This project aims to prevent fraud by checking if a user's image already exists in a bank's database when they apply for a loan. The model detects faces from images, extracts features, and uses dHash and SSIM algorithms to check for similarities between images. The output notifies the bank manager if fraud is detected by displaying matching images and customer details. The model achieves 61% accuracy but performs poorly on low-quality images or images where the user is not facing the camera. Python, OpenCV, Spark, Bottle and Java were used to build and integrate the model.
4.detection of fake news through implementation of data science applicationVenkat Projects
In this project we are using LSTM (Long Short Term Memory) Recurrent Neural Network to predict fake news as huge amount of fake news is gathering in all types of media such as social media or news media and to detect fake news author is training LSTM neural network with past news data label as ‘Genuine’ and ‘Fake’. We downloaded available twitter FAKE NEWS tweets from internet and below is the dataset screen shots
10.sentiment analysis of customer product reviews using machine learniVenkat Projects
10.sentiment analysis of customer product reviews using machine learning In this project author is detecting sentiments from amazon reviews by using various machine learning algorithms such as SVM, Decision Tree and Naïve Bayes. In all 3 algorithms SVM is giving better accuracy and to train this algorithms author has used AMAZON reviews dataset and this dataset is saved inside ‘Amazon_Reviews_dataset’ folder. Below screen shot show example reviews from dataset
Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies.
Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.
In this webinar, we will discuss :
How machine learning can help in identifying anomalies
Steps to approach an anomaly detection problem
Various techniques available for anomaly detection
Best algorithms that fit in different situations
Implementing an anomaly detection use case on the StreamAnalytix platform
To view the webinar - https://bit.ly/2IV2ahC
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" 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/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
4.detection of fake news through implementation of data science applicationVenkat Projects
In this project we are using LSTM (Long Short Term Memory) Recurrent Neural Network to predict fake news as huge amount of fake news is gathering in all types of media such as social media or news media and to detect fake news author is training LSTM neural network with past news data label as ‘Genuine’ and ‘Fake’. We downloaded available twitter FAKE NEWS tweets from internet and below is the dataset screen shots
10.sentiment analysis of customer product reviews using machine learniVenkat Projects
10.sentiment analysis of customer product reviews using machine learning In this project author is detecting sentiments from amazon reviews by using various machine learning algorithms such as SVM, Decision Tree and Naïve Bayes. In all 3 algorithms SVM is giving better accuracy and to train this algorithms author has used AMAZON reviews dataset and this dataset is saved inside ‘Amazon_Reviews_dataset’ folder. Below screen shot show example reviews from dataset
Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies.
Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.
In this webinar, we will discuss :
How machine learning can help in identifying anomalies
Steps to approach an anomaly detection problem
Various techniques available for anomaly detection
Best algorithms that fit in different situations
Implementing an anomaly detection use case on the StreamAnalytix platform
To view the webinar - https://bit.ly/2IV2ahC
Amazon Product Review Sentiment Analysis with Machine Learningijtsrd
Users of Amazons online shopping service are allowed to leave feedback for the items they buy. Amazon makes no effort to monitor or limit the scope of these reviews. Although the amount of reviews for various items varies, the reviews provide easily accessible and abundant data for a variety of applications. This paper aims to apply and expand existing natural language processing and sentiment analysis research to data obtained from Amazon. The number of stars given to a product by a user is used as training data for supervised machine learning. Since more people are dependent on online products these days, the value of a review is increasing. Before making a purchase, a buyer must read thousands of reviews to fully comprehend a product. In this day and age of machine learning, however, sorting through thousands of comments and learning from them would be much easier if a model was used to polarize and learn from them. We used supervised learning to polarize a massive Amazon dataset and achieve satisfactory accuracy. Ravi Kumar Singh | Dr. Kamalraj Ramalingam "Amazon Product Review Sentiment Analysis with Machine Learning" 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/ijtsrd42372.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42372/amazon-product-review-sentiment-analysis-with-machine-learning/ravi-kumar-singh
Disease prediction using machine learningJinishaKG
Github link :
https://github.com/jini-the-coder/Diseaseprediction
Blog link :
http://amigoscreation.blogspot.com/2020/07/disease-prediction-using-machine.html
Youtube link :
https://youtu.be/3YmAbta16yk
Anomaly Detection and Spark Implementation - Meetup Presentation.pptxImpetus Technologies
StreamAnalytix sponsored a meetup on “Anomaly Detection Techniques and Implementation using Apache Spark” which took place on Tuesday December 5, 2017 at Larkspur Landing Milpitas Hotel, Milpitas, CA. The meetup was led by Maxim Shkarayev, Lead Data Scientist, Impetus Technologies along with Punit Shah, Solution Architect, StreamAnalytix and Anand Venugopal, Product Head & AVP, StreamAnalytix, who introduced and summarized the vast field of Anomaly Detection and its applications in various industry problems. The speakers at the event also offered a structured approach to choose the right anomaly detection techniques based on specific use-cases and data characteristics which was followed by a demonstration of some real-world anomaly detection use-cases on Apache Spark based analytics platform.
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
Anomaly Detection - Real World Scenarios, Approaches and Live ImplementationImpetus Technologies
Detecting anomalous patterns in data can lead to significant actionable insights in a wide variety of application domains, such as fraud detection, network traffic management, predictive healthcare, energy monitoring and many more.
However, detecting anomalies accurately can be difficult. What qualifies as an anomaly is continuously changing and anomalous patterns are unexpected. An effective anomaly detection system needs to continuously self-learn without relying on pre-programmed thresholds.
Join our speakers Ravishankar Rao Vallabhajosyula, Senior Data Scientist, Impetus Technologies and Saurabh Dutta, Technical Product Manager - StreamAnalytix, in a discussion on:
Importance of anomaly detection in enterprise data, types of anomalies, and challenges
Prominent real-time application areas
Approaches, techniques and algorithms for anomaly detection
Sample use-case implementation on the StreamAnalytix platform
Part I Machine learning technique
Introduction to Machine Learning
Genetic Algorithm
Monte Calo
Reinforcement Learning
Generative Adversarial Networks
Part II Anomaly Detection technique
Type of Anomaly
RNN
Historical
DB-SCAN
Time Shift Detection
Text Pattern Anomaly Detection
Hand dominant data classification based on smartphone sensitivityAzriidros
Design a data collection tools and install into human samples` mobile phone. Samples are selected from a group of right-handed and left-handed individual to participate in our data collection activities. Collected data will be pre-processed, trained and later tested using data mining technique to measure the accuracy of our technique.
*What is Machine Learning?
-Definition
-Explanation
*Difference between Machine Learning and Standard Programs
*Machine Learning Models
-Supervised Learning
--Classification
--Regression
-Unsupervised Learning
--Clustering
*AI Evolution
-History of AI
-Neural Networks and Deep Learning
-Simple Neural Network and Deep Neural Network
-Difference between AI, Machine Learning, and Deep Learning
We stand on the shoulder of giants when it comes to using the latest and greatest machine learning libraries. However, not much is said about how to deploy and monitor your beautiful model when it's out in production. I want to talk about the successes and potential perils of building real time machine learning solutions, discuss machine learning in general for the non-technical and discuss the architecture and approach we've taken at Ravelin to use machine learning to stop fraud.
Disease prediction using machine learningJinishaKG
Github link :
https://github.com/jini-the-coder/Diseaseprediction
Blog link :
http://amigoscreation.blogspot.com/2020/07/disease-prediction-using-machine.html
Youtube link :
https://youtu.be/3YmAbta16yk
Anomaly Detection and Spark Implementation - Meetup Presentation.pptxImpetus Technologies
StreamAnalytix sponsored a meetup on “Anomaly Detection Techniques and Implementation using Apache Spark” which took place on Tuesday December 5, 2017 at Larkspur Landing Milpitas Hotel, Milpitas, CA. The meetup was led by Maxim Shkarayev, Lead Data Scientist, Impetus Technologies along with Punit Shah, Solution Architect, StreamAnalytix and Anand Venugopal, Product Head & AVP, StreamAnalytix, who introduced and summarized the vast field of Anomaly Detection and its applications in various industry problems. The speakers at the event also offered a structured approach to choose the right anomaly detection techniques based on specific use-cases and data characteristics which was followed by a demonstration of some real-world anomaly detection use-cases on Apache Spark based analytics platform.
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
Anomaly Detection - Real World Scenarios, Approaches and Live ImplementationImpetus Technologies
Detecting anomalous patterns in data can lead to significant actionable insights in a wide variety of application domains, such as fraud detection, network traffic management, predictive healthcare, energy monitoring and many more.
However, detecting anomalies accurately can be difficult. What qualifies as an anomaly is continuously changing and anomalous patterns are unexpected. An effective anomaly detection system needs to continuously self-learn without relying on pre-programmed thresholds.
Join our speakers Ravishankar Rao Vallabhajosyula, Senior Data Scientist, Impetus Technologies and Saurabh Dutta, Technical Product Manager - StreamAnalytix, in a discussion on:
Importance of anomaly detection in enterprise data, types of anomalies, and challenges
Prominent real-time application areas
Approaches, techniques and algorithms for anomaly detection
Sample use-case implementation on the StreamAnalytix platform
Part I Machine learning technique
Introduction to Machine Learning
Genetic Algorithm
Monte Calo
Reinforcement Learning
Generative Adversarial Networks
Part II Anomaly Detection technique
Type of Anomaly
RNN
Historical
DB-SCAN
Time Shift Detection
Text Pattern Anomaly Detection
Hand dominant data classification based on smartphone sensitivityAzriidros
Design a data collection tools and install into human samples` mobile phone. Samples are selected from a group of right-handed and left-handed individual to participate in our data collection activities. Collected data will be pre-processed, trained and later tested using data mining technique to measure the accuracy of our technique.
*What is Machine Learning?
-Definition
-Explanation
*Difference between Machine Learning and Standard Programs
*Machine Learning Models
-Supervised Learning
--Classification
--Regression
-Unsupervised Learning
--Clustering
*AI Evolution
-History of AI
-Neural Networks and Deep Learning
-Simple Neural Network and Deep Neural Network
-Difference between AI, Machine Learning, and Deep Learning
We stand on the shoulder of giants when it comes to using the latest and greatest machine learning libraries. However, not much is said about how to deploy and monitor your beautiful model when it's out in production. I want to talk about the successes and potential perils of building real time machine learning solutions, discuss machine learning in general for the non-technical and discuss the architecture and approach we've taken at Ravelin to use machine learning to stop fraud.
Fake Multi Biometric Detection using Image Quality Assessmentijsrd.com
In the recent era where technology plays a prominent role, persons can be identified (for security reasons) based on their behavioral and physiological characteristics (for example fingerprint, face, iris, key-stroke, signature, voice, etc.) through a computer system called the biometric system. In these kinds of systems the security is still a question mark because of various intruders and attacks. This problem can be solved by improving the security using some efficient algorithms available. Hence the fake person can be identified if he/she uses any synthetic sample of an authenticated person and a fake person who is trying to forge can be identified and authenticated.
Project on fake currency recognition using image processing ppt final (3).pptx426SahithiBaiMiriska
End of the project is the technology of currency recognition basically AIMS for identifying and extracting visible and invisible features of currency notes and till now many techniques have been proposed stering for the fake currency note but the best way to use the visible features of the note or colour and size
The scope of work and idea is this project proposes and approach the 12 detect a currency note be in circular in our country by using their image our project will provide required mobility and compatibility to most of the people and provides a credible accuracy for the fake currency detection we are using machine learning to make it portable and efficient.
The overview of the project is the fake currency detection using machine learning was implemented on matlab features of currency note like serial number security thread identification mark Mahatma Gandhi portrayed what extracted the process star and from image acquisition to calculation of intensity of each extracted feature.
The application the applications are fake currency detection system can be utilised in shops Bank counters and end computerised tailor machine and auto merchant machines and so on the systems are created utilizing diverse techniques and
The future scope of this project is many different adaptation test and innovations have been kept for the future due to lack of time as which a work concerns de per analysis of particular mechanisms new proposal Pride different methods or simply curiosity in future we will be including ammonia for currency conversion and we can implement the system for foreign currencies and tracking of device location through which the currency scan and maintain the same in the database.
Smart Bank Locker Access System Using Iris ,Fingerprints,Face Recognization A...IJERA Editor
In today's modern world, security plays an important role. For that purpose, we proposed advance security systems for banking locker system and the bank customers. This specialized security is proposed through four different modules in combination i.e. face detection technique, Password verification, finger prints and Iris verification .All these steps are followed in the sequence if anything goes wrong he or she is unable to access the system. We are also providing some additional billing with it i.e. it will always for after 1 transaction. It will give the intimation of first transaction is complete and you are left with prescribed number transaction mentioned in system.
Computer Vision - Real Time Face Recognition using Open CV and Python
PROJECT REPORT
1. PROJECT REPORT
-FRAUD DETECTION
Problem Statement: The main objective of this project is to prevent a fraudster from applying
loan twice in a particular bank. When an image comes from the user registration page the
algorithm will check whether the image already exists in the bank’s database or not. If a match is
found a warning message will be displayed to the bank manager who can take appropiate action
based on the details of that particular user.
Dataset Description: The dataset consist of frontal-face of a person’s image. Since the problem
is related to a bank we can safely assume that all the images will be of a good quality(image will
not be of mobile camera quality) and the person will be facing the camera.
Pre-processing: All the images were resized to a common size and then converted to gray-scale
to remove the remove high frequencies and detail.
Model developement: Using OpenCV haarcascade-frontal face classifier we first detect the face
of a person from an image and crop it out and store it in the local filesystem. After this PCA was
used for image re-construction. This step was necessary as it will further remove the noise that
will be present in the image after being converted to gray-scale. After this 2 similarity measures
were used to check for image duplication
a. dHash Algorithm: dHash is a perceptual hashing algorithm. A perceptual
hashing algorithm that takes a fingerprint of a multimedia file by deriving it from
various features from its content so it can take into account transformations on a
given input and yet be flexible enough to distinguish between dissimilar files.
dHash (difference hashing) algorithm computes the difference in brightness
between adjacent pixels, identifying the relative gradient direction. This algorithm
is very fast and we use this algorithm to filter out those images which differ vastly
from the input image.
b. Structural Similarity Index(SSIM): The structural similarity (SSIM) index is a
method for predicting the perceived quality of digital television and cinematic
pictures, as well as other kinds of digital images and videos. SSIM is used for
measuring the similarity between two images. The SSIM index is a full reference
metric; in other words, the measurement or prediction of image quality is based
on an initial uncompressed or distortion-free image as reference. SSIM is
designed to improve on traditional methods such as peak signal-to-noise ratio
(PSNR) and mean squared error (MSE), which have proven to be inconsistent
with human visual perception. Based on this measure we calculate our final
accuracy.
2. Output: our final output displays the proper message to the bank manager based on whether
fraud has been detected or not. Based on the output the bank manager will take appropiate action.
If fraud has been detected on the output window will display the image of the fraudster and
his/her matching image from the bank’s database and the customer id no.
Accuracy of the model: Model accuracy is around 61 %
Drawbacks:
1. The model does not give accurate results if the image quality is poor as OpenCV fails to
detect the face from an image.(eg . if the photo is taken from a mobile phone)
2. The model gives poor results if the user is not facing the camera or standing far away
from the camera.
Tools Used:
1. Python
2. OpenCV for Image Processing
3. Apache Spark for scaling up the model
4. Bottle Framework for Integrating with Java
5. Java for frontend development
References:
1. Wikipedia
2. Github
3. http://blog.iconfinder.com/detecting-duplicate-images-using-python/
4. Apache Spark Documentation
5. Stackoverflow