This document summarizes the problem of image spam detection and existing solutions. It begins by defining image spam as spam where the message text is presented as a picture in an image file, allowing it to evade normal email filtering. It then discusses how image spam undermines current filters' ability to efficiently detect spam within images due to obfuscation techniques. Finally, it outlines the impact of spam, how spammers collect emails, and different types of spam threats like phishing, appending, and blank spam that pose risks to users' inboxes.
Detecting Spambot as an Antispam Technique for Web Internet BBSijsrd.com
Spam which is one of the most popular and also the most relevant topic that needs to be understood in the current scenario. Everyone whether it may be a small child or an old person are using emails everyday all around the world. The scenario which we are seeing is that almost no one is aware or in simple sentence they do not know what actually the spam is and what they will do in their systems. Spam in general means unsolicited or unwanted mails. Botnets are considered one of the main source of the spam. Botnet means the group of software's called bots and the function of these bots is to run on several compromised computers autonomously and automatically. The main objective of this paper is to detect such a bot or spambots for the Bulletin Board System (BBS). BBS is a computer that is running software that allows users to leave a message and access information of general interest. Originally BBSes were accessed only over a phone line using a modem, but nowadays some BBSes allowed access via a Telnet, packet switched network, or packet radio connection. The main methodology that we are going to focus is on Behavioural-based Spam Detection (BSD) method. Behavioral-based Spam Detector (BSD) combines several behaviours of the spam bots at different stages including the behaviour of spam preparation before the spam session when the spammers search for an open relay SMTP service to send e-mails through, and the behaviour of spammers while connecting to the mail server. Detecting the abnormal behaviour produced by the spam activities gives a high rate of suspicion on the existence of bots.
Overview of Existing Methods of Spam Mining and Potential Usefulness of Sende...IDES Editor
This paper gives an overview and analyzes the
different existing anti spamming techniques both content based,
non content based methods and a combination of the duo. It
also suggests that a small change in one of the parameters in
the fuzzy string matching method could be useful to produce
better results.
Collateral Damage:
Consequences of Spam and Virus Filtering for the E-Mail S...Peter Eisentraut
This paper takes a critical look at the impact that contemporary spam
and virus filter techniques have on the stability, performance, and usability
of the e-mail system.
Detecting Spambot as an Antispam Technique for Web Internet BBSijsrd.com
Spam which is one of the most popular and also the most relevant topic that needs to be understood in the current scenario. Everyone whether it may be a small child or an old person are using emails everyday all around the world. The scenario which we are seeing is that almost no one is aware or in simple sentence they do not know what actually the spam is and what they will do in their systems. Spam in general means unsolicited or unwanted mails. Botnets are considered one of the main source of the spam. Botnet means the group of software's called bots and the function of these bots is to run on several compromised computers autonomously and automatically. The main objective of this paper is to detect such a bot or spambots for the Bulletin Board System (BBS). BBS is a computer that is running software that allows users to leave a message and access information of general interest. Originally BBSes were accessed only over a phone line using a modem, but nowadays some BBSes allowed access via a Telnet, packet switched network, or packet radio connection. The main methodology that we are going to focus is on Behavioural-based Spam Detection (BSD) method. Behavioral-based Spam Detector (BSD) combines several behaviours of the spam bots at different stages including the behaviour of spam preparation before the spam session when the spammers search for an open relay SMTP service to send e-mails through, and the behaviour of spammers while connecting to the mail server. Detecting the abnormal behaviour produced by the spam activities gives a high rate of suspicion on the existence of bots.
Overview of Existing Methods of Spam Mining and Potential Usefulness of Sende...IDES Editor
This paper gives an overview and analyzes the
different existing anti spamming techniques both content based,
non content based methods and a combination of the duo. It
also suggests that a small change in one of the parameters in
the fuzzy string matching method could be useful to produce
better results.
Collateral Damage:
Consequences of Spam and Virus Filtering for the E-Mail S...Peter Eisentraut
This paper takes a critical look at the impact that contemporary spam
and virus filter techniques have on the stability, performance, and usability
of the e-mail system.
AN ANALYSIS OF EFFECTIVE ANTI SPAM PROTOCOL USING DECISION TREE CLASSIFIERSijsrd.com
As the internet usage increases in day to day activities, there is an inherent corresponding increase in usage of communication through it with email being the mainstay or rather in the forefront of modern day communication methodologies for businesses and general persons as well. This has led to get customer attention in the form of unwanted and unsolicited bombarding of the customers mail accounts with advertisements, offers, phishing activities, viruses, worms, trojans, generating hate crimes, making the customer to part with sensitive information like passwords, and other media as well which is known as spam. Spam is mass mailing or flooding of mail account servers with unwanted trash data causing damage some times. Spam filters have been in use from the time such mail flooding happens. Most of the spam filters are manual meaning which the user after identifying a mail in his account blocks the sender and henceforth the system will not allow mails to the inbox from such addresses. However the spammers are resilient and send spam mails from different identities and flood the inboxes. This study focuses on algorithms and data mining techniques used to unearth spam mails. They filter the inbox mails as they arrive at the server depending on certain rules which are already defined known as supervised learning methods. Such technologies are known as knowledge engineering techniques. Here a decision classifier is used to train such mails with varying words to filter and identify the words in the mail as spam. The Decision Tree model is used to analyze the mails and identify spam mails and block them. The number of mails sent, content, subject, type whether reply or forward, language etc. are identified using the decision classifier like Naves Bayes and analyzed accordingly to filter the emails.
Now a days Short Message Service(SMS) is most popular way to communication for mobile user because it is cheapest mode or version for communication than other mode.SMS is used for transmitting short length msg of around 160 character to different devices such as smart phones, cellular phones, PDAs using standardized communication protocols. The amount of Short Message Service (SMS) spam is increasing. SMS spam should be put into the spam folder, not the inbox. The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. To avoid this problem SMS filtering Techniques are used. Our proposed approach filters SMS spam on an independent mobile phone on a large dataset and acceptable processing time. There are different approaches able to automatically detect and remove most of these messages, and the best-known ones are based on Bayesian decision theory and Support Vector Machines. Riya Mehta | Ankita Gandhi"A Survey: SMS Spam Filtering" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12850.pdf http://www.ijtsrd.com/computer-science/data-miining/12850/a-survey-sms-spam-filtering/riya-mehta
E-Mail Security Using Spam Mail Detection and Filtering Systemrahulmonikasharma
Electronic mail, also known as email or e-mail, is a method of exchanging digital messages from an author to one or more recipients. Email is the most efficient way to communicate or transfer our data from one to another. While transferring or communicating through email there is the possibility of misbehave. In the existing system Spam method is used to avoid the unwanted Email receiving. Email spam, also known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted email messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. But in Spam method there is no way to prevent the unwanted messages or Email receiving. To solve these unwanted messages or Email receiving we propose the concept Email misbehave blocking system. In the proposed method we permanently prevent the incoming unwanted messages or Email through blocking system.
E-Mail Security Using Spam Mail Detection and Filtering Systemrahulmonikasharma
Electronic mail, also known as email or e-mail, is a method of exchanging digital messages from an author to one or more recipients. Email is the most efficient way to communicate or transfer our data from one to another. While transferring or communicating through email there is the possibility of misbehave. In the existing system Spam method is used to avoid the unwanted Email receiving. Email spam, also known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted email messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. But in Spam method there is no way to prevent the unwanted messages or Email receiving. To solve these unwanted messages or Email receiving we propose the concept Email misbehave blocking system. In the proposed method we permanently prevent the incoming unwanted messages or Email through blocking system.
Have you ever received an e-mail from someone telling you about something you do not want to hear? Well, that is internet spam, and has taken over the personal and business world over the past decade. Spam is a big issue in the US and has cost businesses and people millions of dollars to combat. Please reference my upload describing the effects of Spam on society and also how society plans to deal with this threat.
Analysis of an image spam in email based on content analysisijnlc
Researchers initially have addressed the problem of spam detection as a text classification or
categorization problem. However, as spammers’ continue to develop new techniques and the type of email
content becomes more disparate, text-based anti-spam approaches alone are not sufficiently enough in
preventing spam. In an attempt to defeat the anti-spam development technologies, spammers have recently
adopted the image spam trick to make the scrutiny of emails’ body text inefficient. The main idea behind
this project is to design a spam detection system. The system will be enabled to analyze the content of
emails, in particular the artificially generated image sent as attachment in an email. The system will
analyze the image content and classify the embedded image as spam or legitimate hence classify the email
accordingly.
A multi layer architecture for spam-detection systemcsandit
As the email is becoming a prominent mode of commun
ication so are the attempts to misuse it to
take undue advantage of its low cost and high reach
ability. However, as email communication
is very cheap, spammers are taking advantage of it
for advertising their products, for
committing cybercrimes. So, researchers are working
hard to combat with the spammers. Many
spam detections techniques and systems are built to
fight spammers. But the spammers are
continuously finding new ways to defeat the existin
g filters. This paper describes the existing
spam filters techniques and proposes a multi-level
architecture for spam email detection. We
present the analysis of the architecture to prove t
he effectiveness of the architecture
A multi layer architecture for spam-detection systemcsandit
As the email is becoming a prominent mode of communication so are the attempts to misuse it to
take undue advantage of its low cost and high reachability. However, as email communication
is very cheap, spammers are taking advantage of it for advertising their products, for
committing cybercrimes. So, researchers are working hard to combat with the spammers. Many
spam detections techniques and systems are built to fight spammers. But the spammers are
continuously finding new ways to defeat the existing filters. This paper describes the existing
spam filters techniques and proposes a multi-level architecture for spam email detection. We
present the analysis of the architecture to prove the effectiveness of the architecture.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in
fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data,
incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard
challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier
with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant
features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
AN ANALYSIS OF EFFECTIVE ANTI SPAM PROTOCOL USING DECISION TREE CLASSIFIERSijsrd.com
As the internet usage increases in day to day activities, there is an inherent corresponding increase in usage of communication through it with email being the mainstay or rather in the forefront of modern day communication methodologies for businesses and general persons as well. This has led to get customer attention in the form of unwanted and unsolicited bombarding of the customers mail accounts with advertisements, offers, phishing activities, viruses, worms, trojans, generating hate crimes, making the customer to part with sensitive information like passwords, and other media as well which is known as spam. Spam is mass mailing or flooding of mail account servers with unwanted trash data causing damage some times. Spam filters have been in use from the time such mail flooding happens. Most of the spam filters are manual meaning which the user after identifying a mail in his account blocks the sender and henceforth the system will not allow mails to the inbox from such addresses. However the spammers are resilient and send spam mails from different identities and flood the inboxes. This study focuses on algorithms and data mining techniques used to unearth spam mails. They filter the inbox mails as they arrive at the server depending on certain rules which are already defined known as supervised learning methods. Such technologies are known as knowledge engineering techniques. Here a decision classifier is used to train such mails with varying words to filter and identify the words in the mail as spam. The Decision Tree model is used to analyze the mails and identify spam mails and block them. The number of mails sent, content, subject, type whether reply or forward, language etc. are identified using the decision classifier like Naves Bayes and analyzed accordingly to filter the emails.
Now a days Short Message Service(SMS) is most popular way to communication for mobile user because it is cheapest mode or version for communication than other mode.SMS is used for transmitting short length msg of around 160 character to different devices such as smart phones, cellular phones, PDAs using standardized communication protocols. The amount of Short Message Service (SMS) spam is increasing. SMS spam should be put into the spam folder, not the inbox. The growth of the mobile phone users has led to a dramatic increase in SMS spam messages. To avoid this problem SMS filtering Techniques are used. Our proposed approach filters SMS spam on an independent mobile phone on a large dataset and acceptable processing time. There are different approaches able to automatically detect and remove most of these messages, and the best-known ones are based on Bayesian decision theory and Support Vector Machines. Riya Mehta | Ankita Gandhi"A Survey: SMS Spam Filtering" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12850.pdf http://www.ijtsrd.com/computer-science/data-miining/12850/a-survey-sms-spam-filtering/riya-mehta
E-Mail Security Using Spam Mail Detection and Filtering Systemrahulmonikasharma
Electronic mail, also known as email or e-mail, is a method of exchanging digital messages from an author to one or more recipients. Email is the most efficient way to communicate or transfer our data from one to another. While transferring or communicating through email there is the possibility of misbehave. In the existing system Spam method is used to avoid the unwanted Email receiving. Email spam, also known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted email messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. But in Spam method there is no way to prevent the unwanted messages or Email receiving. To solve these unwanted messages or Email receiving we propose the concept Email misbehave blocking system. In the proposed method we permanently prevent the incoming unwanted messages or Email through blocking system.
E-Mail Security Using Spam Mail Detection and Filtering Systemrahulmonikasharma
Electronic mail, also known as email or e-mail, is a method of exchanging digital messages from an author to one or more recipients. Email is the most efficient way to communicate or transfer our data from one to another. While transferring or communicating through email there is the possibility of misbehave. In the existing system Spam method is used to avoid the unwanted Email receiving. Email spam, also known as unsolicited bulk Email (UBE), junk mail, or unsolicited commercial email (UCE), is the practice of sending unwanted email messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. But in Spam method there is no way to prevent the unwanted messages or Email receiving. To solve these unwanted messages or Email receiving we propose the concept Email misbehave blocking system. In the proposed method we permanently prevent the incoming unwanted messages or Email through blocking system.
Have you ever received an e-mail from someone telling you about something you do not want to hear? Well, that is internet spam, and has taken over the personal and business world over the past decade. Spam is a big issue in the US and has cost businesses and people millions of dollars to combat. Please reference my upload describing the effects of Spam on society and also how society plans to deal with this threat.
Analysis of an image spam in email based on content analysisijnlc
Researchers initially have addressed the problem of spam detection as a text classification or
categorization problem. However, as spammers’ continue to develop new techniques and the type of email
content becomes more disparate, text-based anti-spam approaches alone are not sufficiently enough in
preventing spam. In an attempt to defeat the anti-spam development technologies, spammers have recently
adopted the image spam trick to make the scrutiny of emails’ body text inefficient. The main idea behind
this project is to design a spam detection system. The system will be enabled to analyze the content of
emails, in particular the artificially generated image sent as attachment in an email. The system will
analyze the image content and classify the embedded image as spam or legitimate hence classify the email
accordingly.
A multi layer architecture for spam-detection systemcsandit
As the email is becoming a prominent mode of commun
ication so are the attempts to misuse it to
take undue advantage of its low cost and high reach
ability. However, as email communication
is very cheap, spammers are taking advantage of it
for advertising their products, for
committing cybercrimes. So, researchers are working
hard to combat with the spammers. Many
spam detections techniques and systems are built to
fight spammers. But the spammers are
continuously finding new ways to defeat the existin
g filters. This paper describes the existing
spam filters techniques and proposes a multi-level
architecture for spam email detection. We
present the analysis of the architecture to prove t
he effectiveness of the architecture
A multi layer architecture for spam-detection systemcsandit
As the email is becoming a prominent mode of communication so are the attempts to misuse it to
take undue advantage of its low cost and high reachability. However, as email communication
is very cheap, spammers are taking advantage of it for advertising their products, for
committing cybercrimes. So, researchers are working hard to combat with the spammers. Many
spam detections techniques and systems are built to fight spammers. But the spammers are
continuously finding new ways to defeat the existing filters. This paper describes the existing
spam filters techniques and proposes a multi-level architecture for spam email detection. We
present the analysis of the architecture to prove the effectiveness of the architecture.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data, incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Spam Detection in Social Networks Using Correlation Based Feature Subset Sele...Editor IJCATR
Bayesian classifier works efficiently on some fields, and badly on some. The performance of Bayesian Classifier suffers in
fields that involve correlated features. Feature selection is beneficial in reducing dimensionality, removing irrelevant data,
incrementing learning accuracy, and improving result comprehensibility. But, the recent increase of dimensionality of data place a hard
challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this paper, Bayesian Classifier
with Correlation Based Feature Selection is introduced which can key out relevant features as well as redundancy among relevant
features without pair wise correlation analysis. The efficiency and effectiveness of our method is presented through broad.
Similar to IRJET- Image Spam Detection: Problem and Existing Solution (20)
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.