Anomaly detection, also known as outlier detection, is a technique used to identify unusual patterns in data that do not conform to expected behavior. It is an important part of machine learning, as it helps to detect fraud, risk, and other anomalies. Aishwarya Asesh, a Senior Data Scientist in Utah, United States of America, has developed an innovative anomaly detection algorithm that takes into accounterror, trends, seasonality and other factors to detect anomalies in data.
Aishwarya’s algorithm is a breakthrough in data science and has been widely adopted in the machine learning community. His research is focused on time series analysis and forecasting, and he has been able to make significant contributions to the field. He filed for his patent in the first year of his professional career and now holds multiple patents internationally. One of hispatents called Model Reselection for Accommodating Unsatisfactory Training Data, utilizes a dynamic thresholding technique to identify and adjust the training data to maximize the model performance. This allows for improved results even when the training data is incomplete or otherwise unsatisfactory. With this approach, the model reselection process can be automated and more effective, providing enhanced accuracy in the results. This algorithm has hugely helped the multi-billion market of Anomaly Detection tools which is expected to reach 9.68 billion by 2028.
Building on to his journey of development and collaboration, Aishwarya’s next take on Anomaly Detection algorithm is based on a machine learning model known as recurrent neural networks (RNN). The algorithm takes into account seasonality of the data and does training based on granularity of the data like hourly, daily monthly and so on; the approach also uses other factors such as outliers, to detect anomalies in data. It has been used to identify anomalies in customer feedback data, customer churn data, and other forms of customer data. This has helped companies better understand their customers, improve customer experience, and reduce customer churn.The patented algorithm of Aishwarya, Robust Anomaly and Change Detection Utilizing Sparse Decomposition, is unique as it utilizes a hybrid of two decomposition techniques, Sparse Decomposition and Non-Negative Matrix Factorization (NMF), to detect changes and anomalies in large data sets. The combination of these two decomposition techniques provides an effective way to identify changes and anomalies that may have been overlooked by more traditional methods. Additionally, this algorithm is robust to outliers and can quickly and accurately detect changes and anomalies in a variety of data types and sizes.
These commercial patents and algorithms by Aishwarya is being extensively used in a variety of sectors. Businesses are leveraging the algorithm to detect anomalies in their customer data, such as fraudulent transactions, suspicious behavior, or spikes in customer activity. Additionally, it is being used to detect changes in customer behavior and buying patterns in order to better target marketing campaigns and optimize customer experience. It is being used to automate processes and detect abnormalities in customer service interactions. Finally, it is being used to detect anomalies in financial data, such as fraud or money laundering.By identifying outliers in customer data, companies can take proactive steps to detect fraud and prevent it from happening. This helps to reduce the risk of financial losses due to fraudulent activity.
Real world datasets are tough to handle, and the Anomaly Detection methods developed by Aishwarya provides automatic solution to a problem that has been long-standing in machine learning. For use cases such as network behavior, user behavior, intelligent monitoring across all industries in North America, Europe, Asia Pacific, Latin America and Middle East and Africa, companies were struggling to run an automated insights engine to help their businesses, these developments by Aishwarya are a game changer.
Large, Medium and Small Corporates are using variety of ways to gain insights from the millions of metrics that organizations need to track. Aishwarya described, three primary use cases are application performance, product quality, and user experience. Application performance metrics measure the operational performance of applications and infrastructure components, while product quality and user experience KPIs evaluate the business’s success. Aishwarya’s Anomaly detection algorithms are helpingorganizations detect when real-world business incidents, such as new marketing campaigns or promotional discounts that can cause unexpected changes in their data patterns. With these right analytics programs and management software, organizationsare measuring all aspects of their business activity more effectively than ever before.These algorithms developed by Aishwarya are generating loads of wealth for all major market areas across the globe.
Aishwarya’s algorithm has also been used in the healthcare industry to detect anomalies in patient data. By identifying outliers in patient data, healthcare providers can take early steps to prevent potential problems. This can help to improve patient care and reduce costs.Aishwarya’s contribution to the field of machine learning and anomaly detection is invaluable. His work is helping companies to better understand their customers, improve customer experience, reduce customer churn, and detect fraudulent transactions. He has been recognized by the machine learning community for his work and has been invited to be a judge for various conferences and research forums.
Aishwarya Asesh is a shining star in the field of machine learning and data science. His various algorithmsare helping companies to better understand their customers and improve customer experience. His research is focused on data science, time series analysis and forecasting, and he has made significant contributions to the field of machine learning and artificial intelligence. He is an innovator who has the passion to discover insights from the unknown. Aishwarya is a true leader in the machine learning community and is setting the trend for the future of technical advancements.
