Brief introduction of neural network including-
1. Fitting Tool
2. Clustering data with a self-organising map
3. Pattern Recognition Tool
4. Time Series Toolbox
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Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
Open CV Implementation of Object Recognition Using Artificial Neural Networksijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain's neural networks. It consists of interconnected nodes, often referred to as neurons or units, organized in layers. These layers typically include an input layer, one or more hidden layers, and an output layer.
How to Build a Neural Network and Make PredictionsDeveloper Helps
Lately, people have been really into neural networks. They’re like a computer system that works like a brain, with nodes connected together. These networks are great at sorting through big piles of data and figuring out patterns to solve hard problems or guess stuff. And you know what’s super cool? They can keep on learning forever.
Creating and deploying neural networks can be a challenging process, which largely depends on the specific task and dataset you’re dealing with. To succeed in this endeavor, it’s crucial to possess a solid grasp of machine learning concepts, along with strong programming skills. Additionally, a deep understanding of the chosen deep learning framework is essential. Moreover, it’s imperative to prioritize responsible and ethical usage of AI models, especially when integrating them into real-world applications.
Learn from : https://www.developerhelps.com/how-to-build-a-neural-network-and-make-predictions/
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
X-TREPAN: A MULTI CLASS REGRESSION AND ADAPTED EXTRACTION OF COMPREHENSIBLE D...cscpconf
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible ...csandit
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees
from neural networks. We empirically evaluated the performance of the algorithm on a set of
databases from real world events. This benchmark enhancement was achieved by adapting
Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The
models are then compared with X-TREPAN for comprehensibility and classification accuracy.
Furthermore, we validate the experimentations by applying statistical methods. Finally, the
modified algorithm is extended to work with multi-class regression problems and the ability to
comprehend generalized feed forward networks is achieved.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
Image classification is perhaps the most important part of digital image analysis. In this paper, we compare the most widely used model CNN Convolutional Neural Network , and MLP Multilayer Perceptron . We aim to show how both models differ and how both models approach towards the final goal, which is image classification. Souvik Banerjee | Dr. A Rengarajan "Hand-Written Digit Classification" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42444.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42444/handwritten-digit-classification/souvik-banerjee
This work is proposed the feed forward neural network with symmetric table addition method to design the
neuron synapses algorithm of the sine function approximations, and according to the Taylor series
expansion. Matlab code and LabVIEW are used to build and create the neural network, which has been
designed and trained database set to improve its performance, and gets the best a global convergence with
small value of MSE errors and 97.22% accuracy.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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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
adversary training.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Student information management system project report ii.pdfKamal Acharya
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.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
1. GROUP N0.-7
Rangpal
Prabhat srivastava
Vineet Kumar
Suyash pandey
Vibhav Yadav
NEURAL NETWORK TOOLBOX
GROUP-7
RANGPAL
VINEET KUMAR
PRABHAT SRIVASTAV
SUYASH PANDEY
VIBHAV YADAV
IET LUCKNOW (AKTU)
U. P. 226021
2. Artificial neural networks (ANNs) or connectionist
systems are computing systems vaguely inspired by
the biological neural networks that constitute animal
brains.
Such systems "learn" (i.e. progressively improve
performance on) tasks by considering examples,
generally without task-specific programming. For
example, in image recognition, they might learn to
identify images that contain cats by analyzing example
images that have been manually labeled as "cat" or "no
cat" and using the results to identify cats in other
images. They do this without any a priori knowledge
about cats, e.g., that they have fur, tails, whiskers and
cat-like faces. Instead, they evolve their own set of
relevant characteristics from the learning material that
they process.
3. Neural Network Toolbox provides algorithms,
pretrained models, and apps to create, train,
visualize, and simulate both shallow and deep neural
networks. You can perform classification, regression,
clustering, dimensionality reduction, time-series
forecasting, and dynamic system modeling and
control.
Basically, there are 3 different layers in a neural
network :-
Input Layer (All the inputs are fed in the model
through this layer)
Hidden Layers (There can be more than one
hidden layers which are used for processing the
inputs received from the input layers)
Output Layer (The data after processing is made
available at the output layer)
4.
5. Collect data
Create the network — Create Neural Network
Object
Initialize the weights and biases
Train the network — Neural Network Training
Concepts
Validate the network
Use the network
8. FUNCTIONS
nnstart – neural network getting started GUI
nftool – neural network fitting tool
net(view) – view neural network
net.trainparam.epochs – we can choose how
many epochs we wants
net.trainparam.goal – maximum performance
means we want accuracy
net.trainparam.lr – learning rate
net = train(net,x,y) – train network
9. To define a fitting problem for the toolbox,
arrange a set of Q input vectors as columns in a
matrix. Then, arrange another set of Q target
vectors (the correct output vectors for each of
the input vectors) into a second matrix . For
example, you can define the fitting problem for
a Boolean AND gate with four sets of two-
element input vectors and one-element targets
as follows:
inputs = [0 1 0 1; 0 0 1 1]; targets = [0 0 0 1];
10. Training –these are presented to the network
dfuring training,and the network is adjusted
according to its error.
Validation –these are use to measure network
generalization,and to halt training when
generalization stops improving.
Testing- these are no effect on training and so
provide and independent measure of network
performance during and after training .
11. cluster is a group of objects that belongs to the same class. In other words, similar
objects are grouped in one cluster and dissimilar objects are grouped in another
cluster.
Clustering is the process of making a group of abstract objects into classes of
similar objects.
The notion of a "cluster" cannot be precisely defined, which is one of the reasons
why there are so many clustering algorithms
The notion of a cluster, as found by different algorithms, varies significantly in its
properties. Understanding these "cluster models" is key to understanding the
differences between the various algorithms. Typical cluster models include:
12. Apps
Neural Net Clustering Cluster data by training a self-organizing
maps network
Functions
nnstart Neural network getting started GUI
view View neural network
selforgmap Self-organizing map
train Train neural network
plotsomhits Plot self-organizing map sample hits
plotsomnc Plot self-organizing map neighbor connections
plotsomnd Plot self-organizing map neighbor distances
plotsomplanes Plot self-organizing map weight plane
plotsompos Plot self-organizing map weight positions
plotsomtop Plot self-organizing map topology
genFunction Generate MATLAB function for simulating neural
network
13. #Connectivity models: for example, hierarchical clustering
#Centroid models: for example, the k-means algorithm
#Density models: for
example, DBSCAN and OPTICS defines clusters as
connected dense regions in the data space.
#Subspace Model
#Group Model
#Graph Based Model
#Neural Model
Clusterings can be roughly distinguished as:
#Hard clustering: each object belongs to a cluster or not
#Soft clustering (also: fuzzy clustering): each object belongs
to each cluster to a certain degree (for example, a likelihood
of belonging to the cluster)
14. Biology, computational biology and
bioinformatics Ex- Transcryptomics, sequence
analysis, human genetic clustering
Business and marketing Ex- market research,
Grouping of shopping items
World wide web Ex- social network analysis,
search result grouping
Computer science Ex- software evolution,
image segmentation, recommender system
15. In addition to function fitting, neural networks are also good at
recognizing patterns.
For example, suppose you want to classify a tumor as benign or
malignant, based on uniformity of cell size, clump thickness,
mitosis, etc. You have 699 example cases for which you have 9
items of data and the correct classification as benign or malignant.
As with function fitting, there are two ways to solve this problem:
Use the nprtool GUI, as described in Using the Neural Network
Pattern Recognition Tool.
Use a command-line solution, as described in Using Command-
Line Functions.
It is generally best to start with the GUI, and then to use the GUI
to automatically generate command-line scripts. Before using
either method, the first step is to define the problem by selecting a
data set. The next section describes the data format.
16. To define a pattern recognition problem, arrange a set of Q
input vectors as columns in a matrix. Then arrange another
set of Q target vectors so that they indicate the classes to
which the input vectors are assigned (see"Data
Structures" for a detailed description of data formatting for
static and time-series data).
When there are only two classes; you set each scalar target
value to either 0 or 1, indicating which class the
corresponding input belongs to. For instance, you can define
the two-class exclusive-or classification problem as follows:
inputs = [0 1 0 1; 0 0 1 1]; targets = [1 0 0 1; 0 1 1 0];
When inputs are to be classified into N different classes, the
target vectors have N elements. For each target vector, one
element is 1 and the others are 0. For example, the following
lines show how to define a classification problem that
divides the corners of a 5-by-5-by-5 cube into three classes:
17. The origin (the first input vector) in one class
The corner farthest from the origin (the last input vector) in
a second class
All other points in a third class
inputs = [0 0 0 0 5 5 5 5; 0 0 5 5 0 0 5 5; 0 5 0 5 0 5 0 5]; targets
= [1 0 0 0 0 0 0 0; 0 1 1 1 1 1 1 0; 0 0 0 0 0 0 0 1];
Classification problems involving only two classes can be
represented using either format. The targets can consist of
either scalar 1/0 elements or two-element vectors, with one
element being 1 and the other element being 0.
The next section shows how to train a network to recognize
patterns, using the neural network pattern recognition tool
GUI, nprtool. This example uses the cancer data set
provided with the toolbox. This data set consists of 699 nine-
element input vectors and two-element target vectors. There
are two elements in each target vector, because there are two
categories (benign or malignant) associated with each input
vector.
18. Time series toolbox
• What is time series?
Time series is set of data point listed over
time.
• Why time series toolbox?
It is used for dynamic modelling and
prediction problems.
19. open loop NARX
closed loop NARX
It perform 1 step ahead prediction It
perform multistep ahead prediction
It uses actual value of y for prediction It
uses former value of y for prediction
20. • Time series tool allows to solve 3 kinds of non linear
time series
21. Steps for writing time series code for prediction for NARX output.
(Open loop)
1) Define input and target variables
2) Create a Nonlinear autoregressive network
3) NARXNET function is used to create network
4) NARXNET require (a) InputDelay (b) FeedbackDelay (c)
HiddenlayerSize as input
5) When network got created it require data for training
6) Function PREPARETS prepares time series data and allows to
keep original time series data unchanged
7) PREPARETS uses network ,input series ,target series to preapare
data
8) We know that data prepared is used for three purpose in network
(a) 75% data for training (b)15% data for validation (c)15% data
for testing . So our next step is to divide the data for upcoming
process.
9) Then next step is to train the netwok with the help of TRAIN
function.
10) TRAIN functions uses previously prepared data for training
purpose.
22. Steps for writing time series code for prediction for NAR
output. (closed loop)
1) Closed loop code is not completely different from
open loop
2) Network is always created and trained in open form.
3) So first network is created and trained in open form.
4) Then network is closed using the function
CLOSEDLOOP
5) Further preparets is use to prepare the time series
data
6) Then feedbackdata and input data is updated after
delay
7) Finally performance of network is checked