Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
Artificial Intelligence: Artificial Neural NetworksThe Integral Worm
This presentation covers artificial neural networks for artificial intelligence. Topics covered are as follows: artificial neural networks, basic representation, hidden units, exclusive OR problem, backpropagation, advantages of artificial neural networks, properties of artificial neural networks, and disadvantages of artificial neural networks.
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
Artificial Intelligence: Artificial Neural NetworksThe Integral Worm
This presentation covers artificial neural networks for artificial intelligence. Topics covered are as follows: artificial neural networks, basic representation, hidden units, exclusive OR problem, backpropagation, advantages of artificial neural networks, properties of artificial neural networks, and disadvantages of artificial neural networks.
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Radial basis function network ppt bySheetal,Samreen and Dhanashrisheetal katkar
Radial Basis Functions are nonlinear activation functions used by artificial neural networks.Explained commonly used RBFs ,cover's theorem,interpolation problem and learning strategies.
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
Basics of Neural networks and its image recognition and its applications of engineering fields and medicines and how it detect those images and give the results of those images....
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
Classification by back propagation, multi layered feed forward neural network...bihira aggrey
Classification by Back Propagation, Multi-layered feed forward Neural Networks - Provides a basic introduction of classification in data mining with neural networks
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Radial basis function network ppt bySheetal,Samreen and Dhanashrisheetal katkar
Radial Basis Functions are nonlinear activation functions used by artificial neural networks.Explained commonly used RBFs ,cover's theorem,interpolation problem and learning strategies.
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
Basics of Neural networks and its image recognition and its applications of engineering fields and medicines and how it detect those images and give the results of those images....
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.
Classification by back propagation, multi layered feed forward neural network...bihira aggrey
Classification by Back Propagation, Multi-layered feed forward Neural Networks - Provides a basic introduction of classification in data mining with neural networks
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.
Web spam classification using supervised artificial neural network algorithmsaciijournal
Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that are more efficient, generic and highly adaptive. Neural Network based technologies have high ability of adaption as well as generalization. As per our knowledge, very little work has been done in this field using neural network. We present this paper to fill this gap. This paper evaluates performance of three supervised learning algorithms of artificial neural network by creating classifiers for the complex problem of latest web spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Backpropagation learning, and Levenberg-Marquardt algorithm.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
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.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
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.
2. Background
- Neural Networks can be :
- BiologicalBiological models
- ArtificialArtificial models
- We wish to produce artificial systems capable of
complex calculation similar to the human brain.
3. describe the transmission ofdescribe the transmission of
information and some main ideasinformation and some main ideas
• The brain is consist of a mass of interconnected
neurons
– each neuron is connected to many other neurons
• Neurons transmit signals to each other
• Whether a signal is sent, depends on the strength of
the bond (synapse) between two neurons
4. How Does the Brain Work ? (1)
NEURON
- It is the cell that performs information processing in the
brain.
- Nervous tissue its consist of neurons, which receive and
transmit impulses
5. Each consists of :
SOMA, DENDRITES, AXON, and SYNAPSE.
How Does the Brain Work ? (2)
6. Brain vs. Digital Computers (1)
- Computers require hundreds of cycles to simulate
a firing of a neuron.
- The brain can fire all the neurons in a single step.
ParallelismParallelism
- Serial computers require billions of cycles to
perform some tasks but the brain takes less than
a second.
e.g. Face Recognition
7. Definition of Neural Network
A Neural Network is a system that consist of
many simple processing elements operating in
parallel which can achieve, store, and use
experimental knowledge.
9. Neurons vs. Units (1)
- Each element of NN is a node called unit.
- Units are connected by links.
-Each link has a numeric weight.
10. Neurons vs units (2)
• ANNs incorporate the two fundamental components
of biological neural nets:
1. Neurones (nodes)
2. Synapses (weights)
11. 1- A set of connecting links, each link characterized by a weight: W1,
W2, …, Wm
2- An adder function (linear combiner) which computes the weighted
sum of the inputs:
3- Activation function (squashing function) for limiting the amplitude of
the output of the neuron.
∑=
=
m
1
jjxwnetj
j
Structure of a nodeStructure of a node
12. ActivationActivation Functions
- Use different functions to obtain different models.
- 3 most common choices :
1) Step function
2) Sign function
3) Sigmoid function
- An output of 1 represents firing of a neuron down
the axon.
15. Feed-Forward Neural Network
Architectures
The feed-forward neural network was the first and most simple type of
artificial neural network devised. In this network the information moves in
only one direction—forward: From the input nodes data goes through the
hidden nodes (if any) and to the output nodes. There are no cycles or loops
in the network.
• Two different classes of network architectures
– single-layer feed-forward neurons are organized
– multi-layer feed-forward in acyclic layers
• The architecture of a neural network is linked with the learning algorithm
used to train
16. Single Layer Feed-forward
The simplest kind of neural network is a single-layer perceptron network, which
consists of a single layer of output nodes; the inputs are fed directly to the outputs
via a series of weights. In this way it can be considered the simplest kind of feed-
forward network. The sum of the products of the weights and the inputs is calculated
in each node, and if the value is above some threshold the neuron fires and takes
the activated value; otherwise it takes the deactivated value. Neurons with this kind
of activation function are also called artificial neurons or linear threshold units.
17.
18. This class of networks consists of multiple layers of computational units,
usually interconnected in a feed-forward way. Each neuron in one layer has
directed connections to the neurons of the next layer. In many applications
the units of these networks apply a sigmoid function as an activation function
Multi Layer Feed-forward
19. Supervised Learning Algorithm
• The learning algorithm would fall under this category if the desired
output for the network is also provided with the input while training
the network. By providing the neural network with both an input
and output pair it is possible to calculate an error based on it's
target output and actual output. It can then use that error to make
corrections to the network by updating it's weights.
• Single Layer neural network can be trained by a simple learning
algorithm that is usually called the Delta rule.
• Multi Layer neural network can be trained by a learning algorithm
that is usually called the Bakpropgation.
20. Delta rule
The delta rule is specialized version of backpropagation's learning rule, for
use with single layer neural networks.
It calculates the errors between calculated output and sample output data,
and uses this to create a modification to the weights, thus implementing a
form of gradient descent.
t (target) , o (output)
Mu (learning rate) , beta (error) , x (input)
collect old weight with the new modification weight and thus changing the
networks weight
21. Unsupervised Learning Algorithm
In this paradigm the neural network is only given a set of
inputs and it's the neural network's responsibility to find
some kind of pattern within the inputs provided without
any external aid.
22. Hebb Rule
Hebb method is used in the learning of networks that use
Unsupervised Learning , and the weight is modify by the
equation:
Mu (learning rate) , o (output) , x (input)
Then collect the output with the initial weight :
23. Introduction toIntroduction to
BackpropagationBackpropagation
- In 1969 a method for learning in multi-layer network, Backpropagation,
was invented by Bryson and Ho.
- Backpropagation is generalization of the delta rule to multi-layered
feedforward networks.
- Backpropagation is a common method of training artificial neural networks
used in conjunction with an optimization method such as gradient descent
(a first-order repeated optimization algorithm). It calculates the gradient of a
loss function with respect to all the weights in the network, so that the
gradient is fed to the optimization method which in turn uses it to update
the weights, in an attempt to minimize the loss function.
25. Backpropagation Algorithm – Main
Idea – error in hidden layers
The ideas of the algorithm can be summarized as follows :
1. Computes the error term for the output units using the
observed error.
2. From output layer, repeat
- propagating the error term back to the previous layer
and
- updating the weights between the two layers
until the earliest hidden layer is reached.
26. Forward Propagation of ActivityForward Propagation of Activity
• Step 1: Initialise weights at random, choose a
learning rate η
• Until network is trained:
• For each training example i.e. input pattern and
target output(s):
• Step 2: Do forward pass through net (with fixed
weights) to produce output(s)
– i.e., in Forward Direction, layer by layer:
• Inputs applied
• Multiplied by weights
• Summed
• ‘Squashed’ by sigmoid activation function
• Output passed to each neuron in next layer
– Repeat above until network output(s) produced
32. bias neuron in input layer
Bias Neurons inBias Neurons in BackpropBackpropagationagation
LearningLearning
33. Least-Mean-Square (LMS)Least-Mean-Square (LMS)
AlgorithmAlgorithm
Least mean squares (LMS) algorithms are a class of adaptive filter
used to simulate a required filter by finding the difference between the
desired and the actual signal. It was invented in 1960 by Stanford
University professor Bernard Widrow .
38. Neural Network in Practice
NNs are used for classification and function approximation
or mapping problems which are:
- Tolerant of some imprecision.
- Have lots of training data available.
- Hard and fast rules cannot easily be applied.
39. NETalk (1987)
• Mapping character strings into phonemes so they
can be pronounced by a computer
• Neural network trained how to pronounce each
letter in a word in a sentence, given the three
letters before and three letters after it in a window
• Output was the correct phoneme
• Results
– 95% accuracy on the training data
– 78% accuracy on the test set
40. Other Examples
• Neurogammon (Tesauro & Sejnowski, 1989)
– Backgammon learning program
• Speech Recognition (Waibel, 1989)
• Character Recognition (LeCun et al., 1989)
• Face Recognition (Mitchell)
41. ALVINNALVINN
• Steer a van down the road
– 2-layer feedforward
• using backpropagation for learning
– Raw input is 480 x 512 pixel image 15x per sec
– Color image preprocessed into 960 input units
– 4 hidden units
– 30 output units, each is a steering direction
43. Learning on-the-
fly
• ALVINN learned as the vehicle
traveled
– initially by observing a human
driving
– learns from its own driving by
watching for future corrections
– never saw bad driving
• didn’t know what was
dangerous, NOT correct
• computes alternate views of
the road (rotations, shifts, and
fill-ins) to use as “bad”
examples
– keeps a buffer pool of 200 pretty
old examples to avoid overfitting
to only the most recent images
44. Feed-forward vs. Interactive
Nets
• Feed-forward
– activation propagates in one direction
– We usually focus on this
• Interactive
– activation propagates forward & backwards
– propagation continues until equilibrium is reached in
the network
– We do not discuss these networks here, complex
training. May be unstable.
45. Ways of learning with an ANN
• Add nodes & connections
• Subtract nodes & connections
• Modify connection weights
– current focus
– can simulate first two
• I/O pairs:
– given the inputs, what should the output be?
[“typical” learning problem]
46. More Neural NetworkMore Neural Network
ApplicationsApplications
- May provide a model for massive parallel computation.
- More successful approach of “parallelizing” traditional
serial algorithms.
- Can compute any computable function.
- Can do everything a normal digital computer can do.
- Can do even more under some impractical assumptions.
47. Neural Network Approaches to driving
- Developed in 1993.
- Performs driving with
Neural Networks.
- An intelligent VLSI image
sensor for road following.
- Learns to filter out image
details not relevant to
driving.
Hidden layer
Output units
Input units
•Use special hardware
•ASIC
•FPGA
•analog
49. Actual Products Available
ex1. Enterprise Miner:ex1. Enterprise Miner:
- Single multi-layered feed-forward neural networks.
- Provides business solutions for data mining.
ex2. Nestor:ex2. Nestor:
- Uses Nestor Learning System (NLS).
- Several multi-layered feed-forward neural networks.
- Intel has made such a chip - NE1000 in VLSI technology.
50. Ex1. Software tool - Enterprise Miner
- Based on SEMMA (Sample, Explore, Modify, Model,
Access) methodology.
- Statistical tools include :
Clustering, decision trees, linear and logistic
regression and neural networks.
- Data preparation tools include :
Outliner detection, variable transformation, random
sampling, and partition of data sets (into training,
testing and validation data sets).
51. Ex 2. Hardware Tool - Nestor
- With low connectivity within each layer.
- Minimized connectivity within each layer results in rapid
training and efficient memory utilization, ideal for VLSI.
- Composed of multiple neural networks, each specializing
in a subset of information about the input patterns.
- Real time operation without the need of special computers
or custom hardware DSP platforms
•Software exists.
52. SummarySummary
- Neural network is a computational model that simulate
some properties of the human brain.
- The connections and nature of units determine the
behavior of a neural network.
- Perceptrons are feed-forward networks that can only
represent linearly separable functions.
53. Summary
- Given enough units, any function can be represented
by Multi-layer feed-forward networks.
- Backpropagation learning works on multi-layer
feed-forward networks.
- Neural Networks are widely used in developing
artificial learning systems.
54. ReferencesReferences
- Russel, S. and P. Norvig (1995). Artificial Intelligence - A
Modern Approach. Upper Saddle River, NJ, Prentice
Hall.
- Sarle, W.S., ed. (1997), Neural Network FAQ, part 1 of 7:
Introduction, periodic posting to the Usenet newsgroup
comp.ai.neural-nets,
URL: ftp://ftp.sas.com/pub/neural/FAQ.html