Customer churn is an essential retail metric used in business predictive analytics systems to quantify the number of customers who left a company. All retail and business to consumer companies carefully analyze customer behavior to prevent them to cease their relationship with the company, in other words to make churn. With the latest advancements of Artificial Intelligence and particularly related to Deep Learning, we have a new set of powerful tools ready to employ within multiple horizontal and vertical domain – such as the horizontal of predictive business analytics domain. One of the main goals of predictive analytics is the research and development of the almost-perfect churn detection system. This paper objective is to propose a state-of-the-art churn prediction model based on deep neural models, time-to-next-event models and employing Big Data processing on massive parallel computing using GPU cells.
Keywords — machine learning, business predictive analytics, massive parallel computing, on GPU, deep learning, customer retention, big data, churn prediction
Reference – https://ieeexplore.ieee.org/document/8514153
Predictive analytics systems are currently one of the most important areas of research and development within the Artificial Intelligence domain and particularly in Machine Learning. One of the “holy grails” of predictive analytics is the research and development of the “perfect” recommendation system. In our paper we propose an advanced pipeline model for the multi-task objective of determining product complementarity, similarity and sales prediction using deep neural models applied to big-data sequential transaction systems. Our highly parallelized hybrid pipeline consists of both unsupervised and supervised models, used for the objectives of generating semantic product embeddings and predicting sales, respectively. Our experimentation and benchmarking have been done using very large pharma-industry retailer Big Data stream.
Keywords — recommender systems; efficient embeddings; machine learning; deep learning; big-data; high-performance computing, GPU computing.
Reference – https://ieeexplore.ieee.org/document/8514141
The need for systems capable of conducting inferential analysis and predictive analytics is ubiquitous in a global information society. With the recent advances in the areas of predictive machine learning models and massive parallel computing a new set of resources is now potentially available for the computer science community in order to research and develop new truly intelligent and innovative applications. In our research we present the principles, architecture and current experimentation results for an online platform capable of both hosting and generating intelligent applications – applications with predictive analytics capabilities.
Keywords — artificial intelligence, machine learning ,virtual desktop, predictive analytics
Reference – http://ieeexplore.ieee.org/document/8116994/
On-demand computing, Software-as-a-Service, Platform-as-a-Service, and in general Cloud Computing is currently the main approach by which both academic and commercial domains are delivering systems and content. Nevertheless there still remains a huge segment of legacy systems and application ranging from accounting and management information systems to scientific software based on classic desktop or simple client-server architectures. Although in the past years more and more companies and organizations have invested important budgets in translating legacy apps to online cloud-enabled environment there still remains an important segment of applications that for various reasons (budget related in most cases) have not been translated. This paper proposes an innovative pipeline model architecture for automated translation and migration of legacy application to cloud-enabled environment with a minimal software development costs.
Keywords — automatic programming; cloud computing; migration; machine-learning; automatic translation.
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