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Research Paper

Designing smart and secure ATM card for multiple bank accounts

This project’s main objective is to integrate multiple bank accounts into one single smart card. Many banks charge ATM usage fees from the customers for different transactions. At present day, every customer has an individual ATM card for each and every bank in which he/she maintains an account. So, while handling the cards, many passwords are involved. In order to overcome these difficulties, we embedded more than one bank account of the user in a single ATM smart card, therefore letting the user swipe the card so that the respective individual can select the bank from which he/she is interested to carry out the transaction.

Published by: Mohanraj S., Sanjana R., Ramya Lakshmi R., Durga R., Harini L.

Author: Mohanraj S.

Paper ID: V7I2-1530

Paper Status: published

Published: May 4, 2021

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Research Paper

JavaScript attack detection using machine learning

JavaScript-based attacks have become one of the most common and successful types of attack. Existing techniques for detecting malicious JavaScripts could fail for different reasons. In this project, we propose an efficient method of detecting previously unknown malicious activity using an interceptor at the client-side. In this, we are using a machine-learning algorithm to detect JavaScript attacks. The interceptor monitors the traffic exchanged between the browser and server and extracts the real-time traffic features needed for XSS detection. Supervised machine learning classifiers such as k- NN, Support Vector Machines, Gaussian Naïve Bayes, and Logistic Regression are used on a dataset gathered for achieving high accuracy in webpage classification through learning from patterns. The experimental results obtained, show that this method can efficiently classify malicious code from benign code with promising results.

Published by: Kiran Yadav, Eesha Momin, Komal Bartwal, Mamta Patil

Author: Kiran Yadav

Paper ID: V7I2-1547

Paper Status: published

Published: April 30, 2021

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Research Paper

Biochemical analysis of Siddha mono herbal drug Amukkara Podi( Amukkara Karpam)

Siddhar Yogam or Siddhar Yoga Muraigal are the ancient methods that were advocated by the Siddhars in order to prevent the ailments occurring due to human aging. Also, these methods consisted of physical, mental, and even dietary practices, that must be practiced for a period of a mandala (48 days). The dietary practices, that were advocated by them are the ‘Kaayakarpam’ practices. These usually include the intake of a certain poly or monoherbal drug, for a stipulated span of time. They may either be the drug of choice for general body wellness, or for a specific disease. Considering Amukkara Podi (the powdered root of Withania Somnifera), it has several references as a Kaayakarpam[1] drug for general body wellness. As a special drug, it acts wonderfully towards weight loss for obese patients, when taken in with honey. Obesity[2] or being obese is a health disorder that is characterized by the presence of a BMI of more than 25. These patients tend to be overweight, have a lethargic attitude or a condition of tiredness towards work, and at later stages may suffer from knee, hip pains, etc. Obesity has also been considered to be an uprising health disorder of concern by WHO. The treatment generally includes rigorous exercise in order to lose bodyweight. In cases where obese individuals cannot lose weight through exercise, other options could be taken into consideration. One among them is the intake of Amukkara Podi with Honey. This paper reports the biochemical analysis of the drug, ‘Amukkara Podi’, which could be used as a forerunner for the usage of the drug against obesity.

Published by: Dr. Binthuja G. Dharan, Dr. G. Thirunarayanan, Dr. A. S. Poongodi Kanthimathi

Author: Dr. Binthuja G. Dharan

Paper ID: V7I2-1554

Paper Status: published

Published: April 30, 2021

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Research Paper

Machine Learning Algorithms to Predict Next Day Rain in Australia

This paper predicts whether it will rain the next day or not in Australia. This paper compares 4 machine learning algorithms namely Random Forest Classifier, XGBoost, Light GBM and Logistic Regression models by training and testing them with the data set. The XGBoost Model performed the best when compared to Random Forest Classifier, Logistic Regression and LightGBM. XGBoost produced an accuracy of 94.03%. The models could have performed better if the date was treated as a cyclic continuous feature because the weather itself is cyclic which shows a similar trend during the same seasons.

Published by: P. Sai Dinesh Reddy

Author: P. Sai Dinesh Reddy

Paper ID: V7I2-1543

Paper Status: published

Published: April 30, 2021

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Case Study

Internet of Things Based Smart Society

Internet of Things (IoT) is a fledgling and popular technology, which has given the provision to people for doing daily work like, at just a click or on command. The Internet of Things has helped develop many amazing Things like the smart street where streetlight gets automatically switched on or off with the help of sensor data. Well-sensor is a crucial factor in the field of Internet of Things applications. The great developers and people from the industry are working continuously to develop a system that will help sectors like healthcare, agricultural, industrial, construction, etc. A lot of researchers do research on the Internet of Things as a part of the interest and as a result, they end up developing amazing and unique systems. This paper produces a summary of the Internet of Things system named “Smart Society” which consists of 5 unique modules which are Smart Street Lighting, Disaster Management System, House Safety, Smart Gardening and Healthy Environment. In the further part of the paper, the components and the software systems are discussed which have been utilized to develop this model, and at last, the challenges and future scope of the “Smart Society System”.

Published by: Nitin Nandkumar Sakhare, Dhaval Tanna, Rohit Bhokarikar, Aboli Rode, Aachal Rathod

Author: Nitin Nandkumar Sakhare

Paper ID: V7I2-1531

Paper Status: published

Published: April 30, 2021

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Review Paper

A review on the impact of mobility patterns and prediction on Covid-19 rates

After the outbreak of COVID-19 several prediction models are being used by authorities and officials all over the world to implement appropriate control measures and to make well-informed decisions. Due to an enormous level of ambiguity and shortage of crucial data, conventional models have shown low efficiency for long-term prediction. A coronavirus is a contagious disease that is resulting in the massive growth of COVID-19 cases, therefore, we have used human mobility patterns as the effective factor to take preventive measures to thereby stop the outbreak. In this model, we have used methods such as exponential growth, and prophet models. We here demonstrate the impact of changes in mobility patterns by binding the data in Data Science to efficiently trace the disease, plan strategies, methods and foretell the future growth of the pandemic.

Published by: Priyanka Dongre, Rachna Pazare, Mansi Lanje, Anushka Burewar, Ekta Gajbhiye, Abhishek Kumar

Author: Priyanka Dongre

Paper ID: V7I2-1389

Paper Status: published

Published: April 28, 2021

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