Reflective Student Experience Skills

Reflective Account On Student Experience
a) I have excellent communication skills both written and oral
In my current role, I work closely with a range of colleagues to ensure a high quality of student
experience. My customer communication skills are in evidence when dealing with our 50 students
for each block placement for the C21 Ophthalmology program. For my written skills I have helped
produce an introduction pack for medical students which is subsequently uploaded on to Learning
Central prior to teaching week and is also distributed on the first day of teaching. b) Microsoft
Office Skills
I use Microsoft office daily and produce letters, reports, forms, databases and presentations.
Additionally, we use it to produce materials for our courses. I am confident in using and ... Show
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We have interactive teaching, cultural awareness training, impact on visual awareness workshops
and an interactive clinic with patients with real eye conditions, which has been the highlight for
many of our students.
h) I regularly update the OpenEyes Foundation website and have previously used Immediacy when
working with Inspire Learning
i) I currently do not use any desktop publishing programmes
I did however create student feedback forms for our courses on Google docs.
KNOWLEDGE
J) I do not hold a degree qualification, however, I feel that my experience in my current role in the
education sector has given me a great variety of experience.
k) At present I work in Cardiff University and therefore have experience of working in an education
marketing role l) I have no professional marketing qualification
EXPERIENCE
m) I do have a proven record in producing creative and effective marketing communications as I had
to liaise with an external agency to produce a website to advertise our annual inter–university event

for Oxford–Bristol–Cambridge–Southampton. I also produced the flyer and set up tickets and
registration on
... Get more on HelpWriting.net ...

Within-Class Ability Grouping Argumentative Analysis
In order to grasp the effectiveness of ability groups, it is necessary to point out the differences
amongst them. Mathews (2013) states that ability grouping is classified into two categories which
are between–class ability grouping and within class ability grouping. Between class ability grouping
is essentially leveled groups (high, medium, low) across the grade level and is also known as
"cluster grouping." Each group is assigned to a particular classroom based on their academic ability
or prior performance (gifted, special needs, and language learners). Within–class ability grouping is
"the assignment of students to groups within each classroom based on interest, skill, ability, and
various other factors" (Mathews, 2013, p. 82). Within–class ... Show more content on
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They describe that overall the advantages of ability grouping outweigh the disadvantages of
"reduced overall instructional time and unsupervised seatwork" (Hallinan and Sorenson, 1987, p.
63). However, Hallinan and Sorenson (1987) point out that many studies show that ability grouping
favors students in the high group and disadvantages those who are in the lowest group. This is
because high–ability groups are characterized by students who have a positive attitude towards
learning and who are highly motivated; while, students in the lowest group are those who are easily
distracted and have more behavior problems. If the teacher is spending the group's instructional time
on discipline and redirecting, the group has a smaller amount of instructional time and as a result the
amount of learning taking place is

Gifted Hands Reflection
The portrayal of Dr. Ben Carson's life in 'Gifted Hands' was truly inspirational and motivational.
Through the depiction of his life, we realize that a person can still thrive despite encountering
numerous adversaries. In the first part of the film, the audience saw a younger Ben, a kid who was
hot–tempered and quite gullible, so far from who he is now. As a child, Ben was easily influenced
and pressured by his friends – he succumbed to peer pressure as any adolescents would. Those
specific attributes were manifested when Ben had had enough of his classmates' bullying and it
became more evident when he became friends with a reckless and corrupt boy from his new school.
Although Ben possesses the aforementioned negative traits, we also discovered some outstanding
traits of his. 'Gifted Hands' depicted ... Show more content on Helpwriting.net ...
Ben Carson was truly an exceptional and an accomplished person; however, the audience came to
know that he hasn't always been like that. Ben came from a broken family since his father broke all
contact and left him when he was still young; thus, he lived with his mother and his older brother.
Besides the fact that he comes from a broken family, he is also a man of color. At that certain part of
history, it is common to belittle and discriminate people belonging to the black ethnicity, which
makes Ben's life tougher and more arduous. In addition to the aforementioned issues Ben faced, we
learned that he was having a difficult time reading which explains his unsatisfactory grades and a
little later it was also revealed that his mother also has a learning disability since she also can't read.
Since the said obstacles greatly affected his view of himself and his personality, he had a fairly low
self–concept. Due to the incessant bullying and teasing of his classmates, he was quite ashamed of
himself and he believed that he was not enough – that he was not destined to be someone
outstanding and

What Is CNN Architecture : U-NET
CNN Architecture: U–NET
Built upon the 'fully convolutional network' paradigm, it supplements the contracting network with
successive layers of upsampling operators instead of pooling operators; this provides the network
with the power to localize the learning. The architecture consists of contracting and expanding paths
as seen in fig. The contraction path reduces the original image through a series of convolutions,
ReLU and maxpooling to extract relevant features. This is followed by an expansion path which
through a sequence of upconvolutions, ReLU and concatenation with high–resolution features from
contraction path reproduces original image as a segmented output. Thus the expansive path is almost
symmetric to contracting path yielding ... Show more content on Helpwriting.net ...
The final layer used 1 x 1 convolution to map each 64–component feature vector to the required
number of classes. The network was quite heavy with a total of 23 convolution layers and
necessitates the selection of an appropriate input tile size so that the 2 x 2 max pooling led to pixel
sizes that were even numbers. It may be noted that the unpadded convolutions led to output images
that were smaller than the input by a fixed width.
Ever since the original paper was published, it was highly popular for semantic segmentation. The
absence of U–net related work in earth observation and the success of
U–net for similar semantic segmentation tasks motivated its choice for this project.
III. REINFORCEMENT LEARNING
Reinforcement learning which has its origins in behavioural psychology is a computational approach
to learn from interaction with an environment. It adopts the approach of learning about stimulus or
environment through reward and punishment. Unlike supervised or unsupervised learning, data isn't
independently identically distributed but dynamic and sequential. It is a kind of semi–supervised
learning that lies midway between supervised and unsupervised learning. Adaptive learning through
observed data and predicting future outcome are at the soul of reinforcement learning.
While supervised learning uses class labels for training data and unsupervised learning uses

PBIS
Understanding Positive Behavioral Interventions and Supports (PBIS) and managing student
behavior is an imperative part of all educator responsibility. Starting in the 1980s (Simonsen, 2012),
it was obvious that better, more efficient ways of connecting with students with behavioral
challenges was needed. A form of intervention that went beyond the standard, or traditional means
of being sent out of class, a phone call home which may have led to punishments that escalated the
behaviors instead of working through them, or even being put on suspension which most likely
heighten the stress and allowed for little to no resolution of the behavioral challenges the student(s)
was going through. PBIS is not just for the special needs children of today; the original need was
identified to help the special needs population and has since found its importance throughout most
school systems as a general practice (Simonsen, 2012).
PBIS is referred to as a "framework" where the "emphasis is on a process or approach, rather than a
curriculum, intervention, or practice (Simonsen, 2012)." This 'framework' has grown into schools
and has brought with it numerous professional development to help aide the educators and
assistance for students. With more individual changes, class understanding, and a ... Show more
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"A great deal of evidence shows that the public at large judges the effectiveness of a school in terms
of its management of student behavior" (Marzano, 2003). Understanding that students, often, spend
more time with peers and teachers that within their homes, expectations of learning go beyond
reading, mathematics, science and history lessons. Due to the vast influential areas in a student's life,
rough neighborhoods, unsupervised parks, exedra, schools are viewed as a 'universal' location for
rules and regulation

A Research On Pedestrian Detection
The four papers about pedestrian detection we chose to summarize were great and informative, all
suggested useful techniques and new ideas in deep learning for pedestrian detection. However, there
were few open issues or room for improvement in some of the papers. Here are some of the ideas we
suggested to resolve these issues in each paper.
Joint Deep Learning for Pedestrian Detection (UDN)
Even though the Unified Deep Net (UDN) method learned features by designing hidden layers for
the Convolutional Neural Network such that features, deformable parts, occlusions, and
classification can be jointly optimized, one of its problems is it treats pedestrian detection as a single
binary classification task, which is not able to capture rich pedestrian variations. For example, the
method is not able to distinguish pedestrians from hard negatives due to their visual similarities.
This problem can be resolved by jointly optimizing pedestrian detection with auxiliary semantic
tasks, such as including pedestrian attributes and scene attributes, which was represented in our
previous report.
Another problem with the UDN method is it did not explicitly model mixture of templates for each
body parts, and did not depress the influence of background clutters. Thus, the method could be
improved by explicitly model the complex mixture of visual appearance at multiple levels. For
example, some extra layers can be added into the hierarchy of the UDN, so that at each feature level,
this

Information Systems Record Events On Log Files
Most information systems record events in log files [Abad03]. The type and structure of log files
vary widely by system and platform. For example, weblogs are produced by web servers running
Apache or Internet Information Server (IIS). Operating systems, firewalls, and Intrusion Detection
Systems (IDS) record event information in log files. Applications also record user activities in log
files [Abad03]. Any activities performed during a security breach will most likely result in log
entries being recorded in one or more log files. These attacks cannot be identified by a single log
entry occurrence, but instead can be identified through a series of entries spanning several minutes
[Abad03]. The amount of data logged per system can be in excess of several thousand events per
minute. Additionally, these files are distributed across the network. In order to process and analyze
the log data, it must be integrated. Integrating highly heterogeneous data from multiple sources
requires a massive centralized data repository [Kott13]. This data repository meets the complexity
requirements as defined by Big Data. Big Data is defined by three characteristics: volume, velocity,
and variety. Volume is the size of the data stored, and is measured in terabytes, petabytes, or
exabytes. Velocity is the rate at which data is generated. Variety refers to the types of data, such as
structured, semi–structured, or non–structured [Mahmood13]. Structured data is data that typically
resides in a

Ucb Personal Statement Sample
I'm applying for the Computer Science Ph.D. program at UCSB because I'm interested in machine–
learning technology and its potential to solve a very large range of problems.
Machine–learning fascinated me ever since I discovered the field because, throughout my
mathematical education, I often thought about the idea of fitting functions on pre–existing data to
create generalized solutions. There is a variety of potential applications within machine–learning
that interest me; such as the automation of various tasks performed typically by doctors or the ML
applications to computer software systems. Extraction of information from human–written text
sources (NLP) also interests me because it enables a researcher to quantify and
qualify(?)information ... Show more content on Helpwriting.net ...
I project that in the future, the machine–learning specialists will be increasingly sought after as more
and more of the field's potential is realized. I would like to gain expert–level knowledge by attaining
the Ph.D. program in the Computer Science department of UCSB so that I can make my novel
contribution to this fascinating and growing field, obtain sufficient academic recognition to start
collaborating with an industry research lab and hopefully, work towards innovative entrepreneurial

Computational Advances Of Big Data
In 2013 the overall created and copied data volume in the world was 4.4 ZB and it is doubling in
size every two years and, by 2020 the digital universe – the data we create and copy annually – will
reach 44 ZB, or 44 trillion gigabytes [1]. Under the massive increase of global digital data, Big Data
term is mainly used to describe large–scale datasets. Big data is high–volume, high–velocity and
high–variety information assets that demand cost–effective, innovative forms of information
processing for enhanced insight and decision making [2]. Volume of Big Data represents the
magnitude of data while variety refers to the heterogeneity of the data. Computational advances
create a chance to use various types of structured, semi–structured, and ... Show more content on
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Since big data includes large amount of inconsistent, incomplete, and noisy data, a number of data
preprocessing techniques, including data cleaning, data integration, data transformation and data
reduction, can be applied to remove noise and correct inconsistencies [5]. A good amount of feature
selection algorithms of different models have been developed for multiple fields. Although existing
statistical feature selection methods are useful for normal sized datasets, they may fall short in feture
selection in Big Data due to noise, heterogeneity, and large volume. They become inefficient in
extracting the complex and non–linear patterns generally observed in this kind of data. On the other
hand, the hierarchial structure of Deep Learning techniques allow them to effectively select and
extract meaningful features from Big Data. Some approaches have been tried for learning and
extracting features from unlabeled image data, include Restricted Boltzmann Machines (RBMs) [6],
autoencoders [7], and sparse coding [8] for different fields including image detection. But most of
these techniques were only able to extract low–level features. Hence, to avoid pitfalls and overcome
the challenges, developing and employing computationally efficient algorithms carries high
importance.
Furthermore, most of the proposed feature selection algorithms use batch learning which conducts

Benchmarking Lmdb And Leveldb For Deep Learning
Benchmarking LMDB and LevelDB for deep learning
Weiyue Wang
ABSTRACT
Deep learning is a new emerging area of machine learning research, which has been shown to
produce state–of–the–art results on various tasks. A high performance database management in deep
learning framework will help increase learning efficiency. This work compares the performance of
two key value data storage, Lightning Memory–Mapped Database (LMDB) and google LevelDB,
for deep learning framework. Our key findings are followings. 1.Introduction
Deep Learning (DL) has been shown to outperform most traditional machine learning methods in
fields like computer vision, natural language processing, and bioinformatics. DL seeks to model
high–level abstractions of data by constructing multiple layers with complex structures, which
compose of hundreds millions of parameters to be tuned. For example, a deep learning structure for
processing visual and other two–dimensional data, convolutional neural network (CNN) [1], which
consists of three convolutional layers and three pooling layers, has more than 130 millions of
parameters if the input has 28x28 pixels. While these large neural networks are powerful, we need
high amount of training data. DL tasks need considerable data storage and memory bandwidth.
Key–value stores provide users simple yet powerful interface to data storage, which are often used
in complicated systems. [2] LMDB is a framework that provides high–performance key–value
storage

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

The Negative Use Of Technology And Young Children
Technology and Young Children
Technology is defined as a knowledgeable and skillful tool that provides a service to individuals.
Technology and digital media are tools that are used for teaching and learning. I will show how
technology can positively impact young children and how it can be used to benefit young children.
According to Positive Parenting technology and digital media provides the following benefits:
1. Cause and effect: allows access, curiosity, and exploration at a safe distance when monitored. At a
very young age children can start to perform a variety of actions and at a young age they are aware
of the outcome between those actions.
2. Enhances eye–hand coordination is the movement of eye movement along with hand movements
simultaneously. Students tend to learn more when they interact with hands on learning devices or
tools that educational technology provides.
3. Fine tunes motor skills (pushing buttons, using mouse)
4. Encourages concentration and persistence that can build confidence and self–esteem.
5. Increases technological competence. (BM, 2014)
Tablets are replacing the use of textbooks and social media is a commonplace for even the youngest
of users. When used intentionally and appropriately, technology and interactive media are effective
tools that can support young children's cognitive skills, social and emotional developments. There
are a variety of ways technology can be used at school and at home to support learning. The use of

Social Learning Theory
Social learning theory is the theory that is the closes in attempting to solve why crime happens but
even more why crime happens within unsupervised youth. Social learning theory is basically stating
that people learn from other people through observing, modeling, and imitating (Bandura, 1977). In
a way, it tries to magnify the relationship between social structures and behavior and how they
correlate when it comes to crime. As recently explain in this paper social structures have a good
amount of influence on criminal behavior which this theory also ties to.
When placed in society the location in which is around you has a very significance in your day to
day life in context. For example, what may be viewed as wrong and not approved on one ... Show
One is the area social controls. Under that social controls category polices such as curfews, defined
hours for bars, and abusive substance laws can be enforced that help the routine activities of people
to be more safe and less vulnerable to crime. Another way for policy to incorporated is by having
policies on technology such as, security systems, key fobs, and steering wheel locks just to name a
few. An efficient and effect way to keep the safety at a maximum is to implement public safety
policy in public space. This can include street lighting in dark areas, fences to protect property and
city lines, and blue light systems which are already implemented throughout the

Analyzing The Field Of Big Data
Literature review:
To address the question of how and what techniques has been used to manages this big amount of
data or in the field of Big Data, I review some research papers and review articles in the field of Big
Data. This paper provides the synthesis of those papers which I found relevant to this field. This
paper will focus on the following things:
What are the technologies being used in Big data?
Which technology is suitable for which type of data?
Current trends in Big Data field. Fig: Big Data Sources
4.1 Survey Paper: A survey on data stream clustering and classification
Authors: Hai–Long Nguyen , Yew–KwongWoon , Wee–KeongNg
Published online: 17 December 2014
Purpose:
This paper presents a inclusive survey of the ... Show more content on Helpwriting.net ...
Therefore, to randomly access these datasets, which is commonly assumed in traditional data
mining, is really expensive.
Findings and Learning's:
1) There are some useful, open source software for data stream mining research:.
WEKA: WEKA is the most popular data mining software for the academic environment. WEKA
contains the collection of learning algorithms such as data preprocessing, association rules ,
classification, regression, clustering, and information visualization.
Massive Online Analysis (MOA): This is based on the WEKA framework that is build and designed
for data stream learning.
RapidMiner: RapidMiner is another importantopen source software for data mining.
2) Some important clustering algorithms discussed in this paper to group massive data and can be
useful to industries and organization:
Partitioning methods: This algorithm groups dataset into q clusters, where q is a predefined
parameter.
It continuously reassigns objects from one group to another group so as to r to minimize its
objective function.
Hierarchical methods: In the hierarchical method the aim is to group data objects into a hierarchical
tree of clusters. Hierarchical clustering methods can be further classified as either agglomerative or
divisive, where the hierarchical decomposition is formed in a bottom up(merging) or top
down(splitting) fashion respectively.
Density based methods: Under this method we build up the

The Importance Of Simulation In Nursing
Simulation is an educational approach that allows nurses to refine, expand, and apply skills and
knowledge to sensible medical conditions. The nurses participate in experiences that involve
interactive learning to meet the needs of their education. Nurses who engage in simulated learning
have the potential to identify specific scenarios while at the same time developing competencies and
skills, which improve their learning and experience. Simulation enables healthcare providers to
work without panic, thus minimizing the risk of harm to patients. The teaching strategy optimizes
the outcomes of care and contributes to patient safety, providing the learners with the opportunity to
intervene in clinical situations and experience scenarios with a safe, unsupervised setting without
exposing risks to the patients. In this, simulation in nursing enables the learners to acquire
knowledge and skills, improve their critical thinking skills to rescue lives, and enhance mental
health nursing education. In the teaching career, simulation enables beginners to obtain acquaintance
and skills. It is an important aspect of education for working caregivers and students. The Institute
of Medicine supports simulation as a constant attainment of skills and knowledge (Michelle
Aebersold & Dana Tschannen 2013). When learners use simulation, they acquire knowledge and
skills, enabling them to reduce the rates of mortality and morbidity. The knowledge acquired from
this aspect also enables

The Pros And Cons Of Machine Learning
Machine learning and Deep Learning Some machines are capable to acquire their own knowledge
by extracting patterns from raw data, a phenomenon known as machine learning (ML) (Bengio, Ian
and Aaron 2016). Without question, many aspects of modern society have been deeply impacted by
these machine learning systems. Furthermore, ML claims to accomplish simple results that can be
effortlessly understood by humans (Michie, et al. 1994). Outputs from these systems that are used in
service systems include, but are not limited to offering customers new items and narrowing down
their search based on their interests; language understanding, object recognition, speech perception,
and identifying and favoring significant results of online searches (Yann , Yoshua and Geoffrey
2015). It is important to emphasize that even though human intervention is necessary for
background knowledge, the operational phase is expected to be without human interaction (Michie,
et al. 1994). Consequently, these systems must be able to learn through time. According to Alpaydin
(2004), they must be able to evolve and optimize a performance criterion in order to adapt to the
environmental changes to which they are exposed over time. These systems do that through the use
of past experience or example data. Russell et al. (2004), classified machine learning tasks into three
different groups based on the feedback available to the learning system and the nature of the
learning signal: supervised learning,

Big Data Analysis Using Soft Computing Techniques
Big Data analysis Using Soft Computing Techniques Kapil Patidar Manoj Kumar (Asst. Pro) Dept.
of Computer Science and Engineering Dept. of Computer Science and Engineering ASET, Amity
University ASET, Amity University Noida, U.P., India Noida, U.P., India kpl.ptdr@gmail.com
manojbaliyan@gmail.com
Abstract–Big data is a widespread term used to define the exponential progress and obtainability of
data, both structured and unstructured. Big data may be as important to corporate society, more data
may prime to more precise analyses. More truthful analyses may prime to, more assertive judgment
creation and well judgments can mean greater functioning productivities, reduced cost and risk. In
this paper we discuss about big data analysis using soft computing technique with the help of
clustering approach and Differential Evolution algorithm.
Index Terms–Big Data, K–means algorithm, DE (Differential Evolution), Data clustering
Introduction
Day by day amount of data generation is increasing in drastic manner. Where in to describe the data,
for zetta byte, popular term used is "Big data". The marvelous volume and mixture of real world
data surrounded in massive databases clearly overcome old–fashioned manual method of data
analysis, such as worksheets and ad–hoc inquiries. A new generation of tools and

Voting Based Extreme Learning Machine Essay examples
Real valued classification is a popular decision making problem, having wide practical application
in various fields. Extreme Learning Machine (ELM) pro– posed by Huang et al.[1], is an effective
machine learning technique for real valued classification. ELMis a single hidden layer feedfo5
rward network in which the weights between input and hidden layer are initialized randomly. ELM
uses analytical approach to compute weights between hidden and output layer [2], which makes it
faster compared to other gradient based classifiers ([3, 4]). Various variants of ELM were recently
proposed, which includes Incremental Extreme
10 Learning Machine [5], Kernelized Extreme Learning Machine [6], Weighted Extreme Learning
Machine(WELM) [7], ... Show more content on Helpwriting.net ...
are some of the complex valued classifiers designed for real valued classification problems. CCELM
out– performs other complex valued classifiers for real valued classification problems.
It also performs well when dataset is imbalanced.
35 It has been observed that many practical classification problems have imbalanced data sets[23,
24]. If we classify such data, most of the classifiers favours the majority class due to which most of
the instances belonging to minority class are misclassified. To deal with such dataset, various
sampling approaches [25] as well as algorithmic approaches are used. Sampling approaches includes
over 40 sampling and undersampling techniques. Oversampling replicates a fraction of minority
samples while undersampling approach reduces a fraction of majority samples to make dataset
balanced. But there is problem with sampling approaches. Oversampling [26] increases redundancy
of data and undersampling results in loss of information. In algorithmic approach, classifier design
45 encompasses the measures to handle class imbalance. Most of the neural network based
classifiers like FCRBF [4, 3],CCELM[9] minimizes least square error to find optimal weights.
Recently proposed WELM minimizes weighted least square error function to find optimal weights
between hidden and output layer.
In this classifier, residuals of minority

We Use A Gaussian Function As A Kernel Function
In order to specify the middle layer of an RBF we have to decide the number of neurons of the layer
and their kernel functions which are usually Gaussian functions. In this paper we use a Gaussian
function as a kernel function. A Gaussian function is specified by its center and width. The simplest
and most general method to decide the middle layer neurons is to create a neuron for each training
pattern. However the method is usually not practical since in most applications there are a large
number of training patterns and the dimension of the input space is fairly large. Therefore it is usual
and practical to first cluster the training patterns to a reasonable number of groups by using a
clustering algorithm such as K–means or SOFM and then to assign a neuron to each cluster. A
simple way, though not always effective, is to choose a relatively small number of patterns randomly
among the training patterns and create only that many neurons. A clustering algorithm is a kind of an
unsupervised learning algorithm and is used when the class of each training pattern is not known.
But an RBFN is a supervised learning network. And we know at least the class of each training
pattern. So we'd better take advantage of the information of these class memberships when we
cluster the training patterns. Namely we cluster the training patterns class by class instead of the
entire patterns at the same time (Moody and Darken, 1989; Musavi et al., 1992). In this way we can
reduce at least the

What Is The Backpculation Of Forward Propagation And...
A. Forward Propagation and Backpropagation In case of forward propagation each node has the
same classifier and none of them are fired randomly.Also repeating the input provides the same
output.The question that arises at this point is if every node in the hidden layer receives same input,
why dont all of them produce the same output?The reason is each set of input is modified by unique
weights and biases [6]. Each edge has a specific weight and each node has a unique bias.Thus the
combination of each activation is also unique and hence the nodes fire differently.Prediction of
neural net depends on weight and bias.As prediction should be high it's desired that the prediction
value should be as close to the actual output as ... Show more content on Helpwriting.net ...
IV. PATTERN RECOGNITION USING NEURAL NETS For really complex problems neural
networks outperform their competition.With the aid of GPU's [1], the neural networks can be trained
faster than ever before.Deep learning is specially used to train computers to recognize patterns.For
simple patterns, logistic regression, or SVM are good enough. But when the data has 10s or more
inputs Neural Networks are cut above the rest.For complex patterns, neural networks with a lesser
number of layers become less effective.The reason is the number of nodes required in each layer
grows exponentially with the number of possible patterns in data.Eventually, the training becomes
very expensive and accuracy topples.Hence it can be concluded that for the

Advantages Of Principle Component Analysis
3.4. Principle Component Analysis (PCA)
Principle component analysis, also referred to as eigenvector transformation, Hotelling
transformation and Karhunen Loeve transformation in remote sensing, is a multivariate technique
[66] that is used to decrease dataset dimensionality. In this technique, the original remote sensing
dataset, which is a correlated variable, is distorted into a simpler dataset for analysis. This permits
the dataset to be uncorrelated variables representing the most significant information from the novel
[21]. The computation of the variance covariance matrix (C) of multiband images is expressed as:
Where M and X are the multiband image mean and individual pixel value vectors respectively, and
n is the number of pixels.
In change detection, there are two ways to relate PCA. The first method is counting two image dates
to a single file, and the second methods is subtracting the second image date from the corresponding
image of the first date after performing PCA individually. The disadvantages of PCA can ... Show
For example, Baronti, Carla [39] concerned PCA to examine the changes occurring in multi–
temporal polarimetric synthetic aperture radar (SAR) images. They used association instead of a
covariance matrix in the transformation to condense gain variations that are introduced by the
imaging system and that provide weight to each polarization. In another example, Liu, Nishiyama
[49] evaluated four techniques, including image differencing, image ratioing, image regression and
PCA, from a mathematical perspective. They distinguished that standardized PCA achieved the
greatest performance for change detection. Standardized PCA is better than unstandardized PCA for
change detection because, if the images subjected to PCA are not calculated in the same scale, the
correlation matrix normalizes the data onto the same scale

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

Speaker identification and verification over short...
SPEAKER IDENTIFICATION AND VERIFICATION OVER SHORT
DISTANCE TELEPHONE LINES USING ARTIFICIAL NEURAL
NETWORKS
Ganesh K Venayagamoorthy, Narend Sunderpersadh, and Theophilus N Andrew gkumar@ieee.org
sundern@telkom.co.za theo@wpo.mlsultan.ac.za
Electronic Engineering Department,
M L Sultan Technikon,
P O Box 1334, Durban, South Africa.
ABSTRACT
Crime and corruption have become rampant today in our society and countless money is lost each
year due to white collar crime, fraud, and embezzlement.
This paper presents a technique of an ongoing work to combat white–collar crime in telephone
transactions by identifying and verifying speakers using Artificial Neural Networks (ANNs). Results
are presented to show the potential of this technique.
1. ... Show more content on Helpwriting.net ...
Often after a long legal battle, the victims are left with a worthless judgement and no recovery.
One solution to avoid white collar crimes and shorten the lengthy time in locating and serving
perpetrators with a judgement is by the use of biometrics techniques for identifying and verifying
individuals. Biometrics are methods for recognizing a user based on his/her unique physiological
and/or behavioural characteristics. These characteristics include fingerprints, speech, face, retina,
iris, hand–written signature, hand geometry, wrist veins, etc. Biometric systems are being
commercially developed for a number of financial and securit applications. Many people today have
access to their company's information systems by logging in from home. Also, internet services and
telephone banking are widely used by the corporate and private sectors. Therefore to protect one's
resources or information with a simple password is not reliable and secure in the world of today. The
conventional methods of using keys, access passwords and access cards are being easily overcome
by people with criminal intention.
Voice signals as a unique behavioral characteristics is proposed in this paper for speaker
identification and verification over short distance telephone lines using artificial neural networks.
This will address the white collar crimes over the telephone lines. Speaker identification [1] and
verification [2] over

How The Segmentation Task Would Be Implemented
One of the most important steps towards the completion of this project was the formulation of how
the segmentation task would be implemented. The segmentation strategy should be designed in such
a way that it can efficiently deal with the main challenges of the dataset.
Firstly, the method should be able to ignore background noise, as well as objects that are not
LDL particles. Additionally, it should be able to deal with adjacent and overlapping particles.
A widely used strategy[26, 4] is to face this problem as a binary classification task, with the goal of
classifying each pixel that lies on the surface of the object of interest as 1 and every other pixel as 0.
However, this strategy would lead to loss of spatial information about the ... Show more content on
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Usually, more training data lead to a more robust network that can generalise better and as a result,
make more accurate predictions. However, in some cases, it is difficult to acquire large amounts of
training data, because their collection or production is either very time–consuming or very
expensive. In the case of the micrographs this project is dealing with, the manual annotation process
is very time–consuming (hence the need for the automation of this process) and this is why our
dataset is limited to 41 images.
However, in order to teach the neural network the desired invariance, a data augmentation strategy
was followed. More specifically, left–right and top–down mirroring was applied to each image,
which resulted in tripling the size of the original dataset. The same technique was also used on the
corresponding manually created segmentation masks, resulting in a labeled dataset consisting of 123
examples. This augmentation strategy has been proved to boost the performance of convolutional
networks in similar tasks [5]. Further augmentation of the dataset was initially considered by
rotating the images by 90, 180 and 270 degrees, however, training the neural network on such a
large dataset, turned out to be very computationally expensive, leading to very long training time
and as a result this idea was abandoned.
Additionally, to ensure that the memory requirements of the network during

What Is The Objective Function Of Svtm
. Support Vector Machines: SVM is a state–of–the–art machine learning algorithm which is used in
text analysis. They are universal learners. They exist is various forms– linear and non–linear. It uses
function called kernel. They are not dependent on the dimensionality of feature space. Using an
appropriate kernel, SVM can be used to learn polynomial classifiers, radial basic function(RBF)
[35]. The goal of SVM is to find the large margin hyperplane that divides two classes. Equation of
hyperplane is wT.x+b where class is yϵ{1,–1} depending on features space is x. If the data is
linearly separable, the optimal hyperplane maximizes the margin between the positive and negative
classes of the training dataset. This line splits the data into ... Show more content on Helpwriting.net
...
There are several benefits of SVM– High Dimension Input space – Since the classifier does not
depend on number of features, SVM can handle high dimension space [35]. More importantly, in
sentiment analysis, many features are available. Document Vector Space– Text categorization is
linearly separable [34]. Most document contain few non–zero elements. SVM classifiers worked the
best in majority of papers. In this project, SVM classifier with linear kernel is used. 3.3.3 Rule
Based: This technique was one of the first few methods used for text classification. Human–created
logical rules are applied to categorize the text [39]. Most sentiment analysis use Machine Learning
techniques. Rule based method can detect sentiment polarity. The issue with Rule–Based methods
is, it is difficult to update and the rules and the rules may not be able to cover every scenario [37]. A
rule consists of antecedent and its consequent. It has if–else relation [40]. For instance, Antecedent
=> consequent Antecedent defines the condition of the rule. It can be sequence of tokens or just a
token. Multiple tokens or rules are concatenated by the OR operator "^". This token could be word
or proper noun. The target term represents terms within the context of the text such as person,
company name, brand name etc. Consequent is the sentiment which could be positive, negative or
neutral. It will be the result of the antecedent. The rule looks like: {happy} => positive {angry} =>
negative VADER

Example Of Unsupervised Learning
Supervised learning is fairly common in classification problems because the goal is often to get the
computer to learn a classification system that we have created. Digit recognition, once again, is a
common example of classification learning. More generally, classification learning is appropriate for
any problem where deducing a classification is useful and the classification is easy to determine. In
some cases, it might not even be necessary to give predetermine classifications to every instance of
a problem if the agent can work out the classifications for itself. This would be an example of
unsupervised learning in classification context.
Let's say you are a real estate agent. Your business is growing, so you hire a bunch of new trainee ...
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You know you are supposed to "do something" with the numbers on the left to get each answer on
the right.
In supervised learning, you are letting the computer work out that relationship for you. And once
you know what math was required to solve this specific set of problems, you could answer to any
other problem of the same type!
Unsupervised Learning
Unsupervised learning seems much harder: the goal is to have the computer learn how to do
something that we don't tell it how to do! There are actually two approaches to unsupervised
learning. The first approach is to teach the agent not by giving explicit categorizations, but by using
some sort of reward system to indicate success. Note that this type of training will generally fit into
the decision problem framework because the goal is not to produce a classification but to make
decisions that maximize rewards.
Let's go back to our original example with the real estate agent. What if you didn't know the sale
price for each house? Even if all you know is the size, location, etc of each house, it turns out you
can still do some really cool stuff. This is called unsupervised learning.
Bedrooms Sq. Feet

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

How Does Keller Demonstrate The Value Of Education
Keller was one not only to teach music, but he taught valuable life lessons to Paul. Paul's teacher is
eager to educate him so he can learn from his experiences and be able to succeed in life and his
future ahead. Keller offers Paul a new method of playing piano. His philosophy is that you must be
"cruel to be kind". In this case, Paul must learn to listen well before he begins to play. Keller also
becomes a father figure or mentor to the young Paul. Another lesson taught from his teacher Keller,
is arrogance and humanity. In this regard, Keller wants Paul to have the benefit of his own life
experience. Keller insists that Paul goes back to basics. He has to forget "everything" he has been
taught and learn with such simple fundamental pieces

The Major Roles Of A Primary Teacher
What are the major roles of a Primary Teacher?
Defining the roles and duties of a Primary Teacher can be quite difficult. Gipps et al. (2000) define
teaching as: "a diverse, complex activity with no clear 'rules' except that the teacher should teach
and the children should learn." (Gipps et al. 2000 p.4).
The roles of a primary teacher may be decided by what we believe is effective teaching. The idea of
how children should be taught and how children learn has changed over time. Gipps (1992)
examines how contributions from theorists change the way we look at children's development and
learning. She proposes that there are two main types of models of learning, 'constructivist' and
'transmission' and goes on to suggest that the roles a ... Show more content on Helpwriting.net ...
In this perspective, Carroll suggests the role of the teacher is not to hand down information but to
support children's learning and help them reach higher attainment levels than they would if they
were working alone. Teachers must facilitate discussions to allow pupils to share and co–construct
meanings as well as organise problem–solving activities. Carroll's writing further cements the point
earlier mention by Gipps (1992), suggesting the roles a teacher takes in the classroom depends on
the theory they follow. This can be seen as the roles a teacher performs when following either
perspective are very different.
Having good and substantial knowledge of relevant subjects is often stated in current literature and
documentation as a necessary factor for good and effective primary teaching. It is included in the
Department of Education (2011 p.11) Teachers' Standards, where it is stated that teachers should
have secure subject and curriculum knowledge, and be able to inspire pupils into developing their
own learning and knowledge. A report from Ofsted (2009) looking into the connection between high
quality teaching and subject knowledge in primary education, showed that where lessons were
deemed satisfactory, the teachers weakness in their subject knowledge had negative effects on their
pupils achievement. Writing for the Times Educational Supplement, Morrison (2014) suggests that
subject knowledge is the most important factor for successful teaching in

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

Malware Analysis And Detection Techniques
MALWARE ANALYSIS/DETECTION TECHNIQUES
Sikorski & Honig (2012), explain the fact that when carrying out malware analysis and detection,
only the malware executable is present, which is usually not in natural language form. A variety of
tools and techniques need to be employed to ensure that the underlying information is revealed. Two
basic approaches to malware analysis and detection include: static analysis (observing the malware
without running it), and dynamic analysis (running the malware). They can be done either in the
basic form or more advanced ways.
Static Analysis
In the basic form, static analysis involves carefully observing the executable file without looking at
the actual commands or instructions. This is done to ascertain that a file is indeed malicious, give
information about its functions, and occasionally give information that will enable one produce
simple network signatures. This process is straightforward and can be performed quickly, but in
most cases, it is not effective when dealing with sophisticated malware, and may miss significant
behaviours. An example of static analysis is the use of antivirus software such as AVG for malware
analysis. Unique identifiers called hashes can also be used to identify malware in static analysis.
Dynamic Analysis
In the basic form, dynamic analysis techniques involve both running the malware code and
examining its behaviour on the system or network so as to remove the infection, derive effective
signatures, or

Factors Of Being A Latchkey Children
I. IntroductioN
This paper answers the main question "To what extend is socialisation and the learning process
influenced by being a latchkey child?". In the following you will find reviews of researcher's
definitions for the concept 'latchkey children', and determine the contributory factors to the
phenomenon of 'latchkey' children and the impact (both positive and negative) of the latchkey
situation on children's relationships. The theoretical framework guiding this research focuses on
child development and family theories as well as the ecological perspectives. The discussion of
these theoretical frameworks is essential in this chapter as it provides the rationale and the
framework for the study.
FOCUS ON SOZIALISATION
II. Core
A. Definitions
Before researching the influence of being a latchkey child has on their socialisation and learning
process, the following terms need to be clarified:
1. Socialisation
According to the Oxford dictionary (2016), socialisation is the process by which an individual learns
patterns of behaviour in a way that is acceptable in their society. The process ... Show more content
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Latchkey children
STARTING POINT
Today, whether this is a good thing or not, about 33 percent of all school–age children, an estimated
five million between ages five and 13, are so–called latchkey children, a study of the City of
Phoenix (2003). Furthermore, Your Family Health (2000) highlights a correlation between the
continuing increase in parents working and children being home alone.
As stated in the Oxford Learner's Dictionary (2016), the word latch refers to a small bar fasting a
door or gate. Dowd (1991), Belle (1997) and Robinson (1986), researchers in this field, consider the
term latchkey to be American and link it to the state of a child being in self–care and/or care for
younger siblings on a regular basis during the out of school hours, while their parents are at work.
Latchkey children also labelled unsupervised or home alone children, Belle (1997)

What Is Machine Learning And How It Works?
What is Machine Learning and how it works?
Machines leaning is basically a method of teaching computers to make predictions based on
historical data. The computers then improve its internal programs using this data. To illustrate this
let us consider the example of a normal email filter which automatically filters out spam emails from
an inbox, this is possible as the email engine is programmed to learn to distinguish spam and non–
spam messages. Over time as the program keeps on learning its performance improves drastically.
Other areas where machine learning is used in day to day life are medical diagnosis, self–driving
car, stock market analysis and recommendation engine on any ecommerce website like eBay or
Amazon.
To further elaborate on how it actually works; in machine learning instead of programming of the
computer to solve a problem, the programmer actually writes a series of rigid codes to make the
computer lean to solve a problem from various examples. As you know that computers can solve
complex problems like predicting the pattern of movement of galaxies and so on but it can't perform
easy tasks like identifying objects like a tree or a house, although now a days there are a couple of
search engines and applications that are able to do that like Google Reverse Image Search and Apple
Images, but they still fail when the image is overshadowed by some other image. So machine
learning is basically making the computer think in a way how humans would in this

Sociocultural Theory in Early Childhood Development Essay
Sociocultural is defined as relating to, or involving a combination of social (relating to human
society) and cultural (taste in art and manners that are favored by a social group) factors."
(Socialcultural , 2010) You might ask why we are defining these words. It gives a better
understanding of Vygotsky beliefs "that children seek out adults for interaction, beginning at birth,
and that development occurs through these interactions." (Morrison, 2009 sec 14.6) I agree that his
theory is the best process for learning. Many people feel that social interaction and learning begin at
birth, but there have been research conducted that fetus can learn through parental interaction.
According to Fetal memory "Prenatal memory may be important ... Show more content on
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Lev Vygotsky concept is showing or helping children with a task. They are taught everything
through social interaction no matter what it is. They are taught by example, by getting help with the
task and are expected to be able to complete it by themselves. With this concept of learning every
child is able to learn and evolve into completing activities independently and progressively from
what they have learned. It is important that the environment for the child be set with ideas and task
that will allow them too mentally, educationally and physically develop with or without adult and
peer assistant.
Children are taught to mimic things like sounds and action; by mimicking this will help teachers
understand how a child learns and what they know. It is important to know what each child
understand and what they can progress on from previous teachings; by having them explain what
they are doing. "Intersubjectivity is another concept of Lev Vegotsky "individuals come to a task,
problem, or conversation with their, own subjective ways of making sense of it". (Morrison, 2009
sec 14) This is for the child to verbally discuss their issue, to show that they understand what they
are doing, and that they can talk themselves through a problem. In Early Childhood Development
Today stated that "Lev Vygotsky theory did not focus on children with behavioral issues, learning
disabilities and children whose language is other than

Using Deep Structured Learning Or Deep Machine Learning Essay
INTRODUCTION
Deep Learning (or deep structured learning, or hierarchical learning or deep machine learning) is a
branch of Machine Learning which is based on a set of algorithms that attempts to model high level
abstractions in data by using a deep graph with multiple processing layers which are composed of
multiple non–linear and linear transformations.
Applying Deep Learning to Building Automation Sensors
Sensors such as motion detectors, photocells, CO2 and smoke detectors are used primarily for
energy savings and safety, in building automation. However, next–generation buildings are intended
to be significantly more intelligent, having the capability to analyze space utilization, monitor
occupants comfort, and thereby generate business intelligence. Building–automation infrastructure
that supports such robust features, requires considerably richer information. Since the current
sensing solutions are limited in their ability to address this need, a new generation of smart sensors
are required which enhances the flexibility, reliability, granularity and accuracy of the data they
provide.
Data Analytics at the Sensor Node
The latest era of Internet of Things (IoT), there arises an opportunity to introduce a new approach to
building automation that will decentralize the architecture and push the analytics processing to the
sensor unit instead of a central server or cloud. This is commonly referred to as fog computing, or
edge computing, and this approach provides real–time

An Effective Machine Learning Model
Object recognition is one of the most frontier and potentially revolutionary technologies in computer
science and a central research topic in computer vision. Currently there are increasing number of
researches targeting to give the meaning of our vision to computers. As we move deeper in
understanding the image completely, having more exact and detailed object recognition becomes
crucial. In this context, one cares not only about classifying images, but also about precisely
estimating the class and location of objects contained within the images.
With the improvements in object representations and machine learning models, it is possible to
achieve much advancement in Object Recognition. For the last few years, Deep Neural Network has
... Show more content on Helpwriting.net ...
We achieve this by training a model with a large number of images in the train set. This model is
then passed with a test image to identify the objects contained within that image. The algorithm
performs segmentation on the test image and separate different objects by detecting their
boundaries. All these varied objects are identified with the help of trained model.
In order to accomplish this, we are making use of Torch framework [2]. It is a scientific computing
framework and supports wide range of machine learning algorithms. Torch has built–in support for
large ecosystem of community–driven packages in machine learning. For more efficient and faster
results, we are making use of CUDA, which stands for Compute Unified Device Architecture [3]. It
is a parallel computing platform built by NVIDIA. As the torch framework has support for CUDA
libraries, it is possible to deploy the neural network directly on the GPU instead of the CPU. This
greatly helps in optimizing the performance of neural network and provides much faster results than
standard CPUs. 1.1 Proposed Areas of Study and Academic Contribution
The field of deep learning is picking up steam to the point that it's now inspiring a growing list of
courses in areas such as natural language processing and image recognition. It's also commanding a
growing percentage of

Deep architectures are algorithms that apply successive...
Deep architectures are algorithms that apply successive non–linear transformations to data, in an
attempt to discover higher and higher level representations of this data [1]. The most common
example is an Artificial Neural Network (ANN) with multiple hidden layers. While deep learning is
an old concept, learning algorithms on deep architectures were slow and thus impractical [2].
Discovery of new training methods and improved processing power in recent years have allowed for
state–of–the–art empirical results in computer vision and speech recognition. Most famously, a
group of researchers from Stanford and Google trained a deep network on millions of unlabeled
images, which was then able to detect human faces, bodies, and cats [3]. Deep ... Show more content
on Helpwriting.net ...
Last summer, Google released word2vec, a tool that analyses input text and produces such word
representations using a deep neural network. Interestingly, these representations capture many
linguistic regularities. For example, vector('king')–vector('man')+vector('woman') is close to
vector('queen'). These vector spaces are similar between languages, and could be used for machine
translation. To go further, deeper networks need to be used to understand word phrases and
syntactical relations between phrases. The Stanford NLP Group has for instance studied recursive
neural networks to understand not only words but also how they interact to form a complete
sentence [5]. We come now to the object of my research. What kind of deep architecture is suited to
machine translation? I want a model that can manipulate and understand word phrases and sentences
in the same way that existing systems understand words. Simply expanding upon existing neural
network language models by making them deeper is ineffective because it is too computationally
expensive. We also do not understand well how deep architectures model the data in their hidden
layers, so simply adding layers and neurons seems like a naïve approach. How can I include hand–
engineered features to complement such a system? Can I parallelize computations in this model to
efficiently use GPUs for

Advantages And Disadvantages Of Image Segmentation
Image Segmentation Image segmentation attempts to separate an image into its object classes.
Clustering methods, edge based methods, histogram–based methods, and region growing methods
offer different advantages and disadvantages. The use of a Gaussian mixture expectation
maximization (EM) method has been investigated to realize segmentation specifically for x–ray
luggage scans [131]. Namely, k Gaussian distributions are added to best fit the image histogram,
with each Gaussian distribution corresponding to its own object class. In an x–ray image, high
density objects absorb more x–ray photons and appear more intensely than low density objects. In a
typical x–ray luggage scan image, there will generally be a mix of low density, medium density, and
high density objects. Because of this characteristic, an image segmentation algorithm which requires
knowledge of the number of partitions in the segmentation, such as in EM segmentation, is still a
viable and perhaps even favorable method. By segmenting an x–ray image ... Show more content on
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Both methods work on a 64x64 input window to extract feature and start by first determining edges
oriented at 0, 90, 45, and –45 degrees. In a method known as cell edge distribution (CED), each
64x64 edge map is divided into 16 16x16 cells, and the number of edge pixels in each of these cells
is counted. In another method known as principal projected edge distribution (PPED), edge pixels
are counted along the orientation of the edge map. For example, edge pixels in a horizontal edge
map are counted by counting the number of edge pixels in every 4 rows of the edge map. The result
of either feature extraction method is a 64 element feature vector. A third suggested feature vector is
a simple concatenation of CED and PPED feature vectors into a 128 element feature vector simply
referred to as

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

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