic origin, gender, age, and head orientation of a person shown in face image.
This type of information plays a significant role during face–to–face communication between
humans [1]. Above prospects of facial images can be used in emerging branch of Human Computer
Interaction (HCI). Human age has following characteristics: Aging is uncontrollable process: Aging
cannot be delayed or advanced at will. It is slow and irreversible process. Personal Age Patterns:
The aging factor of a person is defined by his genetic structure as well as external factors like health,
lifestyle, weather conditions, ethnicity, etc. Aging ... Show more content on Helpwriting.net ...
Photo indexing: Automatic indexing of photos is possible based on the age of a person. Missing
individuals: reliable prediction of one's appearance across ages has direct relevance in finding
missing individuals. Age based access control: developing systems which provide age specific
access to an individual at sites like security offices, military areas, social networking, etc. Other
common places: Age estimation system can be helpful at various locations like hospitals, police
stations, banks, government offices, educational institutes, sport events, etc.
Related Work
Various image processing researches related to face have been of keen interest since a long time.
From past decade, though the study related with respect to aging pattern and age estimation has
become important, it is still very challenging. Mostly age estimation is done using shape patterns of
face or using facial texture information such as wrinkles.
Existing methods for facial age estimation typically consists of 2 main steps: image representation
and age prediction [3]. The general models used for representing images are Active Shape Model
(ASM) [4], Active Appearance Model (AAM) [5], Craniofacial Growth Model [6], Aging Pattern
Subspace [2], Manifold Learning [7] whereas for age estimationmulticlass classification problem or
regressing problem.
The ASM model was proposed by Cootes et al [8]. This was used for feature extraction by
characterizing

Neural Networks : An Important Component Of Determining...
Neural Networks in Finance
2600 Words
By Maria L. Vicente
University of Hawaiʻi at Hilo
QBA 362
Fall 2016
Introduction
Predictions are an important component of determining the financial progress of a business.
Business decisions rely on forecasting techniques to predict things such as price movements or
overall success in markets. In the attempt to forecast market predictions, it must be assumed that
future occurrences may be partly based on present and past data (Abu–Mostafa, Yaser S 1996).
Further assumptions must be made to conclude that there is a predictable pattern in past data. There
is evidence for both the idea that financial market forecasting is futile due to the unpredictable
nature of finance, as well as for the idea that financial markets are predictable to an extent. The
consequences of financial decision–making imply an inherent need for the use of forecasting tools in
making predictions about future occurrences. The issue resides in the fact that there is an abundance
of data and information that must be organized and interpreted. A number of techniques may be used
to manage present and past data in order to create a forecast prediction, though with more research
and trials, neural networks have been shown to be superior in performance.
Traditional Techniques Neural networks provide an alternative solution to the traditionally used
statistical methods of forecasting. Traditional method models include variances of linear

Modeling Of Fractal Antenna Using Artificial Neural Network
1. Title:– Modeling of fractal antenna using Artificial Neural Network.
2.Introduction:– In high–performance spacecraft, aircraft, missile and satellite applications, where
size, weight, cost, performance, ease of installation, and aerodynamic profile are constraints, low
profile antennas may be required. Presently, there are many other government and commercial
applications, such as mobile radio and wireless communications that have similar specifications. To
meet these requirements, micro strip antennas can be used [1,2].
The expensive growth of wireless system and booming demand for variety of new wireless
application,it is important to design an antenna whose size,shape,weight and cost will be less. If it is
possible that a single antenna can work on more than one frequency then it is good for us. So
generally fractal antenna is used as multiband antenna.
The fractal geometry concept can be used to reduce antenna size. So fractal shaped antennas are
good choice to reduce antenna size and get multiband behavior.
The fractal antenna can be classified on the basis of iteration as 0 iteration,1st iteration,2nd iteration
etc.
For fulfilling all the requirement introduced above fractal microstrip patch antennas are designed.As
the number of iteration increases the time consume for solving matrix generated in simulator based
on method of moment(IE3D) increases. Due to this reason we are designing an artificial neural
network for microstrip fractal antenna.
2.1 Fractal

What Is Feed-Forward Neural Network Structure?
Feed–forward neural network structure consist of three layers of nodes : input layer, hidden layer ,
and output layer . The input layer nodes denoted $x_i , i in mathbb{N}$ ,where each of one of them
is connected to all nodes in the hidden layer $h_j , j in mathbb{N}$ via connection weight that. Also
, each node in the hidden layer is connected to all nodes in the output layer $ y_k , k in mathbb{N}$
through different connection weights . } label{fig:ffn1} end{figure}
%input{Tikz_Graphic/feedforward.tex} The neural network is a powerful mathematical method that
is capable of representing complex non–linear functions cite{RLStateOfArt_ch10} which has been
used widely in machine learning applications. Figure ef{fig:ffn1} depicts the basic structure of a
neural network, called feed–forward network. It consists of multiple layers of nodes (one input
layer, one or more hidden layer(s) and one output layer). Every node in a layer is connected to all
nodes in the previous layer, where each connection may have a different adjustable weight. The
value of each node in the hidden layer and output layer is a function of a weighted sum of all nodes
in the previous layer.
egin{figure*} centering input{Tikz_Graphic/nn_rl.tex} caption{This figure shows how RL agent
makes decisions inside the framework .At each discrete time $t$, the agent receives file access
pattern arbitrates (from APPM)

Neural Networks Are Used For Forecasting
Abstract– Neural networks are used for forecasting. The purpose of any learning algorithm is to find
a function such that it maps a set of inputs to its correct output. Some input and output patterns can
be easily learned by this neural networks. However, in the learning phase single–layer neural
networks cannot learn patterns that are not linearly separable. Back propagation is a common
method of training the neural networks. We are trying to develope the back propagation (BP) neural
network to form a prediction model for prediction of various shares in stock market.
I. PROJECT DESCRIPTION
The stock market is predictable or not predictable is still a question without an answer. Most
scientists and economists believe in stock is ... Show more content on Helpwriting.net ...
This paper has deep study of the BP neural network in
MATLAB, including how to create a neural network, how to initialize the network, training and
simulation, and using
MATLAB programming function and achieve the designed BP neural network. The last but not the
least, it is proved that the research method and the established model are practical and effective by
empirical analysis of several stocks. It not only simplifies the network structure, but also improves
the prediction accuracy as well, owning good predictive capability and generalization.
Deliverables for Stage1 are as follows:
A general description of the system:
With the help of the prediction model, we are predicting the future price of different stocks over a
future period of time. To achieve this, we need to train our model using the previous stock prices
over a previous period of time, so that our model will predict the future price of the respective
stocks. We are using the yahoo financial data set for training our data. The Back Propagation (BP)
algorithm is used to train the model that we are building using neural networks. We are modelling
our prediction using the MATLAB.
The user will

Business: Artificial Neural Network and Data
1. The independent data marts have inconsistent data definitions and different dimensions and
measures,
2. Which of the following is not a major activity of OLAP? Analytics
3. Which of the following are reports that are similar to routine reports, Ad–hoc reports
4. Clustering techniques involves optimization this is because we want to create group that have
maximum similarity among members within each group...
5. Which of the following is the reason why neural networks have been applied in business
classification problems? Able to learn the data, able to learn the models ' nonparametric nature, its
ability to generalize, All of the above
6. The main processing elements of a neural network are individual neurons
7. A software suite is ... Show more content on Helpwriting.net ...
Common tools used for supervised induction are neural networks , decision trees, and if then else
rules tree
36. Which of the following procedure is used to break datasets into different pairs of training and
testing sets resampling
37. Lotus notes provide online collaboration capabilities
38. Communication occurs when the receiver gets the information at a different time asynchronous
39. A rule–based expert system contains rules in its knowledge base and the rules are used to
generate questions
40. Which of the following is the brain of an expert system inference engine
41. Decisions trees are comprised of essentially a hierarchy of if–then statements
42. A decision tree can be defined as a root followed by internal nodes. each node(including the
root)is labeled with a question
43. Which of the following describes how data are organized and how to use them effectively?
Metadata
44. The difference between the actual output and the desired output for a given set of inputs is an

error named Alpha
45. Data mining provides organizations with an indispensable decision–enhancing environment to
exploit new opportunities by transforming date into a strategic weapon
46. Cluster analysis is a exploratory data analysis tool for solving classification problems
47. A major step in managerial decision making is forecasting. There are many methods to do

Design And Performance Analysis Of And Gate Using Synaptic...
DESIGN AND PERFORMANCE ANALYSIS OF AND GATE USING SYNAPTIC INPUTS FOR
NEURAL NETWORK APPLICATION
[1]S.SOUNDARYA [2] VAMSHI.G [3] SOUNDARAJAN.M [4] RAMKUMAR.A
[1] [2] [3] Students, ece department, saveetha school of engineering, Chennai
[4] Asst.prof, ece department, Saveetha School of engineering, Chennai
ABSTRACT
Logic gates are one of the main constituents to design and integrate as a chip. With advent of vision
and method in neural network, the intricacy can be clear alongside this knowledge to a remarkable
extent. The intention of this paper is to focus on how to design a perceptron that is nothing but a
single layer feed forward neural network to purpose as an AND gate and to examine its
performance. Firstly, the background of ... Show more content on Helpwriting.net ...
It is a kind of linear classifier, i.e. an association algorithm that makes it forecasts established in a
linear predictor purpose joining a set of weights alongside the feature vector. In this paper, the
mathematical background of neural network will be studied firstly. Then, Neural Web Toolbox in
MATLB® will be utilized to develop the neural web for assisting the analyzing of an AND gate.
II.BASIC CONCEPTS
II.A NEURON
The human brain is a collection of concerning 10 billion interconnected neurons. Every single
neuron is a cell that uses biochemical replies to accord, procedure and send information. It is an
electrically excitable cell that procedures and transmits data across mechanical and chemical signals.
These signals amid neurons transpire via synapses, enumerated connections alongside
supplementary cells. Neurons can link to every single supplementary to form neural networks. A
neuron's dendritic tree is related to a thousand bordering neurons. As one of those neuron fires, an
affirmative or negative price is consented by one of the dendrites.
II.B NEURAL NETWORK
Neural network is a computer arrangement of biological neurons, composed of nonlinear
computational agents working in parallel. A neuron's dendritic tree is related to a thousand bordering
neurons. As one of those neurons fire, an affirmative or negative price is consented by one of the
dendrites. The strengths of all the consented prices are added jointly across the procedures of spatial
and temporal

Convolutional Networking Essay
2.1 Styling using Convolutional Neural Networks
The initial work on style transferring using convolutional neural networks was brought forth by
Leon Gatys, Alexander Ecker and Matthias Bethge [1] in which style representations were extracted
from images. This involved superimposing the style image onto the content image such that the
semantic details of the content image were not lost as shown in Figure 1 [1].
Figure 1: Example of using the neural style transfer method advocated by Gatys et al to transfer the
style of the reference photo onto the original photo such that the stylized photo contains the
semantic content of the original photo and the style of the reference photo
The paper provides a detailed explanation of how the ... Show more content on Helpwriting.net ...
2.2. Neural Style Transfer
Becattini et al [2] provide a thorough survey of neural style transfer literature. It highlights the
contemporary problems of the existing models and also discusses future scope. It also presents
various evaluation techniques to contrast outputs obtained through different neural style transfer
methods. A broad classification of neural methods into descriptive and generative neural methods
where the former is used to transfer styles by updating pixel information in the images whereas the
latter optimizes a model and generates an image with varying styles in a single pass. Figure 3
provides a visually contrasting stylized image when a content image is stylized using both the
mentioned methods.
Figure 3: (Starting from the left) a) content image b) style image c) Descriptive method results with
brush size control d) Generative method without brush size control
The paper improves upon the existing methods mentioned above while being intent on process
architecture preservation but improves on the performances by tweaking environmental parameters
such as loss function modification, emphasizing on spatial arrangements to style details. Extensions
to existing transfer methods which were developed for simple images were discussed, this included
style transferring for doodles, head portraits, single user specified objects and video frames. The
parameters mentioned in the paper were also broadly classified

Speech Conversion Using Neural Networks
CS 549 TOPICS IN ARITIFICIAL INTELLIGENCE TEXT TO SPEECH CONVERSION USING
NEURAL NETWORKS Project Report Firstly, Artificial Intelligence was used in 1956, at the
Dartmouth conference and from then it is expanded because of various proposed theories and many
new principles developed by its researchers. It is an area of computer science that focusses on
creating machines that can engage on behaviors of humans, solve the computational models for
complex problems. Here Neural Networks are a computational approach to AI, which is based on
the great collection of Neural Units, which models a Human brain as connected large number of
neurons. These neurons are connected to each other to process the information as of the human
brain. Artificial neural network (ANN) learn by testing and training data. The Artificial Intelligence
in brief can be defined as the study and design of intelligent agents. Text to Speech Conversion
using Neural networks: In this project, we are developing Natural Language Processing (NLP),
which is related to processing human language by computer. The base paper for the implementation
of our project is Natural Language processing techniques in Text–To–Speech synthesis and
Automatic Speech Recognition Here the base paper we referred depicts usage of natural language
processing techniques which includes the production of audio from the input text i.e., text to speech
synthesis and the inverse process i.e., automatic speech recognition. From this paper we

Critical Analysis Of A Neural Network
First, we have encountered one major problem that is how to interpret a neural network given its
black box characteristics. We really wanted to try ourselves, giving interpretation of our results so
that we dug into the existing literature and found out a very interesting research paper written by
Garson in 1991. In « Illuminating the black box: a randomization approach for understanding
variable contributions in artificial neural networks », Olden and al. describes Garson's algorithm
very concisely so that we could create a user–defined function on Python that replicates the method.
The interpretation of the method is provided below. The outputs of the different algorithms in the
context of our study: Decision Tree: features importance ... Show more content on Helpwriting.net
...
The BM ratio allows us to categorize companies into either a growth (low BM ratio) or a value (high
BM ratio) profile and past empirical studies (see Fama and French) suggest that portfolios formed
over value companies tend to out–perform portfolios formed over growth companies over time. We
don't know the influence direction of the variable (given the PCA black–box transformation and
Garson's output) but we can formulate hypotheses on the fact that value companies tend to beat the
market more often than growth companies. Dividend yield and EVtoEBITDA that are our 2nd and
3rd most relevant variables for Neural Nework output also support this previous fact high dividend
yield paying companies characterize generally value companies, while the EVtoEBITDA ratio
characterize the relative price of a company since it returns how many times an investor is willing to
buy the EBITDA (Earnings before interests, taxes, depreciation and amortization) of a company
when he purchases a share of the company. An investor that invests in high EVtoEBITDA
companies has generally good growth prospects views regarding the recurrent earnings of the
company. Finally, the factor size is the 4th most relevant factor that affects our output and this fact is
also supported by Fama and French literature: portfolios formed over small

Artificial Neural Networks ( Anns )
Artificial neural networks (ANNs) are computational algorithms loosely based on the human
biological nervous system which work to model statistical data. An ANN consists of processing
elements known as neurons that are interconnected to each other and work in unison to answer a
particular problem [, and] can be used in places where detecting trends and extracting patterns are
too complex to be detected by either humans or other computer techniques. Although recent in their
explosion in popularity, the underlying logic behind ANNs have existed for nearly a half–century,
however due to the pervasive and ubiquitous adoption of powerful computational tools in our
contemporary society, ANNs have had a sort of renaissance, much to the avail of scientists,
engineers, and consumers.
The current cutting–edge in deep–learning and ANNs focuses highly on both their ability to model
and interpret complex data, and their ability to be put to scale due through optimization and
parallelization. The current framework for designing ANNs is one that is widely available, with a
myriad of tools facilitating their development. Python, C++, Google's Tensorflow, Theano, Matlab,
and Spark all contain a robust set of mathematical operations which necessitate ANNs. Due to the
algorithm behind ANNs, the models are inherently apt in extract[ing] meaning from imprecise or
intricate problems. Speaking reductively, ANNs are data modeling tools which are trained on a
given dataset, and can

Artificial Neural Networks Report Essay
Artificial Neural Networks Report Artificial Neural Networks 1. Introduction Artificial Neural
Networks are computational models inspired by an animal's central nervous systems (brain) that has
the ability of machine learning. Artificial neural networks are generally presented as systems of
interconnected neurons which can compute values from inputs (from wikipedia). 2. Training an
Artificial Neural Network The network is ready to be trained if it had been structured to service a
particular application, meanwhile the initial weights are chosen randomly and after that the training
begins. There are two approaches in training Artificial Neural Networks: supervised and
unsupervised. 2.1 Supervised Training In ... Show more content on Helpwriting.net ...
There are many transfer functions but how we select them, Is there a certain criteria?. There is no
straightforward answer to this question, it depends on the neural network itself and what you want to
achieve from it and the problem that neurons are trying to solve. Transfer function may be linear or
non linear, and It's generally non–linear. linear transfer functions are usually used for inputs and
outputs and also non linear transfer functions (Sigmoid) are used for hidden layers. The transfer
function work as the following: takes the input value compare it to a specific threshold in order to
decide the output value, turn the input value into: 0 or 1, or other numbers in case step function, and
the output value will be in range between 0 to 1 incase sigmoid function (logsig) and between –1 to
+1 in case tan–sigmoid (tansig). Figure1 Hard Limit (Step) Transfer Function[4] Figure2 Linear
Transfer Function[4] Figure3 Sigmoid Transfer Function[4] Table1 Transfer Functions[4] 3.5 Initial
weights in the network Neural networks initial weights usually as random numbers. In [14]
proposed a comparison of different approaches in initialization of neural network weights and the
most of algorithms that were used in multilayer neural networks and they had been based on various
levels of modification of random weight

Types Of Contingencies, Designing A Contingency, And Owner...
According to Gunhan and Arditi (2007), there were three types of contingencies, namely designer's
contingency, contractor's contingency, and owner's contingency. They claimed that the best method
to predict contingency was to use previous experiences. They mentioned that a detailed study of four
factors, namely site conditions, schedule constraints, project scope, and constructability issues could
play an important role either in preventing the CO or reducing the chances of needing a big
contingency money. Smith et al. (1999) stated that the wise decision on the amount of contingency
used while bidding could have effects on whether wining of the contract. They interviewed 12
contractors on the contingency calculation method and found that among these contractors, nobody
was aware of any kind of estimation method for the contingency amount. Whenever, these
contractors used contingency, they simply followed the traditional approach of adding some
percentages to the base cost as contingencies. Mac and Picken (2000) conducted a study on two
types of projects, namely estimating using risk analysis (ERA) and non–ERA projects. They made
comparison between 45 ERA projects with 287 non–ERA projects and found that ERA method
helped to reduce the unnecessary risk allowances in projects. According to the authors, Hong Kong
government was implementing this ERA technique in public construction projects. In the ERA
method, they described that the cost determined for fixed and variable

Neural Networks in Investments Essay example
Neural Networks in Investments
I. ABSTRACT
Investment managers often find themselves overwhelmed with the large amount of data obtained
from the financial markets. Most of the data available is numeric and noisy in nature, making the
decision–making process harder. These decisions usually rely on the integration of statistical
measures that attempt to compress much of the data and qualitative depictions such as graphs and
bar charts with news events and other pertinent information. Investment decisions usually involve
non–linear relationships among the various components of the data. Computers in general, are very
adept at dealing with large amounts of numeric information. However, some algorithms are crucial
in analyzing and ... Show more content on Helpwriting.net ...
At the simplest level, neural networks are a new way of analyzing data. The revolutionary aspect of
neural networks is their ability to learn and trace the complex patterns and trends in data. Neural
networks are made up of neurons and behave like the human brain, and has the ability to apply
knowledge from past experience to new problems. Neural networks acquire this knowledge by
training on a set of data. After the network has been trained and validated, the model may be applied
to data it has not seen previously for prediction, classification, time series analysis or data
segmentation.
Unlike traditional statistical methods, neural networks do not require assumptions about the model
form. A statistical analysis requires a certain form to be assumed such as linearity, which
characterizes relationships between variables. Neural networks are more tolerant of imperfect data,
such as the presence of missing values or other data quality problems. Neural networks perform
better than traditional statistical methods when the model form is unknown or nonlinear, or when the
problems are complex with highly inter–related relationships.
In a dynamic environment, neural networks are flexible tools and have the capacity to learn rapidly
and change quickly. As the data values and outcomes change, the model quickly learns and adapts
itself. Rule based systems

Quantum Neural Network Essay
Chapter 1
Quantum Neural Network
1.1 Introduction and Background
The eld of arti cial neural networks (ANNs) draws its inspiration from the working of human
brain and the way brain processes information. An ANN is a directed graph with highly
interconnected nodes called neurons.Each edge of the graph has a weight associated with it to model
the synaptic eciency. The training process involves updating the weights of the network in such a
way that the network learns to solve the problem.
The neurons in the network work together to solve speci c problems.
The network can be trained to do various tasks like pattern recognition, data classi cation,function
approximation etc. ANNs are widely used in the elds of computer vision ... Show more content on
Helpwriting.net ...
This has been demonstrated by many quantum
1.2. QUANTUM MECHANICS AND ANN 3
Figure 1.2: Quantum analogies for di erent concepts of arti cial neural networks algorithms such
as Grover's search algorithm, Shor's factorisation algorithm etc. We can use this property and our
knowledge of classical neural net– works to create a new computing paradigm called quantum
neural networks
(QNNs). There are many ways of using building a QNN. Figure 1.2 has been taken from [2], which
shows various approaches to achieving
For the purpose of this chapter will will consider only the Menneer and
Narayanan model. This model is based on the parallel universe interpreta– tion of Quantum
mechanics.
1.2.1 The parallel universe interpretation
The parallel universe interpretation or the many world interpretation of quantum mechanics was
proposed by Hugh Everett in the year 1957 [3].
4 CHAPTER 1. QUANTUM NEURAL NETWORK
It says that there exists an in nite number of coexisting universes and each possible outcome of an
event occurs in a parallel universe. According to the theory superposition states of a wavefunction
lies in di erent universes and the quantum system interacts with itself in di erent universes. Any
act of measurement sees the state only in one universe.
This interpretation can help in better understanding of the eld of quan– tum information.

1.3 Quantum Neural Network
We will try to

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

Explanation Of A Neural Network
First of all, we have encountered one major problem that is how to interpret a neural network given
its black box characteristics. We really wanted to try ourselves giving interpretation to our results so
that we dug into the existing literature and found out a very interesting research paper written by
Garson in 1991. In « Illuminating the black box: a randomization approach for understanding
variable contributions in artificial neural networks », Olden and al. describes Garson's algorithm
very concisely so that we were able to create a user–defined function on Python that replicates the
method. The interpretation of the method is provided below. The outputs of the different algorithms
in the context of our study are the following: ... Show more content on Helpwriting.net ...
We don't know the influence direction of the variable (given the PCA black–box transformation and
Garson's output) but we can formulate hypotheses on the fact that value companies tend to beat the
market more often than growth companies. Dividend yield and EVtoEBITDA that are our 2nd and
3rd most relevant variables for Neural Nework output also support this previous fact high dividend
yield paying companies characterize generally value companies, while the EVtoEBITDA ratio
characterize the relative price of a company since it returns how many times an investor is willing to
buy the EBITDA (Earnings before interests, taxes, depreciation and amortization) of a company
when he purchases a share of the company. An investor that invests in high EVtoEBITDA
companies has generally good growth prospects views regarding the recurrent earnings of the
company. Finally, the factor size is the 4th most relevant factor that affects our output and this fact is
also supported by Fama and French literature: portfolios formed over small capitalization companies
tend to out–perform portfolios formed over big capitalization companies over time. It is interesting
to note that this feature is an output of the

Artificial Neural Network Essay
In these project functional models of Artificial Neural Networks (ANNs) is proposed to aid existing
diagnosis methods. ANNs are currently a hot research area in medicine, particularly in the fields
of radiology, cardiology, and oncology. In this an attempt is made to make use of ANNs in the
medical field One of the important goals of Artificial Neural Networks is the processing of
information similar to human interaction actually neural network is used when there is a need for
brain capabilities and machine idealistic. The advantages of neural network information processing
arise from its ability to recognize and model nonlinear relationships between data. In biological
systems, clustering of data and nonlinear relationships are more ... Show more content on
Helpwriting.net ...
Also it includes resizing of image data. 2.2 Image Segmentation: Image Segmentation is concerned
about segmenting the image into various segments using various techniques. In early days a semi–
automatic approach was being used to detect the exact boundaries of the brain tumor. However the
semiautomatic methods were not very successful as they had human induced errors and were time
consuming. A better application of tumor detection was made by introducing fully automated tumor
detection systems. Various methods have been proposed like Markov random fields method, Fuzzy
c–means (FCM) clustering, Otsu's thresholding, K–Mean's, neural network. In this project, four
different algorithms namely Otsu's method, Thresholding, K–means method and Fuzzy c–means and
PSO have been used for designing the brain tumor extraction system. Various segmentation
techniques which will be used in this project to segregate the different regions on the basis of
interest are described as follows: a) K–means: K–means is a clustering technique which aims to
partition a set of observations so as to minimize the within cluster sum of squares (WCSS). The
evaluating function for an image a (m, n) is given as: c(i)=Arg min|mxy2–nxy2| Where i is the no. of
clusters in which the image is to be partitioned. b) Otsu's Method: Otsu's Method divides the image
into two classes of regions namely foreground and background. The background and foreground
regions are selected using the following weighted

Artificial Intelligence (AI) Essay
This research Paper has problems with formatting ABSTRACT Current neural network technology
is the most progressive of the artificial intelligence systems today. Applications of neural networks
have made the transition from laboratory curiosities to large, successful commercial applications. To
enhance the security of automated financial transactions, current technologies in both speech
recognition and handwriting recognition are likely ready for mass integration into financial
institutions. RESEARCH PROJECT TABLE OF CONTENTS Introduction 1 Purpose 1 Source of
Information 1 Authorization 1 Overview 2 T he First Steps 3 Computer–Synthesized Senses 4 Visual
Recognition 4 Current Research 5 Computer–Aided ... Show more content on Helpwriting.net ...
The network – rather like a child – makes up its own rules that match the data it receives to the
result it's told is correct (42). Impossible to achieve in expert systems, this ability to learn by
example is the characteristic of neural networks that makes them best suited to simulate human
behavior. Computer scientists have exploited this system characteristic to achieve breakthroughs in
computer vision, speech recognition, and optical character recognition. Figure 1 illustrates the
knowledge structures of neural networks as compared to expert systems and standard computer
programs. Neural networks restructure their knowledge base at each step in the learning process.
This paper focuses on neural network technologies which have the potential to increase security for
financial transactions. Much of the technology is currently in the research phase and has yet to
produce a commercially available product, such as visual recognition applications. Other
applications are a multimillion dollar industry and the products are well known, like Sprint
Telephone's voice activated telephone calling system. In the Sprint system the neural network
positively recognizes the caller's voice, thereby authorizing activation of his calling account. The
First Steps The study of the brain was once limited to the study of living tissue. Any attempts at an
electronic simulation were brushed aside by the neurobiologist community as abstract conceptions
that bore

Particle Swarm Optimization And Its Range Of Operation
function, their numbers and intersection value determines accuracy of the tool and its range of
operation. If the membership function covers poles values from 0 to 4 then the maximum value for
pole is 4 and the least value is 0 and same thing happens for the constants. Each unit reduces an
order of two to order of one. If a transfer function of higher order is needed to be reduced the
operation is repeated several times. For example an order 8 to 2 function reduction will reduce the 8
poles into 4 poles then the 4 new poles into the final 2 poles. E. Particle Swarm Technique
Particle swarm optimization (PSO) is initialized with a group of random particles (solutions) and
then searches for optima by updating generations. In every iteration, each particle is updated by
following two best values. The first one is the best solution (fitness) each particle has achieved so
far, this value is called Pbest. Another best value that is tracked by the particle swarm optimizer is
the best value, obtained so far by any particle in the population. This best value is a global best and
called Gbest. Each particle consists of: Data representing a possible solution, a velocity value
indicating how much the Data can be changed, a personal best (Pbest) value indicating the closest
the particle 's Data has ever come to the Target.
The particles ' data could be anything. In the flocking birds' example above, the data would be the X,
Y, Z coordinates of each bird. The individual

Artificial Neural Network Advantages And Disadvantages
ABSTRACT– An Artificial Neural Network (ANN) is an information processing paradigm that is
inspired by the way biological nervous systems, such as the brain, process information [1]. Artificial
Neural Networks (ANN) also called neuro–computing, or parallel distributed processing (PDP),
provide an alternative approach to be applied to problems where the algorithmic and symbolic
approaches are not well suited. The objective of the neural network is to transform the inputs into
meaningful outputs. There are many researches which show that brain store information as pattern.
Some of these patterns are very complicated and allows us to recognize from different angles. This
paper gives a review of the artificial neural network and analyses the techniques in terms of
performance. Keywords–ANN, PDP, ITNN, CPN, CNN, CCNN, HN, TSP, RNN, Bi–ANN, CDR,
LRN INTRODUCTION The objective of the neural network is to transform the input to meaningful
output. Neural networks are often used for statistical analysis and data modeling. Neural network
has many uses in data processing, robotics, and medical diagnosis [2]. From the starting of the
neural network there are various types found, but each and every types has some advantages and
disadvantages. Deep learning and –neural network software are the categories of artificial neural
network. The parallel process also allows ANNs to process the large amount of data very efficiently.
The artificial neural network is built with a systematic

Computational Modeling, An Artificial Intelligence...
1. Abstract:
The Ccost estimate is important for projects throughout its life cycle, starting from feasibility studies
till tendering stage and developing BOQ, it provides significant information that even may serve
project budgeting and controlling if reliable.
This paper presents a comparison between two cost estimate predictive modeling, an artificial
intelligence approach using neural networks as non–parametric modeling; and multiplely regression
modeling that represents advanced engineering statics – a parametric modeling.
Results show that neural networks have advantages when dealing with data that for which there is
little a priori knowledge of the appropriate cost estimate relationship to select for regression
modeling. However, in cases where an appropriate CER can be identified, regression models have
significant advantages in terms of accuracy, variability, model creation and model examination. Both
simulated and actual data sets are used for comparison.
Our research is applied for a specific package of construction projects (concrete work) based on 17
projects with a 115 records.
Key words: construction projects, cost estimate. Concrete work, neural network, multiply
regression.
2. Introduction:
Cost estimate is the productive process used to quantify cost and price the resources to achieve
project scope; the output can be used for many purposes throughout project life cycle such as:
Determining the economic feasibility

Literature Review On Biological Neural Network
Chapter–2: Literature Review In this chapter, we discuss a brief introduction of neural network and
biometrics . Traditionally, the term neural network had been used to refer to a network or circuit of
biological neurons. Neural networks are inspired by our brains. The modern usage of the term often
refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus, the
term has two distinct usages:
2.1 Biological Neural Network generally, a biological neural network is consists of a set or sets of
chemically linked or functionally linked neurons. The human brain owns about 1014 synapses and
1011 neurons. A neuron consisting of a soma (cell body),dendrites (receive signal) and axons (send
signal). A synapses ... Show more content on Helpwriting.net ...
(See Figure (2–5)) .
Figure( 2–5): Supervised Learning Rule [1]
2.Unsupervised learning Network works to calculate the output without a previous expectation,
Where we offer network only inputs and it is find target And working on a self–organizing data
Where it competes neurons to get a signal and the neuron Winner we get it on the output and this is
called self–regulation of the network neurons . (See Figure (2–6)). Figure(2–6):Unsupervised
Learning Rule [1]
2.3.3. Transfer Function In behavior of an ANN depends in each of the weights and the input–output
function (transfer function) that is selected for the units. This function usually located into one of
three types[10]: Linear (or slope): The output activity is commensurate to the total weighted output
(see Figure (2–7)).
F(X)=X ; for all x
Figure( 2–7): Linear activation function Threshold: The output is appoint at one of two levels,
depending on whether the total input is less or greater than than some threshold

Artificial Neural Networks ( Ann )
CHAPTER 5 Artificial Neural Networks (ANN) 5.1 Machine Learning In machine learning,
systems are trained to infer patterns from observational data. A particularly simple type of pattern, a
mapping between input and output, can be learnt through a process called supervised learning. A
supervised–learning system is given training data consisting of example inputs and the
corresponding outputs, and comes up with a model to explain those data (a process called function
approximation). It does this by choosing from a class of model specified by the system's designer.
[Nature. ANN 4] 5.1.1 Machine Learning Applied to the Air Engine The rapid growth of data sets
means that machine learning can now use complex model classes and tackle highly non–trivial
inference problems. Such problems are usually characterized by several factors: The data are multi–
dimensional; the underlying pattern is complex (for instance, it might be nonlinear or changeable);
and the designer has only weak prior knowledge about the problem in particular, a mechanistic
understanding is lacking. [Nature, ANN 4] 5.2 Overview of ANN Artificial Neural Networks (ANN)
are a branch of the field known as Artificial Intelligence (AI) which may also consists of Fuzzy
logic (FL) and Genetic Algorithms (GA). ANN are based on the basic model of the human brain
with capability of generalization and learning. The purpose of this simulation to the simple model of
human neural cell is to acquire the intelligent

Artificial Intelligence: An Overview
Artificial Intelligence Computers are everywhere today. It would be impossible to go your entire life
without using a computer. Cars, ATMs, and TVs we use everyday, and all contain computers. It is
for this reason that computers and their software have to become more intelligent to make our lives
easier and computers more accessible. Intelligent computer systems can and do benefit us all;
however people have constantly warned that making computers too intelligent can be to our
disadvantage. Artificial intelligence, or AI, is a field of computer science that attempts to simulate
characteristics of human intelligence or senses. These include learning, reasoning, and adapting.
This field studies the designs of intelligent ... Show more content on Helpwriting.net ...
Expert systems are also known as knowledge based systems. These systems rely on a basic set of
rules for solving specific problems and are capable of learning. The laws are defined for the system
by experts and then implemented using if–then rules. These systems basically imitate the expert's
thoughts in solving the problem. An example of this is a system that diagnosis medical conditions.
The doctor would input the symptoms to the computer system and it would then ask more questions
if need or give diagnoses. Other examples include banking systems for acceptance of loans,
advanced calculators, and weather predictions. Natural language systems interact allow computers
to interact with the user in their usual language. They accept, interpret, and execute the commands in
this language. The attempt is to allow a more natural interaction between the computer and user.
Language is sometimes thought to be the foundation of intelligence in humans. Therefore, it is
reasonable for intelligent systems to be able to understand language. Some of these systems are
advanced enough to hold conversations. A system that emulates human senses uses human sensory
simulation. These can include methods of sight, sound, and touch. A very common implementation
of this intelligence is in voice recognition software. It listens to what the user says, interprets the
sounds, and displays the information on the screen. These are

Applying Medical Procedures For Breast Cancer
ABSTRACT
Medical diagnostic system is a branch in bioinformatics that is concerned with classifying medical
records. Breast cancer is the most common deployed cancer in females worldwide. The main
obstacle is the vagueness and ambiguity involving the breast cancer data. Human nature handles the
vagueness and ambiguity easily. Therefore, doctors diagnose the patient condition using their
expertise. Fuzziness and rough boundary theories simulate the human thinking. The fuzzy rough
hybrids address the uncertainty in terms of membership degree of truth and lower and upper
boundaries of fuzzy rough set theory. This research solves the diagnostic breast cancer problems via
a proposed hybrid model of fuzzy rough feature selection and rough neural networks. The medical
data is preprocessed by the fuzzy rough feature selection algorithm to remove unnecessary
attributes. The reduced data set is applied to the rough neural network to learn the connection
weights iteratively. The test data set are used to measure the proposed model accuracy and time
complexities. Lower and upper approximations of the input features are weighted by input synapses
learnt through training phase. The fuzzy rough proposed model design and implementation are
declared. The experiments used WDBC and WPBC data sets from the UCI machine learning
repository. The experimental results proved the fuzzy rough model ability to classify new instances
compared with the conventional neural network.
Keywords:Data Mining,

The Field Of Serpentine Belt Drive System
The first solution is Parameter Estimation. Actually, in certain engineering problems, vibration
control for an axially moving string focuses on the vibration isolation problem. Controllers are
designed to restrict vibration resulting from external disturbances, such as support pulley
eccentricity or aerodynamic excitation, to areas not requiring high precision positioning.
Some basic works have been done in the field of serpentine belt drives are researches on the
vibration characteristics of axially moving string. Beikmann et al., (1996) applied a mathematical
model to examine the transverse vibration and stability of coupled belt–tensioner systems.
Meanwhile, they modeled and analyzed the serpentine belt drive systems with a dynamic tensioner
shown as figure 3.1.
Fig. 1 three–pulley serpentine belt drive system
This model system includes the essential components mounted in serpentine belt drive systems,
which is a driving pulley, a driven pulley, a dynamic tensioner. Assumptions are made to simplify
the modeling of the belt drive system: (i) Damping is negligible, (ii) Belt bending stiﬀness is
negligible and hence belts are modeled as strings, (iii) Axial translation speed of the belt, c, is
constant and uniform, (iv) Belt slippage is negligible, (v) Pulleys other than the tensioner have fixed
axes, (vi) Belt/pulley contact points are those calculated at equilibrium.
Hamilaton's principle can be applied to derive governing equations and boundary conditions. The

Time Series Forecasting And Neural Networks
Abstract
Time series forecasting plays an important role in the academic and practical domains. Many
researchers have studied on this area from several years. There are many models which are used to
improve the accuracy of time series forecasting. In this paper, I have focused on one method i.e.
Neural Networks. In the first section of the report, I will give brief introduction on time series
forecasting and neural networks. In the next part, I will explain this neural method which is used for
forecasting in the literature review. At last, I will conclude the paper. Moreover, the main aim of this
paper is to define the neural network method among the different methods in the time series
forecasting.
Introduction
Time series forecasting is a quantitative model. It means that historical results are gathered to
produce the results to predict the future. This model is used in that situation where no information is
available or provided data is not satisfactory. Time series is a series of experiments which is
measured to get successive results such as, x1, x2, x3.......Xt–2, Xt–1 Xt. These experiments may be
done every day, every hour or every year. It is important to understand the data pattern to achieve
the aim that hoe variables behaved in the past. It has two types of models which are linear and non
linear. Moreover, linear methods are easy to construct and understand. This is used when there are a
huge number of forecasts to be done. Many researchers have done efforts to

Features Of Space Expansion
Feature Space Expansion
Firstly the feature space would be increased in dimension, by the addition of new features. Due to
the analysis done on feature production, it was noted that by generalising feature production and
consumption (in the neural network), a lot of time could be saved in the long run. This meant when
the feature space was to be expanded, it would be important to create the feature production in a
scalable manner.
Neural Network Expansion
Secondly, the neural network would be extended from a simple input–output neural network to one
with a variable number of inputs, layers, and hidden neurons. The addition of more layers would
allow more complex planes to partition the feature space – as sometimes simple planes cannot
adequately classify data. For example, consider the classification of a data–set similar to the output
of a XOR gate.
Consider four input data–points of (0, 0), (0, 1), (1, 0), and (1, 1), with respective outputs 0,1,1,0,
where 1 represents positive classification, and 0 represents negative classification. This provides an
example of a classification which has very high error when classified without a hidden layer. This is
because a linear combination of the input coefficients can only define a partition which is a straight
line.
Any linear partition of these inputs can at most correctly classify three of four data–points. This is
because the data–points are linearly inseparable. However, with a more complex neural network,
such as one
with

The Training Of A Neural Networks
In order to have a better understanding of the mechanism used in the prediction process, we need to
know what are the stopping conditions, as this process involves a finite number of iterations known
a priori. It has been previously proved one of the problems that occur during the training of a neural
networks is overfitting towards the input data, which generates low quality predictions [56]. In order
to reduce this tendency, the early stopping criteria has been formulated. This involves dividing the
input set in three subsets [57]: –training set, used to estimte the weights; –testing set, used to assess
the prediction sample and –validation set, which is an accuracy indicator of the prediction sample,
although it is not used during the ... Show more content on Helpwriting.net ...
Application using neural networks for the real Gross Domestic Product (GDP) prediction This is an
empirical example which has the role of highlighting the differences between a neural network and a
linear regression model. Lamy Robert estimated a linear regression model to predict the growth of
real GDP in Canada [59]. His model explains 82% of the GDP variation in the period 1978–1998.
The coefficient obtained are statistically significant so, he the model can be used to predict the
values of the GDP for different periods. His model contains six independent variables, which are:
R_(t–1)–is the quarterly growth of the Canadian Financial Index at t–1; F_t–is the employment
growth rate at time t; F_(t–1)–is the employment growth rate at t–1; B_t–is the consumer confidence
index; d_(t–9)–is the first difference of the long–term real interest rate at time t–9 (9 month before
time t); d_(t–3)– is the first difference of the government budget balance as a share of GDP, at time
t–3 (3 months before time t). Four dummy variables have been included in the model to control four
periods (quarter 1 in 1980, quarter 1 in 1981, quarter 4 in 1986 and quarter 3 in 1991). The linear
regression model was estimated based on 62 observations and is given by: 〖GDP〗_t=–
1.695+0.075R_(t–1)+0.3F_t+0.25F_(t–1)+0.019B_t–0.175d_(t–9)–0.32d_(t–3)–
1.15d^1+1.168d^2+0.0906d^3–0.84d^4+ε_t (4.11) where: d^1,d^2,d^3 and d^4 are dummy
variables. The neural

Application Of An Artificial Neural Network Essay
We have the previous workload information so we trained that workload information. First we
normalize the value of the workload information by using the formula Where, M = is the maximum
value along the particular column, X= is the maximum value along the particular column, Q= is the
original value. Q'=is the normalized value. After Normalized the value we design an artificial neural
network. 3.1 Artificial neural network In this structure of ANN we use the one input layer, one or
more hidden layer, and one output layer. That structure we call MLP (multiple layer perceptron). On
the input layer we use input layer 5 neurons. On the first hidden layer we are using the 5 neurons.
And on the second hidden layer we use the 10 neurons. On the output layer we use the one neurons
that predict the future load.NOW we train the workload information by using the Artificial neural
network i.e. MLP structure. To Train the workload information aim is to find the set of weight
values that will cause the output from the ANN to match to the target values as closely as possible.
There several issues are arising when we train the neural network. First is selecting the number of
hidden layer and neurons how much are used on the hidden layer. Second is to avoid the local
minima and finding the globally optimal solution. To training the work load information first we
need to divide the workload information. How much workload information is used to train? How
much workload information for

Overview Of Artificial Neural Networks
Introduction Artificial neural networks are a class of computational structures (Lesk, 2013) made up
of several processing elements, called artificial neurons that are connected and organized in layers
(Larder et al., 2007). They are capable of generating models for the detection of non linear
functions(..). Their algorithms are extensively applied in biology and medicine to solve complex
problems, more specifically for prediction or classification of solutions or to refine methodological
aspects. (Florence and Balasubramanie, 2010). Human immunodeficiency virus (HIV) is a retrovirus
that can lead to acquired immunodeficiency syndrome (AIDS). (Kim et al., 2010). It is a disease in
which the body immune system weakens progressively, ... Show more content on Helpwriting.net ...
Artificial neural networks have been used extensively as a complimentary bioinformatics tool to
make approximations of the cleavage site activity and specificity. First uses of ANNs to solve the
problem The aim of first research study was to develop a classification model that, given a sequence
of eight amino acids, could discriminate between sequences which are either cleavable or
uncleavable by the HIV– 1 protease. (Kim et al., 2010). The neural learning algorithms used most
frequently was back–propagation neural networks (BPNNs) (Thomson et al., 2003) because it
performs well on prediction problems. (Sibanda and Pretorius, 2012). When BBNN was used for the
prediction of the HIV–1 protease cleavage site, it gave a prediction accuracy 92%(Thomson et al.,
2010) However, one of the major disadvantages of using ANNs to analyse biological data referred to
the impossibility of most ANNs of recognizing non– numerical features like amino acids. Hence an
encoding process to model the amino acids was preferable. (Thomson et al., 2003) The advantage of
the Bio basic functional neural networks The peculiarity of this algorithm relates to its ability to
recognise amino acids directly. Thus, avoiding the use of 20 binary bits to represent each amino acid
is advantageous. (2003) The prediction accuracy of BBFNN was proved in a research study using
362 HIV protease sequences, where 114 were with cleavage sites and

The Neural Network Model
In the present chapter determining the radius for a given resonant frequency of centre feed circular
microstrip antenna has been estimated using the FFBP ANN
In the present chapter the radius for a given resonant frequency of a centre feed circular microstrip
patch antenna has been analysed using two layer neural network structures FFBP ANN model. The
Levenberg – Marquardt training algorithm and the transfer function tansig have been used to
implement the neural network model. The simulated values for training and testing the neural
network model are obtained by analysing the circular microstrip patch antenna using CST
Microwave Studio Software (CST–MWS). The results obtained using ANNs are compared with the
simulation findings and found ... Show more content on Helpwriting.net ...
Figure 3.1 shows the training performance graph that indicates the best validation performance and
number of epochs required to achieve the minimum mean square error level. The number of epochs
required for training the aforesaid neural network to achieve mean square error (MSE) is 668. The
training time is 3 seconds and the training algorithm used in the analysis model is Levenberg –
Marquardt training algorithm.
Figure 3.2 shows the training state of the FFBP ANN model, the training state condition is shown by
3 different graphs in the first graph gradient value is shown at different epochs, in the next graph Mu
which indicates the error values at different epochs are shown and the third figure shows the
validation check points at different epochs. All the three graphs are showing the values at epoch 674.
The network is tested for 10 patterns and the table 3.1 shows the comparison of results of CST and
FFBP–ANN with 5 neurons for radius with the variation of the resonant frequency of the circular
patch microstrip antenna computing MSE function with constant substrate height and dielectric
constant.
The network is realized using FFBP architecture. In the model, developed for the analysis of radius
there are 10

Artificial Intelligence Concepts : The Future Of AI
Kevin: Hello, Dr. Ayala, Dr. Ruiz, and Mr. Skep Ticks. Welcome to The Future of AI, and it is my
understanding that each of you are proponents to different concepts. Dr. Ayala, you're a strong
proponent of connectionism, while Dr. Ruiz is a strong supporter of symbol manipulation. Mr. Skep
Ticks is a skeptic of the aforementioned concepts and believes that AIs cannot be intelligent.
Intelligence, he believes, can only be simulated by systems but not created. Having all of you seated
in front of me brings about the unique opportunity to ask if either of you believe that a machine can
achieve intelligence. Ayala: In order to answer this question, one must have a bit of some context
information. For example, what is good old–fashioned AI? ... Show more content on Helpwriting.net
...
The action of the Turing machine determined by the machine's state. Programming a Turing
machine to manipulate symbols is very similar to how human minds process information. Human
intelligence is based on symbolic computation. At close inspection, humans are computers made out
of different material. The brain can function as a manipulator of symbols. This can also be seen
through the idea of multiple realizability. Take a watch as an example. A watch is able to tell time,
correct? Ticks: Yes, a watch is able to tell time. Ruiz: If the time it reads can be taken as a state that
the watch is in, then one can't assume that its structure is the only way to tell that time. The watch on
your wrist is structured differently than mine but is still able to tell time and be at the same state as
my own watch. Even a digital watch can be in the same state of telling time as an analog clock.
Through this hypothetical, there can be more than one way to reach intelligence and the human
mind is not the only formula. AIs can be intelligent in their own right, and they aren't simulations of
intelligence. Ticks: That's all interesting, but have you heard of the Searle's Chinese room thought
experiment? It's a thought experiment that basically implies that genuine intelligence in AI is
impossible to achieve. It's the thought that, since computer programs are purely symbolic, aren't
capable of genuinely understanding the semantics of objects.

Artificial Neural Networks And Predictive Policing

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