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Chap.3 Knowledge Representation Issues Chap.4 Inference in First Order Logic
Knowledge–based agents,
The Wumpus world Logic,
Propositional logic,
Propositional theorem proving
Effective propositional model checking,
Agents based on propositional logic,
First Order Logic,
Forward Chaining/ Resolution,
Backward Chaining/ Resolution,
Unification Algorithm, Resolution,
Clausal Normal Form (CNF)
Artificial Intelligence Chap.5 : Uncertainty
Uncertainty,
Acting under uncertainty,
Basic probability notation,
Bayes’ Rule,
learning with complete data.pdf
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise boosts blood flow and levels of neurotransmitters and endorphins which elevate and stabilize mood.
CV_Chap 6 Motion Representation
Motion Representation
The motion field of rigid objects
Motion parallax
Optical flow
The image brightness constancy equation
Affine flow
Differential techniques
Feature-based techniques
Regularization and robust estimation
Cv_Chap 4 Segmentation
Segmentation
Active Contours
Split and Merge
Watershed
Region Splitting and Merging
Graph-based Segmentation
Mean shift and Model finding
Normalized Cut
CV_Chap 3 Features Detection
Features Detection
Edge Detection
Corner Detection
Line and Curve Detection
Active Contours
SIFT and HOG Descriptors
Shape Context Descriptors
Morphological Operations
CV_2 Fourier_Transformation
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
Recommended
Chap.3 Knowledge Representation Issues Chap.4 Inference in First Order Logic
Knowledge–based agents,
The Wumpus world Logic,
Propositional logic,
Propositional theorem proving
Effective propositional model checking,
Agents based on propositional logic,
First Order Logic,
Forward Chaining/ Resolution,
Backward Chaining/ Resolution,
Unification Algorithm, Resolution,
Clausal Normal Form (CNF)
Artificial Intelligence Chap.5 : Uncertainty
Uncertainty,
Acting under uncertainty,
Basic probability notation,
Bayes’ Rule,
learning with complete data.pdf
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise boosts blood flow and levels of neurotransmitters and endorphins which elevate and stabilize mood.
CV_Chap 6 Motion Representation
Motion Representation
The motion field of rigid objects
Motion parallax
Optical flow
The image brightness constancy equation
Affine flow
Differential techniques
Feature-based techniques
Regularization and robust estimation
Cv_Chap 4 Segmentation
Segmentation
Active Contours
Split and Merge
Watershed
Region Splitting and Merging
Graph-based Segmentation
Mean shift and Model finding
Normalized Cut
CV_Chap 3 Features Detection
Features Detection
Edge Detection
Corner Detection
Line and Curve Detection
Active Contours
SIFT and HOG Descriptors
Shape Context Descriptors
Morphological Operations
CV_2 Fourier_Transformation
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
CV_2 Image Processing
Pixel transforms,
Color transforms,
Histogram processing & equalization ,
Filtering,
Convolution,
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Blurring and noise removal
CV_1 Introduction of Computer Vision and its Application
Image Formation and Representation:
Imaging geometry,
radiometry,
digitization,
cameras and Projections,
rigid and affine transformation
ADA_2_Analysis of Algorithms
Frequency Count, Case Analysis (Average, Best, Worst), Notations: (Big oh, Omega, Theta), Substitution Method, Master Method, Sorting Algorithm: (Bubble Sort, Insertion Sort, Selection Sort, , Shell Sort, Heap Sort, Bucket Sort, Radix Sort, Counting Sort)
ADA_1 Introduction of Algorithm
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise stimulates the production of endorphins in the brain which can help alleviate feelings of stress or sadness.
AI_11 Game playing
Games planning, Mini Max Algorithm,. Alpha beta Pruning, Graph Plan, Goal Stack Planning
AI_11 Understanding
The document discusses understanding in artificial intelligence. It defines understanding as the process of simulating human intelligence through machine learning and algorithms. Machine learning can be supervised, involving labeled training examples to minimize error, or unsupervised, where the system finds common characteristics in unlabeled data. Understanding is also discussed in relation to constraint satisfaction problems, where conditions must be met, like in map coloring or Sudoku puzzles. Backtracking is used to systematically search for solutions that satisfy all constraints.
AI_ 8 Weak Slot and Filler Structure
This document discusses weak slot and filler structures in artificial intelligence. It describes semantic net representation, which represents knowledge as a graphical network of nodes and arcs. It provides examples of representing statements about a cat named Jerry in a semantic net. The document also discusses frame representation, which organizes knowledge into structured records called frames that contain slots and slot values. An example frame is provided for a person named Ram. Advantages and disadvantages of both semantic nets and frames are outlined.
AI_7 Statistical Reasoning
This document provides an overview of uncertainty in artificial intelligence and probabilistic reasoning. It discusses sources of uncertainty like uncertain input, knowledge, and output. Probabilistic reasoning uses probability to represent uncertain knowledge. The document introduces basic probability notation including propositions, atomic events, unconditional probability, conditional probability, independence, and Bayes' rule. It explains how to perform inference using full joint probability distributions and marginalization. The document was prepared by Prof. Khushali B Kathiriya and provides an introduction to representing and reasoning with uncertainty in AI systems.
AI_6 Uncertainty
This document discusses symbolic reasoning under uncertainty. It introduces monotonic reasoning, where conclusions remain valid even when new information is added, and non-monotonic reasoning, where conclusions can be invalidated by new information. For non-monotonic reasoning, it provides an example where concluding a bird can fly is invalidated by learning the bird is a penguin. The document is presented by Prof. Khushali B Kathiriya and outlines introduction to monotonic reasoning, introduction to non-monotonic reasoning, and an example of non-monotonic reasoning logic.
AI_5 Resolution/ Channing
The document discusses knowledge representation using rules in artificial intelligence. It covers procedural versus declarative knowledge, forward chaining and backward chaining. Forward chaining starts with known facts and applies rules to reach a goal, working from bottom to top. Backward chaining starts at the goal and works backwards through rules to find supporting facts, using a top-down approach. An example of selling missiles to prove someone is a criminal is used to illustrate both forward and backward chaining techniques.
AI_ 3 & 4 Knowledge Representation issues
This document discusses knowledge representation in artificial intelligence. It covers various techniques for knowledge representation including logical representation using propositional logic and first-order predicate logic, semantic network representation, frame representation, and production rules. It also discusses issues in knowledge representation such as representing important attributes, relationships, and granularity of knowledge. Propositional logic is introduced as the simplest form of logic where statements are represented by propositions that can be either true or false. The syntax and semantics of propositional logic are also covered.
AI_1 Introduction of AI
Here are the key AI techniques discussed in the document:
- Tree searching techniques like depth-first search, breadth-first search, uniform cost search, A* search, and heuristic search methods.
- Rule-based systems that apply rules to deduce conclusions.
- Constraint satisfaction techniques that find solutions that satisfy constraints.
- Generate and test approaches that generate candidate solutions and test them against requirements.
- Description and matching techniques that describe states and match them to goals.
- Goal reduction techniques that hierarchically reduce goals to subgoals.
The document discusses these techniques as common approaches used to solve different types of AI problems. It provides examples but does not go into detailed explanations of each technique.
DDBMS_ Chap 9 Distributed Deadlock & Recovery Deadlock concept
Deadlock in Centralized systems, Deadlock in Distributed Systems – Detection, Prevention, Avoidance,
Wait-Die Algorithm, Wound-Wait algorithm
Recovery in DBMS - Types of Failure, Methods to control failure, Different techniques of recoverability, Write- Ahead logging Protocol, Advanced recovery techniques- Shadow Paging, Fuzzy checkpoint, ARIES, RAID levels,
Two Phase and Three Phase commit protocols
DDBMS_ Chap 8 Distributed Transaction Management & Concurrency Control
Transaction concept, ACID property, Types of transactions,
Objectives of Distributed Concurrency Control,
Concurrency Control anomalies, Methods of concurrency control, Serializability and recoverability,
Distributed Serializability, Enhanced lock based and timestamp based protocols, Multiple granularity, Multi version schemes,
Optimistic Concurrency Control techniques
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
BRAIN TUMOR DETECTION for seminar ppt.pdf
BRAIN TUMOR DETECTION
AND CLASSIFICATION USING
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CV_2 Image Processing
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CV_1 Introduction of Computer Vision and its Application
Image Formation and Representation:
Imaging geometry,
radiometry,
digitization,
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ADA_2_Analysis of Algorithms
Frequency Count, Case Analysis (Average, Best, Worst), Notations: (Big oh, Omega, Theta), Substitution Method, Master Method, Sorting Algorithm: (Bubble Sort, Insertion Sort, Selection Sort, , Shell Sort, Heap Sort, Bucket Sort, Radix Sort, Counting Sort)
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The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise stimulates the production of endorphins in the brain which can help alleviate feelings of stress or sadness.
AI_11 Game playing
Games planning, Mini Max Algorithm,. Alpha beta Pruning, Graph Plan, Goal Stack Planning
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The document discusses understanding in artificial intelligence. It defines understanding as the process of simulating human intelligence through machine learning and algorithms. Machine learning can be supervised, involving labeled training examples to minimize error, or unsupervised, where the system finds common characteristics in unlabeled data. Understanding is also discussed in relation to constraint satisfaction problems, where conditions must be met, like in map coloring or Sudoku puzzles. Backtracking is used to systematically search for solutions that satisfy all constraints.
AI_ 8 Weak Slot and Filler Structure
This document discusses weak slot and filler structures in artificial intelligence. It describes semantic net representation, which represents knowledge as a graphical network of nodes and arcs. It provides examples of representing statements about a cat named Jerry in a semantic net. The document also discusses frame representation, which organizes knowledge into structured records called frames that contain slots and slot values. An example frame is provided for a person named Ram. Advantages and disadvantages of both semantic nets and frames are outlined.
AI_7 Statistical Reasoning
This document provides an overview of uncertainty in artificial intelligence and probabilistic reasoning. It discusses sources of uncertainty like uncertain input, knowledge, and output. Probabilistic reasoning uses probability to represent uncertain knowledge. The document introduces basic probability notation including propositions, atomic events, unconditional probability, conditional probability, independence, and Bayes' rule. It explains how to perform inference using full joint probability distributions and marginalization. The document was prepared by Prof. Khushali B Kathiriya and provides an introduction to representing and reasoning with uncertainty in AI systems.
AI_6 Uncertainty
This document discusses symbolic reasoning under uncertainty. It introduces monotonic reasoning, where conclusions remain valid even when new information is added, and non-monotonic reasoning, where conclusions can be invalidated by new information. For non-monotonic reasoning, it provides an example where concluding a bird can fly is invalidated by learning the bird is a penguin. The document is presented by Prof. Khushali B Kathiriya and outlines introduction to monotonic reasoning, introduction to non-monotonic reasoning, and an example of non-monotonic reasoning logic.
AI_5 Resolution/ Channing
The document discusses knowledge representation using rules in artificial intelligence. It covers procedural versus declarative knowledge, forward chaining and backward chaining. Forward chaining starts with known facts and applies rules to reach a goal, working from bottom to top. Backward chaining starts at the goal and works backwards through rules to find supporting facts, using a top-down approach. An example of selling missiles to prove someone is a criminal is used to illustrate both forward and backward chaining techniques.
AI_ 3 & 4 Knowledge Representation issues
This document discusses knowledge representation in artificial intelligence. It covers various techniques for knowledge representation including logical representation using propositional logic and first-order predicate logic, semantic network representation, frame representation, and production rules. It also discusses issues in knowledge representation such as representing important attributes, relationships, and granularity of knowledge. Propositional logic is introduced as the simplest form of logic where statements are represented by propositions that can be either true or false. The syntax and semantics of propositional logic are also covered.
AI_1 Introduction of AI
Here are the key AI techniques discussed in the document:
- Tree searching techniques like depth-first search, breadth-first search, uniform cost search, A* search, and heuristic search methods.
- Rule-based systems that apply rules to deduce conclusions.
- Constraint satisfaction techniques that find solutions that satisfy constraints.
- Generate and test approaches that generate candidate solutions and test them against requirements.
- Description and matching techniques that describe states and match them to goals.
- Goal reduction techniques that hierarchically reduce goals to subgoals.
The document discusses these techniques as common approaches used to solve different types of AI problems. It provides examples but does not go into detailed explanations of each technique.
DDBMS_ Chap 9 Distributed Deadlock & Recovery Deadlock concept
Deadlock in Centralized systems, Deadlock in Distributed Systems – Detection, Prevention, Avoidance,
Wait-Die Algorithm, Wound-Wait algorithm
Recovery in DBMS - Types of Failure, Methods to control failure, Different techniques of recoverability, Write- Ahead logging Protocol, Advanced recovery techniques- Shadow Paging, Fuzzy checkpoint, ARIES, RAID levels,
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DDBMS_ Chap 8 Distributed Transaction Management & Concurrency Control
Transaction concept, ACID property, Types of transactions,
Objectives of Distributed Concurrency Control,
Concurrency Control anomalies, Methods of concurrency control, Serializability and recoverability,
Distributed Serializability, Enhanced lock based and timestamp based protocols, Multiple granularity, Multi version schemes,
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