Suche senden
Hochladen
Classification Of Web Documents
•
Als PPT, PDF herunterladen
•
0 gefällt mir
•
2,440 views
H
hussainahmad77100
Folgen
classification
Weniger lesen
Mehr lesen
Bildung
Melden
Teilen
Melden
Teilen
1 von 34
Jetzt herunterladen
Empfohlen
Logic Propositional Logic Propositional Logic: Symbols Propositional Logic: Syntax Propositional Logic: Laws Propositional Logic: Disadvantages Predicate Logic First Order Logic Predicate Logic: Symbols Well-Formed Formula WFF
AI-09 Logic in AI
AI-09 Logic in AI
Pankaj Debbarma
Introduction to Soft Computing
Soft computing
Soft computing
Dr Sandeep Kumar Poonia
In the field of artificial intelligence (AI), planning refers to the process of developing a sequence of actions or steps that an intelligent agent should take to achieve a specific goal or solve a particular problem. AI planning is a fundamental component of many AI systems and has applications in various domains, including robotics, autonomous systems, scheduling, logistics, and more. Here are some key aspects of planning in AI: Definition of Planning: Planning involves defining a problem, specifying the initial state, setting a goal state, and finding a sequence of actions or a plan that transforms the initial state into the desired goal state while adhering to certain constraints. State-Space Representation: In AI planning, the problem is often represented as a state-space, where each state represents a snapshot of the system, and actions transform one state into another. The goal is to find a path through this state-space from the initial state to the goal state. Search Algorithms: AI planning typically relies on search algorithms to explore the state-space efficiently. Uninformed search algorithms, such as depth-first search and breadth-first search, can be used, as well as informed search algorithms, like A* search, which incorporates heuristics to guide the search. Heuristics: Heuristics are used in planning to estimate the cost or distance from a state to the goal. Heuristic functions help inform the search algorithms by providing an estimate of how close a state is to the solution. Good heuristics can significantly improve the efficiency of the search. Plan Execution: Once a plan is generated, the next step is plan execution, where the agent carries out the actions in the plan to achieve the desired goal. This often requires monitoring the environment to ensure that the actions are executed as planned. Temporal and Hierarchical Planning: In more complex scenarios, temporal planning deals with actions that have temporal constraints, and hierarchical planning involves creating plans at multiple levels of abstraction, making planning more manageable in complex domains. Partial and Incremental Planning: Sometimes, it may not be necessary to create a complete plan from scratch. Partial and incremental planning allows agents to adapt and modify existing plans to respond to changing circumstances. Applications: Planning is used in a wide range of applications, from manufacturing and logistics (e.g., scheduling production and delivery) to robotics (e.g., path planning for robots) and game playing (e.g., chess and video games). Challenges: Challenges in AI planning include dealing with large search spaces, handling uncertainty, addressing resource constraints, and optimizing plans for efficiency and performance. AI planning is a critical component in creating intelligent systems that can autonomously make decisions and solve complex problems.
Planning in Artificial Intelligence
Planning in Artificial Intelligence
kitsenthilkumarcse
Introduction to Randomized algorithms
Randomized Algorithm
Randomized Algorithm
Kanishka Khandelwal
The difficulties associated with using mathematical optimization on large-scale engineering problems have contributed to the development of alternative solutions. Linear programming and dynamic programming techniques, for example, often fail (or reach local optimum) in solving NP-hard problems with large number of variables and non-linear objective functions. To overcome these problems, researchers have proposed evolutionary-based algorithms for searching near-optimum solutions to problems. Evolutionary algorithms (EAs) are stochastic search methods that mimic the metaphor of natural biological evolution and/or the social behaviour of species. Examples include how ants find the shortest route to a source of food and how birds find their destination during migration. The behaviour of such species is guided by learning, adaptation, and evolution. To mimic the efficient behaviour of these species, various researchers have developed computational systems that seek fast and robust solutions to complex optimization problems. The first evolutionary-based technique introduced in the literature was the genetic algorithms (Gas). GAs were developed based on the Darwinian principle of the ‘survival of the fittest’ and the natural process of evolution through reproduction. Based on its demonstrated ability to reach near-optimum solutions to large problems, the GAs technique has been used in many applicationsin science and engineering. Despite their benefits, GAs may require long processing time for a near optimum solution to evolve. Also, not all problems lend themselves well to a solution with GAs.
Optimization Shuffled Frog Leaping Algorithm
Optimization Shuffled Frog Leaping Algorithm
Uday Wankar
probabilistic, reasoning, artificial, computer, intelligence, IOE, Sushant, Pulchowk, AI, Statistical techniques used in practical data analysis. e.g. t-tests, ANOVA, regression, correlation; The use of probabilistic models in psychology and linguistics Machine learning and computational linguistics/NLP Measure theory (in fact, almost anything involving infinite sets) Using logic and probability to handle uncertain situation Probability based reasoning is same as understanding directly from the knowledge that a given probability rating based on uncertainty present
Probabilistic Reasoning
Probabilistic Reasoning
Sushant Gautam
Practical Swarm Optimization (PSO)
Practical Swarm Optimization (PSO)
Practical Swarm Optimization (PSO)
khashayar Danesh Narooei
1. Uncertainty and Decision Theory 2. Basic Prob. Theory 3. Prior and posterior probabilities 4. Bayes' Rule 5. Random variable 6. Different types of probability distribution
AI 8 | Probability Basics, Bayes' Rule, Probability Distribution
AI 8 | Probability Basics, Bayes' Rule, Probability Distribution
Mohammad Imam Hossain
Empfohlen
Logic Propositional Logic Propositional Logic: Symbols Propositional Logic: Syntax Propositional Logic: Laws Propositional Logic: Disadvantages Predicate Logic First Order Logic Predicate Logic: Symbols Well-Formed Formula WFF
AI-09 Logic in AI
AI-09 Logic in AI
Pankaj Debbarma
Introduction to Soft Computing
Soft computing
Soft computing
Dr Sandeep Kumar Poonia
In the field of artificial intelligence (AI), planning refers to the process of developing a sequence of actions or steps that an intelligent agent should take to achieve a specific goal or solve a particular problem. AI planning is a fundamental component of many AI systems and has applications in various domains, including robotics, autonomous systems, scheduling, logistics, and more. Here are some key aspects of planning in AI: Definition of Planning: Planning involves defining a problem, specifying the initial state, setting a goal state, and finding a sequence of actions or a plan that transforms the initial state into the desired goal state while adhering to certain constraints. State-Space Representation: In AI planning, the problem is often represented as a state-space, where each state represents a snapshot of the system, and actions transform one state into another. The goal is to find a path through this state-space from the initial state to the goal state. Search Algorithms: AI planning typically relies on search algorithms to explore the state-space efficiently. Uninformed search algorithms, such as depth-first search and breadth-first search, can be used, as well as informed search algorithms, like A* search, which incorporates heuristics to guide the search. Heuristics: Heuristics are used in planning to estimate the cost or distance from a state to the goal. Heuristic functions help inform the search algorithms by providing an estimate of how close a state is to the solution. Good heuristics can significantly improve the efficiency of the search. Plan Execution: Once a plan is generated, the next step is plan execution, where the agent carries out the actions in the plan to achieve the desired goal. This often requires monitoring the environment to ensure that the actions are executed as planned. Temporal and Hierarchical Planning: In more complex scenarios, temporal planning deals with actions that have temporal constraints, and hierarchical planning involves creating plans at multiple levels of abstraction, making planning more manageable in complex domains. Partial and Incremental Planning: Sometimes, it may not be necessary to create a complete plan from scratch. Partial and incremental planning allows agents to adapt and modify existing plans to respond to changing circumstances. Applications: Planning is used in a wide range of applications, from manufacturing and logistics (e.g., scheduling production and delivery) to robotics (e.g., path planning for robots) and game playing (e.g., chess and video games). Challenges: Challenges in AI planning include dealing with large search spaces, handling uncertainty, addressing resource constraints, and optimizing plans for efficiency and performance. AI planning is a critical component in creating intelligent systems that can autonomously make decisions and solve complex problems.
Planning in Artificial Intelligence
Planning in Artificial Intelligence
kitsenthilkumarcse
Introduction to Randomized algorithms
Randomized Algorithm
Randomized Algorithm
Kanishka Khandelwal
The difficulties associated with using mathematical optimization on large-scale engineering problems have contributed to the development of alternative solutions. Linear programming and dynamic programming techniques, for example, often fail (or reach local optimum) in solving NP-hard problems with large number of variables and non-linear objective functions. To overcome these problems, researchers have proposed evolutionary-based algorithms for searching near-optimum solutions to problems. Evolutionary algorithms (EAs) are stochastic search methods that mimic the metaphor of natural biological evolution and/or the social behaviour of species. Examples include how ants find the shortest route to a source of food and how birds find their destination during migration. The behaviour of such species is guided by learning, adaptation, and evolution. To mimic the efficient behaviour of these species, various researchers have developed computational systems that seek fast and robust solutions to complex optimization problems. The first evolutionary-based technique introduced in the literature was the genetic algorithms (Gas). GAs were developed based on the Darwinian principle of the ‘survival of the fittest’ and the natural process of evolution through reproduction. Based on its demonstrated ability to reach near-optimum solutions to large problems, the GAs technique has been used in many applicationsin science and engineering. Despite their benefits, GAs may require long processing time for a near optimum solution to evolve. Also, not all problems lend themselves well to a solution with GAs.
Optimization Shuffled Frog Leaping Algorithm
Optimization Shuffled Frog Leaping Algorithm
Uday Wankar
probabilistic, reasoning, artificial, computer, intelligence, IOE, Sushant, Pulchowk, AI, Statistical techniques used in practical data analysis. e.g. t-tests, ANOVA, regression, correlation; The use of probabilistic models in psychology and linguistics Machine learning and computational linguistics/NLP Measure theory (in fact, almost anything involving infinite sets) Using logic and probability to handle uncertain situation Probability based reasoning is same as understanding directly from the knowledge that a given probability rating based on uncertainty present
Probabilistic Reasoning
Probabilistic Reasoning
Sushant Gautam
Practical Swarm Optimization (PSO)
Practical Swarm Optimization (PSO)
Practical Swarm Optimization (PSO)
khashayar Danesh Narooei
1. Uncertainty and Decision Theory 2. Basic Prob. Theory 3. Prior and posterior probabilities 4. Bayes' Rule 5. Random variable 6. Different types of probability distribution
AI 8 | Probability Basics, Bayes' Rule, Probability Distribution
AI 8 | Probability Basics, Bayes' Rule, Probability Distribution
Mohammad Imam Hossain
Artificial intelligence and knowledge representation
Artificial intelligence and knowledge representation
Artificial intelligence and knowledge representation
Sajan Sahu
This presentation briefly discusses about Bayes Network in Artificial Intelligence
Bayes network
Bayes network
Dr. C.V. Suresh Babu
Artificial Intelligence
FORWARD CHAINING AND BACKWARD CHAINING SYSTEMS IN ARTIFICIAL INTELIGENCE
FORWARD CHAINING AND BACKWARD CHAINING SYSTEMS IN ARTIFICIAL INTELIGENCE
JohnLeonard Onwuzuruigbo
AI and expert system What is TMS? Enforcing logical relations among beliefs. Generating explanations for conclusions. Finding solutions to search problems Supporting default reasoning. Identifying causes for failure and recover from inconsistencies. TMS applications
Truth management system
Truth management system
Mohammad Kamrul Hasan
Lecture slides on RBF as a part of a course on Neural Networks based on Haykin's book.
Neural Networks: Radial Bases Functions (RBF)
Neural Networks: Radial Bases Functions (RBF)
Mostafa G. M. Mostafa
Artificial Intelligence
Artificial Intelligence
Bise Mond
Artificial intelligence reasoning in A.I
Reasoning in AI.pdf
Reasoning in AI.pdf
HarjeetSingh651810
Propositional logic
Propositional logic
Rushdi Shams
This presentation covers the basics of neural network along with the back propagation training algorithm and a code for image classification at the end.
Neural net and back propagation
Neural net and back propagation
Mohit Shrivastava
Deep learning, Convolutional neural network presented by Hengyang Lu at Houston machine learning meetup
Convolutional neural network
Convolutional neural network
Yan Xu
Cluster Han & Kamber
Capter10 cluster basic : Han & Kamber
Capter10 cluster basic : Han & Kamber
Houw Liong The
It presents some geometric algorithms in applied algorithms
Geometric algorithms
Geometric algorithms
Ganesh Solanke
Knowledge Representation in AI
Artificial Intelligence_ Knowledge Representation
Artificial Intelligence_ Knowledge Representation
ThenmozhiK5
Inference rules for quantifiers, Unification and lifting, generalized modus ponens, unification.
Unification and Lifting
Unification and Lifting
Megha Sharma
Unit8: Uncertainty in AI
Unit8: Uncertainty in AI
Unit8: Uncertainty in AI
Tekendra Nath Yogi
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
Artificial Neural Network
Artificial Neural Network
Prakash K
We consider knowledge as a refined kind of information, more general than that found in convention databases. But it may be incomplete or fuzzy as well. We may think of knowledge as a collection of related facts, procedures, models and heuristics that can be used in problem solving or inference systems.[
Knowledge representation and reasoning
Knowledge representation and reasoning
Maryam Maleki
Support Vector Machines
Support Vector Machines
nextlib
This is the course introduction about common sense reasoning. The complete course material can be found as OpenCourseWare at: http://ocw.upm.es/course/common-sense-reasoning
Introduction to common sense reasoning
Introduction to common sense reasoning
Martin Molina
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search, Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A * algorithm, Iterative Deepening A* , Recursive Best First Search, Pruning the CLOSED and OPEN Lists
State Space Search in ai
State Space Search in ai
vikas dhakane
Data.Mining.C.6(II).classification and prediction
Data.Mining.C.6(II).classification and prediction
Margaret Wang
Learning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwise
Hasan H Topcu
Weitere ähnliche Inhalte
Was ist angesagt?
Artificial intelligence and knowledge representation
Artificial intelligence and knowledge representation
Artificial intelligence and knowledge representation
Sajan Sahu
This presentation briefly discusses about Bayes Network in Artificial Intelligence
Bayes network
Bayes network
Dr. C.V. Suresh Babu
Artificial Intelligence
FORWARD CHAINING AND BACKWARD CHAINING SYSTEMS IN ARTIFICIAL INTELIGENCE
FORWARD CHAINING AND BACKWARD CHAINING SYSTEMS IN ARTIFICIAL INTELIGENCE
JohnLeonard Onwuzuruigbo
AI and expert system What is TMS? Enforcing logical relations among beliefs. Generating explanations for conclusions. Finding solutions to search problems Supporting default reasoning. Identifying causes for failure and recover from inconsistencies. TMS applications
Truth management system
Truth management system
Mohammad Kamrul Hasan
Lecture slides on RBF as a part of a course on Neural Networks based on Haykin's book.
Neural Networks: Radial Bases Functions (RBF)
Neural Networks: Radial Bases Functions (RBF)
Mostafa G. M. Mostafa
Artificial Intelligence
Artificial Intelligence
Bise Mond
Artificial intelligence reasoning in A.I
Reasoning in AI.pdf
Reasoning in AI.pdf
HarjeetSingh651810
Propositional logic
Propositional logic
Rushdi Shams
This presentation covers the basics of neural network along with the back propagation training algorithm and a code for image classification at the end.
Neural net and back propagation
Neural net and back propagation
Mohit Shrivastava
Deep learning, Convolutional neural network presented by Hengyang Lu at Houston machine learning meetup
Convolutional neural network
Convolutional neural network
Yan Xu
Cluster Han & Kamber
Capter10 cluster basic : Han & Kamber
Capter10 cluster basic : Han & Kamber
Houw Liong The
It presents some geometric algorithms in applied algorithms
Geometric algorithms
Geometric algorithms
Ganesh Solanke
Knowledge Representation in AI
Artificial Intelligence_ Knowledge Representation
Artificial Intelligence_ Knowledge Representation
ThenmozhiK5
Inference rules for quantifiers, Unification and lifting, generalized modus ponens, unification.
Unification and Lifting
Unification and Lifting
Megha Sharma
Unit8: Uncertainty in AI
Unit8: Uncertainty in AI
Unit8: Uncertainty in AI
Tekendra Nath Yogi
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
Artificial Neural Network
Artificial Neural Network
Prakash K
We consider knowledge as a refined kind of information, more general than that found in convention databases. But it may be incomplete or fuzzy as well. We may think of knowledge as a collection of related facts, procedures, models and heuristics that can be used in problem solving or inference systems.[
Knowledge representation and reasoning
Knowledge representation and reasoning
Maryam Maleki
Support Vector Machines
Support Vector Machines
nextlib
This is the course introduction about common sense reasoning. The complete course material can be found as OpenCourseWare at: http://ocw.upm.es/course/common-sense-reasoning
Introduction to common sense reasoning
Introduction to common sense reasoning
Martin Molina
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search, Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A * algorithm, Iterative Deepening A* , Recursive Best First Search, Pruning the CLOSED and OPEN Lists
State Space Search in ai
State Space Search in ai
vikas dhakane
Was ist angesagt?
(20)
Artificial intelligence and knowledge representation
Artificial intelligence and knowledge representation
Bayes network
Bayes network
FORWARD CHAINING AND BACKWARD CHAINING SYSTEMS IN ARTIFICIAL INTELIGENCE
FORWARD CHAINING AND BACKWARD CHAINING SYSTEMS IN ARTIFICIAL INTELIGENCE
Truth management system
Truth management system
Neural Networks: Radial Bases Functions (RBF)
Neural Networks: Radial Bases Functions (RBF)
Artificial Intelligence
Artificial Intelligence
Reasoning in AI.pdf
Reasoning in AI.pdf
Propositional logic
Propositional logic
Neural net and back propagation
Neural net and back propagation
Convolutional neural network
Convolutional neural network
Capter10 cluster basic : Han & Kamber
Capter10 cluster basic : Han & Kamber
Geometric algorithms
Geometric algorithms
Artificial Intelligence_ Knowledge Representation
Artificial Intelligence_ Knowledge Representation
Unification and Lifting
Unification and Lifting
Unit8: Uncertainty in AI
Unit8: Uncertainty in AI
Artificial Neural Network
Artificial Neural Network
Knowledge representation and reasoning
Knowledge representation and reasoning
Support Vector Machines
Support Vector Machines
Introduction to common sense reasoning
Introduction to common sense reasoning
State Space Search in ai
State Space Search in ai
Ähnlich wie Classification Of Web Documents
Data.Mining.C.6(II).classification and prediction
Data.Mining.C.6(II).classification and prediction
Margaret Wang
Learning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwise
Hasan H Topcu
ppt
ppt
butest
ai
nnml.ppt
nnml.ppt
yang947066
Machine learning and Neural Networks
Machine learning and Neural Networks
butest
Introduction to Classification
Classification Continued
Classification Continued
DataminingTools Inc
Introduction to Classification: Part II
Classification Continued
Classification Continued
Datamining Tools
Artificial Neural Networks
Machine Learning and Artificial Neural Networks.ppt
Machine Learning and Artificial Neural Networks.ppt
Anshika865276
the 16th chapter of the book: foundation of statistical natural language processing
20070702 Text Categorization
20070702 Text Categorization
midi
Lecture 2
Lecture 2
butest
TEXT CLUSTERING
TEXT CLUSTERING.doc
TEXT CLUSTERING.doc
naveenchaurasia
Textmining Predictive Models
Textmining Predictive Models
Textmining Predictive Models
DataminingTools Inc
Textmining Predictive Models
Textmining Predictive Models
Textmining Predictive Models
Datamining Tools
Introduction to Text Mining: Predictive Models
Textmining Predictive Models
Textmining Predictive Models
guest0edcaf
Introduction about Naïve Bayes Classifier
Supervised algorithms
Supervised algorithms
Yassine Akhiat
機器學習旨在讓電腦能由資料中累積的經驗來自我進步,近年來已廣泛應用於資料探勘、計算機視覺、自然語言處理、生物特徵識別、搜尋引擎、醫學診斷、檢測信用卡欺詐、證券市場分析、DNA 序列測序、語音和手寫識別、戰略遊戲和機器人等領域。它已成為資料科學的基礎學科之一,為任何資料科學家必備的工具。 這門課程將由 Appier 首席資料科學家林軒田利用短短的六個小時,快速地帶大家探索機器學習的基石、介紹核心的模型及一些熱門的技法,希望幫助大家有效率而紮實地了解這個領域,以妥善地使用各式機器學習的工具。此課程適合所有希望開始運用資料的資料分析者,推薦給所有有志於資料分析領域的資料科學愛好者。
[系列活動] Machine Learning 機器學習課程
[系列活動] Machine Learning 機器學習課程
台灣資料科學年會
about class in python
python.pptx
python.pptx
GayathriP95
Textmining Retrieval And Clustering
Textmining Retrieval And Clustering
Textmining Retrieval And Clustering
Datamining Tools
Text mining: Text mining Retrieval And Clustering
Textmining Retrieval And Clustering
Textmining Retrieval And Clustering
DataminingTools Inc
Introduction to Text Mining:Textmining Retrieval And Clustering
Textmining Retrieval And Clustering
Textmining Retrieval And Clustering
guest0edcaf
Ähnlich wie Classification Of Web Documents
(20)
Data.Mining.C.6(II).classification and prediction
Data.Mining.C.6(II).classification and prediction
Learning to Rank - From pairwise approach to listwise
Learning to Rank - From pairwise approach to listwise
ppt
ppt
nnml.ppt
nnml.ppt
Machine learning and Neural Networks
Machine learning and Neural Networks
Classification Continued
Classification Continued
Classification Continued
Classification Continued
Machine Learning and Artificial Neural Networks.ppt
Machine Learning and Artificial Neural Networks.ppt
20070702 Text Categorization
20070702 Text Categorization
Lecture 2
Lecture 2
TEXT CLUSTERING.doc
TEXT CLUSTERING.doc
Textmining Predictive Models
Textmining Predictive Models
Textmining Predictive Models
Textmining Predictive Models
Textmining Predictive Models
Textmining Predictive Models
Supervised algorithms
Supervised algorithms
[系列活動] Machine Learning 機器學習課程
[系列活動] Machine Learning 機器學習課程
python.pptx
python.pptx
Textmining Retrieval And Clustering
Textmining Retrieval And Clustering
Textmining Retrieval And Clustering
Textmining Retrieval And Clustering
Textmining Retrieval And Clustering
Textmining Retrieval And Clustering
Kürzlich hochgeladen
38 K-12 educators from North Carolina public schools
On National Teacher Day, meet the 2024-25 Kenan Fellows
On National Teacher Day, meet the 2024-25 Kenan Fellows
Mebane Rash
This presentation is from the Paper 209: Research Methodology and I choose the topic Interdisciplinary Insights: Data Collection Methods.
Interdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptx
Pooja Bhuva
This ppt is useful for B.Ed., M.Ed., M.A. (Education) and Ph.D. students.
Google Gemini An AI Revolution in Education.pptx
Google Gemini An AI Revolution in Education.pptx
Dr. Sarita Anand
Pie
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
heathfieldcps1
cultivation of kodo Millet ppt #kodomillet
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
pradhanghanshyam7136
21st_Century_Skills_Framework_Final_Presentation_2.pptx
21st_Century_Skills_Framework_Final_Presentation_2.pptx
21st_Century_Skills_Framework_Final_Presentation_2.pptx
JoelynRubio1
Brief pharmacology of Remifentanil
REMIFENTANIL: An Ultra short acting opioid.pptx
REMIFENTANIL: An Ultra short acting opioid.pptx
Dr. Ravikiran H M Gowda
The exam questions and answers provided by exact2pass.com for AZ-104 were really helpful in understanding the concepts. I was able to pass the exam with ease and guaranteed success!
latest AZ-104 Exam Questions and Answers
latest AZ-104 Exam Questions and Answers
dalebeck957
𝐋𝐞𝐬𝐬𝐨𝐧 𝐎𝐮𝐭𝐜𝐨𝐦𝐞𝐬: -Discern accommodations and modifications within inclusive classroom environments, distinguishing between their respective roles and applications. -Through critical analysis of hypothetical scenarios, learners will adeptly select appropriate accommodations and modifications, honing their ability to foster an inclusive learning environment for students with disabilities or unique challenges.
Understanding Accommodations and Modifications
Understanding Accommodations and Modifications
MJDuyan
Importance of information and communication (ICT) in 21st century education. Challenges and issues related to ICT in education.
ICT role in 21st century education and it's challenges.
ICT role in 21st century education and it's challenges.
MaryamAhmad92
Here is the slide show presentation from the Pre-Deployment Brief for HMCS Max Bernays from May 8th, 2024.
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
Esquimalt MFRC
Wednesday 20 March 2024, 09:30-15:30.
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)
Jisc
Klinik_ Apotek Onlin 085657271886 Solusi Menggugurkan Masalah Kehamilan Anda Jual Obat Aborsi Asli KLINIK ABORSI TERPEECAYA _ Jual Obat Aborsi Cytotec Misoprostol Asli 100% Ampuh Hanya 3 Jam Langsung Gugur || OBAT PENGGUGUR KANDUNGAN AMPUH MANJUR OBAT ABORSI OLINE" APOTIK Jual Obat Cytotec, Gastrul, Gynecoside Asli Ampuh. JUAL ” Obat Aborsi Tuntas | Obat Aborsi Manjur | Obat Aborsi Ampuh | Obat Penggugur Janin | Obat Pencegah Kehamilan | Obat Pelancar Haid | Obat terlambat Bulan | Ciri Obat Aborsi Asli | Obat Telat Bulan | Pil Aborsi Asli | Cara Menggugurkan Konten | Cara Aborsi Tuntas | Harga Obat Aborsi Asli | Pil Aborsi | Jual Obat Aborsi Cytotec | Cara Aborsi Sendiri | Cara Aborsi Usia 1 Bulan | Cara Aborsi Usia 2 Tahun | Cara Aborsi Usia 3 Bulan | Obat Aborsi Usia 4 Bulan | Cara Abrasi Usia 5 Bulan | Cara Menggugurkan Konten | Kandungan Obat Penggugur | Cara Menghitung Usia Konten | Cara Mengatasi Terlambat Bulan | Penjual Obat Aborsi Asli | Obat Aborsi Garansi | Kandungan Obat Peluntur | Obat Telat Datang Bulan | Obat Telat Haid | Obat Aborsi Paling Murah | Klinik Jual Obat Aborsi | Jual Pil Cytotec | Apotik Jual Obat Aborsi | Kandungan Dokter Abrasi | Cara Aborsi Cepat | Jual Obat Aborsi Bergaransi | Jual Obat Cytotec Asli | Obat Aborsi Aman Manjur | Obat Misoprostol Cytotec Asli. "APA ITU ABORSI" “Aborsi Adalah dengan membendung hormon yang di perlukan untuk mempertahankan kehamilan yaitu hormon progesteron, karena hormon ini dibendung, maka jalur kehamilan mulai membuka dan leher rahim menjadi melunak,sehingga mengeluarkan darah yang merupakan tanda bahwa obat telah bekerja || maksimal 1 jam obat diminum || PENJELASAN OBAT ABORSI USIA 1 _7 BULAN Pada usia kandungan ini, pasien akan merasakan sakit yang sedikit tidak berlebihan || sekitar 1 jam ||. namun hanya akan terjadi pada saatdarah keluar merupakan pertanda menstruasi. Hal ini dikarenakan pada usiakandungan 3 bulan,janin sudah terbentuk sebesar kepalan tangan orang dewasa. Cara kerja obat aborsi : JUAL OBAT ABORSI AMPUH dosis 3 bulan secara umum sama dengan cara kerja || DOSIS OBAT ABORSI 2 bulan”, hanya berbedanya selain mengisolasijanin juga menghancurkan janin dengan formula methotrexate dikandungdidalamnya. Formula methotrexate ini sangat ampuh untuk menghancurkan janinmenjadi serpihan-serpihan kecil akan sangat berguna pada saat dikeluarkan nanti. APA ALASAN WANITA MELAKUKAN ABORSI? Aborsi di lakukan wanita hamil baik yang sudah menikah maupun belum menikah dengan berbagai alasan , akan tetapi alasan yang utama adalah alasan-alasan non medis (termasuk aborsi sendiri / di sengaja/ buatan] MELAYANI PEMESANAN OBAT ABORSI SETIAP HARI, SIAP KIRIM KESELURUH KOTA BESAR DI INDONESIA DAN LUAR NEGERI. HUBUNGI PEMESANAN LEBIH NYAMAN VIA WA/: 085657271886
Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...
Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...
ZurliaSoop
The Graduate Outcomes survey exists to improve the experience of future students.
Graduate Outcomes Presentation Slides - English
Graduate Outcomes Presentation Slides - English
neillewis46
Meaning of Emotional intelligence, Dimension of Emotional Intelligence- Selfawareness, self-motivation, empathy, Social Skills, Mayer &Saloveys(1997) Cognitive model of EI, Golemans (1995) model of EI B. Spiritual intelligence, Methods to learn & develop spiritual Intelligence- Meditation, Detached Observation, Reflection, Connecting, Practice
Unit 3 Emotional Intelligence and Spiritual Intelligence.pdf
Unit 3 Emotional Intelligence and Spiritual Intelligence.pdf
Dr Vijay Vishwakarma
Wizards are very useful for creating a good user experience. In all businesses, interactive sessions are most beneficial. To improve the user experience, wizards in Odoo provide an interactive session. For creating wizards, we can use transient models or abstract models. This gives features of a model class except the data storing. Transient and abstract models have permanent database persistence. For them, database tables are made, and the records in such tables are kept until they are specifically erased.
How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17
Celine George
This Presentation is about the Unit 5 Mathematical Reasoning of UGC NET Paper 1 General Studies where we have included Types of Reasoning, Mathematical reasoning like number series, letter series etc. and mathematical aptitude like Fraction, Time and Distance, Average etc. with their solved questions and answers.
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
Nirmal Dwivedi
SOC 101 Final Powerpoint
SOC 101 Demonstration of Learning Presentation
SOC 101 Demonstration of Learning Presentation
camerronhm
People are more triggered by positive news than negative news. Audience does not want to hear, read or receive any kind of bad news. So these slides show how to convey negative news to someone without affecting their emotions.
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
annathomasp01
https://app.box.com/s/x7vf0j7xaxl2hlczxm3ny497y4yto33i
80 ĐỀ THI THỬ TUYỂN SINH TIẾNG ANH VÀO 10 SỞ GD – ĐT THÀNH PHỐ HỒ CHÍ MINH NĂ...
80 ĐỀ THI THỬ TUYỂN SINH TIẾNG ANH VÀO 10 SỞ GD – ĐT THÀNH PHỐ HỒ CHÍ MINH NĂ...
Nguyen Thanh Tu Collection
Kürzlich hochgeladen
(20)
On National Teacher Day, meet the 2024-25 Kenan Fellows
On National Teacher Day, meet the 2024-25 Kenan Fellows
Interdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptx
Google Gemini An AI Revolution in Education.pptx
Google Gemini An AI Revolution in Education.pptx
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
21st_Century_Skills_Framework_Final_Presentation_2.pptx
21st_Century_Skills_Framework_Final_Presentation_2.pptx
REMIFENTANIL: An Ultra short acting opioid.pptx
REMIFENTANIL: An Ultra short acting opioid.pptx
latest AZ-104 Exam Questions and Answers
latest AZ-104 Exam Questions and Answers
Understanding Accommodations and Modifications
Understanding Accommodations and Modifications
ICT role in 21st century education and it's challenges.
ICT role in 21st century education and it's challenges.
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)
Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...
Jual Obat Aborsi Hongkong ( Asli No.1 ) 085657271886 Obat Penggugur Kandungan...
Graduate Outcomes Presentation Slides - English
Graduate Outcomes Presentation Slides - English
Unit 3 Emotional Intelligence and Spiritual Intelligence.pdf
Unit 3 Emotional Intelligence and Spiritual Intelligence.pdf
How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
SOC 101 Demonstration of Learning Presentation
SOC 101 Demonstration of Learning Presentation
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
80 ĐỀ THI THỬ TUYỂN SINH TIẾNG ANH VÀO 10 SỞ GD – ĐT THÀNH PHỐ HỒ CHÍ MINH NĂ...
80 ĐỀ THI THỬ TUYỂN SINH TIẾNG ANH VÀO 10 SỞ GD – ĐT THÀNH PHỐ HỒ CHÍ MINH NĂ...
Classification Of Web Documents
1.
Classification Chapter# 05
Data Mining The Web By Hussain Ahmad M.S (Semantic Web) University of Peshawar Pakistan
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
Jetzt herunterladen