Future Generation Smart Cities Research: Services, Applications, Case Studies and Policymaking Considerations for Well-Being [Part II]

Submission Deadline: 20 February 2020

IEEE Access invites manuscript submissions in the area of Future Generation Smart Cities Research: Services, Applications, Case Studies and Policymaking Considerations for Well-Being [Part II].

Research on smart cities is maturing and the question of securing the well-being of cities’ inhabitants is attracting increasing attention of researchers, practitioners, and policymakers. Urban computing occupies a central position in this context as advances in this domain will define the scope of possible developments and policymaking strategies in any city. Considering the challenges and opportunities cities/urban spaces generate, today the imperative is to examine how targeted research and cutting-edge innovation can be effectively communicated to all stakeholders so that, ultimately, synergies emerging at the research-innovation-policymaking nexus can be exploited and thus city dwellers’ well-being enhanced. Since urban computing serves as a framework that integrates increasingly sophisticated technologies pertinent to the Internet of Things (IoT), pervasive computing, big data analytics, crowdsourcing, and volunteered geographic information, including user behavior, brand popularity, recommender systems, and social media analytics, it bears the promise and potential that viable solutions to key problems and challenges specific to cities/urban spaces will be worked out. The objective of this Special Section is to examine this promise and potential from a variety of complementary interdisciplinary perspectives, including (but not limited to) computing/ICT, political economy, public policy, innovation, and entrepreneurship.

The topics of interest include, but are not limited to:

  • ICTs and their role in integrative knowledge management systems for smart cities
  • Application of public policies aimed at boosting research and innovation for smart cities
  • Smart generation of volunteered information to finance geared toward research and innovation promotion in smart cities
  • Pervasive computing applied in the transformation from cities to smart cities
  • Smart and collaborative mobile applications to analyze the human dynamics in big cities
  • The role of cryptocurrency technology for Social Economic Growth in smart cities
  • Case studies based on IoT and Big Data Analytics technologies applied to smart cities
  • Enhancement and strategic development of skills and competencies for the required digital transformation to develop public policy making
  • Advanced computing approaches and systems for international business leadership in the context of smart cities
  • Building international innovation networks enabled by sound technological innovative applications for the sustainability of smart cities
  • Blended (concept- and practice-driven) approaches to smart cities research
  • Smart and open data acquisition and processing
  • Volunteered geographic information
  • Pervasive and mobile computing to analyze the social impact in smart cities
  • Cloud computing for smart services inside smart cities
  • Smart healthcare applications in the development of public safety policies
  • Big Data Analytics to smart data from smart cities
  • Virtual and Augmented Reality applied to smart cities applications
  • Cryptocurrency technology to impact the social economic growth in smart cities
  • Crowd-sensing with 5G sensors to smart cities
  • Nanotechnology applied to successful cases in smart cities
  • Cognitive computing to describe behavior in the knowledge society
  • Regulatory and policymaking considerations, including the role of international organizations in context of smart cities and their evolution

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor: Miltiadis D. Lytras, Deree – The American College of Greece, Greece, and  King Abdulaziz University, Saudi Arabia

Guest Editors:

    1. Anna Visvizi, Deree – The American College of Greece, Greece and Effat University, Saudi Arabia
    2. Akila Sarirete, Effat University, Saudi Arabia
    3. Miguel Tores Ruiz, Polytechnic Institue of Mexico, Mexico City, Mexico
    4. Tugrul U. Daim, Portland State University, USA

 

Relevant IEEE Access Special Sections:

  1. Urban Computing & Well-being in Smart Cities: Services, Applications, Policymaking Considerations
  2. Advanced Data Mining Methods for Social Computing
  3. Artificial Intelligence (AI)-Empowered Intelligent Transportation Systems


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact:  mlytras@acg.edu.

Intelligent Information Services

Submission Deadline: 31 January 2020

IEEE Access invites manuscript submissions in the area of Intelligent Information Services.

In the past few years, existing network information service technologies have emphasized and solved the interconnection and interoperability of resources and services, realized the upper application through resource aggregation and collaboration, and effectively promoted the development of network information services. However, in the current internet environment, the diversity of demand and the changeability of environment are more abundant, and the information service technology is setting higher requirements. At present, with the increasing artificial intelligence technology in the field of information services application, and the continuous improvement and upgrading of the current internet infrastructure, application and business model innovations are constantly emerging, and the Internet is further penetrating traditional fields such as finance, transportation, medical treatment, education, etc. Artificial Intelligence technology can already be found in the fields of intelligent transportation, internet finance, and intelligent medical treatment, for example. In the future, some preliminary application results can be expected to be used in a variety of fields, especially in the application areas of Intelligent Information Services, which will have a far-reaching impact.

With the rapid development of computing and communications technology, Intelligent Information Services (IIS) has attracted attention from both research and industrial domains in recent years. The new generation of information technology, represented by Grid Services, Service-oriented Architecture (SOA), Cloud Computing, Internet of Things and Big Data, has brought great opportunities for cross-domain and cross-organizational application integration and information services in the network environment. This Special Section in IEEE Access aims to provide a forum for educators, scientists, engineers, and researchers to discuss and exchange their new ideas, novel results, works in progress and experience on all aspects of Intelligent Information Service, and other related topics, including Cloud Computing, Big Data, Internet+, and Artificial Intelligence.

The topics of interest include, but are not limited to:

  • The model and mechanism of information service
  • Petri net theory and applications
  • Big data and large-scale data driven information service
  • Service-based grid/autonomous/pervasive computing
  • Information service in the mobile Internet
  • Information service in online social networks
  • Service-based Internet+
  • Artificial Intelligence algorithms and their applications in Intelligent information services
  • Crossover research of network information services and other areas, such as intelligent manufacturing, smart city

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  Shouguang Wang, Zhejiang Gongshang University, China

Guest Editors:

    1. Changjun Jiang, Donghua University, China
    2. Mengchu Zhou, New Jersey Institute of Technology, USA
    3. Lu Liu, The University of Leicester, Uk
    4. Kamel Barkaoui, Conservatoire National Des Arts Et Métiers, France
    5. Guanjun Liu, Tongji University, China

 

Relevant IEEE Access Special Sections:

  1. Artificial Intelligence for Physical-Layer Wireless Communications
  2. Distributed Computing Infrastructure for Cyber-Physical Systems
  3. Innovation and Application of Intelligent Processing of Data, Information and Knowledge as Resources in Edge Computing


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact:  wsg5000@hotmail.com.

Artificial Intelligence in Parallel and Distributed Computing

Submission Deadline: 15 January 2020

IEEE Access invites manuscript submissions in the area of Artificial Intelligence in Parallel and Distributed Computing.

Traditional computation is driven by parallel accelerators or distributed computation nodes in order to improve computing performance, save energy, and decrease delays in accessing memory. Recently, artificial intelligent algorithms, frameworks, and computing models are growing to help with high computational performance.

To coordinate communication among professional researchers, engineers and students, and to attract and filter high quality academic contributions recommended from the International Symposium on Advanced Parallel Processing Technology (APPT 2019, http://tc.ccf.org.cn/tcarch/appt2019/), we have organized this Special Section in IEEE Access on “Artificial Intelligence in Parallel and Distributed Computing” (AIPDC). High quality contributions within the field but not presented at the conference are highly encouraged and also considered in this Special Section.

To tackle issues and challenges from the new era of artificial intelligence on computer systems, this Special Section will present innovative solutions and recent advances in the fields of intelligent algorithms, parallel computing methodologies, distributed computing models, new computer architectures, cloud computing, data centers, and so on. We are hoping the articles in this Special Section will guide future applications and research on computer architectures and computer systems.

The topics of interest include, but are not limited to:

  • Parallel Architectures and Hardware Systems
  • Parallel Software
  • Distributed and Cloud Computing
  • Parallel Algorithms and Applications
  • GPU neuromorphic computing, intelligent control and computing on FPGAs

 

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

Associate Editor:  Songwen Pei, University of Shanghai for Science and Technology, China

Guest Editors:

    1. Junjie Wu, National University of Defense Technology, China
    2. Tao Li, Nankai University, China
    3. Yong Chen, Texas Tech University, USA
    4. Stéphane Zuckerman, University of Cergy-Pontoise, France

 

Relevant IEEE Access Special Sections:

  1. Artificial Intelligence in CyberSecurity
  2. Innovation and Application of Intelligent Processing of Data, Information and Knowledge as Resources in Edge Computing
  3. Distributed Computing Infrastructure for Cyber-Physical Systems


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact:  swpei@usst.edu.cn or songwenpei@gmail.com.

Utility Pattern Mining: Theoretical Analytics and Applications

Submission Deadline: 30 April 2020

IEEE Access invites manuscript submissions in the area of Utility Pattern Mining: Theoretical Analytics and Applications.

Utility pattern mining from data has received a lot of attention from the Knowledge Discovery in Data Mining (KDD) community due to the high potential impact in many applications such as finance, biomedicine, manufacturing, e-commerce and social media. Current research in utility mining primarily focuses on discovering patterns of high value (e.g. high profit) in large databases, and analyzing/learning important factors (e.g. economic factors) in a data mining process. One of the popular applications of utility mining is the analysis of large transactional databases to discover high-utility itemsets, which consist of sets of items that generate a high profit when purchased together.

This Special Section in IEEE Access aims at bringing together academic and industrial researchers and practitioners from data mining, machine learning and other interdisciplinary communities, in a collaborative effort to identify and discuss major technical challenges, recent results and potential topics on the emerging fields of Utility-Pattern Mining, by focusing especially on theoretical analytics and applications. Studies about real-world experiences, inherent challenges, and new research methods/applications are also welcome.

The topics of interest include, but are not limited to:

  • Theory, applications, and core methods for utility mining and computing
  • Utility patterns mining in large datasets, e.g., high-utility itemset mining, high-utility sequential pattern/rule mining, high-utility episode mining, and other novel patterns
  • Analysing and learning utility factors of mining and learning processes
  • Predictive modeling/learning, clustering and link analysis that incorporate utility factors
  • Incremental utility mining and computing
  • Utility mining and learning in streams
  • Utility mining and learning in uncertain systems
  • Utility mining and learning in big data
  • Knowledge representations for utility patterns
  • Privacy preserving utility mining/learning
  • Visualization techniques for utility mining/computing
  • Open-source software/libraries/platform
  • Innovative applications in interdisciplinary domains like finance, biomedicine, healthcare, manufacturing, e-commerce, social media, education, etc.
  • New, open, or unsolved problems in utility-pattern mining and computation

 

This Special Section is also published in cooperation with the second “Utility-Driven Mining and Learning (UDML)” workshop held at IEEE ICDM 2019. Important articles from the UDML workshop will be invited for this Special Section, provided that each article has less than 35% similarity to the author’s previous work.

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  Jerry Chun-Wei Lin, Western Norway University of Applied Sciences, Norway

Guest Editors:

    1. Philippe Fournier-Viger, Harbin Institute of Technology (Shenzhen), China
    2. Vincent S. Tseng, National Chiao Tung University, Taiwan
    3. Philip Yu, University of Illinois at Chicago, USA

 

Relevant IEEE Access Special Sections:

  1. Innovation and Application of Intelligent Processing of Data, Information and Knowledge as Resources in Edge Computing
  2. Data-Enabled Intelligence for Digital Health
  3. Data Mining for Internet of Things


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact:  jerrylin@ieee.org.

Data Analytics and Artificial Intelligence for Prognostics and Health Management (PHM) Using Disparate Data Streams

Submission Deadline: 30 November 2019

IEEE Access invites manuscript submissions in the area of Data Analytics and Artificial Intelligence for Prognostics and Health Management (PHM) Using Disparate Data Streams.

As the key technology of condition-based maintenance and autonomous protection for engineering systems and critical components, prognostics and health management (PHM) is of increasing interest to industry, government, and academia. Due to the complexity, integration and intelligence of engineered systems and critical components, there are two significant challenges in implementing fault prediction and health management of complex modern engineered systems. First, health monitoring data obtained by sensing systems have characteristics such as large volume, high velocity, and inhomogeneity. In engineering practice, in most cases, indirect health monitoring data streams are obtained rather than direct health monitoring data. Therefore, it is necessary to extract and derive valuable and meaningful system health indices from a large amount of disorganized/unstructured and difficult to understand raw data through data analytics. Second, the health degradation process of engineered systems and critical components exhibits a high degree of nonlinearity due to significant influencing factors, such as complex structure and varying environments, and uncertain tasks. These nonlinearities are difficult to be analytically derived. By simulating and extending human intelligence, artificial intelligence (AI) can help solve such highly nonlinear problems and achieve more accurate health prediction. Therefore, the application of data analytics and artificial intelligence possesses great potential to advance the innovations and implementation of prognostics and health management using large quantities of disparate data streams.

The topics of interest include, but are not limited to:

  • Advanced information sensing technologies for emitting excitations and receiving degradation data
  • Advanced Big Data Analytics for degradation data
  • Signal processing algorithms for reprocessing degradation data
  • Data fusion for degradation data
  • Extraction of health indicators from engineered systems and critical components
  • Abnormal health detection algorithms
  • Advanced modeling of complex engineered systems with nonlinearity and uncertainty
  • Advanced learning technologies for diagnostics and prognostics
  • Deep learning based diagnostic and prognostic algorithms
  • Uncertainty interpretation, uncertainty quantification, uncertainty propagation and uncertainty management
  • Intelligent performance evaluation
  • Decision Making for maintenance
  • Industrial applications and their success in prognostics and health management

 

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  Zhaojun (Steven) Li, Western New England University, USA

Guest Editors:

    1. Qiang Miao, Sichuan University, China
    2. Faisal Khan, Memorial University of Newfoundland, Canada
    3. Lance Fiondella, University of Massachusetts Dartmouth, USA
    4. Janet Lin, Lulea University of Technology, Sweden
    5. Jeff Voas, National Institute of Standard and Technology, USA

 

Relevant IEEE Access Special Sections:

  1. AI-Driven Big Data Processing: Theory, Methodology, and Applications
  2. Advances in Prognostics and System Health Management
  3. Data Mining for Internet of Things


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: stevenli777@ieee.org.

Feature Representation and Learning Methods With Applications in Large-Scale Biological Sequence Analysis

Submission Deadline: 01 August 2020

IEEE Access invites manuscript submissions in the area of Feature Representation and Learning Methods With Applications in Large-Scale Biological Sequence Analysis.

In recent years, with the rapid growth of biological data (especially biological sequences in the bioinformatics field) data-driven computational methods are increasingly needed to quickly and accurately analyze large-scale biological data.  Machine learning has recently emerged as an “intelligent” method in many specific bioinformatics areas, such as identification of DNA mutations, and protein post-transcriptional modifications, etc. Methods to find discriminative feature representation for biological sequences is quite important, as it greatly impacts the performance of machine learning methods. A good representation is often the one that captures the discriminative information from the data and supports effective machine learning. As a result, it is fundamentally important for the development of new feature representation approaches in machine learning. Over the last few decades, feature representation engineering in different fields has been well studied; however, most are labor intensive and heavily dependent on the professional knowledge of researchers, highlighting the weakness of current learning algorithms. To expand the scope and ease of the applicability of machine learning, it is highly desirable to make learning algorithms less dependent on handcrafted feature engineering so that novel applications could be constructed faster, and more importantly, to make progress toward artificial intelligence (AI).

This Special Section in IEEE Access will target recent feature representation and learning techniques in bioinformatics applications. Applications on large-scale biological data are strongly encouraged. We also encourage authors to contribute their codes and experimental data so they are available to the public, which would make our Special Section more infusive and attractive.

The topics of interest include, but are not limited to:

  • Supervised learning algorithms with applications in large-scale sequence analysis
  • Unsupervised learning algorithms with applications in large-scale sequence analysis
  • Imbalanced learning algorithms for biological sequence data
  • Multi-view feature learning for DNA or Protein classification
  • Deep learning-based feature learning strategies for biological data
  • Feature representation optimization algorithms with applications on bioinformatics.
  • Handcrafted feature representation algorithms for bioinformatic application

 

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  Quan Zou, University of Electronic Science and Technology of China, China

Guest Editors:

    1. Leyi Wei, University of Tokyo, Japan
    2. Qin Ma, Ohio State University, USA
    3. Jijun Tang, University of South Carolina, USA
    4. Dariusz Mrozek, Silesian University of Technology, Poland
    5. Guobao Xiao, Minjiang University, China

 

Relevant IEEE Access Special Sections:

  1. Scalable Deep Learning for Big Data
  2. Deep Learning Algorithms for Internet of Medical Things
  3. Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: zouquan@tju.edu.cn.

Integrative Computer Vision and Multimedia Analytics

Submission Deadline: 30 January 2020

IEEE Access invites manuscript submissions in the area of Integrative Computer Vision and Multimedia Analytics.

In recent years, research is intensifying in computer vision-driven applications such as autonomous vehicles, computer-aided diagnosis and augmented reality. Application-level semantics of streaming video sources are becoming more and more ubiquitous in a wide spectrum of applications. Images, videos and audio can provide rich data sources, from which additional information and context can be surmised. Theoretical, practical, and algorithmic advances have opened up research opportunities that seek higher levels of semantic interpretation of Integrative computer vision and multimedia analytics.

Autonomous vehicles have attracted more attention in recent years because traffic safety is of paramount importance. Also, significant progress in artificial intelligence makes it possible to evolve driving to a more intelligent and autonomous stage. A variety of sensing modalities has become available, including radar, LIDAR, and computer vision. With advances in camera sensing and computational technologies, advances in vehicle detection using monocular vision, stereo vision, and sensor fusion with vision have been extremely active research areas in the intelligent vehicles community.

To deal with the extent and variety of digital media, researchers are combining multimedia analysis and visual analytics to form the new field of multimedia analytics. This Special Section in IEEE Access is aiming to bring attention to the critical new suite of technologies required to analyze images, text, video, geospatial data, audio, graphics, tables, and other forms of information. Multimedia analytics is a critical need for a broad range of applications, including, but not limited to, medicine, economics, social media, and security.

The topics of interest include, but are not limited to:

  • Autonomous vehicle detection
  • Autonomous platoon vehicle modeling
  • Autonomous robots
  • Multimodal medical image registration
  • Image/video summarization and visualization
  • Cross-media retrieval– fine-grained visual search
  • Vision-driven surveillance and monitoring systems
  • Visually-guided manipulation of physical objects
  • Human assistive devices and autonomous design
  • Real-time visual tracking
  • Real-time event detection and understanding
  • Active perception through human-machine interactions
  • Deep learning for multimedia retrieval
  • Applications of multimedia analytics (Healthcare, Fintech, large video archives, etc.

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  Guitao Cao, East China Normal University, China

Guest Editors:

    1. Ye Duan, University of Missouri at Columbia, USA
    2. Chao Ma, Shanghai Jiao Tong University, China
    3. Yin Li, University of Wisconsin-Madison, USA
    4. Vladimir M. Mladenovic, University of Kragujevac, Serbia

 

Relevant IEEE Access Special Sections:

  1. Visual Analysis for CPS Data
  2. Multimedia Analysis for Internet-of-Things
  3. Big Data Learning and Discovery


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: gtcao@sei.ecnu.edu.cn.

Complex Networks Analysis and Engineering in 5G and beyond towards 6G

Submission Deadline: 31 March 2020

IEEE Access invites manuscript submissions in the area of Advances in Complex Networks Analysis and Engineering in 5G and beyond towards 6G.

Modern telecommunications networks represent one of the largest scale construction and deployment efforts with renovations occurring nearly continuously over the course of decades. The resulting networks consist of numerous subsections, each following its own trajectory of development, commingled into a complex ecosystem. Typical attributes used to characterize networks (e.g., interference, coverage, throughput, robustness, cost) fail to fully capture a key feature of future wireless networks, namely the degree of organization. This is increasingly important when we consider the trajectory of the evolution of 5G wireless networks and beyond towards 6G, with respect to densification, heterogeneity and distributed and self-organizing decision-making.

This Special Section tries to shed light on whether such a self-organizing and highly dynamic world can be treated as a complex system and whether complex systems science can give insights on the emergent properties of these kinds of networks and their design and deployment. One of the most widely accepted definitions of complex system, is that of “a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution” (M. Mitchell, “Complexity – A Guided Tour”, Oxford University Press, 2011). This view resonates with the trends we are seeing in wireless networks.

In this Special Section in IEEE Access, we invite submissions of high-quality, original technical and survey papers, which have not been published previously, on complex systems science approaches and techniques and their applications for communications networks.

The topics of interest include, but are not limited to:

  • Network science and statistical mechanics models for self-organizing communication networks
  • Relation between information theory and complex systems science
  • Measuring complexity and organization structure in cellular and IoT networks
  • Cellular automata and agent-based modeling of 5G networks and beyond
  • Application of complex systems science to industrial cyber-physical systems and machine-type communications for control, coordination or optimization
  • Nonlinear-system-based analysis and design in beyond 5G communication networks
  • Chaos-based communication systems for 5G and beyond
  • Design and applications of complex cyber-physical systems based on 5G and beyond
  • Emergence-driven network engineering communication and computation

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  M. Majid Butt, Nokia Bell Labs, France

Guest Editors:

    1. Celso Grebogi, University of Aberdeen, Scotland/UK
    2. Irene Macaluso, Trinity College Dublin, Ireland
    3. Murilo S. Baptista, University of Aberdeen, Scotland/UK
    4. Nicola Marchetti, Trinity College Dublin, Ireland
    5. Pedro H. Juliano Nardelli, LUT University, Finland
    6. Robert Hunjet, Defence Science and Technology Group, Australia
    7. Lt Col Ryan Thomas, US Air Force Academy, USA

 

Relevant IEEE Access Special Sections:

  1. Cyber-Physical Systems
  2. Intelligent and Cognitive Techniques for Internet of Things
  3. Modelling, Analysis, and Design of 5G Ultra-Dense Networks


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: majid.butt@nokia-bell-labs.com.

Advances in Machine Learning and Cognitive Computing for Industry Applications

Submission Deadline: 31 May 2020

IEEE Access invites manuscript submissions in the area of Advances in Machine Learning and Cognitive Computing for Industry Applications.

Over the past few years, great progress has been made due to advances in machine learning and cognitive computing. For example, with the adoption of Convolutional Neural Networks (CNNs), computer vision has surpassed human vision in the task of image recognition. Moreover, the improvement in Natural Language Processing (NLP) makes machine translation, speech recognition, and other sequence applications more powerful than ever. It is the significant progress of machine learning algorithms, computing capability, and big data that makes machine learning and cognitive computing increasingly powerful in many applications. Compared to machine learning, cognitive computing places more emphasis on how the human brain works. Cognitive computing simulates human thought processes with self-learning algorithms that utilize data mining, pattern recognition, and natural language processing. In industrial scenarios, the data amount, as well as data generation speed, is very different compared to standard machine learning data sets. It is a challenge to utilize these heterogeneous data and find meaningful insights for practical applications.

As the basis, the Internet of Things (IoT) middleware platforms, communication, and network ecosystems should be involved. Considering the heterogeneity of industrial data, we are expected to inspect, clean, transform, and model data with the goal of specific industrial applications. Moreover, specialized algorithms, computing architectures, and feature engineering are needed to review, analyze, and present information. With the support of machine learning and cognitive computing, the significant insights and knowledge hidden behind industrial data can be capitalized for process optimization, anomaly detection, energy management, and so on. This Special Section focuses on consolidating research efforts that aim at machine learning and cognitive computing for industrial applications.

The topics of interest include, but are not limited to:

  • Development of new machine learning algorithms for industrial applications
  • Big data analytics, new algorithms, and approaches
  • IoT system engineering
  • Cognitive computing, affective computing (artificial emotional intelligence), and other innovative approaches for industrial scenarios
  • Context-aware, emotion-aware, and other novel information and communication technologies based on machine learning and cognitive computing
  • Machine learning for human, computer and machine interface
  • Multi-modal sensor fusion, unstructured data mining and knowledge discovery in industrial applications
  • Big data analytics software architectures
  • Developing reusable and analytic tools and frameworks
  • Development of autonomous systems for industrial applications
  • New theories and applications of deep learning in industrial informatics

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor: Min Xia, Lancaster University, United Kingdom

Guest Editors:

    1. Zheng Liu, University of British Columbia, Canada
    2. Hong-Ning Dai, Macau University of Science and Technology, China
    3. Yu Zhang, University of Lincoln, UK
    4. Jixiang Yang, Huazhong University of Science and Technology, China
    5. Tsuyoshi Ide, IBM T. J. Watson Research Center, USA
    6. Jiong Jin, Swinburne University of Technology, Australia

 

Relevant IEEE Access Special Sections:

  1. Data Mining for Internet of Things
  2. Artificial Intelligence in CyberSecurity
  3. Intelligent Data Sensing, Collection and Dissemination in Mobile Computing


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact: m.xia3@lancaster.ac.uk.

Scalable Deep Learning for Big Data

Submission Deadline: 29 February 2020

IEEE Access invites manuscript submissions in the area of Scalable Deep Learning for Big Data.

Artificial Intelligence (AI), and specifically Deep Learning (DL), are trending to become integral components of every service in our future digital society and economy. This is mainly due to the rise in computational power and advances in data science. DL’s inherent ability to discover correlations from vast quantities of data in an unsupervised fashion has been the main drive for its wide adoption.

Deep Learning also enables dynamic discovery of features from data, unlike traditional machine learning approaches, where feature selection remains a challenge. Deep Learning has been applied to various domains such as speech recognition and image classification, nature language processing, and computer vision.

Typical deep neural networks (DNN) require large amounts of data to learn parameters (often reaching to millions), which is a computationally intensive process requiring significant time to train a model. As the data size increases exponentially and the deep learning models become more complex, it requires more computing power and memory, such as high performance computing (HPC) resources to train an accuracy model in a timely manner. Despite existing efforts in training and inference of deep learning models to increase concurrency, many existing training algorithms for deep learning are notoriously difficult to scale and parallelize due to inherent interdependencies within the computation steps as well as the training data. The existing methods are not sufficient to systematically harness such systems/clusters.

Therefore, there is a need to develop new parallel and distributed algorithms/frameworks for scalable deep learning which can speed up the training process and make it suitable for big data processing and analysis.

This Special Section in IEEE Access aims to solicit research contributions from academia and industry which addresses key challenges in big data processing and analysis using scalable deep learning.

The topics of interest include, but are not limited to:

  • Distributed architectures/parallel programming models and tools for scalable deep learning/machine learning
  • Parallel Algorithms and models for efficient training of deep learning models for big data (e.g. partition strategies such as using different parallel approaches for network parallelism/model parallelism)
  • Efficient algorithms and architectures to support parameter optimization (e.g. parameter search, hyper parameter search, architecture search)
  • Parameter and Gradient Compression (e.g. Sparsification, Quantization)
  • Applications of deep learning/machine learning to big data (large data such as images/videos, time series data, etc.)
  • Facilitating very large ensemble-based learning on exascale systems
  • Deep Learning systems architectures: edge, cloud-edge integration

We also highly recommend the submission of multimedia with each article as it significantly increases the visibility, downloads, and citations of articles.

 

Associate Editor:  Liangxiu Han, Manchester Metropolitan University, UK

Guest Editors:

    1. Daoqiang Zhang, Nanjing University of Aeronautics and Astronautics, China
    2. Omer Rana, Cardiff University, UK
    3. Yi Pan, Georgia State University, USA
    4. Sohail Jabbar, National Textile University, Faisalabad, Pakistan
    5. Mazin Yousif, T-Systems, International, USA
    6. Moayad Aloqaily, Gnowit Inc., Canada

 

Relevant IEEE Access Special Sections:

  1. Data-Enabled Intelligence for Digital Health
  2. Distributed Computing Infrastructure for Cyber-Physical Systems
  3. Deep Learning: Security and Forensics Research Advances and Challenges


IEEE Access Editor-in-Chief:  Prof. Derek Abbott, University of Adelaide

Article submission: Contact Associate Editor and submit manuscript to:
http://ieee.atyponrex.com/journal/ieee-access

For inquiries regarding this Special Section, please contact:  l.han@mmu.ac.uk.