Deep Learning Algorithms for Internet of Medical Things

Submission Deadline: 30 April 2020

IEEE Access invites manuscript submissions in the area of Deep Learning Algorithms for Internet of Medical Things.

Traditionally, devices used in medical industry predominantly rely on medical images and sensor data; this medical data is processed to study the patient’s health condition and information. Presently, the medical industry requires more innovative technologies to process the large volume of data and improve the quality of service in patient care, and needs an intelligent system to detect early symptoms of diseases in the beginning stage and provide appropriate treatment.

Internet of Medical Things (IoMT) and its recent advancements have included a new dimension towards enhancing the medical industry practices and realizing an intelligent system. In addition, the medical data of IoMT systems is constantly growing because of increasing peripherals introduced in patient care.

Conventionally, medical image processing and machine learning are used for any medical diagnosis, subsequent treatment and therapies. However, with increasing volume of data with increased dimensions and dynamics of medical data, machine learning takes a back seat over another powerful classification mechanism named deep learning. Deep learning can solve more complicated problems, unsolvable by machine learning, and produce highly accurate diagnoses. The medical industry is one of the biggest industries which implements deep learning algorithms. Deep learning can handle the large volume of medical data, including medical reports, patients’ records, and insurance records, helping medical experts to predict the necessary treatment. The scalability of deep learning which helps to process and manipulate this huge volume of data makes an indomitable paradigm for computer-aided diagnosis in medical informatics. The significance of deep learning is compounded by the ever-improving technological aspects towards acquiring precise and multidimensional IoMT data with an eye on improving the accuracy of diagnosis. Overall, incorporating deep learning into IoMT can provide radical innovations in medical image processing, disease diagnosing, medical big data analysis and pathbreaking medical applications.

This Special Section in IEEE Access provides a perfect platform to discuss the prospective developments and innovative ideas in the healthcare domain, with the inception of deep learning-based biomedical systems in IoMT.

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

  • Deep learning for energy management in IoMT devices
  • Deep learning algorithms for medical big data analysis
  • Advancements in deep learning algorithms in health informatics
  • Programmers perspective view on deep learning in IoMT
  • Cognitive deep learning for wearable medical devices
  • Deep learning for data analytics in body sensor networks
  • Monitoring wearable medical devices using deep learning
  • Cyber security and malware detection in IoMT using deep learning
  • Deep neural networks for diagnosing and identifying suspicious lesions in IoMT
  • Evidence-based treatment using IoMT
  • Fraud prevention in healthcare using IoMT based data analytics model
  • Telemedicine using IoMT
  • Patient-centric model using deep learning with IoMT
  • Abnormal identification in patient monitoring system using deep learning with IoMT
  • Advanced medical image processing using deep learning
  • Disease diagnosis using deep learning in IoMT
  • Prioritized medical data transmission in IoMT using deep learning
  • Deep Belief Neural network and IoMT in in Future Medical Industries
  • Deep learning-based privacy preserving and security methods for medical data transmission in IoMT
  • Deep learning algorithm for medical decision support systems in IoMT based big data

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

 

Associate Editor:  Wei Wei, Xi’an University of Technology, China

Guest Editors:

  1. Victor Hugo C. de Albuquerque, Universidade de Fortaleza, Brazil
  2. Naveen Chilamkurti, La Trobe University, Australia
  3. Marcin Woźniak, Silesian University of Technology, Poland
  4. Wael Guibene, Amazon Lab126, USA

 

Relevant IEEE Access Special Sections:

  1. Trends, Perspectives and Prospects of Machine Learning Applied to Biomedical Systems in Internet of Medical Things
  2. Data Mining for Internet of Things
  3. Deep Learning for Computer-aided Medical Diagnosis


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: weiwei@xaut.edu.cn; taneo@126.com.

Advanced Communications and Networking Techniques for Wireless Connected Intelligent Robot Swarms

Submission Deadline: 31 May 2020

IEEE Access invites manuscript submissions in the area of Advanced Communications and Networking Techniques for Wireless Connected Intelligent Robot Swarms.

Robot swarm is one of the hottest topics in both robotics and artificial intelligence, and exciting progress is being achieved. As the key enablers in practical robot swarms, communication and networking are attracting attention. Most applications consider centralized control and reliable communication infrastructure, in order to avoid the significantly increased complexity of distributed task allocation, formation control and collision avoidance in robot swarms.

There are many challenges and problems that are yet to be solved in developing real-world applications of wireless connected robot swarms. For example, collaborations of heterogeneous robot swarms need to function reliably and robustly in the absence of communication infrastructures in remote areas or post-disaster rescues. The research of communications and networking for wireless-connected robot swarms demands joint efforts in robotic and communications disciplines. The objective is to develop technologies that enable efficient management of wireless spectrum resources and highly-networked intelligent behaviors to achieve the full potential of wireless-connected robot swarms.

This Special Section in IEEE Access aims to present recent developments in communications and networking for wireless connected intelligent robot swarms, and their applications, as well as to provide a reference for future research of wireless communication and networking, and their integration with autonomous robotics. The contributions of this Special Section will cover a wide range of research and development topics relevant to autonomous robotic design, cognitive communications, cognitive networking and artificial intelligence. We invite submissions of high-quality original technical and survey articles, which have not been published previously, on the analysis, modeling, simulations and field experiments, as well as articles that can fill the gap between theoretical contributions on intelligent swarms and practical demonstrations and applications.

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

  • Channel modeling and simulation for wireless connected robot swarms
  • Cognitive PHY and MAC protocol design for wireless connected robot swarms
  • Ad hoc networking for wireless connected robot swarms
  • Decentralized control and distributed protocol design for wireless connected robot swarms
  • Energy scavenging and power transfer techniques for wireless connected robot swarms
  • Data-driven optimization of wireless networks for robot swarms
  • Joint design of wireless communications and autonomous robot behaviours, e.g. networked control, network-based fault detection and tolerance, path planning, formation control, data sharing without explicit wireless communications etc.
  • Testbeds and experimental evaluations for communications and networking in wireless-connected robot swarms
  • Field demonstrations and applications of aerial, ground and underwater robotic swarms
  • Resource allocation in wireless-connected robot swarms
  • Applications of deep learning techniques in wireless connected robot swarms
  • Transfer learning and reinforcement learning for networking and communications of robot swarms in complex unknown and unexplored environments
  • Maintaining wireless communication-connectivity in wireless-connected robot swarms
  • Underwater robotic swarm communications and networking design
  • Control algorithm and behavior issues in wireless-connected robot swarms
  • Distributed sensing and precise mapping in wireless-connected robot swarms
  • Effect of smart sensing technologies on communications in wireless-connected robot swarms
  • Control, formation and navigation in wireless-connected robot swarms
  • Swarm intelligence in wireless-connected robot swarms
  • Cooperative robotic swarms for Internet-of-Things ecosystems

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

 

Associate Editor:  Jiankang Zhang, University of Southampton, UK

Guest Editors:

  1. Bo Zhang, National Innovation Institute of Defense Technology, China
  2. DaeEun Kim, Yonsei University, Korea
  3. Hui Cheng, Sun Yat-sen University, China
  4. Jinming Wen, University of Toronto, Canada
  5. Luciano Bononi, University of Bologna, Italy
  6. Venanzio Cichella, University of Iowa, USA

 

Relevant IEEE Access Special Sections:

  1. Networks of Unmanned Aerial Vehicles: Wireless Communications, Applications, Control and Modelling
  2. Network Resource Management in Flying Ad Hoc Networks: Challenges, Potentials, Future Applications, and Wayforward
  3. Artificial Intelligence and Cognitive Computing for Communications and 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: jz09v@ecs.soton.ac.uk.

Advanced Data Mining Methods for Social Computing

Submission Deadline: 31 December 2019

IEEE Access invites manuscript submissions in the area of Advanced Data Mining Methods for Social Computing.

Social networks have become an important way for individuals to communicate with each other. Various kinds of social networks develop explosively, such as online social networks, scientific cooperation networks, athlete networks, airport passage networks, etc. Social networks have increasingly demonstrated their strength due to their large number of participants and real-time information dissemination capability. Social computing has become a promising research area and attracts much attention. Analyzing and mining human behavior, topological structure and information diffusion in social networks can help to understand the essential mechanism of macroscopic phenomena, discover potential public interest, and provide early warnings of collective emergencies.

In the past, to study the characteristics of social networks, questionnaires were designed, and volunteers in the network were invited to complete questionnaires. However, the amount of data collected from questionnaires was not enough to understand the whole perspective and essential mechanism of social events. With the development of mobile sensing, computer networks and artificial intelligence in recent years, it is possible to collect an abundance of data from various social multimedia. Big data in social networks also bring challenges in how to process social data and investigate human behavior. In addition, there are new and complex features in social networks, such as heterogeneous human properties, dynamic network structures and random interpersonal interactions. Therefore, advanced multidisciplinary data collection and data mining methods should be proposed for social computing and developed to study social networks.

This Special Section in IEEE Access welcomes contributions in the quickly growing field of social computing. We encourage articles with multidisciplinary methods for social data mining. The related disciplines include machine learning, information theory, mathematics, computational and statistical physics.

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

  • Network representation learning
  • Streaming social data processing
  • Heterogeneous social network mining
  • Behavior analysis of social networks
  • Social multimedia and image processing
  • Social text analysis
  • Deep learning in social computing
  • Behavior analysis on social networks
  • Pattern recognition of behaviors
  • Human sentiment mining and analysis
  • Individual interest modeling
  • Personalized recommender systems
  • Knowledge graph and its applications
  • Essential mechanism of information diffusion and control
  • Modeling the formation and phase transition of collective phenomena
  • Trend prediction of information propagation
  • Modeling and analysis of interpersonal interactions
  • Multimedia data analysis

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

 

Associate Editor:  Yongqiang Zhao, Northwestern Polytechnical University, China

Guest Editors:

  1. Shirui Pan, Monash University, Australia
  2. Jia Wu, Macquarie University, Australia
  3. Huaiyu Wan, Beijing Jiaotong University, China
  4. Huizhi Liang, University of Reading, United Kingdom
  5. Haishuai Wang, Fairfield University, USA
  6. Huawei Shen, Chinese Academy of Sciences, China

 

Relevant IEEE Access Special Sections:

  1. Applications of Big Data in Social Sciences
  2. AI-Driven Big Data Processing: Theory, Methodology, and Applications
  3. Privacy Preservation for Large-Scale User Data in Social 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: zhaoyq@nwpu.edu.cn.

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Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts

Submission Deadline:  31 August 2019

IEEE Access invites manuscript submissions in the area of Smart Health Sensing and Computational Intelligence: From Big Data to Big Impacts.

Smart health big data is paving a promising way for ubiquitous health management, leveraging exciting advances in biomedical engineering technologies, such as convenient bio-sensing, health monitoring, in-home monitoring, biomedical signal processing, data mining, health trend tracking, evidence-based medical decision support etc. To build and utilize the smart health big data, advanced data sensing and data mining technologies are closely-coupled key enabling factors. In smart health big data innovations, challenges arise in how to informatively and robustly build the big data with advanced sensing technologies, and how to automatically and effectively decode patterns from the big data with intelligent computational methods. More specifically, advanced sensing techniques should be able to capture more modalities that can reflect rich physiological and behavioral states of humans, and enhance the signal robustness in daily wearable applications. In addition, intelligent computational techniques are required to unveil patterns deeply hidden in the data, and nonlinearly convert the patterns to high level medical insights.

This Special Section in IEEE Access invites academic and industrial experts to make their contributions to smart health big data, empowered by biomedical sensing and computational intelligence technologies. Studies are expected to connect the human body, data, and applications, establish an end-to-end information flow, and convert big data to big impacts. Crucial technologies include wearable sensing, in-home sensing, personal health record establishment, biomedical signal processing, deep learning, big data mining, pattern recognition, and other related techniques. This Special Section will allow readers to identify advancements, challenges and new opportunities in cutting-edge smart health big data innovations.

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

  • Wearable sensing for big data: bio-potential sensing, behavioral sensing, optical sensing, ultrasonic sensing, flexible sensing, emerging wearable imaging, etc.
  • In-home sensing for big data: on-bed sensing, sleep quality sensing, activity sensing, mobility sensing, fall detection, rehabilitation monitoring, etc.
  • Personal health big data: cardiac monitoring, cardiopulmonary monitoring, brain function monitoring, mobility monitoring, life style monitoring, etc.
  • Signal quality enhancement in wearable big data sensing
  • Biomedical signal processing for smart health big data
  • Feature extraction and critical feature selection from smart health big data
  • Automatic feature mining using deep learning from smart health big data
  • Dimension reduction for effective learning from smart health big data
  • Time series, image and unstructured data fusion
  • Data mining from large databases for pattern and correlation finding
  • Knowledge discovery in smart health big data
  • Telemedicine, internet of medical things, mobile health, remote health monitoring
  • Precision medicine exploration based on smart health big data
  • Medical relevant insight learning from long-term health records
  • Real-time health alerts and long-term health trend analytics
  • Sleep quality monitoring and analytics with smart health big data
  • Human-computer interaction for rehabilitation and assisted living
  • Lifestyle changing empowered by digital health technologies
  • Smart health big data to empower clinical trials

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

 

Associate Editor: Qingxue Zhang, Indiana University-Purdue University, USA

Guest Editors:

  1. Vincenzo Piuri, University of Milan, Italy
  2. Edward A. Clancy, Worcester Polytechnic Institute, USA
  3. Dian Zhou, University of Texas at Dallas & Fudan University, USA & China
  4. Thomas Penzel, Charite University Hospital, Germany
  5. Walter Hu, University of Texas at Dallas & One-Chip Co., Ltd., USA & China
  6. Hui Zheng, Harvard University & Massachusetts General Hospital, USA

 

Relevant IEEE Access Special Sections:

  1. Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications
  2. Soft Computing Techniques for Image Analysis in the Medical Industry – Current trends, Challenges and Solutions
  3. Human-Centered Smart Systems and Technologies


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

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

For inquiries regarding this Special Section, please contact: qxzhang@iu.edu.

Intelligent Data Sensing, Collection and Dissemination in Mobile Computing

Submission Deadline: 15 October 2019

IEEE Access invites manuscript submissions in the area of Intelligent Data Sensing, Collection and Dissemination in Mobile Computing.

With the application of mobile computing, there could be more intelligent ways to sense and collect data, and to reduce the workload of participants. In addition, the scale of data sensing, collection and dissemination could increase. Mobile computing makes full use of various sensing devices, such as smart phones, wireless body sensors, smart sensing devices in manufacturing, and smart meters. These devices, which sense and collect data (on the order of zettabytes in the near future), together with the computing power of mobile devices, develop a new paradigm to data sensing, collection and dissemination. Mobile Computing has emerged as a prospective computing paradigm to pave the way to pervasive computing for mobile and big-data applications.

To ensure intelligent data sensing, data collection and data processing based on mobile computing over the pervasive computing environment, there are some fundamental challenges. Many open issues remain unresolved, such as how to achieve the expected performance for intelligent data sensing, data collection, data processing, and how to ensure the data quality, data reliability, information security and privacy in data collection/dissemination with intelligence. Other relevant aspects should also be studied, such as the computation cost, the platform, tools, service discovery, data management, and analytics for intelligent data collection and dissemination. These unresolved issues have been major research hotspots for many researchers since they are critical to ensuring rigid and efficient applications for intelligent data sensing, collection and dissemination in mobile computing.

To tackle the above issues and challenges, this Special Section in IEEE Access will present innovative solutions and recent advances in the domain of intelligent data sensing, collection and dissemination in mobile computing, which will provide a guide for the application and future research of mobile computing.

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

  • Science and foundations for computing-based intelligent sensing and data collection/dissemination, including theoretical, computational models, and new standards
  • Application of intelligent data sensing, collection and dissemination based mobile computing Infrastructures, platforms and system architectures to support mobile computing-based sensing and data collection/dissemination
  • Algorithms, schemes and techniques in mobile computing-based intelligent data sensing, data collection/dissemination to provide high performance
  • Infrastructures to support dependable, secure and safe mobile computing-based intelligent sensing and data collection/dissemination
  • Simulating and emulating environments as well as experimental results on mobile edge computing- based intelligent sensing and data collection/dissemination
  • Complexity for mobile computing of intelligent sensing and data collection/dissemination
  • Mobile-computing-based intelligent sensing and data collection/dissemination in the cloud
  • Large-scale data analysis in mobile computing-based intelligent sensing and data collection/ dissemination
  • Scalable data and resource management
  • Knowledge and service discovery in mobile computing-based sensing and data collection/ dissemination with intelligence
  • Business and societal applications of intelligent sensing and data collection/dissemination in mobile computing
  • Challenges and issues of intelligent sensing and data collection/dissemination in mobile computing
  • Other techniques in mobile computing-based intelligent sensing and data collection/dissemination, e.g., intelligent crowd sensing, social networking, and vehicular communications

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

 

Associate Editor:  Xuxun Liu, South China University of Technology, China

Guest Editors:

  1. Anfeng Liu, Central South University, China
  2. John Tadrous, Gonzaga University, USA
  3. Ligang He, University of Warwick, UK
  4. Bo Ji, Temple University, USA
  5. Zhongming Zheng, Ericsson Canada Inc, Canada

 

Relevant IEEE Access Special Sections:

  1. Mobile Edge Computing
  2. Recent Advances in Computational Intelligence Paradigms for Security and Privacy for Fog and Mobile Edge Computing
  3. Trusted Computing


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

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

For inquiries regarding this Special Section, please contact: liuxuxun@scut.edu.cn.

Distributed Computing Infrastructure for Cyber-Physical Systems

Submission Deadline: 01 November 2019

IEEE Access invites manuscript submissions in the area of Distributed Computing Infrastructure for Cyber-Physical Systems.

Advances in information communication technologies have given rise to the Internet of Things (IoT). IoT provides network infrastructures for a number of Cyber-Physical Systems (CPS), and will play an important role in our daily lives. In CPS, the massive number of deployed IoT devices (sensors, actuators, etc.) will be connected to collect data related to energy, transportation, city infrastructure, manufacturing, healthcare, and public safety, among others, supporting numerous smart-world CPS critical infrastructures such as the smart grid, smart transportation, smart health, smart city, and smart manufacturing, to name a few. As IoT devices have only limited computation and storage capacity, this calls for the development of appropriate computing infrastructures that can enable big data computing, intelligence, and storage services to support IoT-based CPS applications.

Generally speaking, more than just the integration of computing and communication with physical systems, CPS can be considered as the vertical integration of command and control, communication infrastructure, and sensing and actuation to realize complex distributed situation awareness, analysis, and decision-making. Deployed to enhance the performance of traditional systems as well as implement novel applications, CPS is characterized by critical service requirements to deliver real-time, low-latency analysis and actuation from the assessment of massive heterogeneous data. Moreover, the geo-distributed nature of sensing and actuation systems requires computing solutions that can meet the critical service needs in a likewise distributed fashion. Because of the diversity of implementations and devices, CPS infrastructures must contend with significant challenges, including the management of massively distributed heterogeneous smart devices, the synchronization of computing and storage across distributed nodes, the interaction and implementation of diverse computing paradigms (e.g., cloud, fog, edge), security and privacy concerns, problems in adaptability and scalability, and the integration of other emerging technologies (5G, machine learning, software defined networking, and network function virtualization), among others. The development of distributed computing infrastructures for CPS thus creates opportunities for novel research and necessitates interdisciplinary efforts to solve these challenges.

The articles in this Special Section in IEEE Access should focus on state-of-the-art research and challenges in the foundations and applications of distributed computing architectures and infrastructures in various CPS domains, including energy, transportation, city infrastructure, manufacturing, healthcare, and public safety.

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

  • Integrated Communication and Distributed Computing Design for CPS
  • Theoretical Computing Foundation and Models for CPS
  • Intelligent Real Time Data Analytics for CPS
  • Security and Privacy Issues in the Distributed Computing Infrastructure of CPS
  • Machine Learning for CPS
  • Communication and Network Architectures and Protocols for Facilitating Distributed Computing Infrastructure Deployment in CPS
  • Data Management, Trading, and Sharing in CPS
  • Integrated Testbed and Case Studies for Computing Infrastructure in CPS
  • Co-Design of Distributed Computing and Physical Systems in CPS

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

 

Associate Editor:   Wei Yu, Towson University, USA

Guest Editors:

  1. Xinwen Fu, University of Central Florida, USA
  2. Jinsong Wu, University of Chile, Chile
  3. Xinyu Yang, Xi’an Jiaotong University, China
  4. Zhen Ling, Southeast University, China
  5. Zheng Chen, University of Houston, USA

 

Relevant IEEE Access Special Sections:

  1. Security and Trusted Computing for Industrial Internet of Things
  2. Towards Service-Centric Internet of Things (IoT): From Modeling to Practice
  3. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things


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

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

For inquiries regarding this Special Section, please contact: wyu@towson.edu.

Artificial Intelligence in CyberSecurity

Submission Deadline: 30 July 2019

IEEE Access invites manuscript submissions in the area of Artificial Intelligence in CyberSecurity.

Recent studies show that Artificial Intelligence (AI) has resulted in advances in many scientific and technological fields, i.e., AI-based medicine, AI-based transportation, and AI-based finance. It can be imagined that the era of AI will be coming to us soon. The Internet has become the largest man-made system in human history, which has a great impact on people’s daily life and work. Security is one of the most significant concerns in the development of a sustainable, resilient and prosperous Internet ecosystem. Cyber security faces many challenging issues, such as intrusion detection, privacy protection, proactive defense, anomalous behaviors, advanced threat detection and so on. What’s more, many threat variations emerge and spread continuously. Therefore, AI-assisted, self-adaptable approaches are expected to deal with these security issues. Joint consideration of the interweaving nature between AI and cyber security is a key factor for driving future secure Internet.

The use of AI in cybersecurity creates new frontiers for security research. Specifically, the AI analytic tools, i.e., reinforcement learning, big data, machine learning and game theory, make learning increasingly important for real-time analysis and decision making for quick reactions to security attacks. On the other hand, AI technology itself also brings some security issues that need to be solved. For example, data mining and machine learning create a wealth of privacy issues due to the abundance and accessibility of data. AI-based cyber security has a great impact on different industrial applications if applied in appropriate ways, such as self-driving security, secure vehicular networks, industrial control security, smart grid security, etc. This Special Section in IEEE Access will focus on AI technologies in cybersecurity and related issues. We also welcome research on AI-related theory analysis for security and privacy.

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

  • Reinforcement learning for cybersecurity
  • Machine learning for proactive defense
  • Big data analytics for security
  • Big data anonymization
  • Big data-based hacking incident forecasting
  • Big data analytics for secure network management
  • AI-based intrusion detection and prevention
  • AI approaches to trust and reputation
  • AI-based anomalous behavior detection
  • AI-based privacy protection
  • AI for self-driving security
  • AI for IoT security
  • AI for industrial control security
  • AI for smart grid security
  • AI for security in innovative networking
  • AI security applications

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

 

Associate Editor:   Chi-Yuan Chen, National Ilan University, Taiwan

Guest Editors:

  1. Wei Quan, Beijing Jiaotong University, China
  2. Nan Cheng, University of Toronto, Canada
  3. Shui Yu, Deakin University, Australia
  4. Jong-Hyouk Lee, Sangmyung University, Republic of Korea
  5. Gregorio Martinez Perez, University of Murcia (UMU), Spain
  6. Hongke Zhang, Beijing Jiaotong University, China
  7. Shiuhpyng Shieh, National Chiao Tung University, Taiwan

 

Relevant IEEE Access Special Sections:

  1. Artificial Intelligence and Cognitive Computing for Communications and Networks
  2. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things
  3. Cyber-Physical Systems


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

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

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

Data Mining for Internet of Things

Submission Deadline: 31 March 2020

IEEE Access invites manuscript submissions in the area of Data Mining for Internet of Things.

The Internet of Things (IoT) has become an important research domain as mature appliances, systems, infrastructures, and their applications have shown their potential in recent years. We can foresee that smart homes and smart cities using these technologies will be realized in the near future. However, many consumers have concerns with the “smart” information system and environment, especially when entering the era of IoT. The expectations of IoT and its relevant products in this new era are quite high. Instead of smartness alone, consumers of IoT products and services would like to see IoT technologies bring about more intelligent systems and environments. The main difference between the “smart thing” and “intelligent thing” is that the former will use predefined rules to provide services to a user whereas the latter will not only use predefined rules, but will also use the analytical results from intelligent mechanisms to discover suitable services for users. More precisely, using only the predefined rules may not be sufficient to consider every possible situation because the number of rules is limited. Using the results obtained after data analysis we can provide additional information to an IoT system to make it better understand the needs of a user. This is why data analytics has become a promising technology for IoT.

Although most researchers of data mining have recognized how to analyze large-scale data is an important research topic for many years, considerations for the IoT environment are quite different from those for the traditional environment because data for the IoT will be created more quickly and in different formats. That is why research on data analytics for IoT have typically been relevant to big data analytics and cloud computing technologies in recent years. This does not mean that traditional data mining and intelligent algorithms are no longer useful for IoT. In fact, how to redesign these algorithms to make them more efficiently and effectively work for IoT has been a critical research trend. In addition to modifying the traditional data mining and intelligent algorithms, an alternative is to develop new data analysis algorithms. Using deep learning technologies for supervised learning to construct a set of classifiers to recognize data entering an IoT system, and using metaheuristic algorithms for unsupervised learning to find out good solutions for classifying unknown data are two promising technologies today. Moreover, how to determine interesting patterns from a series of events of an IoT system is also a critical research topic. In summary, many modern technologies, such as big data analytics, statistical technologies, and other analysis technologies, have also been used for finding out useful information from an IoT system to provide needed services to a user and to enhance the performance of the IoT system as a whole today.

This Special Section in IEEE Access will focus on data mining technologies for the IoT and its applications, such as smart home, smart city, industry, online social network, and even internet of vehicles. We also welcome research on IoT related technologies, such as cloud computing, network security, wireless sensor network, vehicular networking, smart grids, and big data.

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

  • Data Mining for the IoT
  • Machine and Deep Learning for the IoT
  • Metaheuristic Algorithms for the IoT
  • Cloud Computing for the IoT
  • Big Data for the IoT
  • Mobile Computing and Sensing for the IoT
  • Security Framework for the IoT
  • Privacy Protection for the IoT
  • IoT in Smart Home and Smart City
  • IoT in Energy Management
  • Industry IoT
  • IoT in Agriculture and Environment
  • IoT in eHealth and Ambient Assisted Living
  • Internet of Vehicles
  • Edge Computing
  • Applications of the IoT

 

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

 

Associate Editor: Chun-Wei Tsai, National Chung-Hsing University, Taiwan

Guest Editors:

  1. Mu-Yen Chen, National Taichung University of Science and Technology, Taiwan
  2. Francesco Piccialli, University of Naples Federico II, Italy
  3. Tie Qiu, Tianjin University, China
  4. Jason J. Jung, Chung-Ang University, Republic of Korea
  5. Patrick C. K. Hung, University of Ontario Institute of Technology, Canada
  6. Sherali Zeadally, University of Kentucky, USA

 

Relevant IEEE Access Special Sections:

  1. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things
  2. Healthcare Information Technology for the Extreme and Remote Environments
  3. Internet-of-Things (IoT) Big Data Trust Management


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

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

For inquiries regarding this Special Section, please contact: cwtsai0807@gmail.com.

Emerging Approaches to Cyber Security

Submission Deadline: 30 April 2020

IEEE Access invites manuscript submissions in the area of Emerging Approaches to Cyber Security.

The Internet has become a key feature for any business activity. Criminal activity is no exception. Some crimes prior to the Internet, such as theft and scams, have now found the perfect tool for developing their activities- the Internet. The internet allows criminals to hide their real identity and to execute several kinds of offenses (e.g. to sell drugs, to sell private information, child pornography, etc.) and all this is thanks to the possibility of purchasing, in different black markets, specific and advanced tools to facilitate these activities for a low risk and a low economic investment.

In recent years, Internet Crime (e-Crime) has changed its business model, becoming more professional. The most skilled criminals offer their services to other criminals with less IT skills. This is known as CaaS (Crime-as-a-Service). Criminals often offer their skills in forums and markets of the Deep Web and the Dark Net, where advanced anonymization techniques are used to allow users to communicate freely without being traced. In these sites, potential clients can find many types of solutions for illegal activities. For instance, they find software kits, which allow less skilled criminals to infect thousands of computers to steal sensitive information, such as online bank credentials and credit card details.

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

  • Computational and artificial intelligence
  • Internet of Things (IoT)
  • Big Data
  • Social implications of technology
  • Information Security
  • Advances in Traditional System Forensic Methods
  • Multimedia and Artifact Analysis
  • Emerging Approaches to Cyber security
  • Incident Response and Malware Analysis
  • SCADA Forensics and Critical Infrastructure Protection
  • Digital Forensic Science
  • Cyber Crime Law, Psychology and Economics

 

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

 

Associate Editor: Luis Javier García Villalba, Universidad Complutense de Madrid, Spain

Guest Editors:

  1. Ana Lucila Sandoval Orozco, University of Brasilia, Brazil
  2. Mario Blaum, IBM Almaden Research Center, CA, USA
  3. Tai-Hoon Kim, Sungshin Women’s University, South Korea

 

Relevant IEEE Access Special Sections:

  1. Advanced Software and Data Engineering for Secure Societies
  2. Challenges and Opportunities of Big Data Against Cyber Crime
  3. Security Analytics and Intelligence for Cyber Physical Systems


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

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

For inquiries regarding this Special Section, please contact: javiergv@fdi.ucm.es

Data-Enabled Intelligence for Digital Health

Submission Deadline: 15 September 2019

IEEE Access invites manuscript submissions in the area of Data-Enabled Intelligence for Digital Health.

The worldwide increase in the aging population presents an urgent need for new technologies to improve the quality of life for the elderly. In recent years we have seen rapid development of healthcare technologies along with the widespread use of Internet, mobile technologies, data analytics and artificial intelligence in healthcare. These developments have resulted in highly multi-disciplinary research in digital health and smart health, and have also driven the move towards more personalized care.

Digital health aims to apply data sciences, machine learning, artificial intelligence and the internet of things to tackle the health problems and challenges faced by patients and the care professionals. For example, tracking personalized health indicators regularly such as blood pressure, heart rate and others can help with the management of the health and well-being of patients with heart issues.

New technologies developed in the digital industry, particularly in the emerging interfacing area between big data and artificial intelligence, are changing the way healthcare is delivered and can have an enormous economic impact on healthcare provision. We are experiencing extensive research in health care including the development of new smart sensing, new algorithms, and new systems or devices for personalized healthcare. One of the fundamentals of these developments is to ensure that healthcare data can be accessed and analyzed effectively in order to support accurate decision-making. Most digital health system design has been focused on the functionalities defined by the domain expertise. For these types of systems, user experience and effectiveness of the systems will very much depend on the users’ knowledge of the system. This can be a challenging issue for personalized healthcare, particularly for users with disabilities and in an aging society.

Extensive research is currently taking place worldwide in the related areas, which in return raises new scientific questions as well as practical issues; for example (1) what will the next generation of Artificial Intelligence (AI) provide for us to achieve a better quality of life, particularly for our aging society? (2) How can healthcare systems be data-enabled to exploit a learning capability and fit in personal needs? (3) How can data-enabled technologies support effective human-machine cooperation and adapt to each other, and ultimately support humans and machines to work together? and (4) How can human-machine cooperation drive new intelligence to improve the quality of life for people in the healthcare systems?

This Special Section in IEEE Access aims to attract original research articles that advance the state of the art in digital health as well as data science and artificial intelligence. The goal is that it provides an opportunity for us to gain a significantly better understanding of the current developments and the future direction of artificial intelligence and data science in relation to healthcare.

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

  • New technologies and frameworks that support human-machine interaction and human-machine collaborative intelligence
  • Brain-Computer modeling for human-machine cooperation
  • Cognitive computing for healthcare and data intelligence
  • Brain-Computer modeling for cognitive intelligence
  • The design and implementation of personalized healthcare systems
  • The value and challenges of human-machine collaboration in healthcare
  • Data Science and artificial intelligence in digital health, and health management
  • Data science and artificial intelligence in public health
  • Machine learning to understand human behavior and well-being
  • New algorithms for medical and healthcare data analytics
  • Predictive analysis in personalized healthcare
  • Intelligent and predictive analytics for early warning, feedback and in-time intervention for personalized healthcare
  • The cutting edge development of digital health
  • New digital technologies to assist mental healthcare
  • New technology to enable personal data security and effective use in healthcare

 

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

 

Associate Editor:  Yonghong Peng, University of Sunderland, UK

Guest Editors:

  1. Wenbing Zhao, Cleveland State University, United States
  2. Yongtao Hao, Tongji University, China
  3. Yongqiang Cheng, University of Hull, United Kingdom
  4. Linbo Qing, Sichuan University, China
  5. Weihong Huang, Xiangya Hospital, China
  6. Ying Song, West China Hospital, China

 

Relevant IEEE Access Special Sections:

  1. Advanced Information Sensing and Learning Technologies for Data-centric Smart Health Applications
  2. Mobile Multimedia for Healthcare
  3. Healthcare Big Data


IEEE Access Editor-in-Chief: Michael Pecht, Professor and Director, CALCE, University of Maryland

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

For inquiries regarding this Special Section, please contact: Yonghong.Peng@Sunderland.ac.uk