This document summarizes key aspects of arrays and arraylists in Java. It discusses that an array is a fundamental data structure for storing a collection of data elements of the same type. Each element in an array can be referenced using an index. The document also provides examples of declaring and initializing arrays, accessing array elements, and common operations like finding the sum and average of array elements.
Nesc invited presentation: Semantic Provenance and Linked Open DataPaolo Missier
This document discusses Janus, a semantic provenance model for workflow provenance. Janus aims to move from domain-agnostic provenance graphs to domain-aware graphs through explicit annotations. It also aims to move from local provenance graphs to graphs published as linked open data to enable queries across the web of data. The document outlines Janus's structure and how annotations can be propagated through the graph. It also provides examples of extended queries that combine patterns on local provenance graphs with conditions on remote linked open data sources.
Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
This document discusses neural networks and their applications in mobile game programming. It begins with definitions of standard deviation, root mean square, neurons, dendrites, and axons. It then explains the three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. The document also covers standard neural network uses like pattern recognition and control. It provides an in-depth explanation of perceptrons and how they work, including examples of pattern recognition and supervised learning algorithms. Finally, it discusses limitations of single-layer perceptrons and introduces multi-layer perceptrons and backpropagation training.
This document contains lecture notes on sparse autoencoders. It begins with an introduction describing the limitations of supervised learning and the need for algorithms that can automatically learn feature representations from unlabeled data. The notes then state that sparse autoencoders are one approach to learn features from unlabeled data, and describe the organization of the rest of the notes. The notes will cover feedforward neural networks, backpropagation for supervised learning, autoencoders for unsupervised learning, and how sparse autoencoders are derived from these concepts.
This document summarizes research on frequent itemset mining techniques in data mining. It discusses the Apriori algorithm and how the authors improved it by introducing a vertical sort. This improves performance by allowing lower memory usage at all support thresholds and ability to mine lower support thresholds. The document also reviews related work on probabilistic frequent itemset mining and algorithms like U-Apriori and UF-growth for handling uncertainty.
Catching co occurrence information using word2vec-inspired matrix factorizationhyunsung lee
- Factorizing a PMI matrix involves using matrix factorization techniques to represent words in a latent space based on their co-occurrence information, similar to word2vec.
- Recommender systems use matrix factorization to represent users and items numerically in a latent space to predict target values like ratings. Known ratings are used to find latent vectors for users and items that best approximate the rating matrix.
- These latent vectors can then be used to predict unknown ratings by taking the dot product of the user and item vectors.
Variational Autoencoders For Image GenerationJason Anderson
Meetup: https://www.meetup.com/Cognitive-Computing-Enthusiasts/events/260580395/
Video: https://www.youtube.com/watch?v=fnULFOyNZn8
Blog: http://www.compthree.com/blog/autoencoder/
Code: https://github.com/compthree/variational-autoencoder
An autoencoder is a machine learning algorithm that represents unlabeled high-dimensional data as points in a low-dimensional space. A variational autoencoder (VAE) is an autoencoder that represents unlabeled high-dimensional data as low-dimensional probability distributions. In addition to data compression, the randomness of the VAE algorithm gives it a second powerful feature: the ability to generate new data similar to its training data. For example, a VAE trained on images of faces can generate a compelling image of a new "fake" face. It can also map new features onto input data, such as glasses or a mustache onto the image of a face that initially lacks these features. In this talk, we will survey VAE model designs that use deep learning, and we will implement a basic VAE in TensorFlow. We will also demonstrate the encoding and generative capabilities of VAEs and discuss their industry applications.
The document discusses character recognition using convolutional neural networks. It begins with an introduction to classifiers and gradient-based learning methods. It then describes how multiple perceptrons can be combined into a multilayer perceptron and trained using backpropagation. Next, it introduces convolutional neural networks, which offer improvements over multilayer perceptrons in performance, accuracy, and distortion invariance. It provides details on the topology and training of convolutional neural networks. Finally, it discusses the LeNet-5 convolutional neural network and its successful application to handwritten digit recognition.
Nesc invited presentation: Semantic Provenance and Linked Open DataPaolo Missier
This document discusses Janus, a semantic provenance model for workflow provenance. Janus aims to move from domain-agnostic provenance graphs to domain-aware graphs through explicit annotations. It also aims to move from local provenance graphs to graphs published as linked open data to enable queries across the web of data. The document outlines Janus's structure and how annotations can be propagated through the graph. It also provides examples of extended queries that combine patterns on local provenance graphs with conditions on remote linked open data sources.
Class lecture of Data Structure and Algorithms and Python.
Stack, Queue, Tree, Python, Python Code, Computer Science, Data, Data Analysis, Machine Learning, Artificial Intellegence, Deep Learning, Programming, Information Technology, Psuedocide, Tree, pseudocode, Binary Tree, Binary Search Tree, implementation, Binary search, linear search, Binary search operation, real-life example of binary search, linear search operation, real-life example of linear search, example bubble sort, sorting, insertion sort example, stack implementation, queue implementation, binary tree implementation, priority queue, binary heap, binary heap implementation, object-oriented programming, def, in BST, Binary search tree, Red-Black tree, Splay Tree, Problem-solving using Binary tree, problem-solving using BST, inorder, preorder, postorder
This document discusses neural networks and their applications in mobile game programming. It begins with definitions of standard deviation, root mean square, neurons, dendrites, and axons. It then explains the three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. The document also covers standard neural network uses like pattern recognition and control. It provides an in-depth explanation of perceptrons and how they work, including examples of pattern recognition and supervised learning algorithms. Finally, it discusses limitations of single-layer perceptrons and introduces multi-layer perceptrons and backpropagation training.
This document contains lecture notes on sparse autoencoders. It begins with an introduction describing the limitations of supervised learning and the need for algorithms that can automatically learn feature representations from unlabeled data. The notes then state that sparse autoencoders are one approach to learn features from unlabeled data, and describe the organization of the rest of the notes. The notes will cover feedforward neural networks, backpropagation for supervised learning, autoencoders for unsupervised learning, and how sparse autoencoders are derived from these concepts.
This document summarizes research on frequent itemset mining techniques in data mining. It discusses the Apriori algorithm and how the authors improved it by introducing a vertical sort. This improves performance by allowing lower memory usage at all support thresholds and ability to mine lower support thresholds. The document also reviews related work on probabilistic frequent itemset mining and algorithms like U-Apriori and UF-growth for handling uncertainty.
Catching co occurrence information using word2vec-inspired matrix factorizationhyunsung lee
- Factorizing a PMI matrix involves using matrix factorization techniques to represent words in a latent space based on their co-occurrence information, similar to word2vec.
- Recommender systems use matrix factorization to represent users and items numerically in a latent space to predict target values like ratings. Known ratings are used to find latent vectors for users and items that best approximate the rating matrix.
- These latent vectors can then be used to predict unknown ratings by taking the dot product of the user and item vectors.
Variational Autoencoders For Image GenerationJason Anderson
Meetup: https://www.meetup.com/Cognitive-Computing-Enthusiasts/events/260580395/
Video: https://www.youtube.com/watch?v=fnULFOyNZn8
Blog: http://www.compthree.com/blog/autoencoder/
Code: https://github.com/compthree/variational-autoencoder
An autoencoder is a machine learning algorithm that represents unlabeled high-dimensional data as points in a low-dimensional space. A variational autoencoder (VAE) is an autoencoder that represents unlabeled high-dimensional data as low-dimensional probability distributions. In addition to data compression, the randomness of the VAE algorithm gives it a second powerful feature: the ability to generate new data similar to its training data. For example, a VAE trained on images of faces can generate a compelling image of a new "fake" face. It can also map new features onto input data, such as glasses or a mustache onto the image of a face that initially lacks these features. In this talk, we will survey VAE model designs that use deep learning, and we will implement a basic VAE in TensorFlow. We will also demonstrate the encoding and generative capabilities of VAEs and discuss their industry applications.
The document discusses character recognition using convolutional neural networks. It begins with an introduction to classifiers and gradient-based learning methods. It then describes how multiple perceptrons can be combined into a multilayer perceptron and trained using backpropagation. Next, it introduces convolutional neural networks, which offer improvements over multilayer perceptrons in performance, accuracy, and distortion invariance. It provides details on the topology and training of convolutional neural networks. Finally, it discusses the LeNet-5 convolutional neural network and its successful application to handwritten digit recognition.
An image is a representation of a physical entity's properties as a function f(x,y,z) of three variables. A 2D digital image is obtained through perspective projection using a pinhole camera, sampling and quantizing the independent variables and function values. This results in a discrete matrix where each element represents a picture element (pixel) with a finite intensity value.
1. Neural networks are inspired by biological neural networks in the brain and are made up of simple processing units called neurons.
2. Artificial neural networks use a layer of input neurons that receive information and pass it through connections to other neurons.
3. A neural network learns through a process of trial and error adjustment of the weights between neurons to minimize errors between the network's output and the desired output.
DESIGN AND IMPLEMENTATION OF BINARY NEURAL NETWORK LEARNING WITH FUZZY CLUSTE...cscpconf
In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy
Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the
concept of binary neural network and geometrical expansion. Parameters are updated
according to the geometrical location of the training samples in the input space, and each
sample in the training set is learned only once. It’s a semisupervised based approach, the
training samples are semi-labelled i.e. for some samples, labels are known and for some
samples data labels are not known. The method starts with classification, which is done by
using the concept of ETL algorithm. In classification process various classes are formed.
These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.
This document outlines a chapter about functions in C programming. It discusses how functions allow programs to be modularized by breaking them into smaller pieces called modules. Functions can be user-defined or come from standard libraries. Functions take parameters as input, perform operations, and return output. Functions allow for abstraction, reusability, and avoidance of code repetition. The chapter covers defining functions, function prototypes, parameters, return values, and calling functions. It also provides examples of commonly used math library functions.
This document summarizes the Learn++.MF algorithm, which is an ensemble-of-classifiers approach for handling missing data. It trains classifiers on random subsets of available features, rather than estimating missing values. To classify an instance with missing features, it uses the majority vote of classifiers that did not use the missing features. The algorithm assumes redundant and randomly distributed features, conditions often met in practice. It avoids drawbacks of imputation methods and can accommodate substantial missing data with gradual performance decline as missing data increases.
I think this could be useful for those who works in the field of Coputational Intelligence. Give your valuable reviews so that I can progree in my research
This document provides an overview of neural networks and related topics. It begins with an introduction to neural networks and discusses natural neural networks, early artificial neural networks, modeling neurons, and network design. It then covers multi-layer neural networks, perceptron networks, training, and advantages of neural networks. Additional topics include fuzzy logic, genetic algorithms, clustering, and adaptive neuro-fuzzy inference systems (ANFIS).
Bayesian Generalization Error and Real Log Canonical Threshold in Non-negativ...Naoki Hayashi
I have talked in the conference Algebraic Statistics 2020.
As a background of our research, I briefly explained singular learning theory which can be interpretable as an intersection between algebraic statistics and statistical learning theory.
The main part of this presentation is introducing our recent studies for parameter region restriction in singular learning theory. I showed the researches about the learning coefficient (real log canonical threshold) of NMF and LDA. NMF and LDA are typical models whose parameter regions are restricted.
The document summarizes key concepts about perceptrons and perceptron networks:
1) A perceptron is a type of neural network unit that uses a step function as its activation. It takes weighted inputs, sums them, and outputs 1 if the sum is above a threshold and -1 if below.
2) Perceptrons can be organized into single-layer feedforward networks where each output unit is independent.
3) The perceptron learning algorithm updates weights based on errors to minimize misclassifications on the training set. It is guaranteed to converge if the problem is linearly separable.
This work is proposed the feed forward neural network with symmetric table addition method to design the
neuron synapses algorithm of the sine function approximations, and according to the Taylor series
expansion. Matlab code and LabVIEW are used to build and create the neural network, which has been
designed and trained database set to improve its performance, and gets the best a global convergence with
small value of MSE errors and 97.22% accuracy.
The document outlines the rules and topics that will be covered in a data structures and algorithms course. It includes:
- Class rules prohibiting late entry or early exit from classes and announcing unscheduled quizzes.
- An outline of standard data structures and algorithms to be covered, including arrays, stacks, queues, linked lists, trees, sorting, searching, and graphs.
- An introduction to key concepts like data types, algorithms, performance analysis, and asymptotic notation to analyze time and space complexity.
- Artificial neural networks are inspired by biological neural networks and try to mimic their learning mechanisms by modifying synaptic strengths through an optimization process.
- Learning in neural networks can be formulated as a function approximation task where the network learns to approximate a function by minimizing an error measure through optimization of synaptic weights.
- A single hidden layer neural network is capable of learning nonlinear function approximations if general optimization methods are applied to update the synaptic weights.
A Framework for Cross-media Information ManagementBeat Signer
Presentation given at EuroIMSA 2005, International Conference on Internet and Multimedia Systems and Applications. Grindelwald, Switzerland, February 2005
ABSTRACT: Nowadays a user's personal information space is fragmented into multiple repositories on their local machine as well as on remote servers. In order to enable later access to resources managed within such a cross-media information space, information has to be organised in a format that can be processed by automatic retrieval processes. We propose a general framework for personal information management based on extending a cross-media link server with supplemental metadata functionality. In addition to user generated information, our solution automatically derives metadata for classifying and associating resources based on direct interaction with the information space. Resources and metadata can be integrated by referencing external resources or information may be managed directly by the framework. The presented cross-media information management solution is not limited to a fixed set of predefined resources and can be extended based on a resource plug-in mechanism.
Matrix factorization techniques can be used to address some of the limitations of traditional collaborative filtering approaches for recommender systems. Matrix factorization decomposes the user-item rating matrix into the product of two lower-dimensional matrices, one representing latent factors for users and the other for items. This reduced dimensionality addresses data sparsity and scalability issues. Specifically, singular value decomposition is often used to perform this matrix factorization, which can approximate the original rating matrix while ignoring less important singular values and factor vectors. The decomposed matrices can then be multiplied to predict unknown user ratings.
Dr. kiani artificial neural network lecture 1Parinaz Faraji
The document provides a history of neural networks, beginning with McCulloch and Pitts creating the first neural network model in 1943. It then discusses several important developments in neural networks including perceptrons in the 1950s and 1960s, backpropagation in the 1980s, and neural networks being implemented in semiconductors in the late 1980s. The document also includes diagrams and explanations of biological neurons, artificial neurons, different types of activation functions, and key aspects of neural network architectures.
ADAPTIVE BLIND MULTIUSER DETECTION UNDER IMPULSIVE NOISE USING PRINCIPAL COMP...csandit
In this paper we consider blind signal detection for an asynchronous code division multiple access (CDMA) system with Principal component analysis (PCA) in impulsive noise. The blind multiuser detector requires no training sequences compared with the conventional multiuser detection receiver. The proposed PCA blind multiuser detector is robust when compared with knowledge based signature waveforms and the timing of the user of interest. PCA is a statistical method for reducing the dimension of data set, spectral decomposition of the covariance matrix of the dataset i.e first and second order statistics are estimated.
Principal component analysis makes no assumption on the independence of the data vectors PCA searches for linear combinations with the largest variances and when several linear combinations are needed, it considers variances in decreasing order of importance. PCA
improves SNR of signals used for differential side channel analysis. In different to other approaches, the linear minimum mean-square-error (MMSE) detector is obtained blindly; the detector does not use any training sequence like in subspace methods to detect multi user
receiver. The algorithm need not estimate the subspace rank in order to reduce the computational complexity. Simulation results show that the new algorithm offers substantial performance gains over the traditional subspace methods.
Adaptive blind multiuser detection under impulsive noise using principal comp...csandit
The document describes an adaptive blind multiuser detection method for asynchronous code division multiple access (CDMA) systems using principal component analysis (PCA) in impulsive noise environments. PCA is used to extract the principal components from the received signal without requiring training sequences or prior knowledge of channel characteristics. The PCA blind multiuser detector provides robust performance compared to knowledge-based detectors when signature waveforms and timing offsets of users are unknown. Simulation results show the proposed PCA method offers substantial gains over traditional subspace methods for multiuser detection.
Latent factor models for Collaborative Filteringsscdotopen
The document discusses latent factor models for collaborative filtering. It describes how latent factor models (1) map both users and items to a latent factor space to characterize them, (2) approximate ratings as the dot product of user and item vectors, and (3) can be used to predict unknown ratings. It also covers techniques like stochastic gradient descent and alternating least squares for training latent factor models on explicit and implicit feedback data.
Artificial neural networks (ANNs) are inspired by biological neural networks and are composed of interconnected processing elements called neurons. ANNs are configured through a learning process to solve problems like pattern recognition or data classification. Early research in the 1940s and 1950s laid the foundations, like McCulloch and Pitts developing the first neural network model and Hebb developing the first learning rule. ANNs use weighted connections and activation functions to learn from examples through training. Feedforward and feedback networks differ in whether signals travel in one or both directions between layers of neurons. Perceptrons were influential early neural network models that could perform tasks linear programs could not.
A empresa de tecnologia anunciou um novo produto revolucionário que combina hardware, software e serviços em nuvem. O dispositivo é pequeno, portátil e permite acesso a aplicativos e dados em qualquer lugar. Analistas acreditam que o produto pode transformar o mercado e estabelecer a empresa como líder no setor.
An image is a representation of a physical entity's properties as a function f(x,y,z) of three variables. A 2D digital image is obtained through perspective projection using a pinhole camera, sampling and quantizing the independent variables and function values. This results in a discrete matrix where each element represents a picture element (pixel) with a finite intensity value.
1. Neural networks are inspired by biological neural networks in the brain and are made up of simple processing units called neurons.
2. Artificial neural networks use a layer of input neurons that receive information and pass it through connections to other neurons.
3. A neural network learns through a process of trial and error adjustment of the weights between neurons to minimize errors between the network's output and the desired output.
DESIGN AND IMPLEMENTATION OF BINARY NEURAL NETWORK LEARNING WITH FUZZY CLUSTE...cscpconf
In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy
Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the
concept of binary neural network and geometrical expansion. Parameters are updated
according to the geometrical location of the training samples in the input space, and each
sample in the training set is learned only once. It’s a semisupervised based approach, the
training samples are semi-labelled i.e. for some samples, labels are known and for some
samples data labels are not known. The method starts with classification, which is done by
using the concept of ETL algorithm. In classification process various classes are formed.
These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.
This document outlines a chapter about functions in C programming. It discusses how functions allow programs to be modularized by breaking them into smaller pieces called modules. Functions can be user-defined or come from standard libraries. Functions take parameters as input, perform operations, and return output. Functions allow for abstraction, reusability, and avoidance of code repetition. The chapter covers defining functions, function prototypes, parameters, return values, and calling functions. It also provides examples of commonly used math library functions.
This document summarizes the Learn++.MF algorithm, which is an ensemble-of-classifiers approach for handling missing data. It trains classifiers on random subsets of available features, rather than estimating missing values. To classify an instance with missing features, it uses the majority vote of classifiers that did not use the missing features. The algorithm assumes redundant and randomly distributed features, conditions often met in practice. It avoids drawbacks of imputation methods and can accommodate substantial missing data with gradual performance decline as missing data increases.
I think this could be useful for those who works in the field of Coputational Intelligence. Give your valuable reviews so that I can progree in my research
This document provides an overview of neural networks and related topics. It begins with an introduction to neural networks and discusses natural neural networks, early artificial neural networks, modeling neurons, and network design. It then covers multi-layer neural networks, perceptron networks, training, and advantages of neural networks. Additional topics include fuzzy logic, genetic algorithms, clustering, and adaptive neuro-fuzzy inference systems (ANFIS).
Bayesian Generalization Error and Real Log Canonical Threshold in Non-negativ...Naoki Hayashi
I have talked in the conference Algebraic Statistics 2020.
As a background of our research, I briefly explained singular learning theory which can be interpretable as an intersection between algebraic statistics and statistical learning theory.
The main part of this presentation is introducing our recent studies for parameter region restriction in singular learning theory. I showed the researches about the learning coefficient (real log canonical threshold) of NMF and LDA. NMF and LDA are typical models whose parameter regions are restricted.
The document summarizes key concepts about perceptrons and perceptron networks:
1) A perceptron is a type of neural network unit that uses a step function as its activation. It takes weighted inputs, sums them, and outputs 1 if the sum is above a threshold and -1 if below.
2) Perceptrons can be organized into single-layer feedforward networks where each output unit is independent.
3) The perceptron learning algorithm updates weights based on errors to minimize misclassifications on the training set. It is guaranteed to converge if the problem is linearly separable.
This work is proposed the feed forward neural network with symmetric table addition method to design the
neuron synapses algorithm of the sine function approximations, and according to the Taylor series
expansion. Matlab code and LabVIEW are used to build and create the neural network, which has been
designed and trained database set to improve its performance, and gets the best a global convergence with
small value of MSE errors and 97.22% accuracy.
The document outlines the rules and topics that will be covered in a data structures and algorithms course. It includes:
- Class rules prohibiting late entry or early exit from classes and announcing unscheduled quizzes.
- An outline of standard data structures and algorithms to be covered, including arrays, stacks, queues, linked lists, trees, sorting, searching, and graphs.
- An introduction to key concepts like data types, algorithms, performance analysis, and asymptotic notation to analyze time and space complexity.
- Artificial neural networks are inspired by biological neural networks and try to mimic their learning mechanisms by modifying synaptic strengths through an optimization process.
- Learning in neural networks can be formulated as a function approximation task where the network learns to approximate a function by minimizing an error measure through optimization of synaptic weights.
- A single hidden layer neural network is capable of learning nonlinear function approximations if general optimization methods are applied to update the synaptic weights.
A Framework for Cross-media Information ManagementBeat Signer
Presentation given at EuroIMSA 2005, International Conference on Internet and Multimedia Systems and Applications. Grindelwald, Switzerland, February 2005
ABSTRACT: Nowadays a user's personal information space is fragmented into multiple repositories on their local machine as well as on remote servers. In order to enable later access to resources managed within such a cross-media information space, information has to be organised in a format that can be processed by automatic retrieval processes. We propose a general framework for personal information management based on extending a cross-media link server with supplemental metadata functionality. In addition to user generated information, our solution automatically derives metadata for classifying and associating resources based on direct interaction with the information space. Resources and metadata can be integrated by referencing external resources or information may be managed directly by the framework. The presented cross-media information management solution is not limited to a fixed set of predefined resources and can be extended based on a resource plug-in mechanism.
Matrix factorization techniques can be used to address some of the limitations of traditional collaborative filtering approaches for recommender systems. Matrix factorization decomposes the user-item rating matrix into the product of two lower-dimensional matrices, one representing latent factors for users and the other for items. This reduced dimensionality addresses data sparsity and scalability issues. Specifically, singular value decomposition is often used to perform this matrix factorization, which can approximate the original rating matrix while ignoring less important singular values and factor vectors. The decomposed matrices can then be multiplied to predict unknown user ratings.
Dr. kiani artificial neural network lecture 1Parinaz Faraji
The document provides a history of neural networks, beginning with McCulloch and Pitts creating the first neural network model in 1943. It then discusses several important developments in neural networks including perceptrons in the 1950s and 1960s, backpropagation in the 1980s, and neural networks being implemented in semiconductors in the late 1980s. The document also includes diagrams and explanations of biological neurons, artificial neurons, different types of activation functions, and key aspects of neural network architectures.
ADAPTIVE BLIND MULTIUSER DETECTION UNDER IMPULSIVE NOISE USING PRINCIPAL COMP...csandit
In this paper we consider blind signal detection for an asynchronous code division multiple access (CDMA) system with Principal component analysis (PCA) in impulsive noise. The blind multiuser detector requires no training sequences compared with the conventional multiuser detection receiver. The proposed PCA blind multiuser detector is robust when compared with knowledge based signature waveforms and the timing of the user of interest. PCA is a statistical method for reducing the dimension of data set, spectral decomposition of the covariance matrix of the dataset i.e first and second order statistics are estimated.
Principal component analysis makes no assumption on the independence of the data vectors PCA searches for linear combinations with the largest variances and when several linear combinations are needed, it considers variances in decreasing order of importance. PCA
improves SNR of signals used for differential side channel analysis. In different to other approaches, the linear minimum mean-square-error (MMSE) detector is obtained blindly; the detector does not use any training sequence like in subspace methods to detect multi user
receiver. The algorithm need not estimate the subspace rank in order to reduce the computational complexity. Simulation results show that the new algorithm offers substantial performance gains over the traditional subspace methods.
Adaptive blind multiuser detection under impulsive noise using principal comp...csandit
The document describes an adaptive blind multiuser detection method for asynchronous code division multiple access (CDMA) systems using principal component analysis (PCA) in impulsive noise environments. PCA is used to extract the principal components from the received signal without requiring training sequences or prior knowledge of channel characteristics. The PCA blind multiuser detector provides robust performance compared to knowledge-based detectors when signature waveforms and timing offsets of users are unknown. Simulation results show the proposed PCA method offers substantial gains over traditional subspace methods for multiuser detection.
Latent factor models for Collaborative Filteringsscdotopen
The document discusses latent factor models for collaborative filtering. It describes how latent factor models (1) map both users and items to a latent factor space to characterize them, (2) approximate ratings as the dot product of user and item vectors, and (3) can be used to predict unknown ratings. It also covers techniques like stochastic gradient descent and alternating least squares for training latent factor models on explicit and implicit feedback data.
Artificial neural networks (ANNs) are inspired by biological neural networks and are composed of interconnected processing elements called neurons. ANNs are configured through a learning process to solve problems like pattern recognition or data classification. Early research in the 1940s and 1950s laid the foundations, like McCulloch and Pitts developing the first neural network model and Hebb developing the first learning rule. ANNs use weighted connections and activation functions to learn from examples through training. Feedforward and feedback networks differ in whether signals travel in one or both directions between layers of neurons. Perceptrons were influential early neural network models that could perform tasks linear programs could not.
A empresa de tecnologia anunciou um novo produto revolucionário que combina hardware, software e serviços em nuvem. O dispositivo é pequeno, portátil e permite acesso a aplicativos e dados em qualquer lugar. Analistas acreditam que o produto pode transformar o mercado e estabelecer a empresa como líder no setor.
(1) The document describes a current mode quadrature oscillator circuit using a current differencing transconductance amplifier (CDTA). (2) The circuit produces two output currents with a 90 degree phase difference between them. (3) The CDTA is a five-terminal active element that consists of an input current differencing stage and dual output transconductance stage.
The document summarizes an interview with four mechanical engineering students - Karl Kreder, Travis Brubaker, Dan Hursh, and John Dill - about their senior design project called P-Ride, an electric inline skate. They entered P-Ride in the Burton D. Morgan Entrepreneurial Competition. The students explain that P-Ride can travel at 14 mph for an hour on a single battery charge with zero emissions. They are refining the power electronics and remote control functionality. Though the students placed fourth, they felt it was impressive given they were engineers competing against business majors. The competition helped them learn what investors seek regarding marketing and financial analyses of new products.
El documento habla sobre la historia de las carreras de autos y los riesgos y accidentes que conllevan. Comenzaron como una competencia para ver cual auto era más rápido, pero con el tiempo se volvieron más populares y peligrosas, resultando en numerosas muertes a pesar de los esfuerzos por hacerlas más seguras. Eventos como el Rally Dakar y accidentes en la Fórmula 1 y NASCAR han dejado muchos fallecidos, aunque también historias de sobrevivencia milagrosas. Al final plantea si vale la pen
El monitor de computadora muestra los resultados del procesamiento al usuario a través de una interfaz. Los primeros monitores surgieron en 1981 y eran monocromáticos diseñados para modo de texto. Los monitores tienen ventajas como un grosor delgado para portátiles y una geometría siempre perfecta, pero desventajas como solo pueden reproducir fielmente la resolución nativa y necesitan una fuente externa de luz.
This document provides a summary of Finlay A. Keith's experience and qualifications. He has over 14 years of experience in project engineering and management roles in the oil and gas, pharmaceutical, food and drink, and construction industries. His experience includes managing multi-million dollar projects from planning through completion on schedule and budget. He holds a BEng in Chemical Engineering from the University of Strathclyde and is applying for professional certification with APEGA.
La probabilidad permite predecir los resultados de eventos aleatorios al calcular las posibilidades de cada resultado posible, lo que ayuda a tomar mejores decisiones. Un ejemplo es predecir el color de la próxima pelota que saldrá de una caja con 3 pelotas de colores diferentes. La fórmula para calcular la probabilidad es el número de eventos favorables dividido entre el número total de resultados posibles.
This document provides an overview of Sac State's concussion protocol. It discusses the classic definition of concussion, symptoms, concerns like second impact syndrome, and the importance of preventing early return to play. Grading systems and the definition from the 2008 Zurich statement are presented. ImPACT testing, treatment guidelines, and a graduated return to play protocol emphasizing full resolution of symptoms are summarized.
This document discusses demineralized bone matrix (DBM) and its use in orthopedic procedures. It provides information on several DBM products, including StimuBlast, AlloMatrix, DBX, and Grafton. It describes the composition and characteristics of different DBM carriers like reverse phase medium and glycerol. The document also discusses issues like ACL tunnel widening and presents preliminary results of a pilot study examining the effect of DBM on tunnel size in ACL reconstruction. Finally, it introduces the FlexiGraft DBM product line including sponges, cortical fibers, and its applications in procedures like PASTA bridge and RC repair.
1) Planar motion can be represented as a resultant vector from combined component vectors, and the magnitude of this resultant vector must be corrected using the orientation angle between the vector and a reference axis.
2) This correction can be done using properties of a right triangle derived from the unit circle, where the trigonometric functions sine and cosine relate the component vectors to the resultant vector based on the orientation angle.
3) The trigonometric functions serve as correction factors between 0 and 1 (or -1 and 1) to adjust the magnitudes of the component vectors based on the orientation angle and resultant vector.
The study tested the reliability of measuring shoulder internal and external rotation range of motion using a smartphone app versus a clinical goniometer. Three groups of unskilled clinicians measured 12 college students' range of motion twice using both tools. The smartphone app produced good to excellent reliability for both internal and external rotation, with internal rotation reliability being significantly higher. While the clinical goniometer produced fair to good reliability, the smartphone app was found to be more reliable and accurate based on statistical analysis of the results. The study concluded that the smartphone app is a superior tool for measuring shoulder range of motion compared to the traditional goniometer.
This document provides an overview of shoulder labral repair and stabilization procedures. It discusses key steps like portal placement, suture management and fixation options. Specific techniques are demonstrated for SLAP repairs, anterior stabilizations using suture anchors and pushlocks, and posterior stabilizations. Potential complications like a lost suture or wire are also briefly covered. The document serves as a reference for orthopedic surgeons on the technical aspects of arthroscopic shoulder stabilization and labral repair surgeries.
This document discusses various linear data structures in C including arrays, records, pointers, and related operations. It describes declaring and initializing one-dimensional and two-dimensional arrays. Records are used to store related data elements using structures. Pointer arrays and dynamic arrays allocated at runtime are also covered. Searching algorithms like linear search and binary search, and sorting algorithms like bubble sort are summarized.
This document provides information on arrays in Java. It begins by defining an array as a collection of similar data types that can store values of a homogeneous type. Arrays must specify their size at declaration and use zero-based indexing. The document then discusses single dimensional arrays, how to declare and initialize them, and how to set and access array elements. It also covers multi-dimensional arrays, providing syntax for declaration and initialization. Examples are given for creating, initializing, accessing, and printing array elements. The document concludes with examples of searching arrays and performing operations on two-dimensional arrays like matrix addition and multiplication.
OOP Chapter 3: Classes, Objects and MethodsAtit Patumvan
This document discusses object-oriented programming concepts like classes, objects, and methods. It provides an example class for representing fractions that defines instance variables to store the numerator and denominator. It demonstrates creating fraction objects, setting properties on objects by calling methods, and accessing instance variables. The class defines an interface with method signatures and an implementation that includes method definitions to print fractions, set property values, and return property values.
Bca ii dfs u-1 introduction to data structureRai University
This document provides an introduction to data structures. It defines data structures as a way of organizing and storing data in a computer so it can be used efficiently. There are two main types: primitive data structures like integers and characters that are directly operated on by the CPU, and non-primitive structures like arrays and linked lists that are more complex. Key aspects of data structures covered include operations, properties, performance analysis using time and space complexity, and examples of linear structures like arrays and non-linear structures like trees. Common algorithms are analyzed based on their asymptotic worst-case running times.
Introduction to Exploratory Data Analysis with the sci-analysis Python PackageChrisMorrow28
This document introduces the Sci-Analysis Python package for exploratory data analysis. Sci-Analysis makes performing EDA easier by abstracting away specific SciPy, NumPy, and Matplotlib commands and using a single analyze() function. The analyze() function can perform different types of analysis depending on the type and number of arguments passed to it. The document demonstrates analyzing weather data from several cities to determine which has the best overall weather.
Mca ii dfs u-1 introduction to data structureRai University
This document provides an introduction to data structures. It defines data structures as a way of organizing and storing data in a computer so that it can be used efficiently. The document discusses different types of data structures including primitive, non-primitive, linear and non-linear structures. It provides examples of various data structures like arrays, linked lists, stacks, queues and trees. It also covers important concepts like time complexity, space complexity and Big O notation for analyzing algorithms. Common operations on data structures like search, insert and delete are also explained.
Bsc cs ii dfs u-1 introduction to data structureRai University
This document provides an introduction to data structures. It defines data structures as a way of organizing and storing data in a computer so it can be used efficiently. The document discusses different types of data structures including primitive, non-primitive, linear and non-linear structures. It provides examples of common data structures like arrays, linked lists, stacks, queues and trees. It also covers important concepts like time and space complexity analysis and Big O notation for analyzing algorithm efficiency.
Programs are complete in best of my knowledge with zero compilation error in IDE Bloodshed Dev-C++. These can be easily portable to any versions of Visual Studio or Qt. If you need any guidance please let me know via comments and Always Enjoy Programming.
The document discusses arrays, strings, and functions in C programming. It begins by explaining how to initialize and access 2D arrays, including examples of declaring and initializing a 2D integer array and adding elements of two 2D arrays. It also covers initializing and accessing multidimensional arrays. The document then discusses string basics like declaration and initialization of character arrays that represent strings. It explains various string functions like strlen(), strcat(), strcmp(). Finally, it covers functions in C including declaration, definition, call by value vs reference, and passing arrays to functions.
The document provides an overview of learning Bayes networks from data. It discusses learning the structure and conditional probability tables (CPTs) of a Bayes network given training data. When the network structure is known, the CPTs can be directly estimated from sample statistics in the training data, handling both cases of complete and missing data using techniques like expectation-maximization. When the structure is unknown, scoring metrics like minimum description length are used to search the space of possible structures to find the best fitting network. Dynamic decision networks extend this framework to model sequential decision making problems.
This document contains a lab manual for data structures programming. It outlines various exercises including representing sparse matrices using arrays and linked lists, implementing stack and queue data structures using arrays and linked lists, and performing operations on singly, doubly and circular linked lists. It also covers binary tree traversals, binary search tree implementation and operations, and algorithms including heap sort, quick sort, depth first search, breadth first search and Dijkstra's algorithm.
https://telecombcn-dl.github.io/2018-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
The document provides an introduction to a Java programming course. It outlines the course objectives which include understanding core Java concepts like primitive data types, control flow, methods, arrays, object-oriented programming, and core Java classes. It also discusses how upon completing the course students will be able to develop programs using Eclipse IDE and write simple programs using various Java features. The document then covers specific topics that will be taught like methods, object-oriented programming concepts like classes, constructors, and polymorphism.
The document provides information about arrays, strings, and character handling functions in C language. It discusses:
1. Definitions and properties of arrays, including declaring, initializing, and accessing single and multi-dimensional arrays.
2. Built-in functions for testing and mapping characters from the ctype.h library, including isalnum(), isalpha(), iscntrl(), isdigit(), ispunct(), and isspace().
3. Strings in C being arrays of characters terminated by a null character. It discusses common string handling functions from string.h like strlen(), strrev(), strlwr(), strupr(), strcpy(), strcat(), and strcmp().
IRJET- Unabridged Review of Supervised Machine Learning Regression and Classi...IRJET Journal
This document provides an unabridged review of supervised machine learning regression and classification techniques. It begins with an introduction to machine learning and artificial intelligence. It then describes regression and classification techniques for supervised learning problems, including linear regression, logistic regression, k-nearest neighbors, naive bayes, decision trees, support vector machines, and random forests. Practical examples are provided using Python code for applying these techniques to housing price prediction and iris species classification problems. The document concludes that the primary goal was to provide an extensive review of supervised machine learning methods.
Learn how to use arrays in Java, how to enter array, how to traverse an array, how to print array and more array operations.
Watch the video lesson and access the hands-on exercises here: https://softuni.org/code-lessons/java-foundations-certification-arrays
This document provides an overview of Java language fundamentals including:
- The structure of a basic Java program with a main method and use of print statements
- Data types, variables, and arrays in Java
- Operators like arithmetic, relational, and logical operators
- Control structures like if/else statements, switch statements, and loops (while, do-while, for) to control program flow
- Formatting output using escape sequences
The document includes examples of Java code to illustrate these core Java language concepts.
The document discusses separating object-oriented programming code into interface and implementation files. It shows how to define an interface for a Fraction class in a header file and provide the implementation in a separate file. It also covers compiling the code from the command line or using a makefile. The document then demonstrates synthesizing accessor methods to allow accessing properties using dot notation rather than message passing syntax.
Characteristics of Java and basic programming constructs like Data types, Variables, Operators, Control Statements, Arrays are discussed with relevant examples
Similar to Computer Programming Chapter 6 : Array and ArrayList (20)
The document discusses using Internet of Things (IoT) technology for smart agriculture. It provides an overview of IoT and how devices can communicate over a network without human interaction. It then discusses how microcontrollers like Arduino can be used to interface with sensors and actuators to monitor and control the physical environment for applications like smart farming. The document provides examples of using sensors to collect environmental data and controlling devices like motors and lights through a microcontroller.
An Overview of eZee Burrp! (Philus Limited)Atit Patumvan
1) Philus Limited produces restaurant management software called eZee BurrP! which provides a point of sale system, digital menus, and customer feedback system to help restaurants improve operations and customer experience.
2) The software allows restaurants to manage reservations, inventory, sales reporting, payroll and integrate with third parties. It also provides digital menus for customers to view and order from tablets.
3) The customer feedback system allows restaurants to collect surveys and reviews from customers through various methods to build customer loyalty and engagement. It also manages multi-location restaurant chains from one system.
แบบฝึกหัดวิชา Theory of Computation ชุดที่ 1 เซ็ตAtit Patumvan
This document contains an example practice set on sets. It includes questions to determine if elements are members of sets, to find the power set, subset, union, intersection and complement of various sets. Sets are defined using notation such as intervals of integers and set builder notation. Students are asked to write out the elements of sets resulting from operations on the given sets.
Media literacy provides a framework for accessing, analyzing, evaluating, and creating various messages from print to video to the internet. It builds an understanding of media's role in society and teaches important inquiry and self-expression skills for citizens of a democracy. Social media literacy involves having the proficiency to communicate appropriately and responsibly on social networks, and to critically evaluate online conversations. It includes skills like impression management, monitoring one's online reputation, thinking critically about content, having responsible conversations, managing one's social media presence, and managing information and technology.
The document discusses performance measures for total quality management. It outlines several objectives of establishing performance measures such as establishing baselines, determining process improvements needed, and comparing goals to actual performance. Several criteria for effective performance measures are listed, including being simple, relevant to customers, and enabling improvement. Examples of performance measures are provided for strategies involving quality, cost, flexibility, reliability, and innovation. Methods for presenting performance measures like time series graphs and control charts are also mentioned.
This document discusses principles of customer-supplier relationships in total quality management, including partnering, sourcing, supplier selection, supplier rating, and relationship development. The key points are that customers and suppliers should have long-term commitments based on trust and shared visions, methods for evaluating quality and supplier performance are important, and close collaboration through inspection, training, and team approaches helps develop strong relationships.
The document discusses various methods for continuous process improvement, including Juran's Trilogy, the DPSA cycle, Kaizen, and Six Sigma. It describes Juran's Trilogy as a systematic approach involving quality planning, control, and improvement. The DPSA cycle is a method for testing changes through planning, doing, studying, and acting on the results. Kaizen focuses on small, incremental changes to minimize waste and promote continuous improvement. Six Sigma provides a scientific, data-driven approach to process improvement and achieving significant financial results.
This document provides an introduction to Java EE (J2EE) including:
- An overview of the Model View Controller (MVC) design pattern and its core elements.
- A definition of Java EE as an open, standard platform for developing and deploying n-tier, web-enabled enterprise applications.
- An explanation of what comprises Java EE including specifications, implementations, compatibility testing, and more.
This document discusses various aspects of employee involvement in total quality management, including motivation, surveys, empowerment, teams, suggestion systems, and performance appraisal. It describes how understanding employee motivations and establishing clear goals can increase motivation. It also outlines different types of teams, characteristics of successful teams, and the stages of team development. Suggestion systems and performance appraisal are discussed as well.
The document discusses key aspects of customer satisfaction and quality management. It defines internal and external customers and explains how customer perception is influenced by factors like performance, features, service, price and reputation. The document also outlines methods for obtaining customer feedback, using customer complaints to improve, and translating customer needs into requirements. Customer retention is identified as an important goal.
The document discusses key aspects of leadership for Total Quality Management. It defines characteristics of quality leaders as emphasizing customers, prevention, collaboration and coaching. It also outlines the 7 Habits of Highly Effective People and Deming's philosophy. The roles of TQM leaders are described as ensuring decisions align with quality statements and participating in quality celebrations. The quality council duties include developing quality policies and plans.
This document provides an introduction to computer programming and programming languages. It discusses what programming is, the history and evolution of programming languages from machine languages to higher-level languages. It describes assembly languages, third-generation languages like Java and C++, fourth-generation languages, and debates the existence of fifth-generation languages. The document also discusses Java in more detail, including its history, editions, features, environment, and common misconceptions. It provides an example of a simple "Hello World" Java program.
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LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Training: ISO/IEC 27001 Information Security Management System - EN | PECB
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The simplified electron and muon model, Oscillating Spacetime: The Foundation...RitikBhardwaj56
Discover the Simplified Electron and Muon Model: A New Wave-Based Approach to Understanding Particles delves into a groundbreaking theory that presents electrons and muons as rotating soliton waves within oscillating spacetime. Geared towards students, researchers, and science buffs, this book breaks down complex ideas into simple explanations. It covers topics such as electron waves, temporal dynamics, and the implications of this model on particle physics. With clear illustrations and easy-to-follow explanations, readers will gain a new outlook on the universe's fundamental nature.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Computer Programming Chapter 6 : Array and ArrayList
1. Computer Programming
Chapter 6 : Array and ArrayList
Atit Patumvan
Faculty of Management and Information Sciences
Naresuna University
Tuesday, August 2, 2011
2. 2
Collections
• Collection is a group of objects contained in a single
element.
• Example of collection include an array of integer, a vector
of string, or a hash map of vehicles.
• The Java Collection Framework is a unified set of classes
and interfaces defined in the java.util package for storing
collections.
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
3. 3
อาเรย์ (Array)
• เป็นโครงสร้างพื้นฐานในการจัดเก็บชุดข้อมูล
• เป็นการจัดเก็บชุดข้อมูลที่มีชนิดเดียวกันเข้าไว้ด้วยกัน
• ข้อมูลแต่ละตัวเรียกว่า อิลิเมนต์ (element) หรือ เซลล์ (cell)
• การอ้างอิงถึงข้อมูลที่อยู่ในแต่ละอิลิเมนต์ จะใช้ตัวชี้ที่เรียกว่า อิน
เด็กซ์ (index) หรือ ซับสคริปต์ (subscript)
• การอ้างอิงถึงข้อมูลจะต้องอยู่ใน เครื่องหมาย bracket ʻ[ ]ʼ
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
4. 4
อาร์เรย์ (Array)
01: public class PoorScoreProcessing {
02:
03: public static void main(String[] args) {
alice_score 04: int alice_score = 18;
05: int bob_score = 20;
18 06: int carry_score = 35;
07: int dart_score = 21;
int 08: double average_score = averageScore(
bob_score
09: alice_score,
20 10: bob_score,
11: carry_score,
int 12: dart_score);
carry_score 13: System.out.println("Average score is " + average_score);
14: }
35 15:
16: public static double averageScore(
int 17: int s1,
dart_score 18: int s2,
19: int s3,
21 20: int s4) {
21: int sum = s1 + s2 + s3 + s4;
int 22: double avr = sum / 4.0;
23: return avr;
24:
25: }
26: }
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
5. 5
อาร์เรย์ (Array)
score int
18 20 35 21
01: public class ScoreProcessing {
02:
03: public static void main(String[] args) {
04: int[] score = {18, 20, 35, 21};
05:
06: double average_score = averageScore(score);
07: System.out.println("Average score is " + average_score);
08: }
09:
10: public static double averageScore(int[] scores) {
11: int sum = 0;
12: for (int score : scores) {
13: sum += score;
14: }
15: double avr = sum / (double) scores.length;
16: return avr;
17:
18: }
19: }
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
6. 6
การประกาศอาเรย์ (Array Declaration)
Name of array variable length
double [] data = new double [10];
type of array variable element type
data
double double double double double double double double double double
double [ ]
data @AB1F245H
double[ ]
double[ ]@AB1F245H
double double double double double double double double double double
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
7. 7
การประกาศอาเรย์ (Array Declaration)
int [ ] numbers = new int [10]
final int LENGTH = 10;
int [ ] numbers = new int [ LENGTH ];
int length = in.nextInt();
int [ ] numbers = new int [ length ];
int [ ] numbers = { 0, 1, 4, 9, 16};
String [ ] friends = {“Alice”, “Bob”, “Carry”};
Person alice = new Person();
Person bob = new Person();
Person carry = new Person();
Person [ ] persons = {alice, bob, carry};
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
8. 8
Array Reference
01: public class ArrayReference {
02:
03: public static void main(String[] args) {
04: int[] array1 = {1, 2, 3, 4, 5};
05: int[] array2 = array1;
06: array1[0] = 7;
07: System.out.println(array2[0]);
08: }
09: }
int[ ]@AB1F245H
array1 @AB1F245H
int[ ] 1 2 3 4 5
int[0] int[1] int[2] int[3] int[4]
array2 @AB1F245H
int[ ]
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
9. 9
ข้อผิดพลาดที่เกิดขึ้นเป็นประจํา (Common Errors)
• อ้างอิงอินเด็กซ์ผิดพลาด (Bound Error)
01: public class ArrayError1 {
02:
03: public static void main(String[] args) {
04: int[ ] data = new int[10];
05: data[10] = 5.4; // Error: data has 10 elements with index value 0 to 9
06: }
07:}
• ไม่ได้จองพื้นที่ในหน่วยความจํา (Initialized Error)
01: public class ArrayError2 {
02:
03: public static void main(String[] args) {
04: int[ ] data;
05: data[0] = 5.4; // Error: data not initialized
06: }
07:}
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
10. 10
การเติมสมาชิก (filling)
01: public class FillingArray {
02:
03: public static void main(String[] args) {
04: int[] data = new int[5];
05: for (int index = 0; index < data.length; index++) {
06: data[index] = index * index;
07: }
08: }
09:}
0 1 4 9 16
data
int[0] int[1] int[2] int[3] int[4]
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
11. 11
การหาผลรวมและค่าเฉลี่ย (sum and average)
0 1 4 9 16
data
01: public class SumAndAverage { int[0] int[1] int[2] int[3] int[4]
02:
03: public static void main(String[] args) {
04: int[] data = new int[5];
05: for (int index = 0; index < data.length; index++) {
06: data[index] = index * index;
07: }
08:
09: double total = 0;
10: for (int element : data) {
11: total = total + element;
12: }
13: double average = 0;
14: if (data.length > 0) {
15: average = total / (double) data.length;
16: }
17: System.out.println("total: " + total);
18: System.out.println("average: " + average);
19: }
20:}
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
12. 12
หาค่ามากและหาค่าน้อย (maximum and minimum)
01: public class MinMax {
02:
03: public static void main(String[] args) {
04: int[] data = new int[5];
05: for (int index = 0; index < data.length; index++) {
06: data[index] = (int) (Math.random()*100);
07: System.out.println("data["+index+"]="+data[index]);
08: }
09:
10: int min = data[0];
11: int max = data[0];
12: for (int index = 1; index < data.length; index++) {
13:
14: if(min > data[index]){
15: min = data[index]; data[0]=16
16: } data[1]=37
17: data[2]=11
18: if(max < data[index]){
data[3]=32
19: max = data[index];
20: } data[4]=92
21: } min: 11
22: max: 92
23: System.out.println("min: " + min);
24: System.out.println("max: " + max);
25: }
26:}
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
13. 13
หาค่ามากและหาค่าน้อย (maximum and minimum)
01: public class MinMax {
02:
03: public static void main(String[] args) {
04: int[] data = new int[5];
05: for (int index = 0; index < data.length; index++) {
06: data[index] = (int) (Math.random()*100);
07: System.out.println("data["+index+"]="+data[index]);
08: }
09:
10: int min = data[0];
11: int max = data[0];
12: for (int index = 1; index < data.length; index++) {
13:
14: if(min > data[index]){ data[0]=16
15: min = data[index];
16: }
data[1]=37
17: data[2]=11
18: if(max < data[index]){ data[3]=32
19: max = data[index]; data[4]=92
20: }
21: } min: 11
22: max: 92
23: System.out.println("min: " + min);
24: System.out.println("max: " + max);
25: }
26:}
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
14. 14
ค้นหาสมาชิกของอาเรย์ (Search)
01: public class Search {
02:
03: public static void main(String[] args) {
04: int[] data = new int[5]; data[0]=7
05: for (int index = 0; index < data.length; index++) {
06: data[index] = (int) (Math.random()*10); data[1]=4
07: System.out.println("data["+index+"]="+data[index]); data[2]=3
08: } data[3]=6
09: int searchValue = 5;
10: int position = 0;
data[4]=2
11: boolean found = false; search value = 5
12: while(position < data.length && !found){ Not found
13: if(data[position]== searchValue){
14: found = true;
15: } else { data[0]=1
16: position++;
data[1]=3
17: }
18: } data[2]=5
19: System.out.println("search value = " + searchValue); data[3]=8
20: if(found){ data[4]=4
21: System.out.println("Found at "+position );
22: } else {
search value = 5
23: System.out.println("Not found"); Found at 2
24: }
25: }
26:}
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
15. 15
Insert an Element
01: import java.util.Arrays;
02:
03: public class ArrayInsertElement {
04:
05: public static void insertElement(int[] data, int index, int value) {
06:
07: for (int position = data.length - 2; position >= index; position--) {
08: data[position + 1] = data[position];
09: }
10: data[index] = value;
11: }
12:
13: public static void main(String[] args) {
14: int[] data = new int[5];
15: for (int index = 0; index < data.length; index++) {
16: data[index] = (int) (Math.random() * 10);
17: System.out.println("data[" + index + "]=" + data[index]);
18: }
19: data[0]=7
20: System.out.println("Before :" + Arrays.toString(data)); data[1]=3
21: insertElement(data, 1, 99); data[2]=9
22: System.out.println("After :" + Arrays.toString(data)); data[3]=0
23: } data[4]=2
Before :[7, 3, 9, 0, 2]
24: }
After :[7, 99, 3, 9, 0]
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
16. 16
Copy Array
01: import java.util.Arrays;
02:
03: public class ArrayCopy {
04:
05: public static void main(String[] args) {
06: int[] data1 = new int[5];
07: for (int index = 0; index < data1.length; index++) {
08: data1[index] = (int) (Math.random() * 10);
09: System.out.println("data[" + index + "]=" + data1[index]);
10: }
11:
12: System.out.println("data1[] =" + Arrays.toString(data1));
13: int[] data2 = Arrays.copyOf(data1, data1.length);
14: System.out.println("data2[] =" + Arrays.toString(data2));
15: }
16: }
data[0]=9
data[1]=2
data[2]=6
data[3]=7
data[4]=3
data1[] =[9, 2, 6, 7, 3]
data2[] =[9, 2, 6, 7, 3]
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
17. 17
Two Dimensional Arrays
Winter Olympics 206: Figure Skating Medal Counts
0 0 1
Gold Silver Bronze counts
Canada 0 0 1 0 1 1
China 0 1 1
Japan 1 0 0
1 0 0
Russia 3 0 0 3 0 0
number of rows
Name of array variable int [ ] [ ] counts = {
{0,0,1},
{0,1,1},
{1,0,0},
int [ ][ ] data = new int [4][3]; {3,0,0}
};
type of array variable number of columns
element type
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
18. 18
Accessing Two-Dimensional Arrays
01: public class MultiDimensionalArrays {
02:
03: public static final int COUNTRIES = 4;
04: public static final int MEDALS = 3;
05: 0 0 1
06: public static void main(String[] args) { 0 1 1
07: int[][] counts = {
08: {0, 0, 1},
1 0 0
09: {0, 1, 1}, 3 0 0
10: {1, 0, 0},
11: {3, 0, 0}
12: };
13: for (int i = 0; i < COUNTRIES; i++) {
14: for (int j = 0; j < MEDALS; j++) {
15: System.out.printf("%8d", counts[i][j]);
16: }
17: System.out.println();
18: }
19: }
20: }
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
19. 19
Using Objects in Arrays
01: import java.util.Arrays; 01: public class ModalCount {
02: 02:
03: public class Country { 03: public static void main(String[] args) {
04: public static final int GOLD = 0; 04: Country[] countries = new Country[4];
05: public static final int SILVER = 0; 05: countries[0] = new Country("Canada");
06: public static final int BRONZE = 0; 06: countries[1] = new Country("China");
07: 07: countries[2] = new Country("Japan");
08: private String name; 08: countries[3] = new Country("Russia");
09: private int [] counts = new int [3]; 09:
10: 10: countries[0].setModal(Country.BRONZE, 1);
11: public Country(String name){ 11: countries[1].setModal(Country.SILVER, 1);
12: this.name = name; 12: countries[1].setModal(Country.BRONZE, 1);
13: } 13: countries[2].setModal(Country.GOLD, 1);
14: 14: countries[3].setModal(Country.GOLD, 3);
15: public void setModal(int type, int number){ 15:
16: counts[type]=number; 16: System.out.println(countries[0]);
17: } 17: System.out.println(countries[1]);
18: 18: System.out.println(countries[2]);
19: @Override 19: System.out.println(countries[3]);
20: public String toString(){ 20: }
21: return name +"="+ Arrays.toString(counts); 21: }
22: }
23: }
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
20. 20
ArrayList
• ArrayList is a class in the standard Java libraries
• ArrayList is an object that can grow and shrink while your
program is running
• An ArrayList serves the same purpose as an array, except
that an ArrayList can change length while the program is
running
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
21. 21
Declaring and Using Array List
Name of number of rows
array list variable
ArrayList <String> friends = new ArrayList<String>();
type of element type
array list variable
friends.add(“Alice”);
String name = friends.get(i);
friends.set(i, “Bob”);
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
22. 22
Working with Array List
01: import java.util.ArrayList;
02:
+ Person 03: public class AddingElements {
04:
- name : String 05: public static void main(String[] args) {
06: ArrayList<Person> friends = new ArrayList<Person>();
07: friends.add(new Person("Alice"));
+ Person(String) 08: friends.add(new Person("Bob"));
+ getName() : String 09:
+ setName(String) : void 10: System.out.println("Numbers of friends : " + friends.size());
11:
12: Person firstPerson = friends.get(0);
13: System.out.println("Name of first friend is "+
01: public class Person { firstPerson.getName());
02: 14:
03: private String name; 15: friends.set(1, new Person("Carry"));
04: 16: Person secondPerson = friends.get(1);
05: public Person(String name) { 17: System.out.println("Name of second friend is " +
06: this.name = name; secondPerson.getName());
07: } 18:
08: 19: friends.remove(0);
09: public String getName() { 20: firstPerson = friends.get(0);
10: return name; 21: System.out.println("Name of first friend is " +
11: } firstPerson.getName());
12: 22: }
13: public void setName(String name) { 23: }
14: this.name = name;
15: } Numbers of friends : 2
16: } Name of first friend is Alice
Name of second friend is Carry
Name of first friend is Carry
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
23. 23
Copy Array List
01: import java.util.ArrayList;
+ Person 02:
03: public class CopyingArray {
- name : String 04:
05: public static void main(String[] args) {
06: ArrayList<Person> friends1 = new ArrayList <Person>();
+ Person(String) 07: friends1.add(new Person("Alice"));
+ getName() : String 08: friends1.add(new Person("Bob"));
+ setName(String) : void 09:
10: ArrayList<Person> friends2 = (ArrayList) friends1.clone();
11:
12: friends1.add(0, new Person("Carry"));
01: public class Person { 13:
02: 14: Person firstPerson = friends1.get(0);
03: private String name; 15: System.out.println(firstPerson.getName());
04: 16:
05: public Person(String name) { 17: firstPerson = friends2.get(0);
06: this.name = name; 18: System.out.println(firstPerson.getName());
07: } 19:
08: 20: }
09: public String getName() { 21: }
10: return name;
11: }
12:
13: public void setName(String name) {
14: this.name = name;
15: }
16: }
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
24. 24
Wrapper Class and Auto-boxing
Primitive Type Wrapper Class 01: import java.util.ArrayList;
byte Byte 02:
03: public class Wrapper {
boolean Boolean
04:
char Char 05: public static void main(String[] args) {
double Double 06: Double wrapper = 29.35;
07: double x = wrapper;
float Float
08:
int Integer 09: ArrayList<Double> data = new ArrayList<Double>();
long Long 10: data.add(29.35);
short Short 11: double y = data.get(0);
12: }
13: }
Double@AB1F245H
wrapper @AB1F245H value 29.35
Double
Double
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011
25. 25
Compare Array and Array List Operation
Operation Array ArrayList
Get an element. x = data[4] x = data.get(4)
Replace an element. data[4] = 35; data.set(4, 35);
Number of elements. data.length data.size();
Number of filled elements. - data.size();
Remove an element. - data.remove(4);
Add an element, growing the
- data.add(35);
collection.
Initializing a collection int[] data = { 1, 4, 9}; -
Atit Patumvan, Faculty of Management and Information Sciences, Naresuan University
Tuesday, August 2, 2011