This document discusses techniques for detecting and filtering email spam. It begins with an introduction to the growing problem of spam emails and outlines some common characteristics of spam messages like sender information, word usage, and content. It then describes several techniques used for spam filtering, including traditional classification approaches like Naive Bayes and Support Vector Machines, ontology-based methods, graph mining approaches, and neural network methods. Specific algorithms discussed in more detail include Naive Bayes, k-means clustering, and neural networks. The document concludes that machine learning methods show promise for improving spam filtering but that spam remains a significant challenge for internet users and systems.
This document discusses email spam detection. It begins with the objectives of classifying emails as spam or ham (legitimate emails) and providing users knowledge about fake vs real emails. It introduces spam as unsolicited commercial email involving mass mailing, explains how spammers obtain emails and the spam lifecycle. The document also outlines different types of spam filters including header, Bayesian, permission and content filters. It provides a flowchart of the email processing and preprocessing steps like removing stop words and lemmatization (grouping word forms). Finally, it discusses the scope of the project, including increased security and reduced costs.
Evaluation of Spam Detection and Prevention Frameworks for Email and Image Sp...Pedram Hayati
In recent years, online spam has become a major problem for
the sustainability of the Internet. Excessive amounts of spam
are not only reducing the quality of information available on
the Internet but also creating concern amongst search engines
and web users. This paper aims to analyse existing works in
two different categories of spam domains - email spam and
image spam to gain a deeper understanding of this problem.
Future research directions are also presented in these spam
domains.
More info: http://debii.curtin.edu.au/~pedram/research/publications/76-evaluation-of-spam-detection-and-prevention-frameworks-for-email-and-image-spam-a-state-of-art.html
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
The document discusses spam filtering techniques. It begins by defining spam and its purposes. It then discusses the problems caused by spam and some statistics about its prevalence and costs. The document outlines federal regulations regarding spam and how spammers harvest email addresses. It describes different types of spam filters and how Bayesian filtering uses probabilities to classify emails as spam or not spam. The document discusses how data mining can be used for spam filtering and concludes that while no technique is perfect, data mining approaches show promise.
This document discusses web spam detection using machine learning techniques. Specifically, it proposes an improved Naive Bayes classifier that incorporates user feedback and domain-specific features to better detect spam pages. The key points are:
1) Web spam has become a serious problem as internet usage has increased, threatening search engines and users. Spam pages aim to deceive search engines' ranking algorithms.
2) Existing spam detection techniques like content analysis are still lacking and Naive Bayes classifiers are commonly used but have limitations like treating all terms equally.
3) The paper proposes an improved Naive Bayes classifier that assigns different weights to terms based on user feedback and considers domain-specific features to reduce false positives and negatives and improve accuracy
This document summarizes spamming and spam filtering techniques. It discusses how spamming works by sending unsolicited messages from individual email accounts or open relay servers. It then outlines various spam filtering methods like blacklist, whitelist, content-based filters that analyze words or use heuristics. The document implements a simple spam sending program and shows how gmail and outlook spam filters work. It concludes by discussing the effectiveness of different filtering approaches and references further reading on minimizing spam effects.
This document discusses email spam detection. It begins with the objectives of classifying emails as spam or ham (legitimate emails) and providing users knowledge about fake vs real emails. It introduces spam as unsolicited commercial email involving mass mailing, explains how spammers obtain emails and the spam lifecycle. The document also outlines different types of spam filters including header, Bayesian, permission and content filters. It provides a flowchart of the email processing and preprocessing steps like removing stop words and lemmatization (grouping word forms). Finally, it discusses the scope of the project, including increased security and reduced costs.
Evaluation of Spam Detection and Prevention Frameworks for Email and Image Sp...Pedram Hayati
In recent years, online spam has become a major problem for
the sustainability of the Internet. Excessive amounts of spam
are not only reducing the quality of information available on
the Internet but also creating concern amongst search engines
and web users. This paper aims to analyse existing works in
two different categories of spam domains - email spam and
image spam to gain a deeper understanding of this problem.
Future research directions are also presented in these spam
domains.
More info: http://debii.curtin.edu.au/~pedram/research/publications/76-evaluation-of-spam-detection-and-prevention-frameworks-for-email-and-image-spam-a-state-of-art.html
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
The document discusses spam filtering techniques. It begins by defining spam and its purposes. It then discusses the problems caused by spam and some statistics about its prevalence and costs. The document outlines federal regulations regarding spam and how spammers harvest email addresses. It describes different types of spam filters and how Bayesian filtering uses probabilities to classify emails as spam or not spam. The document discusses how data mining can be used for spam filtering and concludes that while no technique is perfect, data mining approaches show promise.
This document discusses web spam detection using machine learning techniques. Specifically, it proposes an improved Naive Bayes classifier that incorporates user feedback and domain-specific features to better detect spam pages. The key points are:
1) Web spam has become a serious problem as internet usage has increased, threatening search engines and users. Spam pages aim to deceive search engines' ranking algorithms.
2) Existing spam detection techniques like content analysis are still lacking and Naive Bayes classifiers are commonly used but have limitations like treating all terms equally.
3) The paper proposes an improved Naive Bayes classifier that assigns different weights to terms based on user feedback and considers domain-specific features to reduce false positives and negatives and improve accuracy
This document summarizes spamming and spam filtering techniques. It discusses how spamming works by sending unsolicited messages from individual email accounts or open relay servers. It then outlines various spam filtering methods like blacklist, whitelist, content-based filters that analyze words or use heuristics. The document implements a simple spam sending program and shows how gmail and outlook spam filters work. It concludes by discussing the effectiveness of different filtering approaches and references further reading on minimizing spam effects.
Understand how a SPAM filter works. In this interactive webinar, we follow the path of an email from your server to the recipient's inbox and explain the end-to-end trials and tribulations of an email message as it flows from your outbox to (hopefully) the recipients inbox. This webinar is more technical than our previous email marketing webinars.-
Topics Covered:
• How current enterprise email filters work
• Tips to avoid getting accidentally blocked
• Tracking an email from send to delivery with possible pitfalls along the way
Presenters: Craig Stouffer, GM | Pinpointe and Mark Feldman, Marketing VP | NetProspex
This document provides an introduction and overview of text analytics for SMS spam filtering classification. It discusses the classification of spam and ham SMS, describes the company Sky Bits Technology which focuses on analytics solutions, and performs Porter's Five Forces and SWOT analyses of the analytics industry and company. It also covers basic concepts in text mining such as preprocessing, transformation, feature selection, and classification methods. The objective is to develop a text classification model using R Studio to automatically categorize SMS as spam or ham.
This document discusses various techniques for filtering image spam in emails. It begins with introducing email spam and image spam, then describes types of image spam and spam content. It discusses the lifecycle of spam and various antispam techniques, including techniques that operate before spam is sent, after it is sent, and after it reaches mailboxes. It also covers existing techniques like analyzing spam characteristics, transmission protocols, local changes, language-based filters, non-content features, content-based classification, and hybrid filters. In the end, it emphasizes that hybrid techniques can effectively combine various filtering models.
Spam and Anti-spam - Sudipta Bhattacharyasankhadeep
The document discusses spam emails and anti-spam techniques. It defines spam emails, describes how spammers earn money and send spam emails. It also discusses the costs of spam emails, various types of spam like email spam, chat spam and search engine spam. The document then covers techniques used by individuals, email administrators and email senders to prevent spam emails. These include filtering, blocking, authentication and legal enforcement. The conclusion states that no single technique can fully solve the spam problem and both users and administrators need to use different anti-spam methods.
An Approach for Malicious Spam Detection in Email with Comparison of Differen...IRJET Journal
This document summarizes a research paper that proposes a model to improve detection of malicious spam emails through feature selection. The model employs a novel dataset for feature selection to optimize classification parameters, prediction accuracy, and computation time. Feature selection is expected to improve training time and classification accuracy. The paper also compares various classifiers, including Naive Bayes and Support Vector Machine, on the selected feature subset. The goal is to automatically learn to detect malicious spam emails, which threaten privacy and security by spreading malware, phishing links, and sensitive data theft.
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 refers to sending unwanted messages in large quantities to many recipients. There are several types of spam, including email spam, social networking spam, blog spam, SMS spam, forum spam, video sharing site spam, and online game messaging spam. Spam is commonly used to advertise dubious products, schemes, or services. To protect from spam, maintain separate public and private email addresses, do not respond to or unsubscribe from spam messages, use strong anti-spam software, update browsers and security patches, and avoid posting email addresses on suspicious websites.
The document discusses spam, including its definition as unsolicited commercial email, statistics on its prevalence, and various types like email spam, web search engine spam, image spam, and blank spam. It also covers how spammers earn money, techniques for sending spam like using botnets and open relays, and anti-spam techniques like using filters and reporting spam messages. The conclusion emphasizes staying informed about spam characteristics to protect systems and data from potential dangers.
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages
Spam emails are a huge problem, with around 130 billion spam emails sent daily. The United States and China account for about 40% of all spam. Spammers obtain email addresses in many ways, like harvesting them from public websites or infecting computers to access address books. To reduce spam, the document recommends using a disposable email address, carefully reviewing privacy policies before sharing your email, reporting spam emails, and not posting your email publicly or opening suspicious attachments. Some anti-spam tools and services mentioned are Mailinator, Spamihilator, MailWasher, and SPAMfighter.
This is the presentation for Machine Learning Assignment in Dublin City University for Spring 2017. In this Project, we made an email spam filtering code using Enron Dataset
This document provides instructions for using various features of Yahoo Mail, including:
- Setting general preferences and adding a signature
- Managing drafts, sent messages, and folders
- Using auto-responds and sending email attachments
- Filtering mail and protecting against spam
- Importing and exporting contacts
- Switching to the Yahoo Mail beta version for additional features
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.
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.
Identification of Spam Emails from Valid Emails by Using VotingEditor IJCATR
In recent years, the increasing use of e-mails has led to the emergence and increase of problems caused by mass unwanted
messages which are commonly known as spam. In this study, by using decision trees, support vector machine, Naïve Bayes theorem
and voting algorithm, a new version for identifying and classifying spams is provided. In order to verify the proposed method, a set of
a mails are chosen to get tested. First three algorithms try to detect spams, and then by using voting method, spams are identified. The
advantage of this method is utilizing a combination of three algorithms at the same time: decision tree, support vector machine and
Naïve Bayes method. During the evaluation of this method, a data set is analyzed by Weka software. Charts prepared in spam
detection indicate improved accuracy compared to the previous methods.
Overview of Anti-spam filtering TechniquesIRJET Journal
1) The document discusses various techniques for anti-spam filtering, including content-based filtering, machine learning algorithms like Naive Bayes, support vector machines, and neural networks.
2) It examines common spammer tricks for avoiding detection and outlines categories of anti-spam solutions like blacklists, whitelists, greylists, and filtering techniques.
3) The document focuses on content-based filtering and machine learning techniques, specifically discussing Naive Bayes algorithms and how user-preferred domain-specific training of filters can improve anti-spam effectiveness.
MINIMIZING THE TIME OF SPAM MAIL DETECTION BY RELOCATING FILTERING SYSTEM TO ...IJNSA Journal
Unsolicited Bulk Emails (also known as Spam) are undesirable emails sent to massive number of users. Spam emails consume the network resources and cause lots of security uncertainties. As we studied, the location where the spam filter operates in is an important parameter to preserve network resources. Although there are many different methods to block spam emails, most of program developers only intend to block spam emails from being delivered to their clients. In this paper, we will introduce a new and efficient approach to prevent spam emails from being transferred. The result shows that if we focus on developing a filtering method for spams emails in the sender mail server rather than the receiver mail server, we can detect the spam emails in the shortest time consequently to avoid wasting network resources.
Email spam, also known as junk email or unsolicited
bulk email(UBE), is a subset of electronic spam involving nearly
identical messages sent to numerous recipients by email. Clicking
on links in spam email may send users to phishing web sites or sites
that are hosting malware. Spam email may also include malware as
scripts or other executable file attachments. Definitions of spam
usually include the aspects that email is unsolicited and sent in bulk
In order to overcome spam problem many researchers have
been conducted and various method of anti-spam filtering have
been implemented. A spam filter is a set of instruction for
determining the status of the received email. Spam filters are used
to prevent spam email passing through the recipient. The main
challenge is how to design an effective spam filter that allows
desired email to pass through while blocking the unwanted email.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
EMAIL SPAM DETECTION USING HYBRID ALGORITHMIRJET Journal
The document describes a study that introduces a hybrid machine learning algorithm for email spam detection. The algorithm combines logistic regression and neural networks. Logistic regression is first used to identify spam indicators in emails, which are then further analyzed using neural networks for deeper analysis and classification. The hybrid approach achieves higher accuracy than individual models alone in differentiating spam from legitimate emails. The document provides background on the problem of email spam, describes related work on spam detection techniques, and outlines the methodology used to develop and evaluate the hybrid machine learning model.
Study of Various Techniques to Filter Spam EmailsIRJET Journal
This document discusses techniques for filtering spam emails. It begins with an introduction explaining the rise of spam emails due to increased internet and email usage. It then describes various techniques used to filter spam, including machine learning methods like Bayesian classification and non-machine learning methods like blacklists, whitelists, and header analysis. Next, it covers how data mining techniques like clustering, classification, and association rule mining can be applied to identify spam emails. Finally, it reviews several papers that have compared the performance of techniques like Naive Bayes, SVM, KNN, and neural networks for spam filtering.
Understand how a SPAM filter works. In this interactive webinar, we follow the path of an email from your server to the recipient's inbox and explain the end-to-end trials and tribulations of an email message as it flows from your outbox to (hopefully) the recipients inbox. This webinar is more technical than our previous email marketing webinars.-
Topics Covered:
• How current enterprise email filters work
• Tips to avoid getting accidentally blocked
• Tracking an email from send to delivery with possible pitfalls along the way
Presenters: Craig Stouffer, GM | Pinpointe and Mark Feldman, Marketing VP | NetProspex
This document provides an introduction and overview of text analytics for SMS spam filtering classification. It discusses the classification of spam and ham SMS, describes the company Sky Bits Technology which focuses on analytics solutions, and performs Porter's Five Forces and SWOT analyses of the analytics industry and company. It also covers basic concepts in text mining such as preprocessing, transformation, feature selection, and classification methods. The objective is to develop a text classification model using R Studio to automatically categorize SMS as spam or ham.
This document discusses various techniques for filtering image spam in emails. It begins with introducing email spam and image spam, then describes types of image spam and spam content. It discusses the lifecycle of spam and various antispam techniques, including techniques that operate before spam is sent, after it is sent, and after it reaches mailboxes. It also covers existing techniques like analyzing spam characteristics, transmission protocols, local changes, language-based filters, non-content features, content-based classification, and hybrid filters. In the end, it emphasizes that hybrid techniques can effectively combine various filtering models.
Spam and Anti-spam - Sudipta Bhattacharyasankhadeep
The document discusses spam emails and anti-spam techniques. It defines spam emails, describes how spammers earn money and send spam emails. It also discusses the costs of spam emails, various types of spam like email spam, chat spam and search engine spam. The document then covers techniques used by individuals, email administrators and email senders to prevent spam emails. These include filtering, blocking, authentication and legal enforcement. The conclusion states that no single technique can fully solve the spam problem and both users and administrators need to use different anti-spam methods.
An Approach for Malicious Spam Detection in Email with Comparison of Differen...IRJET Journal
This document summarizes a research paper that proposes a model to improve detection of malicious spam emails through feature selection. The model employs a novel dataset for feature selection to optimize classification parameters, prediction accuracy, and computation time. Feature selection is expected to improve training time and classification accuracy. The paper also compares various classifiers, including Naive Bayes and Support Vector Machine, on the selected feature subset. The goal is to automatically learn to detect malicious spam emails, which threaten privacy and security by spreading malware, phishing links, and sensitive data theft.
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 refers to sending unwanted messages in large quantities to many recipients. There are several types of spam, including email spam, social networking spam, blog spam, SMS spam, forum spam, video sharing site spam, and online game messaging spam. Spam is commonly used to advertise dubious products, schemes, or services. To protect from spam, maintain separate public and private email addresses, do not respond to or unsubscribe from spam messages, use strong anti-spam software, update browsers and security patches, and avoid posting email addresses on suspicious websites.
The document discusses spam, including its definition as unsolicited commercial email, statistics on its prevalence, and various types like email spam, web search engine spam, image spam, and blank spam. It also covers how spammers earn money, techniques for sending spam like using botnets and open relays, and anti-spam techniques like using filters and reporting spam messages. The conclusion emphasizes staying informed about spam characteristics to protect systems and data from potential dangers.
Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages
Spam emails are a huge problem, with around 130 billion spam emails sent daily. The United States and China account for about 40% of all spam. Spammers obtain email addresses in many ways, like harvesting them from public websites or infecting computers to access address books. To reduce spam, the document recommends using a disposable email address, carefully reviewing privacy policies before sharing your email, reporting spam emails, and not posting your email publicly or opening suspicious attachments. Some anti-spam tools and services mentioned are Mailinator, Spamihilator, MailWasher, and SPAMfighter.
This is the presentation for Machine Learning Assignment in Dublin City University for Spring 2017. In this Project, we made an email spam filtering code using Enron Dataset
This document provides instructions for using various features of Yahoo Mail, including:
- Setting general preferences and adding a signature
- Managing drafts, sent messages, and folders
- Using auto-responds and sending email attachments
- Filtering mail and protecting against spam
- Importing and exporting contacts
- Switching to the Yahoo Mail beta version for additional features
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.
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.
Identification of Spam Emails from Valid Emails by Using VotingEditor IJCATR
In recent years, the increasing use of e-mails has led to the emergence and increase of problems caused by mass unwanted
messages which are commonly known as spam. In this study, by using decision trees, support vector machine, Naïve Bayes theorem
and voting algorithm, a new version for identifying and classifying spams is provided. In order to verify the proposed method, a set of
a mails are chosen to get tested. First three algorithms try to detect spams, and then by using voting method, spams are identified. The
advantage of this method is utilizing a combination of three algorithms at the same time: decision tree, support vector machine and
Naïve Bayes method. During the evaluation of this method, a data set is analyzed by Weka software. Charts prepared in spam
detection indicate improved accuracy compared to the previous methods.
Overview of Anti-spam filtering TechniquesIRJET Journal
1) The document discusses various techniques for anti-spam filtering, including content-based filtering, machine learning algorithms like Naive Bayes, support vector machines, and neural networks.
2) It examines common spammer tricks for avoiding detection and outlines categories of anti-spam solutions like blacklists, whitelists, greylists, and filtering techniques.
3) The document focuses on content-based filtering and machine learning techniques, specifically discussing Naive Bayes algorithms and how user-preferred domain-specific training of filters can improve anti-spam effectiveness.
MINIMIZING THE TIME OF SPAM MAIL DETECTION BY RELOCATING FILTERING SYSTEM TO ...IJNSA Journal
Unsolicited Bulk Emails (also known as Spam) are undesirable emails sent to massive number of users. Spam emails consume the network resources and cause lots of security uncertainties. As we studied, the location where the spam filter operates in is an important parameter to preserve network resources. Although there are many different methods to block spam emails, most of program developers only intend to block spam emails from being delivered to their clients. In this paper, we will introduce a new and efficient approach to prevent spam emails from being transferred. The result shows that if we focus on developing a filtering method for spams emails in the sender mail server rather than the receiver mail server, we can detect the spam emails in the shortest time consequently to avoid wasting network resources.
Email spam, also known as junk email or unsolicited
bulk email(UBE), is a subset of electronic spam involving nearly
identical messages sent to numerous recipients by email. Clicking
on links in spam email may send users to phishing web sites or sites
that are hosting malware. Spam email may also include malware as
scripts or other executable file attachments. Definitions of spam
usually include the aspects that email is unsolicited and sent in bulk
In order to overcome spam problem many researchers have
been conducted and various method of anti-spam filtering have
been implemented. A spam filter is a set of instruction for
determining the status of the received email. Spam filters are used
to prevent spam email passing through the recipient. The main
challenge is how to design an effective spam filter that allows
desired email to pass through while blocking the unwanted email.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
EMAIL SPAM DETECTION USING HYBRID ALGORITHMIRJET Journal
The document describes a study that introduces a hybrid machine learning algorithm for email spam detection. The algorithm combines logistic regression and neural networks. Logistic regression is first used to identify spam indicators in emails, which are then further analyzed using neural networks for deeper analysis and classification. The hybrid approach achieves higher accuracy than individual models alone in differentiating spam from legitimate emails. The document provides background on the problem of email spam, describes related work on spam detection techniques, and outlines the methodology used to develop and evaluate the hybrid machine learning model.
Study of Various Techniques to Filter Spam EmailsIRJET Journal
This document discusses techniques for filtering spam emails. It begins with an introduction explaining the rise of spam emails due to increased internet and email usage. It then describes various techniques used to filter spam, including machine learning methods like Bayesian classification and non-machine learning methods like blacklists, whitelists, and header analysis. Next, it covers how data mining techniques like clustering, classification, and association rule mining can be applied to identify spam emails. Finally, it reviews several papers that have compared the performance of techniques like Naive Bayes, SVM, KNN, and neural networks for spam filtering.
Detecting spam mail using machine learning algorithmIRJET Journal
This document discusses a study that aims to detect spam emails using machine learning algorithms. The researchers developed a model using natural language processing and machine learning techniques. They preprocessed email data by removing stop words and stemming words. They then used a correlation-based feature selection method to extract the most important features. A bagged hybrid classifier combining Naive Bayes and Decision Tree (J48) algorithms was used for classification. The study aims to more accurately classify emails as spam or ham (non-spam) compared to existing methods, which rely on rules-based approaches or single algorithms. It evaluates the performance of different machine learning classifiers like logistic regression, Naive Bayes, and support vector machines.
The document discusses considerations for choosing between building an in-house email infrastructure solution versus outsourcing email services to a third-party provider. Key factors that are assessed include deliverability, cost, data privacy and security, scalability, maintenance and support needs, and analytics capabilities. Maintaining high deliverability requires constant monitoring and optimization of email lists and infrastructure. Costs involve hardware, software, staffing needs for an in-house solution versus monthly service fees for outsourcing. Data security and privacy are also important to consider given regulations. As email volume grows over time, solutions need scalability to process larger amounts of data and automate complex workflows.
Detection of Spam in Emails using Machine LearningIRJET Journal
The document discusses detecting spam emails using machine learning techniques. It evaluates various machine learning algorithms for classifying emails as spam or ham (not spam) and finds that the Multinomial Naive Bayes algorithm provides the highest accuracy. The study uses an email dataset to train classifiers including Naive Bayes, Decision Trees, Random Forest, KNN, SVM and evaluates their performance based on precision, accuracy and other metrics. It finds that while Naive Bayes performs best, it has some limitations and ensemble methods like random forests generally provide more robust performance. The paper contributes to improving email spam detection through comparative analysis of machine learning algorithms.
B2B Email Experts is a leading business to business research studies by creating successful email marketing campaign, data appending techniques and customized email lists.
Improvement of Legitimate Mail Server Detection Method using Sender Authentic...IRJET Journal
This document proposes an improvement to a method for detecting legitimate emails using sender authentication. It aims to reduce false positives where legitimate emails are identified as spam. The proposed method collects forwarding email sources and assumes senders from these sources are legitimate receivers. It introduces a procedure to exclude spam sources that should not be included. The method is evaluated using actual received emails to test its effectiveness in reducing erroneous judgments compared to previous methods.
E-Mail Spam Detection Using Supportive Vector MachineIRJET Journal
The document proposes using a Support Vector Machine model to detect spam emails. It discusses preprocessing email data, extracting features, training an SVM classifier, and testing the classifier on new emails. The authors implemented an SVM model that was able to accurately classify emails as spam or not spam with 98% accuracy, showing SVM is effective for email spam detection.
The document discusses the development of an intelligent spam mail detection system using machine learning. It provides an overview of the challenges of spam emails and how machine learning can be used to analyze large amounts of email data and detect suspicious or spam emails. It then describes the methodology used, including preprocessing the email data, using the Naive Bayes algorithm to classify emails as spam or not spam, and evaluating the performance using metrics like recall, precision and the F1 score. Additional features discussed include scanning email attachments and links using a virus scanning API to identify malicious files and phishing URLs.
A LITERATURE REVIEW ON PHISHING EMAIL DETECTION USING DATA MININGHeather Strinden
This document reviews literature on detecting phishing emails using data mining. It discusses how hybrid features that include both content and header information can be used to effectively classify emails as phishing or legitimate. Various techniques currently used for phishing email detection are examined, including network-level protections, authentication, client-side tools, user education, and server-side filters. Feature selection is important, as phishing emails often resemble legitimate emails, making detection complex. The review finds that server-side filters using machine learning classifiers on selected email features show promise as a solution.
Identifying Valid Email Spam Emails Using Decision TreeEditor IJCATR
The increasing use of e-mail and the growing trend of Internet users sending unsolicited bulk e-mail, the need for an antispam
filtering or have created, Filter large poster have been produced in this area, each with its own method and some parameters are
to recognize spam. The advantage of this method is the simultaneous use of two algorithms decision tree ID3 - Mamdani and Naive
Bayesian is fuzzy. The first two algorithms are then used to detect spam Bagging approach is to identify spam. In the evaluation of this
dataset contains a thousand letters have been analyzed by the software Weka charts provided in spam detection accuracy than previous
methods of improvement
Tracking Spam Mails Using SPRT Algorithm With AAAIRJET Journal
This document proposes a system to detect and block spam emails using AAA (authentication, authorization, and accounting) and SPRT (sequential probability ratio test) algorithms. The system would authenticate users, authorize their ability to send emails, detect spam emails using SPRT, and maintain logs of email activity. Spam emails detected would be blocked without notifying the sender. The system aims to identify "spam zombies" - compromised machines used to send spam emails. It would generate graphs of IP addresses versus number of spam emails to analyze spamming behavior and help administrators take appropriate action against spammers. The proposed system has four modules - staff machine, authentication server, mail server, and admin module for monitoring logs and reports.
The document provides tips for improving email deliverability. It discusses obtaining permission, sending relevant content at an appropriate frequency, managing complaints, using authentication techniques, and maintaining list hygiene to build a good reputation. It also recommends testing emails and using a pre-flight checklist to ensure compliance. The key is focusing on permission, relevance and reputation to maximize deliverability and ROI.
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
Email Spam Detection Using Machine LearningIRJET Journal
This document presents a machine learning approach to detecting email spam using TF-IDF feature extraction and classification algorithms. The proposed system applies TF-IDF to emails to generate numerical vectors, then trains classifiers like SVM, random forest, k-NN and naive Bayes. Evaluation shows SVM achieved 98.47% accuracy. The system aims to enhance email security and productivity by accurately filtering spam from legitimate emails.
A Survey on Spam Filtering Methods and Mapreduce with SVMIRJET Journal
This document summarizes a research paper that surveys different spam filtering techniques, including machine learning approaches like Naive Bayes, KNN, decision trees, and SVM. It discusses how SVM is commonly used for spam filtering due to its ability to handle large datasets, but it has drawbacks of being slow for training on large data. The paper proposes using MapReduce with SVM to address this issue by enabling parallel processing of large input datasets to speed up SVM training for spam filtering. It provides background on various machine learning techniques used for spam filtering and discusses how MapReduce can help improve the scalability of SVM for classification of large volumes of email data as spam or not spam.
Email is quickly becoming the preferred method of communication for businesses across the
world, regardless of industry. Email is faster and less expensive than traditional mail, and many
customers choose to receive messages from the companies they do business with through the
email channel.
Email Marketing What You Dont Know Can Hurt YouEric Salerno
20 slides on email marketing basics and some ideas on how to improve your email marketing practices. Presented by Red Ember Marketing at the Boston Internet Marketing MeetUp.
The document provides guidance on email marketing best practices, including six steps for creating effective email campaigns: authentication and deliverability, template design, email content, testing and optimization, sending best practices, and tracking and reporting. It discusses the importance of deliverability and recommends setting up authentication, warming up new IP addresses gradually over 2-6 weeks, and using opt-in lists. It also provides tips for designing email templates, such as including calls to action, breaking up text, and using images. Effective email template design considers the type of email and ensures the template is clean, easy to read and scan.
Best practices-guide-for-exchange-server-database-corruption-problemsBharat Bhushan
Microsoft Exchange Server Database Corruption Problems & Solution: Comprehensive Guide
Since Microsoft Exchange is a critical asset for any organization, many challenges come up when Exchange server goes down. Due to database corruption, Exchange Server is unable to mount which causes email downtime, loss of productivity, business opportunity loss, and financial loss etc.
Solutions for Exchange Database Corruption
Exchange Server recovery by using built-In utilities: Eseutil And Isinteg
Exchange Server recovery with the help of Stellar Repair For Exchange
Similar to IRJET- Email Spam Detection & Automation (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
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Artificial intelligence (AI) | Definitio