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The Knuth-Morris-Pratt (KMP) algorithm is a pattern matching algorithm used to search for a pattern within a text. It was introduced in 1974 by Donald Knuth, Vaughan Pratt, and James H. Morris, and jointly published by all three in 1977. The KMP algorithm works by comparing each character in the pattern to characters in the text. If a mismatch is found, the pattern is shifted according to a prefix table, which specifies how many positions to shift the pattern. The prefix table is derived from the length of substrings in the pattern.
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Knuth–Morris–Pratt Algorithm | Computer Science
In Software Engineering, the Knuth–Morris–Pratt string looking calculation (or KMP calculation) scans for events of a "word" W inside a principle "content string" S by utilizing the perception that when a jumble happens, the word itself epitomizes adequate data to figure out where the following match could start, accordingly bypassing reconsideration of beforehand coordinated characters. Copy the link given below and paste it in new browser window to get more information on Knuth-Morris-Pratt-Algorithm:- http://www.transtutors.com/homework-help/computer-science/knuth-marris-pratt-algorithm.aspx
IMPLEMENTATION OF DIFFERENT PATTERN RECOGNITION ALGORITHM
IMPLEMENTATION OF DIFFERENT PATTERN RECOGNITION ALGORITHM NETAJI SUBHASH ENGINEERING COLLEGE , KOLKATA
WHAT IS PATTERN RECOGNISITION ?
Given a text string T[0..n-1] and a pattern P[0..m-1], find all occurrences of the pattern within the text.
Example: T = 000010001010001 and P = 0001, the occurrences are:
first occurrence starts at T[1]
second occurrence starts at T[5]
third occurrence starts at T[11]
APPLICATION :
Image preprocessing
• Computer vision
• Artificial intelligence
• Radar signal classification/analysis
• Speech recognition/understanding
• Fingerprint identification
• Character (letter or number) recognition
• Handwriting analysis
• Electro-cardiographic signal analysis/understanding
• Medical diagnosis
• Data mining/reduction
ALGORITHM DESCRIPTION
The Knuth-Morris-Pratt (KMP) algorithm:
It was published by Donald E. Knuth, James H.Morris and Vaughan R. Pratt, 1977 in: “Fast Pattern Matching in Strings.“
To illustrate the ideas of the algorithm, consider the following example:T = xyxxyxyxyyxyxyxyyxyxyxxy
And P = xyxyyxyxyxx
it considers shifts in order from 1 to n-m, and determines if the pattern matches at that shift. The difference is that the KMP algorithm uses information gleaned from partial matches of the pattern and text to skip over shifts that are guaranteed not to result in a match.
The text and pattern are included in Figure 1, with numbering, to make it easier to follow.
1.Consider the situation when P[1……3] is successfully matched with T[1……..3]. We then find a mismatch: P[4] = T[4]. Based on our knowledge that P[1…… 3] =T[1…… 3], and ignoring symbols of the pattern and text after position 3, what can we deduce about where a potential match might be? In this case, the algorithm slides the pattern 2 positions to the right so that P[1] is lined up with T[3]. The next comparison is between P[2] and T[4].
P: x y x y y x y x y x x
q: 1 2 3 4 5 6 7 8 9 10 11
_(q): 0 0 1 2 0 3
Table 1: Table of values for pattern P.
Time Complexity :
The call to compute prefix is O(m)
using q as the value of the potential function, we argue in the same manner as above to show the loop is O(n)
Therefore the overall complexity is O(m + n)
Boyer-Moore algorithm:
It was developed by Bob Boyer and J Strother Moore in 1977. The algorithm preprocesses the pattern string that is being searched in text string.
String pattern matching - Boyer-Moore:
This algorithm uses fail-functions to shift the pattern efficiently. Boyer-Moore starts however at the end of the pattern, which can result in larger shifts.Two heuristics are used:1: if we encounter a mismatch at character c in Q, we can shift to the first occurrence of c in P from the right:
Q a b c a b c d g a b c e a b c d a c e d
P a b c e b c d
a b c e b c d
(restart here)
Time Complexity:
• performs the comparisons from right to left;
• preprocessing phase in O(m+ ) tString Searching and Matching
Novel Approach for String Searching and Matching using American Standard Code for Information Interchange Value
String matching algorithms-pattern matching.
Algorithms Discussed
Knuth–Morris–Pratt algorithm
Boyer–Moore string search algorithm
Bitap algorithm (for exact string searching)
-------------------
Checking whether two or more strings are same or not.
Finding a string (pattern) into another string (text). --> Looking for substring
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An Application of Pattern matching for Motif Identification
Pattern matching is one of the central and most widely studied problem in theoretical computer science. Solutions to the problem play an important role in many areas of science and information processing. Its performance has great impact on many applications including database query, text processing and DNA sequence analysis. In general Pattern matching algorithms are based on the shift value, the direction of the sliding window and the order in which comparisons are made. The performance of the algorithms can be enhanced to a great extent by a larger shift value and less number of comparison to get the shift value. In this paper we proposed an algorithm, for finding motif in DNA sequence. The algorithm is based on preprocessing of the pattern string(motif) by considering four consecutive nucleotides of the DNA that immediately follow the aligned pattern window in an event of mismatch between pattern(motif) and DNA sequence .Theoretically, we found the proposed algorithms work efficiently for motif identification.
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Knuth–Morris–Pratt Algorithm | Computer Science
In Software Engineering, the Knuth–Morris–Pratt string looking calculation (or KMP calculation) scans for events of a "word" W inside a principle "content string" S by utilizing the perception that when a jumble happens, the word itself epitomizes adequate data to figure out where the following match could start, accordingly bypassing reconsideration of beforehand coordinated characters. Copy the link given below and paste it in new browser window to get more information on Knuth-Morris-Pratt-Algorithm:- http://www.transtutors.com/homework-help/computer-science/knuth-marris-pratt-algorithm.aspx
IMPLEMENTATION OF DIFFERENT PATTERN RECOGNITION ALGORITHM
IMPLEMENTATION OF DIFFERENT PATTERN RECOGNITION ALGORITHM NETAJI SUBHASH ENGINEERING COLLEGE , KOLKATA
WHAT IS PATTERN RECOGNISITION ?
Given a text string T[0..n-1] and a pattern P[0..m-1], find all occurrences of the pattern within the text.
Example: T = 000010001010001 and P = 0001, the occurrences are:
first occurrence starts at T[1]
second occurrence starts at T[5]
third occurrence starts at T[11]
APPLICATION :
Image preprocessing
• Computer vision
• Artificial intelligence
• Radar signal classification/analysis
• Speech recognition/understanding
• Fingerprint identification
• Character (letter or number) recognition
• Handwriting analysis
• Electro-cardiographic signal analysis/understanding
• Medical diagnosis
• Data mining/reduction
ALGORITHM DESCRIPTION
The Knuth-Morris-Pratt (KMP) algorithm:
It was published by Donald E. Knuth, James H.Morris and Vaughan R. Pratt, 1977 in: “Fast Pattern Matching in Strings.“
To illustrate the ideas of the algorithm, consider the following example:T = xyxxyxyxyyxyxyxyyxyxyxxy
And P = xyxyyxyxyxx
it considers shifts in order from 1 to n-m, and determines if the pattern matches at that shift. The difference is that the KMP algorithm uses information gleaned from partial matches of the pattern and text to skip over shifts that are guaranteed not to result in a match.
The text and pattern are included in Figure 1, with numbering, to make it easier to follow.
1.Consider the situation when P[1……3] is successfully matched with T[1……..3]. We then find a mismatch: P[4] = T[4]. Based on our knowledge that P[1…… 3] =T[1…… 3], and ignoring symbols of the pattern and text after position 3, what can we deduce about where a potential match might be? In this case, the algorithm slides the pattern 2 positions to the right so that P[1] is lined up with T[3]. The next comparison is between P[2] and T[4].
P: x y x y y x y x y x x
q: 1 2 3 4 5 6 7 8 9 10 11
_(q): 0 0 1 2 0 3
Table 1: Table of values for pattern P.
Time Complexity :
The call to compute prefix is O(m)
using q as the value of the potential function, we argue in the same manner as above to show the loop is O(n)
Therefore the overall complexity is O(m + n)
Boyer-Moore algorithm:
It was developed by Bob Boyer and J Strother Moore in 1977. The algorithm preprocesses the pattern string that is being searched in text string.
String pattern matching - Boyer-Moore:
This algorithm uses fail-functions to shift the pattern efficiently. Boyer-Moore starts however at the end of the pattern, which can result in larger shifts.Two heuristics are used:1: if we encounter a mismatch at character c in Q, we can shift to the first occurrence of c in P from the right:
Q a b c a b c d g a b c e a b c d a c e d
P a b c e b c d
a b c e b c d
(restart here)
Time Complexity:
• performs the comparisons from right to left;
• preprocessing phase in O(m+ ) tString Searching and Matching
Novel Approach for String Searching and Matching using American Standard Code for Information Interchange Value
String matching algorithms-pattern matching.
Algorithms Discussed
Knuth–Morris–Pratt algorithm
Boyer–Moore string search algorithm
Bitap algorithm (for exact string searching)
-------------------
Checking whether two or more strings are same or not.
Finding a string (pattern) into another string (text). --> Looking for substring
Commentz-Walter: Any Better than Aho-Corasick for Peptide Identification?
An algorithm for locating all occurrences of a finite number of keywords in an arbitrary string, also known as multiple strings matching, is commonly required in information retrieval (such as sequence analysis, evolutionary biological studies, gene/protein identification and network intrusion detection) and text editing applications. Although Aho-Corasick was one of the commonly used exact multiple strings matching algorithm, Commentz-Walter has been introduced as a better alternative in the recent past. Comments-Walter algorithm combines ideas from both Aho-Corasick and Boyer Moore. Large scale rapid and accurate peptide identification is critical in computational proteomics. In this paper, we have critically analyzed the time complexity of Aho-Corasick and Commentz-Walter for their suitability in large scale peptide identification. According to the results we obtained for our dataset, we conclude that Aho-Corasick is performing better than Commentz-Walter as opposed to the common beliefs.
Algorithm of Dynamic Programming for Paper-Reviewer Assignment Problem
https://irjet.net/archives/V4/i11/IRJET-V4I11349.pdf
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Pattern matching is one of the central and most widely studied problem in theoretical computer science. Solutions to the problem play an important role in many areas of science and information processing. Its performance has great impact on many applications including database query, text processing and DNA sequence analysis. In general Pattern matching algorithms are based on the shift value, the direction of the sliding window and the order in which comparisons are made. The performance of the algorithms can be enhanced to a great extent by a larger shift value and less number of comparison to get the shift value. In this paper we proposed an algorithm, for finding motif in DNA sequence. The algorithm is based on preprocessing of the pattern string(motif) by considering four consecutive nucleotides of the DNA that immediately follow the aligned pattern window in an event of mismatch between pattern(motif) and DNA sequence .Theoretically, we found the proposed algorithms work efficiently for motif identification.
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Essentials of Automations: Optimizing FME Workflows with Parameters
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Assuring Contact Center Experiences for Your Customers With ThousandEyes
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Connector Corner: Automate dynamic content and events by pushing a button
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
JMeter webinar - integration with InfluxDB and Grafana
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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https://www.hubspot.com/state-of-marketing
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· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
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· TLDR; Get on social, try AI, and align your systems.
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ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
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How Race, Age and Gender Shape Attitudes Towards Mental Health
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
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Technology
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While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
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It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
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Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
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Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
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KMP.ppt
- 2. Knuth – Morris – Pratt algorithm is a PATTERN matching algorithm
- 3. Knuth – Morris – Pratt algorithm is used to search a pattern in a text
- 4. Knuth – Morris – Pratt algorithm was introduced in 1974 by Donald Knuth and Vaughan Pratt, and independently by James H. Morris
- 5. Knuth – Morris – Pratt algorithm was jointly published by all three, in 1977
- 6. Knuth – Morris – Pratt algorithm We use two terms, Pattern and Text Pattern Word which we want to search Text Data in which we want to search for pattern
- 7. How does KMP work Every character in Pattern is compared with characters in Text If all characters in Pattern are matching with characters in text, then search success If any mismatch found then, shift the pattern according to Prefix Table and continue with comparison
- 8. What is Prefix Table A table derived for Pattern, which specifies “How many positions the pattern has to shift” when we found mismatch 0 1 2 3 4 5 6 A B C D A B D 0 0 0 0 1 2 1 Index Pattern Shifting Value
- 9. 0 0 0 0 1 2 1 How to create Prefix Table A prefix table is created using length of substrings in pattern 0 1 2 3 4 5 6 A B C D A B D 0 0 0 0 1 2 1 Index Pattern Shifting Value
- 10. 0 0 0 0 1 2 1 How to create Prefix Table A prefix table is created using length of substrings in pattern 0 1 2 3 4 5 6 A B C D A B D 0 0 0 0 1 2 1 Index Pattern Shifting Value