Simulation in teaching has recently entered the field of education. It is used at different levels of instruction.
The teacher is trained practically and also imparted theoretical learning. In Computer Science, Graph theory
is the fundamental mathematics required for better understanding Data Structures. To Teach Graph theory &
Algorithms, We introduced Simulation as an innovative teaching methodology. Students can understand in a
better manner by using simulation. Graph Tea is one of such simulation tool for Graph Theory & Algorithms.
In this paper, we simulated Tree Traversal Techniques like Breadth First Search (BFS), Depth First Search
(DFS) and minimal cost spanning tree algorithms like Prims.
Slides from my Pittsburgh TechFest 2014 talk, "Machine Learning for Modern Developers". This talk covers basic concepts and math for statistical machine learning, focusing on the problem of classification.
Want some working code from the demos? Head over here: https://github.com/cacois/ml-classification-examples
A Predictive Stock Data Analysis with SVM-PCA Model .......................................................................1
Divya Joseph and Vinai George Biju
HOV-kNN: A New Algorithm to Nearest Neighbor Search in Dynamic Space.......................................... 12
Mohammad Reza Abbasifard, Hassan Naderi and Mohadese Mirjalili
A Survey on Mobile Malware: A War without End................................................................................... 23
Sonal Mohite and Prof. R. S. Sonar
An Efficient Design Tool to Detect Inconsistencies in UML Design Models............................................. 36
Mythili Thirugnanam and Sumathy Subramaniam
An Integrated Procedure for Resolving Portfolio Optimization Problems using Data Envelopment
Analysis, Ant Colony Optimization and Gene Expression Programming ................................................. 45
Chih-Ming Hsu
Emerging Technologies: LTE vs. WiMAX ................................................................................................... 66
Mohammad Arifin Rahman Khan and Md. Sadiq Iqbal
Introducing E-Maintenance 2.0 ................................................................................................................. 80
Abdessamad Mouzoune and Saoudi Taibi
Detection of Clones in Digital Images........................................................................................................ 91
Minati Mishra and Flt. Lt. Dr. M. C. Adhikary
The Significance of Genetic Algorithms in Search, Evolution, Optimization and Hybridization: A Short
Review ...................................................................................................................................................... 103
Self-Organising Maps for Customer Segmentation using R - Shane Lynn - Dublin Rshanelynn
Self-Organising maps for Customer Segmentation using R.
These slides are from a talk given to the Dublin R Users group on 20th January 2014. The slides describe the uses of customer segmentation, the algorithm behind Self-Organising Maps (SOMs) and go through two use cases, with example code in R.
Accompanying code and datasets now available at http://shanelynn.ie/index.php/self-organising-maps-for-customer-segmentation-using-r/.
Slides from my Pittsburgh TechFest 2014 talk, "Machine Learning for Modern Developers". This talk covers basic concepts and math for statistical machine learning, focusing on the problem of classification.
Want some working code from the demos? Head over here: https://github.com/cacois/ml-classification-examples
A Predictive Stock Data Analysis with SVM-PCA Model .......................................................................1
Divya Joseph and Vinai George Biju
HOV-kNN: A New Algorithm to Nearest Neighbor Search in Dynamic Space.......................................... 12
Mohammad Reza Abbasifard, Hassan Naderi and Mohadese Mirjalili
A Survey on Mobile Malware: A War without End................................................................................... 23
Sonal Mohite and Prof. R. S. Sonar
An Efficient Design Tool to Detect Inconsistencies in UML Design Models............................................. 36
Mythili Thirugnanam and Sumathy Subramaniam
An Integrated Procedure for Resolving Portfolio Optimization Problems using Data Envelopment
Analysis, Ant Colony Optimization and Gene Expression Programming ................................................. 45
Chih-Ming Hsu
Emerging Technologies: LTE vs. WiMAX ................................................................................................... 66
Mohammad Arifin Rahman Khan and Md. Sadiq Iqbal
Introducing E-Maintenance 2.0 ................................................................................................................. 80
Abdessamad Mouzoune and Saoudi Taibi
Detection of Clones in Digital Images........................................................................................................ 91
Minati Mishra and Flt. Lt. Dr. M. C. Adhikary
The Significance of Genetic Algorithms in Search, Evolution, Optimization and Hybridization: A Short
Review ...................................................................................................................................................... 103
Self-Organising Maps for Customer Segmentation using R - Shane Lynn - Dublin Rshanelynn
Self-Organising maps for Customer Segmentation using R.
These slides are from a talk given to the Dublin R Users group on 20th January 2014. The slides describe the uses of customer segmentation, the algorithm behind Self-Organising Maps (SOMs) and go through two use cases, with example code in R.
Accompanying code and datasets now available at http://shanelynn.ie/index.php/self-organising-maps-for-customer-segmentation-using-r/.
Using only simple rules for local interactions, groups of agents can form self-organizing super-organisms or “flocks” that show global emergent behavior. When agents are also extended with memory and goals the resulting flock not only demonstrates emergent behavior, but also collective intelligence: the ability for the group to solve problems that might be beyond the ability of the individual alone. Until now, research has focused on the improvement of particle design for global behavior; however, techniques for human-designed particles are task-specific. In this paper we will demonstrate that evolutionary computing techniques can be applied to design particles, not only to optimize the parameters for movement but also the structure of controlling finite state machines that enable collective intelligence. The evolved design not only exhibits emergent, self-organizing behavior but also significantly outperforms a human design in a specific problem domain. The strategy of the evolved design may be very different from what is intuitive to humans and perhaps reflects more accurately how nature designs systems for problem solving. Furthermore, evolutionary design of particles for collective intelligence is more flexible and able to target a wider array of problems either individually or as a whole.
A Review on Non Linear Dimensionality Reduction Techniques for Face Recognitionrahulmonikasharma
Principal component Analysis (PCA) has gained much attention among researchers to address the pboblem of high dimensional data sets.during last decade a non-linear variantof PCA has been used to reduce the dimensions on a non linear hyperplane.This paper reviews the various Non linear techniques ,applied on real and artificial data .It is observed that Non-Linear PCA outperform in the counterpart in most cases .However exceptions are noted.
A Dependent Set Based Approach for Large Graph AnalysisEditor IJCATR
Now a day’s social or computer networks produced graphs of thousands of nodes & millions of edges. Such Large graphs
are used to store and represent information. As it is a complex data structure it requires extra processing. Partitioning or clustering
methods are used to decompose a large graph. In this paper dependent set based graph partitioning approach is proposed which
decomposes a large graph into sub graphs. It creates uniform partitions with very few edge cuts. It also prevents the loss of
information. The work also focuses on an approach that handles dynamic updation in a large graph and represents a large graph in
abstract form.
BEGG’S STAGE II AND ITS MECHANICS /certified fixed orthodontic courses by Ind...Indian dental academy
Description :
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and
offering a wide range of dental certified courses in different formats.for more details please visit
www.indiandentalacademy.com
Using only simple rules for local interactions, groups of agents can form self-organizing super-organisms or “flocks” that show global emergent behavior. When agents are also extended with memory and goals the resulting flock not only demonstrates emergent behavior, but also collective intelligence: the ability for the group to solve problems that might be beyond the ability of the individual alone. Until now, research has focused on the improvement of particle design for global behavior; however, techniques for human-designed particles are task-specific. In this paper we will demonstrate that evolutionary computing techniques can be applied to design particles, not only to optimize the parameters for movement but also the structure of controlling finite state machines that enable collective intelligence. The evolved design not only exhibits emergent, self-organizing behavior but also significantly outperforms a human design in a specific problem domain. The strategy of the evolved design may be very different from what is intuitive to humans and perhaps reflects more accurately how nature designs systems for problem solving. Furthermore, evolutionary design of particles for collective intelligence is more flexible and able to target a wider array of problems either individually or as a whole.
A Review on Non Linear Dimensionality Reduction Techniques for Face Recognitionrahulmonikasharma
Principal component Analysis (PCA) has gained much attention among researchers to address the pboblem of high dimensional data sets.during last decade a non-linear variantof PCA has been used to reduce the dimensions on a non linear hyperplane.This paper reviews the various Non linear techniques ,applied on real and artificial data .It is observed that Non-Linear PCA outperform in the counterpart in most cases .However exceptions are noted.
A Dependent Set Based Approach for Large Graph AnalysisEditor IJCATR
Now a day’s social or computer networks produced graphs of thousands of nodes & millions of edges. Such Large graphs
are used to store and represent information. As it is a complex data structure it requires extra processing. Partitioning or clustering
methods are used to decompose a large graph. In this paper dependent set based graph partitioning approach is proposed which
decomposes a large graph into sub graphs. It creates uniform partitions with very few edge cuts. It also prevents the loss of
information. The work also focuses on an approach that handles dynamic updation in a large graph and represents a large graph in
abstract form.
BEGG’S STAGE II AND ITS MECHANICS /certified fixed orthodontic courses by Ind...Indian dental academy
Description :
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and
offering a wide range of dental certified courses in different formats.for more details please visit
www.indiandentalacademy.com
A Vision for On-the-Job Results: How an Eye Bank Uses Performance-support in ...Human Capital Media
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In this spotlight webinar attendees will explore:
The needs of the modern learner
Striking the right balance of learning modalities within your organization
Items to consider while restructuring learning opportunities and delivery methods
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
Scalable Rough C-Means clustering using Firefly algorithm..................................................................1
Abhilash Namdev and B.K. Tripathy
Significance of Embedded Systems to IoT................................................................................................. 15
P. R. S. M. Lakshmi, P. Lakshmi Narayanamma and K. Santhi Sri
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Janet O. Adekannbi and Testimony Morenike Oluwayinka
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Mahlangu Gilbert, Furusa Samuel Simbarashe, Chikonye Musafare and Mugoniwa Beauty
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Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
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Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
2. 89 International Journal for Modern Trends in Science and Technology
Graph Tea: Simulating Tool for Graph Theory & Algorithms
Fig 1 : Properties Window
Fig 2: Generated Graph
III. ALGORITHM VISUALIZATION
A. Teaching Breadth First Search with Graph Tea
Breadth-first search (BFS) is an algorithm for
traversing or searching tree or graph data
structures in the order of neighbor nodes first,
before moving to the next level neighbors. The
applications of BFS are finding the shortest path,
bipartition and Garbage collection.
The working of BFS can be visualized by using
Graph Tea. To Visualize or simulate the BFS, Select
Algorithms on menu bar. It will popup all the
algorithms that are simulated in Graph Tea tool.
Selecting BFS generates a pop up window. In that
window select Play or Play one step. Play traces the
entire algorithm. Play one step trace the algorithm
step by step.
In Step 1 Algorithm prompt for root vertex. Select
the root vertex in the graph. The algorithm will
explore the root vertex and the visited vertices are
displayed in the pop up window. Once all the
vertices from the root vertex are visited, the next
node will be explored. In this manner all the the
vertices are visited in Breadth First Order.
Given the example above, here are the steps plus
a visualization to play through each step:
Start by exploring vertex 1.
Then visit vertices 2, 3 & 4.
Explore vertex 2
Then visit 0
Exploring 3,4 & 0 does not visit any new
node. Hence the algorithm will be
terminated.
Fig 3: Visualizing Breadth First Search in Graph Theory
3. 90 International Journal for Modern Trends in Science and Technology
Volume: 2 | Issue: 06 | June 2016 | ISSN: 2455-3778IJMTST
B. Teaching Depth First Search With Graph Tea
Depth-first search (DFS) is an algorithm for
traversing or searching tree or graph data
structures that explores as far as possible along
each branch before backtracking. The applications
of DFS are finding Bridges of a graph, topological
sorting and connected components. As similar to
to BFS, to trace DFS Select the root vertex and play
the algorithm.
procedure DFS(G,v):
label v as discovered
for all edges from v to w in G.adjacentEdges(v)
do
if vertex w is not labeled as discovered then
recursively call DFS(G,w)
Figure 4 : Simulating DFS algorithm in Graph Theory
C. Simulating Prim’s algorithm for Minimal
spanning tree:
Let G = (V, E) be a connected graph in which each
edge (u, v) E has an associated cost C(u, v). A
Spanning Tree for G is a subgraph of G that it is a
free tree connecting all vertices in V. The cost of a
spanning tree is the sum of costs on its edges.
In computer science, Prim's algorithm is a greedy
algorithm that finds a minimum spanning tree for a
weighted undirected graph. This means it finds a
subset of the edges that forms a tree that includes
every vertex, where the total weight of all the edges
in the tree is minimized.
Algorithm
1) Create a set mstSet that keeps track of vertices
already included in MST.
2) Assign a key value to all vertices in the input
graph. Initialize all key values as INFINITE. Assign
key value as 0 for the first vertex so that it is picked
first.
3) While mstSet doesn’t include all vertices
….a) Pick a vertex u which is not there in mstSet
and has minimum key value.
….b) Include u to mstSet.
….c) Update key value of all adjacent vertices of u.
To update the key values, iterate through all
adjacent vertices. For every adjacent vertex v, if
weight of edge u-v is less than the previous key
value of v, update the key value as weight of u-v
Fig 5: Simulating Prims algorithm in Graph Theory
4. 91 International Journal for Modern Trends in Science and Technology
Graph Tea: Simulating Tool for Graph Theory & Algorithms
IV. CONCLUSION
Graph Theory & Algorithms are simulated by
using the tool Graph Tea. This Tool is helpful for
delivery of lectures in Graph theory & Algorithms.
We observed, using Simulation as a teaching
methodology improved understanding level of
students
ACKNOWLEDGMENT
We Would like to thank Management of Sasi
Institute of Tech. & Engg., for providing continuous
support in using & delivering the classes using
Graph Tea. It also encourages for preparation &
publication of this paper.
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