The document discusses data structures and algorithms. It explains that algorithms help solve problems through step-by-step processes and that data structures improve algorithm efficiency by organizing data. It also describes techniques for designing algorithms, ways to measure efficiency, and how the chosen algorithm and data structure affect a program's performance.
The document discusses knowledge-based systems and artificial neural networks. It describes an early expert system developed in 1980 to approve credit applications. It also outlines the key components of expert systems, including the knowledge base and rules. Neural networks are discussed as being inspired by the human brain and capable of learning in a similar way. The multi-layer perception model is presented as a way to break tasks into smaller subtasks performed concurrently.
Methodological study of opinion mining and sentiment analysis techniquesijsc
Decision making both on individual and organizational level is always accompanied by the search of
other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum
discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated
content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining
and sentiment analysis are the formalization for studying and construing opinions and sentiments. The
digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is
an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
Neural Network Classification and its Applications in Insurance IndustryInderjeet Singh
This document summarizes the use of neural networks for classification tasks. It discusses the advantages and disadvantages of neural networks for classification. It also presents a case study on using a neural network to classify insurance customers as likely to renew or terminate their policies based on attributes like age and zip code. The neural network achieved higher accuracy than decision trees and regression analysis on the insurance data set.
Neural Network Classification and its Applications in Insurance IndustryInderjeet Singh
This document summarizes a neural networks project report on using neural networks for classification in the insurance industry. The report discusses extracting rules from trained neural networks, using neural networks to predict customer retention and pricing policies. It also discusses using neural networks to detect auto insurance fraud by identifying important fraud indicators.
The document presents a Keras sequential neural network to recognize handwritten digits from the MNIST dataset. It achieves 97.28% accuracy on the test set. The network uses TensorFlow and contains flatten, dense, and softmax layers. It is trained for 3 epochs with Adam optimization and cross-entropy loss. The results demonstrate the network can accurately identify digits while leaving room for improvement by tweaking hyperparameters or using more complex models. Source code and model details are provided.
This document provides an overview of deep learning and common deep learning concepts. It discusses that deep learning uses complex neural networks to determine representations of data, rather than requiring humans to engineer features. It also describes convolutional neural networks and how they are better than fully connected networks for tasks like image recognition. Additionally, it covers transfer learning and how pre-trained models can be adapted to new tasks by retraining final layers, reducing data and computation needs. Common deep learning architectures mentioned include AlexNet, VGG16, Inception and MobileNets.
Random Valued Impulse Noise Elimination using Neural FilterEditor IJCATR
A neural filtering technique is proposed in this paper for restoring the images extremely corrupted with random valued impulse noise. The proposed intelligent filter is carried out in two stages. In first stage the corrupted image is filtered by applying an asymmetric trimmed median filter. An asymmetric trimmed median filtered output image is suitably combined with a feed forward neural network in the second stage. The internal parameters of the feed forward neural network are adaptively optimized by training of three well known images. This is quite effective in eliminating random valued impulse noise. Simulation results show that the proposed filter is superior in terms of eliminating impulse noise as well as preserving edges and fine details of digital images and results are compared with other existing nonlinear filters.
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 discusses knowledge-based systems and artificial neural networks. It describes an early expert system developed in 1980 to approve credit applications. It also outlines the key components of expert systems, including the knowledge base and rules. Neural networks are discussed as being inspired by the human brain and capable of learning in a similar way. The multi-layer perception model is presented as a way to break tasks into smaller subtasks performed concurrently.
Methodological study of opinion mining and sentiment analysis techniquesijsc
Decision making both on individual and organizational level is always accompanied by the search of
other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum
discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated
content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining
and sentiment analysis are the formalization for studying and construing opinions and sentiments. The
digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is
an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.
Neural Network Classification and its Applications in Insurance IndustryInderjeet Singh
This document summarizes the use of neural networks for classification tasks. It discusses the advantages and disadvantages of neural networks for classification. It also presents a case study on using a neural network to classify insurance customers as likely to renew or terminate their policies based on attributes like age and zip code. The neural network achieved higher accuracy than decision trees and regression analysis on the insurance data set.
Neural Network Classification and its Applications in Insurance IndustryInderjeet Singh
This document summarizes a neural networks project report on using neural networks for classification in the insurance industry. The report discusses extracting rules from trained neural networks, using neural networks to predict customer retention and pricing policies. It also discusses using neural networks to detect auto insurance fraud by identifying important fraud indicators.
The document presents a Keras sequential neural network to recognize handwritten digits from the MNIST dataset. It achieves 97.28% accuracy on the test set. The network uses TensorFlow and contains flatten, dense, and softmax layers. It is trained for 3 epochs with Adam optimization and cross-entropy loss. The results demonstrate the network can accurately identify digits while leaving room for improvement by tweaking hyperparameters or using more complex models. Source code and model details are provided.
This document provides an overview of deep learning and common deep learning concepts. It discusses that deep learning uses complex neural networks to determine representations of data, rather than requiring humans to engineer features. It also describes convolutional neural networks and how they are better than fully connected networks for tasks like image recognition. Additionally, it covers transfer learning and how pre-trained models can be adapted to new tasks by retraining final layers, reducing data and computation needs. Common deep learning architectures mentioned include AlexNet, VGG16, Inception and MobileNets.
Random Valued Impulse Noise Elimination using Neural FilterEditor IJCATR
A neural filtering technique is proposed in this paper for restoring the images extremely corrupted with random valued impulse noise. The proposed intelligent filter is carried out in two stages. In first stage the corrupted image is filtered by applying an asymmetric trimmed median filter. An asymmetric trimmed median filtered output image is suitably combined with a feed forward neural network in the second stage. The internal parameters of the feed forward neural network are adaptively optimized by training of three well known images. This is quite effective in eliminating random valued impulse noise. Simulation results show that the proposed filter is superior in terms of eliminating impulse noise as well as preserving edges and fine details of digital images and results are compared with other existing nonlinear filters.
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.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
This document provides lecture notes on data structures that cover key topics including:
- Classifying data structures as simple, compound, linear, and non-linear and providing examples.
- Defining abstract data types and algorithms, and explaining their structure and properties.
- Discussing approaches for designing algorithms and issues related to time and space complexity.
- Covering searching techniques like linear search and sorting techniques including bubble sort, selection sort, and quick sort.
- Describing linear data structures like stacks, queues, and linked lists and non-linear structures like trees and graphs.
The document discusses using a convolutional neural network to recognize handwritten digits from the MNIST database. It describes training a CNN on the MNIST training dataset, consisting of 60,000 examples, to classify images of handwritten digits from 0-9. The CNN architecture uses two convolutional layers followed by a flatten layer and fully connected layer with softmax activation. The model achieves high accuracy on the MNIST test set. However, the document notes that the model may struggle with color images or images with more complex backgrounds compared to the simple black and white MNIST digits. Improving preprocessing and adapting the model for more complex real-world images is suggested for future work.
Image Recognition With the Help of Auto-Associative Neural NetworkCSCJournals
This paper proposes a Neural Network model that has been utilized for image recognition. The main issue of Neural Network model here is to train the system for image recognition. In this paper the NN model has been prepared in MATLAB platform. The NN model uses Auto-Associative memory for training. The model reads the image in the form of a matrix, evaluates the weight matrix associated with the image. After training process is done, whenever the image is provided to the system the model recognizes it appropriately. The weight matrix evaluated here is used for image pattern matching. It is noticed that the model developed is accurate enough to recognize the image even if the image is distorted or some portion/ data is missing from the image. This model eliminates the long time consuming process of image recognition
This document provides an overview of machine learning and neural networks. It begins with an introduction to machine learning concepts like learning, learning agents, and applications. It then covers different types of machine learning including supervised, unsupervised, and reinforcement learning. Specific algorithms like linear discriminant analysis, perceptrons, and neural networks are explained at a high level. Key concepts of neural networks like neurons, network structure, and functioning are summarized.
Object Oriented Methodology (OOM) is a system development approach encouraging and facilitating re-use of software components. We enforce our concern on components re-usability of existing component using Java Language .
IRJET- Intrusion Detection based on J48 AlgorithmIRJET Journal
This document presents a decision tree-based intrusion detection system that uses the J48 algorithm. The system was tested on the NSL-KDD dataset and achieved an accuracy of 96.50% in detecting intrusions. The system uses the Weka tool to implement the J48 decision tree algorithm and generate a classification output identifying normal network connections and different types of attacks. The proposed approach aims to reduce false positives generated by decision trees and outperforms baseline methods according to various evaluation metrics like precision, recall, and accuracy.
Intrusion Detection System for Classification of Attacks with Cross Validationinventionjournals
Now days, due to rapidly uses of internet, the patterns of network attacks are increasing. There are various organizations and institutes are using internet and access or share the sensitive information in network. To protect information from unauthorized or intruders is one of the important issues. In this paper, we have used decision tree techniques like C4.5 and CART as classifier for classification of attacks. We have proposed an ensemble model that is combination of C4.5 and Classification and Regression Tree (CART) as robust classifier for classification of attacks. We have used NSL-KDD data set with binary and multiclass problem with 10-fold cross validation. The proposed ensemble model gives satisfactory accuracy as 99.67% and 99.53% in case of binary class and multiclass NSL-KDD data set respectively.
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
This document discusses artificial intelligence, machine learning, deep learning, and data science. It defines each term and explains the relationships between them. AI is the overarching field, while machine learning and deep learning are subsets of AI. Machine learning allows machines to improve performance over time without human intervention by learning from examples, and deep learning uses artificial neural networks with many layers to closely mimic the human brain. The document provides an example of a fruit detection system using deep learning that trains a neural network to detect ripe fruit for automated harvesting.
Improved Performance of Unsupervised Method by Renovated K-MeansIJASCSE
Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed K-Means algorithm is compared with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means (DWK-Means) algorithm by using Davis Bouldin index, Execution Time and Iteration count methods. Experimental results show that the proposed K-Means algorithm performed better on Iris and Wine dataset when compared with other three clustering methods.
This document discusses various object-oriented methodologies, including Object-Oriented Structured Analysis (OOSA), Object-Oriented Structured Design (OOSD), and Jackson Structured Development (JSD).
OOSA involves splitting a software system into domains and subsystems for analysis. It provides comprehensive coverage of analysis, design, and implementation. OOSD focuses on a single architectural design notation to support software design. Its main entities are classes, modules, and monitors.
JSD focuses on describing the real world through the system by mapping progress over time. It uses three modeling stages - entity action, entity structure, and network stage - to specify the system from states to implementation through various steps.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Comparison of Learning Algorithms for Handwritten Digit RecognitionSafaa Alnabulsi
This document compares different machine learning algorithms for handwritten digit recognition on the MNIST dataset. Convolutional neural networks achieved the best results, with LeNet5 achieving 0.9% error and boosted LeNet4 achieving the lowest error rate of 0.7%. Neural networks required more training time but had faster recognition times and lower memory requirements compared to nearest neighbor classifiers. Overall, convolutional neural networks were best suited for handwritten digit recognition due to their ability to handle variations in size, position and orientation of digits.
IRJET- Art Authentication System using Deep Neural NetworksIRJET Journal
1) The document presents a system to authenticate paintings by artists using deep convolutional neural networks. The system processes images through thousands of neurons to extract patterns and characteristics of an artist's style.
2) A deep convolutional neural network model is implemented and trained on datasets of labeled artworks. The network aims to classify new paintings by artist with 80% accuracy, higher than previous methods.
3) The system was tested on 5 paintings, with a confusion matrix showing correct and incorrect classifications. The 80% accuracy rate is an improvement over previous techniques, but the model has limitations as the number of paintings increases.
This document provides an overview of deep learning concepts including neural networks, supervised and unsupervised learning, and key terms. It explains that deep learning uses neural networks with many hidden layers to learn features directly from raw data. Supervised learning algorithms learn from labeled examples to perform classification or regression on unseen data. Unsupervised learning finds patterns in unlabeled data. Key terms defined include neurons, activation functions, loss functions, optimizers, epochs, batches, and hyperparameters.
Implementation of miml framework using annotatedEditor IJMTER
As MIL (Multi-Instance Learning) considers only input ambiguity and MLL
(Multi-Label Learning) consider only output ambiguity, we require a framework which
consider both ambiguities together and solve the complex problems. MIML (Multi-Instance
Multi-Label) framework can solve this problem, but the implementation of MIML dataset is
more complex as it considers multiple labels and its multiple instances both together. This
research work focuses on implementation of MIML framework using 2014 annotated natural
scene image dataset. An image annotation task is closely related to MIML learning problem.
Multi class SVM (MSVMpack) used to handle classification of more than two classes
without depending on different decomposition methods. Bag of Regions (BoR) is used as a
bag generator which is well known framework to generate local features from images. SIFT
Scale-Invariant Feature Transform (SIFT) good descriptor can handle intensity, rotation and
scale with variations. During experiment for each image SIFT descriptors are extracted for
each shot. As a result it also provide vector of predicted labels, accuracy rate during
classification, hamming loss, one-error, coverage and R-loss after testing the model.
IRJET- Machine Learning based Object Identification System using PythonIRJET Journal
This document presents a machine learning based object identification system using convolutional neural networks (CNNs) in Python. The system is trained on a dataset of cat and dog images and aims to identify objects in input images. The document compares different CNN structures using various activation functions and classifiers. It finds that a model with a ReLU activation function and sigmoid classifier achieved the highest classification accuracy of around 90.5%. The system demonstrates how CNNs can be used for image classification tasks in machine learning.
Unit 1( modelling concepts & class modeling)Manoj Reddy
The document discusses object-oriented modeling and design. It covers key concepts like classes, objects, inheritance, polymorphism, and encapsulation. It also discusses the Unified Modeling Language (UML) which provides standard notation for visualizing, specifying, constructing, and documenting models. The document is a lecture on object-oriented concepts for students to understand modeling using classes, objects, and relationships.
Evaluation of deep neural network architectures in the identification of bone...TELKOMNIKA JOURNAL
This document evaluates the performance of three deep neural network architectures - ResNet, DenseNet, and NASNet - in identifying bone fissures in radiological images. The networks were trained on a dataset of 1000 labeled images of fissured and seamless bones. NASNet achieved the best performance with 75% accuracy, outperforming ResNet and DenseNet. While all networks reduced classification errors, NASNet did so with the fewest parameters. The document concludes NASNet is the best solution for this bone fissure identification task.
The document provides solutions to exercises on algorithms. The first solution describes an algorithm to check if a number is prime by testing all factors from 2 to the number/2. The second generates the first 10 prime numbers by iterating from 2 and checking primality until 10 primes are found. The third accepts a number N and prints N lines with descending numbers from N to 1.
The document discusses developing web applications using ASP.NET. It explains the differences between HTML controls and web server controls, the various types of web server controls, and how to use controls and handle postbacks. It also demonstrates adding and configuring server controls to create interfaces for an online survey application.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
This document provides lecture notes on data structures that cover key topics including:
- Classifying data structures as simple, compound, linear, and non-linear and providing examples.
- Defining abstract data types and algorithms, and explaining their structure and properties.
- Discussing approaches for designing algorithms and issues related to time and space complexity.
- Covering searching techniques like linear search and sorting techniques including bubble sort, selection sort, and quick sort.
- Describing linear data structures like stacks, queues, and linked lists and non-linear structures like trees and graphs.
The document discusses using a convolutional neural network to recognize handwritten digits from the MNIST database. It describes training a CNN on the MNIST training dataset, consisting of 60,000 examples, to classify images of handwritten digits from 0-9. The CNN architecture uses two convolutional layers followed by a flatten layer and fully connected layer with softmax activation. The model achieves high accuracy on the MNIST test set. However, the document notes that the model may struggle with color images or images with more complex backgrounds compared to the simple black and white MNIST digits. Improving preprocessing and adapting the model for more complex real-world images is suggested for future work.
Image Recognition With the Help of Auto-Associative Neural NetworkCSCJournals
This paper proposes a Neural Network model that has been utilized for image recognition. The main issue of Neural Network model here is to train the system for image recognition. In this paper the NN model has been prepared in MATLAB platform. The NN model uses Auto-Associative memory for training. The model reads the image in the form of a matrix, evaluates the weight matrix associated with the image. After training process is done, whenever the image is provided to the system the model recognizes it appropriately. The weight matrix evaluated here is used for image pattern matching. It is noticed that the model developed is accurate enough to recognize the image even if the image is distorted or some portion/ data is missing from the image. This model eliminates the long time consuming process of image recognition
This document provides an overview of machine learning and neural networks. It begins with an introduction to machine learning concepts like learning, learning agents, and applications. It then covers different types of machine learning including supervised, unsupervised, and reinforcement learning. Specific algorithms like linear discriminant analysis, perceptrons, and neural networks are explained at a high level. Key concepts of neural networks like neurons, network structure, and functioning are summarized.
Object Oriented Methodology (OOM) is a system development approach encouraging and facilitating re-use of software components. We enforce our concern on components re-usability of existing component using Java Language .
IRJET- Intrusion Detection based on J48 AlgorithmIRJET Journal
This document presents a decision tree-based intrusion detection system that uses the J48 algorithm. The system was tested on the NSL-KDD dataset and achieved an accuracy of 96.50% in detecting intrusions. The system uses the Weka tool to implement the J48 decision tree algorithm and generate a classification output identifying normal network connections and different types of attacks. The proposed approach aims to reduce false positives generated by decision trees and outperforms baseline methods according to various evaluation metrics like precision, recall, and accuracy.
Intrusion Detection System for Classification of Attacks with Cross Validationinventionjournals
Now days, due to rapidly uses of internet, the patterns of network attacks are increasing. There are various organizations and institutes are using internet and access or share the sensitive information in network. To protect information from unauthorized or intruders is one of the important issues. In this paper, we have used decision tree techniques like C4.5 and CART as classifier for classification of attacks. We have proposed an ensemble model that is combination of C4.5 and Classification and Regression Tree (CART) as robust classifier for classification of attacks. We have used NSL-KDD data set with binary and multiclass problem with 10-fold cross validation. The proposed ensemble model gives satisfactory accuracy as 99.67% and 99.53% in case of binary class and multiclass NSL-KDD data set respectively.
This is a presentation on Handwritten Digit Recognition using Convolutional Neural Networks. Convolutional Neural Networks give better results as compared to conventional Artificial Neural Networks.
This document discusses artificial intelligence, machine learning, deep learning, and data science. It defines each term and explains the relationships between them. AI is the overarching field, while machine learning and deep learning are subsets of AI. Machine learning allows machines to improve performance over time without human intervention by learning from examples, and deep learning uses artificial neural networks with many layers to closely mimic the human brain. The document provides an example of a fruit detection system using deep learning that trains a neural network to detect ripe fruit for automated harvesting.
Improved Performance of Unsupervised Method by Renovated K-MeansIJASCSE
Clustering is a separation of data into groups of similar objects. Every group called cluster consists of objects that are similar to one another and dissimilar to objects of other groups. In this paper, the K-Means algorithm is implemented by three distance functions and to identify the optimal distance function for clustering methods. The proposed K-Means algorithm is compared with K-Means, Static Weighted K-Means (SWK-Means) and Dynamic Weighted K-Means (DWK-Means) algorithm by using Davis Bouldin index, Execution Time and Iteration count methods. Experimental results show that the proposed K-Means algorithm performed better on Iris and Wine dataset when compared with other three clustering methods.
This document discusses various object-oriented methodologies, including Object-Oriented Structured Analysis (OOSA), Object-Oriented Structured Design (OOSD), and Jackson Structured Development (JSD).
OOSA involves splitting a software system into domains and subsystems for analysis. It provides comprehensive coverage of analysis, design, and implementation. OOSD focuses on a single architectural design notation to support software design. Its main entities are classes, modules, and monitors.
JSD focuses on describing the real world through the system by mapping progress over time. It uses three modeling stages - entity action, entity structure, and network stage - to specify the system from states to implementation through various steps.
Dear students get fully solved assignments
Send your semester & Specialization name to our mail id :
“ help.mbaassignments@gmail.com ”
or
Call us at : 08263069601
Comparison of Learning Algorithms for Handwritten Digit RecognitionSafaa Alnabulsi
This document compares different machine learning algorithms for handwritten digit recognition on the MNIST dataset. Convolutional neural networks achieved the best results, with LeNet5 achieving 0.9% error and boosted LeNet4 achieving the lowest error rate of 0.7%. Neural networks required more training time but had faster recognition times and lower memory requirements compared to nearest neighbor classifiers. Overall, convolutional neural networks were best suited for handwritten digit recognition due to their ability to handle variations in size, position and orientation of digits.
IRJET- Art Authentication System using Deep Neural NetworksIRJET Journal
1) The document presents a system to authenticate paintings by artists using deep convolutional neural networks. The system processes images through thousands of neurons to extract patterns and characteristics of an artist's style.
2) A deep convolutional neural network model is implemented and trained on datasets of labeled artworks. The network aims to classify new paintings by artist with 80% accuracy, higher than previous methods.
3) The system was tested on 5 paintings, with a confusion matrix showing correct and incorrect classifications. The 80% accuracy rate is an improvement over previous techniques, but the model has limitations as the number of paintings increases.
This document provides an overview of deep learning concepts including neural networks, supervised and unsupervised learning, and key terms. It explains that deep learning uses neural networks with many hidden layers to learn features directly from raw data. Supervised learning algorithms learn from labeled examples to perform classification or regression on unseen data. Unsupervised learning finds patterns in unlabeled data. Key terms defined include neurons, activation functions, loss functions, optimizers, epochs, batches, and hyperparameters.
Implementation of miml framework using annotatedEditor IJMTER
As MIL (Multi-Instance Learning) considers only input ambiguity and MLL
(Multi-Label Learning) consider only output ambiguity, we require a framework which
consider both ambiguities together and solve the complex problems. MIML (Multi-Instance
Multi-Label) framework can solve this problem, but the implementation of MIML dataset is
more complex as it considers multiple labels and its multiple instances both together. This
research work focuses on implementation of MIML framework using 2014 annotated natural
scene image dataset. An image annotation task is closely related to MIML learning problem.
Multi class SVM (MSVMpack) used to handle classification of more than two classes
without depending on different decomposition methods. Bag of Regions (BoR) is used as a
bag generator which is well known framework to generate local features from images. SIFT
Scale-Invariant Feature Transform (SIFT) good descriptor can handle intensity, rotation and
scale with variations. During experiment for each image SIFT descriptors are extracted for
each shot. As a result it also provide vector of predicted labels, accuracy rate during
classification, hamming loss, one-error, coverage and R-loss after testing the model.
IRJET- Machine Learning based Object Identification System using PythonIRJET Journal
This document presents a machine learning based object identification system using convolutional neural networks (CNNs) in Python. The system is trained on a dataset of cat and dog images and aims to identify objects in input images. The document compares different CNN structures using various activation functions and classifiers. It finds that a model with a ReLU activation function and sigmoid classifier achieved the highest classification accuracy of around 90.5%. The system demonstrates how CNNs can be used for image classification tasks in machine learning.
Unit 1( modelling concepts & class modeling)Manoj Reddy
The document discusses object-oriented modeling and design. It covers key concepts like classes, objects, inheritance, polymorphism, and encapsulation. It also discusses the Unified Modeling Language (UML) which provides standard notation for visualizing, specifying, constructing, and documenting models. The document is a lecture on object-oriented concepts for students to understand modeling using classes, objects, and relationships.
Evaluation of deep neural network architectures in the identification of bone...TELKOMNIKA JOURNAL
This document evaluates the performance of three deep neural network architectures - ResNet, DenseNet, and NASNet - in identifying bone fissures in radiological images. The networks were trained on a dataset of 1000 labeled images of fissured and seamless bones. NASNet achieved the best performance with 75% accuracy, outperforming ResNet and DenseNet. While all networks reduced classification errors, NASNet did so with the fewest parameters. The document concludes NASNet is the best solution for this bone fissure identification task.
The document provides solutions to exercises on algorithms. The first solution describes an algorithm to check if a number is prime by testing all factors from 2 to the number/2. The second generates the first 10 prime numbers by iterating from 2 and checking primality until 10 primes are found. The third accepts a number N and prints N lines with descending numbers from N to 1.
The document discusses developing web applications using ASP.NET. It explains the differences between HTML controls and web server controls, the various types of web server controls, and how to use controls and handle postbacks. It also demonstrates adding and configuring server controls to create interfaces for an online survey application.
A workshop was held for parents on November 27th with a first and second session. Another workshop was also held at the school on November 27th with a first and second session.
PDHPE aims to develop students' understanding and skills to lead healthy, active lives through teaching them about topics like physical activity, personal health, relationships and safety. It helps students develop lifelong skills in areas such as communication, problem solving, and physical skills. The benefits of PDHPE include encouraging self-understanding and valuing others, making informed lifestyle choices, and promoting overall health and safe living.
PDHPE aims to develop skills and understanding in students to lead healthy, active, and fulfilling lives. It helps students develop knowledge about active lifestyles, growth and development, personal health choices, games and sports, safe living and interpersonal relationships. Through PDHPE, students develop lifelong skills in communicating, decision making, problem solving, moving, and interacting that encourage understanding of self and others, informed lifestyle decisions, and healthy, safe, and active living.
Uitslagen polls social media ouderavond 28 novemberestherhagen
This document lists 4 entries with dates, locations, and initials for first and last names. It appears to be scheduling 2 people for workshops on November 28th, with 2 people scheduled for a school workshop and 2 others scheduled for a parent workshop.
The document discusses sorting algorithms and bubble sort. It explains that bubble sort is one of the simplest sorting algorithms that works by repeatedly scanning through a list and swapping adjacent elements that are in the wrong order. The document then provides a step-by-step example of implementing bubble sort on an array to sort it, showing the process of multiple passes to place elements in the correct sorted order.
The document provides solutions to 9 exercises on data structures and algorithms. The exercises cover topics like checking if a number is prime, generating the first 10 prime numbers, printing patterns based on user input, merging sorted arrays, finding the highest common factor of 3 numbers, multiplying matrices, and printing the Fibonacci series recursively. The solutions describe the steps to solve each problem algorithmically in a clear and concise manner.
Download Complete Material - https://www.instamojo.com/prashanth_ns/
This Data Structures and Algorithms contain 15 Units and each Unit contains 60 to 80 slides in it.
Contents…
• Introduction
• Algorithm Analysis
• Asymptotic Notation
• Foundational Data Structures
• Data Types and Abstraction
• Stacks, Queues and Deques
• Ordered Lists and Sorted Lists
• Hashing, Hash Tables and Scatter Tables
• Trees and Search Trees
• Heaps and Priority Queues
• Sets, Multi-sets and Partitions
• Dynamic Storage Allocation: The Other Kind of Heap
• Algorithmic Patterns and Problem Solvers
• Sorting Algorithms and Sorters
• Graphs and Graph Algorithms
• Class Hierarchy Diagrams
• Character Codes
This document provides an introduction to data structures and algorithms. It discusses why they are important for programming and problem solving. It defines key concepts like abstract data types, data structures, algorithms, and algorithm analysis. It also covers different ways of classifying data structures and analyzing the time and space complexity of algorithms. The goal is to help students understand fundamental concepts around organizing data and designing efficient computational procedures.
The document discusses data structures and their importance in organizing data efficiently for computer programs. It defines what a data structure is and how choosing the right one can improve a program's performance. Several examples are provided to illustrate how analyzing a problem's specific needs guides the selection of an optimal data structure.
The document discusses data structures and their importance in organizing data efficiently for computer programs. It defines what a data structure is and how choosing the right one can improve a program's performance. Several examples are provided to illustrate how analyzing a problem's specific needs guides the selection of an optimal data structure.
The document discusses algorithms and data structures. It begins by introducing common data structures like arrays, stacks, queues, trees, and hash tables. It then explains that data structures allow for organizing data in a way that can be efficiently processed and accessed. The document concludes by stating that the choice of data structure depends on effectively representing real-world relationships while allowing simple processing of the data.
A Review on Reasoning System, Types, and Tools and Need for Hybrid ReasoningBRNSSPublicationHubI
This document summarizes a review article about reasoning systems, types of reasoning, and the need for hybrid reasoning systems. It discusses expert systems and how they use knowledge representation and reasoning to emulate expert decision making. The main types of reasoning discussed are deductive, inductive, and abductive reasoning. It also introduces the concept of a hybrid reasoning system that integrates two different types of reasoning to provide both qualitative and quantitative assessments.
This document discusses data structures and their role in organizing data efficiently for computer programs. It defines key concepts like abstract data types, algorithms, and problems. It also provides examples to illustrate selecting the appropriate data structure based on the operations and constraints of a problem. A banking application is used to demonstrate how hash tables are suitable because they allow extremely fast searching by account numbers while also supporting efficient insertion and deletion. B-trees are shown to be better than hash tables for a city database because they enable fast range queries in addition to exact searches. Overall, the document emphasizes that each data structure has costs and benefits, and a careful analysis is needed to determine the best structure for a given problem.
The document discusses key principles of software design including data design, architectural design, user interface design, abstraction, refinement, modularity, software architecture, control hierarchy, structural partitioning, software procedure, and information hiding. These principles provide a foundation for correctly designing software and translating analysis models into implementable designs.
A Development Shell For Cooperative Problem-Solving EnvironmentsJody Sullivan
SCARP is a shell for developing cooperative problem-solving environments. It has a layered architecture with a knowledge base at its core representing tasks, methods, and entities. The knowledge base is accessed by a knowledge handler. A task engine uses the knowledge to manage problem solving, interacting with the user interface through a cooperative dialogue handler when needed. SLOT, a data analysis problem-solving environment, was developed using SCARP to demonstrate its capabilities.
This document provides an overview of the contents of a textbook on object-oriented analysis and design (OOAD). It covers 6 units:
1. Object-oriented concepts, modeling, and the Unified Modeling Language (UML)
2. Iterative development and UML
3. Basic and advanced structural modeling
4. Interaction modeling
5. Architectural modeling
6. Object-oriented programming styles
The first unit introduces object-oriented paradigms and modeling techniques like the data flow diagram, entity relationship diagram, algorithms, and flowcharts. It also discusses object-oriented modeling and the process of object-oriented analysis and design.
This document provides an overview of object oriented analysis and design using the Unified Modeling Language (UML). It discusses key concepts in object oriented programming like classes, objects, encapsulation, inheritance and polymorphism. It also outlines the software development lifecycle and phases like requirements analysis, design, coding, testing and maintenance. Finally, it introduces UML and explains how use case diagrams can be used to model the user view of a system by defining actors and use cases.
The document discusses object-oriented design using UML. It describes the design process, including refining the analysis model into a design model with more implementation details. Key artifacts of design include interfaces, subsystems, and classes. Maintaining both analysis and design models is recommended for large, complex systems. Design axioms aim to maximize independence between components and minimize complexity. Corollaries provide guidelines for loosely coupled, single-purpose classes with strong mappings between analysis and design models.
The document discusses decision trees and the ID3 algorithm. It provides an overview of data mining techniques, including decision trees. It then describes the ID3 algorithm in detail, including how it uses information gain to build decision trees top-down and recursively to classify data. An example of applying the ID3 algorithm to a sample dataset is also provided to illustrate the step-by-step process.
The document summarizes key aspects of architectural design for software systems. It defines software architecture as the structure of system components and relationships between them. Architecture is important for analyzing design effectiveness, considering alternatives, and managing risks. Key architectural styles described include data-centered, data flow, call and return, object-oriented, and layered. The document also discusses defining architectural context diagrams, archetypes, and components to design system architecture.
The document provides an introduction to data structures and algorithms analysis. It discusses that a program consists of organizing data in a structure and a sequence of steps or algorithm to solve a problem. A data structure is how data is organized in memory and an algorithm is the step-by-step process. It describes abstraction as focusing on relevant problem properties to define entities called abstract data types that specify what can be stored and operations performed. Algorithms transform data structures from one state to another and are analyzed based on their time and space complexity.
Course material from my Object-Oriented Development course.This presentation covers the analysis phases and focuses on class discovery, domain modeling, activity diagrams, and sequence diagrams.
Introduction to Data Structure & algorithmSunita Bhosale
Data structures provide an efficient way to organize and store data in a computer. They enable programmers to handle data efficiently through algorithms. As applications and data volumes increase, data structures help address issues like slow processing speeds, inefficient data search, and systems being overwhelmed by multiple requests. Data structures organize data to allow only relevant data to be searched quickly. They improve efficiency, allow reuse, and provide abstraction between client programs and implementation details.
This document discusses design patterns for small devices. It begins by introducing design patterns as recurring solutions to software design problems. It notes that the process of building software should be evolutionary, learning from past experiences. The objective is to apply this theory to embedded systems and suggest three design patterns: Hierarchical State, Virtual Component, and LED Error patterns. It then provides details on each of these patterns, including their structure, implementation, applicability and known uses. The Hierarchical State pattern addresses complexity in state machine designs by organizing states hierarchically. The Virtual Component pattern reduces memory usage by loading components on demand. The LED Error pattern standardizes error handling across modules.
The document discusses software architecture, including definitions, principles, patterns, and modeling techniques. It defines architecture as the structure of a system comprising software elements and relationships. Some key principles discussed are single responsibility, open/closed, and dependency inversion. Common patterns like MVC, layered, and multitier architectures are explained. The document also introduces Unified Modeling Language (UML) for modeling systems using diagrams like class, component, and package diagrams.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Webinar: Designing a schema for a Data WarehouseFederico Razzoli
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
1. Data Structures and Algorithms
Rationale
Computer science is a field of study that deals with solving
a variety of problems by using computers.
To solve a given problem by using computers, you need to
design an algorithm for it.
Multiple algorithms can be designed to solve a particular
problem.
An algorithm that provides the maximum efficiency should
be used for solving the problem.
The efficiency of an algorithm can be improved by using an
appropriate data structure.
Data structures help in creating programs that are simple,
reusable, and easy to maintain.
This module will enable a learner to select and implement
an appropriate data structure and algorithm to solve a given
programming problem.
Ver. 1.0 Session 1
2. Data Structures and Algorithms
Objectives
In this session, you will learn to:
Explain the role of data structures and algorithms in problem
solving through computers
Identify techniques to design algorithms and measure their
efficiency
Ver. 1.0 Session 1
3. Data Structures and Algorithms
Role of Algorithms and Data Structures in Problem Solving
Problem solving is an essential part of every scientific
discipline.
Computers are widely being used to solve problems
pertaining to various domains, such as, banking, commerce,
medicine, manufacturing, and transport.
To solve a given problem by using a computer, you need to
write a program for it.
A program consists of two components, algorithm and data
structure.
Ver. 1.0 Session 1
4. Data Structures and Algorithms
Role of Algorithms
The word algorithm is derived from the name of the Persian
mathematician Al Khwarizmi.
An algorithm can be defined as a step-by-step procedure for
solving a problem.
An algorithm helps the user arrive at the correct result in a
finite number of steps.
Ver. 1.0 Session 1
5. Data Structures and Algorithms
Role of Algorithms (Contd.)
An algorithm has five important properties:
Finiteness
Definiteness
Input
Output
Effectiveness
Ver. 1.0 Session 1
6. Data Structures and Algorithms
Role of Algorithms (Contd.)
A problem can be solved using a computer only if an
algorithm can be written for it.
In addition, algorithms provide the following benefits:
Help in writing the corresponding program
Help in dividing difficult problems into a series of small
solvable problems
Make decision making a more rational process
Help make the process consistent and reliable
Ver. 1.0 Session 1
7. Data Structures and Algorithms
Role of Data Structures
Different algorithms can be used to solve the same problem.
Some algorithms may solve the problem more efficiently
than the others.
An algorithm that provides the maximum efficiency should
be used to solve a problem.
One of the basic techniques for improving the efficiency of
algorithms is to use an appropriate data structure.
Data structure is defined as a way of organizing the various
data elements in memory with respect to each other.
Ver. 1.0 Session 1
8. Data Structures and Algorithms
Role of Data Structures (Contd.)
Data can be organized in many different ways. Therefore,
you can create as many data structures as you want.
Some data structures that have proved useful over the
years are:
Arrays
Linked Lists
Stacks
Queues
Trees
Graphs
Ver. 1.0 Session 1
9. Data Structures and Algorithms
Role of Data Structures (Contd.)
Use of an appropriate data structure, helps improve the
efficiency of a program.
The use of appropriate data structures also allows you to
overcome some other programming challenges, such as:
Simplifying complex problems
Creating standard, reusable code components
Creating programs that are easy to understand and maintain
Ver. 1.0 Session 1
10. Data Structures and Algorithms
Types of Data Structures
Data structures can be classified under the following two
categories:
– Static: Example – Array
– Dynamic: Example – Linked List
Ver. 1.0 Session 1
11. Data Structures and Algorithms
Just a minute
An array is a ___________ data structure, and a linked list
is a ____________ data structure.
Answer:
static, dynamic
Ver. 1.0 Session 1
12. Data Structures and Algorithms
Identifying Techniques for Designing Algorithms
Two commonly used techniques for designing algorithms
are:
Divide and conquer approach
Greedy approach
Ver. 1.0 Session 1
13. Data Structures and Algorithms
Identifying Techniques for Designing Algorithms (Contd.)
Divide and conquer is a powerful approach for solving
conceptually difficult problems.
Divide and conquer approach requires you to find a way of:
Breaking the problem into sub problems
Solving the trivial cases
Combining the solutions to the sub problems to solve the
original problem
Ver. 1.0 Session 1
14. Data Structures and Algorithms
Identifying Techniques for Designing Algorithms (Contd.)
Algorithms based on greedy approach are used for solving
optimization problems, where you need to maximize profits
or minimize costs under a given set of conditions.
Some examples of optimization problems are:
Finding the shortest distance from an originating city to a set of
destination cities, given the distances between the pairs of
cities.
Finding the minimum number of currency notes required for an
amount, where an arbitrary number of notes for each
denomination are available.
Selecting items with maximum value from a given set of items,
where the total weight of the selected items cannot exceed a
given value.
Ver. 1.0 Session 1
15. Data Structures and Algorithms
Just a minute
The ___________ technique involves selecting the best
available option at each step.
Answer:
Greedy
Ver. 1.0 Session 1
16. Data Structures and Algorithms
Designing Algorithms Using Recursion
Recursion:
Refers to the technique of defining a process in terms of itself
Is used to solve complex programming problems that are
repetitive in nature
Is implemented in a program by using a recursive procedure or
function. A recursive procedure or function is a function that
invokes itself
Is useful in writing clear, short, and simple programs
Ver. 1.0 Session 1
17. Data Structures and Algorithms
Just a minute
Identify the problem in the following algorithm that attempts
to find the sum of the first n natural numbers:
Algorithm: Sum (n)
1. s = n + Sum(n – 1)
2. Return (s)
Answer:
There is no terminating condition in the given recursive
algorithm. Therefore, it will call itself infinitely. The correct
algorithm would be:
1. If (n = 1)
Return(1)
2. s = n + Sum(n – 1)
3. Return(s)
Ver. 1.0 Session 1
18. Data Structures and Algorithms
Determining the Efficiency of an Algorithm
Factors that affect the efficiency of a program include:
– Speed of the machine
– Compiler
– Operating system
– Programming language
– Size of the input
In addition to these factors, the way data of a program is
organized, and the algorithm used to solve the problem also
has a significant impact on the efficiency of a program.
Ver. 1.0 Session 1
19. Data Structures and Algorithms
Determining the Efficiency of an Algorithm (Contd.)
The efficiency of an algorithm can be computed by
determining the amount of resources it consumes.
The primary resources that an algorithm consumes are:
– Time: The CPU time required to execute the algorithm.
– Space: The amount of memory used by the algorithm for its
execution.
The lesser resources an algorithm consumes, the more
efficient it is.
Ver. 1.0 Session 1
20. Data Structures and Algorithms
Time/Space Tradeoff
Time/Space Tradeoff:
It refers to a situation where you can reduce the use of
memory at the cost of slower program execution, or reduce the
running time at the cost of increased memory usage.
Example is data storage in compressed/uncompressed form.
Memory is extensible, but time is not. Therefore, time
considerations generally override memory considerations.
Ver. 1.0 Session 1
21. Data Structures and Algorithms
Method for Determining Efficiency
To measure the time efficiency of an algorithm, you can
write a program based on the algorithm, execute it, and
measure the time it takes to run.
The execution time that you measure in this case would
depend on a number of factors such as:
Speed of the machine
Compiler
Operating system
Programming language
Input data
However, we would like to determine how the execution
time is affected by the nature of the algorithm.
Ver. 1.0 Session 1
22. Data Structures and Algorithms
Method for Determining Efficiency (Contd.)
The execution time of an algorithm is directly proportional to
the number of key comparisons involved in the algorithm
and is a function of n, where n is the size of the input data.
The rate at which the running time of an algorithm increases
as a result of an increase in the volume of input data is
called the order of growth of the algorithm.
The order of growth of an algorithm is defined by using the
big O notation.
The big O notation has been accepted as a fundamental
technique for describing the efficiency of an algorithm.
Ver. 1.0 Session 1
23. Data Structures and Algorithms
Method for Determining Efficiency (Contd.)
The different orders of growth and their corresponding big O
notations are:
– Constant - O(1)
– Logarithmic - O(log n)
– Linear - O(n)
– Loglinear - O(n log n)
– Quadratic - O(n2)
– Cubic - O(n3)
– Exponential - O(2n), O(10n)
Ver. 1.0 Session 1
24. Data Structures and Algorithms
Selecting an Efficient Algorithm
According to their orders of growth, the big O notations can
be arranged in an increasing order as:
2 3 n
O(1) < O(log n) < O(n) < O(n log n) < O(n ) < O(n ) < O(2 )
n
< O(10 )
Graphs depicting orders of growth for various big O
notations:
Microsoft Word
Document
Ver. 1.0 Session 1
25. Data Structures and Algorithms
Group Discussion: Dependence of Efficiency on Selected Algorithm
Problem Statement:
You need to write an algorithm to search for a given word in a
dictionary. Discuss how different algorithms and different ways
of organizing the dictionary data affect the efficiency of the
process.
Ver. 1.0 Session 1
26. Data Structures and Algorithms
Summary
In this session, you learned that:
An algorithm can be defined as a step-by-step procedure for
solving a problem that produces the correct result in a finite
number of steps.
An algorithm has five important properties:
– Finiteness
– Definiteness
– Input
– Output
– Effectiveness
An algorithm that provides the maximum efficiency should be
used for solving the problem.
Ver. 1.0 Session 1
27. Data Structures and Algorithms
Summary (Contd.)
Data structures can be classified under the following two
categories:
– Static
– Dynamic
Two commonly used techniques for designing algorithms are:
– Divide and conquer approach
– Greedy approach
Recursion refers to a technique of defining a process in terms
of itself. It is used to solve complex programming problems that
are repetitive in nature.
The primary resources that an algorithm consumes are:
– Time: The CPU time required to execute the algorithm.
– Space: The amount of memory used by the algorithm for
execution.
Ver. 1.0 Session 1
28. Data Structures and Algorithms
Summary (Contd.)
Time/space tradeoff refers to a situation where you can reduce
the use of memory at the cost of slower program execution, or
reduce the running time at the cost of increased memory
usage.
The total running time of an algorithm is directly proportional to
the number of comparisons involved in the algorithm.
The order of growth of an algorithm is defined by using the big
O notation.
Ver. 1.0 Session 1
Editor's Notes
Student already have learnt about SCDs in Module I. Therefore, you can start this topic by asking the following questions to students: What are type 1 SCDs? Given an example to explain type 1 SCDs. This will recapitulate what they have learnt about type 1 SCD in Module 1. Now explain the strategy to load the data into these dimension tables with help of the given diagram. Relate this diagram to the example given in SG.
To start the session, you need to get a set of playing cards in the class. Follow the instructions as given below to begin the game of Rummy. 1. The game begins by dealing a fixed number of cards to all players. The remaining cards are placed face down to form a “stock” pile. 2. There is also a face-up pile called the “discard” pile. 3. Initially, the discard pile contains only one card which is obtained by picking the topmost card from the stock pile. 4. Each player can draw either the topmost card of the stock pile or the topmost card on the discard pile to make a valid sequence in his/her hand. 5. After this, the player must discard one card on top of the discard pile. 6. The next player, can then draw either the topmost card of the draw pile or the topmost card of the discard pile. 7. Therefore, if a player has to draw a card from the discard pile, he/she can draw only the topmost card of the discard pile. 8. Similarly, when a player has to discard a card, he/she must discard it on the top of the discard pile. 9. The discard pile can therefore be considered a Last-In-First-Out list. 10. The last card placed on top of the discard pile is the first one to be drawn. 11. To represent and manipulate this kind of a discard pile in a computer program, you would like to use a list that: a. Contains the details of all the cards in the discard pile. b. Implements insertion and deletion of card details in such a way that the last inserted card is the first one to be removed. This kind of a list can be implemented by using a stack. Ask students to define a stack? Ask them to refer to the game and come up with some characteristics of a stack. Then come to next slide and give them the definition of stacks.
You can give some more explanation of stacks by with the help of the following example. 1. A stack is like an empty box containing books, which is just wide enough to hold the books in one pile. 2. The books can be placed as well as removed only from the top of the box. 3. The book most recently put in the box is the first one to be taken out. 4. The book at the bottom is the first one to be put inside the box and the last one to be taken out.
In this slide you need to show the calculation to determine the sum of an arithmetic progression for bubble sort algorithm. Refer to student guide.
In this slide you need to show the calculation to determine the sum of an arithmetic progression for bubble sort algorithm. Refer to student guide.
In this slide you need to show the calculation to determine the sum of an arithmetic progression for bubble sort algorithm. Refer to student guide.