Fake-Coin Problem
•
0 likes•322 views
Among n identical-looking coins, one is fake and lighter than the others. The algorithm uses a balance scale to compare sets of coins and determine which set contains the fake in O(log n) comparisons. It works by repeatedly splitting the coins into two piles, comparing the piles on the scale, and discarding the heavier pile until the fake coin is found. If n is odd, one coin is kept aside and the rest split into two piles. If even, all n coins are split into two piles.
Report
Share
Report
Share
Recommended
Apriori algorithm
Apriori Algorithm is is basically used Data Mining for generating association rule from a transactional database.
Asymptotic notations
1. Asymptotic notation such as Big-O, Omega, and Theta are used to describe the running time of algorithms as the input size n approaches infinity, rather than giving the exact running time.
2. Big-O notation gives an upper bound and describes worst-case running time, Omega notation gives a lower bound and describes best-case running time, and Theta notation gives a tight bound where the worst and best cases are equal up to a constant.
3. Common examples of asymptotic running times include O(1) for constant time, O(log n) for logarithmic time, O(n) for linear time, and O(n^2) for quadratic time.
sum of subset problem using Backtracking
Given a set of non-negative integers, and a value sum, determine if there is a subset of the given set with sum equal to given sum.
Heaps
This document provides information about priority queues and binary heaps. It defines a binary heap as a nearly complete binary tree where the root node has the maximum/minimum value. It describes heap operations like insertion, deletion of max/min, and increasing/decreasing keys. The time complexity of these operations is O(log n). Heapsort, which uses a heap data structure, is also covered and has overall time complexity of O(n log n). Binary heaps are often used to implement priority queues and for algorithms like Dijkstra's and Prim's.
heap Sort Algorithm
define the heap algorithm and the Max Heap
and Min Heap with example for each type and the insertion for new item .
DESIGN AND ANALYSIS OF ALGORITHMS
This document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem or calculate a quantity. Algorithm analysis involves evaluating memory usage and time complexity. Asymptotics, such as Big-O notation, are used to formalize the growth rates of algorithms. Common sorting algorithms like insertion sort and quicksort are analyzed using recurrence relations to determine their time complexities as O(n^2) and O(nlogn), respectively.
CS8391 Data Structures Part B Questions Anna University
CS8391 Data Structures Part B Questions Anna UniversityP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
Part B CS8391 Data Structures Part B Questions compiled from R2008 & R2013 to help the students of Affiliated Colleges appearing for Ann University ExaminationUnit 1 chapter 1 Design and Analysis of Algorithms
Unit 1 chapter 1 Design and Analysis of AlgorithmsP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
Anna University Regulation 2017
Design and Analysis of Algorithms
Text : Introduction to Design and Analysis of Algorithms by Anany LevitinRecommended
Apriori algorithm
Apriori Algorithm is is basically used Data Mining for generating association rule from a transactional database.
Asymptotic notations
1. Asymptotic notation such as Big-O, Omega, and Theta are used to describe the running time of algorithms as the input size n approaches infinity, rather than giving the exact running time.
2. Big-O notation gives an upper bound and describes worst-case running time, Omega notation gives a lower bound and describes best-case running time, and Theta notation gives a tight bound where the worst and best cases are equal up to a constant.
3. Common examples of asymptotic running times include O(1) for constant time, O(log n) for logarithmic time, O(n) for linear time, and O(n^2) for quadratic time.
sum of subset problem using Backtracking
Given a set of non-negative integers, and a value sum, determine if there is a subset of the given set with sum equal to given sum.
Heaps
This document provides information about priority queues and binary heaps. It defines a binary heap as a nearly complete binary tree where the root node has the maximum/minimum value. It describes heap operations like insertion, deletion of max/min, and increasing/decreasing keys. The time complexity of these operations is O(log n). Heapsort, which uses a heap data structure, is also covered and has overall time complexity of O(n log n). Binary heaps are often used to implement priority queues and for algorithms like Dijkstra's and Prim's.
heap Sort Algorithm
define the heap algorithm and the Max Heap
and Min Heap with example for each type and the insertion for new item .
DESIGN AND ANALYSIS OF ALGORITHMS
This document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem or calculate a quantity. Algorithm analysis involves evaluating memory usage and time complexity. Asymptotics, such as Big-O notation, are used to formalize the growth rates of algorithms. Common sorting algorithms like insertion sort and quicksort are analyzed using recurrence relations to determine their time complexities as O(n^2) and O(nlogn), respectively.
CS8391 Data Structures Part B Questions Anna University
CS8391 Data Structures Part B Questions Anna UniversityP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
Part B CS8391 Data Structures Part B Questions compiled from R2008 & R2013 to help the students of Affiliated Colleges appearing for Ann University ExaminationUnit 1 chapter 1 Design and Analysis of Algorithms
Unit 1 chapter 1 Design and Analysis of AlgorithmsP. Subathra Kishore, KAMARAJ College of Engineering and Technology, Madurai
Anna University Regulation 2017
Design and Analysis of Algorithms
Text : Introduction to Design and Analysis of Algorithms by Anany LevitinBinary Search Tree in Data Structure
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
15 puzzle problem using branch and bound
The document discusses the branch and bound algorithm for solving the 15-puzzle problem. It describes the key components of branch and bound including live nodes, e-nodes, and dead nodes. It also defines the cost function used to evaluate nodes as the sum of the path length and number of misplaced tiles. The algorithm generates all possible child nodes from the current node and prunes the search tree by comparing node costs to avoid exploring subtrees without solutions.
Design and Analysis of Algorithms
This document summarizes a lecture on algorithms and graph traversal techniques. It discusses:
1) Breadth-first search (BFS) and depth-first search (DFS) algorithms for traversing graphs. BFS uses a queue while DFS uses a stack.
2) Applications of BFS and DFS, including finding connected components, minimum spanning trees, and bi-connected components.
3) Identifying articulation points to determine biconnected components in a graph.
4) The 0/1 knapsack problem and approaches for solving it using greedy algorithms, backtracking, and branch and bound search.
Assosiate rule mining
Data mining techniques can uncover useful patterns and relationships in data. Association rule mining finds frequent patterns and generates rules about associations between different attributes in the data. The Apriori algorithm is commonly used to efficiently find all frequent itemsets in a transaction database and generate association rules from those itemsets. It works in multiple passes over the data, generating candidate itemsets of length k from frequent itemsets of length k-1 and pruning unpromising candidates that have infrequent subsets.
Apriori Algorithm
The document discusses the Apriori algorithm, which is used for mining frequent itemsets from transactional databases. It begins with an overview and definition of the Apriori algorithm and its key concepts like frequent itemsets, the Apriori property, and join operations. It then outlines the steps of the Apriori algorithm, provides an example using a market basket database, and includes pseudocode. The document also discusses limitations of the algorithm and methods to improve its efficiency, as well as advantages and disadvantages.
Binary Search - Design & Analysis of Algorithms
Binary search is an efficient algorithm for finding a target value within a sorted array. It works by repeatedly dividing the search range in half and checking the value at the midpoint. This eliminates about half of the remaining candidates in each step. The maximum number of comparisons needed is log n, where n is the number of elements. This makes binary search faster than linear search, which requires checking every element. The algorithm works by first finding the middle element, then checking if it matches the target. If not, it recursively searches either the lower or upper half depending on if the target is less than or greater than the middle element.
Transaction management and concurrency control
The document discusses transaction management and concurrency control in database systems. It covers topics such as transactions and their properties, concurrency control methods like locking, time stamping and optimistic control, and database recovery management. The goal of these techniques is to coordinate simultaneous transaction execution while maintaining data consistency and integrity in multi-user database environments.
Algorithms Lecture 6: Searching Algorithms
We will discuss the following: Search Algorithms, Linear Search, Binary Search, Jump Search, and Interpolation Search
Greedy Algorithm - Knapsack Problem
The document discusses the knapsack problem and greedy algorithms. It defines the knapsack problem as an optimization problem where given constraints and an objective function, the goal is to find the feasible solution that maximizes or minimizes the objective. It describes the knapsack problem has having two versions: 0-1 where items are indivisible, and fractional where items can be divided. The fractional knapsack problem can be solved using a greedy approach by sorting items by value to weight ratio and filling the knapsack accordingly until full.
Clustering, k-means clustering
The document discusses K-means clustering, an unsupervised machine learning algorithm that partitions observations into k clusters defined by centroids. It compares clustering to classification, noting clustering does not use training data and maps observations into natural groupings. The K-means algorithm is then explained, with the steps of initializing centroids, assigning observations to the closest centroid, revising centroids as cluster means, and repeating until convergence. Applications of clustering in business contexts like banking, retail, and insurance are also briefly mentioned.
Planning in AI(Partial order planning)
this presentation is about planning process in AI. The presentation specifically explained POP(Partial order Planning). There are also another planning. In this presentation with help of an example the presentation is briefly explained the planning is done in AI
Apriori algorithm
Apriori is the most famous frequent pattern mining method. It scans dataset repeatedly and generate item sets by bottom-top approach.
Apriori algorithm is given by R. Agrawal and R. Srikant in 1994 for finding frequent itemsets in a dataset for boolean association rule. Name of the algorithm is Apriori because it uses prior knowledge of frequent itemset properties
Floyd Warshall Algorithm
The document discusses the Floyd-Warshall algorithm for finding the shortest paths between all pairs of vertices in a weighted graph. It describes the components used including JavaFX, Jfoenix library, and CSS. It outlines the classes involved like FloydWarshall, MainWindow, MatrixInput, and challenges in graphical representation, positioning, and finding shortest paths. It explains how the algorithm works by initializing the graph and iterating steps to update shortest distances based on intermediate vertices. Advantages are finding all shortest paths with a single execution but it has a complexity of O(n3).
Code generator
The document discusses code generation which is the final phase of a compiler that generates target code such as assembly code by selecting memory locations for variables, translating instructions into assembly instructions, and assigning variables and results to registers, and it outlines some of the key issues in code generation such as handling the input representation, the target language, instruction selection, register allocation, and evaluation order.
Graph coloring using backtracking
This document summarizes graph coloring using backtracking. It defines graph coloring as minimizing the number of colors used to color a graph. The chromatic number is the fewest colors needed. Graph coloring is NP-complete. The document outlines a backtracking algorithm that tries assigning colors to vertices, checks if the assignment is valid (no adjacent vertices have the same color), and backtracks if not. It provides pseudocode for the algorithm and lists applications like scheduling, Sudoku, and map coloring.
All pairs shortest path algorithm
This document describes Floyd's algorithm for solving the all-pairs shortest path problem in graphs. It begins with an introduction and problem statement. It then describes Dijkstra's algorithm as a greedy method for finding single-source shortest paths. It discusses graph representations and traversal methods. Finally, it provides pseudocode and analysis for Floyd's dynamic programming algorithm, which finds shortest paths between all pairs of vertices in O(n3) time.
Floyd Warshall Algorithm
This document presents an overview of the Floyd-Warshall algorithm. It begins with an introduction to the algorithm, explaining that it finds shortest paths in a weighted graph with positive or negative edge weights. It then discusses the history and naming of the algorithm, attributed to researchers in the 1950s and 1960s. The document proceeds to provide an example of how the algorithm works, showing the distance and sequence tables that are updated over multiple iterations to find shortest paths between all pairs of vertices. It concludes with discussing the time and space complexity, applications, and references.
Association rule mining.pptx
This document discusses data mining techniques, including the data mining process and common techniques like association rule mining. It describes the data mining process as involving data gathering, preparation, mining the data using algorithms, and analyzing and interpreting the results. Association rule mining is explained in detail, including how it can be used to identify relationships between frequently purchased products. Methods for mining multilevel and multidimensional association rules are also summarized.
Merge sort analysis and its real time applications
The document provides an analysis of the merge sort algorithm and its applications in real-time. It begins with introducing sorting and different sorting techniques. Then it describes the merge sort algorithm, explaining the divide, conquer, and combine steps. It analyzes the time complexity of merge sort, showing that it has O(n log n) runtime. Finally, it discusses some real-time applications of merge sort, such as recommending similar products to users on e-commerce websites based on purchase history.
Merge sort algorithm
Merge sort is a sorting technique based on divide and conquer technique. With worst-case time complexity being Ο(n log n), it is one of the most respected algorithms.
Merge sort first divides the array into equal halves and then combines them in a sorted manner.
Analysis of optimization algorithms
This document discusses four key concepts related to optimization algorithms:
1) Algorithms are iterative processes that aim to generate improved solutions over time.
2) Algorithms can be modeled as self-organizing systems with exploration and exploitation.
3) Exploration and exploitation are two conflicting components that require balancing.
4) Genetic algorithms use three main evolutionary operators: crossover, mutation, and selection.
Particle swarm intelligence
The particle swarm optimization (PSO) algorithm was developed in 1995 as a metaheuristic algorithm based on swarm intelligence. It uses a population of candidate solutions called particles that search through the problem space to find the best solution. Each particle adjusts its trajectory based on its own experience and the experience of neighboring particles. The algorithm keeps track of each particle's best solution (personal best) and the best solution in the swarm (global best) to determine the particle's new velocity and position in the search space iteration by iteration until an optimal solution is found.
More Related Content
What's hot
Binary Search Tree in Data Structure
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
15 puzzle problem using branch and bound
The document discusses the branch and bound algorithm for solving the 15-puzzle problem. It describes the key components of branch and bound including live nodes, e-nodes, and dead nodes. It also defines the cost function used to evaluate nodes as the sum of the path length and number of misplaced tiles. The algorithm generates all possible child nodes from the current node and prunes the search tree by comparing node costs to avoid exploring subtrees without solutions.
Design and Analysis of Algorithms
This document summarizes a lecture on algorithms and graph traversal techniques. It discusses:
1) Breadth-first search (BFS) and depth-first search (DFS) algorithms for traversing graphs. BFS uses a queue while DFS uses a stack.
2) Applications of BFS and DFS, including finding connected components, minimum spanning trees, and bi-connected components.
3) Identifying articulation points to determine biconnected components in a graph.
4) The 0/1 knapsack problem and approaches for solving it using greedy algorithms, backtracking, and branch and bound search.
Assosiate rule mining
Data mining techniques can uncover useful patterns and relationships in data. Association rule mining finds frequent patterns and generates rules about associations between different attributes in the data. The Apriori algorithm is commonly used to efficiently find all frequent itemsets in a transaction database and generate association rules from those itemsets. It works in multiple passes over the data, generating candidate itemsets of length k from frequent itemsets of length k-1 and pruning unpromising candidates that have infrequent subsets.
Apriori Algorithm
The document discusses the Apriori algorithm, which is used for mining frequent itemsets from transactional databases. It begins with an overview and definition of the Apriori algorithm and its key concepts like frequent itemsets, the Apriori property, and join operations. It then outlines the steps of the Apriori algorithm, provides an example using a market basket database, and includes pseudocode. The document also discusses limitations of the algorithm and methods to improve its efficiency, as well as advantages and disadvantages.
Binary Search - Design & Analysis of Algorithms
Binary search is an efficient algorithm for finding a target value within a sorted array. It works by repeatedly dividing the search range in half and checking the value at the midpoint. This eliminates about half of the remaining candidates in each step. The maximum number of comparisons needed is log n, where n is the number of elements. This makes binary search faster than linear search, which requires checking every element. The algorithm works by first finding the middle element, then checking if it matches the target. If not, it recursively searches either the lower or upper half depending on if the target is less than or greater than the middle element.
Transaction management and concurrency control
The document discusses transaction management and concurrency control in database systems. It covers topics such as transactions and their properties, concurrency control methods like locking, time stamping and optimistic control, and database recovery management. The goal of these techniques is to coordinate simultaneous transaction execution while maintaining data consistency and integrity in multi-user database environments.
Algorithms Lecture 6: Searching Algorithms
We will discuss the following: Search Algorithms, Linear Search, Binary Search, Jump Search, and Interpolation Search
Greedy Algorithm - Knapsack Problem
The document discusses the knapsack problem and greedy algorithms. It defines the knapsack problem as an optimization problem where given constraints and an objective function, the goal is to find the feasible solution that maximizes or minimizes the objective. It describes the knapsack problem has having two versions: 0-1 where items are indivisible, and fractional where items can be divided. The fractional knapsack problem can be solved using a greedy approach by sorting items by value to weight ratio and filling the knapsack accordingly until full.
Clustering, k-means clustering
The document discusses K-means clustering, an unsupervised machine learning algorithm that partitions observations into k clusters defined by centroids. It compares clustering to classification, noting clustering does not use training data and maps observations into natural groupings. The K-means algorithm is then explained, with the steps of initializing centroids, assigning observations to the closest centroid, revising centroids as cluster means, and repeating until convergence. Applications of clustering in business contexts like banking, retail, and insurance are also briefly mentioned.
Planning in AI(Partial order planning)
this presentation is about planning process in AI. The presentation specifically explained POP(Partial order Planning). There are also another planning. In this presentation with help of an example the presentation is briefly explained the planning is done in AI
Apriori algorithm
Apriori is the most famous frequent pattern mining method. It scans dataset repeatedly and generate item sets by bottom-top approach.
Apriori algorithm is given by R. Agrawal and R. Srikant in 1994 for finding frequent itemsets in a dataset for boolean association rule. Name of the algorithm is Apriori because it uses prior knowledge of frequent itemset properties
Floyd Warshall Algorithm
The document discusses the Floyd-Warshall algorithm for finding the shortest paths between all pairs of vertices in a weighted graph. It describes the components used including JavaFX, Jfoenix library, and CSS. It outlines the classes involved like FloydWarshall, MainWindow, MatrixInput, and challenges in graphical representation, positioning, and finding shortest paths. It explains how the algorithm works by initializing the graph and iterating steps to update shortest distances based on intermediate vertices. Advantages are finding all shortest paths with a single execution but it has a complexity of O(n3).
Code generator
The document discusses code generation which is the final phase of a compiler that generates target code such as assembly code by selecting memory locations for variables, translating instructions into assembly instructions, and assigning variables and results to registers, and it outlines some of the key issues in code generation such as handling the input representation, the target language, instruction selection, register allocation, and evaluation order.
Graph coloring using backtracking
This document summarizes graph coloring using backtracking. It defines graph coloring as minimizing the number of colors used to color a graph. The chromatic number is the fewest colors needed. Graph coloring is NP-complete. The document outlines a backtracking algorithm that tries assigning colors to vertices, checks if the assignment is valid (no adjacent vertices have the same color), and backtracks if not. It provides pseudocode for the algorithm and lists applications like scheduling, Sudoku, and map coloring.
All pairs shortest path algorithm
This document describes Floyd's algorithm for solving the all-pairs shortest path problem in graphs. It begins with an introduction and problem statement. It then describes Dijkstra's algorithm as a greedy method for finding single-source shortest paths. It discusses graph representations and traversal methods. Finally, it provides pseudocode and analysis for Floyd's dynamic programming algorithm, which finds shortest paths between all pairs of vertices in O(n3) time.
Floyd Warshall Algorithm
This document presents an overview of the Floyd-Warshall algorithm. It begins with an introduction to the algorithm, explaining that it finds shortest paths in a weighted graph with positive or negative edge weights. It then discusses the history and naming of the algorithm, attributed to researchers in the 1950s and 1960s. The document proceeds to provide an example of how the algorithm works, showing the distance and sequence tables that are updated over multiple iterations to find shortest paths between all pairs of vertices. It concludes with discussing the time and space complexity, applications, and references.
Association rule mining.pptx
This document discusses data mining techniques, including the data mining process and common techniques like association rule mining. It describes the data mining process as involving data gathering, preparation, mining the data using algorithms, and analyzing and interpreting the results. Association rule mining is explained in detail, including how it can be used to identify relationships between frequently purchased products. Methods for mining multilevel and multidimensional association rules are also summarized.
Merge sort analysis and its real time applications
The document provides an analysis of the merge sort algorithm and its applications in real-time. It begins with introducing sorting and different sorting techniques. Then it describes the merge sort algorithm, explaining the divide, conquer, and combine steps. It analyzes the time complexity of merge sort, showing that it has O(n log n) runtime. Finally, it discusses some real-time applications of merge sort, such as recommending similar products to users on e-commerce websites based on purchase history.
Merge sort algorithm
Merge sort is a sorting technique based on divide and conquer technique. With worst-case time complexity being Ο(n log n), it is one of the most respected algorithms.
Merge sort first divides the array into equal halves and then combines them in a sorted manner.
What's hot (20)
Merge sort analysis and its real time applications
Merge sort analysis and its real time applications
More from Gem WeBlog
Analysis of optimization algorithms
This document discusses four key concepts related to optimization algorithms:
1) Algorithms are iterative processes that aim to generate improved solutions over time.
2) Algorithms can be modeled as self-organizing systems with exploration and exploitation.
3) Exploration and exploitation are two conflicting components that require balancing.
4) Genetic algorithms use three main evolutionary operators: crossover, mutation, and selection.
Particle swarm intelligence
The particle swarm optimization (PSO) algorithm was developed in 1995 as a metaheuristic algorithm based on swarm intelligence. It uses a population of candidate solutions called particles that search through the problem space to find the best solution. Each particle adjusts its trajectory based on its own experience and the experience of neighboring particles. The algorithm keeps track of each particle's best solution (personal best) and the best solution in the swarm (global best) to determine the particle's new velocity and position in the search space iteration by iteration until an optimal solution is found.
Nature inspired metaheuristics
The document discusses different types of nature-inspired metaheuristics used for optimization problems. It describes deterministic methods like hill-climbing that always produce the same output for a given input, and stochastic methods like genetic algorithms that can produce different outputs for the same input due to randomness. Gradient-based methods use gradients to find optimal parameters, while gradient-free methods do not. Metaheuristics employ intensification to exploit good solutions locally and diversification to explore globally. Population-based metaheuristics maintain a set of candidate solutions, while trajectory-based methods rely on a single agent. Examples of various metaheuristics are provided.
Darwin's theory of evolution
Genetic algorithms are a family of population-based metaheuristic optimization algorithms inspired by biological evolution, including natural selection and genetics. They maintain and improve a population of candidate solutions by evaluating their fitness and applying operations like selection, crossover and mutation to generate new solutions. Originally developed by John Holland and colleagues in 1960 based on Charles Darwin's theory of evolution, genetic algorithms have become one of the most popular evolutionary algorithms.
no free-lunch theorem
The no-free-lunch (NFL) theorem states that there is no single algorithm that works best for solving all types of problems. While some algorithms may perform better on certain problem types due to their complexity and characteristics, no algorithm will outperform all others across all possible problems. The NFL theorem demonstrates that the performance of any two algorithms will be equal when averaged across all possible problem types, meaning that there is no universally best algorithm and the best choice depends on the specific problem being addressed.
Video and animation
Video and animation involve capturing and displaying sequences of images to depict motion. Video uses real-world images while animation uses drawn or computer-generated images. MPEG standards like MPEG-1, 2, and 4 are used to compress digital video for storage and transmission by removing spatial and temporal redundancies between frames. MPEG compression involves three frame types - I, P, and B frames. I-frames are independent while P and B frames use motion prediction from previous and following frames. Grouping frames into GOPs allows efficient compression. The MPEG encoding and decoding process reconstructs frames using motion vectors and compensating for prediction errors.
Mpeg video compression
MPEG video compression works by reducing both spatial and temporal redundancy. It uses three types of frames - I frames which are intra-coded, P frames which use forward prediction from I or P frames, and B frames which use both forward and backward prediction. I frames provide anchors for the encoding and allow fast forwarding, while P and B frames allow for motion estimation between frames to reduce redundancy over time. MPEG compression groups frames into GOPs and uses motion vectors to predict how objects move between frames, reducing the need to encode redundant pixel data in each frame.
Digital audio
This document discusses digital audio, including its representation and processing. It begins by outlining the uses and applications of digital audio, such as creating moods and enhancing multimedia games. It then covers topics like analog vs. digital audio, producing digital audio through digitization and microphones, representation through sampling and quantization, perceptual encoding, common file formats like MP3 and WAV, and audio streaming using RealAudio. The key aspects of digital audio processing like compression, decompression, and codecs are also summarized.
Designing multimedia
This document discusses the process of designing multimedia projects. It outlines the key phases: conceptualization, analysis, design, development, implementation, and evaluation.
The analysis phase involves studying user needs, content requirements, technical specifications, and constraints. In the design phase, project specifications are created along with storyboards, flowcharts and navigation structures.
The development phase uses authoring tools to create prototypes based on the design. Content like graphics, audio and video are integrated. Formative evaluation tests for bugs during this stage. The implementation phase involves legal/ethical reviews and accessibility testing. Summative evaluation assesses the full project before release.
Testing and Rolling Out Enterprise Applications
The document discusses various aspects of testing and rolling out enterprise applications. It describes different types of testing like functional testing, non-functional testing, white box testing, black box testing and gray box testing. It also discusses different testing levels from unit testing to acceptance testing and production testing. The document then covers testing approaches, environments, performance testing and security testing in detail. Finally, it provides an overview of user acceptance testing and strategies for rolling out enterprise applications.
Constructing Enterprise Applications
The document discusses the process of constructing enterprise applications. It involves translating the design into code components through various activities like achieving construction readiness, constructing solution layers, code review, testing, and creating deployable packages. Construction readiness includes defining a construction plan, package structure, configuration management plan, and development environment. Solution layers include infrastructure, presentation, business, data access, and integration components. Code review and static/dynamic analysis help ensure quality. The overall process aims to fulfill requirements through proper architecture, techniques, and tools.
Architecting and Designing Enterprise Applications
The document discusses the architecture, views, and viewpoints of enterprise applications. It describes how architecture serves as a blueprint and is organized into views and viewpoints representing stakeholder perspectives. The architecture description consists of aspects depicted by models using languages like UML. Enterprise architecture helps conceptualize and analyze business scenarios considering applications, users, and data. Popular frameworks like TOGAF define architecture domains including business, applications, data, and technology. The blueprint of an application includes logical, technical, data, and infrastructure architectures. Logical architecture defines layers like business, data access, and presentation. Technical architecture identifies frameworks, patterns, and integration. Infrastructure services provide common functions.
Incepting Enterprise Applications
This document discusses the key activities involved in incepting an enterprise application, including enterprise analysis, business modeling, requirements elicitation and analysis, requirements validation, and planning and estimation. Enterprise analysis involves identifying business opportunities and stakeholders. Business modeling helps understand business processes and includes creating AS-IS and TO-BE models. Requirements elicitation captures functional and non-functional requirements through use cases and prototypes. Requirements validation ensures requirements meet business needs. Planning and estimation prepares project plans and estimates costs and effort based on techniques like use case points and function points.
Introduction to BEA
This document discusses enterprise applications, including what defines an enterprise application, how they can be categorized, and the challenges they face. Enterprise applications are applications that satisfy an organization's business and functional requirements while also considering efficiency, scalability, security, and other non-functional needs. They can be categorized based on their visibility to end users, the industry domain, type of processing, and whether they are custom-built or readymade. Developing successful enterprise applications requires business case readiness, a clear strategy for execution, and excellence in implementation. It also discusses the key skills needed and how to measure the success of enterprise applications.
Pointers and Structures
Structures in Functions discusses pointers to structures, accessing structure members, passing structures as function arguments by value and by reference, returning structures from functions, arrays of structures, and self-referential structures. Key points include passing a pointer to a structure as a function argument to avoid copying large structures, defining an array of structures to store multiple records, and using a pointer as a member of a structure to create self-referential linked structures like linked lists.
Dynamic memory allocation
The document discusses dynamic memory allocation in C using functions like malloc(), calloc(), free(), and realloc(). It provides:
1) malloc() allocates memory at runtime and returns a pointer to the allocated block. It does not initialize the memory.
2) calloc() also allocates memory at runtime but initializes all bits to zero.
3) free() frees up previously allocated memory so it can be used again.
4) realloc() resizes previously allocated memory blocks, allowing memory usage to grow or shrink as needed.
Function pointer
Pointers can point to functions and be used to call those functions. A function pointer must be initialized with the address of a function before being called. Function pointers allow functions to be passed as parameters and can be used to select between functions at runtime based on some condition.
10. NULL pointer
A null pointer points to nothing or nowhere and is initialized to NULL or 0. Pointers must be initialized to NULL to indicate no memory is allocated yet. Functions returning pointers may need to indicate errors by returning a null pointer. Passing NULL to a function avoids passing an invalid memory address. Dereferencing a null pointer will cause a program to abort, so pointers should be checked against NULL before accessing their value.
13. Pointer and 2D array
A two dimensional array can be accessed using two subscripts, with the first representing the row number and the second representing the column number. Elements are stored in memory row by row. A 2D array can be viewed as a collection of 1D arrays, with each row representing an array of the second dimension size. The base address of the 2D array points to the first element at index [0][0], and any element can be accessed using the generalized form of base_pointer[i][j].
12.string and pointer
String and pointer
https://youtube.com/playlist?list=PLZce0V9wfp454q4iqLYh8AAdLQS36TLWC ---
My Channel for “Pointers in C”
More from Gem WeBlog (20)
Architecting and Designing Enterprise Applications
Architecting and Designing Enterprise Applications
Recently uploaded
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Software Engineering and Project Management - Introduction, Modeling Concepts...
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Hematology Analyzer Machine - Complete Blood Count
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
cnn.pptx Convolutional neural network used for image classication
Convolutional Neural Network used for image classification
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
官方认证美国密歇根州立大学毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国密歇根州立大学毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
学校原版美国波士顿大学毕业证学历学位证书原版一模一样
原版一模一样【微信：741003700 】【美国波士顿大学毕业证学历学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
原版制作(Humboldt毕业证书)柏林大学毕业证学位证一模一样
原件一模一样【微信：bwp0011】《(Humboldt毕业证书)柏林大学毕业证学位证》【微信：bwp0011】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问微bwp0011
【主营项目】
一.毕业证【微bwp0011】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微bwp0011】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Null Bangalore | Pentesters Approach to AWS IAM
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Recently uploaded (20)
Engineering Drawings Lecture Detail Drawings 2014.pdf
Engineering Drawings Lecture Detail Drawings 2014.pdf
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...
Software Engineering and Project Management - Introduction, Modeling Concepts...
Software Engineering and Project Management - Introduction, Modeling Concepts...
Hematology Analyzer Machine - Complete Blood Count
Hematology Analyzer Machine - Complete Blood Count
cnn.pptx Convolutional neural network used for image classication
cnn.pptx Convolutional neural network used for image classication
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
Introduction to AI Safety (public presentation).pptx
Introduction to AI Safety (public presentation).pptx
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Fake-Coin Problem
- 1. Fake-Coin Problem Decrease and Conquer Technique
- 2. Definition Best illustrates the decrease-by-a-constant-factor strategy Among n identical-looking coins, one is fake. Among n identical-looking coins, one is fake.
- 3. Definition Among n identical-looking coins, one is fake. Problem: to design an efficient algorithm for detecting the fake coin detecting the fake coin Assumption: fake coin is known to be lighter than the genuine one
- 4. How to compare? With a balance scale, any two sets of coins any two sets of coins can be compared
- 5. How to compare? Based on tipping to the left, to the right, or staying even, the Balance scale will tell whether Balance scale will tell whether the sets weigh the same or which of the sets is heavier than the other but not by how much.
- 6. How to compare? Staying even, No Fake Coin No Fake Coin
- 7. How to compare? Tipping to the left, Fake Coin - Right Fake Coin - Right
- 8. How to compare? Tipping to the right, Fake Coin - Left Fake Coin - Left
- 9. Basic Idea If n is odd (n−1) is even Keep one extra coin aside Split (n−1) coins into two piles and put each half on the balance scale If the piles weigh the same, the coin put aside must be fake If the balance is not even, then we choose the lightest pile of coins to be the one containing the fake. Proceed in the same manner with the lighter pile - one with the fake coin
- 10. Basic Idea If n is even Split n coins into two piles and put each half on the balance scale If the piles weigh the same, No fake coin If the balance is not even, then we choose the lightest pile of coins to be the one containing the fake. Proceed in the same manner with the lighter pile - one with the fake coin