Machine learning algorithms for social media analysis: A survey

Remaining discharge energy estimation for lithium-ion batteries based on future load prediction considering temperature and ageing effects

The final goal of all industrial machine learning (ML) projects is to develop ML products and rapidly bring them into production. However, it is highly challenging to automate and operationalize ML products and thus many ML endeavors fail to deliver on their expectations. The paradigm of Machine Learning Operations (MLOps) addresses this issue. MLOps includes several aspects, such as best practices, sets of concepts, and development culture. However, MLOps is still a vague term and its consequences for researchers and professionals are ambiguous. To address this gap, we conduct mixed-method research, including a literature review, a tool review, and expert interviews. As a result of these investigations, we contribute to the body of knowledge by providing an aggregated overview of the necessary principles, components, and roles, as well as the associated architecture and workflows. Furthermore, we provide a comprehensive definition of MLOps and highlight open challenges in the field. Finally, this work provides guidance for ML researchers and practitioners who want to automate and operate their ML products with a designated set of technologies.

Machine Learning Operations (MLOps): Overview, Definition, and Architecture

The world is being more and more digitalized with each passing day, making information security a huge concern Through efficient fraud detection using a robust approach, the banking industry will be able to avert fraudulent transactions and save millions of dollars every year Each money transaction is crucial and thus, fraudulent transactions need to be detected at any cost In this paper, we build models to detect fraudulent credit card transactions using five classifiers to find out the best fit classifier for the situation We use two different techniques to tackle the inherent problem of data imbalance The first technique uses data resampling technique to increase the number of samples in the minority class, whereas, the second one uses a cost-based approach where the error function incorporates weights of each class Through the weights, more emphasis can be given to the samples of fraudulent transactions than the normal samples

A Dual Approach for Credit Card Fraud Detection using Neural Network and Data Mining Techniques

A comprehensive survey and analysis of generative models in machine learning

UBMTR: Unsupervised Boltzmann Machine-Based Time-Aware Recommendation System

Severe Acute Respiratory Syndrome Corona Virus 2 or SARS-CoV-2 (COVID-19) has affected 21 million people worldwide and is responsible for 0.75 million deaths (as of August 2020). Declared as a pand...

https://www.tandfonline.com/doi/pdf/10.1080/09720502.2020.1838061

Juxtaposing inference capabilities of deep neural models over posteroanterior chest radiographs facilitating COVID-19 detection

/pdf/arrhythmia-detection-using-deep-belief-network-extracted-4kdv8k85gd.pdf

GM Harshvardhan

Papers

A comprehensive survey and analysis of generative models in machine learning

A Dual Approach for Credit Card Fraud Detection using Neural Network and Data Mining Techniques

UBMTR: Unsupervised Boltzmann Machine-Based Time-Aware Recommendation System

Juxtaposing inference capabilities of deep neural models over posteroanterior chest radiographs facilitating COVID-19 detection

Arrhythmia Detection Using Deep Belief Network Extracted Features From ECG Signals