This document contains self-assessment questions related to algorithms and data structures. It covers topics like algorithm analysis, sorting algorithms, searching algorithms, recursion, dynamic programming, graphs, and more. The questions test understanding of key concepts like time complexity, divide and conquer approach, greedy algorithms, and common algorithms like binary search, merge sort, etc.
This document outlines requirements for a Java program that implements and compares the performance of linear and binary search algorithms as well as several sorting algorithms. It instructs the student to write code that takes user input to determine array sizes, implements the specified search and sorting algorithms on arrays of random integers, and measures the time and number of operations for each. The student is asked to test the algorithms for different array sizes, under ascending and descending sorted and randomly generated input, and to analyze the results, complexity, and relative performance of each algorithm based on processing time graphs and discussions.
The document discusses various algorithm design techniques including greedy algorithms, divide and conquer, and dynamic programming. It provides examples of greedy algorithms like job scheduling and activity selection. It also explains the divide and conquer approach with examples like merge sort, quicksort, and closest pair of points problems. Finally, it discusses running time analysis and big-O notation for classifying algorithms based on time complexity.
This document provides objectives and instructions for a programming lab on arrays in Java. The objectives include learning about declaring, initializing, and manipulating one-dimensional and multi-dimensional arrays. The document also contains sample code segments and questions to test understanding of key array concepts like indexes, loops, passing arrays to methods, and variable argument lists. Students are asked to complete matching, fill-in-the-blank, short answer, and coding questions to prepare for the lab.
Adapted Branch-and-Bound Algorithm Using SVM With Model SelectionIJECEIAES
Branch-and-Bound algorithm is the basis for the majority of solving methods in mixed integer linear programming. It has been proving its efficiency in different fields. In fact, it creates little by little a tree of nodes by adopting two strategies. These strategies are variable selection strategy and node selection strategy. In our previous work, we experienced a methodology of learning branch-and-bound strategies using regression-based support vector machine twice. That methodology allowed firstly to exploit information from previous executions of Branch-and-Bound algorithm on other instances. Secondly, it created information channel between node selection strategy and variable branching strategy. And thirdly, it gave good results in term of running time comparing to standard Branch-and-Bound algorithm. In this work, we will focus on increasing SVM performance by using cross validation coupled with model selection.
Why managing queues is a key to accelerating your product development process. What are the causes of queues and how to reduce batch size to learn as well as launch earlier.
This document discusses machine learning algorithms and their applications. It begins with an abstract discussing supervised, unsupervised, and reinforcement learning techniques. It then discusses machine learning in more detail, explaining that machine learning algorithms represent data instances with a set of features and classify instances based on their labels. The main focus is on supervised and unsupervised learning techniques and their performance parameters. It provides an overview of support vector machines, neural networks, and other machine learning algorithms. In summary, the document provides a survey of different machine learning techniques, how they work, and their applications.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Multi objective genetic algorithm for regression testing reduction eSAT Journals
Abstract Location based authentication is a new direction in development of authentication techniques in the area of security. In this paper, the geographical position of user is an important attribute for authentication of user. It provides strong authentication as a location characteristic can never be stolen or spoofed. As an effective and popular means for privacy protection data hiding in encrypted image is proposed. In our application we are providing secure message passing facilities for this OTP (One Time Password) and Steganoghaphy techniques are used. This technique is relatively new approach towards information security. Keywords: Location based authentication, GPS device, Image encryption, Cryptography, Steganography, and OTP
This document outlines requirements for a Java program that implements and compares the performance of linear and binary search algorithms as well as several sorting algorithms. It instructs the student to write code that takes user input to determine array sizes, implements the specified search and sorting algorithms on arrays of random integers, and measures the time and number of operations for each. The student is asked to test the algorithms for different array sizes, under ascending and descending sorted and randomly generated input, and to analyze the results, complexity, and relative performance of each algorithm based on processing time graphs and discussions.
The document discusses various algorithm design techniques including greedy algorithms, divide and conquer, and dynamic programming. It provides examples of greedy algorithms like job scheduling and activity selection. It also explains the divide and conquer approach with examples like merge sort, quicksort, and closest pair of points problems. Finally, it discusses running time analysis and big-O notation for classifying algorithms based on time complexity.
This document provides objectives and instructions for a programming lab on arrays in Java. The objectives include learning about declaring, initializing, and manipulating one-dimensional and multi-dimensional arrays. The document also contains sample code segments and questions to test understanding of key array concepts like indexes, loops, passing arrays to methods, and variable argument lists. Students are asked to complete matching, fill-in-the-blank, short answer, and coding questions to prepare for the lab.
Adapted Branch-and-Bound Algorithm Using SVM With Model SelectionIJECEIAES
Branch-and-Bound algorithm is the basis for the majority of solving methods in mixed integer linear programming. It has been proving its efficiency in different fields. In fact, it creates little by little a tree of nodes by adopting two strategies. These strategies are variable selection strategy and node selection strategy. In our previous work, we experienced a methodology of learning branch-and-bound strategies using regression-based support vector machine twice. That methodology allowed firstly to exploit information from previous executions of Branch-and-Bound algorithm on other instances. Secondly, it created information channel between node selection strategy and variable branching strategy. And thirdly, it gave good results in term of running time comparing to standard Branch-and-Bound algorithm. In this work, we will focus on increasing SVM performance by using cross validation coupled with model selection.
Why managing queues is a key to accelerating your product development process. What are the causes of queues and how to reduce batch size to learn as well as launch earlier.
This document discusses machine learning algorithms and their applications. It begins with an abstract discussing supervised, unsupervised, and reinforcement learning techniques. It then discusses machine learning in more detail, explaining that machine learning algorithms represent data instances with a set of features and classify instances based on their labels. The main focus is on supervised and unsupervised learning techniques and their performance parameters. It provides an overview of support vector machines, neural networks, and other machine learning algorithms. In summary, the document provides a survey of different machine learning techniques, how they work, and their applications.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Multi objective genetic algorithm for regression testing reduction eSAT Journals
Abstract Location based authentication is a new direction in development of authentication techniques in the area of security. In this paper, the geographical position of user is an important attribute for authentication of user. It provides strong authentication as a location characteristic can never be stolen or spoofed. As an effective and popular means for privacy protection data hiding in encrypted image is proposed. In our application we are providing secure message passing facilities for this OTP (One Time Password) and Steganoghaphy techniques are used. This technique is relatively new approach towards information security. Keywords: Location based authentication, GPS device, Image encryption, Cryptography, Steganography, and OTP
Classification of Machine Learning AlgorithmsAM Publications
The goal of various machine learning algorithms is to device learning algorithms that learns automatically without any human intervention or assistance. The emphasis of machine learning is on automatic methods. Supervised Learning, unsupervised learning and reinforcement learning are discussed in this paper. Machine learning is the core area of Artificial Intelligence. Although a subarea of AI, machine learning also intersects broadly with other fields, especially statistics, but also mathematics, physics, theoretical computer science and more.
Object Detection using Deep Learning with Hierarchical Multi Swarm Optimizationrahulmonikasharma
This document summarizes a research paper that proposes a new method for object detection using deep learning with hierarchical multi swarm optimization. It begins by discussing limitations of existing object detection methods. A visual attention model is introduced that evaluates salient image regions and performs image segmentation for automatic classification. The paper then reviews related work using neural networks for classification. It proposes a new methodology using deep learning and hierarchical multi swarm optimization to identify objects in an image. This combines the benefits of multi swarm optimization and deep learning to overcome issues with existing neural network approaches for object detection. The methodology and implementation steps are described. The paper concludes that the proposed approach can achieve better results for automatic object detection compared to existing methods.
CIS 407 STUDY Inspiring Innovation--cis407study.comKeatonJennings91
This document contains information about various CIS 407 exams, labs, assignments, and case studies for an online course. It includes sample exam questions, lab exercise instructions, assignment descriptions, and a case study on creating bar charts in Java. The assignments involve building an interactive Java application for an insurance agent to generate quotes. Students are tasked with implementing classes, calculating premiums, getting user input, and modifying the application to use different input/output methods.
This document appears to be a sample midterm exam for an operating systems course. It contains 22 multipart questions testing students' knowledge of topics like threads, synchronization, interprocess communication, and scheduling. The exam instructs students to answer directly in the spaces provided below each question and not to begin until told to do so. It also provides instructions on exam policies and wishes students good luck.
Machine learning is a technology design to build intelligent systems. These systems also have the ability to learn from past experience or analyze historical data. It provides results according to its experience.
Alpavdin defines Machine Learning as-
“Optimizing a performance criterion using example data and past experience”.
Data is the key concept of machine learning. We can also apply its algorithms on data to identify hidden patterns and gain insights. These patterns and gained knowledge help systems to learn and improve their performance.
Machine learning technology involves both statistics and computer science. Statistics allows one to draw inferences from the given data. To implement efficient algorithms we can also use computer science. It represents the required model, and evaluate the performance of the model.
Mahout is an Apache project that provides scalable machine learning libraries for Java. It contains algorithms for classification, clustering, and recommendation engines that can operate on huge datasets using distributed computing. Some key algorithms in Mahout include Naive Bayes classification, k-means clustering, and item-based recommenders. Classification with Mahout involves training a model on labeled historical data, evaluating the model on test data, and then using the model to classify new unlabeled data at scale. Feature selection and representation are important for building an accurate classification model in Mahout.
Here is the first set of notes for the first class in Analysis of Algorithm. I added a dedicatory for my dear Fabi... she has showed me what real idealism is....
1)importjavax.swing. = package contains JFrame and JButton class.pdfangelfashions02
1)
importjavax.swing.* => package contains JFrame and JButton classes
2)
javadoc comments begin with the /* symbol, and end with the */ symbol.
3)
If you want to read in values from the user in the console window you must use a
____Scanner______ object.
4)
float, int, long, and double are forms of ____C_____ data types.
5)
An easy way to access every element of a Two dimensional array is to use _____2_nested_____
for loops.
6)
To search the elements of an array, ___linear_____ search can always be used.
7)
There are two types of method calls, void return methods and ___data____ return methods.
8.)
import keyword is used to import built-in and user-defined packages into your java source file.
key word that is used to bring in java.utils is ____Import______
9. When an array name is passed to a method, it is passing the memory address, therefore it is
passing the array by ____reference_____.
because array name refers to the memory address of the array
Solution
1)
importjavax.swing.* => package contains JFrame and JButton classes
2)
javadoc comments begin with the /* symbol, and end with the */ symbol.
3)
If you want to read in values from the user in the console window you must use a
____Scanner______ object.
4)
float, int, long, and double are forms of ____C_____ data types.
5)
An easy way to access every element of a Two dimensional array is to use _____2_nested_____
for loops.
6)
To search the elements of an array, ___linear_____ search can always be used.
7)
There are two types of method calls, void return methods and ___data____ return methods.
8.)
import keyword is used to import built-in and user-defined packages into your java source file.
key word that is used to bring in java.utils is ____Import______
9. When an array name is passed to a method, it is passing the memory address, therefore it is
passing the array by ____reference_____.
because array name refers to the memory address of the array.
Question 1How many parameters does a default constructor haveAn.pdfarccreation001
The document contains 20 multiple choice questions about Java concepts such as constructors, methods, variables, access modifiers, and object passing. Each question is accompanied by a short explanation of the answer. The questions cover topics like default constructors having no parameters, instance methods being associated with individual objects, private instance variables being accessible within the class, and static variables being shared among all class instances.
Analysis on Fraud Detection Mechanisms Using Machine Learning TechniquesIRJET Journal
1) The document discusses using machine learning techniques like Random Forest Classifier and AdaBoost to detect fraud in blockchain transactions through an ensemble model.
2) It analyzes the individual accuracy of Random Forest and AdaBoost classifiers, finding accuracies over 99.99%, then ensembles their predictions using a stacking method.
3) The stacking ensemble model combines the predictions of the Random Forest and AdaBoost models into a new training set to potentially provide even more accurate fraud detection compared to the individual models.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization stratagem. This evolutionary technique always aims to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
This document discusses algorithms and their applications in computer science. It begins with acknowledging those who helped with a course on algorithms. It then provides an introduction to algorithms, describing them as step-by-step procedures for solving general problems. The document provides examples of algorithms for finding the maximum value in a list, searching for a value linearly, and sorting values with bubble sort. It concludes by describing a Java program that uses an algorithm to search for a value within an array.
This document outlines the syllabus for the subject "Design and Analysis of Algorithms" for the 3rd year 1st semester students of the Computer Science and Engineering department with specialization in Cyber Security at CMR Engineering College.
The syllabus is divided into 5 units which cover topics like algorithm analysis, asymptotic notations, algorithm design techniques like divide and conquer, dynamic programming, greedy algorithms etc. It also discusses NP-hard and NP-complete problems. The document provides the textbook and references for the subject. It further includes introductions to different units explaining key concepts like algorithms, properties of algorithms, ways to represent algorithms, need for algorithm analysis etc.
Introduction to Design Algorithm And Analysis.pptBhargaviDalal4
This document contains the syllabus for the subject "Design and Analysis of Algorithms" for the 3rd year 1st semester students of CMR Engineering College. It includes 5 units - Introduction, Disjoint Sets and Backtracking, Dynamic Programming and Greedy Methods, Branch and Bound, and NP-Hard and NP-Complete problems. The introduction covers topics like algorithm complexity analysis and divide and conquer algorithms. The syllabus outlines core algorithms topics and applications like binary search, quicksort, dynamic programming, shortest paths, knapsack etc. that will be covered in the course.
The document discusses algorithms and data structures. It defines an algorithm as a step-by-step procedure for solving a problem using a computer in a finite number of steps. It categorizes common types of algorithms as search, sort, insert, update, and delete algorithms. The document also defines a data structure as a way to store and organize data for efficient use. It distinguishes between linear and non-linear as well as static and dynamic data structures. Finally, it discusses algorithm design strategies like divide and conquer, merge sort, and dynamic programming.
This document summarizes a report on analyzing a stock prediction model using neural networks. The report presents a model that predicts stock prices by extracting stock data, dividing it into training and validation sets, and feeding it into a neural network. Experimental results showed the model could accurately predict stock prices after training on 90% of the data, but predictions on the remaining 10% of data sometimes differed from actual prices. The model allows users to choose different stock attributes or time periods for analysis and prediction.
Understanding the Applicability of Linear & Non-Linear Models Using a Case-Ba...ijaia
This paper uses a case based study – “product sales estimation” on real-time data to help us understand
the applicability of linear and non-linear models in machine learning and data mining. A systematic
approach has been used here to address the given problem statement of sales estimation for a particular set
of products in multiple categories by applying both linear and non-linear machine learning techniques on
a data set of selected features from the original data set. Feature selection is a process that reduces the
dimensionality of the data set by excluding those features which contribute minimal to the prediction of the
dependent variable. The next step in this process is training the model that is done using multiple
techniques from linear & non-linear domains, one of the best ones in their respective areas. Data Remodeling
has then been done to extract new features from the data set by changing the structure of the
dataset & the performance of the models is checked again. Data Remodeling often plays a very crucial and
important role in boosting classifier accuracies by changing the properties of the given dataset. We then try
to explore and analyze the various reasons due to which one model performs better than the other & hence
try and develop an understanding about the applicability of linear & non-linear machine learning models.
The target mentioned above being our primary goal, we also aim to find the classifier with the best possible
accuracy for product sales estimation in the given scenario.
Proposing an Appropriate Pattern for Car Detection by Using Intelligent Algor...Editor IJCATR
Nowadays, the automotive industry has attracted the attention of consumers, and product quality is considered as an
essential element in current competitive markets. Security and comfort are the main criteria and parameters of selecting a car.
Therefore, standard dataset of CAR involving six features and characteristics and 1728 instances have been used. In this paper, it
has been tried to select a car with the best characteristics by using intelligent algorithms (Random Forest, J48, SVM,
NaiveBayse) and combining these algorithms with aggregated classifiers such as Bagging and AdaBoostMI. In this study, speed
and accuracy of intelligent algorithms in identifying the best car have been taken into account.
Lesson 7. The issues of detecting 64-bit errorsPVS-Studio
There are various techniques of detecting errors in program code. Let us consider the most popular ones and see how efficient they are in finding 64-bit errors.
Classification of Machine Learning AlgorithmsAM Publications
The goal of various machine learning algorithms is to device learning algorithms that learns automatically without any human intervention or assistance. The emphasis of machine learning is on automatic methods. Supervised Learning, unsupervised learning and reinforcement learning are discussed in this paper. Machine learning is the core area of Artificial Intelligence. Although a subarea of AI, machine learning also intersects broadly with other fields, especially statistics, but also mathematics, physics, theoretical computer science and more.
Object Detection using Deep Learning with Hierarchical Multi Swarm Optimizationrahulmonikasharma
This document summarizes a research paper that proposes a new method for object detection using deep learning with hierarchical multi swarm optimization. It begins by discussing limitations of existing object detection methods. A visual attention model is introduced that evaluates salient image regions and performs image segmentation for automatic classification. The paper then reviews related work using neural networks for classification. It proposes a new methodology using deep learning and hierarchical multi swarm optimization to identify objects in an image. This combines the benefits of multi swarm optimization and deep learning to overcome issues with existing neural network approaches for object detection. The methodology and implementation steps are described. The paper concludes that the proposed approach can achieve better results for automatic object detection compared to existing methods.
CIS 407 STUDY Inspiring Innovation--cis407study.comKeatonJennings91
This document contains information about various CIS 407 exams, labs, assignments, and case studies for an online course. It includes sample exam questions, lab exercise instructions, assignment descriptions, and a case study on creating bar charts in Java. The assignments involve building an interactive Java application for an insurance agent to generate quotes. Students are tasked with implementing classes, calculating premiums, getting user input, and modifying the application to use different input/output methods.
This document appears to be a sample midterm exam for an operating systems course. It contains 22 multipart questions testing students' knowledge of topics like threads, synchronization, interprocess communication, and scheduling. The exam instructs students to answer directly in the spaces provided below each question and not to begin until told to do so. It also provides instructions on exam policies and wishes students good luck.
Machine learning is a technology design to build intelligent systems. These systems also have the ability to learn from past experience or analyze historical data. It provides results according to its experience.
Alpavdin defines Machine Learning as-
“Optimizing a performance criterion using example data and past experience”.
Data is the key concept of machine learning. We can also apply its algorithms on data to identify hidden patterns and gain insights. These patterns and gained knowledge help systems to learn and improve their performance.
Machine learning technology involves both statistics and computer science. Statistics allows one to draw inferences from the given data. To implement efficient algorithms we can also use computer science. It represents the required model, and evaluate the performance of the model.
Mahout is an Apache project that provides scalable machine learning libraries for Java. It contains algorithms for classification, clustering, and recommendation engines that can operate on huge datasets using distributed computing. Some key algorithms in Mahout include Naive Bayes classification, k-means clustering, and item-based recommenders. Classification with Mahout involves training a model on labeled historical data, evaluating the model on test data, and then using the model to classify new unlabeled data at scale. Feature selection and representation are important for building an accurate classification model in Mahout.
Here is the first set of notes for the first class in Analysis of Algorithm. I added a dedicatory for my dear Fabi... she has showed me what real idealism is....
1)importjavax.swing. = package contains JFrame and JButton class.pdfangelfashions02
1)
importjavax.swing.* => package contains JFrame and JButton classes
2)
javadoc comments begin with the /* symbol, and end with the */ symbol.
3)
If you want to read in values from the user in the console window you must use a
____Scanner______ object.
4)
float, int, long, and double are forms of ____C_____ data types.
5)
An easy way to access every element of a Two dimensional array is to use _____2_nested_____
for loops.
6)
To search the elements of an array, ___linear_____ search can always be used.
7)
There are two types of method calls, void return methods and ___data____ return methods.
8.)
import keyword is used to import built-in and user-defined packages into your java source file.
key word that is used to bring in java.utils is ____Import______
9. When an array name is passed to a method, it is passing the memory address, therefore it is
passing the array by ____reference_____.
because array name refers to the memory address of the array
Solution
1)
importjavax.swing.* => package contains JFrame and JButton classes
2)
javadoc comments begin with the /* symbol, and end with the */ symbol.
3)
If you want to read in values from the user in the console window you must use a
____Scanner______ object.
4)
float, int, long, and double are forms of ____C_____ data types.
5)
An easy way to access every element of a Two dimensional array is to use _____2_nested_____
for loops.
6)
To search the elements of an array, ___linear_____ search can always be used.
7)
There are two types of method calls, void return methods and ___data____ return methods.
8.)
import keyword is used to import built-in and user-defined packages into your java source file.
key word that is used to bring in java.utils is ____Import______
9. When an array name is passed to a method, it is passing the memory address, therefore it is
passing the array by ____reference_____.
because array name refers to the memory address of the array.
Question 1How many parameters does a default constructor haveAn.pdfarccreation001
The document contains 20 multiple choice questions about Java concepts such as constructors, methods, variables, access modifiers, and object passing. Each question is accompanied by a short explanation of the answer. The questions cover topics like default constructors having no parameters, instance methods being associated with individual objects, private instance variables being accessible within the class, and static variables being shared among all class instances.
Analysis on Fraud Detection Mechanisms Using Machine Learning TechniquesIRJET Journal
1) The document discusses using machine learning techniques like Random Forest Classifier and AdaBoost to detect fraud in blockchain transactions through an ensemble model.
2) It analyzes the individual accuracy of Random Forest and AdaBoost classifiers, finding accuracies over 99.99%, then ensembles their predictions using a stacking method.
3) The stacking ensemble model combines the predictions of the Random Forest and AdaBoost models into a new training set to potentially provide even more accurate fraud detection compared to the individual models.
Automatic Unsupervised Data Classification Using Jaya Evolutionary Algorithmaciijournal
In this paper we attempt to solve an automatic clustering problem by optimizing multiple objectives such as automatic k-determination and a set of cluster validity indices concurrently. The proposed automatic clustering technique uses the most recent optimization algorithm Jaya as an underlying optimization stratagem. This evolutionary technique always aims to attain global best solution rather than a local best solution in larger datasets. The explorations and exploitations imposed on the proposed work results to detect the number of automatic clusters, appropriate partitioning present in data sets and mere optimal values towards CVIs frontiers. Twelve datasets of different intricacy are used to endorse the performance of aimed algorithm. The experiments lay bare that the conjectural advantages of multi objective clustering optimized with evolutionary approaches decipher into realistic and scalable performance paybacks.
This document discusses algorithms and their applications in computer science. It begins with acknowledging those who helped with a course on algorithms. It then provides an introduction to algorithms, describing them as step-by-step procedures for solving general problems. The document provides examples of algorithms for finding the maximum value in a list, searching for a value linearly, and sorting values with bubble sort. It concludes by describing a Java program that uses an algorithm to search for a value within an array.
This document outlines the syllabus for the subject "Design and Analysis of Algorithms" for the 3rd year 1st semester students of the Computer Science and Engineering department with specialization in Cyber Security at CMR Engineering College.
The syllabus is divided into 5 units which cover topics like algorithm analysis, asymptotic notations, algorithm design techniques like divide and conquer, dynamic programming, greedy algorithms etc. It also discusses NP-hard and NP-complete problems. The document provides the textbook and references for the subject. It further includes introductions to different units explaining key concepts like algorithms, properties of algorithms, ways to represent algorithms, need for algorithm analysis etc.
Introduction to Design Algorithm And Analysis.pptBhargaviDalal4
This document contains the syllabus for the subject "Design and Analysis of Algorithms" for the 3rd year 1st semester students of CMR Engineering College. It includes 5 units - Introduction, Disjoint Sets and Backtracking, Dynamic Programming and Greedy Methods, Branch and Bound, and NP-Hard and NP-Complete problems. The introduction covers topics like algorithm complexity analysis and divide and conquer algorithms. The syllabus outlines core algorithms topics and applications like binary search, quicksort, dynamic programming, shortest paths, knapsack etc. that will be covered in the course.
The document discusses algorithms and data structures. It defines an algorithm as a step-by-step procedure for solving a problem using a computer in a finite number of steps. It categorizes common types of algorithms as search, sort, insert, update, and delete algorithms. The document also defines a data structure as a way to store and organize data for efficient use. It distinguishes between linear and non-linear as well as static and dynamic data structures. Finally, it discusses algorithm design strategies like divide and conquer, merge sort, and dynamic programming.
This document summarizes a report on analyzing a stock prediction model using neural networks. The report presents a model that predicts stock prices by extracting stock data, dividing it into training and validation sets, and feeding it into a neural network. Experimental results showed the model could accurately predict stock prices after training on 90% of the data, but predictions on the remaining 10% of data sometimes differed from actual prices. The model allows users to choose different stock attributes or time periods for analysis and prediction.
Understanding the Applicability of Linear & Non-Linear Models Using a Case-Ba...ijaia
This paper uses a case based study – “product sales estimation” on real-time data to help us understand
the applicability of linear and non-linear models in machine learning and data mining. A systematic
approach has been used here to address the given problem statement of sales estimation for a particular set
of products in multiple categories by applying both linear and non-linear machine learning techniques on
a data set of selected features from the original data set. Feature selection is a process that reduces the
dimensionality of the data set by excluding those features which contribute minimal to the prediction of the
dependent variable. The next step in this process is training the model that is done using multiple
techniques from linear & non-linear domains, one of the best ones in their respective areas. Data Remodeling
has then been done to extract new features from the data set by changing the structure of the
dataset & the performance of the models is checked again. Data Remodeling often plays a very crucial and
important role in boosting classifier accuracies by changing the properties of the given dataset. We then try
to explore and analyze the various reasons due to which one model performs better than the other & hence
try and develop an understanding about the applicability of linear & non-linear machine learning models.
The target mentioned above being our primary goal, we also aim to find the classifier with the best possible
accuracy for product sales estimation in the given scenario.
Proposing an Appropriate Pattern for Car Detection by Using Intelligent Algor...Editor IJCATR
Nowadays, the automotive industry has attracted the attention of consumers, and product quality is considered as an
essential element in current competitive markets. Security and comfort are the main criteria and parameters of selecting a car.
Therefore, standard dataset of CAR involving six features and characteristics and 1728 instances have been used. In this paper, it
has been tried to select a car with the best characteristics by using intelligent algorithms (Random Forest, J48, SVM,
NaiveBayse) and combining these algorithms with aggregated classifiers such as Bagging and AdaBoostMI. In this study, speed
and accuracy of intelligent algorithms in identifying the best car have been taken into account.
Lesson 7. The issues of detecting 64-bit errorsPVS-Studio
There are various techniques of detecting errors in program code. Let us consider the most popular ones and see how efficient they are in finding 64-bit errors.
Discover the benefits of outsourcing SEO to Indiadavidjhones387
"Discover the benefits of outsourcing SEO to India! From cost-effective services and expert professionals to round-the-clock work advantages, learn how your business can achieve digital success with Indian SEO solutions.
Securing BGP: Operational Strategies and Best Practices for Network Defenders...APNIC
Md. Zobair Khan,
Network Analyst and Technical Trainer at APNIC, presented 'Securing BGP: Operational Strategies and Best Practices for Network Defenders' at the Phoenix Summit held in Dhaka, Bangladesh from 23 to 24 May 2024.
HijackLoader Evolution: Interactive Process HollowingDonato Onofri
CrowdStrike researchers have identified a HijackLoader (aka IDAT Loader) sample that employs sophisticated evasion techniques to enhance the complexity of the threat. HijackLoader, an increasingly popular tool among adversaries for deploying additional payloads and tooling, continues to evolve as its developers experiment and enhance its capabilities.
In their analysis of a recent HijackLoader sample, CrowdStrike researchers discovered new techniques designed to increase the defense evasion capabilities of the loader. The malware developer used a standard process hollowing technique coupled with an additional trigger that was activated by the parent process writing to a pipe. This new approach, called "Interactive Process Hollowing", has the potential to make defense evasion stealthier.
Honeypots Unveiled: Proactive Defense Tactics for Cyber Security, Phoenix Sum...APNIC
Adli Wahid, Senior Internet Security Specialist at APNIC, delivered a presentation titled 'Honeypots Unveiled: Proactive Defense Tactics for Cyber Security' at the Phoenix Summit held in Dhaka, Bangladesh from 23 to 24 May 2024.
1. Self-Assessment Questions 1
1. The rules for performing
arithmetic using Arabic numerals
were originally known as
_____________.
2. The efficiency of algorithms
depends upon ____________,
________ and __________
consumption.
3. An algorithm is considered as
the cornerstone of ____________.
4. To analyze an algorithm is to
determine the amount of
________ necessary to execute it.
5. __________ algorithms are
used to combine to sets of
elements to form a single set
according to some criteria.
6. ___________ is the expression
of an algorithm in a programming
language with all the language
specific codes.
7. An algorithm that invokes itself
within the process is called
_______.
8. ______ is the method of
expressing the upper bound of an
algorithm's running time.
9. _____________ is defined as
the number of memory cells which
an algorithm needs.
10. A ____________ algorithm
converts the solution to a simpler
sub problem to arrive at the
correct solution.
11. A ______________ algorithm
simply tries all possibilities until a
satisfactory solution is found.
12. ___________ algorithms are
based on a depth-first recursive
search.
13. A ______ is a set of data
elements grouped together under
one name.
14. _________ data structure has
all the elements of same data type
in it.
15. For ____________ data
structure, the memory is allocated
at the compile time itself.
Self-Assessment Questions
1. Algorism 2. Speed, size,
resources
3. Good programming
4. Resources 5. Merging
6. Programs 7. Direct recursive
8. Big – O 9. Space complexity
10. Simple recursive
11. Brute force 12.
Backtracking
13. Data structure 14.
Homogeneous
15. Static
Self-Assessment Questions
2
1.The ________ of an algorithm
can be determined by calculating
its performance.
2.___________ of an algorithm is
the amount of time required by an
algorithm to execute.
3.If an algorithm takes least
amount of time to execute a
specific set of inputs then it is
called __________ time
complexity.
4.The method of calculating
primitive operations produces a
computational model called
_______________.
5._________ describes how to
count the maximum number of
primitive operations an algorithm
executes.
6.Recursive procedure should
define a ________ which is small
enough to solve without using
recursion.
7.__________ technique is used to
perform an amortized analysis
method based on a financial
model.
8.If you can setup such a scheme
called amortization scheme then
each operation in the series has
an_______.
9._____________ technique is
used to perform an amortized
analysis method based on an
energy model.
10.The running time of the
algorithm prefixAverages1 is
_______.
11.The running time of the
algorithm prefixAverages2 is
_______.
12.In prefixAverages2 algorithm
________ is the time taken for
initiating the variable s.
Self-Assessment Questions
1.Efficiency 2.Time
complexity
3.Best case
4.Random assess machine
5.Counting primitive operations
6.Base case 7.Accounting
method
8.Amortised running time O(a)
9.Potential function method
10.O(n2) 11.O(n)
12.O(1)
Self-Assessment Questions 3
1. ___________ is more accurate
than Big Oh notation and Omega
notation.
2. ____________ asymptotic
notation is a simple notational
convenience.
3. ___________ depicts the
running time between the upper
bound and lower bound.
4. Tower of Hanoi is a ________
puzzle.
5. The time required to perform a
step should always bound above
by a ________________.
6. ____________ is of no
importance between two
operations for the algorithm’s
basic operation.
Self-Assessment Questions
1. Theta notation 2.
Conditional
3. Theta notation 4.
Mathematical
5. Constant 6. Choice
Self-Assessment Questions
4
1. _________ is defined as the
process that refers itself to simplify
a problem.
2. _____________ need very few
lines of code as it performs the
same process again and again on
different data.
3. In the towers of Hanoi problem,
if the numbers of disks is n, the
number of steps will be
___________.
4. ___________________ of
algorithm means analyzing the
behavior of the algorithm with a
specific set of inputs.
5. We can measure efficiency of
algorithms using
________________ and
_______________ methods.
6. The __________ analysis of the
algorithm makes it easy to study
7. _________________ is defined
as a technique which uses images
to convey the information about
algorithms.
8. ___________ visualization is the
type of visualization which uses
still images to illustrate the
algorithm.
9. ___________ visualization is the
type of visualization which uses
animations to illustrate the
algorithm. This is also known as
algorithm animation.
Self-Assessment Questions
1. Recursion 2. Recursive
algorithms
3. 2n-1 4. Empirical analysis
5. Counters, system clocking
6. Pictorial 7. Algorithm
visualization
8. Static algorithm
9. Dynamic algorithm
Self-Assessment Questions
5
1. A value that satisfies the
constraint is called a ___________.
2. ____________ is a function that
is associated with an optimization
problem determining how good a
solution is.
3. The running time needed to
determine whether a possible
value of a feasible solution is O(n)
and the time required to compute
the objective function is also O(n)
is ________.
4. Selection sort is one of the
simplest and ________ sorting
techniques.
5. Bubble sort has __________,
best and average case run-time of
O(n2).
6. ______________________ is
the simplest sorting algorithm.
7. ________________ is also
known as linear search.
8. We program sequential search
in an array by _________an index
variable until it reaches the last
index.
9. In this pseudocode
implementation, we execute the
__________ only after all list items
are examined with none matching.
10. Exhaustive search
implementation is more important
than _________.
11. Exhaustive search algorithm
gives the ______________ for
every candidate that is a solution
to the given instance P.
12. Exhaustive search is typically
used when the problem size is
___________.
Self Assessment Questions
1. Feasible solution
2. Objective function 3.
O(n2n).
4. Performance oriented 5.
Worst
6. Bubble sort 7. Sequential
search
8. Stepping up 9. Last line
10. Speed 11. Output
12. Limited
Self-Assessment Questions
6
1. Mergesort is a perfect example
of a successful application of the
_________ and ____________
methodology.
2. ____________ is a comparison-
based sorting.
3. What are the three steps
involved in mergesort?
4. If the array has two or more
cells, the algorithm calls the
________ method.
5. Unlike the merge sort, which
breaks up its input elements
according to their position in the
array, quick sort breaks them
according to their ____________.
6. After the partition, if the pivot is
inserted at the boundary between
the ___________ sub-arrays, it will
be in its final sorted position.
7. Binary search is an algorithm for
identifying the position of an
element in a ____________ array.
8. Say if the following statement is
true or false. To find a value in
unsorted array, we need to scan
through only half the elements of
an array.
9. Say if the following statement is
true or false. The benefit of binary
search is that its complexity
depends on the array size
logarithmically.
10. ________________________
methodology can solve most of
the problems regarding binary
trees.
11. The three typical traversals:
2. _____________, _____________,
and _____________ are the most
important Divide and Conquer
algorithms of binary trees.
12. Two kinds of traversal are
_____________ and
_____________.
13. At the expense of a slight
increase in the number of
additions, the strassen’s matrix
multiplication algorithm will
_____________ the total number
of multiplications performed.
14. The normal method of matrix
multiplication of two n X n
matrices takes ___________
operators.
15. By the strassen’s matrix
multiplication algorithm, two 2 X 2
matrices takes 2 only 7
multiplicators and _______
adders.
Self-Assessment Questions
1. Divide, Conquer 2.
Mergesort
3. Divide, recur, conquer
4. Partition 5. Value
6. Left and right 7. Sorted
8. False 9. True
10. Divide and Conquer
11. Pre-order, in-order, post-order
12. Breadth-first traversal, depth-
first traversal 13.
Decrease
14. O( ) 15. 18
Self Assessment Questions
7
1. Decrease and conquer can be
implemented by a
________________ or
____________ approach.
2. Decrease and conquer is also
known as ______________
approach.
3. Decrease and conquer is a
method by which we find a
solution to a given problem based
upon the __________ of a number
of problems.
4. There are ________ major
categories in insertion sort.
5. In insertion sort, the best-case
input is an array that is already
__________.
6. To carry out an insertion sort,
begin at the ______ most element
of the array
7. DFS uses the __________
technique.
8. It is easier to use a ________ to
trace the working of a depth first
search.
9. Depth-first search starts
exploring vertices of a graph at a
_______ vertex.
10. The data structure which is
used to track the working of
Breadth-first search is a
___________.
11. Breadth-first search is an
algorithm which travels in such a
way that all vertices are finished
along every ___________.
12. The data structure which is
used to track the working of
Breadth-first search is a
______________.
13. Topological ordering of a
_________ is a linear ordering of
its nodes.
14. In topological sorting the jobs
are denoted by _________.
15. Being a DAG is also a necessary
condition for
______________sorting to be
possible.
16. Theoretically, the traveling
salesman problem can always be
solved by specifying all
_____________ Hamiltonian
circuits.
17. The cities are represented by
____________ and the roads
between them are shown as
edges.
18. Each of the cities is connected
to another city by a road a
complete
_____________________ is
obtained.
Self Assessment Questions
1. Top down or Bottom-up
2. Incremental 3. Solution
4. Two 5. Sorted
6. Left 7.
Backtracking
8. Time and space 9.
Backtracks
10. Graph 11.
Uninformed
12. Goal 13. Directed
acyclic graph 14. Vertices
15. Topological 16.
Combinatorial
17. Subset 18. Decrease-by-
one
Self Assessment Questions 8
1. _______, ________ & ________
are three instances of transform
and conquer technique.
2. The worst case efficiency of
brute force algorithm is _______.
3. The searching requires _______
comparisons to in the worst case,
when the array is sorted first.
4. Gaussian elimination is an
algorithm for solving systems of
__________ equations.
5. In Gaussian elimination we
make the ________ coefficient
matrix zero.
6. Gaussian elimination can be
used to find the _________of a
matrix.
7. An AVL tree is a _________ tree.
8. The _________________ is the
mirror image of the RL-rotation.
9. The two nodes of 2-3 tree are
___________ and ____________.
10. _________ heap construction
algorithm is less efficient than its
counterpart.
11. A heap can be defined as
________ with keys assigned to its
nodes.
12. The time efficiency of heapsort
is ________ in both worst and
average cases.
13. Greatest common divisor can
be computed easily by ________.
14. The problem of counting a
graph’s paths can be solved with
an algorithm for an appropriate
power of its __________.
15. What is the formula obtained
to find the lcm in the problem
reduction approach to find the
lcm?
Self Assessment Questions
1. Instance simplification, problem
reduction , representation change
2. ө(n2) 3. [log2 n] +1
4. Linear 5. Lower triangular
6. Rank 7. Binary search
8. LR-rotation 9. 2-node, 3-
node
10. Top-down 11. Binary
trees
12. O(n log n) 13. Euclid’s
algorithm
14. Adjacent matrix
15.
Self Assessment Questions 9
1. Input enhancement is based on
___________________ the
instance.
2. The information which is used to
place the elements at proper
positions is accumulated sum of
frequencies which is called as
__________.
3. Sorting is an example of input
enhancement that achieves
___________.
4. Input enhancement is to
_____________ the input pattern.
5. In Horspool’s algorithm, the
characters from the pattern are
matched __________________.
6. The two heuristics in Boyre-
Moore algorithm are
_______________ and
________________.
7. Each slot of a hash table is often
called a _____________.
8. Collision occurs when a hash
function maps two or more keys to
the
____________________________
.
9. When the interval between
probes is computed by another
hash function it is
______________________.
10. As the
______________________
increases the height of the tree
decreases thus speeding access.
11. Access time increases slowly as
the number of records
__________.
12. The insertions in a B-Tree start
from a ________.
Self Assessment Questions
1. Preprocessing 2.
Distribution
3. Time efficiency 4.
Preprocess
5. Right to left
6. Good suffix and bad character
shift
7. Bucket 8. Same hash value
9. Double hashing
10. Branching factor 11.
Increases
12. Leaf node
Self Assessment Questions 10
1. The ____________ and
_____________ are the two
approaches to dynamic
programming.
2. __________ is a technique to
store answers to sub-problems in a
table.
3. The _________________
algorithm is similar to the dynamic
approach.
4. The formula to calculate the nth
Fibonacci series is
_______________.
5. The asymptotic running time
when we first run to calculate the
nth Fibonacci number is _______.
6. To compute the nth Fibonacci
number we followed the
__________ dynamic approach.
7. What formula can we use to find
the value of the binomial
coefficient?
8. The gamma function z! (z
1) allows the binomial coefficient
to be generalized to non-integer
arguments like
_________________.
9. Binomial coefficients are a study
of _____________.
10. Both Warshall’s and Floyd’s
algorithms have the run time as
________.
11. The Warshall’s algorithm is
used to solve _______________
problems.
12. The Floyd’s algorithm is used
to solve ________________
problems.
Self Assessment Questions
1. Top-down, bottom-up
2. Memoization 3. Divide and
conquer
4.
5. O(n) 6. Bottom-up
7.
8. Complex n and k 9.
3. Combinatorics
10. θ(n3) 11. Transitive
closure
12. Shortest path
Self Assessment Questions 11
1. Principle of __________ is
defined as a basic dynamic
programming principle which
helps us to view problems as a
sequence of sub problems.
2. ______________, a
mathematician, invented the
Principle of Optimality.
3. All optimization problems tend
to minimizing cost, time and
maximizing ________.
4. ________________are node
based data structures used in
many system programming
applications for managing dynamic
sets.
5. The Insertion, deletion and
search operations of a binary
search tree has an average case
complexity of _________.
6. The time taken to perform
operations on a binary search tree
is directly proportional to the
_______ of the tree.
7. The __________ expresses the
problem using its sub-instances.
8. ________________ is an NP-
hard optimization problem.
9. The Knapsack problem
minimizes the total _________
and maximizes the total value.
10. The goal of using
______________ is to solve only
the sub problems which are
necessary.
11. Memory functions use a
dynamic programming technique
called _____ in order to reduce
the inefficiency of recursion that
might occur.
12. Memory functions method
solves the problem using
_________approach.
Self Assessment Questions
1. Optimality 2. Richard Ernest
Bellman 3. Profits 4.
Binary search trees 5. O(log n)
6. Height
7. Recurrence relation
8. Bounded Knapsack problem
9. Weight 10. Memory
functions
11. Memoization 12. Top
down
Self Assessment Questions 12
1. The choices made in a greedy
algorithm cannot depend on
_________ choices.
2. The __________________ is
greedy in the sense that at each
iteration it approximates the
residual possible by a single
function.
3. A greedy strategy usually
progresses in a ___________
fashion.
4. The
_______________________ is
obtained by selecting the adjacent
vertices of already selected
vertices.
5. Each _________________
generated by prim’s algorithm is a
part of some other minimum
spanning tree.
6. The greedy strategy in prim’s
algorithm is greedy since the tree
is added with an edge that
contributes the __________
amount possible to the tree's
weight.
7. In Kruskal’s algorithm if the
graph is not connected, then the
algorithm yields a
__________________.
8. The Union-Find data structure is
helpful for managing
_____________ which is vital for
Kruskal’s algorithm.
9. Correctness of Kruskal’s
algorithm can be proved by saying
that the constructed spanning tree
is of _______________.
10. Dijkstra’s algorithm solves the
single-source ____________
problem for a tree.
11. The algorithm finds the path
with lowest cost between the
________ vertex and every other
vertex.
12. The time complexity of
Dijkstra’s algorithm can be
improved for _____________
graphs.
13. Huffman codes are digital
_________________ codes.
14. The Huffman Encoding scheme
falls in the category of
____________.
15. Static Huffman coding is done
with the help of ___________
tables.
Self Assessment Questions
1. Future 2. Pure greedy
algorithm
3. Top-down
4. Minimum spanning tree
5. Sub-tree 6. Minimum
7. Minimum spanning forest
8. Equivalence classes
9. Minimal weight 10. Shortest
path
11. Originating 12. Sparse
13. Data compression
14. Variable length encoding
15. Statistical symbol frequency
Self Assessment Question 13
1. _____________________
means calculating the minimum
amount of work required to solve
the problem.
2. Trivial lower bound is obtained
by the count of the input data that
the algorithm reads and the
output it produces.
3. _______________________
method defines the lower bound
of an algorithm based on the
amount of the comparisons the
algorithm makes.
4. Comparison is the basic
operation of
_____________algorithm.
5. For sorting algorithms the
average case efficiencies are
better than their worst case
efficiencies.
6. We use a ________________
decision tree to represent an
algorithm for searching a sorted
array with three way comparisons.
7. Problems that are solved within
polynomial time are called
_________.
8. ____________ problem finds
the chromatic number of the given
graph.
9. An algorithm in which every
operation is exclusively defined is
called ______________ algorithm.
Self Assessment Questions
1. Lower – bound 2. True
3. Information – theoretic
4. Sorting 5. True
6. Ternary 7. Tractable
8. Graph coloring 9.
Deterministic
Self Assessment Questions 14
1. We can implement Backtracking
by constructing the
_______________.
2. Backtracking, in the _______
case may have to generate all
possible candidates in a problem
state that is growing exponentially.
3. The n-Queens problem, the
_____________ circuit and the
Subset Sum problem are some
examples of problems that can be
solved by Backtracking.
4.
___________________________
organizes details of all candidate
solutions, and discards large
subsets of fruitless candidate
solutions.
5. A ________ is a solution that
satisfies all the constraints of a
problem.
6. In Branch and Bound algorithm,
the ratio of the number of
solutions verified largely
_______________ as the size of
the problem increases.
7. ________________ algorithms
can be used to solve NP-Hard
problems that have small
instances.
8. Minimum Spanning tree
provides us a good basis for
constructing a _________
approximation tour.
9. We select the items in
__________ order of their weights
in order to use the knapsack
capacity efficiently.
Self Assessment Questions
1. State-space tree 2. Worst
3. Hamiltonian4. Branch and
Bound
5. Decreases 6. Optimal
7. Exhaustive search 8. Shortest
9. Decreasing