The document discusses using machine learning models to predict Alzheimer's disease based on Raman spectroscopy data. It finds that neural networks and support vector machines can accurately classify samples as healthy, mild Alzheimer's, or moderate Alzheimer's based on serum and cerebrospinal fluid data, with sensitivities and specificities often over 90%. Feature selection using genetic algorithms helped improve the models' performance. The results support the potential of using Raman spectroscopy as a non-invasive diagnostic tool for Alzheimer's disease.
This document describes a dissertation that aims to improve 3D stereo reconstruction of human faces by combining it with a generic morphable face model. The dissertation first discusses background topics like facial landmark annotation, 3D morphable face models, texture representation, stereo reconstruction and face model deformation. It then describes the proposed scheme which involves steps like landmark annotation, pose estimation, shape fitting, texture extraction, stereo reconstruction from image pairs and deformation of the face model. The results show that fusing the stereo reconstruction with a single image reconstruction using a morphable model leads to a more accurate 3D face model compared to using either method alone. Finally, the deformed face model is visualized on a smartphone using a cardboard viewer.
The document discusses support vector machines (SVMs) for classification. It begins by defining classifiers and the difference between classification and clustering. It then introduces SVMs, explaining that they find optimal decision boundaries that separate classes through mapping data points into higher dimensional space. The document outlines linear and non-linear SVMs, describing how non-linear SVMs can find more complex separating structures through kernels. It also discusses supervised learning with SVMs and how to solve the optimization problem to train linear SVMs, including soft-margin classification to handle non-separable data.
The document summarizes radial basis function (RBF) networks. Key points:
- RBF networks use radial basis functions as activation functions and can universally approximate continuous functions.
- They are local approximators compared to multilayer perceptrons which are global approximators.
- Learning involves determining the centers, widths, and weights. Centers can be randomly selected or via clustering. Widths are usually different for each basis function. Weights are typically learned via least squares or gradient descent methods.
The document reviews concepts related to NP-completeness, including reductions between problems. It provides examples of reducing the directed Hamiltonian cycle problem to the undirected version. It also reduces 3-SAT to the clique problem by transforming a Boolean formula to a graph, then further reduces clique to vertex cover. Hundreds of problems have been shown to be NP-complete through relatively simple reductions like these that leverage previous results.
The document discusses Radial Basis Function (RBF) networks. It describes the architecture of an RBF network which has three layers - an input layer, a hidden layer of radial basis functions, and a linear output layer. It also discusses types of radial basis functions like Gaussian, training algorithms for determining hidden unit centers and radii, and provides an example of how an RBF network can learn the XOR problem.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
This document describes a dissertation that aims to improve 3D stereo reconstruction of human faces by combining it with a generic morphable face model. The dissertation first discusses background topics like facial landmark annotation, 3D morphable face models, texture representation, stereo reconstruction and face model deformation. It then describes the proposed scheme which involves steps like landmark annotation, pose estimation, shape fitting, texture extraction, stereo reconstruction from image pairs and deformation of the face model. The results show that fusing the stereo reconstruction with a single image reconstruction using a morphable model leads to a more accurate 3D face model compared to using either method alone. Finally, the deformed face model is visualized on a smartphone using a cardboard viewer.
The document discusses support vector machines (SVMs) for classification. It begins by defining classifiers and the difference between classification and clustering. It then introduces SVMs, explaining that they find optimal decision boundaries that separate classes through mapping data points into higher dimensional space. The document outlines linear and non-linear SVMs, describing how non-linear SVMs can find more complex separating structures through kernels. It also discusses supervised learning with SVMs and how to solve the optimization problem to train linear SVMs, including soft-margin classification to handle non-separable data.
The document summarizes radial basis function (RBF) networks. Key points:
- RBF networks use radial basis functions as activation functions and can universally approximate continuous functions.
- They are local approximators compared to multilayer perceptrons which are global approximators.
- Learning involves determining the centers, widths, and weights. Centers can be randomly selected or via clustering. Widths are usually different for each basis function. Weights are typically learned via least squares or gradient descent methods.
The document reviews concepts related to NP-completeness, including reductions between problems. It provides examples of reducing the directed Hamiltonian cycle problem to the undirected version. It also reduces 3-SAT to the clique problem by transforming a Boolean formula to a graph, then further reduces clique to vertex cover. Hundreds of problems have been shown to be NP-complete through relatively simple reductions like these that leverage previous results.
The document discusses Radial Basis Function (RBF) networks. It describes the architecture of an RBF network which has three layers - an input layer, a hidden layer of radial basis functions, and a linear output layer. It also discusses types of radial basis functions like Gaussian, training algorithms for determining hidden unit centers and radii, and provides an example of how an RBF network can learn the XOR problem.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document summarizes research on using data mining techniques to predict heart disease. It discusses previous work using classification, clustering, association rule mining and other techniques on several heart disease datasets. Classification algorithms like naive bayes, decision trees and neural networks have been widely used with naive bayes found to often provide the best performance. Feature selection and attribute reduction are also examined. The document provides an overview of the key steps and techniques in medical data mining and predictive analysis for heart disease.
HEART DISEASE PREDICTION USING NAIVE BAYES ALGORITHMamiteshg
This document describes using a Naive Bayes classifier to predict the likelihood of heart disease. It discusses how a web-based application would take in a user's medical information and use a trained dataset to compare and retrieve hidden data to diagnose heart disease. The document provides an example of using Bayes' theorem to calculate the probability of breast cancer based on a positive mammogram. It explains the implementation of the Naive Bayes classifier and concludes that the model could help practitioners make accurate clinical decisions to diagnose and treat heart disease.
Introduction to Radial Basis Function NetworksESCOM
This document provides an introduction to radial basis function (RBF) networks, a type of artificial neural network used for supervised learning problems. It describes how RBF networks are a type of linear model that uses radial basis functions as activation functions for hidden units. While RBF networks are nonlinear, the document emphasizes keeping the underlying mathematics and computations linear to simplify the problem and reduce computational costs compared to other neural network techniques that rely on nonlinear optimization algorithms. It reviews key concepts for RBF networks like least squares optimization, model selection, ridge regression, and forward selection techniques for building networks from data.
Health Prediction System - an Artificial Intelligence Project 2015Maruf Abdullah (Rion)
This document proposes the development of a health prediction system that allows users to get guidance on health issues through an online intelligent healthcare system. It will include features like patient and doctor registration and login, disease prediction based on entered symptoms, doctor search, and admin functions like adding doctors and diseases. The system will use data mining techniques to predict illnesses based on entered symptoms. It will provide a way for patients to get medical advice online when doctors are unavailable. The proposal outlines the requirements, workflow, advantages and disadvantages of the system.
Coursera work: Understanding the Brain: The Neurobiology of Everyday Lifepaulabrillos
Alzheimer's disease is characterized by cognitive impairment and is the most common form of dementia. It begins with mild symptoms and progresses over 10-20 years to severe memory loss and complete dependence. Areas of the brain involved include the temporal and frontal lobes, which control memory and thinking. In early stages, amyloid plaques and tau tangles form in areas for learning and memory. Later, these spread to areas for language, spatial skills, and body awareness. In advanced stages, most of the cortex is damaged, severely shrinking areas for thinking and memory like the hippocampus.
The document is a report on implementing and testing a radial basis function neural network for clustering iris flower data. It introduces RBF networks and the methodology used, which involved locating RBF nodes as cluster centers, calculating Gaussian functions, training the RBF layer unsupervised and a perceptron layer supervised. Results show the network accurately clustered most iris flowers into the three expected categories when trained on the iris data set.
Alzheimer's disease is a progressive brain disorder that causes cognitive decline. It results from amyloid plaques and neurofibrillary tangles in the brain that damage and kill brain cells. Risk factors include increasing age, family history of Alzheimer's, and head injuries. Early signs include memory loss, personality changes, and confusion. Treatment includes medications to improve symptoms as well as non-pharmacological therapies like exercise and cognitive stimulation.
The document discusses Alzheimer's disease and treatments for it. Key points:
- Alzheimer's is the most common form of dementia, caused by nerve cell deterioration in the brain.
- Common symptoms include memory loss, difficulty performing tasks, and mood/behavior changes.
- Current medications aim to slow progression by preventing breakdown of the neurotransmitter acetylcholine or blocking NMDA receptors. Examples given are memantine, donepezil, rivastigmine, and galantamine.
- All treatments can cause side effects like nausea and dizziness but only treat symptoms, not the underlying disease process.
This document provides an overview of Alzheimer's disease, including its causes, symptoms, stages of progression, treatments, nursing considerations, and prevention strategies. Key points include:
- Alzheimer's is the most common form of dementia and causes progressive loss of brain cells and function over time.
- Symptoms start mildly with forgetfulness but progress to include confusion, mood/behavior changes, and impairment of daily living.
- Treatments aim to slow progression using medications and managing symptoms, while nursing focuses on comfort, quality of life, and education.
- Prevention strategies incorporate lifestyle habits like exercise, diet, avoiding smoking/excess alcohol.
Alzheimer's disease is a neurological disorder that destroys memory "dementia" and other important mental functions. Learn the Causes, Symptoms & Treatment for Alzheimer’s Disease here.
This presentation summarizes Alzheimer's disease. It defines Alzheimer's as the most common form of dementia that occurs in the brain. The key points covered are that Alzheimer's causes nerve cell deterioration and death in the brain, leading to problems with brain function. Symptoms include memory loss, difficulty performing tasks, and mood changes. The disease progresses through 7 stages, from normal aging to severe impairment. Currently there is no cure, but drugs can help treat symptoms like memory problems, anxiety, and agitation. The presentation provided an overview of the causes, effects, symptoms and stages of Alzheimer's disease.
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.
Wind farm layout optimization (WFLO) is the process of optimizing the location of turbines in a wind farm site, with the possible objective of maximizing the energy production or minimizing the average cost of energy. Conventional WFLO methods not only limit themselves to prescribing the site boundaries, they are also generally applicable to designing only small-to-medium scale wind farms (<100 turbines). Large-scale wind farms entail greater wake-induced turbine interactions, thereby increasing the computa- tional complexity and expense by orders of magnitude. In this paper, we further advance the Unrestricted WFLO framework by designing the layout of large-scale wind farms with 500 turbines (where energy pro- duction is maximized). First, the high-dimensional layout optimization problem (involving 2N variables for a N turbine wind farm) is reduced to a 6-variable problem through a novel mapping strategy, which allows for both global siting (overall land configuration) and local exploration (turbine micrositing). Sec- ondly, a surrogate model is used to substitute the expensive analytical WF energy production model; the high computational expense of the latter is attributed to the factorial increase in the number of calls to the wake model for evaluating every candidate wind farm layout that involves a large number of turbines. The powerful Concurrent Surrogate Model Selection (COSMOS) framework is applied to identify the best surrogate model to represent the wind farm energy production as a function of the reduced variable vector. To accomplish a reliable optimum solution, the surrogate-based optimization (SBO) is performed by implementing the Adaptive Model Refinement (AMR) technique within Particle Swarm Optimization (PSO). In AMR, both local exploitation and global exploration aspects are considered within a single optimization run of PSO, unlike other SBO methods that often either require multiple (potentially mis- leading) optimizations or are model-dependent. By using the AMR approach in conjunction with PSO and COSMOS, the computational cost of designing very large wind farms is reduced by a remarkable factor of 26, while preserving the reliability of this WFLO within 0.05% of the WFLO performed using the original energy production model.
This document discusses kernel methods and radial basis function (RBF) networks. It begins with an introduction and overview of Cover's theory of separability of patterns. It then revisits the XOR problem and shows how it can be solved using Gaussian hidden functions. The interpolation problem is explained and how RBF networks can perform strict interpolation through a set of training data points. Radial basis functions that satisfy Micchelli's theorem allowing for a nonsingular interpolation matrix are presented. Finally, the structure and training of RBF networks using k-means clustering and recursive least squares estimation is covered.
The document describes using a Hopfield neural network to detect moving objects in videos. The objective is to devise a method to identify differences between frames to detect movements. A Hopfield network is used because it can serve as a content addressable memory. The network consists of neurons corresponding to pixels that are connected to neighboring pixels. Difference frames are obtained and iteratively updated until the network reaches a stable minimum energy state. This allows changed and unchanged pixels to be classified. Applications include video surveillance, people tracking, and traffic monitoring.
Alzheimer's disease is a progressive mental disorder that causes memory loss and cognitive decline. It is the most common form of dementia and is not normally associated with aging alone. The main risk factor is old age, and symptoms begin gradually and progress over time. There are four stages of the disease ranging from predementia to advanced dementia, where independence is lost and basic motor skills decline. Currently, there are no drugs that can stop the disease, but research continues in hopes of finding treatments.
This document discusses Alzheimer's disease (AD), including its definition, etiology, risk factors, pathophysiology, clinical symptoms, diagnosis, and treatment. Some key points include:
- AD is the most common cause of dementia and is characterized by cognitive and behavioral impairment. While the exact cause is unknown, risk factors include age, family history, and genetics such as the APOE E4 allele.
- Pathologically, AD is defined by amyloid plaques and neurofibrillary tangles in the brain. It results from the death of brain cells, affecting processes like memory, thinking, and behavior.
- Diagnosis involves assessing symptoms, ruling out other conditions through tests, and structural imaging of the brain
The document provides an overview of Alzheimer's disease, including:
- It is the most common form of dementia and causes progressive decline in brain function.
- Key hallmarks are amyloid plaques and neurofibrillary tangles which are believed to cause neurodegeneration.
- It predominantly affects areas of the brain involved in memory like the hippocampus and entorhinal cortex.
- Risk factors include age, family history, head injuries, and cardiovascular disease risks. While the cause is unknown, the amyloid hypothesis proposing amyloid plaques lead to the disease is the leading hypothesis driving research.
The document discusses Hopfield networks, which are neural networks with fixed weights and adaptive activations. It describes two types - discrete and continuous Hopfield nets. Discrete Hopfield nets use binary activations that are updated asynchronously, allowing an energy function to be defined. They can serve as associative memory. Continuous Hopfield nets have real-valued activations and can solve optimization problems like the travelling salesman problem. The document provides details on the architecture, energy functions, algorithms, and applications of both network types.
Clustering and Visualisation using R programmingNixon Mendez
Clustering Analysis is a collection of patterns into clusters based on similarity.
Here we will discuss on the following :
Microarray Data of Yeast Cell Cycle
Clustering Analysis :-
Principal Component Analysis (PCA)
Multidimensional Scaling (MDS)
K-Means
Self-Organizing Maps (SOM)
Hierarchical Clustering
1) The document compares the performance of four machine learning techniques (decision tree, random forest, logistic regression, and neural network) on two classification tasks: predicting the winner of tic-tac-toe games and predicting the subcellular location of proteins.
2) For tic-tac-toe, logistic regression had the best performance with 98.3% accuracy, followed by neural network and random forest, while decision tree performed worst.
3) For predicting protein location, random forest performed best with 63.4% accuracy, followed by logistic regression and neural network, while decision tree again had the lowest accuracy.
Survey on data mining techniques in heart disease predictionSivagowry Shathesh
This document summarizes research on using data mining techniques to predict heart disease. It discusses previous work using classification, clustering, association rule mining and other techniques on several heart disease datasets. Classification algorithms like naive bayes, decision trees and neural networks have been widely used with naive bayes found to often provide the best performance. Feature selection and attribute reduction are also examined. The document provides an overview of the key steps and techniques in medical data mining and predictive analysis for heart disease.
HEART DISEASE PREDICTION USING NAIVE BAYES ALGORITHMamiteshg
This document describes using a Naive Bayes classifier to predict the likelihood of heart disease. It discusses how a web-based application would take in a user's medical information and use a trained dataset to compare and retrieve hidden data to diagnose heart disease. The document provides an example of using Bayes' theorem to calculate the probability of breast cancer based on a positive mammogram. It explains the implementation of the Naive Bayes classifier and concludes that the model could help practitioners make accurate clinical decisions to diagnose and treat heart disease.
Introduction to Radial Basis Function NetworksESCOM
This document provides an introduction to radial basis function (RBF) networks, a type of artificial neural network used for supervised learning problems. It describes how RBF networks are a type of linear model that uses radial basis functions as activation functions for hidden units. While RBF networks are nonlinear, the document emphasizes keeping the underlying mathematics and computations linear to simplify the problem and reduce computational costs compared to other neural network techniques that rely on nonlinear optimization algorithms. It reviews key concepts for RBF networks like least squares optimization, model selection, ridge regression, and forward selection techniques for building networks from data.
Health Prediction System - an Artificial Intelligence Project 2015Maruf Abdullah (Rion)
This document proposes the development of a health prediction system that allows users to get guidance on health issues through an online intelligent healthcare system. It will include features like patient and doctor registration and login, disease prediction based on entered symptoms, doctor search, and admin functions like adding doctors and diseases. The system will use data mining techniques to predict illnesses based on entered symptoms. It will provide a way for patients to get medical advice online when doctors are unavailable. The proposal outlines the requirements, workflow, advantages and disadvantages of the system.
Coursera work: Understanding the Brain: The Neurobiology of Everyday Lifepaulabrillos
Alzheimer's disease is characterized by cognitive impairment and is the most common form of dementia. It begins with mild symptoms and progresses over 10-20 years to severe memory loss and complete dependence. Areas of the brain involved include the temporal and frontal lobes, which control memory and thinking. In early stages, amyloid plaques and tau tangles form in areas for learning and memory. Later, these spread to areas for language, spatial skills, and body awareness. In advanced stages, most of the cortex is damaged, severely shrinking areas for thinking and memory like the hippocampus.
The document is a report on implementing and testing a radial basis function neural network for clustering iris flower data. It introduces RBF networks and the methodology used, which involved locating RBF nodes as cluster centers, calculating Gaussian functions, training the RBF layer unsupervised and a perceptron layer supervised. Results show the network accurately clustered most iris flowers into the three expected categories when trained on the iris data set.
Alzheimer's disease is a progressive brain disorder that causes cognitive decline. It results from amyloid plaques and neurofibrillary tangles in the brain that damage and kill brain cells. Risk factors include increasing age, family history of Alzheimer's, and head injuries. Early signs include memory loss, personality changes, and confusion. Treatment includes medications to improve symptoms as well as non-pharmacological therapies like exercise and cognitive stimulation.
The document discusses Alzheimer's disease and treatments for it. Key points:
- Alzheimer's is the most common form of dementia, caused by nerve cell deterioration in the brain.
- Common symptoms include memory loss, difficulty performing tasks, and mood/behavior changes.
- Current medications aim to slow progression by preventing breakdown of the neurotransmitter acetylcholine or blocking NMDA receptors. Examples given are memantine, donepezil, rivastigmine, and galantamine.
- All treatments can cause side effects like nausea and dizziness but only treat symptoms, not the underlying disease process.
This document provides an overview of Alzheimer's disease, including its causes, symptoms, stages of progression, treatments, nursing considerations, and prevention strategies. Key points include:
- Alzheimer's is the most common form of dementia and causes progressive loss of brain cells and function over time.
- Symptoms start mildly with forgetfulness but progress to include confusion, mood/behavior changes, and impairment of daily living.
- Treatments aim to slow progression using medications and managing symptoms, while nursing focuses on comfort, quality of life, and education.
- Prevention strategies incorporate lifestyle habits like exercise, diet, avoiding smoking/excess alcohol.
Alzheimer's disease is a neurological disorder that destroys memory "dementia" and other important mental functions. Learn the Causes, Symptoms & Treatment for Alzheimer’s Disease here.
This presentation summarizes Alzheimer's disease. It defines Alzheimer's as the most common form of dementia that occurs in the brain. The key points covered are that Alzheimer's causes nerve cell deterioration and death in the brain, leading to problems with brain function. Symptoms include memory loss, difficulty performing tasks, and mood changes. The disease progresses through 7 stages, from normal aging to severe impairment. Currently there is no cure, but drugs can help treat symptoms like memory problems, anxiety, and agitation. The presentation provided an overview of the causes, effects, symptoms and stages of Alzheimer's disease.
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.
Wind farm layout optimization (WFLO) is the process of optimizing the location of turbines in a wind farm site, with the possible objective of maximizing the energy production or minimizing the average cost of energy. Conventional WFLO methods not only limit themselves to prescribing the site boundaries, they are also generally applicable to designing only small-to-medium scale wind farms (<100 turbines). Large-scale wind farms entail greater wake-induced turbine interactions, thereby increasing the computa- tional complexity and expense by orders of magnitude. In this paper, we further advance the Unrestricted WFLO framework by designing the layout of large-scale wind farms with 500 turbines (where energy pro- duction is maximized). First, the high-dimensional layout optimization problem (involving 2N variables for a N turbine wind farm) is reduced to a 6-variable problem through a novel mapping strategy, which allows for both global siting (overall land configuration) and local exploration (turbine micrositing). Sec- ondly, a surrogate model is used to substitute the expensive analytical WF energy production model; the high computational expense of the latter is attributed to the factorial increase in the number of calls to the wake model for evaluating every candidate wind farm layout that involves a large number of turbines. The powerful Concurrent Surrogate Model Selection (COSMOS) framework is applied to identify the best surrogate model to represent the wind farm energy production as a function of the reduced variable vector. To accomplish a reliable optimum solution, the surrogate-based optimization (SBO) is performed by implementing the Adaptive Model Refinement (AMR) technique within Particle Swarm Optimization (PSO). In AMR, both local exploitation and global exploration aspects are considered within a single optimization run of PSO, unlike other SBO methods that often either require multiple (potentially mis- leading) optimizations or are model-dependent. By using the AMR approach in conjunction with PSO and COSMOS, the computational cost of designing very large wind farms is reduced by a remarkable factor of 26, while preserving the reliability of this WFLO within 0.05% of the WFLO performed using the original energy production model.
This document discusses kernel methods and radial basis function (RBF) networks. It begins with an introduction and overview of Cover's theory of separability of patterns. It then revisits the XOR problem and shows how it can be solved using Gaussian hidden functions. The interpolation problem is explained and how RBF networks can perform strict interpolation through a set of training data points. Radial basis functions that satisfy Micchelli's theorem allowing for a nonsingular interpolation matrix are presented. Finally, the structure and training of RBF networks using k-means clustering and recursive least squares estimation is covered.
The document describes using a Hopfield neural network to detect moving objects in videos. The objective is to devise a method to identify differences between frames to detect movements. A Hopfield network is used because it can serve as a content addressable memory. The network consists of neurons corresponding to pixels that are connected to neighboring pixels. Difference frames are obtained and iteratively updated until the network reaches a stable minimum energy state. This allows changed and unchanged pixels to be classified. Applications include video surveillance, people tracking, and traffic monitoring.
Alzheimer's disease is a progressive mental disorder that causes memory loss and cognitive decline. It is the most common form of dementia and is not normally associated with aging alone. The main risk factor is old age, and symptoms begin gradually and progress over time. There are four stages of the disease ranging from predementia to advanced dementia, where independence is lost and basic motor skills decline. Currently, there are no drugs that can stop the disease, but research continues in hopes of finding treatments.
This document discusses Alzheimer's disease (AD), including its definition, etiology, risk factors, pathophysiology, clinical symptoms, diagnosis, and treatment. Some key points include:
- AD is the most common cause of dementia and is characterized by cognitive and behavioral impairment. While the exact cause is unknown, risk factors include age, family history, and genetics such as the APOE E4 allele.
- Pathologically, AD is defined by amyloid plaques and neurofibrillary tangles in the brain. It results from the death of brain cells, affecting processes like memory, thinking, and behavior.
- Diagnosis involves assessing symptoms, ruling out other conditions through tests, and structural imaging of the brain
The document provides an overview of Alzheimer's disease, including:
- It is the most common form of dementia and causes progressive decline in brain function.
- Key hallmarks are amyloid plaques and neurofibrillary tangles which are believed to cause neurodegeneration.
- It predominantly affects areas of the brain involved in memory like the hippocampus and entorhinal cortex.
- Risk factors include age, family history, head injuries, and cardiovascular disease risks. While the cause is unknown, the amyloid hypothesis proposing amyloid plaques lead to the disease is the leading hypothesis driving research.
The document discusses Hopfield networks, which are neural networks with fixed weights and adaptive activations. It describes two types - discrete and continuous Hopfield nets. Discrete Hopfield nets use binary activations that are updated asynchronously, allowing an energy function to be defined. They can serve as associative memory. Continuous Hopfield nets have real-valued activations and can solve optimization problems like the travelling salesman problem. The document provides details on the architecture, energy functions, algorithms, and applications of both network types.
Clustering and Visualisation using R programmingNixon Mendez
Clustering Analysis is a collection of patterns into clusters based on similarity.
Here we will discuss on the following :
Microarray Data of Yeast Cell Cycle
Clustering Analysis :-
Principal Component Analysis (PCA)
Multidimensional Scaling (MDS)
K-Means
Self-Organizing Maps (SOM)
Hierarchical Clustering
1) The document compares the performance of four machine learning techniques (decision tree, random forest, logistic regression, and neural network) on two classification tasks: predicting the winner of tic-tac-toe games and predicting the subcellular location of proteins.
2) For tic-tac-toe, logistic regression had the best performance with 98.3% accuracy, followed by neural network and random forest, while decision tree performed worst.
3) For predicting protein location, random forest performed best with 63.4% accuracy, followed by logistic regression and neural network, while decision tree again had the lowest accuracy.
This document provides an overview of machine learning techniques that can be applied in finance, including exploratory data analysis, clustering, classification, and regression methods. It discusses statistical learning approaches like data mining and modeling. For clustering, it describes techniques like k-means clustering, hierarchical clustering, Gaussian mixture models, and self-organizing maps. For classification, it mentions discriminant analysis, decision trees, neural networks, and support vector machines. It also provides summaries of regression, ensemble methods, and working with big data and distributed learning.
Computational Motor Control: Optimal Estimation in Noisy World (JAIST summer ...hirokazutanaka
This is lecure 4 note for JAIST summer school on computational motor control (Hirokazu Tanaka & Hiroyuki Kambara). Lecture video: https://www.youtube.com/watch?v=2-VRBIg5m0w
Several growth factors, cytokines, hormones activate PI3K/Akt pathway. Akt is a key node in this pathway and activates different downstream paths. One such path is Akt/mTORC1/S6K1 that controls protein synthesis, cell survival, and proliferation. Here we show that a negative feedback controls activation of S6K1 through this pathway. Due to this negative feedback, a sustained phospho-Akt signal generates a transient pulse of phospho-S6K1. We have created a mathematical model for this circuit. Analysis of this model shows that the negative feedback acts as a filter and preferentially allows a signal, with sharp and faster rise of phospho-Akt, to induce pronounced activation of S6K1.
The document discusses bioinformatics and computational biology. It describes a lab with over 100 people from diverse backgrounds, including engineers, scientists, technicians, geneticists and clinicians. The lab applies information technology to analyze biological data, focusing on areas like sequence analysis, molecular modeling, phylogeny, medical applications, statistics and more. Specific applications mentioned include analyzing genomes to study genetic diseases and drug design, as well as using the same techniques in agriculture and animal health.
ESAI-CEU-UCH solution for American Epilepsy Society Seizure Prediction ChallengeFrancisco Zamora-Martinez
Presentation given at Cyient Insights (Hyderabad, India).
This work presents the solution proposed by Universidad CEU Cardenal Herrera (ESAI-CEU-UCH) at Kaggle American Epilepsy Society Seizure Prediction Challenge. The proposed solution was positioned as 4th at Kaggle competition.
Different kind of input features (different preprocessing pipelines) and different statistical models are being proposed. This diversity was motivated to improve model combination result.
It is important to note that any of the proposed systems use test set for calibration. The competition allow to do this model calibration using test set, but doing it will reduce the reproducibility of the results in a real world implementation.
Inria Tech Talk - La classification de données complexes avec MASSICCCStéphanie Roger
MASSICCC - Une plateforme SaaS pour le traitement de la classification de données complexes hétérogènes et incomplètes.
Dans ce Tech Talk venez découvrir, tester et apprendre à maîtriser MASSICCC (Massive clustering in cloud computing) une plateforme SaaS orientée utilisateurs, ainsi que ses trois familles d’algorithmes de #classification, fruits des dernières avancées des équipes de recherche Modal & Celeste de Inria, pour analyser et faire de l’apprentissage sur vos "Big Data" (ex : en immobilier, maintenance prédictive, santé, open data, etc. ).
MASSICCC c’est aussi :
- Un accès gratuit pour le test et la recherche sur https://massiccc.lille.inria.fr
- Un "one for all" de la classification
- Une forte interprétabilité des résultats (avec ses graphiques)
- Un mode SaaS qui vous permet un suivi des expériences (en cours ou terminées)
- Et des algorithmes open source qui sont réutilisables indépendamment.
The document describes a generative model for networks called the Affiliation Graph Model (AGM). The AGM models how communities in a network "generate" the edges between nodes. It represents each node's membership in multiple communities as strengths in a membership matrix. The probability of an edge between two nodes depends on the product of their membership strengths in common communities. The maximum likelihood estimation technique can be used to estimate the community membership strengths matrix that best explains a given network.
“Statistical Physics Studies of Machine Learning Problems" by Lenka Zdeborova, Researcher @CNRS
Abstract : We will talk about some insight of the following questions: What makes problems studied in machine and statistical physics related? How can this relation be used to understand better the performance and limitations of machine learning systems? What happens when a phase transition is found in a computational problem? How do phase transitions influence algorithmic hardness?
The document discusses regression models for modeling relationships between input and output variables. It covers linear regression, using linear functions to model the relationship, and nonlinear regression, using nonlinear functions. Maximum a posteriori (MAP) estimation and least squares estimation are described as approaches for estimating the parameters of regression models from data. MAP estimation maximizes the posterior probability of the parameters given the data and assumes prior probabilities on the parameters, while least squares minimizes error. Regularized least squares is also covered, which adds a regularization term to improve stability. Computer experiments are demonstrated applying linear regression to classification problems.
This document summarizes material from the book "Mining of Massive Datasets" by Jure Leskovec, Anand Rajaraman, and Jeff Ullman. It presents an algorithm called BigCLAM that can efficiently detect overlapping communities in large networks. BigCLAM models community membership using a strength matrix and optimizes the likelihood of the model to find communities. It scales to networks with millions of edges using techniques like caching neighbor sums. Experiments show BigCLAM can analyze networks orders of magnitude larger than previous methods in minutes instead of days.
This document contains lecture slides for a course on pattern recognition. It covers linear discriminant functions and multilayer neural networks. For linear discriminant functions, it discusses the two-category and multi-category cases, and optimization methods like gradient descent and Newton's method. For neural networks, it describes feedforward operations, backpropagation learning, and applying these concepts to classify the Iris dataset. Assignments involve building linear and neural network classifiers for the Iris data.
This document discusses machine learning techniques including linear support vector machines (SVMs), data splitting, model fitting and prediction, and histograms. It summarizes an SVM tutorial for predicting samples and evaluating models using classification reports and confusion matrices. It also covers kernel density estimation, PCA, and comparing different classifiers.
. An introduction to machine learning and probabilistic ...butest
This document provides an overview and introduction to machine learning and probabilistic graphical models. It discusses key topics such as supervised learning, unsupervised learning, graphical models, inference, and structure learning. The document covers techniques like decision trees, neural networks, clustering, dimensionality reduction, Bayesian networks, and learning the structure of probabilistic graphical models.
Big Data analysis involves building predictive models from high-dimensional data using techniques like variable selection, cross-validation, and regularization to avoid overfitting. The document discusses an example analyzing web browsing data to predict online spending, highlighting challenges with large numbers of variables. It also covers summarizing high-dimensional data through dimension reduction and model building for prediction versus causal inference.
This document discusses artificial neural networks, specifically multilayer perceptrons (MLPs). It provides the following information:
- MLPs are feedforward neural networks with one or more hidden layers between the input and output layers. The input signals are propagated in a forward direction through each layer.
- Backpropagation is a common learning algorithm for MLPs. It calculates error signals that are propagated backward from the output to the input layers to adjust the weights, reducing errors between the actual and desired outputs.
- A three-layer backpropagation network is presented as an example to solve the exclusive OR (XOR) logic problem, which a single-layer perceptron cannot do. Initial weights and thresholds are set randomly,
This document summarizes a chapter on multilayer perceptrons from a textbook on neural networks. It introduces the limitations of Rosenblatt's perceptron model and how multilayer perceptrons can overcome these limitations by using multiple hidden layers. The backpropagation algorithm is described as a method for training multilayer perceptrons by propagating error signals backward from the output to adjust weights. Examples are given of how a two-layer network can solve the XOR problem that a single-layer perceptron cannot.
The document provides an overview of neural networks. It begins by discussing biological inspiration from the human brain, including key facts about neurons and synapses. It then defines artificial neurons and various components like dendrites, axons, and synapses. The document explores different types of neural networks including feedforward, recurrent, self-organizing maps and time delay neural networks. It also covers common neural network architectures, learning algorithms, activation functions, and applications of neural networks.
The document discusses the history and development of hidden Markov models (HMMs). It describes key concepts such as HMMs consisting of hidden states that produce observable outputs, and how they can be used to model sequential data. The document also provides examples of applying HMMs to problems such as gene finding, multiple sequence alignment, and protein secondary structure prediction. It summarizes algorithms like forward-backward, Viterbi, and Baum-Welch that are used to train and make predictions from HMMs. Finally, it mentions some popular HMM software tools like HMMER and SAM.
1. Machine Learning for Alzheimer Disease
Prediction
Oleksandr Kazakov.
Big Data Dev @Wajam
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 1 / 41
2. Alzheimer Quick Facts
More than 5 million people in US and 35 million worldwide are living
with Alzheimer’s
Every 66 seconds someone in the US develops disease
Kills more than breast and prostate cancer combined
6th leading cause of death in the US and Canada
No current cure
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 2 / 41
3. Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 3 / 41
6. Why Raman Spectroscopy?
It is cheap!
It can provide fingerprinting-type information on the total biochemical
state of blood or cerebrospinal fluid.
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 6 / 41
7. Why Raman Spectroscopy?
It is cheap!
It can provide fingerprinting-type information on the total biochemical
state of blood or cerebrospinal fluid.
It is super cheap and...portable(true if number of handles<=2).
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 6 / 41
17. Biological Neuron
yi = f(
d
i
wixi + w0)
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 16 / 41
18. Feed Forward Neural Network with Backpropagation
Learning
Activity of ith neuron:
xi = f( i)
Where:
i = woi +
j⊂Γi
wijxj
Applying chain rule to compute each weight in the network for any
arbitrary error function E
∂E
∂wij
=
∂E
∂ i
∂ i
∂neti
∂neti
∂wij
Once we know partial derivative of each weight the minimization is done
via gradient descent:
wij(t + 1) = wij − λ
∂E
∂wij
(t)
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 17 / 41
19. Typical three layers feed-forward neural network
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 18 / 41
22. f(x) =
N
i
wih(x)i
Most Popular choices of radial basis function are:
Gaussian: e
−( v2
β2 )
Multiquadric: (v2 + β2)
1
2
Inverse Multiquadric: 1
(v2+β2)
1
2
Where v = x − c and represents distance from the input vector to the
radial basis function centre, and β is a width of a radial basis function.
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 20 / 41
25. Some Theory
Classifier for SVM:
F(x) = sign(
n
j=1
ωjxj − ω0) = sign( ω, x − w0) (1)
Where x = (x1, ...., xn)- evidential description of an object X; vector
ω = (ω1, ...., ωn) and value of ω0 are some parameters of the algorithm.
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 23 / 41
26. Some Theory
Classifier for SVM:
F(x) = sign(
n
j=1
ωjxj − ω0) = sign( ω, x − w0) (1)
Where x = (x1, ...., xn)- evidential description of an object X; vector
ω = (ω1, ...., ωn) and value of ω0 are some parameters of the algorithm.
If set is linear separable then error function can be described as following:
E(ω, ω0) =
l
i=1
[yi( ω, xi − ω0) < 0] (2)
Such condition gives a numerous sets of hyperplanes but we want the one
that will be a maximum distance from the separable set.
−1 < ω, x − ω0 < 1
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 23 / 41
28. Width of The Separation Plane
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 25 / 41
29. Non-linear SVM Classification
The idea is to map given feature space X to the new H using transition
ψ : X → H and if H has enough dimensionality we can hope that set will
be separated. Kernel will be defined as K(x, x ) = ψ(x), ψ(x ) and
classifier can be rewritten as:
F(x) = sign(
l
j=1
λiyiK(xi, x) − w0)
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 26 / 41
30. Non-linear SVM Classification
The idea is to map given feature space X to the new H using transition
ψ : X → H and if H has enough dimensionality we can hope that set will
be separated. Kernel will be defined as K(x, x ) = ψ(x), ψ(x ) and
classifier can be rewritten as:
F(x) = sign(
l
j=1
λiyiK(xi, x) − w0)
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 26 / 41
31. Types of Kernel
Polynomial: K(x, x ) = ( x, x + const)d Example
Radial Basis Function: K(x, x ) = e−γ x−x 2
, γ > 0
Gaussian Radial basis function: K(x, x ) = e−0.5 x−x 2(σ−2)
Sigmoid: K(x, x ) = tanh(k x, x + c), k > 0, c > 0
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 27 / 41
32. Similarity with Neural Networks
SVM structure:
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 28 / 41
34. Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 30 / 41
35. GA Basics
Randomly choose initial population P of N individuals.
Determine the fitness of each individual.
Assign probability of reproduction pi to each individual.
Create a new population selecting individuals according to pi. Then
selected individuals will generate offsprings via crossovers or
mutations.
Repeat if needed.
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 31 / 41
37. RBF and MLP result for Serum
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 33 / 41
38. SVM for AD vs HC
Class Sensitivity(%) Specificity(%)
Healthy Control 87 90
Alzheimer dementia 84 87
Healthy Control 81 93
Mild Alzheimer dementias 67 90
Moderate Alzheimer dementias 74 94
Healthy Control 86 88
Mild Alzheimer dementia 88 87
Healthy Control 82 83
Moderate Alzheimer dementia 81 82
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 34 / 41
39. MLP and SVM for AD vs HC for CSF
For MLP:
Class Sensitivity(%) Specificity(%)
Healthy Control 81 83
Mild Alzheimer dementia 82 81
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 35 / 41
40. MLP and SVM for AD vs HC for CSF
For MLP:
Class Sensitivity(%) Specificity(%)
Healthy Control 81 83
Mild Alzheimer dementia 82 81
And even below 74% Sensitivity and Specificity for SVM.
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 35 / 41
41. MLP for AD vs OD for Serum
Class Sensitivity(%) Specificity(%)
Other dementias 95 98
Alzheimer dementia 96 95
Other dementias 92 97
Mild Alzheimer dementias 95 92
Moderate Alzheimer dementias 93 99
Other dementias 91 94
Mild Alzheimer dementia 84 82
Other dementias 96 95
Moderate Alzheimer dementia 92 99
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 36 / 41
42. SVM for AD vs OD for Serum
Class Sensitivity(%) Specificity(%)
Other dementias 90 95
Alzheimer dementia 95 90
Other demetias 94 87
Mild Alzheimer demetias 89 94
Moderate Alzheimer demetias 93 92
Other dementias 92 93
Mild Alzheimer dementia 84 82
Other dementias 92 93
Moderate Alzheimer dementia 93 92
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 37 / 41
43. Selected Networks Architectures
Type of Network Network Structure Sensitivity(%) Specificity(%)
MLP 5-20-20-1 97 95
MLP 5-50-10-1 96 96
MLP 5-100-1 96 95
MLP 5-200-20-1 96 94
RBF 5-20-20-1 95 94
RBF 5-50-20-1 96 95
RBF 5-100-1 94 94
RBF 5-200-20-1 94 94
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44. Genetic Algorithm Results for Serum
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45. Genetic Algorithm Results for CSF
Oleksandr Kazakov. Big Data Dev @Wajam Machine Learning for Alzheimer Disease Prediction 40 / 41
46. Conclusions
We demonstrated power of Raman Spectroscopy as diagnostic tool
for AD detection.
We demon- strated the high probability for single subject to be
correctly assigned to the particular diagnostic category.
Our results are consistent with current AD biomarker findings.
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