The Murdaugh verdict has been a subject of interest in recent times, with a South Carolina jury finding Alex Murdaugh guilty on two counts of first-degree murder and two counts of weapons possession during a violent crime. This article explores the case from a Bayesian perspective, providing insights into how Bayesian reasoning can be applied …
Time series forecasting has long been regarded as a critical component in myriad fields, ranging from finance and economics to environmental science and engineering. Accurate predictions of future observations enable businesses, researchers, and policymakers to make informed decisions, optimize resource allocation, and mitigate potential risks. However, real-world time series data often exhibit complex patterns and …
Understanding Multivariate Probabilistic Time Series Forecasting with Informer Read More »
In recent years, sequential recommender systems have emerged as a popular approach for providing personalized recommendations to users. These systems take into account the order in which items are consumed by users, enabling them to identify long-term and short-term interests of users and predict their future preferences accurately. However, while sequential recommender systems have proven …
Introduction Frequent Pattern Mining, also known as Association Rule Mining, is a powerful analytical process that helps in discovering frequent patterns, associations, or causal structures from various databases such as relational and transactional databases. It plays a significant role in Market Basket Analysis, a data analysis technique used by retailers to identify patterns in customer …
FP-Growth vs. Apriori: A Comprehensive Comparison of Frequent Pattern Mining Algorithms Read More »
In today’s fast-paced financial world, businesses require accurate and timely financial forecasting to make informed decisions. Multivariate time series forecasting has become a popular technique in finance to predict future values of multiple variables simultaneously. It is a statistical method that models the behavior of several interdependent variables over time, considering the historical data of …
Using Multivariate Time Series Forecasting with Transformers in Finance Read More »
Introduction Recommender systems have revolutionized the way we discover new products, music, movies, and even potential romantic partners. They analyze users’ past interactions with items and provide personalized recommendations based on their preferences. However, traditional recommender systems face several challenges, such as sparsity, cold-start, and scalability. To overcome these limitations, researchers have been exploring the …
Transform Your Recommender System with Temporal Graph Neural Networks Read More »
Introduction The explosion of data in recent years has led to an ever-increasing need for effective recommender systems. These systems are designed to analyze user behavior and preferences and provide recommendations for products, services, or content that are likely to be of interest to them. One of the most promising approaches to building these systems …
Building Recommender Systems with Graph Generative Models Read More »
Introduction Cancer is one of the leading causes of death globally, with millions of people diagnosed each year. Despite significant progress in cancer research, there is still a long way to go in terms of improving diagnosis, treatment, and outcomes. However, with the emergence of artificial intelligence (AI), the future of oncology looks promising, with …
The Future of Oncology: Exploring the Impact of AI on Cancer Research Read More »
Fraud is a major concern for businesses, and it can be challenging to identify fraudulent activities in real-time. Fraudulent activities can cause financial losses, reputation damage, and regulatory fines. The increasing use of digital channels has made it even more difficult to detect fraud. However, advancements in technology have made it possible to identify fraudulent …
Fraud Detection Using Deep Boosting Decision Trees Read More »
