Rating of Various Indian Airlines on various parameters using Twitter Data

Abstract In today’s world, e-commerce markets runs on the sentiments of the customers as they provides real feedback of the products or services. In such scenario, the companies tries to collect data from different social media platform like instagram, Facebook, twitter, and other sites where customers places their reviews and feedback about their experience on various parameters. These organizations analyze these data as per their requirements for market analysis, recommendation systems, feedback of the product/service, sentimental analysis etc. In this proposed work, feedback/reviews of various Indian airlines like Air-India, Indigo, Go-Air, Jet airways, Spice-Jet.is collected from twitter to rate the airlines services on various parameters like food quality, staff, delay in time, turbulence, aircraft condition and value for money parameters.</jats:p

An Efficient Techniques for Fraudulent detection in Credit Card Dataset: A Comprehensive study

Abstract Now a day, credit card transaction is one the famous mode for financial transaction. Increasing trends of financial transactions through credit cards also invite fraud activities that involve the loss of billions of dollars globally. It is also been observed that fraudulent transactions have increased by 35% from 2018. A huge amount of transaction data is available to analyze the fraud detection activities that require analysis of behavior/abnormalities in the transaction dataset to detect and ignore the undesirable action of the suspected person. The proposed paper lists a compressive summary of various techniques for the classification of fraud transactions from the various datasets to alert the user for such transactions.</jats:p

Machine Learning techniques for Cardiovascular Disease

Abstract Machine Learning focuses on developing computer programs that can access data and use it to learn for them. In the medical field, the application of machine learning is used to produce useful patterns and information that can be helpful in diagnosing various diseases. This paper focuses on Cardiovascular Disease (CVDs) with previous information and data. CVDs are the most common cause of death globally. The severity of the disease insists the researchers and medical professionals forecast CVDs threats correctly and precisely as early stage. The best and most effective way of treating CVD is by surgery, yet the test indicates that it’s not only costly but it also has side effects. The main aim is to provide a quantitative and qualitative summary of machine learning techniques in the cardiovascular dataset. This analysis analyzes the selected documents and identifies gaps in the existing literature and assists the researcher in preparing to use machine learning on cardiovascular dataset.</jats:p

Privacy Protection of Digital Images Using Watermarking and QR Code-based Visual Cryptography

The increase in information sharing in terms of digital images imposes threats to privacy and personal identity. Digital images can be stolen while in transfer and any kind of alteration can be done very easily. Thus, privacy protection of digital images from attackers becomes very important. Encryption, steganography, watermarking, and visual cryptography techniques to protect digital images have been proposed from time to time. The present paper is focused on the enhancement of privacy protection of digital images utilizing watermarking and a QR code-based expansion-free and meaningful visual cryptography approach which generates visually appealing QR codes for transmitting meaningful shares. The original secret image is processed with a watermark image (copyright logo, signature, and so on), and then halftoning of the watermarked image has been done to limit pixel expansion. Then, the halftoned image has been partitioned using VC into two shares. These shares are embedded with a QR code to make the shares meaningful. Lossless compression has been performed on the QR code-based shares. The compression method employed in visual cryptography would save space and time. The proposed approach keeps the beauty of visual cryptography, i.e., computation-free decryption, and the size of the recovered image the same as the original secret image. The experimental results confirm the effectiveness of the proposed approach

Early Stress Detection and Analysis using EEG signals in Machine Learning Framework

Abstract Stress, a psychological phenomenon that represents the body’s natural defense against predators and danger, has emerged as the biggest social problem of the 21st century especially during the Covid-19 pandemic. Various techniques or methods such as PET, ECG, EMG, MRI exist to detect and quantify stress. Physiological features produced throughout the brain’s electrical activity are documented by a medical technique known as an electroencephalogram (EEG). In this context, this paper posits a comparative analysis of the above-described methods of stress detection and accentuates on stress detection methodology using EEG signals, as EEG is a perfect non-invasive tool, widely used in clinical and research domains. The fractal dimension (FD) method, which is an indicator of curve irregularities, has been used in the detection of stress for feature extraction, applying three FD algorithms viz. Higuchi, Katz and Permutation Entropy. For classification, this study aims to apply and compare a number of classic machine learning algorithms based on accuracy, precision and sensitivity. This paper also presents a novel architecture, based on EEG analysis in MATLAB, fractal dimension used for feature extraction along with Machine Learning processes for classification i.e., Random Forest and Artificial Neural Network which is useful for early-stage stress detection, analyzing different stress levels viz. mild, moderate and high accuracy and providing ways for people to cope with stress in order to enhance their performance.</jats:p