The document discusses using a probabilistic neural network (PNN) to analyze seismic data and well logs to identify physical attributes, describing the layers and processing of the PNN model as well as examples of preprocessing seismic data and attributes to train the PNN to accurately predict properties like porosity and hydrocarbon volume. The PNN is trained on normalized seismic attribute data and well logs then applied to the full 3D seismic volume to generate property predictions across the area.
Deep learning and neural networks are inspired by biological neurons. Artificial neural networks (ANN) can have multiple layers and learn through backpropagation. Deep neural networks with multiple hidden layers did not work well until recent developments in unsupervised pre-training of layers. Experiments on MNIST digit recognition and NORB object recognition datasets showed deep belief networks and deep Boltzmann machines outperform other models. Deep learning is now widely used for applications like computer vision, natural language processing, and information retrieval.
1. The document discusses the key elements of digital image processing including image acquisition, enhancement, restoration, segmentation, representation and description, recognition, and knowledge bases.
2. It also covers fundamentals of human visual perception such as the anatomy of the eye, image formation, brightness adaptation, color fundamentals, and color models like RGB and HSI.
3. The principles of video cameras are explained including the construction and working of the vidicon camera tube.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
This document discusses edge detection and image segmentation techniques. It begins with an introduction to segmentation and its importance. It then discusses edge detection, including edge models like steps, ramps, and roofs. Common edge detection techniques are described, such as using derivatives and filters to detect discontinuities that indicate edges. Point, line, and edge detection are explained through the use of filters like Laplacian filters. Thresholding techniques are introduced as a way to segment images into different regions based on pixel intensity values.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
This document provides an overview of self-organizing maps (SOM) as an unsupervised learning technique. It discusses the principles of self-organization including self-amplification, competition, and cooperation. The Willshaw-von der Malsburg model and Kohonen feature maps are presented as two approaches to building topographic maps through self-organization. The Kohonen SOM learning algorithm is described as involving competition between neurons to determine a winning neuron, cooperation between neighboring neurons, and adaptive changes to synaptic weights based on Hebbian learning principles.
This document provides an overview of autoencoders and their use in unsupervised learning for deep neural networks. It discusses the history and development of neural networks, including early work in the 1940s-1980s and more recent advances in deep learning. It then explains how autoencoders work by setting the target values equal to the inputs, describes variants like denoising autoencoders, and how stacking autoencoders can create deep architectures for tasks like document retrieval, facial recognition, and signal denoising.
MLPfit is a tool for designing and training multi-layer perceptrons (MLPs) for tasks like function approximation and classification. It implements stochastic minimization as well as more powerful methods like conjugate gradients and BFGS. MLPfit is designed to be simple, precise, fast and easy to use for both standalone and integrated applications. Documentation and source code are available online.
Deep learning and neural networks are inspired by biological neurons. Artificial neural networks (ANN) can have multiple layers and learn through backpropagation. Deep neural networks with multiple hidden layers did not work well until recent developments in unsupervised pre-training of layers. Experiments on MNIST digit recognition and NORB object recognition datasets showed deep belief networks and deep Boltzmann machines outperform other models. Deep learning is now widely used for applications like computer vision, natural language processing, and information retrieval.
1. The document discusses the key elements of digital image processing including image acquisition, enhancement, restoration, segmentation, representation and description, recognition, and knowledge bases.
2. It also covers fundamentals of human visual perception such as the anatomy of the eye, image formation, brightness adaptation, color fundamentals, and color models like RGB and HSI.
3. The principles of video cameras are explained including the construction and working of the vidicon camera tube.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
This document discusses edge detection and image segmentation techniques. It begins with an introduction to segmentation and its importance. It then discusses edge detection, including edge models like steps, ramps, and roofs. Common edge detection techniques are described, such as using derivatives and filters to detect discontinuities that indicate edges. Point, line, and edge detection are explained through the use of filters like Laplacian filters. Thresholding techniques are introduced as a way to segment images into different regions based on pixel intensity values.
The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.
This document provides an overview of self-organizing maps (SOM) as an unsupervised learning technique. It discusses the principles of self-organization including self-amplification, competition, and cooperation. The Willshaw-von der Malsburg model and Kohonen feature maps are presented as two approaches to building topographic maps through self-organization. The Kohonen SOM learning algorithm is described as involving competition between neurons to determine a winning neuron, cooperation between neighboring neurons, and adaptive changes to synaptic weights based on Hebbian learning principles.
This document provides an overview of autoencoders and their use in unsupervised learning for deep neural networks. It discusses the history and development of neural networks, including early work in the 1940s-1980s and more recent advances in deep learning. It then explains how autoencoders work by setting the target values equal to the inputs, describes variants like denoising autoencoders, and how stacking autoencoders can create deep architectures for tasks like document retrieval, facial recognition, and signal denoising.
MLPfit is a tool for designing and training multi-layer perceptrons (MLPs) for tasks like function approximation and classification. It implements stochastic minimization as well as more powerful methods like conjugate gradients and BFGS. MLPfit is designed to be simple, precise, fast and easy to use for both standalone and integrated applications. Documentation and source code are available online.
The document provides an overview of perceptrons and neural networks. It discusses how neural networks are modeled after the human brain and consist of interconnected artificial neurons. The key aspects covered include the McCulloch-Pitts neuron model, Rosenblatt's perceptron, different types of learning (supervised, unsupervised, reinforcement), the backpropagation algorithm, and applications of neural networks such as pattern recognition and machine translation.
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
Neural Networks and Deep Learning BasicsJon Lederman
This document provides an introduction to deep learning and neural networks. It discusses:
- Deep learning learns representations of data rather than relying on hand-engineered features.
- Deep learning architectures include neural networks, convolutional neural networks, and recurrent neural networks.
- Deep learning represents concepts in a nested hierarchy from simple to more abstract, with each layer learning slightly more complex representations. This allows it to learn its own feature detectors from raw data.
Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning ...Zabir Al Nazi Nabil
Industrial pollution resulting in ozone layer depletion has influenced
increased UV radiation in recent years which is a major environmental risk factor for invasive skin cancer Melanoma and other keratinocyte cancers. The incidence of deaths from Melanoma has risen worldwide in past two decades.
Deep learning has been employed successfully for dermatologic diagnosis. In
this work, we present a deep learning based scheme to automatically segment
skin lesions and detect melanoma from dermoscopy images. U-Net was used
for segmenting out the lesion from surrounding skin. The limitation of utilizing
deep neural networks with limited medical data was solved with data augmentation and transfer learning. In our experiments, U-Net was used with spatial
dropout to solve the problem of overfitting and different augmentation effects
were applied on the training images to increase data samples. The model was
evaluated on two different datasets. It achieved a mean dice score of 0.87 and a
mean jaccard index of 0.80 on ISIC 2018 dataset. The trained model was assessed on PH² dataset where it achieved a mean dice score of 0.93 and a mean
jaccard index of 0.87 with transfer learning. For classification of malignant
melanoma, a DCNN-SVM model was used where we compared state of the art
deep nets as feature extractors to find the applicability of transfer learning in
dermatologic diagnosis domain. Our best model achieved a mean accuracy of
92% on PH² dataset. The findings of this study is expected to be useful in cancer diagnosis research.
Published at IJCCI 2018. Source code available at https://github.com/zabir-nabil/lesion-segmentation-melanoma-tl
This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.
The document discusses image restoration techniques. It describes how images can become degraded through phenomena like motion, improper camera focusing, and noise. The goal of image restoration is to recover the original high quality image from its degraded version using knowledge about the degradation process and types of noise. Common noise models include Gaussian, Rayleigh, Erlang, exponential, and impulse noise. Filtering techniques like mean, order statistics, and adaptive filters can be used for restoration by smoothing the image while preserving edges. The adaptive filters change based on local image statistics to better reduce noise with less blurring than regular filters.
The document discusses gradient descent methods for unconstrained convex optimization problems. It introduces gradient descent as an iterative method to find the minimum of a differentiable function by taking steps proportional to the negative gradient. It describes the basic gradient descent update rule and discusses convergence conditions such as Lipschitz continuity, strong convexity, and condition number. It also covers techniques like exact line search, backtracking line search, coordinate descent, and steepest descent methods.
Deep learning uses neural networks, which are systems inspired by the human brain. Neural networks learn patterns from large amounts of data through forward and backpropagation. They are constructed of layers including an input layer, hidden layers, and an output layer. Deep learning can learn very complex patterns and has various applications including image classification, machine translation, and more. Recurrent neural networks are useful for sequential data like text and audio. Convolutional neural networks are widely used in computer vision tasks.
Convolutional neural networks (CNNs) learn multi-level features and perform classification jointly and better than traditional approaches for image classification and segmentation problems. CNNs have four main components: convolution, nonlinearity, pooling, and fully connected layers. Convolution extracts features from the input image using filters. Nonlinearity introduces nonlinearity. Pooling reduces dimensionality while retaining important information. The fully connected layer uses high-level features for classification. CNNs are trained end-to-end using backpropagation to minimize output errors by updating weights.
This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.
Fast R-CNN is a method that improves object detection speed and accuracy over previous methods like R-CNN and SPPnet. It uses a region of interest pooling layer and multi-task loss to jointly train a convolutional neural network for classification and bounding box regression in a single stage of training. This allows the entire network to be fine-tuned end-to-end for object detection, resulting in faster training and testing compared to previous methods while achieving state-of-the-art accuracy on standard datasets. Specifically, Fast R-CNN trains 9x faster than R-CNN and runs 200x faster at test time.
HML: Historical View and Trends of Deep LearningYan Xu
The document provides a historical view and trends of deep learning. It discusses that deep learning models have evolved in several waves since the 1940s, with key developments including the backpropagation algorithm in 1986 and deep belief networks with pretraining in 2006. Current trends include growing datasets, increasing numbers of neurons and connections per neuron, and higher accuracy on tasks involving vision, NLP and games. Research trends focus on generative models, domain alignment, meta-learning, using graphs as inputs, and program induction.
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
1. Autoencoders are unsupervised neural networks that are useful for dimensionality reduction and clustering. They learn an efficient coding of the input in an unsupervised manner.
2. Deep autoencoders, also known as stacked autoencoders, are autoencoders with multiple hidden layers that can learn hierarchical representations of the data. They are trained layer-by-layer to learn increasingly higher level features.
3. Variational autoencoders are a type of autoencoder that are probabilistic models, with the encoder output being the parameters of an assumed distribution such as Gaussian. They can generate new samples from the learned distribution.
This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
This document discusses object detection using the Single Shot Detector (SSD) algorithm with the MobileNet V1 architecture. It begins with an introduction to object detection and a literature review of common techniques. It then describes the basic architecture of convolutional neural networks and how they are used for feature extraction in SSD. The SSD framework uses multi-scale feature maps for detection and convolutional predictors. MobileNet V1 reduces model size and complexity through depthwise separable convolutions. This allows SSD with MobileNet V1 to perform real-time object detection with reduced parameters and computations compared to other models.
The document discusses various neural network learning rules:
1. Error correction learning rule (delta rule) adapts weights based on the error between the actual and desired output.
2. Memory-based learning stores all training examples and classifies new inputs based on similarity to nearby examples (e.g. k-nearest neighbors).
3. Hebbian learning increases weights of simultaneously active neuron connections and decreases others, allowing patterns to emerge from correlations in inputs over time.
4. Competitive learning (winner-take-all) adapts the weights of the neuron most active for a given input, allowing unsupervised clustering of similar inputs across neurons.
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHESVikash Kumar
IMAGE CLASSIFICATION USING KNN, RANDOM FOREST AND SVM ALGORITHM ON GLAUCOMA DATASETS AND EXPLAIN THE ACCURACY, SENSITIVITY, AND SPECIFICITY OF EACH AND EVERY ALGORITHMS
NSL KDD Cup 99 dataset Anomaly Detection using Machine Learning Technique Sujeet Suryawanshi
This document summarizes a presentation given on using decision trees and machine learning techniques for anomaly detection on the NSL KDD Cup 99 dataset. It discusses anomaly detection, machine learning, different machine learning algorithms like decision trees, SVM, Naive Bayes etc. and their application for intrusion detection. It then describes an experiment conducted using the decision tree algorithm on the NSL KDD Cup 99 dataset to classify network traffic as normal or anomalous. The results showed the decision tree model achieved over 98% accuracy on both the full dataset and a reduced feature set.
The document provides an overview of perceptrons and neural networks. It discusses how neural networks are modeled after the human brain and consist of interconnected artificial neurons. The key aspects covered include the McCulloch-Pitts neuron model, Rosenblatt's perceptron, different types of learning (supervised, unsupervised, reinforcement), the backpropagation algorithm, and applications of neural networks such as pattern recognition and machine translation.
After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.
Neural Networks and Deep Learning BasicsJon Lederman
This document provides an introduction to deep learning and neural networks. It discusses:
- Deep learning learns representations of data rather than relying on hand-engineered features.
- Deep learning architectures include neural networks, convolutional neural networks, and recurrent neural networks.
- Deep learning represents concepts in a nested hierarchy from simple to more abstract, with each layer learning slightly more complex representations. This allows it to learn its own feature detectors from raw data.
Automatic Skin Lesion Segmentation and Melanoma Detection: Transfer Learning ...Zabir Al Nazi Nabil
Industrial pollution resulting in ozone layer depletion has influenced
increased UV radiation in recent years which is a major environmental risk factor for invasive skin cancer Melanoma and other keratinocyte cancers. The incidence of deaths from Melanoma has risen worldwide in past two decades.
Deep learning has been employed successfully for dermatologic diagnosis. In
this work, we present a deep learning based scheme to automatically segment
skin lesions and detect melanoma from dermoscopy images. U-Net was used
for segmenting out the lesion from surrounding skin. The limitation of utilizing
deep neural networks with limited medical data was solved with data augmentation and transfer learning. In our experiments, U-Net was used with spatial
dropout to solve the problem of overfitting and different augmentation effects
were applied on the training images to increase data samples. The model was
evaluated on two different datasets. It achieved a mean dice score of 0.87 and a
mean jaccard index of 0.80 on ISIC 2018 dataset. The trained model was assessed on PH² dataset where it achieved a mean dice score of 0.93 and a mean
jaccard index of 0.87 with transfer learning. For classification of malignant
melanoma, a DCNN-SVM model was used where we compared state of the art
deep nets as feature extractors to find the applicability of transfer learning in
dermatologic diagnosis domain. Our best model achieved a mean accuracy of
92% on PH² dataset. The findings of this study is expected to be useful in cancer diagnosis research.
Published at IJCCI 2018. Source code available at https://github.com/zabir-nabil/lesion-segmentation-melanoma-tl
This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.
The document discusses image restoration techniques. It describes how images can become degraded through phenomena like motion, improper camera focusing, and noise. The goal of image restoration is to recover the original high quality image from its degraded version using knowledge about the degradation process and types of noise. Common noise models include Gaussian, Rayleigh, Erlang, exponential, and impulse noise. Filtering techniques like mean, order statistics, and adaptive filters can be used for restoration by smoothing the image while preserving edges. The adaptive filters change based on local image statistics to better reduce noise with less blurring than regular filters.
The document discusses gradient descent methods for unconstrained convex optimization problems. It introduces gradient descent as an iterative method to find the minimum of a differentiable function by taking steps proportional to the negative gradient. It describes the basic gradient descent update rule and discusses convergence conditions such as Lipschitz continuity, strong convexity, and condition number. It also covers techniques like exact line search, backtracking line search, coordinate descent, and steepest descent methods.
Deep learning uses neural networks, which are systems inspired by the human brain. Neural networks learn patterns from large amounts of data through forward and backpropagation. They are constructed of layers including an input layer, hidden layers, and an output layer. Deep learning can learn very complex patterns and has various applications including image classification, machine translation, and more. Recurrent neural networks are useful for sequential data like text and audio. Convolutional neural networks are widely used in computer vision tasks.
Convolutional neural networks (CNNs) learn multi-level features and perform classification jointly and better than traditional approaches for image classification and segmentation problems. CNNs have four main components: convolution, nonlinearity, pooling, and fully connected layers. Convolution extracts features from the input image using filters. Nonlinearity introduces nonlinearity. Pooling reduces dimensionality while retaining important information. The fully connected layer uses high-level features for classification. CNNs are trained end-to-end using backpropagation to minimize output errors by updating weights.
This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.
Fast R-CNN is a method that improves object detection speed and accuracy over previous methods like R-CNN and SPPnet. It uses a region of interest pooling layer and multi-task loss to jointly train a convolutional neural network for classification and bounding box regression in a single stage of training. This allows the entire network to be fine-tuned end-to-end for object detection, resulting in faster training and testing compared to previous methods while achieving state-of-the-art accuracy on standard datasets. Specifically, Fast R-CNN trains 9x faster than R-CNN and runs 200x faster at test time.
HML: Historical View and Trends of Deep LearningYan Xu
The document provides a historical view and trends of deep learning. It discusses that deep learning models have evolved in several waves since the 1940s, with key developments including the backpropagation algorithm in 1986 and deep belief networks with pretraining in 2006. Current trends include growing datasets, increasing numbers of neurons and connections per neuron, and higher accuracy on tasks involving vision, NLP and games. Research trends focus on generative models, domain alignment, meta-learning, using graphs as inputs, and program induction.
You Only Look Once: Unified, Real-Time Object DetectionDADAJONJURAKUZIEV
YOLO, a new approach to object detection. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation.
1. Autoencoders are unsupervised neural networks that are useful for dimensionality reduction and clustering. They learn an efficient coding of the input in an unsupervised manner.
2. Deep autoencoders, also known as stacked autoencoders, are autoencoders with multiple hidden layers that can learn hierarchical representations of the data. They are trained layer-by-layer to learn increasingly higher level features.
3. Variational autoencoders are a type of autoencoder that are probabilistic models, with the encoder output being the parameters of an assumed distribution such as Gaussian. They can generate new samples from the learned distribution.
This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.
This document discusses object detection using the Single Shot Detector (SSD) algorithm with the MobileNet V1 architecture. It begins with an introduction to object detection and a literature review of common techniques. It then describes the basic architecture of convolutional neural networks and how they are used for feature extraction in SSD. The SSD framework uses multi-scale feature maps for detection and convolutional predictors. MobileNet V1 reduces model size and complexity through depthwise separable convolutions. This allows SSD with MobileNet V1 to perform real-time object detection with reduced parameters and computations compared to other models.
The document discusses various neural network learning rules:
1. Error correction learning rule (delta rule) adapts weights based on the error between the actual and desired output.
2. Memory-based learning stores all training examples and classifies new inputs based on similarity to nearby examples (e.g. k-nearest neighbors).
3. Hebbian learning increases weights of simultaneously active neuron connections and decreases others, allowing patterns to emerge from correlations in inputs over time.
4. Competitive learning (winner-take-all) adapts the weights of the neuron most active for a given input, allowing unsupervised clustering of similar inputs across neurons.
IMAGE CLASSIFICATION USING DIFFERENT CLASSICAL APPROACHESVikash Kumar
IMAGE CLASSIFICATION USING KNN, RANDOM FOREST AND SVM ALGORITHM ON GLAUCOMA DATASETS AND EXPLAIN THE ACCURACY, SENSITIVITY, AND SPECIFICITY OF EACH AND EVERY ALGORITHMS
NSL KDD Cup 99 dataset Anomaly Detection using Machine Learning Technique Sujeet Suryawanshi
This document summarizes a presentation given on using decision trees and machine learning techniques for anomaly detection on the NSL KDD Cup 99 dataset. It discusses anomaly detection, machine learning, different machine learning algorithms like decision trees, SVM, Naive Bayes etc. and their application for intrusion detection. It then describes an experiment conducted using the decision tree algorithm on the NSL KDD Cup 99 dataset to classify network traffic as normal or anomalous. The results showed the decision tree model achieved over 98% accuracy on both the full dataset and a reduced feature set.
Types of Machine Learnig Algorithms(CART, ID3)Fatimakhan325
The document summarizes several machine learning algorithms used for data mining:
- Decision trees use nodes and edges to iteratively divide data into groups for classification or prediction.
- Naive Bayes classifiers use Bayes' theorem for text classification, spam filtering, and sentiment analysis due to their multi-class prediction abilities.
- K-nearest neighbors algorithms find the closest K data points to make predictions for classification or regression problems.
- ID3, CART, and k-means clustering are also summarized highlighting their uses, advantages, and disadvantages.
This document provides an overview of deep learning techniques including neural networks, convolutional neural networks (CNNs), and long short-term memory (LSTM) algorithms. It defines key concepts like Bayesian inference, heuristics, perceptrons, and backpropagation. It also describes how to configure neural networks by specifying hyperparameters, hidden layers, normalization methods, and training parameters. CNN architectures are explained including convolution, pooling, and applications in computer vision tasks. Finally, predictive maintenance using deep learning to predict equipment failures from sensor data is briefly discussed.
Artificial neural networks (ANN) consider classification as one of the most dynamic research and
application areas. ANN is the branch of Artificial Intelligence (AI). The neural network was trained by
back propagation algorithm. The different combinations of functions and its effect while using ANN as a
classifier is studied and the correctness of these functions are analyzed for various kinds of datasets. The
back propagation neural network (BPNN) can be used as a highly successful tool for dataset classification
with suitable combination of training, learning and transfer functions. When the maximum likelihood
method was compared with backpropagation neural network method, the BPNN was more accurate than
maximum likelihood method. A high predictive ability with stable and well functioning BPNN is possible.
Multilayer feed-forward neural network algorithm is also used for classification. However BPNN proves to
be more effective than other classification algorithms.
The document discusses several machine learning algorithms: artificial neural networks, naive Bayes classification, and decision trees. It provides examples of applying these algorithms to classify banking customers and compare their performance. Neural networks had the highest accuracy at 88.92% but the longest processing time of 8.01 seconds. Naive Bayes had the shortest processing time of 0.02 seconds but the lowest accuracy at 86.88%. Decision trees achieved 88.98% accuracy with a processing time of 0.04 seconds. The document also provides real-world examples of applying neural networks to tasks like ECG analysis, credit risk management, and environmental modeling.
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.
Deep vs diverse architectures for classification problemsColleen Farrelly
Deep learning study, comparing deep learning methods with wide learning methods; applications include simulation data and real industry problems. Pre-print of paper found here: https://arxiv.org/ftp/arxiv/papers/1708/1708.06347.pdf
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Digital image classification involves sorting pixels into discrete classes based on their spectral values. It can be performed using supervised or unsupervised approaches. Supervised classification involves using training data to define classes, while unsupervised classification uses algorithms to automatically group similar pixels. Accuracy assessment involves comparing the classification to reference data to determine accuracy through an error matrix.
Classification of Breast Cancer Diseases using Data Mining Techniquesinventionjournals
Medical data mining has great deal for exploring new knowledge from large amount of data. Classification is one of the important data mining techniques for classification of data. In this research work, we have used various data mining based classification techniques for classification of cancer diseases patient or not. We applied the Breast Cancer-Wisconsin (Original) data set into different data mining techniques and compared the accuracy of models with two different data partitions. BayesNet achieved highest accuracy as 97.13% in case of 10-fold data partitions. We have also applied the info gain feature selection technique on BayesNet and Support Vector Machine (SVM) and achieved best accuracy 97.28% accuracy with BayesNet in case of 6 feature subset.
An Efficient PSO Based Ensemble Classification Model on High Dimensional Data...ijsc
This summary provides the high-level information from the document in 3 sentences:
The document proposes a Particle Swarm Optimization (PSO) based ensemble classification model to improve classification of high-dimensional biomedical datasets. It develops an optimized PSO technique to select optimal features and initialize weights for base classifiers in the ensemble model. Experimental results on microarray datasets show the proposed model achieves higher accuracy, true positive rate, and lower error rate compared to traditional feature selection based classification models.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Survey on classification algorithms for data mining (comparison and evaluation)Alexander Decker
This document provides an overview and comparison of three classification algorithms: K-Nearest Neighbors (KNN), Decision Trees, and Bayesian Networks. It discusses each algorithm, including how KNN classifies data based on its k nearest neighbors. Decision Trees classify data based on a tree structure of decisions, and Bayesian Networks classify data based on probabilities of relationships between variables. The document conducts an analysis of these three algorithms to determine which has the best performance and lowest time complexity for classification tasks based on evaluating a mock dataset over 24 months.
Data Science - Part IX - Support Vector MachineDerek Kane
This lecture provides an overview of Support Vector Machines in a more relatable and accessible manner. We will go through some methods of calibration and diagnostics of SVM and then apply the technique to accurately detect breast cancer within a dataset.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Feature Subset Selection for High Dimensional Data using Clustering TechniquesIRJET Journal
The document discusses feature subset selection for high dimensional data using clustering techniques. It proposes a FAST algorithm that has three steps: (1) removing irrelevant features, (2) dividing features into clusters, (3) selecting the most representative feature from each cluster. The FAST algorithm uses DBSCAN, a density-based clustering algorithm, to cluster the features. DBSCAN can identify clusters of arbitrary shape and detect noise, making it suitable for high dimensional data. The goal of feature subset selection is to find a small number of discriminative features that best represent the data.
This document discusses artificial neural networks (ANNs) and two neural network algorithms in IBM SPSS: Multilayer Perception (MLP) and Radial Basis Function (RBF). ANNs attempt to develop predictive models by learning correlations between factors and target fields. MLP and RBF are supervised learning techniques that map relationships in data to predict categorical or continuous outcomes. The document explains how neural networks work and provides guidance on choosing an algorithm based on data type and complexity.
1. The document presents a hybrid SVM-KNN classification method for classifying MRI brain images to detect tumors. It combines support vector machines (SVM) and K-nearest neighbors (KNN) to leverage the strengths of both algorithms.
2. The algorithm first uses KNN to classify an MRI image based on its similarity to labeled training images. If KNN is uncertain or confused, it then uses SVM to classify the image by finding the optimal separating hyperplane between tumor and non-tumor classes.
3. The authors implemented this hybrid SVM-KNN algorithm in MATLAB and were able to successfully classify test MRI images as depicting a normal brain or an abnormal brain with a tumor.
Större behov för att effektivt rekrytera
företag och arbetssökande.
Vad menar jag med denna titel? Rekrytera företag hörs ju inte speciellt logiskt ut när vi läser dagligen att företag stänger dörrarna och arbetstagare måste lämna arbetsplatsen, antigen temporärt eller permanent. Det är tyvärr i dessa tider också företag och offentlig verksamhet som sliter med att få den hjälp som dem behöver. Vi har en utmaning, att vi inte klarar matcha arbetslösa med arbetsbehov i nuläget och nära framtid.
Det är ett skriande behov av arbetskraft i kommunerna och regionerna i Sverige i dagsläget.
Behov finns redan
Det finns redan och kommer att finns behov för många tillfälliga jobb i kommuner och regioner när den ordinarie personalen behöver avlastning eller blir sjukskriven. Vi har inte nått kulmen i Corona pandemin, så situationen väntas bli värre.
Kommer coronavirus att störa din jobbsökning?
Arbetssökning är stressande även under de bästa omständigheterna. Den pågående osäkerheten relaterad till coronavirus-pandemin kan göra att utsikterna för att leta efter arbete känns nästan hopplösa, men jobbexperter säger att du borde ge upp.
Ge barnen i Ukraina och Sverige livslånga minnen genom att dela erfarenheter inom kultur och hockey med varandra genom att möts inom hockey.
Genom spelet och vänskap lär barn sig livslektioner som kommer att fortsätta i alla utmaningar nu och senare i livet.
Fritid en essentiell del av flerårsplan 2019-2021 till Falköping kommun. Samverkan och hållbarhet är ord som återkommer rätt så frekventa i denna plan och verkar ha kopplingar till alla verksamhetsområden och planer som dessa har. Alla prioriterade målområden inom flerårsplanen har element som Fritid kan agera inom och ta sin del av utvecklingen och uppfyllande av dessa målbilden.
Trendanalys för att se hur stor intresse det är för Hockeylag inom SHL. En startpunkt för vidare analys som kan vara med att förstärka klubbarnas profil i marknaden och därvid samarbetspartners varumärke. Den sociala profil till klubbarna börjar bli mer och mer viktigt för lagen. Samhällsansvar och deltagande i samhället inom olika områden reflekteras igenom hur klubbarnas namn kändes igen i marknaden och hos folk flest. Det finns många möjligheter, och i denna presentationen har vi gjord ett enkelt försök för att visa att klubbarna är exponerat olika i marknaden under 2017.
En kundresa i det svenska informationssamhället i 2017 baserad på ISS rapporten som heter "Svenskarna och internet 2017". Arbetslöshet och utbildningsnivå spelar en roll i hur många som använder internet, och hur dem använder detta. Det blir allt mer viktig att förstå människors beteende på nätet, speciellt för dem som vill digitalisera sina tjänster och produkt och erbjuda dessa till det svenska samhällets invånare. Allt fler är uppkopplade till internet hemma eller på mobila enheter. Allt fler känner att dem inte tar del i utvecklingen, och det är utbildningsnivå och status i samhället som i stor grad är faktorer till skillnad i använding och hur internet används. Inte minst är det viktigt för hur människor kan påverkas av information igenom att dem har olika trösklar för källkritik.
Vi har kunder med stark digital definition makt, som utmanar föråldrade affärsmodeller och ledningsmetoder. Behov för beteendekunskap och övertygande teknologi och metoder har blivit större en någon gång. Frågan är, vart börjar du, och är det redan för sent? Vad behövs för att lyckas i den nya generation med kunder som redan är här, och som kommer dem nästa 5-15 åren?
Mining industry has developed through various cyvles dependant upon industry conditions. During tougher economic periods the industry has tendency looking for ways to make mining more efficiient and less risk associated with its activities. Exploration activity has always been a long term activity with a timespand oc decades instead of years or even months which the normal operations operate under. This require this part of mining industry to become more innovative and create more accurate prognosis for its discoveries.
Rätt kombination av konstig intelligens på datorn och rätta metoder och teknik kan ge oss en bättre chans att matcha dem som står långt i från arbetsmarknaden. Det är en utmanande uppgift som vi har redan i dag, och som blir större för varje dag som går. Datorn blir smartare igenom programvara och kapacitet, och på samma tid har vi algoritmer som inte fanns bara för 5-10 år sedan som kan vara till hjälp. Företag behöver arbetskraft och tudelningen av arbetsmarknaden har aldrig varit större en i dag, och något måste görs. Jag föreslår en programvara som jag kallar Matchning Arbetssökande Långt Från Arbetsmarknaden - MALFA. Et program som använder tekniker och metoder i samverkan med datorns kraft och algoritm hantering, och som ger oss möjlighet att se vilka möjligheter personer som till exempel har funktionshinder kan hitta en arbetsgivare som vill ha god nytta av denna resursen.
Reaching new frontiers within seismic interpretation and analysis by utilizing deep learning computer vision technology.
VAP4U has worked for a long period how to utilize this technology on normal standard seismic data (SEG/Y or other vendor formats) and propose how to use it through the software s AI s.
Convolutional Neural Networks (CNN) and other machine learning algorithms and methods enables us to make the computer to identify and learn to identify more features in the seismic data than previously done.
Training the computer with the help of some initial input which is done once. Then let the algorithms within limitations and boundaries set by the experts do its work identifying features of interest within the data. Looking for playmodels, lead types or prospects, then let the computer take the grunt of the workload and spend time on more important analysis such as risk and ranking of plays.
Searching the all-time growing amount of global data and research results and retrieving only the relevant and up-to date information becomes more and more challenging. The amount of data including the big data issue in the IoT world makes it even more challenging. How can an employee keeping himself up to date and include the relevant information into his work and ensure his work includes the most relevant and latest information. Most search engines today provide some sort of semantic based answers to the queries you enter into the system. However, most search engines do not know you well enough to provide you with the best answers based on who you are, and what you really want for an answer. Here is today's challenge combined with the growing amount of data and media you find it in. The answer might be closer than you think.
En typ av arbetssökande är den vi kallar för den passiva arbetssökande. Nu är frågan - vem kan tillåta sig att vara passiv, sökaren eller företaget? Svaret är kanske bara sökaren, medan företaget inte kan tillåta sig att vara passiv i rekryteringsarbetet, eller sökandet efter resurser till arbetsplatsen sin.
Vem är den passiva arbetssökande och hur agerar den i dagens och framtidens arbetsmarknad? Denna frågan är kanske den viktigaste och vi försöker att ge våran kommentar i detta bildspelet.
Matchning i arbetsmarknaden har allt blivit hårdare sedan det är svårt för företag att hitta personer med rätt kompetens och större andel av arbetssökare står långt från arbetsmarknaden av olika orsaker. Användning av teknologi som stöd i matchningen har varit gjord dem senaste 10 åren av olika aktörer med olika resultat och bredd. I detta bildspelet försöker jag göra redo för olika algoritmer och metoder som om dem blir integrerad på rätt sätt och använd på rätt sätt, ge båda den arbetssökande och inte minst företagaren mycket större chans att matcha behöv för båda med större grad av succés en man gör i dag.
Det visar sig att arbetsmarknaden sliter med att få parter till att hitta varandra, eller som man säger - matcha med varandra.
Företagen söker personal, medan arbetssökaren söker jobb, men trots detta, så hittar dem inte varandra. Det är många orsaker till att vi har denna situationen, som bland annat saknar rätt kompetens till rätt tid och plats. Andra faktorer kan vara att jobbsökaren inte hittar rätt jobb sedan den inte är lätt att hitta helt enkelt. En platsannons i dag är kanske inte god nog, eller förklarar egentligen inte vad företaget behöver, och jobbsökaren är kanske inte bra nog på att berätta vad den kan eller vill. Jobbsökandet kommer att bli ett av dem viktigaste sökmotorer som vi behöver i framtiden, som redan är här nu!
Rätt jobb vid rätt tidpunkt - så kallad matchning av arbetskraft och behov inom arbetsmarknaden. En utmaning som alltid har varit svår att komma över. Men, nu börjar utvecklingen av verktyg och metoder ta fart och Google har kommit med sin ATS lösning som i form av Google Jobs API ser ut till att vara en lovande teknologi till användning inom matchning av arbetssökande och företagen.
Arbetsförmedling på den svenska marknaden har utmaningar som kanske är unika i världssammanhang.
Stor andel av arbetskraft står långt från arbetsmarknaden, sociala systemet i sverige är unikt och skapar en marknadsfaktor som inte många andra land har inom arbetsmarknaden. Försök på att jämnföra sverige med andra lands arbetsmarknad kan vara mycket svårt och många gånger leda till fel slutsats av vad som behövs eller inte, och vem som borde göra vad.
Frågan som borde ställs är hellre, vad måste görs för att få till en bättre matchning och därvid lägre arbetslöshet speciellt bland dem som är lågutbildade och har ett funktionshinder?
Teknologi har många gånger varit svaret och det industriella samhället fick stor påverkan i arbetsmarknaden, och introduktionen av informations åldern har lett till nya ändringar i arbetsmarknaden som ger oss nya utmaningar på flera fronter.
Införande av konstig intelligens på datorn och användning av kognitiv teknologi blandad med avancerad matematik på stadigt kraftigare datorer ger oss möjligheter som aldrig förut.
Det är nu på tiden att se hur vi kan använda verktygen och metoder som vi har i dag, och som vi inte ens hade för 10 år sen, till att skapa ett paradigm byte inom rekrytering och förmedling av arbete.
Grundvatten av god kvalitet är viktigt för många i regionen, småhushåll, industri, jordbruk, gårdsbruk och liknande.
I och med införandet av nationella miljökvalitetsmål har grundvattnets roll i samhällsplaneringen lyfts fram.
Det finns ett miljökvalitetetsmål med ett fokus på att skydda grundvattnet, dels som en del i vattnets kretslopp, dels som resurs inom den nutida och kommande vattenförsörjningen.
Västra Götalands län har omväxlande geologiska förhållanden. Grundvattentillgångar i jord av betydelse återfinns oftast i större grusavlagringar. Inom stora delar av länet är grusavlagringar sparsamt förekommande. Det är därför väsentligt att dessa avlagringar sparas och skyddas för att kunna användas för grundvattenuttag. I berggrunden finns större grundvattentillgångar endast i den sedimentära berggrunden i delar av centrala Västergötland.
En rad myndigheter och andra organisationer kommer att vara viktiga aktörer. Det är viktigt att också allmänheten och näringslivet tar en aktiv del i arbetet.
Det är nödvändigt att tidigt i arbetet med miljökvalitetsmålen hitta bra arbetsformer, speciellt i ett stort län som Västra Götaland, vilket består av 49 kommuner. För att veta vilka åtgärder som skall sättas in, måste viss grundläggande kunskap sammanställas, främst från kommunerna.
Matcha jobbsökaren till rätta jobbet är alfa och omega för en fungerande arbetsmarknad.
Attrahera personer till att arbeta hos företagen och optimera hur dem kommer i kontakt med personer som kan vara av intresse för dem.
Skapa nya möjligheter för att behålla talanger inom företaget.
Den sökande måste alltid vara i fokus och ses på som en konsument på lik linje med internet användare på en bank, resebyrå eller en som vill hyra en bil eller stuga.
Företag startar sin affär med jobb sök så rekryteringsverktyget måste vara effektivt för företaget som söker talanger.
Vi måste skapa en bättra jobb sök upplevelse både för företag och jobb sökare.
Anställningen är den viktigaste uppgift som företaget gör!
Ett problem som rekryterare och jobb sökare är alltför bekanta med:
Dåligt skrivna jobb beskrivningar som gör lite för att rikta rätt talang till rätt öppna positioner.
För att göra det ännu värre är många resymé är på samma sätt icke informativa, vilket gör det lika utmanande för arbetsgivarna att hitta rätt kandidater genom enkla sökord.
Google gör någonting med denna dubbelriktade röra.
2. A probabilistic neural network (PNN) is a feedforward neural network, which
was derived from the Bayesian network and a statistical algorithm called Kernel
Fisher discriminant analysis. It was introduced by D.F. Specht in the early 1990s.
In a PNN, the operations are organized into a multilayered feedforward network
with four layers:
• Input layer
• Hidden layer
• Pattern layer/Summation layer
• Output layer
3. PNN is often used in classification challenges. When an input is present, the first
layer computes the distance from the input vector to the training input vectors.
This produces a vector where its elements indicate how close the input is to the
training input. The second layer sums the contribution for each class of inputs and
produces its net output as a vector of probabilities. Finally, a compete transfer
function on the output of the second layer picks the maximum of these
probabilities, and produces a 1 (positive identification) for that class and a 0
(negative identification) for non-targeted classes.
4. Input layer
Each neuron in the input layer represents a predictor variable. In categorical variables, N-1
neurons are used when there are N number of categories. It standardizes the range of the values
by subtracting the median and dividing by the interquartile range. Then the input neurons feed the
values to each of the neurons in the hidden layer.
Pattern layer
This layer contains one neuron for each case in the training data set. It stores the values of the
predictor variables for the case along with the target value. A hidden neuron computes the
Euclidean distance of the test case from the neuron’s center point and then applies the RBF kernel
function using the sigma values.
Summation layer
For PNN networks there is one pattern neuron for each category of the target variable. The actual
target category of each training case is stored with each hidden neuron; the weighted value
coming out of a hidden neuron is fed only to the pattern neuron that corresponds to the hidden
neuron’s category. The pattern neurons add the values for the class they represent.
Output layer
The output layer compares the weighted votes for each target category accumulated in the pattern
layer and uses the largest vote to predict the target category.
5. Advantages
There are several advantages and disadvantages using PNN instead of multilayer
perceptron.
PNNs are much faster than multilayer perceptron networks.
PNNs can be more accurate than multilayer perceptron networks.
PNN networks are relatively insensitive to outliers.
PNN networks generate accurate predicted target probability scores.
PNNs approach Bayes optimal classification.
Disadvantages
PNN are slower than multilayer perceptron networks at classifying new cases.
PNN require more memory space to store the model.
6. It is stated that the model
based seismic inversion
has a robust mathematical
platform.
Neural network analysis is
perceived to operates as a
kind of “black-box”.
Is this correct?
7. Inversion technique, which is the most common method to provide acoustic and physical attribute
approximation within seismic data is highly dependent on the selected initial model because of the
inherent non-uniqueness then the solution obtained is one of the many possible solutions which may be
equally valid. There is no reason why a particular solution will have more preference over any other
solution.
For the case at hand, the neural network approach yields a solution that is geologically more meaningful,
because the procedure utilizes the available well log information to estimate the target parameters.
For those areas where the available well-log control is uniformly distributed, the neural network approach
could yield more meaningful impedance estimates that correlate well with the impedance logs. This lends
confidence to the seismic interpreters to believe the impedance estimates away from the control points.
8. Machine learning algorithms use computational methods to “learn”
information directly from data without assuming a predetermined
equation as a model. They can adaptively improve their
performance as you increase the number of samples available for
learning.
Classification, regression, and clustering and build predictive models to discover useful patterns from
observed data.
Use of machine learning tools to detect patterns and build predictive models from data sets.
Machine learning algorithms are used in applications such as:
• computational finance (credit scoring and algorithmic trading),
• image processing and computer vision (face recognition, object detection, object recognition),
• computational biology (tumor detection, drug discovery, and DNA sequencing),
• energy production (price and load forecasting),
• natural language processing, speech and image recognition, and
• advertising and recommendation systems.
Machine learning is an integral part of data analytics, which deals with
developing data-driven insights for better designs and decisions.
9. Build models to train computers ability to classify data
into different categories. This can help perform more
accurately analyze and visualize data.
Classification can be used within area such as image
processing amongst many others.
Common algorithms for performing classification
include support vector machine (SVM), boosted and
bagged decision trees, k-nearest neighbor, Naïve
Bayes, discriminant analysis, logistic regression, and
deep learning and neural networks.
Identify patterns, outliers in the data and determine its
inter-dependencies and geolocations.
TRAIN YOUR DATA with models.
10. Find natural groupings and patterns in data. Clustering
is used on unlabeled data to find natural groupings and
patterns. Applications of clustering include
pattern mining,
medical imaging, and
object recognition.
Common algorithms for performing clustering include
k-means and k-medoids, hierarchical clustering,
Gaussian mixture models, hidden Markov models,
self-organizing maps, fuzzy c-means clustering, and
subtractive clustering.
TRAIN YOUR DATA with models.
11.
12. Organization is required to faster, more efficient and with higher rate of success to
discover, optimize and deploy predictive models by analyzing multiple data sources.
This is required to improve business outcome.
There is a buzz about Big Data, but the requirement for a solution to make senses of
the Big Data, namely the ANALYTICS and INTEGRATION within business
applications, DEPLOYMENT, have been missed.
80% of time spent on preparing data, 20% of time is spent complaining about the
need to prepare the data.
13.
14. • Amount of data associated with many variables (predictors)
• Present day equations not suitable for the Complexity in the data, requires
iteration of algorithms, which can be time-consuming
• Deep Learning, machine learning or Neural Network requires significant technical
expertise
• There is no one fit all solutions, which requires an iterative process approach
• What approach to take, clustering, classification, regression, neural network or
curve fitting algorithms.
• How to best integrate machine learning with data and deployment within Data
Analytics workflows.
15.
16. PREPARATION
• Generate attributes from a 3D seismic volume
• Use wells to compute Acoustic Impedance logs
• Elastic Inversion of 3D Seismic volume
Wells and Seismic are both used as training of impedance logs
and traces
ANALYSIS
Established that inversion alone does not provide enough
temporal resolution to delineate reservoir quality variations.
Use of multi-attributes combined with inversion data in a
Probabilistic Neural Network to provide a high resolution
impedance 3D dataset.
17. PROCEDURE
1. Internal attributes taken from seismic data
2. External attributes from impedance data.
3. The attributes are normalized so input and output traces are
aligned.
4. Establish an operator which can predict log properties from
a seismic attribute.
5. Stepwise regression procedure is used to establish best
correlation between internal (inversion data) and external
attributes.
• Internal attributes could be, but not limited to:
• Integrate
• Quadrature Trace
• Raw Seismic
• Filtered data
• Second derivative
• Instantaneous phased
• Cosine Instantaneous phase
6. Establish training error and validation errors in the neural
network study and select the best one for use in qualifying
reservoir qualities/ variations.
(M.S.Rawat, Viswaja Devalla, T.K.Mathuria and U.S.D.Pandey, 2013)
18. Multi attributes, used as PNN inputs, must be interpreted within a geologic framework, not a statistical
one. Geologically plausible and physically realistic results are necessary to be confidently used for
exploration and exploitation purposes.
Appropriate input seismic attributes are selected via forward step-wise regression as too many input
seismic attributes yields spurious and noisy results (Kalkomey, 1997).
The seismic attributes must be cross validated to avoid overtraining.
Cross validation systematically removes each well used in the training process from the training set.
The multi attribute transform is recalculated with the well absent, or hidden, from the training process.
The average predictive error of all the hidden wells is referred to as the validation error. The validation
error is the error associated with applying the PNN to the entire seismic data volume.
19. 1. Additional seismic attributes are considered valuable as long as the
validation error, the thin line in figure here, is minimized. In this case,
the optimum number of seismic attributes for PNN application is five.
2. Each attribute is further evaluated for its significance within the
established geologic framework
3. The data is trained over intervals of interest.
4. Quantitatively, the PNN does not sufficiently predict the original
Effective porosity values
5. Qualitatively, the PNN predicts the Effective Porosity curve frequency
differences between various facies of interest.
6. The PNN is applied to the 3D seismic data volume yielding a
Effective Porosity 3D volume.
7. Values corresponding to various facies given reason to create facies
trends which has reliability in a temporal domain.
20. 1. First, single-attribute regression was performed to the data. Out of all the attributes,
inverse of Vp/Vs ratio gave highest correlation with hydrocarbon Volume with a
coefficient of 0.64.
2. Then a combination of multi-attribute regression and probabilistic Neural Network
(PNN) method was used to derive a suitable relationship for predicting Hydrocarbon
Volume
3. A multi-attribute stepwise linear regression analysis was performed using Gas
Volume log at fifteen well locations.
4. Validation correlation is computed by excluding one well at a time from the training
data set, calculating correlation at that well and making average of the correlations
after repeating the procedure for all the wells.
5. Validation error was calculated against the number of attributes for the different
operator lengths. The plot illustrates that a thirteen-point operator gave the minimum
validation error with 12 attributes. The attributes were 1/(Vp/Vs), 1/(P-impedance),
Integrated absolute amplitude, Amplitude Envelope (P-impedance), Instantaneous
Phase, Integrate (Vp/Vs), Average Frequency (Vp/Vs), Apparent Polarity (Vp/Vs),
Instantaneous Frequency (P-impedance), Integrate (P-impedance), Cosine
Instantaneous Phase and Amplitude Envelope (Vp/Vs). The network derived from the
multi-attribute linear regression gave an average correlation of 0.80.
Profile showing HC volume along with
actual log at one well location after PNN.
Plot of validation error vs. attributes for
different operator length
(Amit K. Ray1 and Samir Biswal1, 2012)
21. Valioso Ltd
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