Gisvdlsigsbs zkdvdkod kaisvdv kshvdkd h kya hua hai kya hua hai kya hua hai kya hua h tere ko kuch bolna nahi kuch bhi ho raha h kya hai na dhund ke bta do ki nahi kuch puchungiii ho gya h kya hai ki h kya kr rhe h h h h ki mil jayega aur kya ho raha hai ki aap recharge ho jayega aur ek baat ka gussa h kya kar rahe hain aap bataiye ho gai thi na kal ke sath se ho gai hai aap bataiye ho ke aap hi nhi h kya kar rahe hain to aap bataiye hai aur aap hi ho jayegaa se baat karte hain aap ko free h kya aap bataiye hai aap ko bhi free nhi ho skta h tu bhi kuch share kar ye
This document provides an introduction to soft computing. Soft computing is an emerging approach to computing that aims to mimic the human mind's ability to reason and learn with uncertainty and imprecision. The key components of soft computing include neural networks, fuzzy logic, and genetic algorithms. The goals of soft computing are to develop intelligent machines to solve real-world problems that may not have ideal or mathematically modeled solutions, while achieving practicality, robustness, and low cost. Soft computing uses techniques like machine learning, evolutionary computation, and artificial neural networks to approach problems that traditional computing cannot always solve.
The document discusses soft computing and its components. Soft computing aims to solve real-world problems that are difficult for traditional hard computing techniques. It uses fuzzy logic, neural networks, evolutionary computation and other inexact methods. Unlike hard computing which requires precise modeling, soft computing is tolerant of imprecision, uncertainty and approximation. It is well-suited for problems where ideal models are not available, such as pattern recognition, forecasting and control systems. Some key applications of soft computing mentioned include handwriting recognition, image processing, data mining and control systems.
Soft computing is a fusion of methodologies designed to model and solve real-world problems that are too complex for traditional mathematical techniques. It includes fuzzy logic, neural networks, evolutionary computing, and probabilistic computing. The goal of soft computing is to exploit tolerance for imprecision and uncertainty to achieve solutions that resemble human decision making. It allows for approximation, partial truth, and uncertainty where hard computing requires exact values. Soft computing has been applied to problems in areas like control systems, pattern recognition, prediction, and optimization.
This document introduces soft computing and provides an agenda for the lecture. Soft computing is defined as a fusion of fuzzy logic, neural networks, evolutionary computing, and probabilistic computing to deal with uncertainty and imprecision. Hybrid systems combine different soft computing techniques for improved performance. The lecture will cover an introduction to soft computing, fuzzy computing, neural networks, evolutionary computing, and hybrid systems. References are also provided.
Soft computing is an emerging approach to computing that aims to model human-like decision making through techniques like fuzzy logic, neural networks, and genetic algorithms. It allows for imprecision, uncertainty, and approximation to achieve practical and robust solutions. Soft computing deals with problems that are too complex or undefined to model mathematically. It is well-suited for real-world problems where ideal solutions do not exist.
Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
- Fuzzy logic is an extension of classical logic that accounts for partial truth values between "true" and "false". It allows for gradual transitions between values in a membership function.
- Fuzzy logic has been applied to many areas including control systems, decision making, pattern recognition and other areas involving uncertainty. It uses fuzzy "if-then" rules to model imprecise human reasoning.
- The document discusses fuzzy sets, fuzzy relations, applications of fuzzy logic and provides biographical information about Lotfi Zadeh, the founder of fuzzy logic.
This document provides an introduction to soft computing. Soft computing is an emerging approach to computing that aims to mimic the human mind's ability to reason and learn with uncertainty and imprecision. The key components of soft computing include neural networks, fuzzy logic, and genetic algorithms. The goals of soft computing are to develop intelligent machines to solve real-world problems that may not have ideal or mathematically modeled solutions, while achieving practicality, robustness, and low cost. Soft computing uses techniques like machine learning, evolutionary computation, and artificial neural networks to approach problems that traditional computing cannot always solve.
The document discusses soft computing and its components. Soft computing aims to solve real-world problems that are difficult for traditional hard computing techniques. It uses fuzzy logic, neural networks, evolutionary computation and other inexact methods. Unlike hard computing which requires precise modeling, soft computing is tolerant of imprecision, uncertainty and approximation. It is well-suited for problems where ideal models are not available, such as pattern recognition, forecasting and control systems. Some key applications of soft computing mentioned include handwriting recognition, image processing, data mining and control systems.
Soft computing is a fusion of methodologies designed to model and solve real-world problems that are too complex for traditional mathematical techniques. It includes fuzzy logic, neural networks, evolutionary computing, and probabilistic computing. The goal of soft computing is to exploit tolerance for imprecision and uncertainty to achieve solutions that resemble human decision making. It allows for approximation, partial truth, and uncertainty where hard computing requires exact values. Soft computing has been applied to problems in areas like control systems, pattern recognition, prediction, and optimization.
This document introduces soft computing and provides an agenda for the lecture. Soft computing is defined as a fusion of fuzzy logic, neural networks, evolutionary computing, and probabilistic computing to deal with uncertainty and imprecision. Hybrid systems combine different soft computing techniques for improved performance. The lecture will cover an introduction to soft computing, fuzzy computing, neural networks, evolutionary computing, and hybrid systems. References are also provided.
Soft computing is an emerging approach to computing that aims to model human-like decision making through techniques like fuzzy logic, neural networks, and genetic algorithms. It allows for imprecision, uncertainty, and approximation to achieve practical and robust solutions. Soft computing deals with problems that are too complex or undefined to model mathematically. It is well-suited for real-world problems where ideal solutions do not exist.
Soft computing is an approach to computing that aims to model human-like decision making. It deals with imprecise or uncertain data using techniques like fuzzy logic, neural networks, and genetic algorithms. The goal is to develop systems that are tolerant of imprecision, uncertainty, and approximation to achieve practical and low-cost solutions to real-world problems. Soft computing was initiated in 1981 and includes fields like fuzzy logic, neural networks, and evolutionary computation. It provides approximate solutions using techniques like neural network reasoning, genetic programming, and functional approximation.
- Fuzzy logic is an extension of classical logic that accounts for partial truth values between "true" and "false". It allows for gradual transitions between values in a membership function.
- Fuzzy logic has been applied to many areas including control systems, decision making, pattern recognition and other areas involving uncertainty. It uses fuzzy "if-then" rules to model imprecise human reasoning.
- The document discusses fuzzy sets, fuzzy relations, applications of fuzzy logic and provides biographical information about Lotfi Zadeh, the founder of fuzzy logic.
Following topics are discussed in this presentation:What is Soft Computing?
What is Hard Computing?
What is Fuzzy Logic Models?
What is Neural Networks (NN)?
What is Genetic Algorithms or Evaluation Programming?
What is probabilistic reasoning?
Difference between fuzziness and probability
AI and Soft Computing
Future of Soft Computing
Soft computing is a collection of methodologies that aim to exploit imprecision and uncertainty to achieve tractability, robustness, and low solution cost. Its principal constituents are fuzzy logic, neurocomputing, and probabilistic reasoning. Soft computing provides tools to model and solve real-world problems that are too complex for conventional techniques.
The approaches to Artificial Intelligence (AI) in the last century may be labelled as (a) trying to understand and copy (human) nature, (b) being based on heuristic considerations, (c) being formal but from the outset (provably) limited, (d) being (mere) frameworks that leave crucial aspects unspecified. This decade has spawned the first theory of AI, which (e) is principled, formal, complete, and general. This theory, called Universal AI, is about ultimate super-intelligence. It can serve as a gold standard for General AI, and implicitly proposes a formal definition of machine intelligence. After a brief review of the various approaches to (general) AI, I will give an introduction to Universal AI, concentrating on the philosophical, mathematical, and computational aspects behind it. I will also discuss various implications and future challenges.
Soft computing is a field that uses approximate solutions and techniques like fuzzy logic, neural networks, and evolutionary computation to problems that are too complex for traditional binary logic-based computing. It aims to achieve human-like decision making by incorporating uncertainty, imprecision, and partial truth into solutions. The main goal of soft computing is to develop intelligent machines that can provide solutions to real-world problems that are difficult to model mathematically.
Soft-computing refers to computational techniques that study and analyze complex phenomena for which conventional methods have not provided low-cost or complete solutions. It includes fuzzy logic, evolutionary computation, neural networks, Bayesian networks, support vector machines, and hybrid systems. Soft-computing techniques are robust, tolerant of imprecise data, and resemble biological processes more than traditional logical techniques. They provide useful approximations to intractable problems rather than exact solutions.
Neuro-Fuzzy and Soft Computing is a class that teaches techniques for creating intelligent systems that can handle real-world problems involving uncertainty and imprecision. The class will cover multiple soft computing techniques including fuzzy logic, neural networks, genetic algorithms, and probabilistic reasoning. It will present examples of industrial applications and discuss when each technique is applicable. Soft computing combines knowledge from these areas to develop systems that are human-like, adaptable, and able to explain their decisions. The techniques have already been successfully applied in areas like farming, manufacturing, and services.
The document provides an introduction to artificial intelligence (AI), including definitions, concepts, and types of AI. It defines AI as the ability of computers to learn and think like humans. The key concepts discussed are machine learning, deep learning, and neural networks. It describes narrow/weak AI as able to perform specific tasks, general AI as able to perform any intellectual task, and super AI as able to surpass human intelligence. The document also outlines components of AI like learning, reasoning, problem-solving, perception, and language understanding. It presents a three-dimensional model of AI and discusses types based on functionality like reactive machines and those with limited memory.
Soft computing is an emerging approach to computing that aims to solve computationally hard problems using inexact solutions that are tolerant of imprecision, uncertainty, partial truth, and approximation. It uses techniques like fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning to model human-like decision making. Unlike hard computing which requires precise modeling and solutions, soft computing is well-suited for real-world problems where ideal models are not available. The key constituents of soft computing are fuzzy logic, evolutionary computation, neural networks, and machine learning.
This document provides an introduction to artificial intelligence techniques. It discusses key concepts such as what intelligence and AI are, why AI is studied, and different fields of AI like expert systems, neural networks, genetic algorithms and more. It also covers topics such as the advantages of using AI, where it should and shouldn't be applied, the differences between soft computing and hard computing, and more.
Fuzzy logic is a form of many-valued logic that allows intermediate truth values between true and false, such as mostly true. It is used to handle concepts of partial truth and uncertainty. Fuzzy logic algorithms consider all available data to make decisions and mimic how humans reason with possibilities between strict digital values. Fuzzy logic systems use membership functions to quantify linguistic terms, rules and inferences to determine system outputs, and defuzzification to convert fuzzy outputs to non-fuzzy values. Common applications of fuzzy logic include automatic control systems, consumer electronics, and automotive systems.
The document discusses artificial intelligence and robotics. It defines a robot as a machine that senses its environment, thinks to process information, and acts by following instructions to perform work. Artificial intelligence aims to help machines solve complex problems in a human-like way. The document also discusses key concepts in AI like agents, environments, search algorithms, fuzzy logic systems, natural language processing, expert systems, and issues related to AI development.
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
This document provides an introduction to soft computing techniques including fuzzy logic, neural networks, and genetic algorithms. It discusses how these techniques are inspired by human intelligence and can handle imprecise or uncertain data. Examples of applications are given such as fuzzy logic in washing machines to optimize the washing process based on sensor readings, and using genetic algorithms to design optimal robotics.
This document provides biographical information about Şaban Dalaman and summaries of key concepts in artificial intelligence and machine learning. It summarizes Şaban Dalaman's educational and professional background, then discusses Alan Turing's universal machine concept, the 1956 Dartmouth workshop proposal that helped define the field of AI, and definitions of AI, machine learning, deep learning, and data science. It also lists different tribes and algorithms within machine learning.
1. The document discusses an emerging approach to computing called soft computing. Soft computing techniques include neural networks, genetic algorithms, machine learning, probabilistic reasoning, and fuzzy logic.
2. Soft computing aims to develop intelligent machines that can solve real-world problems that are difficult to model mathematically. It exploits tolerance for uncertainty and imprecision similar to human decision making.
3. The document then discusses various soft computing techniques in more detail, including neural networks, genetic algorithms, fuzzy logic, and how they differ from traditional hard computing approaches.
Soft Computing is the fusion of methodologies that were designed to model and enable
solutions to real world problems, which are not modeled or too difficult to model, mathematically. Soft
computing is a consortium of methodologies that works synergistically and provides, in one form or
another, flexible information processing capability for handling real-life ambiguous situations. Its aim is to
exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to
achieve tractability, robustness and low-cost solutions. The guiding principle is to devise methods of
computation that lead to an acceptable solution at low cost, by seeking for an approximate solution to an
imprecisely or precisely formulated problem.Soft Computing (SC) represents a significant paradigm shift
in the aims of computing, which reflects the fact that the human mind, unlike present day computers,
possesses a remarkable ability to store and process information which is pervasively imprecise, uncertain
and lacking in categoricity. At this juncture, the principal constituents of Soft Computing (SC) are: Fuzzy
Systems (FS), including Fuzzy Logic (FL); Evolutionary Computation (EC), including Genetic
Algorithms (GA); Neural Networks (NN), including Neural Computing (NC); Machine Learning (ML);
and Probabilistic Reasoning (PR). In this paper we focus on fuzzy methodologies and fuzzy systems, as
they bring basic ideas to other SC methodologies
Hacking Predictive Modeling - RoadSec 2018HJ van Veen
This document provides an overview of machine learning and predictive modeling techniques for hackers and data scientists. It discusses foundational concepts in machine learning like functionalism, connectionism, and black box modeling. It also covers practical techniques like feature engineering, model selection, evaluation, optimization, and popular Python libraries. The document encourages an experimental approach to hacking predictive models through techniques like brute forcing hyperparameters, fuzzing with data permutations, and social engineering within data science communities.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
Following topics are discussed in this presentation:What is Soft Computing?
What is Hard Computing?
What is Fuzzy Logic Models?
What is Neural Networks (NN)?
What is Genetic Algorithms or Evaluation Programming?
What is probabilistic reasoning?
Difference between fuzziness and probability
AI and Soft Computing
Future of Soft Computing
Soft computing is a collection of methodologies that aim to exploit imprecision and uncertainty to achieve tractability, robustness, and low solution cost. Its principal constituents are fuzzy logic, neurocomputing, and probabilistic reasoning. Soft computing provides tools to model and solve real-world problems that are too complex for conventional techniques.
The approaches to Artificial Intelligence (AI) in the last century may be labelled as (a) trying to understand and copy (human) nature, (b) being based on heuristic considerations, (c) being formal but from the outset (provably) limited, (d) being (mere) frameworks that leave crucial aspects unspecified. This decade has spawned the first theory of AI, which (e) is principled, formal, complete, and general. This theory, called Universal AI, is about ultimate super-intelligence. It can serve as a gold standard for General AI, and implicitly proposes a formal definition of machine intelligence. After a brief review of the various approaches to (general) AI, I will give an introduction to Universal AI, concentrating on the philosophical, mathematical, and computational aspects behind it. I will also discuss various implications and future challenges.
Soft computing is a field that uses approximate solutions and techniques like fuzzy logic, neural networks, and evolutionary computation to problems that are too complex for traditional binary logic-based computing. It aims to achieve human-like decision making by incorporating uncertainty, imprecision, and partial truth into solutions. The main goal of soft computing is to develop intelligent machines that can provide solutions to real-world problems that are difficult to model mathematically.
Soft-computing refers to computational techniques that study and analyze complex phenomena for which conventional methods have not provided low-cost or complete solutions. It includes fuzzy logic, evolutionary computation, neural networks, Bayesian networks, support vector machines, and hybrid systems. Soft-computing techniques are robust, tolerant of imprecise data, and resemble biological processes more than traditional logical techniques. They provide useful approximations to intractable problems rather than exact solutions.
Neuro-Fuzzy and Soft Computing is a class that teaches techniques for creating intelligent systems that can handle real-world problems involving uncertainty and imprecision. The class will cover multiple soft computing techniques including fuzzy logic, neural networks, genetic algorithms, and probabilistic reasoning. It will present examples of industrial applications and discuss when each technique is applicable. Soft computing combines knowledge from these areas to develop systems that are human-like, adaptable, and able to explain their decisions. The techniques have already been successfully applied in areas like farming, manufacturing, and services.
The document provides an introduction to artificial intelligence (AI), including definitions, concepts, and types of AI. It defines AI as the ability of computers to learn and think like humans. The key concepts discussed are machine learning, deep learning, and neural networks. It describes narrow/weak AI as able to perform specific tasks, general AI as able to perform any intellectual task, and super AI as able to surpass human intelligence. The document also outlines components of AI like learning, reasoning, problem-solving, perception, and language understanding. It presents a three-dimensional model of AI and discusses types based on functionality like reactive machines and those with limited memory.
Soft computing is an emerging approach to computing that aims to solve computationally hard problems using inexact solutions that are tolerant of imprecision, uncertainty, partial truth, and approximation. It uses techniques like fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning to model human-like decision making. Unlike hard computing which requires precise modeling and solutions, soft computing is well-suited for real-world problems where ideal models are not available. The key constituents of soft computing are fuzzy logic, evolutionary computation, neural networks, and machine learning.
This document provides an introduction to artificial intelligence techniques. It discusses key concepts such as what intelligence and AI are, why AI is studied, and different fields of AI like expert systems, neural networks, genetic algorithms and more. It also covers topics such as the advantages of using AI, where it should and shouldn't be applied, the differences between soft computing and hard computing, and more.
Fuzzy logic is a form of many-valued logic that allows intermediate truth values between true and false, such as mostly true. It is used to handle concepts of partial truth and uncertainty. Fuzzy logic algorithms consider all available data to make decisions and mimic how humans reason with possibilities between strict digital values. Fuzzy logic systems use membership functions to quantify linguistic terms, rules and inferences to determine system outputs, and defuzzification to convert fuzzy outputs to non-fuzzy values. Common applications of fuzzy logic include automatic control systems, consumer electronics, and automotive systems.
The document discusses artificial intelligence and robotics. It defines a robot as a machine that senses its environment, thinks to process information, and acts by following instructions to perform work. Artificial intelligence aims to help machines solve complex problems in a human-like way. The document also discusses key concepts in AI like agents, environments, search algorithms, fuzzy logic systems, natural language processing, expert systems, and issues related to AI development.
Principle of soft computing.
Soft computing.
Goals of soft computing.
Problem solving techniques.
Hard computing v/s soft computing.
Techniques in soft computing.
Advantages of soft computing.
Applications of soft computing.
This document provides an introduction to soft computing techniques including fuzzy logic, neural networks, and genetic algorithms. It discusses how these techniques are inspired by human intelligence and can handle imprecise or uncertain data. Examples of applications are given such as fuzzy logic in washing machines to optimize the washing process based on sensor readings, and using genetic algorithms to design optimal robotics.
This document provides biographical information about Şaban Dalaman and summaries of key concepts in artificial intelligence and machine learning. It summarizes Şaban Dalaman's educational and professional background, then discusses Alan Turing's universal machine concept, the 1956 Dartmouth workshop proposal that helped define the field of AI, and definitions of AI, machine learning, deep learning, and data science. It also lists different tribes and algorithms within machine learning.
1. The document discusses an emerging approach to computing called soft computing. Soft computing techniques include neural networks, genetic algorithms, machine learning, probabilistic reasoning, and fuzzy logic.
2. Soft computing aims to develop intelligent machines that can solve real-world problems that are difficult to model mathematically. It exploits tolerance for uncertainty and imprecision similar to human decision making.
3. The document then discusses various soft computing techniques in more detail, including neural networks, genetic algorithms, fuzzy logic, and how they differ from traditional hard computing approaches.
Soft Computing is the fusion of methodologies that were designed to model and enable
solutions to real world problems, which are not modeled or too difficult to model, mathematically. Soft
computing is a consortium of methodologies that works synergistically and provides, in one form or
another, flexible information processing capability for handling real-life ambiguous situations. Its aim is to
exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to
achieve tractability, robustness and low-cost solutions. The guiding principle is to devise methods of
computation that lead to an acceptable solution at low cost, by seeking for an approximate solution to an
imprecisely or precisely formulated problem.Soft Computing (SC) represents a significant paradigm shift
in the aims of computing, which reflects the fact that the human mind, unlike present day computers,
possesses a remarkable ability to store and process information which is pervasively imprecise, uncertain
and lacking in categoricity. At this juncture, the principal constituents of Soft Computing (SC) are: Fuzzy
Systems (FS), including Fuzzy Logic (FL); Evolutionary Computation (EC), including Genetic
Algorithms (GA); Neural Networks (NN), including Neural Computing (NC); Machine Learning (ML);
and Probabilistic Reasoning (PR). In this paper we focus on fuzzy methodologies and fuzzy systems, as
they bring basic ideas to other SC methodologies
Hacking Predictive Modeling - RoadSec 2018HJ van Veen
This document provides an overview of machine learning and predictive modeling techniques for hackers and data scientists. It discusses foundational concepts in machine learning like functionalism, connectionism, and black box modeling. It also covers practical techniques like feature engineering, model selection, evaluation, optimization, and popular Python libraries. The document encourages an experimental approach to hacking predictive models through techniques like brute forcing hyperparameters, fuzzing with data permutations, and social engineering within data science communities.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...TechSoup
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
🔥🔥🔥🔥🔥🔥🔥🔥🔥
إضغ بين إيديكم من أقوى الملازم التي صممتها
ملزمة تشريح الجهاز الهيكلي (نظري 3)
💀💀💀💀💀💀💀💀💀💀
تتميز هذهِ الملزمة بعِدة مُميزات :
1- مُترجمة ترجمة تُناسب جميع المستويات
2- تحتوي على 78 رسم توضيحي لكل كلمة موجودة بالملزمة (لكل كلمة !!!!)
#فهم_ماكو_درخ
3- دقة الكتابة والصور عالية جداً جداً جداً
4- هُنالك بعض المعلومات تم توضيحها بشكل تفصيلي جداً (تُعتبر لدى الطالب أو الطالبة بإنها معلومات مُبهمة ومع ذلك تم توضيح هذهِ المعلومات المُبهمة بشكل تفصيلي جداً
5- الملزمة تشرح نفسها ب نفسها بس تكلك تعال اقراني
6- تحتوي الملزمة في اول سلايد على خارطة تتضمن جميع تفرُعات معلومات الجهاز الهيكلي المذكورة في هذهِ الملزمة
واخيراً هذهِ الملزمة حلالٌ عليكم وإتمنى منكم إن تدعولي بالخير والصحة والعافية فقط
كل التوفيق زملائي وزميلاتي ، زميلكم محمد الذهبي 💊💊
🔥🔥🔥🔥🔥🔥🔥🔥🔥
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
1. Soft Computing: An Overview
Arpana Sinhal
Asst. Professor,
Department of Computer Science & Engineering
AIETM, Jaipur
2. Soft Computing, What is it?
• The idea behind soft computing is to model cognitive
behavior of human mind.
• Soft computing is foundation of conceptual intelligence
in machines.
• Use inexact solution to computationally hard tasks (such as
solution for NP-Complete problems, for which there is no known
algorithm that can compute an exact solution in polynomialtime)
• Unlike hard computing, Soft computing is tolerant of
imprecision, uncertainty, partial truth, and approximation.
3. • The idea of softcomputing was initiated in 1981 when
Lofti A. Zadeh published his first paper on soft data
analysis “What is softcomputing”, softcomputing.
Springer-Verlag Germany/ USA.
SoftComputing, What isit?
Lofti A. Zedah, 1992:
“Softcomputing is an emerging approach to computing which
parallel the remarkable ability of human mind to reason and learn
in the environment of uncertainly and imprecision”
6. Hard computing
Deals with precise values
Accurate output is needed
Useful in critical systems
Soft computing
Deals with assumptions
Accuracy is not necessary
Useful for routine,control, decison making tasks
Hard Vs Soft Computing
7. Hard Vs Soft Computing
Hard Computing Soft Computing
Conventionalcomputing requiresa precisely
statedanalyticalmodel.
Softcomputing istolerant ofimprecision.
Often requiresalot of computationtime. Can solve some real world problems
in reasonablylesstime.
Not suited for real world problems for
which ideal model isnotpresent.
Suitablefor real worldproblems.
It requiresfulltruth Canwork with partialtruth
It isprecise andaccurate Imprecise.
Highcostfor solution Lowcostfor solution
Requireprogramsto bewritten Canevolveits own programs
Deterministic Stochasticorrandom
Requireexactinput Candealwith ambiguousandnoisydata
Produceprecise answer Produceapproximateanswers
usestwo-valuedlogic. canusemultivaluedorfuzzy logic
8. Aims of Soft Computing
• The main goal of soft computing is to develop intelligent
machines that provide solutions for real world problems,
which are not modeled/too difficult to model mathematically.
• It’s aim is to exploit the tolerance for Approximation,
Uncertainty, Imprecision, and Partial Truth in order to
achieve close resemblance with human like decision making.
• The guiding principle of soft computing is to exploit these
tolerance to achieve tractability, robustness and low solution
cost.
• The role model for soft computing is the human mind.
9. Models based on human reasoning.
Closer to human thinking and biologically
inspired
Models can be
Linguistic
Comprehensible
Fast when computing
Effective in practice.
Advantages of Soft Computing
10.
11. Applications of soft computing
There are several applications of soft computing where it is used.
Some of them are listed below:
• It is widely used in gaming products like Poker and Checker.
• In kitchen appliances, such as Microwave and Rice cooker.
• In most used home appliances - Washing Machine, Heater,
Refrigerator, and AC as well.
• Apart from all these usages, it is also used in Robotics
work (Emotional per Robot form).
• Image processing and Data compression are also popular
applications of soft computing.
• Used for handwriting recognition.
As we already said that, soft computing provides the solution to
real-time problems and here you can see that. Besides these
applications, there are many other applications of soft computing.
12. Soft Computing Components
Neural Networks
Neural Networks mimic certain processing capabilities of the human
brain and used for learning and adaption.
Fuzzy Logic
Multivalued Logic for treatment of imprecision and vagueness which
used for knowledge representation via fuzzy if-then rules.
Genetic Algorithms
Genetic Algorithms (GAs) are used for evolutionary computation and
to mimic some of the processes observed in natural evolution.
13. Heavy industry
• Robotic arms, Humanoid robots
Home appliances
• Washing machines, ACs,
Refrigerators, cameras
Automobiles
• Travel Speed Estimation, Sleep
Warning Systems, Driver-less cars
Spacecrafts
Maneuvering of a Space Shuttle (FL),
Optimization of Fuel-efficient Solutions for
space craft
APPLICATIONS OF SOFT COMPUTING
14. These methods have in common that :
• they are nonlinear,
• have ability to deal with non-linearities,
• follow more human-like reasoning paths than classical methods,
• utilize self-learning,
• utilize yet-to-be-proven theorems,
• are robust in the presence of noise or errors.
Soft computing is not a concoction, mixture, or combination, rather,
Soft computing is a partnership in which each of the partners
contributes a distinct methodology for addressing problems in its
domain.
In principal the constituent methodologies in Soft computing are
complementary rather than competitive.
Soft Computing Constituents
17. • Fuzzy set theory proposed in 1965 by Lotfi A.
Zadeh is a generalization of classical set theory.
• Uses numeric ranges of sets (fuzzy sets ) to
measure and represent the logical evaluations
of partially accurate findings.
• Most applications in control and decision
making.
FUZZY LOGIC
18. FUZZY LOGIC
In classical set theory, an element either belong to or
does not belong to a set and hence, such set are
termed as crisp set. But in fuzzy set, many degrees of
membership (between o/1) are allowed.
19. Fuzzy mathematics and Fuzzy Set Theory
• Fuzzy mathematics is the branch of mathematics including fuzzy set theory and
fuzzy logic that deals with partial inclusion of elements in a set on a spectrum, as
opposed to simple binary "yes" or "no" (0 or 1) inclusion.
• It started in 1965 after the publication of Lotfi Asker Zadeh's seminal work Fuzzy
sets.t Set Th
• The word “Fuzzy means “ambiguity” Fuzzy set theory permits membership function
valued in the interval{0/1}
• Fuzzy sets can be considered as an extension and gross over simplification of
classical sets.
• It can be best understood in the context of set membership.
• Basically it allows partial membership which means that it contain elements that
have varying degrees of membership in the set. From this, we can understand the
difference between classical set and fuzzy set.
• Classical set contains elements that satisfy precise properties of membership while
fuzzy set contains elements that satisfy imprecise properties of membership.
•
or
• ory
21. Basic concepts of Fuzzy Logics
• Support and Core of a Fuzzy Set
The support S(μ) of a fuzzy set μ ∈ F(X ) is the crisp set that
contains all elements of X that have nonzero membership. Formally
S(μ) = [μ] 0 = {x ∈ X | μ(x) > 0}.
The core C (μ) of a fuzzy set μ ∈ F(X ) is the crisp set that contains
all elements of X that have membership of one. Formally,
C (μ) = [μ] 1 = {x ∈ X | μ(x) = 1}.
Height of a Fuzzy Set
• Definition
The height h(μ) of a fuzzy set μ ∈ F(X ) is the largest membership
grade obtained by any element in that set. Formally,
h(μ) = sup μ(x).
• x ∈X
• h(μ) may also be viewed as supremum of α for which [μ]α is not equal to ∅.
• Definition
• A fuzzy set μ is called normal when h(μ) = 1.
• It is called subnormal when h(μ) < 1.
•
23. Fuzzy Relation
• Fuzzy relation operations are operations that are performed on
fuzzy relations. A fuzzy relation is a mathematical
representation of a relationship between objects where the
degree of the relationship is expressed as a value between 0
and 1. Some common fuzzy relation operations include
composition, union, intersection, and complement. These
operations allow for the manipulation of fuzzy relations in order
to reason about uncertainty and imprecision in data.
24. Operations on Fuzzy Set
1.Union Operation: The union operation of a fuzzy set is defined by:
μA∪B(x) = max (μA(x), μB(x))
2
2. Intersection Operation:The intersection operation of fuzzy set is defined by:
μA∩B(x) = min (μA(x), μB(x))
3. Complement Operation: The complement operation of fuzzy set is defined by:
μĀ(x) = 1-μA(x),
26. FUTURE SCOPE
• Soft Computing can be extended to include
bio-informatics aspects.
• Fuzzy system can be applied to the construction
of more advanced intelligent industrial systems.
• Soft computing is very effective when it’s applied
to real world problems which are not able to
solved by traditional hard computing.