INDEX
WHAT IS FIND-S ALGORITHM IN MACHINE LEARNING?.
HOW DOES IT WORK?.
Find-S Algorithm.
Implementation of Find-S Algorithm.
Limitations of Find-S Algorithm.
This document discusses time advance mechanisms in discrete event simulation. It describes next event time advance where the simulation clock advances to the next imminent event and is executed. It then continues until a stopping rule is satisfied, ignoring periods of inactivity. Fixed increment time advance advances the simulation clock by a fixed time increment Δt after each update. Any events scheduled during the previous interval are considered to occur at the end of that interval. The document also lists the common components of a discrete event simulation model, including the system state, simulation clock, event list, statistical counters, initialization routine, timing routine, event routine, library routines, and report generator.
Graph coloring is an important concept in graph theory. It is a special kind of problem in which we have assign colors to certain elements of the graph along with certain constraints. Suppose we are given K colors, we have to color the vertices in such a way that no two adjacent vertices of the graph have the same color, this is known as vertex coloring, similarly we have edge coloring and face coloring. The coloring problem has a huge number of applications in modern computer science such as making schedule of time table , Sudoku, Bipartite graphs , Map coloring, data mining, networking. In this paper we are going to focus on certain applications like Final exam timetabling, Aircraft Scheduling, guarding an art gallery.
The document discusses the 8-puzzle problem and the A* algorithm. The 8-puzzle problem involves a 3x3 grid with 8 numbered tiles and 1 blank space that can be moved. The A* algorithm maintains a tree of paths from the initial to final state, extending the paths one step at a time until the final state is reached. It is complete and optimal but depends on the accuracy of the heuristic used to estimate costs.
The document discusses parallel algorithms and parallel computing. It begins by defining parallelism in computers as performing more than one task at the same time. Examples of parallelism include I/O chips and pipelining of instructions. Common terms for parallelism are defined, including concurrent processing, distributed processing, and parallel processing. Issues in parallel programming such as task decomposition and synchronization are outlined. Performance issues like scalability and load balancing are also discussed. Different types of parallel machines and their classification are described.
this presentation is about planning process in AI. The presentation specifically explained POP(Partial order Planning). There are also another planning. In this presentation with help of an example the presentation is briefly explained the planning is done in AI
Concept learning and candidate elimination algorithmswapnac12
This document discusses concept learning, which involves inferring a Boolean-valued function from training examples of its input and output. It describes a concept learning task where each hypothesis is a vector of six constraints specifying values for six attributes. The most general and most specific hypotheses are provided. It also discusses the FIND-S algorithm for finding a maximally specific hypothesis consistent with positive examples, and its limitations in dealing with noise or multiple consistent hypotheses. Finally, it introduces the candidate-elimination algorithm and version spaces as an improvement over FIND-S that can represent all consistent hypotheses.
The document outlines the various workflows that make up the software development process, including management, environment, requirements, design, implementation, assessment, and deployment workflows. It describes the key activities for each workflow, such as controlling the process, evolving requirements and design artifacts, programming components, assessing product quality, and transitioning the product to users. The document also notes that iterations consist of sequential activities that vary depending on where an iteration falls in the development cycle.
This document discusses time advance mechanisms in discrete event simulation. It describes next event time advance where the simulation clock advances to the next imminent event and is executed. It then continues until a stopping rule is satisfied, ignoring periods of inactivity. Fixed increment time advance advances the simulation clock by a fixed time increment Δt after each update. Any events scheduled during the previous interval are considered to occur at the end of that interval. The document also lists the common components of a discrete event simulation model, including the system state, simulation clock, event list, statistical counters, initialization routine, timing routine, event routine, library routines, and report generator.
Graph coloring is an important concept in graph theory. It is a special kind of problem in which we have assign colors to certain elements of the graph along with certain constraints. Suppose we are given K colors, we have to color the vertices in such a way that no two adjacent vertices of the graph have the same color, this is known as vertex coloring, similarly we have edge coloring and face coloring. The coloring problem has a huge number of applications in modern computer science such as making schedule of time table , Sudoku, Bipartite graphs , Map coloring, data mining, networking. In this paper we are going to focus on certain applications like Final exam timetabling, Aircraft Scheduling, guarding an art gallery.
The document discusses the 8-puzzle problem and the A* algorithm. The 8-puzzle problem involves a 3x3 grid with 8 numbered tiles and 1 blank space that can be moved. The A* algorithm maintains a tree of paths from the initial to final state, extending the paths one step at a time until the final state is reached. It is complete and optimal but depends on the accuracy of the heuristic used to estimate costs.
The document discusses parallel algorithms and parallel computing. It begins by defining parallelism in computers as performing more than one task at the same time. Examples of parallelism include I/O chips and pipelining of instructions. Common terms for parallelism are defined, including concurrent processing, distributed processing, and parallel processing. Issues in parallel programming such as task decomposition and synchronization are outlined. Performance issues like scalability and load balancing are also discussed. Different types of parallel machines and their classification are described.
this presentation is about planning process in AI. The presentation specifically explained POP(Partial order Planning). There are also another planning. In this presentation with help of an example the presentation is briefly explained the planning is done in AI
Concept learning and candidate elimination algorithmswapnac12
This document discusses concept learning, which involves inferring a Boolean-valued function from training examples of its input and output. It describes a concept learning task where each hypothesis is a vector of six constraints specifying values for six attributes. The most general and most specific hypotheses are provided. It also discusses the FIND-S algorithm for finding a maximally specific hypothesis consistent with positive examples, and its limitations in dealing with noise or multiple consistent hypotheses. Finally, it introduces the candidate-elimination algorithm and version spaces as an improvement over FIND-S that can represent all consistent hypotheses.
The document outlines the various workflows that make up the software development process, including management, environment, requirements, design, implementation, assessment, and deployment workflows. It describes the key activities for each workflow, such as controlling the process, evolving requirements and design artifacts, programming components, assessing product quality, and transitioning the product to users. The document also notes that iterations consist of sequential activities that vary depending on where an iteration falls in the development cycle.
A clear visualization of RGB and CMY color model. How they work and what are their color elements.At the end, you also find the equation of calculating and converting them.
This slides contains assymptotic notations, recurrence relation like subtitution method, iteration method, master method and recursion tree method and sorting algorithms like merge sort, quick sort, heap sort, counting sort, radix sort and bucket sort.
This document discusses video motion analysis techniques. It first defines a motion field as a 2D representation of 3D motion in a scene, where each point is assigned a velocity vector. It then discusses motion templates, which track general movement and are useful for gesture recognition. Motion templates require object silhouettes, which can be obtained through frame differencing, chroma keying using known backgrounds, or active silhouetting using infrared light. The document also discusses estimating motion fields through techniques like matching blocks, objects, or interest points, but that optic flow provides the densest motion field by matching pixel intensities, though it has problems with textureless image areas.
A multi-head Turing machine has a single tape with multiple heads that can read and write to the tape independently. Each head can move left, right, or stay in the same position. This type of Turing machine is as powerful as a standard single-tape Turing machine.
The halting problem asks if it is possible to determine if a Turing machine will halt or run infinitely given its program and input. It is proven to be unsolvable - there is no general algorithm that can correctly determine if all Turing machine programs will halt for all inputs.
The document discusses different approaches for concept learning from examples, including viewing it as a search problem to find the hypothesis that best fits the training examples. It also describes the general-to-specific learning approach, where the goal is to find the maximally specific hypothesis consistent with the positive training examples by starting with the most general hypothesis and replacing constraints to better fit the examples. The document also discusses the version space and candidate elimination algorithms for obtaining the version space of all hypotheses consistent with the training data.
Symmetric encryption uses the same key for both encryption and decryption. It has five components: plaintext, encryption algorithm, secret key, ciphertext, and decryption algorithm. The security depends on keeping the key secret. Symmetric encryption is classified by the type of operations used, number of keys, and how plaintext is processed. Common symmetric algorithms like DES and AES encrypt plaintext in blocks using a substitution-permutation network structure.
This document discusses bottom-up parsing and handle pruning. It begins with an example grammar and string. Bottom-up parsing works by repeatedly reducing substrings matching production bodies until reaching the start symbol. A handle is a substring matching a production body whose reduction moves backward through a rightmost derivation. Handle pruning is removing nonterminal children from a parse tree, allowing reconstruction of a rightmost derivation by working backward from handles. The document provides steps for using handle pruning to rebuild a rightmost derivation in reverse from a given string. An example demonstrates finding the handles in a string and reducing based on the corresponding productions to reach the start symbol.
The document discusses Halstead's software science measures for analyzing programs. It defines key metrics like program vocabulary, length, volume, difficulty level, and effort required. These metrics are calculated based on the number of unique operators and operands in a program. Formulas are provided to estimate values of length, volume, level and effort based on operator and operand counts. An example C program is analyzed to demonstrate calculating these metrics.
The document summarizes key concepts in machine learning including concept learning as search, general-to-specific learning, version spaces, candidate elimination algorithm, and decision trees. It discusses how concept learning can be viewed as searching a hypothesis space to find the hypothesis that best fits the training examples. The candidate elimination algorithm represents the version space using the most general and specific hypotheses to efficiently learn from examples.
This document discusses programming techniques for Turing machines (TMs), including storing data in states, using multiple tracks, and implementing subroutines. It also covers extensions to basic TMs, such as multitape and nondeterministic TMs. Restricted TMs like those with semi-infinite tapes, stack machines, and counter machines are also examined. Finally, the document informally argues that TMs and modern computers are equally powerful models of computation.
Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space.
It helps us to decide which rule has to apply next without getting stuck at any point.
Characteristics of Control Strategies
A good Control strategy has two main
characteristics:
Control Strategy should cause Motion
Control strategy should be Systematic
Co ntrol Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Co ntrol Strategy should be Systematic
Though the strategy applied should create the
motion but if do not follow some systematic
strategy than we are likely to reach the same state
number of times before reaching the solution
which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
Non-monotonic reasoning allows conclusions to be retracted when new information is introduced. It is used to model plausible reasoning where defaults may be overridden. For example, it is typically true that birds fly, so we could conclude that Tweety flies since Tweety is a bird. However, if we are later told Tweety is a penguin, we would retract the conclusion that Tweety flies since penguins do not fly despite being birds. Non-monotonic reasoning resolves inconsistencies by removing conclusions derived from default rules when specific countervailing information is received.
The document discusses key concepts and principles of software engineering practice. It covers the software development lifecycle including requirements analysis, planning, modeling, construction, testing, and deployment. It provides guidance on best practices for communication, modeling, design, coding, testing, and project management. The overall aim of software engineering is to develop reliable, maintainable and usable software that meets customer requirements.
This document discusses different types of voting protocols used in distributed systems:
Static voting protocols proposed by Gifford achieve consensus by replicating data across multiple sites and obtaining the data from other copies if the original fails. Majority-based dynamic voting protocols proposed by Jajodia and Mutchler use version numbers, number of replicas updated, and distinguished site lists to determine consensus. Group-based voting mechanisms proposed by Agarwal and Jalote divide sites into intersecting groups to reduce communication costs when initiating operations.
This document outlines the 10 step process for software project planning. It begins with selecting the project and identifying its scope and objectives. It then covers identifying the project infrastructure, analyzing project characteristics, and identifying products and activities. Steps also include estimating effort for each activity, identifying risks, allocating resources, and reviewing/publicizing the plan. Execution then involves lower level planning. The document also discusses software effort estimation techniques such as algorithmic models, expert judgment, analogy, and top-down and bottom-up approaches.
Parallel and distributed computing allows problems to be broken into discrete parts that can be solved simultaneously. This approach utilizes multiple processors that work concurrently on different parts of the problem. There are several types of parallel architectures depending on how instructions and data are distributed across processors. Shared memory systems give all processors access to a common memory space while distributed memory assigns private memory to each processor requiring explicit data transfer. Large-scale systems may combine these approaches into hybrid designs. Distributed systems extend parallelism across a network and provide users with a single, integrated view of geographically dispersed resources and computers. Key challenges for distributed systems include transparency, scalability, fault tolerance and concurrency.
Informed search algorithms use heuristics to more efficiently find goal nodes in large search spaces. Heuristics estimate how close a state is to the goal and help guide the search. The heuristic function must be admissible, meaning its estimated cost must be less than or equal to the actual cost. Bayes' theorem allows calculating conditional probabilities and is fundamental to probabilistic reasoning, which represents knowledge with uncertainty using probabilities. Fuzzy set theory introduces vagueness by assigning membership degrees between 0 and 1 to represent how well something belongs to a set, like how sunny a day is based on cloud cover.
The document introduces the Find-S algorithm which generates a general hypothesis from positive examples by starting with the most specific hypothesis and replacing attribute values that differ from positive examples with '?' to accommodate more examples. It works by only considering positive examples and eliminating negative examples. However, the algorithm cannot check if the hypothesis is consistent with all data and may be misled by inconsistent training sets since it ignores negative examples. It also lacks backtracking to determine the best changes to improve the hypothesis.
A clear visualization of RGB and CMY color model. How they work and what are their color elements.At the end, you also find the equation of calculating and converting them.
This slides contains assymptotic notations, recurrence relation like subtitution method, iteration method, master method and recursion tree method and sorting algorithms like merge sort, quick sort, heap sort, counting sort, radix sort and bucket sort.
This document discusses video motion analysis techniques. It first defines a motion field as a 2D representation of 3D motion in a scene, where each point is assigned a velocity vector. It then discusses motion templates, which track general movement and are useful for gesture recognition. Motion templates require object silhouettes, which can be obtained through frame differencing, chroma keying using known backgrounds, or active silhouetting using infrared light. The document also discusses estimating motion fields through techniques like matching blocks, objects, or interest points, but that optic flow provides the densest motion field by matching pixel intensities, though it has problems with textureless image areas.
A multi-head Turing machine has a single tape with multiple heads that can read and write to the tape independently. Each head can move left, right, or stay in the same position. This type of Turing machine is as powerful as a standard single-tape Turing machine.
The halting problem asks if it is possible to determine if a Turing machine will halt or run infinitely given its program and input. It is proven to be unsolvable - there is no general algorithm that can correctly determine if all Turing machine programs will halt for all inputs.
The document discusses different approaches for concept learning from examples, including viewing it as a search problem to find the hypothesis that best fits the training examples. It also describes the general-to-specific learning approach, where the goal is to find the maximally specific hypothesis consistent with the positive training examples by starting with the most general hypothesis and replacing constraints to better fit the examples. The document also discusses the version space and candidate elimination algorithms for obtaining the version space of all hypotheses consistent with the training data.
Symmetric encryption uses the same key for both encryption and decryption. It has five components: plaintext, encryption algorithm, secret key, ciphertext, and decryption algorithm. The security depends on keeping the key secret. Symmetric encryption is classified by the type of operations used, number of keys, and how plaintext is processed. Common symmetric algorithms like DES and AES encrypt plaintext in blocks using a substitution-permutation network structure.
This document discusses bottom-up parsing and handle pruning. It begins with an example grammar and string. Bottom-up parsing works by repeatedly reducing substrings matching production bodies until reaching the start symbol. A handle is a substring matching a production body whose reduction moves backward through a rightmost derivation. Handle pruning is removing nonterminal children from a parse tree, allowing reconstruction of a rightmost derivation by working backward from handles. The document provides steps for using handle pruning to rebuild a rightmost derivation in reverse from a given string. An example demonstrates finding the handles in a string and reducing based on the corresponding productions to reach the start symbol.
The document discusses Halstead's software science measures for analyzing programs. It defines key metrics like program vocabulary, length, volume, difficulty level, and effort required. These metrics are calculated based on the number of unique operators and operands in a program. Formulas are provided to estimate values of length, volume, level and effort based on operator and operand counts. An example C program is analyzed to demonstrate calculating these metrics.
The document summarizes key concepts in machine learning including concept learning as search, general-to-specific learning, version spaces, candidate elimination algorithm, and decision trees. It discusses how concept learning can be viewed as searching a hypothesis space to find the hypothesis that best fits the training examples. The candidate elimination algorithm represents the version space using the most general and specific hypotheses to efficiently learn from examples.
This document discusses programming techniques for Turing machines (TMs), including storing data in states, using multiple tracks, and implementing subroutines. It also covers extensions to basic TMs, such as multitape and nondeterministic TMs. Restricted TMs like those with semi-infinite tapes, stack machines, and counter machines are also examined. Finally, the document informally argues that TMs and modern computers are equally powerful models of computation.
Control Strategies
Control Strategy in Artificial Intelligence
scenario is a technique or strategy, tells us about which rule has to be applied next while searching for the solution of a problem within problem space.
It helps us to decide which rule has to apply next without getting stuck at any point.
Characteristics of Control Strategies
A good Control strategy has two main
characteristics:
Control Strategy should cause Motion
Control strategy should be Systematic
Co ntrol Strategy should cause Motion
Each rule or strategy applied should cause the motion because if there will be no motion than such control strategy will never lead to a solution. Motion states about the change of state and if a state will not change then there be no movement from an initial state and we would never solve the problem.
Co ntrol Strategy should be Systematic
Though the strategy applied should create the
motion but if do not follow some systematic
strategy than we are likely to reach the same state
number of times before reaching the solution
which increases the number of steps. Taking care of only first strategy we may go through particular useless sequences of operators several times. Control Strategy should be systematic implies a need for global motion as well as for local motion.
Non-monotonic reasoning allows conclusions to be retracted when new information is introduced. It is used to model plausible reasoning where defaults may be overridden. For example, it is typically true that birds fly, so we could conclude that Tweety flies since Tweety is a bird. However, if we are later told Tweety is a penguin, we would retract the conclusion that Tweety flies since penguins do not fly despite being birds. Non-monotonic reasoning resolves inconsistencies by removing conclusions derived from default rules when specific countervailing information is received.
The document discusses key concepts and principles of software engineering practice. It covers the software development lifecycle including requirements analysis, planning, modeling, construction, testing, and deployment. It provides guidance on best practices for communication, modeling, design, coding, testing, and project management. The overall aim of software engineering is to develop reliable, maintainable and usable software that meets customer requirements.
This document discusses different types of voting protocols used in distributed systems:
Static voting protocols proposed by Gifford achieve consensus by replicating data across multiple sites and obtaining the data from other copies if the original fails. Majority-based dynamic voting protocols proposed by Jajodia and Mutchler use version numbers, number of replicas updated, and distinguished site lists to determine consensus. Group-based voting mechanisms proposed by Agarwal and Jalote divide sites into intersecting groups to reduce communication costs when initiating operations.
This document outlines the 10 step process for software project planning. It begins with selecting the project and identifying its scope and objectives. It then covers identifying the project infrastructure, analyzing project characteristics, and identifying products and activities. Steps also include estimating effort for each activity, identifying risks, allocating resources, and reviewing/publicizing the plan. Execution then involves lower level planning. The document also discusses software effort estimation techniques such as algorithmic models, expert judgment, analogy, and top-down and bottom-up approaches.
Parallel and distributed computing allows problems to be broken into discrete parts that can be solved simultaneously. This approach utilizes multiple processors that work concurrently on different parts of the problem. There are several types of parallel architectures depending on how instructions and data are distributed across processors. Shared memory systems give all processors access to a common memory space while distributed memory assigns private memory to each processor requiring explicit data transfer. Large-scale systems may combine these approaches into hybrid designs. Distributed systems extend parallelism across a network and provide users with a single, integrated view of geographically dispersed resources and computers. Key challenges for distributed systems include transparency, scalability, fault tolerance and concurrency.
Informed search algorithms use heuristics to more efficiently find goal nodes in large search spaces. Heuristics estimate how close a state is to the goal and help guide the search. The heuristic function must be admissible, meaning its estimated cost must be less than or equal to the actual cost. Bayes' theorem allows calculating conditional probabilities and is fundamental to probabilistic reasoning, which represents knowledge with uncertainty using probabilities. Fuzzy set theory introduces vagueness by assigning membership degrees between 0 and 1 to represent how well something belongs to a set, like how sunny a day is based on cloud cover.
The document introduces the Find-S algorithm which generates a general hypothesis from positive examples by starting with the most specific hypothesis and replacing attribute values that differ from positive examples with '?' to accommodate more examples. It works by only considering positive examples and eliminating negative examples. However, the algorithm cannot check if the hypothesis is consistent with all data and may be misled by inconsistent training sets since it ignores negative examples. It also lacks backtracking to determine the best changes to improve the hypothesis.
The Find-S algorithm finds the most specific hypothesis that is consistent with positive training examples by starting with the most specific hypothesis and gradually generalizing it only as far as needed to be consistent with each new positive example seen. The final hypothesis output by Find-S will be the most specific hypothesis within the hypothesis space that is consistent with all positive examples, and also consistent with negative examples if the target concept is representable. Consistency means the hypothesis agrees with all training examples - it outputs the correct label for each example.
Chapter 20 and 21 combined testing hypotheses about proportions 2013calculistictt
This document discusses hypotheses testing and the reasoning behind it. It explains that hypotheses testing involves proposing a null hypothesis and an alternative hypothesis based on a parameter of interest. Data is then analyzed to either reject or fail to reject the null hypothesis. Specifically:
1) The null hypothesis proposes a baseline model or value for a parameter.
2) Statistics are calculated based on the data and compared to what we would expect if the null hypothesis is true.
3) If the results are inconsistent enough with the null hypothesis, we can reject it in favor of the alternative hypothesis. Otherwise we fail to reject the null hypothesis.
The goal is to quantify how unlikely the results would be if the null hypothesis is true,
1. The document discusses machine learning and provides an overview of key concepts like inductive reasoning, learning from examples, and the constituents of machine learning problems.
2. It explains that machine learning problems involve an example set, background concepts, background axioms, and potential errors in data. Common machine learning tasks are categorization and prediction.
3. The document also outlines the constituents of machine learning methods, including representation schemes, search methods, and approaches for selecting hypotheses when multiple solutions are produced.
1. The document discusses machine learning and provides an overview of key concepts like inductive reasoning, learning from examples, and the constituents of machine learning problems.
2. It explains that machine learning problems involve an example set, background concepts, background axioms, and potential errors in data. Common machine learning tasks are categorization and prediction.
3. The document also outlines the constituents of machine learning methods, including representation schemes, search methods, and approaches for selecting hypotheses when multiple solutions are produced.
The document discusses different types of reasoning in artificial intelligence, including deductive reasoning, inductive reasoning, abductive reasoning, common sense reasoning, monotonic reasoning, and non-monotonic reasoning. Deductive reasoning uses known facts to logically deduce conclusions, while inductive reasoning uses specific observations to derive general conclusions. The key difference between deductive and inductive reasoning is that deductive reasoning conclusions are certain if the premises are true, whereas inductive conclusions are probable even if the premises are true.
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
This document provides guidance on writing mathematical proofs. It defines key terms like direct proof and counterexample. It also outlines the standard format for proofs, including stating the theorem, clearly marking the beginning and end, and providing justification for each step. Common mistakes to avoid are arguing from examples rather than a general case, using the same variable to mean different things, and jumping to a conclusion without proper justification.
This document summarizes lecture notes from Andrew Ng on learning theory. It discusses the bias-variance tradeoff in machine learning models and introduces key concepts like generalization error, training error, and hypothesis classes. The document proves that if the hypothesis class H is finite, then with high probability the training error of all hypotheses in H will be close to their true generalization errors, provided the training set is sufficiently large. This uniform convergence guarantee allows relating the performance of the empirical risk minimization algorithm to the best possible hypothesis in H.
The document summarizes key concepts in machine learning, including defining learning, types of learning (induction vs discovery, guided learning vs learning from raw data, etc.), generalisation and specialisation, and some simple learning algorithms like Find-S and the candidate elimination algorithm. It discusses how learning can be viewed as searching a generalisation hierarchy to find a hypothesis that covers the examples. The candidate elimination algorithm maintains the version space - the set of hypotheses consistent with the training examples - by updating the general and specific boundaries as new examples are processed.
The document discusses hypothesis testing and statistical inference. It begins by defining two types of statistical inference - hypothesis testing and parameter estimation. Hypothesis testing determines if sample data is consistent with a hypothesized population parameter, while parameter estimation provides an approximate value of the population parameter.
It then discusses key aspects of hypothesis testing, including stating the null and alternative hypotheses, developing an analysis plan, analyzing sample data, and deciding whether to accept or reject the null hypothesis. Examples are provided to illustrate hypothesis testing methodology and key concepts like p-values, significance levels, directional versus non-directional hypotheses, and applying the steps of hypothesis testing to evaluate a research study's results.
This document introduces Packed Computation, a new approach for solving NP-complete problems. It proposes reducing NP-complete problems to the Rules States Satisfiability (RSS) problem and using Packed Computation algorithms to solve RSS problems quickly. The document outlines the author's research from 2008-2014 in developing this approach. It defines RSS as an NP-complete problem and describes how Packed Computation algorithms work by sometimes selecting single states and sometimes selecting multiple "packed" states. While the algorithms show promise in testing, a mathematical proof of their polynomial runtime complexity is still outstanding. The document serves as an outline for the author's proposed new approach but does not yet prove its mathematical properties.
The document discusses the difference between concept learning and rote learning. Concept learning involves detecting the underlying attributes that define a concept, allowing generalization to new examples. An experiment with chimpanzees demonstrated this by their ability to correctly respond to novel stimuli following concept discrimination training, but not following rote training where the concept was more abstract and involved responding to a number rather than a specific attribute.
A heuristic is a technique for finding an approximate solution to a problem when classic methods fail or are too slow. It sacrifices completeness to increase efficiency. A heuristic search generates and tests possible solutions until it finds one that is good enough. Hill climbing is an example that uses a heuristic function to evaluate states and move to successors that are closer to the goal. It does not backtrack or remember past states, moving only to locally optimal successors until reaching a peak state.
The document discusses machine learning and different types of learning problems. It begins by explaining that machine learning allows systems to learn knowledge that engineers may not know how to provide. It then describes several types of learning including supervised learning (prediction from labeled examples), clustering (finding natural groupings of unlabeled data), and reinforcement learning (learning from rewards/penalties). The document provides examples of different learning problems and algorithms like decision trees. It emphasizes that the goal of learning is to find patterns in data and make accurate predictions, especially for previously unseen examples.
Types of "T-Test" - Research Methodology, Hypothesis Testing
Hypothesis (अनुमानम्) is a predictive statement, capable of being tested by scientific methods, that relates an independent variable to some dependent variable.
The t-test compares the actual difference between two means in relation to the variation in the data (expressed as the standard deviation of the difference between the means).
This document discusses the traditional problem of induction and attempts to justify the inductive method. It presents Hume's view that induction cannot be justified since we cannot infer general laws from specific cases. Two options are considered: obtain knowledge non-inductively or accept induction is irrational. Popper argues for the first option, proposing scientific theories are conjectures subject to falsification, not verification. He claims induction is not needed if we tentatively accept the best theories until falsified. While this avoids Hume's problem, critics argue falsifiability is too weak a criterion and background assumptions are needed for tests. Overall, the document examines Hume's skepticism of induction and Popper's attempt to justify scientific reasoning without relying on induction
Is there any a novel best theory for uncertainty? Andino Maseleno
The document discusses several uncertainty modeling techniques including fuzzy logic, Dempster-Shafer theory, neural networks, and genetic algorithms. It provides background on each technique, how they were developed and applied to problems. Fuzzy logic allows for imprecise variables rather than binary true/false. Dempster-Shafer theory generalizes probability to assign beliefs to sets rather than individual outcomes. Neural networks learn from examples via weighted connections. Genetic algorithms use evolutionary principles to optimize solutions. These soft computing methods complement each other and help model real-world uncertainty.
Genetic algorithms are a search method that uses principles of natural selection and evolution to find optimal solutions to problems. They work by generating an initial population of random solutions, then selecting the fittest to breed a new generation by combining traits and mutating genes. This process is repeated until a satisfactory solution emerges. The eight queens problem can be solved using a genetic algorithm approach by representing board configurations as individuals, evaluating their fitness based on non-attacking queens, breeding the fittest to produce new configurations, and repeating until a solution is found with all queens non-attacking. Semantic networks represent knowledge through nodes and relationships like IS-A and HAS to model how concepts are related, allowing inference of new facts by traversing links
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
2. WHAT IS FIND-S ALGORITHM IN
MACHINE LEARNING?
2
The find-S algorithm is a basic concept
learning algorithm in machine learning.
The find-S technique identifies the
hypothesis that best matches all of the
positive cases. The find-S algorithm
considers only positive cases.
• When the find-S method fails to
categorize observed positive training
data, it starts with the most particular
hypothesis and generalizes it.
3. HOW DOES IT WORK?
3
1.The process starts with initializing ‘h’
with the most specific hypothesis, generally,
it is the first positive example in the data
set.
2.We check for each positive example. If the
example is negative, we will move on to the
next example but if it is a positive example
we will consider it for the next step
3.We will check if each attribute in the
example is equal to the hypothesis value. 4.If
the value matches, then no changes are made.
5.If the value does not match, the value is
changed to ‘?’.
6.We do this until we reach the last positive
example in the data set.
4. Find-S Algorithm
4
1.Initialize h to the most specific
hypothesis in H.
2.For each positive training
instance x For each attribute
constraint a, in h If the constraint
a, is satisfied by x Then do nothing
Else replace a, in h by the next
more general constraint that is
satisfied by x
3.Output hypothesis h
5. Implementation of Find-S
Algorithm
5
To understand the implementation, let us try
to implement it to a smaller data set with a
bunch of examples to decide if a person wants
to go for a walk.
The concept of this particular problem will
be on what days does a person likes to go on
a walk.
6. 6
Looking at the data set, we have six attributes and a final
attribute that defines the positive or negative example. In
this case, yes is a positive example, which means the person
will go for a walk.
So now, the general hypothesis is:
h0 = {‘Morning’, ‘Sunny’, ‘Warm’, ‘Yes’, ‘Mild’, ‘Strong’}
This is our general hypothesis, and now we will consider
each example one by one, but only the positive examples.
h1= {‘Morning’, ‘Sunny’, ‘?’, ‘Yes’, ‘?’, ‘?’}
h2 = {‘?’, ‘Sunny’, ‘?’, ‘Yes’, ‘?’, ‘?’}
7. Limitations of Find-S Algorithm
7
here are a few limitations of the Find-S
algorithm listed down below:
1.There is no way to determine if the
hypothesis is consistent throughout the data.
2. Inconsistent training sets can actually
mislead the Find-S algorithm since it ignores
the negative examples.
3. The find-S algorithm does not provide a
backtracking technique to determine the best
possible changes that could be done to