Pencil Beam And Collapsed Cone Algorithm Calculations For...

Pencil Beam and Collapsed Cone Algorithm Calculations for...
Pencil Beam and Collapsed Cone Algorithm Calculations for a
Lung–type Volume Using CT and the OMP Treatment Planning System
Methods
Measurements have been carried out in both phantom and a specifically designed phantom which
simulated human lung volume. Samples were taken from the Lung Planning CT images for 15
patients using the Oncentra Masterplan OMP Treatment Planning System. The X–axis was,
following convention, taken to be horizontal, and the Y–axis to be vertical; accordingly, abscissa
and ordinate distances to the skin, heart and the lungs were measured (see figure 8). Figures 4 and 5
show typical CT images for a patient's lungs, while Tables 1 and 2 give the beam information and
dose information for typical patients. The X–ray ... Show more content on Helpwriting.net ...
The Oncology Management System: Impac, MOSAIQ was used to transfer the data from the OMP
treatment planning system to the Linac before running the Linac to determine the points' ISO center,
Beam Information and Dose Information, as shown in figure 14, 15, 16 and 17 for the first and
second phantom.
The phantoms were positioned on the Elekta Precise linac, isocentre and aligned with lasers, and the
ion chamber was placed at each dose point, for example Iso, DP1, DP2, DP3 and DP4 (see figure 12
and 13). Doses were measured for the dosimeters and chambers.
The field size and gantry angles chosen are typical of clinical plans for the same 15 patients as used
to design phantom 2. A field size of 10 x 10cm, was used for all fields. Gantry angles of 00–3150–
2700 and 00–600–1200 were used for phantom 1 and 2 respectively. Tables 4 and 5 show beam
information for the first and second phantoms, respectively. The energy used for the plans was 6MV
because lung cancer is treated clinically with 6MV in HOF Hospital 10 MV beam is not used
because considered very high energy and risky to the lungs. Wedges were used for beam one and
three– the angle of the wedge is 60/60 for each beam. Figure 12 and 13 show the plan for phantoms
1 and 2, with the isocentre and dose points measured.
For the first phantom was generated using three 6 MV photon beams, all with a 10 x 10
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Advantages And Limitations Of Genetic Algorithm
1. Introduction
The most popular technique in evolutionary computation research has been the genetic algorithm. In
the traditional genetic algorithm, the representation used is a fixed–length bit string. Each position in
the string is assumed to represent a particular feature of an individual, and the value stored in that
position represents how that feature is expressed in the solution. Usually, the string is "evaluated as a
collection of structural features of a solution that have little or no interactions". The analogy may be
drawn directly to genes in biological organisms. Each gene represents an entity that is structurally
independent of other genes. The main reproduction operator used is bit–string crossover, in which
two strings are used as parents and new individuals are formed by swapping a ... Show more content
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Advantages and Limitations of Genetic Algorithms
The advantages of genetic algorithm includes:
1. Parallelism 2. Liability
3. Solution space is wider
4. The fitness landscape is complex
5. Easy to discover global optimum 6. The problem has multi objective function
7. Only uses function evaluations.
8. Easily modified for different problems.
9. Handles noisy functions well.
10. Handles large, poorly understood search spaces easily
11. Good for multi–modal problems Returns a suite of solutions.
12. Very robust to difficulties in the evaluation of the objective function.
The limitation of genetic algorithm includes:
1. The problem of identifying fitness function 2. Definition of representation for the problem 3.
Premature convergence occurs 4. The problem of choosing the various parameters like the size of
the population, mutation rate, cross over rate, the selection method and its strength.
5. Cannot use gradients.
6. Cannot easily incorporate problem specific information
7. Not good at identifying local optima
8. No effective terminator.
9. Not effective for smooth unimodal functions 10. Needs to be coupled with a local search

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

Wireless Body Area Network ( Wban And Discussion Of...
UTILITY OF COGNITIVE RADIO IN WBAN AND DISCUSSION OF DIFFERENT
ALGORITHMS FOR THE SAME.
Abstract−A WIRELESS BODY AREA NETWORK (WBAN) is a wireless sensor network
technology that is confined to body of a person under supervision. As we use ISM band for
transmission of information for WBAN thus it is a huge possibility that the transmission can
undergo interference that affect the transmission. Hence there is a major utility of combining
cognitive radio with WBAN. In this report, we discuss the role of cognitive radio in WBAN and
how this can enhance the transmission of WBAN. We will also discuss various algorithms for
sensing and thus will compare them.
I. INTRODUCTION
Wireless body area network is a type of wireless sensor network technology that has confined limits
to the body of the person under observation. Being a major area of interest due to variety of
advantageous applications like continuous and flexible health monitoring of patients or performance
analysis of athletes etc, WBAN is one of the major and most interesting area of study of present.
Reviewing the basics of WBAN, the network is a kind of wireless sensor network that consists of
different wireless sensors which can be either mounted on or embedded skin deep into the body of
person under observation, thus the domain further increases study scope and improvements relating
wearable technology advancement.
The sensors are supposed to sense in real time and the processed data can be recorded as well as

A Counter Example For This Algorithm
Ricardo Rigodon and Maulik Patel
Homework 1
Due 9/15
1–6 A counter example for this algorithm can be shown as follows.
U = {27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43}
S1 = {27, 28}
S2 = {29, 30, 31, 32}
S3 = {33, 34, 35, 36, 37, 38, 39, 40, 41}
S4 = {27, 28, 29, 30, 31, 32, 33, 34}
S5 = {35, 36, 37, 38, 39, 40, 41, 42, 43}
The correct answer for this is S4 and S5. However, the algorithm will choose S3, S2, and then S1
which is incorrect. S3 because it has the most uncovered, followed by S2, and then S1.
1–16 Prove n3 + 2n is divisible by 3 BASE CASE : n = 0 Inductive hypothesis : k3 + 2n = 3m =
P(k) Inductive Step : P(k+1)= (k+1)3 + 2(k+1) = k3 + 3k2 + 3k + 1 + 2k + 2 = k3 + 2k + 3k2 + 3k +
3 = 3m + 3k2 + 3k + 3 (k3 + 2n = 3m from Inductive Hypothesis) = 3(m + k2 + k + 1)
1–25 (a) Takes about 100 seconds. If proportional to n2 (102 = 100). (b) n2 is a less efficient
algorithm than n log n should take 100 µs as we know logarithms will divide the input in half until
complete.
2–8 (a) f(n) = log n2; g(n) = log n + 5 f(n) = θ (log n +5) since log n + 5 multiplied by C = 2 is an
upper bound of f(n) and multiplied by C= 1/3 is a lower bound of f(n) (b) f(n) = (n)1/2 g(n) = log n2
f(n) = Ω (log n2) since n1/2 grows faster than log n2 (c) f(n) = log2 n; g(n) = log n f(n) = Ω (log

Nt1310 Unit 1 Algorithm Paper
The fitness value of a string is calculated with the help of the above three estimated QoS parameters.
The objective of our proposed algorithm is summarized to a search for different routing paths which
will increase the values of the QoS parameters at each iteration. In order to generate a comparison
set (C), a certain number of strings are randomly selected from the population. From the population,
two strings are randomly chosen at once and compared with each string in the comparison set. If one
candidate string is better than competitors considering all three QoS parameters, then Pareto–
Optimal set (S) contains this string. On the other hand, if the both competitors are non–dominated,
then a niche count is used to resolve this tie situation. Niche count is estimated as mentioned in [28],
[29]: where Sh[dsi,sj ] is the sharing function and dsi,sj is the distance between the strings s1 and s2.
To keep things simple, a triangular sharing function [28] is used as follows: ... Show more content
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In order to mix the good strings and protect the effective ones simultaneously, the probability of
crossover and mutation are considered as 0.8 and 0.1 respectively. This entire process is repeated,
until the improvement in fitness values (from previous to current Pareto–optimal set) is less than a
chosen precision, ǫ. Undoubtedly, actual precision value (ǫ) will be distinct according to
corresponding QoS parameters. 10−6, 10−5 and 10−3 are chosen as the precision values for the
probabilities of end–to–end delay, total bandwidth and overall energy consumption respectively.
Finally, the strategy returns the results of the discovered QoS–route (whether success or failure and
the set of routes) to the cross–layer QoS–provisioning algorithm shown in Figure

Survey On Application Of De Algorithm
Survey on Application of DE Algorithm
Nasim Taghizadeh Alamdari
Department of Electrical Engineering
University of North Dakota
Grand Forks, ND, USA nasim.taghizadehalam@ndus.edu Dr. Eunjin Kim
Department of Computer Science
University of North Dakota
Grand Forks, ND, USA ejkim@cs.und.edu Abstract–Differential Evolution (DE) is seemingly a
standout amongst the most capable and flexible evolutionary optimizers for the nonstop parameter
spaces as of late. Since the advancement of DE algorithm on late years is quick and the exploration
on and with DE have now achieved a great state, there is an essential need to study late parts of DE
algorithm thoroughly. Considering the tremendous advance of research with DE and its applications
in various areas of science and innovation, we find that it is an imperative to give a basic concepts of
the most recent literary works distributed furthermore to bring up some critical future roads of
research. The motivation behind this paper is to condense and sort out the data on these present
improvements on DE. Starting with a fundamental ideas and definition of differential advancement,
hybridization of DE with different optimizers, furthermore the multi–faceted literature on
applications of DE. The paper likewise displays some of fascinating open issues and future research
issues on DE.
Keywords–Differential evolution; evolutionary optimization; Hybrid differential evolution.
I. INTRODUCTION
While trying to locate the global optimum

Example Of Greedy Algorithm
3 Greedy Algorithm
Greedy technique is an algorithm design policy, built on configurations such as different choices,
values to find objective .Greedy algorithms produce good solutions on mathematical problems. The
main aim is to find some configurations that are either maximized or minimized. Greedy Algorithms
provide a solution for optimization problems that has certain sequence of steps, with a set of choices
for each step. Another solution for Greedy algorithm is dynamic programming . It is also used to
determine the best choices. But greedy algorithm always makes the choice that is best at the moment
to provide the optimal solution for the problem. A greedy algorithm for an optimization always
provides the current sub solution. Basically greedy algorithm always gives an optimal solution to the
MST (Minimum Spanning tree)problem. Some Examples that are solved by greedy algorithm are
Dijkstra's shortest path algorithm and Prim/Kruskal's algorithms.
Greedy algorithm technique relies on following elements:
configurations: It consists of different choices, values to apply on data.
objective : some configurations to be either maximized or minimized to get the predefined objective.
Greedy algorithms are applicable to optimization problems ... Show more content on
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On the other hand it shows that data mining can become both a source of making discrimination and
discovering discrimination items present in the dataset. Discrimination of data is categorized in two
types as direct discrimination and indirect discrimination. Direct discrimination consists of data
mining rules that inherently mention underprivileged groups based on sensitive discriminatory
parameters present in that data set. Indirect discrimination consists of data mining rules that will not
explicitly mentions the discriminatory

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

The Performance Characterization Of Two Multileaf...
Paper title: Head–To–Head Performance Characterization of Two Multileaf Collimator Tracking
Algorithms for Radiotherapy
Method Introduction
The goal of this study was to compare algorithms' performance using the span of motion complexity
and MLC modulation that may be expected during clinical practice.
MLC plans and motion input
Selected abdominal/thoracic and pelvic tumor motions were all obtained by Per Polsen [1, 2] as
representing characteristic three–dimensional motion patterns for those sites. For the lung, motions
represented a "typical motion", high frequency breathing, predominantly lateral motion and
characterized baseline shift from planned position. The represented prostate motions were
continuous drift, persistent excursion from planned position, transient excursion and high frequency
excursion.
Low and high modulation treatment plans differ in MLC modulation to span the MLC complexity
expected during clinical practice. The relevance of modulation in the context of MLC tracking is
that as the complexity of modulation increases, so does the complexity for the fitting algorithm to
calculate the best–fitted aperture given physical constraints. These plans were created by Keall et al
[ref] and used by multiple others studies [ref]. Keall created the MLC plans by forcing constraints
on the optimizer to privilege the PTV for low modulation plan whereas constraints were expanded to
critical structure for the high modulation plan. Four plans were created for each

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
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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

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

Nt1310 Unit 1 Algorithm Report
Exploiting the tensor product structure of hexahedral elements expresses the volume operations as
1D operators. The details are presented in algorithm ref{alg_hexvol}.
begin{algorithm}[h]
caption{Hexahedron volume kernel}
label{alg_hexvol}
KwIn{nodal value of solution $mathbf{u} = left(p, mathbf{v} right)$, volume geometric factors
$partial (rst)/ partial (xyz)$, 1D derivative operator $D_{ij} = partial hat{l}_j /partial x_i$,
model parameters $rho, c$}
KwOut{volume contributions stored in array $mathbf{r}$}
For{each element $e$}
{
For{each volume node $x_{ijk}$} { Compute derivatives with respect to $r,s,t$ $$frac{partial
mathbf{u}}{partial r} = sum_{m=1}^{N+1}D_{im} mathbf{u}_{mjk} qquad frac{partial
mathbf{u}}{partial s} = sum_{m=1}^{N+1}D_{jm} mathbf{u}_{imk} qquad frac{partial
mathbf{u}}{partial s} = sum_{m=1}^{N+1}D_{km} mathbf{u}_{ijm}$$ Apply chain rule to
compute $partial mathbf{u}/partial x, partial mathbf{u}/partial y, partial mathbf{u}/partial z$
$$frac{partial mathbf{u}}{partial x} = frac{partial mathbf{u}}{partial r} frac{partial r}
{partial x} + frac{partial mathbf{u}}{partial s} frac{partial s}{partial x} + frac{partial
mathbf{u}}{partial t} ... Show more content on Helpwriting.net ...
Revisiting figure ref{GLNodes}, we notice that the SEM nodal points already contain the surface
cubature points while the GL nodes do not. Therefore, the SEM implementation is able to utilize the
nodal values to compute the numerical flux, while the GL implementation requires additional
interpolations. In algorithm ref{alg_hexsuf}, we present the procedure of the hexahedron surface
kernel. In both implementations, the solution values on the surface cubature points are pre–
computed and stored in array texttt{fQ}. The lines and variables marked with GL/SEM are the
processes only needed by the GL/SEM implementation

Comparision Of P Self Protection Problem Algorithms For...
Comparision of p–self Protection Problem Algorithms for Static Wireless Sensor Networks Nikitha
Gullapalli
Graduate Student, Dept. of Computer Science and Engineering, University of South Florida
nikitha@mail.usf.edu Abstract – Wireless sensor networks are being widely used in many
surveillance applications. Since sensor nodes are a critical part of sensor networks, certain level of
protection needs to be provided to them. The self–protection problem focuses on using sensor nodes
to provide protection to themselves instead of the target objects or certain target area so that the
sensor nodes can resists the attacks targeting to them directly. In this paper we compare paper [1]
and paper [2]. The key research question being asked, how ... Show more content on Helpwriting.net
...
KEY RESEARCH QUESTIONS
1. How to determine the minimum set of sensors for covering problems of sensor networks?
Efficient centralized and distributed algorithms with constant approximation ratio for the minimum
p–self–protection problem in sensor networks when all sensors have the same sensing radius.
2. Is finding minimum 1–self–protection a NP–complete?
Yes, it is proved that finding minimum 1–self–protection is NP–complete by reducing the minimum
set cover problem.
3. If MIS is selected to provide certain protection to nodes, what happens after some rounds when it
already has p–protections?
Purpose of selecting MIS is to provide certain protections to nodes that are not selected into the
MIS. However, this may not be necessary after some rounds for some nodes when it already has p
protections from selected active nodes Thus, for each node u, we again use p(u) to denote the
protection level (i.e., the number of active sensors that can sense this node) that it already has
achieved via previously activated sensors from MIS's.
4. Is there a smarter way to select the nodes instead of randomly?
Instead of random selection of a sensor to cover each active sensor in MIS, we can use a smarter
method to select the nodes to protect the MIS nodes with less than p protectors in the last steps of
our algorithm.
5. As each sensor has limited power and resources, how to balance the energy consumption?
To balance the energy consumption, one simple method proposed in this paper is

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
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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

Kruskal Algorithm Essay
Kruskal's Algorithm
Introduction:
In 1956, the minimum spanning tree algorithm was firstly described by Kruskal. In same paper
where he rediscovered the Jarnik's algorithm. The algorithm was rediscovered in 1957 by Loberman
and Weinberger, but avoided to be renamed after them. The main idea of Kruskal's algorithms is as
follows:
Scan all the edges in the increase of weight order;
[If any edge is safe, keep it (i.e. add it to the set A)]
Kruskal's Algorithm is directly based on generic MST algorithm. It builds MST in the forest. On the
initial level, each of the vertex is in its own tree in the forest. Then the algorithm consider each of
the edge in turn, order by the increase of weight. If any edge (u, v) connects two of the different ...
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We assume that we use the disjoint–set–forest implementation. With the union by rank and path
compression heuristics, since it is the asymptotically fastest implementation known. Initializing the
first set in line 1 takes O (1) time, and the time to sort the edges in the line 4 is O (E lg E).The for
loop of lines 5–8 performs O (E). Find–set and Union operations on the disjoint–set forest. Along
with the make–set operations, these take total O ((V+E)a(V)) time, where 'a' is the very slowly
growing function. Because we assume that G is connected, we have |E| >=

Application of Genetic Algorithm in the Process of Sound...
This project comprises the application of genetic algorithm in the process of sound evolution using
Darwinian Theory of 'survival of fittest' whereby its emergent behaviour is employed to produce
sound which evolves towards better solution by adapting to the environment over the numerous
generations using its simple operations of selection, crossover and mutation. In the context of sound
evolution, genetic algorithm has been used to evolve musical notes where the process of fitness
function is employed to measure the fitness of candidate solution that takes into consideration the
numbers of criteria need to be exists in candidate solution to make individuals rhythmic in nature.
The role of genetic algorithm in the process of sound evolution is considered to be vital as far as
evolutionary computation is concerned which best suits the application domain in this project. To
produce rhythmic sound, numbers of criteria are considered into the fitness function since it does
shape the population significantly followed by the rating of individuals based on existence of those
criteria into population expected from fitness function. The overall idea of this project and hence the
genetic algorithm in sound evolution is to converge the population towards rhythmic sound since the
criteria are expected from fitness function that leads the function of selection towards the
consideration of those individuals who are most fit followed by the rejection of least ones.
Evolving behaviour of

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

Algorithm Essay
''' @summary: To remove keywords that are similar. This is to bring more diversity to the top ranked
keywords. We use an algorithm called K–means for grouping. This algorithm is implemented and
have not used third party library other than numpy (for computation) and gensim (loading of data
set) Each word in the keyword list is converted to a vector using word2vec. We use GoogleNews
pre–trained dataset to get vector of word. Then, we divide the n keywords into k clusters. Initially,
we randomly choose k centroids. The euclidian distance of each vector is calculated from the
centroid. Each cluster is formed with a centroid and vectors which are closest to it. Once cluster is
formed, a new centroid is found for each cluster. Based on the ... Show more content on
Helpwriting.net ...
centroid_list = random.sample(X,

Gossip-Based Algorithms
1. INTRODUCTION
Gossip–based algorithm plays a major part for distributing simple and efficient information in large
networks. One of the examples of gossip–based algorithm is rumor –spreading model. It is also
called as rumor mongering. It is introduced by Daley & Kendall (D K model) in the context of
duplicated databases. The rumor spreading algorithm is an example of epidemic process. It is mainly
used to examine in the view of mathematics. The algorithm follows synchronous rounds. The main
aim of rumor spreading is to spread a rumor to all nodes in a social network in small no of rounds.
At the beginning of the round, the information is sent to initial node known as start node. Then the
information is sent to all nodes. The node having information will not accept to receive the
information again. While executing the algorithm the graph and degree of nodes must be constant.
In case of dynamic networks, an evolving graph is introduced to study the behavior of graph and
nodes.
Fig. 1 Graph connected with rumors
1.1. Problem statement:
To begin with the rumor spreading algorithm mainly concentrates the broadcasting of message that
is the information should reach all nodes of a graph. Secondly it concerns about the completion time
i.e., within how many rounds the information is reached to all nodes. From the above research the
problem can be stated as :each node transfers the rumor what has but in cases the node might not be
knowing what information that the neighbour

Encryption Vs. Encryption Algorithm
D
The message to be stored in the database is converted to cipher text using encryption algorithm and
key. The resulting cipher text is then saved in database. When the user needs to get the actual
message, he/she decrypts the cipher text with decryption algorithm and the key [3]. Random keys
are generated in encryption process and same keys are used to decrypt the data. The security solely
depends on the choice of encryption algorithm, key size and how the algorithm is implemented. If
any of these criteria goes wrong, it can cause adverse effects in the security of data.
There are different ways by which encryption can be achieved, Encryption by means of algorithms
and through Hashing. Different encryptions based on algorithms and key are DES, RC2, AES_128,
AES_256 etc [13]. Hashing is the process in which the message is converted to hash value using
hash function. For example, the password entered by user is encrypted to hash key value and it is
compared with the encrypted password stored in the database. If the result varies, then an invalid
username/password is entered. Hash functions commonly in use are MD4, MD5, SHA, SHA–1[13].
5.1.2.1 Notations used for Encryption scheme
Key is an important requirement in encryption process.pk is denoted as public key and sk as private
key [5]. Encryption process is denoted by Enc and decryption process is denoted by Dec. Given a
plaintext m and key k, encryption is defined as Enc (m, k) and Decryption is defined as

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

What Is An Algorithm?
What is an algorithm? According to the textbook, an algorithm is a set of specific sequential steps
that describe exactly what the computer program must do to complete the required work. Figure
10.4, on page 426, shows the stages that each programming project follows. Step two is where the
algorithm is, which is "making the plan." The example the book uses is the pharmaceutical
companies designing new drugs. When they design new drugs, they have to use complex computer
programs that model molecules. The computer software programs help chemists create the new drug
in a quicker manner that give the desired pharmacological effects.
Some other things that may be used in the medical field are calculators, flowcharts, look–up tables,
and nomograms (Medical Algorithm). According to the medical dictionary, an algorithm is a model
for making decisions. It shows the step–by–step process that a doctor or pharmacist would use. The
process begins with a patient describing symptoms or problems that he or she has to a doctor. A
doctor's first instinct is to gather all the information and then analyzes the symptoms or problems.
The next step is for the doctor to run a diagnostic test after gathering all of the information in order
to recommend the best solution for the patient's symptoms or problems. There are two different
ways that the diagnostic test can go. The first option is the diagnostic test will tell exactly what is
wrong with the patient and will recommend specific treatments

Limitations And Limitations Of Evolutionary Algorithms
LIMITATIONS OF EVOLUTIONARY ALGORITHMS
Evolutionary algorithms are very promising problem solving techniques but at the same time, they
have a few limitations that can result in loss of efficiency.
Difficult parameter tuning: Any implementation of an Evolutionary algorithms will require the
specification of various parameters, such as population size, mutation rate, and maximum run time,
as well as the design of selection, recombination, and mutation procedures. Finding effective
choices for these is itself a hard problem with little to no theoretical support. In practice researchers
must rely on any available anecdotal reports from related problems, and lots of trial and error.
No assurance of convergence: There is no assurance that the evolutionary algorithm will converge to
a global optimum. There is a possibility that it gets stuck in one of the local optima. This is the
reason why EAs cannot be applied to real–time problems where the accuracy and validity of the
solution cannot be compromised.
Keeping in view, the advantages and limitations of evolutionary algorithms, following three
algorithms have been employed in this work for automatic generations of Quantum and reversible
classical circuits:
1. Genetic Algorithm
2. Quantum–Inspired Evolutionary Algorithm
3. Hybrid Quantum–Inspired Evolutionary Algorithm
GENETIC ALGORITHM
Genetic Algorithm is a heuristic search and optimization algorithm which like any other
Evolutionary Algorithm, mimics the process of

Questions On Algorithms
2) k–means Algorithm k–means is an unsupervised clustering algorithm which is used to classify
input image to k clusters based on the nearest mean. The modified algorithm for k–means derived
from [11] is explained as follows 1. Start.
2. Read the input image A. 3. Resize image A to a fixed size of 256× 256.
4. Divide the image into two 2×2 non overlapping blocks. 5. Represent each block in the form of a
training vector space X. Each block is converted to the training vector Xi= (xi1, xi2, xik).
6. Select k random vectors from the training set and call it as initial codebook C.
7. Select training vector Xi from training vector space. 8. Calculate the squared Euclidean distance
of Xi with the codebook 9. Calculate the n value of ... Show more content on Helpwriting.net ...
Every one of the 89 Images were tried on Intel Core i5–2410M, 3GB RAM utilizing Matlab
R2011b.Picture comes about were gotten in around 7 seconds. To assess execution of our proposed
Algorithm estimations of the three parameters are computedwhichincorporates
Sensitivity,Specificity and Accuracy [12]. These parameters are computed by utilizing (2), (3) and
(4)
Fig. 4. Hard Exudates using LBG (a) Original Image (b) Green Channel Extracted Result (c) Dilated
Image
(d) LBG Result (e) Hard Exudates
Fig. 5. Hard Exudates using k–means (a) Original Image (b) Green Channel Extracted Result (c)
Dilated Image
(d) k–means Result (e) Hard Exudate
sensitivity=TP/TP+FN
Specificity=TN/TN+FP
Accuracy=TP+TN/(TP+FP+TN+FN) where, TP (genuine positive) is number of pixels delegated
Exudates by both the ophthalmologist and the calculation, FP (false positive) is number of non–
exudates pixels which are wrongly recognized as Exudates pixels by the calculation, TN (Genuine
negative) is number of no Exudates pixels which are distinguished as non–Exudates pixels by both
the ophthalmologist furthermore, the calculation, FN (false negative) is number of Exudates pixels
that are not recognized by the calculation but rather are considered as Exudates by
ophthalmologist.The outcome is ideal for most elevated affectability, affectability and ur y's v lue.
At first Hard Exudates

Study Of Data Mining Algorithm For Cloud Computing
ABSTRACT
This technical paper consists of the study of data mining algorithm in cloud computing. Cloud
Computing is an environment created in user's machine from online application stored in clouds and
run through web browser. Therefore, it is essential to manage user's data efficiently. Data mining
also known as knowledge discovery is the process of analyzing data from different perspectives and
summarizing it into useful information where the information can be used to increase revenue, cut
costs of implementation and maintenances, or all. Data mining software and/or algorithms is one of
a number of analytical tools for analyzing data. It allows users to analyze data from many different
dimensions or angles, categorize it, and summarize the relationships identified. Technically, data
mining is the process of finding correlations or patterns among dozens of fields in large relational
databases. The process of mining data can be done in many ways; this paper discusses the
theoretical study of two algorithms K–means and Apriori, their explanation using flow chart and
pseudo code, and comparison for time and space complexity of the two for the dataset of an "Online
Retail Shop".
General Terms
Data Mining, Algorithms et. al.
Keywords
Clusters, data sets, item, centroid, distance, converge, frequent item sets, candidates.
1. INTRODUCTION
Data Mining in Cloud Computing applications is data retrieving from huge collection of data sets.
The process of converting a huge set of data

Optimized Time Quantum For Dynamic Round Robin Algorithm
RESEARCH PAPER
OPTIMIZED TIME QUANTUM FOR DYNAMIC ROUND ROBIN ALGORITHM
Akash Kumar,Avinash Chandra & Sumit Mohan
Department of computer science & Engineering Galgotias College of Engg. & Tech.
Greater Noida ,up, India
Email id –avi.chandra423@gmail.com
Abstract– Round robin is one of the most optimal cpu scheduling algorithm because it is given an
equal amount of static time quantum.But what will be the time quantum is the biggest task. So we
have proposed an improved version of round robin algorithm which will use optimal time quantum
and time quantum is computed with RMS valuesof burst time.
After a result our analysis shows that "Optimized time quantum for dynamic round robin
algorithm"works better than Round Robin algorithm in terms of reducing the number of context
switching(CS),turn around time (TAT),waiting time(WT).
Keywords: Operating System, Scheduling Algorithm, Round Robin, Context switch, Waiting time,
Turnaround time.
INTRODUCTION
A process is an object of a computer program that is being executed. It includes the current values of
the program counter(PC), registers, and variables. The subtle difference between a process and a
program is that the program is a bunch of instructions whereas the process is the activity or action.
The processes waiting to be assigned to a processor are put in a queue called ready queue(RQ). The
time for which a process holds the CPU is known as burst time
Or service time.

Genetic Algorithms And Its Applications Of Cyber Security
Genetic Algorithms and its Applications to Cyber Security Paper By Sameera Chalamalasetty
Guided By Dr. Mario A Garcia
Abstract:
Genetic algorithms (GAs) were initially proposed by John Holland, whose thoughts were connected
and developed by Goldberg. GAs are a heuristic pursuit procedure in view of the standards of the
Darwinian thought of survival of the fittest and characteristic genetics. Holland 's work was
basically an endeavor to numerically comprehend the versatile procedures of nature, however the
general accentuation of GA examination from that point forward has been in discovering
applications, numerous in the field of combinatorial enhancement. Genetic algorithms have been
utilized as a part of science and engineering as versatile algorithms for tackling functional issues and
as computational models of common developmental frameworks. In the latest couple of decades,
this procedure with advancement of cutting edge development has accomplished something new.
Introduction:
"Li [3] describes genetic algorithm as a family of computational models based on evolution and
natural selection." "Bobor [4] has defined a genetic algorithm as a programming technique, which
mimics biological evolution as a problem solving approach."
"An early

The Swarm Based Routing Algorithms
BIOINSPIRED SWARM BASED ROUTING ALGORITHMS IN VANETs
Arshpreet Kaur*, Er. Navroz Kaur Kahlon**
*(Computer Engineering Department, UCOE, Punjabi University, Patiala)
** (Asst. Prof. Computer Engineering Department, Punjabi University, Patiala)
(Email: *arshrai90@gmail.com, **Kahlon.navroz3@gmail.com)
Abstract–Vehicular Ad–hoc Networks (VANETs) play main role in the design and development of
the Intelligent Transportation Systems (ITS) who improves the road safety and transportation
productivity. VANETs include two communication types i.e. Vehicle–to–Vehicle (V2V) and
Vehicle–to–Roadside (V2R) communications. One of the most important challenges of this kind of
network are the timely, safely and reliable dissemination of messages between vehicular nodes
which permits the drivers to take appropriate decisions to improve the road safety. There are many
routing protocols for VANETs which can support the reliability and safety for routing. These
protocols undergo the several limitations including complexity, lack of scalability, end–to–end delay,
routing overheads, etc. To remove these limitations, various bio–inspired methodologies have been
proposed for routing among vehicular nodes in an optimized way. Here in this paper, various bio–
inspired routing algorithms for the VANET are discussed.
1. INTRODUCTION
A Vehicular Ad–hoc Networks (VANET) are considered as a specific type of Mobile Ad–hoc
Network (MANET) which contains the of a set of mobile nodes (Vehicles)

Questions On Advanced Discrete Math And Algorithms
CS7800: Advanced Algorithms. Fall 2016 Homework 1 Solutions Author: Aditeya Pandey,
Collaborators: Micha Schwab,Supraja Krishnan Problems 1–3 are meant as a review of
undergraduate discrete math and algorithms. They shouldn't take you too long, but I recommend
starting these right away to make sure that you have the appropriate background for this course. You
must type your solutions using L A TEX. Please submit both the source and PDF files using the
naming conventions lastname hw1.tex and lastname hw1.pdf. Strive for clarity and conciseness in
your solutions, emphasizing the main ideas over low–level details. I recommend looking at the
introduction in Jeff Erickson's textbook for advice on writing up solutions to algorithms problems.
Do not share written solutions, and remember to cite all collaborators and sources of ideas. Sharing
written solutions, and getting solutions from outside sources such as the Web or students not
enrolled in the class is strictly forbidden. 1Review Problems Problem 1 (Review of Asymptotic
Growth). Arrange the following list of functions in ascending order of growth rate. That is, if
function g(n) immediately follows function f (n) in your list, then it should be the case that f (n) is
O(g(n)). (You do not need to provide proofs.) 2 f 1 (n) = 4n 2 + n log 28 (n) √ f 4 (n) = n + 40 n f 2
(n) = 2 n f 3 (n) = 1024n log 2 (n) f 5 (n) = 10 n f 6 (n) = 3n log 2 (n) f 7 (n) = n 2 log 8 n f 8 (n) =
4096 log 42 (n) f 9 (n) = n log 2 (3) Solution

Mpus: Brute-Force Algorithm. Group Name: Mpus. Members:
MPUS: Brute–Force Algorithm
Group Name: MPUS
Members: Johann Redhead, Tellon Smith, Kevin Lord
I Johann Redhead....................., Tellon Smith....................., and Kevin Lord....................., agree to
attempt and solve the proposed problem to the best of our abilities.
Proposed Problem: Brute–force Algorithm
SUMMARY
Brute force is defined as very strong or forceful. In computer Science, this is essentially the same
definition. Brute–force in Computer Science is the trial and error method used by application
programs to decode encrypted data such as passwords or Data Encryption Standard (DES) keys,
through exhaustive effort (using brute force) rather than employing intellectual strategies. This brute
force method involves an attacker systematically ... Show more content on Helpwriting.net ...
It is possible to design the algorithm both sequentially and in parallel.
During this project, we will solve the problem using both parallel and sequential methods. A
performance analysis will be carried out on both methods to determine the efficiency and speed of
each. The sequential algorithm will be written in both pseudo–code and the C++ language. The
parallel method will also be written in pseudo–code but we will be using C and MPI instructions to
achieve the parallelism of the operation. We would be using the Midwestern State University Turing
cluster and personal Ubuntu virtual machines to implement both the parallel program and the
sequential program. We as a group decided to pursue this topic as passwords play a vital part in how
the world's information is secured and verified. Passwords are used for user authentication to prove
identity or access approval to gain access to a resource. Passwords remain the single most common
point of failure in system security. Ensuring that passwords cannot be efficiently broken is important
for encryption algorithms. Due to this, we decided to design a sequential and parallel algorithm that
would be successful in cracking a password. Given an opportunity to research any topic where
parallel programming may prove useful, we agreed on a brute–force algorithm. In our upcoming
project, we would all attempt to design, implement, and analyze the brute–force algorithm.
STRATEGY
The sequential

The Process Of Spatiotemporal Swarms Mining Algorithms
paragraph{Spatiotemporal Swarms Mining Algorithms}
Algorithm ref{algo:spatiotemporal} shows the process of mining short time and long time
spatiotemporal swarms. Initially, all swarms are empty indicated by Lines 3–5. Then, the trajectory
data is preprocessed to remove inconsistent and inaccurate data at Line 6 and build time to locations
matrix at Line 7. After that, infrequent pruning, row and column pruning methods have been applied
upon matrix data at Lines 8–10 and mines locations based all timestamps short time spatiotemporal
swarms which is objects set size is greater than or equal to $min_{object}$ at Lines 11–17. Mining
long time spatiotemporal swarm generates K–length timestamps candidate swarms from the self
joining of K–1 ... Show more content on Helpwriting.net ...
After that we will mine others swarms. The necessary steps used for mining moving objects swarms
are pointed below where we consider minimum number of objects $min_{object}$ = 2.
At first, our proposed method generates 1–length tiemstamp swarm from the given database shown
in the figure ref{example1}(b) that is called short time spatiotemporal swarms. It has been shown
that the objects in location $L_3$ at timestamps $T_1$ and location $L_4$ at timestemps $T_1,
T_3, T_5$ are removed because only one object exists in the locations which is less than the
minimum number of objects $min_{object}$. In the second step, proposed algorithm begets 2–
length timestamps candidate based on previous 1–length timestamp swarms. Figure ref{example1}
(c) interprets the objects locations based on time where objects are found by the common objects at
particular times of the generated timesets. Suppose at timeset $[T_1, T_2]$ in location $L_1$ begets
objects set $left{o_1, o_2, o_3right}$ from the common objects sets between $left{o_1, o_2,
o_3, o_4right}$ and $left{o_1, o_2, o_3right}$ at time $T_1$ an $T_2$ respectively. In this
way, all the cells of are procreated. If their is no common object, then the cell value is empty. Then,
we apply four efficient pruning techniques: (i) candidate pruning that prune those candidates which
maintain the Apriori pruning principle that is any itemset which is infrequent, its superset should not
be

Time Proprity Analysis: Complexity Analysis Of An Algorithm
"TIME COMPLEXITY" Complexity Analysis In Algorithm:– What is an algorithm?? Algorithm:–
An Algorithm is a major part of data structures which includes linked list, array ,trees, graphs etc
that all are implemented using algorithm. An Algorithm is a sequence of instructions in a finite
manner, written and executed in aspect to find the solution of a problem. For analyzing and defining
an algorithm, we must consider its essential conditions:– 1– The name of Algorithm must be
defined. 2– Algorithm Execution process is defined that how the algorithm runs in terms of
arguments, parameters etc. 3– Algorithm pre–conditions or input(s) must be known from well
defined instructions. 4– Algorithm must be of finite states. 5– Algorithm post condition or output
must be known after execution process. 6– An algorithm must be efficient for counting certain and
each step. 7– We must know the body of Algorithm (which includes general terms N). Algorithm
Efficiency:– To measure an algorithm performance ,we calculate the 'complexity of an algorithm'
which is a function in terms of data that an algorithm must solve and analyze, when input values are
of definite size. A technique for the improvement of memory and space of an algorithm is called
'time and space trade off.' Efficiency of algorithm is measured by its ... Show more content on
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Algorithm Complexity is basically a jagged calculation for the number of procedure steps analyzed
by given problems on pre–conditions that is input data must appropriate for the alternatives
algorithms. The increment of functions generally used 'Big Oh Notation' (O) or 'Worst case
complexity'. Complexity is inversely proportional to efficiency. An algorithm complexity is
measured by its 'time' that defines how much time it takes to perform the work and 'space' which
defines that how much memory it occupies to compute the specified

Natural Selection And Genetic Algorithm
In 1859, Charles Darwin published On the Origin of Species, introducing to the world the
revolutionary concepts of evolution by means of natural selection. As species continue to pass on
their genes from one generation to the next, only those that are most fit to survive and adapt in their
environment will be able to continue this cycle. This is natural selection at work – evolution is the
continuing culmination of this cycle which has inevitably resulted in the complex array of life that
exists today[8]. Beginning in the 1950s, computer scientists began to look towards Darwin's ideas as
being applicable to computer programs. This is because, in an abstract sense, evolution could be
viewed as nature gradually optimizing different species ... Show more content on Helpwriting.net ...
If done properly, after a certain amount of generations there will be members of the population that
contain chromosomes which hold a very accurate approximation to the solution of the problem
being solved[4].
3. A Detailed Look Into a Simple Genetic Algorithm
In order to demonstrate the structure of a GA, it's best to take a look at a simple program which aims
to find the maximum value of a function. This example has been deconstructed from the source code
provided in [7]. Note that employing a genetic algorithm for such a simple problem is actually
unnecessary and time–consuming, and realistically would use a more straightforward approach.
Genetic Algorithms are generally tailored to more complex non–linear problems[6]. Say we have
some function like: f(x)= x^2– a*x+b, and we want to find its maximum value based on a few
constraints. These constraints are, for example:
0≤x≤5.0
0≤a≤5.0

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

Nature Inspired Metaheuristic Optimization Algorithms Essay
Nature–Inspired Metaheuristic Optimization Algorithms–A Review
Pragati Loomba Sonali Tiwari And Neerja Negi
Student, Faculty of Computer Applications Assistant Professor, Faculty of Computer Applications
Manav Rachna International University Manav Rachna International University
Faridabad Faridabad loombapragati.pl@gmail.com sonali.fca@mriu.edu.in neerja.fca@mriu.edu.in
Abstract – Now a day nature–inspired algorithms become a current trend and is applicable to almost
every area. This paper provides a wide classification of existing algorithms as the basis of future
research.. This paper reviewed the existing algorithms Firefly Algorithm (FA), Ant Colony
Optimization (ACO), Bat Algorithm (BA), Cuckoo Search (CS) and Other Nature Inspired
Algorithms. However, the study reveals the existing algorithms to improve the optimization
performance in different analysis. The purpose of this review and comparison is to present a analysis
of all the nature inspired algorithms and to motivate the researchers.
Keyword –Nature Inspired Algorithms, Evolutionary Algorithm, Stochastic global search algorithm
, Swarm Intelligence, Bio–Inspired Algorithms.
I. INTRODUCTION Nature has the ability to solve and optimize the complex problem by logical
and effective ways. Nature has provided us the intelligence ,self learning , pattern recognition,
optimization etc. The mostly followed nature–inspired models of computation are genetic algorithm,
neural computation, and evolutionary

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

What Is Divide And Conquer Algorithm
Background of the problem
1.Divide and Conquer :–
Merge sort runs worst, best and average case in Θ(nlgn).
Divide–and–conquer, breaks a problem into subproblems that are similar to the original problem,
recursively solves the subproblems, and finally combines the solutions to the subproblems to solve
the original problem. You should think of a divide–and–conquer algorithm as having three parts:
1. Divide the problem into a number of sub–problems that are smaller instances of the same
problem.
2. Conquer the sub–problems by solving them recursively. If they are satisfying enough, solve the
sub–problems as base cases.
3. Combine the solutions to the sub–problems into the solution for the original problem.
2.Dynamic Programming

Routing Algorithms For The Network
Routing Algorithms
Routing as we can say is selecting the best path in the network to transfer data from one point to
another. And Routing algorithm on a router in computer networks decides on which incoming line a
packet should travel and thus creating the routing decision. It depends on various factors like
stability, robustness, simplicity, correctness, fairness and optimality.
Routing algorithms are based on two classes namely. Adaptive : In this the routing process is
adapted based on any changes made to the topology or traffic.
Eg: Hierarchical, Link State, distance vector, broadcast and multicast. Non Adaptive: In this process
the routing decisions are mostly computed in advance and will be downloaded to the routers at the
boot ... Show more content on Helpwriting.net ...
This is a level 2 routing system and we can use 3–to 4 level of these kind of routings aswell.
2. Link State Routing Protocols: It calculates the best paths to network by constructing the topology
of the entire network area and then map the best path from this topology or map of all the
interconnected networks. The inputs of LS algorithm i.e network topology and the link costs are
known before hand. This can be achieved by having each broadcast link–state packets to every node
in the network and thus each link–state containing the cost of its attached links. This is termed as
link–state broadcast.
In LS algorithm, every router must do some the following things, Find the clients and record the IP
address Gauze the delays and cost of every client. Build a LS packet to send this packet to each and
every the router on the network. And then find the shortest path on every router.(Sink tree)
3. Distance Vector Routing: DV algorithm is distributed as it receives information of one or more
nodes which are directly attached to it and distributes the same back to its neighbors. It is said to be
asynchronous because as it does not need all of the nodes to operate in lockstep with one other. And
it is iterative because as this process continues as long as there is no information to be exchanged
between the neighbors.
In this routing there are two vectors called a delay node and a successor node, so router ihas
Di =[di1...diN]T and Si=[Si1...SiN]T dij = current estimate of

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