Call for Papers

SCOPE

Deep Learning and Big Data Analytics are two major topics of data science, nowadays. Big Data has become important in practice, as many organizations have been collecting massive amounts of data that can contain useful information for business analysis and decisions, impacting existing and future technology. A key benefit of Deep Learning is the ability to process these data and extract high-level complex abstractions as data representations, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled.

Machine-learning and artificial intelligence are pervasive in most real-world applications scenarios such as computer vision, information retrieval and summarization from structured and unstructured multimodal data sources, natural language understanding and translation, and many other application domains. Deep learning approaches, leveraging on big data, are outperforming state-of-the-art more “classical” supervised and unsupervised approaches, directly learning relevant features and data representations without requiring explicit domain knowledge or human feature engineering. These approaches are currently highly important in IoT applications.

CONFERENCE AREAS

Each of these topic areas is expanded below but the sub-topics list is not exhaustive. Papers may address one or more of the listed sub-topics, although authors should not feel limited by them. Unlisted but related sub-topics are also acceptable, provided they fit in one of the following main topic areas:

AREA 1: MODELS AND ALGORITHMS

• Recurrent Neural Network (RNN)
• Sparse Coding
• Neuro-Fuzzy Algorithms
• Evolutionary Methods
• Convolutional Neural Networks (CNN)
• Deep Hierarchical Networks (DHN)
• Dimensionality Reduction
• Unsupervised Feature Learning
• Deep Boltzmann Machines
• Generative Adversarial Networks (GAN)
• Autoencoders
• Deep Belief Networks

AREA 2: MACHINE LEARNING

• Active Learning
• Meta-Learning and Deep Networks
• Deep Metric Learning Methods
• MAP Inference in Deep Networks
• Deep Reinforcement Learning
• Learning Deep Generative Models
• Deep Kernel Learning
• Graph Representation Learning
• Gaussian Processes for Machine Learning
• Clustering, Classification and Regression
• Classification Explainability

AREA 3: BIG DATA ANALYTICS

• Extracting Complex Patterns
• IoT and Smart Devices
• Security Threat Detection
• Semantic Indexing
• Data Tagging
• Fast Information Retrieval
• Scalability of Models
• Data Integration and Fusion
• High-Dimensional Data
• Streaming Data
• Genomics and Bioinformatics

AREA 4: COMPUTER VISION APPLICATIONS

• Image Classification
• Object Detection
• Face Recognition
• Facial Expression Analysis
• Action Recognition
• Human Pose Estimation
• Image Retrieval
• Semantic Segmentation
• Deep Image Denoising

AREA 5: NATURAL LANGUAGE UNDERSTANDING

• Sentiment Analysis
• Mobile Text Messaging Applications
• Question Answering Applications
• Speech Interfaces
• Language Translation
• Document Summarization
• Content Filtering on Social Networks
• Recommender Systems

KEYNOTE SPEAKERS

PAPER SUBMISSION

PUBLICATIONS

All accepted papers will be published in the conference proceedings, under an ISBN reference, on paper and on digital support.
SCITEPRESS is a member of CrossRef (http://www.crossref.org/) and every paper on our digital library is given a DOI (Digital Object Identifier).

All papers presented at the conference venue will be available at the SCITEPRESS Digital Library (consult SCITEPRESS Ethics of Publication)

SECRETARIAT

DeLTA Secretariat
Address: Avenida de S. Francisco Xavier, Lote 7 Cv. C
             2900-616 Setúbal - Portugal
Tel.: +351 265 520 185
Fax: +351 265 520 186
e-mail: delta.secretariat@insticc.org
Web: http://www.delta.scitevents.org

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