This document discusses methods for detecting fraudulent transactions from online credit card transactions. It first reviews several existing algorithms for fraud detection from literature, including neural networks, rule induction, case-based reasoning and others. It then discusses selecting important attributes from a credit card transaction dataset containing 20 attributes related to transactions and cardholders. Several attribute selection techniques are applied to identify the most important attributes. Finally, various machine learning algorithms including AdaBoost, logistic regression, J48 and naive Bayes are tested on the dataset to identify the best algorithm for detecting fraudulent transactions, with the document concluding that AdaBoost performs best.
The credit card has become the most popular mode of payment for both online as well as
regular purchase, in cases of fraud associated with it are also rising. Credit card frauds are increasing
day by day regardless of the various techniques developed for its detection. Fraudsters are so expert that
they generate new ways for committing fraudulent transactions each day which demands constant
innovation for its detection techniques. Most of the techniques based on Artificial Intelligence, Fuzzy
logic, neural network, logistic regression, naïve Bayesian, Machine learning, Sequence Alignment,
decision tree, Bayesian network, meta learning, Genetic Programming etc., these are evolved in
detecting various credit card fraudulent transactions. This paper presents a survey of various techniques
used in credit card fraud detection mechanisms.
Operationalize deep learning models for fraud detection with Azure Machine Le...Francesca Lazzeri, PhD
Recent advancements in computing technologies along with the increasing popularity of ecommerce platforms have radically amplified the risk of online fraud for financial services companies and their customers. Failing to properly recognize and prevent fraud results in billions of dollars of loss per year for the financial industry. This trend has urged companies to look into many popular artificial intelligence (AI) techniques, including deep learning for fraud detection. Deep learning can uncover patterns in tremendously large datasets and independently learn new concepts from raw data without extensive manual feature engineering. For this reason, deep learning has shown superior performance in domains such as object recognition and image classification.
Although, neural networks have been used for fraud detection for decades, recent advancements in computing technologies along with large volumes of data available today have dramatically improved the effectiveness of these techniques. Using a sample dataset that contains transactions made by credit cards in September 2013 by European cardholders, Francesca Lazzeri and Jaya Mathew explain how to build, deploy, and operationalize a deep learning model to identify and prevent fraud, using Azure Machine Learning Workbench to show the main steps in the operationalization process (from data ingestion to consumption) and the Keras deep learning library with Microsoft Cognitive Toolkit CNTK as the backend.
Online Payment Fraud Detection with Azure Machine LearningStefano Tempesta
Fraud detection is one of the earliest industrial applications of anomaly detection and machine learning. As part of the Azure Machine Learning offering, Microsoft provides a template that helps data scientists easily build and deploy an online transaction fraud detection solution. The template includes a collection of pre-configured machine learning modules, as well as custom R scripts, to enable an end-to-end solution.
This session presents best practices, design guidelines and a working implementation for building an online payment fraud detection mechanism in a SharePoint portal connected to a credit card payment gateway. The full source code of the solution is released as open source.
The credit card has become the most popular mode of payment for both online as well as
regular purchase, in cases of fraud associated with it are also rising. Credit card frauds are increasing
day by day regardless of the various techniques developed for its detection. Fraudsters are so expert that
they generate new ways for committing fraudulent transactions each day which demands constant
innovation for its detection techniques. Most of the techniques based on Artificial Intelligence, Fuzzy
logic, neural network, logistic regression, naïve Bayesian, Machine learning, Sequence Alignment,
decision tree, Bayesian network, meta learning, Genetic Programming etc., these are evolved in
detecting various credit card fraudulent transactions. This paper presents a survey of various techniques
used in credit card fraud detection mechanisms.
Operationalize deep learning models for fraud detection with Azure Machine Le...Francesca Lazzeri, PhD
Recent advancements in computing technologies along with the increasing popularity of ecommerce platforms have radically amplified the risk of online fraud for financial services companies and their customers. Failing to properly recognize and prevent fraud results in billions of dollars of loss per year for the financial industry. This trend has urged companies to look into many popular artificial intelligence (AI) techniques, including deep learning for fraud detection. Deep learning can uncover patterns in tremendously large datasets and independently learn new concepts from raw data without extensive manual feature engineering. For this reason, deep learning has shown superior performance in domains such as object recognition and image classification.
Although, neural networks have been used for fraud detection for decades, recent advancements in computing technologies along with large volumes of data available today have dramatically improved the effectiveness of these techniques. Using a sample dataset that contains transactions made by credit cards in September 2013 by European cardholders, Francesca Lazzeri and Jaya Mathew explain how to build, deploy, and operationalize a deep learning model to identify and prevent fraud, using Azure Machine Learning Workbench to show the main steps in the operationalization process (from data ingestion to consumption) and the Keras deep learning library with Microsoft Cognitive Toolkit CNTK as the backend.
Online Payment Fraud Detection with Azure Machine LearningStefano Tempesta
Fraud detection is one of the earliest industrial applications of anomaly detection and machine learning. As part of the Azure Machine Learning offering, Microsoft provides a template that helps data scientists easily build and deploy an online transaction fraud detection solution. The template includes a collection of pre-configured machine learning modules, as well as custom R scripts, to enable an end-to-end solution.
This session presents best practices, design guidelines and a working implementation for building an online payment fraud detection mechanism in a SharePoint portal connected to a credit card payment gateway. The full source code of the solution is released as open source.
A Survey of Online Credit Card Fraud Detection using Data Mining TechniquesIJSRD
Nowadays the use of credit card has increased, because the amount of online transaction is growing. With the day to day use of credit card for payment online as well as regular purchase, case of fraud associated with it is also rising. To reduce the huge financial loss caused by frauds, a number of modern techniques have been developed for fraud detection which is based on data mining, neural network, genetic algorithm etc. Here a survey of techniques for online credit card fraud detection using Hidden Markov Model, Genetic Algorithm and Hybrid Model, and comparison between them has been shown.
With the growing buzz around data science, many professionals want to learn how to become a data scientist—the role Harvard Business Review called the “sexiest job of the 21st century.” Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.
Francesca and Jaya begin by outlining the typical skillset an exceptional data scientist needs. They then explore common applications of machine learning and artificial intelligence in different business verticals and explore why some companies are much more successful than others at driving analytics-based business transformation. Francesca and Jaya dive into a couple of specific use cases to demonstrate how machine learning and artificial intelligence can help drive business impact within an organization and how the right technology platform can boost employee productivity and help them innovate and iterate rapidly. You’ll learn why a modern cloud analytics environment that makes it easy to collect data, analyze, experiment, and quickly put things into production with a targeted set of customers is becoming a must-have for data-driven organizations and walk through a detailed use case, from how the data typically gets collected to data wrangling, building a model, tuning the model, and operationalizing the model for a business to use in their production environment.
Online Transaction Fraud Detection using Hidden Markov Model & Behavior AnalysisCSCJournals
Card payment are mostly preferred by many for transactions instead of cash. Due to its convenience, it is the most accepted payment method for offline as well as online purchases, irrespective of region or country the purchase is made. Currently, cards are used for everyday activities, such as online shopping, bill pays, subscriptions, etc. Consequently, there are more chances of fraudulent transactions. Online transactions are the prime target as it does not require real card, only card details are enough and can be stored digitally. The current system detects the fraud transaction after the transaction is completed. Proposed system in this paper, uses Hidden Markov Model (HMM), which is one of the statistical stochastic models used to model randomly changing systems. Using Hidden Markov Model, a fraud transaction can be detected during the time of transaction itself and after 3 attempts of verification card can blocked at the same time. Behavior Analysis (BA) helps to understand the spending habits of cardholder. Hidden Markov Model helps to acquire high-level fraud analysis with a low false alarm ratio.
Survey on Credit Card Fraud Detection Using Different Data Mining Techniquesijsrd.com
In today's world of e-commerce, credit card payment is the most popular and most important mean of payment due to fast technology. As the usage of credit card has increased the number of fraud transaction is also increasing. Credit card fraud is very serious and growing problem throughout the world. This paper represents the survey of various fraud detection techniques through which fraud can be detected. Although there are serious fraud detection technology exits based on data mining, knowledge discovery but they are not capable to detect the fraud at a time when fraudulent transaction are in progress so two techniques Neural Network and Hidden Markov Model(HMM) are capable to detect the fraudulent transaction is in progress. HMM categorizes card holder profile as low, medium, and high spending on their spending behavior. A set of probability is assigned to each cardholder for amount of transaction. The amount of incoming transaction is matched with cardholder previous transaction, if it is justified a predefined threshold value then a transaction is considered as a legitimate else it is considered as a fraud.
An Enhanced Automated Teller Machine Security Prototype using Fingerprint Bio...Eswar Publications
The steady growth in electronic transactions has promoted the Automated Teller Machine (ATM) thereby making it the main transaction channel for carrying out financial transactions. However, this has also increased the amount of fraudulent activities carried out on Automated Teller Machines (ATMs) thereby calling for efficient security mechanisms and increasing the demand for fast and accurate user identification and
authentication in ATMs. This research analyses, designs and proposes a biometric authentication prototype for integrating fingerprint security with ATMs as an added layer of security. A fingerprint biometric technique was fused with personal identification numbers (PIN's) for authentication to ameliorate the security level. The prototype was simulated using a fingerprint scanner and Java Platform Enterprise Edition was used to develop an ATM application which was used to synchronize with a fingerprint scanner thereby providing a biometric authentication scheme for carrying out transactions on an ATM.
Score card template for MSME lending with objective to create automatic underwriting engine using flow based lending framework. Data points can e taken from various sources like GST portal, Account statement, UPI, India stack, Bharat bill payment, Digilocker to create robust underwriting engine.
The project sets sight on authenticating the conventional Credit card transaction system. In the prevailing system though the Credit card paves a convenient mode of transactions, it is subjected to more jeopardy. As technology extends its limit, the way of hacking and cracking also goes along the road. In out proposed system, in every transaction with the Credit card a handshaking signal is achieved with the cardholder. The handshaking method is achieved by transferring the transaction time and the purchase details to the mobile of the cardholder by means of a GSM modem. From the acknowledgement and authentication received from the cardholder’s mobile further transaction proceeds. The system used the MCU for the security issues between the Mobile and the Card. Reports can also be generated for every successful authentication.
DEVELOPING PREDICTION MODEL OF LOAN RISK IN BANKS USING DATA MINING mlaij
Nowadays, There are many risks related to bank loans, for the bank and for those who get the loans. The
analysis of risk in bank loans need understanding what is the meaning of risk. In addition, the number of
transactions in banking sector is rapidly growing and huge data volumes are available which represent
the customers behavior and the risks around loan are increased. Data Mining is one of the most motivating
and vital area of research with the aim of extracting information from tremendous amount of accumulated
data sets. In this paper a new model for classifying loan risk in banking sector by using data mining. The
model has been built using data form banking sector to predict the status of loans. Three algorithms have
been used to build the proposed model: j48, bayesNet and naiveBayes. By using Weka application, the
model has been implemented and tested. The results has been discussed and a full comparison between
algorithms was conducted. J48 was selected as best algorithm based on accuracy.
An Adaptive and Real-Time Fraud Detection Algorithm in Online Transactions....................................1
Yiming WU, Siyong CAI
Agronomic Disaster Management using Artificial Intelligence - A Case Study.....................................13
M Sudha
Household Power Optimisation and Monitoring System ...................................................................23
John Batani, Silence Dzambo, Israel Magodi
A Survey of Online Credit Card Fraud Detection using Data Mining TechniquesIJSRD
Nowadays the use of credit card has increased, because the amount of online transaction is growing. With the day to day use of credit card for payment online as well as regular purchase, case of fraud associated with it is also rising. To reduce the huge financial loss caused by frauds, a number of modern techniques have been developed for fraud detection which is based on data mining, neural network, genetic algorithm etc. Here a survey of techniques for online credit card fraud detection using Hidden Markov Model, Genetic Algorithm and Hybrid Model, and comparison between them has been shown.
With the growing buzz around data science, many professionals want to learn how to become a data scientist—the role Harvard Business Review called the “sexiest job of the 21st century.” Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.
Francesca and Jaya begin by outlining the typical skillset an exceptional data scientist needs. They then explore common applications of machine learning and artificial intelligence in different business verticals and explore why some companies are much more successful than others at driving analytics-based business transformation. Francesca and Jaya dive into a couple of specific use cases to demonstrate how machine learning and artificial intelligence can help drive business impact within an organization and how the right technology platform can boost employee productivity and help them innovate and iterate rapidly. You’ll learn why a modern cloud analytics environment that makes it easy to collect data, analyze, experiment, and quickly put things into production with a targeted set of customers is becoming a must-have for data-driven organizations and walk through a detailed use case, from how the data typically gets collected to data wrangling, building a model, tuning the model, and operationalizing the model for a business to use in their production environment.
Online Transaction Fraud Detection using Hidden Markov Model & Behavior AnalysisCSCJournals
Card payment are mostly preferred by many for transactions instead of cash. Due to its convenience, it is the most accepted payment method for offline as well as online purchases, irrespective of region or country the purchase is made. Currently, cards are used for everyday activities, such as online shopping, bill pays, subscriptions, etc. Consequently, there are more chances of fraudulent transactions. Online transactions are the prime target as it does not require real card, only card details are enough and can be stored digitally. The current system detects the fraud transaction after the transaction is completed. Proposed system in this paper, uses Hidden Markov Model (HMM), which is one of the statistical stochastic models used to model randomly changing systems. Using Hidden Markov Model, a fraud transaction can be detected during the time of transaction itself and after 3 attempts of verification card can blocked at the same time. Behavior Analysis (BA) helps to understand the spending habits of cardholder. Hidden Markov Model helps to acquire high-level fraud analysis with a low false alarm ratio.
Survey on Credit Card Fraud Detection Using Different Data Mining Techniquesijsrd.com
In today's world of e-commerce, credit card payment is the most popular and most important mean of payment due to fast technology. As the usage of credit card has increased the number of fraud transaction is also increasing. Credit card fraud is very serious and growing problem throughout the world. This paper represents the survey of various fraud detection techniques through which fraud can be detected. Although there are serious fraud detection technology exits based on data mining, knowledge discovery but they are not capable to detect the fraud at a time when fraudulent transaction are in progress so two techniques Neural Network and Hidden Markov Model(HMM) are capable to detect the fraudulent transaction is in progress. HMM categorizes card holder profile as low, medium, and high spending on their spending behavior. A set of probability is assigned to each cardholder for amount of transaction. The amount of incoming transaction is matched with cardholder previous transaction, if it is justified a predefined threshold value then a transaction is considered as a legitimate else it is considered as a fraud.
An Enhanced Automated Teller Machine Security Prototype using Fingerprint Bio...Eswar Publications
The steady growth in electronic transactions has promoted the Automated Teller Machine (ATM) thereby making it the main transaction channel for carrying out financial transactions. However, this has also increased the amount of fraudulent activities carried out on Automated Teller Machines (ATMs) thereby calling for efficient security mechanisms and increasing the demand for fast and accurate user identification and
authentication in ATMs. This research analyses, designs and proposes a biometric authentication prototype for integrating fingerprint security with ATMs as an added layer of security. A fingerprint biometric technique was fused with personal identification numbers (PIN's) for authentication to ameliorate the security level. The prototype was simulated using a fingerprint scanner and Java Platform Enterprise Edition was used to develop an ATM application which was used to synchronize with a fingerprint scanner thereby providing a biometric authentication scheme for carrying out transactions on an ATM.
Score card template for MSME lending with objective to create automatic underwriting engine using flow based lending framework. Data points can e taken from various sources like GST portal, Account statement, UPI, India stack, Bharat bill payment, Digilocker to create robust underwriting engine.
The project sets sight on authenticating the conventional Credit card transaction system. In the prevailing system though the Credit card paves a convenient mode of transactions, it is subjected to more jeopardy. As technology extends its limit, the way of hacking and cracking also goes along the road. In out proposed system, in every transaction with the Credit card a handshaking signal is achieved with the cardholder. The handshaking method is achieved by transferring the transaction time and the purchase details to the mobile of the cardholder by means of a GSM modem. From the acknowledgement and authentication received from the cardholder’s mobile further transaction proceeds. The system used the MCU for the security issues between the Mobile and the Card. Reports can also be generated for every successful authentication.
DEVELOPING PREDICTION MODEL OF LOAN RISK IN BANKS USING DATA MINING mlaij
Nowadays, There are many risks related to bank loans, for the bank and for those who get the loans. The
analysis of risk in bank loans need understanding what is the meaning of risk. In addition, the number of
transactions in banking sector is rapidly growing and huge data volumes are available which represent
the customers behavior and the risks around loan are increased. Data Mining is one of the most motivating
and vital area of research with the aim of extracting information from tremendous amount of accumulated
data sets. In this paper a new model for classifying loan risk in banking sector by using data mining. The
model has been built using data form banking sector to predict the status of loans. Three algorithms have
been used to build the proposed model: j48, bayesNet and naiveBayes. By using Weka application, the
model has been implemented and tested. The results has been discussed and a full comparison between
algorithms was conducted. J48 was selected as best algorithm based on accuracy.
An Adaptive and Real-Time Fraud Detection Algorithm in Online Transactions....................................1
Yiming WU, Siyong CAI
Agronomic Disaster Management using Artificial Intelligence - A Case Study.....................................13
M Sudha
Household Power Optimisation and Monitoring System ...................................................................23
John Batani, Silence Dzambo, Israel Magodi
Credit card plays a very vital role in todays economy and the usage of credit cards has dramatically increased. Credit card has become one of the most common method of payment for both online and offline as well as for regular purchases of a common man. It is very necessary to distinguish fraudulent credit card transactions by the credit card organizations so their clients are not charged for the purchases that they didn’t make. Despite the fact that using credit card gives huge benefits when used responsibly carefully and however significant credit and financial damages could be caused by fraudulent activities as well. Numerous methods have been proposed to stop these fraudulent activities. The project illustrates the model of a dataset to predict fraud transactions using machine learning. The model then detects if it is a fraudulent or a genuine transaction. The model also analyses and pre processes the dataset along with deployment of multiple anomaly detection using algorithms such as Local forest outlier and Isolation forest. Nikitha Pradeep | Dr. A Rengarajan "Credit Card Fraud Detection" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd41289.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/41289/credit-card-fraud-detection/nikitha-pradeep
A Study on Credit Card Fraud Detection using Machine Learningijtsrd
Due to the high level of growth in each number of transactions done using credit card has led to high rise in fraudulent activities. Fraud is one of the major issues related to credit card business, since each individual do more of offline or online purchase of product via internet there is need to developed a secured approach of detecting if the credit card been used is a fraudulent transaction or not. Pattern involves in the fraud detection has to be re analyze to change from reactive approach to a proactive approach. In this paper, our objectives are to detect at least 95 of fraudulent activities using machine learning to deployed anomaly detection system such as logistic regression, k nearest neighbor and support vector machine algorithm. Ajayi Kemi Patience | Dr. Lakshmi J. V. N "A Study on Credit Card Fraud Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30688.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30688/a-study-on-credit-card-fraud-detection-using-machine-learning/ajayi-kemi-patience
Similar to IRJET- Finalize Attributes and using Specific Way to Find Fraudulent Transaction (20)
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
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.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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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.
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
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.