Improving Phishing URL Detection Using Fuzzy Association Miningtheijes
Phishing is the process to obtain sensitive information such as usernames, passwords, and credit card details by disguising as a trustworthy entity by the use of an electronic communication. Phishing attack continues to pose a solemn risk for web users and annoying threat within the field of electronic commerce. The Phishing detection using fuzzy and binary matrix construction method focuses on discerning the significant features that discriminate between legitimate and phishing URLs. The significant features are extracting the number of dots, length of the host etc., from each URL. These features are then subjected to associative rule mining-apriori and predictive apriori. The rules obtained are interpreted to emphasize the features that are more prevalent in phishing URLs. The key factors for the phished URLs are number of slashes in the URL, dot in the host portion of the URL and length of the URL. The pitfall of binary matrix method is the time complexity. So it impacts the overall speed of the system. The fuzzy based logic association rule mining algorithm was proposed to classify the legitimate and phishing URLs based on the features. The extracted features are converted to fuzzy membership values as “Low”,’ Medium’ and “High”. By applying association rule mining algorithm the rules are generated to detect the phishing URLs. The fuzzy based methodology provides efficient and high rate of phishing detection of URLs
Web phish detection (an evolutionary approach)eSAT Journals
Abstract Phishing is nothing but one of the kinds of network crimes. This paper presents an efficient approach for detecting phishing web documents based on learning from a large number of phishing webs. Phishing means to make something fraud with someone, usually by using internet with the help of emails, to take our personal information, such as credentials. The finest way to protect ourselves and our credentials from phishing attack is to understand the concept of phishing as well as to understand that how to determine a phishing attack. Most of the phishing emails are sent from well-reputed organizations and they ask for your credentials such as credit card number, account number, social security number and passwords of bank account. Mostly the phishing attacks seen from the websites, services and organizations with which we do not even have an account. In this system we are using two classifiers to detect phishing. To recognize the phishing, the Uniform Resource Locator (URL) features of the website are firstly analyzed and then they are classified by using K-means classifier. If the answer is still suspicious then by using parsing of the webpage, its DOM tree is drawn and then the second classifier that is Naive Bayesian (NB) classifier classifies the web page. Key Words: phishing, phishing emails, classifier
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Phishing URL Detection using LSTM Based Ensemble Learning ApproachesIJCNCJournal
Increasing incidents of phishing attacks tempt a significant challenge for cybersecurity personals. Phishing is a deceitful venture with an intention to steal confidential information of an organization or an individual. Many works have been performed to build anti-phishing solutions over the years, but attackers are coming with new manoeuvres from time to time. Many of the existing techniques are experimented based on limited set of URLs and dependent on other software to collect domain related information of the URLs. In this paper, with an aim to build a more accurate and effective phishing attack detection system, we used the concept of ensemble learning using Long Short-Term Memory (LSTM) models. We proposed ensemble of LSTM models using bagging approach and stacking approach. For performing classification using LSTM method, no separate feature extraction is done. Ensemble models are built integrating the predictions of multiple LSTM models. Performances of proposed ensemble LSTM methods are compared with five different machine learning classification methods. To implement these machine learning algorithms, different URL based lexical features are extracted. Mutual Information based feature selection algorithm is used to select more relevant features to perform classifications. Both the bagging and the stacking approaches of ensemble learning using LSTM models outperform other machine learning techniques. The results are compared with other anti-phishing solutions implemented using deep learning methods. Our approaches have proved to be the more accurate one with a low false positive rate of less than 0.15% performed comparatively on a larger dataset.
PHISHING URL DETECTION USING LSTM BASED ENSEMBLE LEARNING APPROACHESIJCNCJournal
Increasing incidents of phishing attacks tempt a significant challenge for cybersecurity personals. Phishing
is a deceitful venture with an intention to steal confidential information of an organization or an
individual. Many works have been performed to build anti-phishing solutions over the years, but attackers
are coming with new manoeuvres from time to time. Many of the existing techniques are experimented
based on limited set of URLs and dependent on other software to collect domain related information of the
URLs. In this paper, with an aim to build a more accurate and effective phishing attack detection system,
we used the concept of ensemble learning using Long Short-Term Memory (LSTM) models. We proposed
ensemble of LSTM models using bagging approach and stacking approach. For performing classification
using LSTM method, no separate feature extraction is done. Ensemble models are built integrating the
predictions of multiple LSTM models. Performances of proposed ensemble LSTM methods are compared
with five different machine learning classification methods. To implement these machine learning
algorithms, different URL based lexical features are extracted. Mutual Information based feature selection
algorithm is used to select more relevant features to perform classifications. Both the bagging and the
stacking approaches of ensemble learning using LSTM models outperform other machine learning
techniques. The results are compared with other anti-phishing solutions implemented using deep learning
methods. Our approaches have proved to be the more accurate one with a low false positive rate of less
than 0.15% performed comparatively on a larger dataset.
In spite of the development of aversion strategies, phishing remains an essential risk even after the
primary countermeasures and in view of receptive URL blacklisting. This strategy is insufficient because of the
short lifetime of phishing websites. In order to overcome this problem, developing a real-time phishing website
detection method is an effective solution. This research introduces the PrePhish algorithm which is an automated
machine learning approach to analyze phishing and non-phishing URL to produce reliable result. It represents that
phishing URLs typically have couple of connections between the part of the registered domain level and the path
or query level URL. Using these connections URL is characterized by inter-relatedness and it estimates using
features mined from attributes. These features are then used in machine learning technique to detect phishing
URLs from a real dataset. The classification of phishing and non-phishing website has been implemented by
finding the range value and threshold value for each attribute using decision making classification. This method is
also evaluated in Matlab using three major classifiers SVM, Random Forest and Naive Bayes to find how it works
on the dataset assessed
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Detecting malicious URLs using binary classification through ada boost algori...IJECEIAES
Malicious Uniform Resource Locator (URL) is a frequent and severe menace to cybersecurity. Malicious URLs are used to extract unsolicited information and trick inexperienced end users as a sufferer of scams and create losses of billions of money each year. It is crucial to identify and appropriately respond to such URLs. Usually, this discovery is made by the practice and use of blacklists in the cyber world. However, blacklists cannot be exhaustive, and cannot recognize zero-day malicious URLs. So to increase the observation of malicious URL indicators, machine learning procedures should be incorporated. In this study, we have developed a complete prototype of Malicious URL Detection using machine learning methods. In particular, we have attempted an exact formulation of Malicious URL exposure from a machine learning perspective and proposed an approach using the AdaBoost algorithm - the proposed approach has brought forward more accuracy than other existing algorithms.
Deep learning in phishing mitigation: a uniform resource locator-based predic...IJECEIAES
To mitigate the evolution of phish websites, various phishing prediction8 schemes are being optimized eventually. However, the optimized methods produce gratuitous performance overhead due to the limited exploration of advanced phishing cues. Thus, a phishing uniform resource locator-based predictive model is enhanced by this work to defeat this deficiency using deep learning algorithms. This model’s architecture encompasses preprocessing of the effective feature space that is made up of 60 mutual uniform resource locator (URL) phishing features, and a dual deep learningbased model of convolution neural network with bi-directional long shortterm memory (CNN-BiLSTM). The proposed predictive model is trained and tested on a dataset of 14,000 phish URLs and 28,074 legitimate URLs. Experimentally, the performance outputs are remarked with a 0.01% false positive rate (FPR) and 99.27% testing accuracy.
A Deep Learning Technique for Web Phishing Detection Combined URL Features an...IJCNCJournal
The most popular way to deceive online users nowadays is phishing. Consequently, to increase cybersecurity, more efficient web page phishing detection mechanisms are needed. In this paper, we propose an approach that rely on websites image and URL to deals with the issue of phishing website recognition as a classification challenge. Our model uses webpage URLs and images to detect a phishing attack using convolution neural networks (CNNs) to extract the most important features of website images and URLs and then classifies them into benign and phishing pages. The accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the proposed model in detecting a web phishing attack.
Typically an IDS refers to a software application that monitors a network for intrusion or malicious
activity. It signals an alarm once an intrusion is detected. In this paper, an IDS based on Distributed
Machine Learning that detects phishing attacks and issues an alarm when the intrusion is detected has
been discussed. This is done by using SVM as the base algorithm. More on why SVM is used and how the
IDS can be applied to detect other types of intrusion has been discussed.
Analyzing the effectualness of Phishing Algorithms in Web Applications Inques...Editor IJMTER
The initial and proficient loss of deception is belief. A wolf in sheep’s clothing is tough
to recognize, similar is the schema of a phishing website. Phishing is the emulsion of social
engineering and technical exploits designed to persuade a victim to provide personal information, for
the fiscal gain of the attacker. It is a new kind of network assault where the attacker creates a spitting
image of an already existing Web Page to delude users. In this paper, we will study two anti-phishing
algorithms, one an end-host based algorithm known as the LinkGuard Algorithm, while the other a
content based approach known as the CANTINA.
Improving Phishing URL Detection Using Fuzzy Association Miningtheijes
Phishing is the process to obtain sensitive information such as usernames, passwords, and credit card details by disguising as a trustworthy entity by the use of an electronic communication. Phishing attack continues to pose a solemn risk for web users and annoying threat within the field of electronic commerce. The Phishing detection using fuzzy and binary matrix construction method focuses on discerning the significant features that discriminate between legitimate and phishing URLs. The significant features are extracting the number of dots, length of the host etc., from each URL. These features are then subjected to associative rule mining-apriori and predictive apriori. The rules obtained are interpreted to emphasize the features that are more prevalent in phishing URLs. The key factors for the phished URLs are number of slashes in the URL, dot in the host portion of the URL and length of the URL. The pitfall of binary matrix method is the time complexity. So it impacts the overall speed of the system. The fuzzy based logic association rule mining algorithm was proposed to classify the legitimate and phishing URLs based on the features. The extracted features are converted to fuzzy membership values as “Low”,’ Medium’ and “High”. By applying association rule mining algorithm the rules are generated to detect the phishing URLs. The fuzzy based methodology provides efficient and high rate of phishing detection of URLs
Web phish detection (an evolutionary approach)eSAT Journals
Abstract Phishing is nothing but one of the kinds of network crimes. This paper presents an efficient approach for detecting phishing web documents based on learning from a large number of phishing webs. Phishing means to make something fraud with someone, usually by using internet with the help of emails, to take our personal information, such as credentials. The finest way to protect ourselves and our credentials from phishing attack is to understand the concept of phishing as well as to understand that how to determine a phishing attack. Most of the phishing emails are sent from well-reputed organizations and they ask for your credentials such as credit card number, account number, social security number and passwords of bank account. Mostly the phishing attacks seen from the websites, services and organizations with which we do not even have an account. In this system we are using two classifiers to detect phishing. To recognize the phishing, the Uniform Resource Locator (URL) features of the website are firstly analyzed and then they are classified by using K-means classifier. If the answer is still suspicious then by using parsing of the webpage, its DOM tree is drawn and then the second classifier that is Naive Bayesian (NB) classifier classifies the web page. Key Words: phishing, phishing emails, classifier
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Phishing URL Detection using LSTM Based Ensemble Learning ApproachesIJCNCJournal
Increasing incidents of phishing attacks tempt a significant challenge for cybersecurity personals. Phishing is a deceitful venture with an intention to steal confidential information of an organization or an individual. Many works have been performed to build anti-phishing solutions over the years, but attackers are coming with new manoeuvres from time to time. Many of the existing techniques are experimented based on limited set of URLs and dependent on other software to collect domain related information of the URLs. In this paper, with an aim to build a more accurate and effective phishing attack detection system, we used the concept of ensemble learning using Long Short-Term Memory (LSTM) models. We proposed ensemble of LSTM models using bagging approach and stacking approach. For performing classification using LSTM method, no separate feature extraction is done. Ensemble models are built integrating the predictions of multiple LSTM models. Performances of proposed ensemble LSTM methods are compared with five different machine learning classification methods. To implement these machine learning algorithms, different URL based lexical features are extracted. Mutual Information based feature selection algorithm is used to select more relevant features to perform classifications. Both the bagging and the stacking approaches of ensemble learning using LSTM models outperform other machine learning techniques. The results are compared with other anti-phishing solutions implemented using deep learning methods. Our approaches have proved to be the more accurate one with a low false positive rate of less than 0.15% performed comparatively on a larger dataset.
PHISHING URL DETECTION USING LSTM BASED ENSEMBLE LEARNING APPROACHESIJCNCJournal
Increasing incidents of phishing attacks tempt a significant challenge for cybersecurity personals. Phishing
is a deceitful venture with an intention to steal confidential information of an organization or an
individual. Many works have been performed to build anti-phishing solutions over the years, but attackers
are coming with new manoeuvres from time to time. Many of the existing techniques are experimented
based on limited set of URLs and dependent on other software to collect domain related information of the
URLs. In this paper, with an aim to build a more accurate and effective phishing attack detection system,
we used the concept of ensemble learning using Long Short-Term Memory (LSTM) models. We proposed
ensemble of LSTM models using bagging approach and stacking approach. For performing classification
using LSTM method, no separate feature extraction is done. Ensemble models are built integrating the
predictions of multiple LSTM models. Performances of proposed ensemble LSTM methods are compared
with five different machine learning classification methods. To implement these machine learning
algorithms, different URL based lexical features are extracted. Mutual Information based feature selection
algorithm is used to select more relevant features to perform classifications. Both the bagging and the
stacking approaches of ensemble learning using LSTM models outperform other machine learning
techniques. The results are compared with other anti-phishing solutions implemented using deep learning
methods. Our approaches have proved to be the more accurate one with a low false positive rate of less
than 0.15% performed comparatively on a larger dataset.
In spite of the development of aversion strategies, phishing remains an essential risk even after the
primary countermeasures and in view of receptive URL blacklisting. This strategy is insufficient because of the
short lifetime of phishing websites. In order to overcome this problem, developing a real-time phishing website
detection method is an effective solution. This research introduces the PrePhish algorithm which is an automated
machine learning approach to analyze phishing and non-phishing URL to produce reliable result. It represents that
phishing URLs typically have couple of connections between the part of the registered domain level and the path
or query level URL. Using these connections URL is characterized by inter-relatedness and it estimates using
features mined from attributes. These features are then used in machine learning technique to detect phishing
URLs from a real dataset. The classification of phishing and non-phishing website has been implemented by
finding the range value and threshold value for each attribute using decision making classification. This method is
also evaluated in Matlab using three major classifiers SVM, Random Forest and Naive Bayes to find how it works
on the dataset assessed
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Detecting malicious URLs using binary classification through ada boost algori...IJECEIAES
Malicious Uniform Resource Locator (URL) is a frequent and severe menace to cybersecurity. Malicious URLs are used to extract unsolicited information and trick inexperienced end users as a sufferer of scams and create losses of billions of money each year. It is crucial to identify and appropriately respond to such URLs. Usually, this discovery is made by the practice and use of blacklists in the cyber world. However, blacklists cannot be exhaustive, and cannot recognize zero-day malicious URLs. So to increase the observation of malicious URL indicators, machine learning procedures should be incorporated. In this study, we have developed a complete prototype of Malicious URL Detection using machine learning methods. In particular, we have attempted an exact formulation of Malicious URL exposure from a machine learning perspective and proposed an approach using the AdaBoost algorithm - the proposed approach has brought forward more accuracy than other existing algorithms.
Deep learning in phishing mitigation: a uniform resource locator-based predic...IJECEIAES
To mitigate the evolution of phish websites, various phishing prediction8 schemes are being optimized eventually. However, the optimized methods produce gratuitous performance overhead due to the limited exploration of advanced phishing cues. Thus, a phishing uniform resource locator-based predictive model is enhanced by this work to defeat this deficiency using deep learning algorithms. This model’s architecture encompasses preprocessing of the effective feature space that is made up of 60 mutual uniform resource locator (URL) phishing features, and a dual deep learningbased model of convolution neural network with bi-directional long shortterm memory (CNN-BiLSTM). The proposed predictive model is trained and tested on a dataset of 14,000 phish URLs and 28,074 legitimate URLs. Experimentally, the performance outputs are remarked with a 0.01% false positive rate (FPR) and 99.27% testing accuracy.
A Deep Learning Technique for Web Phishing Detection Combined URL Features an...IJCNCJournal
The most popular way to deceive online users nowadays is phishing. Consequently, to increase cybersecurity, more efficient web page phishing detection mechanisms are needed. In this paper, we propose an approach that rely on websites image and URL to deals with the issue of phishing website recognition as a classification challenge. Our model uses webpage URLs and images to detect a phishing attack using convolution neural networks (CNNs) to extract the most important features of website images and URLs and then classifies them into benign and phishing pages. The accuracy rate of the results of the experiment was 99.67%, proving the effectiveness of the proposed model in detecting a web phishing attack.
Typically an IDS refers to a software application that monitors a network for intrusion or malicious
activity. It signals an alarm once an intrusion is detected. In this paper, an IDS based on Distributed
Machine Learning that detects phishing attacks and issues an alarm when the intrusion is detected has
been discussed. This is done by using SVM as the base algorithm. More on why SVM is used and how the
IDS can be applied to detect other types of intrusion has been discussed.
Analyzing the effectualness of Phishing Algorithms in Web Applications Inques...Editor IJMTER
The initial and proficient loss of deception is belief. A wolf in sheep’s clothing is tough
to recognize, similar is the schema of a phishing website. Phishing is the emulsion of social
engineering and technical exploits designed to persuade a victim to provide personal information, for
the fiscal gain of the attacker. It is a new kind of network assault where the attacker creates a spitting
image of an already existing Web Page to delude users. In this paper, we will study two anti-phishing
algorithms, one an end-host based algorithm known as the LinkGuard Algorithm, while the other a
content based approach known as the CANTINA.
Similar to Mitigation of Cyber Threats through Identification of Phishing Websites (20)
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
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
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Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
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.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
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.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.