Symbolic mathematics
•Download as PPSX, PDF•
0 likes•106 views
Here's a short slide I prepared. It summarizes how python can be used to solve algebraic expressions ,as well as many other mathematical expression using the sympy library.
Report
Share
Report
Share
Recommended
Daa
There are two main ways to show that an algorithm works correctly: testing and proofs of correctness. Testing an algorithm on sample inputs can find bugs but does not guarantee correctness. Proving correctness mathematically is better but more difficult. To prove the correctness of a recursive algorithm, it must be proved by mathematical induction that each recursive call solves a smaller subproblem and that the base cases are solved correctly. Examples are given of using induction to prove the correctness of recursive algorithms for calculating Fibonacci numbers, finding the maximum value, and multiplying numbers.
Lecture 1
The document discusses the Cilk programming language and its runtime system for parallel programming. Cilk extends C with keywords like spawn and sync to express parallelism. It provides performance guarantees and automatically manages scheduling across processors. The runtime system uses work-stealing to map Cilk threads to processors with near-optimal efficiency. Cilk allows expressing parallelism while hiding low-level details like load balancing.
A peek on numerical programming in perl and python e christopher dyken 2005
This document compares the numerical programming capabilities and performance of Perl and Python with and without numerical libraries like NumPy and PDL. It implements a trapezoidal quadrature rule to integrate three different functions in standard C, optimized C, Python, Python with NumPy, Python with numarray, Perl, and Perl with PDL. The results show that plain Python and Perl are much slower than C, but with numerical libraries their performance is comparable to optimized C for problems that can be formulated as element-by-element array operations. NumPy performs worse for simple functions but the gap decreases for more complex functions that use trigonometric operations. So for numerical problems, Python and Perl with add-on libraries can be viable alternatives to C/C
Ninth session
This document discusses lambda functions and the import keyword in Python. It defines lambda functions as small anonymous functions that can take any number of arguments but only have one expression. Examples are provided to demonstrate lambda functions that add, multiply, and return values. The import keyword is used to import libraries into code. Syntax for importing a whole library, a single method, or all methods using wildcards is described. Examples import the os, matplotlib, scikit-learn, and cryptography libraries.
Big oh Representation Used in Time complexities
This document introduces algorithms and their properties. It defines an algorithm as a precise set of instructions to perform a computation or solve a problem. Key properties of algorithms are discussed such as inputs, outputs, definiteness, correctness, finiteness, effectiveness and generality. Examples are given of maximum finding, linear search and binary search algorithms using pseudocode. The document discusses how algorithm complexity grows with input size and introduces big-O notation to analyze asymptotic growth rates of algorithms. It provides examples of analyzing time complexities for different algorithms.
Algorithms Lecture 1: Introduction to Algorithms
We will discuss the following: Algorithms, Time Complexity & Space Complexity, Algorithm vs Pseudo code, Some Algorithm Types, Programming Languages, Python, Anaconda.
Ch8a
The document discusses intermediate code generation in compiler construction. It covers several intermediate representations including postfix notation, three-address code, and quadruples. It also discusses generating three-address code through syntax-directed translation and the use of symbol tables to handle name resolution and scoping.
Parallel algorithms
This document discusses parallel algorithms and models of parallel computation. It begins with an overview of parallelism and the PRAM model of computation. It then discusses different models of concurrent versus exclusive access to shared memory. Several parallel algorithms are presented, including list ranking in O(log n) time using an EREW PRAM algorithm and finding the maximum of n elements in O(1) time using a CRCW PRAM algorithm. It analyzes the performance of EREW versus CRCW models and shows how to simulate a CRCW algorithm using EREW in O(log p) time using p processors.
Recommended
Daa
There are two main ways to show that an algorithm works correctly: testing and proofs of correctness. Testing an algorithm on sample inputs can find bugs but does not guarantee correctness. Proving correctness mathematically is better but more difficult. To prove the correctness of a recursive algorithm, it must be proved by mathematical induction that each recursive call solves a smaller subproblem and that the base cases are solved correctly. Examples are given of using induction to prove the correctness of recursive algorithms for calculating Fibonacci numbers, finding the maximum value, and multiplying numbers.
Lecture 1
The document discusses the Cilk programming language and its runtime system for parallel programming. Cilk extends C with keywords like spawn and sync to express parallelism. It provides performance guarantees and automatically manages scheduling across processors. The runtime system uses work-stealing to map Cilk threads to processors with near-optimal efficiency. Cilk allows expressing parallelism while hiding low-level details like load balancing.
A peek on numerical programming in perl and python e christopher dyken 2005
This document compares the numerical programming capabilities and performance of Perl and Python with and without numerical libraries like NumPy and PDL. It implements a trapezoidal quadrature rule to integrate three different functions in standard C, optimized C, Python, Python with NumPy, Python with numarray, Perl, and Perl with PDL. The results show that plain Python and Perl are much slower than C, but with numerical libraries their performance is comparable to optimized C for problems that can be formulated as element-by-element array operations. NumPy performs worse for simple functions but the gap decreases for more complex functions that use trigonometric operations. So for numerical problems, Python and Perl with add-on libraries can be viable alternatives to C/C
Ninth session
This document discusses lambda functions and the import keyword in Python. It defines lambda functions as small anonymous functions that can take any number of arguments but only have one expression. Examples are provided to demonstrate lambda functions that add, multiply, and return values. The import keyword is used to import libraries into code. Syntax for importing a whole library, a single method, or all methods using wildcards is described. Examples import the os, matplotlib, scikit-learn, and cryptography libraries.
Big oh Representation Used in Time complexities
This document introduces algorithms and their properties. It defines an algorithm as a precise set of instructions to perform a computation or solve a problem. Key properties of algorithms are discussed such as inputs, outputs, definiteness, correctness, finiteness, effectiveness and generality. Examples are given of maximum finding, linear search and binary search algorithms using pseudocode. The document discusses how algorithm complexity grows with input size and introduces big-O notation to analyze asymptotic growth rates of algorithms. It provides examples of analyzing time complexities for different algorithms.
Algorithms Lecture 1: Introduction to Algorithms
We will discuss the following: Algorithms, Time Complexity & Space Complexity, Algorithm vs Pseudo code, Some Algorithm Types, Programming Languages, Python, Anaconda.
Ch8a
The document discusses intermediate code generation in compiler construction. It covers several intermediate representations including postfix notation, three-address code, and quadruples. It also discusses generating three-address code through syntax-directed translation and the use of symbol tables to handle name resolution and scoping.
Parallel algorithms
This document discusses parallel algorithms and models of parallel computation. It begins with an overview of parallelism and the PRAM model of computation. It then discusses different models of concurrent versus exclusive access to shared memory. Several parallel algorithms are presented, including list ranking in O(log n) time using an EREW PRAM algorithm and finding the maximum of n elements in O(1) time using a CRCW PRAM algorithm. It analyzes the performance of EREW versus CRCW models and shows how to simulate a CRCW algorithm using EREW in O(log p) time using p processors.
Introduction to data structures and Algorithm
This document provides an introduction to algorithms and data structures. It defines algorithms as step-by-step processes to solve problems and discusses their properties, including being unambiguous, composed of a finite number of steps, and terminating. The document outlines the development process for algorithms and discusses their time and space complexity, noting worst-case, average-case, and best-case scenarios. Examples of iterative and recursive algorithms for calculating factorials are provided to illustrate time and space complexity analyses.
Unit ii algorithm
This document provides an overview of algorithms including definitions, characteristics, design, and analysis. It defines an algorithm as a finite step-by-step procedure to solve a problem and discusses their key characteristics like input, definiteness, effectiveness, finiteness, and output. The document outlines the design of algorithms using pseudo-code and their analysis in terms of time and space complexity using asymptotic notations like Big O, Big Omega, and Big Theta. Examples are provided to illustrate linear search time complexity and the use of different notations to determine algorithm efficiency.
Cs2251 daa
/
p
This document provides an overview of the CS-2251 DESIGN AND ANALYSIS OF ALGORITHMS course. It defines algorithms and discusses algorithm design and analysis processes. It covers different algorithm efficiency measures, specification methods, important problem types, classification techniques, and examples like the Euclid algorithm. Key aspects of sorting, searching, graph, combinatorial, and numerical problems are outlined. The features of efficient algorithms and orders of algorithms are defined.
tt
h
Sci py india_conference_2019
Sci py india_conference_2019Electronics and Communication Engineering, Institute of Road and Transport Technology
1. The document discusses digital signal processing concepts and exercises using Python packages like NumPy, SciPy and Matplotlib.
2. It provides instructions on installing the required software packages for Windows, Ubuntu Linux and Android systems.
3. A list of 20 exercises is given covering topics like continuous and discrete sine waves, convolution, DFT using fft and own function, windowing techniques and design of FIR and IIR filters. Lecture 2 data structures and algorithms
The document discusses algorithms and their complexity. It defines an algorithm as a well-defined computational procedure that takes inputs and produces outputs. Algorithms have properties like definiteness, correctness, finiteness, and effectiveness. While faster computers make any method viable, analyzing algorithms' complexity is still important because computing resources are finite. Algorithm complexity is analyzed asymptotically for large inputs, focusing on growth rates like constant, logarithmic, linear, quadratic, and exponential. Common notations like Big-O describe upper algorithm complexity bounds.
Lecture 5: Asymptotic analysis of algorithms
The document discusses asymptotic analysis of algorithms to analyze efficiency in terms of time and space complexity. It explains that the number of basic operations like arithmetic, data movement, and control operations determines time complexity, while space complexity depends on the number of basic data types used. Different algorithms for counting numbers with factorial divisible by 5 are analyzed to find their time complexity. The time complexity of an algorithm can be expressed using Θ notation, which describes the dominant term as the input size increases.
Complexity of Algorithm
This document discusses the complexity of algorithms and the tradeoff between algorithm cost and time. It defines algorithm complexity as a function of input size that measures the time and space used by an algorithm. Different complexity classes are described such as polynomial, sub-linear, and exponential time. Examples are given to find the complexity of bubble sort and linear search algorithms. The concept of space-time tradeoffs is introduced, where using more space can reduce computation time. Genetic algorithms are proposed to efficiently solve large-scale construction time-cost tradeoff problems.
Docase notation for Haskell
Slides from a brief presentation about the 'docase' notation that I did at Haskell Hackathon in Cambridge. The notation makes it easier to work with monads that have some additional operations (such as Par monad or Parsers).
Performance analysis(Time & Space Complexity)
The document discusses algorithms analysis and design. It covers time complexity and space complexity analysis using approaches like counting the number of basic operations like assignments, comparisons etc. and analyzing how they vary with the size of the input. Common complexities like constant, linear, quadratic and cubic are explained with examples. Frequency count method is presented to determine tight bounds of time and space complexity of algorithms.
Register allocation and assignment
This document discusses various strategies for register allocation and assignment in compiler design. It notes that assigning values to specific registers simplifies compiler design but can result in inefficient register usage. Global register allocation aims to assign frequently used values to registers for the duration of a single block. Usage counts provide an estimate of how many loads/stores could be saved by assigning a value to a register. Graph coloring is presented as a technique where an interference graph is constructed and coloring aims to assign registers efficiently despite interference between values.
Presentation(intermediate code generation)
The document discusses three address code, an intermediate representation in compilers that uses at most three operands and one operator per instruction. It explains how three address code works, different implementations like quadruples and triples, and provides an example of converting the expression ((a+b)-((a+b)*(a-b)))+((a+b)*(a-b))) to three address code. Three address code simplifies intermediate code generation and machine code conversion in compilers.
Unit i basic concepts of algorithms
The document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem and get a desired output. Key aspects of algorithms discussed include their time and space complexity, asymptotic analysis to determine best, average, and worst case running times, and common asymptotic notations like Big O that are used to analyze algorithms. Examples are provided to demonstrate how to determine the time and space complexity of different algorithms like those using loops, recursion, and nested loops.
Lua by Ong Hean Kuan
Lua is a lightweight scripting language that can be embedded into C/C++ applications. It is commonly used for extending applications with scripting capabilities. Some key uses of Lua include game scripting, configuration files, extending applications, and implementing logic or rules engines. Lua provides APIs for interacting with the Lua virtual machine from C/C++ code, including pushing and popping values on the Lua stack, calling Lua functions from C, and calling C functions from Lua scripts.
Algorithm Analyzing
This slide explain complexity of an algorithm. Explain from theory perspective. At the end of slide, I also show the test result to prove the theory. Pleas, read this slide to improve your code quality .
This slide is exported from Ms. Power
Point to PDF.
Scientific Python
This document provides an overview and introduction to key Python packages for scientific computing and data science. It discusses Jupyter notebooks for interactive coding and visualization, NumPy for N-dimensional arrays and math operations, SciPy for scientific computing functions, matplotlib for plotting, and pandas for working with labeled data structures. The document emphasizes that NumPy provides foundational N-dimensional arrays, SciPy builds on this with additional mathematical and scientific routines, and matplotlib and pandas complement these with visualization and labeled data functionality.
Data Structure: Algorithm and analysis
This document discusses algorithms and analysis of algorithms. It covers key concepts like time complexity, space complexity, asymptotic notations, best case, worst case and average case time complexities. Examples are provided to illustrate linear, quadratic and logarithmic time complexities. Common sorting algorithms like quicksort, mergesort, heapsort, bubblesort and insertionsort are summarized along with their time and space complexities.
Intermediate code generation in Compiler Design
The document discusses intermediate code generation in compilers. It begins by explaining that intermediate code generation is the final phase of the compiler front-end and its goal is to translate the program into a format expected by the back-end. Common intermediate representations include three address code and static single assignment form. The document then discusses why intermediate representations are used, how to choose an appropriate representation, and common types of representations like graphical IRs and linear IRs.
Beyond lists - Copenhagen 2015
The document contains various notes and information snippets on topics including:
- WiFi login details
- Maslow's hierarchy of needs
- LAMP stack vs 3-tier architecture
- Performance impacts of small delays in page load times
- Quicksort algorithm implementation in Haskell
- Comparisons of Reactive Extensions, F# Observable, and Nessos Streams libraries
- HybridDictionary data structure optimization in .NET
- B+ tree data structure praise
- Phillip Trelford's Twitter handle and blog
Big o
Big O notation describes how efficiently an algorithm or function grows as the input size increases. It focuses on the worst-case scenario and ignores constant factors. Common time complexities include O(1) for constant time, O(n) for linear time, and O(n^2) for quadratic time. To determine an algorithm's complexity, its operations are analyzed, such as the number of statements, loops, and function calls.
Spsl iv unit final
This document provides an overview of the Python programming language. It discusses Python's history, features, and why it is a good programming language. Key points covered include:
- Python was created in the late 1980s and draws from many other languages.
- It is an open source, interpreted, object-oriented, and portable language with a large online community and library support.
- Python code is compiled to bytecode for performance. It has dynamic typing, automatic memory management, and is powerful yet easy to learn.
- The document reviews Python statements, expressions, variables, basic data types, functions, modules and exceptions. It provides examples of Python code.
Spsl iv unit final
This document provides information about the Python programming language. It discusses the features of Python, including that it is object-oriented, open source, portable, powerful, and easy to learn. It also covers Python syntax, statements, functions, modules, exception handling, and how to run Python programs. The outcomes of learning these Python concepts are also listed.
More Related Content
What's hot
Introduction to data structures and Algorithm
This document provides an introduction to algorithms and data structures. It defines algorithms as step-by-step processes to solve problems and discusses their properties, including being unambiguous, composed of a finite number of steps, and terminating. The document outlines the development process for algorithms and discusses their time and space complexity, noting worst-case, average-case, and best-case scenarios. Examples of iterative and recursive algorithms for calculating factorials are provided to illustrate time and space complexity analyses.
Unit ii algorithm
This document provides an overview of algorithms including definitions, characteristics, design, and analysis. It defines an algorithm as a finite step-by-step procedure to solve a problem and discusses their key characteristics like input, definiteness, effectiveness, finiteness, and output. The document outlines the design of algorithms using pseudo-code and their analysis in terms of time and space complexity using asymptotic notations like Big O, Big Omega, and Big Theta. Examples are provided to illustrate linear search time complexity and the use of different notations to determine algorithm efficiency.
Cs2251 daa
/
p
This document provides an overview of the CS-2251 DESIGN AND ANALYSIS OF ALGORITHMS course. It defines algorithms and discusses algorithm design and analysis processes. It covers different algorithm efficiency measures, specification methods, important problem types, classification techniques, and examples like the Euclid algorithm. Key aspects of sorting, searching, graph, combinatorial, and numerical problems are outlined. The features of efficient algorithms and orders of algorithms are defined.
tt
h
Sci py india_conference_2019
Sci py india_conference_2019Electronics and Communication Engineering, Institute of Road and Transport Technology
1. The document discusses digital signal processing concepts and exercises using Python packages like NumPy, SciPy and Matplotlib.
2. It provides instructions on installing the required software packages for Windows, Ubuntu Linux and Android systems.
3. A list of 20 exercises is given covering topics like continuous and discrete sine waves, convolution, DFT using fft and own function, windowing techniques and design of FIR and IIR filters. Lecture 2 data structures and algorithms
The document discusses algorithms and their complexity. It defines an algorithm as a well-defined computational procedure that takes inputs and produces outputs. Algorithms have properties like definiteness, correctness, finiteness, and effectiveness. While faster computers make any method viable, analyzing algorithms' complexity is still important because computing resources are finite. Algorithm complexity is analyzed asymptotically for large inputs, focusing on growth rates like constant, logarithmic, linear, quadratic, and exponential. Common notations like Big-O describe upper algorithm complexity bounds.
Lecture 5: Asymptotic analysis of algorithms
The document discusses asymptotic analysis of algorithms to analyze efficiency in terms of time and space complexity. It explains that the number of basic operations like arithmetic, data movement, and control operations determines time complexity, while space complexity depends on the number of basic data types used. Different algorithms for counting numbers with factorial divisible by 5 are analyzed to find their time complexity. The time complexity of an algorithm can be expressed using Θ notation, which describes the dominant term as the input size increases.
Complexity of Algorithm
This document discusses the complexity of algorithms and the tradeoff between algorithm cost and time. It defines algorithm complexity as a function of input size that measures the time and space used by an algorithm. Different complexity classes are described such as polynomial, sub-linear, and exponential time. Examples are given to find the complexity of bubble sort and linear search algorithms. The concept of space-time tradeoffs is introduced, where using more space can reduce computation time. Genetic algorithms are proposed to efficiently solve large-scale construction time-cost tradeoff problems.
Docase notation for Haskell
Slides from a brief presentation about the 'docase' notation that I did at Haskell Hackathon in Cambridge. The notation makes it easier to work with monads that have some additional operations (such as Par monad or Parsers).
Performance analysis(Time & Space Complexity)
The document discusses algorithms analysis and design. It covers time complexity and space complexity analysis using approaches like counting the number of basic operations like assignments, comparisons etc. and analyzing how they vary with the size of the input. Common complexities like constant, linear, quadratic and cubic are explained with examples. Frequency count method is presented to determine tight bounds of time and space complexity of algorithms.
Register allocation and assignment
This document discusses various strategies for register allocation and assignment in compiler design. It notes that assigning values to specific registers simplifies compiler design but can result in inefficient register usage. Global register allocation aims to assign frequently used values to registers for the duration of a single block. Usage counts provide an estimate of how many loads/stores could be saved by assigning a value to a register. Graph coloring is presented as a technique where an interference graph is constructed and coloring aims to assign registers efficiently despite interference between values.
Presentation(intermediate code generation)
The document discusses three address code, an intermediate representation in compilers that uses at most three operands and one operator per instruction. It explains how three address code works, different implementations like quadruples and triples, and provides an example of converting the expression ((a+b)-((a+b)*(a-b)))+((a+b)*(a-b))) to three address code. Three address code simplifies intermediate code generation and machine code conversion in compilers.
Unit i basic concepts of algorithms
The document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem and get a desired output. Key aspects of algorithms discussed include their time and space complexity, asymptotic analysis to determine best, average, and worst case running times, and common asymptotic notations like Big O that are used to analyze algorithms. Examples are provided to demonstrate how to determine the time and space complexity of different algorithms like those using loops, recursion, and nested loops.
Lua by Ong Hean Kuan
Lua is a lightweight scripting language that can be embedded into C/C++ applications. It is commonly used for extending applications with scripting capabilities. Some key uses of Lua include game scripting, configuration files, extending applications, and implementing logic or rules engines. Lua provides APIs for interacting with the Lua virtual machine from C/C++ code, including pushing and popping values on the Lua stack, calling Lua functions from C, and calling C functions from Lua scripts.
Algorithm Analyzing
This slide explain complexity of an algorithm. Explain from theory perspective. At the end of slide, I also show the test result to prove the theory. Pleas, read this slide to improve your code quality .
This slide is exported from Ms. Power
Point to PDF.
Scientific Python
This document provides an overview and introduction to key Python packages for scientific computing and data science. It discusses Jupyter notebooks for interactive coding and visualization, NumPy for N-dimensional arrays and math operations, SciPy for scientific computing functions, matplotlib for plotting, and pandas for working with labeled data structures. The document emphasizes that NumPy provides foundational N-dimensional arrays, SciPy builds on this with additional mathematical and scientific routines, and matplotlib and pandas complement these with visualization and labeled data functionality.
Data Structure: Algorithm and analysis
This document discusses algorithms and analysis of algorithms. It covers key concepts like time complexity, space complexity, asymptotic notations, best case, worst case and average case time complexities. Examples are provided to illustrate linear, quadratic and logarithmic time complexities. Common sorting algorithms like quicksort, mergesort, heapsort, bubblesort and insertionsort are summarized along with their time and space complexities.
Intermediate code generation in Compiler Design
The document discusses intermediate code generation in compilers. It begins by explaining that intermediate code generation is the final phase of the compiler front-end and its goal is to translate the program into a format expected by the back-end. Common intermediate representations include three address code and static single assignment form. The document then discusses why intermediate representations are used, how to choose an appropriate representation, and common types of representations like graphical IRs and linear IRs.
Beyond lists - Copenhagen 2015
The document contains various notes and information snippets on topics including:
- WiFi login details
- Maslow's hierarchy of needs
- LAMP stack vs 3-tier architecture
- Performance impacts of small delays in page load times
- Quicksort algorithm implementation in Haskell
- Comparisons of Reactive Extensions, F# Observable, and Nessos Streams libraries
- HybridDictionary data structure optimization in .NET
- B+ tree data structure praise
- Phillip Trelford's Twitter handle and blog
Big o
Big O notation describes how efficiently an algorithm or function grows as the input size increases. It focuses on the worst-case scenario and ignores constant factors. Common time complexities include O(1) for constant time, O(n) for linear time, and O(n^2) for quadratic time. To determine an algorithm's complexity, its operations are analyzed, such as the number of statements, loops, and function calls.
What's hot (19)
Similar to Symbolic mathematics
Spsl iv unit final
This document provides an overview of the Python programming language. It discusses Python's history, features, and why it is a good programming language. Key points covered include:
- Python was created in the late 1980s and draws from many other languages.
- It is an open source, interpreted, object-oriented, and portable language with a large online community and library support.
- Python code is compiled to bytecode for performance. It has dynamic typing, automatic memory management, and is powerful yet easy to learn.
- The document reviews Python statements, expressions, variables, basic data types, functions, modules and exceptions. It provides examples of Python code.
Spsl iv unit final
This document provides information about the Python programming language. It discusses the features of Python, including that it is object-oriented, open source, portable, powerful, and easy to learn. It also covers Python syntax, statements, functions, modules, exception handling, and how to run Python programs. The outcomes of learning these Python concepts are also listed.
Chapter08.pptx
This document discusses different types of loops in Python including definite loops using for statements, indefinite loops using while statements, and different patterns for indefinite loops like interactive loops, sentinel loops, and end-of-file loops. It provides examples of using accumulators and loop variables to iterate over a sequence of user-entered numbers and calculate the average. Key loop concepts like pre-test loops, priming reads, and avoiding infinite loops are also covered.
Lecture 5 – Computing with Numbers (Math Lib).pptx
This document provides an overview of numeric data types and operations in Python. It discusses integers and floats, and how they represent numeric values differently. Functions for determining data types like type() and converting between types are presented. Core numeric operations like addition, subtraction, multiplication, division, and modulo are covered. The use of accumulators and loops to calculate factorial and solve other numeric problems is demonstrated. Finally, the math library module is introduced for accessing advanced math functions like square root, trigonometric functions, rounding, and random number generation.
Lecture 5 – Computing with Numbers (Math Lib).pptx
This document provides an overview of numeric data types and operations in Python. It discusses integers and floats, and how they represent numeric values differently. Functions for determining data types like type() and converting between types are presented. Core numeric operations like addition, subtraction, multiplication, division, and modulus are covered. The use of accumulators and loops to calculate factorial and solve other numeric problems is demonstrated. Finally, the math library module is introduced for accessing advanced math functions like square root, trigonometric functions, rounding, and random number generation.
lecture 2.pptx
Python is a general purpose programming language that can be used for both programming and scripting. It is an interpreted language, meaning code is executed line by line by the Python interpreter. Python code is written in plain text files with a .py extension. Key features of Python include being object-oriented, using indentation for code blocks rather than brackets, and having a large standard library. Python code can be used for tasks like system scripting, web development, data analysis, and more.
Revision of the basics of python1 (1).pdf
The document discusses Python programming language. It provides an introduction to Python, describing it as an interpreted, high-level and general-purpose programming language. It then covers various data types in Python including numbers, strings, booleans, lists, tuples, sets and dictionaries. The document also discusses operators in Python like arithmetic, comparison, logical and bitwise operators. Finally, it explains how to work with Python in both interactive and script modes.
Chapter 1 Class 12 Computer Science Unit 1
The document discusses the basics of Python programming language. It provides an introduction to Python, describing how it is an interpreted, high-level and general-purpose programming language. It then covers Python features, data types, operators, and how to work in both interactive and script modes in Python. The document also discusses topics like variables, data handling, arithmetic operators, comparison operators, and more with examples.
Python Workshop
This document contains slides from a Python workshop presentation. It introduces Python, discussing its history, philosophy, features, and how to write Python code. Some key points covered include:
- Python was created in the late 1980s and named after Monty Python.
- It aims to have clear, readable syntax while also being powerful.
- Python code tends to be more concise than languages like Java and C++.
- It uses indentation rather than braces to define code blocks.
- Common data types like lists, dictionaries, and tuples are covered.
- Basic programming constructs like conditionals, loops, functions and file I/O are demonstrated.
Approximation algorithms
The document discusses approximation algorithms for NP-hard problems. It begins with an introduction that defines approximation algorithms as algorithms that find feasible but not necessarily optimal solutions to optimization problems in polynomial time.
It then discusses different types of approximation schemes - absolute approximation where the approximate solution is within a constant of optimal, epsilon (ε)-approximation where the approximate solution is within a factor of ε times optimal, and polynomial time approximation schemes that run in polynomial time for any fixed ε.
The document provides examples of problems that admit absolute approximation algorithms, such as planar graph coloring and maximum programs stored on disks. It also discusses Graham's theorem, which proves that the largest processing time scheduling algorithm generates schedules within 1/3
Data Structures and Agorithm: DS 22 Analysis of Algorithm.pptx
Data Structures and Agorithm: DS 22 Analysis of Algorithm.pptx
GE3171-PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY
GE3171-PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORYANJALAI AMMAL MAHALINGAM ENGINEERING COLLEGE
The document provides a lab manual for the course GE3171 - Problem Solving and Python Programming Laboratory. It includes the course objectives, list of experiments, syllabus, and programs for various experiments involving Python programming concepts like lists, tuples, conditionals, loops, functions etc. The experiments cover problems on real-life applications such as electricity billing, library management, vehicle components, building materials etc. The document demonstrates how to write Python programs to solve such problems and validate the output.Unit 1.pptx
The document provides an introduction and overview of the Design and Analysis of Algorithms course. It covers key topics like asymptotic notations and their properties, analyzing recursive and non-recursive algorithms, divide-and-conquer algorithms like quicksort and mergesort, and sorting algorithms like heap sort. Examples of insertion sort and analysis of its worst-case running time of O(n2) are provided. Asymptotic notation like Big-O, Ω, and Θ are introduced to analyze algorithms' time complexities as the problem size n approaches infinity.
Data Structures- Part2 analysis tools
This document discusses algorithm analysis tools. It explains that algorithm analysis is used to determine which of several algorithms to solve a problem is most efficient. Theoretical analysis counts primitive operations to approximate runtime as a function of input size. Common complexity classes like constant, linear, quadratic, and exponential time are defined based on how quickly runtime grows with size. Big-O notation represents the asymptotic upper bound of a function's growth rate to classify algorithms.
علم البيانات - Data Sience
The document contains a series of slides about Python programming concepts. It begins with an introductory quote from Guido van Rossum about the origins of Python in 1989. The slides then cover various Python topics like data types, variables, operators, conditional statements, loops, functions, modules and libraries. Examples are provided to illustrate each concept. The document serves as a high-level overview of core Python programming principles.
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpally...
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpally...http://algorithmtraining.com/advanced-python-training-hyderabad/
Algorithm Class at KPHB C, C++ Course Training Institute in KPHB, Kukatpally, Hyderabad.
https://sites.google.com/site/algorithmclassAlgorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpall...
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpall...http://algorithmtraining.com/advanced-python-training-hyderabad/
C, C++ Course Training Institute in KPHB, Kukatpally, Hyderabad.
https://sites.google.com/site/algorithmclassAlgorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpally...
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpally...http://algorithmtraining.com/advanced-python-training-hyderabad/
Algorithm Class at KPHB C, C++ Course Training Institute in KPHB, Kukatpally, Hyderabad.
https://sites.google.com/site/algorithmclassAlgorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpally...
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpally...http://algorithmtraining.com/advanced-python-training-hyderabad/
Algorithm Class at KPHB C, C++ Course Training Institute in KPHB, Kukatpally, Hyderabad.
https://sites.google.com/site/algorithmclassSimilar to Symbolic mathematics (20)
Analysis of Algorithms, recurrence relation, solving recurrences
Analysis of Algorithms, recurrence relation, solving recurrences
Lecture 5 – Computing with Numbers (Math Lib).pptx
Lecture 5 – Computing with Numbers (Math Lib).pptx
Lecture 5 – Computing with Numbers (Math Lib).pptx
Lecture 5 – Computing with Numbers (Math Lib).pptx
Data Structures and Agorithm: DS 22 Analysis of Algorithm.pptx
Data Structures and Agorithm: DS 22 Analysis of Algorithm.pptx
GE3171-PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY
GE3171-PROBLEM SOLVING AND PYTHON PROGRAMMING LABORATORY
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpally...
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpally...
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpall...
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpall...
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpally...
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpally...
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpally...
Algorithm Class at KPHB (C, C++ Course Training Institute in KPHB, Kukatpally...
Recently uploaded
一比一原版(osu毕业证书)美国俄勒冈州立大学毕业证如何办理
原版一模一样【微信：741003700 】【(osu毕业证书)美国俄勒冈州立大学毕业证成绩单】【微信：741003700 】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Q/微741003700
【主营项目】
一.毕业证【q微741003700】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【q/微741003700】
◇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
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(osu毕业证书)美国俄勒冈州立大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版(uofo毕业证书)美国俄勒冈大学毕业证如何办理
原版一模一样【微信：741003700 】【(uofo毕业证书)美国俄勒冈大学毕业证成绩单】【微信：741003700 】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Q/微741003700
【主营项目】
一.毕业证【q微741003700】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【q/微741003700】
◇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
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uofo毕业证书)美国俄勒冈大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
OOPS_Lab_Manual - programs using C++ programming language
This manual contains programs on object oriented programming concepts using C++ language.
一比一原版(USF毕业证)旧金山大学毕业证如何办理
原件一模一样【微信：95270640】【旧金山大学毕业证USF学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，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）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，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
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
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理旧金山大学毕业证毕业证学位证USF学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理旧金山大学毕业证USF学位证毕业证学位证【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理旧金山大学毕业证毕业证学位证USF学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理旧金山大学毕业证毕业证学位证USF学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
smart pill dispenser is designed to improve medication adherence and safety f...
Smart Pill Dispenser that boosts medication adherence, empowers patients, enables remote monitoring, enhances safety, reduces healthcare costs, and contributes to data-driven healthcare improvements
This study Examines the Effectiveness of Talent Procurement through the Imple...
In the world with high technology and fast
forward mindset recruiters are walking/showing interest
towards E-Recruitment. Present most of the HRs of
many companies are choosing E-Recruitment as the best
choice for recruitment. E-Recruitment is being done
through many online platforms like Linkedin, Naukri,
Instagram , Facebook etc. Now with high technology E-
Recruitment has gone through next level by using
Artificial Intelligence too.
Key Words : Talent Management, Talent Acquisition , E-
Recruitment , Artificial Intelligence Introduction
Effectiveness of Talent Acquisition through E-
Recruitment in this topic we will discuss about 4important
and interlinked topics which are
Determination of Equivalent Circuit parameters and performance characteristic...
Includes the testing of induction motor to draw the circle diagram of induction motor with step wise procedure and calculation for the same. Also explains the working and application of Induction generator
Bituminous road construction project based learning report
Road construction is not as easy as it seems to be, it includes various steps and it starts with its designing and
structure including the traffic volume consideration. Then base layer is done by bulldozers and levelers and after
base surface coating has to be done. For giving road a smooth surface with flexibility, Asphalt concrete is used.
Asphalt requires an aggregate sub base material layer, and then a base layer to be put into first place. Asphalt road
construction is formulated to support the heavy traffic load and climatic conditions. It is 100% recyclable and
saving non renewable natural resources.
With the advancement of technology, Asphalt technology gives assurance about the good drainage system and with
skid resistance it can be used where safety is necessary such as outsidethe schools.
The largest use of Asphalt is for making asphalt concrete for road surfaces. It is widely used in airports around the
world due to the sturdiness and ability to be repaired quickly, it is widely used for runways dedicated to aircraft
landing and taking off. Asphalt is normally stored and transported at 150’C or 300’F temperature
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
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
* Registration is currently open *
Call for Research Papers!!!
Free – Extended Paper will be published as free of cost.
3rd International Conference on Artificial Intelligence Advances (AIAD 2024)
July 13 ~ 14, 2024, Virtual Conference
Webpage URL: https://aiad2024.org/index
Submission Deadline: June 22, 2024
Submission System URL:
https://aiad2024.org/submission/index.php
Contact Us:
Here's where you can reach us : aiad@aiad2024.org (or) aiadconference@yahoo.com
WikiCFP URL: http://wikicfp.com/cfp/servlet/event.showcfp?eventid=180509©ownerid=171656
#artificialintelligence #softcomputing #machinelearning #technology #datascience #python #deeplearning #tech #robotics #innovation #bigdata #coding #iot #computerscience #data #dataanalytics #engineering #robot #datascientist #software #automation #analytics #ml #pythonprogramming #programmer #digitaltransformation #developer #promptengineering #generativeai #genai #chatgpt #artificial #intelligence #datamining #networkscommunications #robotics #callforsubmission #submissionsopen #deadline #opencall #virtual #conference
SCALING OF MOS CIRCUITS m .pptx
this ppt explains about scaling parameters of the mosfet it is basically vlsi subject
Mechanical Engineering on AAI Summer Training Report-003.pdf
Mechanical Engineering PROJECT REPORT ON SUMMER VOCATIONAL TRAINING
AT MBB AIRPORT
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Recently uploaded (20)
OOPS_Lab_Manual - programs using C++ programming language
OOPS_Lab_Manual - programs using C++ programming language
smart pill dispenser is designed to improve medication adherence and safety f...
smart pill dispenser is designed to improve medication adherence and safety f...
This study Examines the Effectiveness of Talent Procurement through the Imple...
This study Examines the Effectiveness of Talent Procurement through the Imple...
Determination of Equivalent Circuit parameters and performance characteristic...
Determination of Equivalent Circuit parameters and performance characteristic...
5G Radio Network Througput Problem Analysis HCIA.pdf
5G Radio Network Througput Problem Analysis HCIA.pdf
Bituminous road construction project based learning report
Bituminous road construction project based learning report
SENTIMENT ANALYSIS ON PPT AND Project template_.pptx
SENTIMENT ANALYSIS ON PPT AND Project template_.pptx
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
Call For Paper -3rd International Conference on Artificial Intelligence Advan...
Mechanical Engineering on AAI Summer Training Report-003.pdf
Mechanical Engineering on AAI Summer Training Report-003.pdf
openshift technical overview - Flow of openshift containerisatoin
openshift technical overview - Flow of openshift containerisatoin
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
Symbolic mathematics
- 1. Symbolic Mathematics with Python Programming Language Franklin Obasi, Undergraduate, Department Of Chemical Engineering , University Of Port Harcourt, Rivers State, Nigeria.
- 2. Symbolic Mathematics Python programming Many applications in the field of mathematics , Science and Engineering demands the use of symbolic operations, that is, mathematical operations which involves the use of expressions that contain symbolic variables. Evaluating or simplifying these mathematical expression often result to another mathematical expression in terms of the symbolic variable 2𝑡3 + 5𝑡 − 8 6𝑡2 + 5For Instance, the derivative of: With respect to t is:
- 3. Symbolic Mathematics Python programming Python has the capability of handling many types of symbolic operations with the support of a scientific library called SYMPY Library SymPy is short for Symbolic Python SymPy is included with many distributions of Python that are focused on scientists or engineers including the Anaconda Python distribution and Pythonxy. Installation on Linux systems is also straightforward. Further information related to downloading and installing SymPy as well as comprehensive documentation is available on the SymPy website: www.sympy.org.
- 4. Symbolic Mathematics Python programming Sympy Library is imported into Python using the Command: import sympy This enables access to all the available method in the sympy library using sympy.method() format. The next step is to declare the symbolic variables or parameters that will be present in the equations that we plan to manipulate or solve symbolically. To transforms a string that lists the variables or parameters into instances of the SymPy Symbol class, use the command: sympy.symbols() 1. 2.
- 5. Symbolic Mathematics Python programming To Find roots of a polynomial 𝑎𝑥2 + 𝑏𝑥 + 𝑐 output −𝑏 ± 𝑏2 − 4𝑎𝑐 2𝑎 Solve() function is used for solving algebraic expressions
- 6. Symbolic Mathematics Python programming Example Problem The van der Waals equation of state is a common equation for relating the temperature (T), pressure (P), and specific volume (V̂ ) of a nonideal gas . 𝑷 = 𝑹𝑻 𝑽 − 𝒃 − 𝒂 𝑽 𝟐 Our goal is to calculate the specific volume of ammonia (Tc = 405.5 K and Pc = 111.3 atm) at T = 420 K and P = 43.4 atm. Before using SymPy to solve for the specific volume, we need to rewrite our equation(s) so that all terms are on one side of the “=” sign. Thus, we will write the van der Waals equation as 𝟎 = 𝑷 − 𝑹𝑻 𝑽 − 𝒃 + 𝒂 𝑽 𝟐
- 7. Symbolic Mathematics Python programming Solution output The output from this example is [0.70088, 0.06531 - 0.02985*I, 0.06531 + 0.02985*I] The equation is cubic with respect to V so we should not be surprised by getting three solutions (i.e., three roots). In this case, it is simple to determine the correct solution as two of the solutions are complex and obviously not physical
- 8. Symbolic Mathematics Python programming Sympy has lots of methods used for Solving Mathematical Expressions. Enter: >>> help(sympy) In your python Interpreter Shell to view more available Functions associated to Sympy class Email: franklinobasy@gmail.com , LinkedIn : www.linkedin.com/in/franklinobasy