An Overview Of Optimization

An Overview Of Optimization
CHAPTER 1
INTRODUCTION
This chapter is discussed about the project background, the problem of the project, the objective of
the project and project scope.
1.1 PROJECT MOTIVATION
With the development of artificial intelligence in recent years, there has been a growing interest in
algorithms inspired by the observation of natural phenomena. We can see that all the algorithms are
good replacements as method to solve complex computational problems.
Various heuristic approaches have been adopted by researches including genetic algorithm (Holland
1975), simulated annealing (Kirkpatrick et al. 1983), immune system (Farmer et al. 1986), ant
system (Dorigo et al. 1996) and particle swarm optimization (Kennedy and Eberhart 1995; Kennedy
and Eberhart 1997).
All the above heuristics involve the desired problem solution from iterative cycles of iterations
between the population members. Unfortunately, no algorithm can solve all the optimization
problems and have some algorithms can be the best solution for a few problems than the others.
Optimization problems are widely encountered in various fields in science and technology.
Sometimes such problems can be very complex due to the actual and practical nature of the
objective function or the model constraints. Most of power system optimization problems have
complex and nonlinear characteristics with heavy equality and inequality constraints.
Optimization is the important process to many systems in industry and it will produce
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Ada Solution Manual
This file contains the exercises, hints, and solutions for Chapter 1 of the book "Introduction to the
Design and Analysis of Algorithms," 2nd edition, by A. Levitin. The problems that might be
challenging for at least some students are marked by ; those that might be difficult for a majority of
students are marked by .
Exercises 1.1
1. Do some research on al–Khorezmi (also al–Khwarizmi), the man from whose name the word
"algorithm" is derived. In particular, you should learn what the origins of the words "algorithm" and
"algebra" have in common. 2. Given that the official purpose of the U.S. patent system is the
promotion of the "useful arts," do you think algorithms are patentable in this country? Should they
be? 3. a. Write down driving ... Show more content on Helpwriting.net ...
6. Prove that if d divides both m and n (i.e., m = sd and n = td for some positive integers s and t),
then it also divides both n and r = m mod n and vice versa. Use the formula m = qn + r (0 ≤ r < n)
and the fact that if d divides two integers u and v, it also divides u + v and u − v. (Why?) 7. Perform
one iteration of the algorithm for two arbitrarily chosen integers m < n. 9. a. Use the equality gcd(m,
n) = gcd(m − n, n) for m ≥ n > 0. b. The key is to figure out the total number of distinct numbers that
can be written on the board, starting with an initial pair m, n where m > n ≥ 1. You should exploit a
connection of this question to the question of part (a). Considering small examples, especially those
with n = 1 and n = 2, should help, too. 10. Of course, for some coefficients, the equation will have
no solutions. 11. Tracing the algorithm by hand for, say, n = 10 and studying its outcome should
help answering both questions.
3
Solutions to Exercises 1.1
1. Al–Khwarizmi (9th century C.E.) was a great Arabic scholar, most famous for his algebra
textbook. In fact, the word "algebra" is derived from the Arabic title of this book while the word
"algorithm" is derived from a translation of Al–Khwarizmi's last name (see, e.g., [KnuI], pp. 1–2,
[Knu96], pp. 88–92, 114). 2. This legal issue has yet to be settled. The current legal state of affairs
distinguishes mathematical algorithms, which are not

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

Analysis Of Local Search Algorithm For STP
From the tree SP we presented in the algorithm that we have obtained via Local Search Algorithm
for STP, we have generated the matrix of cost. This is done by assigning a cost to all the edges of
tree SP and by assigning a cost on "n" no. of nodes to all the other edges in graph. This assignment
of cost helps in recognizing the cost of the longest possible path between a pair of nodes in any
spanning tree is n−1 (i.e. it passes n−1 edges) while the cost of the shortest path between any pair of
nodes without using of SPT edges is at least "n" (i.e. passes one edge). Consequently, the 802.1d
protocol will produce the intended spanning tree "SP".
3.5 DATA GENERATION
In this section we progress by generating network topologies and traffic ... Show more content on
Helpwriting.net ...
root = 1; in_tree = {root}; considered = ∅; while #in_tree< n do select (u ∈in_tree) and (u !∈
considered); selectnum_branch∈ [min..max] ; foreach i ∈ [1..num_branch] do if #in_tree< n then
select (v ∈ [1..n]) and (u /∈in_tree); creatEdge(u, v); in_tree = in_tree + {v} end end considered =
considered + u; end To the obtained spanning tree from above algorithm we add two types of edges
so that we can get a bi–connected graph. The bi–connected graph has a significance that if any of
the edge becomes down then also the network will be connected via another edge. This gives us
assurance of always up time for a network. This means in case of link failure alternate link will
always be present to ensure the network connectivity.
In this type1 edge connect a leaf with the higher level node while the type 2 edge connect a non–
leaf node (not the root) with the no–leaf node or lower level node of different branch. For each tree
new "n–1" edges are added while the generation of bi–connected graph.
To pretend a network in which a switch has many ports, we define a ratio "r". This means each node
in the tree is connected to at least "r" edges. In each test graph, from the generated bi–connected
graph, we create three more trees with ratio r15 = n/15, r10 = n/10 and r5 = n/5 (where n = no. of
nodes).
3.5.4 The FAT Tree:
Figure shown below depicts the Fat Tree – another topology for DCNs proposed in [35] It is called
Fat Tree because it is not a

Wireless Sensor Networks ( Wsns )
Energy consumption and coverage are common design issues in Wireless Sensor Networks (WSNs).
For that reason, it is vital to consider network coverage and energy consumption in the design of
WSN layouts.
Because selecting the optimal geographical positions of the nodes is usually a very complicated
task, we propose a novel heuristic search technique to solve this problem.
Our approach is a multi–population search algorithm based on the Particle Swarm Optimization
(PSO).
The goal of this algorithm is to search for sensor network layouts that maximize both the coverage
and lifetime of the network.
Unlike traditional PSO, our algorithm assignes a swarm to each sensor in the network and a global
network topology is used to evaluate the ... Show more content on Helpwriting.net ...
The Design of sensor layouts that account for both network coverage and power consumption is a
difficult problem.
Because WSN may have large number of nodes, the task of selecting the optimal geographical
positions of the nodes can be very complicated.
Therefore, we propose a cooperative particle swarm algorithm as a heuristic to address the problem
of wireless sensor layout design.
In our approach we assign a swarm to each node in the network.
Each swarm will search for optimal $x$ and $y$ positions for its associated sensor.
A global network layout, consisiting of the coordinates found by the best particles, will be
maintained by the algorithm.
The lifetime and coverage of this global layout will be used to measure the quality of each particle 's
position.
We hypothesize that by splitting the swarms across the set of sensors, our alorithm with obtain a
finer–grained credit assignment, and reduce the chance of neglecting a good solution for a specific
portion of the solution vector.
We will verify this hypothesis by comparing our algorithm to several tradtional single–population
search techniques.
section{Background}
subsection{Layout Optimization}
Layout optimization for wireless sensor networks consists of finding the coordinates for a set of

sensors that maximizes the lifetime and coverage for the sensor network.
Placement of the sensors is bounded within a two–dimensional square region with an upper left

A Decision Tree Based Rule Formation With Combined Pso...
CHAPTER 3
A DECISION TREE BASED RULE FORMATION WITH COMBINED PSO–GAALGORITHM
FOR INTRUSION DETECTION SYSTEM
3.1 INTRODUCTION The increase in the usage of the computer networks leads to the huge rise in
the threat and attacks. These attackers change, steal and destroy the valuable information and finally
cause complete damage to the computer system of the victim. They affect the performance of the
computer system through the misconfiguration activities and generation of software bugs from
internal and external networks. Irrespective of the existence of various security mechanism,
attackers often attempt to harm the computer system of the intended legitimate users. Hence,
security is a main factor for the efficient operation of the network in various applications such as
healthcare monitoring, military surveillance, etc. The most common security mechanisms are
firewalls, antivirus programs and Intrusion Detection System (IDS).
Firewalls (Fehr, 2013) are the commonly used mechanism for securing the corporate network or
sub–network. The firewall is operated based on a set of rules that can protect the system from the
flooding attacks. The main function is sorting of the packets according to the allow/deny rules,
based on the header–filed information. But the firewalls cannot ensure complete protection of an
internal network, since they are unable to stop the internal attacks. The computer viruses can cause
damage to the computer data that leads to the complete failure of the

What Is The Algorithm For Multi-Networking Clustering...
with most extreme number of sensor nodes in each cluster could be accomplished. The weight
capacities at every sensor node, which is a blend of various parameters including: residual energy,
number of neighbors and transmission control. Basically CFL clustering algorithm is designed for
localization in WSNs. It is unable to work when the distribution of sensor nodes are not good.
3.2.4 FoVs: Overlapped Field of View Authors proposed a clustering algorithm for wireless sight
and sound sensor networks in light of covered Field of View (FoV) areas. The fundamental
commitment of this calculation is finding the convergence polygon and figuring the covered
territories to build up clusters and decide clusters participation. For dense networks, ... Show more
content on Helpwriting.net ...
Along these lines CHs (cluster heads) closest to the BS (base station) can protect more vitality for
between energy transmission. PEZCA give more adjust in energy consumption and and life time of
network correlations with LEACH.
3.2.7 VoGC: Voting–on–Grid clustering In this creator joined voting technique and clustering
algorithm, and grew new clustering plans for secure localization of sensor networks. Authors
likewise found that the recently proposed approaches have great exhibitions on limitation exactness
and the discovery rate of malevolent guide signals. In this plan, malicious guide signals are sifted
through as per the clustering consequence of crossing points of area reference circles. Authors
utilized a voting–on– grid (VOGC) strategy rather than customary clustering calculations to lessen
the computational cost and found that the plan can give great limitation exactness and recognize a
high level of malicious beacon signals. 3.2.8 BARC: Battery Aware Reliable Clustering In this
clustering algorithm authors utilized numerical battery demonstrate for execution in WSNs. With
this battery show authors proposed another Battery Aware Reliable Clustering (BARC) calculation
for WSNs. It enhances the execution over other clustering calculations by utilizing Z–MAC and it
pivots the cluster makes a beeline for battery recuperation plans. A BARC

Essay On Test Data Generation
2. Related works
Some of the recent work related to the automated test data generation is listed below:
Search–based approaches have been extensively applied to solve the problem of software test–data
generation. Yet, test–data generation for object–oriented programming (OOP) is challenging due to
the features of OOP, e.g., abstraction, encapsulation, and visibility that prevent direct access to some
parts of the source code. To address this problem Abdelilah Sakti et al. [26] have presented an
automated search–based software test–data generation approach that achieves high code coverage
for unit–class testing. The test–data generation problem for unit–class testing to generate relevant
sequences of method calls were described at first. ... Show more content on Helpwriting.net ...
A set of search heuristics targeted to OCL constraints to guide test data generation and automate
MBT in industrial applications was proposed. These heuristics for three search algorithms: Genetic
Algorithm, (1+1) Evolutionary Algorithm, and Alternating Variable Method were evaluated.
Empirically evaluated the heuristics using complex artificial problems, followed by empirical
analyses of the feasibility of our approach on one industrial system in the context of robustness
testing.
A feature model is a compact representation of the products of a software product line. The
automated extraction of information from feature models is a thriving topic involving numerous
analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the
use of random feature models. However, these only provide a rough idea of the behaviour of the
tools with average problems and are not sufficient to reveal their real strengths and weaknesses.
Sergio Segura et al. [28] have proposed to model the problem of finding computationally hard
feature models as an optimization problem and solved it using a novel evolutionary algorithm for
optimized feature models (ETHOM). Tool and an analysis operation were given in ETHOM
generated input models of a predefined size maximizing aspects such as the execution time or the
memory consumption of the tool when performing the

Timetable Management System Using Java
MCA
Semester – I
S.No.
Course Code
Course Name
1
2
3
4
5
COMP 712
Programming & Problem Solving with C
COMP 714
COMP 715
MAS 621
BAM 752
Introduction to Softwares
Computer Organization and Architecture
Discrete Mathematics
Business Communication
Total Credits
Semester – II
S.No.
Course Code
Course Name

6
COMP 723
Operating System
7
8
9
10
COMP 724
COMP 725
COMP 726
MAS 661
Data Structures using C++
Information System Analysis and Design
Web Technologies
11
BAM 753
Essentials of Management
Computer based Numerical and Statistical
Techniques
Total Credits
Semester – III
S.No.
Course Code
12
13
14
15
16
17
COMP 731
COMP 732
COMP 733
COMP 736
COMP 837
BAM 796

Semester – IV
S.No.
Course Code
18
COMP 842
19
COMP 843
20 ... Show more content on Helpwriting.net ...
Unit 3: Software
System software, Operating System, Functions of OS, Overview of DOS,
Windows and Unix.
Application software (Word Processor, MS–Excel, MS–PowerPoint)
Unit 4: Programming Languages and Software Development
Generation of Languages, Compiler, Assembler, Linker, Loader, Software
Development Methodology, Software Development Life Cycle
Programming Languages: Programming Language Paradigm, Procedure–Oriented
Language, Object– Oriented Language, Web Based Languages
Unit 5: Network and Data Base Concepts
Definition and Types of Network, Introduction to Internet– Search Engine, Web
Page, Web Browser, Introduction to E–Commerce.
Data Base definition, Data Base Management System, overview of MS–Access
Text Books:
1. Fundamentals of Computer: – V. Raja Raman
2. Fundamentals of Computer: – P. K. Sinha
Reference Books:
1. Microsoft Office Black Book
2. UNIX: The Ultimate Guide: – Sumitabha Das
3. PC Software: – V.K. Jain "O Level"
Computer Organization & Architecture
Code: COMP–715
Credits: 4(3+1+0)
UNIT–1
Introduction: Types of computers: Analog, Digital and Hybrid Computers, Modern Digital
Computer,
Number systems– Binary, Octal, Decimal, Hexadecimal , 1's & 2's Complement.
Digital logic circuits and Components: Logic gates, Boolean Algebra, K–Map Simplification, Half
Adder, Full Adder, Decoder, Encoders, Multiplexers, Demultiplexer, Flip Flops, Registers, Binary
Counters.

Unit 5 Programming Portfolio 1
Student number: 5904068 30.10.2015
Module: 210СCT
Programming Portfolio 1.
Contents
Task 1: Harmonic Series..........................................................3
Task 2..........................................................................................3–4
Task 3: Range Search............................................................4–5
Task 4: Node Delete Function..................................................5
Appendix: Full C++ code...................................................................6–7
Task 1: Harmonic Series
a) def harmonic(n): ... Show more content on Helpwriting.net ...
Input: array arr, int size, int key
Ouput: int position , if found, else –1 def: binary_search(arr, key, l, u)
Where l = 0, r = size – 1 If ( u > l) return –1; mid = (l + u)/2; if (arr[mid] > value) return
binary_search(arr, key, l, mid – 1); else if (arr[mid] < value) return binary_search(arr, value, mid +
1, u); else return mid;
The run–time complexity of this algorithm is O(log(n)).
Task 4:Node Delete Function
void NodeDeleteFunctoin(int l)
{
Node* temp = head; //Point that we need a beginning of the list
Node* prev = NULL; //Previous element is empty
Node* next = NULL; //Next element is empty while (temp != NULL) { if (temp–>value == l) {
//Delete a first node but there are others if (temp == head) { //Point that we need a beginning head =

temp–>next; //We shift the starting of the beginning of the next element if (head != NULL) {head–
>prev = NULL;}
}
else if (temp == tail) //Point that we need a tail
{
prev = temp–>prev; //Move the tail in the back prev–>next = NULL; //Point that there is nothing
before the tail tail = prev; //Remember the address of the removable

Advantages Of Collaborative Differing In Movies
rated a movie that belongs to the comedy genre, then the system can learn to recommend other
movies from this genre. Collaborative filtering: The simplest and original implementation of this
approach recommends to the active user the items that other users with similar tastes liked in the
past. The similarity in taste of two users is calculated based on the similarity in the rating history of
the users. This is the reason why collaborative filtering is often referred as "people–to–people
correlation." Collaborative filtering is considered to be the most popular and widely implemented
technique in Recommendation systems. An item–item approach models the preference of a user to
an item based on ratings of similar items by the same user. Nearest–neighbors methods enjoy
considerable popularity due to their simplicity, efficiency, and their ability to produce accurate and
personalized recommendations. Demographic: This type of system recommends items based on the
demographic profile of the user. The assumption is that ... Show more content on Helpwriting.net ...
It can be used not only by developers, but also by page designers, who can now play a more direct
role in the development life cycle. Another advantage of JSP is the inherent separation of
presentation from content facilitated by the technology, due its reliance upon reusable component
technologies like the JavaBeans component architecture and Enterprise JavaBeans technology. The
purpose of JSP is to provide a declarative, presentation–centric method of developing servlets.
Typically, JSP pages are subject to a translation phase and a request processing phase. The
translation phase is carried out only once, unless the JSP page changes, in which case it is repeated.
Assuming there were no syntax errors within the page, the result is a JSP page implementation class
file. The translation phase is typically carried out by the JSP engine itself, when it receives an
incoming request for the JSP page for the first

Cscc 361 Report On A Maze
Abdulaziz Nasser Almutiri
433105657
CSC 361 Report
Phase #2
Introduction:
We have a maze, robot and goal we should write a code to discover a path for the robot to the goal.
The maze has blocks, hole, charge station and goal.
The robot may fail in the hole the robot has a battery and it might complete we will use 3 algorithms
search
BFS, A* and hill climbing and attempt to discover a path for the robot the search should take care of
the battery
The modeling:
State:
Int x
Int y maze [][] // 2 dimensions' array of char
' ' – empty cell.
R– robot.
T– treasure..
U– treasure and the robot in the empty cell.
H– hole.
X– robot in the hole cell.
Y– treasure in the hole cell
Z– treasure and the robot in the hole cell.
B– blocked cell
Initial state: ... Show more content on Helpwriting.net ...
it begins at a root node and inspects all the neighboring nodes to discover an action and a new state
by the action.
Enqueue the root node then dequeue a root node and examine it. If the node is the goal, quit the
search and return a result.
Otherwise, enqueue any successor that have not yet been discovered. keep Enqueue and Dequeue to
find the goal and quit the search if the queue is empty
A*:
the A* using admissible heuristics and using the priority queue and is an informed algorithm also
known as weighted graphs, the algorithm is optimal.
A* solves problems by starting from the initial node in the priority queue expanding nodes

depending on heuristics until one of the nodes is the goal or the queue is empty.
Hill climbing:
Hill climbing using priority queue the priority is the state heuristics. the hill climbing solves
problems by starting from the initial state and move to a neighbor with better heuristics. Repeat until
all the neighboring states are of lower heuristics Return the current state as the solution state.
The heuristics that can be using in this project it's the cells between the robot (RX | RY) and the
Goal (GX | GY)
Can be counted as:
H = (|RX–GX| +

Computer Literacy Is The Level Of Proficiency And Fluency
1. Explain computer literacy.
Computer literacy is the level of proficiency and fluency someone possesses amid computers.
Nevertheless, computer literacy commonly implies to the capability to manipulate applications
rather than to program. Entities who are vastly computer literate are from time to time identified as
power users (Computer Literacy , n.d.). Computers have affected each of our lives: the manner by
how we work, the manner of how we learn, the manner in which we live, yet even the means we
play. It virtually is unfeasible to progress complete a single day without coming upon a computer, an
apparatus reliant on a computer, information created by a computer, or a word that was established
or whose implication has transformed with the introduction of computers. However, because of the
importance of computers in today's world, it is essential to be computer literate.
2. Explain computer algorithms and its significance.
A computer algorithm is a method or formula for deciphering a problem, based on accomplishing a
sequence of quantified actions. A computer program can be regarded as an intricate algorithm. In
mathematics and computer science, an algorithm ordinarily entails a lesser technique that answers
an intermittent question. Algorithms are commonly managed thru every area of information
technology. A search engine algorithm, selects search strings of keywords and functions as input,
explores its linked database for applicable web pages, and returns findings. An

Nt3110 Unit 1 Algorithm Application Paper
section{Design Procedure}
label{Design}
We divide the system into four main parts as follows.
begin {itemize}
item [1.] Modularize. item [2.] Evaluation. item [3.] Estimation. item [4.] Testing end{itemize}
We represent this fact graphically in the following figure ref{Figure:Phase}. Each part of the figure
describes briefly.
begin{figure}[htp] includegraphics[width=.48textwidth]{figure/arc2.eps} caption{Architecture
of design procedure. } label{Figure:Phase}
end{figure}
subsection{Modularize:}
Social networks are growing day by day. For modular representation of Graph $G(V,E)$ first phase
of the design issue is to modularize the network having border nodescite{newman2006modularity}.
Boarder nodes ... Show more content on Helpwriting.net ...
Then users of these groups are recommended .We use an effective technique of identify the best user
to be recommended. When we are in the distance based group then apply probability based function
and gets the user with high concentration of communication. For example, we need two users but as
many as fifty users have same distance from the recommender. Then we use the probability function
and set a threshold value 2 this will identify the best two users for the best solution. Again if we are
in the probability based group then calculate shortest distance among the users who have same
probability value. For above example, assume 130 nodes have same probability (suppose 0.9) then
run BFS for these nodes (130) the users having shortest distance from the user are recommended.
Though our approach is to work efficiently and succeed to produce a result as much effective as we
want, we have same

Essay On Content Mining
Introduction:
Content mining is tied in with recovering important data from information or content accessible in
accumulations of reports through the recognizable proof and investigation of fascinating examples.
This procedure is particularly beneficial when a client needs to locate a specific kind of incredible
data on the web. Content mining is focusing on the record accumulation. The greater part of content
mining calculation and methods are gone for finding designs crosswise over extensive record
accumulations. The quantity of records can extend from the large number to millions. This paper
goes for talking about some essential content mining strategies, calculation, and apparatuses.
Background:
There are right now excessively various ... Show more content on Helpwriting.net ...
The objective of K–implies calculation is to separate M focuses in N measurements into K bunches.
It looks for a K–parcel with locally ideal inside bunch total of squares by moving focuses called
Euclidean separation starting with one bunch then onto the next [3]. It is a well–known bunch
examination method for investigating a dataset.
2– Naïve Bayes Classifier Algorithm
Characterization in information mining is an assignment of anticipating an estimation of all out
factors. It should be possible by building models in view of a few factors or highlights for the
expectation of a class of a protest on the premise of its traits [4]. A standout amongst the most well–
known learning strategy gathered by likenesses is Naïve Bayes Classifier. It is an administered
machine learning calculations that work on the Bayes Theorem of Probability to manufacture
machine learning models. Guileless Bayes Classifier is extremely useful for breaking down printed
information, for example, Natural Language Processing. It chips away at restrictive likelihood.
What's more, it is the likelihood that something will happen, given that something else has just
happened. By using this, the client will have the capacity to compute the likelihood of a something
utilizing its information [5].
3– Apriori Algorithm
The Apriori algorithm is an important algorithm for mining repeated elements collections especially
for Boolean association rules. It practices methodology known as "bottom up",

Two Step Gravitational Search Algorithm Essay
applied to find out the optimal generation of each unit when the generation cost curves are non–
smooth and discontinuous in nature. Most of the PSO algorithms suffer from the problem of
premature convergence in the early stages of the search and hence are unable to locate the global
optimum. The idea here is to exercise proper control over the global and local exploration of the
swarm during the optimization process. The PSO_TVAC based approach for practical non–convex
ELD problem is tested on four test systems having different sizes and non–linearities. Out the four,
two test systems are with valve point loading effects, one system has POZ and one system has a
large dimension with 38 generating units. The PSO_TVAC is found to ... Show more content on
Helpwriting.net ...
The Lambda iteration method is implemented in three and six generating units. The results are
compared for two different cases with and without losses. In first case generator constraints are
considered along with the lossless system and in second case generator constraints are considered
with the losses. All the programming has been done in MATLAB environment. In this study, three
and six unit thermal power plant is considered which is solved for two different cases with and
without losses. Vo Ngoc Dieu [16], proposed an augmented Lagrange Hopfield network (ALHN) for
real power dispatch on large–scale power systems. The proposed ALHN is a continuous Hopfield
network with its energy function based on augmented Lagrange function. For this combination, the
ALHN method can easily deal with large–scale problems with nonlinear constraints. The proposed
ALHN has been tested on systems from 40 units to 240 units, IEEE 118–bus and IEEE 300–bus
systems, and the obtained results have been compared to those from other methods. The test results
have shown that the ALHN method can obtain better solutions than the com¬pared methods in a
very fast manner. Therefore, the proposed ALHN could be favorable for implementation on the real
power dispatch problems for large–scale systems. The proposed ALHN has been tested on
differ¬ent systems with large number of generating units and buses for two cases neglecting power
loss and including power loss in transmission system. Serhat

A Based System For Diversifying
A Clustering Based System for Diversifying WSRec Results Ms. Apurwa Atre Information
Technology Department , ICOER, SPPU , Pune , India apurwaatre1994@gmail.com Ms. Nayan
Kamble Information Technology Department , ICOER, SPPU , Pune , India
kamble.nayan28@gmail.com Ms. Vineeta Bisht Information Technology Department , ICOER,
SPPU , Pune , India vinitab1994@gmail.com Mr. Tejas Mamarde Information Technology
Department , ICOER, SPPU , Pune , India tejasxs@gmail.com ABSTRACT The use of Web
services for various applications has led to the growth of web services on a large scale. Due to the
increase in usage of web services, it has become of prime importance to design systems for effective
web service recommendation. In our project, we propose a system for effective web service
recommendations incorporating users' preferences regarding quality and diversities amongst web
services. Users' requirements are considered and mined from his usage history. Then we find
functional similarities using clustering techniques followed by applying a ranking algorithm to list
top–k services. To discover high quality Web services, a number of QoS models for Web services
and QoS–driven service selection approaches have been proposed in the service computing field. In
this system user explicitly specifies his/her interests and QoS requirements, and submits them to the
service discovery system. Then the service discovery system matches the user's interests and QoS
requirements with

Software 520 : Differential Evolution Essay
Intro:
Hi, my name is blank and the project I have been working on this year for computing 520 is
differential evolution, DE, on the cloud, under the supervision of blank.
Parallel programming, the utilisation of many small tasks to complete a larger one, has become far
more prevalent in recent times as problems call for systems with higher performance, faster turnover
times, easy access, and lower costs. While this has previously been cost–prohibitive, given that one
would have had to purchase a large number of physical machines to work on, the development of
cloud computing systems has largely answered this call, providing resources and computing power
as a service to users, rather than a product. The addition of hardware virtualisation has further
increased the availability of massively–parallel collections of computers as flexible networked
platforms for computing large–scale problems.
Differential Evolution, or DE, is a cost minimisation method that utilises various evolutionary
algorithm concepts, but can also handle non–differentiable, nonlinear, and multimodal objective
functions that standard evolutionary algorithms cannot. Experiments have shown that DE shows
good convergence properties and outperforms other EA's, converging faster and with more certainty
than many other popular global optimization methods.
DE provides a general optimization function that converges on an optimal set of parameter values
according to some objective function. This is a valuable

Essay On Wifa
In the second part of the thesis, the geometry of the antennas of each WiFi access point is changed
to URA in order to extend the searching area into 2–D search, the well–known subspace MUSIC
algorithm is used for the examination of the received spatial information, and then it estimates each
spatial spectrum in which the Azimuth Angle of Arrival (AOA) and Elevation Angle of Arrival
(EOA) of all the paths at each URA WiFi access point is located. After that, because our system is
considered under very low SNR, a set of spectra at some APs might be influenced, so, a fine–
grained fusion algorithm has been added, it computes the minimum errors between each location in
a known grid dimension and the estimated AOA and EOA at every URA array, ... Show more
A hence about the novel GBSA–MDL source number estimation algorithm and our contribution in
this thesis was set in the first part of the introduction.
In the second part, the wireless based indoor localization which are RSS, TOA, DOA, and TDOA
techniques have been introduced and the grammatical failure of these methods for indoor
localization under the effect of the high existence of multipath signals. Also, we gave a brief
explanation about data fusion multiple sensor techniques and several related works have been
introduced. We motivated our novel indoor positioning algorithm by showing the significant
addition of the multiple sensor data fusion with the recently mentioned wireless based indoor
techniques and the serious need of the 2–D array geometry in the next 5G. At the end, our
contribution in this dissertation has been included.
Chapter 2: Literature Review: In this part, several basic concepts are introduced. We start our
chapter by explaining the meaning of optimization and its two main categories which are local
optimization and global optimization, also the advantages of using the last mentioned category
compared to the first one is mentioned. Accordingly, the most know newly invented global
optimization algorithms based on nature behaviors like the GA, PSO and GBSA are introduced. In
addition of that, the galaxy based search algorithm is studied well and its

Nt1330 Unit 3 Assignment
Assignment Description: For the unit 5 assignment I needed to create a search engine for the
database of terms, documents, and associated data that was created in our previous assignment. The
program needed to implement a "bag of words" query and retrieve documents relevant to the query.
Those documents then needed to have their cosine similarity determined and reported in descending
order, up to the top twenty retrieved results. Additionally, we were to report the number of candidate
documents that were retrieved based on the query. The assignment suggested using "home
mortgage" for testing purposes but that query is not relevant to the corpus of documents we have
been provided. Instead, I used "test run algorithm" for my testing query, though any relevant
terminology for the documents should be fine for your assessment.
Notes on Assignment: ... Show more content on Helpwriting.net ...
As a result, I used a functional code, not my own, from a prior assignment which I minorly modified
to make my search engine meet the requirements of the assignment. I cannot take credit for the
functionality of the contents of the indexer.py file or the sections of it provided in the
indexerandsearch.py file. I would like to provide appropriate credit but was unable to do so due to
the anonymous nature of our assignments. I would not have been able to complete my own
search,py code without a functioning indexer to create the SQLite database so this was necessary. In
indexerandsearch.py the original code for the search engine, based on the example we were
provided, begins at line

Measuring The Quality Of Clusters
Analysis & Comparison
For measuring the quality of clusters four criteria have been used. The first three criteria are
designed so as to measure the quality of cluster sets at different levels of granularity. Ideally it's
needed to generate partitions that have compact, well separated clusters. Hence, the criteria used
presently combine the two measures to return a value that indicates the quality of the partition thus
the value returned is minimized when the partition is judged to consist of compact well separated
clusters with different criteria judging different partition as the best one. The last criterion is based
on time efficiency.
The term compactness and isolation are defined as follows: – Compactness: This is a measure of
cohesion or similarity of objects in an individual cluster with respect to the other objects outside the
cluster. The greater the similarity the greater the compactness. Isolation: This is the measure of
distinctiveness or dissimilarity between a cluster and the rest of the world. The smaller the
similarity, the greater the isolation.
Presently simple distance based measure are used to evaluate compactness and isolation (Manhattan
Distance for the present work) has been used rather than statistical test of significance used in
multivariate analysis of variance. In this section, whilediscussing about each of the criterion R_i
represents an object or search result, C_j represents a cluster and C_jc its centroid. G_cis the global
centroid.

Laona??on Modified Spider Monkey Algorithm
In 2015, K. Lenin et. al. [44] in their study "Modified Monkey Optimization Algorithm for Solving
Optimal Reactive Power Dispatch Problem" expressed that to reduce the real power loss,
modifications were required in local and global leader phase and a Modified Spider Monkey
Algorithm (MMO) was introduced. Paper also upheld that MMO is more favorable for dealing with
non–linear constraints. The algorithm was examined on the IEEE 30–bus system to minimize the
active power loss.
H. Sharma, et al. [45] in 2016, discussed in "Optimal placement and sizing of the capacitor using
Limaçon inspired spider monkey optimization algorithm" that to limit the losses in distribution and
transmission, capacitors of definite sizes are should have been ... Show more content on
Helpwriting.net ...
In 2016, A. Sharma et. al. [48] presented a paper "Optimal power flow analysis using Lévy flight
spider monkey optimization algorithm" in which a Lévy flight spider monkey optimization
(LFSMO) algorithm was proposed to solve the standard Optimal power flow (OPF) problem for
IEEE 30–bus system. The exploitation capacity of SMO was increased in the proposed algorithm.
LFSMO was tested over 25 benchmark functions and its performance was examined. It was found
that LFSMO gave desirable outcomes than the original SMO.
In 2017, S. Kayalvizhi et. al. [49] presented a paper "Frequency Control of Micro Grid with Wind
Perturbations using Levy Walks with Spider Monkey Optimization Algorithm." In this paper, a new
eagle strategy, which is a combination of levy flights and SMO, is utilized in the optimization of the
gains of PI controllers which helps in regulating the frequency of the micro grid. A typical micro
grid test system and a real time micro grid setup at British Columbia are the two case studies
considered, in which the frequency control is implemented. The implementation is done in two–step
search process; in the first place, levy flights do the random search and after that SMO does a
thorough local search. Results demonstrate that the proposed method outperforms the results of
other well–known algorithms and is

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

The Producer Consumer Problem Considered Harmful
The Producer–Consumer Problem Considered Harmful
Shyam Rangrej and Kushang Gonawala
Abstract
In recent years, much research has been devoted to the exploration of DHTs; on the other hand, few
have simulated the construction of architec– ture. In this position paper, we demonstrate the study of
simulated annealing. Our focus in this paper is not on whether the Internet can be made certifiable,
semantic, and optimal, but rather on motivating an analysis of write–ahead logging (INK).
1 Introduction
Recent advances in stable modalities and au– tonomous epistemologies are rarely at odds with
access points. The notion that physicists collude with A* search is usually excellent. On a sim– ilar
note, though such a hypothesis is always a confirmed purpose, it fell in line with our expec– tations.
The development of voice–over–IP would greatly amplify the simulation of the Ethernet.
Motivated by these observations, the under– standing of randomized algorithms and agents have
been extensively developed by scholars. The basic tenet of this method is the deploy– ment of IPv6.
In addition, we allow random– ized algorithms [23] to provide self–learning the– ory without the
compelling unification of sensor networks and A* search. Furthermore, the draw– back of this type
of solution, however, is that the much–touted homogeneous algorithm for the in–
vestigation of spreadsheets by White is Turing complete. Along these same lines, indeed, XML and
link–level

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

Nt1310 Unit 1 Pdf
Figure 3.5: FAST Feature detection in an image patch. The high lighted squares are the pixels used
in the feature detection. The pixel at C is the centre of a detected corner: the dashed line passes
through 12 contiguous pixels which are brighter than
C by more than the threshold. [33]
is applied to find top N points. FAST does not compute the orientation and is rotation variant. It
computes the intensity weighted centroid of the patch with located corner at center. The direction of
the vector from this corner point to centroid gives the orientation. Moments are computed to
improve the rotation invariance. So, in ORB, a rotation matrix is computed using the orientation of
patch and then the BRIEF descriptors are steered according to the orientation. ... Show more content
on Helpwriting.net ...
The Lucas–Kanade method relies on three main assumptions:
Pixel intensity does not change between frames.
Chapter 3. System Composition 26
Figure 3.6: Visualization of optical flow.[11]
Neighboring pixels have similar motion.
Movement is small between frames.
The first assumption is needed as the method operates directly on the intensity of the image, if these
were to change much between each frame the algorithm would fail. The second assumption is
needed as the algorithm uses a window around the point of interest. The window is necessary as (u,
v) cannot be computed from just one pixel. The last assumption is needed as the algorithm uses a
search window to find the vector (u, v). If the movement between frames are too large it may fall
outside of the search window and the vector can not be calculated.
u and v are found by solving the equation shown in (3.6). Ix and Iy are the hori– zontal and vertical
gradient images within the search window. These are found by convolving the image with the
vertical and horizontal Sobel kernels. q1, q2 , . . . , qn are the pixels coordinates of the pixels in the
neighborhood of the point being evaluated. Ix(q1)v + Iy (q1)u = −It(q1)
Ix(q2)v + Iy (q2)u = −It(q2)

Survey On Sentiment Analysis And Opinion Mining
A Survey on Sentiment Analysis and Opinion Mining Abstract– This survey reviews the recent
progress in the field of sentiment analysis with the focus on available datasets and sentiment
analysis techniques. Since many exhaustive surveys on sentiment analysis of text input are
available, this survey briefly summarizes text analysis techniques and focuses on the analysis of
audio, video and multimodal input. This survey also describes different available datasets. In most
of the work datasets are prepared as per specific research requirements. This survey also discusses
methods used to prepare such datasets. This survey will be helpful for beginners to obtain an
overview of available datasets, methods to prepare datasets sentiment analysis techniques, and
challenges in this area. Key words– Sentiment Analysis, Opinion Mining, Multimodal Sentiment
Analysis, datasets 1 INTRODUCTION Opinions always play an important role in decision making.
Businesses seek consumer opinions about their products and services to improvise them, consumers
seek opinions of other consumers to get the best deal. Governors and policy makers of the country
also need to consider opinions of the masses before making some decisions. Emergence of social
networking sites, blogs, forums, e–commerce websites have provided internet users with a platform
where they can express their opinions. Thus a huge source of opinions, views, and sentiments has
been created and is being updated every day. The

What Is The Benchmark Function?
good results regarding the solution quality and success rate in finding optimal solution.
Performances of algorithms are tested on mathematical benchmark functions with known global
optimum. In order evaluate the optimization power of BSA various benchmark functions are taken
into consideration. This dissertation presents the application of GSA on 10 benchmark functions and
GOA on 8 benchmark functions. These benchmark functions are the classical functions utilized by
many researchers. Despite the simplicity, we have chosen these test functions to be able to compare
our results to those of the current meta–heuristics. Benchmark functions used are minimization
functions and are subdivided into the two groups i.e., unimodal and multimodal. ... Show more
Benchmark functions used are minimization functions and are subdivided into the two groups i.e.,
unimodal and multimodal. Multimodal functions are also categorized into fixed dimension and high
dimension multimodal functions. GSA is a heuristic optimization algorithm which has been gaining
interest among the scientific community recently. GSA is a nature inspired algorithm which is based
on the Newton's law of gravity and the law of motion. GSA is grouped under the population based
approach and is reported to be more intuitive. The algorithm is intended to improve the performance
in the exploration and exploitation capabilities of a population based algorithm, based on gravity
rules. However, recently GSA has been criticized for not B.K. Panigrahi [2], presents a novel
heuristic optimization method to solve complex economic load dispatch problem using a hybrid
method based on particle swarm optimization (PSO) and gravitational search algorithm (GSA). This
algorithm named as hybrid PSOGSA combines the social thinking feature in PSO with the local
search capability of GSA. To analyze the performance of the PSOGSA algorithm it has been tested
on four different standard test cases of different dimensions and complexity levels arising due to
practical operating constraints. The obtained results are compared with recently reported methods.
The comparison confirms the robustness and efficiency of the algorithm over other existing
techniques. PSOGSA is formulated by S.

A Master 's Degree Of Computer Science
Statement of Purpose Whether providing light during blackouts, adding a romantic flair to an
evening dinner, or just adding a pleasant fragrance and sense of comfort to a college student's
apartment, candles are an important, yet often overlooked part of our lives. This became clear to me
when my ailing grandmother requested that we bring candles from her house to her in the hospital
so that she could have reminders of home. Google one of the great achievement lives of billions of
people thanks to computer science ,The Internet, one of the many great achievements of computer
science, have changed the way of acquiring information and communicate and perhaps even
thinking process of billions of people. It also advances other disciplines. After spending countless
hours browsing through Google, . I marvel at the untold possibilities computer science could
provide. I have distinction of the exact that I want to pursue a Master's degree in Computer Science.
I want to pursue a Master's degree in computer science. Computer science has brought numerous
changes to the world. Statement of purpose , the exact I have decided to apply for the following
reasons. I have distinction of the exact that I want to pursue a further degree in Computer Science.
In early 2014, I joined a research In my third year, I participated in a project that aimed to develop a
. I use my skills and help programmed the most of the program. Statement of Purpose The state of
mind is obviously the exact have to

The Molecular Docking Method Essay
In silico methods became widely used in the fields of structural molecular biology and structure–
based drug design with the rapid increase in computational power. Molecular docking [2–4] is one
of these in silico techniques.
Docking is a method which predicts preferred orientation (on the basis of binding energy) of one
molecule to the second to form a stable complex. In the field of drug design, first molecule is
usually protein/enzyme/DNA and the second one is small organic molecule/small peptides as
potential drug candidate.
Knowledge of preferred orientation of ligand and protein used to predict binding affinity and to
discriminate high–affinity drug candidates from the low–affinity compounds. 2.6.1Lock and key
analogy
Molecular ... Show more content on Helpwriting.net ...
2.6.3 Search algorithms
The search space which the docking software should take into account theoretically consists of
every possible conformation and orientation of the receptor and ligand. While it is impossible to
exhaustively explore this search space, efficient search algorithm is able to explore its large portion
and identify global extrema (i.e. minima in the energy corresponding to the preferred
conformations) [22]. The docking problem can be handled manually with help of interactive
computer graphics. This solution may work, if we have a good idea of the binding mode of a similar
ligand. Automatic software will be however less biased than a human and will consider many more
possibilities in much shorter time frame.
2.6.4 Genetic algorithms
Genetic algorithms [25] are search methods that mimic the process of evolution by incorporation of
techniques inspired by natural evolution, such as inheritance, mutation or crossover. In genetic
algorithm, an initial population of one–dimensional strings (called chromosomes), which encode
candidate solutions (individuals) evolves toward better solutions. In case of a molecular docking,
each individual may represent one possible system configuration and each string may contain
information about its conformation (e.g. values of angles of rotatable bonds). At the beginning,

Algorithm Of Sequential Gradient Search Essay
3.6. Algorithm of sequential gradient search
Step 1: Set specifications of the inductor
Step 2: Set the values of Bm ,  and no. of core steps. Step 3: 0.45≤ K ≤ 0.6 and 0.3 ≤ Rw ≤ 0.4
Step 4: i = 0 to 30 do: K  0.3  i / 100
Step 5: Calculate cost.
Step 6: If cost shows initially low and after that high, (concavity fails for K ) go to step 28 Step 7: i
= 0 to 20 do: Rw  2  i /10
Step 8: Go to sub–routine, calculate the cost.
Step 9: If cost does not show low value and then high (concavity fails for Rw ) go to step 28
Step 10: i = 0 to 30 do: K  0.3  i / 100
Step 11: Go to sub–routine and calculate the cost.
Step 12: if present cost > previous cost go to step 14
Step 13: end for
Step 14: back to previous value of K
Step 15: For i = 0 to 20 do: Rw  2  i /10
Step 16: Go to sub–routine and calculate the cost.
Step 17: if present cost > previous cost go to step 19
Step 18: end for
Step 19: back to previous value of Rw

Step 20: Go to sub–routine and calculate cost, performance etc.
Step21: check for constraints violation (iron loss &copper loss), if it violets then go to step 25 Step
22: check for temperature rise, if it violets then go to step 25
Step 23: Print out results: go to step 26 Step 24: Stop
Step 25: End
Design optimization or optimal Design means effective and efficient design with minimum
manufacturing cost within certain restriction imposed on it. Optimization is the process of searching
highest and the least values of a given

A Comparative Analysis Of Force Directed Layout Algorithms...
Lauren Peterson
6 December 2016
Term Paper 3 Page Update
Bioinformatics Algorithms: Dr. Kate Cooper
A Comparative Analysis of Force Directed Layout Algorithms for Biological Networks
Brief Description:
I will conduct a comparative analysis of multiple force–directed algorithms used to identify clusters
in biological networks. The analysis will consider topics such as the algorithm process, amount of
preprocessing, complexity, and flexibility of the algorithms for different types and sizes of data. K–
Means, SPICi, Markov Clustering, RNSC, and PBD will be used for the comparison. I will identify
the best algorithm according to my analysis for each type of input data studied.
Background: how to determine if a clustering algorithm is good/if a cluster is good→ modularity
Proteins control all processes within the cell. Though some proteins work individually, most work in
groups to participate in some biochemical event. Examples of these processes include protein–
protein interaction networks, metabolome, correlation/co–expression values, synthetic lethality, and
signal transduction (Cooper, lecture). The study of proteins that work together can allow a greater
understanding of cellular processes. New pathways, proteins, or systems can be identified via
network analysis. In order to recognize groups of proteins that work together, a biological network,
called a graph, is formed.
The study of graphs has a prominent history in mathematics and statistics. Graph Theory

Essay On Better Glass Edu
There are several projects for this research opportunity. I am part of a project called "Better Glass
Edu", a project specifically for the first year researchers to get acclimated to the terms of and usage
of a neural network using machine learning algorithms. Before going in–depth into the research
topic we must first understand what Machine learning is. Machine learning is an application of
artificial intelligence that Provides systems the ability to automatically learn and improve from
experience. It focuses on the development of computer programs that can access data and use it
learn for themselves. A more practical way it is feeding a computer program a set of datapoints. The
computer program uses various algorithms to understand the ... Show more content on
Helpwriting.net ...
Random forests are an ensemble of Decision Trees. This algorithm trains multiple decision trees and
has them vote on the final output of the model. Pros of this algorithms are that it is very unlikely to
overfit. In order for overfitting to occur, a majority of classifiers would have to misclassify an
instance, a majority of weights would have to be incorrect in regression. The basic idea behind a
neural network(similar to a human brain) is to copy, in a simplified but reasonably way, lots of
densely interconnected brain cells inside a computer so you can get it to learn things, recognize
patterns, and make decisions in a humanlike way. The amazing thing about a neural network is that
you don't have to program it to learn explicitly: it learns all by itself, just like a brain!
But it isn't a brain. It's important to note that neural networks are (generally) software simulations:
they're made by programming very ordinary computers, working in a very traditional fashion with
their ordinary transistors and serially connected logic gates, to behave as though they're built from
billions of highly interconnected brain cells working in parallel. Computer simulations are just
collections of algebraic variables and mathematical equations linking them together. They mean
nothing whatsoever to the computers they run inside–only to the people who program them, so there
is no threat of the

General Approaches For Feature Selection
General Approaches for Feature Selection There are 3 types of approaches for feature selection
namely filter, wrapper, embedded method.
Filter method: Filter method does not involve a learning algorithm for measuring feature subset [6].
It is fast and efficient for computation .filter method can fail to select the feature that are not
beneficial by themselves but can be very beneficial when unite with others. Filter method evaluates
the feature by giving ranks to their evaluation value. In filter method it evaluates the correlation
between the features by using criteria such as, mutual information, maximum length, maximum
relevance min redundancy (mRMR), PCA.
Figure 1.2 Filter approach
Wrapper method: wrapper method involve learning algorithm and search for optimal feature subset
from original feature set which discover the relationship between relevance and optimal subset
selection. It performed better than the filter method. The specific training classifier is used to
evaluate the performance of selected features. Figure 1.3 Wrapper approach
Embedded method: Embedded method is a combination of wrapper method and embedded method.
This decreases the computational cost than wrapper approach and captures feature dependencies. It
searches locally for features that allow better discrimination and also the relationship between the
input feature and the targeted feature. It involves the learning algorithm which is used to select
optimal subset among

Features Selection Algorithm For Selecting Relevant Features
Abstract– Process of selecting relevant features from available dataset is known as features
selection. Feature selection is use to remove or reduce redundant and irrelevant features. Various
feature selection algorithms such as CFS (correlation feature selection), FCBF (Fast Correlation
Based Filter) and CMIM (Conditional Mutual Information Maximization) are used to remove
redundant and irrelevant features. To determine efficiency and effectiveness is the aim of feature
selection algorithm. Time factor is denoted by efficiency and quality factor is denoted by
effectiveness of subset of features. Problem of feature selection algorithm is accuracy is not
guaranteed, computational complexity is large, ineffective at removing redundant features. To
overcome these problems Fast Clustering based feature selection algorithm (FAST) is used.
Removal of irrelevant features, construction of MST (Minimum Spanning Tree) from relative one
and partition of MST and selecting representative features using kruskal's method are the three steps
used by FAST algorithm.
Index Terms– Feature subset selection, graph theoretic clustering, FAST
I. INTRODUCTION
Feature subset selection can be viewed as the method of identifying and removing a lot of unrelated
and unnecessary features as probable because (i) unrelated features do not give the predictive
correctness (ii) unnecessary features do not redound to receiving a superior predictor for that they
give main data which is previously

Delta Air Travel
Air travel has become a common part of everyday life. However, one of the common complaints
with air travel is the prices of the tickets. It turns out that people are not the only ones who have
trouble finding the lowest ticket price, computers have a hard time as well. This paper aims to look
at the computational complexity of air travel planning, and find the lowest priced ticket between two
destinations using popular search algorithms. As explained in the introduction, it is not feasible to
attempt to run all the flights across the world through the search algorithms. Instead, flights from
one airline, Delta, were selected. This includes code–share flights. The flight database that was used
is from www.openflights.org and the data is current ... Show more content on Helpwriting.net ...
In doing the research for this project, no such database was able to be found, however.
The final element of this experiment which could use more investigation and changes is the
algorithms used. While the two selected worked for the most part
(excluding the one route A* could not compute), they are likely not the best choice for this type of
search. Finding more robust versions of UCS and A* and including some other algorithms could
have provided more conclusive results as well as giving more interesting data.
The goal of this project was to take a public database of airline flights, select a number of flights,
and run those flights through a couple of the search algorithms studied in class (uniform cost search
and A* search). The algorithms provided some interesting results when run on the ten randomly
selected routes. In almost all of the cases the algorithms were able to find the cheapest path between
the departure and arrival airports, although A* chose a more expensive route once and could not
find one other route. When these findings were compared with
Google's flight search, some of the results were surprising. Almost all of the real–world flights cost
more or less than what the two algorithms found,

Cyber Analytics : Machine Learning For Computer Security
Cyber Analytics – Machine Learning for Computer Security
Arpitha Ramachandraiah, Team CRYPTERS, UBID: 5016 6499
Cyber security is in the forefront of every organizations' core strategy to protect its data and
information systems. This increased awareness about cyber security has been driven partly due to
the increasing number of cyber–attacks and also due to the various government regulations such as
HIPAA, SOX, PCI and so forth. Unlike in the past, attacks on organizations are more targeted,
organized and sophisticated and the target of these attacks on organizations are to obtain proprietary
and sensitive information. The exponential growth in the number of cyber–attacks can no longer be
contained using static, existing standard security ... Show more content on Helpwriting.net ...
Machine Learning uses algorithms for mainly two reasons: one is to predict new data and second, to
analyze existing data. In the first case, once data is gathered, algorithm is applied on it to predict
something new about this data. An application of this in the field of computer security could be
prediction of user's current session based on the information available in the audit logs.
While in the second case, once data is gathered and algorithm applied, it is used to gain fresh
insights into the data which could not have been obtained without having an algorithm that is
powerful enough to process such a large and complex chunk of data. An example of this in
computer security will be understanding of a user's high CPU usage when compared to others
without terming it bad, based on the algorithmic output obtained about the user from the audit logs.
Together with data science, machine learning can be used to gain hidden insights into data and to
build predictive models to process new data.
A couple of security areas where machine learning can be applied in the arena of cyber security are:
1) Network Security: Here, machine learning can be leveraged to build models to find patterns in
traffic that is used to distinguish benign traffic from malicious traffic that signals criminal activity. It
is also possible to detect malicious software such as viruses,

Examples Of Hashing Techniques
A Survey of Hashing Techniques and its Applicability for Efficient Buffer Cache Management
Abstract: Hashing is the convenient way to get access to an item based on the given key which is the
requirement for efficient buffer cache management. Static hashing provides fastest access to an
object at the cost of memory utilization, whereas sequential storage provides most efficient memory
utilization at the cost of access time. To provide balance between two extremes dynamic hashing
schemes are produced. The focus of this paper is to survey various dynamic hashing schemes with
perspective to use it in database buffer cache management. It includes dynamic hashing techniques
like Extendible hashing, Expandable Hashing, Spiral Storage, Linear ... Show more content on
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Hashing has been one of the most effective tools commonly used to compress data for fast access
and analysis, as well as information integrity verification. Hashing techniques have also evolved
from simple randomization approaches to advanced adaptive methods considering locality,
structure, label information, and data security, for effective hashing [19]. The traditional, static,
hashing schemes, requires data storage space to be allocated statically, because of this it did not
work well in a dynamic environment. This meant that as the database grows over time, we have
three options:
1. Choose hash function based on current file size. Get performance degradation as file grows.
2. Choose hash function based on anticipated file size. Space is wasted initially.
3. Periodically re–organize hash structure as file grows. Requires selecting new hash function, re–
computing all addresses and generating new bucket assignments which are very costly.
Some hashing techniques allow the hash function to be modified dynamically to accommodate the
growth or shrinking of the database. These are called dynamic hashing. To eliminate these problems,
dynamic hashing structures have been proposed.
Dynamic means that records are inserted into and deleted from the set, causing the size of the set to
vary [17]. By dynamic we mean that the number of buckets can increase or decrease, according to
the number of

Nt1330 Unit 1 Assignment 1 Algorithm Essay
The algorithm is executed by the owner to encrypt the plaintext of $D$ as follows:
begin {enumerate}
item [1:]for each document $D_i in D$ for $i in [1,n]$ do
item [2:]encrypt the plaintext of $D_i$ using also $textit{El Gamal}$ cipher under $textit{O's}$
private key $a$ and $textit{U's}$ public key $U_{pub}$ as $Enc_{D_i}= U_{pub}^a times D_i $
item [3:]end for
item[4:] return $textit{EncDoc}$
end{enumerate}
subsubsection{textit{textbf {Retrieval phase}}} Include three algorithms as detailed below:
begin{enumerate}
item [I–] $textit{Trapdoor Generator}$: To retrieve only the documents containing keywords $Q$,
the data user $U$ has to ask the $O$ for public key $O_{pub}$ to generate trapdoors; If $O$ is
offline these owners' data can't be retrieved in time. If not, $U$ will get the public key $O_{pub}$
and create one trapdoor for a conjunctive keyword set $Q={q_1,q_2,...,q_l}$, using
$textsf{TrapdoorGen}(Q, PP, PR$) algorithm. Firstly, the data user combines the conjunctive
queries to make them look like one query, $Tq={q_1| q_2|...| q_l}$, then $U$ will compute the
trapdoor of the search request of concatenated conjunctive keywords $textit{Tq}$ under his private
key $b$, $Tw=H_1(Tq)^b in mathbb{G}_1 $. Finally, $U$ submits $Tw$ to the cloud server. ...
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Then $S$ test $textit{BF}$ in all $r$ locations, if all $r$ locations of all independent hash
functions in $textit{BF}$ are 1, the remote server returns the relevant encrypted file corresponding
the $ID_i$ to $U$. In other words searchable index $I_D$ can be used to check set membership
without leaking the set items, and for accumulated

An Overview Of Optimization

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