-
Why algorithms are called algorithms | BBC Ideas
Why are algorithms called algorithms? It's thanks to Persian mathematician Muhammad al-Khwarizmi who was born way back in around AD780.
Want to find out more? Have a watch of this video next... What exactly are algorithms? https://www.youtube.com/watch?v=ZnBF2GeAKbo
Subscribe to BBC Ideas 👉 https://bbc.in/2F6ipav
This video was made by Dayglow Media & Pencil & Pepper.
--------------
Do you have a curious mind? You’re in the right place.
Our aim on BBC Ideas is to feed your curiosity, to open your mind to new perspectives, and to leave you that little bit smarter.
So dive in. Let us know what you think. And make sure to subscribe! 👉https://bbc.in/2F6ipav
Visit our website to see all of our videos: https://www.bbc.com/ideas
And follow BBC Ideas on Twitter: https://twitter.com/bbc...
published: 11 Jul 2019
-
What Is An Algorithm? | What Exactly Is Algorithm? | Algorithm Basics Explained | Simplilearn
🔥Full Stack Developer (MERN Stack): https://www.simplilearn.com/full-stack-developer-course-mern-certification-training?utm_campaign=SCE-FullstackIITM&utm_medium=DescriptionFF&utm_source=youtube
🔥 Post Graduate Program In Full Stack Web Development: https://www.simplilearn.com/pgp-full-stack-web-development-certification-training-course?utm_campaign=WhatIsAnAlgorithm-cuhLSGGV-1k&utm_medium=Descriptionff&utm_source=youtube
🔥 Caltech Coding Bootcamp (US Only): https://www.simplilearn.com/coding-bootcamp?utm_campaign=WhatIsAnAlgorithm-cuhLSGGV-1k&utm_medium=Descriptionff&utm_source=youtube
🔥 Full Stack Java Developer (India Only) - https://www.simplilearn.com/java-full-stack-developer-certification-training-course?utm_campaign=WhatIsAnAlgorithm-cuhLSGGV-1k&utm_medium=Descriptionff&utm_source=...
published: 16 Aug 2021
-
Needlemam Wunsch Algorithm || Dynamic programming || Bioinformatics|| Part #01 (Introduction)
In this you will find:
#DynamicProgramming
#Needleman Wunsch algorithm
#SequenceComparison.
#Matrix filling
#Backtracking
#ibioinormatics.
#BioScholar #biologynotes #biology. #shomurodov 's Biology #iBiology #ArmandoHasudungan. #NeelaBakoreTutorials #VipinSharmaBiologyTutorials #BiomentorsClassesOnline. #biologyexams4u. #khanacademy #amoebasisters #DrNajeebLectures #unacademy #unacademyneet #biologyscienceSK #dr.muhammednaveed #nucleusmedicalmadia I hope this will be helpful to you.
Like||
Share|| &
subscribe to my channel #BioScholar for more concepts and informative videos.
Thanks for watching.
-------------------------------------------------------------------------------------------
More videos links:
Structure of RNA: https://youtu.be/88U1AbWNk...
published: 19 May 2023
-
What is the Ant Colony Optimization Algorithm?
You want to dive deep into the world of finance and management? Visit us:
http://www.frankfurt-school.de/en/home/programmes.html?utm_source=youtube&utm_medium=ACQUISITION
In an internet-based society, online trade is consistently becoming more important. However, an increasing number of parcels also means an increase in logistical complexity.
Let’s assume a German parcel delivery service has to deliver 40 parcels to different addresses all over Germany. To save time, the fastest route between the cities has to be found.
If a computer were to calculate each individual route – from Frankfurt to Düsseldorf to Berlin as well as from Berlin to Frankfurt to Düsseldorf and so on – we would end up with 815 quattuordecillion possibilities. That’s an 8 with 47 zeros. And should one of the cities ...
published: 01 Dec 2016
-
Genetic Algorithm with Solved Example(Selection,Crossover,Mutation)
#geneticalgorithm #softcomputing #machinelearning #datamining #neuralnetwork
If you like the content, support the channel by clicking on Thanks.
What is Genetic Algorithm?
Flow Chart for the Algorithm
Genetic Operators-Selection, Crossover, Mutation
Solved Example
Introduction:1.1 Biological neurons, McCulloch and Pitts models of neuron, Types
of activation function, Network architectures, Knowledge representation, Hebb net
1.2 Learning processes: Supervised learning, Unsupervised learning and
Reinforcement learning
1.3 Learning Rules : Hebbian Learning Rule, Perceptron Learning Rule, Delta
Learning Rule, Widrow-Hoff Learning Rule, Correlation Learning Rule, WinnerTake-All Learning Rule
1.4 Applications and scope of Neural Networks
10
2
Supervised Learning Networks :
2.1 Perception N...
published: 14 Mar 2020
-
Smith Waterman Algorithm || Dynamic Programming|| Bioinformatics||Introduction & Example
In this informative video, we delve into the fascinating world of bioinformatics and computational biology by exploring the Smith-Waterman algorithm. Developed by Temple F. Smith and Michael S. Waterman in 1981, this dynamic programming algorithm has revolutionized the way we compare and analyze DNA, RNA, and protein sequences.
What is the Smith-Waterman Algorithm?
Discover the inner workings of this algorithm that specializes in finding the optimal local alignment between two sequences. Unlike global alignment methods, the Smith-Waterman algorithm focuses on pinpointing the most similar contiguous subsequences, making it an essential tool for identifying functional domains, conserved motifs, and evolutionary relationships.
How Does it Work?
Learn about the scoring matrix, a pivotal co...
published: 16 Aug 2023
-
Bat Optimization Algorithm
Bat Optimization Algorithm is an informative and educational video that explains the concept and implementation of a bio-inspired optimization algorithm called the Bat Algorithm. Through clear and concise explanations, viewers will gain an understanding of how the Bat Algorithm is inspired by the echolocation behavior of bats. The algorithm can be used to solve complex optimization problems in various fields, including management, engineering, economics, biology, etc. The video also provides visual demonstrations of the algorithm in action, making it easy for viewers to follow along and understand the practical applications of the Bat Algorithm. Whether you're a student, researcher, or simply interested in optimization algorithms, this video is a great resource for learning about the Bat A...
published: 06 May 2023
-
Bio-inspired component routing algorithm
Once components are placed on a chip or printed circuit board, you need to draw the tracks that actually connect pins. Tracks must connect groups of pins and they can not cross or overlap. Harder than it seems!
This video shows an alpha version of the algorithm, where track are built by slime mold simulations in a very "organic" way. Only pairs of pins are considered (connecting n is much harder) and a single layer (real PCB have several layers so paths can go up and down to avoid crossings).
published: 13 Jun 2021
-
BLAST Algorithm Explained
Basic local alignment search tools, also known as BLAST, is one of the most used algorithm in Bio-informatics. It allows for quick annotation process for sequenced fragment, how does it actually works?
published: 19 Jun 2019
-
Bioinformatics part 5 FASTA algorithm
For more information, log on to-
http://shomusbiology.weebly.com/
Download the study materials here-
http://shomusbiology.weebly.com/bio-materials.html
FASTA is a DNA and protein sequence alignment software package first described (as FASTP) by David J. Lipman and William R. Pearson in 1985.[1] Its legacy is the FASTA format which is now ubiquitous in bioinformatics.
FASTA takes a given nucleotide or amino-acid sequence and searches a corresponding sequence database by using local sequence alignment to find matches of similar database sequences.
The FASTA program follows a largely heuristic method which contributes to the high speed of its execution. It initially observes the pattern of word hits, word-to-word matches of a given length, and marks potential matches before performing a mor...
published: 29 Oct 2013
3:09
Why algorithms are called algorithms | BBC Ideas
Why are algorithms called algorithms? It's thanks to Persian mathematician Muhammad al-Khwarizmi who was born way back in around AD780.
Want to find out more? ...
Why are algorithms called algorithms? It's thanks to Persian mathematician Muhammad al-Khwarizmi who was born way back in around AD780.
Want to find out more? Have a watch of this video next... What exactly are algorithms? https://www.youtube.com/watch?v=ZnBF2GeAKbo
Subscribe to BBC Ideas 👉 https://bbc.in/2F6ipav
This video was made by Dayglow Media & Pencil & Pepper.
--------------
Do you have a curious mind? You’re in the right place.
Our aim on BBC Ideas is to feed your curiosity, to open your mind to new perspectives, and to leave you that little bit smarter.
So dive in. Let us know what you think. And make sure to subscribe! 👉https://bbc.in/2F6ipav
Visit our website to see all of our videos: https://www.bbc.com/ideas
And follow BBC Ideas on Twitter: https://twitter.com/bbcideas
#mathematics #history #algorithms
https://wn.com/Why_Algorithms_Are_Called_Algorithms_|_BBC_Ideas
Why are algorithms called algorithms? It's thanks to Persian mathematician Muhammad al-Khwarizmi who was born way back in around AD780.
Want to find out more? Have a watch of this video next... What exactly are algorithms? https://www.youtube.com/watch?v=ZnBF2GeAKbo
Subscribe to BBC Ideas 👉 https://bbc.in/2F6ipav
This video was made by Dayglow Media & Pencil & Pepper.
--------------
Do you have a curious mind? You’re in the right place.
Our aim on BBC Ideas is to feed your curiosity, to open your mind to new perspectives, and to leave you that little bit smarter.
So dive in. Let us know what you think. And make sure to subscribe! 👉https://bbc.in/2F6ipav
Visit our website to see all of our videos: https://www.bbc.com/ideas
And follow BBC Ideas on Twitter: https://twitter.com/bbcideas
#mathematics #history #algorithms
- published: 11 Jul 2019
- views: 2727928
13:18
What Is An Algorithm? | What Exactly Is Algorithm? | Algorithm Basics Explained | Simplilearn
🔥Full Stack Developer (MERN Stack): https://www.simplilearn.com/full-stack-developer-course-mern-certification-training?utm_campaign=SCE-FullstackIITM&utm_mediu...
🔥Full Stack Developer (MERN Stack): https://www.simplilearn.com/full-stack-developer-course-mern-certification-training?utm_campaign=SCE-FullstackIITM&utm_medium=DescriptionFF&utm_source=youtube
🔥 Post Graduate Program In Full Stack Web Development: https://www.simplilearn.com/pgp-full-stack-web-development-certification-training-course?utm_campaign=WhatIsAnAlgorithm-cuhLSGGV-1k&utm_medium=Descriptionff&utm_source=youtube
🔥 Caltech Coding Bootcamp (US Only): https://www.simplilearn.com/coding-bootcamp?utm_campaign=WhatIsAnAlgorithm-cuhLSGGV-1k&utm_medium=Descriptionff&utm_source=youtube
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This video explains what is an algorithm in the data structure. This Simplilearn's What Is An Algorithm? tutorial will help beginners to understand what exactly is an algorithm with an example. All of the algorithm basics are explained in this video.
Following topics covered in this video:
00:00 What is an Algorithm?
02:42 What Is An Algorithm? and Characteristics of an Algorithm
05:02 How to write an Algorithm?
07:23 What Is An Algorithm? and it's Analysis
08:30 What Is An Algorithm? and it's Complexity
10:45 Pros and Cons of an Algorithm
11:26 Algorithm vs Programming
✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH
⏩ Check out our Data Structures training videos playlist: https://www.youtube.com/watch?v=27PdRL89A9U&list=PLEiEAq2VkUUJMxIegQ1ge1tcGskjdiwGP
#Algorithm #WhatIsAnAlgorithm #WhatExactlyIsAlgorithm #AlgorithmExplanation #AlgorithmExplained #AlgorithmBasics #AlgorithmExplanation #DataStructureTutorial #DataStructureAndAlgorithmsTutorial #DataStrcutures #Simplilearn
What is an Algorithm?
An algorithm is a finite sequence of well-defined, computer-implementable instructions used to solve a class of specific problems or perform computation in computer science. Algorithms are always clear and are used as specifications for calculations, data processing, automated reasoning, and other tasks.
What Is a Data Structure?
The short answer is: a data structure is a specific means of organizing data in a system to access and use. The long answer is a data structure is a blend of data organization, management, retrieval, and storage, brought together into one format that allows efficient access and modification. It’s collecting data values, the relationships they share, and the applicable functions or operations.
➡️ About Post Graduate Program In Full Stack Web Development
This program will give you the foundation for building full-stack web apps using the Java programming language. You'll begin with the basics of JavaScript, and then venture into some of the more advanced concepts like Angular, Spring Boot, Hibernate, JSPs, and MVC. Now is the perfect time to get started on your career as a full-stack web developer!
✅ Key Features
- Caltech CTME Post Graduate Certificate
- Enrolment in Simplilearn’s JobAssist
- Receive up to 25 CEUs from Caltech CTME
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Attend Masterclasses from Caltech CTME instructors
- Live virtual classes led by industry experts, hands-on projects and integrated labs
- Online Convocation by Caltech CTME Program Director
- 20 lesson-end and 5 phase-end projects
- Capstone Project in 4 domains
- Caltech CTME Circle Membership
- Build your own portfolio on GitHub
✅ Skills Covered
- Agile
- JAVA
- Hibernate and JPA
- Spring Core 50
- DevOps
- HTML5 and CSS3
- AWS
- JavaScript ES6
- Servlets
- SOAP and REST
- JSP
Learn more about Data Structures: https://www.simplilearn.com/data-structures-and-algorithms-article?utm_campaign=WhatIsAnAlgorithm&utm_medium=Description&utm_source=youtube
🔥Explore our FREE Courses with Completion Certificates: https://www.simplilearn.com/skillup-free-online-courses?utm_campaign=WhatIsAnAlgorithm&utm_medium=Description&utm_source=youtube
🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
https://wn.com/What_Is_An_Algorithm_|_What_Exactly_Is_Algorithm_|_Algorithm_Basics_Explained_|_Simplilearn
🔥Full Stack Developer (MERN Stack): https://www.simplilearn.com/full-stack-developer-course-mern-certification-training?utm_campaign=SCE-FullstackIITM&utm_medium=DescriptionFF&utm_source=youtube
🔥 Post Graduate Program In Full Stack Web Development: https://www.simplilearn.com/pgp-full-stack-web-development-certification-training-course?utm_campaign=WhatIsAnAlgorithm-cuhLSGGV-1k&utm_medium=Descriptionff&utm_source=youtube
🔥 Caltech Coding Bootcamp (US Only): https://www.simplilearn.com/coding-bootcamp?utm_campaign=WhatIsAnAlgorithm-cuhLSGGV-1k&utm_medium=Descriptionff&utm_source=youtube
🔥 Full Stack Java Developer (India Only) - https://www.simplilearn.com/java-full-stack-developer-certification-training-course?utm_campaign=WhatIsAnAlgorithm-cuhLSGGV-1k&utm_medium=Descriptionff&utm_source=youtube
This video explains what is an algorithm in the data structure. This Simplilearn's What Is An Algorithm? tutorial will help beginners to understand what exactly is an algorithm with an example. All of the algorithm basics are explained in this video.
Following topics covered in this video:
00:00 What is an Algorithm?
02:42 What Is An Algorithm? and Characteristics of an Algorithm
05:02 How to write an Algorithm?
07:23 What Is An Algorithm? and it's Analysis
08:30 What Is An Algorithm? and it's Complexity
10:45 Pros and Cons of an Algorithm
11:26 Algorithm vs Programming
✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH
⏩ Check out our Data Structures training videos playlist: https://www.youtube.com/watch?v=27PdRL89A9U&list=PLEiEAq2VkUUJMxIegQ1ge1tcGskjdiwGP
#Algorithm #WhatIsAnAlgorithm #WhatExactlyIsAlgorithm #AlgorithmExplanation #AlgorithmExplained #AlgorithmBasics #AlgorithmExplanation #DataStructureTutorial #DataStructureAndAlgorithmsTutorial #DataStrcutures #Simplilearn
What is an Algorithm?
An algorithm is a finite sequence of well-defined, computer-implementable instructions used to solve a class of specific problems or perform computation in computer science. Algorithms are always clear and are used as specifications for calculations, data processing, automated reasoning, and other tasks.
What Is a Data Structure?
The short answer is: a data structure is a specific means of organizing data in a system to access and use. The long answer is a data structure is a blend of data organization, management, retrieval, and storage, brought together into one format that allows efficient access and modification. It’s collecting data values, the relationships they share, and the applicable functions or operations.
➡️ About Post Graduate Program In Full Stack Web Development
This program will give you the foundation for building full-stack web apps using the Java programming language. You'll begin with the basics of JavaScript, and then venture into some of the more advanced concepts like Angular, Spring Boot, Hibernate, JSPs, and MVC. Now is the perfect time to get started on your career as a full-stack web developer!
✅ Key Features
- Caltech CTME Post Graduate Certificate
- Enrolment in Simplilearn’s JobAssist
- Receive up to 25 CEUs from Caltech CTME
- Simplilearn's JobAssist helps you get noticed by top hiring companies
- Attend Masterclasses from Caltech CTME instructors
- Live virtual classes led by industry experts, hands-on projects and integrated labs
- Online Convocation by Caltech CTME Program Director
- 20 lesson-end and 5 phase-end projects
- Capstone Project in 4 domains
- Caltech CTME Circle Membership
- Build your own portfolio on GitHub
✅ Skills Covered
- Agile
- JAVA
- Hibernate and JPA
- Spring Core 50
- DevOps
- HTML5 and CSS3
- AWS
- JavaScript ES6
- Servlets
- SOAP and REST
- JSP
Learn more about Data Structures: https://www.simplilearn.com/data-structures-and-algorithms-article?utm_campaign=WhatIsAnAlgorithm&utm_medium=Description&utm_source=youtube
🔥Explore our FREE Courses with Completion Certificates: https://www.simplilearn.com/skillup-free-online-courses?utm_campaign=WhatIsAnAlgorithm&utm_medium=Description&utm_source=youtube
🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
- published: 16 Aug 2021
- views: 115616
2:38
Needlemam Wunsch Algorithm || Dynamic programming || Bioinformatics|| Part #01 (Introduction)
In this you will find:
#DynamicProgramming
#Needleman Wunsch algorithm
#SequenceComparison.
#Matrix filling
#Backtracking
#ibioinormatics.
#BioScholar #biologyn...
In this you will find:
#DynamicProgramming
#Needleman Wunsch algorithm
#SequenceComparison.
#Matrix filling
#Backtracking
#ibioinormatics.
#BioScholar #biologynotes #biology. #shomurodov 's Biology #iBiology #ArmandoHasudungan. #NeelaBakoreTutorials #VipinSharmaBiologyTutorials #BiomentorsClassesOnline. #biologyexams4u. #khanacademy #amoebasisters #DrNajeebLectures #unacademy #unacademyneet #biologyscienceSK #dr.muhammednaveed #nucleusmedicalmadia I hope this will be helpful to you.
Like||
Share|| &
subscribe to my channel #BioScholar for more concepts and informative videos.
Thanks for watching.
-------------------------------------------------------------------------------------------
More videos links:
Structure of RNA: https://youtu.be/88U1AbWNk-o?sub_conf...
Introduction to science: https://youtu.be/6BE_uAWk4tg?sub_conf...
Microbes and their classification: https://youtu.be/3Vb0saZZoUA
Applications of bioinformatics: https://youtu.be/JQixhya1xpw?Sub-Conf...
Biological databases: https://youtu.be/vFW25cUBKHk
Introduction to Biology: https://youtu.be/zlJDKHgqf14
Animal Husbandry & Fisheries: https://youtu.be/p-FZP4EEi2U
Immunology & immunity types: https://youtu.be/uf5nd7zZQBs
Use of genetic resources: https://youtu.be/7kvpPWWAYSw
Genetic resources& Food security: https://youtu.be/3fILMV-652Y
IT PGRFA: https://youtu.be/eC3vQBDvNAs
PGR: https://youtu.be/isPjRqnqKlc
Different kinds of pgr: https://youtu.be/39k7v_GDNmc
PGR & their conservation: https://youtu.be/gCcU6laStcw
Applications of cryopreservation: https://youtu.be/4AgReUE5j5s
Biodiversity Introduction: https://youtu.be/NlwK6Rs__bs
-------------------------------------------------------------------------------------------
Follow me:
Face book link: https://web.facebook.com/learn8BioSch...
Face book page: learn8BioScholar
Twitter: @ScholarBio
Regards
Bio scholar.
https://wn.com/Needlemam_Wunsch_Algorithm_||_Dynamic_Programming_||_Bioinformatics||_Part_01_(Introduction)
In this you will find:
#DynamicProgramming
#Needleman Wunsch algorithm
#SequenceComparison.
#Matrix filling
#Backtracking
#ibioinormatics.
#BioScholar #biologynotes #biology. #shomurodov 's Biology #iBiology #ArmandoHasudungan. #NeelaBakoreTutorials #VipinSharmaBiologyTutorials #BiomentorsClassesOnline. #biologyexams4u. #khanacademy #amoebasisters #DrNajeebLectures #unacademy #unacademyneet #biologyscienceSK #dr.muhammednaveed #nucleusmedicalmadia I hope this will be helpful to you.
Like||
Share|| &
subscribe to my channel #BioScholar for more concepts and informative videos.
Thanks for watching.
-------------------------------------------------------------------------------------------
More videos links:
Structure of RNA: https://youtu.be/88U1AbWNk-o?sub_conf...
Introduction to science: https://youtu.be/6BE_uAWk4tg?sub_conf...
Microbes and their classification: https://youtu.be/3Vb0saZZoUA
Applications of bioinformatics: https://youtu.be/JQixhya1xpw?Sub-Conf...
Biological databases: https://youtu.be/vFW25cUBKHk
Introduction to Biology: https://youtu.be/zlJDKHgqf14
Animal Husbandry & Fisheries: https://youtu.be/p-FZP4EEi2U
Immunology & immunity types: https://youtu.be/uf5nd7zZQBs
Use of genetic resources: https://youtu.be/7kvpPWWAYSw
Genetic resources& Food security: https://youtu.be/3fILMV-652Y
IT PGRFA: https://youtu.be/eC3vQBDvNAs
PGR: https://youtu.be/isPjRqnqKlc
Different kinds of pgr: https://youtu.be/39k7v_GDNmc
PGR & their conservation: https://youtu.be/gCcU6laStcw
Applications of cryopreservation: https://youtu.be/4AgReUE5j5s
Biodiversity Introduction: https://youtu.be/NlwK6Rs__bs
-------------------------------------------------------------------------------------------
Follow me:
Face book link: https://web.facebook.com/learn8BioSch...
Face book page: learn8BioScholar
Twitter: @ScholarBio
Regards
Bio scholar.
- published: 19 May 2023
- views: 13925
1:59
What is the Ant Colony Optimization Algorithm?
You want to dive deep into the world of finance and management? Visit us:
http://www.frankfurt-school.de/en/home/programmes.html?utm_source=youtube&utm_medium=A...
You want to dive deep into the world of finance and management? Visit us:
http://www.frankfurt-school.de/en/home/programmes.html?utm_source=youtube&utm_medium=ACQUISITION
In an internet-based society, online trade is consistently becoming more important. However, an increasing number of parcels also means an increase in logistical complexity.
Let’s assume a German parcel delivery service has to deliver 40 parcels to different addresses all over Germany. To save time, the fastest route between the cities has to be found.
If a computer were to calculate each individual route – from Frankfurt to Düsseldorf to Berlin as well as from Berlin to Frankfurt to Düsseldorf and so on – we would end up with 815 quattuordecillion possibilities. That’s an 8 with 47 zeros. And should one of the cities change the next day, the routes have to be computed anew all over again. Even modern supercomputers cannot calculate such amounts.
The ant colony optimization algorithm helps to find a solution to this. With a simple mathematical procedure, it simulates the routes in a way that is used by ant colonies to find the best route while foraging for food: An ant that has discovered a short and efficient route will walk to and from the food source more frequently than ants that walk a longer route. The colony notices this by the pheromones emitted by the faster ant, and so can identify the quickest route without having to try each one.
With the ant colony optimization algorithm, the computer learns how to think like an ant colony and can calculate the fastest route much quicker. This helps the parcel delivery service to save on both time, and fuel.
https://wn.com/What_Is_The_Ant_Colony_Optimization_Algorithm
You want to dive deep into the world of finance and management? Visit us:
http://www.frankfurt-school.de/en/home/programmes.html?utm_source=youtube&utm_medium=ACQUISITION
In an internet-based society, online trade is consistently becoming more important. However, an increasing number of parcels also means an increase in logistical complexity.
Let’s assume a German parcel delivery service has to deliver 40 parcels to different addresses all over Germany. To save time, the fastest route between the cities has to be found.
If a computer were to calculate each individual route – from Frankfurt to Düsseldorf to Berlin as well as from Berlin to Frankfurt to Düsseldorf and so on – we would end up with 815 quattuordecillion possibilities. That’s an 8 with 47 zeros. And should one of the cities change the next day, the routes have to be computed anew all over again. Even modern supercomputers cannot calculate such amounts.
The ant colony optimization algorithm helps to find a solution to this. With a simple mathematical procedure, it simulates the routes in a way that is used by ant colonies to find the best route while foraging for food: An ant that has discovered a short and efficient route will walk to and from the food source more frequently than ants that walk a longer route. The colony notices this by the pheromones emitted by the faster ant, and so can identify the quickest route without having to try each one.
With the ant colony optimization algorithm, the computer learns how to think like an ant colony and can calculate the fastest route much quicker. This helps the parcel delivery service to save on both time, and fuel.
- published: 01 Dec 2016
- views: 126242
11:45
Genetic Algorithm with Solved Example(Selection,Crossover,Mutation)
#geneticalgorithm #softcomputing #machinelearning #datamining #neuralnetwork
If you like the content, support the channel by clicking on Thanks.
What is Ge...
#geneticalgorithm #softcomputing #machinelearning #datamining #neuralnetwork
If you like the content, support the channel by clicking on Thanks.
What is Genetic Algorithm?
Flow Chart for the Algorithm
Genetic Operators-Selection, Crossover, Mutation
Solved Example
Introduction:1.1 Biological neurons, McCulloch and Pitts models of neuron, Types
of activation function, Network architectures, Knowledge representation, Hebb net
1.2 Learning processes: Supervised learning, Unsupervised learning and
Reinforcement learning
1.3 Learning Rules : Hebbian Learning Rule, Perceptron Learning Rule, Delta
Learning Rule, Widrow-Hoff Learning Rule, Correlation Learning Rule, WinnerTake-All Learning Rule
1.4 Applications and scope of Neural Networks
10
2
Supervised Learning Networks :
2.1 Perception Networks – continuous & discrete, Perceptron convergence theorem,
Adaline, Madaline, Method of steepest descent, – least mean square algorithm,
Linear & non-linear separable classes & Pattern classes,
2.2 Back Propagation Network,
2.3 Radial Basis Function Network.
12
3
Unsupervised learning network:
3.1 Fixed weights competitive nets,
3.2 Kohonen Self-organizing Feature Maps, Learning Vector Quantization,
3.3 Adaptive Resonance Theory – 1
06
4
Associative memory networks:
4.1 Introduction, Training algorithms for Pattern Association,
4.2 Auto-associative Memory Network, Hetero-associative Memory Network,
Bidirectional Associative Memory,
4.3 Discrete Hopfield Networks.
08
5
Fuzzy Logic:
5.1 Fuzzy Sets, Fuzzy Relations and Tolerance and Equivalence
5.2 Fuzzification and Defuzzification
5.3 Fuzzy Controllers
https://wn.com/Genetic_Algorithm_With_Solved_Example(Selection,Crossover,Mutation)
#geneticalgorithm #softcomputing #machinelearning #datamining #neuralnetwork
If you like the content, support the channel by clicking on Thanks.
What is Genetic Algorithm?
Flow Chart for the Algorithm
Genetic Operators-Selection, Crossover, Mutation
Solved Example
Introduction:1.1 Biological neurons, McCulloch and Pitts models of neuron, Types
of activation function, Network architectures, Knowledge representation, Hebb net
1.2 Learning processes: Supervised learning, Unsupervised learning and
Reinforcement learning
1.3 Learning Rules : Hebbian Learning Rule, Perceptron Learning Rule, Delta
Learning Rule, Widrow-Hoff Learning Rule, Correlation Learning Rule, WinnerTake-All Learning Rule
1.4 Applications and scope of Neural Networks
10
2
Supervised Learning Networks :
2.1 Perception Networks – continuous & discrete, Perceptron convergence theorem,
Adaline, Madaline, Method of steepest descent, – least mean square algorithm,
Linear & non-linear separable classes & Pattern classes,
2.2 Back Propagation Network,
2.3 Radial Basis Function Network.
12
3
Unsupervised learning network:
3.1 Fixed weights competitive nets,
3.2 Kohonen Self-organizing Feature Maps, Learning Vector Quantization,
3.3 Adaptive Resonance Theory – 1
06
4
Associative memory networks:
4.1 Introduction, Training algorithms for Pattern Association,
4.2 Auto-associative Memory Network, Hetero-associative Memory Network,
Bidirectional Associative Memory,
4.3 Discrete Hopfield Networks.
08
5
Fuzzy Logic:
5.1 Fuzzy Sets, Fuzzy Relations and Tolerance and Equivalence
5.2 Fuzzification and Defuzzification
5.3 Fuzzy Controllers
- published: 14 Mar 2020
- views: 359062
4:11
Smith Waterman Algorithm || Dynamic Programming|| Bioinformatics||Introduction & Example
In this informative video, we delve into the fascinating world of bioinformatics and computational biology by exploring the Smith-Waterman algorithm. Developed ...
In this informative video, we delve into the fascinating world of bioinformatics and computational biology by exploring the Smith-Waterman algorithm. Developed by Temple F. Smith and Michael S. Waterman in 1981, this dynamic programming algorithm has revolutionized the way we compare and analyze DNA, RNA, and protein sequences.
What is the Smith-Waterman Algorithm?
Discover the inner workings of this algorithm that specializes in finding the optimal local alignment between two sequences. Unlike global alignment methods, the Smith-Waterman algorithm focuses on pinpointing the most similar contiguous subsequences, making it an essential tool for identifying functional domains, conserved motifs, and evolutionary relationships.
How Does it Work?
Learn about the scoring matrix, a pivotal component of the Smith-Waterman algorithm. We'll break down how match, mismatch, and gap penalties contribute to the calculation of alignment scores, leading to the identification of the best local alignment. Get a visual walkthrough of the dynamic programming process that powers this algorithm.
Explore the wide range of applications that benefit from the Smith-Waterman algorithm. From protein structure prediction to sequence annotation, discover how this algorithm plays a crucial role in various biological analyses. Understand why it's an indispensable tool for researchers and scientists in the field.
Optimization and Performance
Uncover strategies to tackle the computational intensity of the Smith-Waterman algorithm. We'll discuss optimization techniques and heuristic approaches that help speed up the process, making it applicable to real-world, large-scale biological datasets.
Whether you're a budding biologist, a bioinformatics enthusiast, or simply curious about the intersection of algorithms and genetics, this video offers a comprehensive overview of the Smith-Waterman algorithm's significance and practical applications. Join us on this journey into the world of sequence alignment and uncover the secrets behind one of bioinformatics' most powerful tools.
Don't forget to like, subscribe, and hit the notification bell to stay updated with more captivating content on the exciting realm of computational biology and beyond.
Regards:
BioScholar
https://wn.com/Smith_Waterman_Algorithm_||_Dynamic_Programming||_Bioinformatics||Introduction_Example
In this informative video, we delve into the fascinating world of bioinformatics and computational biology by exploring the Smith-Waterman algorithm. Developed by Temple F. Smith and Michael S. Waterman in 1981, this dynamic programming algorithm has revolutionized the way we compare and analyze DNA, RNA, and protein sequences.
What is the Smith-Waterman Algorithm?
Discover the inner workings of this algorithm that specializes in finding the optimal local alignment between two sequences. Unlike global alignment methods, the Smith-Waterman algorithm focuses on pinpointing the most similar contiguous subsequences, making it an essential tool for identifying functional domains, conserved motifs, and evolutionary relationships.
How Does it Work?
Learn about the scoring matrix, a pivotal component of the Smith-Waterman algorithm. We'll break down how match, mismatch, and gap penalties contribute to the calculation of alignment scores, leading to the identification of the best local alignment. Get a visual walkthrough of the dynamic programming process that powers this algorithm.
Explore the wide range of applications that benefit from the Smith-Waterman algorithm. From protein structure prediction to sequence annotation, discover how this algorithm plays a crucial role in various biological analyses. Understand why it's an indispensable tool for researchers and scientists in the field.
Optimization and Performance
Uncover strategies to tackle the computational intensity of the Smith-Waterman algorithm. We'll discuss optimization techniques and heuristic approaches that help speed up the process, making it applicable to real-world, large-scale biological datasets.
Whether you're a budding biologist, a bioinformatics enthusiast, or simply curious about the intersection of algorithms and genetics, this video offers a comprehensive overview of the Smith-Waterman algorithm's significance and practical applications. Join us on this journey into the world of sequence alignment and uncover the secrets behind one of bioinformatics' most powerful tools.
Don't forget to like, subscribe, and hit the notification bell to stay updated with more captivating content on the exciting realm of computational biology and beyond.
Regards:
BioScholar
- published: 16 Aug 2023
- views: 12523
1:53
Bat Optimization Algorithm
Bat Optimization Algorithm is an informative and educational video that explains the concept and implementation of a bio-inspired optimization algorithm called ...
Bat Optimization Algorithm is an informative and educational video that explains the concept and implementation of a bio-inspired optimization algorithm called the Bat Algorithm. Through clear and concise explanations, viewers will gain an understanding of how the Bat Algorithm is inspired by the echolocation behavior of bats. The algorithm can be used to solve complex optimization problems in various fields, including management, engineering, economics, biology, etc. The video also provides visual demonstrations of the algorithm in action, making it easy for viewers to follow along and understand the practical applications of the Bat Algorithm. Whether you're a student, researcher, or simply interested in optimization algorithms, this video is a great resource for learning about the Bat Algorithm and its potential applications.
https://wn.com/Bat_Optimization_Algorithm
Bat Optimization Algorithm is an informative and educational video that explains the concept and implementation of a bio-inspired optimization algorithm called the Bat Algorithm. Through clear and concise explanations, viewers will gain an understanding of how the Bat Algorithm is inspired by the echolocation behavior of bats. The algorithm can be used to solve complex optimization problems in various fields, including management, engineering, economics, biology, etc. The video also provides visual demonstrations of the algorithm in action, making it easy for viewers to follow along and understand the practical applications of the Bat Algorithm. Whether you're a student, researcher, or simply interested in optimization algorithms, this video is a great resource for learning about the Bat Algorithm and its potential applications.
- published: 06 May 2023
- views: 720
0:42
Bio-inspired component routing algorithm
Once components are placed on a chip or printed circuit board, you need to draw the tracks that actually connect pins. Tracks must connect groups of pins and th...
Once components are placed on a chip or printed circuit board, you need to draw the tracks that actually connect pins. Tracks must connect groups of pins and they can not cross or overlap. Harder than it seems!
This video shows an alpha version of the algorithm, where track are built by slime mold simulations in a very "organic" way. Only pairs of pins are considered (connecting n is much harder) and a single layer (real PCB have several layers so paths can go up and down to avoid crossings).
https://wn.com/Bio_Inspired_Component_Routing_Algorithm
Once components are placed on a chip or printed circuit board, you need to draw the tracks that actually connect pins. Tracks must connect groups of pins and they can not cross or overlap. Harder than it seems!
This video shows an alpha version of the algorithm, where track are built by slime mold simulations in a very "organic" way. Only pairs of pins are considered (connecting n is much harder) and a single layer (real PCB have several layers so paths can go up and down to avoid crossings).
- published: 13 Jun 2021
- views: 184
11:40
BLAST Algorithm Explained
Basic local alignment search tools, also known as BLAST, is one of the most used algorithm in Bio-informatics. It allows for quick annotation process for sequen...
Basic local alignment search tools, also known as BLAST, is one of the most used algorithm in Bio-informatics. It allows for quick annotation process for sequenced fragment, how does it actually works?
https://wn.com/Blast_Algorithm_Explained
Basic local alignment search tools, also known as BLAST, is one of the most used algorithm in Bio-informatics. It allows for quick annotation process for sequenced fragment, how does it actually works?
- published: 19 Jun 2019
- views: 17809
20:10
Bioinformatics part 5 FASTA algorithm
For more information, log on to-
http://shomusbiology.weebly.com/
Download the study materials here-
http://shomusbiology.weebly.com/bio-materials.html
FASTA i...
For more information, log on to-
http://shomusbiology.weebly.com/
Download the study materials here-
http://shomusbiology.weebly.com/bio-materials.html
FASTA is a DNA and protein sequence alignment software package first described (as FASTP) by David J. Lipman and William R. Pearson in 1985.[1] Its legacy is the FASTA format which is now ubiquitous in bioinformatics.
FASTA takes a given nucleotide or amino-acid sequence and searches a corresponding sequence database by using local sequence alignment to find matches of similar database sequences.
The FASTA program follows a largely heuristic method which contributes to the high speed of its execution. It initially observes the pattern of word hits, word-to-word matches of a given length, and marks potential matches before performing a more time-consuming optimized search using a Smith-Waterman type of algorithm.
The size taken for a word, given by the parameter ktup, controls the sensitivity and speed of the program. Increasing the ktup value decreases number of background hits that are found. From the word hits that are returned the program looks for segments that contain a cluster of nearby hits. It then investigates these segments for a possible match.
There are some differences between fastn and fastp relating to the type of sequences used but both use four steps and calculate three scores to describe and format the sequence similarity results. These are:
Identify regions of highest density in each sequence comparison. Taking a ktup to equal 1 or 2.
In this step all or a group of the identities between two sequences are found using a look up table. The ktup value determines how many consecutive identities are required for a match to be declared. Thus the lesser the ktup value: the more sensitive the search. ktup=2 is frequently taken by users for protein sequences and ktup=4 or 6 for nucleotide sequences. Short oligonucleotides are usually run with ktup = 1. The program then finds all similar local regions, represented as diagonals of a certain length in a dot plot, between the two sequences by counting ktup matches and penalizing for intervening mismatches. This way, local regions of highest density matches in a diagonal are isolated from background hits. For protein sequences BLOSUM50 values are used for scoring ktup matches. This ensures that groups of identities with high similarity scores contribute more to the local diagonal score than to identities with low similarity scores. Nucleotide sequences use the identity matrix for the same purpose. The best 10 local regions selected from all the diagonals put together are then saved.
Rescan the regions taken using the scoring matrices. trimming the ends of the region to include only those contributing to the highest score.
Rescan the 10 regions taken. This time use the relevant scoring matrix while rescoring to allow runs of identities shorter than the ktup value. Also while rescoring conservative replacements that contribute to the similarity score are taken. Though protein sequences use the BLOSUM50 matrix, scoring matrices based on the minimum number of base changes required for a specific replacement, on identities alone, or on an alternative measure of similarity such as PAM, can also be used with the program. For each of the diagonal regions rescanned this way, a subregion with the maximum score is identified. The initial scores found in step1 are used to rank the library sequences. The highest score is referred to as init1 score.
In an alignment if several initial regions with scores greater than a CUTOFF value are found, check whether the trimmed initial regions can be joined to form an approximate alignment with gaps. Calculate a similarity score that is the sum of the joined regions penalising for each gap 20 points. This initial similarity score (initn) is used to rank the library sequences. The score of the single best initial region found in step 2 is reported (init1).
Here the program calculates an optimal alignment of initial regions as a combination of compatible regions with maximal score. This optimal alignment of initial regions can be rapidly calculated using a dynamic programming algorithm. The resulting score initn is used to rank the library sequences.This joining process increases sensitivity but decreases selectivity. A carefully calculated cut-off value is thus used to control where this step is implemented, a value that is approximately one standard deviation above the average score expected from unrelated sequences in the library. A 200-residue query sequence with ktup2 uses a value 28. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers.
Link- http://en.wikipedia.org/wiki/Main_Page
https://wn.com/Bioinformatics_Part_5_Fasta_Algorithm
For more information, log on to-
http://shomusbiology.weebly.com/
Download the study materials here-
http://shomusbiology.weebly.com/bio-materials.html
FASTA is a DNA and protein sequence alignment software package first described (as FASTP) by David J. Lipman and William R. Pearson in 1985.[1] Its legacy is the FASTA format which is now ubiquitous in bioinformatics.
FASTA takes a given nucleotide or amino-acid sequence and searches a corresponding sequence database by using local sequence alignment to find matches of similar database sequences.
The FASTA program follows a largely heuristic method which contributes to the high speed of its execution. It initially observes the pattern of word hits, word-to-word matches of a given length, and marks potential matches before performing a more time-consuming optimized search using a Smith-Waterman type of algorithm.
The size taken for a word, given by the parameter ktup, controls the sensitivity and speed of the program. Increasing the ktup value decreases number of background hits that are found. From the word hits that are returned the program looks for segments that contain a cluster of nearby hits. It then investigates these segments for a possible match.
There are some differences between fastn and fastp relating to the type of sequences used but both use four steps and calculate three scores to describe and format the sequence similarity results. These are:
Identify regions of highest density in each sequence comparison. Taking a ktup to equal 1 or 2.
In this step all or a group of the identities between two sequences are found using a look up table. The ktup value determines how many consecutive identities are required for a match to be declared. Thus the lesser the ktup value: the more sensitive the search. ktup=2 is frequently taken by users for protein sequences and ktup=4 or 6 for nucleotide sequences. Short oligonucleotides are usually run with ktup = 1. The program then finds all similar local regions, represented as diagonals of a certain length in a dot plot, between the two sequences by counting ktup matches and penalizing for intervening mismatches. This way, local regions of highest density matches in a diagonal are isolated from background hits. For protein sequences BLOSUM50 values are used for scoring ktup matches. This ensures that groups of identities with high similarity scores contribute more to the local diagonal score than to identities with low similarity scores. Nucleotide sequences use the identity matrix for the same purpose. The best 10 local regions selected from all the diagonals put together are then saved.
Rescan the regions taken using the scoring matrices. trimming the ends of the region to include only those contributing to the highest score.
Rescan the 10 regions taken. This time use the relevant scoring matrix while rescoring to allow runs of identities shorter than the ktup value. Also while rescoring conservative replacements that contribute to the similarity score are taken. Though protein sequences use the BLOSUM50 matrix, scoring matrices based on the minimum number of base changes required for a specific replacement, on identities alone, or on an alternative measure of similarity such as PAM, can also be used with the program. For each of the diagonal regions rescanned this way, a subregion with the maximum score is identified. The initial scores found in step1 are used to rank the library sequences. The highest score is referred to as init1 score.
In an alignment if several initial regions with scores greater than a CUTOFF value are found, check whether the trimmed initial regions can be joined to form an approximate alignment with gaps. Calculate a similarity score that is the sum of the joined regions penalising for each gap 20 points. This initial similarity score (initn) is used to rank the library sequences. The score of the single best initial region found in step 2 is reported (init1).
Here the program calculates an optimal alignment of initial regions as a combination of compatible regions with maximal score. This optimal alignment of initial regions can be rapidly calculated using a dynamic programming algorithm. The resulting score initn is used to rank the library sequences.This joining process increases sensitivity but decreases selectivity. A carefully calculated cut-off value is thus used to control where this step is implemented, a value that is approximately one standard deviation above the average score expected from unrelated sequences in the library. A 200-residue query sequence with ktup2 uses a value 28. Source of the article published in description is Wikipedia. I am sharing their material. Copyright by original content developers.
Link- http://en.wikipedia.org/wiki/Main_Page
- published: 29 Oct 2013
- views: 132096