This document provides an overview of system analysis and design. It begins by defining a system, system analysis, and system design. It describes the principal roles and functions of a systems analyst, which include understanding business problems and how technology can solve them. The document then outlines the phases of the system development life cycle, including feasibility analysis, design, development, implementation, and maintenance. It also discusses different types of systems like transaction processing systems, office automation systems, and executive support systems. Finally, it covers topics like integrating new technologies, enterprise resource planning, wireless systems, and open source software.
This document provides an overview of quantum machine learning (QML) and discusses some of its potential advantages over classical machine learning approaches. It begins by introducing common challenges in machine learning like fingerprint recognition and tumor segmentation that are difficult to solve using traditional gradient descent optimization. From a scientist's perspective, these are considered NP-hard problems. The document then explains that QML allows algorithms to execute tasks in parallel rather than sequentially, potentially solving previously unsolvable problems. Several QML algorithms and concepts are introduced that take advantage of quantum computing capabilities, like executing multiple steps simultaneously. Finally, a chart is promised for the next slide that will merge machine learning and QML to better illustrate how QML can address issues that classical ML faces.
The document provides an overview of quantum computing, including its history, data representation using qubits, quantum gates and operations, and Shor's algorithm for integer factorization. Shor's algorithm uses quantum parallelism and the quantum Fourier transform to find the period of a function, from which the factors of a number can be determined. While quantum computing holds promise for certain applications, classical computers will still be needed and future computers may be a hybrid of classical and quantum components.
introduction to deep Learning with full detailsonykhan3
1. Deep learning involves using neural networks with multiple hidden layers to learn representations of data with multiple levels of abstraction.
2. These neural networks are able to learn increasingly complex features from the input data as the number of layers increases. The layers closer to the input learn simpler features while layers further from the input learn complex patterns in the data.
3. A breakthrough in deep learning was developing algorithms that can successfully train deep neural networks by unsupervised learning on each layer before using the learned features for supervised learning on the final layer. This pretraining helps the network learn useful internal representations.
Quantum Computing: Welcome to the FutureVernBrownell
Vern Brownell, CEO at D-Wave Systems, shares his thoughts on Quantum Computing in this presentation, which he delivered at Compute Midwest in November 2014. He addresses big questions that include: What is a quantum computer? How do you build one? Why does it matter? What does the future hold for quantum computing?
-It is a good ppt for a beginner to learn about Quantum
Computer.
-Quantum computer a solution for every present day computing
problems.
-Quantum computer a best solution for AI making
An introduction to quantum machine learning.pptxColleen Farrelly
Very basic introduction to quantum computing given at Indaba Malawi 2022. Overviews some basic hardware in classical and quantum computing, as well as a few quantum machine learning algorithms in use today. Resources for self-study provided.
This document provides an overview of quantum computing and common algorithms used on different types of quantum computers. It discusses how quantum computers work using qubits or qumodes and the existing gate-based and quantum annealing-based architectures. Some examples of algorithms that could run on these quantum computers are presented, including for supervised and unsupervised machine learning tasks as well as graph and network analysis problems. Researchers can access existing quantum computers through the cloud or simulate circuits classically.
This document provides an overview of quantum machine learning (QML) and discusses some of its potential advantages over classical machine learning approaches. It begins by introducing common challenges in machine learning like fingerprint recognition and tumor segmentation that are difficult to solve using traditional gradient descent optimization. From a scientist's perspective, these are considered NP-hard problems. The document then explains that QML allows algorithms to execute tasks in parallel rather than sequentially, potentially solving previously unsolvable problems. Several QML algorithms and concepts are introduced that take advantage of quantum computing capabilities, like executing multiple steps simultaneously. Finally, a chart is promised for the next slide that will merge machine learning and QML to better illustrate how QML can address issues that classical ML faces.
The document provides an overview of quantum computing, including its history, data representation using qubits, quantum gates and operations, and Shor's algorithm for integer factorization. Shor's algorithm uses quantum parallelism and the quantum Fourier transform to find the period of a function, from which the factors of a number can be determined. While quantum computing holds promise for certain applications, classical computers will still be needed and future computers may be a hybrid of classical and quantum components.
introduction to deep Learning with full detailsonykhan3
1. Deep learning involves using neural networks with multiple hidden layers to learn representations of data with multiple levels of abstraction.
2. These neural networks are able to learn increasingly complex features from the input data as the number of layers increases. The layers closer to the input learn simpler features while layers further from the input learn complex patterns in the data.
3. A breakthrough in deep learning was developing algorithms that can successfully train deep neural networks by unsupervised learning on each layer before using the learned features for supervised learning on the final layer. This pretraining helps the network learn useful internal representations.
Quantum Computing: Welcome to the FutureVernBrownell
Vern Brownell, CEO at D-Wave Systems, shares his thoughts on Quantum Computing in this presentation, which he delivered at Compute Midwest in November 2014. He addresses big questions that include: What is a quantum computer? How do you build one? Why does it matter? What does the future hold for quantum computing?
-It is a good ppt for a beginner to learn about Quantum
Computer.
-Quantum computer a solution for every present day computing
problems.
-Quantum computer a best solution for AI making
An introduction to quantum machine learning.pptxColleen Farrelly
Very basic introduction to quantum computing given at Indaba Malawi 2022. Overviews some basic hardware in classical and quantum computing, as well as a few quantum machine learning algorithms in use today. Resources for self-study provided.
This document provides an overview of quantum computing and common algorithms used on different types of quantum computers. It discusses how quantum computers work using qubits or qumodes and the existing gate-based and quantum annealing-based architectures. Some examples of algorithms that could run on these quantum computers are presented, including for supervised and unsupervised machine learning tasks as well as graph and network analysis problems. Researchers can access existing quantum computers through the cloud or simulate circuits classically.
The document summarizes a presentation on quantum information and technologies. It discusses:
1) How quantum computing could enable solving problems in fields like space science, biology, and finance faster than classical computers by taking advantage of quantum properties like superposition and entanglement.
2) Some of the basic concepts in quantum information like qubits, qudits, wavefunctions, error correction, and different methods for building quantum computers like superconducting and optical approaches.
3) The status of quantum computing including cloud access to quantum processors with over 100 qubits now available from IBM, though fully error corrected quantum computers still remain in development.
Machine Learning vs Deep Learning vs Artificial Intelligence | ML vs DL vs AI...Simplilearn
This Machine Learning Vs Deep Learning Vs Artificial Intelligence presentation will help you understand the differences between Machine Learning, Deep Learning and Artificial Intelligence, and how they are related to each other. The presentation will also cover what Machine Learning, Deep Learning, and Artificial Intelligence entail, how they work with the help of examples, and whether they really are all that different.
This Machine Learning Vs Deep Learning Vs Artificial Intelligence presentation will explain the topics listed below:
1. Artificial Intelligence example
2. Machine Learning example
3. Deep Learning example
4. Human Vs Artificial Intelligence
5. How Machine Learning works
6. How Deep Learning works
7. AI Vs Machine Learning Vs Deep Learning
8. AI with Machine Learning and Deep Learning
9. Real-life examples
10. Types of Artificial Intelligence
11. Types of Machine Learning
12. Comparing Machine Learning and Deep Learning
13. A glimpse into the future
- - - - - - - -
About Simplilearn Artificial Intelligence Engineer course:
What are the learning objectives of this Artificial Intelligence Course?
By the end of this Artificial Intelligence Course, you will be able to accomplish the following:
1. Design intelligent agents to solve real-world problems which are search, games, machine learning, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, agent decision making
2. Master TensorFlow by understanding the concepts of TensorFlow, the main functions, operations and the execution pipeline
3. Acquire a deep intuition of Machine Learning models by mastering the mathematical and heuristic aspects of Machine Learning
4. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
5. Comprehend and correlate between theoretical concepts and practical aspects of Machine Learning
6. Master and comprehend advanced topics like convolutional neural networks, recurrent neural networks, training deep networks, high-level interfaces
- - - - - -
Why be an Artificial Intelligence Engineer?
1. The average salary for a professional with an AI certification is $110k a year in the USA according to Indeed.com. The need for AI specialists exists in just about every field as companies seek to give computers the ability to think, learn, and adapt
2. In India, an Engineer with AI certification and minimal experience in the field commands a salary of Rs.17 lacs - Rs. 25 lacs, while it can go up to Rs. 50 lacs - Rs.1 crore per annum for a professional with 8-10 years of experience
3. The scarcity of people with artificial intelligence training is such that one report says there are only around 10000 such experts and companies like Google and Facebook are paying a salary of over $5,00,000 per annum
This document outlines a simulation study conducted by Nora ALHarbi and Enaam ALOtaibi on blood donation drives. It includes an introduction to simulation, definitions, types of simulation, and the simulation process. It then discusses how the Red Cross used simulation to analyze their blood donation process and identify policies to reduce donor wait times. Alternative arrival patterns and policy options like increasing beds were tested. The simulation analysis improved performance and donor satisfaction at Red Cross blood drives.
Simulated annealing is an algorithm for finding good solutions to optimization problems, such as the traveling salesman problem, where the goal is to find the shortest route between cities. It is inspired by annealing in metalworking, where heating and controlled cooling produces strong, defect-free metal. The algorithm starts with a random solution and finds neighboring solutions, accepting worse solutions with probability related to cost difference and iteration number, to avoid local optima. This allows big jumps early on, but the algorithm hones in on a local optimum over many iterations, usually finding a good enough solution. Parameters must be tuned correctly through trial and error. Overall, simulated annealing is considered effective for optimization problems.
Nanotechnology involves manipulating matter at the atomic scale between 1 to 100 nanometers. It has applications in quantum computing which operates at the quantum level using quantum bits that can represent both 1s and 0s through superposition and entanglement. While a quantum computer could solve certain problems much faster than classical computers by processing vast amounts of calculations simultaneously, they still face limitations such as unpredictability, difficulty retrieving data, and requiring total isolation from the environment to maintain fragile quantum states.
Quantum computers perform calculations using quantum mechanics and qubits that can represent superpositions of states. While classical computers use bits that are either 0 or 1, qubits can be both 0 and 1 simultaneously. This allows quantum computers to massively parallelize computations. Some potential applications include simulating molecular interactions for drug development, breaking encryption standards, and optimizing machine learning models. Several companies are working to develop quantum computers, but building large-scale, reliable versions remains a challenge due to the difficulty of controlling qubits.
This document discusses quantum computing, including:
- Quantum computers use quantum phenomena like entanglement and superposition to perform calculations based on quantum mechanics.
- A qubit can represent a 1, 0, or superposition of both, allowing quantum computers to exponentially increase their processing power compared to classical computers.
- Researchers have made progress developing quantum computers, entangling up to 14 qubits and performing calculations with two qubits, but large-scale quantum computers able to solve important problems much faster than classical computers are still a future goal expected to be achieved within 10 years.
Quantum computers use principles of quantum mechanics rather than classical binary logic. They have qubits that can represent superpositions of 0 and 1, allowing massive parallelism. Key effects like superposition, entanglement, and tunneling give them advantages over classical computers for problems like factoring and searching. Early quantum computers have been built with up to a few hundred qubits, and algorithms like Shor's show promise for cryptography applications. However, challenges remain around error correction and controlling quantum states as quantum computers scale up. D-Wave has produced commercial quantum annealing systems with over 1000 qubits, but debate continues on whether these demonstrate quantum advantage. Overall, quantum computing could transform fields like AI, simulation, and optimization if challenges around building reliable large-scale quantum
Software engineering is part of the system engineering process because system engineering involves developing systems from requirements through a top-down approach, with one phase being software engineering where the software is designed to fit the system's functional and physical objectives.
The "software crisis" referred to difficulties in the 1960s-1970s of developing large, complex software systems on time and budget due to rapidly increasing demands and complexity. This often resulted in cost overruns, delays, incomplete functionality, and low quality. The crisis demonstrated a need for new engineering approaches to managing complexity in large software.
The major professional responsibilities of a software engineer include maintaining confidentiality, only taking on work within their competence, protecting intellectual property, and avoiding computer misuse
This document discusses the use of probability in cryptography. It begins with introductions to cryptography and probability. Key probability terms and concepts like events, sample spaces, and Markov models are defined. Public key cryptography using Fermat's Little Theorem is explained. Applications of probability in cryptography are explored, including checksums and the birthday problem, pseudo-random number generators, and code breaking using the Metropolis-Hastings algorithm. The document concludes that probability and cryptography are important fields that help secure communications and protect society from cyber attacks.
1) Quantum computers operate using quantum bits (qubits) that can exist in superpositions of states rather than just 1s and 0s like classical bits.
2) Keeping qubits coherent and isolated from the external environment is extremely challenging as interaction causes decoherence within nanoseconds to seconds.
3) While prototypes of 5-7 qubit quantum computers exist, scaling them up to practical sizes of 50-100 qubits or more to outperform classical computers remains an outstanding challenge due to decoherence issues.
The butterfly effect theory proposes that small initial differences, like the flapping of a butterfly's wings, can lead to large divergences in outcomes over time in highly sensitive systems. Edward Lorenz first theorized this concept and coined the term to describe how seemingly insignificant events can influence complex systems in significant ways. Fractal theory also examines how simple systems can produce complicated results through chaos and sensitive dependence on initial conditions. Chaos theory further develops these ideas, showing how deterministic systems can exhibit unpredictable, chaotic behaviors due to small perturbations in initial values.
The chapter discusses tactics for achieving qualities like availability and modifiability in software architectures. It defines tactics as design decisions that influence quality attribute responses. For availability, common tactics include redundancy, fault detection using techniques like heartbeat monitoring, and fault recovery through approaches such as voting and state synchronization. Modifiability tactics aim to control the time and cost of changes and include localizing modifications, limiting ripple effects, and techniques for managing dependencies between modules. Performance tactics focus on generating responses to events within time constraints.
The document discusses the traveling salesman problem (TSP) and how ant colony optimization (ACO) algorithms can be used to find optimal or near-optimal solutions. It provides an overview of ACO, including how artificial ants deposit and follow pheromone trails to probabilistically construct solutions. The ACO algorithm is described and an example TSP problem with 4 cities (A, B, C, D) is shown across 4 iterations to demonstrate the algorithm. Advantages are noted such as efficiency for small problems and ability to adapt to changes, while disadvantages include slow convergence time for large problems.
Quantum Computers new Generation of Computers part 7 by prof lili saghafi Qua...Professor Lili Saghafi
Quantum algorithm
algorithm for factoring, the general number field sieve
Optimization algorithm
deterministic quantum algorithm Deutsch-Jozsa algorithm
Entanglement
Enigma
Quantum Teleportation
Cyber Security and Post Quantum Cryptography By: Professor Lili SaghafiProfessor Lili Saghafi
Quantum computing has the potential to transform cybersecurity.
Some encryption algorithms are thought to be unbreakable, except by brute-force attacks.
Although brute-force attacks may be hard for classical computers, they would be easy for quantum computers making them susceptible to such attacks.
All financial institutions, government agencies healthcare information are in danger.
How could this new thrust of computing strength give us new tiers of power to analyze IT systems at a more granular level for security vulnerabilities and protect us through more complex layers of quantum cryptography?
Genetic programming is an evolutionary algorithm that uses principles of natural selection and genetics to automatically generate computer programs to solve problems. It works by generating an initial population of random programs, evaluating their performance on the task, and breeding new programs through genetic operations like crossover and mutation. The fittest programs are selected to pass their traits to the next generation, while less fit programs are removed. This process is repeated until an optimal program is found. Genetic programming represents programs as syntax trees and evolves these trees to find solutions without requiring the programmer to specify the form or structure of the solution.
Quantum Computing, Quantum Machine Learning, and Recommendation SystemsSyed Falahuddin Quadri
Introductory guest lecture on Quantum Computing, Quantum Machine Learning, and Recommendation Systems delivered in Data Analysis and Data Mining (Spring 2019) course at UESTC by Dr. Li Xiaoyu, Guest Lecture delivered by Syed Falahuddin Quadri.
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
discuss about System system analysis, system design, system analyst's role, Development of System through analysis, SDLC, Case Tools of SAD, Implementation, etc.
This document provides an overview of system analysis and design. It defines key terms like system, system analysis, and system design. It describes the principal functions of a systems analyst and the phases of the systems development life cycle. It also discusses various data gathering and analysis tools, as well as systems design tools that can be used. The document outlines the roles and qualities of a systems analyst and different types of systems they may work with at the operational, knowledge, and strategic levels of an organization.
The document summarizes a presentation on quantum information and technologies. It discusses:
1) How quantum computing could enable solving problems in fields like space science, biology, and finance faster than classical computers by taking advantage of quantum properties like superposition and entanglement.
2) Some of the basic concepts in quantum information like qubits, qudits, wavefunctions, error correction, and different methods for building quantum computers like superconducting and optical approaches.
3) The status of quantum computing including cloud access to quantum processors with over 100 qubits now available from IBM, though fully error corrected quantum computers still remain in development.
Machine Learning vs Deep Learning vs Artificial Intelligence | ML vs DL vs AI...Simplilearn
This Machine Learning Vs Deep Learning Vs Artificial Intelligence presentation will help you understand the differences between Machine Learning, Deep Learning and Artificial Intelligence, and how they are related to each other. The presentation will also cover what Machine Learning, Deep Learning, and Artificial Intelligence entail, how they work with the help of examples, and whether they really are all that different.
This Machine Learning Vs Deep Learning Vs Artificial Intelligence presentation will explain the topics listed below:
1. Artificial Intelligence example
2. Machine Learning example
3. Deep Learning example
4. Human Vs Artificial Intelligence
5. How Machine Learning works
6. How Deep Learning works
7. AI Vs Machine Learning Vs Deep Learning
8. AI with Machine Learning and Deep Learning
9. Real-life examples
10. Types of Artificial Intelligence
11. Types of Machine Learning
12. Comparing Machine Learning and Deep Learning
13. A glimpse into the future
- - - - - - - -
About Simplilearn Artificial Intelligence Engineer course:
What are the learning objectives of this Artificial Intelligence Course?
By the end of this Artificial Intelligence Course, you will be able to accomplish the following:
1. Design intelligent agents to solve real-world problems which are search, games, machine learning, logic constraint satisfaction problems, knowledge-based systems, probabilistic models, agent decision making
2. Master TensorFlow by understanding the concepts of TensorFlow, the main functions, operations and the execution pipeline
3. Acquire a deep intuition of Machine Learning models by mastering the mathematical and heuristic aspects of Machine Learning
4. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
5. Comprehend and correlate between theoretical concepts and practical aspects of Machine Learning
6. Master and comprehend advanced topics like convolutional neural networks, recurrent neural networks, training deep networks, high-level interfaces
- - - - - -
Why be an Artificial Intelligence Engineer?
1. The average salary for a professional with an AI certification is $110k a year in the USA according to Indeed.com. The need for AI specialists exists in just about every field as companies seek to give computers the ability to think, learn, and adapt
2. In India, an Engineer with AI certification and minimal experience in the field commands a salary of Rs.17 lacs - Rs. 25 lacs, while it can go up to Rs. 50 lacs - Rs.1 crore per annum for a professional with 8-10 years of experience
3. The scarcity of people with artificial intelligence training is such that one report says there are only around 10000 such experts and companies like Google and Facebook are paying a salary of over $5,00,000 per annum
This document outlines a simulation study conducted by Nora ALHarbi and Enaam ALOtaibi on blood donation drives. It includes an introduction to simulation, definitions, types of simulation, and the simulation process. It then discusses how the Red Cross used simulation to analyze their blood donation process and identify policies to reduce donor wait times. Alternative arrival patterns and policy options like increasing beds were tested. The simulation analysis improved performance and donor satisfaction at Red Cross blood drives.
Simulated annealing is an algorithm for finding good solutions to optimization problems, such as the traveling salesman problem, where the goal is to find the shortest route between cities. It is inspired by annealing in metalworking, where heating and controlled cooling produces strong, defect-free metal. The algorithm starts with a random solution and finds neighboring solutions, accepting worse solutions with probability related to cost difference and iteration number, to avoid local optima. This allows big jumps early on, but the algorithm hones in on a local optimum over many iterations, usually finding a good enough solution. Parameters must be tuned correctly through trial and error. Overall, simulated annealing is considered effective for optimization problems.
Nanotechnology involves manipulating matter at the atomic scale between 1 to 100 nanometers. It has applications in quantum computing which operates at the quantum level using quantum bits that can represent both 1s and 0s through superposition and entanglement. While a quantum computer could solve certain problems much faster than classical computers by processing vast amounts of calculations simultaneously, they still face limitations such as unpredictability, difficulty retrieving data, and requiring total isolation from the environment to maintain fragile quantum states.
Quantum computers perform calculations using quantum mechanics and qubits that can represent superpositions of states. While classical computers use bits that are either 0 or 1, qubits can be both 0 and 1 simultaneously. This allows quantum computers to massively parallelize computations. Some potential applications include simulating molecular interactions for drug development, breaking encryption standards, and optimizing machine learning models. Several companies are working to develop quantum computers, but building large-scale, reliable versions remains a challenge due to the difficulty of controlling qubits.
This document discusses quantum computing, including:
- Quantum computers use quantum phenomena like entanglement and superposition to perform calculations based on quantum mechanics.
- A qubit can represent a 1, 0, or superposition of both, allowing quantum computers to exponentially increase their processing power compared to classical computers.
- Researchers have made progress developing quantum computers, entangling up to 14 qubits and performing calculations with two qubits, but large-scale quantum computers able to solve important problems much faster than classical computers are still a future goal expected to be achieved within 10 years.
Quantum computers use principles of quantum mechanics rather than classical binary logic. They have qubits that can represent superpositions of 0 and 1, allowing massive parallelism. Key effects like superposition, entanglement, and tunneling give them advantages over classical computers for problems like factoring and searching. Early quantum computers have been built with up to a few hundred qubits, and algorithms like Shor's show promise for cryptography applications. However, challenges remain around error correction and controlling quantum states as quantum computers scale up. D-Wave has produced commercial quantum annealing systems with over 1000 qubits, but debate continues on whether these demonstrate quantum advantage. Overall, quantum computing could transform fields like AI, simulation, and optimization if challenges around building reliable large-scale quantum
Software engineering is part of the system engineering process because system engineering involves developing systems from requirements through a top-down approach, with one phase being software engineering where the software is designed to fit the system's functional and physical objectives.
The "software crisis" referred to difficulties in the 1960s-1970s of developing large, complex software systems on time and budget due to rapidly increasing demands and complexity. This often resulted in cost overruns, delays, incomplete functionality, and low quality. The crisis demonstrated a need for new engineering approaches to managing complexity in large software.
The major professional responsibilities of a software engineer include maintaining confidentiality, only taking on work within their competence, protecting intellectual property, and avoiding computer misuse
This document discusses the use of probability in cryptography. It begins with introductions to cryptography and probability. Key probability terms and concepts like events, sample spaces, and Markov models are defined. Public key cryptography using Fermat's Little Theorem is explained. Applications of probability in cryptography are explored, including checksums and the birthday problem, pseudo-random number generators, and code breaking using the Metropolis-Hastings algorithm. The document concludes that probability and cryptography are important fields that help secure communications and protect society from cyber attacks.
1) Quantum computers operate using quantum bits (qubits) that can exist in superpositions of states rather than just 1s and 0s like classical bits.
2) Keeping qubits coherent and isolated from the external environment is extremely challenging as interaction causes decoherence within nanoseconds to seconds.
3) While prototypes of 5-7 qubit quantum computers exist, scaling them up to practical sizes of 50-100 qubits or more to outperform classical computers remains an outstanding challenge due to decoherence issues.
The butterfly effect theory proposes that small initial differences, like the flapping of a butterfly's wings, can lead to large divergences in outcomes over time in highly sensitive systems. Edward Lorenz first theorized this concept and coined the term to describe how seemingly insignificant events can influence complex systems in significant ways. Fractal theory also examines how simple systems can produce complicated results through chaos and sensitive dependence on initial conditions. Chaos theory further develops these ideas, showing how deterministic systems can exhibit unpredictable, chaotic behaviors due to small perturbations in initial values.
The chapter discusses tactics for achieving qualities like availability and modifiability in software architectures. It defines tactics as design decisions that influence quality attribute responses. For availability, common tactics include redundancy, fault detection using techniques like heartbeat monitoring, and fault recovery through approaches such as voting and state synchronization. Modifiability tactics aim to control the time and cost of changes and include localizing modifications, limiting ripple effects, and techniques for managing dependencies between modules. Performance tactics focus on generating responses to events within time constraints.
The document discusses the traveling salesman problem (TSP) and how ant colony optimization (ACO) algorithms can be used to find optimal or near-optimal solutions. It provides an overview of ACO, including how artificial ants deposit and follow pheromone trails to probabilistically construct solutions. The ACO algorithm is described and an example TSP problem with 4 cities (A, B, C, D) is shown across 4 iterations to demonstrate the algorithm. Advantages are noted such as efficiency for small problems and ability to adapt to changes, while disadvantages include slow convergence time for large problems.
Quantum Computers new Generation of Computers part 7 by prof lili saghafi Qua...Professor Lili Saghafi
Quantum algorithm
algorithm for factoring, the general number field sieve
Optimization algorithm
deterministic quantum algorithm Deutsch-Jozsa algorithm
Entanglement
Enigma
Quantum Teleportation
Cyber Security and Post Quantum Cryptography By: Professor Lili SaghafiProfessor Lili Saghafi
Quantum computing has the potential to transform cybersecurity.
Some encryption algorithms are thought to be unbreakable, except by brute-force attacks.
Although brute-force attacks may be hard for classical computers, they would be easy for quantum computers making them susceptible to such attacks.
All financial institutions, government agencies healthcare information are in danger.
How could this new thrust of computing strength give us new tiers of power to analyze IT systems at a more granular level for security vulnerabilities and protect us through more complex layers of quantum cryptography?
Genetic programming is an evolutionary algorithm that uses principles of natural selection and genetics to automatically generate computer programs to solve problems. It works by generating an initial population of random programs, evaluating their performance on the task, and breeding new programs through genetic operations like crossover and mutation. The fittest programs are selected to pass their traits to the next generation, while less fit programs are removed. This process is repeated until an optimal program is found. Genetic programming represents programs as syntax trees and evolves these trees to find solutions without requiring the programmer to specify the form or structure of the solution.
Quantum Computing, Quantum Machine Learning, and Recommendation SystemsSyed Falahuddin Quadri
Introductory guest lecture on Quantum Computing, Quantum Machine Learning, and Recommendation Systems delivered in Data Analysis and Data Mining (Spring 2019) course at UESTC by Dr. Li Xiaoyu, Guest Lecture delivered by Syed Falahuddin Quadri.
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
discuss about System system analysis, system design, system analyst's role, Development of System through analysis, SDLC, Case Tools of SAD, Implementation, etc.
This document provides an overview of system analysis and design. It defines key terms like system, system analysis, and system design. It describes the principal functions of a systems analyst and the phases of the systems development life cycle. It also discusses various data gathering and analysis tools, as well as systems design tools that can be used. The document outlines the roles and qualities of a systems analyst and different types of systems they may work with at the operational, knowledge, and strategic levels of an organization.
The document discusses the role of systems analysts and provides an overview of key concepts in systems analysis and design. It covers the types of systems analysts work with, the systems development life cycle, incorporating human-computer interaction considerations, and using computer-aided software engineering (CASE) tools to aid analysts' work.
The document discusses several topics related to information systems including:
1. Eight categories of information systems and examples of each.
2. The systems development life cycle consisting of 7 phases from identifying problems to implementation and evaluation.
3. The importance of system maintenance and how CASE tools can help with maintenance.
The document discusses systems analysis and design. It defines key terms like system, system analysis, and system design. It describes various modeling techniques used in systems analysis and design like the Unified Modeling Language (UML), data flow diagrams, and entity relationship diagrams. It also discusses the systems development life cycle approach, agile approach, and the role of the systems analyst. The document provides an overview of the fundamental concepts in systems analysis and design.
The document discusses systems analysis and design. It defines key terms like system, system analysis, and system design. It describes various modeling techniques used in systems analysis and design like the Unified Modeling Language (UML), Entity Relationship Diagrams, Data Flow Diagrams. It discusses the need for systems analysis and design and the roles of a systems analyst. It also covers the Systems Development Life Cycle (SDLC) approach, the Agile approach, and how the Unified Modeling Language (UML) is used.
The document provides an overview of systems analysis and design. It discusses the roles of systems analysts, different types of information systems, and the phases of the systems development life cycle (SDLC). The SDLC phases include planning, analysis, design, implementation, testing, and maintenance. The document also covers topics like prototyping, joint application design (JAD), rapid application development (RAD), agile methodologies, and object-oriented analysis and design (OOAD).
This chapter discusses systems analysis and design as a disciplined approach to developing information systems. It describes the roles and responsibilities in systems development, including systems analysts, programmers, and management. It also outlines the skills required for systems analysts and discusses different types of information systems. Finally, it introduces the systems development life cycle as a structured process and some alternative development approaches.
The document discusses systems analysis and design. It explains that systems analysis involves understanding an organization's objectives, structure, and processes in order to develop computer-based systems that improve efficiency. The systems development life cycle is a standard methodology used to analyze requirements, design, implement, and maintain information systems through phases like project planning, analysis, design, and maintenance.
System analysis and design involves analyzing an organization's existing system or planned system and designing a better system to improve the organization. The key steps in system analysis and design are analyzing the system needs, designing the recommended system, developing and documenting the software, testing and maintaining the system, and implementing and evaluating the system. System analysts play an important role in information systems development projects by understanding business problems and applying technology to solve them.
The document discusses key concepts in information systems analysis and design. It defines systems analysis and design as the process of developing and maintaining information systems to support business functions. It describes four main types of information systems, the systems development life cycle (SDLC) which includes phases like analysis and design, and alternatives to the traditional life cycle like prototyping. It also covers the roles of systems analysts, programmers, and managers in the systems development process.
This document provides information about a System Analysis and Design course offered by Daffodil International University. The 3-credit, 3-hour per week course focuses on applying information technologies to the system development life cycle including methodologies, analysis, design, and implementation. It aims to teach students how to design and deliver information systems to meet business needs utilizing techniques like requirements documentation. The textbook is Modern Systems Analysis and Design by Hoffer, George, and Valacich which covers topics like system types, the development process, and roles in system development projects.
The document discusses the roles and responsibilities of a systems analyst. It provides definitions for common types of systems such as transaction processing systems, decision support systems, and expert systems. It also outlines the roles of a systems analyst, which include serving as an outside consultant, as an internal support expert, and as an agent of change within an organization. The document emphasizes that systems analysts must have strong problem solving, communication, and technical skills to understand user needs and facilitate the implementation of new information systems.
The document provides an overview of systems analysis and design (SAD). It discusses that SAD is the process of understanding what an information system should do through analysis, and specifying how it will be implemented through design. It also outlines some key aspects of SAD including the importance of good requirements gathering and design. The document aims to give the reader a basic understanding of SAD concepts.
The document discusses various approaches to system analysis including waterfall, prototyping, rapid application development, and agile methods. It describes the typical phases of system analysis as planning, analysis, design, implementation, and support/maintenance. Key aspects of requirements analysis are covered such as information discovery techniques like interviews, questionnaires, and joint application design sessions. The benefits and shortcomings of different system analysis methodologies are also summarized.
The document discusses different approaches to systems building, including the traditional systems lifecycle model consisting of definition, feasibility, design, development, testing, implementation, evaluation and maintenance phases. It also covers prototyping, using application software packages, end-user development, outsourcing, structured methodologies, object-oriented development, computer-aided software engineering and software reengineering.
Chapter01 the systems development environmentDhani Ahmad
This document discusses information systems analysis and design. It covers the modern systems development approach, which includes both process-oriented and data-oriented methods. Four main types of information systems are described: transaction processing systems, management information systems, decision support systems, and expert systems. The systems development life cycle is outlined as having six phases: project identification and selection, project initiation and planning, analysis, design, implementation, and maintenance. Alternatives to the traditional life cycle like prototyping, rapid application development, and joint application design are also discussed.
This document provides an overview of system analysis and design. It discusses key concepts like information, information systems, and information system components. It also describes different types of information systems and system development methods like structured analysis, object oriented analysis, and agile methods. The document then discusses the system development life cycle and provides a detailed example of the SDLC for a clinic management system. It concludes with describing various life cycle models for software development projects.
Learning outcomes of system analysis and design and.pptxSanad Bhowmik
System analysis and design refers to examining a business situation to improve procedures and methods through better computerized information systems. It provides structure for analyzing and designing information systems and involves a series of processes to systematically improve a business. The learning outcomes include being able to define system concepts, develop information systems using various problem-solving tools, write requirements and specifications, and work in groups on system development projects.
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2. Objectives
Define the terms system, system analysis, and system design.
Types of systems.
Describe the principal functions of the systems analyst.
List and describe the phases of the systems development life cycle.
Describe the various data gathering and analysis tools.
Describe a selection of systems design tools.
Alternative approaches to Structured analysis & Design to the SLDC
Explain the role of the maintenance task in the systems development
life cycle.
3. Contents
• What is System Analysis and Design?
• System Analyst.
• System Development Life Cycle.
• Feasibility Analysis.
• Design.
• Development
• Implementation.
4. System Analysis and
Design: what is it?
Firstly we will define the system, than system analysis and system design as
well.
• System
A set of detailed methods, procedures and routines
established or formulation to carry out specify activity,
perform a duty or solve a problem.
Systems Analysis and
Design
5. System Analysis
The dissection of a system into its component pieces to study how
those component pieces interact and work.
(1) The survey and planning
(2) The study and analysis
(3) The definition
System Design
The process of defining the architecture, components, modules,
interfaces and data for a system to satisfy specified requirements.
Systems Analysis and
Design
6. Need for System Analysis and Design
Installing a system without proper planning leads to great user
dissatisfaction and frequently causes the system to fall into
disuse
Lends structure to the analysis and design of information
systems
A series of processes systematically undertaken to improve a
business through the use of computerized information systems
Systems Analysis and
Design
7. Roles of the System Analyst
The analyst plays a key role in information systems development
projects.
Must understand how to apply technology to solve business
problems.
Analyst may serve as change agents who identify the
organizational improvement.
Systems Analysis and
Design
8. Qualities of the System Analyst
Problem solver
Communicator
Strong personal and professional ethics
Self-disciplined and self-motivated
Systems Analysis and
Design
9. System Analyst Recommend, Design, and
Maintain Many Types of Systems for Users
Systems Analysis and
Design
Strategic
Level
Operational
Level
Knowledge
Level
Higher
Level
A system analyst
may be involved
with any or all of
these systems at
each organization
level
10. OPERATIONAL LEVEL
It is a process of large amounts of data for routine business transactions.
Boundary-Spanning
Its concerned with the detection of information. It has two primary sources and two
main sources.
Primary sources of Information
(1) Detect information
(2) Send information into the environment presenting the company in a favorable light.
Main sources of Information
(1) Business intelligence.
(2) Competitive information
Support the day-to-day operations of the company
Example: Payroll Processing, Inventory Management.
Systems Analysis and
Design
Transaction Processing System (TPS)
11. KNOWLEDGE LEVEL
Office Automation System (OAS)
Supports data workers who share information, but do not usually create new
knowledge
Examples: Word processing, Spreadsheets etc.
Knowledge Work System (KWS)
Supports professional workers such as scientists, engineers, and doctors
Examples: computer-aided design systems, virtual reality systems, investment
workstations
Systems Analysis and
Design
12. Higher Level
Management Information System (MIS)
To supports data worker who share information but do not usually create new knowledge.
Example: Word processing, Spreadsheets, Desktop publishing, Email Electronic
scheduling, Communication through voice mail, Email, Video
Decision Support System (DSS)
Aids decision makers in the making of decisions
Examples: financial planning with what-if analysis, budgeting with modeling
Expert System (ES)
Captures and uses the knowledge of an expert for solving a particular problem which leads
to a conclusion or recommendation.
Examples: MYCIN (an early xpert system that used artificial intelligence ;
XCON (eXpert CONfigurer)
Systems Analysis and
Design
13. Executive Support System (ESS)
Helps executives to make unstructured strategic decisions in an informed way
Examples: drill-down analysis, status access
Group Decision Support System (GDSS)
Permit group members to interact with electronic support
Examples: email, Lotus Notes
Computer-Supported Collaborative Work System
(CSCWS)
CSCWS is a more general term of GDSS. It may include software support called
“groupware” for team collaboration via network computers.
Example: video conferencing, Web survey system
Strategic Level
Systems Analysis and
Design
14. Integrating New Technologies into
Traditional Systems
Ecommerce and Web Systems.
Enterprise Resource Planning Systems.
Wireless Systems.
Open Source Software.
Need for Systems Analysis and Design.
Systems Analysis and
Design
15. Systems analysts need to be aware that integrating
technologies affects all types of system
Systems Analysis and
Design
16. Ecommerce and Web Systems
Benefits
Increasing user awareness of the availability of a service,
product, industry, person, or group.
The possibility of 24-hour access for users.
Improving the usefulness and usability of interface design.
Creating a system that can extend globally rather than remain
local, thus reaching people in remote locations without worry
of the time zone in which they are located.
Systems Analysis and
Design
17. Enterprise Resource Planning Systems
(ERPS)
Performs integration of many information systems existing
on different management levels and within different
functions
Example: SAP, Oracle
Systems Analysis and
Design
18. Wireless Systems
System analyst may be asked to design standard or wireless
communication networks that integrate voice, video and email
into organizational intranets or industry extranets
System analyst may also be asked to develop intelligent agents
Example: Microsoft's new software based on Bayesian statistics
Wireless communication is referred as m-commerce (mobile
commerce)
Systems Analysis and
Design
19. Open Source Software (OSS)
An alternative of traditional software development where
proprietary code is hidden from the users
Open source software is free to distribute, share and modify
Characterized as a philosophy rather than simply the process of
creating new software
Example: Linux Operating System, Apache Web Server,
Mozilla Firefox Web browser, Koha, Newgenlib, Evergreen,
OPALS, Greenstone, DSpace, Plone, Drupal, Eprint, and Joomla.
Systems Analysis and
Design
20. SYSTEMS DEVELOPMENT LIFE
CYCLE (SDLC)
Typically the SDLC has 7 steps in the in the development and
improvement of a computer system
Systems Analysis and
Design
21. IDENTIFYING PROBLEMS,
OPPORTUNITIES, AND OBJECTIVES
Activity:
Interviewing user management
Summarizing the knowledge obtained
Estimating the scope of the project
Documenting the results
Output:
Feasibility report containing problem definition and objective
summaries from which management can make a decision on
whether to proceed with the proposed project
Systems Analysis and
Design
22. DETERMINING HUMAN
INFORMATION REQUIREMENTS
Activity:
Interviewing
Sampling and investing hard data
Questionnaires
Observe the decision maker’s behavior and environment
Prototyping
Learn the who, what, where, when, how, and why of the
current system
Output:
Analyst understands how users accomplish their work when
interacting with a computer; and begin to know how to make
the new system more useful and usable. The analyst should
also know the business functions and have complete
information on the people, goals, data and procedure involved
Systems Analysis and
Design
23. ANALYZING SYSTEM NEEDS
Activity:
Create data flow diagrams.
Complete the data dictionary.
Analyze the structured decisions made.
Prepare and present the system proposal.
Output:
Recommendation on what, if anything, should be done.
Systems Analysis and
Design
24. DESIGNING THE RECOMMENDED
SYSTEM
Activity:
Design procedures for data entry
Design the human-computer interface
Design system controls
Design files and/or database
Design backup procedures
Output
Model of the actual system
Systems Analysis and
Design
25. DEVELOPING AND DOCUMENTING
SOFTWARE
Activity:
System analyst works with programmers to develop any original
software
Works with users to develop effective documentation
Programmers design, code, and remove syntactical errors from
computer programs
Document software with help files, procedure manuals, and Web
sites with Frequently Asked Questions
Output:
Computer programs
System documentation
Systems Analysis and
Design
26. TESTING AND MAINTAINING THE
SYSTEM
Activity:
Test the information system
System maintenance
Maintenance documentation
Output:
Problems, if any
Updated programs
Documentation
Systems Analysis and
Design
27. IMPLEMENTING AND EVALUATING
THE SYSTEM
Activity:
Train users
Analyst plans smooth conversion from old system to new
system
Review and evaluate system
Output:
Trained personnel
Installed system
Systems Analysis and
Design
28. THE IMPACT OF MAINTENANCE
Maintenance is performed for two reasons
Removing software errors.
Enhancing existing software.
Systems Analysis and
Design
30. FEASIBILITY ANALYSES
Technical Feasibility: can we build it?
Economic Feasibility: should we build it?
Organizational Feasibility: if we build it, will they come?
Systems Analysis and
Design
31. TECHNICAL FEASIBILITY: CAN WE
BUILD IT?
Familiarity with application: less familiarity more risk.
Familiarity with technology: less familiarity generates more
risk.
Project size: large projects have more risk.
Compatibility: the hard it is so integrate the systems with the
company’s existing technology, the higher the risk will be.
Systems Analysis and
Design
32. ECONOMIC FEASIBILITY: SHOULD WE BUILD IT?
Development Costs.
Annual operating costs.
Annual benefits (cost saving and revenues).
Intangible costs and benefits.
Systems Analysis and
Design
33. ORGANIZATIONAL FEASIBILITY: IF WE BUILD IT,
WILL THEY COME?
Project champion(s).
Senior management.
Users.
Other stakeholders.
Is the project strategically aligned with the business.
Systems Analysis and
Design
34. OBJECT-ORIENTED SYSTEMS
ANALYSIS AND DESIGN (OOSAD)
Analysis is performed on a small part of the system followed by
design and implementation. The development cycle repeats with
analysis, design and implementation of the next part and this
repeats until the project is complete
Systems Analysis and
Design
35. ALTERNATE APPROACHES TO STRUCTURED
ANALYSIS AND DESIGN AND TO THE SYSTEMS
DEVELOPMENT LIFE CYCLE
Agile approach.
Prototyping
Ethics
Project champion Approach
Soft Systems Methodology (SSM)
Multiview
Systems Analysis and
Design
36. SUMMARY
Information is a key resource.
Systems analysts deal with many types of information systems.
Integration of traditional systems with new technologies.
Roles, qualities and skills of the systems analyst.
The systems Development Life Cycle.
Feasibility Analysis.
Alternate Approaches to structured analysis and design and to the
SDLC.
Systems Analysis and
Design
37. REFERENCES
Systems Analysis and Design / by Kenneth E. Kendall and Julie E.
Kendall- 8th ed.- New Delhi: PHI Learning, 2011
Systems Analysis and Design / Alan Dennis, Berbara Haley Wixom and
Roberta M. Roth.-4th th.- New Jersey: John Wiley & Sons, 2010.
Dictionary of Computer and Information Technology / S. K. Bansal.-
New Delhi: A. P. H. Publishing, 2009.
Systems Analysis and
Design
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