43,424 research outputs found
A Novel Business Process Prediction Model Using a DeepLearning Method
The ability to proactively monitor business pro-cesses is a main competitive differentiator for firms. Processexecution logs generated by process aware informationsystems help to make process specific predictions forenabling a proactive situational awareness. The goal of theproposed approach is to predict the next process event fromthe completed activities of the running process instance,based on the execution log data from previously completedprocess instances. By predicting process events, companiescan initiate timely interventions to address undesired devi-ations from the desired workflow. The paper proposes amulti-stage deep learning approach that formulates the nextevent prediction problem as a classification problem. Fol-lowing a feature pre-processing stage with n-grams andfeature hashing, a deep learning model consisting of anunsupervised pre-training component with stacked autoen-coders and a supervised fine-tuning component is applied.Experiments on a variety of business process log datasetsshow that the multi-stage deep learning approach providespromising results. The study also compared the results toexisting deep recurrent neural networks and conventionalclassification approaches. Furthermore, the paper addressesthe identification of suitable hyperparameters for the pro-posed approach, and the handling of the imbalanced nature ofbusiness process event datasets
Advanced Customer Activity Prediction based on Deep Hierarchic Encoder-Decoders
Product recommender systems and customer profiling techniques have always
been a priority in online retail. Recent machine learning research advances and
also wide availability of massive parallel numerical computing has enabled
various approaches and directions of recommender systems advancement. Worth to
mention is the fact that in past years multiple traditional "offline" retail
business are gearing more and more towards employing inferential and even
predictive analytics both to stock-related problems such as predictive
replenishment but also to enrich customer interaction experience. One of the
most important areas of recommender systems research and development is that of
Deep Learning based models which employ representational learning to model
consumer behavioral patterns. Current state of the art in Deep Learning based
recommender systems uses multiple approaches ranging from already classical
methods such as the ones based on learning product representation vector, to
recurrent analysis of customer transactional time-series and up to generative
models based on adversarial training. Each of these methods has multiple
advantages and inherent weaknesses such as inability of understanding the
actual user-journey, ability to propose only single product recommendation or
top-k product recommendations without prediction of actual next-best-offer. In
our work we will present a new and innovative architectural approach of
applying state-of-the-art hierarchical multi-module encoder-decoder
architecture in order to solve several of current state-of-the-art recommender
systems issues. Our approach will also produce by-products such as product
need-based segmentation and customer behavioral segmentation - all in an
end-to-end trainable approach. Finally, we will present a couple methods that
solve known retail & distribution pain-points based on the proposed
architecture.Comment: 2019 22nd International Conference on Control Systems and Computer
Science (CSCS
Deep Neural Net with Attention for Multi-channel Multi-touch Attribution
Customers are usually exposed to online digital advertisement channels, such
as email marketing, display advertising, paid search engine marketing, along
their way to purchase or subscribe products( aka. conversion). The marketers
track all the customer journey data and try to measure the effectiveness of
each advertising channel. The inference about the influence of each channel
plays an important role in budget allocation and inventory pricing decisions.
Several simplistic rule-based strategies and data-driven algorithmic strategies
have been widely used in marketing field, but they do not address the issues,
such as channel interaction, time dependency, user characteristics. In this
paper, we propose a novel attribution algorithm based on deep learning to
assess the impact of each advertising channel. We present Deep Neural Net With
Attention multi-touch attribution model (DNAMTA) model in a supervised learning
fashion of predicting if a series of events leads to conversion, and it leads
us to have a deep understanding of the dynamic interaction effects between
media channels. DNAMTA also incorporates user-context information, such as user
demographics and behavior, as control variables to reduce the estimation biases
of media effects. We used computational experiment of large real world
marketing dataset to demonstrate that our proposed model is superior to
existing methods in both conversion prediction and media channel influence
evaluation.Comment: 6 pages ; It got published in AdKDD 2018 workshop as part of KDD 201
An Interdisciplinary Comparison of Sequence Modeling Methods for Next-Element Prediction
Data of sequential nature arise in many application domains in forms of, e.g.
textual data, DNA sequences, and software execution traces. Different research
disciplines have developed methods to learn sequence models from such datasets:
(i) in the machine learning field methods such as (hidden) Markov models and
recurrent neural networks have been developed and successfully applied to a
wide-range of tasks, (ii) in process mining process discovery techniques aim to
generate human-interpretable descriptive models, and (iii) in the grammar
inference field the focus is on finding descriptive models in the form of
formal grammars. Despite their different focuses, these fields share a common
goal - learning a model that accurately describes the behavior in the
underlying data. Those sequence models are generative, i.e, they can predict
what elements are likely to occur after a given unfinished sequence. So far,
these fields have developed mainly in isolation from each other and no
comparison exists. This paper presents an interdisciplinary experimental
evaluation that compares sequence modeling techniques on the task of
next-element prediction on four real-life sequence datasets. The results
indicate that machine learning techniques that generally have no aim at
interpretability in terms of accuracy outperform techniques from the process
mining and grammar inference fields that aim to yield interpretable models
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction
Stock trend prediction plays a critical role in seeking maximized profit from
stock investment. However, precise trend prediction is very difficult since the
highly volatile and non-stationary nature of stock market. Exploding
information on Internet together with advancing development of natural language
processing and text mining techniques have enable investors to unveil market
trends and volatility from online content. Unfortunately, the quality,
trustworthiness and comprehensiveness of online content related to stock market
varies drastically, and a large portion consists of the low-quality news,
comments, or even rumors. To address this challenge, we imitate the learning
process of human beings facing such chaotic online news, driven by three
principles: sequential content dependency, diverse influence, and effective and
efficient learning. In this paper, to capture the first two principles, we
designed a Hybrid Attention Networks to predict the stock trend based on the
sequence of recent related news. Moreover, we apply the self-paced learning
mechanism to imitate the third principle. Extensive experiments on real-world
stock market data demonstrate the effectiveness of our approach
Artificial Intelligence : from Research to Application ; the Upper-Rhine Artificial Intelligence Symposium (UR-AI 2019)
The TriRhenaTech alliance universities and their partners presented their
competences in the field of artificial intelligence and their cross-border
cooperations with the industry at the tri-national conference 'Artificial
Intelligence : from Research to Application' on March 13th, 2019 in Offenburg.
The TriRhenaTech alliance is a network of universities in the Upper Rhine
Trinational Metropolitan Region comprising of the German universities of
applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, and Offenburg, the
Baden-Wuerttemberg Cooperative State University Loerrach, the French university
network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of
engineering, architecture and management) and the University of Applied
Sciences and Arts Northwestern Switzerland. The alliance's common goal is to
reinforce the transfer of knowledge, research, and technology, as well as the
cross-border mobility of students
Sequential Behavioral Data Processing Using Deep Learning and the Markov Transition Field in Online Fraud Detection
Due to the popularity of the Internet and smart mobile devices, more and more
financial transactions and activities have been digitalized. Compared to
traditional financial fraud detection strategies using credit-related features,
customers are generating a large amount of unstructured behavioral data every
second. In this paper, we propose an Recurrent Neural Netword (RNN) based
deep-learning structure integrated with Markov Transition Field (MTF) for
predicting online fraud behaviors using customer's interactions with websites
or smart-phone apps as a series of states. In practice, we tested and proved
that the proposed network structure for processing sequential behavioral data
could significantly boost fraud predictive ability comparing with the
multilayer perceptron network and distance based classifier with Dynamic Time
Warping(DTW) as distance metric.Comment: KDD2018 Data Science in Fintech Workshop Pape
A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
In this study, a novel Distributed Representation of News (DRNews) model is
developed and applied in deep learning-based stock market predictions. With the
merit of integrating contextual information and cross-documental knowledge, the
DRNews model creates news vectors that describe both the semantic information
and potential linkages among news events through an attributed news network.
Two stock market prediction tasks, namely the short-term stock movement
prediction and stock crises early warning, are implemented in the framework of
the attention-based Long Short Term-Memory (LSTM) network. It is suggested that
DRNews substantially enhances the results of both tasks comparing with five
baselines of news embedding models. Further, the attention mechanism suggests
that short-term stock trend and stock market crises both receive influences
from daily news with the former demonstrates more critical responses on the
information related to the stock market {\em per se}, whilst the latter draws
more concerns on the banking sector and economic policies.Comment: 25 page
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