This document discusses algorithms and their complexity. It provides an example of a linear search algorithm to find a target value in an array. The complexity of this algorithm is analyzed for the worst and average cases. In the worst case, the target is the last element and all n elements must be checked, resulting in O(n) time complexity. On average, about half the elements (n+1)/2 need to be checked, resulting in average time complexity of O(n).
This document outlines the units and topics covered in a course on Design and Analysis of Algorithms. It includes definitions of key algorithm concepts like time complexity, asymptotic notation, and algorithm design approaches. Data structures like arrays, binary search trees, AVL trees, and red-black trees are discussed. Specific algorithms like quicksort, dynamic programming, greedy algorithms, and minimum spanning trees are covered. The document also introduces computational complexity classes like P, NP, NP-complete and NP-hard and some famous unsolved problems in computer science.
This document provides an introduction to data structures and algorithms. It defines data as quantities, characters, or symbols operated on by a computer. Data structures are described as organized ways to store and access data efficiently. Common data structures include arrays, linked lists, trees, stacks, and queues. Algorithms are sets of instructions to solve problems, taking input and producing output. Good algorithms are correct, unambiguous, and efficient. Examples demonstrate data structures like arrays and graphs, as well as a simple maximum-finding algorithm. The conclusion emphasizes the importance of data structures.
Concept and Definition of Data Structures
Introduction to Data Structures: Information and its meaning, Array in C++: The array as an ADT, Using one dimensional array, Two dimensional array, Multi dimensional array, Structure , Union, Classes in C++.
https://github.com/ashim888/dataStructureAndAlgorithm
This document discusses support vector machines (SVM) for classification. It explains that SVM finds a hyperplane that separates classes by maximizing the margin between them. It provides examples of linear, polynomial, Gaussian, and sigmoid SVM kernels. It also discusses tuning SVM parameters like gamma and C, and the pros and cons of SVM, including that it works well with clear margins but does not perform as well on large or noisy datasets. The document is presented by Manish and provides an introduction to SVM for classification.
This document discusses methods for one-shot learning using siamese neural networks. It provides an overview of several key papers in this area, including using siamese networks for signature verification (1993) and one-shot image recognition (2015), and introducing matching networks for one-shot learning (2016). Matching networks incorporate an attention mechanism into a neural network to rapidly learn from small datasets by matching training and test conditions. The document also reviews experiments demonstrating one-shot and few-shot learning on datasets like Omniglot using these siamese and matching network approaches.
This document provides an overview of machine learning concepts including supervised and unsupervised learning. It defines machine learning as a branch of artificial intelligence that uses data to learn. Unsupervised learning can learn more complex models than supervised learning from unlabeled data without explanations. Dimensionality reduction and density estimation are two types of unsupervised learning. Locally linear embedding (LLE) is a nonlinear dimensionality reduction technique that converts high-dimensional data into a lower-dimensional representation while preserving local neighborhoods. The LLE algorithm involves computing neighbors of each data point, weights between points, and vectors to perform the dimensionality reduction.
This document discusses algorithms and their complexity. It provides an example of a linear search algorithm to find a target value in an array. The complexity of this algorithm is analyzed for the worst and average cases. In the worst case, the target is the last element and all n elements must be checked, resulting in O(n) time complexity. On average, about half the elements (n+1)/2 need to be checked, resulting in average time complexity of O(n).
This document outlines the units and topics covered in a course on Design and Analysis of Algorithms. It includes definitions of key algorithm concepts like time complexity, asymptotic notation, and algorithm design approaches. Data structures like arrays, binary search trees, AVL trees, and red-black trees are discussed. Specific algorithms like quicksort, dynamic programming, greedy algorithms, and minimum spanning trees are covered. The document also introduces computational complexity classes like P, NP, NP-complete and NP-hard and some famous unsolved problems in computer science.
This document provides an introduction to data structures and algorithms. It defines data as quantities, characters, or symbols operated on by a computer. Data structures are described as organized ways to store and access data efficiently. Common data structures include arrays, linked lists, trees, stacks, and queues. Algorithms are sets of instructions to solve problems, taking input and producing output. Good algorithms are correct, unambiguous, and efficient. Examples demonstrate data structures like arrays and graphs, as well as a simple maximum-finding algorithm. The conclusion emphasizes the importance of data structures.
Concept and Definition of Data Structures
Introduction to Data Structures: Information and its meaning, Array in C++: The array as an ADT, Using one dimensional array, Two dimensional array, Multi dimensional array, Structure , Union, Classes in C++.
https://github.com/ashim888/dataStructureAndAlgorithm
This document discusses support vector machines (SVM) for classification. It explains that SVM finds a hyperplane that separates classes by maximizing the margin between them. It provides examples of linear, polynomial, Gaussian, and sigmoid SVM kernels. It also discusses tuning SVM parameters like gamma and C, and the pros and cons of SVM, including that it works well with clear margins but does not perform as well on large or noisy datasets. The document is presented by Manish and provides an introduction to SVM for classification.
This document discusses methods for one-shot learning using siamese neural networks. It provides an overview of several key papers in this area, including using siamese networks for signature verification (1993) and one-shot image recognition (2015), and introducing matching networks for one-shot learning (2016). Matching networks incorporate an attention mechanism into a neural network to rapidly learn from small datasets by matching training and test conditions. The document also reviews experiments demonstrating one-shot and few-shot learning on datasets like Omniglot using these siamese and matching network approaches.
This document provides an overview of machine learning concepts including supervised and unsupervised learning. It defines machine learning as a branch of artificial intelligence that uses data to learn. Unsupervised learning can learn more complex models than supervised learning from unlabeled data without explanations. Dimensionality reduction and density estimation are two types of unsupervised learning. Locally linear embedding (LLE) is a nonlinear dimensionality reduction technique that converts high-dimensional data into a lower-dimensional representation while preserving local neighborhoods. The LLE algorithm involves computing neighbors of each data point, weights between points, and vectors to perform the dimensionality reduction.
The document provides an introduction to algorithms presented by Sachin Sharma Bhandari. It defines algorithms, discusses analyzing time and space efficiency, and provides examples of sorting and other problems solved by algorithms. It also reviews data structures like stacks, queues, linked lists, and binary trees that are important for algorithm design.
Me 443 2 tour of mathematica Erdi Karaçal Mechanical Engineer University of...Erdi Karaçal
This document provides an overview of the capabilities of Mathematica, including performing numerical calculations with exact or approximate results, evaluating standard mathematical functions, generating 3D graphics and sound, performing algebraic, calculus, and equation solving operations, working with lists, vectors and matrices, and symbolic computation using transformation rules and definitions.
Me 443 1 what is mathematica Erdi Karaçal Mechanical Engineer University of...Erdi Karaçal
Mathematica is a general computer software system and language intended for mathematical and other applications. It can be used as a numerical and symbolic calculator, a visualization system for functions and data, a high-level programming language, and a modeling and data analysis environment. Mathematica handles numerical, symbolic, and graphical computations in a unified way and has over 800 built-in mathematical operations. It produces both 2D and 3D graphics and incorporates a graphics language. Mathematica also includes a full programming language that allows users to add their own extensions to the system and supports several programming styles.
The document discusses algorithms and their use for solving problems expressed as a sequence of steps. It provides examples of common algorithms like sorting and searching arrays, and analyzing their time and space complexity. Specific sorting algorithms like bubble sort, insertion sort, and quick sort are explained with pseudocode examples. Permutations, combinations and variations as examples of combinatorial algorithms are also covered briefly.
This document presents a VHDL implementation of a complex number multiplier using the ancient Vedic mathematics technique known as Urdhva Tiryakbhyam sutra. The implementation is tested on a Spartan 3 FPGA kit. Simulation results show the resource utilization and delay for 4-bit, 8-bit, and 16-bit complex multipliers designed using this Vedic multiplication method. The results indicate that the Urdhva Tiryakbhyam sutra can efficiently implement complex number multiplication with relatively low resource usage and delay, making it suitable for digital signal processing applications requiring extensive complex number operations.
This document provides instructions for two machine learning homework assignments involving time series prediction and classification. For the first assignment, students are asked to use neural networks to predict chaotic time series data from the Mackey-Glass equation, comparing performance of linear and nonlinear models. For the second assignment, students must classify iris flower types from the Iris data set using a neural network with four input nodes, three output nodes, and logistic output units, evaluating performance through cross-validation and testing.
KNN algorithm is one of the simplest classification algorithm and it is one of the most used learning algorithms. KNN is a non-parametric, lazy learning algorithm. Its purpose is to use a database in which the data points are separated into several classes to predict the classification of a new sample point.
This document discusses data structures and their implementation in C++. It begins by defining the objectives of understanding data structures, their types, and operations. It then defines data and data structures, and describes how data is represented in computer memory. The document classifies data structures as primitive and non-primitive, and describes common operations on each. It provides examples of linear and non-linear data structures like arrays, stacks, queues, and trees. The document concludes by explaining arrays in more detail, including their representation in memory and basic operations like traversing, searching, and sorting.
Independent Component Analysis (ICA) is a statistical technique used to separate mixed signals into independent non-Gaussian components. ICA algorithms estimate the mixing matrix to recover the original source signals up to scaling and permutation ambiguities. ICA has applications in biomedical signal processing such as separating artifacts in MEG data, reducing noise in images, and identifying independent brainwave components from EEG recordings.
1. Data structures organize data in memory for efficient access and processing. They represent relationships between data values through placement and linking of the values.
2. Algorithms are finite sets of instructions that take inputs, produce outputs, and terminate after a finite number of unambiguous steps. Common data structures and algorithms are analyzed based on their time and space complexity.
3. Data structures can be linear, with sequential elements, or non-linear, with branching elements. Abstract data types define operations on values independently of implementation through inheritance and polymorphism.
- Linear algebra is important for image recognition and other fields like physics, economics, and politics. It allows analyzing relationships between multiple variables without calculus.
- Python is a good platform for linear algebra due to libraries like NumPy that allow fast processing of multi-dimensional data like matrices. It also has simple syntax without semicolons.
- Key concepts discussed include vectors, matrices, linear transformations, abstraction, and how linear algebra solves problems in fields like quantum mechanics. Comprehensions provide a concise way to generate sets, lists, and arrays in Python.
This document discusses algorithms and some basic algorithms used in computer science. It begins by defining an algorithm as a step-by-step method for solving a problem or completing a task. It then describes three common constructs used in algorithms: sequence, decision, and repetition. The document also discusses representations of algorithms using UML diagrams and pseudocode. Finally, it provides examples of basic algorithms like summation, product, and finding the smallest/largest values; and describes the general logic and structure of algorithms for addition, multiplication, and minimum/maximum calculations.
Me 443 4 plotting curves Erdi Karaçal Mechanical Engineer University of Gaz...Erdi Karaçal
- The document discusses various plotting and graphics capabilities in Mathematica, including plotting functions, parametric plots, plotting lists of data, options for controlling plot appearance, and two-dimensional and three-dimensional graphics.
- It describes how to control aspects of plots like scales, sampling of functions, and colors using options. It also covers various graphics primitives and directives for customizing plots.
A NEW PARALLEL ALGORITHM FOR COMPUTING MINIMUM SPANNING TREEijscmc
Computing the minimum spanning tree of the graph is one of the fundamental computational problems. In
this paper, we present a new parallel algorithm for computing the minimum spanning tree of an undirected
weighted graph with n vertices and m edges. This algorithm uses the cluster techniques to reduce the
number of processors by fraction 1/f (n) and the parallel work by the fraction O ( 1 lo g ( f ( n )) ),where f (n) is an
arbitrary function. In the case f (n) =1, the algorithm runs in logarithmic-time and use super linear work on
EREWPRAM model. In general, the proposed algorithm is the simplest one.
Parallel processing technique for high speed image segmentation using colorIAEME Publication
This document describes a novel method for high-speed image segmentation using parallel processing and self-learning devices. The method can process video streams at 1000 frames per second. It uses parallel processors that can be trained simultaneously to learn color and grayscale values from example images. After training, the processors can identify colored or grayscale regions in new images in real time. The key advantages are that the parallel processors are easy to program and train for specific tasks like segmentation, unlike other parallel approaches.
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
Kernal based speaker specific feature extraction and its applications in iTau...TELKOMNIKA JOURNAL
This document summarizes kernel-based speaker recognition techniques for an automatic speaker recognition system (ASR) in iTaukei cross-language speech recognition. It discusses kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) for nonlinear speaker-specific feature extraction to improve ASR classification rates. Evaluation of the ASR system using these techniques on a Japanese language corpus and self-recorded iTaukei corpus showed that KLDA achieved the best performance, with an equal error rate improvement of up to 8.51% compared to KPCA and KICA.
A Comparative study of K-SVD and WSQ Algorithms in Fingerprint Compression Te...IRJET Journal
This document compares the K-SVD and WSQ algorithms for fingerprint compression. It provides an overview of both algorithms, including how they work, their advantages, and disadvantages. It also presents results of compressing different sized fingerprint images using each algorithm, showing that K-SVD consistently achieved smaller file sizes than WSQ. The document concludes that K-SVD is superior to WSQ for compressing fingerprint images.
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp –...IRJET Journal
This document summarizes research on developing parallel algorithms to optimize solving the longest common subsequence (LCS) problem. LCS is commonly used for sequence comparison in bioinformatics. Traditional sequential dynamic programming algorithms have complexity of O(mn) for sequences of lengths m and n. The document reviews parallel algorithms developed using tools like OpenMP and GPUs like CUDA to reduce computation time. It proposes the authors' own optimized parallel algorithm for multi-core CPUs using OpenMP.
This document provides an overview of a lecture on designing and analyzing computer algorithms. It discusses key concepts like what an algorithm and program are, common algorithm design techniques like divide-and-conquer and greedy methods, and how to analyze algorithms' time and space complexity. The goals of analyzing algorithms are to understand their behavior, improve efficiency, and determine whether problems can be solved within a reasonable time frame.
The document provides an introduction to algorithms presented by Sachin Sharma Bhandari. It defines algorithms, discusses analyzing time and space efficiency, and provides examples of sorting and other problems solved by algorithms. It also reviews data structures like stacks, queues, linked lists, and binary trees that are important for algorithm design.
Me 443 2 tour of mathematica Erdi Karaçal Mechanical Engineer University of...Erdi Karaçal
This document provides an overview of the capabilities of Mathematica, including performing numerical calculations with exact or approximate results, evaluating standard mathematical functions, generating 3D graphics and sound, performing algebraic, calculus, and equation solving operations, working with lists, vectors and matrices, and symbolic computation using transformation rules and definitions.
Me 443 1 what is mathematica Erdi Karaçal Mechanical Engineer University of...Erdi Karaçal
Mathematica is a general computer software system and language intended for mathematical and other applications. It can be used as a numerical and symbolic calculator, a visualization system for functions and data, a high-level programming language, and a modeling and data analysis environment. Mathematica handles numerical, symbolic, and graphical computations in a unified way and has over 800 built-in mathematical operations. It produces both 2D and 3D graphics and incorporates a graphics language. Mathematica also includes a full programming language that allows users to add their own extensions to the system and supports several programming styles.
The document discusses algorithms and their use for solving problems expressed as a sequence of steps. It provides examples of common algorithms like sorting and searching arrays, and analyzing their time and space complexity. Specific sorting algorithms like bubble sort, insertion sort, and quick sort are explained with pseudocode examples. Permutations, combinations and variations as examples of combinatorial algorithms are also covered briefly.
This document presents a VHDL implementation of a complex number multiplier using the ancient Vedic mathematics technique known as Urdhva Tiryakbhyam sutra. The implementation is tested on a Spartan 3 FPGA kit. Simulation results show the resource utilization and delay for 4-bit, 8-bit, and 16-bit complex multipliers designed using this Vedic multiplication method. The results indicate that the Urdhva Tiryakbhyam sutra can efficiently implement complex number multiplication with relatively low resource usage and delay, making it suitable for digital signal processing applications requiring extensive complex number operations.
This document provides instructions for two machine learning homework assignments involving time series prediction and classification. For the first assignment, students are asked to use neural networks to predict chaotic time series data from the Mackey-Glass equation, comparing performance of linear and nonlinear models. For the second assignment, students must classify iris flower types from the Iris data set using a neural network with four input nodes, three output nodes, and logistic output units, evaluating performance through cross-validation and testing.
KNN algorithm is one of the simplest classification algorithm and it is one of the most used learning algorithms. KNN is a non-parametric, lazy learning algorithm. Its purpose is to use a database in which the data points are separated into several classes to predict the classification of a new sample point.
This document discusses data structures and their implementation in C++. It begins by defining the objectives of understanding data structures, their types, and operations. It then defines data and data structures, and describes how data is represented in computer memory. The document classifies data structures as primitive and non-primitive, and describes common operations on each. It provides examples of linear and non-linear data structures like arrays, stacks, queues, and trees. The document concludes by explaining arrays in more detail, including their representation in memory and basic operations like traversing, searching, and sorting.
Independent Component Analysis (ICA) is a statistical technique used to separate mixed signals into independent non-Gaussian components. ICA algorithms estimate the mixing matrix to recover the original source signals up to scaling and permutation ambiguities. ICA has applications in biomedical signal processing such as separating artifacts in MEG data, reducing noise in images, and identifying independent brainwave components from EEG recordings.
1. Data structures organize data in memory for efficient access and processing. They represent relationships between data values through placement and linking of the values.
2. Algorithms are finite sets of instructions that take inputs, produce outputs, and terminate after a finite number of unambiguous steps. Common data structures and algorithms are analyzed based on their time and space complexity.
3. Data structures can be linear, with sequential elements, or non-linear, with branching elements. Abstract data types define operations on values independently of implementation through inheritance and polymorphism.
- Linear algebra is important for image recognition and other fields like physics, economics, and politics. It allows analyzing relationships between multiple variables without calculus.
- Python is a good platform for linear algebra due to libraries like NumPy that allow fast processing of multi-dimensional data like matrices. It also has simple syntax without semicolons.
- Key concepts discussed include vectors, matrices, linear transformations, abstraction, and how linear algebra solves problems in fields like quantum mechanics. Comprehensions provide a concise way to generate sets, lists, and arrays in Python.
This document discusses algorithms and some basic algorithms used in computer science. It begins by defining an algorithm as a step-by-step method for solving a problem or completing a task. It then describes three common constructs used in algorithms: sequence, decision, and repetition. The document also discusses representations of algorithms using UML diagrams and pseudocode. Finally, it provides examples of basic algorithms like summation, product, and finding the smallest/largest values; and describes the general logic and structure of algorithms for addition, multiplication, and minimum/maximum calculations.
Me 443 4 plotting curves Erdi Karaçal Mechanical Engineer University of Gaz...Erdi Karaçal
- The document discusses various plotting and graphics capabilities in Mathematica, including plotting functions, parametric plots, plotting lists of data, options for controlling plot appearance, and two-dimensional and three-dimensional graphics.
- It describes how to control aspects of plots like scales, sampling of functions, and colors using options. It also covers various graphics primitives and directives for customizing plots.
A NEW PARALLEL ALGORITHM FOR COMPUTING MINIMUM SPANNING TREEijscmc
Computing the minimum spanning tree of the graph is one of the fundamental computational problems. In
this paper, we present a new parallel algorithm for computing the minimum spanning tree of an undirected
weighted graph with n vertices and m edges. This algorithm uses the cluster techniques to reduce the
number of processors by fraction 1/f (n) and the parallel work by the fraction O ( 1 lo g ( f ( n )) ),where f (n) is an
arbitrary function. In the case f (n) =1, the algorithm runs in logarithmic-time and use super linear work on
EREWPRAM model. In general, the proposed algorithm is the simplest one.
Parallel processing technique for high speed image segmentation using colorIAEME Publication
This document describes a novel method for high-speed image segmentation using parallel processing and self-learning devices. The method can process video streams at 1000 frames per second. It uses parallel processors that can be trained simultaneously to learn color and grayscale values from example images. After training, the processors can identify colored or grayscale regions in new images in real time. The key advantages are that the parallel processors are easy to program and train for specific tasks like segmentation, unlike other parallel approaches.
this is a briefer overview about the Big O Notation. Big O Notaion are useful to check the Effeciency of an algorithm and to check its limitation at higher value. with big o notation some examples are also shown about its cases and some functions in c++ are also described.
Kernal based speaker specific feature extraction and its applications in iTau...TELKOMNIKA JOURNAL
This document summarizes kernel-based speaker recognition techniques for an automatic speaker recognition system (ASR) in iTaukei cross-language speech recognition. It discusses kernel principal component analysis (KPCA), kernel independent component analysis (KICA), and kernel linear discriminant analysis (KLDA) for nonlinear speaker-specific feature extraction to improve ASR classification rates. Evaluation of the ASR system using these techniques on a Japanese language corpus and self-recorded iTaukei corpus showed that KLDA achieved the best performance, with an equal error rate improvement of up to 8.51% compared to KPCA and KICA.
A Comparative study of K-SVD and WSQ Algorithms in Fingerprint Compression Te...IRJET Journal
This document compares the K-SVD and WSQ algorithms for fingerprint compression. It provides an overview of both algorithms, including how they work, their advantages, and disadvantages. It also presents results of compressing different sized fingerprint images using each algorithm, showing that K-SVD consistently achieved smaller file sizes than WSQ. The document concludes that K-SVD is superior to WSQ for compressing fingerprint images.
An Optimized Parallel Algorithm for Longest Common Subsequence Using Openmp –...IRJET Journal
This document summarizes research on developing parallel algorithms to optimize solving the longest common subsequence (LCS) problem. LCS is commonly used for sequence comparison in bioinformatics. Traditional sequential dynamic programming algorithms have complexity of O(mn) for sequences of lengths m and n. The document reviews parallel algorithms developed using tools like OpenMP and GPUs like CUDA to reduce computation time. It proposes the authors' own optimized parallel algorithm for multi-core CPUs using OpenMP.
This document provides an overview of a lecture on designing and analyzing computer algorithms. It discusses key concepts like what an algorithm and program are, common algorithm design techniques like divide-and-conquer and greedy methods, and how to analyze algorithms' time and space complexity. The goals of analyzing algorithms are to understand their behavior, improve efficiency, and determine whether problems can be solved within a reasonable time frame.
The document describes developing a model to predict house prices using deep learning techniques. It proposes using a dataset with house features without labels and applying regression algorithms like K-nearest neighbors, support vector machine, and artificial neural networks. The models are trained and tested on split data, with the artificial neural network achieving the lowest mean absolute percentage error of 18.3%, indicating it is the most accurate model for predicting house prices based on the data.
This document provides an overview of machine learning using Python. It introduces machine learning applications and key Python concepts for machine learning like data types, variables, strings, dates, conditional statements, loops, and common machine learning libraries like NumPy, Matplotlib, and Pandas. It also covers important machine learning topics like statistics, probability, algorithms like linear regression, logistic regression, KNN, Naive Bayes, and clustering. It distinguishes between supervised and unsupervised learning, and highlights algorithm types like regression, classification, decision trees, and dimensionality reduction techniques. Finally, it provides examples of potential machine learning projects.
This article aims to classify texts and predict the categories of occurrences, through the study of Artificial Intelligence models, using Machine Learning and Deep Learning for the classification of texts and analysis of predictions, suggesting the best option with the smallest error.
The solution was designed to be implemented in two stages: Machine Learning and Application, according to the diagram below from the Data Science Academy.
Survey on Artificial Neural Network Learning Technique AlgorithmsIRJET Journal
This document discusses different types of learning algorithms used in artificial neural networks. It begins with an introduction to neural networks and their ability to learn from their environment through adjustments to synaptic weights. Four main learning algorithms are then described: error correction learning, which uses algorithms like backpropagation to minimize error; memory based learning, which stores all training examples and analyzes nearby examples to classify new inputs; Hebbian learning, where connection weights are adjusted based on the activity of neurons; and competitive learning, where neurons compete to respond to inputs to become specialized feature detectors through a winner-take-all mechanism. The document provides details on how each type of learning algorithm works.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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.
The document discusses data types, data structures, algorithms, recursion, and asymptotic analysis. It provides definitions and examples of key concepts like abstract data types, data abstraction, algorithms, iterative vs recursive algorithms, complexity analysis using Big-O, Big-Omega and Big-Theta notations. Examples of recursively implementing algorithms to find sum of natural numbers, factorial, GCD, Fibonacci series are presented.
Scalable Rough C-Means clustering using Firefly algorithm..................................................................1
Abhilash Namdev and B.K. Tripathy
Significance of Embedded Systems to IoT................................................................................................. 15
P. R. S. M. Lakshmi, P. Lakshmi Narayanamma and K. Santhi Sri
Cognitive Abilities, Information Literacy Knowledge and Retrieval Skills of Undergraduates: A
Comparison of Public and Private Universities in Nigeria ........................................................................ 24
Janet O. Adekannbi and Testimony Morenike Oluwayinka
Risk Assessment in Constructing Horseshoe Vault Tunnels using Fuzzy Technique................................ 48
Erfan Shafaghat and Mostafa Yousefi Rad
Evaluating the Adoption of Deductive Database Technology in Augmenting Criminal Intelligence in
Zimbabwe: Case of Zimbabwe Republic Police......................................................................................... 68
Mahlangu Gilbert, Furusa Samuel Simbarashe, Chikonye Musafare and Mugoniwa Beauty
Analysis of Petrol Pumps Reachability in Anand District of Gujarat ....................................................... 77
Nidhi Arora
A New Method Based on MDA to Enhance the Face Recognition PerformanceCSCJournals
A novel tensor based method is prepared to solve the supervised dimensionality reduction problem. In this paper a multilinear principal component analysis(MPCA) is utilized to reduce the tensor object dimension then a multilinear discriminant analysis(MDA), is applied to find the best subspaces. Because the number of possible subspace dimensions for any kind of tensor objects is extremely high, so testing all of them for finding the best one is not feasible. So this paper also presented a method to solve that problem, The main criterion of algorithm is not similar to Sequential mode truncation(SMT) and full projection is used to initialize the iterative solution and find the best dimension for MDA. This paper is saving the extra times that we should spend to find the best dimension. So the execution time will be decreasing so much. It should be noted that both of the algorithms work with tensor objects with the same order so the structure of the objects has been never broken. Therefore the performance of this method is getting better. The advantage of these algorithms is avoiding the curse of dimensionality and having a better performance in the cases with small sample sizes. Finally, some experiments on ORL and CMPU-PIE databases is provided.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IMPROVEMENT IN IMAGE DENOISING OF HANDWRITTEN DIGITS USING AUTOENCODERS IN DE...IRJET Journal
This document proposes a deep learning model called Stacked Sparse Denoising Autoencoder (SSDAE) to improve compressed sensing image reconstruction. It combines a denoising autoencoder and sparse autoencoder. The model uses non-linear measurements in the encoder and a decoder network for reconstruction, unlike traditional compressed sensing algorithms. It is trained on corrupted versions of images with added noise. Experimental results on the MNIST handwritten digit dataset show the model achieves better reconstruction quality and peak signal-to-noise ratio than other algorithms, with good denoising ability and robustness to different noise levels.
The document discusses the results of a survey conducted among computer science colleagues to identify the 5 most important algorithms. The survey yielded 32 algorithms across various fields like computer science, mathematics, signal processing and more. Some of the key algorithms identified include binary search, Dijkstra's algorithm, dynamic programming, Euclidean algorithm, gradient descent, maximum flow, merge sort, Newton's method, RSA encryption and singular value decomposition.
Discrete structure ch 3 short question'shammad463061
An algorithm is a finite sequence of precise instructions for performing a computation or solving a problem. There are several key properties of algorithms including that they must have defined input and output, be definite with precisely defined steps, be correct in producing the right output, and be finite so they terminate in a finite number of steps. Different algorithms are analyzed based on their time and space complexity, with a focus on worst-case complexity. Common algorithms include searching, sorting, and algorithms for solving optimization problems. Determining the complexity of algorithms and whether problems can be solved in polynomial time is important for understanding what problems are tractable or intractable.
This document summarizes various algorithms topics including pattern matching, matrix multiplication, graph algorithms, algebraic problems, and NP-hard and NP-complete problems. It provides details on pattern matching techniques in computer science including exact string matching and applications. It also describes how to find the most efficient way to multiply a sequence of matrices by considering different orders of operations. Graph algorithms are introduced including directed and undirected graphs. Popular design approaches for algebraic problems such as divide-and-conquer, greedy techniques, and dynamic programming are outlined. Finally, the key differences between NP, NP-hard, and NP-complete problems are defined.
Neural network based numerical digits recognization using nnt in matlabijcses
Artificial neural networks are models inspired by human nervous system that is capable of learning. One of
the important applications of artificial neural network is character Recognition. Character Recognition
finds its application in number of areas, such as banking, security products, hospitals, in robotics also.
This paper is based on a system that recognizes a english numeral, given by the user, which is already
trained on the features of the numbers to be recognized using NNT (Neural network toolbox) .The system
has a neural network as its core, which is first trained on a database. The training of the neural network
extracts the features of the English numbers and stores in the database. The next phase of the system is to
recognize the number given by the user. The features of the number given by the user are extracted and
compared with the feature database and the recognized number is displayed.
Similar to Efficient Sparse Coding Algorithms (20)
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WhatsApp offers simple, reliable, and private messaging and calling services for free worldwide. With end-to-end encryption, your personal messages and calls are secure, ensuring only you and the recipient can access them. Enjoy voice and video calls to stay connected with loved ones or colleagues. Express yourself using stickers, GIFs, or by sharing moments on Status. WhatsApp Business enables global customer outreach, facilitating sales growth and relationship building through showcasing products and services. Stay connected effortlessly with group chats for planning outings with friends or staying updated on family conversations.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Odoo ERP software
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The Odoo Community serves as a cost-free edition within the Odoo suite of ERP systems. Tailored to accommodate the standard needs of business operations, it provides a robust platform suitable for organisations of different sizes and business sectors. Within the Odoo Community Edition, users can access a variety of essential features and services essential for managing day-to-day tasks efficiently.
This blog presents a detailed overview of the features available within the Odoo 17 Community edition, and the differences between Odoo 17 community and enterprise editions, aiming to equip you with the necessary information to make an informed decision about its suitability for your business.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
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We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
UI5con 2024 - Keynote: Latest News about UI5 and it’s EcosystemPeter Muessig
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
Revolutionizing Visual Effects Mastering AI Face Swaps.pdfUndress Baby
The quest for the best AI face swap solution is marked by an amalgamation of technological prowess and artistic finesse, where cutting-edge algorithms seamlessly replace faces in images or videos with striking realism. Leveraging advanced deep learning techniques, the best AI face swap tools meticulously analyze facial features, lighting conditions, and expressions to execute flawless transformations, ensuring natural-looking results that blur the line between reality and illusion, captivating users with their ingenuity and sophistication.
Web:- https://undressbaby.com/
Microservice Teams - How the cloud changes the way we workSven Peters
A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.
DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.
E-commerce Development Services- Hornet DynamicsHornet Dynamics
For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
2.
A new algorithm for solving L1 – regularized least squares
problem which is more efficient for learning sparse coding
bases
A new approach for L2 – constrained least squares
problem which result in significant speed up for sparse
coding
Goal
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
3. What is Sparse Coding?
Sparse Coding applications in Computer Vision
Image Denoising Image Restoration
Introduction
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
4. Sparse coding is a method for discovering good basis
vectors automatically using only unlabeled data
It learns the basis functions that capture high-level
features in the data
Input Features selected
Sparse Coding Problem
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
5. Sparse coding is a method for discovering good basis
vectors automatically using only unlabeled data
It is similar to PCA
Given a training set of m vectors
where
we attempt to find a succinct representation for each xi
using basis vectors and a sparse vector
such that
Note that the basis can be overcomplete, i.e., n>k
Sparse Coding Problem
1 2, ,[ ], mx xX x L
k
ix R
1 2, , , k
nbb b L R n
sR
1 2
1
[ , , , ]
n
i j j n
j
x s b b b b s
L
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
6. The goal of sparse coding is to present input vectors as
weighted linear combinations of ‘basis vectors’, which capture
high level patterns in input data
The optimization problem in sparse coding
where
and ᶲ is a sparse penalty function (we consider L1 penalty
function).
Sparse Coding Problem
1 1, , , , ,[ ], [ ], [ 1, ]m n mx bB b S sX x s L L L
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
7. The formulation of LASSO
where 𝑥, 𝑦 are vectors, 𝐴 is a matrix and 𝛾 is a constant.
Basic idea of the algorithm is
To get the most useful attributes in a vector (data record)
To guess the sign of each component (attribute) of 𝑥 (data
record), thus, guessing the impact of any changes in the
attribute to the classification of the data record.
A new algorithm to solve
LASSO
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
10. Consider optimization problem (given in the LASSO slide) augmented
with the additional constraint that 𝑥 is consistent with a given active
set and sign vector. Then, if the current coefficients 𝑥 𝑐 are consistent
with the active set and sign vector, but are not optimal for the
augmented problem at the start of Step 3, the feature-sign step is
guaranteed to strictly reduce the objective.
Consider optimization problem (LASSO equation) augmented with the
additional constraint that 𝑥 is consistent with a given active set and
sign vector. If the coefficients 𝑥 𝑐 at the start of Step 2 are optimal for
the augmented problem, but are not optimal for problem (LASSO
equation), the feature-sign step is guaranteed to strictly reduce the
objective.
The feature-sign search algorithm converges to a global optimum of
the optimization problem in a finite number of steps.
Proofs of the Algorithm
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
12. UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
Solving optimization problem over bases B and given fixed
coefficients S.
This is least squares problem with quadratic constraints,
which can be efficiently solved using Lagrange dual.
After the calculations, we find the optimal bases 𝐵 as
follows :
14. Performance of the algorithms was evaluated on four natural
stimulus datasets:
Natural Images
Speech
Stereo Images
Natural Image Videos
All experiments were conducted on a Linux machine with AMD
Opteron 2GHz CPU and 2GB RAM
All the algorithms were implemented in MATLAB
Experiment
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
15. Evaluating Feature sign search algorithm for learning coefficients with L1
sparsity function
Running time and error are compared with other
coefficient learning algorithms
For each dataset, a test set of 100 input vectors and a training set of 1000
input vectors was used
Values: Running Time (Relative Error)
Evaluating Feature Sign
Search Algorithm
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
16. Running time (in seconds) for different algorithm combinations
of coefficient learning and basis learning algorithms using
different sparsity functions is shown below:
Time Taken for learning
Bases
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
17. Using these efficient algorithms they were able to learn
overcomplete bases of natural images
1024 bases 2000 bases
(14 X 14 pixels each) (20 X 20 pixels each)
Learning overcomplete
natural images
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
18. Sparse coding can model the interaction (inhibition)
between the bases (neurons) by sparsifying their
coefficients (activations), and our algorithms enable these
phenomena to be tested with highly overcomplete bases.
They evaluated whether end-stopping behavior could be
observed in sparse coding framework. The results seemed
consistent with the end stopping behavior of the neurons.
Using the learned overcomplete bases, they tested for
center-surround non classical receptive field (nCRF)
effects.
Replicating Complex
Neuroscience phenomena
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
19. They applied sparse coding approaches to self-taught learning,
a new machine learning formalism.
A supervised learning problem along with additional unlabeled
instances that may not have same class labels as labeled
instances.
Sparse coding algorithms are applied to unlabeled data to learn
bases which gives a higher level representation of images, thus
making supervised learning task easier.
This approach proved 11-36% reductions in test error.
Related Work: R. Raina, A. Battle, H. Lee, B. Packer, and A. Y.
Ng. Self-taught learning. In NIPS Workshop on Learning when
test and training inputs have different distributions, 2006
Application to self-taught
learning
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
20. In this paper, sparse coding is formulated as a
combination of two convex optimization problems
Efficient algorithms for these problems were presented:
the feature-sign search for solving the L1-least squares
problem to learn coefficients, and a Lagrange dual
method for the L2-constrained least squares problem to
learn the bases for any sparsity penalty function.
Partially explain the phenomena of end-stopping and
nCRF surround suppression in V1 neurons.
Conclusion
UTA : CSE6363 : Machine Learning Anshu Dipit / Likitha Seeram
2 optimization problems over 2 subset of variables.
Sparse coding provides a class of algorithms for finding succinct representations of stimuli; given only unlabeled input data, it discovers basis functions that capture higher-level features in the data
Digit Recognition. Features capture significant properties of the digits
Sparse coding can be applied for learning over complete basis, in which the number of bases is greater than the input dimension.
Beta is constant. Assuming uniform prior on basis. This objective is to iteratively optimize by alternatingly optimizing w.r.t B and S while holding the other constant.
LARS – Least Angle Regression, Chen et al’s interior point method. Relative error calculation – f obj is final objective value attained by the algorithm and f* is best objective value attained from among all the algorithms
As a result, we can see that Lagrange dual was much faster than gradient descent with projections
1024 bases in 2 hours. 2000 bases in 10 hours. This is not possible using gradient descent method of basis learning.
V1 neurons – primary visual cortex
Paper link - http://ai.stanford.edu/~hllee/nips06-sparsecoding.pdf